low-rate trickling filter effluent: characterisation and ... · hydrodynamic models for trickling...
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Low-rate trickling filtereffluent: characterisationand crossflow filtration
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• A Doctoral Thesis. Submitted in partial fulfilment of the requirementsfor the award of Doctor of Philosophy at Loughborough University.
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- 2 FEB 2000
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LOW-RATE TRICKLING FILTER EFFLUENT
CHARACTERISATION
AND
CROSSFLOW FILTRATION
by
Richard MARQUET
Doctoral Thesis
Submitted in partial fulfilment of the requirements
for the award of the Joint Degree of Doctor of Philosophy of
Loughborough University
and
Institut National Poly technique de Toulouse (France)
January 1999
. © By Richard Marquet 1999
' ..
ABSTRACT
The low-rate trickling filter is the most common biological treatment process used in
small and medium sized sewage works in the UK. It produces an inconsistent effluent
quality, which has traditionally been related to seasonal changes in solids
accumulation, grazing activity and sloughing of microbial film. The final effluent solids
and, organic matter content is then too high for discharge or reuse. Given the
increasingly stringent effluent standards, both in terms of quality and consistency,
tertiary treatment is often required. This study was designed to investigate the key
parameters affecting the performance of low-rate trickling filters and the
characteristics of their effluents in terms of contaminant size, which might influence
the efficiency of crossflow filtration as a tertiary treatment for the trickling filter.
Trickling filter effluent was studied for one year at full-scale and for two years at
pilot-scale. The performance parameters investigated at pilot-scale included
temperature, film accumulation, hydraulic properties and also influent solid content
and size distribution. Correlations and multiple regression analysis were used to rank
these parameters. Particle size distribution (for the particulate fraction), and High
Performance Size Exclusion Chromatography (for the dissolved fraction) were used to
study the effect of the trickling filter on wastewater contaminants, i.e. the changes
from influent to effluent. The two techniques were shown to be valuable tools to
assess the performance of a wastewater treatment process. Crossflow filtration of
trickling filter effluent was also investigated at pilot-scale as a potential tertiary
treatment. The process produced better quality water than current legislation requires.
In-situ membrane cleaning techniques could contribute to an increase in permeate
fluxes.
---- -----
ACKNOWLEDGEMENTS
[ would like to express my gratitude to Professor Andrew Wheatley and Professor
Martine Mietton-Peuchot for supervising this work. [ would also like to thank Or
Margaret [nee and Or Rem: Moletta for accepting to examine this thesis. [ wish also to
thank Or Brigette Vale and Madame Martine Lacoste for their help in preparing the joint
PhD agreement between Loughborough University and [NP Toulouse, and
Loughborough University for a research grant. [ would like to thank all the staff of the
Departement of Civil and Building Engineering laboratories, and more specifically Mrs
Nina Ladner and Messrs Stuart Dale, Geoff Russell, Mick Shonk and Stan Wright.
Finally, [ want to express my extreme gratitude to my parents and to Cynthia for their
encouragements and support during this study.
-----------------------------
T ABLE OF CONTENTS
List of Tables
List of Figures
Abbreviations
Nomenclature
CHAPTER 1: INTRODUCTION
CHAPTER 2: LITERATURE REVIEW
2.1 FUNDAMENTALS OF TRICKLING FILTERS
2.1.1 Biology and mechanisms
2.1.2 Classification of trickling filters
2.1.3 Parameters affecting performance
2.1.4 Modes of operation and other applications
2.2 CHARACTERISTICS OF TRICKLING FILTER EFFLUENT
2.2.1 Particulate content
2.2.2 Dissolved matter
2.3 SECONDARY SETTLEMENT AND TERTIARY TREATMENT
2.3.1 Secondary settlement
2.3.2 Tertiary treatment
2.4 CROSSFLOW FILTRATION OF WASTEWATER
2.4.1 Fundamentals of crossflow filtration
2.4.2 Design and operation
2.4.3 Applications in wastewater treatment
CHAPTER 3: OBJECTIVES
3
3
4
9
9
49
52
53
55
65
65
68
71
72
73
78
89
CHAPTER 4: MATERIAL AND METHODS
4.1 SNARROWS WATER RECLAMATION WORKS
4.2 PILOT-SCALE TRICKLING FILTER
4.2.1 Design and construction
4.2.2 Filter medium
4.2.3 Wastewater storage and distribution
4.2.4 Operation
4.2.5 Hydraulic loading and dosing frequency
4.2.6 Synthetic sewage
4.3 PILOT-SCALE CROSSFLOW FILTRATION UNIT
4.3.1 Apparatus
4.3.2 Instrumentation
4.3.3 Membranes
4.3.4 Experimental procedure
4.3.5 Membrane cleaning
4.4 SAMPLES ANALYTICAL TECHNIQUES
4.4.1 Sampling technique
4.4.2 Samples fractionation
4.4.3 Biochemical Oxygen Demand
4.4.4 Chemical Oxygen Demand
4.4.5 Total Kjeldhal Nitrogen
4.4.6 Solids
4.4.7 Turbidity
4.4.8 Particle Size Distribution
4.4.9 High Performance Size Exclusion Chromatography
4.4.10 pH
4.4.11 Temperature
4.4.12 Other parameters
4.4.13 Analytical programme
91
91
93
93
95
96
97
97
98
99
99
101
102
105
106
107
107
107
108
109
109
109
110
I1I
112
113
113
113
113
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,; ..... Acc C1'tO'l..c~7s6 No.
J
J
J
J
J
J
J
J
j
J
J
J
J
J
J
J
J
J
J
J
4.5 OTHER ANAL YTICAL TECHNIQUES 116
4.5.1 Film accumulation measurement usmg the neutron probe
technique 116
4.5.2 Residence time distribution 117
CHAPTER 5: RESULTS AND DISCUSSION 119
5.1 INTRODUCTION 119
5.2 TRICKLING FILTER AND SECONDARY SETTLEMENT
PERFORMANCES
5.2.1 Trickling filter performances
5.2.2 Performances of secondary settlement
5.2.3 Evolution of ratios throughout treatment
5.2.4 Conclusions
5.3 CHARACTERISATION OF WASTEW A TER AT DIFFERENT
STAGES OF TREATMENT
5.3.1 Characterisation of the particulate fraction by particle size
distribution
5.3.2 Colloidal content of trickling filter effluent
5.3.3 Characterisation of the dissolved fraction by High Performance
120
120
143
149
153
154
155
172
Size Exclusion Chromatography 173
5.3.4 Conclusions 186
5.4 PARAMETERS AFFECTING TRICKLING FILTER PERFORMANCES 188
5.4.1 Temperature 188
5.4.2 Film accumulation 191
5.4.3 Hydrodynamic characteristics 203
5.4.4 Conclusions 220
5.5 DETERMINATION OF THE KEY PARAMETERS AFFECTING
TRICKLING FILTER PERFORMANCE
5.5.1 Correlation analysis
5.5.2 Multiple regression with stepwise estimation
222
223
232
5.6 CROSSFLOW FIL TRA nON OF TRICKLING FILTER EFFLUENT
5.6.1 Pore size selection
5.6.2 Optimisation of the operating parameters
5.6.3 Effect of the pollution load on crossflow microfiltration
239
239
243
performance 245
5.6.4 Influence of crossflow micro filtration on dissolved content of
wastewater 250
5.6.5 Permeate flux enhancement 254
5.6.6 Conclusions 258
5.7 SUMMARY OF THE RESEARCH MAIN FINDINGS 259
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 262
6.1 CONCLUSIONS 262
6.1.1 Trickling filter performance 262
6.1.2 Wastewater contaminant size characterisation 262
6.1.3 Parameters traditionally regarded as affecting trickling filter
performance 263
6.1.4 Determination of the key parameters affecting low-rate trickling
filter performance 263
6.1.5 Assessment of crossflow filtration as a tertiary treatment for low-
rate trickling filter effluent 264
6.2 RECOMMENDA nONS 264
References
Appendix 1: French abstract
Appendix 2: Calibration curve for HPSEC column (using glucose and
dextrans)
Appendix 3: Chromatograms of sodium nitrate
Appendix 4: Results of paired-stepwise analyses
. ,
LIST OF TABLES
Table Title Page
2.1 A Typical design and operation infonnation for trickling filters 10
2.18 Types of filter media used in percolating filters in 8ritain. 12
and their percentage occurrence
2.1C
2.l'D
2.1E
2.2A
2.28
2.2C
2.2D
2.3A
2.38
2.3C
2.4A
2.48
2.4C
2.40
2.4E
2.4F
Categories of wastewater contaminants
Values of constants in retention time models
Hydrodynamic models for trickling filters
Nature distribution of COD m high-rate trickling filter
effluent
Advantages and inconveniences of the different dissolved
matter fractionation techniques
DOC fractionation of trickling filter influent and effluent
DOC fractionation of trickling filter influent and effluent
Typical design and operation values for trickling filter
settlement tanks
Tertiary treatment processes and applications
Proportion by population-equivalent treated and by number
of plants of low-rate single-pass trickling filter plants
followed by tertiary treatment
Pore size and molecular weight cut-off ranges for the various
type of membranes
Advantages and inconveniences of CFF application m
waste water treatment
Quality results achieved usmg dynamic filtration as
secondary treatment
Membrane bioreactors references
Tertiary treatment applications of CFF
Quality results achieved using RO as tertiary treatment of
trickling filter effluent
17
45
46
56
59
61
61
66
69
70
73
78
80
81
82
83
2.4G Quality results achieved using MF as tertiary treatment of 84
biofilter effluent
2.4H Quality results achieved using MF as tertiary treatment of 85
trickling filter effluent
2.41 Quality results achieved using dynamic filtration as tertiary 85
treatment of trickling filter effluent
4.2A Characteristics of the trickling filter medium 95
4.2B Composition of the synthetic sewage 98
4.3A Parameters measured in the filtration loop and sensors 101
4.3B General characteristics of the membrane modules 103
4.3C Structure of the 0.1 ~m membrane module 104
4.30 Structure of the 0.2 ~m membrane module 104
4.3E Clean membrane water flux 10S
4.3 F Operating conditions of clean water flux determinations 10S
4.3G Membrane cleaning protocol 106
4.4A Fractions of contaminants defined for this study 107
4.4B Average frequency of analyses of trickling filter performances lIS
4.4C Frequency of analyses during a filtration run 114
4.5A Sampling program for residence time distributions 118
5.2A Influent characteristics - Phase 1,2 and 3 122
5.2B Unsettled effluent characteristics - Phase 1,2 and 3 126
5.2C Regression BOO removed - BOO influent 131
5.20 Regression COD removed - COD influent 136
5.2E Settled effluent characteristics - Phase 1',2 and 3 144
5.2F Unsettled and settled effluent characteristics - Full-scale 149
study
S.2G
5.2H
5.21
S.3A
Evolution of COD/BOO ratio through treatment IS I
Evolution of CODf/BODf ratio through treatment ISI
Evolution of the proportion ofVSS through treatment IS2
Example of PS Os characteristics for unsettled trickling filter 159
effluent
5.3B
5.3C
5.3D
5.3E
5.3F
5.3G
5.3H
Example of particulate fractionation for unsettled trickling 159
filter effluent
Parameters of regression models 160
PSD by volume mean and median diameters and SD at 164
different stages of treatment - Phases 1-2-3
PSD by number inean and median diameters and SD at 164
different stages of treatment - Phases 1-2-3
Average coefficient of determination for the 2 tested models - 167
Pilot-scale study
PSD by volume characteristics at different stages of 168
treatment
PSD by number characteristics at different stages of 168
treatment
5.31 Average parameters for the 2 tested models - Full-scale 170
study
5.3J Average ~-potential values (Phase 2) 172
5.3K Eluents tested for HPSEC 174
5.3L Identification of influent peaks on RI detector chromatogram 179
5.3M Aborbance peaks of potential SMPs 183
5.3N Compounds presenting an absorbance peak at around 220 nm 185
5.4A Regression T TF - Ta and Ti 189
5.4B Variables used for film accumulation correlations 201
5.4C Results of correlation analysis for film accumulation 202
parameters - Phases 2'-3
5.4D Estimation of a" and c for tr = tm and tr = t50 212
S.4E Parameters extracted from the three hydrodynamic models 214
used
S.4F
S.4G
S.SA
Variables used for hydrodynamic characteristics-film 218
accumulation correlations
Results of correlation analysis for hydrodynamic 219
characteristics
Plots of 'Y = f(X)' found in the literature for BOD 222
5.5B Results of correlation analysis - concentrations 226
5.5C Results of correlation analysis - removal efficiencies 229
5.50 Results of correlation analysis - characteristics 231
5.5E Points allocation for paired stepwise analysis 234
5.5F Results of stepwise analysis for eBOO 236
5.5G Results of stepwise analysis for eCOO 237
5.5H Results of stepwise analysis for eTSS 238
5.51 Results of stepwise analysis for RE BOO 238
5.5} Results of stepwise analysis for RE COD 238
5.5K Results of stepwise analysis for seBOO 239
5.6A Comparison of permeate quality for filtration with 0.2 and 242
0.1 Ilm membranes.
5.6B Values at permeate flux in feed and bleed operation mode at 256
various time.
LIST OF FIGURES
Figure
2.IA
2.1B
4.2A
4.3A
4.3B
5.2A
5.2B
5.2C
5.20
5.2E
5.2F
5.20
5.2H
5.21
5.2J
5.2K
5.2L
5.2M
5.2N
5.20
5.2P
Title ~
Schematic representation of the phenomena involved ill 6
biofilm reactors
Typical organic constituents in settled municipal wastewater 17
Pilot-scale trickling filter 94
Crossflow filtration pilot 100
Structure of a membrane module and liquid flu:xes 103
Cumulative frequency of influent and unsettled TFE BOO 127
Cumulative frequency of influent and unsettled TFE BOOf 128
BOO fractionation 130
Regression removed BOO - influent BOO 1'7 ~-
Cumulative frequency of influent and unsettled TFE COD 134
Cumulative frequency of influent and unsettled TFE COOf 135
CO 0 fractionation 137
Regression removed COD - influent COD 138
Cumulative frequency of influent and unsettled TFE TSS 140
Cumulative frequency of influent and unsettled TFE TKN 142
Regression removed TKN - influent TKN 142
Cumulative frequency of unsettled and settled TFE BOO 145
Cumulative frequency of unsettled and settled TFE COD 147
Cumulative frequency of unsettled and settled TFE TSS 148
Cumulative frequency of unsettled and settled TFE BOO - 150
Full-scale
Cumulative frequency of unsettled and settled TFE COD - 150
Full-scale
5.2Q Cumulative frequency of unsettled and settled TFE TSS - 150
Full-scale
5.3A Representations of PSD by number 157
5.38 Representations of PSD by volume 158
5.3C Modelling of frequency distribution by number - dp > 0.1 161
~m
5.3D Modelling of frequency distribution by number - dp > 1.2 161
~m
5.3E Average PSDs by volume 163
5.3F Particulate fractionation by number - Phases 1-2-3 166
5.30 Particulate fractionation by volume - Phases 1-2-3 166
5.3H Average PSDs by volume - Full-scale 169
5.31 Particulate fractionation by number - Full-scale 171
5.3J Particulate fractionation by volume - Full-scale 171
5.3K Absorbance spectrum of influent (Phase 2) and unsettled 176
TFE
5.3L Influent (prepared with ultrapure water, Phase 3) 178
chromatograms
5.3M Influent (Phase 3) chromatograms 180
5.3N Trickling filter effluent chromatograms 181
5.4A Correlation trickling filter T - ambient T (April 1995) 190
5.48 Correlation trickling filter T - influent T (April 1995) 190
5.4C Evolution of TF moisture content profile - Phases I, 2 and 3 192,
5.4D
5.4E
5.4F
5.40
5.4H
193,
195
Average moisture content and t/b ratio - Phases I, 2 and 3 198
Typical RTD measured in the pilot-scale trickling filter 205
Median and mean residence times - Phases 1-2-3 208
RTD standard deviations - Phases 1-2-3 209
Regressions median and mean residence times (without film) 211
- hydraulic loading
5.41
5.41
5.6A
5.68
5.6C
5.60
5.6E
5.6F
5.6G
5.6H
5.61
5.61
5.6K
Comparison experimental RTO - nMFR, SR and SSR 216
models output (lanuary 97)
Liquid volumes - nMFR, SR and SSR models 217
Influence of membrane pore size on transmembrane pressure 241
Influence of membrane pore size on permeate flux 241
Influence of initial transmembrane pressure on 244
transmembrane pressure
Influence of initial transmembrane pressure on permeate flux
Influence of crossflow velocity on permeate flux
Influence of feed nature on permeate flux
244
246
248
Influence of feed nature on permeate turbidity 249
Influence of feed nature on permeate COO 249
Trickling filter effluent chromatograms 251
Permeate (after 4 h filtration) chromatograms 252
Concentrate (after 4 h filtration without bleed) 253
chromatograms
5.6L Influence of bleed flow on permeate flux 255
5.6M Influence of permeate flux control on permeate flux 255
5.6N Influence of permeate flux control on transmembrane 257
pressure
5.60 Influence of polystyrene beads addition on permeate flux 257
AB BREVIA TIONS
AD
AS
ASC
AWSP
BSA,
CFF
ECPs
FD
FV
GPC
HPSEC
LV
MBR
MF
MFR
MW
MWCO
NF
OCD
PFR
PSD
RBC
RO
RTD
SMPs
SR
SSR
Anaerobic digestion
Activated sludge
Aerobic solids contact
Anoxic waste stabilisation pond
Bovine serum albumin
Crossflow filtration
Extracellular polymers
Frequency distribution
Flowing volume
Gel permeation chromatography
High performance size exclusion chromatography
Liquid volume
Membrane bioreactor
Microfiltration
Mixed-flow reactor
Molecular weight
Molecular weight cut-off
Nanofiltration
Oversize cumulative distribution
Plug-flow reactor
Particle size distribution
Rotating biological contactor
Reverse osmosis
Residence time distribution
Soluble microbial products
Stagnant reactor
Simplified stagnant reactor
SV
TFE
TFSC
UCD
UF
Stagnant volume
Trickling filter effluent
Trickling filter - solids contact
Undersize cumulative distribution
Ultrafiltration
NOMENCLATURE
Symbol
lip
aT
dso(n)
dso(v)
de
dfm
dm(n)
dm(v)
dp
dpe
Xd
eX
Xf
g
h
iX
n
nMFR
nMFR'
nMFR2
r
seX
s,
Particle surface area
Ambient temperature
Median particle diameter for a PSD by number, for particles
ranging from 0.1 to 900 ~m
Median particle diameter for a PSD by volume, for particles
ranging from 0.1 to 900 ~m
Membrane channel internal diameter
Filter medium diameter
Mean particle diameter for a PSD by number, for particles
ranging from 0.1 to 900 ~m
Mean particle diameter for a PSD by volume, for particles
ranging from 0.1 to 900 ~m
Particle diameter
Centre of particle sizer channels
Dissolved value of parameter X (i.e. after filtration at 0.1
~m)
Value of parameter Xfor unsettled trickling filter effluent
Filtered value of parameter X (i.e. after filtration at 1.2 ~m)
Acceleration of gravity at sea level
Trickling filter depth
Value of parameter X for influent
Number of distribution arms
Number of MFRs in series (nMFR model)
Number of MFRs in series (SR model)
Number ofMFR2s in series (SSR model)
Radius of membrane module channels
Value of parameter X for settled trickling filter effluent
Rotating arm rotation speed
~m
~m
mm
mm
~m
~m
~m
~m
mls2
m
m
rev/min
Xss
t
tIb ratio
lm(7 h)
tm(90%)
t,
t50
t50/t 16
vp
VI
AO.I
Suspended value of parameter X (i.e. removed by filtration at
1.2 f.!m)
Time
Ratio top half of the filter AMC / bottom half of the filter
AMC
Mean residence time, calculated up to an arbitrary cut-off
time = 7 h
Mean residence time, calculated up to a 90% tracer recovery
Retention time
Median residence time
Ratio median residence time / time required to recover in the
effluent 16% of the injected amount of tracer
Particle volume
Terminal velocity of particles
First parameter of the power-law distribution function used
to model PSDs by number for particles> 0.1 f.!m
A 1.2 First parameter of the power-law distribution function used
AMCb
AMCt
BOI
BOO
BODf
BOOs
BOOse
to model PSDs by number for particles> 1.2 f.!m
Membrane area
Total particulate surface area
Average moisture content
Average moisture content in the bottom half of the filter
Average moisture content in the top half of the filter
Second parameter of the power-law distribution function
used to model PS Os by number for particles> 0.1 f.!m
Second parameter of the power-law distribution function
used to model PS Os by number for particles> 1.2 f.!m
Biochemical oxygen demand
filtered BOO
settleable BOO
supracolloidal BOO
s
min
mm
mm
mm
f.!mJ
rnIs
% saturation
of voids
% saturation
of voids
% saturation
of voids
mgll
mgll
mgll
mgll
BODss
C
C(t)
COD
CODe
CODd
CODf
CODs
CO Dsc
CODss
Dair
DOC
E
H
PI
P2
Pp
Ppol-u(n)
PpOI-12(V)
Pp l.2-100( n)
Pp 1.2-100( v)
suspended BOO
Tracer concentration vs. time curve
Tracer concentration in the effluent at time t
Chemical oxygen demand
colloidal COD
dissolved COD
filtered COD
settleable COD
supracolloidal COD
suspended COD
Natural draft of air
Dissolved organic carbon
Normalised tracer concentration versus time curve
Volumetric concentration factor
mg'!
mg'!
mg'!
mg'!
mg'!
mg'!
mg'!
mg'!
mg'l mm of water
mg'!
Trickling filter depth m
Permeate flux lIm2 h
Permeate permeability m'/m'.h.Pa
Total particulate number
Proportion of parameter X in the a fraction %
Proportion of volatile X %
Pressure in the filtration loop before the membrane module Pa
Pressure in the filtration loop after the membrane module Pa
Permeate pressure Pa
Proportion of total number of particles in the range [0.1;1.2] %
~m
Proportion of total particulate volume in the range [0.1; 1.2] %
~m
Proportion of total number of particles in the range [1.2;100] %
~m
Proportion of total particulate volume in the range [1.2; 1 00] %
~m
Proportion of total number of particles III the range %
[l 00;900] ~m
Pp IOO-900( V) Proportion of total particulate volume in the range [100;900] %
Ilm
Q Area under a C curve
Q Hydraulic loading m3/m3 d
Q' Hydraulic loading m3/m2_d
Qp Permeate flow rate m3Jh
Qr Recirculation flow rate in the filtration loop m3Jh
R Correlation coefficient
R2 Coefficient of determination
Rc Resistance due to the colloidal fraction m'_Pa.h/m'
Ra Resistance due to the dissolved fraction m'.Pa.h/m'
RF Resistance due to membrane fouling m'.Pa.h/m'
RM Intrinsic resistance of the membrane m 2.Pa.h1mJ
Rs Resistance due to the settleable fraction m'.Pa.h/m'
Rss Resistance due to the suspended fraction m'.Pa.h/m'
RT Total equivalent resistance to transport m'.Pa.him'
REX Removal efficiency for parameter X %
REs X Removal efficiency by settlement for parameter X %
Re Reynolds number
Se Effluent substrate concentration mg/!
Si Influent substrate concentration mg/!
T Temperature °C
Tx Temperature in the trickling filter at depth x from the top °C
surface of the filter bed
Tp Periodicity of water spraying mm
TTF Trickling filter temperature °C
TKN Total Kjeldhal nitrogen mg/!
TS Total solids
TSS Total suspended solids mg/!
Ucf Crossflow velocity mls
Vo Dead volume of CFF rig m3
VE Volume of exchange zone for each MFR (SR model) m3
VE• Volume of exchange zone for MFRl (SSR model) m]
VMFR Volume of 1 MFR (nMFR model) m]
VMFR· Volume of 1 MFR (SR model) m]
VMFR1 Volume of the MFR with exchange (SSR model) m]
VMFR2 Volume of 1 MFR of the series of MFR2s in series (SSR m]
model)
Vp Total particulate volume ~m]
VS Volatile solids mg!!
VSS Volatile suspended solids mg!!
A Specific surface area of trickling filter medium m2/m]
LlP( Transmembrane pressure Pa
LlP(c Critical transmembrane pressure Pa
~ Viscosity of fluid centipoise
P Density of fluid kg/m]
Ps Density of particle kg/m]
O'p(n) Standard deviation of a PSD by number, for particles ranging ~m
from 0.1 to 900 ~m
O'p(v) Standard deviation of a PSD by volume, for particles ranging ~m
from 0.1 to 900 ~m
0'tC7 h) Standard deviation of a residence time distribution, calculated mm
up to an arbitrary cut-off time = 7 h
0'(90%) Standard deviation of a residence time distribution, calculated mm
up to a 90% tracer recovery
0'/ Variance of a residence time distribution min2
CHAPTER 1: INTRODUCTION
Biofilm reactors are currently regarnmg popularity in the fields of water and
wastewater treatment. Higher activity and higher resistance to changes in
environmental conditions are among their advantages over suspended processes
(Lazarova and Manem, 1995). The trickling filter constitutes the most traditional type
of biofilm reactor. It originated more than lOO years ago from tests on the percolation
of sewage through soils. Work published by the Lawrence Experiment Station in
Massachussets (Mills, \890, quoted by IWEM, 1988) showed that percolation
through a gravel or stone bed led to the potential application of higher rates of sewage
than through standard soil. This work inspired Corben (1902) (quoted by IWEM,
1988) who built the first large-scale trickling filters at Salford sewage works. They
featured spraying of wastewater onto the medium surface, a drainage system and
medium voidage permitting efficient ventilation of the bed. Since then, and with
relatively little modifications in principle, the trickling filter has become the most
commonly used waste water treatment process for small and medium sized sewage
works in the UK.
The trickling filters strong points lie in its:
• Simplicity of operation;
• Low numing costs (normally no energy requirements for low-rate single-stage
systems; low maintenance requirements);
• Resistance to variable loadings and toxic material.
The process drawbacks include relatively important land requirements, and odour and
fly nuisances in hot climates. The key drawback is the trickling filters inability to
consistently achieve a satisfactory effluent quality, even if it is recognised that often
the downstream secondary settlement is responsible for poor effluent quality (Norris
et ai., 1980). Fine particles in the effluent can pass through the secondary clarifier to
deteriorate the quality of the final effluent.
1
In spite of the wide use of the trickling filter process, the parameters affecting trickling
filter performance are still not fully understood. For example, the conventional design
parameters for trickling filters are hydraulic and organic (as BOO) loadings. But
primary effluent TSS as well as organic concentration could also play a role in trickling
filter performance. Recent findings on the degradation mechanisms of dissolved and
solid matter in biofilm reactors suggest that contaminant size distribution in the
influent also affects performance. Other parameters known to affect performance
include temperature, film accumulation in the filter and hydrodynamic characteristics
of the filter. Therefore the parameters affecting trickling filter performance remain a
topic worthy of further research, especially in terms of assessment of their relative
importance.
In order to comply with the new requirements set by the 91127IIEEC Urban
Wastewater Treatment Directive (Commission of the European Communities, 1991),
it will probably be necessary to complement the biological filtration step in order to
get rid of both non-settleable suspended solids and BOO in the underflow. Given the
existing tertiary treatment techniques, it is necessary to search for alternative
processes that could produce effluents of a more reliable quality. Membrane processes
represent a way of meeting those increasingly stringent environmental quality
objectives.
In particular, crossflow filtration, a solid/liquid separation process originally
developed for the process industry, seems promising. It is a pressure-driven process
where the build-up of a cake layer at the membrane surface is hypothetically
suppressed or limited by the crossflow velocity-induced shear stress. Increasing
applications in combination with or after biological wastewater treatment are reported,
although few examples are reported of use as a tertiary treatment for low-rate trickling
filter effluent. As the contaminants size distribution in the filtered suspension affects
cross flow filtration performance, their study is a prerequisite to a crossflow filtration
study.
2
CHAPTER 2: LITERATURE REVIEW
This chapter is composed of four sections. The first section deals with the
fundamentals of the trickling filter process, insisting on the parameters affecting
performance. In the second section, the current knowledge in terms of trickling filter
effluent' characterisation is reviewed. Thirdly, the options currently in use for the
further treatment of trickling filter effluent are examined. Finally, in the fourth section,
crossflow filtration, a technique that is increasingly attracting interest in waste water
treatment, is discussed.
2.1 FUNDAMENTALS OF TRICKLING FILTERS
The trickling filter, a fixed-film biological reactor, is the most common wastewater
treatment process in the UK. It consists of a bed of medium over which wastewater is
distributed. The percolation of the wastewater through the filter bed induces the
development of a biological film at the surface of the medium. The activity of the
community constituting the film is responsible for pollution removal.
Before 1950, low-rate trickling filters were widely used for secondary treatment,
largely because of the process stability and ease of operation. During the 1960s and
the early 1970s in the USA, the use of trickling filters declined in favour of activated
sludge (AS), largely because of the ability of the activated sludge process to produce a
higher quality, more polished effluent (Matasci et ai., 1988). By the late 1970s
however, the rising cost of energy and advances in biological filter design (deeper
filters with higher loading rates), which were possible because of developments of
plastic media, caused a resurgence in the use of biological filters, at higher rates
(WPCF, 1988). Low-rate trickling filters are however still the most widely used
secondary treatment process in Europe, being equally common in other temperate
regions induding the USA where they are employed at over 3700 separate municipal
sewage treatment plants (Gray. 1992). In England and Wales, the proportions of
population served by trickling filter and activated sludge are about the same, but more
3
sewage treatment plants use percolating filters rather than the activated sludge process
(Gray, 1992).
This section on the fundamentals of trickling filters includes details of the biology and
mechanisms of the process, a classification of trickling filters, a description of the
parameters affecting their performances, and finally the trickling filter mode of
operations and other applications.
2.1.1 Biology and mechanisms
Wastewater applied to a trickling filter has usually undergone pretreatment and
primary settlement, to remove the bulk of suspended solids. The function of the
trickling filter is then to biochemically convert soluble and colloidal (i.e. non settleable)
organic matter into theoretically settleable solids (i.e. biological sludge) that can be
removed by secondary settlement. This conversion is carried out by a wide range of
organisms constituting the biological film at the surface of the filter medium.
2.1.1.1 Film composition and morphology
The biological film consists of single cells or microcolonies embedded in an
exopolymeric matrix of microbial origin which are attached to the filter medium. The
physical and chemical properties of biofilms produce a structure similar to a porous
gel containing 90-95% of water (Carlson and Silverstein, 1998). The film hosts a
complex community of micro-organisms, which live by oxidising the inorganic and
organic compounds in wastewater. The film also hosts invertebrate macro-organisms.
They are known as grazers or grazing fauna, because they feed upon compounds
released by other organisms, and on lower forms of life or their dead remains.
The structure of the film community has been well identified and described
(Tomlinson and Hall, 1955; Hawkes, 1963; Curds and Cockbum, 1970; Curds and
Hawkes, 1975; Solbe, 1975; Wheatiey, 1976). The micro-organisms found in trickling
filters include:
• Bacteria, both autotrophic and heterotrophic;
• Fungi;
• Algae;
4
• Cyanobacteria (,blue-green algae');
• Protozoa (the principal classes found in filter and effluents being flagellates, amoebae
and ciliates);
• Pathogens.
The invertebrates composing the grazing fauna include:
• Rotifers;
• Nematodes;
• Worms (annelids);
• Insects and mites (arthropods), at both larvae and adult stage.
Although the maximal nutrient removal may be achieved much earlier, it is believed
that the development of a stable ecological habitat within a filter takes at least two
years (Wheatley, 1976).
Mature biofilms have traditionally been assumed to be uniform in thickness, but this
has been challenged in the past few years. The total thickness of biological film in
aerobic fixed-film reactors often exceeds 500 ~m (Zhang and Bishop, 1994). Biofilms
have been found to be highly porous, containing interstitial voids, channels and cell
clusters which complicate the water-biofilm interface and the internal transport in the
biofilm (de Beer et al., 1983; Lewandowski et al., 1994; Stoodley et al, 1994; quoted
by Carlson and Silverstein, 1998). The biofilm structure is highly stratified,
characterised by an increase of biofilm density, a decrease of metabolically active
biomass, and a decrease of porosity with biofilm depth (Bishop et al., 1995). Pore
diameters range from about 350-400 ~m at the surface to pores 40-50 ~lm wide for
biofilm depths of about 500-600 ~m (Waleed and Ganczarczyk, 1993; quoted by
Massone and Rozzi, 1995).
2.1.1.2 Mechanisms of pollution removal
The trickling filter as a whole can be divided into five zones (Figure 2.IA):
• Interstitial air;
• Free flowing liquid film (or bulk water);
• Captured liquid film (or liquid film);
5
- - ----------
• Biological film;
• Inert support material.
Figure 2.1A: Schematic representation of the phenomena involved in biofilm reactors (after Sarner, 1986)
Air Bulk woter Liquid film Biofilm
0-. Ad· ~.~ 'ryl "
I sorp· ~: !~'j
fig;- ::; ~ "
Electron- <J- :."!o ,. ;",-'" Aerarion donor- •• ,.',
acceptor Liquid ";, r:: "nutrients film "
Biofilm it~
<:r dilfu.ion~ ,.~.
bulk h ~
... Iner, tranport •• support
~ Reaction ~::. ~:.:. material
<l (7- BiOfilm~ r1. ()o. Liquid diffusion t·: film :.:.'
Aeration :.::. iffusion .::
Products ~ .. : C> :":.
I t';, I> '.:'
The micro-organisms within the biological film use organic matter in wastewater as
their food source, and dissolved oxygen to metabolise this food.
The free flowing liquid film corresponds to the liquid flowing freely over the biological
film. To reach the biofilm, organic material must pass from this free flowing liquid film
through the captured liquid film to the biofilm itself. The captured liquid film
corresponds to the liquid fraction retained between the micro tentacles and cavities of
the biofilm surface, through which organic material is brought in contact with the
biofilm.
Small dissolved organics are transported by diffusion within the biofilm where the
reactions take place (Levine et al., 1991). Molecules with a molecular weight of less
than 1000 Dalton can be taken up and metabolised intracellularly by bacteria (Levine
et at., 1985), the relative biodegradability of these low molecular mass compounds
being governed by their molecular structure.
6
The degradation of particulate matter, polymers and macromolecules require a
preliminary step since they cannot be transported across bacterial membranes. They
are first adsorbed on to the biofilm surface, where extracellular enzymes synthesised
by bacterial cells hydrolyse these adsorbed particulates and macromolecules into
smaller sub units (Confer and Logan, 1998, 1997a, 1997b). These sub units diffuse
through the film, are transported across the cell membrane and metabolised. The
amount of enzyme required and the rate of reaction are related to the size and
structure of the substrate.
It is generally assumed that no bulk liquid hydrolysis of particulate/macromolecular
material take place within a trickling filter. Larsen and Harremoes (1994), however,
challenged the general assumption that surface adsorption is the first step in the
sequence of degradation of non-diffusible matter in biofilms. In a study using lab-scale
biofilm reactors, they found that bulk liquid hydrolysis was responsible for
conversion 'of colloidal substrate to diffusible substrate. The hypothesis given was
that micro-organisms in the biofilm produce free and membrane bound extracellular
hydrolytic enzymes. The free extracellular enzymes diffuse out of the biofilm into the
captured liquid film at the surface of the biofilm. The presence of hydrolytic enzymes
in the liquid results in non-diffusible substrate being hydrolysed to diffusible substrate
in the bulk liquid phase. This diffusible substrate is then able to diffuse into the
biofilm where it is further hydrolysed by membrane-bound extra-cellular enzymes to
degradable substrate. The low molecular weight degradable substrate can then be
transported across the bacterial cell membranes to be metabolised intracellularly.
Whatever the mechanisms required for particulate matter hydrolysis, the kinetics for
removal of particulate organic matter are generally assumed to be slower than the
kinetics for soluble substrates. This is attributed to the slower mass transfer of
particles compared to soluble species and to the additional time required for the initial
hydrolysis reaction (Bouwer, 1987). This hydrolysis reaction is actually composed of
succession of cycles of hydrolysis and release of hydrolytic fragments into solution
until fragments are small enough « \000 Oalton) to be assimilated by cells (Confer
and Logan, 1998, 1997a, 1997b).
7
As a trickling filter is predominantly an aerobic system, oxygen is required by micro
organisms to metabolise the organic matter. Oxygen is dissolved into the wastewater
from the air that circulates through the filter, and diffuses from the wastewater into
the surface of the biological film and then inside the biofilm. Both mathematical
modelling (Arvin and Harremoes, 1990) and experimental verification using oxygen
micro-electrodes (Kuhl and Jorgenson, 1992, quoted by Lens et al., 1995; van
Lo<;>sdrecht et al., 1995) indicate that the maximal oxygen penetration depth in
biofilms is about 150 I-lm. This is due to the low solubility of mole~ular oxygen in
water, the high oxygen consumption rate of biofilm microbiota and diffusional
resistance to oxygen transport within the biofilm. This limited oxygen penetration
depth governs aerobic biofilm reactor performance since micro-organisms in the deeper
regions are not provided with oxygen and can no longer contribute to aerobic
degradation of pollutants (Arvin and Harremoes, 1990).
2.1.1.3 Film sloughing
Film sloughing is the most important cause of solids production by a trickling filter,
and as such heavily influences trickling filter performance. It results in a well-operated
filter from the continual cycle of film growth followed by death and detachment from
the media. When the biofilm becomes too thick or the bacterial activity in the external
layers too rapid to allow organic matter and/or oxygen to reach the internal layers of
biofilm, anaerobic onditions arise in the innermost biofilm layer. Deprived of food and
oxygen, the aerobic microorganisms in this layer die and lose their ability to cling to
the media. At the same time, anaerobic microorganisms feed on the aerobic
microorganisms and produce gases that tend to loosen the biofilm attachment to the
media (Hawkes, 1957; quoted by Howell and Atkinson, 1975). The shearing force of
the applied wastewater then scours the film off the media, a process called sloughing,
and new biofilm begins to grow. At equilibrium, the net rate of biomass growth equals
the rate of sloughing. It is governed by several factors including organic matter
concentration in the wastewater, biofilm activity and shear stress (Rao Bhamidimarri
and See, 1992, quoted by Biddle, 1994).
8
The scoured film is carried out of in the filter effluent through the underdrain system
for settlement in a secondary settlement tank.
2.1.2 Classification of trickling filters
Trickling filters are classified as low (or standard) rate, intermediate rate, high-rate,
super high-rate or roughing filters, as a result of their design and operational
parameters. Design parameters include filter medilUTI, filter depth and distribution
sys,tems, while operational parameters include loadings to the filter and dosing
frequency or periodicity. The key parameters for the following classification are
loadings. They are expressed as hydraulic loading, in vollUTIetric (m'/m'.d) or surfacic
(ml/ml.d) form, and as organic loading (g BOD/m3 d). With increasing hydraulic
loading the liquid residence time ought to decrease, which results in a decrease in the
proportion of oxidisable matter removed. However, it is known that increasing
hydraulic loading may increase the efficiency of distribution and wetting of high
surface area media. Some media manufacturers quote minimlUTI wetting rates for this
reason. The amount of organic matter removed per unit volume of filter increases with
increasing BOO loading (IWEM, 1988).
The terms of low-rate and high-rate trickling filters are therefore well distinguished in
terms of the intended purpose of the filter: low-rate filters are designed and operated
to achieve full purification, while high-rate filters are usually intended for partial
treatment.
Table 2.IA describes the characteristics of the various types of filters.
The most common trickling filters in the UK are of the low-rate type for small to
medium populations, containing a mineral medium of 50-100 mm diameter to a depth
of 1.8 m. They are the ones on which this study focuses.
2.1.3 Parameters affecting performance
The performances of a trickling filter are commonly defined in terms of organics and
solids removal efficiency, but nutrient removal (nitrogen and phosphorus) has become
9
T, hI 2 lA T a e vpica I I . I . kl' fI ~ I lfi (eslgll mu opera/lOll III o/'ma/101l or /nc IIlg. 1 /ers Il( {lp/e( . /'om M If I E Id 1991 WPCF 1986 P k 19 8) e/cll (//U ( v. , i e, 7 -
Parameter Low-rate I ntermediate-ra te High-rate Super high-rate Roughing Two-stage
Hydraulic loading 1 - 4 4 - 1 0 10-40 15 - 8 5 50-190' 10-40'
(m 3 /m 2.d)
Hydraulic loading 0.4 - 2.2 1.7 - 5.6 5.6 - 44.4 1.3 - 28.3 4.2 - 41.3 • 4.2 - 22.2 •
(m 3 /m 3.d)
Organic loading 80-400 240-480 480-960 48-1600 1600-8000 960-1920
(g BOD/m3 .d)
Medium rock slag rock, slag rock plastic plastic redwood rock, slag, plastic
Depth (m) 1.8-2.4 1.8-2.4 0.9-1.8 3 - 1 2 4.6-12 1.8-2.4
Recirculation 0 0- 1 1 - 2 1 - 2 1 - 4 0.5-2 o ratio
Filter flies many some few few or none few or none few or none
Sloughing intermittent intermittent continuous continuous continuous continuous
BOO removal 80- 90 50-70 65 -85 65 -80 40 - 65 85-95
efficiencv (%)
Effluent good partial poor poor no good
nitrification
Dosing intervals ,; 5 min 15-60 s ,; 15 s
(intermittent) (continuous) jcontinuous)
': does not include recirculation
more important recently. The parameters affecting these performances can be divided
into three different categories:
• Design parameters;
• Operational parameters;
• Parameters resulting from interactions between design and operational parameters
and environmental conditions.
2.1..3.1 Design parameters
Design parameters affecting trickling filter performance include filter medium. filter
depth and waste water distribution.
A. Filter medium
The function of the filter medium is to provide support to the growth of the biofilm
and the associated fauna. It must also enable sufficient ventilation for the process to
stay aerobic, and ensure maximum contact time between active film and wastewater.
Trickling filter media are characterised by their specific surface area. shape, SIze,
voidage and nature. Surface area, shape, size and voidage are related. The specific
surface area gives an indication of the amount of medium surface available for biofilm
colonisation, but the shape of the medium and the hydraulic loading determine the
proportion of this surface which is actually wetted. In terms of size, the choice
usually requires a compromise between the conflicting requirements to provide a large
specific surface area (small medium) and large interstitial voids to allow sufficient
ventilation and prevent clogging (large medium). As a result, 50 mm grading has
generally become the accepted medium size in the UK (IWEM, 1988: Hawkes and
Jenkins, 1955).
Regarding the material for the medium, the choice is usually dictated by local
availability since transport costs are generally higher than the cost of the medium
itself.
t 1
Table 2.1 B summarises the occurrence of the different types of media in British low
rate trickling filters as for 1975. The data are not recent but are very likely to still be
valid.
Table 2.1 B: Types of filter media used in percolating filters In Britain, and their percenta[(e occurrence (L 19r) earner, J
Tvpe of medium Occurrence (%)
Granite 26 Clinker 24
Blast furnace slaq 23 Rounded qravel 6
Limestone and clinker 6 Limestone 4
Coke 4 Clinker and gravel 3
Slaq and coke 1 Saqer chippinqs 1
A British Standard specification for medium has been in place for some years (BS
1438; British Standards Institution, 1977) but in a more recent review, Bryers and
Characklis (1990) outlined the specifications for an ideal support medium as:
• Low cost;
• Large surface area per unit volume;
• Sufficient void space to allow for air flow and removal of excess biofilm.
Plastic media (random or modular) were introduced in the 1950s because the low
voidage (20-50%) of traditional mineral media restricts the hydraulic and orgaruc
loadings applicable. High voidage plastic media are therefore used when the loading
applied to the filter is likely to generate excessive biofilm development and subsequent
ponding. Random plastic media are similar to mineral media, being composed of small
elements randomly positioned in the filter. Modular plastic media are composed of
thin sheets of corrugated plastic that are separated from one another, except at a few
points where the adjacent sheets' corrugations touch.
B. Filter depth
Most of the models that have been proposed to describe the purification process in a
trickling filter assume that efficiency increases with filter depth (Samer, 1986). It has
been shown by Tebbutt (1977). quoted by Gray (1983), that the highest rates of
1 2
oxidation occur in the top section of the trickling filter, where the limiting factor is
usually the amount of oxygen which can be provided by natural ventilation. Bruce
(1969) (quoted by Gray and Leamer, 1984) found that roughly 90% of biodegradable
matter is removed in the upper 600 mm.
Gray (1992) studied the performances of low and high-rate trickling filters at various
depths. In single pass low-rate filters, 1.8 m deep, he found that 75% of the influent
BOJ;) was removed in the top 0.9 m of the bed, 45-50% removal occurring in the top
0.3 m. Little BOO removal occurred in the second half of the filter. In terms of TSS,
most of the removal happened in the top 0.9 m at both low and high-rate loadings. and
it was connected to the adsorption capacity of the film. As for nitrification, it was
restricted to depths above 0.9 m for low-rate filters, and above 1.5 m or excluded in
the case of high-rate operation. The author concluded that a minimum depth of 0.9 m
was required to ensure adequate BOO and TSS removal in low-rate filters, although if
nitrification is required a minimum depth of 1.5 m is necessary. At higher loadings, a
greater depth is required to provide enough surface area for maximum BOO removal.
Samer (1986) found that the higher the load of particles. the lower the removal of
dissolved organics. The author explained the phenomenon by the fact that in a
trickling filter, the largest sludge production occurs in the top part, where the organic
loading is the highest. This sludge has to pass through the lower levels of the filter and
probably affects the removal of dissolved organics at these lower levels. The author
concluded that depths that exceed a certain value have little influence on treatment
efficiency.
In the case of high-rate filters, Bruce and Merkens (1970) found no significant
difference in performance between two filters operated at the same hydraulic loadings
(ranging between 3 and 18 m3/m3.d) and containing plastic media over a depth of 7.2 m
for one and 2.1 m for the other. The authors quoted similar findings by Chipperfield
(1968), who observed similar BOO removal in filters of 1.8 m and 5.4 m depth
operated at the same hydraulic loadings (6 to 24 mJ/m3 d). It therefore seems that
depth influences performances of trickling filters up to certain loadings only.
1 3
C. Waste water distribution
An effective distribution system is required to ensure that the entire filter receives the
same hydraulic loading. This is a prerequisite to achieve maximum treatment
efficiency. Indeed, zones of filter media remaining unwetted and therefore not
colonised by biomass and not contributing to substrate reduction can result in low
filter performance. Various techniques are used for the application of wastewater to
the surface of trickling filters. Full-scale systems are usually equipped with either
rotating arms (for circular filters) or travelling distributors (for rectangular filters).
They are dosed continuously or periodically using a siphon or a tipping trough for
smaller filters, or constant head channels or chambers for larger installations. In the
case of high-rate trickling filters, wastewater is applied using a fixed distribution
network of pipes fitted with either nozzles or splash-plates
2.1.3.2 Operational parameters
Operational parameters influencing trickling filter performance include hydraulic and
organic loading, solid loading and size distribution, and dosing frequency.
A. Hydraulic loading, organic loading and wastewaler r;fzaracterislics
Hydraulic loading
The higher the hydraulic loading, the greater the proportion of the waste water passing
over the film surface, which results in a lower hydraulic residence time and an inferior
fmal effluent quality (Gray, 1992). On the other hand, low hydraulic loading rates
increase the hydraulic retention time and increase biofilm depth. In the UK, it is
recognised that, in order to produce a Royal Commission standard effluent (20:30, i.e.
20 mg!! BOO and 30 mg!! TSS) with a 95% compliance after settlement and a high
degree of nitrification, single-stage filters treating domestic sewage should receive a
hydraulic loading between 0.12 and 0.60 m'/m'.d (Gray, 1992).
Organic loading
The organic loading influences the amount of organic matter removed by the filter and
the film growth rate, because a part of the organic matter removed is converted into
film. The organic (and sometimes the inorganic) constituents of wastewater are the
food and energy source for the micro-organisms in any biological process. As a result,
1 4
trickling filter BOO loading expressed on a volumetric basis or filter bed surface basis
is the conventional design parameter for trickling filters. Organic loading can vary from
as low as 70 g BOD/mJ.d for a low-rate filter to well over 1600 g BOD/mJ.d for a
roughing filter. However, an organic loading of between 70 and 100 g BOD/mJ.d is
recommended to achieve the Royal Commission standard effluent (Gray, 1992)
mentioned before.
Organic loading increases can lead to decrease in treatment. For example, Matasci et al.
(1986) found a positive correlation between organic loading and filter effluent TSS in a
study at full scale of the trickling filter - solids contact process. Indeed, higher organic
loadings lead to thicker film growths. Siirner (1986) quoted results from a previous
study (Siirner, 1980) in which a high organic loading in a pilot-scale trickling filter was
found to accelerate film growth. This also increases the likelihood of undesirable odour
production. Finally, organic loading has an influence on the state of dispersion of the
solids leaving the filter. At higher organic loadings, suspended solids in the effluent
have been found to be more dispersed and less well flocculated (Parker et al., 1993).
Other parameters
Wastewater variations also affect the performance of a trickling filter at given organic
and hydraulic loadings. Although resistance to shock load is one of the advantages of
trickling filters other activated sludge, extreme changes in pH may retard process
efficiency. In general bacteria survive between pH 5.5 and 9, and thrive between pH
6.5 and 8.5 (WPCF, 1988). In terms of nutrients, ratios (on mass or concentration
basis) commonly required for aerobic biological processes, in particular trickling
filters, are BOD:N:P = 100:5:1.
B. Solid loading and size distribution of organic material
Even if trickling filter BOO loading expressed on a volumetric basis or media surface
area basis is the conventional design parameter for trickling filters, influent TSS also
plays a significant role in trickling filter performance (Matasci et al. 1986). Indeed, as
seen in § 2.1.1.2, the efficiency of wastewater treatment processes depends on the
removal of both dissolved and particulate fractions (Bouwer, 1987). The size of
contaminants is therefore important since the rate and efficiency of removal of
1 5
dissolved substances differs from that for particles (Adams and Asano, 1978; Sarner,
1986; quoted by Levine et al., 1991). The kinetics for removal of particulate organic
matter are usually assumed to be slower than the kinetics for soluble substrates
because of slower mass transfer of particles compared to soluble species and because
the initial hydrolysis reaction can be relatively slow (Bouwer, 1987). And since the
dissolved organics' flux into biofIim is proportional to molecular size (Logan et aI.,
1987a, 1987b), recent trickling fIlter models calculate performance of trickling fIlters as
a f~nction of the size distribution of biodegradable dissolved organics. As a result,
advanced characterisation of the influent to trickling filters (usually primary effluent)
is required. This is an important part of the research discussed in this thesis.
PrimarY effluent size characterisation
The composition of the influent to a trickling fIlter depends on the upstream
treatment. The latter generally includes preliminary treatment (coarse screening, grit
removal) and primary treatment (primary settlement). Exceptionally, influent
treatment can include chemically assisted primary treatment (for further reduction of
TSS and BOO loadings), neutralisation (for pH control) or pre-aeration (for odour
control).
The contaminants that must be removed from primary effluent are complex mixtures
of particulate and dissolved constituents (Figure 2.1 B).
1 6
Figure 2.1H: Typical organic constituents in settled municipal wastewater (after Levine et al., 1985)
Recalcitrant compounds
a."o,ft ,,,_,,,
Humic acids
Nutrients RNA .0'" ... ".11 ... . ." ........... . Viruses
Ca'~.!.ales .. POIYSaCCharides '.11 ..• ,~c ••• , •.•. , .u.c'",c ............ c" ..
10' ,
.... CII... Ploteins
Fa.!!!..!~.idS E_ocelluiar enzymes IlIle .... ,1)
Approximate molecular mass,amu
10' 10' 10' 10' 10' 10' 10' , I
I I I I I
10-· '0-) 10- l 10- 1
_
~A~I.~.~ •. ~P~'O~I~O'~O~' __ ~ - ..-aacteria
Bacterial lIocs ,--::=':;::";;'::'::':"'-.-.. Organic debris
'''00 .... ~ .. "' ... •• IUI
-~ ...
10' I I
I 100 10' 10 2
Particle size, microns
The size fractionation of contaminants IS based on the hypothesis that the
biodegradability of organic matter is closely linked to its size (Nogueira et at., 1998).
Historically, four size categories have been used to describe wastewater contaminants
(Rickert and Hunter, 1971). The operational definitions of the size categories are given
in Table 2.1 C.
Table 2.1 C: CateKories of wastewater contaminants (after Rickert and Hunter, 1971) Fraction Contaminants size (Ilm)
dissolved < 0.001 colloidal 0.001·1
supracolloidal 1 ·1 00 settleable > 100
However, the most common (because technically simple) fractionation boundaries
found in the literature are 1.2 and 0.1 ~m. The 1.2 ~m boundary is traditionally used
because it corresponds to the pore size of the glass-fibre filter used for TSS
determination (Standard methods; APHA-A WW A-WEF, 1995). It separates a
'suspended' and a 'filtered' fraction, that correspond respectively to the settleable
supracolloidal and colloidal-dissolved fractions. The boundary of 0.1 ~m is also
commonly found in the literature. It also represents a convenient filter pore size, at
the boundary between micro and ultrafiltration. Levine et al. (1985), reviewing the size
ranges of organic matter in wastewater and the available particle sizing techniques,
1 7
concluded that the contaminants m wastewater could be separated into two size
categories: larger than 0.1 ~m (,particulate') and smaller than 0.1 ~m ('dissolved').
As indicated in Figure 2.1 B, the fraction of organic material above 1.2 ~m includes
protozoa, algae, bacterial flocs and single cells, waste products and other
miscellaneous debris. In the size range [0.1; 1.2] can be found some bacterial cells, cell
fragments, viruses and inorganic particles such as clays. Organic matter smaller than
0.1 ~m is composed of cell fragments, Viruses, miscellaneous debris and
macromolecules. The macromolecules include polysaccharides, proteins, lipids and
nucleic acids; they are generally defined by molecular weight because of the difficulties
associated with the analysis of molecular sizes.
In terms of organic matter distribution, Levine et al. (1985) found that 30 to 85% of
organic matter in primary effluents was associated with particles bigger than 0.1 ~m.
Influence of solid loading on trickling filter performance
Some references to the influence of suspended solid loading on trickling filter
performance can be found in the literature. According to Matasci et al. (1986),
suspended solids entering a trickling filter play a key-role in its performance. They
found for four full-scale trickling filter plants that increases in primary effluent TSS
were always correlated with increases in trickling filter effluent TSS. Similar results
were mentioned by Siirner (1986), who noted that for a given organic loading, fmal
effluent TSS from a pilot-scale trickling filter increased as TSS loading to the filter
increased. Earlier, Tomlinson and Hall (1953) (quoted by Pike, 1978) studied the
performances of alternative double filtration with or without primary and intermediate •
settlement. They found an increase in the amount of solids discharged from the·
secondary filter when the intermediate settlement was omitted. This emphasises the
importance of reliable primary treatment (or intermediate clarification in the case of
double filtration) on final effluent quality.
