development of microalgae based biorefinery

DEVELOPMENT OF MICROALGAE BASEDBIOREFINERY TO IMPROVE ITS ENERGY

EFFICIENCY

AUTHOR: DANIEL FOZER

SUPERVISOR: PROF. DR. PETER MIZSEY

PHD THESIS

FACULTY OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY

DEPARTMENT OF CHEMICAL AND ENVIRONMENTAL PROCESS ENGINEERING

BUDAPEST, BME, 2019

Acknowledgements

First and foremost, I would like to express my sincere and greatest gratitude to my

supervisor, Prof. Dr. Peter Mizsey for guiding my research under his supervision. I’m grateful

for mentoring me since I joined the Environmental and Process Engineering Research group

as a bachelor student. I appreciate his time, ideas and comments that made the present work

possible.

I’m also deeply thankful to Dr. Aron Nemeth for his continuous help and plenty of

advice in algae cultivation.

My sincere thanks also goes to Bernadett Kiss, Nora Valentinyi, Tibor Nagy, Andras

Jozsef Toth, Eniko Haaz, Anita Andre and Asmaa Selim for their assistance through my studies

and research.

I would like to thank Dr. Edit Szekely and Laszlo Lorincz for their help in hydrother-

mal gasification.

I am grateful to Dr. Jozsef Balla for his help in gas chromatography and Dr. Sandor

Tomoskozi for helping me in the determination of N content.

Herewith, I would like to thank all the colleagues at the Department of Chemical and

Environmental Process Engineering, for helping me in technical and scientific issues and for

providing a friendly atmosphere.

I would like to thank my parents and my brother for always providing supportive

background and giving continuous encouragement.

I would like to thank the financial support for the New National Talent Program

2018 (NTP-NFTÖ-18-B-0154), the New National Excellence Program 2017 (ÚNKP-13-3-I-BME-

022), Campus Mundi short study trip, OTKA-112699 and OTKA-128543. The dissertation was

supported by the European Union and the Hungarian State, co-financed by the European

Regional Development Fund in the framework of the GINOP-2.3.4-15-2016-00004 project,

iii

Acknowledgements

aimed to promote the cooperation between the higher education and the industry.

iv

Abstract

Biorefineries have received a great attention in recent decades due to the possible

application of biomass as energy carrier. Microalgae are a suitable feedstock to produce

biofuels with high energy density, however, it is questionable whether net energy gain is

achievable considering the whole conversion route. Cradle-to-grave overall energetic analysis

of microalgae based biorefinery alternatives are investigated in detail to determine the energy

balance and to identify technological bottlenecks. Based on the detected bottleneck points 3

main areas are investigated for the development of microalgae based biorefineries to improve

its efficiencies: (1) carbon capture from flue gas to provide CO2 for the cultivation of algae, (2)

cultivation of microalgae biomass and (3) the elimination of drying process or the application

of hydrothermal conversion technologies.

The carbon capture related bottleneck is evaluated conducting life cycle, PESTLE

and Multi-Criteria Decision analyses. It is found that the environmental effects of Carbon

Capture and Storage technology can be upgraded if fossil based energy carriers are excluded

and replaced by renewable sources and if the required heat for MEA absorber regeneration is

decreased by proper process improvements.

In the case of the cultivation step, it is demonstrated that the efficiency of the process

can be greatly affected by the illumination regimes. The light factorial optimization of Chlorella

vulgaris cultivation shows that applying ideal wavelength and optimized intensity levels

enhance the biomass productivity, while the biological composition (lipid and carbohydrate

content) of the biomass can be shifted according to the objectives of the refinery.

Hydrothermal conversion of the biomass is a favourable conversion technology

due to the possible elimination of the drying step. Throughout the investigation of targeted

cultivation and hydrothermal gasification (HTG) of microalgae biomass it is found that biogas

yield and gas composition (H2, CH4, CO2, CO) can be indirectly influenced already at the culti-

vation step, which highlights the importance of the application of ideal artificial illumination

conditions.

v

Résumé

Les bioraffineries ont fait l’objet d’une grande attention au cours des dernières

décennies en raison de l’utilisation possible de la biomasse en tant que vecteur d’énergie. Les

microalgues sont une matière pertinent pour la production de biocarburants à haute densité

énergétique. Cependant, on peut se demander si un gain d’énergie net est réalisable compte

tenu de l’ensemble du processus de conversion. L’analyse énergétique des alternatives de

bioraffinerie à base de microalgues est étudiée en détail afin de déterminer le bilan énergétique

et d’identifier les goulots d’étranglement technologiques. Sur la base des points de goulot

d’étranglement détectés, trois domaines principaux sont étudiés pour le développement de

bioraffineries à base de microalgues afin d’améliorer son efficacité : (1) capture du carbone à

partir des gaz de combustion pour fournir du dioxyde de carbone pour la culture d’algues, (2)

la production de microalgues et (3) l’élimination du processus de séchage ou l’application des

technologies de conversion hydrothermale.

