facultative anaerobic granular sludge for textile dyeing waste water treatment

BAHAGIAN A – Pengesahan Kerjasama *

Adalah disahkan bahawa projek penyelidikan tesis ini telah dilaksanakan melalui

kerjasama

antara_________________________dengan____________________________

Disahkan oleh :

Tandatangan : ………………………………….. Tarikh : ………………….

Nama : …………………………………..

Jawatan : …………………………………..

(Cop Rasmi)

* Jika penyelidikan tesis/projek melibatkan kerjasama

Bahagian B – Untuk Kegunaan Pejabat Sekolah Pengajian Siswazah

Tesis ini telah diperiksa dan diakui oleh:

Nama dan Alamat Pemeriksa Luar : Prof. Dr. Hamidi bin Abdul Aziz Pusat Pengajian Kejuruteraan Awam, Kampus Kejuruteraan, Universiti Sains Malaysia,

14300 Nibong Tebal, Seberang Prai Selatan,

Pulau Pinang.

Nama dan Alamat Pemeriksa Dalam : Prof. Ir. Dr. Mohd Azraai bin Kassim Timbalan Naib Canselor (Akademik), Pejabat Timbalan Naib Canselor, (Akademik), UTM Skudai. Prof. Madya Dr. Johan Sohaili Fakulti Kejuruteraan Awam, UTM Skudai.

Nama Pemeriksa Lain (jika ada) : -

Disahkan oleh Timbalan Pendaftar di Sekolah Pengajian Siswazah:

Tandatangan : ………………………………….. Tarikh : ……………………….

Nama : ZAINUL RASHID BIN ABU BAKAR

FACULTATIVE ANAEROBIC GRANULAR SLUDGE FOR TEXTILE DYEING

WASTEWATER TREATMENT

KHALIDA MUDA

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Civil Engineering)

Faculty of Civil Engineering

Universiti Teknologi Malaysia

JANUARY 2010

ii

I declare that this thesis entitled “Facultative Anaerobic Granular Sludge for Textile

Dyeing Wastewater Treatment” is the result of my own research except as cited in

the references. The thesis has not been accepted for any degree and is not

concurrently submitted in candidature of any other degree.

Signature : ………………………………….

Name : KHALIDA MUDA

Date : 5 January 2010

iii

Dedicated to my precious love

AKMAL, AIMAN & AMMAR

iv

ACKNOWLEDGEMENT

In the name of Allah the Most Gracious, the Most Merciful. First and foremost I am truly grateful for the blessings of Allah that gives me the strength to complete this thesis.

I would like to convey my highest gratitude to all my supervisors; Professor Dr. Mohd Razman Salim, Associate Professor Dr. Zaharah Ibrahim and Dr. Azmi Aris for their excellent supervision, encouragement, understanding and patience throughout my study. May Allah bless and reward all of them. Without them, my PhD experience would be a very difficult one.

A special thanks to Dr Adibah for allowing me to use all the equipment and facilities at the Faculty of Bioscience and Bioengineering laboratories. To Dr. Arifah and Dr. Robiah of the Mathematics Department, Faculty of Science, thank you very much for assisting me on the statistical analysis. I would also like to express my appreciation to all the laboratory staff; Pak Usop, Ramli, Muzafar and Kak Ros of the Environmental Engineering Laboratory; Kak Fatimah, Awang and Yus of the Faculty Bioscience and Bioengineering laboratories, Encik Jefry of Mechanical Faculty Laboratory. I would also like to extend my thanks to all my seniors; Dr. Ismid, Kak Tim, Kak Farid, Kak Mala, Kak Fauziah, Kak Ngah, Kak Su for the continuous support, advice and encouragement during my study. To my colleagues and juniors, Shamila, Nana, Yati, Isal, Zana, Linda, Muzafar, Norly, Azlan, Zaini, Rosnani, Rosnita, Fairuzah, An and others from IPASA and MP2, thank you very much for their immeasurable friendship, motivation and support. To my dear friend, Aloes, thank you very much for all the support that you have given to me.

I am sincerely indebted to my sisters, brothers and other family members especially Kak Hasmah for all the love, support and du'a. Lastly to my precious heroes, Akmal, Aiman and Ammar, thank you very much for all the unconditional love, support, sacrifice and du'a during the hard times. All of you are my reasons to continue striving and overcome all obstacles.

Special thanks to the Ministry of Science, Technology and Innovation (ScienceFund-79137), Ministry of Higher Education (FRGS-78122) and UTM (IRGS-75221) for funding this research.

v

ABSTRACT

Dye residuals found in textile dyeing wastewater contribute to the difficulty

in treating such wastewater. Conventional biological processes failed to treat the wastewater while physico-chemical processes, although successful, are often costly in practice. Sequential anaerobic-aerobic process has been found to succeed in treating the textile wastewater. This study looks at the possibility of developing and applying facultative anaerobic granular sludge (FAnGS) in treating the wastewater in a single reactor under intermittent anaerobic and aerobic conditions. Synthetic textile wastewater which comprised of a mixture of Sumifix Navy Blue EXF, Synozol Red K-4B and Sumifix Black EXA, were used throughout the study. Different sludge and anaerobic granule mixture with the addition of specialized dye degrader microbes customized to treat dyeing wastewater were used at the initial stage. The initial development took place using a 4 L column reactor. Some of the studies were conducted in the same reactor while the remaining was conducted in a smaller scale, all under intermittent anaerobic and aerobic phases. After about 70 days of development, mature FAnGS were developed possessing excellent granules quality. The average size of the FAnGS was 2.3 ± 1.0 mm with average settling velocity of 80 ± 8 m/h resulting in settling velocity index (SVI) of 69 mL/g. Such properties have caused a significant increase in the biomass concentration to 7.3 ± 0.9 g/L, which was observed to be beneficial to the performance of the system. At the end of the development process, the biogranules were able to achieve 94% of COD, 95% of ammonia and 62% of color removal. The oxygen uptake rate (OUR) /specific oxygen uptake rate (SOUR) and specific methanogenic activity (SMA) indicate the presence of facultative, anaerobic and aerobic bacteria within the granules. Six bacteria were identified within the FAnGS which include Bacillus cereus, Pseudomonas veronii, three species of Pseudomonas genus and Enterobacter sp., all are considered in the literature as dye degrader microbes. With the aid of statistical experimental design, subsequent studies showed that the microbial activity of the FAnGS and their performance in removal of organics (in terms of COD) and color were affected by several factors which include substrate concentration, pH, temperature, hydraulic retention time (HRT) and concentration of redox mediator. Interaction effects between the factors were also observed. The magnitude and the direction (positive or negative) of the effects are however dependent on the reacting conditions. Several statistical models describing the relationship between some of the variables were developed. From the study, the highest removal of color (87%) and COD (94%) were achieved by the FAnGS biomass in IFAnGSBioRec system operated with 24 hours HRT with an intermittent of anaerobic (18 hours) and aerobic (6 hours) reactions.

vi

ABSTRAK

Lebihan baki pewarna dalam air sisa tekstil menyumbang kepada kesukaran

dalam olahan airsisa tersebut. Olahan konvensional biologi gagal mengolah airsisa ini manakala olahan kimia-fizikal, walaupun berhasil, melibatkan kos yang tinggi. Olahan berselang seli anaerobik-aerobik telah didapati berjaya mengolah airsisa tekstil. Dalam kajian ini, keupayaan menghasil dan menggunakan enapcemar granul fakultatif anaerobik (FAnGS) bagi mengolah airsisa tekstil dalam satu reaktor dengan fasa anaerobik dan aerobik secara berselang seli diterokai. Air sisa tekstil yg mengandungi campuran pewarna Sumifix Navy Blue EXF, Synozol Red K-4B dan Sumifix Black EXA digunakan sepanjang kajian. Di awal kajian, enapcemar yang berbeza, granul anaerobik dan beberapa mikrob pengurai pewarna dicampurkan dan digunakan dalam mengolah airsisa pewarna. Pembentukan granul dilakukan dalam reaktor berisipadu 4 L. Kesemua kajian yang dijalankan adalah secara berselang seli bagi fasa anaerobik dan aerobik samaada dengan menggunakan reaktor yang sama atau dalam skala yang lebih kecil. Setelah lebih kurang 70 hari, pembentukan FAnGS matang yang memiliki ciri granul yang baik berjaya dihasilkan. FAnGS yang terbentuk mempunyai saiz purata 2.3 ± 1.0 mm dengan halaju enapan 80 ± 8 m/j dan menghasilkan index halaju enapan (SVI) 69 mL/g. Dengan memiliki ciri-ciri tersebut, kepekatan biomas telah meningkat dengan signifikan kepada 7.3 ± 0.9 g/L. Di akhir pembentukan granul, peratus penyingkiran terhadap permintaan oksigen biokimia (COD), ammonia dan warna adalah masing-masing 94%, 95% dan 62%. Analisis bagi kadar pengambilan oksigen (OUR)/ kadar pengambilan oksigen spesifik (SOUR) mengesahkan kehadiran bakteria fakultatif, anaerobik dan aerobik dalam granul yang dihasilkan. Enam bakteria daripada FAnGS dikenal pasti sebagai Bacillus cereus, Pseudomonas veronii, tiga spesis Pseudomonas genus dan Enterobacter sp. Dengan menggunakan kaedah rekabentuk eksperimen, kajian menunjukkan aktiviti mikrob dari FAnGS dan keupayaan menyingkirkan bahan organik (berdasarkan kepada COD) dan warna adalah dipengaruhi oleh beberapa faktor termasuk kepekatan substrat, pH, suhu, masa tahanan hidraul dan kepekatan perantara redox. Kesan interaksi yang wujud antara faktor juga telah diperhatikan. Magnitud dan arah (positif dan negatif) sesuatu kesan adalah bergantung kepada keadaan tindakbalas yang berlaku. Beberapa model statistik telah dibina menghubungkan beberapa faktor yang dikaji. Daripada kajian ini, penyingkiran tertinggi terhadap warna (87%) dan COD (94%) telah dicapai oleh biojisim FAnGS dalam system IFAnGSBioRec yang beroperasi secara tindakbalas olahan berselang seli anaerobik (18 jam) dan aerobik (6 jam) dengan masa tahanan hidraul (HRT) selama 24 jam.

vii

TABLE OF CONTENTS

CHAPTER TITLE

PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xvi

LIST OF FIGURES xx

LIST OF ABBREVIATIONS xxx

LIST OF SYMBOLS xxxiii

LIST OF APPENDICES

xxxv

1 INTRODUCTION

1

1.1 Preamble 1

1.2 Objectives of the Study 3

1.3 Scope of the Study 4

1.4 Significance of the Study 5

1.5 Organization of Thesis

7

2 BIOGRANULATION TECHNOLOGY IN

WASTEWATER TREATMENT

9

2.1 Introduction 9

viii

2.2 Biogranulation 10

2.3 Development of Aerobic Granules 12

2.3.1 Aerobic Granules from Aerobic Activated

Sludge

14

2.3.2 Aerobic Granules Seeded with Anaerobic

Granular Sludge

15

2.4 Microbial Structure and Diversity of

Microorganisms

18

2.4.1 Microbial Structure 18

2.4.2 Microbial Diversity 20

2.5 Characteristics of Aerobic Granules 21

2.5.1 Size and Morphology 21

2.5.2 Settleability 23

2.5.3 Density and Strength 24

2.5.4 Cell Surface Hydrophobicity 25

2.5.5 Specific Oxygen Utilization Rate 26

2.5.6 Storage Stability 27

2.5.7 Exopolysaccharides 28

2.6 Factors Affecting the Formation of Aerobic Granules 29

2.6.1 Substrate Composition 29

2.6.2 Organic Loading Rate 30

2.6.3 Hydrodynamic Shear Force 31

2.6.4 Feast and Famine Regime 33

2.6.5 Hydraulic Retention Time 34

2.6.6 Presence of Inorganic Composition 34

2.6.7 Concentration of Dissolved Oxygen 35

2.6.8 Slow Growing Organisms 36

2.6.9 Settling Time 37

2.6.10 Reactor Configuration 38

2.6.11 Volumetric Exchange Ratio 38

2.7 Applications of Aerobic Granule in

Wastewater Treatment Systems

39

2.7.1 High Strength Organic Wastewater 39

ix

Treatment

2.7.2 Simultaneous Organic and Nitrogen

Removal

40

2.7.3 Phosphorus Removal 42

2.7.4 Phenol Wastewater Treatment 43

2.7.5 Biosorption of Heavy Metals and Nuclear

Waste

44

3 DYE DEGRADATION PROCESS

46

3.1 Textile Industry 46

3.2 Characteristics of Textile Wastewater 48

3.2.1 Quantity 49

3.2.2 Quality 50

3.3 Dye and Environmental Problems 52

3.4 Treatment of Dyes 54

3.4.1 Biodegradation of Dyes 57

3.4.2 Bacterial Degradation of Dyes 59

3.4.3 Mechanisms of Biodegradation of Azo Dyes 60

3.4.3.1 Aerobic Dye Degradation Process 60

3.4.3.2 Anaerobic Dye Degradation

Process

62

3.4.3.3 Anoxic Dye Degradation Process 63

3.4.4 Mineralization of Aromatic Amines 65

3.5 Treatment System for Biodegradation of Azo Dyes 67

3.5.1 The Sequential Anaerobic/Aerobic Reactor

System

68

3.5.2 The Integrated Anaerobic/Aerobic Reactor

System

70

4 DEVELOPMENT OF FACULTATIVE ANAEROBIC

GRANULES

75

4.1 Introduction 75

x

4.2 Materials 76

4.2.1 Wastewater Composition 79

4.2.2 Granules Precursor 81

4.2.3 Reactor Set-up 83

4.3 Analytical Methods 86

4.3.1 Biological Characteristics 86

4.3.1.1 Morphological and Structural

Observation

86

4.3.1.2 Microbial Activity 89

4.3.2 Physical Characteristics 91

4.3.2.1 Settling Velocity 91

4.3.2.2 Sludge Volume Index 91

4.3.2.3 Granular Strength 92

4.3.2.4 Biomass Concentration 92

4.3.2.5 Sludge Retention Time 93

4.3.3 Chemical Characteristics 94

4.3.4 Removal Performance 95

4.3.4.1 Color 95

4.3.4.2 Chemical Oxygen Demand 95

4.3.4.3 Ammonia 96

4.4 Experimental Procedures 96

4.5 Results and Discussion 99

4.5.1 Morphology of Facultative Anaerobic

Granular Sludge

99

4.5.2 Cellular Characterization of Facultative

Anaerobic Granular Sludge

103

4.5.3 Microbial Activity 106

4.5.4 Size of the Facultative Anaerobic Granular

Sludge

108

4.5.5 Settling Velocity of the Facultative


109

4.5.6 Granular Strength of the Facultative


110

xi

4.5.7 Biomass Concentration and Sludge

Retention Time

113

4.5.8 Mineral and Metal Content 114

4.5.9 Removal Performance 117

5

4.6 Conclusions

EFFECT OF AGGREGATION AND SURFACE

HYDROPHOBICITY BY SELECTED MICROBES

FROM FACULTATIVE ANAEROBIC GRANULAR

SLUDGE

121

123

5.1 Introduction 123

5.2 Materials 124

5.3 Analytical Methods

5.3.1 Chemical Oxygen Demand and Color

Removal

126

126


5.4.1 Isolation Procedure of Bacteria Strain 127

5.4.2 Morphological Characterization 128

5.4.3 Identification of Microorganisms Isolated

from Facultative Anaerobic Granular Sludge

131

5.4.4 Specific Growth and Screening for Dye-

Degrading Bacteria

131

5.4.5 Autoaggregation Assay 132

5.4.6 Surface Hydrophobicity Assay 133

5.4.7 Effect of Substrate Concentration, pH and

Temperature on Coaggregation and Surface

Hydrophobicity

134

5.4.8 2-Level Factorial Experimental Design 135

5.4.9 Response Surface Methodology (Central

Composite Design)

137


5.5.1 Morphological and Cellular Characterization 139

xii

of Bacteria Isolation from Facultative


5.5.2 Screening for Dye Degrader and

Autoaggregator From Bacteria Strain

Isolated from Facultative Anaerobic

Granular Sludge

139

5.5.3 Analysis of the Isolates from Facultative


141

5.5.4 Effect of Substrate, pH and Temperature on

Coaggregation and Surface Hydrophobicity

147

5.5.4.1 Factorial Analysis: The Main

Effect of Substrate on

Coaggregation

152


Effect of pH on Coaggregation

156


Effect of Temperature on

Coaggregation

157

5.5.4.4 Factorial Analysis: The Interaction

Effect on Coaggregation

157


Effect of Substrate on Surface

Hydrophobicity

159


Effect of pH on Surface

Hydrophobicity

161


Effect of Temperature on Surface

Hydrophobicity

162

5.5.4.8 Factorial Analysis: The Interaction

Effect on Surface Hydrophobicity

164

5.5.5 Response Surface Analysis 167

5.6 Conclusions 176

xiii

6 THE EFFECT OF HYDRAULIC RETENTION TIME

ON FACULTATIVE ANAEROBIC GRANULAR

SLUDGE

180


6.2 Materials 182



6.3.2 Physical Characteristics 186

6.3.3 Removal Performances 186




6.5.2 Physical Profile of the Reactor System 189

6.5.3 Effect of Hydraulic Retention Time on

Physical Properties of the Granular Biomass

196

6.5.4 Effect of Hydraulic Retention Times on

Chemical Oxygen Demand Removal

202

6.5.5 Effect of Hydraulic Retention Time on

Color Removal

204

6.5.6 Effect of Hydraulic Retention Time on the

Biokinetics of Facultative Anaerobic

Granular Sludge during Biodegradation of

Dye

207

6.6 Conclusions

212

7 EFFECT OF SUBSTRATE AND RIBOFLAVIN ON

FACULTATIVE ANAEROBIC GRANULAR

SLUDGE

214


7.2 Materials 215

xiv

7.2.1 Granular Precursor 216


7.3.1 Chemical Oxygen Demand and Color

Removal

216


7.4.1 Screening for Concentration of Redox

Mediator

218

7.4.2 Batch Experiment for Chemical Oxygen

Demand and Color Removal Using

Facultative Anaerobic Granular Sludge

218

7.4.3 2-Level Factorial and Central Composite

Design Composite Experiment

218


7.5.1 Screening for Redox Concentration 221

7.5.2 Factorial Design Analysis of Chemical

Oxygen Demand Removal

223


Effect of Substrate Chemical


226


Effect of Riboflavin on Chemical


228

7.5.2.3 Factorial Analysis: The

Interaction Effect of Substrate and

Riboflavin on Chemical Oxygen

Demand Removal

230

7.5.3 Central Composite Design Analysis of

Chemical Oxygen Demand Removal

232

7.5.4 Factorial Design Analysis of Color

Removal

234

7.5.4.1 Factorial Analysis: Main Effect of

Substrate on Color Removal

237

7.5.4.2 Factorial Design Analysis: Main 241

xv

Effect of Riboflavin on Color

Removal

7.5.4.3 Factorial Analysis: Interaction

Effect

242

7.5.5 Central Composite Design Analysis of

Color Removal

246

7.6 Conclusions

257

8 CONCLUSIONS AND RECOMMENDATIONS

259

8.1 Conclusions 260

8.2 Recommendations

264

REFERENCES

267

Appendices A-G 301-350

xvi

LIST OF TABLES

TABLE NO. TITLE PAGE

3.1 Characteristics of textile wastewater (Bisschops and

Spanjers, 2003 and Dos Santos et al., 2006a)

52

3.2 Release of typical pollutants associated with various

textile manufacturing processes (Crini, 2006 and Dos

Santos et al., 2006a)

53

3.3 Advantages and disadvantages of the current methods of

dye removal from industrial effluents (Robinson et al.,

2000 and Crini, 2006)

56

3.4 Sequential anaerobic-aerobic treatment system for dye

degradation

71-72

3.5

Integrated anaerobic-aerobic sequential treatment

system for dye degradation

73-74

4.1

4.2

Sequential batch reactor system with intermittent

anaerobic/aerobic/anoxic reaction phase treating

different types of wastewater

List of reagents used in the experiment

77-78

79

xvii

4.3 List of equipment used in the experiment

80

4.4 One complete cycle of the IFAnGSBioRec

99

4.5 The OUR levels during the aerobic reaction phase of

one complete cycle

108

4.6 Characteristics of seed sludge and FAnGS

112

4.7 Comparison of mineral content at different stages

during the development of FAnGS

115

5.1 List of reagents used in the experiment

125

5.2 List of equipment used in the experiment

126

5.3 The variables and their range of high and low values

used in the factorial experiment

136

5.4 Two-level fractional factorial design with three

variables (in coded levels) conducted in duplicate (not

in randomized order)

136

5.5 Two-level of CCD experimental run in coded units

138

5.6 Morphological and cellular characterization of the

twelve isolated bacteria from FAnGS

140

5.7 Characteristics and performance of the isolated bacterial

from the FAnGS

142

5.8 Taxonomic and phylogenetic characteristic of the

isolates from FAnGS

148

xviii

5.9 Characteristics of identified selected bacteria strains

from FAnGS

149

5.10 Experimental results for 2-level factorial design analysis

151

5.11 The P-values of the estimated main and interaction

effects of variables substrates, pH and temperature on to

the percentage of coaggregation and surface

hydrophobicity after six hours aeration phase

152

5.12 Experimental results for CCD analysis

168

5.13 Summary of the P-value of the response surface

modeling analysis

169

5.14 Mathematical models in terms of actual factors

170

6.1 Dye degradation process using integrated reactor system

183-184

6.2 Operation steps during single cycle operation

188

6.3 Details of experimental condition of the IFAnGSBioRec

192

6.4 Oxidation Reduction Potential

192

6.5 Biomass concentrations at different stages of the

experiment

193

6.6 Physical properties of the granular biomass at different

stage of experiment

197

6.7 Profile of COD and color removal percentage at

different stages of experiment

207

xix

6.8 Coefficient of biokinetic parameters

209

6.9 Kinetic coefficients of FAnGS at different stages of the

experiment

210

7.1 Experimental runs of factorial design and CCD in actual

and coded values (not in random order)

220

7.2 Experimental results for factorial design analysis

224


effects of substrates and riboflavin for the percentage of

COD removal

224


233


modeling analysis

234

7.6 Experimental results for factorial design analysis

236


effects of variables substrates and redox mediator for

the percentage of color removal

237


246


modeling analysis

247

7.10 Mathematical models in terms of actual values

249

xx

LIST OF FIGURES

FIGURES NO. TITLE

PAGE

1.1 Outline of the study 8

2.1 Design principles of sequencing batch reactor

(Jern, 1989)

13

2.2 Schematic diagram of aerobic granulation

developed without any carrier material (Beun et al.,

1999)

16

2.3 Granulation development supported by ciliates. A:

Formation of floc where the ciliates settle on other

organisms or particles (Phase 1). B: Arrow shows

the colonization of bacteria on the ciliate stalks

(Phase 2). C: Granules grown into bigger sizes with

dense core. Zooids of the ciliates stalks completely

overgrown by bacterial, die and act as the

“backbone” structure (Phase 3). D: Unstalked free

swimming ciliates detach from the biofilm to

escape death. Smooth and compact granules are

formed. E: The surviving swarming ciliate cells

get attached to the matured surface granules

(shown by arrow) (Weber et al., 2007)

17

xxi

2.4 The flowchart of the morphological and physical

changes of the anaerobic granules in the process of

aerobic granule formation in SBR system (Linlin et

al., 2005)

18

4.1 Location of textile industry; Ramatex Industry Sdn.

Bhd., Sri Gading Industrial Park, Batu Pahat and

sewage treatment plant; Indah Water Konsortium

Treatment Plant System, Taman Sutera, Skudai.

82

4.2 Schematic layout of the IFAnGSBioRec system (Wang et al. (2004) and Zheng et al. (2005)

84

4.3 The IFAnGSBioRec system used in the study

85

4.4 Preparation frame work for granule development 87

4.5 Characterizations of FAnGS 88

4.6 The morphological development of facultative

anaerobic granular sludge (scale bar at steady-state

equals to 1mm) Pictures were taken using stereo

microscope with magnification of 6.3X (a)

Granules developed from the activated sludge (b)

Granules developed from anaerobic granules

patches

100

4.7 Pictures of sludge particles during the initial stage

of the experiment (a) and matured FAnGS granules

at the 66 days of the experiment (b). Pictures were

taken using stereo microscope with magnification

of 6.3X (scale bar equals to 1 mm)

102

xxii

4.8 FESEM microstructure observations on mature

facultative anaerobic granular sludge under the

magnification of 10,000K. (a) Coccoid bacteria

tightly linked to one another. (b) Cavities that

appear between bacteria clumped inside the

granules

104

4.9 The changes on the microbial population during the

process development of the FAnGS observed by

gram staining procedures under microscopic

magnification of 1000K (a) The sludge being

dominated by the filamentous organisms. (b)

Changes in the domination species within the

FAnGS

105

4.10 The profile of dissolved oxygen and oxygen uptake

rate in one complete cycle of the IFAnGSBioRec

system (♦) Dissolve oxygen, (□) Oxygen uptake

rate (PI and PIII-Anaerobic phase; PII and PIV-

Aerobic phase)

107

4.11 The relationship between the biomass

concentrations retained in the reactor with the

settling velocity of the FAnGS (■) Settling

velocity; (○) Biomass concentration

110

4.12 The relationship between the SVI values and

settling velocity of the FAnGS (○) SVI, (■)

Settling velocity

111

4.13 The profile of integrity coefficient representing the

granular strength of the FAnGS

112

xxiii

4.14 The profile of biomass concentration in the SBR.

(●) MLSS, (□) MLVSS

114

4.15 The settling velocity profile in relation to mean cell

residence time (SRT). (○) SVI, (■) SRT

115

4.16 Profile of COD removal during FAnGS

development in IFAnGSBioRec system. (▲)

Influent COD, (■) Effluent COD, (○) COD

removal

118

4.17 Profile of Ammonia removal during FAnGS

development in IFAnGSBioRec system. (▲)

Influent ammonia, (■) Effluent ammonia, (○)

Ammonia removal

119

4.18 Profile of color removal during FAnGS

development in IFAnGSBioRec system. (100

ADMI ≈ 1 Platimun-Cobalt). (▲) Influent color,

(■) Effluent color, (○) Color removal

119

4.19 The removal for COD, ammonia and color in one

complete cycle of the SBR system (■) Color, (○)

COD, (▲) Ammonia

120

5.1 Characterization of microbes isolated from the

FAnGS granules

129

5.2 Experimental work for the investigation on the

effect of substrate concentration, pH and

temperature on the percentage of coaggregation

and surface hydrophobicity og the mixed culture

130

xxiv

5.3 Agarose gel electrophoresis of DNA extraction

144

5.4 Agarose gel electrophoresis of purified PCR

amplification product

145

5.5 The pareto chart of the percentage of (a)

coaggregation and (b) surface hydrophobicity after

six hours of aeration phase (A: substrate; B: pH; C:

temperature; α: 0.1)

155

5.6 Main effects plot on the coaggregation

158

5.7 Interaction effects plot on the coaggregation

process (• Centre point)

159

5.8 Main effects plot of variables for the percentage of

SHb

161

5.9 Interaction effect plots for the percentage of SHb

(• Centre point)

165

5.10 Predicted versus actual data for (a) coaggregation

and (b) surface hydrophobicity

171

5.11 (a) Contour and (b) 3D response surface plots

representing relationship between pH, temperature

and percentage of coaggregation

172


representing relationship between the concentration

of substrate, pH and percentage of surface

hydrophobicity

174

xxv



of substrate, temperature and percentage of surface

hydrophobicity

175


representing relationship between pH, temperature

and percentage of surface hydrophobicity

177

6.1 Experimental analyses on the effect of HRT on

granular biomass in treating synthetic textile

dyeing wastewater

185

6.2 OUR profile of (a) Stage I (Aerobic phase 2.84

hours), (b) Stage II (Aerobic phase 5.84 hours) and

(c) Stage III (Aerobic phase 11.84 hours)

190

6.3 OUR profile of (a) Stage IV (Aerobic phase 11.84

hours), (b) Stage V (Aerobic phase 5.84 hours), (c)

Stage VI (Aerobic phase 17.84 hours)

191

6.4 Profile of biomass concentration at different stages

of the experiment. Stage I: anaerobic (2.8 h):

aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic

(5.8 h); Stage III and Stage IV: anaerobic (11.8 h):

aerobic (11.8 h); Stage V: anaerobic (17.8 h):

aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic

(17.8 h)

195

6.5 Distribution of size particles at different stages of

the experiment. Stage I: anaerobic (2.8 h): aerobic

(2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);

Stage III and Stage IV: anaerobic (11.8 h): aerobic

200

xxvi

(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8

h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)

6.6 Profile of sludge volume index throughout the

experiment. Stage I: anaerobic (2.8 h): aerobic (2.8

h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);




201

6.6 Profile of COD removal performance of the reactor

system at different stages of the experiment. Stage

I: anaerobic (2.8 h): aerobic (2.8 h); Stage II:

anaerobic (5.8 h): aerobic (5.8 h); Stage III and

Stage IV: anaerobic (11.8 h): aerobic (11.8 h);

Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage

V: anaerobic (5.8 h): aerobic (17.8 h)

191

6.7 Profile of COD removal performance of the reactor

system at different stages of the experiment. (○)

Influent COD; (■) Effluent COD, (▲) COD

removal. Stage I: anaerobic (2.8 h): aerobic (2.8

h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);




203

6.8 Profile of color removal performance of the reactor

system at different stages of the experiment. (♦)

Influent color, (■) Effluent color, (○) Color

removal. (100 ADMI ≈ 1 Pt-Co). Stage I:

anaerobic (2.8 h): aerobic (2.8 h); Stage II:

anaerobic (5.8 h): aerobic (5.8 h); Stage III and

205

xxvii

Stage IV: anaerobic (11.8 h): aerobic (11.8 h);

Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage

V: anaerobic (5.8 h): aerobic (17.8 h)

7.1 Experimental works for the investigation on the

effect of substrate concentration and redox

mediator on COD and color removal via the aid of

experimental design

217

7.2 Color removal at different concentrations of

riboflavin. Absorbance at 600 nm (♦), absorbance

at 542 nm (□)

222

7.3 The Pareto chart of COD removal for (a) anaerobic,

(b) aerobic and (c) total removal (A: substrate; B:

riboflavin; α: 0.1)

225

7.4 Main effect plot of substrate and riboflavin for (a)

anaerobic, (b) aerobic and (c) total COD removal

229

7.5 Interaction plot for the percentage of COD removal

for (a) anaerobic, (b) aerobic and (c) total removal

(Substrate: ____ 2633.88 m/L; ____ 866.12 mg/L; ●

Centre point)

231

7.6 The relationship between substrate, riboflavin and

percentage of total COD removal after 24 hours of

experimental run, (a) Contour plot and (b)

Responses surface plot

235

7.7 Pareto chart of Sumifix Navy Blue EXF removal at

(a) 5 and (b) 12 hours (α: 0.1; A: Substrate; B:

Riboflavin)

238

xxviii

7.8 Pareto chart of Synozol Red K-4B removal at (a) 5

and (b) 12 hours (α: 0.1; A: Substrate; B:

Riboflavin)

239

7.9 Main effect plot of substrate and riboflavin on the

color removal of Sumifix Navy Blue EXF at (a) 5

and (b) 12 hours of experiment under anaerobic

condition

243

7.10 Main effect plot of substrate and riboflavin on

color removal of Synozol Red K-4B at (a) 5 and (b)

12 hours of experiment under anaerobic condition

244

7.11 Interaction of variables substrate and riboflavin for

Sumifix Navy Blue EXF at (a) 5 and (b) 12 hours

of the experimental conditions (Substrate: ____

2366.88 m/L ; ____ 866.12 mg/L; ● Centre point)

245

7.12 Interaction of variables substrate and riboflavin for

Synozol Red K-4B at (a) 5 and (b) 12 hours of the

experimental conditions (Substrate: ____ 2366.88

m/L; ____ 866.12 mg/L; • Centre point)

245

7.13 Predicted versus actual data for Sumifix Navy Blue

EXF removal at (a) 5 hours and (b) 12 hours

250

7.14 Predicted versus actual data for Synozol Red K-4B

removal at (a) 5 hours and (b) 12 hours

251



of substrate, riboflavin and color removal of

Sumifix Navy Blue EXF removal at 5 hours

253

xxix

(Reduced Quadratic Model)


representing relationship between the

concentrations of substrate, riboflavin and color

removal of Sumifix Navy Blue EXF removal at 12

hours

254




removal of Synozol Red K-4B removal at 5 hours

255




removal of Synozol Red K-4B removal at 12 hours

256

xxx

LIST OF ABBREVIATIONS

16S rRNA - 16 subunit ribosomal ribonucleic acid

ADMI - American Dye Manufacturing Index

AnAHR - Anaerobic-aerobic hybrid reactor

AnFBR - Anaerobic fluidized bed reactor

ANOVA - Analysis of variance

AO7 - Acid orange 7

AR151 - Acid red 151

BLASTn - Basic local alignment search tool

CAg - Coaggregation

CCD - Central Composite Design

COD - Chemical oxygen demand (C-mmoL or mg/L or g/L)

CR - Congo red

CSTR - Continuous stirring tank reactor

DB79 - Direct Blue 79

DGGE - Denaturing gradient gel electrophoresis

DNA - Deoxyribonucleic acid

DNT - Dinitrotoluene

DO - Dissolved oxygen (mg/L)

EBPR - Enhanced biological phosphorus removal

EPA - Environmental Protection Act

EPS - Extracellular polymeric substances

FAD - Flavin adenine dinucleotide

FESEM - Field-Emission Scanning Electron Microscope

FAnGS - Facultative anaerobic granular sludge

FLAA - Flame Atomic Absorption Spectrophotometer

xxxi

FMN - Flavin mononucleotide

FSIH - Fluorescent in situ hybridization

GAO - Glycogen-accumulating organism

GDP - Gross domestic product

HRT - Hydraulic retention time (h or day)

IC - Integrity coefficient

IFAnGSBioRec - Intermittent facultative anaerobic granular sludge

biological reactor

IPC - Integrated pollution control

IPPC - Integrated Pollution and Prevention Control

LOFT - Lack of fit test

MD - Mixed dye

MG - Malachite green

MIDA - Malaysian Industrial Department Authority

MLSS - Mixed liquor suspended solid (mg/L or g/L)

MLVSS - Mixed liquor volatile suspended solid (mg/L or g/L)

N & P - Nitrogen & Phosphorus

N/COD - Nitrogen/Chemical oxygen demand

NA - Nutrient agar

NAD - Nicotinamide adenine dinucleotide

NCBI - National Center of Biotechnology Information

OLR - Organic loading rate (mg/L·day or kg/m3·day)

ORP - Oxidation reduction potential

OUR - Oxygen uptake rate (mg/L.h)

P/COD - Phosphorus/Chemical oxygen demand

PAO - Polyphosphate-accumulating organism

PCR - Polymerase chain reaction

Pt-Co - Platinum Cobalt

PN - Exoprotein

POVH - Poly(vinyl alcohol)

PS - Polysaccharide

RB - Reactive black

RDR - Rotating disc reactor

RG - Residual granules (mg)

xxxii

RSM - Response surface method

SBCR - Sequencing biofilm configured reactor

SBR - Sequencing batch reactor

SDS - Sodium dodecylsulfate

SG - Settled granules (mg)

SHb - Surface hydrophobicity

SMA - Specific methanogenic activity

SOUR - Specific oxygen uptake rate (mg DO/g VSS.h)

SRB - Sulfate reducing bacteria

SRT - Sludge retention time (day)

STDW - Synthetic textile dyeing wastewater

STDW - Synthetic textile dyeing wastewater (mL or L)

SVI - Sludge volume index (mL/g)

TAA - Total aromatic amines

TOC - Total organic carbon (C-mmoL or mg/L or g/L)

UAFB - Upflow anaerobic fixed bed

UASB - Up-flow anaerobic sludge blanket

VER - Volumetric exchange rate

xxxiii

LIST OF SYMBOLS

Ce - COD concentration in the effluent (C-mmoL or mg/L or g/L)

Ci - COD concentration in the influent (C-mmoL or mg/L or g/L)

kd - Endogenenous decay rate

M - Biomass concentration (mg VSS/L)

Ox - Theoretical chemical oxygen demand which is assume as 1.42

mg O2/ mg biomass

Qe - Effluent flowrate (L/d)

tc - Cycle time of SBR operation (d)

tc - Cycle time of the SBR operation (d)

Vd - Manually discharge mixture volume (L)

Ve - Effluent volume in SBR operating cycle (L)

Ve - Effluent volume of the SBR operating cycle (L)

Ve - Working volume of the SBR system (mL or L)

Vr - Working volume of the SBR system (L)

VT - Total working volume in reactor (L)

Xd - Biomass concentration of manually discharged mixture

(g VSS/L)

Xe - Effluent volatile solid concentration (g VSS/L)

Xe - Effluent volatile solid concentration (g VSS/ L)

Xr - Mixed liquor volatile suspended solid in reactor (mg/L)

Xvss - Volatile solid concentration in the reactor system (g VSS/L)

XVSS1 - Volatile solid concentration at the beginning of

operation in SBR reactor (g VSS/L)

XVSS2 - Volatile solid concentration at the end of cycle operation in

SBR reactor (g VSS/L)

xxxiv

Y - Theoretical biomass yield

Yobs - Observed biomass yield

- Solid retention time (d)

μ - Biomass growth rate

xxxv

LIST OF APPENDICES

APPENDIX TITLE

PAGE

A Data and Calculation

301

B Molecular Procedure of 16S Sequence Analysis

314

C Morphology of Bacteria

319

D Molecular Data Analysis

320

E Factorial Design and Response Surface Methodology

Data Analysis for Coaggregation and Surface

Hydrophobicity Assay

335

F Factorial Design and Response Surface Methodology

Data Analysis for COD Removal

341

G Factorial Design and Response Surface

Methodology Data Analysis for Color Removal

344

1

CHAPTER 1

INTRODUCTION

1.1 Preamble

Dyes have been one of the most demanding compounds in many industrial

sectors with textile industries as the leading and biggest consumers. According to

Dos Santos et al. (2003), nearly one million metric tonnes of dyes are annually

produced throughout the world with azo dyes representing about 70% of the total

production. Dyes are manufactured in such a way to provide long lasting attractive

color design to suit a variety of customer needs. Dyes are designed with high

stability towards light, heat, and sweat, and resistance to oxidizing agents (Ravi

Kumar et al., 1998 and Sun and Yang, 2003). These criteria make the dyes very

recalcitrant to degradation, and impose threats to the environment.

With huge consumptions and demand, treatment of textile industrial effluents

presents an arduous task. There have been a number of techniques used in treating

textile industrial effluent. At present, the main methods in textile wastewater

treatment involve physical and chemical processes. However, such methods are

often costly. Some treatments, even though capable of removing color, just merely

transfer the contaminants from one form to another. The generation of concentrated

2

sludge from coagulation process, for example, would generate another environmental

issue which is the disposal of sludge (Pearce et al., 2003). Excessive chemical usage

in the textile wastewater treatment process might create secondary pollution

problems to the environment. Due to the high cost on operation, maintenance and

disposal problems, the present treatment technologies are not favored to be applied at

large scale for textile industries (Ghoreishi and Haghighi, 2003). Furthermore,

according to the Integrated Pollution Control (IPC) regulations, any decoloration

systems involving destruction technologies that prevail as transferal of pollution

from one part of the environment to another is prohibited (Willmott et.al., 1998).

The biological treatment process has been a major unit in wastewater

treatment plants. However, a variety of chemicals are used in the textile industry and

due to stringent effluent requirements by the authority, conventional biological

treatment seems to be ineffective in treating wastewater. Furthermore, dyes are

known for their complex chemical structure and mostly are of synthetic origin. Due

to the recalcitrant nature of these compounds, the conventional treatment system fails

to remove sufficient color and other pollutants that are present in the textile

wastewaters (Stolz, 2001; Pandey et al., 2007; van der Zee and Cervantes, 2009).

Studies have shown that complete mineralization of dye compounds requires

both anaerobic and aerobic biological treatment approaches (Melgoza et al., 2004).

The former will cause the cleavage of the azo bond. The latter performs complete

mineralization of the dye compounds to form harmless and stable byproducts.

Nowadays, there is a trend to use microorganisms in the form of aggregates

as compared to suspended cells. These aggregates perform degradation process

either through cell-to-cell interaction or in combination with other particulates,

forming biofloc known as granules. The granular system is endowed with some

characteristics of good settling ability, high concentration of microorganisms with

strong and compact structure and high biomass retention that could withstand

significantly higher organic loading (Morgenroth et al., 1997 and Moy et al., 2002).

3

Having such characteristics, the granular system has great advantages over

conventional activated sludge.

Granules consist of millions of microorganisms that clump together with

anaerobic microorganisms occupying the inner layer of the granules and with aerobic

microbes at the outer layer. With the presence of both types of microorganisms

within the granules, the granular system can be used for complete degradation of

textile wastewater. However, studies on the applications of granular systems treating

textile wastewater are apparently lacking. Hence, more research needs to be

conducted in this area to provide a better understanding on the mechanisms and

capability of the treatment system.

The aim of this study is to develop an effective bioprocess that is able to treat

textile wastewater. The study is focused on the use of facultative anaerobic granular

sludge as the treatment process. The development of facultative anaerobic granular

sludge and identification of factors affecting their effectiveness in degradation

process is the emphasis in this study.

1.2 Objectives of the Study

The specific objectives of this study are:

i. To develop facultative anaerobic granular sludge (FAnGS) under

intermittent anaerobic and aerobic reaction mode in a sequential batch

reactor system with the use of synthetic textile dyeing wastewater.

4

ii. To characterize the physical, chemical and biological properties of the

developed FAnGS and to identify the most suitable mixed bacteria

consortia isolated from the FAnGS that are capable of being an

aggregator and dye degrader.

iii. To characterize the aggregation and surface hydrophobicity properties

of the selected mixed bacteria consortia as a function of substrate

concentration, pH and temperature.

iv. To study the effect of hydraulic retention time with variation of

intermittent reaction mode on the performance of FAnGS in terms of

chemical oxygen demand (COD) and color removal.

v. To investigate the effect of substrate concentration and riboflavin as

the redox mediator on the performance of FAnGS in terms of COD

and color removal.

1.3 Scope of the Study

This study covers the design and application of a laboratory-scale reactor

system identified as Intermittent Facultative Anaerobic Granular Sludge Biological

Reactor (IFAnGSBioRec). The design and operation of the reactor system are based

on the sequential batch reactor system. The FAnGS is developed using synthetic

textile dyeing wastewater containing a mixture of three dyes namely Sumifix Black

EXA, Sumifix Navy Blue EXF and Synozol Red K-4B.

5

The matured FAnGS is characterized for its physical, chemical and biological

properties. The microorganisms present in the FAnGS are identified through the

application of a molecular technique and are used for further detailed investigation

with respect to the aggregation and surface hydrophobicity, the important aspects of

the initial mechanism that takes place in the development of FAnGS. The

performance of the FAnGS in treating textile dyeing wastewater are investigated

based on COD and color removal. The effects of different substrate and redox

mediator concentration as well as variation of hydraulic retention times on the COD

and color removal are also explored. The performance of the FAnGS in COD and

color removal has also been studied with the variation on substrate and redox

mediator concentration in synthetic textile dyeing wastewater (STDW). In addition

to the IFAnGSBioRec, some of the experiments are conducted in serum bottles.

Some of these experiments also involve the use of statistical experimental design.

1.4 Significance of the Study

Biogranular systems either anaerobic or aerobic granules have been studied

for the degradation of different types of wastewater (Beun et al. 1999; Moy et al.,

2002; Arrojo et al., 2004; Lemaire et al., 2007 and Chen et al. 2008a). The

applications of granular system for the treatment of textile wastewater have been

reported by many researchers (Razo-Flores et al., 1997; Tan et al., 2000; van der

Zee, 2001a; and Dos Santos et al., 2003). However, most of the research on the

degradation of dye stuff in textile wastewater is focused on the applications of

anaerobic granular system. Apparently, the use of FAnGS for the dye degradation

process appears to be missing. The importance of this study is therefore listed as

follows;

i. The study provides the design and procedural input of a compact

laboratory-scale reactor system known as IFAnGSBioRec, fabricated

6

specifically for the formation of facultative anaerobic granular sludge

for treating textile wastewater.

ii. The study provides the procedures for the formation of FAnGS and its

physico-chemical and biological characteristics.

iii. The study provides details in relation to the effect of substrate, pH and

temperature on aggregation and surface hydrophobicity of the mixed

bacteria culture selected from FAnGS. The findings would provide

knowledge on suitable conditions for the development of the FAnGS,

customized for degradation of textile wastewater.

iv. The study provides information regarding the most suitable

combination time for anaerobic and aerobic reaction phases in the

IFAnGSBioRec cycle tailored for degradation of textile dyeing

wastewater.

v. The study also provides the biokinetic parameters including biomass

growth rate (μ), endogenenous decay rate (kd), observed biomass yield

(Yobs) and theoretical biomass yield (Y) in relation to changes of HRT

during textile dyeing degradation by the FAnGS.

vi. The study also provides the effect of using different concentrations of

substrate and redox mediator in relation to dye degradation by the

FAnGS.

7

1.5 Organization of Thesis

The thesis is presented in eight chapters. Chapter 1 provides the overview of

problems generated from the textile wastewater contamination and setbacks in

effluent treatment. The chapter also points out the importance of biological

treatment in degrading the dyes from the textile wastewater. The literature review is

divided into two parts i.e. Chapter 2 and 3. Chapter 2 mainly discusses on the outline

of the granulation process including the theoretical features of granules development,

factors affecting the granulation process and also the applications of granular

systems. Chapter 3 highlights the mechanisms involved in dye degradation process

and the biological treatment system used for textile wastewater treatment.

Chapters 4, 5, 6 and 7 present the works that have been conducted in this

study. Chapter 4 presents the study on the development and characterization of the

FAnGS and also the performance of the FAnGS reactor system on COD and color

removal. Chapter 5 focuses on the initial stage of the granulation process by looking

into the aggregation and surface hydrophobicity of the selected mixed bacteria

isolated from the FAnGS. Chapter 6 presents the effect of hydraulic retention time

on the performance of COD and color removal. Chapter 7 focuses on the applications

of redox mediator to enhance color removal by the FAnGS. Lastly, Chapter 8

presents the conclusions of this study. Figure 1.1 gives the flowchart illustrating the

overall outline of the experimental work for this study. This chapter also provides

recommendation for future research exploration in relation to the findings of this

study.

8

Study on Facultative Anaerobic Granular Sludge for Textile Wastewater

Treatment

Development of facultative anaerobic granular sludge for textile wastewater

treatment

(Refer Chapter 4)

Characterization of Facultative


Study on the effect of substrate concentration, pH and temperature on

coaggregation and surface hydrophobicity

(Refer Chapter 5)

Study on the effect of hydraulic retention time on facultative anaerobic granular

sludge

(Refer Chapter 6)

Study on the effect of substrate and

riboflavin on facultative anaerobic

granular sludge

(Refer Chapter 7)

Coaggregation (Refer 5.5.4.2-

5.5.4.4 and 5.5.5)

Factorial Design

(Refer 5.4.8)

Central Composite

Design (Refer 5.4.9)

Central Composite

Design (Refer 7.4.3)

Factorial Design

(Refer 7.4.3)

Data Analysis

Surface Hydrophobicity (Refer 5.5.4.6-

5.5.4.8 and 5.5.5)

Removal Performance

Biokinetic parameter

(Refer 6.5.6)

Biological Characteristic

Physical Characteristic

Chemical Characteristic

Removal Performance

Morphological & Structural (Refer 4.5.1-

4.5.2)

COD Removal (Refer 5.5.9 &

6.5.4)

Granular Biomass &

SRT (Refer 4.5.7 &

6.5.2-6.5.3)

Microbial Activities

(Refer 4.5.3 and 6.5.1)

Characterization of Microbes (Refer 5.5.1-

5.5.3)

Settling Velocity & SVI

(Refer 4.5.5)

Mineral & Metal Content (Refer 4.5.8)

Granular Strength

(Refer 4.5.6)

Ammonia Removal

(Refer 5.5.9)

Color Removal (Refer 5.5.9 and 6.5.5)

COD Removal

(Refer 7.5.2-7.5.3)

Color Removal

(Refer 7.5.4-7.5.5)

Conclusions

Figure 1.1 Outline of the study

9

CHAPTER 2

BIOGRANULATION TECHNOLOGY IN WASTEWATER TREATMENT

2.1 Introduction

The technology of cell immobilization has been used for decades in the

environmental engineering and bioengineering fields (Liu and Tay, 2002). Microbial

immobilization can be classified into three different categories which are biofilm,

entrapped microorganisms and microbial aggregates. Biofilms are formed when

bacteria adhere to surfaces in aqueous environments and begin to excrete a slimy,

glue-like substance that can anchor them to all kinds of material such as plastics,

polymers, ceramics, rocks, basalts, activated carbon or any other solid surface

(Costerton et al., 1995 and Kwok et al., 1998).

Entrapped or encapsulation of the microorganisms is another form of

microbial agglomeration where microbes can be trapped in hydrophobic gels or other

types of gels such as polyacrylamide, chitosan, alginate, agar cellulose acetate and

polyvinyl-alcohol (Kim et al., 2000). These gel substances confine the migration and

maintain high concentration of microorganisms in the reactor system. The

performance of the reactor system would be expected to reach a higher removal

efficiency due to the presence of high cell concentration.

10

Granular sludge, also regarded as the special case of biofilm formation, has

been successfully developed either through anaerobic conditions (Lettinga et al.,

1980; Schmidt and Ahring, 1996; Zhang et al., 2007a) or aerobic conditions

(Morgenroth et al., 1997, Bao et al., 2009; Shi et al., 2009). Microbial granulation is

a self-immobilization community that can be formed with or without support

material. It is estimated that about 900 anaerobic granular sludge systems have been

successfully operated all over the world (Alves et al., 2000).

2.2 Biogranulation

Granules, defined as discrete macroscopic aggregates consist of dense

microbial consortia packed with different bacterial species amounting to millions of

organisms per gram of biomass (Weber et al., 2007). They are formed through self-

immobilization microorganisms which involves cell to cell interactions inclusive of

biological, physical and chemical processes. According to Calleja (1984), microbial

granulation is gathering of cells to form a fairly stable, contiguous, multicellular

association under physiological condition.

The granulation system is first recognized in an up-flow anaerobic sludge

blanket (UASB) system as anaerobic granular sludge. Since then, extensive

investigations have been carried out by many researchers for the past two decades via

the innovative upflow sludge bed (USB) type reactor (Bachman et al., 1985 and

Lettinga et al., 1997). The application of anaerobic granulation system is relatively

well known through the successful demonstration particularly in removing

biodegradable organic matter from municipal and industrial wastewater (Lettinga et

al., 1980; Fang and Chui, 1993; Schmidt and Ahring, 1996).

11

The success of granulation systems is related to their capacity of good settling

property of the biomass without the need of a biomass carrier. This would allow

high solids retention time and process stability with simple and low-cost equipment

(Ahn and Richard, 2003). Large size and high density of microorganisms have led to

rapid settling capacity which simplifies the separation of treated effluent from the

biomass (Liu and Tay, 2004). Such characteristics enable the granular sludge to

handle high liquid flows with long biomass residence times and minimal suspended

solids released in the effluent (Wirtz and Dague, 1996).

Since the microorganisms within the granules are closely clumped together,

this generates syntrophic associations which occur due to optimum distances

between microbial associates at appropriate substrate levels. Such condition enables

high and stable performance of metabolism activities (Batstone et al., 2004).

Granules also consist of extracellular polysaccharides substances (EPS)

produced by the microorganisms within the granules that help to strengthen the

granular structure. The presence of EPS covers the granular structure and acts as

protection shield to the microbes against shock loading and toxic compound that may

be present in the wastewater (Tay et al., 2005a). With such characteristics,

granulation system can also be regarded as an efficient device in the removal process

of xenobiotic from wastewater (Bathe et al., 2004 and Wuertz et al., 2004).

Despite the successful performance of anaerobic granular sludge systems,

attention is later diverted to the development and application of aerobic granulation.

This is due to several drawbacks that have been observed in the application of the

anaerobic, including long start-up period, operations at relatively high temperatures

and are not suitable for nutrient removal and low strength organic wastewater (Liu

and Tay, 2004).

12

2.3 Development of Aerobic Granules

Development of aerobic granules has been first reported in a continuous

aerobic up-flow sludge blanket reactor by Mishima and Nakamura (1991). The

granules are claimed to exhibit very good settling property. Aerobic granules are

compact, regular and of smooth rounded shape with an apparent outer surface which

can easily be differentiated from the loose, fluffy and irregular flocs of conventional

suspended sludge. Some researchers have also claimed that aerobic granules are an

extension and a special case of biofilm formation (El-Mamouni et al., 1995).

Aerobic granulation system has been used for organics, nitrogen, phosphorus

and toxic substances removal, especially of the high strength wastewater (Moy et al.,

2002; Arrojo et al., 2004; Qin and Liu, 2006; Yi et al., 2008; Kishida et al., 2009).

Bacteria normally do not aggregate naturally to each other due to repulsive

electrostatic forces via the presence of negatively-charged protein compounds of the

cell wall (Voet and Voet, 2004). However, under selective environmental condition,

microorganisms are capable of attaching to one another and thus form aggregates.

These aggregates, consisting millions of microorganisms with different functioning

roles are responsible for degrading a mixture of organic compounds within the

wastewater as well as removing the nutrients.

Most research and reports on aerobic granulation are developed in sequencing

batch reactor (SBR) systems (Morgenroth et al., 1997; Beun et al., 1999; Hailei et

al., 2006; Chen et al., 2008a; Chen et al., 2008b; Kim et al., 2008). The reaction

phase can be in the condition of anaerobic, aerobic or anoxic with or without mixing

depending on the purpose of the treatment process. Figure 2.1 shows the steps

involved in one complete cycle of the SBR system.

13

Figure 2.1 Design principles of sequencing batch reactor (Jern, 1989)

Aerobic granulation involves multiple-step processes engaged with both

physicochemical and biological forces that make significant contributions in the

granules development (Calleja, 1984; Linlin et al., 2005; Weber et al., 2007).

Aerobic granules can be developed purely from activated sludge, as described by

Beun et al. (1999) and they can also be developed using anaerobic granules as the

seed sludge (Linlin et al., 2005). The mechanisms for development of aerobic

granules from activated sludge are slightly different from the development of

granules seeded with anaerobic granules. This is discussed in the following sections.

Aeration/mixing

Fill

React

Settle Draw

Idle

Decanting

14

2.3.1 Aerobic Granules from Aerobic Activated Sludge

Aerobic granulation is not a standalone process but resulted from the

integration of many aspects such as physical, chemical and biological processes and

interaction with the surrounding environment. Liu and Tay (2002) explain the

involvement of different types of physicochemical and biological forces that are

responsible for the development of aerobic granules. The first stage of the

granulation process is the cell-to-cell or cell-to-solid surface interaction initiated by

diffusion of mass transfer, hydrodynamic forces of the surrounding areas,

thermodynamic effects, gravitational force, as well as the competency of cells to

move towards one another (Pratt and Kolter, 1998). In the second step several

physical (for instance, the Van der Waals forces, surface tension, hydrophobicity,

opposite charge attractions, thermodynamic of surface free energy, bridges by

filamentous bacteria), chemical and biochemical (cell surface dehydration, cell

membrane fussions and signals among microbial communities) attractive forces are

involved in stabilizing the multicell links that are formed in the earlier step (Bossier

and Verstraete, 1996 and Tchobanoglous et al., 2004). The third step is the maturing

stage which involves the production of substances that facilitate the interaction of

cell-to-cell and results in the development of highly organized microbial structure.

During this stage there are changes in the mechanisms of metabolite production such

as higher production of extracellular polymer, growth of cellular cluster, metabolite

change and environmental-induced genetic effects. The final step in aerobic

granulation involves shaping of the three dimensional aerobic granules by

hydrodynamic shear forces (Chisti, 1999a).

Beun et al. (1999) has also described the path of aerobic granules formation.

Immediately after inoculation, the reactor system is found to be dominated by

bacteria and fungi. Mycelial pellets that are formed by the dominating fungi manage

to retain in the reactor due to the good settling ability. Bacteria which do not hold

this characteristic are discarded with the effluent. With the shear force imposed by

aeration, the filaments are detached from the surface of the pellets. The pellets grow

bigger and bigger until they manage to reach up to 5-6 mm in diameter. With time,

15

when the pellets have grown too big, they will be defragmented. The matured pellet

start to rupture into smaller pieces when there is limitation of oxygen to penetrate

into the inner parts of the pellets. The fragment of mycelial pellet acts as the

immobilized matrix for the bacteria to grow and form new colonies. At this stage,

the bacteria can be considered big enough to settle at faster speed and able to escape

washout. The bacterial colonies grew larger and developed granules. When the

granules are formed, the whole system is governed by bacterial growth. Figure 2.2

illustrates the steps in the development of aerobic granules, as explained by Beun et

al. (1999).

Weber et al. (2007) have illustrated three consecutive phases of granular

mechanism development with the involvement of several eukaryotic organisms.

Microscopic analysis has revealed that eukaryotic organisms play a key role in

aerobic granule formation seeded with sludge from municipal wastewater treatment

plants. Most frequently seen, stalked ciliates of the subclass Peritrichia and

occasionally, the fungi, are found to be involved in the granulation process

development. Figure 2.3 shows the development of aerobic granules with the ciliates

as the main foundation (Weber et al., 2007).

2.3.2 Aerobic Granules Seeded with Anaerobic Granular Sludge

Development of aerobic granules using anaerobic granular sludge as seeding

material has been demonstrated by Linlin et al. (2005). Through microscopic

observation, the mechanisms involving morphological and physical changes of the

anaerobic granular sludge into the formation of aerobic granules in the SBR system

is demonstrated in a flow chart shown in Figure 2.4. During the initial stage, the

anaerobic granular seeds disintegrate into irregular smaller flocs and debris when

exposed to hydrodynamic shear force during aerobic conditions. Some of these flocs

and debris are washed out. The remaining flocs and debris act as a precursor that

16

initiates the growth of new aerobic granules. The hydrodynamic shear force also

help in shaping the formation of the structural community of the microbial

aggregates during the maturing stage (Di Iaconi et al., 2006). The optimal

combination of the shear force and the growth of the microorganisms within the

aggregates govern the stable structural formation of the granules (Chen et al., 2008a).

The morphology of these aerobic granules is slightly different as compared to the

aerobic granules developed without the presence of anaerobic granular seed sludge.

Small patches of defragmented anaerobic granular seeds are clearly observed within

the developed aerobic granules.