Particulate matter has been found to interfere with dissolved organic matter removal in
biofilm reactors. The negative effect of adsorption of organic particles on a biofilm
surface on dissolved organics removal was first observed by Samer (1981). He studied
1 8
------------
the removal of dissolved and particulate organic matter in pilot-scale high-rate trickling
filters (plastic media; depth: 3 m; hydraulic loading: 2-4 m3/m2 d). He observed that a
heavy load of fine suspended and colloidal particles disturbed the removal of dissolved
organic matter, but gave no explanation.
Siirner and Marklund (1984) studied at laboratory-scale the influence of particulate
organics on the removal of dissolved organics in fixed-film biological reactors. They
use~ starch and particles obtained from digested sewage sludge as model particulate
organics, and glucose as model dissolved organics. They found that for high glucose
concentrations and high temperatures the removal of glucose was reduced when
particles were adsorbed on the biofilm surface. This reduction was proportional to the
amounts of adsorbed particles: the more particles the worse the performance. The
authors explanation was that a high temperature gives a high removal rate of glucose
and consequently a high demand for oxygen. Thus, any reduction of the oxygen
concentration close to the biofilm surface results in an oxygen shortage inside the
biofilm. The negative effect of the adsorbed organic particles is due to the fact that the
degradation mechanism required to facilitate their further transport in the biofilm
generates a local oxygen consumption at the biofilm surface. This results in a local
oxygen deficit in the biofilm and therefore in a local reduction in glucose removal rate.
Similarly, Figueroa and Silverstein (1991) investigated the effect of particulate and
soluble organic matter on nitrification in biofilm reactors, in this case Rotating
Biological Contactors (RBCs). They found that the particulate organic matter
inhibited nitrification to the same degree as soluble organic matter. They also found
that total influent organic matter was a better predictor of nitrification than soluble
influent organic matter only. To explain the fact that particulate matter inhibits
nitrification, they used the theory advanced by Siirner and Marklund (1986) that the
adsorption of the particulates to the biofilm mechanically interferes with oxygen
transfer as well as resulting in local oxygen depletion. The biofilm absorptive capacity
is regenerated by the eventual solubilisation and uptake of the particulate organics by
bacteria. With regards to inhibition due to soluble organic matter, the authors thought
that the mechanism was competitive inhibition by heterotrophs, previously identified
1 9
by Wanner and Gujer (1986) and by Strand (1986) (quoted by Figueroa and
Silverstein, 1992).
Influence of contaminants size distribution on performance
The size distribution of organic matter has also been found to affect trickling filters
performance.
Alt"ring particle size distribution (PSD) of influent has been found to improve the
performances and treatment capacity of trickling filters. Levine et al. (1985) used
pulsed bed filtration to remove the large, less readily degradable material in primary
effluent before a high-rate trickling filter. This resulted in effective BOO and TSS
removal at hydraulic loadings of up to 160 m'/m'.d, while the design loading of the
filter was between 10 and 40 m'/m'.d. The authors explanation was that, during the
treatment of unfiltered primary effluent, enzymatic hydrolysis in the liquid stream
and recirculation pumping contributes to reducing the size of larger particles for more
efficient removal by adsorption. Using pulsed bed filtration to remove the larger
particles enables efficient treatment of the remaining material without recirculation.
Similarly, Sarner (1980), quoted by Sarner (1986), compared the percentage removal
of dissolved BOO as a function of influent BOO concentration in a pilot-scale high
rate trickling filter fed with either settled or strained raw sewage. Straining resulted in a
feed to the trickling filter richer than settled sewage in fme suspended and colloidal
particles. Lower removal rates were found in the case of the strained feed. Similar
results were found in a smaller-scale pilot filter (Sarner, 1981). The higher the load of
particles, the lower the removal of dissolved organics. The author explained the
phenomenon by the fact that in a trickling filter, the largest sludge production occurs
in the top part, where the organic loading is the highest. This sludge has to pass
through the lower levels of the filter and probably affects the removal of dissolved
organics at these lower levels.
Similar results were obtained by Matsumoto and Weber (1988) who used a fine
grained, shallow-bed, air-pulsed filter to reduce primary effluent TSS and BOO prior
to treatment by a trickling filter. They found that significant reductions in trickling
20
---- -----
filter area and significant increases in hydraulic and orgaruc loading rates could be
achieved while keeping similar levels of performance.
Molecular size and size distribution of dissolved organic matter also affect
performance. As indicated in § 2.1.1.2, dissolved organics removal require adsorption
("biosorption", Carlson and Silverstein, 1998) on and diffusion within the biofilm.
Even if the boundary between the two phenomena is difficult to define, both have
beep. found to be affected by molecular size. Carlson and Silverstein compared the _
biosorption of several dissolved compounds of different MW: cellobiose (MW = 343
Dalton), chondrosine (MW = 355 Dalton), Dextran (MW = 6000 and 40000 Dalton)
and hyaluronic acid (MW = 50000 Dalton). They observed that sorption was
inversely proportional to MW for the studied compounds. In terms of sorption rate
(compared for uncharged molecules), they also found a decrease with increasing MW.
They concluded that these findings were consistent with reports by other researchers
which conceptualise biofilrn structure as a molecular sieve through which the rate of
diffusion decreases with increasing molecular size, reaching a maximum sorbate size
where internal diffusion into the biofilm cannot occur. Indeed, it has been
demonstrated that the dissolved organics flLLX into a biofilrn is inversely proportional
to molecular size (Logan et al., 1987b). La Cour Jansen and Harremoes (1984) studied
the removal of soluble substrates in a lab-scale fixed film reactor. They used methanol,
acetic acid and glucose as model soluble substrate and experimented on denitrification
and on oxidation of organics. They found diffusion coefficients for nitrate, oxygen
methanol, acetic acid and glucose in fLxed films to be in the range of 15-80% of the
values found in pure water. Lawrence et at. (1994) (quoted by Carlson and Silverstein,.
1998) showed that diffusion coefficients of different MW dextran polymers (therefore
of bigger MW than any of the compounds tested La Cour Jansen and Harremoes,
1984) within biofilms are 2-13% of their respective diffusion coefficient in water, with
decreasing biofilm diffusion coefficients as molecular size increases.
Modelling of particle size distribution of wastewater
All the above mentioned references show the importance of influent particle size and
particle size distribution (PSD) on fixed-film reactors in general (and more specifically
trickling filters) performance. But even if it is admitted that studies of PSDs at each
21
treatment stage give an insight into the behaviour of particulates and solids separation
performance (Boiler and Blaser, 1998; Boiler, 1993), few attempts have been made to
express the link between influent PSO and fixed-film biological reactors performance.
And PSO modelling can be seen as a way of extracting of PS Os parameters that could
be correlated to performance parameters.
Lawler (1997) summarised the concept of PSD in water and waste water. PSDs have
two. main characteristics: they are continuous, i.e. particles exist in every Size
increment no matter how finely one divides the overall size range; and there is
presumably an upper limit to the size of particles in any suspension.
In the sense of mathematical statistics, PSOs can be treated as random distributions of
a continuous random variable (Bernhardt, 1994). Continuously distributed random
variables X have the property that the probability of assuming a fixed value x, is zero:
The probability of X being situated between x, and x2 is:
The function q ofx is called the frequency distribution (FO) or PSD function, and has
to fulfil the conditions:
Vx, q(x) ~ 0
and
fo- q(x) dx = 1
This implies that, in order to compare distributions, it is essential to normalise them
so that the total area under the curve is 1 (Alien, 1997). As a result, the probability of
X being smaller than x,is:
22
The probability Q understood as a function of x can be called the undersize cwnulative
distribution (UCD) function; tberefore, it can be written tbat:
q = dQ or 'ix, q(x) =(dQ)(X) dx dx
The function q is sometimes introduced as the slope of Q (Lawler et at., 1980),
Another function of x can be defined and has been used in tbe field of particle size:
R(x) = [- Q(x) = P(X ~ x) = r q(t) dt
It can be called the oversize cumulative distribution (OCD) function,
For particle sizing of water or wastewater, the value x can be eitber the particle
diameter (dp), surface area (ap) or volwne (vp)' The diameter dp is the most commonly
used. Depending on tbe sizing technique used, the quantity Q is eitber the nwnber of
particles (Np), surface area of particles (Ap), volwne of particles (V p) or mass of
particles (Mp), The most commonly counted value is number of particles, the
corresponding PSDs being known as PSDs by nwnber. Distributions in terms of the
other parameters are then extrapolated from distributions by numbers by assuming
circularity (distributions by surface area) or sphericity (distributions by volwne) of
the particles, with a diameter equal to tbe logaritbmic average (logaritbmic or not) of
the particle diameters measured in each size range:
• A', p'
and
, 1[ d
p'
a =-p 4
23
.y. p'
and
J 1t d,
v =--, 6
Distributions by mass are either extrapolated from distributions by volume usmg
p~icle density measurements, or are measured directly gravimetrically (usually by
successive filtration).
PSD modelling in the field of water and wastewater was pioneered for solid-liquid
separation applications in water treatment. Kavanaugh et af. (1980) used PSD
functions to characterise water at various stages of a water treatment plant:
flocculation, settlement and granular-media filtration. PSDs (particle size range: 2 -
100 /-lm) were plotted as FDs. They were found to follow a power-law distribution
function of the form:
where
A, B = empirical coefficients
Similar results were found by Lawler et af. (1980) for PSDs measured in the range 0.3
- 30 /-lm, and by Ginn and Amitharajah (1990) (particle size range: I - 60 /-lm).
According to both Kavanaugh et af. (1980) and Lawler et af. (1980), A and B are
related to the characteristics of the suspension. A can be interpreted as a scale factor
that is related to the total number or concentration of particles in the system. B is
related to the number of particles in each size class. Kavanaugh et af. (1980) remarked
that extrapolation of the power-law function to the submicron size had been extended
to 0.01 /-lm in the field of oceanography (Harris, 1977; Sharp, 1973; quoted by
Kavanaugh et af., 1980):
PSD modelling of wastewater mainly concerned activated sludge. Adin et af. (1989)
used PSDs to characterise the effect of deep-bed filtration on wastewater particulate
24
content. They determined PSDs (particle size range: I - 300 !lm) of various types of
secondary effluents. They plotted PSDs in number as OCDs, and tested the validity
of the models:
and
where
a, b, n, B = empirical coefficients
They found that in most cases, the exponential function described distributions better
than the power-law function. They however considered that the improvement in
description was not as radical as to prevent the use of the power-law function, and
used it in further work (Adin and Alon, 1993; Alon and Adin, 1994). Alon and Adin
(1994) highlighted the fact that modelling OCDs (as R) was similar to modelling FDs
(as q), since:
dQ dR q=-=--
dx dx
The connections between the A and B values of the Kavanaugh et al. (1980) and the a
and b values of the Adin et al. (1989) model are therefore:
A=a b
B=b+1
Li and Ganczarczyk (1991) modelled PSD of activated sludge suspensions. For floes
ranging in size from 0.5 to more than 500 !lm. they tested the validity of 4 models for
PSDs in number plotted as FDs. These models were power-law, exponential, half
normal and Rosin-Rarnmler. They found that the best fit was given by the Rosin
Ramrnler distribution function closely followed by the power-law, and then by the
half-normal and the exponential distribution functions. They however noted that
caution should be given in interpretation of the Rosin-Rarnmler distribution: this
function involves the calculation of the logarithm of the data logarithm, which reduces
scatter and results in higher correlation. The authors concluded that the power-law
25
distribution is the most suitable model for the PSD of activated sludge flocs across the
whole size spectrum. They also tested the validity of 4 other distribution functions
(namely log-normal, Gamma, Weibull and exponential) for particulates larger than 10
~m only. They found that the best fit was given by the log-normal distribution. This
last part of their work was repeated by Barbusinski and Koscielniak, 1995, who also
found a good fit between PSD of activated sludge suspensions (size range: 10 - 900
~m) and a log-normal distribution function.
Recently, Wilen and Balmer (1999) modelled PSDs of activated sludge suspensions
and supematant after 20 min and 60 min of settlement. In the case of activated sludge
suspensions, they found that PSDs in volume measured over a range of 11.6-1128 ~m
could be successfully fitted to a log-normal distribution function. They however gave
no details about whether they had modelled frequency or cumulative distributions.
The supematant PSDs in number expressed as FDs and measured over the size range
1-100 ~m by light obscuration were fitted to a power-law function.
As a conclusion, the power-law function appears to be the distribution function the
most commonly used to model PSDs in the field of water and wastewater.
C. Dosing periodicity
Dosing periodicity, i.e. the interval between two deliveries of wastewater in one point
of the filter surface has been shown to affect trickling filter performance. The dosing
to low-rate trickling filters is usually intermittent. The interval between wettings
varies with the wastewater flow rate, but should be short enough to prevent the
biological film from drying out (WPCF, 1988). The dosing becomes continuous for
high-rate trickling filters and above.
Under low-rate conditions, Gray (1992) recommends to use large doses at infrequent
intervals, i.e. low speeds of rotation. This results in each segment of filter bed being
subjected to a surge of liquid followed by a long period without any flow before the
next dose. According to Gray (1992), the surge of wastewater ensures a more uniform
distribution of organic distribution within the bed, extending the depth of
heterotrophic activity and encouraging a more even distribution of film.
26
On the contrary, low periodicity dosing approaches the conditions found with
continuous dosing with a large accumulation of microbial film at the surface. It has
been found (Gray, 1992) ideal for random plastic medium or media with a high voidage
that is able to support a high film accumulation in the top 900 mm.
2.1.3.3 Parameters resulting from interactions between design and operational
parameters and environmental conditions
The interactions between design and operational parameters and the environmental
conditions (essentially the ambient temperature) generate parameters influencing the
perfonnances of a trickling filter. These parameters include filter temperature,
ventilation, film accumulation and hydrodynamic characteristics of the reactor.
A. Filter temperature
Filter temperature is a factor that affects the film (biofilm and grazers) activity and, as
a result, the perfonnances of the trickling filter. Indeed, optimum temperatures for
bacterial activity are in the range from about 25 to 35'C (Metcalf and Eddy, 1991).
Filter temperature is a function of the ambient temperature, the wastewater
temperature, and the heat retention properties of the filter and its medium.
Pierce (1978) (quoted by WPCF, 1988) reported that relatively low temperatures
during winter generally reduced overall BOO removal by approximately 33% at nine
studied trickling filter plants.
The effect of temperature on trickling filter performance is dealt with in the models
predicting trickling filter performance. For example, Howland (1953) (quoted by
Shriver and Bowers, 1975) expressed the temperature (as waste water temperature)
effect on performance in the following equation:
RE BOOT = RE B0020 x 1.035 T·20
where
T = wastewater temperature (0C)
RE BOOT = BOO removal efficiency at T (%)
27
An other expression was given by 10hnson (1971):
L035T- 1U
RE BOO = 1-(1- RE B0020 ) T 100
The 10hnson equation gives more realistic predicted efficiencies than the Howland
expression. For example, using the latter, a filter system designed for 85% removal at
20°C would obtain slightly over 100% removal if the waste temperature went to
25°C.
B. Ventilation - dissolved oxygen content
Ventilation also affects trickling filter performance. Indeed, the oxygen required for the
metabolism of the biofilm's micro-organisms is supplied to trickling filters by air
circulation, caused by natural ventilation. It is made available to the microorganisms
through dissolution in the waste water and diffusion through the biofilm (§ 2.1.1.2). If
oxygen is not present in sufficient concentrations, system performance is poor and
odours occur.
The distribution of air in the filter occurs by circulation through the void spaces in the
filter medium. Ventilation currents are caused by the temperature differences between
the ambient air and the air in the void space of the filter medium, which causes a draft
of air through the medium. The draft can be expressed as the pressure head resulting
from the temperature difference. An empirical formulation has been given by
Schroeder and Tchobanoglous (1976):
where
o ' -, H (1 1) '"' = J.)J Tc + 273 - Th + 273
Dui, = natural air draft (mm of water)
H = filter depth (m)
Tc = cold temperature (0C)
T h = hot temperature (0C)
28
In winter, the external temperature is lower than the filter's. As a result, the air
entering the trickling filter will be warmed by the wastewater passing through the bed
causing a net upward flow. In summer, the opposite may occur, resulting in a
downward flow of air. More likely, during the warmest period of the year, there are
periods when nearly no airflow occurs through the trickling filter because temperature
differences are negligible. Consequently, during these periods, trickling filters are the
mo~t stressed from the point of view of their oxygen resources (Parker et al., 1993).
At full scale, draught is encouraged by draught tubes located in the sides of filters
(Horan, 1990).
C. Film accumulation
As mentioned previously in § 2.1.1, the film in a trickling filter is composed of active
biofilm, inactive humus and macro-invertebrate grazers. Film accumulation within a
trickling filter is an important parameter affecting its performance. In particular, under
temperate conditions, the winter accumulation of film in many filters impairs their
efficiency.
Measurement of film accumulation
Two basic approaches to film accumulation measurement can be found in the
literature. The first is direct measurement using removable baskets of medium,
contained in sampling tubes or shafts within the trickling filter. Film accumulation is
then estimated by a number of gravimetric methods including total film weight, total
dry solids, volatile solids and percentage settlement of solids (Heukelekian, 1945;
Hawkes and Shephard, 1970; Gray and Learner, 1984; Battistoni et al., 1992; Thorn et
al., 1996).
The second approach is indirect measurement by neutron scattering, which enables the
measurement of film quantity within the filter without disturbing the medium (Harvey
et al., 1963 ; Solbe et al., 1967; Sullins and Galler, 1968; Bruce, 1970; Bruce and
Merkens, 1970 ; Gray and Leamer. 1984 ; Upton and Cartwright, 1984 ; Biddle,
1994). The technique originated in the USA in the late 1950s - early 1960s where it
29
was initially developed for the measurement of moisture content in soils, but it was
rapidly applied to trickling filters by Harvey et al. (1963).
The high water content of trickling filter film, between 92.3 and 97.0% in the case of
plastic medium, and between 95.0 and 98.2% for a mineral medium (Gray and Learner,
1984), makes it possible to measure film accumulation by neutron scattering. This is
done by measuring the abundance of hydrogen atoms (present mostly in water
mol~cules) using a neutron probe. The neutron probe consists of a source of highly
excited neutrons that is lowered into aluminum access tubes inserted vertically into the
filter bed. Emitted fast neutrons (i.e. having a very high-energy content) are preferably
moderated by hydrogen atoms associated to oxygen in water molecules, which
produce a cloud of slow neutrons. The latter are detected and counted by the
equipment to give a number that is proportional to the water content of the
surrounding volume. Assuming that the proportion of water to total film remains
uniform, then the count is proportional to the amount of film. In this way, estimation
can be made of the relative amount and distribution of film throughout the depth of
the filter.
The results of neutron probe measurements are expressed as moisture content or
percentage saturation of medium voids (Gray and Learner, 1984).
In the case of excessive film accumulation, liquid can be trapped in the interstices of
the medium and included in the measurement. High percentage saturation of voids is
therefore usually symptomatic of ponding, and includes both film and retained water.
Solbe et al. (1967) carried out a 2.5 years study on a low-rate pilot-scale trickling
filter, 1.8 m deep, filled with 63 mm graded blast furnace slag. It was fed with
domestic settled sewage at a hydraulic loading of 0.47 m3/m3 d and an organic loading
between 100 and 150 g/m3.d. The authors monitored the film accumulation within the
filter using both neutron scattering and a gravimetric method. The filter was fitted with
metal shafts containing six perforated metal baskets containing medium; film
accumulation was estimated by measuring the volatile solids of the organic matter
30
removed from the medium contained in the baskets of one shaft. The authors found a
good correlation between the results obtained using the two techniques.
Gray and Learner (1984) monitored the film quantity in pilot-scale filters containing a
mineral medium (50 mm-graded blast furnace slag) and a random plastic medium over
two one-year periods. The hydraulic and organic loading were 1.68 ml/m'.d - 280 g
BOD/m'.d for one year and 3.37 ml/ml.d - 630 g BOD/m'.d for the other. The authors
compared five methods of determining film accumulation: four gravimetric methods
(total film weight, total dry solids, volatile solids, percentage settlement of solids), and
the neutron scattering technique. Similarly to Solbe et al. (1967), they found good
correlation between all methods at the lower loading. They however found no
significant correlation between neutron scattering and gravimetric results at the higher
loading.
The major advantage of the neutron scattering technique over gravimetric
determination is that it allows direct and rapid measurement of film accumulation
without disturbing the medium. This allows easy determination of changes in film
accumulation.
Influence of film accumulation on trickling filter performance
Accumulation of film in a trickling filter is thought to limit trickling filter performance
by influencing the following factors:
• Residence time and hydrodynamic properties of the filter;
• Ventilation/aeration of the filter;
• Rate of adsorption of suspended and dissolved organic matter.
Residence time and hydraulic properties (i.e. distribution of wastewater) of the filter
have been shown to be affected by film accumulation. According to Gray (1983),
heavy weights of film alters the flow pattern by creation of both 'reservoirs' and
channeling of waste water within the filter, resulting in a loss of performance.
Excessive film accumulations can lead to ponding: the wastewater is prevented from
trickling through the filter medium, resulting in water accumulating at the tilter surface.
31
This therefore prevents the wastewater from being treated. Moreover, excessive film
accumulation creates substantial anaerobic conditions in the biofilm by preventing
ventilation (Hawkes and Shepard, 1972). This markedly reduces the degradation
efficiency of the predominantly aerobic microorganisms of the biofilm, and can lead to
odor problems from the filter. Even without reaching total blockage and ponding, high
biofilm accumulation within a filter results in poor ventilation and poor performance.
Fil.ql. accumulation is also an expression of biofilm thickness, which affects diffusion
of substrate. Several authors (Komegay and Andrews, 1968; LaMotta, 1976; quoted
by Lazarova and Manem, 1995) found that biofilm activity was not proportional to
the quantity of fixed biomass, but increased with the thickness of biofilm up to a
determined level called the "active thickness". Above this level, the diffusion of
nutrients becomes a limiting factor, differentiating an "active" biofilm from an
"inactive" biofilm. This is why a stable, thin and active biofilm offers optimal
performances, and measurement of biofilm accumulation is a good indicator of
performance.
Finally, film accumulation and subsequent unloading through sloughing was also cited
by Howell and Atkinson (1976) as a major contributor to trickling filter performance,
because effluent solids contribute to the variations in BOO removal.
Parameters controlling biofilm accumulation:
Variability in terms of film accumulation is observed with both time and depth. The
variability with time is seasonal. As for the variability with depth, the film is usually
not distributed evenly throughout the depth of the filter bed, being more abundant in
the upper areas of the bed where food is a non-limiting factor (Gray, 1992).
The dual variability in film accumulation results from interactions between design
parameters (notably the characteristics of the filter medium), operational parameters
(organic and hydraulic loading, dosing frequency) and environmental parameters
(mainly temperature), the latter influencing the activity of both the microbiological
population of the biofilm and the grazing fauna.
32
The mam variability in film accwnulation is that caused by seasonal changes,
attributed primarily to temperature variations. It has been observed by nwnerous
authors (e.g. Solbe et aI., 1967; Gray and Leamer, 1984). The total film accwnulation
in a trickling filter follows a seasonal pattern, being maximal in winter. As the
temperature increases in spring, there is a discernible sloughing of the film that has
accwnulated over the winter months. This results in minimal film accwnulation in the
summer months.
As mentioned above, decrease in temperature reduces the metabolic activity of both
the microbiological population of the biofilm and the grazing fauna. Honda and
Matsumoto (1983) found that the growth rate of individual microorganisms was
reduced, but that the growth capacity of film in a model trickling filter (in absence of
grazing fauna) increased as the temperature decreased. This was linked to the fact that
the biofilm autolysis coefficient was decreased at low temperatures. This reduction of
cell lysis at lower temperatures has been for a long time in the US regarded as the key
parameter iiilluencing film accwnulation (Lackey, 1925; Holtje, 1943; Heukelekian,
1945; Cooke and Hirsch, 1958; quoted by Hawkes and Shephard, 1970).
However, in the UK, it is the reduction of grazing activity due to low temperatures
that was thought to be responsible for winter film accwnulation (Harrison, 1908;
Reynoldson, 1939, 1942 ; Lloyd, 1945; Tomlinson, 1946 ; Hawkes, 1961, 1965 ;
Williams and Taylor, 1968 ; Hawkes and Shephard, 1972 ; quoted by Shephard and
Hawkes, 1976, and Honda and Matsumoto, 1983).
To establish the pre-eminence of one or the other, Shephard and Hawkes (1976)
compared the relative importance of microbial activity and grazing activity on biofilm
accwnulation in a pilot-scale trickling filter. They found that, at a high temperature
(20°C), the amount of biofilm was controlled at a more uniform and lower level in the
presence of grazing activity than in a system without grazers. At a low temperature
(5°C), the grazing activity was inhibited and the biofilm accumulation was similar with
or without grazers in the filter. It would therefore seem that grazing activity is really
important in summer, when it keeps the film accumulation to a lower level than in the
absence of grazers. On the other hand, in winter, the film accwnulation is similar
33
whether grazers are present or not. The WPCF (1988) concluded that grazing activity
was particularly important for low-rate trickling filters.
Design and operating parameters also influence the film accumulation within trickling
filters. They affect seasonal variation, but also distribution with depth. The filter
medium characteristics is one of the design parameters that has been found to
markedly influence film accumulation. Truesdale and Eden (1963) compared the
performances of eight mineral media (rounded gravel, clinker, blast furnace slag and
rock, each with a size of 25 mm and 64 mm) in low-rate trickling filtration (hydraulic
loading rate: 0.6 m3/m3 .d). They studied film accumulation within the various media
using a neutron probe, and found that filters with small media displayed on average
accumulations of film 1.85 times higher than filters with bigger media. Similarly, Bruce
(1970) found a much greater film accumulation in two mineral media trickling filters
than in four plastic media trickling filters, all of them operated at high-rate (hydraulic
loading = 6 m3/m 3 d). More recently, Biddle (1994) compared the film accumulation
within four pilot-scale nitrifying trickling filters filled with different media (three
plastic media, one mineral medium: grade 20-25 mm blast furnace slag) using neutron
scattering. The filters were initially fed for 7 months with primary effluent at a loading
rate of 1.4 m3/m3.d, then for 18 months with secondary effluent at a loading rate of 6
m3/m3 d. The author found a rapid start-up in biomass accumulation during the first
phase of the research within the blast-furnace slag filter, while it occurred more slowly
and to a lesser extent within the plastic media filters. He also observed that the blast
furnace slag filter exhibited even biomass colonisation throughout the filter depth and
at all times of the year, whereas the plastic media filters showed an extreme variation
in the biomass content with time. This was to be connected to a lower rate of biomass
growth rate on the plastic media and to the higher voidage in the plastic media reducing
physical retention of the solids. In terms of distribution throughout the depth of the
filters, colonisation appears to have began in the upper layers and then to have
gradually spread downwards.
Operating parameters have also been shown to influence biofilm accumulation. Dosing
frequency is one of them. Tomlinson and Hall (1955) first reported that the winter
accumulation of film was reduced by low frequency dosing. This was confirmed by
34
Hawkes and Shepherd (1972), who studied the seasonal fluctuations and vertical
distribution of accumulated solids and grazing fauna populations (using a gravimetric
technique) in two low-rate filters having different dosing periodicities: 0.3 rnin and 14
min. This difference in dosing periodicity possibly also resulted in differences in
hydraulic and organic loading, but this is not mentioned by the authors. They found a
lower winter solids accumulation in the surface layers (upper 30 cm) of the high
periodicity dosed filter than in the low periodicity one. The film accumulation within
the ,rest of the filter was however less affected by the dosing periodicity. In the low
periodicity dosed filter there was a reduction in the amount of solids with depth,
whereas in the high periodicity dosed filter the solids were at times evenly distributed
throughout the depth of the filter, or accumulated to a greater extent at depths within
the filter. In the low periodicity dosed filter, there was each year a marked reduction in
the amount of solids within the filter in the early spring followed by a reduction in the
solids in the surface layers. In the high periodicity dosed filter, this unloading took
place later and occurred in the upper layers first, followed by a discharge from the
depths of the filter. The differences in the average amounts of solids throughout the
depth of the filter was less marked, and the decline of the average amounts of solids
throughout the filters took place earlier in the low periodicity dosed filter than in the
high periodicity dosed one.
Organic and hydraulic loadings are also important with respect to film accumulation.
The organic loading the film growth rate, because a part of the organic matter removed
is converted into film. In high-rate filters, especially those employing modular plastic
media, the film development is mainly controlled by hydraulic loading, which scours
the film from the smooth surfaced media as it reaches critical thickness. Bruce (1970)
compared theperforrnances of s1,( different types of media (two mineral, including
graded 13-8 cm blast furnace slag, and four plastic media) in high-rate pilot-scale
trickling filters (hydraulic loading: 6 m3/m3 .d) over a twelve month period. Film
accumulation in each filter was determined at regular intervals using a neutron probe.
No significant variation in film accumulation was observed with depth, and little
fluctuation occurred with time of operation. This was attributed to the high hydraulic
loading of the filter. More recently, Upton and Cartwright (1984) used neutron
scattering to measure film accumulation in a full-scale nitrifying filter (3.66 m deep,
35
filled with granite: 20 mm-graded, apart from the top 0.3 m and the bottom 0.6 m: 100
mm graded) fed with activated-sludge settled effluent at a hydraulic loading of 2.54
m3/mJ d. Measurements obtained in the summer and winter months showed no
significant differences in film accumulation. There was also no significant difference in
distribution along the depth of the filter. These uniform low levels indicated that there
was very little degree of film build-up in a nitrifying trickling filter. This was thought
to be consistent with the low organic and high hydraulic loadings to the filter.
Finally, film accumulation is also influenced by solids loading. Vieira and Melo (1995)
studied at laboratory scale the effect of inorganic particles (clay) on the behaviour of
biofilms; They found that the presence of inorganic particles led to higher biofilm
accumulation in the system, to a greater stability in the biofilm when the substrate
was suppressed, and to an increase in the mass transfer rates throughout the biofilm
thickness. They concluded that inorganic particles enhanced microbial activity in the
biofilm and probably caused changes in the physical structure of the biofilm, making it
stronger with a more open matrix.
In conclusion, in operational terms, a knowledge of the pattern of film accumulation
within a filter is required if maximum performance efficiency is to be maintained
(Honda and Matsumoto, 1983). An ideal situation would be the ability to control the
quantity and quality of active biomass within the filter, as is the case in the activated
sludge process. This can only be achieved in trickling filters by having a greater
fundamental understanding of the kinetics of film growth, and regular determination of
film accumulation.
D. Hydrodynamic characteristics
The importance of residence time (also known as retention time or contact time) on
the efficiency of trickling filters has been acknowledged from the early ages of the
process (Royal Commission on Sewage Disposal, 1908, quoted by Gray, 1992). As
the waste passes over the biological film, organic matter is removed by the biomass. It
therefore seems reasonable to assume that residence time is in some way related to
organic removal efficiency in a trickling filter. This view is expressed in many models
36
---------
for trickling filters design, including the modified Velz equation, which are based on
first-order kinetics of biological oxidation, as shown below:
where
S -k' -=e r
So
S = effluent substrate concentration
So = influent substrate concentration
k = reaction rate constant for treatment
t, = retention time
It is also thought that hydrodynamics conditions of wastewater flowing over the
biofilrn have a distinct effect on oxygen penetration and oxygen availability for
biological reactions (Suschka, 1987).
However, contrasting results led Atkinson et al. (1963) (quoted by Sushka. 1987) to
state that 'residence time analysis of trickling filters are irrelevant and serve only to
cloud the basic issues'.
For example, Cook and Katzberger (1977) examined the effect of liquid residence time
on organic removal efficiency in a model trickling filter. They found that small
variations in liquid residence time had a pronounced effect on COD removal efficiency
in high-rate trickling filters, while organic removal efficiencies at low-rate are
essentially independent of liquid residence time. This latter result corroborates
findings by Craft and Ingols (1973). They found two similar residence time
distributions (RTDs) on a full-scale low-rate trickling filter in two different instances,
while organic removal efficiency was near zero on one occasion and 50-60% on the
other. They concluded that organic removal efficiency by a low-rate trickling filter is
independent of the liquid residence time. By contrast, Ferchichi et al. (1994) found on
a low-rate trickling filter (hydraulic loading: 1-2.5 ml/m2 d) filled with plastic medium
that the treatment efficiency (in terms of dissolved BOO and COD removal
efficiency) improved with increasing residence time.
37
Most of the work to date has been done on liquid residence time. This neglects the
fact that up to 85% of organic matter in wastewater is represented by material bigger
than 0.1 ~m (Levine et al., 1991). Solids residence time is therefore probably an
important parameter affecting overall trickling filter perfonnance. Recent experiments
using fluorescent microbeads as solid tracer showed a fast penetration of microbead
tracer particles into the biofilm and a residence time of the beads in the biofilm which
was much longer than that of bacterial cells (Drury et al., 1993; Tijhuis et aI., 1994;
v!lI1 Benthum et aI., 1994). It is reasonable to assume that solids are not only attached
or detached at the surface of the biofilm, but are also transported in the biofilm either
through the pore structure of rough biofilrns (Drury et al., 1993), or through
temporary cracks and fissures in dense biofilms (Tijhuis et aI., 1994; van Benthum et
aI., 1994).
Detennination of Residence Time Distribution and residence time
The Residence Time Distribution (RTD) is widely used to evaluate the mean
residence time in all kinds of reactors. The hydrodynamic analysis of chemical reactors
was initiated by Danckwerts (1953) who developed the residence time concept. An
inert chemical, known as a tracer, is injected into the reactor at a time t = 0, and the
tracer concentration is then measured in the effluent stream as a function of time: C(t).
The residence time of a fraction of tracer was defined as the time required for it to exit
the reactor, or time spent by it in the reactor. The effluent concentration-versus-time
curve is known as the C curve. If we call Q the area under the C curve, Q is calculated
by:
Q= rC(t)dt
The RTD function E results from the nonnalisation of C. It is defined by:
As a result,
E(t) = C(t) .Q
38
J.- J.+~ C( t) ! J.-E(t)dt= -dt=- C(t)dt=! o 0 Q Q 0
Danckwerts assumed that a tracer introduced in a system follows the same path as the
fluid. As a result, the RTD of a reactor is a characteristic of the mixing that occurs in
this reactor.
The selection criteria for tracers are: initial absence in the reactor and in the feed to the
reactor; solubility in the feed to the reactor; non-reactivity in the reactor; ease of
detection. The two most used methods of tracer injection are pulse input and step
input. For step inputs, tracer addition to the feed starts at time t = 0 at a
concentration CO; the concentration of tracer in the feed to the reactor stays at this
concentration and rate until the concentration in the effluent is indistinguishable from
that in the feed. But the most common method is pulse input: a knO\,TI amount of
tracer is injected in one shot into the feed stream entering the reactor in as short a time
as possible. The outlet concentration is then measured as a function of time.
An other technique, based on drainage distribution, was used by Tariq (1975) to
estimate Residence Time Distributions in trickling filters. He determined a full curve
of liquid drainage over a relatively long period of time after interruption of liquid
application. But this technique presents the disadvantage of requiring to interrupt the
process for relatively long periods of time, and therefore tracer studies are more
popular.
The usual parameters extracted from RTDs include mean residence time and standard
deviation.
The mean residence time tm is an indicator of the location of the distribution. It IS
given by the centre of gravity of the E or the C curve, i.e. by:
rtE(t)dt CtC(t)dt t = 0 = .:;0'--__ _
m fo-E(t)dt fo-C(t)dt
39
The standard deviation cr represents the spread of the distribution. It is calculated by:
where
cr2 = variance of the distribution
However, because of the skewness of the distribution usually obtained, determining
the centroid of the curves and hence the mean residence time and the standard
deviation implies selecting arbitrary cut-off points, or extrapolating the missing part of
the curves. Reservations on this parameter were expressed by Sheikh (1971), who
stated that mean residence time was not an accurate enough parameter. The results of
tracer studies are also expressed as percentile, the x percentile residence time being the
time at which x% of the injected tracer has exited the reactor. The median residence
time, where x = 50, is the most commonly used value.
The modal residence time (or peak time) is also sometimes used. It is the time at
which the peak tracer concentration is detected in the effluent flow (Cook and
Katzberger, 1977).
Other factors has been used in the past. For example, Truesdale et al. (1962) used
RTD measurements to establish a "degree of short-circuiting", defined as the
proportion of tracer recovered I h after injection.
Characteristics of trickling filter residence time distributions
Long tailing which affects the evaluation of mean residence time has been observed for
biofilm reactors in general, and trickling filter in particular (Massone and Rozzi, 1995;
Lens et al., 1995; Suschka, 1987; Stevens et al., 1986). Bloodgood et al. (1958)
(quoted by Massone and Rozzi, 1995) found that in fully colonised trickling filters,
retention time measured by RTD analysis increased considerably compared to results
found on the same reactors before biofilm colonisation. These variations were much
40
higher at low flow rates than at high flow rates. The authors concluded that the
increase in retention time was probably mainly due to the increase of the total liquid
hold-up in the reactors, and therefore of the reactor liquid volume. More recently,
Massone and Rozzi (1995) emitted the hypothesis that tracer retention in the
captured liquid film is the main cause of differences that may exist between R TDs for
a given fixed-film reactor.
A cpmplementary interpretation is that tailing is due to tracer diffusion in the biofilm.
Stevens et al. (1986) studied RTDs in a fluidised bed with and without biofilm. They
found that when the sand particles where coated with biofilm, the RTDs had long tails
that could not be predicted by simple hydraulic mixing models. Their explanation was
that tailing was caused by tracer diffusing into the biofilm when the concentration was
high and escaping from the film when the bulk concentration was low. The relative
importance of this phenomenon is controlled by biofilm thickness, the diffusion
decreasing with the depth of the biofilm (Wa1eed and Ganczarczyk, 1993). It is also
controlled by diffusivity of tracer, diffusivities in the biofilm being nearly equal to
diffusivities in water (Williamson and McCarty, 1976).
Tracer diffusion within the biofilm led Jimenez et al. (1988) to compare 5 tracers for
RTD determination in a submerged filter. Having assumed a plug-flow model for their
reactor, they found that only Blue dextran gave a satisfactory RTD. The RTDs
obtained with the other tracers tested showed significant tailing. This tailing was an
evidence of different rates of diffusion of the latter tracers into and out of the biofilm.
Blue dextran was thought to be an ideal tracer for this submerged filters because of its
high molecular weight (2 x 106
Dalton). The authors however conceded that this might
not be the case for all biofilm reactors, especially those with low flow velocities.
Parameters affecting Residence Time Distribution in trickling filters
Several parameters affect residence time in a trickling filter. They are medium (Bruce,
1970), filter depth (Sheikh, 1970), hydraulic loading (Cook and Katzberger, 1977) and
biofilm accumulation within the filter (Gray and Learner, 1984).
41
• Medium:
The medium characteristics clearly affect the retention time within a filter. The
distribution of the surface area (i.e. the proportion of vertical, inclined and horizontal
surfaces) may have an effect (Bruce and Merkens, 1970): the greater the specific
surface area of any media, the longer the residence time is likely to be. Assuming
similar surface tension forces, the larger the surface area available, the greater the
volume of liquid that may be retained. The pore size distribution also has a very
sig~ificant effect upon residence time curves. The more uniform the pore sizes within
a filter, the less the spread of the tracer concentration versus time curve, which means
that any fraction of the applied liquid is more likely to receive biological purification.
The pore itself can have an important effect upon the residence time of a liquid, film
accumulation in smaller pores having a greater effect in retaining the liquid than in
larger pores.
• Depth of the filter:
Filter depth has an impact on retention time since it IS related to the volume of
medium.
• Hydraulic loading:
The hydraulic loading applied to a filter also affects the retention time. Increasing the
hydraulic loading has a similar effect as reducing the available surface area of the
media. As a result, increasing the hydraulic loading tends to reduce the retention time
and may also reduce the overall length of time over which all the liquid is retained, as
shown by Meltzer (\962).
• Biofilm accumulation:
Biofilm accumulation has a very significant impact on the retention characteristics of a
trickling filter. Truesdale et af. (\962) found that tracer studies were useful as means
of assessing the amount of film in a filter. Eden et af. (1964), quoted by Gray and
Learner (\984), found that residence time increased directly with film accumulation up
to an optimum weight, after which retention characteristics changed due to excessive
film. Solbe et af. (\974) also showed that film accumulation can double, sometimes
treble the residence time on low-rate filter. Excessive uneven accumulation of film may
42
result in short circuiting (Truesdale et a/., 1961), i.e. channelling of the liquid within
the filter resulting in reduced residence times.
Cook and Katzberger (1977), using a fixed film biological reactor simulating a trickling
filter, reported that the amount of biomass in the reactor has a pronounced effect on
the liquid residence time. Gray and Learner (1984) reported a direct correlation
between median residence time and film accwnulation (monitored gravimetrically) in a
pilot-scale trickling filter filled with 50 mm-graded blast furnace slag and loaded at
1.68 m3jm3.d, but they did not nwnerically express the correlation. The correlation
remained true when the loading was increased until so much film accwnulated that the
interstices within the medium became blocked and channelling occurred reducing the
median residence time. Battistoni et a/. (1992) expressed a mathematical relationship
between film accwnulation (measured gravimetrically) and median residence time in a
pilot-scale high-rate (hydraulic loading: 19.5-28.1 m3jm'.d) trickling filter filled with
plastic medium.
However, according to Gray (1992), the effects of film accumulation on residence time
are not clear, and since no direct relationship has been fully established it would
appear that the film modifies the flow pattern of the wastewater once it reaches
particular accwnulations only. Other results have also contradicted the idea of a
simple relationship between residence time and biofilrn accwnulation. As mentioned in
the previous section, Craft and Ingols (1973) found similar RTDs in a full-scale
trickling filter on two occasions (June 1971 and August 1971). The filter was ponding
on the first occasion due to excessive a1urniniwn hydroxide accumulation, while in the
second occasion the pilot was not encountering excessive film accumulation. The
authors therefore concluded that in this instance residence time was completely
independent of film accwnulation.
Modelling ofliquid residence time
As mentioned before, retention time has been considered by many researchers to be
directly related to the depth of the filter bed, and to decrease with increasing hydraulic
loading (Bloodgood et al., 1959; Meltzer, 1962; Duddles et a/., 1974). Typical
equations in the literature are of the form:
43
where
t, = retention time
H = filter depth (m)
Hb
t =a, Q'
Q = hydraulic loading (m'/m'.d)
a, b, c = empirical coefficients
The importance of the medium specific surface area was also stressed. For example,
Bruce (1970) noted that residence time was related in a general way to specific surface
area, but expressed no precise relation. Other authors gave equations of a similar form,
but in which:
where
A = specific surface area of the medium (m'/m3)
a', d= empirical coefficients
More recently, the influence ofbiofilm accumulation on residence time has been put in
equation by Battistoni et at. (1992) (Table 2.1 D).
Table 2.1D summarises values for a, b, c, a' and d found in the literature.
For a given filter, the medium characteristics (therefore A) and filter depth CH) are
constant. Therefore, the changes in retention time in a particular trickling filter are due
to variations in hydraulic loading and biofilm accumulation. Furthermore, since in
normal operation the hydraulic loading is also constant, biofilm accumulation is the
main factor affecting residence time for a given filter.
44
Table 2.1 D: Values of constants in retention time models
Residence a time
undefined 30.2
undefined k dim 1.67
undefined
undefined
undefined
undefined -
undefined
undefined
undefined
undefined -
mean -
undefined 2
mean
mean -median
median 0.54 eO.2 S
median 0.57e0.Q18 F
mean -
'. . quoted by Sheikh (1971) ": quoted by Suschka (1987)
"': quoted by Rowlands (1979) a = hydraulic loading (m'/m'.h) S = amount of biofilm (kg TS/m')
b c
1.08 0.5
1 0.67
0.408
1 0.76
0.575
1 0.33 ' 0.67
- 0.48
2
0.93
- 1
- 0.78
1 0.75
1 0.71
- 0.5 + 0.1 log (a I 0.0011
1 0.78
1 0.46
1 0.51
- ·1
F = amount of biofllm after 10 min drainage (kglm3)
dim = filter medium diameter
Hydrodvnamic modelling
a'
-
-
-
-
--
-
0.22 x 10
-
-
-
d Reference
Mc Dermatt ,
(1957) Sloodgood
et al. , ,
(1959) - Burgess et
'" al. (1961)
- MeUzer (1962)" Bryan and
Moeller " ,
(1963)
- Howland
(1963)'"
- Germain (1966)'"
- Rincke ( 1967)"
Rincke and Waiters
" , (1970)
- Oleskiewickz , ,
(1975)
- Ganczarczyk " (1963)
- Suschka (1987) ., 0.91 Muslu (1986)
- Massone and Rozzi (1996)
1 Sheikh (1971 )
- Battistoni et al. (1992)
Battistoni et al. (1992)
- Hinton and Stensel (1991 )
The mi'<ing of single-pass trickling filters has been regarded as typical of plug flow
(Bryers and Characklis, 1990; lWEM, 1988), because it was thought that the
waste water circulates over and within the film with little mi'<ing. However, it is
usually considered as non-ideal, neither ideal plug flow nor ideal mixed flow (Suschka,
45
1987). As a result, various hydrodynamic models combining plug-flow reactors (PFR)
and/or mixed-flow reactors (MFR) have been used to attempt to simulate trickling
filters and model trickling filters. They are summarised in Table 2.IE.
T, bl ) lE H d d dlfi . kl" fil a e _. y ro ynamlc mo e s or tnc mg" ters Model Reactor type Reference PFR Trickling filter (plastic Vase I and Schrobiltgen
medium) (1991) Dispersion Inclined rotating tubular Lens et al. (1995)
reactor Trickling filter (plastic Vasel and Schrobiltgen
medium) (1991) Dispersion with exchange Trickling filters (mineral Seguret (1998)
and plastic media) MFR Trickling filter (plastic Vase I and Schrobiltgen
medium) (1991) n MFR Inclined rotating tubular Lens et al. (1995)
reactor Nitrifying trickling filter Wik et al. (1995)
(plastic medium) (n+ 1) MFR Inclined rotating tubular Lens et al. (1995)
reactor n MFR with dead-space Trickling filter (plastic Ferchichi (1 991)
medium) Simplified stagnant Inclined rotating tubular Lens et al. (1995)
reactor (SSR) reactor Model trickling filter Massone and Rozzi (inclined channel laid (1995) with qeotextile mat)
Biodiffusion Trickling filters (mineral Seguret (1998) and plastic media)
The basis of the modelling is the fitting of the output of a given model to an
experimental RTD using usually the least square method. The fitting involves the
optimisation of the model parameters.
The Dispersion model consists of a PFR on which some degree of backmixing or
intermixing is superimposed, the magnitude of which is independent of the position
within the reactor (Levenspiel, 1972). This implies that there are no stagnant pockets
and no major short-circuiting in the reactor.
The Dispersion with exchange model (or Model I, Levenspiel (1972» corresponds to
a PFR with dispersion connected to a stagnant reactor.
46
The n MFR model (or tanks-in-series model) consists of a succession of n MFRs in
series. It is often used in water and waste water treatment processes and surface-water
quality modelling (Peng, 1997).
The (n + 1) MFR model consists in n MFR in series, connected to one MFR (Wang,
1991, quoted by Lens et aI., 1995).
The n MFR with dead-space model is based on the n MFR model, but with each
MFR including dead-space, i.e. a fraction of each MFR is inaccessible to the liquid
flow and therefore to the tracer.
The Stagnant reactor model (or Model G, Levenspiel (1972» consists of n MFRs in
series, each connected to a stagnant reactor (SR). The amount of liquid flowing in and
out of one SR from the corresponding MFR allows to simulate adsorption and release
of the tracer in the biofilm. This model has been shown in the past to be a good model
for the hydrodynamics of trickling filters (Beccari and Rozzi, 1979, quoted by Lens et
al., 1995).
The Simplified stagnant reactor model (Lens et al., 1995), as its name indicates, is a
simplification of the Stagnant reactor model. It consists of n MFRs in series with only
the first MFR connected to a SR.
The Biodiffusion model was developed by Riemer (1980) to model a submerged
downflow denitrification filter. The main flow is assumed to be plug flow with
longitudinal dispersion. However, the tracer is exchanged with the biomass by
diffusion through an area equal to the specific surface area. The tracer concentration in
the water becomes a function of distance along the filter axis and time, while the
concentration in the biofilm is a function of the same parameters and the distance from
the biofilm surface.
As indicated in Table 2.1E, Vasel and Schrobiltgen (1991) tested the validity of three
hydrodynamic models for a pilot-scale trickling filter: PFR, dispersion and MFR. The
47
filter was filled with plastic medium, and RTDs were measured using pulse injections
of LiCI on clean medium (with tap water) and on medium covered with biofilm at 4
different hydraulic loadings: 0.9, 1.7,2.6 and 3.4 m3/m3.d. The authors did however
not conclude on which of the models gave the best representation of the filter.
Ferchichi (1991) used the n MFR with dead-space model as a hydrodynamic model
for a pilot-scale trickling filter filled with plastic medium. He measured RTDs on the
filter with and without film accumulation, using pulse injections of tracer (not defmed)
and under hydraulic loadings of 10, 16.7 and 20 m3/m3.d. The author found a good
adequation between model output and experimental RTDs. However, he
acknowledged the fact that the model used was possibly not the most theoretically
adequate because it does not simulate zones of slow exchange in the filter. The author
suggested that a more theoretically acceptable model would comprise a slow-exchange
zone, dead-space and an active flowing fraction.
Wik et at. (1995) measured RIDs using again pulse injection of LiCl as a tracer on a
pilot-scale nitrifYing trickling filter filled with plastic medium and hydraulically loaded
at 15.3 and 30.4 m3/m3.d. They found that the n MFRs model gave a good fit to their
experimental RTDs. The value of n was found to be around 4.3, and the amount of
water in the filter was calculated to be around 4.7 m3 (the total volume of the filter
being 41.2 m\ They however acknowledged the fact that the process should not be
described theoretically using the n MFRs model since the water flows downwards and
therefore very little back mixing is likely to occur.
Lens et at. (1995) measured RTDs on rotating tubular biofilm reactors hydraulically
loaded to simulate high-rate trickling filters. They used pulse injections of NaCl as a
tracer, and tested the validity of 4 hydrodynamic models on their experimental data:
dispersion, n MFRs, (n+ 1) MFRs and SSR. They found high dispersion of tracer in
their reactors (dispersion numbers> 0.1), but did not compare the output of the
dispersion model with their experimental RTDs. Regarding the other models, they
found that neither the n MFRs nor the (n+ I) MFRs agreed well with the experimental
data. By contrast, the plot of the SSR model output gave a good fit to the
experimental RTDs. They concluded that the better agreement between the calculated
48
and measured RTDs after extending the n MFRs model with one stagnant reactor
supported the importance of tracer absorption and desorption inlby the biofilm.
Recently, Seguret (1998) used both the Dispersion with exchange model and the
Biodiffusion model to model RTDs of 8 full-scale high-rate trickling filters: 2 filled
with mineral medium and operated at hydraulic loadings of 4.5 and 6.1 m3/m3.d; 6
filled with plastic media and operated at hydraulic loadings ranging from 9.3 to 15.1
m3/m3.d. He used pulse injections ofLiCl as tracer. He found that both models gave an
adequate representation of the studied trickling filters.
In conclusion, it can be seen that several of the tested models adequately represent the
hydrodynamics of high-rate trickling filters. However, no specific model appear to
have been unanimously recognised to best describe the hydrodynamics of low-rate
trickling filters.
2.1.4 Modes of operation and other applications
2.1.4.1 Filter staging
The original trickling filter design was single-stage without recirculation. It is the most
common in the UK (IWEM, \988) and in the US (WPCF, \988). To improve its
performances, the trickling filter can also be operated:
• With recirculation;
• As double filtration;
• As two-stage filtration;
• As alternating double filtration.
The effect of recirculation is to increase the effective hydraulic loading on the filter in
proportion to the sum of feed and recirculated flows, causing greater scouring effect.
The strength of the applied combined feed is correspondingly reduced. Recirculation
also results in bringing the filter effluent back in contact with the biological population
for further treatment. Itis almost always included in high-rate trickling filter systems.
Recirculation does not concern settled solids: the majority of the active
microorganisms are attached to the filter media and do not pass out of the reactor as in
49
the activated sludge process, and recirculating solids has no beneficial Impact on
performance.
In double filtration, two similar beds are used in series, usually with senlement after
both stages. Senlement after the first stage avoids the risk of filter ponding in the
second stage (Pike, 1978). Absence of intermediate senlement has only slight effect on
performance, but there is an increase in the amount of solids discharged from the
secondary filter (Tomlinson and Hall, 1953; quoted by Pike, 1978).
In two-stage filtration, the first stage consists of a roughing or high-rate trickling filter,
followed after senlement by filtration using a low-rate filter. Without intermediate
clarification, the system does not provide bener treatment than the same two filters in
parallel because the overall organic loading and detention time is the same. Two stage
systems are also used when nitrification is required. The first-stage filter and
intermediate clarifier reduce the organic loading that allow nitrifying bacteria to grow
and to reproduce in the second-stage filter (WPCF, 1988).
Alternating double filtration consists of double filtration complemented by flow
diversion valves and additional pumping. The result is that the order of operation of
the two filters can be changed, usually at intervals of 1-2 weeks. The use of alternating
double filtration enables the doubling of the organic loading with negligible effect on
effluent quality (Pike, 1978).
2.1.4.2 Other applications
Other applications for trickling filters include:
• Combination with fixed-film processes;
• Nitrifying trickling filter.
A. Combination with suspended-growth processes
Trickling filters have been used in combination with suspended growth systems. The
most recent and successful application is known as the Trickling Filter-Solids Contact
process (TFSC). It has been widely described in the literature (Parker et aI., 1993;
Matasci et al;, 1986; Feodotoff, 1983; Norris et al., 1980). First tested in 1979, it
50
includes a trickling filter, an aerobic solids contact (ASC) tank, a flocculation zone and
a secondary clarifier.
In the process, the trickling filter is the primary organic removal unit: it reduces
filtered BOD and develops solids that can be flocculated. The role of the ASC tank is
then to encourage flocculation through contact between non settleable finely divided
solids in the trickling filter effluent and recycled biological solids from the secondary
clarifier. With sufficient retention, the ASC tank can also remove additional filtered
BOD. Fine bubble aeration is used to minimise turbulence. In some plants, the
recycled biological solids after secondary settlement are also reaerated prior to their
reintroduction in the ASC tank. Further solids flocculation is provided in the clarifier
(called flocculator-clarifier), through mild stirring in a specially designed centre well.