Le goulot d’étranglement lié au capture du carbone est évalué lors de l’analyse du

cycle de vie, de PESTLE et de la décision multicritères. Il est trouvé que les effets environne-

mentaux de la technologie de captage et de stockage du carbone peuvent être améliorés si

les sources d’énergie fossile sont exclues et remplacées par des sources renouvelables et si

l’énergie requise pour la régénération de l’absorbeur MEA est réduite par des améliorations

appropriées du processus.

Dans le cas de l’étape de culture, il est démontré que l’efficacité du processus peut

être grandement affectée par les régimes d’éclairage. L’optimisation factorielle en lumière

de la culture de Chlorella vulgaris montre que l’application d’une longueur d’onde idéale

et de niveaux d’intensité optimisés améliore la productivité de la biomasse, tandis que la

composition biologique (lipides et glucides) de la biomasse peut être modifiée en fonction des

objectifs de la bioraffinerie.

La conversion hydrothermale de la biomasse est une technologie de transformation

favorable en raison de l’élimination possible de l’étape de séchage. Lors de l’investigation de

la culture et de la gazéification hydrothermale de la biomasse de microalgues, il est déterminé

vii

Acknowledgements

que le rendement en biogaz et la composition en gaz peuvent être indirectement influencés

dès la phase de culture, ce qui souligne l’importance de l’application de conditions idéales

d’éclairage artificiel.

viii

Contents

Acknowledgements iii

Abstract (English/Français) v

List of figures xiv

List of tables xviii

1 Introduction 1

2 Literature review 3

2.1 Microalgae biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Properties of Chlorella vulgaris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2.1 Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2.2 Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 Biological composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.4 Influencing cultivation factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.4.1 Light . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.4.2 Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.4.3 Aeration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

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2.5 Microalgae biorefineries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.6 Upstream technologies-Production and harvesting algae . . . . . . . . . . . . . 9

2.6.1 Microalgae cultivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.6.1.1 Open system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.6.1.2 Closed photobioreactors . . . . . . . . . . . . . . . . . . . . . . . 11

2.6.2 Harvesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.7 Downstream technologies-Processing algae biomass . . . . . . . . . . . . . . . . 14

2.7.1 Biochemical conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.7.1.1 Fermentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.7.1.2 Transesterification of lipids . . . . . . . . . . . . . . . . . . . . . . 15

2.7.2 Thermochemical conversion . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.7.2.1 Pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.7.2.2 Atmospheric gasification . . . . . . . . . . . . . . . . . . . . . . . 16

2.7.3 Hydrothermal technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.7.3.1 Hydrothermal liquefaction (HTL) . . . . . . . . . . . . . . . . . . 18

2.7.3.2 Hydrothermal gasification (HTG) . . . . . . . . . . . . . . . . . . 19

2.7.3.3 Hydrothermal carbonization (HTC) . . . . . . . . . . . . . . . . . 21

2.8 The energy efficiency of biorefineries . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.9 Carbon Capture and Storage (CCS) . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.9.1 Carbon capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.9.2 Carbon storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3 Materials and Methods 27

3.1 Calculating the energy balance of an algae-based biorefinery . . . . . . . . . . . 27

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3.1.1 Energy requirements for the cultivation step . . . . . . . . . . . . . . . . . 27

3.1.2 Nutrients and related energy requirements for the cultivation . . . . . . 29

3.1.2.1 Energy requirements related to CO2 . . . . . . . . . . . . . . . . . 30

3.1.2.2 Energy requirements related to the nitrogen and phosphorus

sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.1.3 Harvesting and dewatering . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.1.4 Pretreating for lipid extraction . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.1.5 Extraction of lipids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.1.6 Transesterification of lipids . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.1.7 Thermochemical conversion of algae cake . . . . . . . . . . . . . . . . . . 35

3.1.8 Hydrotermal conversion of wet biomass . . . . . . . . . . . . . . . . . . . 36

3.1.9 The calculation of energy efficiency . . . . . . . . . . . . . . . . . . . . . . 37

3.2 Life Cycle Analysis (LCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2.1 Goal & Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2.2 Life Cycle Inventory (LCI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2.3 Life Cycle Impact Assessment (LCIA) . . . . . . . . . . . . . . . . . . . . . 39