● ●

● ● ●

Figure 2.2 Schematic diagram of aerobic granulation developed without any carrier

material (Beun et al., 1999)

Inoculation Pellet

formation Shear force

Lysis Granules of bacteria coloni

Oxygen limitation

Colonisation of bacteria

17

Figure 2.3 Granulation development supported by ciliates. A: Formation of floc

where the ciliates settle on other organisms or particles (Phase 1). B: Arrow shows

the colonization of bacteria on ciliate stalks (Phase 2). C: Granules grow into bigger

sizes with dense core. Zooids of the ciliate stalks completely overgrown by bacteria,

die and act as the “backbone” structure (Phase 3). D: Unstalked free swimming

ciliates detach from the biofilm to escape death. Smooth and compact granules area

formed. E: The surviving swarming ciliate cells get attached to the matured surface

granules (shown by arrow) (Weber et al., 2007)

18

Figure***: The

Figure 2.4 The flowchart of the morphological and physical changes of the

anaerobic granules in the process of aerobic granule formation in SBR systems

(Linlin et al., 2005)

2.4 Microbial Structure and Diversity of Microorganisms

The structural and diversity of microorganisms have been one of the main

focuses in a granulation study. A variety of micro-scale techniques and instrument

together with molecular biotechnological approaches have been applied by

researchers in order to obtain a better understanding on the interaction and

mechanisms involved in the process and development of granulation systems.

2.4.1 Microbial Structure

The microscopic structure of aerobic granules have been examined using a

wide range of micro-scale techniques including scanning and transmission electron

Anaerobic granular

seeding; regular shape, black color; D=1.1mm

“Steady stage” granules

D=1.2 mm

Small aerobic granules appear with filamentous

dominancy

Settling time

decreased to 5 min; most SS were washed

out

Yellow colored granules; dominancy by

aerobic microbes, SS increased and recombined

Anaerobic granular seed shrunk and disintegrated due to aerobic condition Day 7 Day 21

Day 35

Day 42Day 50

19

microscopy, confocal laser scanning microscopy combined with fluorescent in situ

hybridization (FISH) and specific fluorochromes. Different arrangement either by

reactor configuration or substrate utilized as the sole carbon source in media

preparation or even different microorganisms specifically used in the granulation

process reveal different microbial structures of the aerobic granules (Toh et al., 2003;

Weber et al., 2007; Lemaire et al., 2008a).

The structure of an aerobic granule consists of different layers occupied by

different types of microorganisms or substances depending on its individual function

in the granulation development (Tsuneda et al., 2004; de Kreuk et al., 2005; Abreu et

al., 2007). Usually the outer layer of the aerobic granule will be conquered by

aerobic or obligate aerobic microbes. For example, ammonium-oxidizing bacterium

Nitrosomonas spp. has been found mainly at a depth of 70 to 100 µm from the

granule surface (Tay et al., 2002a). At the deeper area of the aerobic granules where

oxygen could not penetrate, anaerobic bacteria Bacteroides spp. is found 800 to 900

µm below the granules surface (Tay et al., 2002b).

Most of the structures of the aerobic granules contain channels and cavities

covering thickness areas of 900 µm from the surface of the granules. The pore

structures assist and create pathways for the exchange of nutrient, metabolites and

oxygen moving into and out of the inner parts of the granules to the surrounding

areas. However, at the depth of 300 to 500 µm from the granules surface, the pores

are denser (Tay et al., 2003). The pores at the depth of 400 µm below the granule

surface are filled with polysaccarides. Due to the dense structure, the movement of

oxygen and nutrient are obstructed and has resulted in death to the microorganisms at

the core of the aerobic granules. The layer of the dead microbes is located at 800 to

1000 µm (Toh et al., 2003).

The optimal diameter for aerobic granules is less than 1600 µm in order to

obtain full utilization of the aerobic microbes. This is twice the distance from the

granule surface area before reaching the anaerobic region within the aerobic granules

20

(Tay et al., 2002c). Mushroom-like structures are observed to be present in the

development of aerobic granules mediated with high substrate N/COD ratios (Liu et

al. 2004a). The observation of thick layers of differential mushroom-like structure

has been reported earlier by Costerton et al. (1995) in biofilms of mixed bacterial

communities. Complex microbial community distributes themselves to increase

accessibility towards nutrients, which increases survivality and stability of its micro-

structure (Watnick and Kolter, 2000). A matured granule comprises of two separate

layers which are a dense core zone and a fringe zone. The core zone is consisted of a

mixture of dense rods and cocci bacterial cells and EPS. While, the fringe zone is

represented with a loose structure that comprises of bacteria and stalked ciliates of

fungal filaments (Weber et al., 2007).

2.4.2 Microbial Diversity

Molecular biotechnology has been used in the investigation of microbial

diversity developed within granular biomass (Jang et al., 2003; Lemaire et al.,

2008a; Zhu et al., 2008). Nitrifying, denitrifying, glycogen-accumulating bacteria

and phosphorus-accumulating bacteria are among the identified bacteria that can be

present in the aerobic granules operating under different experimental conditions

(Tsuneda et al., 2003a; Meyer et al., 2006; Lemaire et al., 2008a).

Different types of microbes are observed to dominate within granules which

are closely related to the composition of the culture media. Jiang et al. (2004a) has

used denaturing gradient gel electrophoresis (DGGE) analysis techniques to study

the microbial diversity of the aerobic granules. There was a major shift in the

microbial community as the seed sludge developed into granules. Culture isolation

and DGGE assays confirmed the dominance of beta-Proteobacteria and high-G+C

gram-positive bacteria in the phenol-degrading aerobic granules. By using

Fluorescence in situ hybridization identified by Lemaire et al. (2008a),

21

Accumulibacter spp. (a polyphosphate-accumulating organism, PAO) localized at the

outermost 200 μm region of the granule while Competibacter spp. (a glycogen-

accumulating organism, GAO) dominated in the granule central zone, the area that

could not be penetrated by the oxygen molecules.

2.5 Characteristics of Aerobic Granules

Aerobic granules are known for their characteristics that represent their

outstanding features required for excellent stability and high efficiency performance

of a reactor system making it an innovative modern technology for the wastewater

treatment industry.

2.5.1 Size and Morphology

The size of a granule is an important parameter that can influence the

performance and stability of the reactor system. Granules with bigger diameter can

easily be defragmented under high shear force resulting in high biomass washout.

Meanwhile, granules that are too small cannot develop good settling property which

may end up with higher suspended substances in the effluent. Granules with bigger

sizes will be developed in the SBR system supplied with low superficial air velocity

while smaller granular sizes will be observed formed in systems aerated at higher

superficial air velocity (Chen et al., 2007). Different granular sizes ranging from as

small as 0.3 mm to as big as 8.8 mm in diameter possessing different granular

characteristics were reported by various researchers (Dangcong et al., 1999; Tay et

al., 2003; Zheng et al., 2005).

22

According to Chisti (1999a), the size of the suspended biosolids is controlled

by the hydrodynamic shear force of the reactor system. The size of the aerobic

granule varies depending on the balance between the growth and shear force imposed

by superficial air velocity that give the hydrodynamic shear force on the newly

developed granules. The observed growth of microbial aggregates is the net result of

the interaction between growth and shear forces (Yang et al., 2004a). The usual

reported average diameter of an aerobic granule is in the range of 0.2 mm to 5 mm

(Liu et al., 2003a). Bigger size of aerobic granules has been reported with size of 7-

10 mm (Morgenroth et al., 1997 and Wang et al., 2004). Based on the biological

viability and physical properties, Toh et al. (2003), suggested that for the optimal

performance and economic purposes, the operational size range for effective aerobic

SBR granular sludge should be in the diameter of 1.0-3.0 mm.

The usual structure of the aerobic granule is normally spherical in shape with

smooth surface areas (Peng et al., 1999; Zhu and Wilderer, 2003; Adav and Lee,

2008a). The morphology of the granules can be influenced by the type and

concentration of substrate used in the media compositions. Based on the electron

microscope (SEM) observations, glucose-fed granules appeared with fluffy outer

surface due to the predominance of filamentous bacteria growth. On the other hand,

the acetate-fed granules showed compact microstructure and smooth outer surface.

The non-filamentous and rodlike bacteria were observed dominating the acetate-fed

granules that are tightly linked together (Tay et al., 2001a).

Since difference type of microorganisms may predominate at different

substrate concentration levels, using different concentrations of substrate in granules

development may influence the structural and morphology of the developed granules.

The growth rate of filamentous organisms is shown to be higher at lower substrate

concentrations as compared to floc forming organisms (Schwarzenbeck et al., 2005).

Fluffy and loose morphology, mainly occupied by filamentous bacteria are observed

in granules cultivated at low organic loading rate (OLR). However, the granular

structures evolved into smooth irregular shapes with fold, crevices and depressions at

higher loading rate. The irregular structures are thought to allow better diffusion and

23

penetration of nutrients into the internal part of the granules and the same goes for

the metabolites excreted out from the granules to the surrounding areas (Moy et al.,

2002).

2.5.2 Settleability

Settleability of a granular sludge shows the aptitude of the granule to settle

down within a specified period of time. Good settleability properties are indicated

by fast and clear separation between the sludge biomass and the effluent. It can be

represented by the settling velocity and sludge volume index (SVI) of a particular

granule. The settling velocity of aerobic granules is in the range of 30 to 70 m/h

depending on the size and structure of the granules. The settling velocity of the

aerobic granules is comparable to the anaerobic granules. Settling velocity of

activated sludge flocs is in the range of 8 to 10 m/h which is three times lower than

to those of aerobic granules. Good settleability profile of aerobic granules is

desirable in wastewater treatment plants as good settling properties facilitate a high

percentage of sludge retention in the reactor system. Superior characteristic of

settleability assist to maintain the stability performance of the reactor system, show

high removal efficiency and can be used for wastewater with high hydraulic loading

(Beun et al., 2000 and Tay et al., 2001b).

Sludge volume index represents the volume of 1 g of sludge that can settle

within 30 min (Tchobanoglous et al., 2004). The SVI of conventional bioflocs is

very much higher as compared to the SVI of the aerobic granules indicating very

poor settling property. The bioflocs with average diameter around 70 µm have the

SVI value of 280 ml/g which is mainly dominated by filamentous bacteria (Tay et

al., 2001b). The SVI value of flocs in an activated sludge system is observed to be

above 150 mL/g (Crites and Tchnobanoglous, 1998). Granular sludge, on the other

hand, has SVI of lower than 100 mL/g (Peng et al., 1999, Liu et al., 2003b and Qin et

24

al., 2004a). Most of the sludge biomass will be retained in the clarifier and can avoid

washout.

2.5.3 Density and Strength

In environmental engineering, the density of microbial aggregates is

frequently used to describe the strength and compactness of the microbial interaction.

The observed density of microbial aggregates is the consequence of balance

interaction between cells (Liu and Tay, 2004). The density of the aerobic granule is

reported to be in the range of 32.2 to 110 g VSS/L (Beun et al., 2002; Arrojo et al.,

2006; Di laconi et al., 2006). The biomass density of detached bioflim particles from

the biofilm airlift suspension reactor is 15 g/L particles, lower compared to 48 g/L of

the biomass density of aerobic granules developed in the sequential batch airlift

reactor. Both of the reactor systems are operated at the same organic loading rate

and same superficial air velocity (Beun et al., 1999).

The specific gravity of aerobic granules is in the range of 1.004 to 1.065

(Etterer and Wilderer, 2001, Liu et al., 2004a; Yang et al., 2004a). It is observed that

when the aerobic granules grow bigger the compactness of the granules decreases

revealing less solid and loose architectural assembly. In other words, granules with

smaller sizes are more compact as compared to larger aerobic granules (Toh et al.,

2003). Liu et al. (2004a) reported that, as the specific growth rate reduce from

0.1/day to 0.04/day, smaller aerobic granular size but with higher specific gravity

(1.065 to 1.015) are formed. This indicates a compact and stronger formation of

microbial structure.

Granules which can withstand high abrasion and shear force are considered as

granules that possess high physical strength. The physical strength of the granules is

25

expressed as the integrity coefficient, an indirect quantitative measurement on the

ability of the granules to endure the hydrodynamic shear force often imposed on to

the granules during the reactor operations (Ghangrekar et al., 2005). This is

measured by placing the granules in a conical flask subjected to 200 rpm agitation

speed for 5 minutes. The parts that are loosely attached within the granules will be

detached and known as the residual of the granules. The ratio of residual granules to

the total weight of the granular sludge represents the integrity coefficient of the

granules. A good granular strength is indicated with the integrity coefficient of

lower than 20.

2.5.4 Cell Surface Hydrophobicity

It has been reported that the formation of biofilm and anaerobic granules are

very much affected by the changes in the physico-chemical properties of cell surface

(Zita and Hermansson, 1997 and Kos et al., 2003). When the bacterium approaches

another bacterium, there will be energy involved as the crucial force (hydrophobic

interaction) in the formation of the adhesive connection (Liu et al., 2004b).

Cell adhesion process is governed by three important forces which are

electrostatic force, Van der Waals force and polymeric interaction (Azeredo and

Oliveira, 2000). Since all bacteria cells have negative surface potential, electrostatic

force caused repulsion between cells. Meanwhile, the Van der Waals force and

polymeric interaction are the attractive forces. However, the Van der Waals force is

considered as an independent environmental factor so the adhesion of cell is

governed more by the electrostatic force and polymeric interaction. The polymeric

interaction is enhanced by the presence of the EPS. Any changes in the EPS

production and composition will be reflected by alteration of the physicochemical

characteristic of the cellular surface including surface charges, hydrophobicity and

other properties (Wang et al., 2006a).

26

Aerobic granulation can be regarded as a microorganism-to-microorganism

self-immobilization process, in which cell hydrophobicity could be used as a decisive

parameter in determining the microbial interaction and the structural compactness of

aerobic granules (Liu et al., 2004b). Cell hydrophobicity is an important affinity

force in cell self-immobilization which governed the mechanisms of cell adhesion

(Daffonchio et al., 1995 and Kos et al., 2003). The cell hydrophobicity is believed to

be the main triggering force in the initial stage of the biogranulation process and

strengthen the cell-to-cell interaction (Liu et al., 2003b). Lin et al. (2003) reported

that the formation of heterotrophic and nitrifying granules show nearly two fold

higher of cell surface hydrophobicity as compared to the bioflocs.

2.5.5 Specific Oxygen Utilization Rate

Measurements on the activity of certain enzymes or specific products of the

bacterial metabolism are among methods available to evaluate the activity of the

activated sludge (Lazarova and Manem., 1995). Specific oxygen utilization rate

(SOUR) is a useful parameter that can be used as an indicator of microbial activity of

the microorganisms. The effect of any alteration on the physical and chemical

conditions of the reactor system on microbial activity can be represented by

measuring the SOUR. The value can be regarded as an important parameter that can

be used to assist the permissibility of substance loading rate most importantly onto

treatment of toxic chemicals such as phenol or any petrochemical substances. The

SOUR values measured for aerobic granules have been reported by many

researchers.

The values vary depending on various aspects such as biomass density of the

microbes involved, types and concentration of substrates used as well as the

conditions of experiment (Zhu and Wilderer, 2003; Ergurder and Demirer, 2005a;

Liu and Tay, 2007a; Chen et al., 2008b). Granules that contain high concentrations

27

of Ca2+ seem to give lower SOUR values as compared to the non-Ca2+-accumulated

granule. It has been suggested that the presence of too much of Ca2+ might give a

negative effect on the bioactivity of the granules (Ren et al., 2008). Increase in the

hydrodynamic shear force in terms of superficial air velocity will significantly

increase the SOUR level by increasing the respiration activities of the

microorganisms (Tay et al., 2001a). This may be due to the fact that the high

hydrodynamic shear force causes increment on rate of the oxygen transfer between

the granules and the liquid interface (Chisti, 1999b). Linear correlation is observed

on the biochemical reaction between the oxygen consumption that represents the

bioactivity of microbial metabolisms with the production of carbon dioxide. At high

metabolism rates where high oxygen utilization occurred, less cell mass is produced

and more of the substrate is converted into carbon dioxide (Tay et al., 2004).

The SOUR values are inversely related to the settling time imposed by

hydraulic selection pressure onto the microorganisms in the reactor system (Qin et

al., 2004b). Changes in the hydraulic selection pressure are able to regulate the

respiratory activity of the microorganisms. The SOUR of microorganisms is also

affected by the long storage of aerobic granules under anaerobic conditions. The

SOUR value of granular sludge decreased after a long storage (Zhu and Wilderer,

2003).

2.5.6 Storage Stability

The condition during the storage of granules is another important aspect that

needs to be considered. Without proper storage, granules may lose its stability and

microbial activities within the granules may deteriorate. These may affect the

characteristics and performances of the granules. The obvious changes that could be

observed after a long storage in tap water are the changes in color of the aerobic

granules which turn from brownish-yellowish (fresh aerobic granules) to gray and

28

black. However, aerobic granules stored in phosphate buffer saline solution

experienced less color changes. It is expected that the color changes are due to the

anaerobic metabolisms generated from stored aerobic granules (Ng, 2002 and Tay et

al., 2002c).

Granules may lose its microbial activity and stability when stored for an

extended period and are also closely related to the storage temperature. The granules

experience an endogenous respiration and disintegration of the granules during

storage at high temperatures and without any supplement of external carbon sources.

Long storage periods at cool temperatures were reported to cause decrease in the

granular strength as compared to fresh aerobic granules. The strength of glucose-fed

and acetate-fed granules both reduced by 7-8% after four months stored at 4oC (Tay

et al., 2002c and Liu and Tay, 2004).

2.5.7 Exopolysaccharides

The extracellular polysaccharides substances (EPS) are metabolic products

secreted by microorganisms in the form of sticky material (Liu et al, 2004c). EPS

consists of a variety of organic substances such as polysaccharide (PS), exoprotein

(PN), deoxyribonucleic acid (DNA), humic acid, uronic acid and other materials

(Matthew and John, 1997 and Wang et al., 2005a). They act as a buffering stratum

for cells against a harsh exterior environment. Under nourished conditions, EPS

would serve as carbon and energy source (Liu et al., 2002 and Zhang and Bishop,

2003). EPS are thought to act as the glue that holds the bioflocs together to form

bigger aggregates (Matthew and John, 1997). They are responsible to mediate both

the cells cohesion and adhesion, and play a crucial role in maintaining structural

integrity of the biofilm matrix. The networking between cell and EPS would form

aggregates that led to the formation of biofilm (Nielsen et al., 1997 and Zhang et al.,

2007b). The aggregates combine through several binding interaction such as specific

29

protein–polysaccharide interactions, hydrophobic interactions, hydrogen bonding,

and ionic interactions (Zhang et al., 2007a).

2.6 Factors Affecting the Formation of Aerobic Granules

The formation of aerobic granules can be affected by many factors and

conditions. Factors that have been identified to influence the granular formation

include substrate composition, OLR, hydrodynamic shear force, feast and famine

regime, feeding strategy, SRT, concentration of dissolved oxygen, reactor

configuration, settling time and volumetric exchange ratio. Of all the listed factors,

the major selection pressures responsible for the successful aerobic granular

formations are the settling time and volumetric exchange ratio (Liu et al. 2005a).

Unsuitable adjustment on the values for the settling time and the volumetric

exchange ratio will lead to the failure of granules formation.

2.6.1 Substrate Composition

Different substrate composition used as the source of energy in the aerobic

granules development resulted with different granular structures and microbial

diversity found within the granules. The microstructure and species diversity of

aerobic granules are closely related to the type of carbon source used. Glucose-fed

aerobic granules exhibit a filamentous structure, while acetate-fed aerobic granules

are dominated by the rodlike bacteria with very compact structure. Formation of

aerobic granules is a process independent of the characteristics of the feed

wastewater (Beun et al., 1999; Moy et al., 2002; Jiang et al., 2002; Arrojo et al.,

2004).

30

The size of developed granules is also affected by the type of substrate

presence in the media used as the feed solution. Amongst the four substrates (i.e

glucose, glucose with acetate acid, acetate acid and ethanol), ethanol-fed granules

appeared to be the largest and most stable granules as compared to others (Erguder

and Demirer, 2005b).

Aerobic granules developed with the presence of nitrogen and carbon sources

have resulted with the co-existing of heterotrophic, nitrifying and denitrifying

microniches within the granules. The activities exhibited by the different

microniches are found governed by the substrate N/COD ratio. The nitrifying

activity is significantly enhanced with the increase of the substrate N/COD ratio,

while the heterotrophic activity is decreasing (Yang et al., 2004b). More compact

granular structure is developed with high substrate N/COD ratio. The Kagg value that

represents the equilibrium position of a microbial aggregation process and ρeq, the

density of aerobic at equilibrium, shows an increasing trend as the substrate N/COD

ratio increases (Liu and Tay, 2004).

2.6.2 Organic Loading Rate

Aerobic granules can be cultivated in a wide range of organic loading rates

(2.5 -15 kg COD/m3·day) demonstrating that the level of organic loading rates have

insignificant effect on the formation of the aerobic granules (Moy et al., 2002; Liu et

al., 2003d; Yang et al., 2004b). However, different concentrations of the OLR

greatly influenced the characteristic of the formed granules.

At OLR of 1.68 kg COD /m3·day, smaller granules are developed containing

mainly bacteria microcolonies with minimal settling velocity of 9.6 m/h. When the

OLR is increased to 4.2 kg COD/m3·day, denser and more compact granular structure

31

with improved settling velocity are observed. The granular structure of higher OLR

is dominated by bacteria with different types of morphotypes (Li et al., 2006a). Liu

et al. (2003b) reported, increase in organic loading from 3 to 9 kg COD/m3·day

resulted in an increase of the mean granular size from 1.6 to 1.9 mm. However, the

physical strength of aerobic granules decreased as the organic loading rate is

increased. This is associated with the increased in the biomass growth rate that has

caused reduction in the strength of the three-dimensional structure of the microbial

community (Liu et al., 2003a). Tay et al. (2004) observed when the OLR was lower

than 1-2 kg COD /m3·day, the development of granules would be a failure.

However, at too high OLR (more the 8 kg COD /m3·day), unstable granules with

destruction on granular strength and structural integrity would appear. Having OLR

set at about 4 kg COD /m3·day, stable granules are developed, characterized by high

removal performance (99% of soluble COD removal) and good settleability

properties with SVI of 24 mL/g MLVSS (Tay et al., 2004).

From the perspective of microbiological surface properties, a higher ratio of

extracellular protein to polysaccharides showed more percentage of surfaces

hydrophobic and less negative surface charge. This condition is suitable for

granulation development. At higher OLR (4-12 g COD/L·day), decrease of the

protein secretion and increase in the polysaccharides concentration in the sludge EPS

have been observed indicating a low extracellular protein to polysaccharides ratio.

This condition is inappropriate for granules formation. This explains why the

disintegration of granules occurred when the OLR increases between 10-12 g

COD/L·day (Zhang et al., 2007b).

2.6.3 Hydrodynamic Shear Force

Through observation, diverse characteristic with respect to the physical

changes on the granular structure is developed under different pressure imposed by

32

the hydrodynamic shear force. Aerobic granules can be formed at hydrodynamic

shear force in terms of superficial upflow air velocity of above 1.2cm/s in a SBR

column. When the system is operated with superficial air velocity of less than 0.3

cm/s, it is only filled with bioflocs (Tay et al., 2001c).

Stable and robust granular structure for long-term reactor operation could be

achieved in the system operated with high hydraulic shear force (2.4-3.2 cm/s).

Granules developed at higher hydraulic shear force will be smaller in size but more

regular, rounded and compact. However, large-sized filamentous granules with

irregular shape and loose structure can lead to poor performance, and operation

instability can occur in systems run with low hydraulic shear force (0.8-1.6 cm/s)

(Chen et al., 2007). In terms of equilibrium size and size-dependent growth rate, the

growth of aerobic granules are inversely related to shear force imposed to microbial

community, while a high organic loading favors the growth of aerobic granules,

leading to large size granules (Yang et al. 2004b). The compactness of the granular

structure formed under high superficial air velocity is due to the excretion of the EPS

and reduction of surface free energy (Beun et al., 1999 and Liu et al., 2004d).

The hydraulic shear force not only influences the physical structure of the

formed granules but also affect the metabolic behavior of the microbes within the

granules. When the superficial gas velocity increases, the respiration activity

(SOUR) and the ratio of sludge polysaccharides to sludge-proteins are also increased

(Tay et al., 2001c). The changes on the bioactivity among the microorganisms under

high shear force are directed with the formation of larger, compact and stable

granules. Mild shear force at agitation rates of 400-600 rpm exerts biological floc

with higher surface hydrophobicity, larger floc size and lower sludge volume index,

indicating a favorable condition for settleable floc growth (Liu et al., 2005b). From

an engineering point of view, hydrodynamic shear force can be manipulated, as a

control parameter, to enhance the microbial granulation process (Liu and Tay, 2002).

However, Liu and Tay (2002) have added further that the hydrodynamic shear force

is not a primary inducer for the aerobic granulation in the SBR.

33

2.6.4 Feast and Famine Regime

Sequential batch reactor which operates with intermittent feeding strategy can

cause the microorganisms within the reactor to experience periodic starvation

through feast and famine regimes. Under periodic starvation conditions, the

microorganisms become more hydrophobic and high cell hydrophobicity that

facilitates microbial aggregation (Bossier and Verstraete, 1996 and Liu et al., 2004b).

Periodic feast and famine regimes can be regarded as a kind of selection

pressure for the microbes that could cause alteration of the cell surface properties and

lead to more of cell aggregation. According to Liu et al. (2005b), high feast-famine

ratio feeding applied to SBR systems may influence the characteristic of the

developed granules that lead to the formation of dense and compact aerobic granules.

Prolonging famine regime means an increase in the starvation period. Under

starvation conditions, bacteria become more hydrophobic and facilitate more

microbial adhesion and aggregation. Since aggregation is an effective strategy of

cells against starvation, utilizing prolong starvation treatment would improve the

efficiency of bioaugmentation. The starvation phase has caused a decrease in surface

negative charge from 0.203 to 0.023 meq/ g VSS and an increase in the relative

hydrophobicity from 28.8 to 60.3% of aerobic granules. The EPS, especially protein

concentrations, are well correlated with surface charge and relative hydrophobicity.

It is concluded that a reasonable amount of EPS should be controlled to form and

maintain aerobic granules and starvation is important in initiating the aerobic

granulation (Li et al., 2006b). However, according to Liu et al. (2007b), the

starvation phase in aerobic granulation is not a prerequisite since granules are

developed in SBR systems operated with 1 hour cycle time operation. However,

prolonged starvation times exhibited more stable granule formation. The starvation

time of a system may need to be controlled in a reasonable range.

34

2.6.5 Hydraulic Retention Time

Hydraulic retention times of 4 to 6 hours have resulted in a good granulation

process while higher HRTs (i.e 24 hours) could not support granulation. HRTs in the

range between 2 to 12 hours are favorable for formation of stable aerobic granules

with good settling properties and activity (Pan et al., 2004). At shorter HRTs (i.e 1.5

hours), the granulation process speeds up due to strong hydraulic selective pressure.

However, the structures of the granules are fluffy and exhibit poor settling ability

that demonstrated very unstable granules (Liu and Tay, 2008). Granules cultivated at

HRT ranging between 6 to 12 hours, possess high percentage of cell hydrophobicity

as compared to the granules developed with HRT of 24 hours (Liu et al., 2003c).

2.6.6 Presence of Inorganic Composition

The presence of divalent ions are reported to enhance microbial aggregation.

Ca2+ probably acts as a constituent of extracellular polysaccharides or proteins used

as linking materials (Grotenhuis et al., 1991a).

The additional Ca2+ has accelerated the aerobic granulation process and

produced better settling and strength of aerobic granular sludge properties, and also

exhibited higher polysaccharide contents (Bruus et al., 1992). Augmentation with

100 mg of Ca2+/L, speed up the granulation development from 32 days to 16 days.

The Ca2+ binds to the negatively-charged groups present on the bacterial surface and

the EPS to form a strong and sticky non deformable polymeric gel-like matrix (Jiang

at el., 2003).

35

Ca2+-rich granules have successfully developed after 3 months operation in

SBR systems supplied with media-rich Ca2+ (40 mg/L). The granules exhibited

higher granular strength with increasing granular size. Calcium-fed granules with

calcium content from 89.8 to 151 mg/g SS show compressive strength of 0.16-0.42

N/mm2. However, high accumulation of Ca2+ has reduced microbial activity (Ren et

al., 2008).

Aerobic granules are also capable of absorbing inorganic compounds. The

increased absorption of Ni2+ is observed with the increase of pH from pH 2 to pH 6

with the maximum absorption occurring at pH 6. Large quantities of K+, Mg2+ and

Ca2+ are released when Ni+ is being absorbed into the granules indicating an ion

exchange mechanism that take place (Xu et al., 2006).

2.6.7 Concentration of Dissolved Oxygen

The concentrations of DO in the reactor for aerobic granules development is

not considered as the decisive parameter. This is because aerobic granules can be

developed at DO concentrations as low as 0.7-1.0 mg/L and as high as 6 mg/L (Yang

et al., 2003; Qin et al., 2004a; Tsuneda et al., 2004).

Aerobic granules developed under low DO concentrations (0.5 -2.0 mg/L)

produce sludge with poor settling properties and high turbidity in the effluent.

Deterioration on settling properties of the sludge is associated with excessive growth

of filamentous bacteria and the formation of porous flocs (Martins et al., 2003 and

Liu and Liu, 2006). High oxygen concentrations are required to obtain stable

granules (de Kreuk and van Loosdrecht, 2004).

36

The percentage of dissolved oxygen that enables penetration into the granules

depends on the size of the granules. The diffusivity of dissolved oxygen becomes a

major limiting factor for metabolic activity when the size of the granules is more

than 0.5 mm (Li and Liu, 2005).

Different types of substrate used to feed the granules formation exhibit

different properties with respect to oxygen diffusivity. Acetate-fed granule sizes

between 1.28-2.5 mm show oxygen diffusivity coefficients of 1.24-2.28×10-9 m2/s

while phenol-fed granule sizes between 0.42-0.78 mm exhibit oxygen diffusivity

coefficients of 2.50-7.65×10-10 m2/s. Oxygen diffusivity declines as the diameter of

the granules increase. The diffusivity of oxygen for acetate-fed granules is

proportional to the granular size. Meanwhile, for phenol-fed granules the diffusivity

is proportional to the square of the granule diameter. The different patterns of oxygen

diffusivity among the two types of granules is due to higher secretion of extracellular

polymer content by phenol-fed granules, yielding lower oxygen diffusivity (Chiu et

al., 2006).

2.6.8 Slow Growing Organisms

Slow growing organisms can be used to improve the stability and removal

efficiency of the aerobic granular sludge as seen in the SBR system supplemented

with low oxygen concentrations (Beun et al., 2002). Slow growing bacteria with a

low growth yield are more capable to grow as granules than fast growing aerobic

heterotrophic bacteria. Selection of slow growing bacteria can be achieved by having

a long anaerobic feeding period followed by an anaerobic reaction phase (Brdjanovic

et al., 1998 and de Kreuk and van Loosdrecth, 2004).

37

By having substrate concentrations with a high ratio of N/COD, the nitrifying

population will be enriched. This would enhance the slow growth rate of the aerobic

granules. At this condition, a smaller size of matured granules are observed with

strong structure and good settleability as compared to the granules generated with

high growth microbial rates (Liu et al., 2004a).

2.6.9 Settling Time

Settling time is considered as one of the most decisive factor in the formation

of aerobic granules in a SBR system (Liu et al., 2005a). For a successful formation

of aerobic granules, short settling time is compulsory. If the settling time is not short

enough, the dominancy of granular biomass will not happen.

Aerobic granules are successfully cultivated and become dominant in SBR

systems operated with 5 minutes settling time. Whereas, a mixture of aerobic

granules and suspended sludge is observed in a reactor system operated at settling

times longer than 10 minutes (Qin et al., 2004b). When the SBR system is operated

with 2 to 5 minutes settling time, complete granular biomass is formed in the reactor.

These granules contain higher EPS protein with improved cell surface

hydrophobicity (McSwain et al., 2005 and Qin et al., 2004b).

A short setting time exerts a major hydraulic selection pressure that would

select good settling bioparticles for granulation. Short settling time allows more

sludge floc to retain in the reactor and overcome the presence of bioparticles. This

will result in a successful development of the granulation process (Lin et al., 2003;

Wang et al., 2004; Hu et al., 2005).

38

2.6.10 Reactor Configuration

The configuration of the reactor gives an impact on the liquid flow pattern

and able to cause microbial aggregates in the reactor. In a column type reactor,

allowing the air or liquid upflow, creates a relatively homogenous circulation flow

and localized vortex along the reactor height (Beun et al., 2002 and Liu and Tay,

2002). This pattern forces the microbial aggregation to adopt a regular granular shape

that has minimum free surface energy. The SBR should have a high H/D ratio to

improve selection of granules by the difference in settling velocity (Beun et al.,

1999).

Successful development of stable aerobic granules is observed in an airlift

reactor operated at superficial air velocity of 1.2 cm/s. The reactor is designed with

an additional draft tube placed in the reactor column. Such configuration is believed

to be able to increase the shear forces built up in the reactor system compared to a

bubble column reactor configuration (Liu et al., 2007b).

2.6.11 Volumetric Exchange Ratio

The volumetric exchange rate (VER) and settling time are the most important

factors that determine the successful development of the aerobic granules (Liu et al.,

2005c). The fraction of aerobic granules in the total biomass is proportional to the

VER. The reactor is dominated by aerobic granules when the VER of the reactor

system is designed with higher percentage of about 60-80%. However, when the

VER is 40% or less, a mixture of aerobic granules and suspended sludge will be

present. The production of EPS is stimulated significantly by high VER (Wang et

al., 2006b and Zhu and Wilderer, 2003).

39

2.7 Applications of Aerobic Granules in Wastewater Treatment Systems

Aerobic granules are a rapidly emerging technology in biological wastewater

treatment. The applications of aerobic granules improve process efficiency by

allowing high and stable biodegradation conversion rates, efficient biomass

separation and high microorganism accumulation within the aggregates. Aerobic

granular sludge systems have been reported capable of treating high-strength organic

wastewater and could be used to remove a wide range of pollutants. Biogranulation

is capable of accommodating a wide range of treatment capacities with varying

loading rates, wastewater composition and treatment goals. Biogranules could be

used for a specific treatment target by developing specific granules for specific

treatment.

.

2.7.1 High Strength Organic Wastewater Treatment

One of the prominent characteristics of aerobic granular sludge is the ability

to retain high concentrations of biomass. Such ability enables the aerobic granular

sludge system to withstand and treat high strength organic loading wastewater. The

feasibility on treating high strength organic loading by using this system has been

demonstrated by several researchers such as Moy et al. (2002), Jiang et al. (2004b),

Eckenfelder et al. (2006) and Chen et al. (2008a). An average of 3.3 mm of aerobic

granules was developed with biomass density of 11.9 gVSS/L granule in systems

supplied with organic loading substrate of 7.5 kg COD/m3·day. When higher organic

loading is applied, a balance between the hydrodynamic shear force and the level of

organic loading rate used is very important for the production of more compact

granules and stability of the reactor system (Buen et al., 1999).

40

Moy et al. (2002) has successfully cultivated glucose-fed aerobic granules at

higher organic loading rates (6–15 kg COD/m3·day). At OLR of 15 kg COD/m3·day,

the performance of the reactor system showed more than 92% of COD removal. The

COD removal rate was observed high at various organic loading rates (6 to 12 kg

COD/m3·day) indicating high granular bioactivity with good reactor performance

(Chen et al., 2008a).

Jiang et al. (2004) reported that as the concentration of phenol loading

increases from 1.0 to 2.5 kg phenol/m3·day, an obvious influence on the structure,

activity and the metabolism of the aerobic granules was observed. The granular

system shows complete phenol degradation at all different phenol concentration

levels used in the study. The structure, activity and the rate of metabolisms of the

granules, increased and at a peak when the concentration of phenol loading is

increased from 1.0 to 2.0 kg phenol/m3·day. Compact granules with good setteability

properties are sustained at the loading rate until 2.0 kg phenol/m3·day. However,

those characteristics start to decrease when the loading is increased to more than 2.0

kg phenol/m3·day. The granular structure starts to weaken when the phenol loading

is increased to 2.5 kg phenol/m3·day, due to the toxic effect of the phenol compound.

2.7.2 Simultaneous Organics and Nitrogen Removal

Nitrification and denitrification for complete mineralization of nitrogen

compound is observed to be carried out by aerobic granules. Arrojo et al. (2004) had

successfully showed the simultaneous removal of organics and nitrogen with

removal efficiencies of 80% and 70% respectively. This has been conducted at high

organic and nitrogen loading rates of 7 g COD/ (L·d) and 0.7g NH4+-N/(L·d),

respectively, of a dairy wastewater. Beun et al. (2001) had also reported on the

application of aerobic granules in the nitrification and denitrification processes. The

ammonia oxidizing bacteria responsible for the nitrification process has been found

41

localized at the upper layer of the granules and down to 300 μm into the granular

thickness. The denitrification process takes place at the inner core of the granules

where the oxygen could not penetrate. The coexistence of heterotrophic and

nitrifying populations in aerobic granules has also been reported by Jang et al.

(2003).

Qin and Liu (2006) have successfully used microbial granules cultivated

under alternating anaerobic and aerobic reaction phases in the SBR system. The

system shows an effective percentage removal for organic carbon (95-97%) and

complete conversion of ammonia to nitrogen gas (99-100%). The results give an

indication on the coexistence of heterotrophic, nitrifying and denitrifying populations

in the microbial granules.

The activities of the nitrifying and denitrifying populations are very much

affected by the N/COD ratio and the levels of dissolved oxygen. High N/COD ratios

enhance the performance of the nitrifiers. However, the activities of the heterotropes

population within the granules are reduced with the increase in the N/COD ratio. A

sufficient level of dissolved oxygen concentration is required for a sufficient mass

transfer between the liquid and granules during denitrification (Yang et al. 2003).

The successful cultivation and performance of nitrifying organisms within the

granular structure show that the aggregates are able to act as an effective protection

for the sensitive nitrifying population. This shows that granulation systems are

adaptable to treatment for different types of wastewater compositions.

2.7.3 Phosphorus Removal

Removal of phosphorus by aerobic granules has been studied by several

researchers including Cassidy and Belia (2005), De Kreuk et al. (2005) and Lemaire

42

et al. (2007). Aerobic granules can be designed to treat different types of pollutants.

This could be achieved through incorporating specific degrader microbes during the

development of the aerobic granules. Lin et al. (2003) had successfully developed

aerobic granules containing phosphorus-accumulating microbes by applying

substrate P/COD ratio ranging from 1/100 to 10/100 in a SBR system. Phosphorus-

accumulating microbial granules are developed with an attempt to improve the

problems associated with phosphorus removal by conventional biological treatment.

Phosphorus-accumulating microbial granules are reported capable of adsorbing

phosphorus in the range of 1.9% to 9.3% by weight of the granules. There would be

an uptake of soluble organic carbon and release of phosphate during the anaerobic

stage followed by rapid phosphate uptake in the aerobic stage. This typical profile of

soluble COD and PO4-P could be an indication for the typical P-accumulating

characteristics. The accumulated phosphorus decreased with an increase in the

substrate P/COD ratio. A 2.5% of influent P/COD ratio resulted in an accumulation

of 6% of P in the granules. The same percentage of accumulated P has been reported

by Cassidy and Belia (2005) with the use of influent P/COD ratio of 2.8%. Over

98% of COD and P removal and over 97% removal of N and VSS are reported in

treating abattoir wastewater by using aerobic sludge granules.

De Kreuk et al. (2005) has claimed that at low oxygen concentrations (20%),

simultaneous COD, N and P removal could occur since heterotrophic growth was

able to develop inside the granules. Accumulibacter spp (a polyphosphate-

accumulating organism, PAO) and Campetibacter spp (a glycogen non-

polyphosphate-accumulating organism, GAO) are incorporated in the development

of aerobic granules in a SBR system with alternating aerobic and anaerobic reaction

periods by Lemaire et al. (2008b). The PAO spp. dominated the 200 μm of the outer

region of the granule while the Campetibacter spp. dominated in the core zone of the

granule. This aerobic granule is able to demonstrate a good phosphorus and nitrogen

removal.

43

2.7.4 Phenol Wastewater Treatment

Biological degradation of phenolic wastewater is generally preferred due to

lower cost and the possibility of having complete mineralization process.

Degradation at low concentrations of phenol was successful but dealing with high

concentrations of phenol-containing wastewater exerted toxicity effect by the

substrate itself. High concentrations and fluctuations of phenol load cause

breakdown of the activated sludge processes (Watanabe et al., 1999) and death to the

phenol–degrading bacteria. However, phenol degradation by using aerobic granules

displays an excellent degradation performance (Chou et al., 2004, Chou and Huang,

2005; Tay et al., 2005a).

Chou et al., (2004) has reported the percentage of COD removal of phenol-

containing wastewater was high with an average removal of 93.9% when the system

operated at 25oC. The COD removal is higher and reached 97.9 to 98.2% when the

temperature is increased from 30 to 40oC. Tay et al., (2005b), reported the phenol

degradation rate of aerobic granular biomass was not affected by the increase of

phenol loading rate from 0 to 2.4 kg/ m3·day.

The microbial aggregation matrix within the compact granules is likely to

serve as an effective protection barrier against high phenol concentrations. Due to

the diffusion limitation, a substrate concentration gradient is developed at the surface

of the granular matrix. This condition seems to be able to protect the

microorganisms from toxicity effect by means of diluting the chemical compound

below some threshold value and avoid substrate inhibition (Rittmann and McCarty,

2001 and Liu and Tay, 2004). Adav et al. (2007) reported that aerobic granules are

capable of degrading phenol at 1.18 g phenol/g VSS/d. An addition of co-substrate

such as glucose and ethanol is capable of treating phenol-containing wastewater

(Wang et al., 2007 and Zhang et al., 2008).

44

2.7.5 Biosorption of Heavy Metals and Nuclear Waste

Aerobic granules seem to have the ability as a biosorbent towards some

heavy metals that are often found in a wide variety and range of industrial

wastewaters. Aerobic granules have shown capability in absorbing the heavy metals

as revealed by other biomaterials such as fungus (Kumari and Abraham, 2007 and

Patel and Suresh, 2007), marine algae (Daneshyar et al., 2007), waste activated

sludge (Otero et al., 2003) and biosludge (Wang et al., 2006c). Granules have large

surface area, high porosity and good settling properties that can be responsible for

the performance as a good biosorbent.

Concentration gradient has become the driving force for the absorption of

metals onto aerobic granules. The maximum biosorption capacities of individual

Cu2+ and Zn2+ by aerobic granules are closely related to the initial concentrations of

the metals in the reactor i.e. 246.1 mg/g and 180 mg/g respectively (Xu et al., 2006).

Sun et al., (2008a) revealed that in the adsorption mechanisms, the functional groups

such as alcoholic and carboxylate of the aerobic granules would be the active binding

sites for the biosorption of Co2+ and Zn2+. The maximal adsorption capacity of the

granules was 55.25 mg/g of Co2+ at pH 7 and 62.50 mg/g of Zn2+ at pH 5.

The ability as novel biomaterials for nuclear waste (soluble uranium) removal

by the aerobic granular sludge has been demonstrated by Nancharajah et al. (2006).

The effect of different pH levels (pH 1 to 8) and initial uranium concentrations (6 to

750 mg/L) are among the main focus of the study. In less than one hour, almost

complete removal of uranium at concentrations ranging between 6-100 mg/L is

reported. Rapid biosorption occurred in a pH range of 1 to 6 as compared to pH 7

and above. In the biosorption of uranium, an ion-exchange mechanism is observed

to take place. Light metal ions such as Na2+, K2+, Ca2+ and Mg2+ are simultaneously

expelled from the granules during the absorption of the uranium. The maximum

biosorption capacity of uranium is reported to be at 218 ± 2 mg/g dry granular

biomass.

45

As a conclusion, granulation systems offer good removal performance of

various types of pollutant. However, the behavior with respect to the removal

performance, changes in the physical characteristics of the granules as well as the

stability of the reactor systems varies in treating different types of pollutants. The

biological activity and microbial diversity within the granules may also differ with

the granules used in treating different types of wastewater. Even though there are

many studies being reported on the use of anaerobic granular biomass in treating

textile wastewater, the knowledge of the use of aerobic or facultative anaerobic

granular biomass particularly with the application of intermittent anaerobic and

aerobic reaction phase is still lacking. Therefore, this study is conducted with the

objective of filling the insufficient knowledge with regard to the use of facultative

anaerobic granular biomass in treating textile wastewater.

CHAPTER 3

DYE DEGRADATION PROCESS

3.1 Textile Industry

The textile industry, which is known as one of the main industrial trades,

established its first textile processing factory way back in the 1500s (Neefus, 1982).

The world production of natural and chemical fibres in 2003, have reached almost 63

million tonnes, 1.8 million tonnes or 2.4% more as compared to the production in

2002 which provided huge advantages for world economic values (Aizenshtein,

2004). In social terms, it gives benefit to more than 2.2 million workers through

114,000 textile-related companies with a turnover of about 198 billion Euros. In

2001, the European textile and clothing industries have contributed to about 3.4% of

the EU manufacturing industry’s revenue and granted 6.9% work opportunity to the

citizens (IPPC, 2003).

Malaysia is also known for its textile and apparel, recognized around the

world for quality and reliability. It has become one of the important industrial

activities of this country. When the country started to embark on a path of export-

oriented in the early 1970s, the growth on Malaysian’s textile and apparel industry

shot up very high and accelerated with an export valued at RM 10.3 billion. This has

47

listed the textile industrial activities as the ninth largest contributor to total earnings

from manufactured exports in 2007. The industry has provided more than 67,000

work opportunities through 637 licensed companies in textile production with

investments of RM7.8 billion (MIDA, 2007).

Apart from running textile manufacturing as a large scale activity, quite a

number of Malaysian fabric productions are conducted on small scales. One

common practice from conventional cottage textile industries is that the textile

effluents are mainly discharged directly into the drainage system without proper

treatment. Even though textile manufacturing has added great value in terms of its

economic and social aspects, it has also been identified as one of the significant

environmental polluters. One of the greatest contributors to Malaysia’s GDP, textile

industry activities have also been listed as the fourth industrial wastewater polluter

discharging significant quantities of high level pollutants amounting up to 7.4% into

streams. Fiber manufacturing and dyeing textile sectors are predominant for its

contribution both to the economy and environmental emissions (Haroun and Azni,

2009).

The textile industry is also known for its longest and most complicated

industrial chains in the manufacturing industry. It has diverse sectors in its

production with respect to the raw material, processes and products equipment. The

textile industry can be divided into different fragmented groups that produce and

process textile-related products such as fiber, yarn or fabric for further processing

into apparel, home furnishings and industrial goods. The most important stage that

has been identified to contribute significant adverse impacts to environmental water

pollution problems is the dyeing and finishing stage. This stage covers the

bleaching, dyeing, printing and stiffening of textile products conducted in various

processing steps. The purpose of the dyeing and finishing stage is to improve the

serviceability and increase the durability of products to suit the demands of fashion

and function (IPPC, 2000 and Savin and Butnaru, 2008). The finishing stage is also

known as the “wet processing”. The term is given as “wet” due to the huge amount

of water usage in most of the processes. In order to achieve the desired effect, a wide

48

range of chemicals, dyes and chemical auxiliaries are used. Textile processing

employs a variety of chemicals depending on the nature of the raw material and the

desired product (Aslam et al., 2004). Any impurity from each of the processing

stages will be discarded into wastewater treatment systems. The dyeing and

finishing process of the textile industry has been recognized as the main contributor

with respect to the amount of water usage and its quality (Savin and Butnaru, 2008).

3.2 Characteristics of Textile Wastewater

Dyes are used in many manufacturing activities as coloring agents to produce

many types of goods such as textile, plastic, paper printing, leather, food and in

specialized applications such as drugs, cosmetics and photochemical products

(Zollinger, 1987). Among these, the textile industry is the largest consumer. Due to

the high consumer demand, there are over 100,000 commercially obtainable dyes

existing with more than 700,000 tonnes of dyes produced annually (McMullan et al.,

2001 and Pearce et al., 2003). This scenario has resulted with the high generation of

colored wastewater. The characteristics of textile wastewater for both its quantity

and quality vary greatly depending on the type of raw materials, chemicals,

techniques or specific process operations at the mill, equipment used and production

design of the textile processes (Bisschops and Spanjers, 2003 and Dos Santos et al.,

2006a). The prevailing management philosophy of a company also influences the

amount of water usage.

Lacasse and Baumann (2006) reported that the textile industry gave adverse

impact to the environment through its high pollutant discharge. In textile processing

activities, about 10% of the chemicals in the pre-treatment and dyeing operation will

remain, giving the desired design and color on the fabric. Meanwhile, the other 90%

of chemicals will be discharged as textile effluent (IPPC, 2003). Due to the

inefficiency of the treatment system, the textile industry has been recognized as one

49

of the main pollutant discharge. Apart from that, textile industrial activities involve

large amount of water in its processes. Due to these factors, textile industrial

processes have been listed as one of the top pollution contributor to the environment.

3.2.1 Quantity

One of the prominent profiles of the textile industry is its high water usage. It

has been estimated that nearly one million metric tonnes of dyes is produced

annually (Dos Santos et al., 2003). The average wastewater generation from a

dyeing facility is estimated at between 3785-7570 million m3 per day. The dyeing

and rising processes for disperse dyeing generate about 100 to 142 L of wastewater

per kilogram of product. The textile industry presents its biggest impact on the

environment through its primary water consumption that could reach up to 80-100

m3/ton of finishing textile (Savin and Butnaru, 2008).

Dye and pigments from printing and dyeing operations are the principal

sources of color in textile effluent. Dyes and pigments are highly colored materials

used in relatively small quantities (a few percent or less of the weight of the

substrate) to impart color to textile materials for aesthetic or functional purposes.

Desizing, scouring, bleaching, mercerizing and dyeing are the common cotton wet

textile processing. Among these processes, the mercerizing and dyeing processes

consume large volumes of water with a water usage of 232-308 L and 8-300 L for

every kilogram of textile processed, respectively (Dos Santos et al. 2007). In typical

dyeing and printing processes, 50 to 100% of the color is fixed on the fiber and the

remainder is discarded in the form of spent dye baths or in the wastewater from

subsequent textile-washing operations (EPA, 1997). The amount of dye lost into the

wastewater depends upon the type of dyestuff used, methods and application route in

the textile processing operation. It also depends on the intended color intensity that

is required for each particular design (Willmott et al., 1998). During the dyeing

50

process, a range of 5-20% of acid dyes is lost in the effluent and in numerous cases

these dyes are directly flushed into the receiving water body (Trovaslet et al., 2007).

The release of dyes into the effluent may vary greatly leading to a wide range of total

annual discharge between 30,000 and 150,000 tonnes (Faraco et al., 2009).

High content of salts in textile dyeing wastewater has been identified as a

potential environmental problem. Many types of salt are either used as raw materials

or produced as by-products of neutralization or other reactions in textile wet

processes. Typical cotton batch dyeing operations use quantities of salt that range

from 20 to 80% of the weight of goods dyed, with usual concentrations between

2,000 mg/L to 3,000 mg/L. Sodium chloride and sodium sulfate constitute the

majority of the total salts used, while other salts such as magnesium chloride and

potassium chloride are used as raw materials in lower concentrations (EPA, 1997).

3.2.2 Quality

Dye industrialized wastewater are normally characterized by high chemical

and biological oxygen demand, suspended solids, high values of conductivity and

turbidity and intense color owing to the presence of dye intermediates or residues and

auxiliary chemicals added in many stages in textile processing (Mohan et al., 2007a

and Miranda et al., 2009). Pollution from this manufacturing is very much related to

the type and origin of the fiber involved. Textile processes with natural fibers

generate higher pollution loads as compared to synthetic fibers. The usage of

pesticide as preservation of natural fibers contributes to high COD concentrations in

natural fibers textile processing wastewater. These pesticides are released into the

wastewater during washing and scouring operations (Correia et al., 1994). Finishing

processes generate wastewater containing natural and synthetic polymers and a range

of other potentially toxic substances (Snowden-Swan, 1995).

51

Common characteristics of textile wastewater for cotton textile wet

processing for different processing categories are shown in Table 3.1. The highest

organic loading is generated from the scouring process with 8 g COD/L followed by

bleaching and desizing processes. The desizing process produces the highest

concentration of total solids which may come from impurity of the previous

processes. The primary sources of biological oxygen demand (BOD) of the desizing

process include waste chemicals or batch dumps, starch sizing agents, knitting oils,

and degradable surfactants. Desizing, which is the process of removing size

chemicals from textiles, is one of the industry’s largest sources of wastewater

pollutants in the U.S textile industry. More than 90% of the size used is disposed of

in the effluent streams. The remaining 10% is recycled (EPA, 1997). Desizing

processes often contribute up to 50% of BOD loading in wastewater from wet

processing (Snowden-Swan, 1995). The dyeing process is a process where color is

added to the fibres which normally require a large amount of water usage. Table 3.1

shows the dyeing process releasing the colored effluent with very high dye

concentration of 1450-4750 of ADMI units (Bisschops and Spanjers, 2003; Dos

Santos et al., 2006a; Dos Santos et al., 2007).

Table 3.2 summarizes the typical pollutant released by various associated

textile manufacturing processes. Desizing, scouring and dyeing are among the

processing steps that contribute to the most pollutant in textile waste stream. Source

of metals such as copper, cadmium, chromium, nickel and zinc found in textile mill

effluents include fiber, dyes, plumbing, and chemical impurities (IPPC, 2003). In

some dyes, metals are the functional group which forms an integral part of the dye

molecule. However, in most textile effluents, the metals present are simply from

impurities generated during dye manufacturing.

52

Table 3.1: Characteristics of textile wastewater (Bisschops and Spanjers, 2003; Dos

Santos et al., 2006a)

Process COD (g/L) BOD (g/L) TS (g/L) TDS (g/L) pH Color (ADMI)

Desizing 4.6-5.9 1.7-5.2 16.0-32.0 - - -

Scouring 8 0.1-2.9 7.6-17.4 - 10--13 694

Bleaching 6.7-13.5 0.1-1.7 2.3-14.4 4.8-19.5 8.5-9.6 153

Mercerising 1.6 0.05-0.10 0.6-1.9 4.3-4.6 5.5-9.5 -

Dyeing 1.1-4.6 0.01-1.80 0.5-14.1 0.05 5-10 1450-4750

Bleaching and

Dyeing* 0.2-5.5 2.0-3.0 0.1-5.0 - 2-10 280-2000

*Characterization of textile wastewater in Malaysia (Ahmed et al., 2005; Lau and Ismail, 2009; Ibrahim et al.,

2009; Ibrahim et al. (in review))

3.3 Dye and Environmental Problems

In textile dyeing processes, dyes are lost into the effluent due to the

incomplete exhaustion of dyes on the fibres. Approximately 2% of dyes are

discharged directly into the aqueous effluent and 10 % are subsequently lost during

the dyeing process of textile (Easton, 1995 and Pearce et al., 2003). The problem

related to the dye lost is very much accelerated when the annual market for dye was

reported to be more than 109 kg (Zollinger, 1987 and Dos Santos et al., 2007).

Reactive dyes are among the popular type of dyes used especially in textile

dyeing processes of cellulose fibres and make up approximately 30 % of the total dye

market (Kamilaki, 2000). Dyeing with reactive dyes contribute more problems due

53

to its low degree of fixation ability which causes as much as 50% of the dye lost into

the wastewater stream. Losses of dye are due to the relatively low levels of dye-fiber

fixation degree and to the presence of unreactive hydrolyzed dye in the dyebath. Dye

hydrolysis takes place when the dye molecule reacts with water rather than with the

hydroxyl groups of the cellulose. These problems become more complex with high

water solubility and characteristics of the dyes involved (Pearce et al., 2003).

Table 3.2: Release of typical pollutants associated with various textile manufacturing

processes (Crini, 2006 and Dos Santos et al., 2006a)

Steps in Textile Processing Main pollutants

Sizing BOD, COD, metals, cleaning waste, size

Desizing BOD from water-soluble sizes, lubricants, biocides, antistatic compounds, size agents, enzymes, starches, waxes, ammonia,

Scouring/ washing

Disinfectants and insecticides residues, NaOH, surfactants, soaps, fats, waxes, pectin, oils, sizes and antistatic agents, detergents, knitting lublicants, spin finishes, spent solvents

Bleaching Adsorbable organic halogens, sodium silicate or organic stabilizers, Hydrogen peroxide,high pH

Mercerizing NaOH and other salts, high pH

Dyeing Color, metals, salts, surfactants, sulphide, formaldehyde, toxics, organic processing aids, cationic materials, BOD, COD, sulfide, acidity/alkalinity, spent solvents

Finishing BOD, COD, suspended solids, toxics, spent solvents

The presence of very small amount of dyes in water even less than 1 mg/L for

certain types of dyes is highly visible and undesirable (Robinson et al., 2001 and

54

Crini et al., 2007). The release of dyes into the environment has become a public

concern since its presence gives adverse effects on aesthetic merit, water

transparency and gas solubility in lakes, rivers and other water bodies (Banat et al.,

1996). Since textile processing wastewaters typically contain dye concentration in

the range of 10-200 mg/L (Pandey et al., 2007) these can be considered as highly

colored and could impose aesthetic problems of the environment. Dyes that are

released in the water body in the form of colored wastewater could lead to several

adverse effects to the environment. Aquatic organisms are exposed to acute effects

due to toxicity of the dyes. The intensity of light that penetrate into the water body

will be reduced which will end up with reduction in the photosynthesis process by

the plants in the aquatic ecosystem. Additionally, the dyes are chemically complex

and photolytically stable which will make the dyes highly persistent in the

environment for a long while. As the dye components are very difficult to be

degraded, they persist in the environment. These conditions are even worse with the

fact that many of the dyes are made from known carcinogens such as aromatic

compounds and benzidines (Clarke and Anliker, 1980 and Brown and DeVito, 1993).

Based on the European criteria for the classification of dangerous substances,

the acute toxicity effect of azo dyes is considered rather low with the LD50 values of

250-2000 mg/kg of body weight (Clarke and Anliker, 1980). However, the

degradation products of the azo dyes include the aromatic amines and the impurities

of the textile wastewater, are the compounds of concern due to their potential

carcinogenicity (Novotny et al., 2006). Due to these circumstances, the release of

dye-contained wastewater has become a source of public concern.

3.4 Treatment of Dyes

There are a number of approaches that have been used in treating textile

industrial effluents. At present, the major techniques in textile wastewater treatment

55

mainly involve physical and/or chemical processes. However, such methods are

often costly. Some treatments may remove color by just transferring one problem to

another when those treatments produce accumulation of concentrated sludge which

creates disposal problems (Pearce et al., 2003). Excessive use of chemicals in dye

treatment creates secondary pollution problems to the environment.

Some of the physico-chemical techniques that have been applied for textile

wastewater treatment are coagulation and flocculation (Harrelkas et al., 2009),

electrokinetics coagulation (Kobya et al., 2003 and Alinsafi et al., 2005),

precipitation (Solmaz et al., 2007), adsorption (Ong et al., 2008a and Sayed and

Ashtoukhy, 2009), membrane filtration and nanofiltration (Miranda et al., 2009 and

Unlu et al., 2009), ion exchange (Wu et al., 2008), ultrasonic mineralization

(Maezawa et al., 2007) and electrolysis (De Jonge et al., 1996). Treatment using

ozonation, Fenton’s reagent, electrochemical destruction and photocatalysis are some

of the emerging techniques reported to have potential use for decolorization (Tang

and Chen, 2004; Faouzi et al., 2006; Papadopoulos et al., 2007; Ay et al., 2009; Ma

and Zhou, 2009). However, such technologies usually involve complicated

procedures and are economically unfeasible (Chang and Lin, 2000). Table 3.3 shows

some of the advantages and disadvantages of using physical and chemical

technologies in color removal (Robinson et al, 2001 and Crini 2006).