The resulting large settleable flocs are then removed during secondary settlement.
The size of the ASC tank and the duration of the aerobic periods depend on the
design and loading of the trickling filter (Parker et at., 1993). The residence time in
ASC tanks varies from 3 to 60 min, against 120 to 480 for AS tanks.
Three major process variations can handle a wide range of TSS and filtered BO D
loadings in the trickling filter effluent. Two factors govern the choice of mode: need for
particulate removal, and need for filtered BOD removal.
To distinguish it from other biological filter/suspended growth processes, the TFSC
process has been defined by the US Environment Protection Agency (US EP A) has
having the following characteristics:
• The primary function of the ASC tank is to increase solids capture and flocculation
and reduce particulate BOD;
• Most of the filtered BOD removal occurs in the biological filter;
• The ASC tank is not designed to nitrify, although nitrification may occur ill the
biological filter;
• The aerated solids contact period is I h or less, based on total flow including recycle:
• The solids residence time of the ASC tank is less than about two days (the typical
range being 0.2-\'0 day, depending on bioflocculation objectives).
51
Good quality effluent has been found, for example 10 mg/I BOO - 10 mg/I TSS (Norris
et al., 1980).
B. NitrifYing trickiingjilter
Nitrifying trickling filters are employed as an additional treatment to remove ammonia
from secondary effluent, i.e. which has received full carbonaceous oxidation by
trickling filtration or activated sludge (Thorn et al., 1996; Wik et al., 1995; Upton and
Cartwright, 1984). The disadvantage of low growth rate of nitrifiers due to
competition with heterotrophs is reduced. Indeed, the growth of heterotrophs IS
depressed due to the low level of organic material, previously removed by secondary
treatment. The design and operation of nitrifying trickling filters used to be similar to
those of low-rate trickling filters, apart from the use of smaller graded media and
higher hydraulic loadings, with continuous dosing (Upton and Cartwright, 1984).
More recently, standard high-rate designs have been used, with plastic medium (Wik
et al., 1995).
2.2 CHARACTERISTICS OF TRICKLING FILTER EFFLUENT
The nature and size distributions of contaminants in the influent to a trickling filter
(normally primary effluent) are modified through treatment. This is due to several
processes, including new cell synthesis, flocculation, adsorption, enzymatic
breakdown of macromolecules and biochemical oxidation (Levine et al., 1985).
These processes, classified by Gray (1992) as physical (adsorption) and biochemical
(bio-oxidation and synthesis) processes, generate a variety of end-products. Some of
these end-products are soluble or gaseous, e.g. nutrient salts or carbon dioxide, or
soluble products from the lysis of the micro-organisms comprising the film. The
remainder are present as solids that require separation from the effluent. These solids
are of three types: flocculated solids, detached fragments of the accumulated film and
the grazing fauna (fragments of their bodies and faeces).
S2
Characterisation of both the particulate and dissolved fractions of these end-products
in the effluent is important. With regard to the particulate fraction, the generally
quoted inferior performance of trickling filters as compared with activated sludge
treatment has been related to the unsatisfactory settlement of solids that are washed
out of the filter (Bank et aL, 1976; Foster, 1977; Sfuner, 1986; quoted by Zahid and
Ganczarczyk, 1990). These solids are found over the whole range of possible
contaminant size, but it is the colloidal and supra-colloidal fractions that can pass
through the secondary settlement tank which cause deterioration in the suspended
solids and organic content of the final effluent. Concerning the dissolved fraction, there
is growing concern regarding the generation of soluble microbial products (SMPs) by
biological waste water treatment processes since they could be refractory to further
treatment. Furthermore, the membrane fouling encountered during CFMF of trickling
filter effluent is partly related to the properties of the suspension to be filtered,
particularly the nature and size of contaminants.
It was therefore intended to characterise the trickling filter effluent as precisely as
possible, in terms of both the particulate and the dissolved fractions.
2.2.1 Particulate content
The particulate content of wastewater is modified during treatment through a trickling
filter. As a result, the trickling filter effluent particulate content includes:
• Unchanged influent material;
• Influent material partially degraded during treatment;
• Material generated during treatment.
As noted in § 2.1.1.3, film sloughing is the most important cause of solids production
by a trickling filter, and as such heavily influences trickling filter performance. It is a
cyclical seasonal phenomenon which can overload the humus tanks with solids and
cause a deterioration in effluent quality. The growth of the grazing fauna is slow in
winter when they also retreat deeper in the filter. However, the growth of the micro
organisms within the filter does not slow down to the same extent and the thickness of
the film increases. As the temperature rises in spring, the activity of the grazers
increases and this loosens the thick film from the medium. As a result the film is
53
dislodged from the medium and washed out ill large amounts, overloading the
secondary. Treatment performance is severely reduced during this period (Horan,
1990).
2.2.1.1 Composition of trickling filter effluent particulate matter
As mentioned before, effluent from trickling filters contain some residual wastewater
solids and some biological solids. However, most of the suspended solids washed
from a filter are fragments of biofilm detached from the filter (Howell and Atkinson,
1976). The readily settleable fraction is composed mainly of pieces of biological film,
scouring organisms and grazer debris (Bruce and Merkens, 1970). They have been
divided by Solbe et al. (1967) as animal (macro-invertebrates) and non-animal solids.
As a result, sludge from low-rate-filters, referred to as humus, contain a large
proportion of grazing fauna and animal fragments (Gray, 1992).
On the other hand, suspended solids from high-rate filters are more dispersed (Parker
et al., 1993), and high-rate filters produce sludge mainly composed of flocculated
solids and detached fragments of film.
2.2.1.2 Particle size distribution of trickling filter effluent
The concept of particle size distribution (PSD) has been introduced in § 2.\.3.2.B.
Few data are available on PSD of trickling filter effluent.
Adin et at. (1989), Adin and Alon (1993) and Alon and Adin (1994) studied PSD (in
number and in volume, over a particle size range of I to 300 ILm) of various types of
waste water, including trickling filter effluent. They found median diameters of
approximately 20 ILm for trickling filter effluent for PSDs by number. As mentioned
in § 2. \.3.2.B, they fitted their curves of oversize cumulative distributions (OCDs)
with exponential and power law functions via least square correlation analysis. They
found that in most cases the exponential function described the distributions slightly
better than power law function, but used the latter for modelling since a similar
function had been applied to various other types of water and waste water (e.g.
Kavanaugh et al., 1980).
54
Zahid and Ganczarczyk (1991) studied some physical properties of suspended solids
in high-rate trickling filter effluents (organic loading rate = 480 gBOD/m3 d; hydraulic
loading rate = 8 m3/m2 d). The sizing and counting technique used was microscopy
followed by image analysis and two magnifications were used to cover a \Vide range of
particle size. On average 300 particles manually selected at random were measured for
each sample. The authors found, for distributions by number, mean values of particle
longest dimensions, breadths and equivalent diameter of 106, 62 and 68 Ilm
respectively. They also found that on average, about 80% of the measured particles
had their equivalent diameters in the range up to 100 Ilm.
It therefore appears that more work is required in terms of particle sizing analysis of
trickling filter effluent.
2.2.2 Dissolved matter
The dissolved content of primary effluent is modified during biological treatment
through a trickling filter. As a result, and similarly to the particulate content, the
dissolved content of trickling filter effluent is composed of:
• Material unchanged by biological treatment;
• Influent materials partially degraded during treatment;
• New material produced during treatment.
2.2.2.1 Composition of trickling filter effluent dissolved matter
No results were found in the literature on the detailed composition or identity of
organic matter in low-rate trickling filter effluent.
Rebhun and Manka (1971) investigated the composition of dissolved organic matter in
settled high-rate trickling filter effluent. The trickling filter was fed for 80% with a
mostly domestic sewage, the remaining 20% accounting for industrial wastewater from
light industries. The settled effluent was characterised by a COD/BOO ratio of 30.
After sampling, samples were centrifuged, concentrated and filtered at 0.45 Ilm prior
to fractionation. The authors found for three samples the average composition of
dissolved organics (as a proportion of total COD) given in Table 2.2A:
55
Table 2.2A: Nature distribution of COD in high-rate trickling filter eJjluent (after Rebhun and Manka 1971) ,
Fraction Proportion of total COD (%)
Ether extractable 8.3 Anionic detergents 13.9
Carbohydrates 11 .5 Tannins 1.7 Proteins 22.4
Humic substances 41.6
It appears that humic substances accounted for 40 to 50% of the effluent total COO,
while proteins were the second major group of effluent organics. Unfortunately, the
authors did not study the influent to the trickling filter, and it is not possible to see
the effect of the trickling filter process on the wastewater composition.
The terms "humic substances" was used in this paper to describe biologically resistant
organic material in soil and surface waters. These substances are divided into three
subgroups: humic acids, fulvic acids and hymathomelanic acids. They are formed by
microbial action in a complex, two-stage process. The two stages were decomposition
of original plant and animal residues to simpler compounds, and subsequent synthesis
of specific high molecular weight substances, so called humic substances (Kononova
and Aleksandrova, 1959, quoted by Rebhun and Manka, 1971). However, since the
authors had not studied the composition of wastewater prior to biological treatment, it
is difficult to confirm that humic substances were actually generated during biological
treatment, or that were already present in the feed to the trickling filter.
More recently it has been found in various laboratory-scale studies that the bulk of
soluble organic matter in biological wastewater effluents is of microbial origin rather
than originating from the influent to the treatment (Boero et aI., 1996, 1991; Noguera
et al., 1994; Rittmann et aI., 1987; Narnkung and Rittmann, 1986; Gaudy and Blachly,
1985; Grady et aI., 1984; Parkin and McCarty, 1981; Siber and Eckenfelder, 1980;
quoted by Kuo and Parkin, 1996). This matter is referred to as soluble microbial
products (SMPs).
SMPs have been subdivided into two categories, based on the bacterial phase from
which they are derived (Boero et aI., 1991):
56
• Substrate utilisation associated products (or growth-associated); they are
intermediates or end products of substrate degradation, cell metabolism or cell growth;
• Biomass associated products (or non growth-associated); these result from
endogenous respiration, i.e. cell lysis and decay.
SMPs are composed of a wide range of organic compounds that differ in structure and
MW: hwnic and fulvic acids, polysaccharides, proteins, nucleic acids, organic acids,
amino acids, antibiotics, steroids, enzymes, structural components of cells, and other
products of metabolism (Rittmann et aI., 1987; Manka and Rebhun, 1982; Parkin and
McCarty, 1981; Saunders and Dick, 1981; Chudoba et aI., 1980; quoted by Kuo and
Parkin, 1996).
Kinetic models describing the formation of SMPs by both suspended cultures and
fixed-film reactors are currently in the early stages of development because of the lack
of fundamental information. Most of the reports in the literature describing
production of SMPs during aerobic treatment concern the activated sludge process
(Pribyl et al., 1997; Grady et al., 1984; Parkin and McCarty, 1981). Data from earlier
studies indicates that a considerable amount of SMPs are generated during endogenous
metabolism phases. Starvation and biomass decay therefore appear to be the major
factors responsible for SMP formation (Pribyl et aI., 1997). Studies in suspended
culture systems have also shown that a significant portion of the SMPs generated by
activated sludge systems are not readily biodegradable. These compounds are often
refractory to secondary treatment (Ohron et al., 1989; Hejzlar and Chudoba, 1986).
They can however be partly metabolised over longer periods of time (Gaudy and
Blachly, 1985). There is by contrast little data on SMPs production by aerobic
biofilm reactors, and the limited research to date has focused primarily on laboratory
scale reactors.
In conclusion, the balance of the literature indicates that a majority of SMPs produced
through biological treatment are generated during endogenous respiration. These
conclusions have been drawn for aerobic processes mostly from research on lab-sale
activated sludge systems. Further research is required to establish the significance of
SMP generation in pilot and full-scale research, particularly in low-rate trickling filter.
57
2.2.2.2 Dissolved matter size fractionation techniques
The main characterisation techniques for wastewater dissolved matter have been
developed to study the molecular size distribution of organic contaminants. The three
main techniques found in the literature are:
• Successive ultrafiltration (UF) in stirred cells, which generates discrete distributions;
• Gel Permeation Chromatography (GPC, or Gel Chromatography, Gel Filtration
Chromatography or Size Exclusion Chromatography), which generates continuous
distributions;
• High Performance Size Exclusion Chromatography (HPSEC), which also generates
continuous distributions.
The sizes of dissolved organics are referred to as apparent molecular weight, shortened
to molecular weight (MW) because the separation techniques used are calibrated with
compounds of known molecular weight and not size.
In the case of GPC and HPSEC, the separation results from the distribution of the
sample between the moving mobile phase and the stagnant portion of the mobile
phase retained within the porous structure of the stationary phase. Residence time
differences are controlled by the extent to which the different fractions of the sample
can diffuse through the pore structure of the stationary phase. This depends on the
ratio of molecular dimensions to the distribution of pore-size diameters (Poole and
Poole, 1991). Very large molecules never enter the stationary phase and thus move
quickly through the void volume of the column, whereas smaller molecules can enter
the stationary phase pores, thus retarding the movement of the molecules in order of
decreasing molecular size.
The results of an analysis come as a succession of more or less discrete elution peaks.
Each peak is generated by one or several compounds of similar size. After calibration
of the column with samples of known molecular weight, it is possible to estimate the
molecular weight of the different detected peaks.
Table 2.28 summarises the advantages and inconveniences of the three techniques.
58
Table 2.2E: Advantages and inconveniences of the different dissolved matter [ractionation techniques
Technique Advantaoes Inconvenience
Successive U F • production of samples big • membrane fouling, resulting in enough for various analysis the potential retention by a
• relatively simple equipment membrane of compounds smaller than its MW cut-off
• necessity of distribution adjustments by calculation of
membrane rejection coefficients • length of analysis
C?fC • possibility at analysis on • usually necessity to collected fractions concentrate samples because
dilution in the mobile phase results in low concentrations that make detection difficult
• potential interactions between eluent, sample and
column; great diversity of nature and properties of
separated compounds, making the connection between
molecular size and elution time difficult
• lenoth of analvsis HPSEC • rapidity of analysis • potential interactions
• no sample concentration between eluent, sample and requirement column; great diversity of
• better separation than GPC nature and properties of due to higher pressure separated compounds, making
the connection between molecular size and elution time
difficult • detection limited to typical
LC detection
HP SEC has been used by various authors for the characterisation of water and
wastewater. For example, Kainulainen et al. (1994) used this technique to analyse lake
water before and after treatment by chemical coagulation, sand filtration, ozonation,
chlorination, granular activated carbon filtration and nanofiltration (I run). The
technique was used for similar studies on reservoir water by Kim et at. (1997) and on
surface water by Lund and Ormerod (1995). Other examples include characterisation
of land fill I each ate before and after aerobic or anaerobic biological treatment (Gourdon
et al., 1989) and analysis of water extracts from sewage sludge-soil mixtures at
different stages of decomposition (Katayama et al., 1986; Hashimoto et al., 1983), .
2.2.2.3 Fractionation of trickling filter effluent dissolved matter
There is little data on dissolved matter size fractionation in trickling filter effluent.
Manka et al. (1974) further characterised the humic substances extracted from high-
59
- ---------
rate trickling filter effluent in terms of molecular weight distributions. They used Gel
Permeation Chromatography (Sephadex gels G-25, G-50 and G-75 with respective
exclusion limits: 100-5000, 500-10000, 1000-50000) and absorbance at 360 nm for
detection. They found that most of the hwnic substances in high-rate trickling filter
effluent had a molecular weight between 1000 and 5000 Dalton.
Sachdevet al. (1976) studied the apparent molecular weight distribution of dissolved
organics in various secondary effluents, including trickling filter effluent (no further
details about the process). The samples were filtered at 0.45 ~m to eliminate the
particulate fraction, and concentrated by freeze-drying with an associated organic
carbon recovery of 92%. The samples were then fractionated by Gel Permeation
Chromatography (Sephadex gels G-IO, G-15 and G-25), and the fractions were
analysed for TOC. The authors found a bi-modal distribution for TOC: 62.2% of the
soluble TOC had a MW smaller than 700 Dalton, and 20.4% had a MW bigger than
5000 Dalton. It also appeared that there were no organics with MW between 1500
and 5000 Dalton. This result is contradictory with the finding by Manka et al. (1974)
that most of the hwnic substances in high-rate trickling filter effluent had a molecular
weight between 1000 and 5000 Dalton. This is possibly due to differences in design
and operation of the two different trickling filters. This could also be due to alterations
of the samples MW distribution brought by sample preparation.
Logan and Jiang (1990) studied the molecular size distribution of dissolved organic
matter of ground water samples and of trickling filter influent and effluent (no details
on design and operation). Trickling filter influent and· effluent samples were
sequentially prefiltered at 1.2 ~m and 0.2 ~m to remove particulate matter. They were
then fractionated using parallel ultrafiltration in stirred cell at MW cut-off of 10000
and 1000 Dalton. The various fractions were analysed for total organic carbon (TOC).
The distributions obtained were adjusted using a model based on membrane
permeation coefficients. This was to take into account membrane rejection, i.e. the fact
that dissolved organics smaller than the membrane MW cut-off can be retained by this
membrane. Indeed, experiments by Alleman (1986) (quoted by Logan and Jiang, 1990)
on paper-mill wastewaters indicated that dissolved organics smaller than the
membrane pores could be underestimated by as much as 70% when membrane
60
rejection was not determined. The dissolved organic carbon (DOC) fractionation in
wastewater before and after a trickling filter is given in Table 2.2C.
Table 2.2C: DOCfractionation of trickling filter influent and effluent (after Logan and 1" 1990) IOn/<,
DOe (moll) Fraction (MW) Influent Effluent
< 1000 7.0 (51.1 " 10.1 (63.5) 1000-10000 1.0 (7.5 2.8 (17.3)
> 10000 5.6 (41.4) 3.1 (19.2) Total 13.611 00.0) 16 (100.0)
": In parentheses, proportion (%) of total concentration.
It appears that the proportion of compounds with low MW is higher in trickling filter
effluent than in the influent. However, these results are questionable because the
effluent DOC was higher than that of the influent.
Confer et af, (1995) used the same technique as Logan and Jiang (1990) to analyse the
molecular weight distribution of trickling filter influent and effluent from the same
plant. The only difference in sample preparation comes from the fact that samples
were prefiltered at 0.45 IlII1 prior to ultrafiltration, instead of at 1.2 then 0.2 I!m for
the previous paper. The distributions were also adjusted using rejection coefficients,
and are presented in Table 2.2D.
Table 2.2D: DOC fractionation of trickling filter influent and effluent (after Confer et al 1995) .,
Doe mg/I) Fraction (MW) Influent Effluent
< 1000 13.6 (68.2)" 5.1 (60.9) 1000-10000 4.6 (22.7) 0.9 (10.9)
> 10000 1.8 (9.1) 2.4 (28.2)
Total 20.0 (100.0) 8.4 (100.0)
": In parentheses, proportion (%) of total concentration.
In this case again, DOC in the trickling filter influent was predominantly small
molecular weight « 1 000 Dalton). DOC concentrations decreased in the low and
intermediate MW fractions, but increased in the large molecular weight fraction
compared to influent values. The predominance of low MW DOC in trickling filter
effluent agrees with data previously obtained at the same plant (Logan and Jiang,
6 1
1990; Amy et al., 1987a). However, the results in terms of effect of treatment on
DOC fractions contradict those by Logan and Jiang (\ 990). The latter had found an
increase in DOC through treatment for both the small and intermediate fractions, and a
decrease for the high molecular fraction through treatment by the same trickling filter.
This tended to prove that SMPs had a small MW « 1000 Dalton). On the other hand,
the results from Confer and Logan (\ 995) tend to prove that SMPs are of larger MW
(> 10000 Dalton). This contradiction could partly be explained by the fact that in
1990, the total DOC increased from influent (13.6 mg/l) to effluent (16 mg!l), while in
1995, the pattern conformed more to logic with DOC reduction from 20 mg!l in the
influent to 8.4 mg/l in the effluent.
This review on MW distribution of trickling filter effluent shows variolls, sometimes
contradictory, findings. This highlights the difficulties associated with the field of
MW distribution. However it seems that these distributions for TF effluent are
bimodal, with most of the organic matter smaller than 700-1000 Dalton, relatively few
matter in the 1000 to 5000-10000 range, and a little bit more above 10000 Dalton.
2.2.2.4 Fractionation of model biofilm reactor effluent dissolved matter
Given the limited availability of data on soluble residues in trickling filter effluent, it
was decided to extend the literature review to model fixed-film reactors. Indeed, even if
the transport of water, substrates and gas vary with the type of fixed-film reactor, the
processes occurring within the biofilm are similar for all reactors of this type (la Cour
Jansen and Harremoes, 1984). The substrates have to be transported in soluble form
inside the biofilm to the bacteria where the reaction takes place, and the reaction
products have to be transported out again.
Namkung and Rittmann (1986) studied SMPs formation kinetics in a lab-scale biofilm
reactor fed with phenol (MW; 94 Dalton). After prefiltration at 0.45 J.lm, molecular
weight distributions were carried out using successive ultrafiltration in stirred cell at
MW cut off of 10000, 1000 and 500 Dalton. The authors found that more than 80%
of the reactor effluent DOC had a molecular weight greater than 500 Dalton. Since the
sole organic carbon source in the feed was phenol, they concluded that the majority of
effluent DOC consisted of SMPs, with only a small fraction of the effluent DOC
62
being the residual original substrate. They also concluded that the SMPs consisted
mostly of high MW compounds rather than low MW compounds. They reported that
70% of the effluent DOC had a MW greater than 1000 Dalton, with 30-64% greater
than 10000 Dalton. As a result, the effluent DOC distribution was bimodal, with a
lowest DOC concentration in the 500-1000 Dalton range. The authors also found
through both experiments and modelling that the concentrations of the effluent SMP
and DOC were directly proportional to the influent substrate concentration for the
conditions tested.
Confer and Logan (1997a, I 997b) used UF to study the molecular weight distribution
of hydrolysis products during biodegradation of model macromolecules in both
suspended and biofilm cultures. The samples were centrifugated and prefiltered at 0.2
J.Lm prior to UF at a MW cut-off of 10000 Dalton. Further UF at a MW cut-off of
1000 Dalton was used in the study of polysaccharide degradation (I 997b ). The model
macromolecules were, in one case (l997a) a protein (Bovine Serum Albumin: BSA),
and in the other case (I 997b ) polysaccharides (dextran and dextrin). In the case of
BSA (MW = 65000 Dalton), the biofilm reactor was a lab-scale batch reactor matured
with high-rate trickling filter influent (which included recycled effluent), before being
acclimated to BSA just before the experiments. Molecular weight distribution showed
that very little intermediate molecular weight (2000-10000 Dalton) fragments
accumulated in the bulk solution: a maximum of 3 mgil during the five hour time
required to totally degrade a solution with an initial concentration of BSA of 150 mgil.
Parallel experiments were run with batch suspended culture reactors inoculated in
three different ways to represent a range of bacterial diversity: with a protein
degrading isolate, a commercial BOO test inoculum (limited diversity culture) and a
wastewater inoculum. The authors found that intermediate molecular weight protein
hydrolytic fragments were produced and released in solution in the three suspended
culture reactors. In batch reactors with initial BSA concentrations of 100 mgil,
maximum concentrations of intermediate molecular weight hydrolytic fragments of 25,
10 and 6 mgil were found respectively in pure, limited diversity and waste water
cultures. Since the concentration of material in the intermediate fraction decreased as
culture diversity increased, the authors suggested that the involvement of many
microbial species during protein degradation limits the accumulation of hydrolytic
63
fragments under conditions typical of wastewater treatment systems. The authors
also concluded that because the concentration of protein hydrolysis fragments released
in high diversity wastewater inoculated reactor was low and these fragments were
rapidly degraded, then the release of hydrolysis fragments should have little impact on
overall macromolecule degradation kinetics in domestic waste water treatment systems.
Two conclusions can be drawn which are of interest for our study: fixed film reactors
like the trickling filter, acclimatised with real sewage i.e. possessing a high bacterial
diversity, produce hardly any hydrolysis products during protein degradation. On the
other hand, in the case of suspended culture reactors, the lower the culture bacterial
diversity, the higher the concentration of hydrolysis products.
As mentioned before, similar experiments were performed on polysaccharides (Confer
and Logan, 1997b): dextran (MW = 70000 Dalton) and dextrin (MW = 86000 Dalton).
In all reactor configurations, and for all inocula, small molecular weight
polysaccharides « \000 Dalton) accumulated in solution during polysaccharide
degradation. Particularly, in the case of the biofilm reactor fed with dextran, 25% of
polysaccharides in solution were smaller than \000 Dalton after only 0.1 h. This
molecular weight transformation occurred even though less than 10% of total substrate
was assimilated into the biofilm. This accumulation of hydrolytic fragments was
consistent with results from both batch and continuous suspended culture
experiments, and the authors concluded that this was evidence that there can be a
change in molecular weight distribution without substrate uptake and that hydrolysis
and uptake are not tightly coupled. The appearance of hydrolytic fragments ill
solution has been noted before during polysaccharide degradation by various types of
reactors (Banerji et aI., 1968; Haldane and Logan, 1994; Larsen and Harremoes, 1994;
Confer and Logan, 1997b; quoted by Confer and Logan, 1997a). It appears that the
concentration of hydrolysed material accumulated during the degradation of
polysaccharides is relatively large in comparison to that accumulated during the
degradation of proteins (1997a).
64
2.3 SECONDARY SETTLEMENT AND TERTIARY TREATMENT
2.3.1 Secondary settlement
As mentioned previously, the role of a trickling filter is to convert dissolved pollution
into particulate, settleable pollution. They are therefore traditionally followed by
secondary settlement tanks, also known in the case of low-rate trickling filters as
humus tanks, which function is to produce a clarified effluent. Good performance of
this secondary settlement is regarded as a key parameter to overall trickling filter
plants performance.
2.3.1.1 Parameters affecting secondary settlement of trickling filter effluent
Secondary settlement tanks are therefore needed to remove solids sloughed off
cyclically during periods of unloading with low-rate filters, and for removal of lesser
amounts of solids sloughed off continuously by high-rate filters (Metcalf and Eddy,
1991). Various parameters influence the performance of this settlement.
A. Rate of up flow tricklingfilter
The rate at which the trickling filter process is run influences the effluent settle ability .
For example, settling characteristics of sludge from high-rate trickling filters seem to be
typically poor (Samer, 1986). This is explained because the most active organisms at
the top of the biological film are continuously washed out of the filter because of the
shear stress exerted by wastewater. As a result a large portion of the washed out
sludge is relatively "young", or has a low "sludge age"; and it has been proved in the
case of activated sludge (Pavoni et al., 1972; Beccari et at., 1980) that the higher the
sludge age, the better its settleability and the lower the effluent TSS concentration.
Samer (1986) also quoted results from a previous study (Samer, 1980) in which it was
found that a high organic loading in a pilot-scale trickling filter accelerates biofilm
growth. The age of the sludge washed out of a trickling filter influencing effluent
settleability, an increase in organic loading rate (increasing biofilm growth rate) results
in higher TSS concentration in the settled effluent.
65
Low-rate trickling filter still encounter settlement problems. An explanation given by
Gray (1992) is that low-rate trickling filters usually achieve high levels of nitrification,
and a long sludge retention time within the humus tank may give rise to anoxic
conditions and problems of denitrification. This results in the carry over of sludge in
the final effluent.
B. Tank design
In common with primary and activated sludge settlement tanks, the settlement tanks
following trickling filters can be divided into three types according to flow pattern:
longitudinal flow, radial flow or upward flow. According to Gray (1992), deep square
tanks of small cross-sectional area with an inverted pyramid bottom are common in
the UK. They differ from activated sludge settlement tanks in that sludge
recirculation, essential to the activated-sludge process, is lacking. All the sludge from
trickling filter settlement tanks is removed to sludge-processing facilities.
The four most important design parameters influencing secondary settlement tanks
performance are surface-settling rate, solids-loading rate, weir overflow rate, and
detention time (WPCF, 1988). Typical values used for trickling filter settlement tanks
in the USA are given in Table 2.3A.
Table 2.3A: Typical desiJIn and operation values for tricklin!lfilter seUlemenl tanks
Averac e value Parameter WPCF (1988) Metcalf and Eddv (1991)
Su rfaCrsettli~)g rate 15- 5 5 16 - 25 (peak: 41-49) m 3/m'
Solids-loading rate Ikn/m'.ci\
50-120 70 - 117 (peak: 187)
Weir overflow rate 125-250 -·lm 3/m.d)
Residence time (h) 1 - 3 -Deoth (m) 2.4 - 5.5 3.0 - 4.6
C. Temperature
Secondary settlement of trickling filter effluent is affected by temperature. This effect
is mainly due to the temperature influence on the physical characteristics of
wastewater (Adin et aI., 1984).
66
Shriver and Bowers (1975) studied secondary settlement of trickling filter effluent in
winter temperatures. As the temperature of water decreases the viscosity mcreases.
This is also true of density at the usual temperature range of wastewater. From the
specific gravity of the solids plus the viscosity and density of wastewater at various
temperatures, the terminal velocity for various particle sizes can be calculated, using
Stokes law:
where
V - k g (p, - p) dp
,- J.!
vt = terminal velocity of particle (m/s)
g = acceleration of gravity (m/s2)
Ps = density of particle (kg/m3)
p = density of fluid (kg/m3)
dp = particle diameter (J.!m)
J.! = viscosity of fluid ( centipoise)
2
The terminal velocity of a given particle size therefore decreases with temperature
because of increased fluid viscosity. These theoretical conclusions have been
confirmed by the authors at three different full-scale trickling filter plants.
D. Film accumulation and fauna
Gray and Learner (1984) found that sludge production by a low-rate trickling filter
was directly liked to seasonal film accumulation, low at times of high adsorption of
solids when the rate of film accumulation was the greatest and high during times of
maximum film accumulation when fewer solids were being adsorbed and also during
sloughing.
The good settleability of TF effluent solids has been linked with the activity of the
grazing fauna in the trickling filter. Solbe et al. (1967) studied the effect of macro
invertebrates on settlement of trickling filter effluent. They carried out a 2.5 years
study on a pilot-scale low-rate trickling filter, 1.8 m deep, filled with 63 mm graded
blast furnace slag, and fed with settled domestic sewage at a hydraulic loading of 0.47
67
mJ/mJ.d and an organic loading between 100 and 150 g/mJ.d. The settleability of
trickling filter effluent was studied monthly over a period of 1.5 year, using a 1 m
deep settlement column. The authors carried out 24 h settlement tests, and divided the
effluent TSS into non-settleable solids, aninJal and non-animal settleable solids. They
found that the non-animal settleable solids represented the biggest part of effluent
TSS, and followed the same seasonal pattern as the effluent TSS, being maximum in
March-April-May. The aninJal settleable solids reached a peak of nearly 40 of total
TSS in August, but decreased to about 5% in December and January. The authors also
found that the animal material settles more rapidly than the other fraction, with nearly
70% settling within the first 15 min compared with only 35% of the non-aninJal
fraction. Comparing rapidity of settlement at high and low (> lOO mg/!, < 100 rng/!)
TSS concentrations in the effluent before settlement, they found that settlement was
faster at periods of high concentrations.
Poor settleability of trickling filter effluent solids has also been linked by Parker et al.
(1992) to the fact that some of the solids originate from the inner anaerobic layers of
the biofilm, and therefore display poor bioflocculation potential.
Whereas the bulk of the solids settle easily, there is a fraction of fine solids that do not
and are carried out of the humus tank in the fmal effluent. These fine solids are
responsible for a significant portion of the residual BOO in the fmal effluent, and
some form of tertiary treatment is then required.
2.3.2 Tertiary treatment
Increasingly stringent standards are imposed for discharges of waste water treatment
plant. In particular, the European Union Wastewater Treatment Directive of 1991
created more stringent requirements for various parameters, including BOO, COD
(125 mg/l and/or 75% removal efficiency) and TSS. Since these new standards cannot
be met even after optimisation of secondary treatment processes, add-on treatments
or tertiary treatments have to be added after these secondary treatment processes. A
good recent review of existing tertiary treatment techniques has been given by lWEM
(1994). They include:
• Lagooning (conventional or raft lagoons);
68
• Irrigation over grassland or grassplots;
• Constructed wetlands (overland-flow, vertical-flow or rootzone-flow wetlands);
• Straining systems (drum filters or microstrainers);
• Sand filters (slow, rapid gravity, moving-bed, automatic);
• Upward flow clarifiers.
Some techniques are designed specifically for disinfection. They include:
• Chemical techniques (addition of chlorine, peracetic acid, ozone or lime);
• Coagulation-flocculation;
• Membrane filtration;
• UV irradiation.
Nutrient removal is usually achieved by coagulation-flocculation or nitrifying trickling
filters.
Table 2.36 summarises the existing processes and their application.
Ta bl e 2.38: Tertiary treatment processes an
Process TSS, BOO, COD removal
Laaoonina 1 Grass olots 1 Reed beds 1
Strainina systems 1 Sand filters 1 Upward flow 1
clarifiers Chlorination
Ozonation Membrane 2 filtration
UV irradiation
Coagulation- 2 flocculation
Nitrifvino filters
1: process pnmary obJecllve 2: also partly efficient for
d I appl ications
Nutrients removal
2 2
1
1
Disinfection
2 2
2
1 1 1
1
2
Data by Griffin (1998) indicate the proportion of low-rate single-pass trickling filter
followed by built tertiary treatment. They are summarised in Table 2.3C.
69
Table 2.3C: Proportion by population-equivalent treated and by number of plants of low-rate sin~le-pass tricklin filter plants followed by tertiary treatment
Proportion (%) Tertiary treatment by Population-equivalent by Number of plants
treated No tertiary treatment 60.2 77.6 Upward flow clarifiers 4.2 1.2
Grass plots 10.7 7.3 Reed beds 24.9 13.9
It appears that 40% of the effluent from low-rate single-pass trickling filters of the
largest UK water authority require built tertiary treatment.
Reports from the literature also mention the use of chemical addition as tertiary
treatment for trickling filter effluent. The chemicals used as coagulant-flocculant for
trickling filter plants are typically ferric sulphate and chloride, aluminium sulphate and
lime (IWEM, 1994).
Shriver and Bowers (1975) investigated the use of alum as a coagulant for trickling
filter effluent. They also investigated ferric chloride and a combination of alum and a
polymer, but focussed on alum because of handling, performance and economic
considerations. They ran series of Jar tests on settled trickling filter effluent samples
to determine the effect of alum treatment on BOO, TSS and soluble phosphate. They
found BOO and TSS concentrations decreasing with increasing alum concentrations
above 10 and 20 mgll respectively, without pH contro!' They also observed
phosphate removals of over 70% at 80 mgll alum with initial pH adjustement to 7.0
before alum treatment. They concluded that dosage requirements of 50 to 150 mgll
would probably satisfy most needs for trickling filter effluent applications
Ineson (1997) reported on full scale trials of chemical phosphorus removal on a double
trickling filtration plant. Ferric sulphate was used as a coagulant-flocculant. Three
dosing locations were tested, individually or combined, i.e. for single or dual point
dosing: the primary settlement tank, and the first and second-stage secondary
settlement tanks. Trials of single-point dosing at the second-stage secondary
settlement tank were rapidly aborted because of the formation in the effluent of non
70
settling micro flocs. The dose was optimised at 360 kg/day as iron, which gives a
concentration of 13.8 mgll. Tests of single and dual dosing showed that the best P
removal was achieved by single point dosing on the first-stage secondary settlement
tank, while the BOO and COD removal were increased from respectively 60 to 77%
and 56 to 70%. However, no improvement was noted on TSS removal. Similar results
in terms of P removal were achieved with dual dosing.
Nickerson et al. (1974) also reported on the use of chemical addition to improve the
quality of trickling filter effluent. The solution adopted was the dosing of ferric
chloride and cationic polymer before primary clarification, followed by anionic
polymer dosing before secondary settlement. The average doses were of 110, 1.35 and
0.75 mg/I for respectively ferric chloride, cationic polymers and anionic polymers. The
chemical additions to the primary clarifiers achieved 85% and 60% TSS and BOO
removal respectively. Altogether, the treatment efficiency averaged above 90% BOO
removal. However, the sludges resulting from the use of chemicals are more
voluminous and more difficult to dewater than normal sludge.
The known limitations of the current tertiary treatment techniques (for example high
sludge production in the case of chemical addition) suggest that alternatives have
worthy of investigation. One of these alternatives is crossflow filtration.
2.4 CROSSFLOW FIL TRA TION OF WASTEWATER
Crossflow filtration (CFF) is a pressure-driven membrane separation process. In CFF,
the feed stream flows tangentially to the upstream surface of the membrane at high
velocity, and the generated shear stress limits the build-up of a filter cake at the
membrane surface.
CFF has had a number of industrial applications. They include separation in the food
industry (concentration of fruit juices and maple syrup, separation of milk proteins
from cheese whey, recovery of proteins and carbohydrates from corn-processing
effluents), treatment of industrial effluent (separation of electroplating and metal
71
finishing wastewater), desalination of brackish and seawater. More recently,
applications in domestic wastewater treatment have been reported.
This section will start with a brief summary of the fundamentals of CFF. The current
applications of the technique to domestic wastewater treatment, and particularly the
previous work done on CFF of trickling ftiter and more generally fixed ftim reactors
effluent will then be reviewed.
2.4.1 Fundamentals of crossflow filtration
2.4.1.1 Mechanisms
In conventional membrane dead-end ftitration, the suspension to be filtered flows
perpendicularly to the membrane under an applied pressure. As a result, clogging of
the membrane occurs in a relatively short time because of build-up of retained material
constituting a ftiter cake at the surface of the membrane.
By contrast, in CFF, the flow of the suspension is parallel to the membrane surface,
and because the system is pressurised, particle-free fluid is forced through the ftiter.
Particles larger than the membrane pores are retained on the high pressure side. But, as
a result of the crossflow, the formation of the ftiter cake is limited by the scouring
action of the suspension flowing across the membrane. The stream that passes
through the membrane is known as the permeate (or ftitrate), while the stream that
does not go through the membrane, i.e. corresponding to the feed minus the permeate,
is known as the concentrate (or retentate).
2.4.1.2 Membrane fouling
The problem inherent to membrane filtration in general, and to CFF in particular, is
the decrease of permeate flux due to membrane fouling. The control of membrane
fouling is necessary to extend continuous running time and reducing the frequency of
interruption of the process for chemical cleaning (Otaki et al., 1998).
Membrane fouling has been related by Visvanathan et al. (1986) (quoted by Pouet,
1994) to three phenomena at the membrane-suspension interface:
72
• Deposition of particles and colloids bigger than the pore sizes on the surface of the
membrane, resulting in the build-up of a cake; this type of fouling is usually easily
reversible;
• Concentration polarisation, i.e. high soluble material concentration at the membrane
surface reaching the point where some of the soluble matter comes out of solution
which leads to the formation of a gel layer; this fouling is also reversible;
• Internal clogging of the membrane pores by adsorption inside the structure of the
membrane of colloidal material; this type of fouling is less reversible (Wiesner, 1992).
2.4.2 Design and operation
2.4.2.1 Membrane categories and configurations
A. Membrane pore size/molecular weight cut-off
Membranes are categorised according to their rejection characteristics, rated on the
basis of their nominal pore size or molecular weight cut-off (MWCO). Table 2.4A
gives the ranges of pore size anellor MWCO for the various types of membranes.
Table 2.4A: Pore size and molecular weight cut-off ranges for the various type of membranes
Membrane Pore size (!!m) MWCO (Dalton)
Microfiitration (MF) 0.1 - 5 > 5 x 107
Ultrafiltration (UF) 0.005 - 0.2 10'-5x107 Nanofiltration (NF) 0.002 - 0.005 200 - 10'
Reverse osmosis (RO) undetectable < 200
RO is primarily used for the separation of macromolecules, colloids, molecules and
ions from a liquid. UF retains material bigger than the order of the nanometer,
including bacteria, macromolecules and colloids, the lower range of UF !higher range of
RO being known as NF. MF is capable of removing particulate solids, bacteria,
macromolecules and colloids from a liquid. Another process, dynamic filtration, is a
type of MF in which the effective membrane consists in a dynamic layer pre-coated
onto a woven fabric; the precipitated precoat plus the filter cake form the dynamic
membrane. The process has to be stopped every few hours to remove the pre-coat
and renew it.
73
There are many similarities between UF and MF: the hydrodynamic pattern is similar,
the hardware is also similar, and the fouling problems of the membrane are nearly the
same.
B. Membrane configurations
The membranes used for CFF can be classified into 4 broad categories:
• Plate and frame units;
• Spiral wound units;
• Tubular units;
• Hollow fibre units.
Plate and frame units were the earliest manufactured, using flat sheets membranes.
This configuration ahs been refined with the years and is still successfully used today.
Spiral wound membrane elements were developed to overcome the high-cost of plate
and frame systems by fit into tubular pressure vessel. They offer savings in energy
and space.
Tubular membrane units were developped early in the history of CFF, and very
popular due to their ability to tolerate high level of suspended solids. Polymeric
tubular membranes are cast onto porous media such as non-woven fabric or glass
reinforced plastic. Inorganic tubular membranes, however are usually self-supporting
due to the greater strength of the membrane material.
Hollow fibre membranes were developed to increase the filtration area per unit
volume. The surface is anisotropic, with the active membrane either on the inside or
on the outside of the hollow fibre. The hollow-fibres are self-supporting, enabling the
use of backwashing for cleaning. The units are composed of bundles of thousands of
fibres, potted at both ends in a resin header and inserted in a cylindrical shell.
2.4.2.2 Operating parameters affecting performance
The performances of CFF are estimated in terms of both permeate quality and
permeate flux, i.e. permeate volumetric flow rate per unit area of membrane. The
74
permeate quality is mainly function of the membrane pore size or molecular weight
cut-off and of the size distribution of contaminants. The permeate flux is a function of
several operating parameters (AI Malack and Anderson, 1996), including:
transmembrane pressure, crossflow velocity, temperature, pore size of the membrane.
Other parameters also affect the permeate flux, result from the interactions between
nature of membrane and of the suspension to be filtered. In particular, electrostatic or
zeta-potential attraction or repulsion between the solids of the filter medium and the
particles in the fluid is important (lohnston, 1990).
Extensive literature reviews on the parameters affecting performance can be found in
the literature (Lojkine et al., 1992; Tarleton and Wakeman, 1993, 1994a, 1994b). The
main findings of these reviews are summarised below.
A. Transmembrane pressure
Although the transmembrane pressure (LlPt) provides the driving force for filtration,
increases in M t result in an increased permeate fllL'( only to a certain point. Indeed,
flux increases linearly with M t at low pressures, up to a critical or limiting pressure
(LlPtC). Above LlPtc , the rate of increase, the flux is independent of the pressure or the
flux decreases with increasing Mt. It appears that, above Mtc, increases in the
filtration driving force are counteracted by resistance due to deposition of the material
on the membrane surface and by fouling of the membrane. It is particularly the case
with concentration polarisation and the potential formation of a gel layer. The
permeate flowrate is limited to the rate at which it diffuses through the gel layer, and
increasing the pressure does not increase fllL'<.
The value of Mtc depends on crossflow velocity, pore Size and suspensIOn
concentration.
B. Cross flow velocity
Crossflow velocity (Ucr) is the second major parameter affecting the performance of
CFF. It directly affects the shear-stress at the surface of the membrane, and hence the
cake removal rate.
75
A large number of workers have found that increasing the crossflow velocity increases.
Several workers report a power law relationship between flux and cross flow velocity :
permeate flux = a Vel
where
a, b = arbitrary parameters
However, the effects of increased crossflow velocity can be confounded with the
effects of the simultaneous increase in transmembrane pressure, thus the benefits of
increased crossflow velocity may be reduced by pressure. Increasing the crossflow
velocity may also increase the rate of fouling, because the increased flux carries more
material to the membrane surface.
C. Temperature
Temperature as an effect on the permeate flux through viscosity. According to the
theory of flow through porous media, the filtration flux varies in inverse proportion to
the viscosity. Thus, when the temperature increases, the viscosity of the suspension
decreases (as long as it stays in the Newtonian area), and the permeate flux increases.
2.4.2.2 Expression of membrane resistance
The total hydraulic resistance to transport through the membrane is traditionally
expressed using the concept of additive resistances (Bhave, 1991 b). The permeability
of the membrane structure can be expressed as the ratio of driving force
(transmembrane pressure) to the total equivalent resistance to transport:
where
with
L =M, P R
T
Lp = permeability (ml /m2 h.Pa),
RT = total equivalent resistance to transport (m2.Pa.hlm3)
76
where
~ = intrinsic resistance of the membrane (m2 Pa.hlm3)
RF = resistance due to membrane fouling (m2 Pa.hlm3)
2.4.2.3 Antifouling techniques
Techniques have been developped to counteract and/or control membrane fouling.
They have been classified by Richaud-Patillon (1990) into two categories:
• Mechanical techniques, to increase or complement (by generating perturbations) the
shear stress generated by crossflow velocity;
• Physical-chemical techniques, to prevent fouling or modify it.
Mechanical techniques include: use of ultra-sounds (Porter, 1977; FJemming, 1988,
Tarleton, 1989); addition of mobile (Milisic and Bersillon, 1986; Rios et aI., 1986;
Clavaguera et al., 1991) or fixed (Jaffrin et aI., 1990; Field et al., 1992) turbulence
promoters; application of pulsated flow (Hlavacek, 1990; Richaud-Patillon, 1990;
Jaffrin et aI., 1988; Bersillon, 1986); backwashing (Lacoste, 1992; Moulin, 1990;
Richaud-Patillon, 1990; Milisic and Bersillon, 1986) (all quoted by Pouet, 1994).
Physico-chemical techniques include: flocculation-coagulation (Mallevialle et aI., 1993;
Lahoussine-Turcaud et al., 1990; Moulin, 1990; Richaud-Patillon, 1990);
electrofiltration (Jurado et al., 1993; Visvanathan et aI., 1990); ozone (Moulin, 1990)
(all quoted by Pouet, 1994). The chemical regeneration of membranes, required when
then permeate flux has reached an unacceptably low level, has also been put in this
category.
However most antifouling techniques make the CFF process uneconomical. This is for
example the case for the use of coagulants on a continuous basis, i.e. in-line
coagulation-flocculation prior to CFF (AI Malack and Anderson, 1996).
77
2.4.3 Applications in wastewater treatment
CFF has been applied to a range of wastewater streams, including pnmary and
secondary effluent and also sludge (Bindoff et al, 1988), digester supernatants (Al
Malack el al., 1996), landfill leachate (Visvanathan el al., 1994), for various industrial
and domestic effluents. This review wil focus on domestic wastewater treatment.
The pros and cons of the application of CFF in wastewater treatment are summarised
in 'fable 2.4B.
Table 2.4B: Advanlages and inconveniences of CFF applicalion in waslewaler «if! C d D 19n Irealmen! a ler ooper an ee, J
Benefits Constraints can be easily fitted as an add-on to high capital costs
existinq processes selection of membranes to suit the size high energy costs resulting from the use
of organic molecules present in effluent of hiqh pressures dynamic membranes can be designed to potential further treatment required for absorb COD and avoid need for further concentrate
treatment compactness in size as opposed to depth
filters possibility of simultaneous clarification
and disinfection without the risk of orqano-haloqenated compound formation
modularity: possibility to add more membrane modules
One of the drawbacks mentioned (further treatment for the concentrate) can easily be
tackled in the field of wastewater treatment: the concentrate can be treated by
anaerobic digestion (Cooper and Dee, 1995) or by reintroduction at the head of the
plant for primary sedimentation (Olivieri el al., 1991).
One of the mam advantages of membrane processes in wastewater treatment
(especially for tertiary treatment applications) is their modularity. For example, to
meet a 30-25 standard today using a membrane system which provides better than 5-
10 standards, it would not be necessary to treat all the discharge. When the standard
are tightened. additional modular units could be installed. On the other hand, using
existing process makes it virtually impossible to respond to tighter future standards
(Monk. 1993).
78
2.4.3.1 Secondary treatment applications
CFF has been used for the secondary treatment of domestic wastewater either purely
or as a part of a physico-chemical treatment, or combined with a biological process
(usually activated sludge).
A. Crossjlow filtration as a part of a purely physico-chemical treatment
Ultrafiltration
Recent applications of CFF include greywater recycling: CFF is used in buildings to
treat the building wastewater with the exception of toilet-flushed water (and
sometimes combined with rainfall runoff) in order to produce toilet-flushing and/or
irrigation water.
Microfiltration
Pouet (1994) used MF (0.2 !lm, ceranuc membranes) to treat wastewater having
undergone preliminary treatment and either:
• No further treatment;
• Primary settlement (2 h);
• A combination of electrocoagulation and flottation.
Under a ~, of 1.45 x 105 Pa and Ucr of 2.25 mls, the permeate fllL'{es obtained for the
first two suspensions had values of approximately 20 IIm2 h, while CFF of
waste water having undergone electrocoagulation and flotation gave permeate fluxes
just below 200 IIm2.h. The author concluded that settleable matter in raw sewage had
little effect on membrane fouling, most of the fouling being due to supra-colloidal and
colloidal matter. She however gave no data on permeate quality.
Dvnamic filtration
Gosling and Realey, 1992 (quoted by Cooper and Dee, 1995) used dynamic filtration
to treat primary effluent. They used aluminium sulphate as a membraning material.
The results found in terms of permeate quality are summarised in Table 2.4C.
79
Table 2.4C: Quality results achieved using dynamic filtration as secondary treatment (after Gosling and Rea/ey, /992)
Parameter (moll) Feed Effluent EO) 147 65 an 348 102 TSS 60 7
NH3-N 25 25
B. CFF in combination with biological treatment
The main attempts of using CFF in domestic wastewater treatment have been in
conjunction with biological·treament. Indeed, after removal of soluble biodegradable
material in a conventional biological treatment system, biomass must be separated
from the liquid stream to produce a high quality effluent. A secondary settlement tank
is used for the solid/liquid separation step, and often constitutes the effluent quality
limiting step. CFF has been used directly to replace secondary settlement, in reactors
known as membrane bioreactors, or as an add-on process after secondary settlement,
i.e. for tertiary treatment.
Membrane bioreactors
Membrane bioreactors (MBRs) integrate a biological process and a membrane unit for
solid-liquid separation. In MBRs, the microorganisms responsible for biological
reactions are separated from the effluent using the membrane and recycled or
maintained in the reactor. The biological process used in MBRs are usually of the
suspended culture type, mainly activated sludge, because the recycling of the biomass
in the reactor is beneficial to the process.
The main advantages of membrane bioreactors are that the process can be significantly
more compact than conventional processes, higher biomass concentrations can be
achieved which result in reduced quantities of excess sludge, and the effluent can be
particulate free and partially disnfected (Fane, 1996).
Numerous applications of MBRs can be found in the litterarure at lab-scale and pilot
scale. Table 2.40 gives some examples.
80
Table 2 4D· Membrane bioreactors references Biological process Filtration process Reference
PS UF Arika et al. (1977), Fane et al. (1980), Roullet
(1987), Krauth and Staab (1988, 1993), Chaize
and Huyard (1991), Magara and Itoh (1991),
Arnot et al. (1993), Ishiguro et al. (1994), Tardieu et al. (1996), Van Dijk and Roncken (1997), Cicek et al.
(1998) PO UF Choo and Lee _ (1996) PS suction UF Praderie (1996), Cote et
al. (1998) PS UF-MF Muller et al. (1995) PS MF Krauth and Staab (1988),
Vaid et al. (1991), Suwa et al. (1992), Trouve et
al. (1994), Kishino et al. (1996), Wisniewski
(1996) PO MF Brockmann and Seyfried
(1996) PS suction MF Yamamoto et al. (1989),
Benitez et al. (1995), Nagaoka et al. (1996), Davies et al. (1998),
Engelhardt et al. (1998), Gunder and Krauth
(1998) PS dynamic filtration Bailey et al. (1994b) PO dynamic filtration Bailey et al. (1994a)
AS = activated sludge AD = anaerobic digestion
It is however important to note that the latest developments in aerobic membrane
bioreactors concern suction filtration. Membrane modules, either in plate and frame
(manufactured by Kubota: Davies et al., 1998; Engelhardt et at., 1998: Gunder and
Krauth, 1998) or hollow-fibre (manufactured by Zenon: Cote et at., 1998; Engelhardt
et al., 1998; Gunder and Krauth, 1998) configuration, are immersed inside the aeration
tanle The permeate is extracted from the tank by means of a permeate extraction
pump that creates suction. As a result, there is no crossflow circulation.
81
2.4.3.2 Tertiary treatment applications
The use of membranes as a tertiary treatment process after existing plants has a great
potential (Vigneswaran et al., 1991; quoted by Ben Aim et al., 1993). Indeed, even if
membrane bioreactors, enabling the recycling of the biomass directly inside the reactor,
offer a great interest, they require a completely new design.
CFF has been tested as a way of helping to meet increasingly stringent standards
(Cooper and Rees, 1995). It has also been tested as a treatment for tenninal .'
disinfection (Langlais et aI., 1993; Olivieri et aI., 1991; ) in the case of discharge to
recreational or sea farming (fish, shellfish) areas, or simply to improve the water
quality of lakes and rivers (Rautenbach et al., 1996). One of the main interests of the
use of CFF in wastewater treatment is the potential for water recycling, increasingly
seen as a new water resource (Vera et aI., 1998; Richardson and Trussell, 1997; Fane,
1996; Neal, 1996; Okun, 1996; Stenstrom et al., 1982; Bailey et a/., 1974).
Applications for recycled wastewater are both industrial and domestic. Industrial
applications include cooling tower make-up and construction. Domestic applications
concern mostly irrigation of agricultural crops and non-agricultural land like golf
courses, parks, athletic fields, motorway medians and borders, nurseries. Table 2.4E
summarises applications of CFF as a tertiary treatment after other secondary
treatments than fixed-film processes.
Table 2.4E: Tertiary treatment applications ofCFF Preliminarv treatment CFF process
PS UF
PS MF
PS dynamic filtration
AWSP dynamic filtration
AS = activated sludge; AD = anaerobic digestion AWSP = anoxic waste stabilsation pond
82
Reference
Mandra et al. (1992), Vial et al. (1992), Olsen and Haagensen L1983~
Vera et al. (1998), Dittrich et al. (1996),
Jolis et al. (1996). Oesterholt and Bull
(1993). Dizer et al. (1993), Kolega et al.
(1991') AI·Malack and Anderson
(1997). Bailey et al. (1994b), Gosling and
Brown (1993) AI·Malack et al. (1998).