3.2.3.1 IPCC 2007 (100a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2.3.2 Eco-indicator 99 (EI 99) . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2.3.3 IMPACT 2002+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.2.3.4 EPS 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3 PESTLE analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.1 Political & Legal factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.2 Economic factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

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3.3.3 Social factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.4 Technological factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.5 Environmental factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.4 MCDA analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.5 Microalgae cultivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.5.1 Organism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.5.2 Microtiter plate (MTP) and RGB-LED panel set up and operation . . . . 45

3.5.3 Photobioreactors (PBRs) set up and operation . . . . . . . . . . . . . . . . 46

3.5.4 Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.5.4.1 Optical Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.5.4.2 Cell number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.5.4.3 Dry weight content and biomass productivity . . . . . . . . . . . 49

3.5.4.4 Calibration curves . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.5.4.5 Light intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.5.4.6 Ultimate analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.5.4.7 Proximate analyis . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.6 Biological composition of the algal biomass . . . . . . . . . . . . . . . . . . . . . 51

3.7 Hydrothermal gasification (HTG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.7.1 Design of equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.7.2 Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.7.3 Analitical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.7.3.1 Biogas Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.7.3.2 Total Carbon (TC) determination . . . . . . . . . . . . . . . . . . 54

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3.8 Experimental design and statistical analysis . . . . . . . . . . . . . . . . . . . . . 55

3.8.1 DoE for Light wavelength . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.8.2 DoE for Light intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.8.3 DoE for the investigation of light intensity and aeration rate in PBR . . . 55

4 Results and Discussion 57

4.1 Energy balance of a third generation biorefinery . . . . . . . . . . . . . . . . . . . 57

4.2 Energy Balance, Net Energy Ratio of alternatives . . . . . . . . . . . . . . . . . . . 63

4.3 Identified refinery’s bottlenecks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.4 Life Cycle Analysis of Carbon Capture and Storage process alternatives . . . . . 66

4.4.1 CCS system setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.4.2 CCS via fossil fuel based absorbent regeneration . . . . . . . . . . . . . . 68

4.4.3 CCS via process improvement . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.4.4 CCS via application of renewable energy . . . . . . . . . . . . . . . . . . . 72

4.5 PESTLE analysis of CCS process alternatives . . . . . . . . . . . . . . . . . . . . . 75

4.5.1 Political & Legal aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.5.2 Economic aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.5.3 Social aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.5.4 Technological aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.5.5 Environental aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.6 Multi-Criteria Decision Analysis of CCS alternatives . . . . . . . . . . . . . . . . 85

4.7 Investigation of illumination conditions to influence the biomass productivity of

Chlorella vulgaris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.7.1 Optimal wavelength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

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4.7.2 The effect of light intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.7.3 Scaled up fermentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

4.8 Using targeted cultivation to increase the yield and composition of HTG biogas 98

5 Conclusions 103

6 Major New Results 105

References 110

Appendix 132

A List of symbols 133

B Supplementary materials 137

Declarations 141

xiv

List of Figures

2.1 Schematic ultrastructure of Chlorella vulgaris (Safi et al., 2014) . . . . . . . . . . 4

2.2 Daughter cell-wall formation during reproduction in Chlorella vulgaris (Ya-

mamoto et al., 2004, 2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 Biochemical composition of microalgae biomass (Schmid-Staiger, 2009) . . . . 5

2.4 Spectra of electromagnetic radiation, where the photosynthetically active radia-

tion (PAR) ranges from 400 to 750 nm (Hall and Rao, 1999) . . . . . . . . . . . . . 6

2.5 Energy carriers and process units converting microalgae biomass (Amin, 2009) 9

2.6 A schematic figure of a raceway-pond unit with scaling. (a) Raceway-pond, (b)

paddlewheel. (Sompech et al., 2012). . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.7 A schematic figure of a tubular photobioreactor (Chisti, 2008a). . . . . . . . . . 12

2.8 Other types of photobioreactors, (a) Airlift reactors, (b) Stirred tank photobiore-

actor, (c) Flat plate PBR (Singh and Sharma, 2012) . . . . . . . . . . . . . . . . . . 13

2.9 Phase diagram of water and static dielectric constant of water in function of

temperature at 200 bars (Tran, 2016). . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.10 The mechanism of biomass conversion via hydrothermal gasification (Made-

noglu et al., 2016). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.11 Three different approaches to capture carbon dioxide (Rackley, 2017). . . . . . 24

3.1 The flowsheet of the investigated Carbon Capture and Storage technology. MEA:

monoethanolamine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

xv

List of Figures

3.2 Detailed representation of damage model of Eco-indicator 99 LCIA method

(MHSPE, 2000). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.3 Overall scheme of IMPACT 2002+ LCIA method (Humbert et al., 2012). . . . . . 42