The technique finally chosen for a particular textile wastewater treatment

usually depends on types of dyes used in the textile processing, quantity and quality

of the textile effluent, operational cost from energy consumptions, equipment,

chemical requirement and as well as cost of handling the generated waste products.

Environmental fate over the chosen treatment for textile wastewater is one of the

significant factors that need to be taken into account. Treatment systems that can

offer effective dye removal from large volumes of wastewater at low cost is a more

preferable alternative in solving the textile wastewater problem and this can be

achieved through biological and/or combination treatment processes.

56

Table 3.3: Advantages and disadvantages of the current methods of dye removal from industrial effluents (Robinson et al. 2001 and Crini , 2006)

Categories Technology Advantages Disadvantages

Physical treatment

Membranes filtration

Removes all types of dyes, produce a high quality treated effluent

High pressure, expensive, incapable of treating large volumes.

Adsorption on activated carbon

The most effective adsorbent, produce a high-quality treated effluent

Ineffective for disperse and vat dyes, require regeneration that increase in the expenses, loss of adsorbent, non-destructive process

Chemical treatment

Coagulation Simple, economically feasible High sludge generation, handling and disposal problems

Ozonation Applied in gaseous state: no alteration of volume Short half-life (20 min)

Irradiation Effective oxidation at lab scale Requires a lot of dissolved O2

Ion exchange Regeneration; no adsorbent loss Not effective for all dyes

Photochemical No sludge production Formation of byproduct

Electrochemical destruction

Breakdown compounds are non-hazardous Very expensive

Fenton reagent Effective decolonization of both soluble and insoluble dyes

Sludge generation

Biological treatment

Biomass Low operating cost, good efficiency and selectivity, no toxic effect on microorganisms

Slow process, performance depends on some environmental conditions such as temperature, pH, salts

Biosorbents Peat Good adsorbent due to cellular structure Requires long retention time

Chitin and chitosan

Low cost, abundant, renewable, biodegradable resources Resulted with pressure drop in sorption columns, cannot be used as insoluble sorbent under acidic conditions

57

Bioremediation using microbial biocatalysts to reduce the dyes present in the

effluent offer potential advantages over physico-chemical processes. Such a system

has become the main focus of recent research. However, many aspects have to be

investigated in order to really gain advantages over the system especially when it

comes to applying the treatment system in-situ. Each of the technique mentioned has

its own limitations. Complete dye degradation seems to be difficult to achieve if the

treatment only depends on a single treatment process. At present, a combination of

different treatment techniques is being practiced in order to achieve complete

mineralization of dyes.

3.4.1 Biodegradation of Dyes

Biodegradation of dyes means using a biological approach in decolorizing the

dyes. It can be carried out using bacteria, algae or fungi. Advantages achieved by

using biological approaches have been claimed by many researchers either by having

partial or complete degradation of dyes (Kudlich et al., 1999; Chen et al., 2005;

Frijters et al., 2006; Dos Santos et al., 2007; Mohan et al., 2007a; Ertugrul et al.,

2008). Effective dye removal from large volumes of wastewater at a considerable

low cost as compared to other techniques can be obtained through biological

approaches. Furthermore, biodegradation does not contain complicated procedures

(Pearce et al., 2003).

Microalgae which acted as the primary producers to the aquatic food chains

were found to be competent as an ideal biosorbent for color removal from textile

wastewater (Daneshvar et al., 2007). However, its application was limited due to

low chemical and heat resistance (Ertugrul et al., 2008). Due to the advantages of

using bacterial in dye degradation, extensive research have been conducted for the

58

past two decades involving the isolation and identification of bacteria capable of

degrading various types of dyes (Rai et al., 2005).

The application of fungi is influenced by the bioadsorption capacity of the

biomass. The absorbed dye compounds are degraded by the powerful extracellular

ligninolytic enzymatic system (Dias et al., 2007). However, fungi are ineffective for

the removal of reactive dyes. Reactive dyes are usually changed into a hydrolyzed

form, losing its aptitude to bind to cellulose. In this condition, these dyes may no

longer be successfully absorbed by the fungal biomass. The regeneration process by

extraction with methanol is required to regain back the absorption capacity.

However, the regeneration process may slightly reduce the percentage of absorption

(Carliell et al., 1994).

Pretreatment process is required in order to increase the biomass absorption

capacity. The pretreatment methods may include autoclaving, contacting with

chemicals, either organic chemicals (formaldehyde) or inorganic chemicals (natrium

oxide, calcium chloride, hydrogen sulfide) (Fu and Viraraghavan, 2001). The

pretreatment process may increase the cost of the operation set-up and increase the

operating time. The application of fungi in a large scale system has become a

limitation since high growth of fungi may inhibit the growth of other useful

microorganisms. Furthermore, fungi require low pH levels for optimum activity and

need long hydraulic retention times for high dye removal (Chen et al., 2003). The

main principle of dye removal using algae and fungi depends on the adsorption rather

than the degradation process. As a result, the dyes still remain in the environment

(Wang et al., 2009a). Due to the drawbacks in using algae and fungi as the

biodegradation agent, many studies have focused on the biodegradation process by

bacteria.

In recent reports, a number of studies have focused on the immobilized

microorganisms able to decolorize textile wastewater (Kornaros and Lyberatos,

2006; Sirianuntapiboon et al., 2007; Somasiri et al., 2008; Sun et al., 2008b; Zhu et

59

al., 2008). The immobilization of dye-removing microorganisms provides important

advantages including degradation at higher dye concentrations without lost of cell

viability, protective environment for the dye degradation activities against changes in

temperature, pH and effect of toxic compounds that co-exists in the wastewater

(Ertugrul et al., 2008).

3.4.2 Bacterial Degradation of Dyes

Isolation of decolorizing bacteria started in the 1970s with cultured bacteria

Bacillus subtilis able to degrade azo dyes (Horitsu et al., 1977), followed by isolation

of Aeromonas hydrophila (Idaka et al., 1978) and Bacillus cereus (Wuhrmann et al,

1980). Nowadays, numerous capable dye degrader bacteria have been reported

(Chen et al., 2003; Xu et al., 2007; Hsueh and Chen, 2007; Moosvi and Madamwar,

2007; Dave and Dave, 2009). A bacterial strain, Citrobacter sp. CK3, isolated from

activated sludge from a textile paper mill was found capable of degrading Reactive

Red 180 with 95% color removal within 36 hour incubation under anaerobic

condition. It also exhibited high tolerance to dye concentration up to 1000 mg/L

(Wang et al., 2009a). Some of the isolates are capable of degrading a broad

spectrum of dyes which include azo, anthraquinone and triphenylmethane dyes such

as Aeromonas hydrophila (Ren et al., 2006), Bacillus cereus strain DC11 (Deng et

al., 2008) and Bacillus thuringiensis (Dave and Dave, 2009).

Most of the researches on color degradation mechanisms have been

conducted and focused on the biotransformation of azo dyes, as, among the 10,000

dyes applied in textile processing industries, 60-70% are azo compounds (van der

Zee, 2003a). Hence, most study in relation to dye degradation has been focused

more on the azo dyes. Azo dyes are molecules with one or more azo (N=N) bridges

linking substituted aromatic structures (Carliell et al., 1995). However, there have

been several reports focusing on other types of dyes such as degradation of

60

anthraquinone dyes (Lee and Pavlostathis, 2004 and Trovaslet et al., 2007),

phtalocyanine dyes (Nilsson et al., 2006) and triphenylmethane dyes (Shedbalkar et

al., 2008).

3.4.3 Mechanisms of Biodegradation of Azo Dyes

Biodegradation of azo dyes can occur under aerobic, anaerobic

(methanogenic), and anoxic conditions by many different trophic groups of bacteria

(Pandey et al., 2007). Biodegradation of azo dyes can occur as a direct degradation

process through the presence of enzymes or as an indirect mechanism process. The

indirect mechanism process involves the presence of other substances that could aid

the degradation process. Many research studies have been concentrated under

anaerobic degradation process due to the higher removal percentage of dye

degradation as compared to aerobic condition.

3.4.3.1 Aerobic Dye Degradation Process

Aerobic biodegradation of azo dyes involve enzymatic reduction process. In

this mechanism, enzymes play an important role in transferring reducing equivalents

from the oxidation of organic substances or from the coenzyme to the azo dyes. Azo

reductase is a specialized azo dye reducing enzyme found in some aerobic and

facultative bacteria that are able to degrade simple molecular structure of the azo dye

as sole carbon and energy sources (Zimmermann et al., 1984). Since the enzymes

react specifically to the dye molecule, the reaction is known as specific enzymatic

reaction (Blumel and Stolz, 2003 and Chen et al., 2004).

61

Pseudomonas aeroginosa can decolorize a few types of azo dyes under

aerobic condition with the presence of external carbon sources (Nachiyar and

Rajkumar, 2003). An obligate aerobic strain, Shingomonas 1CX, is able to grow on

Acid Orange 7 (AO7) and used it as the source of energy, carbon and nitrogen

(Coughlin et al, 2003). However, these bacterial strains could only cleave the N=N

bond of a simple dye structure and utilize the amines as the source of carbon and

energy for bacterial growth but not on complex azo dye structure. Xenophilus

azovorans KF46 could utilize azo dye carboxy-orange I (Zimmermann et al., 1982)

but this strain could not grow on more complex dye structure such as analogous

sulfonated dyes, Acid Orange 20 and Acid Orange 7.

The azo dye, Acid red 151 (AR151) was successfully degraded under aerobic

condition when this dye acted as the sole carbon source for microorganisms in a

sequencing batch biofilter with a porous volcanic rock. The system showed a high

percentage of color removal (99%) with initial concentration of 50 mg/L of AR151

(Bruiton et al., 2004). Arora et al. (2007) reported extensive degradation (95-98%)

of monoazo disperse dye under shaking aerobic condition by bacterial strain Bacillus

firmus isolated from local sewage.

Azo dye degradation has also been observed to occur under microaerophilic

conditions (Sandhya et al., 2005; Xu et al., 2007; Franciscon et al., 2009; Khalid et

al., 2008, Elisangela et al., 2009). Microaerophilic condition is where the percentage

of oxygen is only 5% (Engelkirk et al., 1992). Several azo dyes were degraded in

sequential microaerophilic-aerobic treatment condition by a facultative Klebsiella sp.

Strain VN-31 with 94% of color removal (Franciscon et al., 2009). Another

facultative Staphylococcus arlettae bacterium isolated from an activated sludge

process of the textile industry, has successfully decolorized four different azo dyes

under micraerophilic condition with percentage dye removal of more the 97%

(Elisangela et al., 2009). However, since the aerobic biodegradation of azo dyes is

restricted to the presence of azoreductase and only suitable for the degradation of

simple molecule dye structures. This has led many researchers to conclude that azo

62

dyes are persistent under aerobic conditions (Pagga and Taegar, 1994 and Ong et al.,

2008).

3.4.3.2 Anaerobic Dye Degradation Process

Dye degradation under methanogenic (anaerobic) condition could be

participated by various types of bacteria trophic groups including acidogenic,

acetogenic and methanogenic bacteria (Talarposhti et al., 2001; Bras et al., 2005;

Ong et al., 2005a; Somasiri et al., 2008). Extensive studies on dye degradation have

been carried out by many researchers using diverse groups of bacteria. Based on the

molecular characterizations of microbial populations in anaerobic baffled reactors

treating industrial textile wastes, sulfate reducing bacteria (SRB), γ-proteobacteria

and Mehanosaeta species and Methanome donthylovorans hollandica of the

methanogenic populations were among the prominent bacteria groups identified in

the treatment of dye waste (Plumb et al., 2001). Dos Santos et al. (2006b) confirmed

the ability of fermentative bacteria to use humic acid as electron acceptor in the

reduction of azo dyes. The anaerobic treatment of wool dyeing effluents which

contained predominantly methanogenic cultures have shown higher performance for

color removal of more than 88% at the HRT of 24 hours (Bras et al., 2005). Ong et

al. (2008b) had reported 100% degradation of 625 mg/L of initial concentration of

Acid Orange 7 under limited oxygen supply (DO below 0.25 mg/L) without any

addition of external carbon sources in granular activated carbon-biofilm configured

sequencing batch reactor system. Study on the methanogenic consortia on

decolorization has been studied earlier on by Carliell et al. (1996), Razo-Flores et al.

(1997) and many others.

Based on many documented research reports, various types of azo dyes could

be degraded by anaerobic bacteria. This gives an indication that azo dye reduction

mechanisms are non-specific reactions (Moutaouakkil et al., 2003 and Hong et al.,

63

2007). Under this condition, in order for dye degradation to occur, organic carbon

or energy source from simple substrates such as glucose, starch, acetate or ethanol or

complex substrates such as whey and tapioca are required (Chinwetkitvanich et al.,

2000, Isik and Sponza, 2005a, van der Zee and Villaverde, 2005). The rate of

degradation defers depends on the addition of co-substrate and the dye structure itself

(Pandey et al., 2007).

First-order kinetics with respect to dye concentrations has been reported by

many researchers for the mechanisms of dye decolorisation (Isik and Sponza, 2004a;

Ong et al., 2005b; Lourenco et al., 2006). According to van der Zee (2001a), the

biological reduction of mono-azo dyes by anaerobic bioreactors followed the first

order kinetics without any lag phase. Multiphase kinetics was observed for biological

reduction of dyes containing azo linkages such as diazo and polyazo dyes.

Meanwhile, Wijetunga et al., (2007) reported biodegradation of Acid Red 131 and

Acid Yellow 79 under anaerobic condition using mixed anaerobic granular sludge

followed by the first order and second order kinetics, respectively. Other researchers

found dye decolorization occurring according to zero-order kinetics (Brown, 1981

and Dos Santos et al., 2004). Degradation of azo dyes (Acid Orange 7 and Reactive

Red 2) by anaerobic granular sludge demonstrates zero order kinetics for biological

dye reduction and second order kinetics for chemical dye reduction as a function of

sulfide and dye concentrations (van der Zee, 2003b). The contradictory findings may

be due to the different experimental conditions that imposed different rate-limiting

step in the reduction of azo dye.

3.4.3.3 Anoxic Dye Degradation Process

Degradation of azo dyes could also occur under anoxic condition by

maintaining the oxygen concentration levels in the range of 2.5-3.0 mg/L (Mohan et

al., 2007b). Dye degradation under anoxic condition is considered as a non-specific

64

enzymatic reaction (Pearce et al., 2003 and Silveira et al., 2009). Azo dye

decolorizations by mixed aerobic or facultative anaerobic microbes are reported to

occur under anoxic conditions (Kapdan et al., 2000, Moosvi et al., 2005, Ren et al.,

2006; Dafale et al., 2008). Aeromonas hydrophila strain, new isolated species found

that could decolorize triphenylmethane, azo and anthraquinone dyes with more than

85% decolorization for azo and anthraquinone dyes within 36 hours under anoxic

condition (Ren et al., 2006).

Decolorization of Remavol Black-B under anoxic condition was found to fit

the first order kinetics with respect to dye concentration and electron donor (carbon

source) as well as operational parameters, pH and temperature (Dafale et al., 2008).

Significant degradation of azo dyes by a specific bacterial consortium was achieved

in a two stage anoxic-oxic reactor system with 84% and 80% for color and COD

removal in raw textile wastewater (Dafale et al., 2008). A novel bacterial strain

isolated from soil samples contaminated with textile wastewater, identified as

Pseudomonas sp. SUK.1, is capable of decolorizing Red BLI (50 mg/L) up to

99.28% within 1 hour under static anoxic condition at a pH range of between 6.5 to

7.0 and temperature at 30oC (Kalyani et al., 2008).

Dye degradation under anoxic conditions by facultative, anaerobic and

fermentative bacteria seems to be affected by the type of substrate used as the

external carbon sources (Pandey et al., 2007). The azo dye decolorization (Reactive

Red 22) under anoxic condition was significantly decreased when glucose was used

as the main carbon source for Pseudomonas luteola (Chang et al., 2001).

65

3.4.4 Mineralization of Aromatic Amines

The mineralization of aromatic amines can be considered as the second stage

of dye degradation process after the cleavage of the azo bond. At this stage the

amine compounds will be converted into harmless end products under aerobic

condition. This degradation stage is very important since the persistent occurrence

of these substances may impose significant adverse effects on the ecosystem.

Many of the aromatic amines released from the anaerobic degradation of azo

dye are further mineralized in the aerobic conditions. Under aerobic treatment

conditions, aromatic amines can be mineralized by non-specific enzymes through the

hydroxylation and ring opening of the aromatic compounds by incorporating two

oxygen atoms (Zissi and Lyberatos, 1996 and Pandey et al., 2007). The aerobic

degradation of aromatic amines has been extensively studied such as the degradation

of aniline (Konopka, 1993), chlorinated aromatic amines (Loidle et al., 1990),

carboxylated aromatic amines (Russ et al., 1994), sulfonated aromatic amines (Sen

and Demirer, 2003), benzene-based aromatic amines (Cinar et al., 2008) and

nitroaniline (Khalid et al., 2009). Biodegradation of sulfanilic acid and 1-amino-2-

napthol has been successfully demonstrated by a bacterial culture in an aerobic

rotating biological contactor (Coughlin et al., 2002). However, there are certain

types of aromatic compounds that cannot be further degraded even in aerobic

condition especially byproducts from the cleavage of reactive azo dye Reactive

Black 5, Reactive Violet 5 and Direct Black 8 (Panswad and Luangdilok, 2000; Libra

et al., 2004; Sponza and Isik, 2005).

The conversion of the aromatic amines is generally carried out by specialized

aerobes. Under microaerophilic conditions where DO concentrations are in the range

of 0.2 to 0.5 mg/l, certain aromatic amines including aniline, 1,4-diaminobenzene, 1-

amino-2-naphthol; catechol and 4-amonobenzoic acid can be completely degraded by

Shewanella decolorationis S12 via the oxidative cleavage of the aromatic ring (Xu et

al., 2007). Mixed bacterial culture identified as Acinetobacter sp., Citrobacter

66

freundii and Klebsiella oxytoca, showed complete degradation of 100 mg/L of

nitroaniline within 72 hours under aerobic conditions (Khalid et al., 2009).

Aromatic amines are considered as stable biotransformation byproduct of azo

dye degradation under anaerobic conditions due to the resistance for further

degradation under anaerobic conditions (Stolz, 2001 and Sponza and Isik, 2005).

However, amines with simple structure were reported able to be degraded under

anaerobic condition such aniline and nitroaromatic compounds (De et al., 1994 and

Razo-Flores et al., 1999). Complete degradation of aniline by methanogenic granular

sludge was reported by Kato et al. (1993) and Tan et al. (1999). Amines that could

not be further degraded will remain and contribute to the untreated COD level in the

effluent.

The amine compounds can be autoxidized when exposed to air. This reaction

will cause the colorless anaerobic dye degradation byproduct to become colored with

difference color intensity. The increase of color during autoxidation of aromatic

amines was confirmed by several researchers (Cruz and Buitron, 2001; Libra et al.,

2004; Sponza and Isik, 2005). This reaction may reduce the overall percentage of

color removal and further investigations are required to overcome this problem.

As discussed in the earlier sections, dye degradation mainly occurred under

anaerobic or anoxic conditions while further degradation of the aromatic amines

mainly takes place under aerobic conditions. Hence, many researchers are focusing

more on having both anaerobic and aerobic conditions in textile wastewater

treatment processes for complete and effective dye degradation either as integrated or

sequential treatment systems.

67

3.5 Treatment System for Biodegradation of Azo Dyes

The requirement of azo dye cleavage prior to degradation through oxidative

reaction of the aromatic amines allow the process of anaerobic and aerobic reaction

phase as the logical prerequisite requirement for complete biodegradation for most

colored substances through biological treatment (Knackmuss, 1996 and van der Zee

and Villaverde, 2005). The applications of both anaerobic and aerobic conditions are

indeed required for complete degradation of azo dyes (Melgoza et al., 2004). These

conditions have become important features that need to be considered in designing

the treatment system for textile wastewater.

The treatment condition that would be best for complete mineralization of the

azo dye could be appropriate in two approaches. It uses two separate compartments

or a reactor that separates the sludge in sequential anaerobic/aerobic treatment

system (Khelifi et al., 2008) or integrated anaerobic/aerobic treatment in a single

reactor system (Frijters et al., 2006 and Cinar et al., 2008). The study on the dye

degradation process has been conducted by many researchers either in sequential

treatment system such as a series of reactor systems or integrated treatment in a

single reactor using sequential batch reactor. The study on dye biodegradation is

also conducted under limited oxygen supply such as anoxic or microaerophilic

condition, either in sequential or integrated reactor system. Different types of media

have also been used as the degradation agents such as suspended biomass, biofilms

and granules in different types of reactor systems to achieve the most effective

treatment system for dye degradation of textile wastewater.

68

3.5.1 The Sequential Anaerobic/Aerobic Reactor System

In sequential anaerobic and aerobic reactor system, two separate reactors are

used. The wastewater is first treated under anaerobic condition in an anaerobic

reactor followed by an aerobic reactor for the aerobic treatment phase. The studies

on the conversion of azo dyes using a sequential anaerobic and aerobic reactor

system have been extensively studied by many researchers (Brown and Hamburger,

1987; Rajaguru et al., 2000; Kapdan et al. 2003; Libra et al. 2004; Mohanty et al.,

2006). Materials such as charcoal and calcium alginate beads have been used in dye

degradation process as the support materials for the immobilization of

microorganisms in the reactor system. Activated sludge and anaerobic granular

sludge are some of the common biomass used as the source of the microorganisms

for biodegradation of textile wastewater. Table 3.4 summarizes some of the studies

conducted on dye degradation using a sequential anaerobic and aerobic reactor

system.

In this sequential treatment process, the main substrate source that represents

the organic loading are consumed anaerobically and at the same time the cleavage of

the azo dye takes place producing the aromatic amines as the byproduct. During the

aerobic reaction phase, the amines are used as the additional carbon and energy

sources for the microbes in the aerobic reactor. However, in the case of recalcitrant

amines such as the sulphonated aromatic amines (Tan and Field, 2000 and Tan et al.,

2005), these amines either remain in the reactor system or escape with the released

effluents.

The first anaerobic–aerobic full-scale treatment of textile wastewater was

applied in the Netherlands in 1999 by a textile company known as Ten Cate Protect

dealing with vat, disperse and reactive dyes. The company managed to remove 80-

95% of the dyes in the anaerobic reactor system. The removal of reactive dyes took

place in the anaerobic treatment while the absorption of vat and disperse dye and also

69

the mineralization of the aromatic amines occurred in the aerobic reactor (Frijters et

al., 2006).

Overall observation shows that high color removal is achieved under

anaerobic condition while higher aromatic amines and COD removal can be

accomplished under aerobic condition. A coupled system with anaerobic and aerobic

reaction mode has confirmed to be a successful strategy in achieving complete

biodegradation of azo dyes. Isik and Sponza (2006) suggested that having post

aerobic treatment step would provide even more complete mineralization of textile

wastewater. Increase in the retention time under anaerobic condition would increase

the COD and color removal (Ong et al., 2005a). Most of the degradation of organic

compound occurred at the early stage of aerobic reaction, so increase in the HRT of

the aerobic system does not really effect the COD removal.

The addition of organic loading rate would improve the percentage of color

removal (Ong et al., 2005b). This is because an increase in the organic loading rate

means more of substrate is added into the treatment system. The presence of

substrates such as glucose, sucrose, acetic acid acted as the electron donor. Increase

of these substances multiplies the amount of electron donor transferred to the N=N

bond and results with increase in color removal. Increase in organic loading rate

showed a slight increase in COD removal due to the increase in the biomass

production under aerobic condition. However, the COD removal deteriorates in the

anaerobic treatment system when the organic loading rate increases. The reduction

effect on COD removal is more obvious when there is increase in loading rate due to

increase in the concentration of dyes. When comparing between static and shaking

conditions, the static condition would exhibit more percentage color removal

(Moosvi and Madamwar, 2007).

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3.5.2 The Integrated Anaerobic/Aerobic Reactor System

Complete degradation and mineralization of dye-containing wastewater

require both anaerobic and aerobic conditions. As discussed in the earlier section,

many researches have been conducted with both anaerobic and aerobic treatment

conditions for textile wastewater treatment. However, using two or more separate

reactor systems may not be economical as it requires more area for reactor set-up.

In order to achieve complete dye degradation for textile wastewater within a suitable

working area, an economical budget integrated system has been introduced. In the

integrated treatment system, the most important part is the development of different

microniches occupied by different types of microorganisms. Control on the oxygen

level is very important in order to develop different microniches in the reactor

column. Type and concentration of co-substrate present in the wastewater may also

influence the oxygen level within the reactor system during the degradation process

(Tan et al., 2000). This means choosing the appropriate type and amount of substrate

for use in the treatment system are also important aspects that need to be considered

to achieve the desired removal and stability performance of the reactor system.

The integration of different microniches will be useful for the degradation of

several types of pollutants in a single compartment or reactor system. Table 3.5

shows the summary of the integrated system of anaerobic and aerobic dye

degradation process in a sequential batch reactor system. Based on the results of the

integrated treatment system, most of the cleavage of the azo bond takes place under

anaerobic condition while mineralization of the aromatic amines occurs mainly under

aerobic condition. The results are comparable to the treatment systems that used

separate reactor system.

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Table 3.4: Sequential anaerobic-aerobic treatment system for dye degradation

Treatment system Dye/Wastewater Biomass/ Microorganisms Removal Performance Reference

AnAHR-UASB Azo dyes (Siriusgeib and Siriuslichtbraun).

Mesophilic anaerobic granular sludge and activated sludge

56-90% (color); Methane production rate 22 mg COD/L/day

Kalyuzhnyl and Sklyar (2000)

UASB (HRT:24h) → Aerobic tank (HRT:16 h)

Simulated textile effluent; Reactive azo dye + modified starch

Granules UASB: 73% (COD); 84% (BOD); 64% (Color); Aerobic tank: 12% (COD); 11% (BOD); 11% (Color). Overall: 84% (COD); 96% (BOD); 75% (Color)

O'Neill et al., (2000a)

UASB→CSTR Remazol Black-5 (100 mg/L)+ glucose

Anaerobic granule UASB: 92-87% (COD); 50-76% (Methane Gas); CSTR: 28%, 42%, 90% (SRT: 1.7, 5.7 and 11d)(COD); 90-95% (Color)

Sponza and Isik (2002)

UASB (HRT: 0.5d) → CSTR(HRT: 2d)

2,4 dinitrotoluene (DNT) (2-500 mg/L) + Molasses

Partially granulated anaerobic sludge; activated sludge

85% (COD); 90% (Color) Sponza and Atalay (2003)

RDR (HRT: 15h) Reactive Black 5 (530 mg/L) + acetic acid

- 65% (Color); 85% (methane gas) Libra et al. (2004)

UASB (HRT: 30h) → CSTR (HRT: 4.5 d)

Raw cotton textile wastewater + glucose


UASB: 9-15% (COD); 46-55% (Color); Overall: 40-85% (COD); 39-81% (Color)

Isik and Sponza, (2004a)

UASB:→CSTR Reactive Black 5 (RB 5) and Congo Red (CR)


RB5: 94.1-45.4% (COD); 79-73% (Color); CR: 92.3% (COD); 95.3-92.2% (Color) (HRT 3.5-0.5d)

Isik and Sponza, (2004b)

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Table 3.4: Sequential anaerobic-aerobic treatment system for dye degradation (Continued)


UASB (HRT: 16.5-15 h) → SBR (HRT: 2.5-2.3 h)

Direct Black 38 (6.1-213 g/m3·h) + glucose


UASB: 49% (COD); 80% (Color)(HRT: 15 d) CSTR: 67%(COD) (HRT:2.3d); Overall: 84% (COD); 52% (Color) (HRT: 2.9d).

Sponza and Isik (2005)

UASB (HRT: 24 h) → SBR (HRT:24 h)

Orange II (0-100 mg/L); STWW + sucrose

Activated sludge UASB: 25-45% (COD); >95% (Color); SBR: 90% (COD)

Ong et al. (2005a)

SBR1 (aerobic)→ SBR2 (anaerobic); HRT(24 h)

Orange II (0-100 mg/L); STWW + sucrose

Activated sludge 15% (COD-SBR1); 80% (COD-SBR2) Ong et al. (2005b)

Full scale: AnFBR (anaerobic) → Aerobic basin

Mixture of vat, disperse, reactive, anthraquinone and indigoids (40 mg/L)

- 80-90% (COD); 80-95% (Color) Frijters et al. (2006)

Fermenter (anaerobic) → Aerobic tank

Reactive Black 5(100-3000 mg/L)

Activated sludge >90% (COD); 46% (amines);HRT: 2 days Mohanty et al. (2006)

UAFB (HRT: 5-15 days)→Aerobic tank

Effluent from manufacturing textile and pharmaceuticals

UAFB (charcoal support material); Aeration tank + P. aeroginosa ;5%, (v/v)

UAFB: 37-70% (COD); 7-58%(color); Aerobic Tank: 40-45% (COD); 35-40% (color) ; Overall: 94% (COD); 89% (color).

Moosvi et al. (2007)

UASB (HRT: 6-100 days)→CSTR (HRT: 15-1days)

Mixed dyes (Reactive Black 5, Direct Red 28, Direct Black 38, Direct Brown 2 and Direct Yellow) (250 mg/L)

UASB: Partially granulated anaerobic sludge; CSTR: activated sludge from dye industry

Overall removal: 97% (COD) and 91% (Color). CSTR: TAA (70-85%); HRT: 9-6 days.

Isik and Sponza (2008)

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Table 3.5: Integrated anaerobic-aerobic sequential treatment system for dye degradation


SBR: Fill: <5min; React: Anaerobic (18/6 h); Aerobic (5 h); Settle: 55 min; Decant: 5 min.

Remazol Black B (10 mg/L) + Nutrient broth + Sodium acetate + glucose

Sewage treatment plant

73-77% (Color-PAOs); 59-64% (Color-GAOs). 66% (18h HRT); 59% (6h HRT) of anaerobic contact time.

Panswad et al., (2001a)

SBR: Fill: 15min:React: 18.5 h; 0.5 h (aerobic); Settle: 4 h; Decant: 0.25 h; Idle: 0.5 h.

Remazol Black. STWW (+ starch,+ polyvinyl- alcohol (POVH),+ carboxymethyl cellulose)

Anaerobic granules from UASB

66% (TOC); 94% (Color) Shaw et al. (2002)

SBR: VER: 75%. Intermittent agitation: 180 rpm

STWW (propionate : DB79 [1:50]); Dye (50% dye + 50% dispersing agent)

Activated sludge 65% (Color); 96% (amines); Overall removal: 96% (Color)

Melgoza et al. (2004)

SBR: Fill: 50 min; React: Anaerobic (10.5 h) / Aerobic (10 h) ; Settle: 1.5 h; Draw: 35 min; Idle: 10 min

Azo dye: Remozol Brilliant Violet 5 and Acid Orange; STWW + starch derivative

Activated sludge 80% (COD); 90-99% (Color) removal and 80% of COD removal

Albuquerque et al. (2005)

SBR: Fill: 48 h (static or mixed); Anaerobic: Aerobic (8-12h); Settle: 2 h; Draw: 48 min; Idle: 24 min

Jane Sandolane MF-RL & Rouge Sandolane MF-2BL dyes + antraquinone

Raw wastewater + aerobic sludge.

85% (soluble COD); 95% (BOD5) Goncalves et al. (2005)

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Table 3.5: Integrated anaerobic-aerobic sequential treatment system for dye degradation (Continued)


SBR: Different anaerobic/aerobic residence time (2-19 h)

Vinylsulphonyl, monochlortriazine, Remazol Rot RR.

Alcaligenes faecalis and Comamonas acidovorans

90% (Color-4-6 h of anaerobic cond. At 60 mg/L of dye conc.); >85% (COD); >90% (Color) at 500 mg/L dye conc.

Kapdan and Oztekin (2006)

SBCR: Fill: 3 h; React: 20 h; Draw: 0.45 h; Idle: 0.15 h.

Acid Orange 7; STWW + sucrose

Granular activated carbon; dye degrading microbes

88% (COD); 100% (Color) Ong et al. (2008b)

SBR: Fill: 3 min; Anaerobic: Aerobic reaction 48 hrs(24:23.9); 12 hrs(12: 11.9); 12 hrs(6:5.9); Draw (3 min)

Remazol Brilliant Violet 5R; STWW + glucose

Facultative mixed bacterial culture

Anaerobic: 75% (COD); 72%, 89% and 86% (color); Aerobic:64%, 92% and 89% (amines)

Cinar et al. (2008)

SBR: HRT: 8 h; Working vol.: 2 L; VER: 50%,

Malachite green (MG) as main carbon source

Aerobic digested sludge (+nutrient + micronutrient);

92.3% (Color) Mondal and Ahmad (2009)

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CHAPTER 4

DEVELOPMENT OF FACULTATIVE ANAEROBIC GRANULES

4.1 Introduction

In recent years, the ability of biodegradation process for treating different

types of wastewater involving both anaerobic and aerobic processes has been widely

reported in the literature (Jang et al., 2003; Arrojo et al., 2004; Su and Yu, 2005;

Yilmaz et al., 2007; Wang et al., 2009b). Table 4.1 summarizes some of the studies

conducted on anaerobic and aerobic/anoxic treatment process in SBR system

treating different types of wastewater. The intermittent anaerobic and aerobic

treatment approach showed high percentage of COD and nutrient removal. The

intermittent anaerobic and aerobic treatment process in wastewater treatment is

important in obtaining complete degradation process particularly for treating

recalcitrant compounds.

Since color removal and complete mineralization of the dyestuff could be

achieved through the combination of both processes as discussed in Chapter 3, many

studies have been conducted on the textile wastewater using the intermittent

anaerobic and aerobic treatment approach (Kapdan et al., 2003; Goncalves et al.,

2005; Albuquerque et al., 2005; Franciscon et al., 2009). Nevertheless, the

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integrated process used so far, requires the use of two separate reactors to suit the

different oxygen requirements of the degradation processes.

An attempt was therefore made to develop FAnGS which are capable to live

and work under both anaerobic and aerobic conditions. While achieving both

conditions fulfill the requirements to treat textile wastewater, the use of microbial

granules enhances the treatment system as discussed in the previous chapter.

Furthermore, the application of FAnGS under anaerobic and aerobic conditions

requires the use of a single reactor.

In this study, the progress of the FAnGS development was monitored and

their properties were determined. The effectiveness of the FAnGS in treating

synthetic wastewater during the development process was also assessed. This

chapter presents the results of the work conducted.

4.2 Materials

All of the chemicals/reagents used in this experimental work are listed in

Table 4.2 while the analytical equipments are listed in Table 4.3. Distilled water

generated from a water distiller was used throughout the experiment. Trace

elements and mixed dye were prepared as stock solutions and were kept at room

temperature for daily use. Synthetic textile dyeing wastewater (STDW) was

prepared three times a week for influent supply to the reactor system. In order to

avoid contamination, the mixture of the synthetic wastewater, trace elements and

dye were carried out in a laminar flow.

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Table 4.1 Sequential batch reactor system with intermittent anaerobic/aerobic/anoxic reaction phase treating variety types of wastewater Reference Biomass Wastewater Reaction phase/HRT Performance/Remark

Jang et al. (2003)

Activated sludge of MWTP

SWW (Carbon source: glucose)

HRT: 6 hours; Feed: 15 min; Aerobic: Anoxic: 4.75 hours (2:1); Settling: 45 min; Decant: 15 min

Removal: Nitrification (97%); COD (95%)

Lin et al. (2003)

Sludge from MWTP

SWW (Carbon source: acetate)

HRT: 6 hours; Feed: 5 min; Anaerobic: 120 min; Aerobic: 226; Settling: 5 min; Decant: 4 min.

P-accumulating organisms were enriched in the granule as the substrate P/COD ratio was increased.

Meyer et al.(2003)

Sludge enriched with GAO

SWW (Carbon source: acetate)

HRT: 8 hours; Feed: 5 min; Anaerobic 1.5 hours; Aerobic: 2 hours; Settling: 25 min; Decant: 5 min

Dense and highly active aggregates of microbes can lead to mass transport limitation

Zhu and Wilderer (2003)

Activated sludge

SWW (Carbon source: glucose & pepton)

HRT: 6 hours; Feed: 15 min; Anaerobic: 1 hour 40 min; Aerobic: 3.5 hours; Settling: 20 min; Decant: 15 min

Removal: COD (78-94%)

Arrojo et al. (2004)

Sludge from industrial wastewater

Industrial WW and SWW (Carbon source: acetate)

HRT: 3 hours; Feed: 3 min; Anoxic/Aerobic: 171 min; Settling: 1 min; Decant: 30 min; Idle: 3 min

Removal: Nitrification (70%); COD (85-95%)

Schwarzenbeck et al. (2005)

Sludge biomass

Dairy WW HRT: 8 hours; Feed: 5 min; Anaerobic: 0-60 min; Aerobic: 335-345 min; Settling: 5 min; Decant: 4 min; Idle: 5 min.

Removal:90% (CODtotal); 80% Ntotal; 67% Ptotal

Su and Yu (2005)

Activated sludge

Soy-bean WW HRT: 4 hours; Feed: 5 min; Aerobic: 220 min; Settling: 5 min; Decant: 10 min

Removal: COD (98-99%- after 7 days)

Cassidy and Belia (2006)

Flocculating sludge

Abattoir WW HRT: 7.2-6.2 hours; Fill: 120 min (anaerobic); Aerobic : 220 min; Settle: 2-60 min; Decant/Idle: 15 min.

Removal: COD and Phosphates (>98%); Nitrogen (>97%)

Qin and Liu (2006)

Activated sludge

SWW (Carbon source: ethanol)

HRT: 6 hours; Feed: 5 min; Aerobic: 230; Anaerobic: 119 min;Settling: 2 min; Decant: 4 min.

Removal: COD (95-97); Nitrogen (99-100%)

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Table 4.1 Sequential batch reactor system with intermittent anaerobic/aerobic/anoxic reaction phase treating variety types of wastewater (Continued)

Reference Biomass Wastewater Reaction phase/HRT Performance/Remark Zhang et al.

(2006) Activated sludge of MWTP

Raw swine manure WW

HRT: 3.3 days; Cycle time: 8 hours; Anaerobic: 1hour 15 min; Anoxic/aerobic: 2 hour 45 min; Anaerobic: 1 hour 30 min; Anoxic/aerobic: 2 hours; Settling: 30 min

Removal: Total Nitrogen (97.5%); Total Phosphorus (95%); COD (96%); BOD5 (100%); Turbidity (95%)

Wang et al. (2007)


SWW (Carbon source: glucose & 2,4-dichlorophenol)

HRT: 8 hours; Cycle time: 4 hours; Feed: 4 min; Anoxic (no stirring): 30 min; Aerobic: 200-210 min: Settle: 1-11 min; Decant: 5 min

Removal: COD (95%); 2,4-dicholophenol effluent (94%)

Yilmaz et al. (2007)

Aerobic granule

Abattoir WW/SWW (0-100% ratio)

HRT: 6.7-13.3 hours; Feed: 18 min; Anaerobic (50-60 min); Aerobic (160-400 min); Post Aerobic (80 min)

Removal: Soluble Nitrogen (93%); Soluble COD (85%); Soluble Phosphorus (89%)

Kim et al. (2008)


SWW (Carbon source: glucose & acetate)

HRT: 6H; Aerobic: 4.75 hours; Anaerobic: 1.25 hours; Feed: 0.25 hours; Settle: 0.75 hours; Decant: 0.25 hours

Removal: COD (95-98%); NH4+-N (47-99%)

depending on the NH4+-N loading rate

Lemaira et al. (2008a)

Aerobic granules

SWW; different ratio of SWW and abattoir WW

HRT: 13.3 hours; 8 hour cycle; Feed: 18 min; Anaerobic/anoxic: 60 min; Aerobic: 315 min; mixed anoxic: 80 min; Settle: 2 min; Decant: 5 min

Total removal: Soluble COD (85%); Ammonia (99%); Phosphates (98%)

Wichern et al. (2008)

Flocculating sludge

Dairy WW HRT: 8 hrs; Feed: 60 min; Anaerobic: 60-0 min; Aerobic: 334-405 min; Settling: 15-4 min; Decant: 5 min; Idle: 5 min

Nitrogen removal rate: 4.5-9.0 kgCOD/m3·d.

This Study (2009)

Mixed sludge with anaerobic granule

Synthetic textile dyeing WW

HRT: 6 hours: Feed: 5 min; Anaerobic: 80 min; Aerobic: 260 min; Settling/Decant/Idle: 5 min

Removal: COD (93%); Ammonia (95%); Color: (62%)

*MWTP-municipal wastewater treatment plant; WW-wastewater; SWW-synthetic wastewater; GAO-glycogen accumulating organisms.

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Table 4.2: List of reagents used in the experiment

Chemical Solutions/Reagents Applications

Synthetic wastewater Wastewater model compounds (Section 4.2.1) Trace element

Mixed dyes

Sludge sewage Granular precursor (Section 4.2.2) Anaerobic granules

Dye degrader microbes

COD reagent COD measurement (Section 4.2.4.4(b))

Nessler reagent Ammonia measurement (Section 4.2.4.4.(c))

Concentrated H2SO4 Digestion of mineral (Section 4.3.3) Nitric Acid

Gold sputter Sample coating (FESEM)

Crystal violet

Gram Staining (Section 4.2.4.5(a)) Iodine

Ethyl alcohol

Safranin

Nitrogen gas Anaerobic condition (Section 4.2.4.5 (b))

Oil emulsion Observation under 100x microscope magnification

Sodium hydroxide, NaOH pH adjustment

Hydrogen cloride, HCl

4.2.1 Wastewater Composition

Synthetic wastewater with the following composition was used: NH4Cl 0.16

g/L, KH2PO4 0.23 g/L, K2HPO4 0.58 g/L, CaCl2⋅2H20 0.07 g/L, MgSO4⋅7H2O 0.09

g/L, EDTA 0.02 g/L and trace solution 1 ml/L. The carbon sources used in this

experiment were glucose (0.5 g/L), ethanol (0.125 g/L) and sodium acetate (0.5 g/L).

The trace elements used were based on the composition recommended by Smolders

et al. (1995). The composition of the trace element was H3BO3 (0.15 g/L),

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FeCl3⋅4H2O (1.5 g/L), ZnCl2 (0.12 g/L), MnCl2⋅4H2O (0.12 g/L), CuCl2⋅2H2O (0.03

g/L), NaMoO4⋅2H2O (0.06 g/L), CoCl2⋅6H2O (0.15 g/L), and KI 0.03 g/L. Mixed

azo dyes consisted of Sumifix Black EXA, Sumifix Navy Blue EXF and Synozol

Red K-4B with total concentrations of 50 mg/L was used in this study. The mixture

gave an initial COD of 1270 mg/L; 1020 ADMI (≈102 Pt-Co) and average ammonia

concentration of 38 mg/L. The pH of the synthetic wastewater was adjusted to 7.0 ±

0.5 before feeding.

Table 4.3: List of equipment used in the experiment

Equipments Manufacturer/Product

IFAnGSBioRec UTM/Fabricated

Filter paper Wartmann/125 mm diameter

Filter apparatus Sartorius/DOA-P504-BN Incubator ELBA/EMO-1706 Furnace Interscience Sdn. Bhd./Carbolite

Glass column UTM/Fabricated Orbital shaker Protech/Orbital Shaker Model 720

Spectrophotometer HACH/DR/4000U COD reflux HACH/DRB 200

Flame Atomic Absorption Spectrophotometer Perkin Elmer/Analyst 400

Stereo microscope Leica Mycrosystems Wetzlar GmbH/Leica DMLS

Digital image management and analyzer ARC PAX-CAM/PAX-ITv6

Scanning electronic microscope Carl Zeiss/Zeiss Supra 35 VPFESEM

Coating system Biorad/Polaron Division SEM Coating System

DO meter & Data acquisition software ISTEK®/PH/ISE/DO Meter Model 125 PD

Water bath Memmert / Julabo PT30511 Autoclave Hirayama/Hiclave HV-50 Air pump RESUN/LP-100 air-pump

Water distiller Apex/Water Distiller

Laminar flow ERLA/CFM Series Air Cabinet Laminar Flow

Sieve WYKEHAM FARRANCE SLOUGH ENGLAND/British Standard Test Sieve (0.1-2.0 mm)

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As discussed in Chapter 3, the most commonly used dyes in the textile

processing are the azo dye. Hence, a mixed azo dye was applied in this study in order

to provide a closer resemblance to the real textile wastewater that usually consist of

several mixtures of dyes. Furthermore, synthetic textile wastewater was used in this

study in order to ensure the consistency of the wastewater quality with respect to the

compositions and concentrations. High variation in the wastewater compositions

may result in difficulty in analyzing and interpreting the results of the study.

4.2.2 Granules Precursor

The development of the FAnGS involved the mixture of sludge sewage

treatment plant (Taman Sutera, Indah Water Konsortium Treatment Plant System)

and textile wastewater treatment plant (Ramatex Industry Sdn. Bhd., Sri Gading

Industrial Park). Sludge from sewage treatment plant was used since the sludge

contains very high concentration of microbial populations that may be useful for

granule development. The sludge from textile wastewater was used since the

microorganisms that are present in the textile sludge may have already acclimatized

to the textile wastewater and would be easier to grow in the synthetic textile

wastewater used in the study. The acclimatized microbes in the textile sludge may

also have high capability in dye degradation process. This is important for the

development of granules that is going to be used for treating textile wastewater. An

equal volume of sludge from a municipal sewage treatment plant and a textile mill

wastewater treatment plant were mixed. The sludge inoculums were sieved with a

mesh of 1.0 mm to remove large debris and inert impurities. Figure 4.1 shows the

location of the sewage treatment plant (Indah Water Konsortium Treatment Plant

System, Taman Sutera) and textile wastewater treatment plant (Ramatex Industry

Sdn. Bhd., Sri Gading Industrial Park) where the sludge samples were taken.

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Figure 4.1 Location of textile industry; Ramatex Industry Sdn. Bhd., Sri Gading

Industrial Park, Batu Pahat and sewage treatment plant; Indah Water Konsortium

Treatment Plant System, Taman Sutera, Skudai.

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Anaerobic granules collected from a UASB treating paper mill industrial

effluent (Denmark) were used as the seed sludge. For every 1 L of sludge mixture,

about 100 mL of anaerobic granules of sizes less than 1 mm diameter were used as

seed for the granulation process. The MLSS of the anaerobic granules were 3.3 g/L.

Dye degrader microbes were also added into the sludge mixture. These microbes

were obtained from previous research that has successfully isolated the microbes

from textile wastewater treatment plants (Nawahwi, 2009 and Ibrahim et al., 2009).

The sludge mixtures were acclimatized with synthetic textile dyeing wastewater for

two months prior to the experimental start-up.

4.2.3 Reactor Set-up

The schematic representation of the IFAnGSBioRec set-up is given in Figure

4.2. The design of the reactor used in this study was based on several studies

conducted by previous researchers such as Wang et al. (2004) and Zheng et al.

(2005) with several modifications. A water jacketed column reactor was used in the

study. The column was designed for a working volume of 4 L with internal diameter

of 8 cm and a total height of 100 cm. The water jacketed column was designed to

provide temperature controlled conditions by allowing the circulation of hot water

from a water heating circulation system (Julabo PT30511) to the water jacketed

column of the reactor system. The temperature of the heating system was set at 30oC.

The wastewater was fed into the reactor from the bottom of the reactor. Air was

supplied into the reactor by a fine air bubble diffuser also located at the bottom of the

reactor column. The decanting of the wastewater took place via an outlet sampling

port located at 40 cm height from the bottom of the reactor. The reactor system was

equipped with dissolved oxygen meter and pH meter (Istek ®, Korean Model 125

PD) for the continuous monitoring of the DO and pH level throughout the

experiment. The 4-L laboratory scale of IFAnGSBioRec used in this study is shown

in Figure 4.3. Figure 4.4 shows the preparation carried out in the development of

FAnGS for textile wastewater treatment.

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1. Influent tank 2-5. Peristaltic pumps

6. Mass-flow controller 7. Air pump 8. Timer controller 9. Effluent tank Figure 4.2 Schematic layout of the IFAnGSBioRec system (Wang et al. (2004)

and Zheng et al. (2005)

Sampling point

DO probe

pH probe

Effluent

Influent

o o o o o

o o o o

o o

o o o o o

o o o o o

o o

1

7

3

9

5

8

6

2

4

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Figure 4.3 The IFAnGSBioRec system used in this study

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The FAnGS was analyzed for biological, physical and chemical

characteristics. The biological characteristic of the FAnGS was investigated in terms

of morphological, structure as well as cellular observation of the microbes within the

FAnGS. The microbial activities of the granules were investigated with respect to

their specific oxygen uptake rate (SOUR) and specific methanogenic activity (SMA).

The physical characteristics of the FGS tested include settling velocity, granular

strength and sludge volume index (SVI). The chemical aspect of the FAnGS

analyzed includes their mineral content. Profiles of the reactor system were

evaluated on the biomass concentration retained in the reactor as well as the sludge

retention time. Figure 4.5 shows the experimental analysis conducted for

characterization of the FAnGS.

4.3.1 Biological Characteristics

4.3.1.1 Morphological and Structural Observation

The morphological and structural observations of the granules were carried

out by using a stereo microscope equipped with digital image management and

analyzer (PAX-ITv6, ARC PAX-CAM). The microbial compositions of the granules

were observed qualitatively with a scanning electronic microscope (FESEM-Zeiss

Supra 35 VPFESEM). The granules were left dried at room temperature prior to

gold sputter coating (Bio Rad Polaron Division SEM Coating System) with coating

current of 20 mM for 45 s.

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Figure 4.4 Preparation frame work for granule development

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88

Figure 4.5 Characterizations of FAnGS

89

The cellular observation of the microorganisms present within the FAnGS

was carried out using gram staining. The gram staining procedures were carried out

by preparing a smear onto a glass slide. The slide was heated in order to fix the

smear onto the slide. The fixed smear was covered with one or two drops of crystal

violet for one minute. The stained smear was poured off and carefully washed with

distilled water. Then, iodine was added and left for 1 minute before once again

washed off with distilled water. The preparation was then washed with ethyl alcohol

of 95%. The slide was counterstained with safranin for 1 to 2 minutes prior to

rinsing with distilled water. A drop of oil was placed on the slide and was examined

under 100x magnification of stereo microscope (Leica DMLS). Gram-positive cells

appear violet and gram negative cells appear red when observed under the light

microscope.

4.3.1.2 Microbial Activity

The microbial activity of the FAnGS was conducted by measuring the

oxygen utilization rate (OUR), specific oxygen utilization rate (SOUR) and specific

methanogenic activity (SMA). The OUR and SOUR measurements were performed

by following Standard Methods (APHA, 2005). The OUR value was measured

during both the first and second stage of the aerobic reaction phase. The profile of

dissolved oxygen (DO) concentration in the reactor was measured continuously

online using a DO electrode (Istek®, Model 125 PD). The data were electronically

recorded using data acquisition software (Istek®, Model 125 PD).

The OUR measurement was conducted as soon as the aeration phase started.

Sample for the reactor system was taken from the sampling port and used to fill the

300 mL BOD bottle. Then, the DO probe was immediately inserted into the BOD

bottle. The sample in the BOD bottle was mixed using a magnetic stirrer at 20 rpm.

The initial DO was measured as DOa. The time taken for the DO to reduce to 2

90

mg/L was measured as T. DOb was measured at the end of each OUR measurement

(i.e. when the DO level in the BOD bottle nearly reaching to 2 mg/L). Then the

sample in the BOD bottle was returned back into the reactor. The DO measurement

is repeated with 10 minutes time interval with new samples from the reactor until the

aeration phase completed. For the SOUR measurement, the biomass concentrations

collected in the BOD bottle were quantified and measured as M. The measurement

for OUR and SOUR were conducted at room temperature. The calculations for OUR

and SOUR are given in the equations below:

where

OUR = Oxygen uptake rate (mg/L.h)

SOUR = Specific oxygen uptake rate (mL CH4/g VSS.h)

DOa = Initial dissolved oxygen (mg/L)

DOb = End dissolved oxygen (mg/L)

T = Time (min)

M = Granular biomass (mg/L)

The SMA analysis was conducted according to Erguder and Demirer (2008) with

several modifications where a 500 mL BOD bottle seeded with FAnGS with final

concentrations of 1-2 g VSS/L and basal medium (250 mL effective volume). The

bottle was purged with N2 gas mixture for 5 minutes to obtain an anaerobic

condition. The bottle was then sealed with a rubber septum. Acetate acid (HAc) was

fed into the serum bottle at the concentration of 3000 mg/L. The experiments were

conducted at room temperature (28 ± 2oC). The production of methane gas (CH4)

was determined daily for 5-7 days by using liquid displacement methods containing

concentrated KOH stock solution (20 g/L) (Erguder and Demirer, 2005a). After

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each gas measurement, the bottle was manually shaken. At the end of the SMA

assay, the VSS content in the bottle was measured. The SMA was calculated as the

maximum CH4 produced per gram of VSS per hour (mL CH4 g/VSS.h) (Zitomer and

Shrout 1998).

4.3.2 Physical Characteristics

4.3.2.1 Settling Velocity

The settling velocity was determined by recording the average time taken for

the individual granule to settle at a certain height in a glass column filled with tap

water (Linlin et al., 2005).

4.3.2.2 Sludge Volume Index

The SVI value could be calculated by measuring the bedvolume of the sludge

biomass in the reactor divided with the dry weight of the biomass in the reactor. The

bedvolume can be obtained by measuring the bed height of the sludge biomass that

settled in the reactor 5 minutes after the aeration phase stopped. The bedvolume is

obtained by multiplying the bedheight with the surface area of the bedcolumn (Beun

et al., 1999).

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4.3.2.3 Granular Strength

Determination of the granules’ strength was based on Ghangrekar et al.

(1996). Shear force on the granules was introduced through agitation using an

orbital shaker (Protech Orbital Shaker Model 720) at 200 rpm for 5 minutes. At

certain degree of the shear force, parts of the granules that were not strongly attached

within the granules will detach. The ruptured granules were separated by allowing

the fractions to settle for 1 minute in a 150 ml measuring cylinder. The dry weight

of the settled granules (SG) and the residual granules in the supernatant (RG) were

measured. The ratio of the solids in the supernatant (RG) to the total weight of the

granular sludge (SG+RG) used for granular strength measurement was expressed as

the integrity coefficient (IC) in percent. This percentage indirectly represents the

strength of the granules. The higher the IC value the lesser the strength of the

granules and vice versa. The calculation for the IC value is given in the equation

below.

where

IC = Integrity coefficient

RG = Residual granules (mg)

SG = Settled granules (mg)

4.3.2.4 Biomass Concentration

The biomass concentration in term of mixed liquor suspended solid (MLSS)

and mixed liquor volatile suspended solid (MLVSS) were measured according to

93

Standard Methods (APHA, 2005). Ten (10) mL of samples were filtered using 45μm

filter paper (Wartmann) using a filter apparatus (DOA-P504-BN). The filter papers

with samples were then weighed (Ma) before heated at 150oC for one hour. After the

samples were allowed to cool in the desiccators, the filter papers were weighed again

(Mb). Then the filter papers were heated at 550oC for 15 min and were weighed (Mc)

after the filter papers were allowed to cool in the desiccators. The MLSS and

MLVSS were measured using Eq. 4.4 and Eq. 4.5, respectively.

where,

MLSS = Mixed liquor suspended solid (mg/L)

MLVSS = Mixed liquor volatile suspended solid (mg/L)

Ma = Weight of filter paper with sample before heating at

150oC (mg)

Mb = Weight of filter paper with sample after heating at

150oC (mg)

Mc = Weight of filter paper with sample after heating at

550oC (mg)

V = Sample volume (mL)

4.3.2.5 Sludge Retention Time

Sludge retention time (SRT) is determined as follows (Beun et al., 1999).

94

where,

Xr = Mixed liquor volatile suspended solid in reactor (mg/L)

VT = Total working volume in reactor (L)

Qe = Effluent flowrate (L/d)

Xe = Mixed liquor volatile suspended solid in effluent (waste

sludge) (mg/L)

4.3.3 Chemical Characteristics

The granules were analyzed chemically for their mineral content which

includes Ca2+, Mg2+, Na+, K+, Fe2+, Ni2+ and Co2+. The procedures for acid digestion

of the granular sample were based on Ghangrekar et al. (2005) with some

modification. The granular sample was evaporated to dryness in an incubator

(EMO-1706) at 105oC. About 5 g of the dry sludge were dissolved in a minimum

quantity of concentrated sulfuric acid giving a brownish solution. Then, concentrated

nitric acid was added into the solution until it turned colorless. The solution was

diluted with distilled water to a total volume of 25 mL for mineral and metal

determination. The mineral contents were determined by using a Perkin Elmer

Analyst 400 Flame Atomic Absorption Spectrophotometer (FLAA).

95

4.3.4 Removal Performance

Synthetic wastewater samples of the influent and effluent from the

IFAnGSBioRec were used for the quantification of removal performance of the

Chemical Oxygen Demand (COD), color and ammonia. All samples were

centrifuged prior to measurement. This step is carried out to prevent any

interference that may caused by the presence of suspended particles in the samples.

4.3.4.1 Color

A quantitative estimation of the color intensity was carried out by

calorimetric approach. Color was analyzed using a HACH Spectrophotometer

(DR/4000U) according to Procedure No.1660. Using distilled water as blank, the

method gives color value in terms of American Dye Manufacturing Index (ADMI)

4.3.4.2 Chemical Oxygen Demand

Chemical oxygen demand was quantified using a HACH Spectrophotometer

(DR/4000U) according to Procedure No. 2720. Each sample was added to the COD

reagent (High Range Digestion for COD, Cat. 21259-15) and was digested at 150oC

for 2 hours in COD reactor (Model DRB 200). After the digestion was completed,

the sample was allowed to cool at room temperature before the COD levels were

measured using the spectrophotometer.

96

4.3.4.3 Ammonia

The determination of ammonia was according to the Nessler Method (APHA

2005). One (1) mL of sample was diluted using 25 mL deionized water. Then three

drops of mineral stabilizer was added into the samples and mixed. This was followed

by adding another three drops of dispersing agent and mixed. Lastly, 1 mL of

Nessler reagent was added into the sample and mixed again before the sample was

left to react for 1 minute. The sample was then measured using a HACH

Spectrophotometer (DR4000/U) according to Method No 2400.