Johari et al. (1996)
It appears that MF seems to be the most considered option. We will focus in this
review on the use of CFF as a tertiary treatment after fixed film reactors in general,
and trickling filters in particular.
Reverse osmosis - nanofiltration (RO - NF)
Stenstrom et al. (1982) used RO to treat effluent from a high-rate trickling filter
treating a purely domestic wastewater. The RO unit consisted of tubular cellulose
ace.tate membranes. It was operated at a pressure of 40.8 x 105 Pa and a crossflow
velocity of 1.05 rnJs. It was cleaned every three days using a mixed chemical (citric
acid, enzyme detergent) and physical (spongeball cleaning) procedure. The feed to the
unit was trickling filter effluent having undergone flocculation, settlement, filtration
through a mixed filter media (anthracite coal, silica sand, gamet sand), chlorination and
prefiltration at 30 Ilm. The permeate flux ranged between 4 and 40 Vm2 h, with a
stabilized value after cleaning of 2 \.2 IIm2.h. Table 2.4F gives typical values of
performances of the system.
Table 2.4F: Quality results achieved using RO as tertiary treatment of trickling filter effluent (after Stenstrom et al., 1982)
Parameter (mg/I) Feed Permeate m:; 26.5 1 .5 TDS 671 98
total N 26 4.1 total P 9.3 0.21
Microfiltration
Langlais et al. (1992, 1993) used crossflow micro filtration as a tertiary treatment after
a biofilter producing poor quality effluent. They used the Memcor system, consisting
of hollow fibre modules made of polypropylene with a pore size of 0.2 Ilm.
Membrane cleaning was achieved by air backwashing every 15 min. Chemical cleaning
(using successively a solution produced by the membrane manufacturer, phosphoric
acid and hydrogen peroxide) was used if air cleaning had not brought the permeate flux
to a set value. The 8P, ranged between 0.1 and 0.5 x 105 Pa, and filtration runs were
made with and without crossflow, the membrane unit being fed with unsettled biofilter
effluent. A comparison between dead end runs and runs with crossflow velocity
showed little difference in permeate flux (only 9% more permeate after 70 h of running
83
for a run with crossflow velocity than for a run without). Mean flow rates were 75
IIm2 h without crossflow and 82 IIm2 h with crossflow. The performances of the unit
in terms of pollution removal are summarised in Table 2.40.
Table 2.4G: Quality results achieved using MF as tertiary treatment of biofilter effluent (after Langlais et al.. 1992)
Parameter Feed Effluent
BOO (mg/I 30-100 21 COD (mg/I 100-400 80-100 TSS (mg/I 1 1 - 8 8 -
turbidity (NTU) 17 -6 9 < 1 Faecal coliforms 1.6 x 106 - 1.5 X 109 < 1
(counts/1QO ml) Faecal streptococci 1.8 x 106_7.8x 107 < 1 (counts/1QO mll
It appears that average removal efficiencies of 70 and 60% were achieved for
respectively BOO and COD. as well as near total disinfection.
Olivieri et al. (1991) used crossflow micro filtration as a tertiary treatment after a
trickling filter. They also used the Memcor system, consisting of hollow fibre modules
made of polypropylene with a pore size of 0.2 J,1m. Membrane cleaning was achieved
by air backwashing at either a preset time interval or pressure drop. The feed to the
microfiltration unit was settled trickling filter effluent pre-screened at 200 J.1ffi. The
system operated at a low transmembrane pressure (6.P, = 0.4-0.9 x 105 Pa) in the dead
end or cross flow mode, and produced permeate fluxes between 80 and 130 IIm2 h.
Table 2.4H gives the average performances of the system over a two months period.
Table 2.4H: Quality results achieved using MF as tertiary treatment of trickling filter effluent (after Olivieri et al.. 1991)
Parameter Feed Effluent
BOO (moJl) 34.4 3.3 TSS (moJl) 77.9 0.92
Total coliforms 1.1 x 106 < 1 (counts/1QO ml) Faecal coli forms 0.4 x 10· < 1
(counts/100 mll Faecal streptococci 24752 < 1 (counts/1QO ml)
Coliphage (counts/100 11562 <5 ml)
84
Dvnamic filtration
Gosling and Realey (1992) (quoted by Cooper and Dee, 1995) used the Renovexx
process as a tertiary treatment after a trickling filter treating mostly domestic
wastewater. They used Aluminium sulphate as a membraning material and found the
following performance results. The performances of the process in terms of permeate
quality are presented in Table 2.41.
Table 2.41: Quality results achieved using dynamic jiltration as tertiary treatment of tricklingjilter ejjluent (after Goslinf< and Realey, 1992)
Parameter Feed Effluent BOO (mall) 1 7 3 COD (mgll) 120 48 TSS (mg/l) 32 2
turbidity (NTU) 23 0.4 P04-P (mall 6.7 0.4 NH3-N (mall 2.6 1 .9
2.4.3.3 Contributions of the various fractions of wastewater to membrane
fouling
The resistance Rr offered to membrane filtration is a combination of the resistances
offered by the membrane itself and by the fractions of the filtered suspension, i.e. by
the dissolved and the suspended fraction (respectively: RM, RD and Rs).
The initial assumption of the additivity of these three resistances (model of the
resistances-in-series) was proved insufficient by Fane et af. (1980), who carried out
UF experiments in a stirred cell on separated dissolved and suspended solids
solutions. By creating mixed solutions of dissolved and suspended solids, they
showed that the total resistance found was smaller than the sum of the individual
resistance; they justified it by a reduction of RDS due to the presence of suspended
solids (protection of some pores by big solids, preventing them from a more effective
obstruction by dissolved macromolecules).
Fane et al. (1980) showed that for a given concentration, the dissolved solids exert a
greater resistance to flow than do the suspended solids. Indeed, in order to determine
the relative significance of the dissolved and suspended solids in an activated sludge
85
mixed liquor, they carried UF experiments in a stirred cell on separated dissolved and
suspended solutions. It appears that, at a constant mass transfer coefficient
Defrance (1996) carried out crossflow micro filtration of activated sludge (prescreened
at 500 f.lm) using a 0.1 f.lffi ceramic membrane.
To study the influence of the different fractions of the suspension on filtration, she
filtered:
• Raw activated sludge (TSS = 1300 mgll) (containing the settleable fraction, the
colloidal fraction and the dissolved fraction);
• Settled activated sludge (containing the colloidal and dissolved fractions);
• The latter after flocculation with FeCI) (400 mgll) (containing only the dissolved
fraction).
The operating conditions were Ucr = 3 mls, 6.P, = I X 105 Pa, T = 15°C.
After 4 hours filtration, the permeate fluxes were respectively of 64, 120 and 550
Vm2.h. Using Darcy law, she calculated the resistance due to each fraction, and she
concluded that the proportion of fouling due to the settleable fraction is similar to the
one due to the colloid fraction. On the other hand the fouling due to the dissolved
fraction was negligible.
Meanwhile she admitted that the assumption that:
where
Rr = RM + Rs + Rc + Ro
Rr = total resistance
~ = membrane resistance
Rs = resistance due to the settleable fraction
Rc = resistance due to the colloidal fraction
Ro = resistance due to the dissolved fraction
is not fully valid, since the different fractions interract during the process of membrane
fouling.
86
Furthermore she quoted results by Wisniewski (1996) who found that the dissolved
fraction was the major contributor to membrane fouling (in the case of a membrane
bioreactor, the membrane operating with backwashing (which affects the nature of
membrane fouling».
Wisniewski and Grasmick (1996) studied the influence of the various fractions of an
activated sludge suspension on membrane fouling. They used ceramic membranes
(pore size = 0.2 ~m) to filter:
• An acti vated sludge suspension,
• The supematatant of this suspension after 2 h senlement,
• The permeate obtained after filtration of the original suspension using a membrane
with a 0.05 ~m pore size.
The operating conditions were: M't = 105
Pa, Ucf = 4 m/s, backwash periodicity = 2
mm.
After I h filtration, the permeate fluxes were respectively 110, 140 and 210 IIm2.h for
respectively the original suspension, the settled suspension and the prefiltered
suspension. They then calculated specific resistances to filtration of the various
fractions of the suspension. They found that the dissolved fraction « 0.05 ~)
accounted for more than 50% of the specific resistance to filtration (and therefore of
the total membrane fouling), the settleable and (supra-colloidal - colloidal) fractions
accounting each for 25%. These results seem to show the essential role of the soluble
fraction on membrane fouling. However they note that the contribution of the non
settleable and settleable fractions would increase for longer filtration runs and without
backwashing (since backwashing limits the contribution of particulates to fouling).
2.4.3.4 Potential contribution of extracellular polymers to membrane fouling of
exocellular polymers
One of the obvious by-products of biological wastewater treatment usmg biofilm
reactors is extracellular polymers (ECPs). They play an essential role in biofilm
structure, activity and performance. They are mostly composed of polysaccharides
(up to 65%), and the main structural monomers of these polysaccharides include
glucose, galactose, mannose, galacturonic acid and glucuronic acid (Bryers and
87
Characklis, 1990; Horan and Eccles, 1986). ECPs also contain pyruvate, uronic acids
and neutral sugars which dramatically affect overall biofilm chemical characteristics
(Fazio et al., 1982; Goodwin and Forster, 1989; Morgan et aI., 1990; quoted by
Carlson and Siverstein, 1998) . .other substances are also present, such as proteins,
nucleic acids and lipids (Goodwin and Foster, 1989).
The dominant groups of sugar-acid residues in ECPs are carboxyl and hydroxyl acids,
which are ionized at neutral pH values producing negative biofilm surface charge at
conditions found in most environments (Horan and Eccles, 1986; Morgan et aI., 1990;
quoted by Carlson and Silverstein, 1998). The net negative surface charge of make
them contribute or not to membrane fouling, depending of the charge of the membrane
surface. The net negative surface charged of biofilms is likely to influence sorption and
transport of charged aqueous contaminants, particularly decreasing diffusion of
negatively charged natural organic matter molecules within the biofilm, compared with
neutral or positively-chatged molecules which would have more favorable electrostatic
interaction with the negatively-charged ECPs.
ECPs could be major contributors to membrane fouling in the case of cross flow
filtration of trickling filter effluent.
88
CHAPTER 3: OBJECTIVES
Low-rate trickling filters have been criticised for generating an influent of inconsistent
quality. The nature of trickling filter effluent solids and/or poor performance of
secondary settlement are responsible for this inconsistency. Solids production is related to
trickling filter performance, which is in turn thought to be affected by several key
parameters. Studies to date on trickling filter performance have usually presented the
effect of single parameters. However, there are several parameters affecting performance,
which often have an interactive effect and cannot therefore be considered in isolation.
There is still-uncertainty regarding which of the parameters have the greatest influence on
performance. With respect to the problem of non-settleable solids, tertiary treatment is
often a necessary solution. Crossflow filtration is a promising alternative to existing
tertiary treatment options. This study therefore has four main objectives:
• To study the performance of a low-rate trickling filter.
This was done at pilot-scale under partially controlled conditions. The design and
operational parameters were chosen to simulate a st~ndard UK low-rate trickling filter.
The filter was exposed to ambient conditions, and fed with synthetic sewage under
different balances of filtered to total organic matter. The efficiency of secondary
settlement was also studied at pilot-scale, and at full-scale at a local low-rate trickling
filter works .
• To characterise the particulate and dissolved fraction of waste water.
The objective was to investigate the influence of the various process stages on the size
and size distribution of wastewater contaminants. The literature review has shown that
contaminant size and size distribution affects performance of fixed-film reactors in
general and of trickling filters in particular. Contaminant size distribution is also an
important factor influencing the performance of solid-liquid separation processes.
89
• To understand the key parameters affecting low-rate trickling filter perfonnance.
The literature review has shown that 3 categories of parameters affect trickling filter
perfonnance: design parameters, operational parameters, and parameters resulting from
interactions between the two previous types and ambient conditions. The design
parameters and some operational parameters (hydraulic loading, dosing periodicity)
having been set at standard UK values, it was intended to clarify the respective role of:
characteristics of the influent, mostly in terms of balance of filtered to total organic
matter;
size and size distribution of the influent particulate matter;
filter temperature;
film accumulation in the filter;
hydrodynamic characteristics of the filter.
The interactions between the last three parameters also reqUire clarification, as
contradictory results have been reported in the literature .
• To assess the performance of crossflow filtration as a tertiary treatment for low-rate
trickling filter effluent.
The literature review has shown that crossflow filtration is increasingly considered as a
solid-liquid separation option after or in combination with biological wastewater
treatment. The objective was to investigate the perfonnance of the technique as a tertiary
treatment for low-rate trickling filter effluent.
90
.'
CHAPTER 4: MATERIAL AND METHODS
During the course of this research, work was carried out at full and pilot-scale. The
full-scale research consisted of the study of secondary settlement performance of low
rate trickling filter effluent over a period of one year. The bulk of the research was
however done at pilot-scale; this was:
• To study the organic and solid removal efficiency pattern of a low-rate trickling
filter and of secondary settlement for this type of effluent;
• To characterise the effect of the trickling filter on the dissolved and particulate
matter of wastewater;
• To investigate the influence of solid content and size distribution on filter
performance, and to compare and contrast it with the influence of temperature, film
accumulation in the filter and hydrodynamic characteristics of the filter;
• To assess the potential of crossflow filtration as a tertiary treatment after a low-rate
trickling filter.
4.1 SNARROWS WATER RECLAMATION WORKS
The full-scale part of the research (15 months: October 93 - December 94) was done
at Snarrows Water Reclamation Works (WRW), Coalville (Leics.), operated by
Severn Trent Ltd and selected as a typical low-rate trickling filter plant. At the time of
the study, the works was treating combined domestic and industrial effluents from a
population-equivalent of approximately 32000. The final effluent was discharged to a
brook, with a consented quality of:
• BOD = 15 mg/I;
• TSS = 20 mg/I;
• NHJ-N = 5 (surnmer)/IO (winter) mg/I.
The treatment processes included:
• Preliminary treatment (screening, grit chamber);
• Primary treatment in sedimentation tanks;
91
• Secondary treatment, in trickling filters (75% of the total incoming flow) and
oxidation ditch (treating the remaining 25%); both biological treatments were
followed by secondary settlement;
• Tertiary treatment in grass plots;
• Sludge pre-treatment in consolidation tanks prior to off-site disposal.
Twelve trickling filters were in operation at the time of the study. They were circular
(25.8 m diameter), filled with 50 mm-graded granite and fed with rotating double arm
distributors. Eight of the filters were 1.8 m deep, the other four having a bed depth of
2.5 m. The trickling filter effluent was subsequently distributed to five humus tank of
the radial-flow type with rotating half-bridge scrapers, although some unsettled
effluent was returned to the trickling filter influent chamber at times of low flow to
ensure nitrification. Settled effluent was directed either to tertiary treatment (grass
plots) or directly to the brook.
During the period of study, the trickling filters were operated at an organic loading
between 0.04 and 0.07 kg BOO/m3 .. d and a hydraulic loading between 0.22 and 0.45
m3/m3 .. d. The distribution rotating arms were operated at a rotation speed of 0.1
rev/min, giving a dosing periodicity of 5 min. The secondary settlement tanks were
operated at an upward flow velocity between 0.16 and 0.66 mIh with an approximate
retention time of 3 h.
For 1991192, the average BOO strength ofraw sewage was 256.3 mg/I (lones, 1992),
reduced to 174.2 mg/I after primary settlement. Results from the 1991-1992 campaign
showed that without tertiary treatment the BOO consent of 15 mg/I was not achieved
in 14.5% of the settled effluent samples. In terms of TSS the settled trickling filter
effluent was above consent for 31 % of the analysed samples. After tertiary treatment,
the final effluent complied with the 20: 15 (BOO:TSS) consent limit 95% of the time,
although many samples were close to the limit.
Trickling filter effluent samples for this project were taken in a chamber receiving the
effluent of two of the 1.8 m-deep trickling filters. Samples of settled trickling filter
effluent were collected directly after the humus tank fed from the above mentioned
chamber
92
4.2 PILOT-SCALE TRICKLING FILTER
A pilot-scale trickling filter was built for this research in the compound of the
Department of Civil and Building Engineering, Loughborough University. It was
designed and operated for 30 months to simulate a fraction of a full-scale standard
low-rate trickling filter, but under controlled conditions of influent.
4.2.1 Design and construction
A diagram of the pilot-scale trickling filter is given in Figure 4.2A. The trickling filter
consisted of a 2.6 m-tall, 0.9 m-diameter glass-fibre cylinder of 2-4 mm wall
thickness. The cylinder was filled over a depth of 1.8 m with mineral medium (blast
furnace slag). This depth is traditional for full-scale plants using conventional media,
because it is recognised as offering a good compromise between treatment efficiency,
hydraulic head loss and cost (Gray, 1989). The resulting filter bed volume was 1.14
mJ, with a surface area of 0.64 m2
The medium was supported 30 cm from the base of the cylinder by a fine grid of
plastic-coated wire. Three plastic columns were positioned in the base of the filter to
provide additional structural support for the filter bed. The effluent outlet had a
diameter of 110 mm. Nine 58 mm-diameter holes were drilled in the 0.3 m gap
between the bottom of the cylinder and the filter base to provide additional
ventilation. Altogether, the outlet hole and the ventilation holes provided an aeration
area of 0.03 m2, equivalent to 5.23% of the upper surface. The top 0.5 m of the
cylinder left above the surface of the filter bed acted as a wind-shield, preventing
disturbance of wastewater distribution to the filter.
The filter was fitted with 3 sampling ports at heights of 0.45, 0.90 and 1.35 m from
the base of the filter bed. They consisted of 0.5 m lengths of 38 mm-diameter PVC
pipe cut in half to form a collecting trough. Even though they were not used for the
93
Figure 4.2A: Pilot-scale trickling filter
tracer injection
po~oleno~ve
.. III(~--: thermocouple
feed tank
stirrer
neutron probe access tube
constant head tank
~/" 1\\" '1 , / \",
, 11 ,
""11\'" , , ',"11'\,'
, I \' I I I \ ,
centrifugal pump
94
sampling
stirrer
sludge withdrawal
study, the sampling ports were included to offer the possibility of monitoring changes
in the quality of wastewater as it passes through the filter.
Prior to installing the medium, a 50 mm-diameter aluminium access-tube extending
through the depth of the filter was installed vertically in it. The tube was positioned to
guarantee that it would be surrounded by a minimum radius of 0.3 m of medium
(Biddle, 1994). It was plugged at the bottom and sealed at the top when not in use to
keep it perfectly dry and prevent any film growth inside it. It enabled measurement of
film accumulation profiles within the filter using the neutron scattering technique (see
§ 4.5.1).
The installation was fitted with 5 K-type thermocouples, enabling the hourly
recording of 5 temperatures:
• Ambient temperature, at mid-height of the filter;
• Influent temperature;
• Trickling filter temperature (at 0.3 m from the outside wall) at depth of 0.05, 0.90
and 1.75 m from the top surface of the filter bed.
The thermocouples were connected to a data logger (Comark, model Compuface)
linked to a Personal Computer (IBM, model PS/2).
4.2.2 Filter medium
The characteristics of the filter medium are given in Table 4.2A.
Table 4.2A: Characteristics of the tricklin~ filter medium Medium blast-furnace slaq
Grade(mml Specific surface area (m2
' m3) Voidaae (%)
Volumetric mass (ko/m3)
': 9.S. 1438:1971 Specification, quoted by IWEM (1988) ": Gray and Learner (1984)
50 101-118' 45-50' 886.3"
Blast-furnace slag was chosen for this study because it is one of the most common
media used in British trickling filters (Learner, 1975). Blast-furnace slag is a by
product of the steel industry; it results from the high-temperature fusion of fluxing
stone with coke ash and the siliceous and aluminous residues remaining after
95
reduction and separation of iron from the ore (Emery, 1978, quoted by Biddle, 1994).
The precise composition and nature of slag varies between production sources and is
dependent upon the composition of iron ore, the iron extraction process, and the
method of cooling the slag.
With respect to the size of the chosen medium, most filter media are within the range
38-51 mm (Bruce, 1976; Learner, 1976; quoted by Gray and Leamer, 1984). Hawkes
and lenkins (1955) (cited by Gray and Learner, 1984) compared four grades of
mineral medium. They concluded that a rough textured medium of 50 mm nominal
size offered the best compromise between a large surface area and the provision of a
high voidage. Blast furnace slag with a 50 mm grading is therefore the most
appropriate mineral medium to use in pilot-scale filters. It has been extensively used
for previous pilot-scale (Gray and Leamer, 1983, 1984) and full-scale (Coombs et al.,
1996) studies, ensuring that the information resulting from this study could be useful
and compared to others.
4.2.3 Wastewater storage and distribution
The influent to the filter was stored in two large tanks (capacity: 600 I) connected to
each other by a siphon. One of the large tartks was connected to a smaller constant
head tank (volume at constant head: 48 I). Wastewater was continuously pumped
from this constant head tartk using a centrifugal pump (Brook Compton Parkinson
Motors). A by-pass in the steel piping allowed the recycling of the pumped influent in
the tartk, while the pressure built up in the piping before dosing to the filter (Figure
4.2A). Mixing of the small constant head tartk was enabled by the return of pumped
influent through the by-pass.
The filter was fed intermittently, using· a timer-controlled solenoid valve (RS
Components). This was to simulate the dosing periodicity found on a full-scale filter
fed by rotating arms, gravity siphon or tipping trough systems, where at time t only a
portion of the filter is sprayed with wastewater.
The sp,raying on top of the filter bed was obtained by means of a downward-facing
nozzle (Delavan, model BNM 46), generating a solid-cone spray. The height of the
96
--- ---
nozzle over the exposed surface of the filter bed was adjusted according to the chosen
hydraulic loading rate and dosing frequency, and to the diameter of the pilot-scale
filter. This was to give optimal distribution over the entire surface of the filter bed,
and to minimise liquid flow along the cylinder wall.
4.2.4 Operation
Operation of the pilot-scale trickling filter was initiated with a start-up phase (May -
June 1994). It consisted of seeding and maturation of the trickling filter, with the
objective being to ensure complete wetting of the medium and rapid colonisation of
all the available surface area. Starting up in summer was recommended since it
favoured the rapid establishment of an active biofilm on the medium. The start-up
phase was followed by three experimental phases, under different operating
conditions:
• Phase I: July 1994 - August 1995;
• Phase 2: September 1996 - March 1997;
• Phase 3: April 1997 - October 1998.
The operating conditions were moderated from September 1995 to June 1996, due to
the absence of the investigator. Normal operation was resumed in August 1996 (under
Phase 2 operating conditions) as a start-up period for Phase 2.
4.2.5 Hydraulic loading and dosing frequency
The objective being to simulate a fraction of a low-rate single-pass trickling filter, the
pilot was operated at a hydraulic loading of 0.5 m3/m3.d (0.9 m 3/m2.d) with no
effluent recirculation. This value was kept over the 3 phases of research. It was
controlled by controlling the intensity of by-pass, and checked (and if necessary
adjusted) daily. The value of 0.5 m3/m 3.d was chosen because it is standard for low
rate trickling filters (Gray, 1989). The hydraulic loading was reduced to 0.3 m3/m]d
between Phase 1 and 2, to make the operation of the pilot easier and keep some film
activity within the filter.
Throughout the study, the influent was sprayed on the filter bed surface for 5 s at
intervals of 66.7 s; this gave a dosing periodicity of 1.2 min and corresponded to an
97
equivalent rotation speed of 0.84 rev/min. These conditions were in the range used for
full-scale low-rate trickling filters: fixed-nozzle distribution systems are operated at
full-scale with interval between doses varying from 0.5 to 5 min, while rotational
speed for rotary distribution systems (having two arms or more) is usually between
0.1 and 2 rev/min (WPCF, 1988).
4.2.6 Synthetic sewage
The pilot-scale trickling filter was fed during the three phases of research with
synthetic sewage formulated to represent domestic settled sewage (i.e. primary
effluent). The advantages of synthetic sewage over real sewage were:
• Relatively uniform characteristics, ensured by regular preparation (every two days);
• No risk of toxic or industrial components;
• The possibility to be reproduced in any laboratory;
• The possibility to modifY composition in a controlled way.
The synthetic sewage option was also considered more economical than transporting
large quantities of settled sewage or building the plant at a local sewage works.
The composition of the synthetic sewage (Table 4.2B) was based on that used by
Phanapavudhikul (1978) for a study at laboratory and pilot-scale of the effect of
temperature on activated sludge. It was itself adapted from Williams and Taylor
(1968), who used synthetic sewage for a study at pilot-scale of the effect of macro
invertebrates on the efficiency of trickling filters ..
Table 4.2B: Composition of the synthetic sewa>re
Concentration (mall' Constituent Phase 1 Phase 2 Phase 3
dextrin 150 150 150 ammonium chloride 130 130 130
veast extract 120 120 120 alucose 100 100 200
soluble starch 100 0 0 maize starch 0 100 75
sodium carbonate 100 100 100 detergent 50 50 50
(commercial) sodium dihydrogen 20 20 20
orthophosphate Dotassium sulphate 8.3 8.3 8.3
98
As shown in Table 4.2B, the composition of the synthetic sewage was modified for
the three phases. The objective of these modifications was to vary both the solid
content and the 'filtered to total organic matter' ratio in the synthetic sewage. This was
to study the influence of the size of organic matter on trickling filter performance.
During the start-up phase, and as recommended in the literature (Pike, 1978), the pilot
was initially seeded for I day using 80 I of activated sludge collected at
Loughborough Sewage Treatment Works, with a recycling ratio of I. It was then fed
for two months with synthetic sewage for the rest of the start-up phase and for Phase
I. For Phase 2, the composition of the synthetic sewage was modified by replacing
soluble starch by maize starch. During Phase 3, the composition of the synthetic
sewage was changed again, the concentration of maize starch being reduced by a
quarter and the concentration of glucose doubled.
The influent to the filter was prepared every two days. A concentrate mixture was
prepared by dilution in hot tap water of the requisite amount of 'dry' chemicals (pre
prepared in plastic bags) and of yeast extract. This concentrate mixture was then
diluted with tap water in one of the two 600 I continuously stirred tanks. The
operation was repeated for the other 600 I tank.
Regular cleaning of the system was required to remove excess biological growth. The
influent tanks, solenoid valve and nozzle were cleaned monthly. The intermediate
pipework was dismantled and cleaned every three months.
4.3 PILOT-SCALE CROSSFLOW FILTRATION UNIT
4.3.1 Apparatus
The pilot-scale crossflow filtration (CFF) unit is presented in Figure 4.3A. Its
configuration was of the 'closed system' type (Bhave, 1991 a). It comprised a filtration
loop made of stainless steel, including a cross flow microfiltration membrane module,
that was fed from a PVC-made feed tank.
99
o o
feed tank
temperature control --
Figure 4.3A: Crossflow filtration pilot
valve for concentrate bleed
heat exchanger (cold tap wate~
circulation)
thermocouple
feed pump
flowmeter
membrane module
valve for cross flow velocity adjustment
recirculation pump
valve for pressure adjustment
Pl
Pp
PZ
rotameter
The feed tank was cylindrical with a conical bottom and had a volume of 25 I. It was
temperature-controlled at 20°C and stirred using a heater-stirrer (Techne, model
Tempette Junior TE-8J), enabling constant feed temperatures to be maintained during
experiments.
The feed pump (Grundfos model CRN2-50) was located between the feed tank and
the filtration loop. It was centrifugal multistage, designed to generate high pressures
at low flow. It was mono speed, and a valve located between the pump and the
filtration loop enabled pressure adjustment in the loop.
The filtration loop was made of stainless steel. It had a volume of 3.8 I. It included a
recirculation pump, a valve to adjust the recirculation flow rate, a heat exchanger and
the crossflow filtration module. The recirculation pump (Servinox, model S-28S), also
centrifugal (but monostage) and mono speed, generated a high flow and low pressure.
The permeate could be collected in a tank connected to a compressed air line,
enabling backwashing of the module.
4.3.2 Instrumentation
The parameters measured during a filtration run and the equipment used to measure
them are indicated in Table 4.3A.
Table 4.3A: Parameters measured in the filtration 1000 and sensors Parameter Measurement eauipment
Pressure P" Po and P: pressure aauaes (Bourdon) Temoerature in the 1000 K-tvoe thermocouole (RS Components)
Recirculation flow in the filtration loop flowmeter(Krohne, model DW 182) Permeate flow flowmeter (Platon)
': see Figure 4.3A
The crossflow velocity Ucf was controlled by adjusting the recirculation flow rate in
the filtration loop.
101
, ,
It is given by:
where
U = Q,
of ( 2) 3600 191t ~'
Ucf = crossflow velocity (m/s)
Qr = recir:ulation flow rate in the filtration loop (mJIh)
de = diameter of membrane channels (mm)
The transmembrane pressure L'.P, was calculated using:
where
L'.P, = transmembrane pressure (Pa)
PI = pressure in the loop before the membrane (Pa)
P2 = pressure in the loop after the membrane (Pa)
Pp = permeate pressure (Pa)
The permeate flux was obtained by dividing the permeate flow rate by the membrane
area:
J = Qp P A
m
where
Jp = permeate flux (mJ/m2.h),
Am = membrane area (m2).
Qp = permeate flow rate (mJ.h)
4.3.3 Membranes
The membrane modules used for this study were tubular multichannel (SeT-US
Filter, model Membralox) (Figure 4.3B). They are composed of a ceramic support
102
with a coarse porosity on which a membrane is deposited as a fine layer of metallic
oxides. The general characteristics of the membrane modules are given in Table 4.38.
Figure 4.3B: Structure of a membrane module and liquid fluxes
support
FEED -
I , PERMEATE channel
Table 4.3B: General characteristics o{the membrane modules Module length (m) 0.85
Number of channels 19 Channel internal diameter (mm) 4
Membrane area (m2) 0.2
The membrane modules casing was made of stainless steel. Polypropylene gaskets
provided a water-tight seal at the contact between the ceramic module and its casing.
Tubular membranes were selected for this study since, of the currently available
membrane module configurations, the tubular configuration is the least affected by
fouling because of the relatively large diameter of the membrane channels.
Ceramic membranes were chosen because they have a good chemical resistance for
cleaning, withstanding a pH range of 0.5 - 14.5 (Vigneswaran et aI., 1996), and
because they are capable of sustaining high mechanical stress (burst pressure> 3 x
106 Pa; ). They also offer additional advantages over other types of membranes:
• Hydrophilic properties;
103
• Non biodegradability;
• Wide chemical compatibility;
• Long lifetime.
These advantages of inorganic membranes over organic membranes have been
highlighted in the literature (e.g. Dittrich et at., 1996).
Membranes with two different pore sizes were tested: 0.1 !!m and 0.2 !!m. The
structures of the membrane modules are given in Tables 4.3C and 4.3D:
Table 4.3C: Structure of the 0.1 wn membrane module Layer Composition Thickness (Jlm) Pore size (Jlm)
support alumina 2000 12 AI,O,
intermediate alumina 40 0.8 AI2O,
membrane zircon 10 0.1 zr02
Table 4.3D: Structure of the 0.2 wn membrane module
Laver Composition Thickness (Jlm) Pore size (Ilm)
support alumina 2000 12 AI,O,
intermediate alumina 40 0.8 AI2O,
membrane alumina 20 0.2 AI,O,
Mineral oxides with a general formula of MOl (zircon) and M20 J(alumina) are
amphoteric. Given the pH of the aqueous solution in which they are, the reactions
involved in the creation of surface charge are:
pH < 7: M-OH (surface) + W (water) -> M-OH2•
pH>7: M-OH (surface) + OH" (water) -> M-O· (surface) + H20
This results in either attraction or repulsion of the negatively charged colloids.
Table 4.3E gives clean water fluxes found in the literature for the membranes used.
104
I
Table 4.3E: Clean membrane water (lux Clean water flux (x 10.3 m3! m2 .h.Pa)
Source 0.1 I'm membrane 0.2 I'm membrane
Manufacturer 15 20 Moulin (1990) 14.5 12
Richaud-Patillon (1990) 9 14
4.3.4 Experimental procedure
Before the beginning of each filtration run, the membrane module was mounted on , the pilot and rinsed with 0.25 J.!m cartridge-filtered tap water for 5 min with Ucf = 0
and llP, = 0.5 x 105 Pa. The clean water flux was then measured, using filtered tap
water. Four values were taken, under the conditions given in Table 4.3F.
Table 4.3F: Operating conditions of clean water flux de terminations UCf (m!s) I <lP, (x 10' Pal I
0 1 0 2 4 1 4 2
The clean water flux was then calculated as an average of these 4 values.
The filtration loop was then drained. The feed tank of the CFF pilot was filled with 25
I of freshly pre-collected trickling filter effluent (after gentle shaking for resuspension
of settled matter), and left to stabilise at 20°C while being mixed. When a temperature
of 20°C was reached, the trickling filter effluent was gently pumped into the filtration
loop under a very low pressure (2 x 10' Pa), the permeate outlet of the membrane
being closed, and the high point of the loop being opened for air drainage. The outlet
of the trickling filter was then connected to the membrane filtration unit feed tank, the
membrane permeate outlet opened, and the filtration run was started by starting the
pumps and adjusting the valves to obtain the required Ucf and llP,.
Three types of operation mode were used:
• The determination of optimum operating conditions (IlP,j , Ucr) was based on a
series of runs carried out over a period of 90 min with recycling of the permeate in the
feed tank, to maintain a constant solid content in the system. In order to carry out each
105
series of tests on water with fixed characteristics, 120 I of settled pilot scale trickling
filter effluent were collected before the beginning of a series and stored in a single
tank at 4°C (the suspension properties were only marginally altered during the two
days necessary for the realisation of a series). Before each run of a series, the
suspension in the storage tank was resuspended by gentle stirring, and 25 I of it were
collected and poured into the influent tank of the membrane filtration pilot.
• Longer runs (240 min) were carried out in true 'closed system' configuration (Bhave,
199Ia). The feed tank was continuously fed by gravity with settled (or unsettled)
trickling filter effluent from the pilot-scale humus tank, and the permeate was not
returned to the feed tank .
• Runs were also carried out in the 'continuous' mode of the 'feed and bleed'
configuration (Bhave, 1991 a). A portion of the concentrate was permanently bled out
of the filtration loop and returned to the feed tank.
4.3.5 Membrane cleaning
At the end of each filtration run, the filtration pilot and membrane were cleaned in
situ. The filtration pilot was first purged of the filtration concentrate, and rinsed with
cartridge filtered tap water (at 0.25 !lm).
The cleaning protocol then applied is described in Table 4.30. It combines alkaline
and acid cleaning, at low M, , as recommended by the manufacturer (Bhave, 1991 a).
To bl 3G \.t b I a e 4. : 1 em rane c eanin~ protoco I Duration Solution Concentration t.P, U" T Permeate
(min) (x la' Pal (m/s) (OC) outlet
25 sodium 2% v/v 0 6 80 closed hydroxide
.2 sodium 2% v/v 0.5 0 80 opened hydroxide
5 filtered - 0.5 0 ambient opened water
25 nitric acid 2% v/v 0 6 60 closed
2 nitric acid 2% v/v 0.5 0 60 opened
5 filtered - 0.5 0 ambient opened water
106
After cleaning, the membrane module was removed from the filtration loop and stored
in sodium hypochlorite at 200 ppm. All the cleaning solutions (sodium hydroxide,
nitric acid and sodium hypochlorite) were prepared in water having undergone reverse
osmosis.
4.4 SAMPLES ANALYTICAL TECHNIQUES
4.4.1 Sampling technique
Since a reliable composite sampler was not available, all the analyses were done on
grab samples. This type of sampling was regarded as appropriate for the pilot-scale
study since:
• The trickling filter was fed with synthetic wastewater stored in fully mixed tanks,
with a controlled composition;
• The hydraulic loading to the filter was constant.
Grab samples were also taken for the crossflow filtration study and for the full-scale
settlement study.
All samples were stored at 4°C, and all analyses were done within 8 h of sampling.
4.4.2 Samples fractionation
Particle size and its influence on trickling filter performance was an important part of
this research, and limits of particle size ranges were defined according to commonly
used boundaries, summarised in Table 4.4A.
Table 4. 4A: Fractions of contaminants defined for this study Particle I I I
size 0.1 1.2 100
(Llm) I I I filtered
1 suspended
Fraction If) (ss) dissolved
: colloidal :
supracolloidal
: settleable
Id) (c) (se) (s)
The raw samples were analysed homogenised, unsettled and unfiltered, in conformity
with the Urban Waste Water Treatment Directive (Commission of the European
Communities, 1991).
107
Settled samples were obtained using the Standard Methods protocol defined for
Settleable Solids determination (ref. 2540 F) (APHA-AWWA-WPCF, 1989). The
process consists of quiescent settlement at laboratory temperature in a glass vessel of
not less than 1 I volume, a diameter of not less than 90 mm and a minimum depth of
200 mm. After I h, 250 ml of settled sample was siphoned from the centre of the
container at a point halfway between the surface of the settled material and the liquid
surface.
Filtered samples were obtained by vacuum filtration at 1.2 and 0.1 ~m. The filters
used were Whatrnan GF/C (1.2 ~m) and Millipore Isopore (0.1 ~m). The importance
of the filter load (i.e. of the volume filtered) on the concentration of colloidal and
dissolved matter passing through membrane and glass-fibre filters has been
highlighted by Danielsson (1982). He found on a lake water sample filtrate (at 0.45
~m) concentrations of iron ranging from 6.5 to 0.1 mg/I, depending on the volume of
sample filtered. As a result, for this study, loads to the filters were controlled. The
sample volumes filtered were 100 ml for the 1.2 ~m filters, and 20 ml for the 0.1 ~m
filters.
4.4.3 Biochemical Oxygen Demand
The Biochemical Oxygen Demand (BOO) is an index of the biodegradable organic
matter present in a wastewater sample. It measures the oxygen utilised during an
incubation period of 5 days for the biochemical degradation of organic material
(carbonaceous demand) and the oxygen used to oxidise inorganic material such as
sulphides and ferrous iron. Nitrification was inhibited (using allylthiourea) to prevent
interference in the measurement due to further use of oxygen for oxidation of reduced
forms of nitrogen (nitrogenous demand) in the sample. Dissolved oxygen in the
samples was measured by Winkler titration. The technique used was a Standard
Method (ref. 5210 B) (APHA-A WW A-WPCF, 1989). BOO is normally expressed in
mg 0 2/1, simplified to mg/1.
108
..
4.4.4 Chemical Oxygen Demand
The Chemical Oxygen Demand (COD) is a measure of the oxygen equivalent of the
organic matter content of a sample that is susceptible to oxidation by a strong
chemical oxidant. The technique used was Open Reflux. Organic substances in the
sample are oxidised by potassium dichromate in sulphuric acid solution at reflux
temperature. Silver sulphate was used as a catalyst and mercuric sulphate was added
to prevent chloride interference. The excess dichromate was then titrated with
standard ferrous ammonium sulphate using ferroin as an indicator. The technique .'
used was a Standard Method (ref. S220 B) (APHA-AWWA-WPCF, 1989), and
samples were analysed in duplicate. COD is also normally expressed in mg 0 2/1,
simplified to mg/1.
4.4.5 Total Kjeldhal Nitrogen
Total Kjeldhal Nitrogen (TKN) includes both organic nitrogen and ammonia. Organic
nitrogen is defined functionally as organically bound nitrogen in the trinegative
oxidation state. It includes such natural material as proteins and peptides, nucleic
acids and urea, and numerous synthetic organic materials. Ammonia is produced
largely by deamination of organic nitrogen-containing compounds and by hydrolysis
of urea. Analysis were run in duplicates using a Kjeltec System I unit (Tecator)
composed of a 1007 Digestion System, a 1002 Distillling Unit and a Titration Unit.
The technique used was a Standard Method (ref. 4500-Norg B) (APHA-A WW A
WPCF, 1989).
4.4.6 Solids
Solids refer to matter suspended or dissolved in waste water. Total Solids (TS) is the
term applied to the material residue left in a silica dish after evaporation of a sample
and its subsequent drying in an oven at losec. The increase in weight over that of the
empty dish represents the TS. TS includes Total Suspended Solids (TSS) and Total
Dissolved Solids (TDS).
TSS were measured by filtering the sample through a standard glass-fibre filter
(Whatman GF IC) and drying the filter at 10SeC. The smooth side of the filter was
109
used uppennost (Wheatley, 1976). The increase in weight of the filter represents the
TSS. TDS can be obtained by difference between TS and TSS:
TDS =TS - TSS
Volatile Solids (VS) or Volatile Suspended Solids (VSS) were obtained by igniting to
constant weight at 550°C the silica dish or glass-fibre filter used for the detennination
of respectively TS or TSS. The remaining solids represent the fixed fraction, while the
weight lost on ignition is the volatile fraction of either TS or TSS.
When VS and VSS were to be detennined, the silica dish and glass-fibre filter used
for detenninations were pre-ignited at 550°C to prevent any weight loss during the
effective measurement.
Settleable Solids (SS) were determined gravimetrically by applying the TSS
detennination protocol to a settled sample. The settlement protocol has been defined
in § 4.4.2. The TSS of the settled sample represented the Non-Settleable Solids
(NSS), the SS being obtained by difference between TSS of the original sample and
NSS:
SS = TSS - NSS
All analyses were carried out according to Standard Methods (ref. 2540 8, D, E and
F)(APHA-AWWA-WPCF,1989).
4.4.7 Turbidity
Turbidity is an expression of the amount of light scattered and absorbed by
particulates rather than transmitted in straight lines through the sample. Turbidity
reflects the number of particles per unit volume scattering the incident light.
Measurements were made using a Hach Portable Turbidimeter, operating under the
nephelometric method. This method is based on a comparison of the intensity oflight
scattered by the sample under defined conditions with the intensity of light scattered
by a standard reference under the same conditions. The higher the intensity of
scattered light, the higher the turbidity. The results are expressed in nephelometric
turbidity units (NTU).
110
4.4.8 Particle Size Distribution
Particle Size Distribution (PSD) was carried out using a Coulter LS 130 particle size
distribution analyser (Coulter Electronics Ltd.), measuring size over a range of 0.1 to
900 Ilm. The principle of measurement is light diffraction. When a beam of parallel
light is directed onto a particle, light is diffracted. The diffraction angle depends upon
the size of the particle: the smaller the particle, the wider the diffraction angle. The
diffracted light is then focaJised by means of a Fourier lens to an array of detectors,
located at the lense's Fourier distance. In the LS 130, the light used is laser light at a
wavelength of 750 nm, and two Fourier lenses collect the diffracted light and focus it
onto three different sets of detectors: low-angle (mainly for large particles), mid-angle
(for average particles) and high-angle (mainly for small particles).
Diffraction is meaningful only for particles down to 0.4 Ilm because it is difficult to
distinguish particles 0 f different sizes by diffraction patterns alone when the particles
are smaller than 0.4 Ilm in diameter.
As a result, the measurement between 0.1 and 0.4 J..lm are done in the LS 130 by
Polarisation Intensity Differential Scattering (PIDS). PIDS is based on polarisation of
light which is not used in ordinary diffraction measurements. Light can be
decomposed into two components, the vertically and horizontally polarised
components (vertical and horizontal referring to the oscillation of the light's electric
field). For particles in the size ranges close to the wavelength of light, the difference
in scattering of vertically versus horizontally polarised light (when the scattering is
observed at roughly the direction of propagation of light) is dependent on the ratio of
particle size to the wavelength of light. PIDS measures the pattern of difference in
scattering of vertically and horizontally polarised light at three different wavelengths
(450,600 and 900 nm).
Data from PIDS and Fraunhofer diffraction are combined to provide a particle size
distribution from 0.1 to 900 Ilm, in 100 logarithmic channels progressively wider in
span. The centre of each channel dpc is calculated using:
. { log d p lowmds, + log d P uppe"dg' J dpc = anti 10 2
1 1 1
The samples were restored to room temperatures before measurement and were
analysed without dilution. The results obtained are in the form of particle size
distributions, either in number or in volume.
4.4.9 High Performance Size Exclusion Chromatography
HPSEC was carried out on an HP 1050 Series High Performance Liquid
Chromatography system (Hewlett Packard). It consisted of an HP 1050 Pump, a
Rheodyne 7125 injection valve fitted with a lOO IJ.I sample loop, an HP 1050 Variable
Wavelength Detector operating at 220 nm, and an HP 1047A Refractive Index (RI)
Detector. The column used was 7.5 x 300 mm PL Aquagel OH-30 column (Polymer
Laboratories). The mobile phase used was ultrapure water (quality: 18 mQ cm), at a
flow rate of 1 mllmin. The temperature of both the column and the detectors was
controlled at 35°C. Prior to injection, the eluent was sparged with helium for I h to
remove oxygen in order to improve the performance of the detectors, and then
pumped through the set-up for 2 h to ensure stability of the system. Before injection,
the samples were filtered through 0.45 IJ.m syringe filters (Hewlett Packard), and since
the volume of sample filtered affects the concentration of dissolved matter in the
filtrate (see § 4.4.2), the sample volumes filtered were always of 8 m!.
A calibration curve for the column (Appendix 2) was obtained under the same
analytical conditions using glucose and several polysaccharides (Dextran, Pharmacia)
of known MW; a linear relationship exists between the log MW of the compounds
and the elution volume from the chromatographic column. This calibration curve is
given as an indication only, since dissolved compounds in wastewater have different
structures: roughly spherical (e.g. proteins), long rods (e.g. viruses) or random-coil
structures (e.g. polysaccharides).
The use of the two detectors is required to make absolute molecular weight
determinations. Indeed, the two detectors fall into two categories: the UV
spectrophotometer measures a property specific to the solute, i.e. UV absorbance,
while the RI detector measures a change in a bulk property of the mobile phase: its
RI. The spectrophotometer offers fast responses to changes in molecular weight, has
small mixing volumes and is sufficiently sensitive to determine molecular weights at
112
concentrations typical of those found in trickling filter effluents. The intensity oflight
scattering is proportional to both molecular weight and concentration. As for the
differential refractometer, it records the difference in refractive index between the
pure eluant and the solute containing eluant. The measured property is directly
proportional to the concentration, and this type of sensor is essential whenever the
substances chromatographed do not show any light absorption. Therefore, by using
both types of detectors in series, the molecular weight distribution can be evaluated
across the whole chromatogram.
4.4.10 pH
The pH was measured using a Gallenkamp pH stick - combined electrode. The meter
was calibrated before each set of readings with a standard buffer at pH 7. Readings
were taken immediately after sampling.
4.4.11 Temperature
For each set of analyses, the temperature of the various samples was recorded at
collection, using a mercury thermometer.
4.4.12 Other parameters
Other measurements were made during the course of the research to further
characterise the studied suspensions. They included 1; potential (detector: Malvern
Instruments Ltd., model Zetamaster S version PCS), and absorbance spectrum using a
diode array spectrophotometer (Hewlett Packard, model HP 8453), both being
measured on samples prefiltered at 0.45 Ilm.
4.4.13 Analytical programmes
4.4.13.1 Snarrows WRW
Samples of trickling filter effluent before and after secondary settlement were
collected on a monthly basis for a year at Snarrows WRW. The parameters measured
for each sample were: BOO, COD, TKN, TS, VS, TSS, VSS, turbidity, PSD, pH and
T. A few samples of primary effluent were analysed for the same parameters
(approximately every 3 months).
113
4.4.13.2 Pilot-scale trickling filter
The analysis programme varied during the three phases of research. Table 4.4B gives
the frequency of analysis on the pilot-scale trickling filter during the three phases.
In addition to those indicated in Table 4.4B, other analyses were carried out less
regularly. During Phase I, measurements were made of influent and effluent TKN,
both neat and filtered at 1.2 Ilm. A few measurements were made on TKN of settled
samples during Phase 2 and 3.
Analysis of samples by HPSEC was carried only during Phase 3 of the research, the
technique having been developed during Phase 2.
4.4.13.3 Crossflow filtration
Analyses were carried during a number of CFF runs. The parameters measured were
COD and turbidity for the three types of samples, TSS being measured only for the
feed and concentrate samples. The frequency of measurement during a filtration run is
indicated in Table 4.4C.
Table 4.4C: Freauency of analyses durinf! a filtration run Time Analvsis done on (minI Feed Permeate Concentrate
0 x . -30 · x -60 · x .
120 x x . 240 · x x
114
f Table 4.48: Average ji-eqllellcy of analyses 0 trickling filter performances
Averaqe frequency of analyses ·per month durinq Analysis done on sample Phase 1 Phase 2 and 3
neat filtered at filtered at settled Influent Effluent Influent Effluent 1.2 Ilm 0.1 Ilm
0Cll 3 3 2 2 0Cll 3 3 2 2
0Cll 0 3 • 0 2
aD 3 3 2 2 aD 3 3 2 2
aD 0 0 2 2 aD 0 3 • 0 2
TKN - - 2 2 TSS 3 3 14' - 7· 14' - 7·
TSS 0 3 • 0 14' - 7"
\!SS 3 3 2 2 \!SS 0 3 • 0 2
lS 3 3 2 2 lS 0 3
• 0 2
IS 3 3 2 2 IS 0 3 • 0 2
turbidity 3 3 14' - 7" 14' - 7" turbidity 3 3 2 2
tu rb id i ty 0 0 2 2 turbidity 0 3 • 0 14' - 7"
PSD 2 2 1 1 PSD 0 1 • 0 1
from May 1995 ': during Phase 2 ": during Phase 3
4.5 OTHER ANALYTICAL TECHNIQUES
4.5.1 Film accumulation measurement using the neutron probe
technique
The film relative amount and distribution throughout the depth of the pilot-scale
trickling filter was measured using the neutron probe technique.
The film in trickling filters is comprised mainly of water. Values between 9S.0 and
98.2% were found by Gray and Learner (1984) for biofilm growing on a trickling
filter containing SO mm graded blast furnace slag. As indicated in the literature
review, the technique consists of measuring the abundance of hydrogen atoms
(present mostly in water molecules) using neutron scattering. A radioactive source of
neutrons is lowered into the SO mm diameter aluminium access-tube inserted
vertically into the filter medium. Emitted fast neutrons collide with the hydrogen
atoms within the water molecules and produce a cloud of slow neutrons. These are
detected and counted by the equipment to give a number which is proportional to the
water content of the surrounding volume of medium. Assuming that the proportion of
water to dry films remains uniform, the count is proportional to the amount of dry
film. The major advantage of this technique over gravimetric techniques (Gray and
Learner, 1984) is that it allows direct measurement of film accumulation without
disturbing the medium. The moisture content of a filter was expressed as percentage
saturation of voids.
The probe used in this study was an Institute of Hydrology Neutron Probe System II
(Didcot Instrument Company Ltd.). The source of neutrons was composed of a
mixture of americium and beryllium. Readings were taken at 10 cm intervals
throughout the depth of the filter. Each value was the mean of two values each of
which being a mean count per second integrated over a 16 s sample period.
Before readings, the percolating liquid was left to drain out of the filters for 2.5 h,
once the distribution system had been shut off. This was done to measure only water
trapped either within organisms or in pockets of the medium (Biddle, 1994). Previous
studies (Marais and Smit, 1960; Burn, 1961; quoted by Row1ands, 1979) have
116
indicated that a radius of 20 mm around the aluminium shaft is sufficient to avoid
introduction of errors due to any edge effects.
The probe was calibrated before each set of readings in a 90 cm diameter drum fitted
with an aluminium access-tube and containing 80 cm deep of medium at three
different moisture levels:
• Completely dry material (0% saturation of voids);
• Moist (drained) medium;
• "Saturated" medium, I.e. with the voids completely filled with water (100%
saturation of voids).
Calibration readings were taken midway through the depth of medium. The drum had
a conical bottom fitted with a tap at its base to facilitate water drainage after
measurement of the moisture content of the "saturated" medium.
Before each use of the probe a reading was also taken in a water drum. This water
standard count rate was used to normalise (by division) the filter and calibration
counts. This was recommended by the manufacturer to take into account the potential
drift in readings due to the ageing of the probe.
Readings were always taken within 24 h of tracer study (for retention time analysis)
and 48 h of full analysis of the filter performances. Film accumulation was measured
every two months during Phase 1, and monthly during Phase 2 and 3.
4.5.2 Residence time distribution
The retention times of the pilot-scale trickling filter were determined by tracer study.
Residence time distributions were obtained using lithium (as LiCl) as the tracer.
Various tracers have been used to study liquid residence time in trickling filters. They
include a variety of radioactive elements, dyes and salts. Lithium was selected for this
study because:
• It is not present in the synthetic sewage;
• It can be detected quickly, accurately and at low concentrations by flame
photometry;
• It does not react with the constituents of the biofilm to any significant extent, being
inert and not consumed by bacteria (Biddle, 1991);
117
• It is non-toxic at the concentrations used and does not affect the biofilm (Tomlinson
and Chambers, 1979; Gray, 1981);
• Lithium chloride is cheap, stable and readily soluble in water.
It has been used by several authors in the past for tracer studies in fixed-film
biological reactors (Seguret, 1998; Fenandez-Polanco et al., 1996; Wik et al., 1995;
Leighton and Forster, 1995; Biddle, 1991; Vasel and Schrobiltgen, 1991), and also to
study mass transfer through biofilm (Vieira and Melo, 1993).
For each experiment, 5 g of LiCI (representing 0.819 g of Li) were dissolved in 60 ml
of synthetic sewage. The resulting solution was introduced in the piping by
substituting it to the same volume of influent just before the solenoid valve, and this
just before the opening of the solenoid valve. Samples of the filter effluent were then
taken following the sampling progranune given in Table 4.5A, from the outlet stream
immediately after the filter.
Table 4.5A: Samplinf.( prowamfor residence time distributions Time following injection (h) Sampling period (min)
0-1 1 1-2 5 2-6 10 6-7 15
Samples were allowed to settle overnight before measuring the lithium concentration
using a flame photometer (Ciba-Corning model 410). Readings from the flame
photometer were adjusted by substracting from them the average value of three
control samples taken before the tracer was added to the filter. The resulting values
were converted into lithium concentrations using a calibration curve. Calibration
curves were calculated for each experiment using standard lithium solutions. The
residence time distributions were obtained by plotting lithium concentration against
time.
118
CHAPTER 5: RESULTS AND DISCUSSION
5.1 INTRODUCTION
Low-rate trickling filters are known to cyclically produce excessive amounts of solids
(Howell and Atkinson, 1976). This frequently poses a problem with respect to the
95% compliance required by the new effluent standards set by the EU Urban
Wastewater Directive (Commission of the European Communities, 1991). The
approach taken to study this problem can be divided into two parts. The first
consisted of a study of low-rate trickling filter performances and of the
characterisation of the key parameters affecting these. The aim was to develop further
understanding of trickling filter performance, particularly in terms of the linkage
between organics and solids removal. The second part of the project was to investigate
the use of cross flow micro filtration as a tertiary treatment to remove the excessive
solids generated. Characterisation of trickling filter effluent in terms of particle size
and dissolved matter content was undertaken to aid both the aspects of performance
characterisation and tertiary treatment.