3.4 Microscopic image of Chlorella v. microalgae isolate with oil immersion lens

(1000x zoom, Bel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.5 The structure of the RGB-panel and LED module. (a) The schematic figure of the

RGB-LED panel, (b) Monitoring biomass growth in microplate cells. . . . . . . . 47

3.6 Laboratory scaled stirred tank photobioreactor. (1) Photobioreactor, (2) RGB-

LED stand, (3) LED strip, (4) magnetic stirrer, (5) magnet, (6) sampling manifold,

(7) inlet air manifold, (8) sparger, (9) clucking, (10) sampling glass, (11) air filter,

(12) air filter, (13) B.Braun control unit, (14) rotameter . . . . . . . . . . . . . . . 48

3.7 The P&I diagram of microalgae cultivation and hydrothermal gasification. PBR:

photobioreactor, P-1,2,3,4,5,6,7,8,9,10,11,12,13,14: pipe sections, v-1,2,3,4,5,6:

needle valves, PUMP-1,2,3: pumps, HX-1,2,3: heat exchangers, R-1: HTG reactor

section, T-I2-I3: thermocouples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.1 Flowsheet of the investigated microalgae-based biofuel plant . . . . . . . . . . . 59

4.2 Energy demand distribution in the case of the dry route . . . . . . . . . . . . . . 60

4.3 Energy demand distribution in the case of the wet route . . . . . . . . . . . . . . 61

4.4 Energy gaining by products. Biodiesel and glycerol are produced through trans-

esterification; biochar, bio-oil and biogas are produced through atmospheric

gasification, Upgraded bio-oil, HTL bio-char and HTL bio-gas are produced via

hydrothermal liquefaction, bio-oil stabilization and hydroprocessing. . . . . . . 62

4.5 Energy demand and net energy ratio of the biorefinery alternatives. (NER values

are highlighted above the columns) (a) Wet route, ORP; (b) Wet route, tPBR; (c)

Dry route, tPBR; (d) Wet route, tPBR with flue gas purification; (e) Wet route, ORP

with flue gas purification; (f ) Dry route, ORP; (g) Dry route; tPBR with flue gas pu-

rification; (h) Dry route, ORP with flue gas purification, (i) Dry route, horizontal

tPBR with flue and (j) Wet route, horizontal tPBR with flue gas purification. . . 63

4.6 Total environmental impacts of the CCS Fossil alternative compared to the

uncontrolled CO2 release in case of (a) IPCC 2007, (b) Eco-indicator 99 and (c)

IMPACT 2002+ LCIA methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

xvi

List of Figures

4.7 The results of the multi-perspective impact assessment methods by impact

categories in case of CCS fossil LCA alternative. (a) Eco-indicator 99, (b) IMPACT

2002+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.8 Total environmental impacts of the CCS Improved alternative compared to the

uncontrolled CO2 release in the case of (a) IPCC 2007, (b) Eco-indicator 99 and

(c) IMPACT 2002+ LCIA methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.9 The total environmental impacts of different renewable energy sources for MEA

solvent regeneration. a.) Heavy fuel oil, burned in industrial furnace 1MW, non-

modulating/CH U; b.) Heat, at cogeneration with biogas engine, agricultural

covered, allocation exergy/CH U; c.) Heat, at cogeneration with biogas engine,

allocation exergy/CH U; d.) Heat, at cogeneration with ignition biogas engine,

agricultural covered, alloc. energy/CH U; e.) Heat, at cogeneration ORC 1400

kWth, wood, emission control, allocation energy/CH U; f.) Heat, at cogeneration

6400kWth, wood, allocation energy/CH U; g.) Heat, central or small-scale, other

than natural gas (Europe without Switzerland) heat production, wood pellet, at

furnace Alloc Def, U . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.10 Total environmental impacts of the CCS Renewable alternative compared to the

uncontrolled CO2 release in the case of (a) IPCC 2007, (b) Eco-indicator 99 and

(c) IMPACT 2002+ LCIA methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.11 CCS and quota trading cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.12 CO2 CCS and quota trading cost by three quota trading rate (AC5/t;AC18/t;AC25/t)

producing 1 MWhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.13 MCDA result of the reduced PESTLE factors using Multi Attribute Value Theory

(MAVT) for the evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

4.14 Radial graph of the overall MCDA results . . . . . . . . . . . . . . . . . . . . . . . 87

4.15 Overall result of the Multi-Criteria Decision Analysis . . . . . . . . . . . . . . . . 87

4.16 Fermentations to determine ideal wavelength settings. Data are arithmetic

means of 3 replicates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.18 Productivity under different intensity levels, surface plot . . . . . . . . . . . . . . 92

xvii

List of Figures

4.17 Testing adequacy of the fitted statistical model for investigation of light intensity.

(a) Normal probability plot, (b) Predicted vs Observed values, (c) Raw residuals

vs Case number plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

4.19 Predicted versus experimental values, model verification . . . . . . . . . . . . . 94

4.20 Fermentations with the scaled up fermentors. . . . . . . . . . . . . . . . . . . . . 95

4.21 Specific component production of biomass . . . . . . . . . . . . . . . . . . . . . 96

4.22 Detecting interaction between aeration and light intensity parameters in case

of (a) biomass productivity, (b) protein, (c) carbohydrate and (d) lipid content.