4.4 Experimental Procedures

During the start up period, 2 L of mixed sludge and 2 L of synthetic textile

wastewater were added into the reactor system making the final volume of 4 L with a

total sludge concentration after inoculation of 5.5 g/L. The system was supplied with

external carbon sources consisting of glucose, sodium acetate and ethanol which

gave a substrate loading rate of 2.54 kg COD/m3·d. The calculation for the OLR is

given in Appendix A. The hydraulic retention time (HRT) of the reactor was 6 hours

and was divided into several phases.

The reactor was operated in successive cycles of 6 hours, each one with an

intermittent anaerobic and aerobic reaction phase. All of the operation of peristaltic

pumps, circulation of influent, air diffuser and decanting process were controlled by

means of a timer. The reaction phase was started with an anaerobic phase for 40

min, followed by an aerobic reaction phase for 130 min. The reaction phase was

repeated with another 40 min of anaerobic phase and 130 min for a second aerobic

reaction phase. During the anaerobic reaction phase, the wastewater in the reactor

system was allowed to circulate. The wastewater from the upper level of the reactor

97

system was pumped out of the reactor column and returned back through the valve

located at the bottom of the reactor. The circulation process was carried out by using

a peristaltic pump (Cole-Parmer System Model; 6-600 rpm). The wastewater was

circulated at a rate of 18 L/h. The circulation system was stopped when the

anaerobic phase ended. The circulation process is required to achieve a

homogeneous distribution of substrate as well as a uniform distribution of the

granular biomass and restricts the concentration gradient. Each of the cycle

comprised of 5 min filling, 340 min reaction, 5 min settling, 5 min decanting and 5

min idle.

Samples of the synthetic wastewater from the IFAnGSBioRec were taken

twice a week. Fifteen (15) mL of influent sample as the initial value was taken from

the influent tank before the new cycle operation started, while another 15 mL of the

effluent sample was taken from the effluent tank after the effluent was released

during the decanting phase as the final values. The samples were filled in a separate

15 mL centrifuge tube. Samples were centrifuged for 5 min at 4000 rpm at 4oC in

order to pellet down all of the suspended particles from the samples. The

supernatant was used to measure the removal performance of the Chemical Oxygen

Demand (COD), color and ammonia removal.

Ten (10) mL of sample was taken into 15 mL of centrifuge tube from the top

portion of the reactor about 10 minutes after the filling stage ended. The sample was

measured as the initial concentration of the suspended solids. Another 10 mL of

sample was taken from the effluent after the decanting stage. When conducting

sampling for the measurement of the suspended solids, the effluent from the reactor

was collected in a 5L beaker. The effluent in the beaker was mixed before the

sample taken with the purpose to get a correct representative value of the suspended

particles in the effluent. These samples were used for the measurement of the

suspended particles in the influent and in the effluent. Lastly, another 10 mL of

sample volume was taken during the aeration phase. In order to get a representative

value, the samples were taken at two sampling points i.e. the upper and lower

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sampling ports. Both samples were then mixed in a beaker and analyzed for the

MLSS and MLVSS.

The bedheight of the biomass in the reactor was also measured twice a week

for the calculation of the SVI. The bedheight was measured immediately after the

settling time ended and before the wastewater was drained out during the decanting

time. The measurement of the OUR and the SMA were conducted a few days before

the experiment ended which is after the FGS has reached the maturation stage. The

granular sample was taken almost once in two weeks for the measurements of the

physical properties of the FAnGS.

Table 4.4 shows the successive phase for one complete cycle of the

IFAnGSBioRec. The dissolved oxygen (DO) concentrations remained low during

the anaerobic condition (0.2 mg/L) and reached saturation concentrations during the

aerobic phase. The superficial air velocity during the aerobic phase was 1.6 cm/s.

The calculation of the superficial air velocity is given in Appendix A. The pH

during the reaction process varied in the range of 6.0 to 7.8 and the temperature of

the experiment was at 30oC. The reactor system was operated for a period of 66

days. Two litres of the wastewater remained in the reactor after the decanting stage

yielding a volumetric exchange rate (VER) of 50%. At this settling time (5 min),

only particles with settling velocity larger than 4.8 m/h remained in the column. Any

particles having smaller settling velocity would be washed out in the effluent.

99

Table 4.4: One complete cycle of the IFAnGSBioRec

Successive Phase One complete cycle (6 hours)

Filling 5 min

React

Anaerobic Aerobic

1st phase 40 130

2nd phase 40 130

Settling 5 min

Decant 5 min

Idle 5 min

Total cycle length 360 min

4.5 Results and Discussion

4.5.1 Morphology of Facultative Anaerobic Granular Sludge

A week after inoculation in the reactor, visual and microscopic observations

of granules formation were made. At this stage, the developed granules were

composed more of loosely clumped sludge which could easily break up into pieces if

placed under vigorous shaking. Within a week, the anaerobic seed granules

underwent morphological changes from spherical in shape and black in color with

average diameter of 1 mm into smaller grey granules due to exposure to the aeration

as mentioned earlier. On day 30, two different types of granules were clearly

observed in the reactor as shown in Figure 4.6.

100

Figure 4.6 The morphological development of facultative anaerobic granular

sludge (scale bar at steady-state equals to 1mm). Pictures were taken using a stereo

microscope with magnification of 6.3X. (a) Granules developed from the activated

sludge. (b) Granules developed from anaerobic granules patches.

a

b

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Figure 4.6a shows mainly irregular-shaped with yellow colored granules

(Type A) that are solely developed from the activated sludge. In Figure 4.6b, the

anaerobic granules that have fragmented into smaller pieces have formed different

sizes of granules (Type B) that contained pieces of anaerobic granules. The outer

layer of the latter were yellow in color indicating the domination of aerobic or

facultative microorganisms while the darker spots within the granules indicate the

presence of anaerobic fragments originated from the anaerobic granules. The

formation of Type A granules could be elucidated by the mechanisms explained by

Beun et al. (1999). The development was initiated from the mycelial pellets that

were retained in the reactor due to high settling velocity. These mycelial pellets

eventually become the support matrix for the bacteria growth. Bacteria that were

able to attach to this matrix were retained and suppressed the growth of filamentous

microorganisms and became the dominant species in the reactor.

The formation of Type B granules has been discussed by Linlin et al. (2005).

These granules were formed through a series of physical and morphological changes.

The anaerobic granules initially disintegrated into smaller size flocs and debris when

exposed to aeration forces in the SBR column. Some of the granules and debris that

were too small were washed out with the effluent while the heavier ones were

retained in the column and acted as nuclei for the formation of new granules. Having

these types of granules that consisted of the combination of aerobic and anaerobic

portions within the granules could increase the possibility of degradation process that

requires both aerobic and anaerobic conditions for complete degradation particularly

for textile wastewater. Figure 4.7 shows the obvious morphological differences

between sludge particles during the initial stage of the experiment and matured

FAnGS at the final stage (day 66) of the experiment. The average sludge particles

are 0.02 ± 0.01 mm (Figure 4.7a), while the FAnGS developed with the average

particle diameter size of 2.3 ± 1.0 mm with maximum size reaching up to 4 mm

(Figure 4.7b).

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Figure 4.7 Pictures of sludge particles during the initial stage of the experiment (a)

and matured FAnGS granules at the 66 days of the experiment (b). Pictures were

taken using a stereo microscope with magnification of 6.3X (scale bar equals to 1

mm)

a

b

103

The microstructure of the FAnGS that was examined using SEM is shown in

Figure 4.8. The SEM observation of the mature granules shows the domination of

non-filamentous coccoid bacteria that is tightly linked and embedded to one another

and form a rounded shape on the surface of the granule and covered with

extracellular polysaccharides substances (EPS) (Figure 4.8a). The absence of

filamentous bacteria in the developed granules may be due to the experimental

conditions that did not favor their growth such as high concentrations of DO during

the aerobic phase (i.e. 7.0 ± 0.5 mg/L) and considerably high organic loading rates

(2.4 kg COD/ m3·d) (Chudoba, 1985; Eckenfelder, 2000; Zheng et al., 2006). Figure

4.8b shows the presence of cavities between the clumped bacteria. These cavities are

anticipated to be responsible in allowing a smooth mass transfer of substrates or

metabolite products in and out of the granules (Tay et al., 2003 and Toh et al., 2003).

4.5.2 Cellular Characterization of Facultative Anaerobic Granular Sludge

The seeding sludge under microscopic observation showed a typical

morphological structure of conventional activated sludge that contained the

filamentous bacteria as the typical dominant species. The gram staining of the

seeding sludge in Figure 4.9a shows the presence of filamentous bacteria as the main

dominating species and mostly resulted with negative gram staining. The mature

FAnGS reveal more of fat-rod and coccal-types bacterial morphotypes. The

microbial presence resulted with both positive and negative gram stains (Figure

4.9b). This shows that there are changes in the dominancy of microorganisms in the

development of the FAnGS.

104

Figure 4.8 FESEM microstructure observations on mature facultative anaerobic

granular sludge under the magnification of 10,000K. (a) Coccoid bacteria tightly

linked to one another. (b) Cavities that appear between bacteria clumped inside the

granules

b

a

Cavities

105

Figure 4.9 The changes on the microbial population during the process development

of the FAnGS observed by gram staining procedures under microscopic

magnification of 1000K (a) The sludge being dominated by the filamentous

organisms. (b) Changes in the domination species within the FAnGS

a

b

106

4.5.3 Microbial Activity

A typical DO concentration profile for one complete cycle and the OUR

profile during both of the aerobic reaction phases are shown in Figure 4.10. OUR

gives an indication of the performance of biological activity of microorganisms in

the reactor in terms of oxygen utilization. Higher OUR values designate of high

biological activity and vice versa.

PI and PIII show the first and second stages of anaerobic reaction phase,

respectively. At these anaerobic stages, most of the dye degradation process

occurred where amines as the byproduct, were released (Sponza and Isik, 2005),

while PII and PIV represent the first and second stage of aerobic reaction phase,

respectively. Most of the co-substrates provided to the reactor system were

consumed within few minutes of the first aerobic reaction phase (PII) and is known

as the feast period. During the feast period, the DO concentrations in the reactor

were low (about 4 mg/L). The high utilization of DO during the feast period was

also indicated by the high OUR which was 281 mg/L.h. The amines, which were

produced during the anaerobic reaction phase (PI), were mineralized under this

aerobic condition (PII) as they cannot be further degraded under anaerobic phases

(Stolz, 2001 and Sponza and Isik, 2005).

When all the carbon sources (substrate and amines) in the wastewater have

being utilized, an endogenous respiration process took place, referred as the famine

period. The DO concentration immediately increased to around 7.0 mg/L which was

closed to the DO saturation level. The OUR also reduced to 14 mg/L.h indicating

low utilization of DO. The transition from the feast to famine phase was clearly

observed with the drastic increase of the dissolved oxygen and the extreme drop of

the OUR within few minutes of the aerobic reaction phase (PII).

107

Figure 4.10 The profile of dissolved oxygen and oxygen uptake rate in one

complete cycle of the IFAnGSBioRec system (♦) Dissolve oxygen, (□) Oxygen

uptake rate (PI and PIII-Anaerobic phase; PII and PIV- Aerobic phase)

Since there was no addition of substrate during the second aerobic reaction

phase (PIV), the consumption of DO during this phase was also low. This is shown

by high DO levels reaching saturation values of 7.6 mg/L. A sharp increase in the

OUR was also observed at the beginning of this phase. Apparently, the residual dyes

which were not degraded in Stage PI and PII were transformed into smaller

molecules (e.g. amines) during the second stage of the anaerobic phase (PIII). These

smaller molecules were further mineralized in Stage IV which resulted in a sharp

increase in the OUR. As the concentration of these molecules were reduced, the

OUR also became lower until it reached a minimum of 11 mg/L.h. Table 4.5 shows

the OUR value during both of the aerobic reaction phases.

PI PII PIII PIV

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Table 4.5: The OUR levels during the aerobic reaction phase of one complete cycle

Aerobic reaction phase OUR (mg/L.h)

1st stage (PII) 2nd stage (PIV)

Begin react 281 ± 39 167 ± 51

End react 14 ± 2 11 ± 2

The SOUR of the FAnGS was determined before the termination of the

experiment. The SOUR was 51.1 ± 6.8 mg DO/g VSS.h. This value was slightly

lower than those of the aerobic granules reported by Tay et al. (2001a) which ranged

from 55.9- 69.4 mg DO/g VSS.h and higher than the coupled granules reported by

Erguder and Demirer (2005a) (6-47 mg DO/g VSS.h). The SMA of the FAnGS is

lower (10.3 mL CH4/g VSS.h) than the one reported by Erguder and Demirer

(2005a) (14-42 mL CH4/g VSS.h). However, despite the low SMA emission, it

provides the evidence of the existence of methanogens within the FAnGS.

4.5.4 Size of the Facultative Anaerobic Granular Sludge

The shear force imposed in the development of granules in this experiment,

in terms of superficial upflow air velocity (i.e. 1.6 cm/s), resulted in the development

of FAnGS with average diameter of 2.25 mm. This is in the range normally observed

for anaerobic and anoxic granules. According to Peng et al. (1999), the diameter of

the developed aerobic granule is in the range of 0.3 to 0.5 mm which is much smaller

as compared to anaerobic and anoxic granules that could develop up to 2 to 3 mm.

The strong shearing force produced by mixing and aeration during the aerobic phase

prevents the development of bigger aerobic granules. However, reduction in famine

period may also lead to the formation of bigger aerobic granular’ sizes (Liu and Tay,

2006). Other factors and conditions of the experimental set-up could also resulted

to much bigger sizes (eg. 5 mm) as reported by Liu and Tay (2004).

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4.5.5 Settling Velocity of the Facultative Anaerobic Granular Sludge

The average settling velocity of the sludge and anaerobic granular sludge

used as the seeding in this experiment were 9.9 ± 0.7 m/h and 42 ± 8 m/h

respectively. The average settling velocity of the anaerobic granular seed is in

accordance with those reported by Schmidt and Ahring (1996) which was in the

range of 18-100 m/h. The settling velocity of the FAnGS increased from 17.8 ± 2.6

m/h to 83.6 ± 2.6 m/h at the end of experiment. The average settling velocity of the

mature FAnGS at the end of the experiment reached almost 80 ± 7.6 m/h. The

settling velocity obtained from this study was almost three times greater than the

settling velocity of the aerobic granules reported by Zheng et al. (2005).

The increase in settling velocity has given significant impact on the biomass

concentration in the reactor. The relationship between the concentration of the

MLSS and settling velocity of the granules is shown in Figure 4.11. Despite the

short settling time (5 min in this experiment), the high settling velocity possessed by

the developed FAnGS enabled the granules to escape from being flushed out during

the decanting phase. Such conditions have caused more FAnGS to retain in the

reactor and resulted in the increase of biomass concentration.

The SVI value has also improved from 276.6 mL/g at the initial stage of the

experiment to 69 mL/g at the end of the experiment indicating the good settling

properties of the granules which is favorable in wastewater treatment plant operation.

Figure 4.12 demonstrates the SVI profile along with settling velocity. As the SVI

value improved, the granular settling properties increased from 50 m/h to about 80

m/h.

The SVI value achieved in this experiment is in agreement with the result

reported by McSwain et al. (2004) with SVI values of 115 ± 36 ml/g (settling time

10 min) and 47 ± 6 ml/g (settling time 2 min). The higher settling velocity and lower

110

SVI value of the mature FAnGS as compared to previous reports by other

researchers indicate that the formation of granules seeded with anaerobic granules

would develop better settling properties of the granules.

Figure 4.11 The relationship between the biomass concentrations retained in the

reactor with the settling velocity of the FAnGS (■) Settling velocity; (○) Biomass

concentration

4.5.6 Granular Strength of the Facultative Anaerobic Granular Sludge

The granular strength of the granules was measured based on the integrity

coefficient (IC) as mentioned earlier. The smaller the value of IC, the higher the

strength and ability of the granules to clump themselves from being broken due to

shear force of the aeration. Figure 4.13 shows the profile of IC of the developed

FAnGS as a function of time. The IC reduced as the granules developed. With an

initial value of 30, the IC was reduced to about 9 at the termination of the

111

experiment. A sharp reduction of IC was observed after 40 days of the experimental

run. According to Ghangrekar et al. (2005), granules with integrity coefficient of

less than 20 were considered high strength granules. The reduction in IC value

indicates the increase in the strength of the bond that holds the microorganisms

together within the developed granules.

Figure 4.12 The relationship between the SVI values and settling velocity of the

FAnGS (○) SVI, (■) Settling velocity

During the early stage of the granule development, the microbes within the

granules were loosely bounded to each other. At this stage, the granules may consist

of more cavities which make the granules less dense, as manifested by low settling

velocity. As more microbes are linked together, the granules increase in size. Under

certain selective pressures (i.e. short settling time, hydrodynamic shear force,

starvation of the microbial cell) within the reactor, microbes may produce more

extrapolysaccarides (EPS) (Lin et al., 2003 and Qin et al., 2004a). As reported by

Zhang et al. (2007b) and Adav and Lee, (2008a), the EPS contribute greatly to the

strength and the stability of aerobic granules. When more EPS are being produced

112

by the microbial cells, they form a cross-linked network and further strengthen the

structural integrity of the granules. The cavities within the granules will be filled

with the EPS as it is a major component of the biogranule matrix material. This

caused the granules to become denser and stronger as shown by their high settling

velocity and low IC value at the end of the experiment.

Figure 4.13 The profile of integrity coefficient representing the granular strength of

the FAnGS

The physical characteristics of the seed sludge and the matured FAnGS are

summarized in Table 4.6. The developed FAnGS possess the biomass characteristics

that are desirable in the biological wastewater treatment system.

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Table 4.6: Characteristics of seed sludge and FAnGS

Characteristics Seed Sludge FAnGS

SVI (mL/g) 276.6 69

Average diameter (mm) 0.02 ± 0.01 2.3 ± 1.0

Average settling velocity (m/h) 9.9 ± 0.7 80 ± 8

IC 92 ± 6 9.4 ± 0.5

MLSS (g/L) 2.9 ± 0.8 7.3 ± 0.9

MLVSS (g/L) 1.9 ± 0.5 5.6 ± 0.8

4.5.7 Biomass Concentration and Sludge Retention Time

The profile of the biomass concentration (i.e. MLSS) after seeding with the

anaerobic granules is shown in Figure 4.14. During the first few days of the

experiment, almost half of the sludge was washed out from the reactor causing a

rapid decrease in the biomass concentration. The MLSS reduced from initial

concentrations of 5.5 g/L to 2.9 g/L mainly due to the short settling time used in the

cycle (i.e 5 min). During this initial stage, the anaerobic granules were also observed

to disintegrate into smaller fragmented granules and small debris resulted from shear

force caused by the aeration during the aerobic stage. These small fragments have

poor settling ability and were washed out from the reactor. This caused an increase

in the suspended solids concentration in the effluent. However, as the experiment

continued, the concentration of the biomass increased and finally reached 7.3 g

MLSS/L when the experiment was discontinued on the 66th day. The profile of

MLVSS follows the same trend of MLSS, ranging from 1.9 g/L to 5.6 g/L.

114

Figure 4.14 The profile of biomass concentration in the SBR. (●) MLSS, (□) MLVSS

The mean cell residence time (SRT) also increased from 1.4 days at the initial

stage to 8.3 days on the 66th day, indicating the accumulation of the biomass in the

reactor. As less biomass was washed out during the decanting period, the increase in

SRT is another manifestation of good settling characteristics resulting from the high

settling velocity. Nonetheless, the benefit of high SRT will depend on the goal of the

treatment process (Tchobanoglous et al., 2004). The SRT is affected by the settling

velocity. The profiles of the settling velocity and the SRT as a function of time are

given in Figure 4.15.

4.5.8 Mineral and Metal Content

The concentration of mineral and metal contents in sludge, newly developed

and matured FGS were determined in mg/g of dry sludge and presented in Table 4.7.

The trend of the concentration of the metal differs according to specific metals.

115

Figure 4.15 The settling velocity profile in relation to mean cell residence time

(SRT). (○) SVI, (■) SRT

The concentration of Na+ and K+ shows not much difference in the newly

developed and matured granules as compared to the content in the sludge. However,

there is an obvious increment on the concentration of Ca2+ and Mg2+ within the

matured granule. The concentration of Fe2+ is slightly reduced in newly-developed

and matured FAnGS as compared to the concentration in the sludge. As for the

concentration of Ni2+ and Co2+, there is not much difference when compared

between the newly-developed and matured granules. The concentration for both of

these metals is lower as compared to the concentration in the sludge.

Stable concentrations of Na+ and slight decrease of K+ concentrations in the

sludge and the matured granules may indicate that these monovalent cations are not

involved in the granulation process. However, it was reported that at high

concentrations, Na+ and K+ may cause adverse effect on the granules formation. It

could cause reduction in sludge concentration, settling velocity of the sludge,

granular strength and treatment efficiency (Ghangrekar et al., 2005). The

116

monovalent cation, Na+ could become the reason for detrimental impact on the

flocculation system (Sobeck and Higgins, 2002).

Table 4.7: Comparison of mineral content at different stages during the development of FAnGS

Mineral contents (mg/g of dry sludge)

Mineral / Metal Sludge Newly developed FAnGS (1 week)

Matured FAnGS (10 weeks)

Ca2+ 1.53 ± 0.02 2.01 ± 0.58 4.65 ± 0.04

Mg2+ 0.13 ± 0.01 0.322 ± 0.003 1.75 ± 0.08

Na+ 0.22 ± 0.06 0.25 ± 0.49 0.24 ± 0.05

K+ 1.31 ± 0.06 1.15 ± 0.03 0.93 ± 0.05

Fe2+ 2.32 ± 0.02 1.90 ± 0.04 1.98 ± 0.08

Ni2+ 0.60 ± 0.02 0.22 ± 0.01 0.23 ± 0.01

Co2+ 0.149 ± 0.003 1.021 ± 0.002 0.02 ± 0.01

The developed FAnGS in this study showed higher accumulation of Ca2+ and

Mg2+ as compared to the newly-developed and the seed sludge. This may indicate

the involvement of these elements in the granulation process. Based on the divalent

cation bridging theory, the presence of Ca2+ and Mg2+ promotes equivalent floc

properties (Soberck and Higgins, 2002). The presence of Ca2+ was reported to

intensify the granular strength and enhance the granulation process (Grotenhuis et

al., 1991b; Jiang et al., 2003; Ghangrekar et al., 2005). These divalent cations are

postulated to be able to stimulate granulation by neutralizing the negative charges on

the bacterial cell surfaces that were developed due to the strong “van der vaal”

attraction forces (Grotenhuis et al., 1991a and Tay et al., 2000). The Ca2+ acts as a

cation bridge that interconnects between the EPS molecules and bacterial surfaces.

The connections form as stiff polymeric gel-like matrix that could augment the

117

granulation development (Costerton et al., 1987; Sutherland, 2001; Jiang et al.

2003).

The presence of Ca2+ in the form of calcium carbonate and calcium

phosphate produced an inert support for the bacteria to grow in the granulation

process (Yu et al., 2001). Calcium uptake around 60 mg/g of Ca2+ in the granule

has been reported to be the ideal concentration for good granule characterization

with high strength and good settling property (Ghangrekar et al., 2005). Anything

higher than this concentration was not recommended since it could cause increase in

the ash content in the sludge due to chemical precipitation. Too much of Ca2+ could

also give a detrimental effect on the performance and stability of the reactor systems

and decrease in specific activity of the sludge system (Yu et al., 2001). Ca2+ at

concentrations higher than 780 mg/L could caused 60-90% reduction in COD

removal and serious cementation in the sludge bed through the high precipitation of

Ca2+ (Langerak et al., 1998).

The Ni2+ and Co2+ uptake into the granules might be influenced by the metal

requirement of the variety of microorganisms present in the reactor system

(Ghangrekar et al., 2005). However, at higher concentration, these metals could

inhibit some of the biological mechanisms of organisms (Bae et al., 2000).

4.5.9 Removal Performance

The performance of the FAnGS (after acclimatization stage) based on the

removal of COD, color and ammonia is given in Figure 4.16 to 4.18. Figure 4.16

and 4.17 shows that, at the initial stage of the operation, the percentage removal for

COD and ammonia were 71% and 67%, respectively. The removal efficiency has

increased to 94% for COD and 95% for ammonia at the end of the experiment. The

118

increase in removal efficiency indicates the occurrence of high biological activity in

the reactor system. During the first month, the removal efficiency for COD and

ammonia was fluctuating. However, the removal became more stable for the

remaining period.

Figure 4.16 Profile of COD removal during FAnGS development in

IFAnGSBioRec system. (▲) Influent COD, (■) Effluent COD, (○) COD removal

The removal efficiency for color as shown in Figure 4.18 was fluctuating

almost throughout the experiment. The percentage of color removal was about 25%

during the start up and increased to 62% at the end of the experiment. The average

color removal was 55%. This low percentage of the color removal may be due to

insufficient HRT. As dye substances are recalcitrant and difficult to be degraded,

more time is required by the organisms to degrade the dyes. The inconsistent

percentage for color removal may be also contributed by the unstable condition of

the aromatic amines, the byproduct of dye degradation which easily oxidized when

exposed to oxygen during the aerobic phase. As will be discussed in Chapter 6, the

removal of color becomes better at higher HRT.

119

Figure 4.17 Profile of Ammonia removal during FAnGS development in

IFAnGSBioRec system. (▲) Influent ammonia, (■) Effluent ammonia, (○)

Ammonia removal

Figure 4.18 Profile of color removal during FAnGS development in

IFAnGSBioRec system. (100 ADMI ≈ 1 Platimun-Cobalt). (▲) Influent color, (■)

Effluent color, (○) Color removal

120

Figure 4.19 shows the percentage of removal of COD, ammonia and color in

a complete 340-minute reaction mode of the SBR system recorded on the 66th days

of experiment. The profile and the percentage of removal for COD and ammonia

were almost the same while the removal of color was much lower. After 340

minutes of intermittent anaerobic and aerobic modes, about 93%, 95% and 62% of

COD, ammonia, and color respectively were removed.

Figure 4.19 The removal for COD, ammonia and color in one complete cycle of

the SBR system (■) Color, (○) COD, (▲) Ammonia

During the first anaerobic phase (PI) (0-40 min), approximately 15% and 4%

of COD and ammonia respectively, were removed. In the first aerobic phase (PII)

(40-170 min), about 69% of the COD was removed while 80% of the ammonia was

oxidized. In the second anaerobic phase (PIII) (170-210 min), only about 5% of

COD and ammonia were removed while the remaining (about 4% for COD and 6%

for ammonia removal) were removed in the second aerobic phase (PIV) (210 to 340

min). As for color, about 46% and 16% were removed during the anaerobic and

aerobic phases respectively.

PIII PI PII PIV

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The degradation and decolorization of dye during the anaerobic condition has

been widely reported in the literatures (van der Zee et al., 2001a and Dos Santos et

al., 2007). The electrons from the electron donor are transferred to the N=N bond of

the azo dye causing the cleavage of the bond forming aromatic amines in anaerobic

condition. The amines are then degraded under aerobic condition reducing the COD

value of the wastewater. In addition to the degradation mechanism, dye removal

may also occur via adsorption onto the biomass. The removal of ammonia which

mainly takes place during the first aerobic stage is expected to be caused by the

nitrification process.

Based on the removal performance of the system, it has been proven that the

developed FAnGS is capable of performing the degradation process during the

anaerobic and aerobic phases. This indicates the presence of facultative and

anaerobic microorganisms in the FAnGS. According to Li and Liu (2005), when the

granules grew to a size larger than 0.5 mm, the diffusion of oxygen into the inner

part of the granules became a limitation. This may give an indication of the presence

of anaerobic microorganisms within the centre of the FAnGS since the average size

of FAnGS developed in this study was 2.3 ± 1.0 mm. Aerobic microorganisms may

be present at the outer layer of the granules which easily access the oxygen molecule

and mainly responsible for the COD removal. Meanwhile, the facultative

microorganisms may be present in any part of the FAnGS due to their capability to

live under both anaerobic and aerobic conditions.

4.6 Conclusions

Several conclusions could be drawn from the study and they are as follows:

i. Stable FAnGS could be cultivated in the SBR system with the application of

intermittent anaerobic and aerobic reaction modes during the reaction

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phase. The matured granules showed the domination of non-filamentous

bacteria that were tightly linked and embedded to one another and

covered with EPS. The morphology of the developed granules is affected

by the seed used in the development process.

ii. After 66 days of operation, matured FAnGS has reached an average diameter

of 2.3 ± 1.0 mm with settling velocity of 80 ± 8 m/h. The SVI value of

the biomass has decreased from 276.6 mL/g to 69 mL/g at the end of 66

days, also indicating the excellent settling properties of the granules. The

SRT increased from 1.4 days during the initial stage to about 9 days at the

end of the experiment demonstrating the accumulation of biomass in the

reactor system. The FAnGS is also structurally strong as shown by a low

IC value of 9.4 ± 0.5. By comparing the settling property of the FAnGSs’

with the granules developed by other researchers, the cultivation of

granules seeded with anaerobic granules resulted in better granules.

iii. The development of FAnGS is positively-correlated with the accumulation of

divalent cationic Ca2+ and Mg2+ in the granules suggesting the role played

by the cations in the granulation process.

iv. The developed FAnGS was able to remove COD and ammonia in the

wastewater up to more than 93%. Although the average removal of color

was only about 55%, the results indicate the viability of the granulation

system in treating textile wastewater under intermittent anaerobic and

aerobic phase strategy.

v. OUR/SOUR and SMA analysis proved the presence of anaerobic and aerobic

microorganisms activities in the FAnGS and capable of performing the

degradation process both in anaerobic and aerobic conditions.

123

CHAPTER 5

EFFECT OF AGGREGATION AND SURFACE HYDROPHOBICITY BY

SELECTED MICROBES FROM FACULTATIVE ANAEROBIC GRANULAR

SLUDGE

5.1 Introduction

Granulation is a complex process. Development of compact aerobic granules

is initialized by the formation of small aggregates (Adav et al., 2008a) that does not

rely on the need for carriers or artificial surfaces for cell attachment (Liu and Tay,

2002). The mechanisms involved in granulation are subjected to a multiple-step

process that involves many aspects of physicochemical and microbiological features.

These steps may be affected by many factors including types and concentrations of

substrate in the influent, nature of the seed sludge, availability of essential nutrients,

presence of extracellular polymeric substrates (EPS), composition of the media that

may contain different concentrations of divalent cations, pH and temperature of the

experiments and also the operational set-up of the reactor system (Dignac et al.,

1998; Linlin et al., 2005; Liu and Tay, 2007c; Yi et al., 2008; Wang et al., 2009b).

The interaction of microorganisms during the initial stage of the granulation process

may play a significant role in the assurance of successful development of the

granulation process. Investigation on factors that may affect the mechanisms during

124

the initial stage of the granulation process should be considered as a crucial aspect to

be explored.

Aggregation ability and the surface hydrophobicity (SHb) of bacteria are two

independent traits that can be used as an indirect method for evaluating the adhesion

ability of bacteria (Marin et al., 1997; Ibrahim et al., 2005; Rahman et al., 2008).

Since the adhesion ability is postulated to be involved in the granulation process,

study on autoaggregation or coaggregation (CAg) and SHb of the granules and/or

among the bacteria found within the granules have become one of the main focuses

in the foundation for a better understanding on the granulation mechanisms. Few

attempts have been made by researchers concentrating on the effect of aggregation

and SHb (Dabert et al., 2005; Wang et al., 2005a; Ivanov et al., 2006). However,

the knowledge on this aspect particularly on the development of dye degrading

granule is very limited. Not much research has been carried out to study the factors

affecting aggregation and SHb among the microbes involved in the granulation

process.

This study was conducted to investigate the response on CAg and SHb of

selected mixed bacteria which have been isolated from FAnGS. Deeper

investigation at microscopic level was carried out to determine the effect of substrate

concentration, pH and temperature on the CAg and the SHb of the microbes isolated

from the FAnGS developed in the IFAnGSBioRec system described in Chapter 4.

5.2 Materials

Some of the chemicals/reagents and equipments used in this study are listed

in Section 4.2 and are described in Section 4.3. In addition, another list of chemicals

125

and reagents and analytical equipment specified used in this experiment of this

chapter are given in Tables 5.1 and 5.2, respectively.

Table 5.1: List of reagents used in the experiment

Chemical/Reagent Applications

Nutrient agar Spread plate (Section 5.3.1)

Potassium phosphate, K2HPO4 Washing buffer (Section 5.3.6)

Xylene (C6H4(CH3)2) Surface hydrophobicity assay (Section

5.3.6)

Nutrient broth Bacteria culture (Section 5.3.7)

Promega DNA extraction kits

DNA extraction and purification (Appendix C-1) Promega DNA purification kits

Ethanol

Agarose gel Gel eletrophoresis (Appendix C-2)

TAE buffer Buffering system for electrophoresis (Appendix C-2)

Bromophenol blue

Loading dye (Appendix C-2) SDS

Glycerol

Gene Ruler Ladder Mix Marker

Reverse and Forward primer PCR amplification (Appendix C-3)

PCR reaction solution

Promega Wizard®SV gel PCR product purification (Appendix C-4)

PCR clean-up system

126

Table 5.2: List of equipment used in the experiment

Equipment Manufacturer/Product

Microcentrifuge Sartorius/Sigma 1-14

Turbidity Meter HANNA Instrument/ H 93703 Microprocessor

Optical Density Meter BUCK Scientific/100 VIS Spectrophometer

Vortex IKA/MS 1 Minishaker

0.2 μm Filter Sartorius (M) Sdn. Bhd./Minisart-Ny25

Syringe filter Sartorius (M) Sdn. Bhd./Minisart-Ny25

Microwave ELBA/EMO-1706

Eletrophoresis Apparatus BIO-RAD Laboratores/MiniSub-Cell

Power pack BIO-RAD Laboratores/PowerPac Basic

UV Transilluminator Vilber Lourmat/ TFX-35 Vilber Lourmat

Thermocycler Perkin Elmer/GeneAmp PCR System 9700


5.3.1 Chemical Oxygen Demand and Color Removal

In this study, the removal efficiency of COD and color of each of the isolated

bacteria from the FGS were conducted by batch experiment. The COD and color

removal were measured according to the analytical methods described in Section

127

4.3.4. The specific COD and color degradation rate were obtained by dividing the

percentage of COD or color removal with the biomass concentration for each of the

individual bacteria and time taken for the removal to take place. The biomass

concentrations for each of the bacteria were measured according to the analytical

methods as described in Section 4.3.2.4.


The experiments in this study were conducted in two stages. The first stage

involved the isolation of microorganisms from the FAnGS and screening of

microorganisms based on their ability to degrade COD and dyes. Figure 5.1 shows

the experimental work conducted on the characterization of the microbes isolated

from the FAnGS. The degradation of dyes was measured as the percentage of color

removal. In the second stage the effect of substrate concentration, pH and

temperature on CAg and SHb of selected bacteria as a mixed culture were

investigated using factorial and response surface methods. The summary of the

experimental work for this second stage is shown in Figure 5.2.

5.4.1 Isolation Procedure of Bacteria Strain

The synthetic textile dyeing wastewater (STDW) was prepared as explained

in Section 4.2.1. For the bacteria isolation, the synthetic dyeing wastewater which

was used as the growth medium was sterilized by autoclaving the media for 20 min

at 121oC. The mixed dyes, glucose and minerals were filtered through 0.2 μm

membrane filter for sterilization purposes. Then, these materials were added

128

separately into the autoclaved medium. Using aseptic techniques, a small amount of

mature granules were added to synthetic dyeing medium (15 mL) and mixed in a

sterilized beaker in order to disintegrate the granules. The supernatant was serially

diluted with medium (101 to 108 fold dilutions) before inoculation on to nutrient agar

(NA). Eight dilution bottles filled with 9 mL of sterilized distilled water were

prepared for sample dilution process. The samples were serially diluted by

transferring 1 mL of samples from the lower serial dilution (10-1) to the next serial

dilution bottle (10-2) until the eighth serial dilution bottle (10-8). Finally, about 1 mL

of each dilution was spread onto nutrient agar using a glass spreader. The isolation of

microorganisms was carried out by the spread plate method (Madigan et al., 2000).

The plate was inverted and incubated at room temperature and were monitored over

several weeks. Pure bacterial cultures were obtained by repeatedly subculturing onto

new nutrient agar plates until single pure colonies were obtained. The single

bacterial colonies were investigated for morphological and cellular characteristics.

5.4.2 Morphological Characterization

The pure culture bacteria isolated from FAnGS were characterized based on

the colony and cellular morphology of single colonies obtained. The colony

morphology on the nutrient agar was characterized for their size, shape, color,

margin and elevation. The colonies were examined using a stereo microscope (Leica

Zoom 2000) and the Pax-it Image Analyzing System. The analytical method of

gram-staining for the cellular morphology examination was according to Section

4.3.1.1.

129

Figure 5.1 Characterization of microbes isolated from the FAnGS granules

130

Figure 5.2 Experimental work for the investigation on the effect of substrate concentration, pH and temperature on the percentage of coaggregation and surface hydrophobicity of the mixed culture

131

5.4.3 Identification of Microorganisms Isolated from Facultative Anaerobic

Granular Sludge

Bacteria isolated from the FAnGS were further investigated for identification

of the microorganisms. The identification of the microorganisms involved several

stages beginning with the DNA extraction by using the DNA extraction kits

(Promega) in order to obtain the genomic DNA of the microbes. The successful

isolations of the genomic DNA were identified by running the gel electrophoresis. In

order to increase the magnitude of the isolated DNA, the genomics DNA were then

amplified by using the polymerase chain reaction (PCR) amplification process. In

the PCR process, two universal primers were used to amplify the 16S rRNA gene of

the selected bacteria. The amplified DNA was purified using PCR purification kits

before the sequencing of the genomic DNA was performed. The genomic DNAs

were sent to Vivantis Sdn. Bhd. for the sequencing purposes. Through the result of

the sequencing process, the identifications of the bacteria were obtained through the

BLASTn search which was carried out at the National Center of Biotechnology

Information (NCBI). The detailed procedure of microbial identification by using 16S

rDNA sequence analysis is provided in Appendix B.

5.4.4 Specific Growth and Screening for Dye-Degrading Bacteria

The cells used in the specific growth observation were at the exponential

growth in mixed dye (MD) medium. Each bacterium was inoculated in separate

bottles with 10% v/v of pure culture into the sterilized synthetic textile dyeing

wastewater. The experiments were performed in duplicate in 1 L schott bottle.

Each bottle contained 1L of MD medium with an initial mixed dye concentration of

100 mg/L. A mixture of substrate consisted of glucose; acetate and ethanol were

added at the concentration of 1500 mg/L. Individual strains were introduced into the

132

medium and were cap immediately and incubated at room temperature. The

decolorizations of the mixed dye were measured at regular intervals during

incubation. The cell growth was monitored by optical density (OD) measurement

with an Optical Density Meter (100 VIS Spectrophometer) at wavelength of 600 nm.

The specific growth rate and dye degradation rate were calculated by performing a

linear regression on initial curves of cell growth and dye decolorization, respectively

against time.

5.4.5 Autoaggregation Assay

Samples which were used for the growth and dye degradation rate were used

for CAg and hydrophobicity assay. The synthetic textile dyeing wastewater in each

of the schott bottle was allowed to reach complete decolorization (more than 90%)

before autoaggregation assay was carried out. The aggregation assay was conducted

based on the procedure presented by Rahman et al. (2008) with several

modifications. Each of the samples was aerated using an air diffuser which was

connected to an air pump at a rate of 5 L/min. The aeration was stopped 15 min after

the growth of the bacteria reached the stationary phase. Fifteen (15) mL of samples

from each bottle was taken for the autoaggregation assay.

The initial turbidity of each sample was measured using a turbidity meter (H

93703 Microprocessor) as the initial reading. Then, the samples were centrifuged at

a slow centrifugation speed of 650 g for 2 min as described by Malik et al. (2003)

before the turbidity measurements were taken again. The CAg ability was expressed

as coaggregation percentage and calculated by using the equation below:

133

(5.1)

where,

CAg% = Percentage of coaggregation

CA0 = The absorbance of cultured media at 0 h

CAi = The absorbance of cultured media after centrifugation

From the CAg%, the mixed culture could be classified into three groups: high

coaggregation (HCAg: >70% CAg), medium coaggregation (MCAg: 20-70% CAg)

and low coaggregation (LCAg:<20% CAg) cultures. A high aggregation index

denotes a strong tendency of the cells to agglomerate into an aggregate (Adav and

Lee, 2008b).

5.4.6 Surface Hydrophobicity Assay

The surface hydrophobicity (SHb) of the bacterial strains either in the form of

single or as mixed cultures were based on the microbial adhesion to hydrocarbon

assay. SHb of the mixed bacteria was determined according to the methods

described by Zavaglia et al. (2002) and Canzi et al. (2005). Fifteen (15) mL of

samples were taken from the sample bottles used for growth and dye degradation rate

tests in the previous experiments. The bacterial cells were harvested by

centrifugation at 14,000xg for 5 min. The samples were washed twice with 50 mM

K2HPO4 (pH 7.0) and then resuspended in the same buffer to obtain an absorbance of

about 0.5 at 660nm. Five (5) mL of bacterial suspension was mixed with 1 mL of

xylene (C6H4(CH3)2) by vortexing for 120s and then allowed to stand for 1 hour at

room temperature. The absorbances of the bacterial suspension in the aqueous phase

after mixing (Ai) were compared to the absorbance taken at the initial stage of the

experiment (A0). Changes in absorbance due to the bacterial adhesion to the

hydrocarbons were measured as 660 nm by using an Optical Density Meter (OD)

134

(Jenwai-6300 Spectrophometer). The surface hydrophobicity was expressed as

surface hydrophobicity percentage (SHb%) and calculated by using the following

equation:

(5.2)

where,

SHb% = Percentage of surface hydrophobicity

A0 = The absorbance of sample before mixing with xylene

Ai = The absorbance of sample after extraction with xylene.

5.4.7 Effect of Substrate Concentration, pH and Temperature on

Coaggregation and Surface Hydrophobicity

Based on the results of the previous analysis which includes dye degradation

tests, autoaggregation and SHb assay, six out of twelve isolated bacteria were

selected for further study. These selected bacteria were labels as bacteria BS1FAnGS,

BS6FAnGS, BS7FAnGS, BS10FAnGS, BS11FAnGS and BS12FAnGS.

These bacteria were used as a mixed culture to determine the effect of

substrate concentration, pH and temperature on the CAg of and SHb of the mixed

culture. Each of the selected single bacteria cell were cultured separately in nutrient

broth until the measurement of the OD was near to 1. Then, the cell cultures were

harvested and re-suspended in saline water. The individual bacteria were then mixed

together and about 25 mL (10%v/v) of sample culture were inoculated in a separate

500 mL schott bottle containing STWW with the concentration of dye at 10 ppm.

135

The substrates containing glucose, acetate and ethanol were used as the

mixed external carbon sources. The final pH of the culture media, the temperature

for sample incubation and the concentration of substrate used in the experiment were

varied depending on the experimental design. The synthetic textile dyeing

wastewater which has been inoculated with the selected mixed culture was allowed

to decolorize before the CAg and SHb assay were carried out. After the synthetic

textile dyeing wastewater was decolorized more than 90%, the sample were aerated

by supplying air at a flow rate of 5 L/min. The samples were aerated continuously

for 5 hours. Ten (10) mL samples were taken hourly throughout the aeration phase

and used for the CAg and SHb assay.

5.4.8 2-Level Factorial Experimental Design

The effects of substrate concentration, pH and temperature on CAg and SHb

of the selected mixed culture in the synthetic textile dyeing wastewater were

investigated using a 2-level factorial experimental design. The range values of the

factors considered in the experiments are listed in Table 5.3. For a three variables

factorial design, a complete matrix would have been 23 which are equal to 8

experimental runs. Since the experiments were conducted in duplicate, a total of 16

experiments were carried out. Table 5.4 shows the factorial design of the study.

Each of the independent variable was investigated at a high (+1) and a low (-1) level.

The experimental design and the analysis were carried out using MINITAB®

statistical software.

136

Table 5.3: The variables and their range of high and low values used in the factorial

experiment

Variables Unit Low Value High Value

A: Substrate mg/L 500 3000

B: pH 5 9

C: Temperature oC 20 40

Table 5.4: Two-level fractional factorial design with three variables (in coded

levels) conducted in duplicate (not in randomized order)

Run No. Factor 1 Factor 2 Factor 3

A: Substrate B: pH C: Temperature

CASE01 -1 -1 -1

CASE02 -1 -1 -1

CASE03 +1 -1 -1

CASE04 +1 -1 -1

CASE05 -1 +1 -1

CASE06 -1 +1 -1

CASE07 +1 +1 -1

CASE08 +1 +1 -1

CASE09 -1 -1 +1

CASE10 -1 -1 +1

CASE11 +1 -1 +1

CASE12 +1 -1 +1

CASE13 -1 +1 +1

CASE14 -1 +1 +1

CASE15 +1 +1 +1

CASE16 +1 +1 +1

137

5.4.9 Response Surface Methodology (Central Composite Design)

The Response Surface Method (RSM), namely Central Composite Design

(CCD) was used to investigate the possibility of non-linear effect of the selected

variables. In addition to the factorial trials, the CCD was run with five replicate at

the central point together with +α and –α points. This was employed to fit the

second–order polynomial models and to obtain an experimental error of the

experiment. The range and levels of experimental variables investigated in this study

are the same used in the factorial design process as presented in Table 5.3. For CCD,

a total of twenty experimental runs were carried out. Table 5.5 shows the design

matrix for substrate, pH and temperature as the variables in coded units for the CCD

experimental run.

Based on the CCD design matrix, the experiments were divided into three

parts; a 23 factorial design point run (CASE01 to CASE08), the star point run

(CASE09 to CASE14) and the centre point runs (CASE15 to CASE20). In this

study, the responses were the percentage of CAg and SHb of the mixed

microorganisms selected from the FAnGS. The main and synergistic effects of the

factors were determined based on the factorial points and centre point runs. The non-

linear response behavior was analyzed using the star points and centre points runs.

The centre point runs were repeated six times in order to achieve a better estimation

of the experimental error (pure error). The experimental runs were conducted in

randomized order. The main reason of randomization is to reduce bias of unexpected

element during experimental runs.

138

Table 5.5: Two-level of CCD experimental run in coded units

Run No. Factor 1 Factor 2 Factor 3

A: Substrate B: pH C: Temperature

CASE01 -1 -1 -1

CASE02 1 -1 -1

CASE03 -1 1 -1

CASE04 1 1 -1

CASE05 -1 -1 1

CASE06 1 -1 1

CASE07 -1 1 1

CASE08 1 1 1

CASE09 -1.682 0 0

CASE 10 1.682 0 0

CASE 11 0 -1.682 0

CASE 12 0 1.682 0

CASE 13 0 0 -1.682

CASE 14 0 0 1.682

CASE 15 0 0 0

CASE 16 0 0 0

CASE 17 0 0 0

CASE 18 0 0 0

CASE 19 0 0 0

CASE 20 0 0 0

139


5.5.1 Morphological and Cellular Characterization of Bacteria Isolation from

Facultative Anaerobic Granular Sludge

A total of twelve microorganisms have been successfully isolated as pure

culture bacteria from the FAnGS developed in synthetic textile wastewater in the

IFAnGSBioRec system. All of the 12 bacteria were characterized in terms of form,

shape, edges and colony surface for their colony characteristics. Gram staining

procedure was carried out for each bacterium in order to characterize the cellular

morphology of the isolated bacteria. The pure bacteria isolates were named

accordingly as BS1FAnGS to BS12FAnGS. Almost all of the isolated pure culture showed

the presence of EPS indicated by slimy and gleaming emergence. Table 5.6 shows

the results of the cellular and colony morphology of the isolated pure culture from

FAnGS. The examples that illustrate the colony and cell morphology used to

describe the characteristics of the isolated bacteria are given in Appendices C-2 and

C-3.

5.5.2 Screening for Dye Degrader and Autoaggregator from Bacteria Strain

Isolated from Facultative Anaerobic Granular Sludge

All of the 12 pure bacteria cultures isolated from FAnGS were screened for

the ability of dye degradation and autoaggregation. The specific COD and dye

degradation rates were also determined to assist the selection of the isolated bacteria.

Table 5.7 shows the bacterial growth rate, percentage of color and COD removal,

specific COD and dye degradation rate, percentage of aggregation and

hydrophobicity of the individual bacteria. The percentage of COD removal was

140

measured during the decolorization of the mixed dye medium where most of the

decolorization took place after seven days of incubation period. Not much of the

COD was removed during the decolorization that occurred under anaerobic

condition. Most of the individual bacteria isolate showed a slight increase in the

COD during the anaerobic phase. COD reduction was higher during the aerobic

reaction phase.

Table 5.6: Morphological and cellular characterization of the twelve isolated

bacteria from FAnGS

Bacteria

strain

Gram

staining

Cellular

morphology

Colony morphology

Form Edge Surface Elevation

BS1 FAnGS - Palisades Circular Entire Smooth Convex

BS2 FAnGS + Streptobacilli Punctiform Entire Smooth Convex

BS3 FAnGS + Coccobacilli Punctiform Entire Smooth Convex

BS4 FAnGS ₋ Long rod

streptobacilli Irregular Undulate

Dry,

powdery Raised

BS5 FAnGS + Palisades Circular Entire Dry Flat

BS6 FAnGS

+ Rod Irregular Undulate Dry Flat

BS7 FAnGS

₋ Rod Irregular Undulate

Dry,

powdery Raised

BS8 FAnGS + Streptobacilli Punctiform Entire Smooth Convex

BS9FAnGS ₋ Palisades Circular Entire Smooth Convex

BS10FAnGS - Long rod

streptobacilli Irregular Undulate

Dry,

powdery Raised

BS11 FAnGS ₋

Coccobacilli Irregular Undulate Dry,

powdery Raised

BS12 FAnGS ₋ Coccobacilli Circular Entire Smooth Convex

141

Based on the ability to degrade dye and the competency of the individual

bacteria to form aggregates, the six bacteria i.e. BS1FAnGS, BS6FAnGS, BS7FAnGS,

BS10FAnGS, BS11FAnGS and BS12FAnGS are selected for further analysis. These six

selected bacteria show high capability to degrade the dye with the percentage of

more than 85% and a moderate range of COD removal with the percentage of 15-

56%. Based on the percentage of autoaggregation and SHb, all the selected bacteria

are of high CAg and high SHb with more than 70% for both CAg and SHb. The

identity of the selected bacteria was investigated to classify the genus and species of

the bacteria through 16S rDNA sequence analysis.

5.5.3 Analysis of the Isolates from Facultative Anaerobic Granular Sludge

Molecular techniques of bacteria identification involved several steps which

include bacteria isolation, PCR amplification process either by using specific or

universal primer, characterization and finally determination of taxonomic and

phylogenetic class of bacteria isolates via 16S rDNA sequence analysis (Margarita et

al., 2001). PCR is a powerful tool with its ability to exponentially amplify specific

nucleic acid sequences in a short period of time. The amplification of

deoxyribonucleic acid (DNA) via PCR is achieved through multiple cycles of in vitro

DNA replication (Kuslich et al., 2008). The result of nucleic acid sequence analysis

will complement and confirmed the conventional bacteria identification assay. In

this study, the corresponding 16S ribosomal DNA (rDNA) sequence analysis was

applied rather than using the ribosomal RNA (rRNA) since the former approach

would grant stable and more informative analysis as compared to the latter.

Furthermore, the analysis of 16S ribosomal DNA (rDNA) would be able to provide

information of bacteria identification up to the genus and species level via its

sequence polymorphisms (Fox et al., 1993).

142

Table 5.7: Characteristics and performance of the isolated bacterial from the FAnGS

Bacteria strain

Bacterial growth rate Max biomass (mg /L)

Color removal (ADMI)

(%)

Specific dye degradation

rate (mg/g/h)

COD removal

(%)

Specific COD

degradation rate

(mg/g/h)

Autoaggregation (%)

Surface Hydrophobicity

(%) Anaerobic phase

Aerobic phase

BS1FGS 0.026 0.229 2.28 86.5 0.287 47.3 1.80 97.3 ± 1.4 82.8 ± 1.6

BS2FGS 0.024 0.233 2.05 83.0 0.278 10.3 4.40 40.2 ± 0.9 39.6 ± 1.8

BS3FGS 0.027 0.173 1.73 77.9 0.040 10.2 0.29 20.4 ± 1.5 61.4 ± 2.5

BS4FGS 0.027 0.147 1.42 82.6 0.221 40.6 2.06 35.7 ± 1.1 35.5 ± 1.3

BS5FGS 0.030 0.179 1.74 82.7 0.045 27.3 0.77 18.5 ± 1.8 90.2 ± 2.8

BS6FGS 0.044 0.072 1.48 91.5 0.229 15.2 0.73 77.4 ± 1.2 97.3 ± 1.4

BS7FGS 0.030 0.195 2.28 87.1 0.285 37.6 1.52 77.9 ± 1.5 87.4 ± 1.1

BS8FGS 0.077 0.141 1.40 82.1 0.150 12.1 0.32 31.4 ± 1.1 12.5 ± 2.7

BS9FGS 0.027 0.206 1.60 79.3 0.136 40.0 1.94 12.5 ± 1.4 21.5 ± 3.2

BS10FGS 0.035 0.143 2.07 86.7 0.150 54.5 2.29 85.5 ± 1.4 85.7 ± 1.8

BS11FGS 0.032 0.174 2.19 88.0 0.425 33.3 1.32 82.8 ± 1.8 90.2 ± 4.2

BS12FGS 0.030 0.159 2.05 82.7 0.196 40.9 1.69 61.4 ± 1.5 88.4 ± 3.3

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As discussed earlier, six bacteria have been selected for further analysis for

their taxonomic and phylogenetic status. The bacteria were extracted for their

ribosomal DNA and were amplified through the PCR amplification process by using

universal primers with forward and reverse primer. In this study, all the DNA of the

selected bacteria was successfully extracted individually. These were shown by the

clear formed band on the agarose gel electrophoresis. The amplified 16S rDNA

sequences were purified using protein purification kits (Promega PCR Clean-up

System). The purification kits were used to remove impurities that may interfere the

recovery of clear DNA band. Figure 5.3 shows the qualitative analysis of the PCR

products. The observation of visible and clear bands from Lane II to VII indicates

the successful isolation of high concentration and purity of the genomic DNA.

Based on the comparison with the DNA marker Ladder, the obtained genomic DNA

extraction was more than 10kbp indicating that the DNA samples are pure enough to

be used as a template in the PCR amplification process for further analysis.

The amplification of the PCR products after purification by using Vivantis

GF-1 Gel DNA Recovery Kit is shown in Figure 5.2. Lane II to VII shown in

Figure 5.4 represents the genomic DNA PCR product of bacteria strain BS1FAnGS,

BS6FAnGS, BS7FAnGS, BS10FAnGS, BS11FAnGS and BS12FAnGS, respectively. Lane I

represent the DNA ladder marker. The PCR product of the selected bacteria strain

were successfully amplified and purified based on the comparison with the DNA

ladder. The results indicated that all PCR products could be observed at

approximately 1.5 kbp.

The amplified 16S rDNA of the PCR products were sent to Vivantis

Technologies Sdn. Bhd. for the sequencing process. The 16s rDNA sequencing

results were then compared with the Genbank database at the National Center for

Biotechnology Information (NCBI) using the nucleotide-Basic Local Alignmentt

Search Tool (BLASTn) program for the identification of genus and species of the

isolated bacteria (Altschul et al., 1990). Through the BLASTn analysis, the

alignment scores based on forward and reverse sequence of partial 16S rDNA of the

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selected bacteria strains were determined. BLASTn is a nucleotide-matching

program with improved search speed and firm statistical establishment to support the

database searching. A reliable justification of a homology is obtained through high

percentage of DNA nucleotide similarity, an E-value of less than one and a high

score of more than 80 bits (Altschul et al., 1990 and Bergeron, 2002).

Figure 5.3 Agarose gel electrophoresis of DNA extraction

Lane I: DNA ladder marker

Lane II: Genomic DNA extraction of BS1FAnGS

Lane III: Genomic DNA extraction of BS6FAnGS

Lane IV: Genomic DNA extraction of BS7FAnGS

Lane V: Genomic DNA extraction of BS10FAnGS

Lane VI: Genomic DNA extraction of BS11FAnGS

Lane VII: Genomic DNA extraction of BS12FAnGS

10 kbp

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Figure 5.4 Agarose gel electrophoresis of purified PCR amplification product

Lane I: DNA ladder marker

Lane II: Amplified 16S rDNA of BS1FAnGS

Lane III: Amplified 16S rDNA of BS6FAnGS

Lane IV: Amplified 16S rDNA of BS7FAnGS

Lane V: Amplified 16S rDNA of BS10FAnGS

Lane VI: Amplified 16S rDNA of BS11FAnGS

Lane VII: Amplified 16S rDNA of BS12FAnGS

The sequencing result analysis reveals that BS1FAnGS could be identified as

Pseudomonas veronii. A full length of sequencing containing 1394 nucleotides base

pair was obtained from the NBCI showing 100% similarity with strain Pseudomonas

veronii UFZ-B547. The results showed that the E value was zero with a very high

total score of 2555 indicating a reliable justification of a homology. Based on the

alignment scores of sequence generated from the forward primer, 338 sequence

nucleotides were obtained. From this sequence, it shows that BS6FAnGS has 94%

similarity with Bacillus cereus strain S10 with a total score of 525 and E-value of 5e-

145 generated from forward primer. Meanwhile, based on the 595 nucleotides

1.5 kbp

I II III IV V VI VII VIII

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sequence result generated from the reverse primer has 98% similarity to Bacillus

cereus with a total score of 1066 and E-value of zero. Based on the forward and

reverse sequence analysis that show high percentage of similarity, high total score

and less than one of E-value, BS6FAnGS is identified as Bacillus cereus.

Strains BS7FAnGS, BS10FAnGS and BS12FAnGS were identified as Pseudomonas

sp. However, based on the alignment score of the sequence generated by the forward

and reverse primers were not from the same species since there were differences in

the arrangement of nucleotides bases. Based on the obtained sequences, these three

strains can be grouped under the same type of Pseudomonas genus. The analysis

sequence for strain BS7FAnGS showed the forward and reverse sequences with 383 and

620 nucleotides based pair, respectively. Based on the 383 nucleotides forward

sequence, strain BS7FAnGS demonstrate 98% similarity, total score of 686 and zero E-

value with Pseudomonas citronellolis strain NK 2.C2-1. Meanwhile, the reverse

primer generated 620 nucleotides sequence that showed 99% similarity with

Pseudomonas sp.J9(2007). The total score was high (1110) and the E-value was

zero. Based on the obtained result, therefore strain BS7FAnGS was identified as

Pseudomonas sp. Strain BS10FAnGS was identified as Pseudomonas sp. based on the

99% similarity and zero E-value of the forward (538 nucleotides) and reverse (787

nucleotides) sequences with high total score of 966 and 1443, respectively. The

closest relative to the strain identified from the forward nucleotides sequences was

Pseudomonas trivials strain BIHB 745. Meanwhile, the Pseudomonas sp. mandelli

was identified as the closest relative to strain BS10FAnGS based on the reverse

nucleotides sequence. BS12FAnGS was also identified as Pseudomonas species based

on the full length sequences with 1403 based pair nucleotides. The generated full

sequences exhibit 99% similarity to strain Pseudomonas species with a very high

total score of 2536 and zero E-value.