The experimental work can therefore be divided into four main sections:
• A study of the performances of a low-rate trickling filter at pilot-scale, and of
secondary settlement of trickling filter effluent at both pilot and full-scale;
• A characterisation of both the dissolved and particulate fractions of low-rate trickling
filter effluents;
• A study at pilot-scale of the key parameters affecting performances, and an attempt
to rank these parameters by order of importance;
• An assessment of crossflow filtration as a potential tertiary treatment for low-rate
trickling filter effluent.
1 1 9
5.2 TRICKLING FILTER AND SECONDARY SETILEMENf
PERFORMANCES
Low-rate trickling filter perfonnances were investigated using a pilot-scale trickling
filter based at Loughborough University. The perfonnances of secondary settlement
were investigated for both the pilot-scale trickling filter and a full-scale low-rate
trickling filter at a local sewage works (Snarrows Water Reclamation Works).
5.2.1 Trickling filter performance
This section reports the results of investigation of the perfonnances of a pilot-scale
low-rate trickling filter over a period of two years. It is divided into two main
sections. The first (Section 5.2.1.1) describes the development of a synthetic sewage
recipe used for operation of the pilot-scale trickling filter, and the changes made to this
recipe to allow the study of trickling filter perfonnance under different influent solids
loadings and characteristics. The second section (5.2.1.2) deals with the perfonnances
of the trickling filter, as measured by standard perfonnance indicators such as BOD,
COD, TSS and others.
5.2.1.1 Influent characteristics
The characteristics of the influent have long been suspected of influencing the
perfonnances of trickling filters. It is therefore important to fully characterise the
influent with respect to parameters such as COD:BOD ratio, proportion of filtered
organic matter, etc .. In order to control and manipulate these parameters the pilot scale
trickling filter was fed with synthetic sewage, representing settled sewage! primary
effluent.
The operation of the pilot began with a start-up phase (May - June 1994), which
consisted of seeding and maturation of the trickling filter. The bulk of the study were
then subdivided in three phases, corresponding to three different composition of
synthetic sewage:
• Phase I: July 1994 - August 1995;
• Phase 2: September 1996 - March 1997;
• Phase 3: April 1997 - October 1998.
120
.,
During the start-up phaSe, the pilot-scale filter was initially seeded with activated
sludge, at a recycling ratio of 100%. It was then fed with synthetic sewage (recipe I)
for the rest of the start-up phase and for Phase 1. During Phase 2, the pilot was fed
with synthetic sewage of a composition similar to that of Phase I, with the exception
that soluble starch was replaced by less soluble maize starch (recipe 2). During Phase
3, the composition of the synthetic sewage was altered again, with the concentration
of maize starch reduced by 25% and the concentration of glucose doubled (recipe 3).
The objective of these modifications was to change both the solid content and the
'filtered to total' organic matter ratio in the synthetic sewage. The resulting general
characteristics of the synthetic sewage for the three phases are summarised in Table
S.2A.
121
Table 5 2A' Influent characteristics - Phase 1 7 and 3 ,-
Family of Parameter Phase 1 Phase 2 Phase 3 oarameter
EO:> BOO (mg/l) 163.5 ± 48.5 144.~ ± 54.0 158.7 ± 23.3 ( 35) 1 1 ) ( 1 0)
BOof (mg/I) 91.6 ± 52.2 27.4 ± 27.0 115.7 ± 31.3 ( 26) ( 1 1 \ ( 1 0)
P BOof (%) 55.5 ± 25.9 18.1 ± 15.5 74.1 ± 20.9 ( 2 6 ) ( 1 1) ( 1 0)
CID COD (mg/I) 352.2 ± 94.1 354.9 ± 138.8 424.0 ± 48.9 ( 34) ( 1 2 ) ( 1 1 )
CO Of (mg/I) 165.2 ± 73.8 118.4 ± 51.5 237.3 ± 41.3 , ( 2 8 ) ( 1 1 ) ( 1 0 )
P COof (%) 48.1 ± 19.0 36.6 ± 13.9 57.4 ± 12.6 ( 2 8 ) ( 1 1 ) ( 1 0 )
COoIBOo COoIBOo 2.2 ± 0.4 2.5 ± 0.5 2.7 ± 0.3 (34) ( 1 1 ) ( 1 0 )
COofIBOof 2.4 ± 1.4 8.3 ± 6.6 2.1 ± 0.4 ( 2 6 \ ( 1 1 \ ( 1 0 )
TSS TSS (mg/I) 152.5 ± 61.7 221.9 ± 75.8 174.4 ± 49.1 ( 3 7 ) ( 78) ( 4 1 )
VSS (mg/I) 143.0 ± 65.0 233.0 ± 109.1 163.3 ± 54.4 (29) ( 1 1 ) ( 9 )
P VSS (%) 95.1 ± 2.5 95.7 ± 2.7 96.3 ± 1.6 (29)- ( 1 1 )- ( 9 )
lS TS (mg/l) 828.9 ± 92.3 845.1 ± 150.4 838.7 ± 51.1 ( 36) ( 1 1 ) ( 1 0 )
VS (mg/I) 331.4 ± 55.3 378.9 ± 119.1 365.4 ± 41.0 ( 2 6 ) ( 1 1 ) ( 9 )
P VS (%) 40.4 ± 3.9 44.2 ± 6.5 43.2 ± 3.6 ( 2 6 ) ( 1 1 ) ( 9 )
Tu rbidity t~~bid1:y 60.2 ± 33.2 51.0 ± 19.7 55.6 ±19.3 NTU ( 37) (42) ( 4 1 )
turbidityf 9.3 ± 14.6 2.3 ± 2.7 10.4 ± 11.7 (NTU)' ( 2 9 ) ( 1 1 ) ( 1 0)
P t~rbiditYf 13.~ ± 13.0 3.~ 1±1 ~.4 24.7 ± 27.7 %\ 29\ ( 1 0)
TKN TKN (mg/I) 42.2 ± 7.6 44.4 ± 2.9 46.0 ± 5.5 ( 1 8 ) ( 6 ) ( 6 )
TKNf (mg/I) 33.~ ± 3.3 - -7 \
P TKNf (%) 76.2 ± 9.5 - -( 7 )
T T ('C) 12.? ± 4.4 8.27 ± 2.0 16.9 ± 4.8 36\ -( 7 8 ) ( 4 1 )
pH pH 7.4 ± 0.5 7.3 ± 0.5 6.8 ± 0.3 ( 3 6 ) ( 1 2) ( 1 0)
Tabulated values are: mean ± standard deViation (number of observations) Xf: filtered value of parameter X (i.e. after filtration at 1.2 Ilm) P Xf: Proportion of Xa (P Xa = 100 Xf/X) P VX: Proportion of volatile X (P VX = 100 VXlTX)
122
------ - -------
The aim of using synthetic sewage was to provide a constant controlled influent
composition throughout the study. However, some variability was unavoidable as the
influent was stored outdoors and therefore exposed to climatic variations. This did not
affect the measurement of performances since they were calculated using the specific
influent and effluent characteristics measured on that day.
A. Organic content
The mean values of BOO over Phase 1,2 and 3 were respectively 163.5, 144.5 and .. 158.7 mg/l. These values are within the range given by the Institute of Water Pollution
Control (1980) (quoted by Gray, 1992) for domestic settled sewage. Since the
hydraulic loading rate was kept constant throughout the study (at 0.5 mJ/mJ.d), the
organic loading rates applied to the filter were respectively 81.8, 72.2 and 79.4 g
BOO/mJ.d. These values also correspond to typical values of organic loading for low
rate trickling filters.
Table 5.2A indicates that during Phase I, 55.5% of the BOO of the synthetic sewage
was represented by BOOf, i.e. attributable to filtered matter (smaller than 1.2 Ilm).
This is similar to values found in real settled sewage. For example, Bruce and Merkens
(1973) found that about half of the BOO of settled sewage is attributable to
suspended matter, and the other half to filtered constituents. Similar results had been
found by Sorrels and Zeller (1953) (quoted by Bruce and Merkens, 1973). In another
study, Levine et al. (\ 991) carried out organic fractionation of wastewater at different
stages of treatment. According to their data, the organic pollution in settled sewage
(primary effluent) was approximately equally distributed between classes below and
above I Ilm, the fraction below Illm accounting for between 27 and 59% of organic
pollution.
For Phase 2 and 3, the composition of the synthetic sewage was changed to alter this
balance between the filtered and suspended organic matter. ~uring Phase 2, the
objective was to operate with a feed with a proportionally low filtered BOO content:
the distribution was 18.1% filtered - 81.9% suspended. On the other hand, during
Phase 3 the balance was reversed: 74.1 % filtered - 25.9% suspended.
123
---------
A similar pattern was found for COO distributions: the proportions of filtered COD
were of 48.1 %, 36.5% and 57.4% during Phase 1, 2 and 3 respectively. These
proportions of COOf are similar to values found by Neis and Tiehm (1997) for real
primary effluent at three full-scale plants: 31 %,46% and 52% (the COD values being
respectively 406, 431 and 696 mg/l).
B. Solid content
The synthetic sewage was characterised by TSS values averaging at 152.5, 221.9 and .'
174.4 mg/l for respectively Phase 1,2 and 3. These values are also in the range of TSS
values found for domestic settled sewage.
The synthetic sewage could also be characterised by a BOO/TSS ratio of 1.153 for
Phase 1,0.615 for Phase 2 and 1.011 for Phase 3. This was comparable to the values
of 0.95 and 0.45 found respectively by Bruce and Merkens (1973) and Tomlinson and
Hall (1950).
C. Nitrogen content
Nitrogen was analysed by means of Total Kjeldhal Nitrogen (TKN) which represents
the sum of organic and ammonia nitrogen. TKN concentrations in the synthetic
sewage averaged at 42.2, 44.4 and 46.1 mg/l respectively during Phases 1,2 and 3.
D. Other characteristics
In terms of temperature, it can be noted that the influent temperatures averaged at
12.0,8.3 and 16.9°C during Phases 1,2 and 3 respectively. This is explained by the
fact that Phase I covered a full year, while Phase 2 corresponded to Autumn and
Winter months of another year, and Phase 3 to the following Spring and Summer
months. These temperatures are within the ranges given by Painter (1971) for sewage
temperature in moderate climates: 8-12'C in winter and 17-20'C in summer.
pH values averaged at 7.4, 7.3 and 6.8 respectively during Phase I, 2 and 3. They
were in the optimum range of values (6.5-7.5) for bacterial growth (Metcalf and Eddy,
1991 ).
124
5.2.1.2 Performances of trickling filter
The performance of the pilot-scale trickling filter was monitored by the measurement
and comparison of influent and effluent characteristics such as BOO, COD and solids.
The objective was three-fold. Firstly, to establish whether the performance of the
pilot-scale filter exhibited similar characteristics to that of a full-scale trickling filter.
Secondly, to investigate the effect of different proportions of filtered:total influent
organic matter on filter performance. Thirdly, to generate performance data directly
related to measurements of biofilm accumulation, residence time and particle size
distribution, to enable the investigation of the key parameters affecting filter
performance (Section 5.5).
Table 5.2B gives the average characteristics of unsettled trickling filter effluent during
Phase 1,2 and 3.
125
Table 5 2B' Unsettled effluent characteristics Phase I 7 and 3 - , -Family a! Parameter Phase 1 Phase 2 Phase 3
parameters
EO) BOO (mqll) 21.1 :: 7.6 (35) 32.1 ± 17.1 (11) 9.2 - 2.8 (10)
RE BOO (%) 85.7:: 7.1 (35) 75.5 :: 14.9 (11) 94.2 :: 1.4 (10)
BOO! (mall) 4.5 ± 1.5 (27) 3.8 ± 5.2 (11) 1.6 = 0.6 (10)
RE BOO! (%) 89.4 :: 12.1 (25) 84.9 ± 14.9 (10) 98.5 ± 0.9 (10)
P BOO! (%) 22.8 ± 7.5 (27) 12.6 :: 12.6 (11) 18.5 :: 10.6 (10)
CXD COD (mafl) 89.6 ± 24.2 (38) 124.7 ± 54.5 (14) 70.7 - 20.0 (11)
RE COD 1%) 72.8 ± 9.7 (33) 58.3 :: 20.2 (12) 83.5 • 3.8 (11)
COD! (mq/I) 49.3 ± 16.6 (28) 56.3 ± 11.8 (11) 41.0 ± 12.9 (10)
RE COD! (%) 64.0 ± 20.8 (28) 45.0 = 23.2 (11) 82.1 :: 7.0 (10)
.. P COD! (%) 56.3 ± 19.5 (28) 42.0 ± 10.9 (11) 60.1 :: 10.0 (10)
COOIBOO COOIBOO 4.7 ± 1.7 (34) 5.0:: 1.7 (11) 7.5 = 2.1 (10)
COO!/BOO! 11.1 ± 3.5 (26) 58.1 ± 34.6 (11) 38.7 :: 35.9 (10)
TSS TSS (mall) 56.6 = 23.0 (59) 64.7 ± 31.2 (90) 31.1 ± '13.6 (43)
RE TSS 1%) 61.0 :: 17.5 (36) 70.1 ± 15.2 (78) 81.8 - 7.2 (41)
VSS (mq/I) 42.0 ± 17.2 (37) 72.2 + 40.8 (11) 25.3 - 7.4 (9)
P VSS (%) 71.3 ± 10.8 (36) 83.6 ± 5.5 (11) 84.6 ± 7.4 (9)
TS TS (mqll) 822.9 ± 70.2 (36) 790.6 ± 101.7(11) 778.5" 61.0 (10)
VS (mq/I) 310.9" 46.3 (26) 284.2 :: 63.6 (11) 272.4 = 54.3 (9)
P VS (%) 37.7 ± 3.6 (26) 35.6 ± 3.9 (11) 34.9 = 5.3 (9)
Turbidity turbidity (NTU) 17.5:: 6.6 (41) 17.2 ± 7.3 (52) 8.5 = 3.8 (41)
turbidity! (NTU) 0.9 :: 0.2 (29) 0.6 ± 0.3 (11) 0.7 :: 0.2 (10)
P turbidity! (%) 5.1 :: 1.5 (29) 3.3 ± 1.3 (11) 9.4 :: 5.9 (9)
TKN TKN (mall) 9.5 " 3.5 (18) 14.2 ± 3.4 (6) 6.2 = 1.6 (6)
RE TKN (%) 77.3 ± 7.0 (18) 68.1 ± 6.2 (6) 86.2 - 4.5 (6)
TKN! (mqll) 7.8 " 1.9 (7) · RE TKN! (%) 76.3 ± 6.1 (7) · P TKN! (%) 73.7 :: 8.2 (7) · .
T T (OC) 9.1 ± 4.4 (36) 6.1 ± 2.9 (80) 14.0:: 4.3 (41)
d-l d-l 7.7 :: 0.3 (36) 7.3 :: 0.1 (15) 7.2 " 0.2 (10)
Tabulated values are: mean ± standard deViation (number of observallOns) Xi: influent value of parameter X Xe: effluent value of parameter X RE X: removal efficiency for the parameter X (i.e. RE X = 100 (Xi - Xe)/Xi)
A. Organic content
Figure S.2A and 5.2B show the cumulative frequency of influent and unsettled
trickling filter effluent BOO and BOOf respectively, for Phase 1,2 and 3. Treatment
by the trickling filter reduced BOO and BOOf values: on average from 163.5 to 21.1
mg/l and 91.6 to 4.5 mg/l respectively during Phase I; from 144.5 to 32.1 mg/l and
27.4 to 3.8 mg/l during Phase 2; and from 158.7 to 9.2 mg/l and 115.7 to 1.6 mg/l
during Phase 3. Variations in the unsettled effluent BOO corresponded generally with
fluctuations in the BOO of synthetic sewage.
126
Figure 5.2Aa: Cumulative frequency of influent and unsettled TFE BOO -Phase 1
100
f *->- 80 0 c Q) :0 60 CT
~ ~ 40
~ "3 20 E :0
c..J 0 ,.
0 50
.. / 100 150
BOO (mg/I)
200
I. Influent IJ Unsettled TFE
-
250 300
Figure 5.2Ab: Cumulative frequency of influent and unsettled TFE BOO -Phase 2
100 IJ • *- IJ • >-0
80 IJ • c IJ • Q) :0 60 IJ • CT
~ IJ • '" 40 IJ • > IJ • ~ "3 20 IJ • E IJ • :0
c..J 0 IJ •
0 50 100 150 200 250 300 BOO (mg/I)
I· Influent IJ Unsettled TFE
Figure 5.2Ac: Cumulative frequency of influent and unsettled TFE BOO -Phase 3
100 IJ • ~ IJ • >-
0 80 IJ • c
Q) IJ • :0 60 CT IJ • ~ IJ • Q) 40 IJ • . 2:
;;; IJ • "3 20 E IJ • :0
IJ • <..l 0 0 20 40 60 80 100 120 140 160 180 200
BOO (mg/I)
I· Influent IJ Unsettled TFE
127
Figure 5.2Ba: Cumulative frequency of influent and unsettled TFE BODf -Phase 1
100 -c ~ ••
>. 80 .. " ~ .. c Cl> ::l 60 0-
•• • ~ .' Cl> 40 > • ~ •• "S 20 •• • E ::l I (J • 0
0 20 40 60 80 100 120 140 160 180 BOOt (mg/I)
I· Influent 0 Unsettled TFE
Figure 5.2Bb: Cumulative frequency of influent and unsettled TFE BODf -Phase 2
100 0 • ~ c 0 • >.
" 80 0 • c 0 • Cl>
::l 60 0 • 0-
~ 0 • Cl> 40 0 • > 0 • ~ "S 20 0 • E o • ::l (J
0 • 0 10 20 30 40 50 60 70 80 90 100
BOOt (mg/I)
I • Influent 0 Unsettled TFE
Figure 5.2Bc: Cumulative frequency of influent and unsettled TFE BODfPhase 3
100 0
~ ~ 0 >. 80 0 " c Q) 0 ::l 60 0- 0 ~
~ .~ 40
1ii ~ "S 20 E ::l (J
0 0 20 40
• •
•
60 80 100 BOot (mg/I) I. Influent 0 Unsettled TFE
128
• •
120
• •
•
140
• •
160
The BOD removal efficiency averaged at 85.7, 75.5 and 94.2% respectively during
Phases 1, 2 and 3. This corresponded to the proportion of soluble BOD in the
influent, which was 55.5% in Phase 1, 18.1 % in Phase 2 and 74.1 % in Phase 3. This is
understandable since soluble organic matter is known to be rapidly catabolised in a
biofilm whereas particulate BOD is slower to biodegrade (Ekama et aI., 1986). The
relationship between influent soluble BOD and BOD removal efficiency is further
corroborated by the fact that the BODf removal efficiency was similar for Phases 1
and 2 (89.4 and 84.9% respectively), therefore, being largely irrespective of the
different proportions of soluble BOD in the influent. That is, removal of soluble BOD
remained fairly constant, suggesting that differences in total BOO removed were
related to the removal of particulate BOD. The BODf removal efficiency for Phase 3
was higher than that for the previous phases, being 98.5%. This increased efficiency is
probably the result of a number of inter linked factors, viz, the highest proportion of
influent soluble BOD, higher average ambient temperatures as Phase 3 corresponds
with spring and summer, and a well established biofilm as this was the last phase of
operation.
The fact that soluble BOD is removed more effectively than particulate BOD agrees
with the literature and full-scale operational experience. Parker et al. (1993) reported
that full-scale, low-rate trickling filters treating sewage effluent reduced BODf to a
very low level. A similar trend was also reported by Bruce and Merkens (1973) for
high-rate, pilot-scale trickling filters, filled with mineral media (blast furnace slag and
basalt). The removal efficiency of BODf was 39%, while that of BODss was only
17%.
The BOO results were analysed further by studying the evolution of the proportions
of filtered and suspended BOD (BODf and BODss) fractions during treatment (Figure
5.2C). For the three phases the proportion of BODf decreased during trickling
filtration, while the proportion of BODss increased. On average, over the whole
period of research the proportion of BODf diminished from 50.7% in the influent to
19.5% in the effluent, the proportion of BODss increasing from 49.3% to 80.5%. A
similar pattern was observed by Lin et al. (1986) for a Rotating Biological Contactor
(a type of fixed-film reactor). Their explanation was that a fraction of the particulate
129
Figure S.2Ca: BOD fractionation - Phase 1
100%
'" 80%
" 0
t5 60%
~ 40% Cl 0 20%
'" 0%
Inliuent Unsettled TFE
• Filtered o Suspended
Figure S.2Cb: BOD fractionation - Phase 2
100%
>'! e..- 80%
" .2 60% "6 ~ 40% Cl 0 20%
'" 0%
Inliuent Unsettled TFE
• Filtered o Suspended
Figure S.2Cc: BOD fractionation - Phase 3
100%
~ 80%
" 0
ti 60%
~ 40% Cl 0 20%
'" 0%
Influent Unsettled TFE
• Filtered o Suspended
130
organic matter adsorbed in the trickling filter is hydrolysed and converted to dissolved
organic matter. This is then available as a food source for microbial growth, resulting in
solids production in the form of cells. The authors concluded that, as a result, the
global filtered BO D removed is greater than that measured by the difference between
influent and effluent BODf. It is the sum of the influent BODf removed and the
influent colloidal and supra colloidal BOD that has been hydrolysed in the reactor.
The BOD removed was plotted as a function of the influent BOD, for both total and
filtered BOD (Figure 5.2D). The removed BOD increased linearly with the influent
BOD, and Table 5.2C summarises the determination coefficients found during the
various phases of study. The correlation between influent and removed BODf is
higher than that for total influent and removed BOD. This suggests a simple
mechanism for regulation of BODf in the effluent, i.e. biodegradation of soluble
organic matter, while assessment of total (soluble and particulate) BOD removal is
complicated by solids production as well as removal.
Table 5.2C: Rezression BOD removed - BOD influent Average determination coefficient for regression between removed and influent concentration for
Phase OCD BOOf
1 - 2 - 3 0.937 156\ 0.999 145\ 1 0.934 (35) 0.999 (25) 2 0.901 -(11) 0.998 (10) 3 0.988 (10) 1.000 (10)
Tabulated values are: mean(number of observations)
Jenkins (1957) reported a linear correlation between BOD removed and influent BOD
for low-rate trickling filters treating a wide range of sewage types under various
climatic conditions. He suggests that this is strong evidence that the relationship is not
fortuitous. Similar linear relationships between BOD removed and influent BOD have
been found for high-rate filters at full-scale by Schulze (1960) and pilot-scale by Bruce
and Merkens (1973). Biddle (1994) also demonstrated a linear relationship between
rate of BOD removal and the filter loading for several pilot-scale nitrifYing trickling
filters.
1 31
Figure 5.2Da: Correlation removed BOO - influent BOD -Phases 1-2-3
275 r-------------------------------------------------~
250
225
200
'a,175 .S-8 150 ID "0125 g? ~ 100 f!?
75
50
25
Y = ·18.842 + .986' X; R"2 = .937
75 100 125 150 175 200 225
Figure 5.2Db: Correlation removed BOOr - influent BODf - Phase 1-2-3
o
250 275 300
180 r-----------------------------------------------~
160
140
'a, 120
.S-0 100 o ID "0 80 g? 0 E 60 f!?
40
20
0
Y = -3.383 + 1 • X; R"2 = .999
0 20 40 60 80 100 120 140 160 180
132
In tenns of filtered BOO, Sarner (1986) found that the removal of filtered BOO was
linearly correlated to filtered BOO concentration in the influent of a high-rate filter.
Richards and Reinhart (1986) studied the perfonnance of 2 high-rate trickling filters. A
linear relationship existed between influent BODf and BODf removed up to an
influent BODf concentration of 40 mgll, at which point the curve started to flatten.
This change in the relationship between influent BODf and BODf removed was also
noted by Parker and Merrill (1984) who attributed this to oxygen transfer limitation in
the biofilm. These authors quoted Wiliamson and McCarty (1976) as saying that
BOO removal can become oxygen-limited when the biofilm is exposed to soluble BOO
concentrations of greater than 40 mg/l. It is important to note that no oxygen-transfer
limitation has been found in the results presented in this thesis, in spite of influent
BODf concentrations of up to 160 mgll. This is due to the fact that the filter was
operated at low-rate.
COD
While BOO is the traditional indicator of organic pollution in domestic wastewater
treatment, COD has been widely used in the UK as a pollution parameter to assess
industrial wastewaters but not routinely for domestic wastewaters. COD is however
becoming more important, this is mostly due to the European Union Urban Waste
Water Treatment directive 91/271IEEC (Commission of the European Communities,
1991) which sets requirements for discharges from urban waste water treatment plants
not only in terms of BOO and TSS, but a maxima.\ COD concentration of 125 mgll
and/or minimum reduction of 75% as a 95 percentile.
As indicated in Table 5.2B, COD removal efficiencies averaged at 72.8% during Phase
1,58.3% during Phase 2 and 83.5% during Phase3 (Figure 5.2E and 5.2F). The pattern
was similar to that observed for BOO: the higher removal efficiencies were observed in
parallel with the higher P CODf in the influent (48.1 % for Phase I, 36.6% for Phase 2
and 57.4% for Phase 3; Figure 5.2F).
COD removal efficiencies throughout the research averaged at 71.8% for COD and at
63.4% for CODf, compared to 85.2% for BOO and 90.4% for BODf. The lower
removal efficiency for COD compared to that for BOO was anticipated; it could be
133
Figure S.2Ea: Cumulative frequency of influent and unsettled TFE COD - Phase 1
100 ~ f , ~
1"" >- 80 " c CD ::l 60 , c-~
CD 40 > ~
~~ "5 20 E j ::l
U • • 0 0 100 200 300 400 500
COD (mg/l)
I· Influent 0 Unsettled TFE
Figure S.2Eb: Cumulative frequency of influent and unsettled TFE COD - Phase 2
100 0
*- 0 • >- 80 0 • " 0 • c 0 CD
0 • ::l 60 c- o • ~ 0 • CD 40 0 • > ~ 0 • 0 "5 20 0 • E 0 • ::l
11 U 0 0
0 100 200 300 400 500 COD (mgm
I· Influent 0 Unsettled TFE
Figure S.2Ec: Cumulative frequency of influent and unsettled TFE COD - Phase 3
100 0 ~ ~ 0 >-"
80 0 c 0 Q) ::l 60 0 <:T
~ 0 • CD 40 0 • > 0 • ~ "5 20 0 • E 0 • ::l
U 0 0 •
0 100 200 300 400 COD (mg/l)
I· Influent 0 Unsettled TFE
134
600
• • • •
-
600 700
•
700 800
•
500 600
Figure 5.2Fa: Cumulative frequency of influent and unsettled TFE COnf -Phase 1
100 ~
rP u . , • • e..->- 80
.I 0
" Q) j ... :0 60 <:r
~ Q) 40
~ • • '5 20
n £. .'-E :0 (J
0 0 50 100 150 200 250 300 350
COOl (mg/I)
I· Influent 0 Unsettled TFE 1
Figure 5.2Fb: Cumulative frequency of influent and unsettled TFE COnf -Phase 2
100 0 ~ • ~ 0 • >-0
80 0 • " 0 • Q) :0 60 0 • <:r
~ 0 • Q) 40 0 • > 0 • ~ '5 20 0 • E 0 • :0 (J 0 • 0
0 50 100 150 200 250 300 COOl (mg/I)
I· Influent 0 Unsettled TFE
Figure 5.2Fc: Cumulative frequency of influent and unsettled TFE COnf -Phase 3
100 0 • ~ e..- o • >-
0 80 0 • " Q) 0 • :0 60 <:r 0 • ~ 0 • Q) 40 0 • >
.~ 0 • '5 20
E 0 • :0 0 • (J
0 0 50 100 150 200 250 300
COOl (mg/I)
I· Influent 0 Unsettled TFE
135
due to either the generation of refractory soluble microbial products (SMPs) during
treatment (Chudoba, 1985a), or refractory residues from the influent. It is also
interesting to note that, while BOOf removal efficiency was higher than BOO removal
efficiency, the trend was inverted in the case of COD. COD removal efficiency was
higher than COOfremoval efficiency. This could indicate the generation of refractory
SMPs rather than refractory soluble COD from the influent.
Figure 5.2G shows the evolution through treatment of the wastewater COD
fractionation between filtered and suspended fractions. On average throughout the
research, the fraction of COD carried by particulates (ssCOO) was reduced from
52.6% in the influent to 46.1% in the unsettled TFE, while the COOf fraction
increased from 47.4% to 53.9%. The trend was similar for each of the 3 phases of the
research, while the initial fractions of COOf represented on average 48.1 %, 36.6% and
57.4% of the total COD for Phase 1,2 and 3 respectively. This seems to be further
evidence that the trend of increase in the proportion of COOf through treatment was
independent of the proportion of COOf in the influent.
Similarly to the analysis done for BOO, the removed COD was plotted as a function
of the influent COD (Figure 5.2H). As in the case of BOO, the COD removed
increased linearly with the COD applied to the filter. Table 5.20 summarises the
average determination coefficients found for both COD and COOf during the various
phases of study.
Table 5.2D: ReJ;!ression Dremove - in lIent CO d COD if! Average determination coefficient for regression between removed and influent concentration for
Phase CID CClDf 1 - 2·3 0.853 (57) 0.957 (49)
1 0.851 (34) 0.980 (28) 2 0.857 (12) 0.958 (11) 3 0.854 ( 11) 0.891 (10)
Tabulated values are: mean (number of observations)
As in the case of BOO, the high coefficients of determination indicate that the
regressions are significant. That is, the more COD in the influent, the more COD is
removed by the trickling filter. This would be expected to hold true until the influent
136
Figure 502Ga: COD fractionation - Phase 1
100%
~ 80%
c: 0 60% ti Jg 40% 0 0 20% U
0%
Influent Unsettled TFE
• Filtered o Suspended
Figure 502Gb: COD fractionation - Phase 2
100%
->!! a.. 80%
c: 0 60% ti Jg 40% 0 0 20% U
0%
Influent Unsettled TFE
• Filtered o Suspended
Figure 502Gc: COD fractionation - Phase 3
100%
#. 80%
c: 0 60% ti Jg 40% 0 0 20% U
0%
Influent Unsettled TFE
• Filtered D Suspended
137
----------
Figure S.2Ha: Correlation removed COD - influent COD - Phases 1-2-3
600 r-------------------------------------------~----~ Y = ·74.612 + .948' X; R"2 = .853
500 o
o
100
oL---~~----~----~----~------~----~--~ 100 200 300 400 500 600 700 800
influent COD (mg/lO
Figure S.2Hb: Correlation removed CODe - influent CODe - Phase 1-2-3
300 Y = -45.97 + .981 • X; R"2 = .957
250
0 ~200 .S-o 0150 () "0 Cl) > 0 E 100 ~
50 0
0 0 50 100 150 200 250 300 350
influent CO Of (mg/l)
138
COD is so high that the filter would be 'overloaded'. As was previously mentioned,
COD is not a parameter usually measured for sewage effluents, therefore, not much
information exists in the literature with respect to the relationship between influent
and removed COD for a trickling filter. However, results reported for other types of
fixed-film reactors agree with the linear correlations found during this research. Both
d'Antonio et at. (1997) and Pastorelli et al. (1997) reported linear relationships
between influent COD and COD removed, for an RBC and for moving-bed biofilm
reactors, respectively. The coefficients were again higher for filtered COD than for
total COD. This can be explained in part by the sensitivity of the COD test to solids:
given the small volume of sample (5 ml) used for COD analysis, the presence of solids
in the sample can have a high impact. Although, to take this factor into account,
analyses were done in duplicates. Alternatively, this could be explained (as in the case
of BOO) by the production of biofilm solids as well as degradation of influent
particulates, which can complicate the calculation of COD removal.
B. Solids content
Figure 5.21 shows the cumulative frequency of influent and unsettled effluent TSS
concentrations during Phase 1, 2 and 3.
TSS removal efficiency by the trickling filter averaged at 61.0, 70.1 and 81.8% during
respectively Phases 1,2 and 3. The TSS concentration decreased through treatment
from average values of 152.5,221.9 and 174.4 mgll to average values of 56.6, 64.7 and
31.1 mg/l during respectively Phase 1, 2 and 3. It is interesting to note that the highest
removal of TSS occurred during Phase 3, although this phase was characterised neither
by the highest or lowest concentration of TSS in the influent. Phase 3 was, however,
characterised by the highest proportion of influent filtered BOO and the highest
average ambient temperatures. Consequently, it is possible that the increased removal
efficiency of TSS during Phase 3 could be related to increased overall activity of the
biofilm as a result of increased readily biodegradable substrate and favourable ambient
temperatures.
No marked relationship seems to exist between concentration of TSS in the influent
and TSS concentration in the effluent for Phases I, 2 and 3. This contrasts with
139
Figure 5.2Ia: Cumulative frequency of influent and unsettled TFE TSS -Phase 1
100 dl -;R /-e....
>- 80 " If c: Ql
" 60 0-
~ !l! 40
~ :; 20 E " , u 0
0 50 100 150 200 250 300 350 400 450 TSS (mg/I)
- Influent Il Unsettled TFE
Figure 5.2Ib: Cumulative frequency of influent and unsettled TFE TSS -Phase 2
100 .- -;R e.... >-"
80 c: Ql
" 60 0-
~ Ql 40 > ~ :; 20 E " u
300 400 500 600 700 TSS (mg/l)
I - Influent Il Unsettled TFE
Figure 5.2Ic: Cumulative frequency of influent and unsettled TFE TSS -Phase 3
100 III -. *- -->- 80 ,,-" // c: Ql
" 60 0-
~ Q) 40 > ~ :; 20 ---E
~ ,/ " u 0 0 50 100 150 200 250 300 350
TSS (mg/l)
I- Influent Il Unsettled TFE
140
fmdings by Matasci et a/. (1986), who reported that TSS entering a trickling filter
played a role in its performance. For four full-scale low-rate trickling filter plants they
found that an increase in primary effluent TSS was correlated with an increase in
trickling filter effluent TSS. This was also found to be the case by Samer (1986),
reporting on research conducted with a pilot-scale trickling filter. Thus, theoretically,
the higher the concentration ofTSS in the influent, the higher the concentration of TSS
in the trickling filter effluent.
The difference between the results reported in this thesis, and those reported by
Matasci et a/. (1986) and Samer (1986) could be related to the nature of the solids.
The TSS in the synthetic sewage is composed of starch and dextrin, both of which are
reasonably biodegradable. However, the suspended solids in primary settled effluent
(used by Matasci et al. (1986) and Samer (1986)) are of a different nature to that used
in the synthetic influent of this study. It should also be remembered that the size of
the particles in the influent could have an effect on performance. This aspect is dealt
with in the later section on particle size distribution.
Table 5.2B also shows that the mineralisation ratio (i.e. proportion of VSS) of the
trickling filter effluent was relatively constant, the organic fraction (VSS) accounting
on average for 71.3, 83.6 and 84.5% of the TSS respectively for Phases I, 2 and 3.
The literature gives similar values, between 75 and 80% (Pitter and Chudoba, 1990,
quoted by Figueroa and Silverstein, 1992).
C. Nitrogen content
Figure 5.21 shows the cumulative frequency of TKN concentrations in the influent
and unsettled TFE during the whole period of research.
The TKN removal efficiency averaged at 77.3% throughout the period of study.
The removed TKN was reasonably correlated with the influent TKN, with a
coefficient of determination of 0.663 (Figure 5.2K). This correlation was not as good
as that observed for organic pollution parameters (BOO and COD) or TSS. However,
it was still in agreement with similar correlations found in the literature for fixed-film
141
Figure S.2J: Cumulative frequency of influent and unsettled TFE TKN -Phases 1-2-3
100 t1 • • 0 • 0 • 0 •
80 0 • 0 • ~ 0 • ~ 0 • '"
0 • " 0 • <= 60 0 • Cl) 0 • " 0 • c- o • ~ 0 • 0 • Cl)
40 0 • > 0 • .~ 0 • ,~
0 • 0 • 0 • " 0 • Ll 20 0 • 0 • 0 • 0 • 00 • •
0 0 10 20 30 40 50 60
TKN (mgn)
I· Influent 0 Unsettled TFE
Figure S.2K: Correlation removed TKN - influent TKN - Phases 1-2-3
142
bioreactors, for example by Biddle (1994) and Upton and Cartwright (1984).
5.2.2 Performances of secondary settlement
A trickling filter converts soluble and non-settleable organic matter to settleable
material which is then removed during secondary settlement. Consequently, secondary
settlement performance is considered to be an important part of the overall
performance of the trickling filter process.
5.2:2.1 Pilot-scale results
The performances of secondary settlement of trickling filter effluent were studied
during the last third of Phase I (Phase 1 ') and during Phases 2 and 3, using quiescent
settlement for 1 h (see Materials and Methods). The results of this study are
presented in Table S.2E for Phase 1',2 and 3.
143
Table 5 2E' Settled effluent characteristics - Phase I' 2 and 3 ,
Family of Parameter Phase l' Phase2 Phase3 parameter
800 BOO (mg/l) 9.4 ± 1.2 19.0 ± 12.6 4.7 ± 1.6 (7) ( 1 1 ) ( 1 0 )
REs BOO (%) 25.6 ± 6.6 42.~ ± 10.2 45.6 ± 20.9 ( 7) 1 1 ) ( 1 0 )
a::o COD (mg/I) 54.9 ± 12.3 81.2 ± 37.2 48.0 ± 16.2 ( 8 ) ( 1 1 ) ( 1 1 )
REs COD (%) 23.2 ± 17.2 43.4 ± 9.2 32.8 ± 7.7 ( 8 ) ( 1 1 ) ( 1 1 )
CODIBOD CODIBOD 5.7 ± 1.4 4.9 ± 1.4 11.4 ± 6.6 (7) ( 1 1 ) ( 1 0)
TSS TSS (mg/I) 15.3 ± 3.2 24.0 ± 16.2 9.6 ± 3.8 ( 8') (76) ( 4 1 )
REs TSS (%) 59.0 ± 14.8 64.6 ± 11.0 64.5 ± 16.2 ( 8 ) (77) ( 41 )
VSS (mg/I) 13.3 ± 2.8 32.~ 1±1 ~6.4 10.4 ± 3.4 ( 8) ( 8 )
P VSS (%) 87.2 ± 7.5 92.7 ± 5.0 89.9 ± 15.2 ( 8 ) ( 1 1 ) ( 8 )
1S TS (mg/l) 795.8 ± 61.3 747.1 ± 86.3 750.5 ± 57.0 ( 8 ) ( 1 0 ) ( 1 0 )
VS (mg/l) 302.3 ± 47.4 256.8 ± 40.3 250.6 ± 60.4 ( 8 ) ( 1 0 ) .1..9 )
P VS (%) 37.9 ± 4.5 34/1 ~ )2.7 33.3 ± 6.7
( 8 ) ( 9 ) Turbidity turbidity 5.0 ± 1.1 (8) 8.1 ± 4.5 3.5 ± 1.3
(NTU) (42) ( 41 ) TKN TKN (mg/I) - 13.3 ± 2.5 4.4 ± 2.9 (4)
( 5 ) REs TKN (%) - 11.3 ± 3.1 36.3 ± 19.6
( 5 ) ( 4 )
Tabulated values are: mean ± standard deViation (number of observations)
A. Organic content
Figure 5.2L shows the cumulative frequency of unsettled and settled trickling filter
effluent BOO. Secondary settlement' removed on average an additional 25.6, 42.6 and
45.6% of unsettled TFE BOO, bringing the settled effluent BOO to average levels of
9.4, 19.0 and 4.7 respectively during Phase I', 2 and 3. It therefore appears that
secondary settlement consistently bring the effluent BOO below the required
standards (only two BOO values above 25 mg/I out of the 28 measurements).
144
Figure 5.2La: Cumulative frequency of unsettled and settled TFE BOO -Phase I'
100 • ~ IJ • >!1 ,~ !!..->- 80 IJ
) " c Q) IJ :J 60 •• a-
.. 1 ~ IJ Q) 40 IJ > ~
( :; 20 IJ E :J IJ ()
0 • 0 5 10 15 20 25 30 35 40 45
BOO (mg~)
I· Unsettled TFE IJ Settled TFE
Figure 5.2Lb: Cumulative frequency of unsettled and settled TFE BOO -Phase 2
100 ~ IJ • C IJ • >- 80 IJ • " c IJ • Q) :J 60 IJ • a-~ IJ • Q) 40 IJ • > IJ • ~ :; 20 IJ • E IJ • :J () IJ • 0
0 10 20 30 40 50 60 70 80 BOO (mg/I)
I· Unsettled TFE IJ Settled TFE
Figure 5.2Lc: Cumulative frequency of unsettled and settled TFE BOO -Phase 3
100 IJ • *- IJ • >- 80 IJ • " c
Q) IJ • :J 60 a- IJ • i!? - IJ • Q) 40 IJ • > ~ IJ • :; 20 E IJ • :J IJ • ()
0 0 2 4 6 8 10 12 14
BOO (mgn)
I· Unsettled TFE IJ Settled TFE
145
COD
The performances of secondary settlement in terms of COD removal were also
expressed using cumulative frequency graphs (Figure 5.2M).
From the graph and Table 5.2E it appears that secondary settlement removed on
average 23.2, 43.4 and 32.8% of unsettled TFE COD during respectively Phase 1', 2
and 3. The settled effluent COD averaged at 54.9, 81.2 and 48.0 mgll during the three
phases. Again the values were consistently below standards (breached on one occasion
out of 30 measurements).
B. Solid content
Figure 5.2N shows the unsettled and settled TFE TSS cumulative graphs during Phase
1', 2 and 3. Secondary settlement removed on average 59.0, 64.6 and 64.5% of
unsettled TFE TSS. As a result, the TSS values of final effluent averaged at 15.3, 24.0
and 9.6 mg/I during respectively Phase 1',2 and 3. They were higher than 35 mgll only
on 7 occasions out of 125 measurements. The performance of the pilot-scale filter
followed by settlement were therefore very satisfactory at the low loadings applied.
Visual observations showed that the readily settleable solids were composed mainly
of pieces of biological film, scouring organisms and animal debris, all of which were
discharged continuously from the filter at a fairly steady state. Long stringy growths
were also occasionally shed from the filter.
C. Nitrogen content
TKN removal by secondary settlement was studied during Phase 2 and 3. It appears
from Table 5.2E that secondary settlement removed on average 11.3 (Phase 2) and
32.8% (Phase 3) of unsettled TFE TKN.
5.2.2.2 Full-scale results
As indicated in the Materials and Methods, the performance of secondary settlement
was also monitored at full-scale over a period of 13 months. Table 5.2F summarises
the characteristics of unsettled and settled TFE during the period of study.
146
Figure 5.2Ma: Cumulative frequency of unsettled and settled TFE COD - Phase l'
100 ~ c
.~ C c >- 80
,..1 " c: C
" :::l 60 r:r c ~. ~ " 40 c ... > ,. ~ c :; 20 c ~I E :::l C () • • 0
0 20 40 60 80 100 COD (mg/I)
I· Unsettled TFE C Settled TFE
Figure 5.2Mb: Cumulative frequency of unsettled and settled TFE COD - Phase 2
100 c 1ft. c • • >- 80 c • " c: C • " :::l 60 c • r:r • ~ c • " 40 c • > c • ~ c • :; 20 • E c • :::l ()
0 c •
0 50 100 150 200 COD (mgn)
I· Unsettled TFE c Settled TFE
Figure 5.2Mc: Cumulative frequency of unsettled and settled TFE COD - Phase 3
100 c ;R ~ c >- 80 c " c: C • " :::l 60 c • r:r ~ C • .~ 40 c • c • 1ii c • :; 20 E c • :::l () C • 0
0 20 40 60 80 COD (mgn)
I· Unsettled TFE c Settled TFE
147
120
• •
••
140 160
•
250 300
•
100 120
Figure 5.2Na: Cumulative frequency of unsettled and settled TFE TSS -Phase I'
100 •• <fl c ",. ..
>- 80 c 0
"./ c c Q)
" 60 CT c ~ c Q) 40 > I ~ c :; 20 c ~.; E " c .. (J
0
•
0 20 40 60 80 100 120 140 TSS (mg/I)
I· Unsettled TFE c Settled TFE
Figure 5.2Nb: Cumulative frequency of unsettled and settled TFE TSS -Phase 2
100 IPCI III C .. " >- 80 /,--0 c Q)
" 60 CT
~ Q) 40 > ~ :; 20 E " (J
25 50 75 100 125 150 175 200 TSS (mg/I)
I· Unsettled TFE c Settled TFE
Figure 5.2Nc: Cumulative frequency of unsettled and settled TFE TSS -Phase 3
100 • <fl ~ >- 80 ~~ u c
/ Q)
" 60 CT
~ Q) 40 / > ~ :; 20 J E " (J
0 0 10 20 30 40 50 60 70 80
TSS (mg/I)
I· Unsettled TFE C Settled TFE
148
I
Table 5.2F: Unsettled and settled effluent characteristics - Full-scale study
Family of Parameter Unsettled TFE Settled TFE ~~arameter
EO) BOO (mg/I) 23.5 ± 15.9 10.2 ± 5.6 ( 1 5 ) ( 1 5 )
REs BOO (%) - 48.9 ± 19.9 ( 1 5)
CID COD (mg/I) 96.1 ± 45.1 54.6 ± 23.5 ( 1 3 ) ( 1 3 )
REs COD (%) - 39.9 ± 23.3 ( 1 3 )
CODlBOD CODIBOD 4.9 ± 1.4 5.9 ± 2.7 ( 1 3 ) ( 1 3 )
TSS TSS (mg/I) 55.4 ± 52.7 12.7 ± 7.2 ( 1 5 ) ( 1 5 )
REs TSS (%) - 67.9 ± 23.1 ( 1 5 )
TS TS (mg/I) 763.3 ± 144.2 734.2 ± 114.8 ( 1 4 ) ( 1 4 )
Tu rbidity turbidity (NTU) 28.3 ± 18.1 8.6 ± 5.3 ( 1 5 ) ( 1 5 )
TKN TKN (mg/I) 4.3 ± 1.1 2.5 ± 1.0 ( 6 ) ( 5 )
REs TKN (%) - 44.9 ± 25.7 ( 5 )
T T (OC) 11.0 ± 4.0 11.1 ± 4.3 ( 1 5 ) ( 1 4 )
pH pH 7.7 ± 0.2 7.7 ± 0.2 ( 1 5 ) ( 1 4 )
Tabulated values are: mean ± standard deViation (number of observations)
The cumulative frequency plots of unsettled and settled trickling filter are presented in
Figure 5.20, 5.2P and 5.2Q respectively for BOO, COD and TSS.
The performance of full-scale secondary settlement confirmed the average results
found at pilot-scale during the three phases of research. The values were very similar
in terms of settled effluent quality. The average full-scale removal efficiencies (48.9%
for BOO, 39.9% for COD and 67.9% for TSS) were only marginally better than that
obtained throughout the pilot-scale study (39.4% for BOO, 34.1 % for COD and
64.2% for TSS). Secondary settlement is, therefore, generally efficient at removing the
solids generated during biological treatment.
5.2.3 Evolution of ratios through treatment
The performance results are concluded with the following section, III which the
performance parameters (COD, BOO and solids) are cross-referenced in order to gain
149
Figure 5.20: Cumulative frequency of unsettled and settled TFE BOO -Full-scale
100 0 • ~
~ 0 • 0 • '" 80 0 • 0 c: 0 • CD 0 • " 60 er 0 • ~ 0 • CD 40 0 • > 0 • ~ 0 • :; 20 0 • E 0 • " o • ()
0 o •
0 10 20 30 40 50 60 70 BOO (mg/l)
I· Unsettled TFE 0 Settled TFE
Figure 5.2P: Cumulative frequency of unsettled and settled TFE COD -Full-scale
100 0 • <t- o • '" 80 0 • 0 0 • c: CD 0 • " 60 0 • er ~ 0 • ~ 40 0 •
0 • .~ 0 • :; 20 0 • E 0 • " ()
0 0 •
0 20 40 60 80 100 120 140 160 180 200 COD (mgA)
I· Unsettled TFE 0 Settled TFE
Figure 5.2Q: Cumulative frequency of unsettled and settled TFE TSS -Full-scale
100 0 • <t- o • 0 • '" 80 0 • 0 c: 0 • CD 0 • " 60 er 0 • ~ 0 • CD 40 0 • .2: 0 • 1ii 0 • :; 20 0 • E 0 • " 0 • ()
0 0 •
0 25 50 75 100 125 150 175 200 225 250 TSS (mg/l)
I· Unsettled TFE 0 Settled TFE
150
more information from the data. The evolution of these ratios through the trickling
filter and, where appropriate, through secondary settlement is presented.
S.2.3.1 COD/BOO ratio
The evolution of COD to BOO ratio throughout the treatment provides information
on the biodegradability of organic matter. A high COD/BOO ratio indicates a poorly
biodegradable organic matter, while reciprocally a low COD/BOO ratio indicates a
very biodegradable organic matter.
Table 5.2G shows the evolution of CODIBOD ratio throughout treatment, while
Table 5.2H shows the CODflBOOf ratio before and after treatment by the trickling
filter.
Table 5.2G: Evolution oj"COD/BOD ratio through treatment
COD/BOO ratio Phase Influent Unsettled TFE Settled TFE
1 - 2 -3 2.4 ± 0.5 (55) 5.3 ± 2.1 (56) 7.4 ± 5.0' (28) 1 2.2 ± 0.4 (34) 4.7 ± 1.7 (35) 5.7 + 1.4" (7) 2 2.5 + 0.5 11 5.0 + 1.7 11 4.9 + 1.4 (11) 3 2.7 + 0.3 10 7.5 + 2.1 10 11.4 + 6.6 (10)
Tabulated values are: mean ± standard devlallOn (number of observations) ': Phase 1 '-2-3 ": Phase l'
Table 5.2H: Evolution oj"CODjlBODj"ratio through treatment
CODI/BODf ratio
Phase Influent Unsettled TFE
1 - 2 - 3 3.7 + 4.1 (47) 27.6 + 17.4 (48) 1 2.4 + 1.4 (26) 11 .1 + 3.5 (27 2 8.3 + 6.6 (11) 58.1 + 34.6 (11) 3 2.1 + 0.4 (10) 38.7 + 35.9 (10)
Tabulated values are: mean ± standard devlallOn (number of observations)
It appears that, on average throughout the study, the CODIBOD ratio increased with
the level of treatment: the ratio for settled effluent is higher than that for unsettled
effluent, itself higher than that for the influent. This pattern was specifically observed
during the individual Phases I and 3, the increase of ratio through settlement being
confirmed by the full-scale results. However, during Phase 2, a slight decrease in
COD/BOO through settlement ofTFE could be observed. This means that, in the case
151
of Phase 2, and by opposition to Phase I and 3, the settled TFE was marginally more
biodegradable than the unsettled TFE.
The decrease in biodegradability of wastewater through treatment was even more
noticeable for the filtered fraction: on average throughout the study, the CODf/BODf
ratio increased from 3.7% in the influent to 27.9% in the unsettled TFE. The readily
available fractions are degraded and removed first, leaving the more recalcitrant
co~pounds in the waste water.
5.2.3.2 VSSITSS ratio
VSS represents the organic fraction of the solids in the waste water, which consists of
influent organic solids and cellular material (biomass). The calculation of the VSSITSS
ratio expresses the proportion of VSS in the total solids, i.e. the proportion of
organic:inorganic solids. Table 5.21 shows the evolution of P VSS % through
treatment.
T, bl - 21 El' if h ifVSS h h a e). vo utlOn 0 t e proportIOn 0 t rougl treatment
P VSS (%)
Phase Influent Unsettled TFE Settled TFE
1 - 2 - 3 95.5 ± 2.4 75.9 ± 9.2 90.3 + 8.8a
1 95.1 ± 2.5 71.3 ± 10.8 87.2 + 7.5b
2 95.7 + 2.7 83.6 + 5.5 92.7 + 5.0 3 96.3 + 1.6 84.6 + 7.4 89.9 + 15.2
Tabulated values are: mean ± standard deViation (number of observations) a: Phases 1 '-2-3 b: Phase l'
The proportion of VSS (organic material) decreases as a result of treatment in the
trickling filter, i.e. particulate organic material is degraded during treatment. The
proportion of VSS in the settled TFE increases again, although is still lower than the
value for the influent. This indicates that a high proportion of the non-settleable
material is in fact of an organic nature, most likely material generated by the trickling
filter.
152
5.2.4 Conclusions
• Operational performance of a trickling filter was assessed in terms of BOO and COD
removal and solids production and removal. The pilot-scale trickling filter used in this
study was shown to exhibit similar performance characteristics to full-scale low-rate
trickling filters. A linear relationship was obtained between influent BOO and BOO
removed during treatment, as has been reported for full-scale trickling filters. Similarly,
a linear relationship was demonstrated between influent COD and COD removed.
Particulate solids in the influent were found to be partially removed during treatment,
the proportion of volatile solids being reduced from an average of 95% in the influent
to an average of 76% in the trickling filter effluent. Secondary settlement following the
pilot-scale trickling filter was found to reduce BOO, COD, solids and TKN of the
effluent. The results were slightly lower but proportionately comparable to those
found during the full-scale study at Snarrows STW.
• The effect of different proportions of filtered:total influent organic matter on filter
performance was assessed with the use of three different synthetic sewage recipes,
corresponding to Phases 1, 2 and 3. With respect to BOO, a higher proportion of
filtered BOO: particulate BOO was found to increase BOO removal efficiency, as has
been found for full-scale trickling filters. The filtered BOO was found to be removed
preferentially to the particulate BOO. Both BOO and BODf exhibited a linear
relationship between influent BOO (BOOt) and BOO (BOOt) removed. COD
removal efficiency exhibited a different relationship to BOO removal efficiency, as
total COD removal was higher than filtered COD removal. CODf was found to
increase during treatment, suggesting that effluent CODf was independant of influent
CODf. The different concentrations of particulate material (TSS) in the influent
during Phases 1, 2 and 3 did not exhibit a direct relationship with TSS in the trickling
filter effluent.
• In general, it appears that the pilot-scale low-rate trickling filter operated for two
years during this study performed well and produced a quality of effluent satisfactory
with regards to the new EU directive on Urban Wastewater Treatment (Commission
of European Communities, 1991). Although, at times effluent results were found to be
above the required standards for BOO, COD and TSS. The highest occurrence of these
153
poor quality results was during Phase 2, which was characterised by lower
temperatures and a high proportion of suspended organic matter in the influent.