Intensity level -1: 178.90 (Red) & 64.82 (Blue) µmol m−2 s−1; 1: 256.88 (Red) &

102.10 (Blue) µmol m−2 s−1. Aeration level -1: 0.50 vvm; 1: 0.75 vvm. . . . . . . 97

4.23 Yields of H2, CH4, CO2 and CO biogas components at 550°C, 30.0 MPa and

average 120 sec residence time. (a) 256.88(R) 102.10(B) µmol m−2 s−1, 0.50 vvm;

(b) 256.88(R) 102.10(B) µmol m−2 s−1, 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m−2

s−1, 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m−2 s−1, 0.75 vvm. . . . . . . . . . . . 99

4.24 Comparing biomass productivity, total gas and total specific yield of hydrother-

mal gasification. (a) 256.88(R) 102.10(B) µmol m−2 s−1, 0.50 vvm; (b) 256.88(R)

102.10(B) µmol m−2 s−1, 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m−2 s−1, 0.50

vvm; (d) 178.90(R) 64.82(B) µmol m−2 s−1, 0.75 vvm. . . . . . . . . . . . . . . . . 100

4.25 Specific gas yield of biogas components. (a) 256.88(R) 102.10(B) µmol m−2 s−1,

0.50 vvm; (b) 256.88(R) 102.10(B) µmol m−2 s−1, 0.75 vvm; (c) 178.90(R) 64.82(B)

µmol m−2 s−1, 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m−2 s−1, 0.75 vvm. . . . . 100

B.1 Calibration in the MTP device. (a) Chlorella vulgaris MACC555,Dry Weight (g

L−1) in function of (255-Green) code, (b) Chlorella vulgaris MACC555, Cell num-

ber (cells mL−1) in function of 255-Green code, (c) Chlorella vulgaris MACC555,

Optical Density (OD560) in function of 255-Green code. . . . . . . . . . . . . . . 138

B.2 Calibration for the laboratory scaled stirred tank photobioreactors (a) Chlorella

vulgaris MACC555, Dry Weight content (g L−1) in function of Optical density

(OD560), (b) Chlorella vulgaris MACC555, Cell number (cells mL−1) in function

of Optical density (OD560). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

xviii

List of Tables

2.1 Biological composition of different algae strains (Shuba and Kifle, 2018; Carpio

et al., 2015; Chia et al., 2013; Ho et al., 2013) . . . . . . . . . . . . . . . . . . . . . 5

2.2 Water properties under different states (Kumar et al., 2018) . . . . . . . . . . . . 18

3.1 Properties of open Raceway-ponds. All data refer to one unit of raceway-pond.

(Sompech et al., 2012; Jorquera et al., 2010) . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Properties of helical tubular photobioreactor (tPBR). All data refer to one unit of

tPBR. (Chisti, 2008b; Molina et al., 2001; Gómez-Pérez et al., 2015) . . . . . . . . 29

3.3 Characterization in the case of Eco-indicator 99 method. . . . . . . . . . . . . . 40

4.1 Energy demand by operational units . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.2 Higher Heating Values (HHVs) of the produced energy carriers . . . . . . . . . . 59

4.3 Net energy ratios of biomass based refineries and energy fuels. ORP, Open

Raceway-ponds, tPBR, tubular photobioreactor, HTL, Hydrothermal liquefaction. 65

4.4 Life cycle inventory data of the investigated CCS process related to 774.5 kgCO2/MWhe 67

4.5 Cost analysis for 1 MWhe production; a(Zauba, 2016), b(BAC, 2016), c (Wesnaes

and Weidema, 2006), d (Eurostat, 2014), e(Metz et al., 2005) . . . . . . . . . . . . 77

4.6 Economic factors of each alternatives . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.7 Social factors of each CCS alternatives . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.8 Technological factors of each alternatives . . . . . . . . . . . . . . . . . . . . . . . 80

xix

List of Tables

4.9 Midpoint impacts of CO2 release and CCS technology using IMPACT 2002+

method. Fossil fuel: Heavy fuel oil, burned in industrial furnace; Renewable

energy source: biogas from agricultural waste. . . . . . . . . . . . . . . . . . . . 82