BS11FAnGS is identified as Enterobacter sp. This was also based on the full

length sequencing result analysis that show 99% similarity, high total score of 2603

and zero E-value with Enterobacter sp. VET-7, Enterobacter sp. L3R3-1 and

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Enterobacter asburiae strain J2S4. The detailed results on the DNA sequencing

analysis and the BLASTn analysis for selected bacteria strain are provided in

Appendix D. Table 5.8 shows the result of the alignment scores of sequences

generated as the percentage of similarity and the confirmed identification of the

bacteria strains BS1FAnGS, BS6FAnGS, BS7FAnGS, BS10FAnGS, BS11FAnGS and BS12FAnGS.

The detailed characterizations of the identified bacteria strain describing the physical

and chemical distinctiveness is given in Table 5.9 (John et al., 1994).

5.5.4 Effect of Substrate, pH and Temperature on Coaggregation and Surface

Hydrophobicity

In this study, aeration was applied to generate shear force effect rather than

using physical shaking as commonly reported in previous aggregation research

papers (Rahman et al., 2008; Nishiyama et al., 2007; Adav and Lee, 2009). The

reason of using aeration is to allow a close resemblance of a real condition that took

place in the granulation process. As discussed in Chapter Four, the aeration was

used to introduce the shear effect onto the microorganisms in the reactor to allow the

initialization of the granulation process.

In this chapter, focus is made on investigating the effect of substrate, pH and

temperature (terms as variables) on CAg and SHb (terms as responses) of the

selected mixed culture from FAnGS in synthetic textile dyeing wastewater.

MINITAB™ statistical software is used as the analytical tool for factorial design

analysis. The effect of these variables was presented by the response produced by a

change in the level of the factors investigated. When the effect of one variable is

affecting the responses of other variables, there is an interaction effect between the

variables studied.

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Table 5.8: Taxonomic and phylogenetic characteristic of the isolates from FAnGS

Isolates No. of bases used

to establish identity

16S rRNA gene sequence identity (%)

E value Total score Closest relative Taxonomic affliation(s)

BS1FGS 1394 99 0 2555 Pseudomonas veronii 6S rRNA Pseudomonas veronii

BS6FGS Forward seq. 338 94

5.00E-146 525 Bacillus cereus strain S10

Bacillus cereus Reverse seq. 595 98 1066 Bacillus cereus partial


0 686 Pseudomonas citronellolis

strain NK 2.C2-1 Pseudomonas sp.

Reverse seq. 620 98 1110 Pseudomonas sp.J9(2007)


0 966 Psedumonas trivialis strain

BIHB 745 Pseudomonas sp.

Reverse seq. 787 99 1443 Pseudomonas sp. mandelii

BS11FGS 1429 99 0 2603 Enterobacter sp. VET-7 Enterobacter sp.

BS12FGS 1403 99 0 2536 Pseudomonas veronii strain INA06 Pseudomonas sp.

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Table 5.9: Characteristics of identified selected bacteria strains from FAnGS

Bacteria strain

Characteristics

Bacillus cereus (BS1FAnGS)

• gram positive or facultative anaerobic spore forming rod • well grown in anaerobic condition; usually found in soil, air and water• frequently found in pasteurized milk, causing spoilage because of the production lipases and proteases • grew and produced a protease using wool as sole of carbon and nitrogen • associated with rice based food poisoning • denitrifying bacteria; associated with the formation of "clumping" in secondary clarifiers and forming in anaerobic digester. • identified to have capability in degrading the azo dye compounds (Khehra et al., 2005; Pourbabaee et al., 2005; Deng et al., 2008)

Pseudomonas veronii (BS6FAnGS)

• gram positive • non-spore forming • facultative anaerobic • capable in degrading ketones (Onaca et al., 2007)

Pseudomonass sp. (BS7FAnGS, BS10FAnGS, BS12FAnGS)

• gram negative with non-spore forming • obligated aerobic; facultative anaerobic • floc forming bacteria that could initiate floc formation in the activated sludge process • used as bioaugmentation in sanitary sewer system and biological treatment plan • have the capability to degrade phenol and phenolic compounds • easily degrade sulfur containing compounds that are associated with malodor production • capable as denitrifying species • many species from genus Psuedomonas were identified capable in degrading dye compounds (El-Naggar et al., 2004; Khehra et al., 2005; Barragan et al., 2007) • widely distributed in nature • some species are pathogenic for humans, animals or plants

Enterobacter sp. (BS11FAnGS)

• gram negative with non-spore forming • aerobic, facultative anaerobic, optimal temperature 30-37oC • capable as denitrifying species and involve in fermentation process • acted as phosphorus accumulating organisms (PAO) in enhance biological phosphorus removal system • many species from genus Enterobacter sp. capable in degrading dye compounds (Moutaouakkil et al., 2003; Barragan et al., 2007) • commonly distributed in fresh water, soil sewage, plants vegetables, animal and human faces

150

The results from the factorial design are presented in the form of an ANOVA

table and table that provides information on the estimated effects with coefficients.

The ANOVA table gives a summary of the significance of the main and interaction

effects by observation on the P-value. Table of the estimated effects with

coefficients shows the P-value associated with each individual model term. In this

factorial design, the responses obtained were statistically evaluated with the

confidence levels of above 90% (P-value less than 0.1).

The experimental results for factorial design analysis are given in Table 5.10.

The summary table of the ANOVA that shows the main, two and three ways

interaction effect is given in Table 5.11. Detailed discussions for both responses of

this study are based on the results obtained after five hours exposure to shear force

through the aeration process. With respect to the experimental conditions exploited

in the study, the P-value (at 0.1 level of significant) indicates that all factors, i.e.

substrate, pH and temperature show significant effect on CAg. The significant

interaction effects are only observed between pH and temperature (pH ×

Temperature) while the three way interaction (Substrate × pH × Temperature) is not

significant. As for the responses of SHb, all model terms (main, 2-way and 3-way

interaction) except for the 2-way interaction between substrate and temperature

(Substrate × Temperature), are all significant. Figure 5.5 shows the Pareto chart

generated by MINITAB™ for CAg and SHb of the mixed culture. The vertical

disconnected red line represents the significance level determined by the statistical

software. The horizontal column bar that does not reach the disconnected red line

would imply the insignificance of the model terms. The detailed results of the

analysis constructed by the software are given in Appendices E1 to E6.

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Table 5.10: Experimental results for 2-level factorial design analysis

Run No. Coaggregation (%) Surface Hydrophobicity (%)

CASE01 49.6 19.0

CASE02 44.7 21.7

CASE03 67.2 33.8

CASE04 64.1 29.4

CASE05 46.8 37.3

CASE06 44.1 41.2

CASE07 65.6 39.6

CASE08 62.9 36.9

CASE09 65.2 32.1

CASE10 61.9 31.5

CASE 11 84.5 53.9

CASE 12 85.4 52.0

CASE 13 38.8 20.4

CASE 14 38.7 18.2

CASE 15 57.4 8.6

CASE 16 57.6 6.8

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Table 5.11: The P-values of the estimated main and interaction effects of variables

substrates, pH and temperature on to the percentage of coaggregation and surface

hydrophobicity after six hours aeration phase

Effects Coaggregation (%) Significanta

Surface Hydrophobicity

(%) Significanta

The Main

Substrate < 0.0001 Yes 0.001 Yes

pH < 0.0001 Yes < 0.0001 Yes

Temperature 0.0004 Yes 0.0019 Yes

The 2-way interaction

Substrate × pH 0.5607 No < 0.0001 Yes

Substrate × Temperature 0.4827 No 0.8624 No

pH × Temperature < 0.0001 Yes < 0.0001 Yes

The 3-way interaction

Substrate × pH × Temperature 0.4679 No 0.0008 Yes

a significant at α = 0.1

5.5.4.1 Factorial Analysis: The Main Effect of Substrate on Coaggregation

With respect to the main effect on the percentage of CAg, the substrate shows

a significant effect with P-value of less than 0.0001. The substrate gave a positive

effect with estimated main effect of 19.36. This means that increase in the

153

concentration of substrate will cause an increase in the CAg process. Such

phenomenon may due to the increase in the cell growth of the mixed culture when

the substrate is increased. The presence of more cell biomass increases the collision

among the cells and may cause more CAg to take place. Liu and Tay (2002)

reported that the collision between particles is one of the key factors that influences

the formation and stabilization of biofilms, anaerobic and aerobic granules.

The cell surfaces and their characteristics change with the alteration of the

surrounding environmental condition (van Loosdrecht et al. 1989). van Loosdrecht

et al. (1987) reported that increase in the substrate flux which cause increase in the

bacterial growth is capable in changing the cell SHb. Hence, in order to accelerate

the granulation start-up process, it was suggested that high OLR should be applied

during the granulation process. Moy et al. (2002) reported that there was no negative

effect on the granulation process when the OLR was increased as high as 15

kg/m3·day.

The effect of collision among the microorganisms which was due to high

biomass production at high substrate concentration was prominent during the initial

stage of the aeration phase where the amount of substrate was still high. During the

first hour of aeration phase, the percentage of CAg was about 57%, with the

estimated significant effect of +7.775 and the P-value of 0.005 (Detailed results for

one to four hours of CAg assay are given in the Appendices E1 to E4). As the

aeration phase was prolonged up to six hours, the effect of substrate has increased

(estimated effect +19.36) with the percentage of CAg at high substrate

concentrations increased to 68%. The difference may due to the production of EPS

during the starvation the phase after long aeration times. Microorganisms will

produce more EPS when undergoing starvation conditions when most of the

substrate in the medium is being utilized (Wang et al., 2006a). The formation of EPS

that covers the cell surface from physicochemical point of view could be regarded as

polyelectrolyte adsorbed onto a colloidal particle. The presence of EPS on the cell

surface could alter the characteristics of the physicochemical properties of the cell

154

surface which includes the surface charge, surface hydrophobicity and others. Varon

and Choder (2000) reported that there is physical change of the bacterial surface

during starvation condition. Bacteria were observed to produce connecting fibrils

that emerged on the surface of the cell. The connecting fibrils acted as a form of

microbial communication which is believed to be involved in the initial stage of cell

aggregation. The changes in the bacterial surface properties are important aspects

with regard to the flocculation, adhesion and granulation process (Veiga et al., 1997

and Liu et al., 2004d).

Under the condition where cells are poor with EPS, the cell surface will be

dominated by electrostatic interactions resulting with the repulsion among the cells

and cause separation between cells. Cells with poor EPS will follow the Derjaguin-

Landau-Verwey-Overbeek (DLVO) theory which indicated that increase in surface

charge will lead to increase in the repulsion electrostatic interactions between

approaching surfaces, resulting with a weakening of the bonding within cell. This

condition may occur in the flocculation process that is poor with EPS. On the other

hand, cells which are rich with EPS would be dominated with the polymeric

interaction that resulted with enhancement of cell adhesion (Tsuneda et al., 2003b).

Furthermore, the interaction effect between polymers is much greater as compared to

the repulsion effect due to the increase of the surface charge. The amino of

exoprotein (PN) which is one of the components of the EPS that are able to

neutralize the surface charge. With the presence of EPS, the effect of surface charge

will be too weak to inhibit sludge flocculation. Since increase in substrate

concentration is associated with increase in cell biomass, the effect of substrate

concentration could indirectly affect the aggregation of cells through the changes on

the production of EPS particularly during the starvation phase.

155

Figure 5.5 The pareto chart of the percentage of (a) coaggregation and (b) surface

hydrophobicity after six hours of aeration phase (A: substrate; B: pH; C:

temperature;

α: 0.1)

156

5.5.4.2 Factorial Analysis: The Main Effect of pH on Coaggregation

The effect of pH is also significant on the percentage of CAg with the P-

value of less than 0.0001. The pH gave an opposite effect onto the CAg process as

compared to the effect of substrate. It was observed that the pH increase caused a

decrease in CAg. The pH variables gave a -13.84 of estimated main effect onto the

CAg process.

Under neutral conditions, microorganisms would be negatively-charged due

to the ionization of carboxyl, sulphate and phosphate that acted as functional groups

in the cell’s surface (Sutherland, 1982 and Wiley et al., 2008). According to the

DLVO theory, when two surfaces possess the same charges, there will be a repulsive

force between the two surfaces resulting with prohibition of cell aggregation. This

repulsive force is known as Gibbs Free Energy. In acidic conditions, there will be an

excess of ions H+ which will cause neutralization of the cell surface charge. Such

condition will reduce the free Gibbs energy. Reduction in the electrical repulsion in

turn favors cell-to-cell approach and would initiate the formation of cell aggregation

(Derjaugin and Landau, 1941). When the pH is higher, the excess of OH- would

enhance the surface charges of the bacteria cell and increased the free Gibbs energy

and caused the cell to be driven even further apart.

Nonetheless, aggregation was also observed to occur under neutral pH

conditions. This may suggest that beside the electrostatic repulsion, other

mechanisms such as SHb may also be involved in cell aggregation (Adav and Lee,

2008b). There is evidence that cell hydrophobicity is inversely correlated to the

quantity of the surface charge of the microorganisms (Liao et al., 2001). An increase

of cell hydrophobicity reflects reduced negative charges on the bacterial surface.

157

5.5.4.3 Factorial Analysis: The Main Effect of Temperature on Coaggregation

Temperature was also found to have a mainly positive significant effect on

the CAg (P-value less than 0.001). However, the estimated main effect of

temperature was only +5.56 suggesting that the effect can be considered weaker as

compared to the other two variables. Increase in temperature within the range of this

experimental condition would increase the microbial activities which include

metabolisms and mobility of the microorganisms. In other words, increase in

temperature may increase the specific growth rate of the microorganisms. According

to Liu et al. (2004a), the microbial aggregation process is faster at higher specific

growth rates of the microorganisms. Increase in cell growth rate will lead to increase

in cell biomass and the percentage of collisions between the cells resulting in

increase in the probability of cell aggregation.

Increase in temperature will also result in decrease of the free Gibbs energy.

As mentioned earlier, reduction of the Gibbs free energy will favor the aggregation

of cell. The same observation was reported by Ibrahim et al. (2005) who said that as

the temperature of the incubation increased, the ability of the Bifidobacteria to

perform aggregation also increased. Figure 5.6 showed the main effect of variables

substrate, pH and temperature on the CAg process.

5.5.4.4 Factorial Analysis: The Interaction Effect on Coaggregation

As shown in Figure 5.7, among the three variables, the interaction effect was

only observed to be significant between variable pH and temperature with a P-value

less than 0.0001. At low temperatures, the percentage of CAg was almost the same

at low and high values of pH (pH 5.81 and 8.19) which was about 53%. However,

158

when the temperature increased, the percentage of CAg in acidic conditions

increased up to more than 70% while the percentage of CAg in alkaline conditions

reduced to less than 50%. The main effects of acidic and high temperature

conditions have caused the percentage of CAg to increase to 66% and 62%,

respectively. The interaction between pH and temperature has increased CAg up to

about 75%.

Figure 5.6 Main effects plot on the coaggregation

When the pH of the experimental condition was alkaline, the increase in the

temperature has reduced the percentage of CAg. As discussed earlier, when the

temperature increases, the free Gibbs energy between two same surfaces charge

particles decreases and eventually would enhance CAg. However, in alkaline

conditions, there are possibly high concentrations of OH- ions which cause the

repulsive force between cells to become even greater and could have overshadowed

the effect of high temperatures. Increase in temperature would also cause the

movement of the particles to increase based on the theory of Brownian movement

(Tchobanoglous et al. 2004) causing the cell particles driven even more apart. This

could be the possible reason of why during high temperature and high pH conditions,

the CAg among the cell reduced.

159

Figure 5.7 Interaction effects plot on the coaggregation process (• Centre point)

The relationship between substrate and pH and between substrate and

temperature shows no significant interaction effect since the lines are almost parallel

to each other. The 3-way interaction effect between variable substrate, pH and

temperature were insignificant with a P-value of 0.4679.

5.5.4.5 Factorial Analysis: The Main Effect of Substrate on Surface

Hydrophobicity

Surface hydrophobicity represents one of the physicochemical characteristics

of cellular surface of the microorganisms. Hydrophobicity of sludge is believed to

play a crucial role and acted as the triggering force towards aggregation (Mahoney et

al., 1987; Liu et al., 2004b, Wang et al., 2005a). According to Liu et al. (2004b), the

••

160

surface cell hydrophobicity could be induced by the conditions of the cultures and

capable of initiating the cell-to-cell aggregation. It is believed that the

hydrophobicity of the cell is one of the most important attraction forces in the

microbial aggregation and high cell hydrophobicity seems to be a prerequisite for

biogranulation to take place.

Previous studies indicate that changes in cell SHb is affected by many factors

that cause stress to the culture condition such as starvation, growth rate, growth

substrate, pH and temperature (Liu et al., 2004b). However, different types of

bacteria, as single or as mixed culture may response differently especially when the

bacteria is found in different types of solution or wastewater. According to Liu et

al., (2004b), the knowledge regarding the role of cell hydrophobicity in the

biogranulation process is far from complete. Under stressful culture conditions,

bacterial cells would change its cell hydrophobicity (Bossier et al., 1996 and

Mattarelli et al., 1999). The bacteria would become more hydrophobic and lead to

the strengthening of the cell to cell interaction of a microbial structure. It is a form

of protective mechanisms of the cells against unfavorable environmental conditions.

The result of the 2-level factorial design experimental run shows that

substrate has significant effect on SHb with the P-value of 0.001 and estimated effect

of +4.95. Figure 5.8 shows the main effect of the variables on the SHb of the mixed

culture used in this study. The substrate has caused positive effect on the SHb where

as the increase in substrate has caused increase in SHb. Increase in the substrate

concentration means more food supplied to the microorganisms which will increase

the bacterial growth. When more bacteria are present, more EPS will be produced

when faced with the starvation stage. Increase in substrate may also induce the

bacterial growth rate which would increase the production of the EPS. Increase in

the EPS would cause increase in the SHb and the bacteria would become more

hydrophobic which may facilitate adhesion or aggregation process (Kjelleberg et al.,

1987; Liu et al., 2003c; Jiang et al., 2004b). The effect of substrate has the same

pattern to the percentage of CAg and SHb with the presence of EPS playing a

161

prominent role for both responses. The EPS is known to be capable in mediating

both the cohesion and adhesion of cells and play a fundamental role in sustaining the

structural integrity in the development of biofilm, anaerobic granules and aerobic

granules (Tsuneda et al., 2001 and Flemming et al., 2007).

Figure 5.8: Main effects plot of variables for the percentage of SHb

5.5.4.6 Factorial Analysis: The Main Effect of pH on Surface Hydrophobicity

The pH caused a highly significant effect on SHb with P-value less than

0.0001 and an estimated effect of -8.05. Increase in pH from pH 5.81 to pH 8.19 has

caused a decrease in the percentage of SHb from 34% to 27.5%. During acidic

condition, the presence of H+ ions in the media solution would cause neutralization

of the surface charge of the bacterial cell and reduced the electrostatic force. Since

the cell hydrophobicity is inversely correlated to the quantity of the surface charge of

microorganisms (Liao et al. 2001), the percentage of SHb increased and would

enhance the aggregation of the cells as the surface charge of the microorganisms is

reduced. When the pH of the media solution increases (alkaline), there will be more

162

of OH- presence in the solution. This will enhance the repulsive force of the

bacterial surface charge and reduced the chances of the bacteria cell forming

aggregates subsequently reducing the percentage of SHb.

Difference in pH of the media can also be associated with the type of growth

substrate used in the growth media. It was reported that the formation of anaerobic

granules in the UASB was highly affected by the substrate composition of the media

solution (Liu et al., 2003e). This is because different types of growth substrate may

cause acidic or alkaline conditions when being hydrolyzed. The concentration of H+

and OH- will have an effect on the surface tension of the liquid media and eventually

affecting the SHb of the bacteria cell.

The effect of pH on the SHb may basically depend on the net surface charge

of the exoprotein of the bacteria cell which eventually depends on the type of protein

of the bacteria cell wall. However, further investigations are required for a better

understanding on the effect of pH on the SHb particularly on the association of

different proteins of the bacteria cell wall.

5.5.4.7 Factorial Analysis: The Main Effect of Temperature on Surface

Hydrophobicity

The P-value of 0.002 indicates that the temperature of the experimental

conditions has a highly significant effect on the SHb. The estimated effect of -4.43

showed that temperature has caused a negative effect on the SHb. In this

experiment, when the temperature was increased from about 24oC to 36oC, there was

a significant reduction of the percentage of SHb from 32.5% to 28%.

163

As temperature increases, the liquid surface tension decreases (Moraes et al.,

2008), allowing the adhesion of hydrophilic cells and resulted with reduction in the

percentage of cell SHb. The same observation was reported in the study carried out

by Blanco et al. (1997), on 42 strains of Candida albicans. The majority of the

strains become hydrophobic at lower temperature (20oC) as more cell-to-cell

aggregation takes place. When the temperature was increased to 37oC, the strains

may undergo changes in their surface property and become hydrophilic with less

aggregation taking place. In the development of anaerobic granules in the UASB

reactor system, the bacteria cells become more hydrophilic and grow in rather loose

association when the liquid surface tension in the UASB is lesser than 50 mN/m.

When the liquid surface tension is larger than 56 mN/m, the adhesion of more

hydrophobic bacteria cells will take place and form bigger aggromeration (Thaveesri

et al., 1995 and Grootaerd et al., 1997).

The effect of temperature has given an opposite result for the percentage of

aggregation and SHb eventhough it was claimed that the SHb is the triggering force

for cell aggregation (Liu et al., 2004b). Rahman et al. (2007) has categorized

bacteria (Bifidobacteria) into high, medium and low autoaggregator groups. Among

the high autoaggregator, increase in temperature has caused a decrease in the

percentage of autoaggregator. As for the medium and low autoaggregators, increase

in temperature has caused an increase in the percentage of autoaggregator. In

addition to temperature, other factors such as pH of the media, types of protein

bound on cell surface of the different bacteria strains may give a different effect on

the ability to autoaggregate. Further investigation is required in order to investigate

the effect of temperature onto the autoaggregation and SHb among different bacteria

strains under different experimental conditions.

Furthermore, different types of bacteria may response differently when

exposed to different environmental conditions with regard to the ability to adhere

either among cells or on to a solid surface. Different types of protein may also be

secreted or produced at different temperature conditions resulting with a different

164

degree of the cell hydrophobicity (Maclagan and Old, 1980). Mattarelli et al. (1999)

reported the production of lipoteichoic acid and other mechanisms have caused high

hydrophobicity of cells incubated at low temperature (25oC) as compared to 37oC

incubation temperature condition. This study involved the use of a mixed selected

bacteria culture which individually may give different responses towards the

temperature changes. The overall effect may be affected by the most dominant

species in the group and may differ from the expected outcome.

5.5.4.8 Factorial Analysis: The Interaction Effect on Surface Hydrophobicity

The interaction effect between variables is given in Figure 5.9. The

interaction effect between substrate with pH and pH with temperature were highly

significant with the P-value of less than 0.0001. The values of the estimated effects

show a strong relationship for both the above significant interactions. The

interaction effect between substrate and temperature is insignificant with the P-value

of 0.862. The insignificant interaction is shown by the parallel line representing the

SHb responses of SHb during high and low levels of substrate and temperature

variables. The plot in Figure 5.9 describes the interaction between substrate and pH,

showing at high concentrations of substrate, change in pH from a lower value to a

higher value has caused the SHb to reduce. When the concentration of substrate is

low, the increase in pH has caused a slight increase in the SHb of the mixed

microorganisms.

The phenomenon observed is believed to be caused by the media solution

used in preparing the synthetic textile dyeing wastewater which consists of ethanol,

glucose and sodium acetate. During the aeration phase, when the degradation of

substrate took place, hydrolysis of acetate released more of OH- that could cause the

media to become more alkaline (Voet and Voet, 2004). At high pH conditions,

165

where there will be more of OH- in the solution, hydrolysis of acetate will further

increase the concentration of OH- and make the media solution more alkaline.

Increase in the OH- will increase the surface tension due to the repulsive electrostatic

force. This condition will make the cell to be driven apart and reduce the changes to

form aggregation and that probably could elucidate on why the percentage of

aggregation reduce even more when the substrate increase in the alkaline condition

(high pH value). In this condition, it is believed that the electrostatic force is much

stronger as compared to the polymeric interaction eventhough during high substrate

concentrations.

Figure 5.9: Interaction effect plots for the percentage of SHb (• Centre point)

At low pH, hydrolysis of acetate that produces more OH- will neutralize the

existing H+. In this situation, the electrostatic force is reduced and when the

substrate concentration is increased, the production of EPS may contribute to cells

hydrophobicity and make the polymeric interaction force become predominant and

able to promote cell adhesion. This explanation is based on the report of Tsuneda et

al. (2003b) who claim that if the amount of EPS is relatively small, the cell adhesion

will be inhibited by the electrostatic interaction (referring to the alkaline condition of

• •

166

this experiment) and if the EPS is relatively large, cell adhesion will be relatively

enhanced by the polymeric interaction.

For the interaction effect of pH and temperature, the plot in Figure 5.9 shows

that when the pH of the media (synthetic textile dyeing wastewater) was acidic,

increase in temperature caused the SHb to increase from about 25% up to more than

40%. However, when the pH of the media was alkaline, increase in temperature has

caused reduction on SHb from almost 40% to less than 10% of SHb. Increase in

temperature has enhanced the effect of pH either in acidic or basic conditions. In the

acidic media where the ion H+ is abundant, most of the negative surface charges of

the bacteria cell are neutralized. This will reduce the free Gibbs energy and reduce

the repulsive effect between the cells. Increase in temperature will cause an increase

in the collision between the cells (increase in the Brownian movement) which will

encourage the interaction between cells and enhances the aggregation process. In the

alkaline media condition, abundance of ion OH- may increase the repulsive effect of

surface charge. Increase in temperature which increased the cell movement may

cause the cell with high free Gibbs energy (due to increase in the surface charges) to

be driven further apart from one another and reduce the changes to form aggregates.

A significant 3-way interaction effect of substrate, pH and temperature is also

within the experimental condition with the P-value of 0.001. The curvature effect of

the significant interaction was further investigated by using the response surface

experimental design and will be discussed in the next section.

167

5.5.5 Response Surface Analysis

The results of the CCD analysis are shown in Table 5.12. The analysis was

carried out using full quadratic terms including linear, square and interaction with the

aid of Design-Expert 7P statistical software. The summarized results of the analysis

of variance (ANOVA) for the percentage of CAg and SHb are shown in Table 5.13.

The detailed results consisting of estimated regression coefficient and ANOVA table

are given in Appendices E7 to E9.

For CAg, based on the P-value, all the linear terms show significant values.

The model exhibits significant non-linearity for substrate and pH with the P-values

of 0.0042 and 0.0427, respectively. However, temperature shows non-curvature

effect with the P-value of 0.2397. The interaction between pH and temperature are

found to be highly significant with the P-value of 0.02 while the other two

interactions are not significant (P-values more than 0.2). The R-squared value of the

model is acceptable (86.63%) but the P-value for the Lack of Fit Test (LOFT) is

significant (0.0029). This implies that the analytical understanding of the model is

not statistically accurate. It may indicate that the process appears to be too complex

to model.

In order to improve the model, another attempt was carried out by omitting

two interaction terms (Substrate × pH, Substrate × Temperature) and one square term

(Temperature × Temperature). All the omitted terms were selected based on the

insignificant result obtained from the full quadratic terms analysis. The results of

this analysis with omitted terms are given in Appendix E8. Omitting the selected

insignificant terms lowered the P-value of all the included terms (Reduced quadratic

terms) compared to the previous analysis (Full quadratic terms). The R-squared

term reduced from 86.63% to 84.06% and the LOFT increased from 0.0029 to

0.048%. The drop of the R-squared value points out that the omitted terms only cost

2.5% in goodness of fit and this is acceptable. Eventhough the LOFT P-value has

168

improved by more than sixteen times higher compared to the analysis of the full

quadratic terms, the value still implies the inadequacy of the fitted model.

Table 5.12: Experimental results for CCD analysis

Run Coaggregation (%) Surface Hydrophobicity (%)

CASE01 49.6 19.0

CASE02 67.2 33.8

CASE03 46.8 37.3

CASE04 65.6 39.6

CASE05 65.2 32.1

CASE06 84.5 53.9

CASE07 38.8 20.4

CASE08 57.4 8.6

CASE09 68.8 50.9

CASE10 73.0 70.7

CASE11 54.2 62.4

CASE12 5.50 7.8

CASE13 29.3 71.2

CASE14 58.3 83.9

CASE15 59.4 75.2

CASE16 56.4 77.2

CASE17 55.1 72.7

CASE18 60.3 74.0

CASE19 57.5 76.2

CASE20 60.2 72.2

169

Table 5.13: Summary of the P-value of the response surface modeling analysis

Term

Coaggregation (%) Surface Hydrophobicity

(SHb) (%) Full

Quadratic Terms

Linear + Square + pH ×

Temperature

The P-valuea

Substrate 0.0067 0.0037 0.3582

pH 0.0022 0.0009 0.075

Temperature 0.0369 0.0262 0.9194

Substrate*pH 0.6329 - 0.3596

Substrate*Temperature 0.8147 - 0.8854

pH*Temperature 0.02 0.0133 0.1221

Substrate*Substrate 0.0042 0.0016 0.0423

pH*pH 0.0427 0.0352 0.0014

Temperature*Temperature 0.2397 - 0.3393

R-squared value 86.63% 84.06% 75.89%

Lack of Fit (LOFT) 0.0029 0.048 <0.0001 0.01 – 0.04: Highly significant; 0.05 – 0.1: significant; 0.1 – 0.2: less significant; < 0.2: insignificant (Vecchio,

1997)

The response surface analysis of SHb shows that among the investigated

variables, only pH shows a significant effect with the P-value of 0.0750. The model

also shows significant non-linearity for both substrate and pH with the P-value of

0.0423 and 0.0014. Temperature shows the same response for the square term as in

the CAg assay with insignificant effect with P-value of 0.3393. The R-squared value

of this model eventhough slightly lower as compared to the value for responses of

coaggregation is still considered within acceptable range with a percentage of

75.88%. However, the P-value for the Lack of Fit Test (LOFT) is significant (less

than 0.0001) for this model indicating the inaccuracy of the statistical model.

Nonetheless the best statistical models that can be used to represent the responses

170

(CAg and SHb) within the range of the experimental conditions in this study as

accomplished from the CCD design analysis are given in Table 5.14.

Table 5.14: Mathematical models in terms of actual factors

Responses Statistical Model

(Coagggregation)2 (%) = -30449.2 - 4.02×A + 7114.5×B + 843.1×C – 108.1×BC + 1.4×10-3×A2 –329.1×B2

Surface Hydrophobicity (%)

= -1122.2 + 0.12×A + 239.7×B + 18.1×C – 6.5×10-3×AB – 2.01×10-4×AC – 1.4×BC – 1.9×10-5×A2 – 13.8×B2 – 0.1×C2

A: Substrate (mg/L); B: pH C: Temperature (oC)

The predicted versus actual plots for the responses of CAg and SHb are

shown in Figure 5.10. The plots reveal that the actual values are distributed

relatively near to the straight line in both cases. The predicted and actual values

obtained from this experiment are considered to be statistically acceptable indicated

by the high value of the R-squared calculated from the analysis of variance of this

study conditions.

The response surface and contour plots which illustrate the relationship

between the percentage of CAg and independent variables of pH and temperature is

shown in Figure 5.11. The figure shows the surface plots that represent the effect of

varying pH and temperature at fixed substrate concentrations (1750 mg/L). It can be

seen that increasing the temperature value at low pH levels, has caused an increase in

the CAg process with a high percentage of 69.5%. However, at high pH levels, the

percentage of CAg was slightly decreased with increase in the temperature. Having a

condition at high temperature levels and low pH conditions would be the best

condition for achieving the maximum percentage of CAg.

171

Actual

Pred

icte

d

29.75

1808.33

3586.91

5365.49

7144.08

29.75 1808.33 3586.91 5365.49 7144.08

Actual

Pred

icte

d

4.97

24.69

44.41

64.13

83.85

4.97 24.69 44.41 64.13 83.85

Figure 5.10: Predicted versus actual data for (a) coaggregation and (b) surface

hydrophobicity

(a)

(b)

172

(a)

(b) Figure 5.11: (a) Contour and (b) 3D response surface plots representing relationship between pH, temperature and percentage of coaggregation

173

Figures 5.12 to 5.13 show the relationship between SHb and independent

variables of substrate, pH and temperature, respectively. The contour plots of the

RSM are drawn as a function of two variables at a time, holding other remaining

factors or variables at a fixed level.

The surface plots of the interactions between variables for the percentage of

SHb as the observed response was found to be a symmetrical mound shape shown in

Figures 5.12 and 5.13. Meanwhile, the contour plot in Figure 5.14 appears to be of

elliptical shape. As shown in Figure 5.12 where the variable temperature was held at

30oC, at any substrate concentration level, the percentage of SHb was low both at

high and low levels of pH (8.19 and 5.81). However, the response increased as the

pH level approached the neutral pH condition (pH 7). The percentage of SHb was

the highest (77.34%) when the pH and substrate level were at 6.7 and 1967.9 mg/L,

respectively.

Figure 5.13 illustrates the percentage of SHb as a response in a contour and

response surface plots for the interaction between varying concentrations of substrate

and temperature. The interaction was observed at constant pH level (pH 7) as the

hold value. The symmetrical mound shape plot shows that at any temperature level,

the percentage of SHb increases as the substrate concentration increased from

1006.75 mg/L to1750 mg/L. However, as the concentration of substrate increases

from 1750 to 2493.25 mg/L, the response started to reduce. The highest percentage

of response is 75.91% obtained at substrate concentration of 1909.63 mg/L and

temperature of 30.05oC. The point that shows the highest percentage of SHb was

positioned at the middle of the symmetrical mound shape plots.

174

(a)

(b) Figure 5.12: (a) Contour and (b) 3D response surface plots representing relationship between the concentration of substrate, pH and percentage of surface hydrophobicity

175

(a)

(b)

Figure 5.13: (a) Contour and (b) 3D response surface plots representing relationship between the concentration of substrate, temperature and percentage of surface hydrophobicity

176

Having substrate variables at constant concentration of 1750 mg/L, Figure

5.14 demonstrates the contour and response surface plots of surface hydrophobicity

at varying pH and temperature values. The percentage of response reduces from

71.34 to 50.17% when the temperature reduced from 35.95 to 24.05oC at a pH level

of 5.81. However, at a high pH level (8.19) the response increases from 32.96 to

52.02% when the temperature of the experiment reduced from 35.95 to 24.05oC. The

graph also shows that at a high temperature (35.95oC), the percentage of response

was doubled when pH reduced from 8.19 to 5.81 at a temperature of 35.95oC. As the

temperature of the experiment was set at 24.05oC, there was not much change on the

percentage of response as the pH level reduced in the same manner. When the

temperature and pH levels were at 32.68 and 6.6, respectively, the predicted

percentage of SHb shows the highest value of 77.14. The response illustrated in

Figures 5.12 to 5.14 shows that the maximum predicted percentage of SHb is

indicated by the surface confined in the smallest curve of the contour diagram.

5.6 Conclusions

i. The FAnGS is consisted of different types of bacteria where among the

twelve bacteria successful isolates demonstrated different morphological and

cellular characteristics.

177

(a)

(b)

Figure 5.14: (a) Contour and (b) 3D response surface plots representing relationship between pH, temperature and percentage of surface hydrophobicity

178

ii. Among the twelve bacteria isolated from FAnGS, all of the isolates show the

same growth pattern under anaerobic (slow growth) and aerobic condition

(fast growth). However, they show different capability in degrading the COD

and dye compound with different degradation rate. The twelve isolated

bacteria also demonstrate different SHb and ability in performing aggregation

when imposed under high shear force. Most of the isolated bacteria that

exhibit high percentage of SHb also show high aptitude to form aggregate.

iii. With the aid of molecular technology via PCR application, the six selected

bacteria that show considerable high capacity in degrading dye and COD and

at the same time also able to perform aggregation and high SHb, have been

identified as Bacillus cereus, Pseudomonas veronii, three species of

Pseudomonas genus and Enterobacter sp. Based on the literature, all of the

six selected isolates are capable to grow both under anaerobic and aerobic

conditions. This ability may become as one of the reasons for the survival of

these bacteria to grow within the FAnGS.

iv. Within the experimental condition of this study, all the selected variables

investigated imposed a significant linear effect on the CAg process. The

substrate concentration and temperature show a positive effect on CAg as the

concentration of substrate and the degree of the temperature increased, while

pH imposed negative effect on CAg when the pH level change from acidic to

alkaline condition. However, the interaction effect onto CAg was only

significant between pH and temperature. Substrate concentration and pH

showed significant non-linear effect on CAg, while the effect of temperature

was not significant. The three way interaction between the three variables

was not significant.

v. All of the three variables, substrate, pH and temperature demonstrate a

significant effect on SHb. Variable substrate shows an increase in percentage

179

of SHb as the concentration of substrate increased. However, the percentage

of SHb reduced as the degree of temperature reduced and the pH change from

acidic to alkaline condition. The interactive effects were observed to occur

between pH and substrate and between pH and temperature. All of the

variables exhibit significant three way interaction effect. With respect to the

non-linearity effect of variables, pH and substrate indicated significant effect

but the result for temperature was opposite.

CHAPTER 6

THE EFFECT OF HYDRAULIC RETENTION TIME ON FACULTATIVE

ANAEROBIC GRANULAR SLUDGE

6.1 Introduction

The applications of granulation techniques for dye degradation have been

reported by many researchers. Most of the studies were focused on using anaerobic

granules under anaerobic condition since major decolorization process occurs under

this condition (Bras et al., 2005; Isik and Sponza, 2005b; Somasiri et al., 2008). The

UASB reactor system containing anaerobic granules was capable in treating raw

textile wastewater with 90% and 92% of COD and color removal, respectively with

24 hours of HRT (Somasiri et al., 2008). Color removal higher than 88% was

reported by Bras et al. (2005) treating mixed monoazo and diazo in a methanogenic

laboratory-scale UASB system at 24 hours HRT.

There are also several reports on the application of aerobic condition for color

removal. Most of the studies use activated sludge, suspended cells or biofilms as the

biomass compositions (Vives et al., 2003; Buitron et al., 2004; Sandhya et al., 2005;

Sirianuntapiboon and Srisornsak, 2007; Sirianuntapiboon and Sansak, 2008). Almost

100% of color removal was reported for the degradation of Direct Blue and Direct

181

Red in a series of granular activated carbon system and sequential batch reactor

system operated under aerobic condition at HRT of 7.5 days (Sirianuntapiboon and

Sansak, 2008). Buiton et al. (2004) reported an average of 80% color removal was

observed for the degradation of Acid Red 151 in a sequential biofilter packed with

porous volcanic rock-pozolane under aerobic condition.

Since complete mineralization of dye containing wastewater requires both

anaerobic and aerobic conditions, several attempts have been conducted to

investigate the removal efficiency under both operating conditions using anaerobic

granules with a series of anaerobic and aerobic reactor systems (Sponza and Atalay,

2003; Isik and Sponza, 2004a; Isik and Sponza, 2004b). However, the application of

granular system in an integrated textile wastewater treatment system has not been

much reported (Shaw et al., 2002).

Table 6.1 shows previous research studies on dye degradation process in

integrated reactor systems using different forms of biomass under different reaction

phase conditions. The table shows that the overall color removal percentage is very

much affected by the HRT particularly during the anaerobic phase. Color removal

increased as the retention time of anaerobic phase increased (Panswad et al., 2001a;

Buitron et al., 2004; Goncalves et al., 2005). However, different types of biomass

either in the form of suspended cells, activated sludge or granules, used in the

treatment system for treating different types of dyes (single or mixed) may result in

different removal efficiencies.

Successful cultivation of FAnGS has been achieved using synthetic textile

dyeing wastewater in an integrated reactor system under intermittent anaerobic and

aerobic reaction phases as reported in Chapter 4. The FAnGS consisting of

anaerobic, aerobic and facultative microorganisms may become the most suitable

biomass for treating the textile wastewater. However, knowledge on the

performance of color removal using FAnGS under different HRT is lacking. The

changes in terms of the granular properties, performance of COD and color removal

182

as well as the performance of the reactor system towards the effect of different HRT

are the main focus of this chapter. Biokinetic parameters such as biomass growth

rate (μ), endogenous decay rate (kd), observed biomass yield (Yobs) and theoretical

biomass yield (Y) were also investigated in relation to the changes of HRT of the

anaerobic and aerobic reaction phases.

6.2 Materials

All of the chemical or reagents and equipments used in this study were given

in Section 4.2. In addition, an Orion 2 Star pH-Benchtop meter (SN-016655) was

used to measure the redox potential of the wastewater while conducting the

experiment. However, in this experiment the concentration of the carbon sources

(glucose, acetate and ethanol) was increased giving an initial OLR of 2.5 kg

COD/m3·day.


The effect of HRT was investigated with respect to the changes in the

microbial activity, physical characteristics and removal performance of the granular

biomass in the IFGSBioRec. Figure 6.1 shows the experimental analysis conducted

for this study.

183

Table 6.1: Dye degradation process using integrated reactor system

Reactor system Biomass Dye Reaction phase/HRT Performance Reference

SBR Activated sludge

Raw wastewater containing disperse, sulfur & reactive dyes

Conventional SBR (Aerobic, 10 hrs); Anoxic+ anaerobic (2 hrs) / Aerobic (8 hrs)

Color reduction was not so good due to less contact hour during anaerobic condition

Pansuwan et al. (1999)

SBR Sludge Reactive Black 5 & Reactive Blue 5,19, 198

Anoxic/Anaerobic (18 hrs)/ Aerobic (5hrs)

Color removal: at 20 mg/L of dyes (63-66%); at 100 mg/L of dyes (32-58%)

Luangdilok and Panswad (2000)


Reactive azo dye Procion Red H-E7B

Anaerobic (24 hrs ) /Aerobic(16 hrs)

63.9% (anaerobic stage) and 11.1% (aerobic stage)

O’Neil et al. (2000b)


Remazol Black B Anoxic+Anaerobic/ Aerobic: (0/11; 2/9; 4/7; and 8/3 hrs)

Color removal: 26.9-60.5% (anaerobic); 1.9-16.7% (aerobic)

Panswad et al. (2001a)


Remazol Black B Anaerobic (18/6 hrs) /Aerobic (5hrs)

73-77% (enriched with PAOs); 59-64% (enriched with GAOs) of color removal

Panswad et al. (2001b)


Remazol Brilliant Violet 5R & Remazol Black B

Anaerobic (9-11 hrs) /Aerobic (8-12 hrs)

90% removal for RBV; 75% removal for RBB

Lourenco et al. (2001)

SBR Anaerobic granules

Remazol Black reactive dye

Anaerobic (18.5 hrs) /Aerobic (30 min)

94% (color removal); 66% (TOC removal)

Shaw et al. (2002)

RDBR Aerobic biofilm

Acid Orange 7 HRT: 1.5 hrs 90% of color removal Coughlin et al. (2002)

184

Table 6.1: Dye degradation process using integrated reactor system (Continued)

Reactor system Biomass Dye Reaction phase/HRT Performance Reference

SBR Activated carbon

Reactive dyes Anoxic (14;17.5 hrs)/ Oxic (6; 2.5 hrs)

>80% (COD removal); 18-25% (color removal).

Pasukphun and Vinitnantharat (2003)

SBBR Porous volcanic rock

Azo dyes Acid Red 151

HRT: 4-24 hrs 99% color removal (14-16% contributed by porous material )

Buitron et al. (2004)

SBR Aerobic sludge

Azo dyes Anaerobic (12;8 hrs)/ Aerobic (8;12;12 hrs)

85% (COD removal); 95% (BOD5 removal)

Goncalves et al. (2005)

SBR Bio-sludge Vat dye Aerobic (19 hrs) /Anaerobic (3 hrs) /Anoxic (0.5 hrs); HRT : 3 d

Color; COD; BOD5; TKN removal: STIWW (98.5%; 96.9%; 98.6%; 93.4%); RWW (75%; 71; 96.7;63%)

Sirianuntapiboon et al. (2006)


Acid Black Azo dye Anoxic (30 min)/ Aerobic (23 hrs)/Anoxic (30 min)

100% (color removal); 92% (COD removal)

Mohan et al. (2007b)

Reaction vessel

Sludge Reactive azo dyes Anoxic (8 hrs) / Aerobic (16 hrs)

12-85% (color removal ); 95% (COD removal )

Smith et al. (2007)

SBR Granular activated carbon

Acid Orange 7 Anaerobic (20 hrs) 100% (color removal); 88% (COD removal)

Ong et al. (2008b)

SBR Facultative anaerobic granule

Mixed azo dyes Anaerobic/Aerobic HRT=3:3;6:6;12:12; 18:6; 6:18 hrs

Highest color removal (86.5%), Highest COD removal (94.1%)

This study (2009)

185

Figure 6.1 Experimental analyses on the effect of HRT on granular biomass in treating synthetic textile dyeing wastewater

186


The microbial activities as the observation of the biological characteristics

were measured based on the OUR of the granular biomass used in this experiment.

The procedure for OUR measurement is as described in Section 4.3.1.2.

6.3.2 Physical Characteristics

The physical changes were investigated in terms of the concentration of the

MLSS and MLVSS, SRT, settling velocity and the sizes of the granular biomass.

The settling velocity of the granules was measured as described in Section 4.3.2.1.

The measurement of MLSS and MLVSS concentrations were according to Standard

Methods (APHA, 2005) as given in Section 4.3.2.4. The granular sizes were

measured by sieving 250 mL of suspended granular biomass using different sizes of

sieve mesh. The amount of the sieved granules were divided with the total granular

biomass and measured as a percentage of volume fractions.

6.3.3 Removal Performances

The removal performances of the reactor system with respect to color and

COD removal were analyzed according to the description in Section 4.3.4.1 and

Section 4.3.4.2, respectively. The redox potential levels were measured throughout

the experiment using an Orion 2 Star pH-Benchtop meter (SN-016655).

187


The FAnGS which was developed in the IFAnGSBioRec as discussed in

Chapter 4 was used as the granular biomass in this study. The size of FAnGS

selected for this experiment was in the range of 0.3-2.5 mm. The FAnGS was

inoculated into the bioreactor at a ratio of 1:4 of the working volume of the reactor

system. One (1) L of acclimated mixed sludge as described in Section 4.2.2 was also

added into the reactor system. During the start-up of the experiment, 2 L of synthetic

textile dyeing wastewater was filled into the reactor. This has made the

concentration of MLSS and MLVSS during the start up of the experiment as 23.2 g/L

and 18.4 g/L respectively. During the first two month of the start-up, 10% (v/v) of

selected dye degrader microbes were added twice a week into the reactor.

The operation steps of one complete cycle of the IFAnGSBioRec are shown

in Table 6.2. The HRT were varied between 6 to 24 hours in order to study the effect

of HRT towards the removal of COD and color by the FAnGS in the continuous

operation of IFAnGSBioRec. During the anaerobic react phase, circulation of

wastewater was carried out using peristaltic pump (Cole-Parmer System Model, 6-

600 rpm), pumping out the wastewater from the upper part of the reactor system and

entering back into the system at the bottom of the reactor. The circulation was

conducted at a flow rate of 18 L/h. Throughout the aerobic react phase, air pump

was used to provide the oxygen to the system. The air was supplied at a superficial

air velocity of 2.5 cm/s. The reaction phases were operated intermittently starting

with anaerobic and followed by aerobic. The reaction phase was then continued with

a second anaerobic phase followed by a second aerobic phase. Then, the biomass

was allowed to settle during the settling phase. The detailed descriptions on the

reaction phase variation are discussed in Section 6.5.2.

188

Table 6.2: Operation steps during single cycle operation

Sequence phase Phase period Air supply Recirculation

Fill 15 min Off Off

Reaction

Anaerobic Varies* Off On

Aerobic Varies* On Off

Settle 5 min Off Off

Decant 5 min Off Off

Idle 5 min Off Off



The microbial activity was measured based on the oxygen uptake rate of a

complete one cycle operation. The OUR of a complete cycle was measured several

times before each of the stages ended. The OUR measurement of each stage of the

experiment showed that most of the external substrate was consumed more or less

within the first 30 minutes of each aerobic reaction phase. Figures 6.2 to 6.3 show

the profiles of the OUR throughout the experiment from Stage I to Stage VI. The

OUR profile (Figure 6.2) shows that the initial measurement of the OUR reduced as

the HRT increased (Stage I to Stage III). This is due to the reduction in the OLR as

the HRT increased. Less oxygen is required as the concentration of the organic

loading reduced. After a sharp increase of OUR at the beginning of each cycle in all

stages, the OUR measurement was constantly low until the end of the cycle. The low

measurement of OUR gives an indication that most of the external substrates have

been consumed. This also means that the microorganisms in the reactor system were

under starvation phase. At this phase, no further degradation was observed

189

eventhough the HRT was extended. During the starvation phase, endogenous

respiration will take place, except at the beginning of the second phase of aerobic

reaction where there was a short increase in the OUR. In this case, the short increase

on the OUR measurement was due to the mineralization of amines, the byproduct of

dye degradation during the second anaerobic reaction phase. As the duration of

anaerobic reaction phase increased, the short pulse increased as shown in Figure 6.3

(a and b) of Stage IV and V, respectively. Stage IV and Stage V were operated with

the same HRT and organic loading but different in the anaerobic and aerobic reaction

phase ratio.

6.5.2 Physical Profile of the Reactor System

The details of the experimental conditions of the reactor system are shown in

Table 6.3. The HRT of the experiment was increased from 6 hours in Stage I to 24

hours in Stage III. The increase in the HRT resulted with a reduction of the OLR

supplemented into the reactor system from 2.5 to 0.6 kg COD/m3·day. The HRT for

Stage III to VI was kept constant i.e. 24 hours, but the duration of anaerobic and

aerobic reaction phases was varied. From Stage III onwards, the OLR was increased

to 0.8 kg COD/m3·day by increasing the concentration of the carbon sources in the

synthetic textile dyeing wastewater. The temperature of the treatment system was

kept constant at 30.0 ± 2.0oC while the pH throughout the experiment was between

6.3 and 8.0.

Table 6.4 shows the oxidation reduction potential (ORP) values measured

during the second phase of the anaerobic and aerobic reactions during the

experiments. The ORP profile of all the stage corresponded very well with the

dissolved oxygen. The ORPs were recorded with more negative values when the

anaerobic reaction phase increased while during the aerobic phase the ORP varies

between +98 to +177 mV.

190

Figure 6.2 OUR profile of (a) Stage I (Aerobic phase 2.84 hours), (b) Stage II

(Aerobic phase 5.84 hours) and (c) Stage III (Aerobic phase 11.84 hours)

(c)

191

Figure 6.3 OUR profile of (a) Stage IV (Aerobic phase 11.84 hours), (b) Stage V

(Aerobic phase 5.84 hours), (c) Stage VI (Aerobic phase 17.84 hours)

(b)

(c)

192

Table 6.3: Details of experimental condition of the IFAnGSBioRec

Stage Days covered

Phase (hours) HRT (hrs)

OLR (kg COD/ m3·day)

1st 2nd

Anaerobic Aerobic Anaerobic Aerobic

I 49 1.42 1.42 1.42 1.42 6 2.5

II 43 2.92 2.92 2.92 2.92 12 1.3

III 51 5.92 5.92 5.92 5.92 24 0.6

IV 43 5.92 5.92 5.92 5.92 24 0.8

V 46 8.92 2.92 8.92 2.92 24 0.8

VI 46 2.92 8.92 2.92 8.92 24 0.8

, where X = COD concentration of the influent (mg/L); Vadd= Volume of influent added in

each cycle operation (mL); Vtotal = Total working volume of the experiment (mL); T = Hydraulic retention time (hour).

Table 6.4: Oxidation Reduction Potential

Stage Anaerobic Reaction Phase Aerobic Reaction Phase

I -124 ± 27 125 ± 19

II -219 ± 33 129 ± 24

III -358 ± 29 174 ± 34

IV -355 ± 51 151 ± 17

V -407 ± 21 112 ± 21

VI -225 ± 28 177 ± 15

The biomass profile at steady state with stepwise increment of HRT (Stage I

to III) and variation of reaction phases (Stage IV to VI) are shown in Table 6.5. As

shown in Table 6.5, it is apparent that the biomass concentration (MLSS) in the

reactor decreased and the VSS in the effluent was also reduced with the increase in

the HRT (Stage I to III). The reduction of the biomass concentration in the reactor

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may be due to the lower value of OLR applied in the reactor system as the HRT

increased.

Table 6.5: Biomass concentrations at different stages of the experiment

Reaction Phase

Stage

I II III IV V VI

Anaerobic (hours) 2.8 5.8 11.8 11.8 17.8 5.8

Aerobic (hours) 2.8 5.8 11.8 11.8 5.8 17.8

MLSS (g/L) 35.3 ± 1.6 28.7 ± 0.6 25.2 ± 1.8 30.5 ± 3.4 31.6 ± 3.7 23.3 ±0.8

MLVSS (g/L) 31.9 ± 1.8 24.5 ± 2.2 18.5 ± 2.2 26.0 ± 3.4 22.4 ± 2.0 20.2 ± 0.8

VSS/SS 0.90 0.85 0.73 0.85 0.71 0.87

Effluent (VSS g/L) 0.34 ± 0.16 0.31 ± 0.11 0.26 ± 0.19 0.34 ± 0.11 0.33 ± 0.10 0.55 ± 0.22

SRT (day) 27.6 ± 13.4 42.4 ± 10.2 78.9 ± 23.9 70.1 ± 23.9 72.5 ± 23.3 41.6 ± 18.4

When the OLR was increased to 0.8 kg COD/m3·day, there was an

improvement in the biomass concentration where the biomass concentration have

increased to 30.5 ± 3.4 g/L and 31.6 ± 3.7 g/L in Stage IV and Stage V as compared

to 25.2 ± 1.8 g/L of biomass concentration in Stage III which run at the same HRT

(24 hours) but with OLR 0.6 kg COD/m3·day. The increase in OLR has caused an

increment in the biomass concentration in the reactor. A slight increase in the

biomass concentration was also observed along with the longer period of the

anaerobic phase (Stage V), i.e. 18 hours.

The ratio of the volatile biomass (MLVSS) to total biomass (MLSS) reduced

from Stage I to Stage III mainly due to decrease in the OLR as the HRT increased

from 6 to 24 hours, whereas the MLVSS/MLSS ratio of the Stage III and Stage IV

194

with 12 hours aerobic reaction phase was observed higher with the ratio of 0.73 and

0.85, respectively. The increment may be due to the increase of the OLR from 0.6 to

0.8 kg COD/m3·day (Stage III to Stage IV). Increase in the OLR means more carbon

sources were supplied to the microorganisms in the reactor. When more food is

available, more growth will take place and this is indicated by the increase in the

MLVSS/MLSS ratio.

However, when the anaerobic period of the HRT is extended, the

MLVSS/MLSS ratio decreased (0.71). Decrease in MLVSS/MLSS ratio may

indicate an increase of inorganic accumulation within the granulation biomass. The

same observation was reported by Panswad et al. (2001a) that increase of inert solids

in the biomass was observed when the system was exposed to high anoxic/anaerobic

condition in the SBR cycle. When the duration of aeration phase was increased up to

18 hours, the biomass started to reduce again (Stage VI) and increase of VSS in the

effluent was once again observed. This may give an indication that too long of

aerobic reaction phase is not suitable for granular biomass system. Prolong of

aeration time may result in instability of the reactor performance. The profile of

biomass concentration of the reactor system throughout the experimental process is

given in Figure 6.4.

The sludge retention time (SRT) of the biomass in the SBR system can be

calculated by using Equation 6.1.

where,

= Solid retention time (d)

= Volatile solid concentration in the reactor system

(g VSS/L),

195

= Working volume of the SBR system (L),

= Biomass concentration of manually discharged mixture

(g VSS/L)

= Manually discharge mixture volume (L),

= Effluent volatile solid concentration (g VSS/L)

= Effluent volume of the SBR operating cycle (L)

= Cycle time of the SBR operation (d)

Figure 6.4 Profile of biomass concentration at different stages of the experiment.

(●) MLSS, (□) MLVSS. Stage I: anaerobic (2.8 h): aerobic (2.8 h); Stage II:

anaerobic (5.8 h): aerobic (5.8 h); Stage III and Stage IV: anaerobic (11.8 h): aerobic

(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage V: anaerobic (5.8 h):

aerobic (17.8 h)

196

Since in the operating system there was no physical sludge discharging at any

of the operating time, Eq. 6.1 can be simplified as Eq. 6.2 (Liu and Tay, 2007b).

cee

rvss

/tVXVX

θ = (6.2)

Based on Eq. 6.2, the SRT of the reactor system increased from 27.6 ± 13.4

to 78.9 ± 30.8 d when the length of the HRT increased from 6 to 24 hours (Stage I to

Stage III). With HRT of 24 hours, increase of anaerobic reaction phase up to 18

hours (Stage IV to Stage V) has slightly increased the SRT from 70.1 ± 23.9 to 72.5

± 23.3 d. The SRT value changes in each stage of the experiment. According to

Wijffels and Tramper (1995), the favorable sludge age for high removal efficiency

for COD and nitrification process is more than 4 days. Based on the SRT obtained,

this granular system is capable of the simultaneous degradation of nitrification

process and COD removal. Since the treatment goal is to remove recalcitrant dyeing

compound, the SRT value of all stages evaluated in this experiment was in the

acceptable range from degradation of xenobiotic compounds (Grady et al. 1999).

6.5.3 Effect of Hydraulic Retention Time on Physical Properties of the

Granular Biomass

In this experiment, the HRTs were between 6 to 24 hours with variation time

for anaerobic and aerobic conditions during the reaction phase. The effects of this

variation on the physical properties of the granules are given in Table 6.6.

197

The mean granular size in the reactor was the largest (i.e 843 ± 44 μm) during

Stage I which was at the shortest HRT and highest OLR. When the HRT increases

from 6 to 24 hours, the OLR was reduced from 2.5 to 0.6 kg COD/m3·day. This

condition may contribute to the smaller granules formation due to the reduction in

the food supply. Furthermore, as the HRT increased from 6 to 24 hours, the mean

granular size was reduced may also be due to the long exposure of the granules to the

shear force imposed by high superficial air velocity during the increasing aerobic

reaction phase. When the OLR were increased from 0.6 to 0.8 kg COD/m3·day from

Stage III to Stage IV, the mean granular size slightly increased due to increase in

food supply.