5.3 CHARACTERISATION OF WASTEWATER AT DIFFERENT
STAGES OF TREATMENT
As ,indicated in the literature reVIew, wastewaters are heterogeneous mixtures of
materials with a wide range of particle size and molecular weight (MW). The size of
contaminants is important since most of the processes involved in treatment (mass
transfer, adsorption, diffusion, biochemical reactions, sedimentation, filtration) are
affected by particle dimensions.
With regard to biological filtration, the rate and efficiency of removal of dissolved
substances in fixed-film biological processes such as the trickling filter differs from
that of particles. It is assumed that dissolved organics are transported by diffusion
into the biofilm, where the degradation reactions take place. On the other hand,
particulate organics are adsorbed on the biofilm surface and have first to be
hydrolysed into a dissolved form in order to be transported into the biofilm (Samer
and Marklund, 1984).
Crossflow filtration is also affected by the size of contaminants. The particulate
matter bigger than the membrane pore size constitutes a filter cake at the membrane
surface, while the small molecular weight dissolved matter is carried through the
membrane. The behaviour of material of a size similar to that of the pore size is
uncertain. This material is known to contribute to membrane fouling, externally as a
part of a filter cake and/or internally, inside the membrane structure.
The wastewater was therefore characterised in terms of contaminant size at the
various stages of treatment. The particulate fraction was characterised by particle size
distribution, the objective being to follow PSD evolution through treatment. This was
achieved by extracting parameters from PSDs, parameters that could be correlated to
each other and to general trickling filter performance parameters. The dissolved
154
fraction of wastewater was fractionated using a High Performance Size Exclusion
Chromatography (HPSEC system). As for the particulate fraction, the objective was
to monitor the effect of the various stages of treatment on the dissolved fraction.
The characterisation of the particulate fraction was undertaken throughout the study,
at pilot-scale and at full-scale. The HPSEC technique was developed during Phase 2
and was applied during Phase 3 of the pilot-scale study.
5.3.1 Characterisation of the particulate fraction by particle size
distribution
The objective of this part of the research was to characterise the evolution of the
particulate content of wastewater through the different stages of treatment: influent,
unsettled and settled trickling filter effluent.
5.3.1.1 Presentation of results
As indicated in the literature review, PSDs can be expressed by number, surface area,
volume or mass (APHA-AWWA-WEF, 1995), either as frequency distributions
(FDs) or cumulative distributions: undersize (UCDs) or oversize (OCDs). In order to
compare distributions at different stages of treatment, it is essential to normalise FDs
so that the area under the curve is 100% (Alien, 1997).
The parameters extracted from PSDs were:
• General PSD characteristics (central tendency and spread indicators);
• PSD fractionation into three size ranges (0.1-1.2 jlm, 1.2-100 jlm, 100-900 jlm);
• Parameters extracted from PSD modelling.
This is illustrated in the following sections (A to C) with a typical set of data
(measured on unsettled trickling filter effluent). The objective being to illustrate the
influence of the data analysis techniques on the results, using a simple example, before
presentation of the actual data in Section 5.4.1.2.
155
A. PSD profile and general characteristics
Figures 5.3A and 5.38 show typical representations of PSDs expressed by number
(Np) and by volume (Vp) for unsettled trickling filter effluent. They include plots of
frequency distributions (FDs): d(Np)/d(dp) and d(Np)/d(log dp) (and d(Vp)/d(dp) and
d(V p)/d(log dp» versus dp, and plots of undersize and oversize cumulative
distributions (UCDs and OCDs) versus dp, both by number and by volume. A
logarithmic scale was chosen for the abscissa, because of the width of the particle
diameter range. It can be seen that plotting by number and volume give different and
complementary information. When plotting by number, the distributions indicate the
dominance of the small particulates (smaller than I lAm) in terms of number. On the
contrary, the plots by volume show skewness towards the smaller volumes. This
shows that the bulk of the particulate matter contributing to total particulate volume
has a diameter between 30 and 300 lAm, with a mode value of about 100 lAm. The
complementarity of PSD plots by number and by volume has been highlighted in the
literature. Adin et af. (1989) reported that PSD curves by number and by volume
(measured over a 1-300 J.lffi range for effluent from an activated sludge plant) looked
totally different from one another. They concluded that, even if most of the particles
were smaller than 10 lAm, the largest fraction of total particulate volume was
contributed by the 10-80 lAm particle size range. Similarly, Li and Ganczarczyk (1991)
compared PSD by volume and by number for activated sludge flocs over a particle
size range of 0.5 to 512 lAm. They found that, although the number of floes in the
system was overwhelmingly occupied by those smaller than 2 lAm (over 95% of the
total number), more than 80% of the total floc volume was due to floes larger than 128
lAm.
The data that can be extracted from distributions are representative of either the
average or the spread of the distributions. The data representative of the average are
mean (dm) and median (dso) , i.e. respectively abscissa of the centre of gravity of the
distribution, and abscissa corresponding to 50% size of the cumulative distribution
curves. The spread was represented by the standard deviation value (0'). These data
were extracted for both distributions by number and distributions by volume, on the
total range of particulate (0.1-900 lAm). Table 5.3A gives these values for the PSD
shown in Figure 5.3A and 5.38.
156
Figure 5.3Aa: Plot of d(Np)/d(log dp) vs. dp (in log)
300
250 a: "0 200 0> S1 150 :0 -a: 100 z :0
50
0
0.1 10 100 1000
dp (~m)
Figure 5.3Ab: Plot of d(Np)/d(dp) vs. dp (in log)
1400
1200
a: 1000 \ • ::!. BOO \ "0
" -a: 600 • z ill :0 400 •
200
0
0.1 10 100 1000
dp (~m)
Figure 5.3Ac: Plot of UCD and OCD in number vs. dp (in log)
100
BO ~, ~ 60 ~ ~ c. 40 >
.-~ 20 • Or. • "<-4
0 0.1 10 100 1000
dp (~m)
---- UCD --0--- =
157
Figure 5.3Ba: Plot of d(Vp)/d(log dp) vs. dp (in log)
120
100 CL "0
80 Cl .2
60 :0 -CL 40 > :0
20
0
0.1 10 100 1000
dp (~m)
Figure 5.3Bb: Plot of d(Vp)/d(dp) vs. dp (in log)
0.7 ~--------------------------------------------------,
0.6
2 0 .5 "0 ::0 0.4 -g- 0.3
:0 0.2
0.1
oL---------~----~--------------~~Dm~ 0.1
100
80
~ 60
~ 40
20
o 0.1
10 100 1000
Figure 5.3Bc: Plot od UCD and QCD in volume vs. dp (in log)
l:iJn.,.. ..." ~f ~ 7~ ,
10 100 1000
dp (~m)
-11----- UCD -0- OCO
158
Table 5.3A: Example of PSDs characteristics for unsettled trickling filter effluent
PSD d m (~m) dso (~m) 0, (~m)
by number 0.28 0.17 0.61 bv volume 117 .40 91.24 108.54
The contrast observed between plots of PSD by number and by volume is expressed
again by the difference in values found for dm, d;Q and Gp. It is understandable since
particle volume is a cubic power of particle diameter.
B. PSD fractionation
The second type of information that can be extracted from PSD of wastewater is the
fraction of total particulate number or volume in different particle size: 0.1-1.2, 1.2-
100 and 100-900 !-Im. These ranges were chosen because the range of the particle sizer
was 0.1-900 !-Im, and also because, as indicated in the literature review, 0.1, 1.2 and
100 !-Im represent typical particle size boundaries. Based on the definition given by
Levine et al. (1991), the [0.1;1.2], [1.2; 100] and [lOO; 900] fractions represent
respectively a fraction of the colloidal fraction, the supra-colloidal fraction and the
settleable fraction. Table 5.3B gives the particle fractionation characteristics for the
unsettled TFE PSD described in the previous section.
To bl - 3B E I >/ . I fr a e). xamp/e 0 r part/cu ate . fi Id· kl" fil if! actlOnatlOn or unsett e trlC rng. Iter e uent
Fraction (%) of total Darticulate Size range (~m) Number Volume
0.1 - 1.2 98.16 0.32 1.2 - 100 1.84 54.99 100 - 900 0.00 (2.1 X 10"4) 44.68
C. PSD modelling
The last way used in this study to quantitatively characterise PSDs was to model
them, using power laws. The power-law distribution function was selected because it
has been found to give the most adequate representation of PSD by number for water
and waste water suspensions over a range of particles smaller than 1 J.Lm to bigger than
several hundred !-Im (Wilen and Balmer, 1999; Alon and Adin, 1994; Adin and Alon,
1993; Li and Ganczarczyk, 1993, 1991; Ginn and Amitharajah, 1990; Adin et al.,
1989; Kavanaugh et al., 1980; Lawler et al., 1980).
159
The distributions by number were therefore modelled using:
where
A, B = empirical coefficients
This was done by plotting 10g(d(Np)/d(dp)) versus log(dp), and by calculating the
parameters of the regression between the two variables. Because of the high skewness
of the distributions to the right, the values of 10g(d(Np)/d(dp)) considered for the
regression corresponded to Np of up to 99.99% of the total particulate number.
The modelling was attempted both across the range [0.1; 9001l-lm and across the range
[1.2; 90011-lm. This was done because some of the models reported in the literature
have been estimated for particles bigger than 1 I-lm only, and it was thought that the
high number of particulate found below 1 I-lm could greatly influence the values of the
model parameter. Figure 5.3C (particles> 0.1 I-lm) and 5.3D (particles > 1.2 I-lm)
show the log-log plot of d(Np)/d(dp) vs. dp respectively, for the unsettled trickling
filter effluent PSD data previously used, and also for data resulting from the influent
and settled effluent samples analysed on the same day. Table 5.3C give the outputs of
the modelling, i.e. the parameters of the models, as well as the regressions' coefficients
of determination.
'[, bl - 3e p a e ). r d I arameters 0 regressIOn mo e S
Model parameters for particles > 0.1 um > 1.2 gm
Suspension AO.1 BO.1 R2 A1.2 B12 R2
Influent 0.837 3.381 0.987 613.152 3.212 0.983 Unsettled 4.030 2.737 0.998 343.237 2.961 0.998
1FE Settled 1.073 3.254 0.993 570.298 3.178 0.984
1FE
The values of coefficients of determination indicated that PSDs of the wastewater at
the various stages of treatment can be accurately described by power law functions. It
can be noted that values of R2 for modelling for particles> 1.2 I-lm are marginally
higher than that for the modelling over the whole size range.
160
10000
1000
100
c: 10
~ "C -c: 0.1 Z .-"C
0.01
0.001
0.0001
0.00001
1000
100
10
0.1
0.01
0.001
0.0001
0.00001
0.1
Figure 5.3C: Modelling of d(Np)/d(dp) by a power-law at various stages of treatment (particles> 0.1 I'm)
10
dp (~m)
--t.t-- Influent ~ Unsettled TFE ----<.>-- Settled TFE
Figure 5.3D: Modelling of d(Np)/d(dp) by a power-law at various stages of treatment (particles> 1.2 I'm)
.. ~
~~
10
dp (~m)
• Influent ~ Unsettled TFE Settled TFE
161
100
100
D. Conclusions
The example given in this section has shown that different parameters can be extracted
from PSO data according to the method of analysis of that data. No single method of
analysis fully expresses the information given by a PSO. Instead, extraction of
parameters using all 3 methods gives the maximum amount of complementary
information. Therefore all 3 methods have been used to interpret the distribution data
gathered during this project, the results of which are presented in the following
section.
5.3.1.2 Evolution of particle size distribution characteristics through treatment
A. Pilot-scale stZldy
PSO profile and general characteristics
Figure 5.3E shows the average PS Os (expressed as FD by volume) at the different
stages of treatment over the three phases of study. This type of plot was chosen
because it gives the easiest visual representation of particle size evolution through
treatment. Table 5.30 and 5.3E summarise the values dm, dso and c;p for distributions
respectively by volume and by number.
In terms of values calculated for distributions by volume, it appeared for the three
phases of study that trickling filtration caused a shift in the granulometric distribution
towards larger particle diameters. The mean particulate diameters were on average
shifted from 45.1, 41.3 and 36.3 ~m to 116.5, \15.5 and 131.9 ~m respectively during
Phase 1,2 and 3. A similar shift was observed for both median diameters and standard
deviations. The small particulates of the influent undergo hydrolysis and degradation
during their percolation through the filter, whereas the solids found after treatment are
larger (flocculated solids, detached fragments ofbiofilm and of grazing fauna).
A marked reduction in the proportion of these large particles could also be observed
after settlement. with almost complete disappearance of particles bigger than 250 ~m;
the PSO mean diameters were brought down respectively to 47.2, 52.8 and 52.5 ~m.
The remaining solids were therefore mostly in the supra-colloidal range. It IS
interesting to note that the mean values found for the settled trickling filter effluent
162
Figure 5.3Ea: Average PSD by volume - Phase 1
140
c: 120 "0
100 Cl .2 80
:e 60 c: 40 > :0- 20
0,
0.1 10 100 1000
--- Influent ---0- Unsettled TFE --+-- Settled TFE
Figure 5.3Eb: Average PSD by volume - Phase 2
140
c: 120 "0
100 Cl .2 80 :0- 60 -c: 40 > :0- 20
0 0.1 10 100 1000
--- Influent ---0- Unsettled TFE --+-- Settled TFE
Figure 5.3Ec: Average PSD by volume - Phase 3
160
c: 140 "0 120 Cl 100 .2 80 :0-- 60 c: > 40 :0- 20
O. 0.1 10 100 1000
dp (~m)
--- Influent ---0- Unsettled TFE --+-- Settled TFE
163
Table 5. 3D: PSD by volume mean and median diamelers and SD al difjerenl slages of Irealmenl - Phases J -2-3
Phase 1 Phase 2 Phase 3
Nature of dm(v) dso(v) a p( v) dm(v) dso(v) a p( v) dm(v) dso(v) a p(v)
suspension (~m) (~m ) (~ m) (~ m) ( ~m) (~m ) (~m) (Jl m) (~ m)
Influent 45.1±19.3 27.9±15.0 67.3±25.1 41.3±8.5 30.0±5.6 51.6±19.3 36.3±12.9 23.5±8.3 53.7±41.1
( 2 0 ) (20) (20) ( 6 ) ( 6 ) ( 6 ) ( 6 ) ( 6 ) ( 6 )
Unsettled 116.S±14.9 85.1±11.4 113.9±31.9 IIS.S±12.1 87.7±8.6 111.9±13.7 131.9±26.9 74.8±13.1 162.3±31.3
TFE ( 2 0 ) (20) ( 20) ( 6 ) ( 6 ) ( 6 ) ( 6 ) ( 6 ) ( 6 )
Settled TFE 47.2±6.1 40.0±7.7 36.1±11.9 52.8±6.2 47.0±5.5 38.1 ±1 0.5 52.5±4.4 41.8±4.6 55.3±28.8
( 5 ) ( 5 ) ( 5 ) ( 6 ) ( 6 ) ( 6 ) ( 5 ) ( 5 ) ( 5 )
T, bl 5 3£ PSD b b a e . )y lIum er mean all d d' d' I me lGn lGme ers an dSD Idffi t I >/1 I I Ph a I. eren sages 0 rea men - ases J 23 - -
Phase 1 Phase 2 Phase 3
Nature of dm(n) dso(n) a p( n) dm(n) dso(n) a p( n) dm(n) dso(n) a p( n)
suspension ( um) (~m ) (u m) (~m ) ( um) (~m ) (um) (~m ) ( ~m)
Influent 0.2±0.0 0.1 ±O.O 0.2±0.0 0.2±0.0 0.1 ±O.O 0.2±0.0 0.2±0.1 0.2±0.1 0.2±0.1
( 2 0 ) (20) (20) ( 6 ) ( 6 ) ( 6 ) ( 6 ) ( 6 ) ( 6 )
Unsettled 1.1±0.8 0.7±0.5 1. 9± 1.4 1.0±0.5 0.6±0.3 1.8±0.8 0.7±0.6 0.4±0.3 1.3±1.0
TFE (20) (20) ( 2 0) ( 6 ) ( 6 ) ( 6 ) ( 61 ( 6 ) ( 6 )
Settled TFE 0.6±0.5 0.4±0.3 0.8±0'? 0.2±0.0 0.1 ±O.O 0.2±0.0 0.2±0.0 0.1 ±O.O 0.2±0.0
( 5 ) ( 5 ) ( 5 ) ( 6 ) ( 6 ) ( 6 ) ( 5 ) (5 ) ( 5 )
For both tables: TFE = trickling filter effluent; Tabulated values are: mean ± standard deviation (number of observations)
were almost identical for the three phases of the research, indicating consistency and
relative independence from the influent and unsettled effluent characteristics.
Similar trends were observed for distributions by volume and by number: the mean
particle diameter (and median diameter and standard deviation) of wastewater
increased through treatment by trickling filtration, and then decreased through
secondary settlement to reach a value usually still larger than the influent's value.
Ho)Vever, the values obtained from distributions by number were much lower than
those obtained from distributions by volume. This highlighted the fact that the large
majority of particulate matter had a very small particle size, but they only accounted
for a very small fraction of the total particulate volume, since volume is a cubic value
of diameter.
These results contrast with those obtained by Zahid and Ganczarczyk (1990). As
mentioned in the literature review, they measured some physical properties of
suspended solids in unsettled effluents from high-rate trickling filters (hydraulic
loading: 14 m3/m3.d; organic loading: 480 g BOD/m3.d). They used microscopy
followed by image analysis to size particles of more than 10 !-lm in longest dimension.
They found mean values for particles longest dimension and equivalent diameter
distributions by number of 106 and 68 !-lm respectively. These values contrast with
the mean values of 0.7 to 1.1 !-lm found in this study for mean diameter of low-rate
trickling filter effluent PSD by number. There are several explanations for this
discrepancy. The main is probably related to the difference in particle size range
studied: Zahid and Ganczarczyk (1990) only considered particles larger than 10 !-lm,
while the equipment used in this study sized particles down to 0.1 /lm. The fact that
they sized particles from high-rate trickling filter effluent (by opposition to the low
rate trickling filter effluent of this study) could also contribute to the difference: the
higher shear generated by the higher hydraulic loading probably resulted in bigger
particles and fragments of film in the effluent than in the case of a low-rate filter.
PSD fractionation
Figure 5.3F shows the fractionation of total particulate number into colloidal (from 0.1
to 1.2!-lm), supra-colloidal (from 1.2 to 100 !-lm) and settleable (above 100 !-lm)
165
Figure 5.3F: Particulate fractionation in number - Phases 1-2-3
100
C Q; 95 .0 E " c:
90 Cl)
<; "5 u t: 85 ., c.
'<; § 80 '0 c: 0
ti 75 e! u.
70
Influent Unsettled TFE Settled TFE
• 0, H ,2 ~m 0 1.2·1 00 ~m III 1 00·900 ~m
Figure 5.3G: Particulate fractionation in volume - Pbases 1-2-3
100
~ 90
Cl) 80 E
" '0 70 >
" <; 60 "5 u t: 50 ., c. 0; 40 § '0 30
c: 0 20 U e!
10 u.
0
Inlluent Unsettled TFE Settled TFE
• 0,H,2 ~m 0 1.2·100 ~m • 100·900 ~m
166
fractions, for Phases 1,2 and 3. Figure 5.3G gives the same information for PSD by
volume.
The plots by volume show a decrease in the proportion of the colloidal and supra
colloidal fractions and an increase in the proportion of the settleable fraction through
treatment by the trickling filter. This was followed through settlement by an increase
for both the colloidal and supra-colloidal fractions, and a logical decrease in the
settleable fraction. By contrast, the plot by number show an increase in proportion of
the supra-colloidal through treatment by the trickling filter. This was followed by a
strong reduction of the supra-colloidal fraction. This last result tends to prove that a
portion of the supra-colloidal by number is in effect settleable.
It can be noted that, even if the settleable fraction is insignificant in terms of
proportion of the total particulate number for both influent (simulating settled
sewage) and settled trickling filter effluent, it still accounts for approximately 6 to 8%
of the total particulate volume of both suspensions.
PSD modelling
Table 5.3F gives the average coefficient of determination for the 2 models, and for the
wastewater at different stages.
T, bl - 3F A a e). It h 2 veraKe parameters or t e teste d d I P'I I d mo es- I at-sea e stu y
Averaqe parameters for PSD model for particles > 0.1 !lm > 1.2 um
Suspension Ao., Bo , R2 . A'2 B'2 R2
Influent 1.187 3.371 0.988 534.579 3.224 0.976 Unsettled 5.112 2.885 0.994 312.263 2.844 0.992
TFE Settled 4.564 3.238 0.987 401.945 2.903 0.978
TFE
The first comment is that the average values of determination coefficients were high,
indicating the validity of the use of power-law models to model PSDs of wastewater
at various stages of treatment. The models showed on average a better fit over the full
range of particle diameter (> 0.1 j.Lm) than for particles bigger than 1.2 j.Lm only.
Finally, the highest values ofR2 were obtained for unsettled TFE, whatever the model.
167
L __ _
The values of B are in the range 1.8 - 5.0 quoted by Kavanaugh et at. (1980) for PSD
of various waters and wastewaters. They are also similar to the values extracted from
Adin et at. (1989): between 1.83 and 3.81 for settled trickling filter effluent, and
between 2.73 and 4.43 for various other wastewaters including aerated lagoons and
activated sludge effluent.
B. F,ull-scale study
To validate the pilot-scale results, a study of the evolution ofPSD with treatment was
also carried at full-scale.
PSD profile and general characteristics
The results are presented in Figure 5.3H in terms of evolution of PSD, while Table
5.3G and 5.3H give the mean and median diameters and standard deviations at the
various stages of treatment.
Table 5.3IG: PSD by volume characteristics at different stages of treatment
Nature of dm(v) d 5o (v) ap(v) suspension ().l m) (!.l m) (!.l m)
Influent 70.59 ± 12.92 42.24 ± 12.17 98.30 ± 2.41 ( 5 ) (5 ) ( 5 )
Unsettled TFE 144.68 ± 22.05 98.86 ± 20.83 149.55 ± 17.32 ( 1 1 ) ( 1 1 ) (1 1 )
Settled TFE 78.65 ± 27.02 63.18 ± 22.19 77.82 ± 33.03 ( 1 1 ) ( 1 1 ) ( 1 1 )
TFE = trickling filter effluent Tabulated values are: mean value ± standard deviation (number of observations)
~ bl - 3H PSD b a e J. ynum b h er c aracteristics at diffi i erent stages 0 treatment Nature of dm (n) d 50( n) ap(n)
suspension (!.l m) (!.lm) (!.l m)
Influent 0.19 ± 0.00 0.15 ± 0.00 1.88 ± 1.02 ( 5 ) ( 5 ) ( 5 )
Unsettled TFE 1.36 ± 1.06 0.93 ± 0.72 2.14 ± 1.65 ( 1 1) ( 1 1 ) ( 1 1 )
Settled TFE 0.37 ± 0.61 0.27 ± 0.41 0.50 ± 0.88 ( 1 1 ) ( 1 1 ) L 1 1 )
TFE = trlcklmg filter effluent Tabulated values are: mean value ± standard deviation (number of observations)
168
Figure 5.3H: Average PSD by volume - Full-scale
120
100
80 c: u C>
~ .2 60 U en ~
<D c: G u
40
20
o ' .. _'_1.1-1 :.11 I.I.I.!.II.LLI- J U LLLl ___ ·_ .
0.1 10 100 1000
dp (~m)
.. . •..... Influent ·1· 1··_··· Unsettled TFE .. . • .. . Settled TFE
The mean (and median) particle size were 70.6 (and 42.2) Ilm, 144.7 (and 98.9) Ilm,
and 78.6 (and 63.2) Ilm for respectively the influent, unsettled and settled trickling
filter effluent. These values were in the same order of magnitude as the ones found for
the pilot-scale study, although slightly larger. This was probably due to the fact that
the influent to the trickling filter was a combination of primary effluent and recycled
unsettled trickling filter effluent.
PSD fractionation
Figures 5.3! and 5.31 give the fractions of respectively total particulate number and
total particulate volume represented by colloidal (from 0.1 to 1.2 Ilm), supra-colloidal
(from 1.2 to 100 Ilm) and settleable matter (above 100 Ilm).
The full-scale trends confirmed the pilot-scale ones. They showed a decrease in the
proportion of the colloidal and supra-colloidal fractions and an increase in the
proportion of the settleable fraction through treatment by the trickling filter. This was
followed through settlement by an increase for both the colloidal and supra-colloidal
fractions, and a logical decrease in the settleable fraction. For the fractionation by
number, the proportion of particulates in the settleable fraction stayed negligible
throughout the study (less than 1%).
PSD modelling
The modelling was also applied to full-scale results. The average coefficients of
determination found for the different models on wastewater at different stages of
treatment are indicated in Table 5.3!.
Table 5.3!: AveraR'e parameters or t e 2 teste fi h mo e s- u -sea e stu y d dl Fll I d
Averaqe parameters for PSD model for oarticles > 0.1 urn > 1.2 um
Suspension Ao., Bo., R2 A'2 B1.2 R2
Influent 1. 137 3.327 0.996 589.526 3.343 0.990 Unsettled 1.385 2.959 0.996 341.609 2.902 0.995
TFE Settled 1.280 3.158 0.993 465.290 3.077 0.990
TFE
170
Figure 5.31: Particulate fractionation in number - Full-scale
100
~ 95
" E ::> "0
90
>
" 85 ;;; :; .~ t: 80 '" 0.
«i 75 El "0 c: 70 0
U 11 65 u.
60
Influent Unsettled TFE Settled TFE
• 0.1-1.2 ~m 0 1.2-100 ~m • 100·900 ~m
Figure 5.3J: Particnlate fractionation in volnme - Full-scale
100
~ 90
Q) 80 E ::> "0 70 > Q)
;;; 60 :; 0 ;:: 50 '" 0.
«i 40 El "0 30 c: 0 20 U 11 10 u.
0
Influent Unsettled TFE Settled TFE
• 0.1-1.2 ~m 0 1.2-100 ~m 11100-900 ~m
1 71
The results confirm the pilot-scale results. Values of A and B are similar to the ones
found at pilot-scale, and reported in the literature. The average values of determination
coefficients are also high, indicating the validity of the use of power-law models to
model PSDs of wastewater at various stages of treatment. The model show on average
a better fit over the full range of particle diameter (> 0.1 Ilm) than over the
supracolloidal and settleable fractions only (> 1.2 Ilm). The best fit of both models
was again found in the case of the unsettled trckling filter effluent.
53.2 Colloidal content of trickling filter effluent
The separation of finer, near colloidal sized (0.001 to 1.0 Ilm) particle suspensions is
problematic with respect to secondary settlement. These particles do not settle
readily, and the suspension is said to be stable (Wakeman et al., 1989). Colloidal
particles can also affect filtration systems. In a biological system, colloidal particles
can be generated, for example, by the production of exocellular polymers (ECPs),
which play an essential role in biofilm structure. ECP's have negatively charged
surface functional groups (Sutherland, 1977, quoted by Zhang et at., 1998), which
allows them to bind cationic species such as heavy metals. As colloidal particles are
characterised by having a surface charge, they were measured by measuring the zeta
potential of the trickling filter effluent. This was done on a few occasions during Phase
2 of the research. The average values for influent and trickling filter effluent (both pre
filtered at 0.45 Ilm) are given in Table 5.3J.
Table 5 3J' Average (-potential values (Phase 2)
Suspension 1;; 'potential (mV)
Influent . 16.3 + 1.9 Tricklinq-filter effluent . 17.4 + 2.0
The values measured were similar to that found by Adin and Alon (1993) and Alon
and Adin (1994). They had found zeta potential values ranging from -14 to -16 m V for
trickling filter effluent, with an average value of -15 m V.
172
-------------
5.3.3 Characterisation of the dissolved fraction by High
Performance Size Exclusion Chromatography
The evolution of the dissolved fraction of wastewater through treatment was followed
during Phase 3 using HPSEC, the technique having been developed during Phase 2. As
mentioned in the literature review, the technique theoretically offers the possibility to
estimate the MW distribution of macromolecular compounds present in wastewater
(Katayama er al., \986). In comparison with the more traditional GPC technique, it
allows rapid size separation without sample preparation and/or concentration which
itself may alter the dissolved content of the sample. But in the size separation of
water-soluble compounds by HPSEC and GPC, ionic and hydrophobic interaction
between solutes and the stationary phase of the column can interfere by accelerated or
retarded transportation of water-soluble compounds through the column. This can
lead to the erroneous estimation of the molecular size (Pfannkoch er al., \980). As a
result the choice of the operating conditions of the technique is very important.
5.3.3.1 Choice of operating conditions
A. Choice of column
HPSEC was preferred over the more traditional GPC because the soft gels used as
immobile phase in GPC have poor mechanical strength and require long, high-capacity
columns at a relatively low flow-rate. The static phase of the column used was
characterised by a hydrophilic polyhydroxyl surface.
B. Choice of eluanr
Salts and buffers are commonly added to GPC and HPSEC mobile phases because
they tend to counteract solute-column interactions due to charge effects that cause
deviations from true molecular size fractionations (Gardner and Landrum, 1983): some
substances are eluted earlier or later than predicted due to their interactions with the
support matrix. Solvent ionic strength, pH and compound composition are factors
that can affect retention time of eluted molecules from size exclusion columns with
distilled water. Ionic adsorption and exclusion effects between sample and supports
are well known with cationic and anionic samples (Kato, 1989). This is because
negatively charged groups are present in many support matrices. This is the case for
hydrophilic-polymer based supports, originating from carboxyl groups, as well as for
173
silica-based supports (which generally carry negatively charged residual silanol
groups). In addition, hydrogen bonding interactions also sometimes occur.
As a result, several eluents found in the literature were tried. They are summarised in
Table 5.3K.
Table 5 3K- Eluents tested/or HPSEC Eluent Reference
200 mM NaNO" 1 0 mM NaH,PO , pH 7 column manufacturer recommendation 20 mM NaNO" 10 mM NaH,PO , pH 7 column manufacturer recommendation 10 mM NaGI, 0.33 mM Na,PO" 0.66 Frolund et al. (1994)
mM NaH2P04 , pH 6.8 2.5 mM phosphate buffer Frimmel et al. (1992), Gourdon et al.
(1989) 10 mM GH,GOONa, pH 7 Kainulainen et al. (1994)
10 mM NaGI, pH 7 Harada et al. (1994), Brodsky and Prochazka '(1975)
50 mM sodium phosphate buffer, 300 Katayama et al. (1986) mM NaGI,~H 7
However, they all resulted in a chromatogram for the Refractive Index Detector with a
very important negative peak at about the exclusion time of the column. This is due to
the fact that water (the solvent for wastewater samples) has a much lower refractive
index than any salt or buffer solution. By comparison, deionized water as an eluent
generated a small negative peak. Deionised water is a commonly used eluent for
HPSEC of biopolymers using rigid polymer gels as static phase (Makino and Hatano,
1988). It has been used as an eluent in HPSEC and GPC of water and wastewater, for
example by Takeuchi et al. (1997), Guggenberger (1989), Gardner and Landrum
(1983). It offers the further advantage of separating compounds having similar
molecular sizes, but different chemical characteristics. It exploits potential sample
column interactions and thus can enhance resolution of chemically different
components (Urano et al., 1980). As a result, deionised water was selected as the
eluent for this study.
C. Choice 0/ a wavelength/or the UV spectrophotometer
A spectrophotometer measures the loss in intensity on passage of a beam of light
through a sample. The traditional wavelength used for detection of UV -absorbing
organic constituents in water and wastewater is 253.7 nm, rounded off at 254 nm
174
(Standard Method ref. 5910 A-B; APHA-AWWA-WEF, 1995). It is close to the
absorbance maximum of nucleic acids and of some aromatic compounds, e.g. benzene.
But it is recognised in the Standard Method that this choice of wavelength was
arbitrary, Since it does not correspond to any particular compound or type of
compound.
To optimise the response of the spectrophotometer, it was decided to operate the
detector at the wavelength at which the absorbance of the suspensions to be studied is
maximum. Since the study was carried at pilot-scale using synthetic sewage, each
compound of the synthetic sewage, as well as their mixture (i.e. the synthetic sewage)
and the trickling filter effluent, were analysed individually in a diode-array
, spectrophotometer over a range of 190-800 nm, to find out at which wavelength the
maximum absorbance peak was observed. The absorbance spectra analysis of the
individual compounds of the synthetic sewage (Phase 2 composition), prepared in
de ionised water, as well as that of their mixture (i.e .. the synthetic sewage) also
prepared in deionised water, showed that the main substance contributing to the
absorbance spectrum of synthetic sewage is yeast extract.
Figure 5.3K presents the absorbance spectra of the synthetic sewage (Phase 2
composition) prepared in ultrapure water and in tap water, and of trickling filter
effluent. The first observation was that the absorbance spectra of synthetic sewage
prepared in de ionised water and in tap water are very similar. But while the maximum
absorbance was observed at a wavelength of 200 nm for synthetic sewage, two peaks
were observed for the trickling filter effluent: at 205 nm, and at 220 nm. This was an
indication that compounds absorbing at 220 nm are generated by the trickling filter.
Since the objective of the study was to focus on the changes brought about in the
dissolved organic maner content of wastewater through treatment in a trickling filter,
and since the wavelengths of200 and 205 nm are close, it was decided to operate the
absorbance detector of the HPSEC system at 220 nm.
175
3
2.5
2
1.5 2
0.5
0-
Figure 5.3K: Absorbance spectrum of influent (Phase 2) and trickling filter effluent
1: Influent (prepared in ultrapure water)
2: Influent (preparedJn tap water).
3: Trickling filter effluent
~~~~--~=--==.-=-~--. .---~-..---.--'-.- -'--~--'-----------------'--T----------'--- ------
L-______ ~2~()()!'L ______ ____'22""0!!..... ______ 224~0'_ _____ . _ _"2~60~ _______ .~2~80<_ .~300'i!L. __ ..:Wavele!!9!I]J!!'!!
5.3.3.2 Characterisation of the synthetic sewage
The chromatograms of the synthetic sewage prepared in deionised water are presented
in Figure 5.3L.
The first observation is that RI detector peaks do no necessarily match UV
absorbance peaks for the same sample. This is due to the fact that the two detectors
fall into two different categories. The absorbance detector measures a property
specific to the solute, which is detected only if it contains a chromophore absorbing at
the operating wavelength. By contrast, the RI detector measures a change in a bulk
property of the mobile phase: the difference in RI between a sample of the pure
mobile phase and that eluting from the column, this difference being proportional to
the concentration of solute. The RI detector is regarded as a universal detector,
because virtually all solutes have a different RI to that of the eluent (Determann,
1969). This type of sensor is essential whenever the substances analysed do not show
any absorbance at the chosen wavelength. Therefore, by using both detectors, the
MW distribution can be evaluated across the whole chromatogram, complementary
information being given in terms of detected solutes' properties and concentration.
It is interesting to note that the response of the RI detector has been found in the
literature to be related to the COD of the detected peaks. Zuckerman and Molof
(1970) used GPC to study the MW distribution of wastewater at various stages of
treatment. They used for detection a RI detector and automatic COD analysis. The
chromatograms obtained in terms of both RI and COD showed similar peaks at similar
retention times.
The response of the RI detector for the synthetic sewage shows 6 peaks or groups of
peaks, as well as one negative peak. To identify the peaks in the synthetic sewage
chromatogram, solutions of each individual compound of the synthetic sewage were
prepared using de ionised water, at the concentration at which they are found in the
synthetic sewage (Phase 3 composition). By comparing this chromatogram with the
RI detector chromatogram for each of the individual compounds of the synthetic
sewage, it is possible to identify each of these peaks (the analysis of the peaks of the
absorbance chromatogram showed that they were mostly generated by yeast extract
177
Figure 5.3L: Influent (Phase 3, prepared with uItrapure water) chromatograms
ADCl A, RID Of HPITESTOI90.D
RI It>
-
60-
55
50
... ... 45-
co ci -
40
35-
30 ... '" It> ... <D <D
25 y-, 3r-t . I
2 6 ' 20- 1 4 5
O' 10 ~ 30 mi VWDl A. Wavelength=220 nm of HPITESTOI90.D
mAU -
)
20-~ !'
15 -
... .., '" <D
10 -
- . '" en ui 0 co
Cl 0
5
N It>
co
U
0
0 10 20 30 ml
178
--------------------
material). Results of this identification for the RI detector chromatogram are given in
Table 5.3L.
Table 5.3L: Identification of influent peaks on RI detector chromatof!;ram
Peak number Comcound
1 Yeast extract 2 Yeast extract 3 Glucose 4 Ammonium chloride 5 Yeast extract or maize starch 6 Sodium carbonate
The first peak, coming out after 3.6 mm, corresponds to matter excluded by the
stationary phase. This is due to its molecular size being greater than the biggest pore
size of the stationary phase. On the other hand, the negative peak probably
corresponds to the total exclusion limit of the column, i.e. to elution of the water
which constitutes the solvent of the wastewater samples. The samples being colder
than the operating temperature of the system (3 S°C), the water which constitutes
their solvent has therefore a lower RI than the water used as eluent (the same negative
peak was obtained by making an injection of ultrapure water at room temperature
(20°C) and with the system operating at 35°C). The negative peak therefore
corresponds to the lower limit of the column. The sixth peak, coming out after the
total exclusion limit, corresponds to material retained in the column due to non-size
exclusion effects, probably being adsorbed on the stationary phase and therefore
slowly eluted.
5.3.3.3 Chromatograms of influent and effluent
Figures 5.3M and 5.3N show typical chromatograms measured during Phase 3 of the
research, respectively for synthetic sewage and unsettled trickling filter effluent, in
terms of both absorbance (at 220 nm) and RI detection.
The response of the RI detector for the influent is similar to the one found for the
synthetic sewage prepared with deionised water. The si:'( peaks observed for the
synthetic sewage are detected in the influent. The only major difference comes from
the fact that Peak 5, relatively small on the synthetic sewage chromatogram, is quite
179
Figure 5.3M: Influent (Phase 3) chromatograms
ADCl A. RID of HPITEST0102.D
RI -0>
"l -36
34
32 ... 0 It! 0>
~ 30 , F
28
26 Y4 3
5 24 hrt;t -' . . ~
2 22- 1
20
0 10 20 30 mir VWDl A. Wavelength=220 nm of HPITEST0102.D
mAU
160
140
120
100
60
60
40
... ... .. 20 0 ..
": 0 "! ~ '":
1 - ... "! "!'! -; A .... .... -0
0 10 20 30 mi"
180
Figure 5.3N: Trickling filter effluent chromatograms
ADCl A, RID of HPITESTOO99.D
RI 0
'" '" " 36-
34
32-
30 '" .. .. .,;
28
'" ... .. 26- ..
U ...
24 • A - . r
22- 7
20-
0 10 20 30 mir VWD1 A, Wavelength=220 nm of HPITESTOO99.D
mAU - -'" 160 - ~ 140
120 -
100 -
60-
60-
40 ... '" -
20 '" "," "'-- "! "! '" 0
0 10 2D 30 mir
1 81
large on the influent chromatogram. The corresponding compounds are unidentified
but are probably due to storage of the synthetic sewage.
The response of the RI detector for the effluent shows peaks at the same elution times
as the influent, but smaller. Since the heights of the peaks are proportional to the
concentrations of the solutes in the samples, this shows that these solutes have been
partially consumed by the biomass in the trickling filter. This is particularly noticeable
for peak number 4, which has practically disappeared through treatment by the
trickling filter. On the other hand, a seventh peak has appeared on the chromatogram,
well after the total exclusion limit of the column. It therefore also corresponds to
material retained in the column due to non-size exclusion effects.
The chromatograrns obtained in terms of absorbance at 220 nm confirm the above
results. The influent chromatogram displays a succession of peaks that disappear on
the effluent chromatogram, while two new peaks have appeared. The biggest of those
two peaks corresponds to peak 7 on the effluent chromatogram given by the RI
detector. It is very large in comparison with the others, and it can be concluded, from
this result and the result of analysis by diode array spectrophotometry of both
influent and effluent, that the compounds in this fraction are strong absorbers at 220
nm. The size of peak 7 on the RI detector response indicates however that these
compounds are not present in large quantities in comparison with the remaining
dissolved maner from the influent. A second smaller peak has appeared on the
effluent chromatogram obtained with the absorbance detector. However, the
compounds generating this peak are present in such low concentrations that they have
generated no peak on the RI detector chromatogram.
5.3.3.4 Identification of peak 7
It has been shown in the literature review that SMPs can be expected to be generated
by biological treatment of wastewater in general, and by fixed-film reactors and
trickling filters in particular.
The potential nature for SMPs include amino acids. proteins, polysaccharides, nucleic
acids, hwnic acids. The absorbance properties of these various types of compounds
182
were studied, to try to match peak 7 with one of them. Table 5.3M summarises the
absorbance characteristics of the potential organic matter composing SMPs.
To bl 53M Ab b a e sor ance pea ksif . ISMP o· potentza s Compound Wavelength at Comments Reference
maximal absorbance (nm)
amino acids • 175-190 • strongest band, Brown (1980) probably due to an n->n* transition
• 205-210 • band of very low intensity
carbohydrates, 0 no significant APHA-AWWA-WEF carboxylic acids absorbance in the (1995)
UV wavelenqths nucleosides, 240-275, with associated with Brown (1980)
nucieotides and peak at 260 1t->n* transitions nucieic acids
proteins • = 280 Brown (1980) • = 190 • oeotide-bond
Rem: The electronic transitions of most concern in organic chemistry are: - n->1t*, .in which the electron of an unshared pair goes to an unstable (anti bonding) 1t orbital; - 1t->1t*, in which an electron goes from a stable (bonding) 1! orbital to an unstable 1t orbital.
Many organic compounds in water and wastewater, including carboxylic acids and
carbohydrates, do not absorb significantly in the UV wavelengths (APHA-A WW A
WEF, 1995). The hydroxyl groups of carbohydrates do not absorb at wavelengths
above 200 nm (Bauer and Voeltler, 1985).
Regarding amino acids, two absorbance bands of their carboxyl group are known. The
strongest band (probably due to an n->n* transition) occurs in the region of 175-190
nm, but this part of the spectrum is difficult to study because the deuterium lamps
usUally used have a bottom ofrange of no less than 190 nm. The other band due to an
n->n* transition occurs in the region around 205-210 nm, but is of very low intensity.
In the case of proteins, the peptide-bond absorbance peak occurs at around 190 nm.
However, in general, protein absorbance is maximal at about 280 nm, and
measurements at around this wavelength are widely used when amino acids, peptides,
proteins, enzymes or protein hydrolysates have to be determined in samples (Gorog,
183
\995). The 280 run absorbance is due principally to tryptophan and tyrosine, the
absorbance spectra of proteins resulting largely from the presence of the aromatic
amino acids tryptophan, tyrosine and phenylalanine (Owen, \996). Much higher
sensitivity can be attained at shorter wavelengths (around 210 run), where the
aromatic amino acids possess more intense absorbance bands than that around 280 run
and the -NH-CO- groups of the peptide chain contributes to the overall absorbance,
(Gorog, \995). The value of these results is limited, partly because of the general
problems of absorbance measurements at short wavelengths and also as a consequence
of the interference from other constituents (mainly water and nucleic acids). However,
measurement around 2\ 0 run is frequently used in the spectrophotometric analysis
during HPLC of amino acids and derivatives, peptides and proteins.
Regarding nucleosides and nucleic acids, both the pyrimidine and purine ring systems
are associated with 1t->n* transitions giving rise to absorbance in the region of 240-
275 run. As a result, nucleosides, nucleotides (nucleoside phosphates) and nucleic
acids absorb strongly in the region 240-270 run, with a peak close to 260 run (Brown,
1980).
Humic substances are naturally-occurring macromolecules, formed from plant
components by multistage processes, and constitute a general class of acidic, yellow
black coloured organic polyelectrolytes with molecular weight ranging from as low as
several hundred to perhaps several hundred thousands (Becher, 1987). They are three
major fractions of humic substances, defmed in terms of their solubilities (Aiken et al.,
1985): humin (fraction not soluble in water at any pH value); humic acid (fraction
insoluble in water under acid conditions, below pH 2, but becoming soluble at greater
pH); fulvic acid (fraction soluble under all pH conditions). The use of UV-VIS
spectrophotometry to identify specific functional groups of humic substances is not
feasible, because the complex nature of humic substances gives a featureless spectrum
(resulting from the overlap of the absorbances of various chromophores) decreasing
monotonically with increasing wavelength of up to 600-700 run (Summers et al., 1987,
MacCarthy and Rice, 1985). The wavelength usually used during analysis
(chromatographic or not) of humic substances is therefore 254 run (Vartiainen et aI.,
\987; Gardner and Landrum, 1983).
184
Since no potential SMP compounds displayed the main characteristic of peak 7
(strong absorbance at 220 nm), organic and inorganic substances presenting an
absorbance peak at 220 nm were then looked for in the literature. The results are
summarised in Table 5.3N.
To bl - 3N C a e). d b b ompoun s presenting an a sor k ance pea at around 220 nm
Wavelength at Compound Nature Reference maximal (chromophore)
absorbance (nm)
• 219 (tt->tt') tryptophan amino acid Brown (1980) • 280 (tt->tt') (indole) • 222 (tt->tt') tyrosine amino acid Brown (1980) • 274 (tt->tt') (phenolic) (aromatic)
220 nitrate APHA-AWWA-WEF (1995)
It appears that the only organic compounds having an absorbance peak at 220 nm also
have a second absorbance peak at another wavelength, but not at 200 nm. Nitrate and
nitrite are the only compounds having a single absorbance peak at 220 nm. Indeed, this
property is used in a Standard Method for determination of nitrate in water and
wastewater (Standard Method Ref. 4500-N03- B.; APHA-AWWA-WEF, 1995).
Since dissolved organic matter may also absorb at 220 nm and N03- does not absorb
at 275 nm, a second measurement made at 275 nm may be used to correct the N03-
value. The results of analysis of effluent sample at 275 nm showed no absorbance at
this wavelength. The assumption of peak 7 being generated by nitrate and/or nitrite
was checked by injecting nitrate salts solution in the HPSEC system. The
chromatograms obtained by injection of sodium nitrate are given in Appendix 3. The
shape of the peak on the UV spectrophotometer response match. A similar result was
obtained with potassium nitrate.
5.3.3_5 Generation of SMPs by the trickling filter
It appears from this study that treatment by a low-rate trickling filter results in
degradation of dissolved matter, shown by a size reduction of peaks on the effluent RI
detector chromatogram. It also results in production of a new type of soluble matter:
185
nitrate (and nitrite), by nitrification of the anunonium chloride present in the influent.
This is detected mostly by UV absorbance at 220 nm. As mentioned previously,
biological treatment of wastewater is often thought to result in the production of
SMP's, i.e. refractory organic compounds, or 'hard COD'. There is however no
indication in this study of SMPs generation through treatment. SMP generation was
noted by Namkung and Rittmann (1986) in a lab-scale biofilm reactor fed with phenol
as a sole carbon source. This biofilm reactor could probably be said to have a limited
culture diversity due to the sole carbon source feed. It is therefore interesting to note
the findings of Confer and Logan (1997 a and b), who reported that metabolic
intermediates from protein degradation increased as microbial culture diversity
decreased. A pure culture reactor (lab-scale, suspended growth) generated the most
metabolic intermediates (which could be considered SMPs), while a wastewater
microbial culture generated far fewer metabolic intermediates. Continuing in this vein,
a lab-scale biofilm reactor which was matured with trickling filter influent, showed
almost no accumulation of metabolic intermediates (SMPs) in the bulk liquid. The
pilot-scale trickling filter studied during Phases I, 2 and 3 was originally seeded with
wastewater activated sludge, i.e. high microbial diversity. The synthetic sewage
simulates real synthetic sewage and therefore has a range of carbon sources, both
protein and carbohydrate, which is turn will have ensured cultural diversity within the
trickling filter. It is, therefore, possible that the lack of SMPs found during this study
could be related to the microbial diversity of the biofilm, as found by Confer and
Logan (1997 a, b). It is also possible, therefore, that SMP production is not a feature
oflow rate trickling filters. To date, there have been no reports of SMP production in
full-scale trickling filters. It would be interesting to apply this technique of
wastewater characterisation to a range of full-scale trickling filters in order to gain
more information on SMP generation, which could add to the findings of this research.
5.3.4 Conclusions
Characterisation of the trickling filter influents and effluents m terms of PSO has
shown the following:
• Expression of PSO's by number or volume yields different, but often complementary
information. It is, therefore, beneficial to express PSD's by both number and volume.
186
It must be remembered, when comparing results in the literature, that only PSD's
expressed by the same method are directly comparable.
• With respect to wastewater characterisation, it can be seen that treatment by a
trickling filter causes a shift to bigger particles, which are then removed during
secondary settlement. This has been demonstrated at pilot- and full-scale.
• The study has also shown that a portion of supracolloidal particles generated by the
trickling filter are settleable.
• M,odelling of the PSD's using the power-law distribution function has allowed the
extraction of parameters CA and B) which will be used to determine whether PSD is a
parameter which has an effect on trickling filter performance.
Characterisation of the dissolved fraction of trickling filter int1uents and eft1uents has
shown the following:
• HPSEC is a useful tool for characterising the dissolved fraction of wastewater, both
organic and inorganic. The method is simple to use, rapid, and doesn't suffer from the
drawbacks of sample preparation and/or concentration which is required for the more
traditional GPC technique.
• The use of water as an eluant in HPSEC was found to be the best option for aqueous
waste water samples.
• The dual use of Rl and UV detectors in HPSEC was shown to be beneficial,
producing complementary information on both relative concentration of matter and
strength of absorbance at 220 nm. This was found to be especially useful in
identifYing the nitrate peak, for example.
• Treatment of the synthetic sewage by the pilot-scale trickling filter was found to
reduce the concentration of all the int1uent compounds, and result in the production of
nitrite and nitrate.
• No SMPs were detected as being produced by the trickling filter in this study.
187
5.4 PARAMETERS AFFECTING TRICKLING FILTER
PERFORMANCES
As indicated in the literature review, several parameters are known to affect trickling
filter performance. These include design and operational parameters, and parameters
resulting from the interactions between these parameters and environmental
conditions. For this study based on a pilot-scale trickling filter, design and operational
parameters were fixed, apart from the organic loading which fluctuated slightly
according to the concentration in the influent. This variability was monitored by
regular measurements of the influent characteristics, and was probably not higher than
that encountered at a full-scale plant. In parallel to following the performance of the
pilot-scale low-rate trickling filter, parameters resulting from interactions between
design/operation and environmental conditions were monitored. The objective of this
part of the study was to analyse these parameters, and to express them as variables
that could be related to performance indicators. The parameters studied were:
• Temperature;
• Film accumulation within the filter;
• Hydrodynamic properties of the filter.
5.4.1 Temperature
As indicated in the literature review, temperature in the filter is regarded as one of the
parameters affecting trickling filter performance. Five temperatures were logged hourly
during the period of study. They were:
• Temperature in the trickling filter at 0.05, 0.90 and 1.75 m from the top surface of
the filter bed (TO.05 , TO.90 , Tl.75);
• Influent temperature (iT);
• Ambient temperature (at mid-height of the filter) (aT).
The three temperatures measured in the trickling filter enabled the calculation of an
average trickling filter temperature: TIF , where:
T - ToOl + 2 To.90 + Tu, TF -
4
188
Simple regressions were calculated between T TF and both aT and iT (themselves
correlated to each other because the influent to the trickling filter was stored
outdoors). Examples of correlations are given in Figure 5.4A and 5.4B. The results
over the whole period of research and for each phase (minus the data lost through data
logging problems) are presented in Table 5.4A.
T, hi - 4A R a e) .• egressIOn T TF-a T dT an I
Parameters of regression between T TF and aT iT
Phase a b R2 a b R2
1'-22-3 0.544 8.703 0.705 0.892 3.389 0.745 1 ' 0.536 8.315 0.711 0.981 1.949 0.787 22 0.635 5.886 0.778 0.940 2.291 0.853 3 0.480 11.698 0.635 0.702 6.706 0.583
, : from October 94 2 : from November 96
It appears that trickling filter temperature is highly correlated (given the number of
observations: hourly logging) with both ambient and influent temperature. The
coefficient of determination is generally higher in the case of influent temperature than
in the case of ambient temperature. This means that the temperature of the influent is
generally of greater importance than that of the air in controlling the temperature of
the filter. A similar fmding was reported by Truesdale and Eden (1963) in a
comparative study at pilot-scale of the relative efficiency of eight media for low-rate
trickling filters. More recently, Battistoni et al. (1992) also found that the temperature
of the biofilm in a trickling-filter was strongly correlated to influent temperature while
it responded more slowly to changes in ambient temperature. It is worth noting that
the use of an above-ground filter results in a greater influence of ambient temperature
on trickling filter temperature than in the case of a full-scale (under-ground) filter,
because of greater heat transfer between the filter medium and the outside air. This
effect was however minimised by the fact that the filter medium (50 mm graded blast
furnace slag) is characterised by a voidage between 45 and 50%, providing a better
heat retention than higher voidage media. Gray ( 1980) (quoted by Gray, 1992) found
on above-ground pilot-scale trickling filters (media depth: 1.8 m; filters diameter: 1.6
m) that the core temperature in a 50 mm graded blast-furnace slag tilter showed small
changes in temperature (less than I°C) over periods in excess of 6 h, while that of
plastic medium filter (voidage: 91.3%) showed variations of up to IOC within 30 min.
189
Figure 5.4A: Plot ofTtfvs. aT (April 1995)
•
'r:
5
-5 o 5 10 15 20 25
aT ( C)
Figure 5.4B: Plot ofTtfvs. iT (April 1995)
25
• 20
15 U
r: 10 •
l1li l:· • 5
o o 2 4 6 8 10 12 14 16 18
iT ( C)
190
Given the significance of the regressions found between trickling filter temperature and
both influent and ambient temperatures, the latter two were used for further
correlations.
S.4.2 Film accumulation
As indicated in the literature review, film accumulation within the filter is one of the
parameters affecting trickling filter performance. The film accumulation profile within
the·, filter was monitored during this study using the neutron scattering technique,
which gives the moisture content profile within the filter. The moisture content was
related to film accumulation, since film is composed of 95 to 98% of water. Readings
throughout the depth of the filter were taken every two months during Phase 1 (from
December 94) of the research, then monthly during Phase 2 and 3. These readings
were made within a day of measurement of the hydrodynamic characteristics of the
filter by tracer study, and of the full range of analyses of trickling filter performance
including particle size distribution.
5.4.2.1 Evolution of film accumulation profile within the trickling filter
The neutron probe measurements enabled the determination of the extent of film
accumulation through the depth of the filter. Figure 5.4C shows the evolution of the
film accumulation profile in the trickling filter during Phases 1 (from December 94), 2
and 3.