4.10 Midpoint impacts of CO2 release and CCS technology using Eco-indicator 99

(H) method. Fossil fuel: Heavy fuel oil, burned in industrial furnace; Renewable

energy source: biogas from agricultural waste. . . . . . . . . . . . . . . . . . . . 83

4.11 Summary of impact assessment results, IPCC 2007 GWP 100a method . . . . . 84

4.12 Summary of impact assessment results, weighting, IMPACT 2002+ method . . . 84

4.13 Summary of impact assessment results, weighting, Eco-indicator 99(H) method 84

4.14 Proximate and ultimate analysis of Chlorella vulgaris . . . . . . . . . . . . . . . . 88

4.15 Analysis of variance (ANOVA) for the investigation of wavelength effect. . . . . . 90

4.16 Biomass productivity under illumination by different wavelengths. Data are

arithmetic means (±S.E.) of 3 different experiments. . . . . . . . . . . . . . . . . 90

4.17 Central composite design. Data are arithmetic means (±S.E.) of 3 different

experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

4.18 Analysis of variance (ANOVA) for response surface model. . . . . . . . . . . . . . 92

4.19 Microalgae cultivation in stirred tank photobioreactor and final biological com-

position of Chlorella vulgaris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

4.20 Hydrothermal gasification of microalgae biomass at 550°C and 30.0 MPa. . . . . 98

B.1 Estimated regression coefficients of the fitted response surface model in case of

intensity optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

B.2 Experimental and predicted results for model verification. Data are arithmetic

means (±S.E.) of 3 different experiments. . . . . . . . . . . . . . . . . . . . . . . . 140

xx

1 Introduction

The worldwide growing population and rising global energy demand enlarge the

necessity of finding alternative energy carriers and new sustainable solutions to prevent energy

shortages and climate change (Demirbas, 2011; IAE, 2011; Duic, 2015). Renewable energy

sources (such as wind, solar, geothermic, biomass) have the potential to mitigate the emission

of greenhouse gases and the negative effects of global warming. Microalgae based biorefineries

are able to contribute for achieving a greener economy by producing biofuels (bioethanol

via fermentation, biodiesel via transesterification, biogas via gasification) that pose lower

environmental damages compared to fossil fuels. Algae have many attractive characteristics

including high growth rate and photosynthetic efficiency, it does not compete with food

market and it can be cultivated on non-arable land. On the other hand the transformation

of microalgae into high energy density biofuels have several technological and economic

obstacles. These barriers are related to the cultivation of algae (it can be cultivated in extremely

dilute suspensions, difficult to control biological systems) and to the downstream processing

which consists of complex process units. Algae are capable to absorb the CO2 content of the

flue gas if it does not contain toxic compounds in high concentration. If it does other options

should be applied for the fixation of carbon dioxide to prevent the uncontrolled release of this

primary greenhouse gas (GHG). Fossil based power plants are responsible for around 30-40%

of the total annual anthropogenic carbon dioxide emission (Gohar and Shine, 2007) and these

point like emission sources provide an excellent opportunity for significant and efficient GHG

emission reduction. The separated CO2 can be stored and subsequently used in the algae

cultivation step which consequently decrease environmental pollution and support circular

economy.

The main motivation of the dissertation is addressing the above mentioned essential

environmental and technological concerns. The aim of the work is to improve the efficiency of

microalgae based biorefineries according to the following points:

1

Chapter 1. Introduction

• Investigation of cradle-to-grave energetic analysis of microalgae based biorefinery alter-

natives.

• Identification of possible refinery bottleneck(s) that can influence significantly the

energy efficiency of a biorefinery.

• Evaluation of Carbon Capture and Storage (CCS) technology using life cycle, PESTLE

and Multi-Criteria Decision Analyses.

• Reducing the environmental effects of the CCS process chain.

• Investigation of microalgae cultivation using artificial illumination.

• Improving the biomass productivity and biological composition (lipid and carbohydrate

content) of algae biomass.

• Influencing positively the yields of hydrothermal gasification and the biogas composi-

tion via the optimization of microalgae cultivation.

The dissertation is partitioned into 6 chapters. Chapter 1 contains the introduction

of the dissertation including the motivation and the aims of the work. The state-of-the-art and

the literature review are organized into Chapter 2. Chapter 3 describes the applied data and

methods throughout the research, while the results can be found in Chapter 4. The conclusions

of the dissertation are summarized in Chapter 5. The main findings of the dissertation are

presented in Chapter 6.