Table 6.6: Physical properties of the granular biomass at different stages of

experiment

Reaction Phase

Stage

I II III IV V VI

Anaerobic (hours) 2.8 5.8 11.8 11.8 17.8 5.8

Aerobic (hours) 2.8 5.8 11.8 11.8 5.8 17.8

Mean size (μm) 843 ± 44 590 ± 55 440 ± 40 567 ± 79 575 ± 46 385 ± 22

SVI (mL/g) 13.1 ± 0.4 18.8 ± 1.5 21.4 ± 1.6 16.8 ± 1.3 15.5 ± 1.3 24.8 ± 0.9

SV (m/h) 41.3 ± 3.1 35.1 ± 0.8 24.5 ± 1.1 28.4 ± 1.3 33.4 ± 2.5 21.3 ± 0.5

At Stage V, even though the HRT was 24 hours, the mean granular size was

observed to increase. This is due to the short aerobic reaction phase (i.e. 6 hours) as

anaerobic reaction phase was prolonged up to 18 hours and with increase in the OLR.

This shows that bigger granules could still be maintained in the reactor system at

higher HRT provided that aerobic reaction phase is reduced by increasing the

198

anaerobic reaction phase. This condition seems to be suitable for treating textile

wastewater that requires both anaerobic and aerobic phases.

With respect to the development of the granulation process, increase in the

anaerobic reaction phase would cause changes in the EPS component within the

granular sludge enhancing the granulation process. Aerobic granules may consist of

layers of aerobic and facultative anaerobic granules (Jang et al., 2003 and Tay et al.,

2002a). The aerobic microorganisms are able to produce more EPS as compared to

the anaerobic microorganisms (Foster, 1991). As the aerobic microorganisms are

responsible in producing the EPS, under anaerobic condition the facultative

microorganisms suppressed the EPS production and encourage the consumption of

EPS. Fermentation of EPS and disruption of microorganisms would take place

inside the granules during anaerobic condition leading to reduction of the EPS in the

granule. Reduction of the EPS in the inner part of the granules resulted in the

reduction of the surface negative charges, the steric interaction and entanglement of

the EPS and increase in hydrophobicity which means less water trapped (Foster,

1991). With the effect of shear force, the compacting process onto the granule would

have taken place and the granules continue to grow. The increase of the granular

size is stabilized when the balance between growth and detachment due to shear

force effect is reached. The interaction between the production of EPS by the

aerobic microorganisms during aerobic reaction phase may has been balanced with

the consumption of the EPS by the anaerobic or facultative microbes during the

anaerobic reaction phase that caused increase in the granular size during Stage V.

This means having an intermittent reaction phase of anaerobic and aerobic process

would be a good strategy for the application of granulation system for textile

wastewater treatment. Balance in bioactivity of the production and consumption of

EPS could maintain a reasonable amount of EPS within the granules (Li et al.,

2006b) which are important for the successful development and growth of the

granular biomass.

The SVI value of the granular sludge was used to evaluate the granular

settling ability. It is anticipated that bigger granules would have higher settling

velocity and hence, reduce the SVI value, indicating good settling ability. The SVI

199

value changes with the same pattern as the granular biomass concentration as well as

the mean particle size of the granules. As the granular particles decreased in size, the

SVI value increased. The SVI value improved when the anaerobic reaction phase

was prolonged in Stage V indicating such reaction pattern would help to develop

granules with better settling profile. As stated earlier, according to Panswad et al.

(2001a), inert biomass increased as the anoxic/anaerobic condition was prolonged. It

could be possible that the accumulation of inert particles within the granules

increased and resulted with improved SVI properties of the granular biomass.

Figure 6.5 shows the particle size distribution of granular biomass in the

reactor at each stage of the experiment. The figure shows that the particle size

distribution was clearly affected by the HRT and aeration time which imposed shear

force to the granules. As shown earlier in Table 6.4, when the HRT increased,

without increasing the concentration of substrate in the influent, will cause reduction

in the OLR. This means less food is supplied into the reactor. The granular biomass

in the reactor will be exposed to a longer starvation period when the HRT is

increased. The starvation effect become more obvious when the aeration time is

longer as the HRT increased. When there was no more food to be consumed

(starvation phase), the microorganisms will undergo endogenous respiration where

the EPS within the granules will be used as the alternative of the energy sources (Liu

et al., 2005a). The granular biomass, then, would experience microbial decay and

lyses. This would lead to increase in the granules porosity and weaken the granular

structure. Empty holes in the granules would be observed with fragile granule

structure. In these circumstances, the granules would easily defragment into smaller

sizes under operational condition. Control over the granular sizes and the length of

starvation time are among the important factors to be considered for maintaining

performance stability of the granular reactor system.

Hydraulic retention time is an important parameter that control the contact

time between the biomass and the wastewater in a reactor system. The HRT of a

system must be long enough for the degradation process by the microorganisms to

take place. However, in the application of granular biomass in the treatment system,

200

the HRT should not be too long as it may cause the disintegration of the granules.

According to Tay et al. (2002b) and Wang et al. (2005b), a short HRT is favorable

for rapid granulation process, while too long HRTs may lead to granulation system

failure due to high biomass lost (Pan et al., 2004). An optimum HRT of

biogranulation systems would be able to stabilize the reactor performance with good

biomass retention and high removal performance. According to Pan et al. (2004),

the optimum HRT for aerobic granulation systems ranging from 2 to 12 hours

enabled the formation and maintenance of stable aerobic granules with good

settleability and microbial activities. However, the optimum HRT for the treatment

of different types of wastewater may vary depending on the type of wastewater and

the targeted degradation compound.

Figure 6.5 Distribution of size particles at different stages of the experiment. Stage

I: anaerobic (2.8 h): aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h); Stage

III and Stage IV: anaerobic (11.8 h): aerobic (11.8 h); Stage V: anaerobic (17.8 h):

aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)

201

Figure 6.6 shows the profile of SVI throughout the experiments. The SVI

value in Stage V was reduced from 16.8 ± 1.3 mL/g (in Stage IV) to 15.5 ± 1.3 mL/g.

This is expected to be due to the accumulation of more inert solids within the

granules as shown with low levels of MLVSS/MLSS ratio in Stage V (0.71). Despite

changes in HRT that caused decrease in the granular sizes, the SVI values of the

whole experiments were good except for Stage VI. During Stage VI, the prolonged

duration of the aerobic phase (i.e. 17.8 hours) which was operated at high superficial

air velocity (2.5 cm/s), cause the granular biomass to rupture. At this stage, size of

the granular biomass becomes smaller causing the settleability of the particles to

reduce and was demonstrated with increase in SVI value.

Figure 6.6 Profile of sludge volume index throughout the experiment. Stage I:

anaerobic (2.8 h): aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h); Stage

I II III IV V

202

III and Stage IV: anaerobic (11.8 h): aerobic (11.8 h); Stage V: anaerobic (17.8 h):

aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)

6.5.4 Effect of Hydraulic Retention Times on Chemical Oxygen Demand

Removal

The profile for COD concentration in the influent, effluent and removal

performance for all six stages of experiment is given in Figure 6.7. The biogranular

system showed consistent COD degradation performance with 84.2 ± 0.9% removals

after about 50 days of start-up period (acclimatization phase). The overall

performance was almost consistent despite the fact that the duration of the

experimental process was increased from 6 hours to 24 hours. This phenomenon

may be due to the decreasing biomass concentration and also due to the decrease in

the OLR as mentioned earlier. When the OLR was increased from 0.6 kg

COD/m3·day to 0.8 kg COD/m3·day on the 194th day of the experiment (Stage III to

Stage IV), the COD removal efficiency increased from about 84.4 ± 0.4% at the end

of Stage III (day 193) to 90.7 ± 0.2% at the end Stage IV(day 236).

Mohan et al. (2007b) reported that the performance efficiency of the system

was found to be affected by the operating OLR. The SBR system operating at higher

OLR resulted with a high substrate uptake rate at the end of the cycle period. This

was also observed by Ong et al. (2005b).

An increase in the percentage of COD removal efficiency was also observed

when the period of anaerobic phase was increased from 12 hours to 18 hours. The

removal increased from 90.7 ± 0.2 % in Stage IV to 94.1 ± 0.6 % in Stage V.

Psukphun and Vinitnantharat (2003) claimed that the increase in the non-aeration

phase in the SBR system would cause an alteration in the population of anaerobic

microorganisms in the system which is expected to produce good COD and color

removal for textile wastewater. However, according to Kapdan and Oztekin (2006),

when the duration of anaerobic phase is too long, the contribution of aerobic reaction

203

phase might be decreased. This is possibly due to the toxic effect of aromatic amines

produced during dye degradation.

Figure 6.7 Profile of COD removal performance of the reactor system at different

stages of the experiment. (○) Influent COD; (■) Effluent COD, (▲) COD removal.

Stage I: anaerobic (2.8 h): aerobic (2.8 h); Stage II: anaerobic (5.8 h): aerobic (5.8 h);

Stage III and Stage IV: anaerobic (11.8 h): aerobic (11.8 h); Stage V: anaerobic (17.8

h): aerobic (5.8 h); Stage V: anaerobic (5.8 h): aerobic (17.8 h)

Owing to the condition in the SBR system where different reaction phases

occur in the same column, too long anaerobic reaction periods will cause high

accumulation of aromatic amine in the same compartment. High concentrations of

aromatic amines may inhibit the activity of aerobic microorganisms during the

aerobic phase. In this study, eventhough the anaerobic reaction phase was extended

up to 18 hours, there was no reduction in COD removal. This shows that there was

204

no inhibition on the activity of aerobic microorganisms by the long accumulation of

the byproduct produced from anaerobic degradation of the dye compound. It might

be that the concentration of dye used during this experiment was not that high to

produce enough concentration of the aromatic amines that may cause toxic effect

towards the microorganisms within the biogranules. Furthermore, the biogranules

might not be affected by the dyestuff degradation byproducts due to the structural

form of the biogranules. The biogranules structure which consisted of EPS acts as a

shield for microorganisms within the granules against any shock loading or toxic

compound.

At the final stage (Stage VI) of the experiment, a surge drop of COD removal

efficiency was observed. As the aeration time was increased from 6 to 18 hours, the

COD removal reduced from 94.1 ± 0.6% to 82.6 ± 0.8%. The drop in the COD

removal efficiency was due to the increase in biomass loss into the effluent. The

MLSS in Stage VI was 23.3 ± 0.8 g/L as compared to 31.6 ± 3.7 g/L observed in the

previous stages.

6.5.5 Effect of Hydraulic Retention Time on Color Removal

Color removal was observed to increase from 66.7 ± 1.6 % to 76.5 ± 0.8 % as

the HRT increased from Stage I to Stage III. Increase in the HRT allows longer

contact time between the granules and the wastewater resulting in better color

removal. Furthermore, when the OLR was increased from 0.6 kg COD/m3·day

(Stage III) to 0.8 kg COD/m3·day (Stage IV), a significant improvement in color

removal from 76.5 ± 0.8 % to 83.1 ± 1.4 % was observed. This may be caused by the

increase in the microbial population. Ong et al. (2005b) reported that the percentage

of color removal efficiency increased by 16% in anaerobic and 50% in aerobic SBR

reactor systems when the OLR rate was increased from 2.66 to 5.32 g COD/L·day.

An increase from 82% to 90% of color removal efficiency was observed by

205

Talarposhiti et al. (2001) when the COD loading was increased in a two-phase

anaerobic packed bed reactor from 0.25 to 1 kg COD/m3·day.

Since more color removal took place in anaerobic condition (Banat et al.,

1996; van der Zee et al., 2001a and Dos Santos et al., 2007), the percentage of color

removal was once again increased from Stage IV (83.1 ± 1.4%) to Stage V (86.5 ±

0.5%) when the anaerobic reaction phase was extended from 12 to 18 hours of the 24

hours reaction cycle. Improved decolorization process that occurs during the

anaerobic stage enhances the overall wastewater biodegradation since more readily

biodegradable substances could be degraded in the following aerobic treatment

(Stolz, 2001). Figure 6.8 shows the profile of the color removal performance.

Figure 6.8 Profile of color removal performance of the reactor system at different

stages of the experiment. (♦) Influent color, (■) Effluent color, (○) Color removal.

(100 ADMI ≈ 1 Pt-Co). Stage I: anaerobic (2.8 h): aerobic (2.8 h); Stage II:

anaerobic (5.8 h): aerobic (5.8 h); Stage III and Stage IV: anaerobic (11.8 h): aerobic

I II III IV V

206

(11.8 h); Stage V: anaerobic (17.8 h): aerobic (5.8 h); Stage V: anaerobic (5.8 h):

aerobic (17.8 h)

With respect to the mechanisms that are involved in color degradation, the

addition of electron–donating substrate could considerably improve the

decolorization reductive rate (Bras et al., 2001, Dos Santos et al., 2005). Their

studies using anaerobic and aerobic sequential wastewater treatment system indicated

that the anaerobic stage was the main step for color degradation while the aerobic

phase acted as the polishing step and enhancement in COD removal. Higher initial

COD concentration did not improve color removal but caused deterioration in COD

removal in the anaerobic-aerobic SBR system (Kapdan and Oztekin, 2006).

Psukphun and Vinitnantharat (2003) reported that the duration of the

anaerobic phase should be long enough to obtain better COD and color removal.

Increase in the HRT would provide enough time of the COD and intermetabolites of

simulated textile wastewater in anaerobic or/and anaerobic/aerobic systems (Isik and

Sponza, 2008). This means biodegradation of the azo bonds may require a certain

contact time in order to achieve high removal efficiency. Depending only on the

filling stage to provide anaerobic condition for the cleavage of azo bond compounds

may not be adequate for textile wastewater treatment. However, the time requires for

the cleavage of the azo bond may be affected by the complexity of the dye molecule

structures. Higher contact time for the anaerobic reaction phase can be provided by

having the anaerobic reaction stage during the react phase in the SBR cycle as

proposed in this experimental study. The suitable contact time of anaerobic and

aerobic reaction phase may provide high removal performance for the cleavage of the

N=N bond (anaerobic condition) and mineralization of aromatic amines (aerobic

phase). Furthermore, the reduction of COD is more effective during the aerobic

stage as compared to the anaerobic reaction condition (Smith et al., 2007). From this

study, it shows that having longer anaerobic (18 hours) and shorter aerobic (6 hours)

reaction phase resulted with the highest removal for color and slight improvement in

the efficiency of COD removal. The effect of HRT on the COD and color removal

performance by the FGS biomass at different stages of the experiment is given in

Table 6.7.

207

Table 6.7: Profile of COD and color removal percentage at different stages of

experiment

Reaction Phase

Stage

I II III IV V VI

Anaerobic (hours) 2.8 5.8 11.8 11.8 17.8 5.8

Aerobic (hours) 2.8 5.8 11.8 11.8 5.8 17.8

COD (%) 84.2 ± 0.9 84.6 ± 1.1 84.4 ± 0.4 90.7 ± 0.2 94.1 ± 0.6 82.6 ± 0.8

Color (%) 66.7 ± 1.6 74.3 ± 0.4 76.5 ± 0.8 83.1 ± 1.4 86.5 ± 0.5 75.4 ± 0.3

6.5.6 Effect of Hydraulic Retention Time on the Biokinetics of Facultative

Anaerobic Granular Sludge during Biodegradation of Dye

The total solid biomass concentration in a biological reactor system is

governed by the rate of substrates utilization and biomass production by the

microorganisms. The rates of such processes which are known as the biokinetic

parameters would give prediction on the performance of the biological process in

wastewater treatment. The understanding and information on the rate of biological

reactions and basic principles governing the growth of microorganisms are very

important in developing an effective design and operation of the biological reactor

system (Tchobanoglous et al., 2004).

208

In this study, the biokinetic parameters of the FAnGS were also investigated

in relation to the effect of different HRTs. The biokinetic parameters that were

investigated are the overall specific biomass growth rate (μoverall), endogenous decay

rate , observed biomass yield , and theoretical biomass yield All of

the calculations for the biokinetic parameters are according to the equations listed in

Table 6.8 and are based on the reports by Liu and Tay (2007a) and Chen et al.

(2008b). The results of biokinetic parameters for all stages in this experiment are

given in Table 6.9.

When the experiment moved from Stage I to Stage III, the SRT was increased

from 27.6 ± 13.4 to 78.9 ± 30.8 d, these have caused the μoverall to reduce from 0.036

to 0.013/d. The results are in accordance with the report stated by Li et al. (2006b)

that sludge biomass will loose their bioactivity when the SRT is increased. The

reduction of the μoverall as the HRT increased was also observed by Liu and Tay

(2007a). As mentioned earlier, the OLR was reduced when the HRT was increased

from 6 to 24 hours (from Stage I to Stage III). The reduction in the OLR may also

contribute to the reduction of μoverall from Stage I to Stage III. The μoverall of Stage IV

and Stage V was the same when the SRT of these two stages slightly increased from

70.1 ± 23.9 to 72.5 ± 23.3 d, respectively. The μoverall of Stage VI increased as the

SRT was reduced to 41.6 ± 18.4 d eventhough Stage VI was operated with the same

HRT as Stage IV and V. The reduction of the SRT in Stage VI may be contributed

by the increase in the sludge washout that was shown by the increase in the

suspended solids concentration in the effluent discharge.

The rate of biomass lost due to endogenous respiration is represented by

endogenous decay rate kd, as given in Equation 6.4. The OUR that was measured

during the last 10 min or before the second aeration phase stop of one cycle operation

was used to calculate the kd. As the HRT increased from 6 to 24 hours (Stage I to

Stage III), the kd values reduced. However, since the reduction was also very small,

the kd can be considered as constant when the HRT was increased. Furthermore, the

kd value during 24 hours HRT of Stage III to V can also be considered constant

(0.0075 to 0.0076/d). It can thus be concluded that the kd is considered constant

209

throughout the experiment. The kd values calculated from this study were very small

as compared to the kd values of aerobic granules (Chen et al., 2008b) and of the

activated sludge (Tchobanoglous, 2004).

Table 6.8: Coefficient of biokinetic parameters

Biokinetic Coefficient Units Formula Equation

Overall specific biomass growth rate

Per day

θ = sludge retention time

(6.3)

Endogenous decay rate

Per day

= oxygen uptake rate (mg/L.h)

= theoretical chemical oxygen

demand which is assume as 1.42 mg O2/ mg biomass M= biomass concentration (mg VSS/L)

(6.4)

Observed biomass yield

mg VSS/mg COD

= Effluent volatile solid concentration (g VSS/L) Ci = COD concentration in the influent (mg/L) Ce = COD concentration in the effluent (mg/L)

(6.5)

210

Theoretical biomass yield

mg VSS/mg COD

(6.6)

Table 6.9: Kinetic coefficients of FAnGS at different stages of the experiment

Kinetic coefficients of facultative granules

Stage I Stage II Stage III Stage IV Stage V Stage VI

Observed specific biomass growth rate (μoverall) (per day)

0.036 0.024 0.013 0.014 0.014 0.024

Endogenous decay rate kd (per day) 0.0096 0.0086 0.0075 0.0075 0.0076 0.0060

Observed biomass yield (Yobs) (mg VSS/ mg COD)

0.316 0.298 0.242 0.269 0.217 0.412

Theoretical biomass yield Y (mg VSS/ mg COD)

0.399 0.395 0.385 0.410 0.338 0.515

The observed biomass yield Yobs is the ratio of the biomass production rate to

the substrate removal rate and is calculated according to Equation 6.5. The Yobs is

one of the most important parameter used in biological kinetic models. Equation 6.5

is derived from the equation below (Liu and Tay, 2007a):

where,

211

XVSS1 = Volatile solid concentration at the beginning of cycle

operation in SBR reactor (g VSS/L)

XVSS2 = Volatile solid concentration at the end of cycle operation in

SBR reactor (g VSS/L)

Ve = Working volume of the SBR system

tc = Cycle time of SBR operation (d)

Xe = Effluent volatile solid concentration (g VSS/ L)

Ve = Effluent volume in SBR operating cycle (L)

Equation 6.5 is simplified from Equation 6.7 when the reactor system reached

steady state and the biomass was maintained at a constant value (Chen et al., 2008b).

The Yobs can be used to describe the sludge productivity which relates to the net

sludge production.

The results in Table 6.9 show that the sludge production is inversely related

to the value of SRT as shown in Stage I to III. As SRT increased, the Yobs, value

decreased. Since the biomass activity is reduced when the SRT increased, this has

caused the reduction of the biomass yield. The results obtained from this experiment

are in accordance with the ones reported by van Loosdrecht and Hence (1999). It is

well known that the net sludge production in an activated sludge system decreases

with increasing sludge age. The biokinetic parameters could give a good indication

for the system performance. It can be used as a basis for the design and product

optimization of a system reactor. The Yobs value of Stage IV to Stage V, decreased

from 0.269 to 0.217 mg VSS/ mg COD as the SRT of those stages was increased

from 70.1 ± 23.9 to 72.5 ± 23.3 d, respectively. Eventhough Stage IV and Stage V

were operated with the same HRT, the ratio of anaerobic/aerobic reaction phase was

different. It shows that when the ratio of anaerobic/aerobic time was increased, the

Yobs decreased.

The theoretical value is calculated using Equation 6.6. It is expected that

the theoretical Y value will be higher as compared to Yobs. The difference between

212

the and theoretical Y value is contributed by endogenous metabolism, predation,

death and lysis process. The theoretical Y value obtained in this study shows the

same pattern as given by the Yobs. The results for the Yobs and theoretical Y value

obtained in this experiment are within the typical reported range of conventional

activated sludge system (Al-Malack, 2006 and Tchobanoglous et al., 2004).

6.6 Conclusions

i. The granular biomass concentration of the reactor system reduced as the HRT

increased which is mainly due to the reduction in the OLR. However, with

HRT of 24 hours, the biomass slightly increased when the period of anaerobic

reaction phase was longer then the aerobic reaction. The ratio of

MLVSS/MLSS increased during Stage V with anaerobic/aerobic reaction

phase was set with 17.8/5.8 hour which may be due to the increased

accumulation of inert particles within the granules. Eventhough with increase

in the HRT, the concentration of granular biomass can be improved with

increase in the anaerobic reaction time and reduction in the aerobic reaction

time as shown in Stage V.

ii. The size and the SVI of the FAnGS reduced as the HRT of the system

increased (Stage I to Stage III) due to increase in the aeration time that

resulted with the disintegration of the FAnGS. Too long aerobic reaction

times exposed the granules under prolonged starvation condition causing

instability of the granular structure that lead to disruption of the granules.

The size and the SVI value were improved with the increase in the OLR

(Stage IV). The size and the SVI were also improved with increase in the

anaerobic and reduction in the aerobic reaction phases (Stage V).

iii. The percentage of COD removal in this study was not likely affected by the

increase in the HRT which was mainly due to the decrease in the granular

biomass and OLR (Stage I to Stage III). However, the COD removal was

improved with the increase in the anaerobic reaction phase shown in Stage V

213

as compared to Stage IV. The percentage of color removal has improved

with the increase in the HRT.

iv. Stage V (17.8 and 5.8 hours of anaerobic and aerobic reaction phase,

respectively) can be considered as the best condition for the removal of color

and the organic compound since the percentage of color and COD removal

are the highest.

v. Increase in the HRT resulted with an increase in the SRT. Since the SRT is

inversely related to the μoverall, increase in the HRT will cause a reduction in

the μoverall. Increase in the HRT has caused a reduction in the bioactivity of

the granular sludge shown by the reduction of the μoverall, Yobs and Y values.

A slight increase in the SRT was observed with increase in the

anaerobic/aerobic time ratio. This has caused a reduction in the Yobs and Y

values but the μoverall is considered constant. The kd is also considered

constant throughout the experiment.

CHAPTER 7

EFFECT OF SUBSTRATE AND RIBOFLAVIN ON FACULTATIVE

ANAEROBIC GRANULAR SLUDGE

7.1 Introduction

The biodegradation process of azo dyes can be influenced by many factors.

Among them, the presence of redox mediator and the use of different compositions

as the primary substrate were identified as factors that may give effect on the rate of

dye degradation process (van der Zee et al., 2001b; Keck et al., 2002; van der Zee

and Cervantes, 2009). Despite many studies conducted on biodegradation of azo

dyes, the effectiveness of these two factors in enhancing the decolorization of textile

wastewaters is still ambiguous. Most of the study on the effect of redox mediator

focused on either single or mixed bacteria cultures in degrading the dye. Anaerobic

granular sludge has been frequently used as the source of biomass in dye degradation

under anaerobic condition while only few studies were conducted by using aerobic

biomass (Keck et al., 1997 and Kudlich et al., 1997).

Furthermore, knowledge on the effect of the redox mediator and primary

substrate concentration on the use of facultative granules for decolorization of the

azo dye through the application of experimental design is still lacking. The presence

215

of substrate in the textile wastewater will release electrons during its degradation

process. These electrons are required for the degradation of dye which will be

mediated by the redox mediator. Redox mediators are responsible in transferring the

electrons to the dye compounds. However, the understanding on the interactions

between these factors is still indistinct and need to be explored further. In this study,

the impacts of the redox mediator and primary substrate concentration were

investigated. Mixed azo dye consisted of Sumifix Black EXA, Sumifix Navy Blue

EXF and Synozol Red K-4B were again used as the model compound. A similar

substrate which has been used in studies presented in Chapters 4 through 6 was used

and riboflavin was used as the redox mediator. Riboflavin was chosen as the redox

mediator since it represents the redox active moiety in ubiquitous enzyme cofactors

such as flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), which

have been implicated in the azo dye reduction. FMN and FAD are not practical to be

used since they are expensive biochemical materials. Riboflavin is an affordable

vitamin which could be added into the bioreactor to stimulate azo dye reduction

during anaerobic treatment (Field and Brady, 2003). This chapter presents the

findings on the study.

7.2 Materials

Most of the chemical or reagents and equipment used in this study are as

described in Section 4.2. In addition, a 150 mL serum bottle with rubber stopper was

used as a batch test in this experiment. A sealer (E-Z Crimper-20 mm) was used to

seal the serum bottle with a metal cap. A 30 mL syringe with needle was used for

sampling purposes. Synthetic textile dyeing wastewater as described in Section 4.2.1

was used as the media solution of the experiment. The concentration of the mixed

dyes in the wastewater was prepared at 100 mg/L. Riboflavin was obtained from

Sigma (St Louis, USA). Substrate and riboflavin concentration were varied

according to the experimental design.

216

7.2.1 Granular Precursor

A range of 0.3 to 0.6 mm of FAnGS biomass with an average granular size of

0.45 mm was used in this experiment. The FAnGS used in this study was developed

as presented in Chapter 4. About 10% (v/v) of granules were inoculated into the

serum bottle containing 130 mL of synthetic medium which give about 1.94 g/L of

volatile suspended solids (VSS). The FAnGS was always kept in the synthetic

dyeing wastewater prior to conducting experiments for acclimatization purposes.


7.3.1 Chemical Oxygen Demand and Color Removal

In this study, the removal efficiency of COD through the batch experiment

using FAnGS was conducted according to the analytical methods described in

Section 4.3.4.2. The percentage of color removal was estimated quantitatively by

measuring the absorbance reduction at the maximum absorbance wavelength of the

dyes used in the experiment. The individual dye was measured at 600 and 542 nm

wavelength using a UV-visible spectrophotometer (Shimadzdu Model UV-2450).

Distilled water was used as the blank.

217


Screening of redox mediator was conducted as the preliminary test in order to

determine the suitable range of redox concentration to be used in the experimental

design study. Figure 7.1 show the experimental work carried out in this study.

Figure 7.1 Experimental works for the investigation on the effect of substrate concentration and redox mediator on COD and color removal via the aid of experimental design

218

7.4.1 Screening for Concentration of Redox Mediator

The screening process was conducted using batch experiment. Forty-five

(45) mL of synthetic dye wastewater with the organic substrate concentration at 1500

mg/L and dye concentration of 100 mg/L were filled in a 50 mL centrifuge tube. Ten

(10) % (v/v) of granular biomass was added into the synthetic media. Different

concentrations of riboflavin ranging from 0.001 to 0.01 mM were added in each of

the centrifuge tube. A half (0.5) mL of sample was taken hourly for an interval of 24

hours and measured for the color reduction. The experiments were conducted at

room temperature with the samples kept under static condition. The samples were

centrifuged at 5,000 rpm for five minutes to pellet down any suspended particles

prior to color measurement.

7.4.2 Batch Experiment for Chemical Oxygen Demand and Color Removal

Using Facultative Anaerobic Granular Sludge

Batch experiments were conducted to study the effects of substrate and redox

mediator concentration on COD and color removal under anaerobic and aerobic

conditions. One hundred and thirty (130) mL of synthetic wastewater was added to

the serum bottle. Then, 10% (v/v) of granular biomass was added to the serum

bottle. The bottles were capped with rubber stoppers and then were sealed with

metal caps using a sealer (E-Z Crimper).

The anaerobic condition was established by purging the headspace with

N2/CO2 (80%/20%) for 2 min into the serum bottle. By doing this, the concentration

of DO was kept lower than 0.2 mg/L. After purging, the redox mediator was added

into the wastewater sample in the serum bottle. The concentration of redox mediator

219

and substrate in the synthetic wastewater were based on the concentration set by the

experimental design as will be explained in the next section. During the anaerobic

reaction phase, the serum bottles were placed on an orbital shaker and were shaken at

a speed of 100 rpm for continuous contact of granules with the synthetic wastewater.

The experiment under anaerobic condition was conducted for twelve hours. After

the anaerobic reaction phase was completed, the samples were then exposed to the

aeration phase by supplying air bubbles at an air flow rate of 10 ml/h for another

twelve hours.

Samples were taken every two hours during the 24 hours of the reaction

period. During the anaerobic phase, the samples were taken using a 30 mL syringe

with needle. To avoid introducing oxygen into the reactor during the anaerobic

phase, the needle was plugged through the rubber cap after removing the top part of

the metal cap.

7.4.3 2-Level Factorial and Central Composite Design Experiment

Two-level factorial and Central Composite Experimental design was used in

this study. The concentrations of substrates and redox mediator were used as the

variables while the removal of COD and color were the responses. The

concentrations of the substrate and redox mediator were in the range of 500 to 3000

mg/L and 1 to 150 μM, respectively. MinitabTM Statistical Software was used for the

design and analysis of the factorial experiment while Design Expert Statistical

Software was used for the CCD experiment.

Table 7.1 shows the experimental runs used in the factorial design and CCD

in actual and coded values. The two variables of 2-level factorial design comprised

220

of four experimental runs (CC01 to CC04). Since the experiments were conducted in

duplicate, a total of eight runs were carried out. The results provide the linear effect

as well as the interaction effects of the variables. Star point (CC05 to CC08) and

centre point (CC09 to CC013) values were added for the CCD experiments that

present any non-linearity effect of the variables in the reaction process.

Table 7.1: Experimental runs of factorial design and CCD in actual and coded

values (not in random order)

Run Factor 1 Factor 2

A: Substrate B: Riboflavin

CC01 -1 (866.1) -1 (22.8)

CC02 1 (2633.8) -1 (22.8)

CC03 -1 (866.1) 1 (128.2)

CC04 1 (2633.8) 1 (128.2)

CC05 -1.414 (500) 0 (75.5)

CC06 1.414 (3000) 0 (75.5)

CC07 0 (1750) -1.414 (1)

CC08 0 (1750) 1.414 (150)

CC09 0 (1750) 0 (75.5)

CC10 0 (1750) 0 (75.5)

CC11 0 (1750) 0 (75.5)

CC12 0 (1750) 0 (75.5)

CC13 0 (1750) 0 (75.5)

221


7.5.1 Screening for Redox Concentration

The percentage of color removal from the mixed dye at different

concentrations of riboflavin is shown in Figure 7.2. As indicated earlier, the mixed

dye consisted of Sumifix Black EXA, Synozol Red K-4B and Sumifix Navy Blue

EXF with the highest peak measured at wavelength of 480, 542 and 600 nm,

respectively. Since riboflavin is a colored compound that has a peak wavelength

absorbance at 475 nm, the reading for Sumifix Black EXA dye at 480 nm

wavelength has been interfered by the color of the riboflavin. Hence, only the results

of Sumifix Navy Blue EXF and Synozol Red K-4B are shown in the figure.

As shown in the figure, a very low concentration of riboflavin is capable of

increasing the color removal of the mixed dye. Up to riboflavin concentration of 0.1

mM, increase in its concentration, increased the extent of color removal. The highest

color removal (80%) was achieved when the concentration of redox mediator was at

0.1 mM. The color removal was then reduced when the concentration of the redox

mediator was further increased. A minimum percentage of color removal was

observed at the redox mediator dose of 1 mM. In the control sample where there was

no addition of riboflavin, the color reduction can be considered as reasonably high

with about 60% removal. This may be due to the presence of other accelerating dye

degradation agent that may naturally have been produced in the sludge or in the

granules (Dos Santos et al., 2006b).

Different concentrations of redox mediator have been used to accelerate the

dye degradation process in previous studies. For example, 0.012 -0.024 mM and 1

mM of AQDS has been used by Dos Santos et al. (2004) in batch experiments. van

der Zee et al. (2001a) has used AQDS at the concentration of 19 μM to 155 μM in a

222

continuous experiment for the degradation of the Reactive Red 2 by anaerobic

granular sludge in an UASB system. Riboflavin in the range between 0.091 to 0.546

mM was used by Field and Brady (2003) in enhancing the reduction of Mordant

Yellow 10 by anaerobic granular sludge in batch experiments.

Figure 7.2: Color removal at different concentrations of riboflavin. Absorbance at

600 nm (♦), absorbance at 542 nm (□)

Most of the previous experiments were conducted at very low concentrations

of redox mediator. A small amount of redox mediator is capable of achieving high

color removal. Furthermore, the results for color removal would be interfered when

high concentrations of redox mediator are applied since most of the redox mediators

are colored chemical compounds. Only few types of redox mediator are in the form

of colorless compounds such as methyl vilogen and NAD. However, as mentioned

earlier, the application of NAD is limited due to its economical constraint.

223

As a result of the screening study, a further study on the effect of redox

mediator on dye degradation was conducted at low concentrations of 1 to 150 μM of

riboflavin.

7.5.2 Factorial Design Analysis of Chemical Oxygen Demand Removal

The experimental results for the factorial runs are given in Table 7.2. The

overall COD removal was higher under the anaerobic reaction phase reaching almost

80% as compared to 68% of the highest COD removal for the aerobic reaction phase.

The lowest percentage of COD removal of anaerobic and aerobic reaction conditions

was about 24% and 29%, respectively. The percentage of total COD removal varied

between 66 to 86%.

The summary table of the ANOVA that shows the main and two-way

interaction effect is given in Table 7.3. Figure 7.3 shows the Pareto chart generated

by MINITAB™ for anaerobic, aerobic and total COD removal. The detailed results

of the analysis constructed by the software are given in Appendices F1 to F3. The

analyses show that at 90% confidence level (P-value less than 0.1), substrate and

riboflavin have significant main and interactive effects on aerobic COD removal and

total COD removal. As for COD removal during the anaerobic stage, only substrate

was found to have a significant effect.

224

Table 7.2: Experimental results for factorial design analysis

Run Anaerobic COD removal

Aerobic COD removal

Total COD removal

CC01 32.9 67.8 78.4

CC02 79.4 29.7 85.5

CC03 28.8 52.2 66.0

CC04 78.4 27.9 84.4

CC05 25.9 68.3 76.5

CC06 78.9 28.8 85.0

CC07 24.4 55.3 66.2

CC08 77.1 37.5 85.7

Table 7.3: The P-values of the estimated main and interaction effects of substrates

and riboflavin for the percentage of COD removal

Effect Anaerobic

COD removal

SignificantaAerobic

COD removal

SignificantaTotal COD

removal Significanta

Main

Substrate < 0.0001 Yes < 0.0001 Yes < 0.0001 Yes

Riboflavin 0.373 No 0.001 Yes < 0.0001 Yes

2-way interaction

Substrate× Riboflavin 0.755 No 0.002 Yes 0.001 Yes

a significant at α = 0.1

225

Figure 7.3 The Pareto chart of COD removal for (a) anaerobic, (b) aerobic and (c)

total removal (A: substrate; B: riboflavin; α: 0.1)

226

7.5.2.1 Factorial Analysis: The Main Effect of Substrate on Chemical Oxygen

Demand Removal

With respect to the experimental conditions used in this study, the substrate

concentration shows highly significant effects for both the anaerobic and aerobic

reaction phases with P-value of less than 0.0001. The effect was also significant for

total COD removal where the P-value was also less than 0.0001. However, the

direction of the effect was opposite when compared between the anaerobic and

aerobic reaction phases.

During the anaerobic reaction phase, the estimated effect was +50.45 while

during the aerobic reaction phase the value was -32.45. This means during the

anaerobic reaction phase, the percentage of COD removal increased as the substrate

was increased. Under the aerobic reaction phase, the percentage of COD removal

decreased with increase in substrate concentration. Substrate concentration was

found to cause a positive effect on total COD removal with an estimated effect of

13.4.

When the substrate concentrations were increased, more food was supplied to

the microorganisms. Such condition was postulated to cause increment in the

concentration of the suspended biomass in the serum bottle which leads to increase in

the percentage of COD removal under anaerobic condition. Increase in COD

removal from 73% to 88% has been also reported by Siman et al. (2004) when the

OLR was increased from 1.5 to 5.4 g COD/L·day in an anaerobic sequencing biofilm

batch reactor system.

The negative effect of substrate concentration on aerobic COD removal can

be due to several reasons. Since the experiment for aerobic reaction phase was a

continuation from the anaerobic reaction phase, the same sample was used for the

227

aerobic phase after the anaerobic reaction was completed. Since most of the

substrate has been degraded during anaerobic reaction, so the amount of substrate left

for aerobic reaction was lesser. In other words, there was only a little amount of

substrate left to be used during the aerobic phase eventhough the initial concentration

of substrate at the start of the experiment was high.

Another possible reason for the lower percentage of COD removal as the

substrate increased under aerobic reaction phase was insufficient oxygen supplied.

As the substrate increased, the demand for oxygen is expected to be greater in order

to degrade more substrate. Since the rate of the aeration was not increased

throughout the experiment, this might contribute to the decreasing COD removal

since the supply of oxygen is not enough to oxidize the increasing concentration of

substrate.

Another possibility might be due to the uncleavage dye compounds and the

accumulation of non-mineralized or slow degradation of amines compounds as

reported by Tan and Field (2000). It can be due to the toxic effect of the

accumulation of aromatic amines, the byproduct produced during the increasing

anaerobic degradation of the mixed dye presence in the synthetic media. Aromatic

amines are known as toxic compounds (Weisburger, 2002) which may affect the

microbial degradation activity of the aerobic microorganisms localized at the outer

layer of the granules.

228

7.5.2.2 Factorial Analysis: The Main Effect of Riboflavin on Chemical Oxygen

Demand Removal

The concentration of riboflavin did not have a significant effect on the

percentage of COD removal for the anaerobic reaction phase since the P-value was

0.373. As the concentration of redox mediator increased, more of the N=N bond are

expected to be cleaved (Field and Brady, 2003 and Mendez-Paz et al., 2005) and

more amines were released during the anaerobic reaction phase. Since amine could

not be further degraded under anaerobic condition, the concentration of COD did not

reduce significantly.

However, for the aerobic reaction phase, the redox mediator has a significant

effect on the percentage of COD removal with the P-value of 0.001. The percentage

of COD removal during the aerobic reaction phase was reduced when the

concentration of redox mediator was increased with the estimated effect of -7.95. As

stated earlier, under anaerobic condition, when the redox mediator was increased,

more cleavage of the azo bond will take place and hence increased the release of the

amines. Since the aeration rate was kept constant throughout the experiment, as the

concentration of redox mediator increased, the oxygen supply was not enough to

degrade the increasing concentration of the amines thus causing reduction in COD

removal. The presence of high concentrations of amines may also impose a toxicity

effect to the microorganisms in the serum bottle that lead to reduction in aerobic

COD removal.

The effect of increasing redox mediator on the percentage of total COD

removal was also significant where the P-value is less than 0.0001 and the estimated

effect was -5.78. This means, as the concentration of the redox mediator increased,

the percentage of total COD removal was reduced. Based on the value of the

estimated effect, it shows that the significant effect of redox mediator in total COD

removal was mainly contributed by the aerobic reaction phase. The reasoning for the

229

reduction of the total COD removal was the same as explained for the reduced COD

removal under aerobic condition. Figure 7.4 shows the main effect of substrate and

riboflavin during the anaerobic and aerobic reaction phases and also for the total

COD removal.

Figure 7.4 Main effect plot of substrate and riboflavin for (a) anaerobic, (b) aerobic

and (c) total COD removal

230

7.5.2.3 Factorial Analysis: The Interaction Effect of Substrate and Riboflavin

on Chemical Oxygen Demand Removal

The interaction effect between substrate and riboflavin was insignificant for

percentage of COD removal in the anaerobic reaction phase but was significant for

the aerobic reaction phase and the total COD removal with P-values of 0.02 and

0.001, respectively.

At low substrate concentration, increase in riboflavin is expected to result in

increase in the production of amines. Amines that can be further degraded under

aerobic condition contributed to the increased concentration of COD. Since there

was no increment on the aeration rate, the amount of oxygen may not be enough for

the microorganisms to degrade the increasing COD level resulting in decreasing

percentage of COD removal as the riboflavin increased. Due to the same reason,

increase in the concentration of riboflavin has also caused a reduction but only with a

slight decrease in the percentage of COD removal at high concentrations of substrate.

Higher percentages of total COD removal were observed at higher

concentrations of substrate (2633.88 mg COD/L) as compared to the lower

concentrations of substrate (866.12 mg COD/L) at low concentrations of riboflavin.

However, increase of riboflavin at higher substrate levels has barely caused any

changes on the percentage of total COD removal as the riboflavin itself has caused

an increase in the COD value (through the formation of amines). The high removal

percentage during high substrate concentrations was due to the anaerobic degradation

process.

The reduction of the total COD removal was observed at low substrate

concentration as the riboflavin increased, mainly contributed by the increase in the

concentration of the aromatic amines and the insufficiency of oxygen supply. Figure

231

7.5 shows the interaction plot of substrate and riboflavin for anaerobic, aerobic and

total COD removal.

Figure 7.5 Interaction plot for the percentage of COD removal for (a) anaerobic, (b) aerobic and (c) total removal (Substrate: ---- 2633.88 m/L; ___ 866.12 mg/L; ● Centre point)

•

232

7.5.3 Central Composite Design Analysis of Chemical Oxygen Demand

Removal

Table 7.4 shows the experimental results of the CCD for COD removal. The

analysis was carried out using full quadratic terms including linear, square and

interaction. The results of the ANOVA for COD percentage removal at different

reaction conditions are shown in Table 7.5. The detailed results for estimated

regression coefficient and ANOVA table are given in Appendices F4 to F6.

Based on the P-value, the results showed that only the linear term of substrate

(P-value 0.094) of the anaerobic reaction phase and the square term of substrate (P-

value of 0.045) of the aerobic reaction phase are significant. This shows that

substrate concentration only caused a linear effect on the COD removal during the

anaerobic reaction phase and has a non-linear effect during the aerobic reaction

phase. Both the models of COD removal under anaerobic and aerobic reaction phases

are insignificant with R-squared values of 46% and 47.9%, respectively.

Figure 7.6 shows the contour and surface plot of the significance defined

model for total COD removal. The figure clearly shows that as the substrate

concentration increases, the percentage of COD removal also increases. With respect

to the effect of riboflavin, at low substrate concentrations, increase in the

concentration of riboflavin has caused a reduction in the total COD removal from

71.2% to 61.8%. At high concentrations of substrate, increase in the concentration of

riboflavin have caused a slight increase in the total COD removal from 82.3 to

84.1%. The highest percentage removal of total COD was observed at substrate and

riboflavin concentrations of 2165.8 mg/L and 23.4 μM, respectively with 84.5%

removal.

233


Run Anaerobic reaction phase

Aerobic reaction phase Total COD removal

CC01 32.9 67.8 78.4

CC02 79.4 29.7 85.5

CC03 28.8 52.2 66.0

CC04 78.4 27.9 84.4

CC05 23.9 32.0 48.2

CC06 31.6 66.8 77.3

CC07 24.4 73.7 80.1

CC08 21.3 73.1 78.8

CC09 22.1 75.4 80.8

CC10 25.6 72.5 79.5

CC11 24.3 73.7 80.1

CC12 23.9 72.6 79.2

CC13 24.0 73.6 79.9

The best statistical model that can be used to represent the total COD removal

for this process based on the experimental conditions in this study obtained from the

response surface analysis is:

Total COD removal = +51.26 + 0.04 × A - 0.23 × B + 6.07×10-5 × AB (7.1)

- 8.58×10-6 × A2 + 5.92×10-4 × B2

where:

A = Substrate in mg/L

B = Ribofalvin in uM

234


Term

Anaerobic COD removal

Aerobic COD removal

Total COD removal

The P-valuea

Substrate 0.094 0.799 0.002

Riboflavin 0.868 0.725 0.295

Substrate×Substrate 0.249 0.045 0.008

Riboflavin×Riboflavin 0.385 0.538 0.396

Substrate×Riboflavin 0.939 0.707 0.277

R-squared value 46% 47.90% 85.80%

Lack of Fit <0.0001 <0.0001 <0.0001

0.01 – 0.04: Highly significant; 0.05 – 0.1: significant; 0.1 – 0.2: less significant; < 0.2: insignificant (Vecchio,

1997)

7.5.4 Factorial Design Analysis of Color Removal

In the investigation of color removal, focus was made on the anaerobic

reaction phase only. This is because when the measurements for color removal were

made during the aerobic reaction phase, negative responses were obtained. The

negative response was due to the resurgence of color in the samples under the

aerobic reaction phase. As mentioned earlier, since the absorbance for Sumifix

Black dye is at 480 nm which is near to the absorbance of the riboflavin which is at

475 nm, the measurement for Sumifix Black dye was interfered by the color of the

riboflavin. Hence, further analysis and discussion only focused on Sumifix Navy

Blue EXE and Synozol Red K-4B only.

235

(b)

Figure 7.6 The relationship between substrate, riboflavin and percentage of total

COD removal after 24 hours of experimental run, (a) Contour plot and (b) Responses

surface plot

(a)

236

Table 7.6 shows the experimental results for factorial runs for the percentage

of color removal measured at absorbance 600 nm and 542 nm for Sumifix Navy Blue

EXF and Synozol Red K-4B, respectively. Table 7.7 shows the summary of the

ANOVA of the factorial runs. The detailed results for the table of estimated effect

and coefficient and ANOVA table are given in Appendices G1 to G4. The Pareto

chart that shows the effects of substrate and riboflavin concentration on the

percentage of color removal of Sumifix Navy Blue EXF and Synozol Red K-4B at

five and twelve hours of the experimental runs are given in Figures 7.7 and 7.8,

respectively. The results of color removal were investigated at the early and end

stages of the experiment in order to observe any time dependent effect among these

variables. Figure 7.8 shows both variables give significant effect on color removal

except for the interaction effect at twelve hours experiment for Sumifix Navy Blue

EXF. Figure 7.8 demonstrates that both variables are significant at both five and

twelve hour experiments for Synozol Red K-4B.

Table 7.6: Experimental results for factorial design analysis

Run Sumifix Navy Blue EXF Synozol Red K-4B

5 hours 12 hours 5 hours 12 hours

CC01 74.7 77.0 68.5 75.2

CC02 80.0 76.7 77.5 75.8

CC03 77.2 83.9 62.3 82.3

CC04 83.5 81.0 78.0 80.0

CC05 75.0 78.8 69.3 76.0

CC06 79.7 76.4 76.1 76.0

CC07 76.5 84.0 62.8 82.5

CC08 84.0 82.0 79.4 81.0

237

Table 7.7: The P-values of the estimated main and interaction effects of variables

substrates and redox mediator for the percentage of color removal

Effect

Sumifix Navy Blue EXF Synozol Red K-4B

5 hours Significanta 12 hours Significanta 5 hours Significanta 12

hours Significanta

Main effect

Substrate <0.0001 Yes 0.022 Yes <0.0001 Yes 0.071 Yes

Riboflavin <0.0001 Yes <0.0001 Yes 0.015 Yes <0.0001 Yes

Interaction effect

Substrate× Riboflavin 0.017 Yes 0.351 No 0.002 Yes 0.028 Yes

asignificant at α = 0.1

7.5.4.1 Factorial Analysis: Main Effect of Substrate on Color Removal

The results of the factorial design analysis showed that at five hours after the

experiment started, the substrate was observed to give a significant main effect for

both dyes; both with P-values of less than 0.0001 and an estimated effect of +5.95

and +12.02 for Sumifix Navy Blue EXF and Synozol Red K-4B, respectively. The

percentage of color removal increased as the concentration of substrate was increased

from 866.1 mg/L to 2633.9 mg/L. The percentage of color removal was a bit higher

by about 4% for Sumifix Navy Blue EXF as compared to Synozol Red K-4B.

238

Figure 7.7 Pareto chart of Sumifix Navy Blue EXF removal at (a) 5 and (b) 12

hours (α: 0.1; A: Substrate; B: Riboflavin)

239

Figure 7.8 Pareto chart of Synozol Red K-4B removal at (a) 5 and (b) 12 hours

(α: 0.1; A: Substrate; B: Riboflavin)

240

The effect of substrate concentration in this study is in accordance with the

results reported by other researchers. It has been reported that the percentage of

color removal increased as the substrate supplement to the system was increased (Fu

et al., 2002 and Sirianuntapiboon and Srisornsak, 2007). Dafale et al. (2008)

measured the kinetic constant of dye degradation of azo dyes with and without the

presence of substrate glucose and reported that the kinetic constant was increased

fivefold for the decolorization of Remazol Black B with the addition of 2 g/L of

glucose. The use of substrate is essential in obtaining a good percentage of dye

degradation (Delee et al., 1998 and Ozsoy et al., 2005). It was reported that the

azoreductase enzyme system is responsible for the decolorization of dyes by bacteria

with the presence of substrate under anaerobic conditions (Yoo, 2002).

The presence of external carbon sources will donate or produce electrons

after being oxidized under the catabolism process. The released electrons will be

used for the formation of the reducing equivalent or the reduced co-factor (Carliell et

al., 1995). These reducing equivalent or reduced co-factor will involve in the

electron transfer to the N=N bond of dye chemical structure and resulted with the

cleavage of the double bond. Therefore, when the concentration of substrate is

increased, more electrons will be donated to the azo bond and this improved the

percentage of color removal.

The effect of substrate concentration on color removal was different at twelve

hours for both dyes. As the effect of substrate was found to cause a positive effect at

five hours reaction, the effect was negative at twelve hours reaction (i.e -1.9 for

Sumifix Navy Blue EXF and -0.8 for Synozol Red K-4B).

The negative effect is probably due to the accumulation of amines as they are

colored compounds which can cause different color intensity. The presence of these

colored amines may not affect the color removal at the early stage of the experiment

since the intensity of color due to the presence of dye was still high and the amines

241

were still relatively low in concentration. However, as the degradation of the dyes

continued and reached the maximum removal at the later stage of the experiment

(twelve hours), more amines are being produced and accumulated. Hence, the color

of the wastewater was no longer due to the dye compounds alone. At twelve hours,

the wastewater in the serum bottle did not become colorless but has turned greenish.

This greenish color may be due to the accumulation of the aromatic amines and

resulted with a slight increase in color intensity of the wastewater.

7.5.4.2 Factorial Design Analysis: Main Effect of Riboflavin on Color Removal

In this study, the results showed that the concentration of riboflavin has a

significant effect on the color removal of the mixed azo dyes. The P-values for the

effect of riboflavin on the color removal of Sumifix Navy Blue EXF is less than

0.0001 for both five and twelve hours of the experimental period. The color removal

of Synozol Red K-4B is also significant at five and twelve hours of the experiment

with the P-values of 0.015 and less than 0.0001, respectively.

At twelve hours of the experiment, the effect of riboflavin shows nearly the

same positive magnitude for both types of dyes with an estimated effect of +5.5 and

+5.7 for Sumifix Navy Blue EXF and Synozol Red K-4B, respectively. This mean,

at twelve hours of the experimental run, as the concentration of riboflavin increased,

the percentage of color removal for both types of dyes were also increased.

The results obtained at five hours of the experiment showed that the

estimated effect for Sumifix Navy Blue EXF removal is positive with magnitude of

2.95. However, the result obtained for the removal of Synozol Red K-4B shows a

negative value of the estimated effect (-2.225). It shows that the addition of

242

riboflavin has caused a reduction in the percentage of Synozol Red K-4B removal at

the earlier stage of the degradation process. It is possible that Synozol Red K-4B has

a more complex chemical structure as compared to Sumifix Navy Blue EXF. Due to

the complexity, the degradation of Synozol Red K-4B at five hours of the experiment

was lesser than Sumifix Navy Blue. As discussed earlier in Section 7.4.1, riboflavin

is a colored chemical compound; so, increase in the concentration of riboflavin will

add color to the synthetic media. While the color of Synozol Red K-4B is not being

sufficiently removed at five hours of the experiment, the addition of riboflavin has

caused the overall color reduction for Synozol Red K-4B to decrease.

The addition of redox mediator has been reported to accelerate the transfer of

electron from a primary electron donor to the azo dye bond that acted as the terminal

electron acceptor (Kudlich et al., 1997 and Keck et al., 2002). Besides, the presence

of redox mediator is also capable of minimizing the steric hindrance due to high

density of the electrons in the azo bond dye molecule structure (Moir et al., 2001)

and cause decreasing energy of the chemical reaction (Dos Santos et al, 2007). This

would help to increase the dye degradation process.

Figures 7.9 and 7.10 show the main effect of substrate and riboflavin for both

dyes at both 5 and 12 hours of anaerobic experimental condition.

7.5.4.3 Factorial Analysis: Interaction Effect

The concentration of substrate and riboflavin show a significant interaction

effect for both dyes. However, for the removal of Sumifix Navy Blue EXF, the

effect was not significant where the P-value is 0.351 at twelve hours of the

experimental runs. The interaction effect at five hours for the removal of Sumifix

243

Navy Blue EXF gave the P-value of 0.017. Since Sumifix Navy Blue EXF is

presumed to have a simpler molecular structure, the transfer of the reducing

equivalence is not a limiting factor. The transfer of electron to the N=N bond of the

Sumifix Navy Blue EXF was not so much dependent on the presence of the electron

transfer. Furthermore, the amount of reducing equivalence produced due to the

degradation of substrate could be high and easily being transferred to the dyes. Such

condition may result with non-interaction effect between the two variables at twelve

hours of the experimental conditions. The biodegradation of dye is a time dependent

degradation process. This has been proven in the previous chapter (Chapter 6) where

the percentage of color removal increase at the HRT increased. This could be

explained by the increasing magnitude effect on the color removal between five and

twelve hours of the reaction process.

Figure 7.9 Main effect plot of substrate and riboflavin on the color removal of

Sumifix Navy Blue EXF at (a) 5 and (b) 12 hours of experiment under anaerobic

condition

244

Figure 7.10 Main effect plot of substrate and riboflavin on color removal of

Synozol Red K-4B at (a) 5 and (b) 12 hours of experiment under anaerobic condition

The P-values of the interaction effect at five hours and twelve hours are 0.002

and 0.028, respectively, indicating significant interaction results. As for the Synozol

Red K-4B, the dye is assumed to have more complex molecule structure as compared

to Sumifix Navy Blue EXF. This has caused a reduction in color removal with the

addition of riboflavin at the early stage (i.e five hours). At higher substrate

concentration, more reducing equivalence would be released and this could be seen

with a slight increase in the percentage of color removal for Synozol Red K-4B. At

twelve hours, the presence of riboflavin has significantly accelerated the percentage

of Synozol Red K-4B and Sumific Navy Bleu EXF removals at both low and high

substrate concentrations.

245

The interaction effect for Sumifix Navy Blue EXF and Synozol Red K-4B at

five and twelve hours of the experimental conditions are given in Figures 7.11 and

7.12, respectively.

Figure 7.11 Interaction of variables substrate and riboflavin for Sumifix Navy Blue EXF at (a) 5 and (b) 12 hours of the experimental conditions (Substrate: ---- 2366.88 m/L; ____ 866.12 mg/L; ● Centre point)

Figure 7.12 Interaction of variables substrate and riboflavin for Synozol Red K-4B

at (a) 5 and (b) 12 hours of the experimental conditions (Substrate: ---- 2366.88 m/L;

____ 866.12 mg/L; • Centre point)

246

7.5.5 Central Composite Design Analysis of Color Removal

The color removal was measured at five and twelve hours of the experimental

conditions for both type of dyes and the results are given in Table 7.8. The analysis

was carried out using full quadratic terms including linear, square and interaction.

The results of the ANOVA for color removal at different reaction conditions are

shown in Table 7.9. The detailed results for estimated regression coefficient and

ANOVA table are given in Appendices G5 to G9.


Run Sumifix Navy Blue EXF Synozol Red K-4B

5 hours 12 hours 5 hours 12 hours

CC01 74.7 77 68.5 75.2

CC02 80.0 76.7 77.5 75.8

CC03 77.2 83.9 62.3 82.3

CC04 83.5 81.0 78.0 80.0

CC05 79.8 86.9 66.5 85.5

CC06 82.3 81.8 73.9 81.1

CC07 78.9 71.8 68.8 71.3

CC08 83.3 81.4 82.3 79.8

CC09 82.8 79.5 76.2 78.1

CC10 82.4 80.9 79.7 80.1

CC11 87.3 85.9 82.5 83.9

CC12 83.2 81.5 79.1 80.5

CC13 85.8 79.8 79.8 79.1

247


Term

Sumifix Navy Blue EXF Synozol Red K-4B

The P-value

5 hours

12 hours 5 hours 12 hours Full Quadratic

terms

Linear + Square

Substrate 0.0512 0.0369 0.1221 0.0219 0.1769

Riboflavin 0.0998 0.078 0.0041 0.3008 0.0031

Substrate2 0.0396 0.0275 0.1717 0.0177 0.1322

Riboflavin2 0.0413 0.0289 0.0121 0.1988 0.0069

Substrate× Riboflavin 0.8326 - 0.5543 0.4552 0.4624

R-squared value 74.36% 74.19% 83.49% 75.04% 85.59%

Lack of Fit (LOFT) 0.3865 0.5007 0.8885 0.0456 0.7938

a0.01 – 0.04: Highly significant; 0.05 – 0.1: significant; 0.1 – 0.2: less significant; < 0.2: insignificant (Vecchio,

1997)

Statistical models were developed to relate the concentration of substrate and

redox mediator with color removal. Since the effects of both variables are time

dependent, the models were developed at five and twelve hours of the experimental

condition. The modeling attempts were developed with the aid of Design Expert 7P.

Based on the P-value, the results associated with substrate concentration were

significant at five hours of the experiment and became trivial at twelve hours. The

results showed the same patterns for both linear and square terms. With regard to the

248

effect of redox mediator (riboflavin), the variable shows a significant effect for the

color removal of Sumifix Navy Blue EXF at both five and twelve hours of the

experimental conditions for both linear and square terms.

The color removal of Synozol Red K-4B for the effect of riboflavin was

opposite as compared to the effect of substrate concentrations. The effect of the

redox mediator was insignificant at five hours but became significant at twelve hours

for both linear and square terms. The effect of substrate and redox mediator with

respect to the interaction terms for Sumifix Navy Blue EXF and Synozol Red K-4B

were all insignificant for both five and twelve hours. The R-squared values for all

models were in the acceptable ranges (74-86%). For the color removal of Sumifix

Navy Blue EXF at both five and twelve hours of the experiment, the P-values for the

LOFT was insignificant with the value of 0.3865 and 0.8885, respectively.