During Phase 1, the profile of film accumulation within the filter appears to have
varied only slightly in the top half of the filter. The only noticeable difference could be
observed in February 95, where the upper section of the top half of the filter showed
high film accumulation. This corresponded to visual observations of growth of fungi at
the surface of the filter, a common feature at the cold winter temperatures (IWEM,
1988). The surface of the filter became pink, a condition usually associated with
growth of Fusarium (Wheatley, 1976). On the other hand, a progressive increase in
moisture content could be observed in the bottom half of the filter throughout Phase 1.
During Phase 2, the pro tile measured in October 96 differed significantly from the
later profiles, with increases in moisture content of approximately 3 to 4% saturation
1 91
Figure S.4Ca: Evolution of TF moisture content profile- Phase 1
... "0 '5 > -0 c: .Q
~ :0
~ ~ 'E .,
<i) 'E I\) 8
e! ~ '5 ::;
.0
Filter depth <cm) Date
160
IcDec, 94 . Feb, 95 . Apr,95 o Jun, 95 Aug 951
Figure 5.4Ch: Evolution of moisture content profile - Phase 2
.. :g 0 > '0 c: .2 "§ ::> 1ii '" ~ !!.-
~ E ~
CD c: W 0
" i!! ~ ·0 :;;
97
Filter depth (cm) Date
160
I c Oct. 96 • Dec. 96 • Jan. 97 D Feb. 97 Mar. 97 I
.,
of voids throughout the depth of the filter between October 1996 and December 1996.
This was probably due to the fact that, for most of the period which separated Phase
I and 2, the pilot had been operated at low hydraulic and organic loadings. As a result,
the film accumulation within the filter was probably maintained at a low level.
However, standard hydraulic and organic loadings were reapplied to the filter two
months before the beginning of Phase 2, in August 96. This resulted in a strong
increase in film accumulation, that reached a near steady state in December 96. There
wer~ less variations between the following accumulation profiles. The main variations
were observed in the upper section of the top half of the filter. Higher moisture
contents at the filter surface could be observed in January and February 97 by
comparison with December 96; the values were then reduced in March 97. This
corroborated the results found during Phase I, that had shown high film accumulation
in the upper section of the top half of the trickling filter in winter, related to
proliferation of Fusarium type fi.mgi. The bottom half of the filter displayed slightly
less variability. It showed, as the top half, a significant increase in moisture content
between December 96 and January 97, and a progressive migration of moisture
content towards the base of the filter in Februray and March 97.
Figure 5.4Cc shows the distribution of film accumulation with depth during Phase 3 of
the research. The moisture content profile for April 97 (beginning of Phase 3) was
similar to the one obtained in March 97 (end of Phase 2), with only slight increases at
the surface and in the lower part of the bottom half. The May and June 97 profiles
could be characterised by a progressive decrease in moisture content in the top half of
the filter, while July and August 97 profiles confirmed the trend of decrease in film
accumulation in both the top and bottom half of the filter. By contrast, the September
97 profile showed a slow increase in film accumulation in the top half of the filter.
In summary, it was observed that during Phase I (from December 94), the film
accumulation in the trickling filter quickly reached equilibrium in the top half of the
filter, while colonization of the bottom half of the filter was slower, reaching a
maximum during the summer months. Phase 2 (Autumn 96 to Spring 97) was
characterised by a rapid increase in film accumulation throughout the filter, to reach a
near steady state in December 96. From then to March 97 increase was only recorded
194
Figure S.4Cc; Evolution of TF moisture content profile - Phase 3
<ii' "0 .g '0 c: .2 ~ '" ~ C
~ ~ CD 1:: 01 8
~
'" 10 '0 :;
.7
Filter depth <cm) Date
160
IC Apr. 97 • May. 97 . Jun. 97 C Jul. 97 . Aug 97 . Sep. 971
in the higher section of the top half of the filter. During Phase 3 (Spring to Autumn
97), there was reduction in film accumulation first in the top half of the filter, then in
the bottom half, and finally a slow increase in the top half recorded in September 97.
The observations of Phase 1 were comparable to those of Biddle (1994). He found a
rapid start-up in film accumulation during the first 7 months of his research within a
blast-furnace slag (20-25 mm graded) filled filter, loaded with primary effluent at 1.4
m3hn3.d. In terms of distribution throughout the depth of the filter, colonisation
appears to have begun in the upper layers and then to have gradually spread
downwards, which is similar to that observed in Phase 1 of this study. The faster
colonisation of the top half of the filter by comparison with that of the bOIlom half is
to be expected. Nutrients concentrations are highest at the filter surface, therefore
growth rates are faster. Biddle (1994) also operated plastic media filled filters under
identical conditions to the blast furnace slag filter mentioned above. It is interesting to
note that film accumulation occurred much more slowly and to a lesser extent within
the plastic media filled filters. This was connected to a lower rate of biomass gro",1h
on the plastic media due to the higher voidage in the plastic media that reduces both
heat retention within the filter and physical retention of solids.
The seasonal variability observed during Phases 2 and 3 conforms to results in the
literature. The pattern of increase of film accumulation in Winter followed by decrease
in Spring has been previously observed by several authors (including Solbe et al.,
1967; Gray and Learner, 1984). The winter increase has been explained by a decrease
in biomass and grazing fauna activity, combined with overgrowth of fungi in the upper
layers of the filter. The subsequent biomass decrease in Spring is due to a combination
of sloughing of excess winter biomass and a resurgence of grazing activity.
The results found in Phase 3 were similar to the ones found by Hawkes and Shepherd
(1972), who studied the seasonal fluctuations and vertical distribution of accumulated
solids and grazing fauna populations in low-rate trickling filters filled with 3.7-5 cm
graded mineral medium (gravel), under different dosing periodicities: 0.3 min and 14
min. They found that in the high frequency (i.e. low periodicity) dosed filter the
196
unloading first occurred below the surface layer with the warmer seasons, followed by
an unloading of the surface solids. On the other hand, the unloading in the low
frequency dosed filter first occurred in the surface layers and proceeded downward.
This latter result is similar to that observed during Phase 3 of this study. This is
relatively surprising since the dosing periodicity used in this study (1.2 min) is closer
to 0.3 min than to 14 min.
Gray and Learner (1984) found a similar vertical distribution of film in a pilot-scale
trickling filter containing the sarne media as used in this study, at a hydraulic loading
of 1.68 m3/m3 d. The found that most of the film was in the top 30 cm and also in the
lower half of all the filters below 90 cm, which confirms the findings of this study that
the biggest variability in film accumulation can be observed in these areas. However,
subsequent analysis showed that the film recorded in the lower half was comprised
mainly of humus and debris, and not of active biofilm. This latter result sheds light on
the observed variations. The variations observed in the upper section of the top half
were probably due to variations in thickness of biofilm attached to the medium. On
the other hand, the variations observed in the lower half of the filter were probably
due to accumulation and/or discharge of detached biofilm and grazers debris from the
more active surface layers.
5.4.2.2 Mean film content
Figure 5.40 shows the evolution of the average moisture content (AMC. expressed as
percentage of saturation of voids) and of the top half of the filter AMC I bottom half
of the filter AMC ratio (t/b ratio) in the trickling filter during Phases 1,2 and 3. This
ratio, which gives an indication of the relative film accumulation between the top and
bottom halves of the filter, was thought to be significant given the differences in film
accumulation observed throughout the study between the two halves of the filter.
The values recorded throughout the 3 phases of study were comprised on average
between 8 and 13% saturation of voids, with maximal values of about 16% saturation
of voids. These values are relatively small in comparison to those reported by Gray
and Learner (1984) for a trickling filter with a medium identical to the one used in this
study (50 mm graded blast furnace slag). They found average values of23.0 and
197
0 "ifl. c ., - 0 uo:> "0
::;; !!' ·0 « ~ >
as '"
0 ""- c: -- 0 '" U ~
"0
::;; ·0 « " >
1ii '"
C; ;,!! c: ., L 0 U ~
"0
::;: ·0 « " >
1ii '"
13
12
1 1
10
9
B
Figure 5.4Da: Average moisture content aud tlb ratio - Pbase 1
----I I I
Dec. 94 Feb.95
I I
I AMC
Apr.95
Date
I Jun. 95
--1 ..... - Vb ratio
Aug. 95
1 .1 1.05 1 0.95 ~ 0.9 !!' 0.B5 ~ O.B 0.75 0.7
Figure 5.4Db: Average moisture content and tlb ratio - Phase 2
13
12
11
10
9
B
13
12
1 1
10
9
B
- ..... f--" --I I
Oct. 96 Dec. 96
--Jan. 97
Date
I--
Feb. 97
--1 ..... - lib ratio
1.1 1.05 1 -. 0.95 g 0.9 !!' 0.B5 O.B
~ 0.75 0.7
Mar. 97
Figure 5.4Dc: Average moisture content and tlb ratio - Phase 3
rr===;------------------, 1.1 1.05 1 o
'0.95 ~ 0.9 0.85 ~ O.B 0.75
~----~~--~--~--~~----~~----~~--~~0.7
Apr. 97 May. 97 Jun. 97 Jul. 97 Aug. 97 Sep. 97
Date
--1 ..... - tlb ratio
198
26.5% at hydraulic loading rates of 1.68 m3/m3.d and 3.37 m3/m3 d respectively, under
continuous dosing. These higher values can probably be explained by the fact that the
loadings used for their study were far in excess of the loadings normally associated
with single-pass 50 mm slag medium filters.
The film accumulation was lower during the first winter of operation than during the
second winter. This result was similar to that found by Heukelekian (1945), and is due
to the required maturation period of the trickling filter. More generally, it can be seen
that the filter contained less film throughout Phase I than Phases 2 and 3.
During Phase I, the average moisture content within the filter was comprised between
8.5 and 10%. It showed a constant increase from December 94 to June 95, followed
by a slight decrease in August 95. As for the tIb ratio, it was always below I, ranging
from 0.77 to 0.93. After a slow decrease from December 94 to February 95, it showed
a constant increase until the end of the phase (August 95). The proportion of film
(solids and biofilm) therefore appears to be permanently higher in the bottom half of
the filter than in the top half. These average values confirm the findings of the film
accumulation profiles study: the filter was progressively colonised by film throughout
Phase I, the increase in moisture content being localised in the bottom half of the filter
for most of the phase.
During Phases 2 and 3, the measurements, made on a monthly basis instead of bi
monthly, gave a clearer picture of the evolution of film accumulation. Results show an
increase in the overall average moisture content of the filter from October 96 until
April 97, then a steady decrease until August 97, and a new increase in September 97.
The value of the tIb ratio was mostly below I, apart from in three instances: ID
January and February 1997, and in September of the same year. These values ID
January and February 1997 confirm the accumulation of film in the filter during
Winter 1997, the accumulation being mostly located in the top half of the filter. This
was followed from March 1997 by a progressive unloading of the top half of the filter,
marked by a decrease of the tIb ratio while the average moisture content throughout
199
--- ---------------
the filter remained nearly content. This transfer of film from the top half to the
bottom half continued until April 97, the process of discharge of film in the effluent
starting at about this time with a constant reduction in average moisture content until
August 1997. This was followed by an overall build-up of film within the filter from
September 1997, the process having earlier started in the top half of the filter as
indicated by and earlier increase in t/b ratio.
The seasonal pattern in film accumulation observed mostly during Phase 2 and 3 (with
maXimum film accumulation in the winter and minimum film in the summer) confmned
prevIOus observations on low-rate (Gray and Learner, 1984; SollJe et al., 1967;
Hawkes, 1963) and high-rate filters (Bruce and Merkens, 1973). With falling
temperatures in the late autumn, the film commenced to accumulate. This continued
throughout the winter to produce the greater accumulation later in winter. With rising
temperatures in the spring, there was a rapid decrease in film resulting in very low
accumulation in the filter throughout the summer period.
However, seasonality is not the only parameter influencing film accumulation:
Heukelekian (1945) found a direct relation between film quantity in a trickling filter
and BOO and TSS loading to the filter. It is logical that the organic loading influences
the amount of organic matter removed by the filter and the film growth rate since a
part of the organic matter removed is converted into film.
The change of feed between the various phases of research could therefore also
contribute to changes in film accumulation. In particular, the contrast in film
accumulation between Phase 2 and 3 could possibly be due to a combination between
environmental conditions and feed characteristics. Indeed, Phase 2 was characterised
by low temperatures and a mostly suspended BOO, while Phase 3 was characterised
by higher temperatures and a mostly soluble BOO.
5.4.2.3 Investigation of the parameters affecting film accumulation
A correlation analysis was carried out in order to clarify the relative importance of
environmental conditions and feed characteristics on film accumulation. Based on the
findings from the literature, the parameters tested were temperatures and influent
200
characteristics. The variables used for correlation analysis are presented in Table 5.4B
(parameter to explain = dependent variable; possible explanatory parameter = independent variable).
Table 5.4B: Variables used (or film accumulation correlations Variable type Nature Variable
Dependent variables Film accumulation -AMC parameters - AMCt
-AMCb - t/b ratio
Independent variables Temperatures - aT - iT
Influent characteristics - iBOD - P iBODf
-iCOD - P iCODf
- iTS - P iVS - iTSS
- P iVSS
- i(TSS/TS) Influent PSD characteristics - idso(v), id m (v)
- idso(n), idm (n) - iPpo., _1.2(v), iPp1.2.'oo(v) ,
iPp,00.'OO(v) - iPPO.l_,.2(n), iPP12-100(n),
i P P 100.'00 ( n ) - iAn , iB, , iA " is ,
See Nomenclature
The quality of the correlation between two variables was assessed by the correlation
coefficient R. R measures of the degree of closeness of the linear relationship between
two variables, and indicates the strength of the association between the variables. It is
generally admitted that the relationship between two variables is probably not of
much importance if the absolute value of their correlation coefficient is smaller than
0.5 (Hair et al., 1998). The validity of the correlations was further checked by
determining if the correlation coefficients were statistically different from 0, using the
Fisher's r to z transformation. This transforms the correlation coefficient to a variable
with a standard normal distribution, allowing a probability level (p-value) to be
calculated for the null hypothesis that the correlation is equal to O. The higher the p
value, the less significant the correlation. Only correlations with p-values lower than
0.1000 were considered.
201
The results of the correlation analysis over the whole period of study gave little
evidence of any correlation. A further study of regression plots showed different
patterns for the data from Phase i and the first data set from Phase 2 (October 96) on
the one hand and for the rest of Phase 2 (: Phase 2') and Phase 3 on the other hand.
This is understandable since Phase 1 corresponded to the maturation period of the
filter, while October 96 corresponded to an early after the restart of normal operation
of the pilot after a year of operation at low loadings. On the other hand, the filter was
clo~e to maturity during Phases 2' and 3.
The results of the correlation analysis for Phases 2' and 3 are presented in Table S.4c.
Table 5.4C: Results of correlation analysis for film accumulation parameters -Phases 2'-3
Parameter R Number of p-value observations
!WC iT ·0.925 10 <0.0001 aT ·0.815 10 0.0025
id,,(v) 0.814 8 0.0109 idm(v) 0.786 8 0.0178
i(TSS/TS) 0.675 10 0.0301 P iBODf ·0.605 10 0.0639
iTSS 0.598 10 0.0678 AMCt iT ·0.928 10 <0.0001
aT ·0.824 10 0.0020 AMCb idm(v) 0.962 8 <0.0001
id,,(v) 0.925 8 0.0003 iPP1,2_100(V) ·0.830 8 0.0078 i(TSS/TS) 0.823 10 0.0020
iT ·0.816 10 0.0025 iTSS 0.788 10 0.0047
P iBODf ·0.739 10 0.0121 iSu ·0.716 8 0.0443 aT ·0.715 10 0.0175
P iCODf ·0.696 10 0.0231 id (n) ·0.634 8 0.0946
lib ratio 0
The best correlation coefficients obtained for the average moisture content of the filter,
representing film accumulation, were negative with temperatures, and positive with
size characteristics of the influent particulate matter.
The film accumulation in the top half of the filter appeared to be highly negatively
correlated with temperatures. As for film accumulation in the bottom half of the filter,
it appeared to be highly positively correlated to the size characteristics of the influent
particle matter.
202
It can therefore be concluded that film accumulation in a trickling filter is controlled by
temperature and by influent characteristics in terms of particle size. It appears that
accumulation in the top half of the filter is mainly controlled by temperature, while
accumulation in the bottom half of the filter was mainly controlled by the influent size
characteristics.
These findings are conform to the observations by Gray and Learner (1984) that fihn
in the top half of the filter is mainly active biofilm, while the film accumulated in the
bottom half is mainly detached biofilm and grazers debris. The accumulation in the top
half of the filter is controlled by temperature, that affects the activity of the biomass
and of the grazers. On the other hand, the accumulation in the bottom half is related to
the size of the influent particulate matter, probably because big solids in the influent
contribute to easier detachment of biofilm, and also because these bigger solid can pass
through the filter untreated and accumulate in the lower half.
5.4.3 Hydrodynamic characteristics
The hydrodynamic characteristics of a trickling filter are also considered an important
factor affecting trickling filter performance. The objective of this part of the study was
to extract parameters of the trickling filter residence time distributions (RTDs) that
could be correlated to performance indicators. Furthermore, the interactions between
hydrodynamic characteristics and film accumulation within the filter were
investigated.
Tracer studies (by pulse injection of Liel) were performed to determine the RTDs of
the pilot-scale trickling filter. As indicated in the Materials and methods section, this
was done every two months during Phase I, and monthly during Phases 2 and 3 of the
research.
Eden et at. (1964) highlighted the main limitation of tracer studies: tracers can only
measure their own retention, and not necessarily the retention characteristics of waste
liquids. One of the main problems encountered with the use of tracers is that they can
suffer from prolonged retention due to adsorption onto the biofilm and/or absorption
in the biofilm. This is why tracers like dyes, salt solutions or ammonium salts became
203
for a while less popular for retention time studies of biofilm reactors. Radioactive
tracers were introduced in the late 1950s (Eden and Melbourne, 1960) and proved to
be efficient, but the technical, fmancial and environmental problems inherent to their
use limited their application. Lithium as lithium chloride has been used by several
authors in the past for tracer studies in fixed-film biological reactors (Seguret, 1998;
Buffiere et al., 1998; Fenandez-Polanco et al., 1996; Wik et aI., 1995; Leighton and
Forster, 1995; Biddle, 1991; Vasel and Schrobiltgen, 1991), and also to study mass
tran~fer through biofilm (Vieira and Melo, 1993). Lithium chloride was therefore
chosen for this study.
It is important to keep in mind the fact that lithium chloride diffuses through biofilm.
As mentioned before, Vieira and Melo (1993) used this substance to study mass
transfer through biofilm. They selected LiCl because it is an inert substance not
consumed by bacteria. On established biofilms (more than 100 h old), they found
mass transfer coefficients of Li in the biofilm ranging between 2 x 10-6 and 6 x 10-6
m/s (17.3 and 51.8 cm/d). This indicates that LiCl is absorbed in biofilms, and tailing
can therefore be anticipated on RTDs ofbiofilm reactors.
The diffusion of tracer within the biofilm is encouraged by biofilm structure. The
concept of mature biofilms being uniform in thickness has been challenged in the past
few years. Biofilm has been found to contain interstitial voids, channels and cell
clusters which complicate the water-biofilm interface and the internal transport in the
biofilm (de Beer et aI., 1983; Lewandowski et aI., 1994; Stoodley et aI., 1994; quoted
by Carlson and Silverstein, 1998). It appears that the contact between biofilm and
water is greater than previously assumed, increasing the transport of contaminants
through biofilm pores and channels.
SA.3.1 General shape of residence time distributions
Figure 5.4E shows a typical RTD found for the trickling filter. It is characterised by a
rapid increase in concentration of tracer in the effluent, reaching a peak and followed
by an extended tail. The tailing is usually explained by assuming that some of the
flowing fluid is held back by adsorption on the surface of the medium or in the
biofilm, by being trapped within pores, or by being held up in the many little stagnant
204
I\)
o 01
'" Cl .s
14
12
10
c: 8 o ., ~ c ~ 6 c: o o ::;
4
2
o
o 50
Figure 5.4E: Typical pilot-scale trickling filter RTD (August 1995)
100 150 200 250 300 350 400 450
Time (min)
regions present at the contact points of the solid (Levenspiel, 1972). As anticipated in
the case of a biofilm reactor like the trickling filter, it can be interpreted as resulting
from the slow exchange of tracer between the liquid film and the biofilm.
5.4.3.2 RTD parameters
A. Expression of average residence times
As indicated in the Literature review, the values traditionally extracted from and used
to 1escribe distributions include indicators of central tendency (e.g. mean, mode,
median) and spread of the distribution (standard deviation).
Mean residence time and standard deviation (Formulas 2.F and 2.G) are theoretically
calculated by integrating on time between 0 and +00. However, because of the tailing of
RTDs, it is necessary to select an arbitrary cut-off point for determining the centre of
gravity of the curves and hence the mean residence time and standard deviation. This
was thought by certain authors (e.g. Sheikh, 1970) to make these parameters partially
inaccurate.
For this study, the tracer sampling programme at the outlet of the filter lasted 7 h
from injection time. Because of the tailing, the tracer recovery after 7 h did not exceed
75% on certain occasions. As a result, it was required to extrapolate the tail to
calculate. The approach taken was first used by Tomlinson and Hall (1950): the
descending arm of the RTD can be approximated by a logarithmic curve, modelled
using a power-law function:
where
c(t) = A t B
c(t) = tracer concentration at the trickling filter outlet at time t (mg/I)
t = time (min)
A, B = arbitrary coefficients
The values of A and B were determined by calculating the regression of log (c(t»
versus log t. Only the values of t and c(t) acquired during the last two hours of
sampling were used (i.e. between t = 300 min an ct = 420 min) for the regression.
206
Indeed, it was noticed that calculating the regression using the whole decreasing ann of
the RTD curve (i.e. between t = mode and t = 420 min) resulted in infinite tailing.
Mean residence time (tm) was then calculated using two types of arbitrary cut-off
time:
• t = 7 h (duration of sampling);
• Time required to recover 90% of the quantity of tracer initially injected (with
rec~nstitution of the tail of the RTD as previously described).
Figure 5.4F shows the evolution throughout the study of the median retention time
(t50) comparatively to that of the mean residence time calculated using the two
arbitrary cut-off time described before (tm(7 h), tm(90%). It appeared that the values
of mean residence time were highly dependent on the cut-off time chosen. However,
the pattern followed by the mean residence time calculated for a 90% tracer recovery
was similar to that followed by the median residence time.
B. Expression of the spread of residence time distributions
The spread of the distributions is traditionally expressed by the standard deviation. It
was calculated for each RTD using the two arbitrary cut-off times described in the
previous section, yielding two values: crtC7 h) and crt(90%).
In the case of trickling filters the spread of RTDs has also been classically expressed
by the t50ft 16 value, ratio between the median residence time and the 16 percentile
retention time (time required to recover at the outlet 16% of the injected quantity of
tracer) (Sheikh, 1971; Bruce and Merkens, 1970). The values of tl6 and t50 were
selected because the authors assumed that trickling filters RTDs follow log-normal
distnbutions, and the standard deviation of such a distribution is given by log
(t50/tI6).
Figure 5.4G shows the evolution throughout the study of the RTDs standard
deviation, calculated using the two arbitrary cut-off time described before. Again it
appears that the value of standard deviation is very much affected by the choice of
this arbitrary cut-off time.
207
I\)
o (»
Figure S.4F: Median and mean residence times - Phases 1-2-3
Time (min)
u " .0 o ~ 0.. c
« " . ...., g> « g
o 1ij ....,
Date
t-O> t-.ci 0>
" <;; U. :::
o Im(7 h) ~ 150
t-
'" t-
ci 0>
,;.. « .. c: ::: " "3 t-...., C. '" ...., " ci. «
Q) Cl)
.lm(90%)
I\)
o <D
Figure S.4G: Standard deviation - Phases 1-2-3
Standard deviation (min)
"u
.ci o LL" • C.
et: g 0> ..., " . ..: :;:
o ~ ~ ~ .
LL :a Date :!: ~ ~
~ g
o SD (7 h) ~ SD(90%)
..., :; ...,
C. Residence times without biofilm
RTDs were first measured before the beginning of operation of the pilot-scale trickling
filter, i.e. before colonisation of the filter medium by biofilm. This was done to
generate "reference data". Indeed, Suschka (1987) stated that there is "unfortunately
very little evidence on comparison of measured residence time on biological filters
with and without biological film".
It h~ been seen in the literature review that the retention time tr in a trickling filter
could be expressed by:
where
Hb A" t =a'--, Q'
H = filter depth (m),
A = specific surface area of the medium (m2/mJ)
Q' = hydraulic loading (mJ/m2 d)
a', b, 'c, d = arbitrary coefficients
In the case of the trickling filter used for this study, the values of H and A are fixed.
Therefore, Equation (5.1) can be simplified into:
where
a" = constant for a given filter.
a" t =, Q'
In the literature, tr represents either the mean or median residence time. The values of
a" and of c for t50 and tm (calculated using the two arbitrary cut-off times described
before) were determined for the dry medium by measuring RTDs at three different
hydraulic loadings: Q = 0.25, 0.5 and I ml /m3 d (i.e. Q' = 0.39, 0.78 and \.57 ml /m2 d).
Results are presented in Figure 5.4H and summarised in Table 5.40.
210
C I 0 !!l
C I :R 0 0
'" §
Figure 5.4Ha: Median residence time = f(hydraulic loading)
1000 ,-----------------------------------------------------,
100
10
0.1 10
0' (m3/m2.d)
Figure 5.4Hb: Mean residence time - 7 h = f(hydraulic loading)
1000 ,-----------------------------------------------------,
100
10
1
0.1
1000
100
1 0
0.1
• • • -
10
0' (m3/m2.d)
Figure 5.4Hc: Mean residence time - 90% = f(hydraulic loading)
10
0' (m3/m2.d)
21 1
..
Table 5.4D: Estimation of a" and cfor tr = tm and tr = t50
Ir a" c R2
15 0 71.01 1 .152 1.000
Im (7 h) 90.12 0.691 0.998
Im(90%) 90.85 1.025 0.999
The values of c found in this study (in the absence of film accumulation): between 0.7
and l.l, are slightly higher than that found in the literature (usually with film
accurnulation): between 0.4 and I (Table 2.IE). This could mean that film
accumulation within the filter reduces the effect of changes in hydraulic loading on
retention time. These results generally confirm that the retention time in a trickling
filter (with or without film accumulation) is inversely proportional to a power of the
hydraulic loading to the filter.
5.4.3.3 Hydrodynamic modelling of trickling filter
Hydrodynamic modelling was used to extract model parameters from experimental
RTDs, parameters that could be correlated to performance parameters.
A. Limitations of modelling due to intermittent flow
It is important to keep in mind that the theory of hydrodynamic modelling was
developed for continuous flow reactors. The pilot-scale trickling filter used in this
study was fed intermittently using a solenoid valve, but the outlet flow was
continuous and constant. Intermittent feeding was chosen to simulate a fraction of a
full-scale trickling filter. Indeed in a full-scale circular trickling filter fed with a rotating
arm, the periodicity T p at which a fraction of the trickling filter surface is sprayed
with water is defined by the speed of rotation Sr of the rotating arm:
where
T =_1_ P n s ,
T p = periodicity of water spraying (min)
n = number of distributor rotating arms
s, = rotating arm rotation speed (rev/min)
212
In the case of a pulse injection of tracer in the distribution system of a full-scale
trickling filter, the tracer is therefore sprayed only on a fraction of the filter surface.
And it takes Tp minutes for water to be sprayed again on this fraction, while water is
continuously sprayed on other fractions of the filter. The main difference between the
pilot-scale filter used in this study and a full-scale filter is the lack in the outlet flow of
this contribution of water coming from other fractions of the filter. It can therefore be
assumed that the RTD of the pilot-scale filter is the "concentrated" RTD of a full
scal\! trickling filter, this being confirmed by the similar curve shapes found between
this study and studies at full-scale (e.g. by Seguret (1998), Craft and Ingols (1973».
Furthermore, hydrodynamic modelling has previously been applied to intermittently
fed reactors. For example, Richards and Reinhart (1986) studied the hydrodynamic
characteristics of high-rate filters fed intermittently with a dosing periodicity of 12 s.
B. Hydrodynamic modelling
The interpretation of RTDs can be carried out by fitting them to some theoretical
models (Fenandez-Polanco et al., 1996). Given the shape of the RTDs found in this
study, three models have been applied to the trickling filter. These models, previously
described in the Literature review, were:
• The n MFR model;
• The Stagnant reactor (SR) model;
• The Simplified stagnant reactor (SSR) model.
The nMFR model was chosen because it is the most used (Vasel and Schrobiltgen,
1991; Lens et aI., 1995; Wik et al., 1995). It is however important to keep in mind that
this model is not theoretically the most appropriate. Indeed, as highlighted by Wik et
al. (1995), the proportion of backmixing is likely to be small since the water flows
downwards by gravity. The SR model was also tested because the concept of
exchange between each MFR and an exchange zone intuitively satisfies the
assumption of a fraction of the tracer being absorbed in and released by the biofilm.
Finally, the SSR model (introduced by Lens et aI., 1995) was tested because the
neutron scattering results have shown that the top and bottom half of the filter
exhibited different pattern in terms of film accumulation. The SSR model, breaking
213
down the reactor between one MFR with exchange and n MFRs in series, was
therefore thought to provide an adequate representation of the trickling filter.
The parameters of each model are summarised in Table 5.4E.
Table 5 4£' Parameters eXlractedfrom the three hydrodynamic models used Model Parameters n MFR • VMFR: volume of 1 MFR
• nU".: number of MFRs in series Stagnant reactor (SR) • VMFR,: VOlume of 1 MFR
• VE: volume of exchange zone for each MFR
• nMFR,: number of MFRs in series Simplified stagnant reactor (SSR) • VMFR,: volume of the MFR with exchange
• VE,: volume of exchange for MFR1 • VMFR2 : volume of 1 MFR of the series of
MFR2s • nMFR2: number of MFR2s in series
These parameters enable for each model the calculation of a liquid volume (LV), and
for the SR and SSR models, of stagnant volume (SV) and flowing volume (FV):
where
where
SV(SR) = nMFR' VE
FV(SR) = nMFR' VMFR,
LV(nMFR) = nMFR VMFR
L V(SR) = SV(SR) + FV(SR)
LV(SSR) = SV(SSR) + FV(SSR) = SV(SSR) + FVl(SSR) + FV2(SSR)
SV(SSR) = YE'
FVI(SSR) = VMFR1
FV2(SSR) = nMFR:! VMFR2
The models parameters were calculated using the DTS software (PROGEPl - LSGC
Nancy, France), described by Leclerc et al. (1995), Each theoretical model can be
represented by a network of interconnected elementary units. The software is fed
with the description of this network, and then derives and solves the corresponding
214
mass-balance equation in the Laplace domain. The model parameters, intervening in
the equation, are then optimised by fitting the model outlet response to an
experimental RTD curve. Before optimisation of the parameters, their initial values
were set to I, and the two criteria for optimisation were set at 0.001 for the accuracy
of the parameters and at 1000 for the maximal number of iterations. A second
optimisation with the same criteria was then carried out, using as initial values of
parameters the values found by the first optimisation.
Figure 5.41 shows typical results of the modelling. The best agreement between the
calculated and measured RTDs was obtained using the simplified stagnant reactor
model, followed at the same level by both the SR model and the n MFR model. In a
purely mathematical sense, this is perfectly understandable because the higher the
number of parameters in a model, the better the fit of the mathematical model to the
experimental data. Nonetheless, the values of the parameters extracted from each
model make sense. Furthermore, it can be advanced that the better agreement between
the calculated and measured RTDs after extending the n MFR model with one
stagnant' reactor (to create the SSR model) supported the importance of tracer
absorption and de sorption inlby the film. A similar improvement in agreement had
been observed by Lens et al. (1995) in the case of RTD modelling of a rotating tubular
biofilm reactor.
The number of MFRs in series varied between 1.4 (for the reactor without film) and
2.6. By contrast, Wik et al. (1995) had found values of around 4.3. However, they
were working on RTDs measured on a nitrifying filter filled with plastic medium of
high specific surface area, under hydraulic loadings of5.7 and 11.3 m3/m2 h.
The total liquid volumes calculated using the three models varied between 30 and 90 I
and are plotted on Figure 5.4J.
5.4.3.4 Correlation hydrodynamic characteristics - biofilm accumulation
As mentioned in § 5.4.3.1.1, the only variable during the operation of the pilot-scale
trickling filter was the film accumulation. Furthermore, contradictory findings were
reported in the literature on the influence of film accumulation on hydrodynamic
215
Figure 5.4Ia: Comparison experimental RTD - nMFR modelled RTD (January 97)
0.00573
E(t) 0.00430
0.00286
0.00143
0.00000
0.00000
(--"
96.000
'"
192 _ 00
Light grey: Experimental RID Dark grey: Modelled RID
288.00 384.00
Time (min)
Figure 5.4lb: Comparison experimental RTD - SR modelled RTD (January 97)
0.00573
E(t) 0.00430
0.00286,
0.00143
0.00000 i ,
0.00000
','
t"
96.000
"
"
192.00
Light grey: Experimental RID Dark grey: Modelled RID
288.00 384.00
Time (min)
Figure 5.4le: Comparison experimental RTD - SSR modelled RTD (January 97)
0.00573
E(t) 0.00430
0.00286
0.00143 I i
0.00000
/~
I !
0.00000 96.000 192.00
216
Light grey: Experimental RID Dark grey: Modelled RID
288.00 384.00
Time (rnin)
Liquid volume (I) Liquid volume (I) Liquid volume (I) N .. '" ex> N .. '" ex> ..... NC,o),l:Io.OIC»""""CD<o
0 0 0 0 0 0 0 0 0 0 0000000000
No No No biolilm biofilrn biofilm
Dec. 94 Dec. 94 Dec. 94
.' Feb. 95 Feb. 95 Feb. 95
(JO
~'e .., 0; .., Apr. 95 Apr. 95
<C .., <i<j' ~ <i<j' ~ <i<j' C ,. Jun 95 C • Jun. 95 C Jun. 95 ... ... t!> .c' t!> ...
t!> Ul c Ul (JO :... is: Aug 95 :... 0; Aug. 95 Ul Aug. 95 <C :... ... ... ~ ., ,., ~ ... ..
D Dec. 96 I .. ,.
Dec. 96 ;r
Oec. 96 t"" t"" < t"" .E' I\)
11 :!l .E' 0
~ 0 c ..c- C
~. Jan. 97 C 3 Jan. 97 C Jan. 97 Q; '" 0 Q; " 0 0
'" '" !!< Q; '" ..,
. .0' '" ..,
D CD .., '" 0 Feb. 97 0 Feb. 97 Feb. 97 ;: c ;: 0
is: :!l ;: 8 8 0
Mar. 97 ~. Mar. 97 8 Mar. 97 t!> t!> ~ t!> <C = I!I '" < a: Apr. 97 '"
0 Apr. 97 '" Apr. 97 :!l :o:l
c :o:l .., 0 3
:o:l ~ 8 " 8 s· May. 97 May. 97 May. 97 <C 0 0 8
Q- Q- 0 .c' t!> !!. Q-c Jun. 97 - Jun. 97 Jun. 97 !!. is: N
Jut 97 Jul. 97 Jul. 97
Aug. 97 I1I Aug. 97 Aug. 97
Sap. 97 I1I Sep. 97 Sep.97
properties. It was therefore intended to look for correlations between the two types
of parameters.
The parameters used as independent variables were the hydrodynamic chatacteristics
of the trickling filter (extracted from RTDs), while the independent variables were the
film accumulation parameters. They are summarised in Table 5.4F.
Table j.4F: Variables used for hydrodynamic characteristics-film accumulation correlations
Variable type Nature Variable
Dependent variables Hydrodynamic ° 150 characteristics ° Im(7h)
° tm(90%) ° cr,(7h)
° cr ,( 9 0 %)
° t50/t16 ° LV(nMFR)
° LV(SR) ° LV(SSR) ° SV(SR)
° SV(SSR) ° FV(SR)
° FV(SSR) ° FV1 (SSR)
° FV2(SSRt Independent variables Film accumulation oAMC
parameters ° AMCt ° AMCb
° lib ratio
See Nomenclature
The results of the correlation analysis are surnrnarised in Table 5.4G. The criteria of
selection for significant correlations were IRI > 0.5 and/or p-value < 0.1000.
218
Table 5 "G' Results 0/ correlation analysis/or hydrodynamic characteristics Parameter R Number of p-value
observations
t50 IWC 0.631 1 5 0.0101 AMCt 0.600 1 5 0.0163 AMCb 0.581 1 5 0.0215
tm(7h) 12)
tm(90%) AMCt 0.765 1 5 0.0005 IWC 0.738 1 5 0.0011
AMCb 0.612 1 5 0.0136 lIb ratio 0.568 1 5 0.0254
cr,(7h) 12)
cr I( 9 0 %) AMCt 0.785 1 5 0.0002 IWC 0.714 1 5 0.0019
t/b ratio 0.664 1 5 0.0056 AMCb 0.543 1 5 0.0352
t50/t16 12)
LV(nMFR) 12)
LV(SR) 12)
LV(SSR) 12)
SV(SR) 12)
SV(SSR) 12)
FV(SR) 12)
FV(SSR) 12)
FV1 (SSR) 12)
FV2(SSR) 12)
It appears that tso, tm(90%) and crt(90%) were positively linearly correlated to film
accumulation indicators. This means that both central tendency and spread of RTDs
are affected by film accumulation in the low-rate trickling filter studied. This result can
be compared to results by Gray and Learner (1984). They also found a direct
relationship between median retention time and tilm accumulation in a pilot-scale
trickling filter containing 50 mm-graded blast furnace slag, and loaded at 1.68 ml/mJd.
They however had not expressed this relationship mathematically.
This also conforms to findings by Cook and Katzberger (1977), who found that the
amount of film in a model trickling filter has a pronounced effect on the liquid
residence time in the filter.
On the contrary, however contradictory results were published by Eden et al. (1964)
who had found that there was no direct correlation between film weight and retention
219
times and concluded that the use of retention time to predict the film weight within a
filter was misleading.
A second comment is that, between the arbitrary cut-off times of 7 h and time
required to recover 90% of the injected mass of tracer, the latter seems to be the most
significant because correlations (confIrmed by correlations found with t50) were found
in this case.
The lack of significant correlation between liquid volume extracted from
hydrodynamic modelling and film accumulation indicators was disappointing. An
explanation could be that the data used for modelling were the data gathered from t = 0
to t = 7h, and it appeared that 7 h was potentially not the best cut-off time. Results
could have been different had the data used for hydrodynamic modelling been extended
up to a tracer recovery of 90%. However, the RTDs tailing already had to be extended
using a power function, and it was thought that modelling using reconstructed data
could be unsatisfactory.
5.4.4 Conclusions
With respect to temperature:
• The temperature of the trickling filter was found to be controlled by the influent
temperature, rather than the ambient temperature.
With respect to film accumulation:
• Initial film colonisation of the trickling filter started in the upper layers, and spread
downwards.
• Typical seasonal variability of film accumulation was not demonstrated during Phase
I, in which film accumulation was influenced primarily by the colonisation process
and maturation of the film.
• Seasonal variability of film accumulation was observed during Phases 2 and 3, with
maximum film accumulation in winter and minimum film accumulation in the summer
months.
• Film accumulation could also have been influenced by the different compositions of
synthetic sewage in Phases 2 and 3.
220
• The ratio of moisture content in the top and bottom half of the filter shows that the
proportion of film is almost always higher in the bottom half of the filter than in the
top half. Exceptions were noted when decreasing ambient temperatures resulted in
film accumulation in the top half of the filter.
• Film accumulation in the top half of the filter is related mainly to active biofilm,
while the film in the bottom half of the filter is composed of solids and biofilm.
• The best correlation coefficients obtained for film accumulation were temperature
(negative) and size of influent particles (positive). Temperature was mostly correlated
to film accumulation in the top half of the filter, while influent particle size influenced
film accumulation in the bottom half of the filter.
With respect to hydrodynamic characteristics:
• RTD curves for the pilot-scale trickling filter were characterised by a rapid increase
in concentration of tracer in the effluent, reaching a peak and followed by an extended
tail.
• The tailing can be interpreted as resulting from the slow exchange of tracer between
the liquid film and the biofilm.
• Mean residence time calculations were influenced by the chosen 'cut-off time' (t=7h
or t=90%). The mean residence time value for t=90% tracer recovery was found to be
more satisfactory, being similar to the median residence time.
• Measurement of residence times in the trickling filter without biofilm generated
reference data and showed that changes in hydraulic loading had more effect on the
RTDs than when measured with biofilm.
• With respect to hydrodynamic modelling of the RTDs, the best agreement between
calculated and measured RTDs was obtained using the simplified stagnant reactor
model, followed by the SR model and then the nMFR model.
• Correlation between the hydrodynamic characteristics and biofilm accumulation
showed that both the central tendency and spread of RTDs were affected by film
accumulation in the low-rate trickling filter.
• There was a lack of significant correlation between liquid volume extracted from
hydrodynamic modelling and film accumulation indicators.
221
. '
5.5 DETERMINATION OF THE KEY PARAMETERS AFFECfING
TRICKLING FILTER PERFORMANCE
The objective of this section is to detennine to what extent each of the parameters
previously studied affect treatment perfonnance of a low-rate trickling filter. Indeed,
as seen in the Literature review, all these parameters are known to affect the
perfonnance of trickling filter in some way. However, most of the studies found in the
literature have presented the effect of one parameter at a time, without considering the
interactive effect of several parameters. For example, Table 5.5A summarises various
relations found in the literature for BOO .
Table 5.5A: Plots o['Y = f(X)'found in the literature for BOD Y (unit) X (unit) Reference
effluent BaD (mg/I) BaD loading (kg/m'. d) Tomlinson and Hall (1950), Gray (t+s) and Learner (1984)
effluent BaD (mg/I) influent BaD (mgll) Tomlinson and Hall (1950) (t+s)
effluent BaD (mg/I) effluent TSS (mg/I) Bruca and Merkens (1970) (t+s) (t+s)
BOO removed (kg/m'.d) BaD loading (kg/m'.d) Tomlinson and Hall (1950). (t+s) Schulze (1960), Bruce and
Merkens (1970). Biddle (1994) RE. BaD (%) specific surface area of medium Truesdale et al. (1962), Bruce
(t+s) . (m'/m') and Merkens (1970) RE. BaD (%) depth (m) Schulze (1960)
(t+s) RE. BaD (%) hydraulic loading (m'/m'.d) Schulze (1960)
(t+s) R.E. BaD (%) hydraulic loading (m'/m'.d) Bruce and Merkens (1970), WRC
(t+s) ( 1976) R.E. BaD (%) effluent T (OC) Adin et al. (1984)
(t+s) R.E. BaD (%) film accumulation (g/ml) Sullins (1968)
(t+s) effluent BODf (mg/I) depth (m) Logan et al. (1987a.b) effluent BODf (mg/I) BODf loadino (kolm'.d) Harrison and Daigger (1987)
BODf removed (kg/m'.d) influent BODf (mgll) Parker and Merrill (1984), Richards and Reinhart (1986),
Sarner (1986). Logan et al. (1987a)
R.E. BODf (%) hvdraulic loadine fm'/m'.d) Looan et al. (1987a.b) RE. BODf (%) BODI loading (kg/m'.d) Richards and Reinhart (1986) RE. BaD! (%) median retention time (min) Richards and Reinhart (1986) RE. BODf (%) mean retention time (min) Ferchichi (1991)
As indicated in the litterature review, the effect of influent particulate content and size
distribution on trickling filter perfonnance has also been mentioned (Figueroa and
Silverstein, 1992; Samer and Marklund, 1984), but has not been studied in conjunction
with other parameters.
222
One way to clarify the relative importance of the various parameters affecting
performance would be to study each individual parameter with the others completely
controlled. This is theoretically possible at laboratory-scale but virtually impossible at
pilot or full-scale. In this study, the design parameters (specific surface area of
medium, depth) and some of the operating parameters (hydraulic loading, dosing
frequency) were fixed. However, the objective of the pilot-scale study was to simulate
the operation of a full-scale trickling filter exposed to external temperatures under
partially controlled conditions (synthetic sewage).
Correlation and regression analysis were therefore used to study the influence of
operating parameters and parameters resulting from interactions between the previous
parameters and ambient conditions (independent variables) on trickling filter
performance (dependent variables). The first step of the analysis was the calculation
of correlation matrixes including independent and dependent variables. From these
matrixes were extracted the independent variables best correlated to each dependent
variable. In the second part of the analysis, multiple regression (using forward
step wise selection) was used to rank the extracted independent variables in terms of
their relative influence on each dependent variable. It is important to emphasise that in
this study regression was not used as a modelling tool, but as a mean of ranking the
parameters affecting performance and therefore as a tool contributing to improve the
understanding of the studied phenomena.
5.5.1 Correlation analysis
5.5.1.1 Dependent variables
The parameters used as dependent variables were of different natures. They can be
grouped in three categories:
• Concentrations:
- effluent BOO, BOOf, COD, COOf, TSS;
- settled effluent BOO, COD, TSS;
The effluent, filtered effluent and settled effluent concentrations in organic pollution
indicators (BOO, COD), as well as the effluent and settled effluent TSS
223
concentrations were used to determine which were the parameters affecting the
effluent characteristics in its different fractions.
• Removal efficiencies:
- RE BOO (%); RE COD (%); RE TSS (%);
- REs BOO (%); REs COD (%); REs TSS (%);
Removal efficiencies in terms of BOO, COD and TSS through trickling filtration
(biQlogical process) and secondary settlement (physico-chemical process) were used
to determine which parameters influenced trickling filter performance with respect to
removal efficiency.
• Suspension characteristics:
- eCOO/eBOO; eCOOf/eBOOf; seCOO/seBOO;
- P eBOOf (%); P eCOOf (%);
The COD/BOO ratio for the effluent, filtered effluent and settled effluent was used to
determine the parameters affecting the effluent biodegradability. The proportion of
filtered organic matter in.the effluent (for both BOO and COD) was also used, since it
represents the fraction of organic matter that would remain in the effluent after
filtration. This was done to further understand the mechanisms involved in the
process of trickling filtration.
5.5.1.2 Independent variables
The parameters used as independent variables, I.e. to potentially explain the
dependent variables were grouped in six categories:
• Operational parameters:
1) Pollutants loadings, expressed as concentrations since the hydraulic loading
was constant: influent concentrations (in the case of RE and effluent
concentrations); filtered influent concentrations (in the case of filtered effluent
concentrations); effluent concentrations (in the case of REs and settled
effluent concentrations);
2) Feed characteristics: Influent and effluent P BOOf or COOf (in the case
respectively of effluent and settled effluent P BOOf or CODf); influent,
224
-------------------
filtered influent and effluent COD/BOO ratio (in the case respectively of
effluent, filtered effluent and settled effluent CODIBOD ratio);
3) Particle size distribution characteristics: for both PSD in number and m
volume: mean and median dp; PSD standard deviation; proportion of total
particulate content represented by particulate beteween 0.1 and 1,2 J.lm,
between 1.2 and 100 J.lm, and bigger than 100 J.lm; A and B parameters
extracted from PSD modelling .
• Parameters resulting of the interactions between other parameters:
4) Temperature: ambient, influent and effluent temperature;
5) Biofilm accumulation: average moisture content; ratio top half valuelbottom
half value;
6) Hydrodynamic properties: mean and median residence time; ratio tSO/q 6;
RTD standard deviation; parameters extracted from hydrodynamic modelling.
As mentioned before, the first stage of the analysis was to calculate a correlation
matrix for each category of dependent variables with each category of independent
variable. From each correlation matrix, and for each dependent variable, the
independent variables best correlated (IRI > 0.5 and p-value < 0.1000) were extracted.
5.5.1.3 Correlations with concentrations
The significant correlations found between effluent concentrations and independent
variables are summarised in Table S.SB.
A.BOD
The effluent BOO (eBOD) appeared to be positively correlated to the influent TSS,
and negatively correlated to the proportion of BODf in the influent. With regards to
PSD parameters, the effluent BOO was positively correlated to median and mean
influent diameters for PS Os in volume, and to the proportion of total particulate
number and volume represented by particulates bigger than 100 J.lm.
As for temperature (T) parameters, eBOD was negatively correlated to both ambient
and influent T. Finally, eBOD was also negatively correlated to parameters of central
tendency of RTDs.
225
I\) I\)
0>
Table 5.58: Results
PI80Df
T
"/,'/,,,.,,/ concentrations
(0.0001)
-0.526 (0.0029)
o
o aT ·0.425
. ,
The filtered effluent BOO was negatively correlated to the average moisture content in
the filter, and to parameters expressing both central tendency (mean and median
residence time) and spread of RTDs.
The settled effluent BOO (seBOD) was highly positively correlated to the effluent
BOO and TSS. It was also negatively correlated to the effluent COD/BOO ratio. In
terms of PSD parameters, seBOD was positively correlated to the B parameter
extr;:tcted from modelling of PSDs by numbers using power laws. By contrast, it was
negatively correlated to the standard deviation of the PS Os by volume. Finally,
seBOD was negatively correlated to ambient and effluent temperature .
B COD
The results found for the effluent COD (eCOD) were similar to those found for
eBOD. The effluent COD was positively correlated to influent TSS, and negatively
correlated to the proportion of CODf in the influent. With regards to PSD parameters,
eCOD was positively correlated to median influent diameters for PSDs in volume, and
negatively correlated to the proportion of total particulate volume represented by
particles smaller than 1.2 J.1m. However, no correlations were found with T
parameters, by opposition to that found for eBOD.
The filtered effluent COD (eCODf) appeared to be only correlated with RTD
parameters: negatively with parameters expressing the spread of the RTDs.
Similarly again to that found for seBOD, the settled effluent COD was highly
positively correlated to the effluent COD and TSS. It was also negatively correlated to
the standard deviation of effluent PSD by volume, and positively correlated to the B
parameter extracted from PSD by numbers modelling using power laws.
C. TSS
The effluent TSS was negatively correlated to the proportion of both filtered BOO
and COD in the influent, and positively correlated to the influent TSSITS ratio. It was
also positively correlated to the PSD by volume median diameter, and negatively
227
correlated to the ambient and influent temperatures and to the mean residence time in
the trickling filter.
As for the settled effluent (seTSS), it was highly positively correlated to the effluent
BOO, COD and TSS. It was on the other hand negatively correlated to PSD
parameters: standard deviation of distribution by volume and B parameter extracted
from PSD by numbers modelling using power laws. Finally, seTSS was negatively
correlated to both ambient and effluent temperatures.
5.5.1.4 Correlations with removal efficiencies
The significant correlations found between removal efficiencies and independent
variables are summarised in Table S.Sc.
A.BOD
The BOO removal efficiency through the trickling filter was positively correlated to
the influent filtered BOO, in terms of both its proportion of the total influent BOO
and of its absolute value. It was also negatively correlated to COD/BOO ratio of the
influent filtered fraction. In terms of PSD parameters, the BOO removal efficiency
appeared to be negatively correlated to the mean and median diameter of the influent
PSD by volume, indicating that the bigger the particulate matter, the lower the BOO
removal efficiency. This was corroborated by a negative correlation with the
proportion of influent total particulate volume represented by particulate matter
bigger than 100 Ilm. Positive correlations were also found between BOO removal
efficiency and temperature, and with mean and median residence times. On the other
hand, there appeared to be a negative correlation with spread of RTD (expressed by
RTSO/16).
The fiftered BOO removal efficiency was positively correlated to the proportion of
filtered BOO in the influent and to parameters expressing central tendency of RTD. It
was also negatively correlated to a parameter expressing the spread of RTDs.
As for effluent BOO removal efficiency by settlement, it was negatively correlated to
the A parameter extracted from PSD by numbers modelling using power laws.
228
Table 5.5C: Results removal
too
-0.571 (0.0009)
o o
.,
B. COD
The parameters affecting COO removal efficiency were similar to those affecting BOO
removal efficiency. They included influent filtered COD/BOO ratio (negative
correlation), COD (positive correlation), and median diameter for PSOs by volume
(negative correlation). R.E. COD was also negatively correlated to the RT50116 ratio,
and positively to the mean residence time.
The COOf removal efficiency was positively correlated to the proportion of COOf in
the influent, and negatively correlated to the influent filtered COOIBOO ratio.
As for the effluent COD removal by settlement, it was negatively correlated to the
proportion of filtered COD in the effluent. It appeared to be mostly correlated to
characteristics of the effluent PSO: positively to the median diameter of the PSO by
volume and to the proportion of total particulate volume represented by particles
bigger than 100 Iolm; negatively to the A parameter extracted from PSO by numbers
modelling using power laws and to the proportion of total particulate volume
represented by particles between 1.2 and 100 Iolm.
C. TSS
The TSS removal efficiency by trickling filter appeared to be negatively correlated to
the RT50116 ratio, and positively correlated to mean and median residence time.
As for the TSS removal efficiency by settlement, it appeared to be correlated to
effluent PSO characteristics: negatively to the proportion of total particulate volume
represented by particles between 1.2 and 100 Iolm; positively to the median diameter
of the distribution by volume and the proportion of total particulate volume
represented by particles bigger than 100 Iolm.
5.5.1.5 Correlations with effluent characteristics
The significant correlations found between effluent characteristics and independent
variables are summarised in Table 5.50.
230
Table 5.5D: Results
Type of
Cone.
PSD
T
RTD
Im(7 h)
I"
12) iCODfliBOD -.!', •. c'
-0.507
12)
12)
0.608
characteristics
eCOD/eBOD 0.514
12) 12)
aT 0.529 12) 12)
eT 0.517
0,(90%) -0.59 cr,(7 h) -0.525
-0.534 -0.517
. ,
A. COD/BOD ratio
The effluent COD/BOO ratio appears to be correlated mostly to temperature (aT, iT)
and RTD (t;o/tI6, tm(7 h), t;o) parameters. It is interesting to note that for the latter
parameters, the correlation is positive with parameters related to the central tendance
of the distributions, and negative with a parameter expressing the spread of the
distributions. A correlation was also found with the mean diameter of the influent
PSD in volume.
The filtered effluent COD/BOO ratio was only correlated to the filtered influent
CODIBOD ratio .
The settled effluent COD/BOO ratio appeared to be correlated to similar parameters
as the effluent COD/BOO ratio. Positive correlations were found with temperatures
and with the unsettled effluent COD/BOO ratio.
It therefore appears that the production of effluent with high COD/BOO ratio, ie with
low biodegradability, was correlated to high temperatures, long mean residence time
and shortly spread feed RTDs. In the case of unsettled effluent, a high COD/BOO
ratio was emphasized by small influent mean diameter, while for the settled effluent,
low biodegradability was correlated to high spread of PSD by number and low
biodegradability of the feed.