2

2 Literature review

2.1 Microalgae biomass

Microalgae are a simple autotrophic aquatic organisms that are capable to fix the

carbon dioxide from atmosphere and transform it into high value compounds such as proteins,

carbohydrates and lipids. The size of an algae cell ranges from a few micrometers to hundreds

of micrometers, they exist in unicellular and multi-cellular forms. Microalgae can grow in

marine and fresh water and in a wide range of climate zones varies from tundra to desert.

Microalgae are characterized by a wide range of biochemical properties which can be traced

back to their huge biodiversity with over 40 000 species (Rico et al., 2017).

Algae have many attractive characteristics over terrestrial biomass, such as high pho-

tosynthetic efficiency, possible cultivation on non-arable land, high growth rate, relatively low

land-use requirement, accumulation of lipids and absorption of carbon dioxide. Autotrophic

microalgae including bacteria (cyanobacteria), diatoms (Chromalveolata) and unicellular

organisms (e.g., Chromista, Chlorophyta) can be found in either marine or freshwater environ-

ments (Spolaore et al., 2006).

2.2 Properties of Chlorella vulgaris

2.2.1 Morphology

Chlorella vulgaris is classified as follows: Domain: Eukaryota, Kingdom: Protista,

Division: Chlorophyta, Class: Trebouxiophyceae, Order: Chlorellales, Family: Chlorellaceae,

Genus: Chlorella, Specie: Chlorella vulgaris. The structural elements of C. vulgaris are similar

to plants. It contains cell wall, mitochondrion, chloroplast, cytoplasm, Golgi body, chlorophyll,

carotenoids, thylakoids and nucleus as it is illustrated in Figure 2.1. The spherical cells

3

Chapter 2. Literature review

diameter is about 2-10 µm. The thickness and the structure of the cell wall depend on the

actual growth phase (Yamamoto et al., 2007).

Figure 2.1 – Schematic ultrastructure of Chlorella vulgaris (Safi et al., 2014)

2.2.2 Reproduction

C. vulgaris reproduces asexually as it is detailed in Figure 2.2. One cell can multi-

ply within 24 hours by autosporulation. In the first steps the cell is growing (a,b), then the

chloroplast (c) and protoplast (d,e) dividing. After the maturation 4 daughter cells are formed

with their own cell wall (f) and finally the mother cell wall ruptures and the daughter cells are

liberated. The asexual reproduction of algae comes to pass quickly. Thanks to the high growth

rate the total algae biomass can be doubled within 24 hours (Gikonyo, 2013).

Figure 2.2 – Daughter cell-wall formation during reproduction in Chlorella vulgaris (Yamamotoet al., 2004, 2007)

4

2.3. Biological composition

2.3 Biological composition

Microalgae biomass consists of proteins, carbohydrates, lipids and other valuable

components such as pigments and antioxidants as it is detailed in Figure 2.3. The high

biodiversity and great number of microalgae species result in various biological compositions

as it is shown in Table 2.1. The lipid, carbohydrate and protein ratios can be influenced by

applying different cultivation parameters which makes possible to produce wide ranges of

biofuels using algae (e.g., bioethanol, biodiesel, biogas and biochar).

Figure 2.3 – Biochemical composition of microalgae biomass (Schmid-Staiger, 2009)

Table 2.1 – Biological composition of different algae strains (Shuba and Kifle, 2018; Carpioet al., 2015; Chia et al., 2013; Ho et al., 2013)

Strain Lipids (wt.%) Proteins (wt.%) Carbohydrates (wt.%)

Chlorella vulgaris 5.9-28 15-52 12.16-33.7Chlorella protothecoides 55 10-52 10-15Spirulina maxima 6-7 60-71 13-16Spirulina platensis 4-9 46-63 8-14Chaetoceros calcitrans 39 58 10Euglena gracilis 4-20 39-61 14-18Spirogyra sp. 11-21 6-20 33-64Chaetoceros muellerii 33 44-65 11-19Synechoccus sp. 11 63 15

5


2.4 Influencing cultivation factors

2.4.1 Light

The energy for photosynthesis is provided by external light irradiation. The elec-

tromagnetic radiation can be split up into different regions based on the wavelength as it is

illustrated in Figure 2.4. The energy is inversely correlated to wavelength thus a photon of blue

light (400-500 nm) is more energetic than red light (700 nm) (Richmond, 2007).