For the color removal of Synozol Red K-4B, the P-value of the LOFT is only

insignificant at twelve hours (P-value 0.7938) and significant (P-value 0.0456) at

five hours of the experimental conditions. The significant P-value for the LOFT

implies that the predictive understanding of the model is not statistically accurate and

that the process appears to be too complex to model.

An attempt was made in order to improve the statistical model by removing

all the insignificant terms for the full quadratic terms. However, the insignificant

terms were only removed for the five hours reaction phase of Sumifix Navy Blue

EXF. Removing the interaction term has improved the statistical model since the P-

value of the LOFT has increased to 0.5007 as compared to 0.3865 for the full

quadratic term model. All the insignificant terms for Synozol Red K-4B and the

twelve hours reaction phase of Sumifix Navy Blue EXF were unchanged since

removing those insignificant terms will cause reduction for the R-squared and P-

value of the LOFT.

249

The best statistical models that can be used to represent the color removal

process based on the experimental conditions in this study as attained from the CCD

experimental design analysis are given in Table 7.10. The mathematical model of

Sumifix Navy Blue EXF for five hour reaction phase was generated based on the

reduced quadratic model.

Table 7.10: Mathematical models in terms of actual values

Dye Time (hours) Statistical model

Sumifix Navy

Blue EXF

5

= 65.38 + 0.01×A + 0.15×B - 2.79 × 10-6×A2

– 7.77× 10-4×B2

12

= 77.1 – 5.8 × 10-3×A + 0.23×B + 1.54 × 10-6×A2

– 9.61× 10-4×B2 – 1.4×A.B

Synozol Red K-

4B

5

= 49.0 + 0.02×A + 0.09×B – 6.34 × 10-6×A2

– 8.21× 10-4×B2 + 3.6×A.B

12

= 75.3 – 5.34 × 10-3×A + 0.22×B + 1.54 × 10-6×A2

– 9.62×B2 – 1.56 × 10-5×A.B

A: Substrate (mg/L); B: Riboflavin(μM)

The predicted versus actual plots for the COD removal for Sumifix Navy

Blue EXF and Synozol Red K-4B are shown in Figures 7.13 to 7.14. The observed

points of the plots reveal that the actual values can be considered distributed

relatively near to the straight line for the color removal of dyes at both five and

twelve hours of the experimental conditions. The predicted and actual values

obtained from this experiment are considered to be fit.

250

Actual

Pred

icte

d

74.70

77.85

81.00

84.15

87.30

74.70 77.85 81.00 84.15 87.30

Actual

Pred

icte

d

71.80

75.58

79.35

83.13

86.90

71.80 75.58 79.35 83.13 86.90

Figure 7.13: Predicted versus actual data for Sumifix Navy Blue EXF removal at (a)

5 hours and (b) 12 hours

(b)

(a)

251

Actual

Pred

icte

d

62.30

67.35

72.40

77.45

82.50

62.30 67.35 72.40 77.45 82.50

Actual

Pred

icte

d

70.88

74.53

78.19

81.84

85.50

70.88 74.53 78.19 81.84 85.50

Figure 7.14 Predicted versus actual data for Synozol Red K-4B removal at (a) 5

hours and (b) 12 hours

(a)

(b)

252

The response surface and contour plots based on the significant model are

given in Figures 7.15 to 7.18. The response surface and contour plots show that the

reactions that took placed in the experiment changed with time with respect to the

observed variables for both dyes. The reactions were also different as the structure

of the dyes differed. Figure 7.15 shows the surface plot and response surface plots of

substrate and riboflavin for the percentage of Sumifix Navy Blue EXF at five hours

reaction time with a reduced quadratic model. The response was found to be a

symmetrical mound shape. The maximum prediction of the percentage of color

removal was indicated by the surface confined in the smallest curve of the contour

diagram. The maximum percentage of color removal was 85% that occurred when

the concentration of substrate and riboflavin were at 2111.8 mg/L and 96.1 μM,

respectively.

Based on Figure 7.16, the surface plot indicating the best predicted

decolorization for Sumifix Navy Blue EXF (85.3%) at twelve hours reaction phase

was obtained with substrate and riboflavin concentrations of 866.1 mg/L and 128.2

μM, respectively. The predicted value agrees with the actual removal (82.9) which

deferred by only 3% obtained experimentally under the same condition.

The pattern of the contour and response surface plot for the color removal of

Synozol Red K-4B as shown in Figures 7.17 to 7.18 are almost the same as shown

for Sumifix Navy Blue EXF except at five hours of the reaction period. The pattern

of the plots for Synozol Red K-4B at five hours appears as an elliptical shape (Figure

7.17). At low substrate concentration, increase in the concentration of riboflavin did

not cause significant change on the color removal. At the lowest concentration of

riboflavin (22.8 mg/L), the percentage of color removal was about 67.83%. The

percentage of color removal slightly increased to 70.22% when the concentration of

riboflavin reached the centre point (75.5 μm) before reducing again to 67.83% when

the concentration increased to the highest value of riboflavin (128.2 μm). The

highest percentage of color removal was 81.12% that occurred at substrate and

riboflavin concentrations of 2238.4 mg/L and 104.07 μm, respectively.

253

(a)

866.117 1308.06 1750 2191.94 2633.8

22.8205

49.1603

75.5

101.84

128.179

Substrate

Rib

ofla

vin

77.949

79.3552

80.7615

80.7615

82.1678

83.574

55555

(b) 866.117

1308.06

1750

2191.94

2633.88

22.8205

49.1603

75.5

101.84

128.179

74.7

77.85

81

84.15

87.3

Substrate Riboflavin

Figure 7.15 (a) Contour and (b) 3D response surface plots representing relationship

between the concentrations of substrate, riboflavin and color removal of Sumifix

Navy Blue EXF removal at 5 hours (Reduced Quadratic Model)

Riboflavin (μM)

Substrate (mg/L)

Col

or R

emov

al (%

)

Rib

ofla

vin

(μM

)

Substrate (mg/L)

254

(a) 866.117 1308.06 1750 2191.94 2633.8

22.8205

49.1603

75.5

101.84

128.179

77.2823

78.8952

80.5081

82.121

83.7339

55555

(b) 866.117

1308.06

1750

2191.94

2633.88

22.8205

49.1603

75.5

101.84

128.179

71.8

75.575

79.35

83.125

86.9


Figure 7.16: (a) Contour and (b) 3D response surface plots representing relationship between the concentrations of substrate, riboflavin and color removal of Sumifix Navy Blue EXF removal at 12 hours

Rib

ofla

vin

(μM

)

Substrate (mg/L)

Substrate (mg/L)

Riboflavin (μM)

Col

or R

emov

al (%

)

255

(a)

866.117 1308.06 1750 2191.94 2633.8

22.8205

49.1603

75.5

101.84

128.179

A S b t t

B: R

ibof

lavi

n

70.043

70.043

72.257774.4724

74.4724

76.687

78.9017

55555

866.117

1308.06

1750

2191.94

2633.88

22.8205

49.1603

75.5

101.84

128.179

62.3

67.35

72.4

77.45

82.5


(b)

Figure 7.17: (a) Contour and (b) 3D response surface plots representing relationship between the concentrations of substrate, riboflavin and color removal of Synozol Red K-4B removal at 5 hours

Col

or r

emov

al (%

)

Rib

ofla

vin

(μM

)

Substrate (mg/L)

Substrate (mg/L)

Riboflavin (μM)

256

866.117 1308.06 1750 2191.94 2633.8

22.8205

49.1603

75.5

101.84

128.179

A: Substrate

B: R

ibof

lavi

n

76.2462

77.752

79.2578

80.7635

80.763582.2693

55555

(a)

866.117

1308.06

1750

2191.94

2633.88

22.8205

49.1603

75.5

101.84

128.179

71.3

74.85

78.4

81.95

85.5


(b)

Figure 7.18: (a) Contour and (b) 3D response surface plots representing relationship between the concentrations of substrate, riboflavin and color removal of Synozol Red K-4B removal at 12 hours

Col

or R

emov

al (%

)

Rib

ofla

vin

(μM

)

Substrate (mg/L)

Substrate (mg/L) Riboflavin

(μM)

257

Figure 7.18 shows the contour and response surface plots for color removal of

Synozol Red K-4B at 12 hours reaction. At this reaction time, the plots have

changed to more of a saddler plot. The plot shows that as the concentration of redox

mediator increased, the percentages of color removal were also increased. The

highest and lowest removal percentages at this hour were predicted to be 83.8% and

74.7%. The highest color removal was observed when keeping the substrate and

riboflavin concentrations at 866.1 mg/L and 128.18 μm, respectively. The lowest

color removal occurred at riboflavin concentration of 22.8 μm but with the same

substrate concentration. This may give an indication that at this hour the

concentration of riboflavin affects the color removal as compared to the

concentration of substrate. The percentages of color removal obtained from the

actual experiment were 82.3% and 75.2% at the same substrate and riboflavin

concentrations. The percentages of color removal given by the statistical model were

only deferred by less than 1% indicating high prediction efficiency of the developed

model. Both results indicate the accuracy of the model developed.

7.6 Conclusions

i. Only a small amount of riboflavin as the redox mediator is required to

accelerate a high percentage of color removal. Too much of the compound

may overshadow the color removal since riboflavin itself is a colored

compound.

ii. The effect of substrate concentration was significant for COD removal at both

anaerobic and aerobic reaction phases as well as for total COD removal. The

magnitude and direction of the effect were different among the reaction

phases. The effect of substrate was positive for the COD removal under

anaerobic condition and also for total COD removal. However, the effect

was negative under aerobic condition.

258

iii. The interaction effect was insignificant for COD removal under anaerobic

condition but significant for aerobic reaction and total COD removal.

iv. The effect of riboflavin was insignificant for COD removal under anaerobic

condition but was significant for the reaction under aerobic condition and

total COD removal. The direction of the effect was negative for COD

removal under all experimental conditions.

v. The effect of substrate on the color removal of Sumifix Navy Blue EXF and

Synozol Red K-4B was significant for both five and twelve hours of the

reaction phase. However, the magnitude and direction of the effect were

opposite when compared between the two reactions. The five hour reaction

demonstrates a positive effect while negative for twelve hours reaction.

vi. The effect of riboflavin was also significant at both five and twelve hours

reaction phases for color removal of both dyes. All responses were positive

with increasing magnitude as the reaction time move from five to twelve

hours of reactions. Except for the five hours reaction phase for Synozol Red

K-4B, riboflavin was negatively affecting the percentage of color removal.

vii. The interaction effect of substrate and riboflavin were significant for both 5

and twelve hours of the reaction for Synozol Red K-4B. As for Sumifix Navy

Blue EXF, the interaction effect was only significant for five hours of the

reaction. The direction of the interaction effect was positive for five hours

reaction but negative at twelve hours reaction phase. This applied for both

dyes.

CHAPTER 8

CONCLUSIONS AND RECOMENDATIONS

Granulation system is a feasible treatment process that can be used to treat

recalcitrant pollutant containing wastewater such as those found in the textile dyeing

wastewater. This study was aimed at developing granular biomass that is capable of

treating such wastewater. Synthetic wastewater comprising of a mixed dye of

Sumifix Navy Blue EXF and Synozol Red K-4B and Sumifix Black EXA was used

in this study. The biogranules were developed using a mixture of sludge from a

sewage treatment plant and a textile mill wastewater treatment plant, anaerobic

granules from a paper mill, and with the addition of a specialized dye degrader

microbes customized to treat dye containing wastewater. Different types of study

were conducted in several types of reactor. However, the different reactors shared a

common feature, i.e. intermittent anaerobic and aerobic conditions in which the

FAnGS were developed. With the aid of statistical experimental design, the effects of

some variables were assessed.

The following are the conclusions that can be derived from this study. The

recommendations to improve the findings of the study are given in the following

section.

260

8.1 Conclusions

Several conclusions that could be derived from the experimental results of this study

are given below:

a. Successful development of FAnGS has been demonstrated in the

IFAnGSBioRec system with specialized features of the intermittent anaerobic

and aerobic reaction mode during the reaction process. The FAnGS

developed from the mixture of sludge and anaerobic granules were compact,

strong in structure and possessed good settling properties. Such properties

have increased the concentration of the biomass in the reactor which was

observed to improve the performance of the system. The anaerobic granules

seeding have created a noticeable different on the morphological features of

the granules by having fragmented anaerobic granules within the FAnGS as

compared to granules developed without the addition of anaerobic granular

seeding.

b. Under intermittent anaerobic and aerobic reaction phase strategy, the FAnGS

was capable of eliminating pollutants that required both anaerobic and

aerobic treatment conditions. Within 6 hours of HRT, the FAnGS was able to

remove more than 95% ammonia, 62% color and 94% COD. Based on

OUR/SOUR and SMA analyses, the granules seem to have facultative,

anaerobic and aerobic microorganisms that degrade under both anaerobic and

aerobic conditions.

c. Several aerobic and facultative anaerobic types of microorganisms were

identified within the granules and they are Bacillus cereus, Pseudomonas

veronii, three species of Pseudomonas genus and Enterobacter sp. They are

among those considered in the literature as dye degrader microbes.

261

d. Further investigation on the isolated microorganisms within the FAnGS

showed that they are capable of forming aggregates and exhibit reasonably

high to moderate range of surface hydrophobicity. The percentage of

aggregation and surface hydrophobicity of the mixed culture are higher when

compared to individual microorganisms.

e. Through the aid of factorial design and response surface modeling, substrate

concentration, pH and temperature imposed significant effect on the

coaggregation and surface hydrophobicity. Substrate concentration shows a

positive significant effect on coaggregation and surface hydrophobicity while

pH caused a negative effect. As the substrate concentration increased, the

percentage of coaggregation and surface hydrophobicity also increased. As

the pH value increased from acidic to alkaline, the percentage of

coaggregation and surface hydrophobicity decreased. The temperature

caused a positive effect on the coaggregation process but a negative effect on

the surface hydrophibicity. Among the three variables, the significant

interaction was only observed between pH and temperature for coaggregation

process. While for surface hydrophobicity, the interaction effect was

significant between pH and substrate concentration and between pH and

temperature. The 3-way interaction effect of substrate concentration, pH and

temperature was only significant for surface hydrophobicity. Based on the

central composite analyses, substrate concentration and pH have a non-linear

effect on coaggregation process and surface hydrophobicity.

f. Increase in the HRT has affected the biomass profile and the physical

properties of the granules. Not only the concentration of the granular

biomass has reduced, the size and settling properties of the FAnGS have also

reduced with the increase in the HRT. Due to the reduction of the biomass

profile, the removal performance for COD was also reduced. However, the

removal percentage of color improved as the HRT increased.

g. In the application of FAnGS with intermittent reaction phase, increase in the

anaerobic reaction time with minimum aerobic reaction time can be

262

considered as a good strategy to maintain and improve the performance of the

granular reactor system particularly for the removal of color for textile

wastewater treatment.

h. Changes in the HRT have also affected the microbial activity of the granular

biomass. Increase in the HRT has caused a reduction in the microbial activity

with the reduction in the μoverall, Yobs and Y values. More stable condition of

the granular biomass can be achieved through the increase in ratio of

anaerobic reaction time to aerobic reaction time. The kd value also remains

unchanged.

i. Substrate concentration imposed a significant effect for COD removal. In the

anaerobic condition, more COD is being removed as the substrate

concentration increased. However, the result was opposite for COD removal

under aerobic condition. The direction of total COD removal followed the

removal under anaerobic condition but with lesser magnitude. Substrate

concentration has a linear effect on COD removal under anaerobic condition

and non-linear under aerobic condition. The effects of substrate

concentration on total COD removal were found to be non-linear.

j. The amount of redox mediator that is required to accelerate the color removal

is very small. Within the range used in the experiment, the concentration of

redox mediator did not give any significant effect for anaerobic COD

removal. However, the effect was highly significant for aerobic and total

COD removal. The concentration of riboflavin was negatively affecting the

removal of COD under aerobic condition and total COD removal. The

concentration of substrate and riboflavin also response interactively for

aerobic and total COD removal.

263

k. With respect to color removal, the effect of substrate caused a positive

significant effect at the early stage of the experiment but the opposite

direction was observed at the later stage. Hence, the effect of substrate

concentration on color removal was also considered as time dependent. The

non-linear effect of substrate on color removal was only significant during the

early stage of the experiment.

l. The riboflavin has a significant positive effect on the color removal of

Sumifix Navy Blue EXF throughout the experiment. The negative significant

effect of riboflavin was found on Synozol Red K-4B at the early stage of the

experiment and change to positive at the later stage of the experiment. The

effect of riboflavin was found to be non-linear for the color removal of

Sumifix Navy Blue EXF throughout the experiment. As for Synozol Red K-

4B, the non-linear effect of riboflavin was only significant at the later stage of

the experiment.

m. The application of experimental design (i.e. factorial design and response

surface) could provide more reliable results where a significant effect either

as the main or/and interaction can be obtained and quantitatively identified.

The use of experimental design is a more effective approach that could

provide more information on the association of the variables towards the

responses through less experimental work as compared to one-factor-at-a-

time approach. However, the selection of values and the variables that will

be included in the statistical model may affect the result of the experiment. A

misleading conclusion may occur when the selection of values and variables

are not carefully made. The development of the statistical model from the

experimental design is also dependent on the complexity of the process. A

process can become difficult to model when the outcomes of the mechanisms

involved in the process are not directly associated with the investigated

variables.

264

8.2 Recommendations

In order to improve the performance of the FAnGS in treating textile wastewater,

further studies are needed and are given as follows:

a. Since the size of the granules is an important aspect in the degradation of the

dye, a study focusing on the effect of the granule’s size on dye degradation is

needed in order to obtain maximum removal rate.

b. Knowledge on the kinetics of decolorization and COD removal with the

effect of the environmental factors such as temperature, pH, co-substrate,

with and without the presence of redox mediator are severely lacking. The

results of kinetic studies would be able to assist on the design and operation

of the reactor system. This information is important in order to develop an

efficient biodegradation process for the dye containing wastewater.

c. In this study, the presence of aromatic amines with its autoxidation effect has

caused interference in the quantification of the color removal. A study

approach on the fate of the aromatic amines with respect to the detection,

degree of mineralization and autoxidation as well as the toxicity effect to the

microorganisms and the surrounding environment are important aspects that

need to be investigated. Further investigations are needed to overcome the

problem of recolorization so that true measurement on the color removal

under aerobic condition could be precisely determined.

d. The application of biogranules treatment approach is affected by many

factors. Cost effect analyses with the exploitation on the affecting variables

are required in order to obtain the most economical application of biogranular

treatment system at the most optimal condition.

APPENDIX A: DATA AND EXAMPLES OF CALCULATIONS

A-1: Organic Loading Rate

,

where X = COD concentration of the influent (mg/L) Vadd= Volume of influent added in each cycle operation (mL) Vtotal = Total working volume of the experiment (mL) T = Hydraulic retention time (hour).

X Vadd Vtotal T OLR (kg/m3·d)

FAnGS development

1270 2 4 6 2.54

Stage X Vadd Vtotal T OLR

(kg/m3·d) I 1270 2 4 6 2.54 II 1270 2 4 12 1.27 III 1270 2 4 24 0.635 IV 1604 2 4 24 0.802 V 1604 2 4 24 0.802 VI 1604 2 4 24 0.802

A-2: Superficial Air Velocity

Air flow rate (L/min)

Diameter of column (m)

Surface area (m2) Superficial air velocity (cm/s)

5 0.08 5.024 x10-3 1.66

Air flow rate (L/min)

Diameter of column (m)

Surface area (m2) Superficial air velocity (cm/s)

7.5 0.08 5.024 x10-3 2.5

302

A-3: Oxygen Uptake Rate

where OUR = Oxygen uptake rate (mg/L.h) DOa = Initial dissolved oxygen (mg/L) DOb = End dissolved oxygen (mg/L) T = Time (min)

Stage I Stage II

Time DOa DOb OUR Time DOa DOb OUR 32 6.79 2.00 538.65 210 7.85 3.30 78.00 57 7.28 1.97 335.37 91 5.85 2.33 139.25 90 7.67 1.98 227.60 71 8.18 2.99 263.15 77 7.12 1.99 239.84 101 7.72 2.96 169.66103 7.48 1.99 191.88 119 8.00 2.97 152.17 108 7.59 2.00 186.33 172 7.75 2.98 99.84 180 7.80 2.00 116.00 210 8.14 2.99 88.29 230 7.81 1.99 91.10 298 7.51 3.00 54.48 250 7.40 1.97 78.19 385 8.13 2.98 48.16 280 7.47 1.99 70.46 498 8.13 2.97 37.30 340 7.55 1.99 58.87 617 7.78 3.00 27.89 500 7.78 1.98 41.76 698 8.56 3.00 28.68 530 7.80 2.00 39.40 818 8.29 2.99 23.33 600 7.57 1.99 33.48 926 7.74 3.00 18.43 640 7.40 2.00 30.38 936 8.15 2.98 19.88 680 7.20 2.00 27.53 1110 8.17 2.68 17.81720 7.30 1.96 26.70 1129 9.16 3.00 19.64

1136 8.48 2.69 18.35 75 6.10 4.92 56.64 1186 8.38 3.00 16.33 109 6.38 2.00 144.66 450 7.60 2.00 44.80 190 8.19 2.92 99.85 540 7.50 1.98 36.80 470 8.25 2.98 40.37 740 7.40 1.99 26.32 850 8.12 2.33 24.52780 7.20 1.98 24.09 930 7.94 3.00 19.12 830 7.50 2.00 23.86 1200 8.49 3.00 16.47 870 7.40 2.00 22.34 1300 8.59 3.00 15.48 900 7.10 2.40 18.80 1350 8.41 2.56 15.60

1623 8.70 3.00 12.64 1500 8.70 3.00 13.68 1240 7.30 3.00 12.48

303

Stage III Stage IV

Time DOa DOb OUR Time DOa DOb OUR 500 7.50 3.00 32.40 85 7.00 2.00 211.76 150 7.50 1.80 136.80 169 6.90 2.00 104.38 185 7.40 2.41 97.10 240 7.20 1.98 78.30 400 7.50 3.30 37.80 173 7.60 2.00 116.53 207 7.30 2.00 92.17 293 7.30 2.00 65.12 370 7.80 2.00 56.43 415 7.30 1.98 46.15 400 7.40 2.00 48.60 500 7.30 2.00 38.16 400 7.50 2.00 49.50 660 7.40 2.00 29.45 500 7.50 2.00 39.60 690 7.30 2.00 27.65 600 7.50 2.00 33.00 890 7.30 2.00 21.44 550 7.40 2.41 32.66 835 7.40 1.98 23.37 640 7.60 2.41 29.19 880 7.40 2.00 22.09 760 7.70 2.41 25.06 940 7.40 1.98 20.76 800 7.70 2.41 23.81 970 7.40 1.98 20.12 1200 7.80 2.41 16.17 990 7.50 1.98 20.07 900 7.70 2.41 21.16 1000 7.50 2.00 19.80 1100 7.80 2.41 17.64 1020 7.30 2.00 18.71 1220 7.50 2.42 14.99 1090 7.40 2.00 17.83 1920 7.80 2.42 10.09 1100 7.30 1.98 17.41 2200 7.70 2.42 8.64 1180 7.50 2.00 16.78 2300 7.80 2.44 8.39 1190 7.40 2.00 16.34 2400 7.80 2.48 7.98 1200 7.60 2.00 16.80 2300 7.80 3.00 7.51 1400 7.50 1.98 14.19

1500 7.50 2.00 13.20 430 7.50 2.58 41.19 1650 7.60 1.98 12.26 1800 7.76 2.58 10.36 1900 8.06 3.00 9.59 235 7.71 1.98 87.78 2200 7.74 3.00 7.76 375 7.82 2.00 55.87 2000 7.98 3.00 8.96 420 7.30 1.98 45.60 2300 7.85 3.00 7.59 420 7.50 2.00 47.14 2500 7.81 3.00 6.93 800 7.70 2.00 25.65 2000 8.00 3.00 9.00 1000 7.60 2.00 20.16 2750 8.20 3.00 6.95 640 7.60 1.98 31.61 2800 8.00 3.00 7.07 840 7.40 2.00 23.14

900 7.30 2.00 21.20 650 7.50 2.00 30.46 690 7.40 2.00 28.17 870 7.80 1.98 24.08 910 7.60 1.98 22.23 980 7.70 1.98 21.01 990 7.50 1.98 20.07 930 7.50 2.00 21.29 950 7.30 2.00 20.08 1300 7.50 1.98 15.29 1440 7.80 2.00 14.50 1600 7.60 2.00 12.60 1700 7.40 2.00 11.44 1800 7.70 2.00 11.40

304

Stage V Stage VI

Time DOa DOb OUR Time DOa DOb OUR 129 6.83 2.00 134.79 500 7.91 3.00 35.4 107 8.11 2.00 205.57 720 8.11 3.00 25.6 125 7.01 1.98 144.86 166 8.90 2.99 128.2 190 7.81 2.00 110.08 678 8.70 3.00 30.3 170 8.32 2.00 133.84 820 8.80 2.99 25.5 160 8.05 2.00 136.13 932 8.90 2.92 23.1 165 7.59 2.00 121.96 1179 9.20 2.31 21.0180 7.45 2.00 109.00 1100 8.90 2.99 19.3 260 7.67 2.00 78.51 1070 8.70 2.84 19.7 290 7.41 2.00 67.16 1110 9.30 3.00 20.4 350 7.37 2.00 55.23 1500 9.00 2.99 14.4 390 7.22 2.00 48.18 1950 9.22 2.99 11.5 480 7.60 2.00 42.00 1700 9.14 2.36 14.4 580 7.60 2.00 34.76 1620 9.60 2.99 14.7 630 7.44 2.00 31.09 1890 9.44 2.98 12.3 780 7.30 2.00 24.46 1650 9.10 2.89 13.5 860 7.60 2.00 23.44 1780 8.98 2.99 12.1 900 7.40 2.00 21.60 1600 8.50 2.99 12.4 860 7.20 2.00 21.77 1680 9.40 2.98 13.8

1000 7.41 2.00 19.48 1640 9.20 2.97 13.7 1100 7.37 2.00 17.57 1570 8.90 2.80 14.0 1050 7.17 2.00 17.73 1690 9.10 2.90 13.2

1640 9.20 2.97 13.7 124 6.50 2.00 130.65 1690 9.30 2.80 13.8 170 7.20 2.00 110.12 300 7.10 2.00 61.20 194 7.60 3.50 76.1 350 7.10 2.00 52.46 480 7.40 3.00 33.0 450 7.30 2.00 42.40 300 6.01 2.98 36.4 430 7.40 2.00 45.21 600 8.59 2.91 34.1 600 7.30 2.00 31.80 720 8.41 3.00 27.1800 7.40 2.00 24.30 740 8.20 2.98 25.4 900 7.20 2.00 20.80 1260 8.37 2.99 15.4 980 7.60 2.00 20.57 1300 8.22 3.00 14.5

1080 7.30 2.00 17.67 900 8.30 2.49 23.2 1100 7.60 2.00 18.33 1050 8.30 2.33 20.5 1600 7.70 2.00 12.83 2100 8.41 3.00 9.3 1900 7.40 2.00 10.23 2100 8.20 2.98 8.9

2000 8.23 2.99 9.4 2100 8.12 3.00 8.8 2200 8.10 2.98 8.4 2200 8.00 2.99 8.2 2300 8.00 2.98 7.9 2200 8.10 2.98 8.4 2400 8.20 3.00 7.8 2500 8.20 3.00 7.5 2590 8.20 3.00 7.2

305

A-4: Sludge Retention Time

cee

rvss

/tVXVX

θ =

where,

= Solids retention time (d) = Volatile solids concentration in the reactor system

(g VSS/L) = Working volume of the SBR system (L) = Effluent volatile solids concentration (g VSS/L) = Effluent volume of the SBR operating cycle (L) = Cycle time of the SBR operation (d)

Stage SRT average sd

I

32.9 4 0.30 2 3 27.4

27.6 13.4 29.8 4 0.52 2 3 14.3

32.9 4 0.20 2 3 41.1

II

24.5 4 0.27 2 6 45.4

42.4 10.2 26.7 4 0.43 2 6 31.0

22.3 4 0.22 2 6 50.7

III

20.9 4 0.20 2 12 104.5

78.9 30.8 17.9 4 0.40 2 12 44.8

16.6 4 0.19 2 12 87.4

IV

26.7 4 0.52 2 12 51.3

70.1 23.9 22.3 4 0.36 2 12 61.9

29.1 4 0.30 2 12 97.0

V

24.3 4 0.30 2 12 81.0

72.5 23.3 20.3 4 0.44 2 12 46.1

22.6 4 0.25 2 12 90.4

VI

20.2 4 0.59 2 12 34.2

41.6 18.4 19.4 4 0.31 2 12 62.6

21 4 0.75 2 12 28.0

306

A-5: Sludge Volume Index

where,

SVI = Sludge volume index (mL/g SS) Bedvolume = Volume of settled biomass in reactor (L) d.w = Dry weight of biomass in reactor (g SS/L ) 4 = Working volume (L) 1000 = Conversion factor (L to mL)

Stage Average

dry weight

Bedheight (cm) at 5 min

settling

A (cm2)

Bedvolume (cm3)

Bedvolume (L)

SVI (mL/g MLSS)

SVI (average) SD

I

34.7 37.0 50.14 1855.17 1.855 13.4

13.1 0.4 36.7 37.0 50.14 1855.17 1.855 12.6

34.5 37.0 50.14 1855.17 1.855 13.4

II

26.3 43.0 50.14 2156.01 2.156 20.5

18.8 1.5 29.5 43.0 50.14 2156.01 2.156 18.3

30.4 43.0 50.14 2156.01 2.156 17.7

III

23.6 43.0 50.14 2156.01 2.156 22.8

21.4 1.6 27.3 43.0 50.14 2156.01 2.156 19.7

24.8 43.0 50.14 2156.01 2.156 21.7

IV

30.1 41.0 50.14 2055.73 2.056 17.1

16.9 1.3 28.3 41.0 50.14 2055.73 2.056 18.2

33.1 41.0 50.14 2055.73 2.056 15.5

V

30.4 39.0 50.14 1955.45 1.955 16.1

15.5 1.3 34.8 39.0 50.14 1955.45 1.955 14.0

29.6 39.0 50.14 1955.45 1.955 16.5

VI

23.9 46.0 50.14 2306.43 2.306 24.1

24.8 0.9 22.4 46.0 50.14 2306.43 2.306 25.7

23.6 46.0 50.14 2306.43 2.306 24.4

307

Day Stage Average dry weight


settling A (cm2) Bedvolume

(cm3) Bedvolume

(L) SVI

(mL/g MLSS)

0

23.0 39.5 50.14 1980.52 1.981 21.53 3 15.4 38.0 50.14 1905.31 1.905 30.91 5 17.5 38.0 50.14 1905.31 1.905 27.19 7 14.1 46.0 50.14 2306.43 2.306 40.78

14 15.7 42.0 50.14 2105.87 2.106 33.45 17 19.2 41.0 50.14 2055.73 2.056 26.77 21 23.1 43.0 50.14 2156.01 2.156 23.35 24 25.9 40.0 50.14 2005.59 2.006 19.36 28 26.5 40.1 50.14 2010.61 2.011 18.97 30 27.3 30.5 50.14 1529.26 1.529 14.00 32 28.1 24.0 50.14 1203.36 1.203 10.72 33 26.5 25.0 50.14 1253.50 1.253 11.83 36 28.8 27.5 50.14 1378.85 1.379 11.97 37 28.9 37.0 50.14 1855.17 1.855 16.05 38 29.8 37.5 50.14 1880.24 1.880 15.77 39 31.3 38.5 50.14 1930.38 1.930 15.42 43 30.8 42.0 50.14 2105.87 2.106 17.08 46 32.6 42.0 50.14 2105.87 2.106 16.15 47 33.3 34.5 50.14 1729.82 1.730 13.00 50

I

32.1 35.5 50.14 1779.96 1.780 13.86 56 33.3 35.0 50.14 1754.89 1.755 13.17 72 33.7 35.0 50.14 1754.89 1.755 13.02 78 34.8 29.0 50.14 1454.05 1.454 10.45 84 35.3 31.0 50.14 1554.33 1.554 11.01 88 35.5 33.0 50.14 1654.61 1.655 11.65 92 35.4 36.5 50.14 1830.10 1.830 12.92 95 35.3 38.0 50.14 1905.31 1.905 13.49 99 35.3 37.0 50.14 1855.17 1.855 13.14 103

II

35.1 41.5 50.14 2080.80 2.081 14.82 106 33.3 41.0 50.14 2055.73 2.056 15.44 111 33.2 41.0 50.14 2055.73 2.056 15.47 119 30.8 44.0 50.14 2206.15 2.206 17.91 124 29.7 42.0 50.14 2105.87 2.106 17.73 129 29.9 42.0 50.14 2105.87 2.106 17.61 135 28.7 44.0 50.14 2206.15 2.206 19.21 142 28.7 43.0 50.14 2156.01 2.156 18.78 148

III

27.9 44.0 50.14 2206.15 2.206 19.74 155 27.2 44.0 50.14 2206.15 2.206 20.28 161 25.6 42.0 50.14 2105.87 2.106 20.57 169 24.8 44.0 50.14 2206.15 2.206 22.24 175 25.3 44.0 50.14 2206.15 2.206 21.79 187 25.2 43.0 50.14 2156.01 2.156 21.39 193

IV

25.4 39.0 50.14 1955.45 1.955 19.25 201 26.6 39.0 50.14 1955.45 1.955 18.38 209 28.4 38.0 50.14 1905.31 1.905 16.77 215 27.7 38.0 50.14 1905.31 1.905 17.19 224 30.5 40.0 50.14 2005.59 2.006 16.44 230 30.5 40.0 50.14 2005.59 2.006 16.44 236 30.5 41.0 50.14 2055.73 2.056 16.83

308

Day Stage Average dry weight


settling A (cm2) Bedvolume

(cm3) Bedvolume

(L) SVI

(mL/g MLSS)

241

V

29.6 41.0 50.14 2055.73 2.056 17.36 248 30.4 40.0 50.14 2005.59 2.006 16.49 255 30.5 38.0 50.14 1905.31 1.905 15.64 262 30.8 38.0 50.14 1905.31 1.905 15.47 268 30.5 40.0 50.14 2005.59 2.006 16.44 276 31.4 39.0 50.14 1955.45 1.955 15.57 282 31.6 39.0 50.14 1955.45 1.955 15.47 286

VI

29.4 42.0 50.14 2105.87 2.106 17.92 292 24.8 43.0 50.14 2156.01 2.156 21.73 299 23.7 43.0 50.14 2156.01 2.156 22.74 307 20.3 42.0 50.14 2105.87 2.106 25.93 314 22.7 44.0 50.14 2206.15 2.206 24.25 320 23.0 45.0 50.14 2256.29 2.256 24.52 328 23.3 46.0 50.14 2306.43 2.306 24.75

A-6: Removal Performance (COD, Ammonia and Color Removal)

Day COD (mg/L) Color(ADMI) Ammonia (mg/L)

Influent Effluent %

Removal Influent Effluent %

Removal Influent Effluent %

Removal 1 1270 390 69.3 1010 761 24.6 35.0 11.6 66.8 3 1260 278 78.0 1010 703 30.4 37.2 15.1 59.5 8 1275 241 81.1 1005 495 50.7 34.7 2.2 93.7

11 1265 187 85.2 1020 458 55.1 33.3 3.2 90.5 14 1255 237 81.1 1050 426 59.4 29.4 1.7 94.3 17 1260 258 79.5 1010 403 60.1 37.5 1.5 95.9 21 1270 160 87.4 1020 473 53.6 35.9 1.4 96.0 24 1255 136 89.1 1020 370 63.8 39.5 0.9 97.7 28 1280 161 87.4 1030 373 63.8 40.9 1.3 96.931 1275 181 85.8 1020 429 58.0 30.2 2.0 93.334 1250 89 92.9 1020 407 60.1 31.9 1.1 96.4 52 1265 104 91.8 1040 339 67.4 41.2 1.3 96.7 55 1260 89 92.9 1030 433 58.0 49.8 3.7 92.6 59 1270 120 90.6 1020 418 59.0 40.7 2.5 93.9 66 1270 80 93.7 1010 388 61.6 32.3 1.5 95.2

309

Day Influent COD

Effluent COD Average COD

(Effluent)

COD removal Average COD

removal sd

a b c a b c

0 1270 345 357 348 350 72.8 71.9 72.6 72.4 0.5 3 1265 325 331 329 328 74.3 73.8 74.0 74.0 0.3 5 1268 290 287 316 298 77.1 77.4 75.1 76.5 1.3

14 1260 261 258 256 258 79.3 79.5 79.7 79.5 0.2 17 1270 268 262 236 255 78.9 79.4 81.4 79.9 1.3 21 1275 240 231 223 231 81.2 81.9 82.5 81.9 0.7 24 1277 222 226 221 223 82.6 82.3 82.7 82.5 0.2 28 1270 226 234 231 230 82.2 81.6 81.8 81.9 0.3 30 1272 212 212 215 213 83.3 83.3 83.1 83.2 0.1 32 1266 211 197 214 208 83.3 84.4 83.1 83.6 0.7 36 1270 210 204 199 204 83.5 83.9 84.3 83.9 0.4 38 1269 209 207 226 214 83.5 83.7 82.2 83.1 0.8 39 1270 192 208 217 206 84.9 83.6 82.9 83.8 1.0 43 1271 206 215 208 210 83.8 83.1 83.6 83.5 0.4 46 1268 207 209 202 206 83.7 83.5 84.1 83.8 0.3 47 1273 211 201 205 206 83.4 84.2 83.9 83.8 0.4 50 1275 203 205 194 201 84.1 83.9 84.8 84.3 0.556 1280 198 224 229 217 84.5 82.5 82.1 83.0 1.372 1274 199 214 186 200 84.4 83.2 85.4 84.3 1.1 78 1272 198 216 197 204 84.4 83.0 84.5 84.0 0.8 84 1269 193 212 193 199 84.8 83.3 84.8 84.3 0.9 88 1266 186 189 195 190 85.3 85.1 84.6 85.0 0.4 92 1270 199 192 187 193 84.3 84.9 85.3 84.8 0.5 95 1270 199 194 203 199 84.3 84.7 84.0 84.3 0.4 99 1281 191 199 214 201 85.1 84.5 83.3 84.3 0.9 103 1268 193 188 209 197 84.8 85.2 83.5 84.5 0.9 106 1271 193 184 194 191 84.8 85.5 84.7 85.0 0.4 111 1274 200 203 187 197 84.3 84.1 85.3 84.6 0.6 119 1277 203 194 202 200 84.1 84.8 84.2 84.4 0.4 124 1270 217 206 202 208 82.9 83.8 84.1 83.6 0.6 129 1270 202 202 196 200 84.1 84.1 84.6 84.3 0.3 135 1275 201 196 193 197 84.2 84.6 84.9 84.6 0.4 142 1275 189 207 215 204 85.2 83.8 83.1 84.0 1.1 148 1270 198 202 197 199 84.4 84.1 84.5 84.3 0.2 155 1270 212 215 230 219 83.3 83.1 81.9 82.8 0.8161 1270 199 216 231 215 84.3 83.0 81.8 83.0 1.3 171 1264 193 211 202 202 84.7 83.3 84.0 84.0 0.7 180 1276 202 198 193 197 84.2 84.5 84.9 84.5 0.4 190 1275 185 196 207 196 85.5 84.6 83.8 84.6 0.9 195 1605 116 169 149 144 92.8 89.5 90.7 91.0 1.7 202 1600 149 155 166 157 90.7 90.3 89.6 90.2 0.6 209 1608 125 159 174 153 92.2 90.1 89.2 90.5 1.5 215 1608 172 145 148 155 89.3 91.0 90.8 90.4 0.9 224 1611 153 150 147 150 90.5 90.7 90.9 90.7 0.2 230 1605 162 141 135 146 89.9 91.2 91.6 90.9 0.9 236 1600 146 152 149 149 90.9 90.5 90.7 90.7 0.2 241 1608 127 137 125 130 92.1 91.5 92.2 91.9 0.4248 1606 132 117 114 121 91.8 92.7 92.9 92.5 0.6255 1608 119 93 116 109 92.6 94.2 92.8 93.2 0.9

310

262 1608 108 122 117 116 93.3 92.4 92.7 92.8 0.5 268 1607 117 87 125 110 92.7 94.6 92.2 93.2 1.3 276 1603 99 83 99 94 93.8 94.8 93.8 94.1 0.6282 1600 106 90 88 94 93.4 94.4 94.5 94.1 0.6 286 1605 124 141 138 134 92.3 91.2 91.4 91.6 0.6 292 1608 146 169 150 155 90.9 89.5 90.7 90.4 0.8 299 1605 218 209 220 216 86.4 87.0 86.3 86.6 0.4 307 1606 210 226 222 219 86.9 85.9 86.2 86.3 0.5 314 1606 236 234 239 237 85.3 85.4 85.1 85.3 0.2 320 1600 290 280 261 277 81.9 82.5 83.7 82.7 0.9 328 1608 296 275 270 280 81.6 82.9 83.2 82.6 0.9

Day Influent Color

Effluent Color Average Color

(Effluent)

Color removal Average color

removal sd a b c a b c

0 1051 796 802 790 796 24.3 23.7 24.8 24.3 0.6 3 1054 736 729 740 735 30.2 30.8 29.8 30.3 0.5 5 1047 620 601 640 620 40.8 42.6 38.9 40.8 1.9

14 1050 562 569 548 560 46.5 45.8 47.8 46.7 1.0 17 1049 543 541 536 540 48.2 48.4 48.9 48.5 0.4 21 1055 567 572 586 575 46.3 45.8 44.5 45.5 0.9 24 1050 617 629 637 628 41.2 40.1 39.3 40.2 1.0 28 1055 544 554 552 550 48.4 47.5 47.7 47.9 0.5 30 1052 482 498 485 488 54.2 52.7 53.9 53.6 0.8 32 1052 465 472 472 470 55.8 55.1 55.1 55.3 0.4 36 1050 547 562 571 560 47.9 46.5 45.6 46.7 1.2 38 1051 490 479 496 488 53.4 54.4 52.8 53.5 0.8 39 1050 558 562 563 561 46.9 46.5 46.4 46.6 0.3 43 1055 554 560 536 550 47.5 46.9 49.2 47.9 1.2 46 1050 446 467 453 455 57.5 55.5 56.9 56.6 1.0 47 1049 461 471 473 468 56.1 55.1 54.9 55.4 0.6 50 1055 383 392 407 394 63.7 62.8 61.4 62.6 1.2 56 1050 377 386 395 386 64.1 63.2 62.4 63.2 0.9 72 1048 327 321 319 322 68.8 69.4 69.6 69.3 0.4 78 1052 294 322 315 310 72.1 69.4 70.1 70.5 1.4 84 1052 311 306 313 310 70.4 70.9 70.2 70.5 0.4 88 1051 327 343 322 330 68.9 67.4 69.4 68.6 1.0 92 1050 344 349 334 342 67.2 66.8 68.2 67.4 0.7 95 1050 321 326 289 312 69.4 69 72.5 70.3 1.9 99 1054 344 370 337 350 67.4 64.9 68 66.8 1.6 103 1055 350 344 322 339 66.8 67.4 69.5 67.9 1.4 106 1059 325 331 309 322 69.3 68.7 70.8 69.6 1.1 111 1055 319 318 291 309 69.8 69.9 72.4 70.7 1.5 119 1052 307 309 290 302 70.8 70.6 72.4 71.3 1.0 124 1055 332 329 310 324 68.5 68.8 70.6 69.3 1.1 129 1050 282 288 266 279 73.1 72.6 74.7 73.5 1.1 135 1050 273 270 291 278 74 74.3 72.3 73.5 1.1 142 1052 270 266 275 270 74.3 74.7 73.9 74.3 0.4 148 1050 247 254 271 257 76.5 75.8 74.2 75.5 1.2 155 1054 284 277 310 290 73.1 73.7 70.6 72.5 1.6

311

161 1053 249 266 284 266 76.4 74.7 73 74.7 1.7 171 1050 268 277 282 276 74.5 73.6 73.1 73.7 0.7 180 1050 257 248 265 257 75.5 76.4 74.8 75.6 0.8 190 1056 259 242 250 250 75.5 77.1 76.3 76.3 0.8 195 1053 250 237 258 248 76.3 77.5 75.5 76.4 1.0 202 1050 181 190 180 183 82.8 81.9 82.9 82.5 0.6 209 1050 160 161 171 164 84.8 84.7 83.7 84.4 0.6 215 1057 186 189 194 190 82.4 82.1 81.6 82.0 0.4 224 1050 159 155 171 162 84.9 85.2 83.7 84.6 0.8 230 1054 174 194 180 183 83.5 81.6 82.9 82.7 1.0 236 1050 191 179 162 177 81.8 83 84.6 83.1 1.4 241 1052 157 161 183 167 85.1 84.7 82.6 84.1 1.3 248 1050 162 155 172 163 84.6 85.2 83.6 84.5 0.8 255 1048 175 176 159 170 83.3 83.2 84.8 83.8 0.9 262 1047 146 150 160 152 86.1 85.7 84.7 85.5 0.7 268 1050 143 154 150 149 86.4 85.3 85.7 85.8 0.6 276 1050 140 135 158 144 86.7 87.1 85 86.3 1.1 282 1050 141 145 134 140 86.6 86.2 87.2 86.7 0.5 286 1049 232 215 234 227 77.9 79.5 77.7 78.4 1.0 292 1050 222 214 212 216 78.9 79.6 79.8 79.4 0.5 299 1050 232 235 246 238 77.9 77.6 76.6 77.4 0.7 307 1059 268 264 254 262 74.7 75.1 76 75.3 0.7 314 1055 236 248 263 249 77.6 76.5 75.1 76.4 1.3 320 1050 270 254 279 268 74.3 75.8 73.4 74.5 1.2 328 1050 258 260 255 258 75.4 75.2 75.7 75.4 0.3

A-7: Coaggregation

where,

CAg% = Percentage of coaggregation CA0 = The absorbance of cultured media at 0 h CAi = The absorbance of cultured media after centrifugation

312

CA0 = The absorbance of cultured media at 0 h Cai = The absorbance of cultured media after centrifugation

No 1 hour 2 hour 3 hour 4 hour 5 hour 1 hour 2 hour 3 hour 4 hour 5 hour

1 117 113 115 127 135 71 59 63 64 68

2 69 72 68 67 72 40 38 33 37 40

3 115 131 135 165 189 50 42 52 44 62

4 98 114 128 147 128 38 39 40 37 46

5 68 68 69 65 74 39 42 51 42 39

6 62 70 85 79 71 39 42 32 28 40

7 85 80 84 83 80 49 40 34 30 28

8 81 80 83 80 76 48 37 32 27 28

9 86 107 97 110 111 29 30 34 33 39

10 85 102 99 100 108 38 43 40 44 41

11 115 207 210 234 243 35 33 35 37 38

12 126 178 211 188 244 34 31 33 35 36

13 83 81 82 85 85 37 31 41 39 52

14 97 94 88 97 93 43 45 49 53 57

15 117 111 123 118 113 48 47 46 47 48

16 93 89 96 94 92 45 48 37 32 39

% of AGG 1 hour 2 hour 3 hour 4 hour 5 hour

1 39.3 47.8 45.2 49.6 49.6 2 42.6 47.0 52.0 45.3 44.7 3 56.9 67.6 61.5 73.1 67.2 4 60.8 65.4 69.0 75.0 64.1 5 43.1 37.7 26.1 36.0 46.8 6 36.5 40.5 62.6 64.5 44.1 7 42.4 50.6 59.8 64.3 65.6 8 41.3 53.9 61.9 65.9 62.9 9 65.9 71.7 64.6 69.8 65.2

10 55.0 57.7 59.8 55.8 61.9 11 69.5 84.2 83.1 84.0 84.5 12 73.1 82.3 84.6 81.5 85.4 13 55.1 62.2 50.3 54.6 38.8 14 55.6 52.2 44.8 45.4 38.7 15 59.4 57.3 62.7 59.9 57.4 16 51.9 46.3 61.8 66.4 57.6

313

A-8: Surface Hydrophobicity

where, SHb% = Percentage of surface hydrophobicity A0 = The absorbance of sample before mixing with xylene Ai = The absorbance of sample after extraction with xylene.

No A0 Ai % of Hydrophobicity

1 0.625 0.506 19.0

2 0.456 0.357 21.7

3 0.647 0.428 33.8

4 0.868 0.613 29.4

5 0.217 0.136 37.3

6 0.233 0.137 41.2

7 0.366 0.221 39.6

8 0.393 0.248 36.9

9 0.629 0.427 32.1

10 0.61 0.418 31.5

11 1.255 0.579 53.9

12 1.128 0.541 52.0

13 0.426 0.339 20.4

14 0.413 0.338 18.2

15 0.771 0.705 8.6

16 0.673 0.627 6.8

314

APPENDIX B: MOLECULAR PROCEDURE OF 16S SEQUENCE

ANALYSIS

B-1: Genomic DNA Isolation

Each of the selected bacteria (BS1FAnGS, BS6FAnGS, BS7FAnGS, BS8FAnGS,

BS10FAnGS, BS11FAnGS and BS12FAnGS) was extracted separately in order to obtain the

DNA genomic of the bacteria. The genomic DNA was extracted from the cell pellets

by using genomic DNA extraction and purification kit (Promega). Each of the

bacteria was culture in nutrient broth for 24 hours before 1 ml of each bacterium

cultures with optical density (OD at 660 nm) of more than 0.5 was taken and

centrifuged at 13,000-16,000xg for 2 minutes in order to obtain the cell pellet. The

supernatant was removed before 600 μl of Nuclei Lysis Solution was added to the

pelleted cell. The cells were gently resuspended using pipetters. The solution was

incubated at 80oC for 5 minutes in order to lyses the cells. The solution was then left

to cool at room temperature.

Following this, 3 μl of RNase solution was added to the cell lysate before the

tube was inverted for 2-5 times to well mix the solution. The solution was incubated

at 37oC for 30 minutes. Then the sample was to cool again at room temperature.

After that, 200 μl of Protein Precipitation solution was added to the RNase-treated

cell lysate. The solution was vortex vigorously at high speed for 20 second to mix the

Protein Precipitation solution with the cell lysate. The sample was then incubated in

ice for 5 minutes before centrifugation at 13,000-16,000 g for 3 minutes.

The supernatant containing the DNA was transferred to a clean 1.5 mL

microcentrifuge, which contain 600 μl of room temperature isopropanol. The

solution was gently mixed by inversion until the thread-like strands of DNA form

appeared as a visible mass. The sample was then centrifugation at 13000-16,000 g

for 2 minutes. The supernatant was carefully poured off after centrifugation. Then

the tube was drained on clean adsorbent paper before 600 μl of 70% ethanol was

added into the tube. Then the tube was gently inverted for several times for washing

the DNA pellet. Then the sample was centrifuged again at 13,000-16,000 g for 2

minutes. The ethanol was aspirate carefully. The tube was drained on clean

315

absorbent paper and the pellet was air-dry for 15 minutes. Then 100 μl of DNA

Rehydration solution was added to the tube. The DNA was rehydrated by

incubating at 65oC for 1 hour. The successful isolated DNA solution was stored at

4oC.

B-2: Analysis of Genomic DNA

The analysis of the genomic DNA of each of the bacteria was measured

qualitatively via agarose gel electrophoresis. 1% (w/v) of agarose was dissolved in 1

x TAE buffer (Table B-1) in microwave oven until there were no solid particles in

the solution and the solution is completely homogenous. Table B-1 showed the TAE

buffer that was prepared as concentrated stock solution (50x). The solution was

allowed to cool to approximately 50oC and then was poured into the gel base sealed

at both with masking tapes. A suitable comb was placed vertically at one end of gel

base. The comb was removed after the gel agar was left solidified for 30 minutes.

The gel was then submerged using 1 x TAE running buffer in the electrophoresis

tank. The sample was mixed with 1 μL of loading dye which consisted of

bromophenol blue 0.25% (w/v), SDS 1% (w/v) and glycerol 80 % (v/v). The sample

and 5 μl of universal DNA marker (Gene Ruler Ladder Mix Marker) were loading

into the performed wells. The gel was run at 80 V and 45 W for 60 minutes. Lastly,

the gel was stained for 30 minutes in running buffer containing 0.5 μg/mL ethidium

bromide (EtBr) before the extracted DNA was examined and photographed on a UV

transilluminator (TFX-35 Vilber Lourmat).

B-3: Polymerase Chain Reaction (PCR)

Polymerase chain reaction (PCR) is a method for amplification of DNA in

vitro where through polymerase chain reaction the DNA can be multiply up to a

billion fold (Madigan et al., 2000). The PCR copies the DNA using basic elements

of natural DNA synthesis and replication processes (McPherson and Moller, 2006).

316

Table B-1: Composition of the TAE buffer (50x)

Components Volume

Tris-Base 242.0 g Glacial acetic acid 57.1 ml

0.5 M EDTA (pH 8.0) 1000 mL

Deionized water Top up until 1000 ml

The extracted DNAs were then amplified via PCR. In the PCR amplification

process, universal primers that normally consisted of at least 17 to 25 nucleotides

were used since they are homologous to broadly conserved sequences. Primers are

needed that act as sites for initiation of DNA synthesis by the DNA polymerase.

These primers define the region of the template DNA that need to be copied.

Primers or also known amplimers are complementary to the regions of known

sequence on opposite strands of the DNA template (McPherson and Moller, 2006).

The universal primer used in this amplification is listed in Table B-2.

The PCR process consisted of three distinct steps that are governed by

temperature. The first step is known as the denaturation. At this stage, the double-

stranded template DNA is denatured by heating typically at 94oC. Here the double-

stranded DNA is separated into two complementary single strands. The second step

is the annealing process where the oligonucleotide primers will be hybridized to the

DNA template. The temperature used during this stage normally is between 40-72oC.

The last step is the DNA synthesis process where the thermostable DNA polymerase

will be effectively synthesis the DNA at reaction temperature of 72oC (McPherson

and Moller, 2006).

Table B-2: Reverse and forward of universal primers

Primer Sequence

16S-Reverse primer 5’- cgg cta cct tgt tac gac tt - 3’

16S-Forward primer 5’- gag ttt gat cct ggc tca g - 3’

100 μl of the reaction solution was prepared in PCR reaction tube. The

reaction solution used for the amplification process was shown in Table B-3. The

317

preparation of the reaction solutions were carried out on ice. The samples were

resuspended gently to homogenize the mixtures well. Then samples were placed in a

thermocycler (Perkin Elmer GeneAmp PCR System 9700) where polymerase chain

reaction took place. Parameters for PCR cycle were shown in Table B-4.

Table A-B: Composition of PCR reaction solution

Components Volume (μl )

Genomic DNA 5

16S-Reverse primer 3

16S-Forward primer 3

Buffer 10

MgCl2 8

dNTP mix 2

Taq polymerase 1

dH2O 68 Total 100

B-4: PCR Product Purification

PCR product was purified using Promega Wizard®SV gel and PCR clean-up

system. According to this procedure an equal volume of Membrane Binding Solution

and PCR reaction were mixed homogenously. The prepared PCR product was

poured to SV minicolumn assembled to a clean 2 mL collection tube. After

incubation at room temperature for 1 minute, the sample was centrifuged at 16,000 x

g for 1 minute. The flow-through liquid was discarded and the minicolumn was

reinserted back into the collection tube.

Then 700 μL of Membrane Wash Solution that has been diluted with ethanol

for washing the column were added into the minicolumn. The mixed solution was

centrifuge at 16,000 x g for 1 minute. The washing procedure was repeated once

again with 500 μL of Membrane Wash Solution and centrifuged at 16,000 x g for 5

minutes. All the flow-through was discarded. The column assembly was

318

recentrifuged for another 1 minute with the microcentrifuge lid opened for

evaporation of any ethanol residual. Then the minicolumn was transferred to a clean

1.5 mL microcentrifuge tube and was added with 50 μL of Nuclease-Free Water.

The solution was incubated for 1 minute at room temperature before centrifuge at

16,000 x g for 1 minute. Finally the minicolumn was discarded and the eluted

purified DNA was store 4oC or -20oC.

Table B-4: Parameter of PCR cycle

Parameter Temperature (oC) Time

Pre-denaturation 94 4 min Denaturation 94 1 min

Annealing 55 45 s Extension 72 1 min

Final Extension 72 7 min Preservation 4 Infinite

B-5: Purified DNA Estimation

Yields of the purified DNA were determined using agarose electrophoresis

analysis. Agarose with 1% (w/v) was used and was run at 80 V and 45 W for 60

minutes. Then, the gel was stained in running buffer plus 0.5 μg/ml ethidium

bromide (EtBr) for 30 minutes. The result of the DNA electrophoresis was examined

using UV transilluminator (TFX-35 Vilber Lourmat) and photographs were taken

using digital camera. Gene Ruler Ladder Mix Marker was used as the universal

marker.

B-6: Sequencing of the 16S rRNA Gene and Homology Analysis

The purified DNA (PCR product) was sent to Vivantis Laboratories Sdn Bhd

for sequencing purposes. The identification of selected bacteria were determined via

comparing the 16S rDNA sequences obtained in this study to the GenBank database

of National Center for Biotechnology Information (NCBI) websites

319

(http://www.ncbi.nlm.nihgov/BLAST). The nucleotides were compared through

Basic Local Alignment Search Tool (BLASTn) search program.