B. Proportion of filtered organic matter
The proportion of filtered COD in the effluent was positively correlated to the same
proportion in the influent. The proportion of filtered organic matter (for both BOO
and COD) was negatively correlated to the spread of RTOs, and, in the case of BOO
only, to a central tendency parameter ofRTOs.
5.5.2 Multiple regression with stepwise estimation
The correlation analysis has shown that some of the dependent variables were
correlated to more than one individual independent variable. However, it was difficult
to rank these independent variables by order of importance, because the number of
observations (i.e. the sample size) used for each correlation was not the same for all
232
variables. As a result, in order to analyse the relationship between each dependent
variables and the independent variables individually correlated to it, the analysis was
supplemented by multiple regression analysis with forward step wise selection of the
independent variables.
In this technique, a model selection procedure helps choose the independent variables
that are most useful in explaining a given dependent variable (Abacus Concepts,
1992). Forward selection starts with an empty model and adds independent variables
in order of their ability to predict the dependent variable. The criteria for adding
variables is the partial F-ratio, square of the value obtained from a Hest for the
hypothesis that the coefficient of the variable in question is equal to zero. The variable
assignment algorithm starts with the calculation of the partial F -ratio for each variable
not in the model. If the maximum of these values is greater than the F-to-enter
specified, the corresponding variable is entered, completing the current step. If no
variable is entered, the stepwise procedure stops.
This method was used here to search for the multiple linear regression best explaining
the variance of each dependent variable, indicator of trickling filter performance. This
method has previously been used by Desbordes and Servat (1983) to explain the
variance of average concentrations ofTSS, BOO and COD during the runoff of rainfall
events.
The quality of the multiple regression models was assessed by means of the adjusted
R 2, which takes into account the number of independent variables included in the
regression equation and the sample size (Hair et at., 1998).
There are pitfalls to keep in mind while using multiple regression:
- it is important that the independent variables are not strongly correlated (maximum
correlation between any two independent variables should be less than 0.8);
- the minimum ratio of observations to independent variables should not fall below 5
to I (Hair et al., 1998).
233
As a result, and since the size of samples sometimes did not exceed 10, a direct
step wise analysis with introduction of S or 6 potential explanatory variable was not
possible. Instead, for each dependent variable, the 4 to 6 potential variables most
individually correlated to the dependent variable were used for a step wise analysis by
pair. For each pair of independent variables, the output of the analysis can be
summarised by the order of entry of each variable, and the adjusted R2 of the model.
The different variables were then marked using the following method:
• For each pair of independent variables used, the two independent variables tested
were allocated points, based on their order of entry in the model. The point allocation
was as described in Table S.SE.
T, bl 55E P . a e omtsa II ocation fi . d or palre I . stepwlse ana YS1S Variable Points allocated
entered first in the model 2 entered second in the model 1
entered on its own in the model 3 not entered in the model 0
To take into account the validity of the model calculated by the paired stepwise, its
adjusted R2 value was used to moderate the points allocated to each variable of the
pair. Therefore, the mark for each variable of the pair is calculated as:
Mark = (Points allocated to the variable) x (Adjusted R2 of the model) .
• The global mark for each independent variable was calculated as the sum of all the
marks calculated. during the various stepwise analyses by pair.
The results are summarised in the tables below (the details of the paired-stepwise
analyses for the dependent variables considered are given in Appendix 4).
5.5.2.1 Results for effluent concentrations
A. BOD
The parameters selected for the paired stepwise analysis were extracted from the
tables summarising the results of the correlation analyses. The ranking generated by
234
the stepwise analysis shows that two main parameters affect effluent BOO. These are
mean residence time and influent TSS. These parameters are closely followed by
influent median particle diameter and ambient temperature. Out of the 5 parameters
tested the proportion of the filtered BOO in the influent appears to be the least
significant. The effect of mean residence time on performance is logical, as the longer
the contact time of the BOO with the biofilm, the greater the biodegradation and the
less BOO in the effluent. The next two parameters are interesting as they confirm the
effect of influent solids on performance, in terms of both concentration and size.
Sarner and Marklund (1984) noted a similar phenomenon in a lab-scale fixed film
reactor. They reported that the removal of glucose by the biofilm was reduced as a
result of increased TSS in the influent. In this case, the TSS was composed of both
starch and particles from digested sewage sludge. The effect was proportional to the
amount of particles adsorbed to the biofilm and was also more pronounced at higher
temperatures. It is thought that the adsorption of particulates to a biofilm
mechanically interferes with oxygen transfer, as well as resulting in local oxygen
depletion as a result of hydrolysis reactions. This results in an oxygen shortage in the
biofilm, which in turn decreases the degradation of soluble material and results in
increased effluent BOO. Temperature can exacerbate this phenomenon by initially
increasing the biological activity and hence the oxygen demand. Samer (1980), quoted
by Sarner (1986) also found that an increase of fme suspended and colloidal particles
in the influent of a pilot-scale trickling filter resulted in a decreased efficiency of
dissolved BOO removal. It was found that the higher the load of particles, the lower
the removal of dissolved organics by the trickling filter, as was predicted in the
stepwise analysis above. The stepwise analysis also ranked, just behind concentration
of solids in the influent, the median particle size. That is, the bigger the particles, the
more pronounced the effect of these particles on BOO removal. Levine et al. (1985)
found that removal of larger particulates from influent resulted in increased BOO
removal for a high-rate trickling filter. The effect of particle size on BOO in the
effluent can be explained simply by the fact that larger particles are more resistant to
breakdown by hydrolytic enzymes.
It is interesting to note that the mean residence time is the main determining factor for
effluent BOO, because, theoretically at least, an increase in residence time could be
235
beneficial for treatment of a high solids waste by trickling filtration. Although, the
simple theory of hydrolysis being the rate-determing step in total BOO removal is
complicated by the theory that particulates can interfere with oxygen transfer and
result in local oxygen shortages. In this case, increasing the residence time may not
counteract these effects. Temperature is negatively correlated to effluent BOO,
however, as demonstrated above, the beneficial effects of temperture on biomass
activity can be detrimental in some circumstances. The positive correlation of influent
filtered BOO with effluent BOO has been dealt with in the earlier section of trickling
filter performances. It suffices to say that when the BOO of the influent is present
largely as dissolved matter, i.e. the particulate matter is reduced, BOO removal is
improved.
Table 5.5F: Results of step wise analysis for eBOD
Variable Mark I ,(7 h) 2.978
iTSS 2.945 id,n(v) 2.840
aT 2.728 P iBODf 1.908
As found for BOO, the particle size of the influent TSS is important in influencing
trickling filter effluent quality in terms of COD. However, in the case of the effluent
COD, the proportion of particles (1 - 1.2 Ilm) in the influent was found to be the main
factor influencing effluent COD. The greater the proportion of small particles in the
influent, the less COD in the effluent. Conversely, the higher the median particle
diameter, the more COD in the effluent. Thus, it seems that higher proportions of
small particles were actually beneficial to effluent COD. It is interesting to note that
the influent filtered COD seems 10 have the least effect on the effluent COD, in
contrast to that observed for BOO. This was also noted in the section on trickling
filter performances, in which it was potentially attributed to the production of
refractory soluble COD by the trickling filter. However, HPSEC characterisation of
dissolved matter in the trickling filter effluent did not find any obvious sources of
SMPs in the effluent.
236
Table 5.5G: Results o/stepwise analysis/or eCOD Variable Mark
iPPo1-1,(v) 2.238 id,o(v) 1.530
iTSS 1.362 P iCODf 0.000
The stepwise analysis for effluent TSS has ranked four parameters, with proportion
of filtered BOO in the influent being the most significant parameter affecting effluent
TSS. The correlation is negative, i.e. the higher the influent filtered BOO, the lower the
effluent TSS. This could be explained simply by the fact that increasing
concentrations of filtered BOO in the synthetic sewage means decreasing
concentrations of particulate BOO, which means less carry-over of influent particles
into the effluent. However, no marked correlation has been found between influent
TSS and effluent TSS. Alternatively, increasing concentrations of readily
biodegradable BOO in the influent may give rise to a more active biofilm which can
more rapidly degrade influent particulate matter, resulting in less carry-over of influent
TSS into the effluent. However, as the trickling filter is an aerobic system, a more
active biofilm also means more cell production, which in turn could mean more TSS in
the effluent. The connection between influent filtered BOO and effluent TSS is
therefore not completely clear and warrants futher investigation. The second
parameter affecting effluent TSS is the ambient temperature, i.e. an increase in
temperature results in a decrease in effluent TSS. This can be explained by increased
degradation of particulate matter by a more active biofilm at higher ambient
temperatures. The residence time is also shown to affect effluent TSS. As particulate
matter is particularly slow to degrade, it makes sense that carry-over of particulate
matter into the effluent should be quite strongly influenced by the residence time of
treatment. Lastly, the size of the influent TSS has been shown to effect effluent TSS.
As was mentioned earlier, Levine et at. (\985) showed that removal of large particles
from the influent increases the overall efficiency of TSS removal of a trickling filter.
Consequently, the bigger the particles in the influent, the less total TSS removal will
take place and the higher the effluent TSS.
237
- - _._------
Table 5.5H: Results of stepwise analysisfor eTSS
Variable Mark P iBODf 3.813
aT 2.631 t ,(7 h) 0.648 id,n(v) 0.588
Stepwise analysis with respect to BOO removal efficiency has shown that influent
filtered BOO has by far the greatest impact on BOO removal efficiency. This was
covered in the section of trickling filter performances, in which it was found that
filtered influent BOO was removed more efficiently than particulate BOO. The
influent filtered COD to filtered BOO ratio ranks as the second most important
parameter affecting BOO removal efficiency, representing the relative biodegradability
of the soluble matter in the trickling filter influent. This is followed in turn by the
mean residence time, the influent particle size, and the ambient temperature. The
correlation between BOO removal efficiency and ambient temperature was also
reported by Gray and Leamer (1984) who found a significant correlation between
ambient temperature and BOO removal for a low-rate trickling filter.
Table 5.5/: Results of stepwise analysis for RE BOD
Variable Mark
P iBODf 7.074 iCODf/iBODf 4.911
I , (7 h) 3.414 id,n(v) 2.181
aT 2.038 I,n/l, • 1.484
Stepwise analysis for COD removal efficiency shows Ihat influent COD is Ihe top
ranking parameler. This is followed by the filtered COD to BOO ratio of the influent,
mean residence time, the spread of the residence time distribution and the median
particle diameter of the influent.
Ta bl I if I fi RECOD e 5.5J: Resu ts 0 stepwise ana ysis or
Variable Score
iCOD 5.857 iCODfliBODf 3.840
I ,(7 h) 1.470
1'0/1 " 1.128 id,o(v) 0.545
238
Stepwise analysis for settled effluent BOO shows that the effluent BOO has the
greatest effect on settled effluent BOO. The second parameter is the B value from
power law modelling of PS Os. Thirdly, the temperature is shown to influence
settlement, probably by influencing the viscosity of water.
Table 5.5K: Results of stepwise analysis for seBOD Variable Score
eBOO 7.731 eB, , 3.480 aT 2.106
eo ,(v) 0.000
5.6 CROSSFLOW FILTRATION OF TRICKLING FILTER
EFFLUENT
The objective of this section was to assess the feasibility of using cross flow filtration
as a tertiary treatment after a low-rate trickling filter. The previous sections of the
study have shown that, even if a low-rate trickling filter generally produces an effluent
of consistently good quality, the new standards set by the EU Directive on Urban
Wastewater are sometimes not reached. In the first two parts, the optimisation of the
design and operating conditions of crossflow filtration are described. In the third part,
the effect of the pollution load and size distribution are presented, followed in the
fourth part by a study of the effect of crossflow filtration on the wastewater
dissolved content. Finally, the fifth part presents techniques to try to prevent and/or
control membrane fouling.
5.6.1 Pore size selection
Given the findings of the Literature review, it was decided at the beginning of the
research to use crossflow micro filtration (pore size between 0.1 and 10 Ilm) instead of
ultrafiltration (pore size between 0.001 and 0.1 J..lm), because of the lower pressures
involved and therefore lower operating cost of the process. The influence of two
standard pore sizes (0.1 and 0.2 Ilm) was compared on both permeate fl\LX (Jp) and
permeate quality. These pore sizes were selected because previous studies using these
239
membranes proved to be efficient in removing micro-organisms. For example, Duclert
et a/. (1990) found 100% removal efficiency for total and faecal coliforms during
filtration of raw sewage, activated sludge and lagoon effluent using a SeT 0.2 IlIll
membrane.
Figures 5.6A and 5.6B compare the respective evolution of transmembrane pressure
«(\Pt) and permeate flux during crossflow filtration of settled trickling filter effluent
(TSS = IS mg/I) with 0.1 and 0.2 Ilm pore size membranes. The operating conditions
were the same for both runs: M,i = 2 x 105 Pa, Ucr = 3 rnIs. Figure 5.6A shows that
the M, followed a similar pattern in the two cases, increasing quickly from M,i =
2 x 105 Pa to stabilise at about 3.6 x 105 Pa within 25 min. The rapid initial increase
was probably due to rapid initial fouling of the membrane; this provoked a rapid
decrease in permeate flux, and therefore resulted in a pressure increase within the
system.
The permeate flux profiles shown on Figure 5.6B are typical of crossflow
microfiltration: they exhibit a rapid decrease due to initial membrane fouling, followed
by a slow, almost linear, decrease with time. The slow long-term declines in flux of the
second period are a feature of most real systems, true steady-state fluxes being rarely
achieved.
Pillay and Buckley (1992) determined experimentally the processes responsible 'for
the evolution of the permeate flux with time. They concluded that the initial rapid flux
decrease is due to a rapid formation of a filter cake. Thereafter, the cake thickness
becomes limited to a near steady-state value, due to the scouring action of the
tangential circulation. The authors noted that the slow decline in performance during
the near steady-state was due to compression of the cake.
The data showed that the permeate flux was higher for the 0.1 Ilm membrane than for
the 0.2 Ilm membrane. The flux therefore does not necessarily increase with increasing
pore size. This result confirmed those of Tarleton and Wakeman (1993) who also
found that increasing the membrane pore size caused a reduced filtration performance
240
4
3.5
3
'" 0.. 2.5
'" CS 2
~
ii: 1.5 <l
0.5
0
0
600
500
400 ? N < E 300 ~ 0. ...,
200
100
0
0
Figure 5.6A: Influence of membrane pore size on transmembrane pressure evolution
50 100 150 200
Time (m in)
• 0.1 IJm --0--- 0.2 IJm
Figure 5.6B: Influence of membrane pore size OD permeate flux
k L
-_"""1..
50 100 150 200
Time (min)
• 0.1 IJrn --0--- 0.2 IJm
241
250
250
in terms of flux levels, because small solids were increasingly able to penetrate the
inner structure of the membrane.
The performance of the two pore sizes were also compared in terms of quality of the
permeate. This was monitored by detennining turbidity and COD at different times
during the filtration runs. The results are presented in Table 5.6A.
Table 5.6A: Comparison of permeate quality for filtration with 0.2 and 0.1 ).lm membranes
0.2 um membrane 0.1 um membrane turbidity CID turbidity CID
Time (NTU) (mg/l) (NTU) (m /I) (min) F P F P F P F P
0 3.4 - 75.8 - 5.4 - 67.3 -30 - 0.48 - 53 - 0.35 - 28.5 60 - 0.58 - 45.3 - 0.33 - 39.4 120 4 0.57 64.9 38.4 3.3 0.66 82.6 49.2 240 - 0.64 - 42.5 - 0.42 - 35
F: feed P: permeate
-Turbidity:
For both runs the settled trickling filter effluent had a turbidity between 3.3 and 5.4
NTU. The permeate turbidity obtained was below 0.7 NTU for both membranes.
-COD:
The COD of the feed to the membrane varied between 50 and 80 mgll. Fouling of the
membrane is known to reduce the effective pore size; this could potentially lead to an
increase in removal efficiency with time, since the fouling increases with time. In these
results, there was however no significant increase of COD removal with the decrease
in the filtrate flux brought by fouling; COD removal efficiency averaged at 40%
throughout the experimental run (yielding an average permeate COD of 42 mg02 I-I).
These results showed that there were no significant differences in permeate quality
between filtration with the 0.1 and 0.2 I-lm membranes. This confirmed that the
pollution fraction between these two particle sizes is negligible, more likely below the
level of sensitivity of the analytical methods (turbidity and COD).
242
Since the permeate flux was higher in the case of the 0.1 I!m membrane and there were
negligible differences in permeate quality, it was decided to carry out the bulk of the
experimental work with this membrane.
5.6.2 Optimisation of the operating parameters
The main operating parameters that can be controlled in crossflow filtration are 6Pt
and Vcf. To study the influence of both parameters on permeate flux, series of
filtration runs were done on the same suspension: unsettled trickling filter effluent
(TSS = 60-65 mgll), in a 'batch - closed system' configuration, ie. with initial
introduction of 25 I of suspension in the feed tank and recycling of the permeate in it.
5.6.2.1 Influence of initial transmembrane pressure
The influence of the initial M, (6P,i) on the evolution of M, and of Jp was tested by
comparing the evolution of both parameters under various 6P,i. Three 6P,i (1, 2 and 3
x 10; Pa) were tested, with Vcf = 3 mls.
Figure 5.6C and 5.6D show the respective evolution of M, and of Jp with time, with
the three different 6P,i.
Figure 5.6C shows that during a crossflow filtration run with the system used, the 6P,
increased during the first moments of filtration to stabilise after a few minutes at a
relatively steady value, then decreased slightly from time td to stabilise again at a
smaller value. The rapid initial increase was due to rapid initial fouling of the
membrane; this quickly decreased the permeate flux through the membrane, and
therefore provoked a pressure increase within the system. After that, the time td at
which the 6P, started decreasing slightly corresponds to the time necessary for all the
initial suspension to have entered the filtration loop: from time td, all the solids
present in the initial 25 I suspension were within the loop, and the new liquid pumped
into the loop was recycled permeate. Therefore, the values of permeate flux and
pressure normalised fllL,( to be taken into account for the optimisation of the
parameters were those at td (td '" 25 min).
243
4
3.5
3
ro Cl. 2.5
"' ~ 2
~
c:: 1.5 <l
0.5
0
0
500
450
400
350 ?
300 N < E 250 "'-c. 200 -,
150
100
50
0
0
Figure 5.6C: Influence of initial transmembrane pressure on transmembrane pressure
10 20 30 40 50 60 70 80
Time (min)
---1.1-- TMPi = 1 bar ----0---- TMPi = 2 bar --<'1-- TMPi = 3 bar
Figure 5.6D: Influence of initial transmembrane pressure on permeate flux
10 20 30 40 50 60 70 80
Time (min)
• L\.Pti = 1 bar ---G-- APti = 2 bar • .6.Pti = 3 bar
244
90
90
I
Figure 5.6D show that the higher the value of D.P,i, the higher the penneate flux. These
results were in accordance with the literature. Indeed, the M, provides the driving
force for filtration. However many authors (for example Lojkine et al. (1992)) quoted
the existence of a critical or limiting transmembrane pressure (D.P,c), above which the
penneate flux does not increase linearly with M,. This has been linked to the fact that,
above D.P,C, increases in the filtration driving force are counteracted by increases in the
resistance due to the deposition of material on the membrane and the degree of
membrane fouling.
To keep the value of D.P, at moderate levels, a value of M,i = 2 x 105 Pa was adopted
for further experiments.
5.6.2.2 Influence of cross flow velocity
Experiments were also carried out to verify the influence of Ucf on Jp .. M,i being
adjusted at 2 x 105 Pa, filtration runs were done at Ucf = 2, 3 and 4 mls.
Figure 5.6E presents the evolution of Jp with time. The results are in accordance with
the known phenomenon that the higher U cf, the higher the penneate flux. However,
higher values of Ucf mean high power consurnptions. For further experiments, it was
therefore decided to operate the pilot unit at Ucf = 3 mls.
5.6.3 Effect of the pollution load on crossflow microfiltration
performance
The efficiency of crossflow filtration for different types of wastewaters was
investigated. The results of crossflow filtration runs of influent on the pilot-scale
trickling filter (i.e. synthetic sewage, simulating settled sewage), unsettled and settled
trickling filter effluent were compared. The runs were done with the SeT O.IJ.!m
membrane under the operating conditions previously defined (D.P,i = 2 x 105 Pa,
Ucf = 3 mls). A 'closed system' configuration was used for these runs: the feed tank
was continuously fed at a rate of 0.4 IImin (with 25 I of suspension initially poured in
the feed tank), the penneate was not recycled in the feed tank, and no concentrate was
245
Figure 5.6E: Influence of crossflow velocity on permeate flux
500
450
400
350 ~
:2 300
'" < I\) E 250
"" 2:: Ol
~200
150
~ ~-
.~~. • :: : : : -{] ..... 0
100
50
0
0 10 20 30 40 50 60 70 80 90
Time (min)
• Ufc = 2 mls ----0-- Ufc = 3 mls • Ufc = 4 mls I
bled from the filtration loop. This configuration was adopted to simulate the
production mode that could be used at a full-scale plant.
The results were compared in tenns of penneate flux and of penneate quality.
Figure 5.6F shows that the lower the solid load, the higher the penneate flux. For
example, after 60 minutes, the values of Jp were of 83, 100 and 121 lIm2.h for the
filtration of respectively influent, unsettled and settled trickling filter effluent (with
respective TSS concentration of 124, 37 and 15 mg/l). After 240 minutes, the values of
Jp were respectively of41, 53 and 71l1m2.h.
The results in tenn of penneate quality, expressed in Figure 5.6G and 5.6H, were
different depending on the parameters followed:
- Turbidity:
The penneate turbidity appeared to be independent of the filtered suspension
turbidity. These were respectively 70 NTU for the primary effluent, 15 NTU for the
unsettled trickling filter effluent, and 4.4 NTU for the settled trickling filter effluent.
In the three cases, the penneate values were below 0.7 NTU for turbidity. Crossflow
filtration at 0.1 ~m was therefore very effective at removing typical sewage solids,
whatever the level of preliminary treatment.
-COD:
The COD removal efficiency by crossflow filtration was very similar in the three
cases: 35, 36 and 40% in the case of filtration of respectively influent, unsettled
trickling filter effluent and settled trickling filter effluent.
The remaining COD level was satisfactory according to Urban waste water directive
standards when crossflow filtration was operated after the biological treatment (51.4
mgll for unsettled trickling filter effluent and 38 mgll for settled trickling filter
effluent). These penneate COD values are similar to that found by Vera et al. (1998)
in the case ofCFF (mineral membranes; 0.14 ~m pore size) of secondary effluent from
an activated sludge plant: the penneate COD was on average 34 mg /I for a feed
(secondary effluent) COD of 89 mg /1 on average.
247
I\)
-I> Cl)
::: t ~ ~
400
E 300 :::,
Co ....,
200
100
o
u
o
Figure 6.F: Influence offeed nature on permeate flux
~i , r---r,
50 100 150 200 250
Time (min)
,-- -. Influent ---0--- Unsettted TFE • Settled TFE
~ o
E o o u
Figure 5.6G: Influence of feed nature on turbidity
o 30 60 120 240
Time (min)
E3 Feed·; El Perm·; m Feed·e m Perm·e ~ Feed·se ~ Perm·se
Figure 5.6H: Influence of feed nature on COD
5
o 30 60 120 240
Time (m in)
~ Feed·; t;l Perm·; lID Feed.. lID Perm.. ~ Feed·.e ~ Perm-se
249
However, the remaining COD level found in this study was still high in the case of
CFF of influent (simulating primary effluent): the permeate COD remained in this
case at a value of 218.7 mgll.
This result confirmed the fact that crossflow micro filtration cannot be installed
directly as a sole secondary treatment after primary settlement. Microfiltration
membranes do not provide dissolved organic pollution removal which requires
biological preliminary treatment.
Further characterisation of the dissolved fraction of wastewater was undertaken, to
further the understanding of the effect of crossflow micro filtration on this fraction of
wastewater.
5.6.4 Influence of crossflow filtration on dissolved content of
wastewater
HPSEC was used to characterise the impact of CFF on the dissolved content of
unsettled trickling filter effluent. The operating conditions were the same as the one
described in § 5.6.3. Figures 5.6! and 5.6J show the chromatograms of unsettled
trickling filter effluent before and after 4 h of CFF at 0.1 lAm; Figure 5.6K gives the
chromatograms of concentrate after 4 h of CFF without bleed. The chromatograms of
the feed and of the permeate were similar. This was with the exception of peak 1,
which corresponds to the high MW material excluded by the stationary phase. Peak 1
disappeared on the permeate chromatogram, proving that the solutes generating it have
been stopped by the CFMF membrane.
The chromatograms obtained for the CFF concentrate (Figure 5.6K) show the
accumulation in the filtration loop of solutes constituting peak 1, which had been
stopped by the membrane. The height of peak 3 (glucose) has also increased. This
result was surprising but was confirmed by similar observations in three occasions, to
a lesser extent however. A potential explanation is that some glucose is generated in
the filtration loop by further hydrolysis of dextrin and starch remaining in trickling
filter effluent.
250
5.61: Trickling filter effluent chromatograms
ADC 1 A. RID of HPITESTOO99.D
RI 0
'" - ... .. 36
34
32
30-'" ~ "': .,
28 ... '" ~
26 .. '"
U 24-
'" "-- ~
22
20
0 10 20 30 ml VWDl A, Waveleng1h-220 nm of HPITESTOO99.D
mAU '" ...
lOO - ~ 140
120 -
lOO
eo
60
40
'" '" 20
'" ..... .,-- '"! '"! ., \..
0
0 10 20 30 mi
251
S.6J: Permeate (after 4 h filtration) chromatograms
ADCl A. RID of HPITEST010l.D
RI v .. .., ..;
36
34-
32
30-
'" .. ~
28 en
26
24 - ~V 22
20
0 10 :20 30 mir VWDl A, Wavelength=220 nm of HPITESTOl 01.0
mAU
~ -
160
140 -
120
100
80
60-
40 en
'" "! .., 20 ur
"' \.
0 -0 10 20 30 ml
252
5.6K: Concentrate (after 4 h filtration without bleed) chromatograms
AOC1 A. RID 01 HPITEST01 00.0
RI ., -'" .;
U> ,.. 36 ~
en
34
32 '" ,.. U> .;
30
28
'" 1t ,.. ~
26 - ~
24 VV '" ~
22
20-
0 10 20 30 mi VW01 A, Wavelength=220 nm 01 HPITEST01oo.0
mAU
~ -
160
140
120
100
60-
60 U> U>
'" 40-
.;
;r .. 20- "'.; ,,-
~
'" 0
0 10 20 30 ml
253
These results were obtained with a CFF membrane at 0.1 ~m. Harada et at. (1994)
reported the use of GPC to characterise the reactor liquor and the membrane permeate
in the case of an anaerobic-ultrafiltration membrane bioreactor (MW cut-off: 3 x 106
Dalton). They found great differences in terms of molecular size between the two
solutions: while major portions of organics present in the reactor liquor were occupied
by substances of MW as large as 106 Dalton (eluted at the void volume), the permeate
consisted of substances with MW less than only 1500 Dalton. The authors concluded
that the membrane was capable of not only separating suspended or colloidal matters,
but also fractionating dissolved organics by molecular size. They attributed the
exclusion of higher molecular size materials from the permeate to their contribution to
the formation of gel layers on the membrane surface. However, the authors used two
columns of different types to analyse reactor liquor and membrane permeate. More
definite conclusions would probably have been drawn had the two types of samples
been analysed using the same type of chromatographic column. The increase of peak 1
observed in the concentrate chromatograms of this study shows that, in the case of
CFF, these "higher MW compounds do not only accumulate at the membrane surface
because their concentration increases in the concentrate.
5_6.5 Permeate flux enhancement
5.6.5.1 Change in operation mode
As indicated in the Materials and methods, most runs were performed in 'closed
system' configuration, i.e. without bleed from the filtration loop and no return of the
permeate to the feed tanle A negative aspect of such a configuration is the rapid
concentration effect in the recirculation loop, which can accelerate fouling and thus
reduce the filtrate flux (Bhave, 1991 a). As a result it was tried to operate filtration
runs in the 'continuous' mode of the 'feed and bleed' configuration, i.e. by constantly
recycling a fraction of the concentrate (i.e. of the content of the filtration loop) in the
feed tank. The results in terms of permeate flux are presented in Figure 5.6L. It
appears that operating in the 'feed and bleed' configuration brought little improvement
to the permeate flux. Only a slight increase in the pseudo steady-state permeate flux
value could be observed. The values of permeate flux at various time are indicated in
Table 5.6B.
254
800
700
SOO
? 500
'" < E 400 <=. 0- 300 ...,
200
100
0
450
400
350
:;; 300
'" 250 < :§
200 0-...,
150
100
50
0
Figure 5.6L: Influence of bleed flow on permeate flux
>
R
1
~ . " . --"
o 50 100 150 200 250
Time (min)
---1.1-- Ob = 0 ---0--- Ob = 0.5 I/min
---+--Ob= 1 I/min
--C-Qb=l.S I/min
o
1
Figure 5.6M: Influence of permeate flux control on permeate flux
""'aa...~ ~-..." ~
~~--..n c:;........,
50 100 150 200
Time (min)
---1.1-- Jp uncontrolled ---0--- Jp controlled at 150 1/m"2.h
255
250
Table 5.68: Values at permeate flux infeed and bleed operation mode at various time. Values of Jp (l/m2 .h) for ab =
Time (min) 0 I/min 0.5 I/min 1 I/min 1.6 I/min
30 124 1 1 8 1 21 133
60 98 103 98 1 1 5
240 47 74 • 80 80
• : value at time = 180 min
5.6.5.2 Control of permeate flux
One of the reasons for the rapid initial fouling of crossflow micro filtration membranes
is the fact that a clean membrane exposed to a particulates and colloid rich suspension
gets instantaneously fouled because of the initial high permeate fllL"es. A way to
control this problem, i.e. to limit the rapid initial membrane fouling, could be to
control the permeate fllL" level. Figure S.6M presents the average value of permeate
flux measured during two runs for which the permeate fllL" was fL"ed at a maximal
value of ISO IIm2.h, and contrasts it with the value of permeate fllL" measured within a
day interval without permeate flux control:
In the case of absence of permeate fllL" control, the permeate flux profile was classical
with a rapid initial decrease in permeate flux stabilising after about I h to a pseudo
steady-state level. The permeate fllL" controlled at a maximal level of 150 IIm2 h
exhibited a much slower decrease, but eventually after 240 min reached a value similar
to the one reached without control. This shows that the control of permeate flux
generated a flux profile without rapid initial decrease, but that eventually reached a
flux value identical to the one reached without control.
The profile observed for LlP, is interesting (Figure S.6N). It appears that control of the
permeate flux slowed down the membrane fouling expressed by the a slower increase
of /lP, during the filtration run. However, the LlP, values stabilised to reach a pseudo
steadt state value after about 110 min and increased slowly to reach the value obtained
in the case of an absence of Jp control.
256
2.5
2
~ 1.5
B-
a: <l
0.5
0
0
700
I 600
500
"? C\I 400 <
1
E ;::. 300
Q. ,
...., 200
100
0
o
Figure 5.6N: Influence of permeate flux control on transmembrane pressure
50 100 150 200
Time (min)
--1.1-- Jp uncontrolled .--a-- Jp controlled at 0.5 I/mA2.h
h
Figure 5.60: Influence of polystyrene beads addition on permeate flux
. ~~"-Q...:"""'~.J..; ~ -
50 100 150 200
Time (min)
I
--1.1-- No beads .--a-- 100 rnl beads - ....... >-- 200 ml beads
257
250
250
This test showed that, even if permeate flu.x control limits initial membrane fouling,
this positive effect was eventually lost and the controlled permeate flux equalled the
uncontrolled one after 240 min.
5.6.5.3 Antifouling techniques
Pillay and Buckley (1992) showed that the cake formation at the membrane surface is
apparently irreversible, i.e. once the cake has been formed its thickness cannot be
easily decreased. This was explained by Pillay (1991) as being due to irreversible
compression of the cake. To try to slow down the cake build up, polystyrene beads
were introduced as scrubbers inside the filtration loop. This was inspired from
membrane cleaning by foam swabbing described by Deqian (1987). The diameter of
the membrane module channels being 4 mm, the polystyrene beads were sieved at 3.35
mm before use to prevent channel blocking.
Figure 5.60 presents the comparative permeate flu.x measured during a control run
(settled trickling filter effluent, ~P,i = 2 x 10; Pa, Ucr = 3 m1s) and two test runs done
under the same operating conditions but with addition in the recirculation loop of
respectively 100 ml and 200 ml of polystyrene beads.
It appeared that polystyrene beads had very little effect on the efficiency of the
process at the lower level (lOO ml in the loop), whereas at a level of200 ml in the loop
the permeate flux was 25.3% higher after 1 h of filtration, and 30% higher after 4 h.
5.6.6 Conclusions
• The permeate flux was higher for the O.IJ.lm membrane than the 0.2 J.lffi membrane,
allowing the conclusion that increased membrane pore size can result in reduced
permeate flu.x due to small solids being able to penetrate the inner structure of the
bigger pore size membrane.
• Fouling of the membrane did not significantly increase COD removal.
• Increasing the irtitial transmembrane pressure and crossflow velocity increases the
permeate flux.
258
• The crossflow membranes could not remove enough influent COD to be considered
as a viable secondary treatment option after primary settlement. The main benefit of
cross flow micro filtration is therefore as a tertiary treatment.
• Characterisation of the soluble content of trickling filter effluent and permeate
showed that the highest molecular weight dissolved material was removed by
crossflow microfiltration. This material was found to not only accumulate at the
membrane surface, but also to be present in the concentrate.
• Controlling the permeate flux was found to be initially beneficial by limiting
membrane fouling, however, after 240 minutes of operation, the controlled permeate
flux equalled the uncontrolled permeate flux.
• The introduction of polystyrene beads into the loop appeared to reduce membrane
fouling, the permeate flux being 25% higher after I h of filtration and 30% higher after
4 hours.
5.7 SUMMARY OF THE RESEARCH MAIN FINDINGS
Solids production by low-rate trickling filters is the central theme of the research
presented in this thesis. Investigation of this phenomenon was divided into two main
areas of research. Firstly, the source of the problem has been tackled by undertaking a
thorough investigation of factors' affecting trickling filter performance, including
particle size analysis, biofilm accumulation within the filter and residence time
distribution. Multiple regression with stepwise estimation has then been used to
determine the key parameters affecting trickling filter performance. Secondly, solids
production and removal of solids by crossflow filtration have been assessed.
Characterisation of both the solid and liquid fractions of trickling filter effluent has
been undertaken to aid both the understanding of trickling filter performance and the
use of cross flow filtration as a tertiary treatment.
Multiple regression with stepwise estimation showed that performance of the pilot
scale trickling filter, when assessed in terms of effluent solids (TS5), was as follows.
The most significant factor influencing the amount of solids in the effluent was the
proportion of filtered BOO in the influent. That is, the higher the proportion of
259
filtered BOO in the influent, the lower the concentration of solids in the effluent.
Although this can be attributed partially to less solids carry-over from influent to
effluent, no marked correlation was found between influent TSS and effluent TSS. A
high proportion of easily degradable substrate gives rise to an active biological
population which may, in turn, result in more rapid degradation of influent particulate
material. This explanation is supported by the fact that higher ambient temperature
(and therefore biofilm temperature and activity) also results in a better effluent quality
with respect to solids. The size of the influent particles was also shown to influence
effluent TSS, with an increase in influent particle size resulting in higher effluent TSS
values. Increased influent particle size was also shown to decrease BOO removal
efficiency of the trickling filter, demonstrating again that filter performance and solids
production are closely linked.
Extending these observations to full-scale operation of low-rate trickling filters, it is
interesting to note that the practice of recirculating unsettled effluent solids to the
influent wastewater stream could increase the problem of suspended solids in the
effluent and reduce the efficiency of filter performance.
Particle size analysis of the pilot-scale trickling filter effluent has shown an increase
in particle size through treatment, which can be explained by the conversion of small,
biodegradable influent particulates and dissolved matter to larger cell matter. Thus, a
marked proportion of solids in a trickling filter effluent is cell debris and excess
biofilm. It, therefore, follows that effluent TSS is influenced by biological activity and
biofilm accumulation. Although biofilm renewal (growth and sloughing) is a constant
process, resulting in small amounts of solids being continually shed into the effluent, it
is the seasonal pattern of biofilm accumulation which has the greatest impact on
effluent TSS. Measurement of biofilm accumulation, using the neutron probe
technique, showed that excessive film accumulation occurred during the winter
months, as a result of decreased activity of the grazing fauna combined with
overgrowth of fungi in the upper layers of the filter. The onset of warmer
temperatures in spring resulted in solids production as a result of sloughing of the
excess biomass. Thus, a deterioration in trickling filter effluent quality (with respect
to eft1uent TSS) can often be experienced during the spring months, resulting in a
260
breach of consent limits at a sewage treatment works which operates satisfactorily for
the remainder of the year. Therefore, a form of tertiary treatment may be necessary to
ensure effluent compliance. A desirable characteristic of the tertiary treatment system
appears to be the ability to be operated only for certain periods of the year.
In addition to removal of effluent solids, increasingly strict consent limits for effluent
COD and BOO mean that removal of dissolved substances during tertiary treatment is
also desirable. In particular, the production of soluble refractory carbonaceous material
(SMPs) during biological wastewater treatment is an area of incn::asing concern.
Characterisation of the dissolved fraction of trickling filter effluent was undertaken
during this research project, using HPSEC. No SMPs were detected in the effluent
from the pilot-scale trickling filter; however, HPSEC proved to be valuable in
determining that a proportion of the highest molecular weight dissolved material in the
trickling filter effluent could be removed using crossflow filtration. Thus, crossflow
filtration as a form of tertiary treatment was found to produce a high quality effluent,
removing not only suspended solids but also a portion of dissolved effluent matter.
The comparison between crossflow filtration and other tertiary treatment techniques
should be based on a variety of parameters including effluent quality, availability, ease
of operation, maintenance requirements and capital costs. An advantage of crossflow
filtration is a good and constant effluent quality. However, this superior permeate
quality involves a constant energy requirement, and is accompanied by flux decline
due to membrane fouling. This contributes to increasing the costs of the process by
reducing the length of filtration cycles, and requiring input of labour and chemicals for
cleaning on a regular basis. With respect to capital costs, crossflow filtration has the
advantage of being a relatively compact and modular process, which reduces land
requirement. It offers the possibilities of mobility and intermittent use.
These advantages and disadvantages, in comparison with other techniques such as
upward flow clarification, grass plots, reed beds and sand filters, mean that the use of
crossflow filtration is not yet well established in the wastewater industry. However,
tighter regulations and a growing emphasis on wastewater recycling make of crossflow
filtration an option worthy of consideration.
261
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS
6.1 CONCLUSIONS
6.1.1 Trickling filter performance
• Investigations were made over 2 years on the performances of a pilot-scale low-rate
trickling filter exposed to seasonal climatic variations, under 3 compositions of influent.
The best organic matter removal efficiencies were obtained during Phase 3 of research,
characterised by Spring-Summer temperatures and a 60-70% proportion of filtered
organic matter in the influent. By contrast, the worst organic matter removal efficiencies
occurred during Phase 2, under Autumn-Winter temperatures, and a 20-40% proportion
of filtered organic matter in the influent.
• The performances of secondary settlement of low-rate trickling filter in terms of organic
matter removal efficiency were independent of the unsettled effluent characteristics. The
pilot-scale results compared wel1 with ful1-scale results. A high quality effluent in terms
ofBOD, COD and TSS was general1y produced throughout the study. This tends to prove
that a low-rate trickling filter performs satisfactorily under normal operating conditions.
6.1.2 Wastewater contaminant size characterisation
• In terms of particle size distribution, results show a shift towards larger particle sizes
through trickling filtration, most of the large solids being then removed by secondary
settlement. The particle size distributions of influent, unsettled and settled effluent could
be satisfactorily fitted to a power-law distribution function. The best fit was obtained
when model1ing frequency distribution by number for particles bigger than 0.1 ~m. These
results obtained at pilot-scale were confirmed by analyses at ful1-scale.
262
• The study (by High Performance Size Exclusion Chromatography) of the evolution
through treatment of the wastewater dissolved content showed no generation of Soluble
Microbial Products by the trickling filter.
6.1.3 Parameters traditionally regarded as affecting trickling filter
performance
• The temperature of the trickling filter was found to be more correlated to the influent
temperature than to the ambient temperature.
• After maturation, the film accumulation within the filter followed a seasonal pattern,
with maximal accumulation in winter and minimum accumulation in summer.
Accumulation in the top half of the filter was highly correlated (negatively) to influent
temperature, confirming that film in this part of the filter is mainly active biofilm and
grazers. By contrast, accumulation in the bottom half of the filter was highly correlated
(positively) to size of the influent particulate matter; this confirmed that the film located
in this part is mostly detached fragments of film and non-degraded particulate matter
from the influent.
• The hydrodynamics of the trickling filter could satisfactorily be modelled using the
Simplified stagnant reactor. The mean and median residence time in the filter were found
to be correlated to film accumulation within the filter. However, no significant
correlations were found between the liquid volumes (stagnant and flowing) calculated
from hydrodynamic modelling and the measurements of film accumulation.
6.1.4 Determination of the key parameters affecting low-rate trickling
filter performance
• The key parameters affecting low-rate trickling filter performance were ranked by order
of importance. The 5 main parameters affecting organic matter removal efficiency were:
proportion of filtered (: < 1.2 /lm) organic matter in the influent, biodegradability of the
filtered organic matter, mean residence time in the filter, median diameter of the
263
particulate matter in the influent and ambient temperature. The order of the key
parameters affecting effluent organic matter content is slightly different: median diameter
of the particulate matter in the influent, mean residence time in the filter, influent TSS,
proportion of filtered (: < 1.2 Ilm) organic matter in the influent and ambient temperature.
In both cases it appears that influent particle size, a parameter not traditionally
considered, provided valuable information on the operation of the plant.
6.1.5 Assessment of crossflow filtration as a tertiary treatment for low
rate'trickling filter effluent
• The performance of crossflow filtration as a tertiary treatment for low· rate trickling
filter effluent was very good in terms of permeate quality. Direct filtration of synthetic
sewage (simulating settled sewage) confirmed the necessity of dissolved pollution
removal by conversion to a particulate form prior to crossflow filtration. The use of
polystyrene beads in the filtration loop contributes to performance improvements in terms
of permeate flux.
6.2 RECOMMENDATIONS
• Biofilm activity in laboratory-scale reactors is often assessed by specialised techniques,
including microprobes for oxygen transfer, DNA measurements and enzymatic activity
assays. These techniques could be used to further the understanding of biological activity
in pilot and full-scale trickling filters, and correlations made with other performance
parameters.
• Further work is required to confirm or infirm the influence of influent particle size on
trickling-filter and other fixed-film reactors performances. The work could be extended
by the study, for various particle sizes, of the influence ofparticulate biodegradability on
fixed-film reactor performance.
264
• The non-production of SMPs by the low-rate trickling filter should be checked over a
longer period oftime, to take into account seasonal variation. The findings should then be
confirmed at full-scale.
• Characterisation of the dissolved fraction of the wastewater by HPSEC could be
enhanced by the use of a diode array detector instead of a single wavelength absorbance
detector. This would ease the identification of the fractionated substances.
• Regarding cross flow filtration, more work is required in terms of permeate flux control.
In particular, more can be done on the optimisation of the addition of mobile scouring
devices in the crossflow filtration loop to reduce membrane fouling.
• Current practice for treatment of crossflow filtration concentrate include recirculation
with raw sewage at the head of the works, and direct treatment with sludge by anaerobic
digestion. Further work is required to characterise the concentrate and identify the best
treatment/disposal options.
• More generally, crossflow microfiltration was shown to produce a high quality effluent.
However, it is still debatable as to whether it is the best available technology not entailing
excessive cost. With this in mind, it is recommended that crossflow microfiltration is
compared with other tertiary treatment techniques for the treatment of trickling filter
effluent.
265
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Appendix 1:
French abstract
RESUME
Le lit bacterien a faible charge est le procede d'epuration biologique le plus utilise en
Grande-Bretagne dans les stations d'epuration de faible a moyenne capacite. Ce
procede genere des effluents de qualite fluctuante, traditionnellement expliquee par des
variations d'accumulation de film biologique dans le lit bacterien. La qualite de
l'effluent clarifie (pollution organique, matieres en suspension) est alors trop faible
pour le recyclage ou le rejet dans les eaux receptrices, et un traitement tertiaire est
souvent necessaire pour respecter les normes de plus en plus strictes. Les travaux
presentes ont pour objectif l'etude des parametres-cles influenyant les performances de
lit bacteriens a faible charge, et l'etude de la taille des contaminants dans leurs
effluents. Ce dernier parametre pourrait influencer l'efficacite de la filtration
tangentielle en tant que traitement tertiaire. Des effluents de lits bacteriens ont ete
etudies pendant un an a echelle reelle et deux ans a echelle pilote. Les parametres
influenyant les performances du procede, etudies a echelle pilote. incluent temperature,
accumulation de film biologique dans le lit bacterien. caracteristiques
hydrodynamiques du lit, contenu en matieres en suspension et granulometrie de
l'influent. L'influence relative de ces parametres sur les performances a ete analysee
par correlation et regression multiple. L'impact du lit bacterien sur la taille des
contaminants a ete analyse par granulometrie (fraction particulaire) et par
chromatographie d'exclusion a haute performances (fraction dissoute). Les deux
technique contribuent a une meilleure comprehension du procede d'epuration. La
filtration tangentielle a egalement ete etudiee en tant que traitement tertiaire potentiel
pour les effluents de lits bacteriens. Le permeat produit est de qualite superieure aux
norrnes actuelles de rejet. Une technique de nettoyage de la membrane en cours de
filtration semble augmenter le flux de permeat.
MOTS-CLES:
Lit bacterien a faible charge - Granulometrie - Chromatographie d'exclusion -
Hydrodynamique - Biofilm - Filtration tangentielle
Appendix 2:
Calibration curve for HPSEC column (using glucose and
dextrans)
APPENDIX 2: Calibration curve for HPSEC column (obtained using glucose and dextrans)
10000000
• 1000000
• C-
100000
S tii 0
E 10000
Cl 'Qi ;: tu 1000 :; u Ql '0 ::;:
100
10
4 5 6 7 8 9 10 1 1 12 13 14
Elution time (min)
Appendix 3:
Chromatograms of sodium nitrate
Sodium nitrate chromatograms (concentration = 25 mgll)
ADCl A, RID of HPITEST0202.D
RI
36-
34-
32
30
.. 28 '" 0 ~
'" 0 ~
~ '" 26
24 1\ -
22
20
0 10 20 30 mi VWDl A, Wavelength=220 nm Of H"II "" lu:lll2.D
mAU
~ 160
140
120 -
lOO
80
60-
40
20
\. 0
0 10 20 30 mi
Appendix 4:
Results of paired-stepwise analyses
Variable Variables tested Variables in model Counts R R2 R2 adjusted
eBOO iTSS, P iBODf 1. iTSS, 2. P iBODf 47 0.709 0.503 0.480 iTSS, idso(v) 1. idso(v) 30 0.647 0.419 0.398 iTSS, aT 1. iTSS 2. aT 44 0.785 0.616 0.597 iTSS, t"(7h) 1. t "(7h)' 2. iTSS 15 0.906 0.820 0.791 P iBODf, id,o(v) 1. id,o(v) 25 0.681 0.463 0.440 P iBODf aT 1. P iBODf, 2. aT 43 0.682 0.465 0.439 P iBODf, tm(7h) 1. U7h), 2. P iBODf 14 0.787 0.620 0.550 id 50 (v) aT 1. idso(v) 23 0.701 0.492 0.467 id,o(v), t ,(7h) 0 1 0 - - -aT, tm(7h) 1. aT, 2. U7h) 1 5 0.932 0.868 0.846
Scores'
Variable Score
iTSS 2x0.480 + o + 2xO.597 + 1 xO.791 ; 2.945 P iBODf 1x0.480 + o + 2x0.439 + 1 xO.550 ; 1.908 id,o(v) 3xO.398 + 3xO.440 + 3x0.467 + 0 ; 2.840 aT 1 xO.597 + 1x0.439 + 0 + 2xO.846 ; 2.728 t ,(7h) 2xO.791 + 1xO.550 + 0 + 1xO.846 ; 2.978
Variable Variables tested Variables in model Counts R R2 R2 adiusted
am iTSS, P iCOD! 1. iTSS 49 0.682 0.465 0.454 iTSS, iPPn . ,(v) 1. iPPn,. ,(v) 29 0.526 0.277 0.250 iTSS. id n(V) 1. idso ( v) 29 0.523 0.274 0.247 P iCOD!, iPpo ,., ,(v) 1. iPPo1-1,(v) 25 0.527 0.278 0.246 P iCOD!, id,o(v) 1. id,o(v) 25 0.542 0.294 0.263 i PPo ,., ,(v) id,o( v) 1. iPpo ,., ,(v) 29 0.526 0.277 0.250
Scores
Variable Score
iTSS 3xO.454 +0 +0 = 1.362 P iCOD! 0+0+0=0 iPpo ,., ,(v) 3xO.250 +3xO.246 + 3xO.250 = 2.238 id,o(v) 3xO.247 + 3xO.263 + 0 = 1.530
Variable Variables lesled Variables in model Counls R R2 R2 adiusled
eTSS P iBOOf id,o( v) 1. P iBOOf 2. id,o(v) 26 0.788 0.621 0.588 P iBODf aT 1. P iBOOf 43 0.679 0.461 0.447 P iBOOf, Im(7h) 1. P iBOOf, 2. Im(7 h) 14 0.838 0.702 0.648 id,o(v), aT 1. aT 25 0.581 0.338 0.309 id,o(v), 1,(7 h) 0 1 0 aT 10 (7 h) 1. aT 1 5 0.774 0.599 0.568
Scores
Variable Score
P iBOOf 2xO.588 +3xO.447 +2xO.648 = 3.813 id,o(v) 1 xO.588 +0 +0 = 0.588 aT o +3xO.309 +3xO.568 = 2.631 I ,(7 h) lxO.648 +0 +0 = 0.648
Variable Variables lesled Variables in model Counls R R2 R2 adjusled
REBOD P iBODf, iCODf/iBODf 1. P iBODf, 2. iCODf/iBODf 47 0.753 0.567 0.547 P iBODf, id<n(v) 1. P iBODf, 2. id,o(v) 25 0.769 0.592 0.555 P iBODf, aT 1. P iBODf 43 0.712 0.507 0.495 P iBODf, Im(7h) 1. P iBODf, 2. Im(7h) 1 4 0.912 0.831 0.800 P iBODf, 1,0/1" 1. P iBODf 14 0.791 0.626 0.595 iCODf/iBODf, id,n(V) 1. iCODfliBODf, 2. id,n(V) 25 0.829 0.688 0.660 iCODf/iBODf aT 1. iCODf/iBODf, 2. aT 43 0.750 0.563 0.541 iCODf/iBODf, I n(7h) 1. iCODfliBODf, 2. Im(7h) 14 0.826 0.683 0.625 iCODfliBODf, I,n/I,. 1. iCODf/iBODf, 2. I,n/I,. 14 0.637 0.405 0.356 id,n(v}, aT 1. id,n(v) 23 0.594 0.353 0.322 id.n(vl, L(7h) 0 10 - - -id.n(v) Iso/I,. 1. I.n/I, R 1 0 0.668 0.446 0.376 aT I J7h) 1. 1,(7h), 2. aT 1 5 0.743 0.552 0.477 aT Iso/I, B 1. aT 15 0.623 0.388 0.340 Im(7h}, I,D/I,. 1. Im(7h) 1 5 0.626 0.392 0.345
Scores Variable Score
P iBODf 2xO.547 + 2xO.555 + 3xO.495 + 2xO.800 + 3xO.595 = 7.074 iCODfliBODf 1 xO.547 + 2xO.660 + 2xO.541 + 2xO.625 + 2xO.356 = 4.911 id.n(v) 1xO.555 + 1xO.660 + 3xO.322 + 0 +0 = 2.181 aT o + 1xO.541 + 0 + 1x0.477 + 3xO.340 = 2.038 ImU h) 1 xO.800 + 1 xO.625 + 0 + 2x0.477 + 3xO.345 = 3.414
1'0/1, • o + 1 xO.356 + 3xO.376 + 0 + 0 = 1.484
Variable Variables tested Variables in model Counts R R2 R2 adiusled
RECXJD iCODf/iBODf iCOD 1. iCODfliBODf 2. iCOD 47 0.725 0.526 0.505 iCODf/iBODf id,o(v) 1. iCODfliBODf, 2. id,o(v) 26 0.763 0.582 0.545 iCODf/iBODf I'n/l" 1. iCODf/iBODf 14 0.587 0.345 0.290 iCODf/iBODf Im(7 h) 1. iCODf/iBODf 2. Im(7 h) 14 0.723 0.522 0.435 iCOD id,o(v) 1.iCOD 31 0.728 0.530 0.514 iCOD, I,,"IIA 1. iCOD 1 5 0.813 0.661 0.635 iCOD, In(7 h\ 1. iCOD 1 5 0.813 0.661 0.635 id50 (v). 1'0/1, fi 1. 150/1,< 10 0.668 0.446 0.376 id,o(v), 1,(7 h) 0 10 - - -I'n/l,", 1,(7 h) 1.1,(7 h) 1 5 0.626 0.392 0.345
Scores
Variable Score
iCODfliBODf 2xO.505 +2xO.545 +3xO.290 +2xO.435 = 3.840 iCOD 1 xO.505 +3xO.514 +3xO.635 +3xO.635 = 5.857 id,n(v) 1 xO.545 +0 +0 +0 = 0.545 I,nlt , < o +0 +3xO.376 +0 = 1.128 Im(7 h) 1 x0.435 +0 +0 +3xO.345 = 1.470
Variable Variables tested Variables in model Counts R R2 R2 adjusted
seBOD eBOD, eB" 1.eBOD 15 0.921 0.848 0.836 eBOD ean{v) 1.eBOD 15 0.921 0.848 0.836 eBOD,aT 1. eBOD 28 0.953 0.909 0.905 eB,? eaJv) 1. eB,? 1 5 0.821 0.673 0.648 eB'2. aT 1. eB'2, 2. aT 15 0.895 0.801 0.768 ea,(v), aT 1. aT 15 0.697 0.485 0.446
Scores
Variable Score
eBOD 3xO.836 +3xO.836 +3xO.905 = 7.731 eB .. o + 3xO.648 +2xO.768 = 3.480 eaotv) 0+0+0-0 aT o + 1 xO. 768 +3xO.446 = 2.106