APPENDIX C: MORPHOLOGY OF BACTERIA Appendix C-1: Morphology of Bacteria Colony

Figure C-1 Example of classification of bacteria colony morphology (Wiley et al.,

2008)

Appendix C-2: Morphology of Bacteria Cell

Figure C-2 Example of classification of bacteria cellular morphology (Lester and

Birkett, 1999)

320

APPENDIX D: MOLECULAR DATA ANALYSIS

D1: BLASTn Analysis Result for the Determination of the Alignment Scores

of the Full Sequence of 16S rDNA for BS1FAnGS

BS1FAnGS– Full length sequence (1394 nucleotides) CGAGCGGTAGAGAGAAGCTTGCTTCTCTTGAGAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGATAACGTTCGGAAACGGACGCTAATACCGCATACGTCCTACGGGAGAAAGCAGGGGACCTTCGGGCCTTGCGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGTGATTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTAGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGGAACACCAGTGGCGAAGGCGAACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAGCTCTTAGTGGCGCAGCTAACGCATTAAGTTGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGCCTTGACATCCAATGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACATTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCACGTAATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCTGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGAATCAGAATGTCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCACCAGAAGTAGCTAGTCTAACCTTCGGGGGG Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the full length sequence from NCBI Accession Description

Max score

Total score

Query coverage

E value

Max ident

Links

AY144583.1 Pseudomonas veronii strain UFZ-B547 16S ribosomal RNA gene, partial sequence

2555 2555 100% 0.0 99%

AF064460.1 Pseudomonas veronii 16S ribosomal RNA gene, complete sequence

2555 2555 100% 0.0 99%

FJ594447.1 Pseudomonas sp. BS2(2009) 16S ribosomal RNA gene, partial sequence

2551 2551 100% 0.0 99%

AB365063.1 Pseudomonas sp. Pi 3-62 gene for 16S rRNA, partial sequence

2549 2549 100% 0.0 99%

EU099375.1 Pseudomonas sp. J7 16S ribosomal RNA gene, partial sequence

2549 2549 100% 0.0 99%

321

Accession Description Max score

Total score

Query coverage

E value

Max ident

Links

AY179328.1 Pseudomonas veronii 16S ribosomal RNA gene, partial sequence

2549 2549 100% 0.0 99%

AF539745.1 Pseudomonas veronii UFZ-B549 16S ribosomal RNA gene, partial sequence

2549 2549 100% 0.0 99%

AF195777.1 Pseudomonas sp. H1 16S ribosomal RNA gene, partial sequence

2547 2547 100% 0.0 99%

FM162562.1 Pseudomonas veronii partial 16S rRNA gene, strain MT4

2543 2543 100% 0.0 99%

FJ184354.1 Uncultured soil bacterium clone T7_3 16S ribosomal RNA gene, partial sequence

2543 2543 100% 0.0 99%

Phylogenetic Analysis

The phylogenetic tree showed the interrelationship between BS1FAnGS and top 10 Blast hits from NCBI. D2: BLASTn Analysis Result for the Determination of the Alignment Scores of Forward and Reverse Sequences of Partial 16S rDNA for BS6FAnGS

BS6FAnGS - Forward sequence (338 nucleotides) CCTGCCCATAAGACTGGCATAACTCCGGGAAACCGGGGCTAATACCGGATAAAATTTTGAACCGCATGGTTCGAAATTGAAAGGCGTATTCGATTGTCACTTATGGATGGACCCGCGTCGCGTTAGCTAGTTGGTGAGGTAACGGCTCACCAAGGGAACGATGCGTAGCCGACCTGAGAGGGTGATCGGCCACACTGGCACTGAGACTCGGCCCAGACTCCTACGGGAGGCATCAACAGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGCGAGTGATTGAGGCTTTCTGGTCGTAAAACTCTGGTGTTATGGAAGAACAAGTGC

Nucleotides marked in RED had been edited for sequence analysis.

AF064460 Pseudomonas veronii

AF539745 Pseudomonas veronii UFZ-B549

FJ594447 Pseudomonas sp. BS2

BS1FAnGS

FM162562 Pseudomonas veronii

AF195777 Pseudomonas sp. H1

AB365063 Pseudomonas sp. Pi 3-62 gene

AY144583 Pseudomonas veronii strain U...

AY179328 Pseudomonas veronii

EU099375 Pseudomonas sp. J7

FJ184354 Uncultured soil bacterium cl...

13

5

4

2

4

11

6330

322

Top 10 Blast hits sequence on the forward sequence from NCBI Accession Description

Max score

Total score

Query coverage

E value

Max ident

Links

FM873953.1 Uncultured bacterium partial 16S rRNA gene, clone MB04A04

525 525 100% 5e-146

94%

EU558974.1 Bacillus sp. cp-h31 16S ribosomal RNA gene, partial sequence

525 525 100% 5e-146

94%

EF422070.1 Bacillus cereus strain S10 16S ribosomal RNA gene, partial sequence

525 525 100% 5e-146

94%

EF113618.1 Bacillus thuringiensis strain IYM2 16S ribosomal RNA gene, partial sequence

521 521 100% 6e-145

94%

FJ598018.1 Bacillus cereus strain Bc6301 16S ribosomal RNA gene, partial sequence

520 520 100% 2e-144

94%

FJ528077.1 Bacillus sp. BM1-4 16S ribosomal RNA gene, partial sequence

520 520 100% 2e-144

94%

FJ598437.1 Bacillus sp. PM-3 16S ribosomal RNA gene, partial sequence

520 520 100% 2e-144

94%

FJ598436.1 Bacillus cereus strain PM-2 16S ribosomal RNA gene, partial sequence

520 520 100% 2e-144

94%

EU429664.1 Bacillus thuringiensis serovar ostriniae 16S ribosomal RNA gene, partial sequence

520 520 100% 2e-144

94%

FJ581461.1 Bacillus cereus strain HWB1 16S ribosomal RNA gene, partial sequence

520 520 100% 2e-144

94%


The phylogenetic tree showed the interrelationship between BS6FAnGS _For and top 10 Blast hits from NCBI.

BS6FAnGS -For

FM873953 Uncultured bacterium

EF422070 Bacillus cereus strain S10

FJ598436 Bacillus cereus strain PM-2

FJ598437 Bacillus sp. PM-3

FJ581461 Bacillus cereus strain HWB1

FJ528077 Bacillus sp. BM1-4

EF113618 Bacillus thuringiensis strai...

EU558974 Bacillus sp. cp-h31

FJ598018 Bacillus cereus strain Bc6301

EU429664 Bacillus thuringiensis serov...

FM162562 Pseudomonas veronii (outgroup)

3358

10

13

4

2

3

3

4

323

BS6FAnGS- Reverse sequence (595 nucleotides) CCGCGATTACTAGCGATTCCAGTTTCATGTAGGCGAGTTGCAGCCTACAATCCAAACTGAAAACGGTTTTATGAGATTAGCTCCACCTCGCGGTCTTGCACCTCTTTGTACCGTCCATTGTAGCACGTGTGTAGCCCAGGTCATAAGGGGCATGATGATTTGACGTCATCCCCACCTTCCTCCGGTTTGTCACCGGCAGTCACCTTAGAGTGCCCAACTTAATGATGGCAACTAAGATCAAGGGTTGCGCTCGTTGCGGGACTTAACCCAACATCTCACGACACGAGCTGACGACAACCATGCACCACCTGTCACTCTGCTCCCGAAGGAGAAGCCCTATCTCTAGGGTTTTCAGAGGATGTCAAGACCTGGTAAGGTTCTTCGCGTTGCTTCGAATTAAACCACATGCTCCACCGCTTGTGCGGGCCCCCGTCAATTCCTTTGAGTTTCAGCCTTGCGGCCGGACTCCCCAGGCGGAGTGCTTAATGCGTTAACTTCAGCACTAAAGGGCGGAAACCCTCTAACACTTAACACTCATCGTTTACGGCGTGGACTACCAGGGTATCTAATCCCTGTTTGCTCCCCACGCTTTCGC Nucleotides marked in RED had been edited for sequence analysis. BS6FAnGS- Reverse complementary sequence GCGAAAGCGTGGGGAGCAAACAGGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGAGTGTTAAGTGTTAGAGGGTTTCCGCCCTTTAGTGCTGAAGTTAACGCATTAAGCACTCCGCCTGGGGAGTCCGGCCGCAAGGCTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGTCTTGACATCCTCTGAAAACCCTAGAGATAGGGCTTCTCCTTCGGGAGCAGAGTGACAGGTGGTGCATGGTTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTGATCTTAGTTGCCATCATTAAGTTGGGCACTCTAAGGTGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAATCATCATGCCCCTTATGACCTGGGCTACACACGTGCTACAATGGACGGTACAAAGAGGTGCAAGACCGCGAGGTGGAGCTAATCTCATAAAACCGTTTTCAGTTTGGATTGTAGGCTGCAACTCGCCTACATGAAACTGGAATCGCTAGTAATCGCGG Top 10 Blast hits sequence on the reverse sequence from NCBI


Total score

Query coverage

E value

Max ident

Links

FM209283.1 Bacillus cereus partial 16S rRNA gene, strain TC4

1066 1066 100% 0.0 98%

EU239120.1 Bacillus cereus strain KNUC260 16S ribosomal RNA gene, partial sequence

1061 1061 100% 0.0 98%

AY461756.2 Bacillus sp. H-15 16S ribosomal RNA gene, partial sequence

1061 1061 100% 0.0 98%

AY461752.2 Bacillus sp. H-11 16S ribosomal RNA gene, partial sequence

1061 1061 100% 0.0 98%

DQ067207.1 Bacillus sp. A36 16S ribosomal RNA gene, partial sequence

1061 1061 99% 0.0 98%

EU429669.1 Bacillus thuringiensis serovar toumanoffi 16S ribosomal RNA gene, partial sequence

1059 1059 100% 0.0 98%

EU429668.1 Bacillus thuringiensis serovar thuringiensis 16S ribosomal RNA gene, partial sequence

1059 1059 100% 0.0 98%

EU429666.1 Bacillus thuringiensis serovar cameroun 16S ribosomal RNA gene, partial sequence

1059 1059 100% 0.0 98%

EU429665.1 Bacillus thuringiensis serovar berliner 16S 1059 1059 100% 0.0 98%

324


Total score

Query coverage

E value

Max ident

Links

ribosomal RNA gene, partial sequence

EU429662.1 Bacillus thuringiensis serovar galleriae 16S ribosomal RNA gene, partial sequence

1059 1059 100% 0.0 98%


The phylogenetic tree showed the interrelationship between BS6FAnGS_Rev and top 10 Blast hits from NCBI.

D3: BLASTn Analysis Result for the Determination of the Alignment Scores of Forward and Reverse Sequences of Partial 16S rDNA for BS7FAnGS

BS7FAnGS - Forward sequence (383 nucleotides) TAACACATGCGAGTCGAGCGGATGAAGGGAGCTTGCTCTCTGATTCAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGACAACGTTCCGAAAGGGACGCTAATACCGCATACGTCCTACGGGAGAAAGTGGGGGATCTTCGGACCTCACGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTAGGTGGGGTAATGGCTCACCTAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAATGCACT Nucleotides marked in RED had been edited for sequence analysis.

EU239120 Bacillus cereus strain KNUC260

AY461752 Bacillus sp. H-11



AY461756 Bacillus sp. H-15


FM209283 Bacillus cereus



BS6FAnGS-Rev

DQ067207 Bacillus sp. A36

FM162562 Pseudomonas veronii (outgroup)

63

11

2

1

1

2

0

0

2

325

Top 10 Blast hits sequence on the forward sequence from NCBI Accession Description

Max score

Total score

Query coverage

E value

Max ident

Links

FJ393104.1 Uncultured proteobacterium clone MFC-B162-E02 16S ribosomal RNA gene gene, partial sequence

697 697 100% 0.0 99%

EU352759.1 Pseudomonas citronellolis strain NK 2.C2-1 16S ribosomal RNA gene, partial sequence

686 686 100% 0.0 98%

EU170480.1 Pseudomonas aeruginosa strain L-4 16S ribosomal RNA gene, partial sequence

686 686 100% 0.0 98%

EU170479.1 Pseudomonas sp. LF-1 16S ribosomal RNA gene, partial sequence

686 686 100% 0.0 98%

EF593111.1 Pseudomonas citronellolis 16S ribosomal RNA gene, partial sequence

682 682 99% 0.0 98%

EU312076.1 Pseudomonas sp. Pds-5 16S ribosomal RNA gene, partial sequence

680 680 98% 0.0 99%

EU287480.1 Pseudomonas sp. J9(2007) 16S ribosomal RNA gene, partial sequence

680 680 100% 0.0 98%

EF660333.1 Pseudomonas sp. LFJS3-9 16S ribosomal RNA gene, partial sequence

680 680 100% 0.0 98%

AB007999.1 Pseudomonas sp. WAS2 gene for 16S rRNA, partial sequence

680 680 100% 0.0 98%

EF379150.1 Uncultured bacterium clone AA4 32 16S ribosomal RNA gene, partial sequence

676 676 98% 0.0 98%


The phylogenetic tree showed the interrelationship between BS7FAnGS_For and top 10 Blast hits from NCBI.

EU170480 Pseudomonas aeruginosa strai...

EF379150 Uncultured bacterium

EU170479 Pseudomonas sp. LF-1 16S

EF593111 Pseudomonas citronellolis

AB007999 Pseudomonas sp. WAS2

EU352759 Pseudomonas citronellolis st...


EF660333 Pseudomonas sp. LFJS3-9

BS7FAnGS-For

FJ393104 Uncultured proteobacterium c...

EU312076 Pseudomonas sp. Pds-5

8865

50

62

6022

62

19

326

BS7FAnGS -Reverse sequence (620 nucleotides) TGGTGACCGTCCCCCCGAAGGTTAGACTAGCTACTTCTGGAGCAACCCACTCCCATGGGGTGACGGGCGGTGTGTACAAGGCCCGGGAACGTATTCACCGTGACATTCTGATTCACGATTACTAGCGATTCCGACTTCACGCAGTCGAGTTGCAGACTGCGATCCGGACTACGATCGGTTTTGTGGGATTAGCTCCACCTCGCGGCTTGGCAACCCTCTGTACCGACCATTGTAGCACGTGTGTAGCCCTGGCCGTAAGGGCCATGATGACTTGACGTCATCCCCACCTTCCTCCGGTTTGTCACCGGCAGTCTCCTTAGAGTGCCCACCTTAACGCGCTGGTAACTAAGGACAAGGGTTGCGCTCGTTACGGGACTTAACCCAACATCTCACGACACGAGCTGACGACAGCCATGCAGCACCTGTGTTCCGATTCCCGAAGGCACTCCCACATCTCTGCAGGATTCCGGACATGTCAAGGCCAGGTAAGGTTCTTCGCGTTGCTTCAAATTAAACCACATGCTCCACCGCTTGTGCGGGCCCCCGTCAATTCATTTGAGTTTTAACCTTGCGGGCCGTACTCCCCAGGCGGTCGACTTATCGCGTTAGCTGCGCCACTA Nucleotides marked in RED had been edited for sequence analysis. BS7FAnGS -Reverse complementary sequence TAGTGGCGCAGCTAACGCGATAAGTCGACCGCCTGGGGAGTACGGCCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTTGAAGCAACGCGAAGAACCTTACCTGGCCTTGACATGTCCGGAATCCTGCAGAGATGTGGGAGTGCCTTCGGGAATCGGAACACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCGCGTTAAGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCAGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCACAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGTGAATCAGAATGTCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCTCCAGAAGTAGCTAGTCTAACCTTCGGGGGGACGGTCACCA Top 10 Blast hits sequence on the reverse sequence from NCBI


Total score

Query coverage

E value

Max ident

Links

EU287480.1 Pseudomonas sp. J9(2007) 16S ribosomal RNA gene, partial sequence

1110 1110 100% 0.0 99%

AF039488.1 Pseudomonas sp. 273 small subunit ribosomal RNA gene, partial sequence

1105 1105 100% 0.0 98%

DQ339153.1 Pseudomonas sp. RLD-1 16S ribosomal RNA gene, partial sequence

1099 1099 100% 0.0 98%

EU043324.1 Pseudomonas sp. SBR3-tpnb 16S ribosomal RNA gene, partial sequence

1096 1096 98% 0.0 99%

DQ926686.1 Uncultured bacterium clone N3 16S ribosomal RNA gene, partial sequence

1094 1094 100% 0.0 98%

327


Total score

Query coverage

E value

Max ident

Links

EU312076.1 Pseudomonas sp. Pds-5 16S ribosomal RNA gene, partial sequence

1092 1092 99% 0.0 98%

FJ470323.1 Uncultured bacterium clone SLB16 16S ribosomal RNA gene, partial sequence

1088 1088 100% 0.0 98%

DQ136054.2 Bacterium PT09 16S ribosomal RNA gene, partial sequence

1088 1088 100% 0.0 98%

EU170480.1 Pseudomonas aeruginosa strain L-4 16S ribosomal RNA gene, partial sequence

1088 1088 100% 0.0 98%

EU170479.1 Pseudomonas sp. LF-1 16S ribosomal RNA gene, partial sequence

1088 1088 100% 0.0 98%



D4: BLASTn Analysis Result for the Determination of the Alignment Scores of Forward and Reverse Sequences of Partial 16S rDNA for BS10FAnGS

BS10FAnGS– Forward sequence (538 nucleotides) GGCCCTACACATGCGAGTCGAGCGGTAGAGAGAAGCTTGCTTCTCTTGAGAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGATAACGTTCGGAAACGGACGCTAATACCGCATACGTCCTACGGGAGAAAGCAGGGGACCTTCGGGCCTTGCGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGAGATTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCCCAATTACTGGGCGTAAAGCGCGCGTAGGTGG

AF039488 Pseudomonas sp. 273

DQ926686 Uncultured bacterium clone N3

DQ339153 Pseudomonas sp. RLD-1

EU312076 Pseudomonas sp. Pds-5


BS7FAnGS- Rev EU043324 Pseudomonas sp. SBR3-tpnb

FJ470323 Uncultured bacterium clone S...

DQ136054 Bacterium PT09

EU170480 Pseudomonas aeruginosa strai...

EU170479 Pseudomonas sp. LF-1

74

50

78

67

58

79

98

69

328

Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the forward sequence from NCBI


Total score

Query coverage

E value

Max ident

Links

DQ536516.1 Pseudomonas trivialis strain BIHB 745 16S ribosomal RNA gene, partial sequence

966 966 100% 0.0 99%

EF379138.1 Uncultured bacterium clone Mat Z4 19 16S ribosomal RNA gene, partial sequence

965 965 99% 0.0 99%

DQ264528.1 Uncultured bacterium clone BANW559 16S ribosomal RNA gene, partial sequence

965 965 99% 0.0 99%

EU086550.1 Bacterium THCL4 16S ribosomal RNA gene, partial sequence

963 963 99% 0.0 99%

FM162562.1 Pseudomonas veronii partial 16S rRNA gene, strain MT4

961 961 99% 0.0 99%

FJ434132.1 Pseudomonas sp. IMER-A2-24 16S ribosomal RNA gene, partial sequence

961 961 99% 0.0 99%


961 961 99% 0.0 99%


961 961 99% 0.0 99%


961 961 99% 0.0 99%


961 961 99% 0.0 99%


The phylogenetic tree showed the interrelationship between BS10FAnGS_For and top 10 Blast hits from NCBI.

EF379138 Uncultured bacterium clone M...


EU086550 Bacterium THCL4


DQ536516 Pseudomonas trivialis strain...

DQ264528 Uncultured bacterium clone B...

FM162562 Pseudomonas veronii


FJ434132 Pseudomonas sp. IMER-A2-24


BS10FAnGS

AE005177 Escherichia coli (outgroup)

10

6

6

3

0

1

1

48

8

329

BS10FAnGS -Reverse sequence (787 nucleotides) GTCCCCCCGAAGGTTAGACTAGCTACTTCTGGTGCAACCCACTCCCATGGTGTGACGGGCGGTGTGTACAAGGCCCGGGAACGTATTCACCGCGACATTCTGATTCGCGATTACTAGCGATTCCGACTTCACGCAGTCGAGTTGCAGACTGCGATCCGGACTACGATCGGTTTTCTGGGATTAGCTCCACCTCGCGGCTTGGCAACCCTCTGTACCGACCATTGTAGCACGTGTGTAGCCCAGGCCGTAAGGGCCATGATGACTTGACGTCATCCCCACCTTCCTCCGGTTTGTCACCGGCAGTCTCCTTAGAGTGCCCACCATTACGTGCTGGTAACTAAGGACAAGGGTTGCGCTCGTTACGGGACTTAACCCAACATCTCACGACACGAGCTGACGACAGCCATGCAGCACCTGTCTCAATGTTCCCGAAGGCACCAATCCATCTCTGGAAAGTTCATTGGATGTCAAGGCCTGGTAAGGTTCTTCGCGTTGCTTCGAATTAAACCACATGCTCCACCGCTTGTGCGGGCCCCCGTCAATTCATTTGAGTTTTAACCTTGCGGCCGTACTCCCCAGGCGGTCAACTTAATGCGTTAGCTGCGCCACTAAAGAGCTCAAGGCTCCCAACGGCTAGTTGACATCGTTTACGGCGTGGACTACCAGGGTATCTAATCCTGTTTGCTCCCCACGCTTTCGCACCTCAGTGTCAGTATCAGTCAGGTGGTCGCCTTCGCCACTGGTGTTCCTTCCTATATCTACGCATTTCACCGCTACACAGGAAATT Nucleotides marked in RED had been edited for sequence analysis BS10FAnGS -Reverse complementary sequence AATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAGCTCTTTAGTGGCGCAGCTAACGCATTAAGTTGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGCCTTGACATCCAATGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACATTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCACGTAATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCTGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGAATCAGAATGTCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCACCAGAAGTAGCTAGTCTAACCTTCGGGGGGAC Top 10 Blast hits sequence on the reverse sequence from NCBI


Total score

Query coverage

E value

Max ident

Links

EU934227.1 Pseudomonas sp. LaGso27g, 16S ribosomal RNA gene, partial sequence

1443 1443 100% 0.0 99%

AY512607.1 Pseudomonas sp. A3YXyl2-4 16S ribosomal RNA gene, partial sequence

1443 1443 100% 0.0 99%

AY263479.1 Pseudomonas sp. R1enr 16S ribosomal RNA gene, partial sequence

1443 1443 100% 0.0 99%

AY144583.1 Pseudomonas veronii strain UFZ-B547 16S ribosomal RNA gene, partial sequence

1443 1443 100% 0.0 99%

330


Total score

Query coverage

E value

Max ident

Links

AY882021.1 Pseudomonas sp. GD100 16S ribosomal RNA gene, partial sequence

1443 1443 100% 0.0 99%

AF058286.1 Pseudomonas mandelii 16S ribosomal RNA gene, complete sequence

1443 1443 100% 0.0 99%

DQ339583.1 Pseudomonas sp. Enf22 16S ribosomal RNA gene, partial sequence

1443 1443 100% 0.0 99%

AF064460.1 Pseudomonas veronii 16S ribosomal RNA gene, complete sequence

1443 1443 100% 0.0 99%

FJ594447.1 Pseudomonas sp. BS2(2009) 16S ribosomal RNA gene, partial sequence

1437 1437 100% 0.0 99%

FJ517635.1 Pseudomonas sp. DM2 16S ribosomal RNA gene, partial sequence

1437 1437 100% 0.0 99%



D5: BLASTn Analysis Result for the Determination of the Alignment Scores of the Full Sequence of 16S rDNA for BS11FAnGS

BS11FAnGS -Full sequence (1429 nucleotides) TGCGGCAGGGCTACACATGCAGTCGAGCGGTAGCACAGAGAGCTTGCTCTCGGGTGACGAGCGGCGGACGGGTGAGTAATGTCTGGGAAACTGCCTGATGGAGGGGGATAACTACTGGAAACGGTAGCTAATACCGCATAACGTCGCAAGACCAAAGAGGGGGACCTTCGGGCCTCTTGCCATCAGATGTGCCCAGATGGGATTAGCTAGTAGGTGGGGTAACGGCTCACCTAGGCGACGATCCCTAGCTGGTCTGAGAGGATGACCAGCCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGGGGAGGAAGGTGCTGAGGTTAATAAC

BS10FAnGS Rev

FJ594447 Pseudomonas sp. BS

AF058286 Pseudomonas mandelii

DQ339583 Pseudomonas sp. Enf22

AY263479 Pseudomonas sp. R1enr

EU934227 Pseudomonas sp. LaGso27g

AY512607 Pseudomonas sp. A3YXyl2-4

AY144583 Pseudomonas veronii strain U...

AF064460 Pseudomonas veronii

AY882021 Pseudomonas sp. GD100

FJ517635 Pseudomonas sp. DM2

13

10

9

8

3

0

2

11

331

CTCAGCAATTGACGTTACCCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCGACTTGGAGGTTGTGCCCTTGAGGCGTGGCTTCCGGAGCTAACGCGTTAAGTCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCAGAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTCTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTATCCTTTGTTGCCAGCGGTTCGGCCGGGAACTCAAAGGAGACTGCCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGCATACAAAGAGAAGCGACCTCGCGAGAGCAAGCGGACCTCATAAAGTGCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTAGATCAGAATGCTACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCAAAAGAAGTAGGTAGCTTAACCTTCGGGAGGGCGCTCACCATC Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the full length sequence from NCBI


Total score

Query coverage

E value

Max ident

Links

EU781735.1 Enterobacter sp. VET-7 16S ribosomal RNA gene, partial sequence

2603 2603 99% 0.0 99%

EU331414.1 Enterobacter sp. L3R3-1 16S ribosomal RNA gene, partial sequence

2603 2603 99% 0.0 99%

EU221358.1 Enterobacter asburiae strain J2S4 16S ribosomal RNA gene, partial sequence

2603 2603 99% 0.0 99%

DQ068845.1 Uncultured bacterium clone 5s2 16S ribosomal RNA gene, partial sequence

2591 2591 99% 0.0 99%

DQ068880.1 Uncultured bacterium clone bb2s4 16S ribosomal RNA gene, partial sequence

2588 2588 99% 0.0 99%

AB244469.1 Enterobacter cloacae gene for 16S rRNA, partial sequence, strain: NC1111

2586 2586 99% 0.0 99%

AB114621.1 Uncultured Enterobacteriaceae bacterium gene for 16S rRNA, partial sequence, clone:ER-9

2586 2586 99% 0.0 99%

AM184307.1 Pantoea agglomerans partial 16S rRNA gene, strain WAB1969

2582 2582 99% 0.0 99%

FJ445214.1 Pantoea sp. NIIST-186 16S ribosomal RNA, partial sequence

2580 2580 99% 0.0 99%

EF655641.1 Uncultured bacterium clone B12 16S ribosomal RNA gene, partial sequence

2580 2580 99% 0.0 99%

332


The phylogenetic tree showed the interrelationship between BS11FAnGS and top 10 Blast hits from NCBI. D6: BLASTn analysis result for the determination of the alignment scores of the full sequence of 16S rDNA for BS12FAnGS

BS12FAnGS -Full Length sequence (1403 nucleotides) GTCGAGCGGTAGAGAGAAGCTTGCTTCTCTTGAGAGCGGCGGACGGGTGAGTAATGCCTAGGAATCTGCCTGGTAGTGGGGGATAACGTTCGGAAACGGACGCTAATACCGCATACGTCCTACGGGAGAAAGCAGGGGACCTTCGGGCCTTGCGCTATCAGATGAGCCTAGGTCGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCGTAACTGGTCTGAGAGGATGATCAGTCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGTGATTGCTTTGACGTTACCGACACAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCTTAATTACTGGTCATAAAGCGCGCGTAGGTGGGTTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCAACTAGCCGTTGGGAGCCTTGAGCTTTTAGTGGCGCAGCTAACGCATTAAGTTGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGAAGCAACGCGAAGAACCTTACCAGGCCTTGACATCCAATGAACTTTCTAGAGATAGATTGGTGCCTTCGGGAACATTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGTAACGAGCGCAACCCTTGTCCTTAGTTACCAGCACGTAATGGTGGGCACTCTAAGGAGACTGCCGGTGACAAACCGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCCTTACGGCCTGGGCTACACACGTGCTACAATGGTCGGTACAGAGGGTTGCCAAGCCGCGAGGTGGAGCTAATCCCAGAAAACCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGAATCA

DQ068845 Uncultured bacterium clone 5s2

FJ445214 Pantoea sp. NIIST-186

EF655641 Uncultured bacterium clone B12

EU221358 Enterobacter asburiae strain...

AB244469 Enterobacter cloacae gene

DQ068880 Uncultured bacterium clone b...

EU331414 Enterobacter sp. L3R3-1

AM184307 Pantoea agglomerans

BS11FAnGS

EU781735 Enterobacter sp. VET-7

AB114621 Uncultured Enterobacteriacea...

60

51

50

4421

12

27

50

333

GAATGTCGCGGTGAATACGTTCCCGGGCCTTGTACACCCCGCCCGTCACACCATGGGAGTGGGTTGCACCAGAAGTAGCTAGTCTAACCCTCGGGAGGACGGTACCA Nucleotides marked in RED had been edited for sequence analysis. Top 10 Blast hits sequence on the full length sequence from NCBI


Total score

Query coverage

E value

Max ident

Links

AJ583501.3 Pseudomonas extremaustralis 16S rRNA gene, strain CT14-3

2536 2536 100% 0.0 99%

DQ136048.2 Bacterium PT03 16S ribosomal RNA gene, partial sequence

2536 2536 100% 0.0 99%

AB056120.1 Pseudomonas veronii gene for 16S rRNA, strain:INA06

2536 2536 100% 0.0 99%

AM421982.1 Pseudomonas sp. NJ-61 16S rRNA gene, strain NJ-61

2531 2531 100% 0.0 99%

AY014806.1 Pseudomonas sp. NZ024 16S ribosomal RNA gene, partial sequence

2531 2531 100% 0.0 99%

FJ179366.1 Pseudomonas trivialis strain BIHB 750 16S ribosomal RNA gene, partial sequence

2525 2525 100% 0.0 99%

EU086570.1 Bacterium TLCL3 16S ribosomal RNA gene, partial sequence

2525 2525 100% 0.0 99%

AB334768.1 Pseudomonas veronii gene for 16S ribosomal RNA, partial sequence

2525 2525 100% 0.0 99%

DQ536516.1 Pseudomonas trivialis strain BIHB 745 16S ribosomal RNA gene, partial sequence

2525 2525 100% 0.0 99%

AY599721.1 Pseudomonas sp. TB3-6-I 16S ribosomal RNA gene, partial sequence

2525 2525 100% 0.0 99%


The phylogenetic tree showed the interrelationship between BS12FAnGS and top 10 Blast hits from NCBI.

AJ583501 Pseudomonas extremaustralis

AY014806 Pseudomonas sp. NZ024

DQ136048 Bacterium PT03

AB056120 Pseudomonas veronii gene

AM421982 Pseudomonas sp. NJ-61

BS12FAnGS

AY599721 Pseudomonas sp. TB3-6-I

FJ179366 Pseudomonas trivialis strain...

DQ536516 Pseudomonas trivialis strain...

EU086570 Bacterium TLCL3

AB334768 Pseudomonas veronii gene

EF422070 Bacillus cereus (outgroup)

53

17

12

51

49

33

37

3593

334

Service Report Microorganism Identification By 16S/18S rRNA Sequencing

Customer Name : Dr. Azmi Aris/ Date: 12th February 2009 Ms Khalida Muda Institute/Company : Dept of Environmental Eng.

Faculty of Civil Eng. UTM

Sample ID Our Label Comment Remark

1. BS1FAnGS G1 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas veronii

Sequencing results, phylogenetic trees, multiple alignment and top 10 sequences match were supplied.

2. BS6FAnGS 02 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Bacillus cereus


3. BS7FAnGS 03 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas sp.


4. BS10FAnGS N2 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas sp.


5. BS11FAnGS N3 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Enterobacter sp.


6. BS12FAnGS N4 Phylogenetic analysis using both 5’ (forward) and 3’ (reverse) sequences showed that this bacteria is closely related to Pseudomonas sp.


In order to view the sequencing result, the Chromas software need to be used. The Chromas freeware is available at http://www.technelysium.com.au/chromas_lite.html Prepared by: Chan Toh Theng Lab Manager Vivantis Technologies Sdn. Bhd.

VIVANTIS TECHNOLOGIES SDN. BHD. (587389-D)

Suite 2-B, No. 23, Jalan U1/15, Hicom Glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia. Tel: 603-5569 5785 Fax: 603-5569 5786 URL: http://www.vivantis.com E-mail: [email protected]

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APPENDIX E: FACTORIAL DESIGN AND RESPONSE SURFACE METHODOLOGY DATA ANALYSIS FOR COAGGREGATION AND

SURFACE HDROPHOBICITYASSAY E-1: Factorial Design Analysis for Coaggregation Assay (1 hour) Fractional Factorial Fit: CAgg_1h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_1h (coded units) Term Effect Coef SE Coef T P Constant 53.025 1.006 52.70 0.000 Substrat 7.775 3.887 1.006 3.86 0.005 pH -9.725 -4.862 1.006 -4.83 0.001 Temperat 15.325 7.663 1.006 7.62 0.000 Substrat*pH -6.600 -3.300 1.006 -3.28 0.011 Substrat*Temperat -2.200 -1.100 1.006 -1.09 0.306 pH*Temperat -0.650 -0.325 1.006 -0.32 0.755 Substrat*pH*Temperat 1.325 0.663 1.006 0.66 0.529 Analysis of Variance for CAgg_1h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 1559.53 1559.53 519.843 32.10 0.000 2-Way Interactions 3 195.29 195.29 65.097 4.02 0.051 3-Way Interactions 1 7.02 7.02 7.023 0.43 0.529 Residual Error 8 129.57 129.57 16.196 Pure Error 8 129.57 129.57 16.196 Total 15 1891.41 E-2: Factorial Design Analysis for Coaggregation Assay (2 hour) Fractional Factorial Fit: CAgg_2h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_2h (coded units) Term Effect Coef SE Coef T P Constant 57.775 1.318 43.83 0.000 Substrat 11.350 5.675 1.318 4.31 0.003 pH -15.375 -7.687 1.318 -5.83 0.000 Temperat 12.925 6.463 1.318 4.90 0.001 Substrat*pH -7.475 -3.737 1.318 -2.84 0.022 Substrat*Temperat -4.775 -2.388 1.318 -1.81 0.108 pH*Temperat -4.100 -2.050 1.318 -1.56 0.159 Substrat*pH*Temperat -4.500 -2.250 1.318 -1.71 0.126 Analysis of Variance for CAgg_2h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 2129.07 2129.07 709.69 25.53 0.000 2-Way Interactions 3 381.95 381.95 127.32 4.58 0.038 3-Way Interactions 1 81.00 81.00 81.00 2.91 0.126 Residual Error 8 222.41 222.41 27.80 Pure Error 8 222.41 222.41 27.80 Total 15 2814.43

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E-3: Factorial Design Analysis for Coaggregation Assay (3 hour) Fractional Factorial Fit: CAgg_3h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_3h (coded units) Term Effect Coef SE Coef T P Constant 59.363 2.417 24.56 0.000 Substrat 17.375 8.687 2.417 3.59 0.007 pH -11.225 -5.612 2.417 -2.32 0.049 Temperat 9.200 4.600 2.417 1.90 0.094 Substrat*pH -1.775 -0.887 2.417 -0.37 0.723 Substrat*Temperat 0.800 0.400 2.417 0.17 0.873 pH*Temperat -6.900 -3.450 2.417 -1.43 0.191 Substrat*pH*Temperat -1.700 -0.850 2.417 -0.35 0.734 Analysis of Variance for CAgg_3h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 2050.12 2050.12 683.37 7.31 0.011 2-Way Interactions 3 205.60 205.60 68.53 0.73 0.561 3-Way Interactions 1 11.56 11.56 11.56 0.12 0.734 Residual Error 8 747.75 747.75 93.47 Pure Error 8 747.75 747.75 93.47 Total 15 3015.04 E-4: Factorial Design Analysis for Coaggregation Assay (4 hour) Fractional Factorial Fit: CAgg_4h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_4h (coded units) Term Effect Coef SE Coef T P Constant 61.944 2.134 29.02 0.000 Substrat 18.637 9.319 2.134 4.37 0.002 pH -9.637 -4.819 2.134 -2.26 0.054 Temperat 5.462 2.731 2.134 1.28 0.236 Substrat*pH -4.637 -2.319 2.134 -1.09 0.309 Substrat*Temperat -2.088 -1.044 2.134 -0.49 0.638 pH*Temperat -6.562 -3.281 2.134 -1.54 0.163 Substrat*pH*Temperat 1.238 0.619 2.134 0.29 0.779 Analysis of Variance for CAgg_4h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 1880.31 1880.31 626.769 8.60 0.007 2-Way Interactions 3 275.72 275.72 91.907 1.26 0.351 3-Way Interactions 1 6.13 6.13 6.126 0.08 0.779 Residual Error 8 583.03 583.03 72.878 Pure Error 8 583.03 583.03 72.878 Total 15 2745.18

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E-5: Factorial Design Analysis for Coaggregation Assay (5 hours) Fractional Factorial Fit: CAgg_5h versus Substrate, pH, Temperature Estimated Effects and Coefficients for CAgg_5h (coded units) Term Effect Coef SE Coef T P Constant 58.406 0.4839 120.69 0.000 Substrat 19.362 9.681 0.4839 20.01 0.000 pH -13.837 -6.919 0.4839 -14.30 0.000 Temperat 5.562 2.781 0.4839 5.75 0.000 Substrat*pH -0.587 -0.294 0.4839 -0.61 0.561 Substrat*Temperat 0.713 0.356 0.4839 0.74 0.483 pH*Temperat -12.287 -6.144 0.4839 -12.70 0.000 Substrat*pH*Temperat -0.737 -0.369 0.4839 -0.76 0.468 Analysis of Variance for CAgg_5h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 2389.30 2389.30 796.432 212.56 0.000 2-Way Interactions 3 607.34 607.34 202.447 54.03 0.000 3-Way Interactions 1 2.18 2.18 2.176 0.58 0.468 Residual Error 8 29.98 29.98 3.747 Pure Error 8 29.97 29.97 3.747 Total 15 3028.79

E-6: Factorial Design Analysis for Surface Hydrophobicity Assay Fractional Factorial Fit: SHb (%) versus Substrate, pH, Temperature Estimated Effects and Coefficients for SHb (coded units) Term Effect Coef SE Coef T P Constant 30.15 0.4889 61.66 0.000 Substrat 4.95 2.47 0.4889 5.06 0.001 pH -8.05 -4.02 0.4889 -8.23 0.000 Temperat -4.43 -2.21 0.4889 -4.53 0.002 Substrat*pH -11.25 -5.62 0.4889 -11.50 0.000 Substrat*Temperat -0.18 -0.09 0.4889 -0.18 0.862 pH*Temperat -20.82 -10.41 0.4889 -21.30 0.000 Substrat*pH*Temperat -5.13 -2.56 0.4889 -5.24 0.001 Analysis of Variance for SHb (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 435.54 435.54 145.181 37.96 0.000 2-Way Interactions 3 2241.09 2241.09 747.032 195.30 0.000 3-Way Interactions 1 105.06 105.06 105.063 27.47 0.001 Residual Error 8 30.60 30.60 3.825 Pure Error 8 30.60 30.60 3.825 Total 15 2812.30

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E-7: Response Surface Modeling Analysis for Coaggregation Assay (Transforms) (Full Quadratic Terms) (5 hours) Response Surface Regression: Coaggregation versus Substrate, pH and Temperature Transform: Power Lambda: 2.05 Constant: 0 Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 6.307E+007 9 7.007E+006 7.20 0.0024 significant A-Substrate 1.128E+007 1 1.128E+007 11.59 0.0067 B-pH 1.623E+007 1 1.623E+007 16.67 0.0022 C-Temperature 5.639E+006 1 5.639E+006 5.79 0.0369 AB 2.362E+005 1 2.362E+005 0.24 0.6329 AC 56360.44 1 56360.44 0.058 0.8147 BC 7.442E+006 1 7.442E+006 7.65 0.0200 A2 1.322E+007 1 1.322E+007 13.58 0.0042 B2 5.244E+006 1 5.244E+006 5.39 0.0427 C2 1.521E+006 1 1.521E+006 1.56 0.2397 Residual 9.734E+006 10 9.734E+005 Lack of Fit 9.242E+006 5 1.848E+006 18.81 0.0029 significant Pure Error 4.912E+005 5 98249.40 Cor Total 7.280E+007 19 Std. Dev. 986.59 R-Squared 0.8663 Mean 4133.89 Adj R-Squared 0.7460 C.V. % 23.87 Pred R-Squared 0.0221

PRESS `7.119E+007 Adeq Precision 11.147 Coefficient Standard 95% CI 95% CI

Factor Estimate df Error Low High VIF Intercept 4113.74 1 402.38 3217.19 5010.30 A-Substrate 908.95 1 266.97 314.11 1503.80 1.00 B-pH -1090.06 1 266.97 -1684.90 -495.21 1.00 C-Temperature 642.60 1 266.97 47.75 1237.45 1.00 AB -171.85 1 348.81 -949.05 605.36 1.00 AC 83.93 1 348.81 -693.27 861.14 1.00 BC -964.48 1 348.81 -1741.68 -187.27 1.00 A2 957.62 1 259.89 378.56 1536.69 1.02 B2 -603.20 1 259.89 -1182.26 -24.13 1.02 C2 -324.93 1 259.89 -903.99 254.14 1.02

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E-8: Response Surface Modeling Analysis for Coaggregation Assay (Transforms) (5 hours) (Linear + Square + pH x Tempareture) Response Surface Regression: Coaggregation versus Substrate, pH and Temperature Transform: Power Lambda: 2. Constant: 0 Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 3.912E+007 6 6.520E+006 11.43 0.0002 significant A-Substrate 7.112E+006 1 7.112E+006 12.47 0.0037 B-Ph 1.047E+007 1 1.047E+007 18.36 0.0009 C-Temperature 3.590E+006 1 3.590E+006 6.29 0.0262 BC 4.676E+006 1 4.676E+006 8.20 0.0133 A2 9.044E+006 1 9.044E+006 15.85 0.0016 B2 3.152E+006 1 3.152E+006 5.53 0.0352 Residual 7.416E+006 13 5.705E+005 Lack of Fit 7.105E+006 8 8.881E+005 14.25 0.0048 significant Pure Error 3.116E+005 5 62320.34 Cor Total 4.654E+007 19 Std. Dev. 755.31 R-Squared 0.8406 Mean 3363.73 Adj R-Squared 0.7671 C.V. % 22.45 Pred R-Squared 0.4608 PRESS 2.509E+007 Adeq Precision 13.947 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 3143.25 1 261.49 2578.33 3708.17 A-Substrate 721.65 1 204.39 280.10 1163.19 1.00 B-pH -875.70 1 204.39 -1317.25 -434.15 1.00 C-Temperature 512.68 1 204.39 71.13 954.23 1.00 BC -764.56 1 267.04 -1341.47 -187.65 1.00 A2 788.26 1 197.98 360.55 1215.98 1.01 B2 -465.39 1 197.98 -893.10 -37.67 1.01

340

E-9: Response Surface Modeling Analysis for Surface Hydrophobicity Assay

ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 9065.62 9 1007.29 3.50 0.0321 significant A-Substrate 267.37 1 267.37 0.93 0.3582 B-pH 1137.96 1 1137.96 3.95 0.0750 C-Temperature 3.10 1 3.10 0.011 0.9194 AB 265.65 1 265.65 0.92 0.3596 AC 6.30 1 6.30 0.022 0.8854 BC 822.15 1 822.15 2.85 0.1221 A2 1559.95 1 1559.95 5.41 0.0423 B2 5472.23 1 5472.23 18.99 0.0014 C2 290.17 1 290.17 1.01 0.3393 Residual 2881.90 10 288.19 Lack of Fit 2862.19 5 572.44 145.17 < 0.0001 significant Pure Error 19.72 5 3.94 Cor Total 11947.53 19 Std. Dev. 16.98 R-Squared 0.7588 Mean 51.96 Adj R-Squared 0.5417 C.V. % 32.67 Pred R-Squared -0.8204 PRESS 21749.41 Adeq Precision 5.870 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 75.43 1 6.92 60.01 90.86 A-Substrate 4.42 1 4.59 -5.81 14.66 1.00 B-pH -9.13 1 4.59 -19.36 1.11 1.00 C-Temperature 0.48 1 4.59 -9.76 10.71 1.00 AB -5.76 1 6.00 -19.14 7.61 1.00 AC -0.89 1 6.00 -14.26 12.49 1.00 BC -10.14 1 6.00 -23.51 3.24 1.00 A2 -10.40 1 4.47 -20.37 -0.44 1.02 B2 -19.49 1 4.47 -29.45 -9.52 1.02 C2 -4.49 1 4.47 -14.45 5.48 1.02

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APPENDIX F: FACTORIAL DESIGN AND RESPONSE SURFACE METHODOLOGY DATA ANALYSIS FOR COD REMOVAL

F1: Factorial Design Analysis for COD Removal Fractional Factorial Fit: ANA_COD versus Substrate, RM Estimated Effects and Coefficients for ANA_COD (coded units) Term Effect Coef SE Coef T P Constant 53.225 1.048 50.78 0.000 Substrate 50.450 25.225 1.048 24.07 0.000 RM -2.100 -1.050 1.048 -1.00 0.373 Substrate*RM 0.700 0.350 1.048 0.33 0.755 Analysis of Variance for ANA_COD (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 5099.22 5099.22 2549.61 290.14 0.000 2-Way Interactions 1 0.98 0.98 0.98 0.11 0.755 Residual Error 4 35.15 35.15 8.79 Pure Error 4 35.15 35.15 8.79 Total 7 5135.35 Estimated Coefficients for ANA_COD using data in uncoded units Term Coef Constant 28.1989 Substrate 0.0589095 RM -0.00113257 Substrate*RM 6.625473E-07 F2: Factorial Design Analysis for Aerobic COD Removal Fractional Factorial Fit: AER_COD versus Substrate, RM Estimated Effects and Coefficients for AER_COD (coded units) Term Effect Coef SE Coef T P Constant 44.68 0.4131 108.15 0.000 Substrate -32.45 -16.22 0.4131 -39.28 0.000 RM -7.95 -3.97 0.4131 -9.62 0.001 Substrate*RM 6.35 3.17 0.4131 7.69 0.002 Analysis of Variance for AER_COD (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 2232.41 2232.41 1116.20 817.73 0.000 2-Way Interactions 1 80.64 80.64 80.64 59.08 0.002 Residual Error 4 5.46 5.46 1.37 Pure Error 4 5.46 5.46 1.36 Total 7 2318.51

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F3: Factorial Design Analysis for Total COD Removal Fractional Factorial Fit: Total COD versus Substrate, RM Estimated Effects and Coefficients for Total (coded units) Term Effect Coef SE Coef T P Constant 78.175 0.2767 282.53 0.000 Substrate 12.800 6.400 0.2767 23.13 0.000 RM -6.350 -3.175 0.2767 -11.47 0.000 Substrate*RM 5.000 2.500 0.2767 9.04 0.001 Analysis of Variance for Total (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 408.325 408.325 204.162 333.33 0.000 2-Way Interactions 1 50.000 50.000 50.000 81.63 0.001 Residual Error 4 2.450 2.450 0.613 Pure Error 4 2.450 2.450 0.612 Total 7 460.775

F4: Central Composite Design Analysis for Anaerobic COD Removal Response Surface Regression: Anaerobic COD removal versus Substrate, RM The analysis was done using coded units. Estimated Regression Coefficients for Anaerobic COD removal Term Coef SE Coef T P Constant 23.940 8.723 2.744 0.029 Substrate 13.374 6.896 1.939 0.094 RM -1.186 6.896 -0.172 0.868 Substrate*Substrate 9.299 7.395 1.257 0.249 RM*RM 6.849 7.395 0.926 0.385 Substrate*RM 0.775 9.753 0.079 0.939 S = 19.51 R-Sq = 46.0% R-Sq(adj) = 7.5% Analysis of Variance for Anaerobic COD removal Source DF Seq SS Adj SS Adj MS F P Regression 5 2270.79 2270.79 454.159 1.19 0.400 Linear 2 1442.09 1442.09 721.044 1.90 0.220 Square 2 826.30 826.30 413.151 1.09 0.388 Interaction 1 2.40 2.40 2.402 0.01 0.939 Residual Error 7 2663.31 2663.31 380.473 Lack-of-Fit 3 2657.02 2657.02 885.673 563.05 0.000 Pure Error 4 6.29 6.29 1.573 Total 12 4934.10

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F5: Central Composite Design Analysis for Aerobic COD Removal Response Surface Regression: Aerobic COD removal versus Substrate, RM The analysis was done using coded units. Estimated Regression Coefficients for Aerobic COD removal Term Coef SE Coef T P Constant 73.56 7.883 9.332 0.000 Substrate -1.65 6.232 -0.264 0.799 RM -2.28 6.232 -0.366 0.725 Substrate*Substrate -16.33 6.683 -2.443 0.045 RM*RM -4.33 6.683 -0.648 0.538 Substrate*RM 3.45 8.813 0.391 0.707 S = 17.63 R-Sq = 47.9% R-Sq(adj) = 10.7% Analysis of Variance for Aerobic COD removal Source DF Seq SS Adj SS Adj MS F P Regression 5 2000.32 2000.32 400.063 1.29 0.366 Linear 2 63.36 63.36 31.679 0.10 0.904 Square 2 1889.35 1889.35 944.674 3.04 0.112 Interaction 1 47.61 47.61 47.610 0.15 0.707 Residual Error 7 2174.92 2174.92 310.702 Lack-of-Fit 3 2169.46 2169.46 723.155 530.56 0.000 Pure Error 4 5.45 5.45 1.363 Total 12 4175.23

F6: Central Composite Design Analysis for Total COD Removal Response Surface Regression: Total COD removal versus Substrate, RM The analysis was done using coded units. Estimated Regression Coefficients for Total COD removal Term Coef SE Coef T P Constant 79.900 2.144 37.267 0.000 Substrate 8.332 1.695 4.916 0.002 RM -1.917 1.695 -1.131 0.295 Substrate*Substrate -6.706 1.818 -3.690 0.008 RM*RM 1.644 1.818 0.904 0.396 Substrate*RM 2.825 2.397 1.179 0.277 S = 4.794 R-Sq = 85.8% R-Sq(adj) = 75.7% Analysis of Variance for Total COD removal Source DF Seq SS Adj SS Adj MS F P Regression 5 974.42 974.416 194.883 8.48 0.007 Linear 2 584.75 584.746 292.373 12.72 0.005 Square 2 357.75 357.747 178.874 7.78 0.017 Interaction 1 31.92 31.923 31.923 1.39 0.277 Residual Error 7 160.88 160.881 22.983 Lack-of-Fit 3 159.38 159.381 53.127 141.67 0.000 Pure Error 4 1.50 1.500 0.375 Total 12 1135.30

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APPENDIX G: FACTORIAL DESIGN AND RESPONSE SURFACE METHODOLOGY DATA ANALYSIS FOR COLOR REMOVAL

G1: Factorial Design Analysis for Color Removal (Sumifix Navy Blue_600 nm_5 hour) Fractional Factorial Fit: 600_5h versus Substrate, RM Estimated Effects and Coefficients for 600_5h (coded units) Term Effect Coef SE Coef T P Constant 78.8250 0.1199 657.45 0.000 Substrate 5.9500 2.9750 0.1199 24.81 0.000 RM 2.9500 1.4750 0.1199 12.30 0.000 Substrate*RM 0.9500 0.4750 0.1199 3.96 0.017 Analysis of Variance for 600_5h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 88.2100 88.2100 44.1050 383.52 0.000 2-Way Interactions 1 1.8050 1.8050 1.8050 15.70 0.017 Residual Error 4 0.4600 0.4600 0.1150 Pure Error 4 0.4600 0.4600 0.1150 Total 7 90.4750

G2: Factorial Design Analysis for Color Removal (Sumifix Navy Blue_600 nm_12 hour) Fractional Factorial Fit: 600_12 versus Substrate, RM Estimated Effects and Coefficients for 600_12 (coded units) Term Effect Coef SE Coef T P Constant 79.9750 0.2604 307.11 0.000 Substrate -1.9000 -0.9500 0.2604 -3.65 0.022 RM 5.5000 2.7500 0.2604 10.56 0.000 Substrate*RM -0.5500 -0.2750 0.2604 -1.06 0.351 Analysis of Variance for 600_12 (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 67.7200 67.7200 33.8600 62.41 0.001 2-Way Interactions 1 0.6050 0.6050 0.6050 1.12 0.351 Residual Error 4 2.1700 2.1700 0.5425 Pure Error 4 2.1700 2.1700 0.5425 Total 7 70.4950

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G3: Factorial Design Analysis for Color Removal (Synozol Red K-4B_542 nm _5 hour) Fractional Factorial Fit: 542_5h versus Substrate, RM Estimated Effects and Coefficients for 542_5h (coded units) Term Effect Coef SE Coef T P Constant 71.738 0.2741 261.68 0.000 Substrate 12.025 6.013 0.2741 21.93 0.000 RM -2.225 -1.112 0.2741 -4.06 0.015 Substrate*RM 4.125 2.063 0.2741 7.52 0.002 Analysis of Variance for 542_5h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 299.103 299.103 149.551 248.73 0.000 2-Way Interactions 1 34.031 34.031 34.031 56.60 0.002 Residual Error 4 2.405 2.405 0.601 Pure Error 4 2.405 2.405 0.601 Total 7 335.539

G4: Factorial Design Analysis for Color Removal (Synozol Red K-4B_542 nm _12hour) Fractional Factorial Fit: 542_12h versus Substrate, RM Estimated Effects and Coefficients for 542_12h (coded units) Term Effect Coef SE Coef T P Constant 78.6000 0.1639 479.46 0.000 Substrate -0.8000 -0.4000 0.1639 -2.44 0.071 RM 5.7000 2.8500 0.1639 17.38 0.000 Substrate*RM -1.1000 -0.5500 0.1639 -3.35 0.028 Analysis of Variance for 542_12h (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 2 66.2600 66.2600 33.1300 154.09 0.000 2-Way Interactions 1 2.4200 2.4200 2.4200 11.26 0.028 Residual Error 4 0.8600 0.8600 0.2150 Pure Error 4 0.8600 0.8600 0.2150 Total 7 69.5400

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G5: Response Surface Modeling Design Analysis for Color Removal (Sumifix

Navy Blue EXF_600 nm_5 hours) (Full Quadratic Term)

ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 105.45 5 21.09 4.06 0.0475 significant A-Substrate 28.64 1 28.64 5.51 0.0512 B-Riboflavin 18.67 1 18.67 3.60 0.0998 AB 0.25 1 0.25 0.048 0.8326 A2 33.1 1 33.10 6.37 0.0396 B2 32.3 1 32.34 6.23 0.0413 Residual 36.36 7 5.19 Lack of Fit 18.04 3 6.01 1.31 0.3865 not significant Pure Error 18.32 4 4.58 Cor Total 141.81 12 Std. Dev. 2.28 R-Squared 0.7436 Mean 81.63 Adj R-Squared 0.5605 C.V. % 2.79 Pred R-Squared -0.1063 PRESS 156.89 Adeq Precision 4.849 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 84.30 1 1.02 81.89 86.71 A-Substrate 1.89 1 0.81 -0.013 3.80 1.00 B-Riboflavin 1.53 1 0.81 -0.38 3.43 1.00 AB 0.25 1 1.14 -2.44 2.94 1.00 A2 -2.18 1 0.86 -4.22 -0.14 1.02 B2 -2.16 1 0.86 -4.20 -0.11 1.02

347

G6: Response Surface Modeling Design Analysis for Color Removal (Sumifix

Navy Blue EXF_600 nm_5 hours) (Reduced Quadratic Term-Linear + Square

Terms)

ANOVA for Response Surface Reduced Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 105.20 4 26.30 5.75 0.0176 significant A-Substrate 28.64 1 28.64 6.26 0.0369 B-Riboflavin 18.67 1 18.67 4.08 0.0780 A2 33.10 1 33.10 7.23 0.0275 B2 32.34 1 32.34 7.07 0.0289 Residual 36.61 8 4.58 Lack of Fit 18.29 4 4.57 1.00 0.5007 not significant Pure Error 18.32 4 4.58 Cor Total 141.81 12 Std. Dev. 2.14 R-Squared 0.7419 Mean 81.63 Adj R-Squared 0.6128 C.V. % 2.62 Pred R-Squared 0.1786 PRESS 116.48 Adeq Precision 5.847 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 84.30 1 0.96 82.09 86.51 A-Substrate 1.89 1 0.76 0.15 3.64 1.00 B-Riboflavin 1.53 1 0.76 -0.22 3.27 1.00 A2 -2.18 1 0.81 -4.05 -0.31 1.02 B2 -2.16 1 0.81 -4.03 -0.29 1.02

348

G7: Response Surface Modeling Design Analysis for Color Removal (Sumifix Navy Blue EXF_600 nm_12 hour) ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 158.58 5 31.72 7.23 0.0109 significant A-Substrate 13.55 1 13.55 3.09 0.1221 B-Riboflavin 76.73 1 76.73 17.50 0.0041 AB 1.69 1 1.69 0.39 0.5543 A2 10.16 1 10.16 2.32 0.1717 B2 49.45 1 49.45 11.28 0.0121 Residual 30.69 7 4.38 Lack of Fit 4.08 3 1.36 0.20 0.8885 not significant Pure Error 26.61 4 6.65 Cor Total 189.26 12 Std. Dev. 2.09 R-Squared 0.8379 Mean 80.62 Adj R-Squared 0.7220 C.V. % 2.60 Pred R-Squared 0.6270 PRESS 70.59 Adeq Precision 9.821 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 81.52 1 0.94 79.31 83.73 A-Substrate -1.30 1 0.74 -3.05 0.45 1.00 B-Riboflavin 3.10 1 0.74 1.35 4.85 1.00 AB -0.65 1 1.05 -3.13 1.83 1.00 A2 1.21 1 0.79 -0.67 3.09 1.02 B2 -2.67 1 0.79 -4.54 -0.79 1.02

349

G8: Response Surface Modeling Design Analysis for Color Removal (Synozol Red K-4B_542 nm _5 hours ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 377.90 5 75.58 4.21 0.0437 significant A-Substrate 154.57 1 154.57 8.61 0.0219 B-Riboflavin 22.42 1 22.42 1.25 0.3008 AB 11.22 1 11.22 0.62 0.4552 A2 170.80 1 170.80 9.51 0.0177 B2 36.16 1 36.16 2.01 0.1988 Residual 125.71 7 17.96 Lack of Fit 105.54 3 35.18 6.98 0.0456 significant Pure Error 20.17 4 5.04 Cor Total 503.61 12 Std. Dev. 4.24 R-Squared 0.7504 Mean 75.01 Adj R-Squared 0.5721 C.V. % 5.65 Pred R-Squared -0.5528 PRESS 782.01 Adeq Precision 5.778 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 79.46 1 1.90 74.98 83.94 A-Substrate 4.40 1 1.50 0.85 7.94 1.00 B-Riboflavin 1.67 1 1.50 -1.87 5.22 1.00 AB 1.68 1 2.12 -3.34 6.69 1.00 A2 -4.95 1 1.61 -8.75 -1.16 1.02 B2 -2.28 1 1.61 -6.08 1.52 1.02

350

G9: Response Surface Modeling Design Analysis for Color Removal (Synozol Red K-4B_542 nm _12 hours ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model 144.60 5 28.92 8.31 0.0074 significant A-Substrate 7.85 1 7.85 2.26 0.1769 B-Riboflavin 67.98 1 67.98 19.54 0.0031 AB 2.10 1 2.10 0.60 0.4624 A2 10.10 1 10.10 2.90 0.1322 B2 49.59 1 49.59 14.25 0.0069 Residual 24.35 7 3.48 Lack of Fit 5.04 3 1.68 0.35 0.7938 not significant Pure Error 19.31 4 4.83 Cor Total 168.95 12 Std. Dev. 1.87 R-Squared 0.8559 Mean 79.44 Adj R-Squared 0.7529 C.V. % 2.35 Pred R-Squared 0.6092 PRESS 66.03 Adeq Precision 10.475 Coefficient Standard 95% CI 95% CI Factor Estimate df Error Low High VIF Intercept 80.34 1 0.83 78.37 82.31 A-Substrate -0.99 1 0.66 -2.55 0.57 1.00 B-Riboflavin 2.92 1 0.66 1.36 4.47 1.00 AB -0.72 1 0.93 -2.93 1.48 1.00 A2 1.21 1 0.71 -0.47 2.88 1.02 B2 -2.67 1 0.71 -4.34 -1.00 1.02

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