Figure 2.4 – Spectra of electromagnetic radiation, where the photosynthetically active radiation(PAR) ranges from 400 to 750 nm (Hall and Rao, 1999)

Light energy for photosynthesis can be provided by the sun or artificial sources. Sun-

light is free and abundant but the cultivation is influenced by day/night cycles, weather and

seasonal changes. Artificial light sources can balance these limitations but their application

increase the cost of cultivation. Various artificial light sources can be applied for algae cultiva-

tion such as: high intensity discharge lamps (HID), fluorescent tubes and light emitting diodes

(LEDs). HID and LED have the highest photosynthetically active radiation (PAR) efficiency,

1.87 µmol-ph s−1 W−1 and 1.91 µmol-ph s−1 W−1, respectively (Blanken et al., 2013). LED

technology is rapidly increasing and in contrast to the HID technology it is not attained yet the

technical maximum in regard of PAR efficiency.

6

2.4. Influencing cultivation factors

The efficiency of LEDs must be developed in three different areas (Pimputkar et al.,

2009; Narukawa et al., 2010): (1) Overheating due to high currents decreases the lifetime of

LEDs, while high temperature of the light source can increase the temperature of culture in

case of closed system decreasing the biomass productivity. (2) The wall plug efficiency (WPE),

the ratio between radiant flux and electrical input power, decreases with increasing currents

limiting the efficient irradiation of photobioreactor surfaces. (3) High internal reflection of

photons should be decreased to prevent light loss.

Closed indoor systems require artificial light sources which expands the number of

possible influencing factors on cultivation with light’s wavelength distribution and intensity.

Light emitting diodes (LEDs) emerged recently as one of the most appropriate light sources for

microalgae cultivation (Helena et al., 2016; Atta et al., 2013; Schulze et al., 2017). LEDs provide

longer lifetime and better energy efficiency compared to fluorescent lamps or tubs. Cultivation

experiments under LEDs were successfully carried out in a number of works (Hu and Sato,

2017; Ra et al., 2016; Severes et al., 2017). Several studies showed that different wavelengths

influence growth and photosynthetic metabolism in algae (Aguilera et al., 2000; Samudra

et al., 2015; Carneiro et al., 2009; Teo et al., 2014). Photon flux density (PFD) and wavelength

are important cultivation factors (Schulze et al., 2014) because they have high impact on

photosynthesis: low light intensities can cause photolimitation, while excessive intensities lead

to photoinhibition, therefore as a consequence, economic microalgae cultivation demands

optimized illumination conditions.

2.4.2 Nutrients

One of the most important nutrient for microalgae growth is the nitrogen. Nitrogen

limitation as a stress condition affects lipid metabolism in algae. Nitrogen deficiency enhances

the accumulation of lipids in cells. Li et al. (2011) found that following nitrogen depletion starch

content of the biomass decreased while neutral lipid content increased up to 52.1% of cell

dry weight in case of Pseudochlorococcum sp. Nitrogen starvation can also affect astaxanthin

content in certain strains. Zhekisheva et al. (2002) reported that the astaxanthin and also the

fatty acid content was increased due to nitrogen limitation in case of Haematococcus pluvialis.

Phosphorus limitation is also increasing TAG content and influence lipid composi-

tion. Higher phosphorus content in the cultivation medium results in increasing amount of

16:0 and 18:1 in cells. Though, at the same time, deprivation was found in cases of 18:4ω3,

20:5ω3 and 22:6ω3 (Khozin-Goldberg and Cohen, 2006; Hu et al., 2008).

Osmotic environment changes the biological composition of algae. Takagi et al.

(2006) found that increasing the salt concentration (NaCl) from 0.5 to 1.0 M results in higher

intracellular lipid content (67%) though it is also reported that the photosynthesis and cell

7


growth rate was inhibited by high osmotic stress (up to 1.5 M NaCl concentration). Pal et al.

(2011) reported that the TFA content was increased to 47% of the DW at 13 g L−1 NaCl in case

of Nannochloropsis sp.

2.4.3 Aeration

Beyond sufficient illumination conditions, algae requires efficient gas transfer to

achieve high rates of photosynthesis. Carbon is an essential element for adequate algae growth

and it accounts to around 50 wt.% of algae biomass. CO2 is the primary carbon source for

phototrophic algae cells. Elevating the atmospheric concentration of CO2 in inlet air (0.039%)

to 1-5 % increases the biomass and chlorophyll productivities as well (Chinnasamy et al., 2009).

Tang et al. (2011) cultivated Scenedesmus and Chlorella sp. with 0.03%, 5%, 10%, 20%, 30%,

50% CO2. The investigated microalgae strains showed the best growth potential at 10% CO2.

Cheng et al. (2019) examined the effects of simulated flue gas containing 10% CO2, 200 ppm

NOx and 100 ppm SOx in the case of Chlorella sp. The results showed that the strain is able

to tolerate the toxic compounds during the cultivation, and the maximum CO2 fixation rate

was 1.2 g L−1 d−1. These results show that the CO2 biofixation of microlagae biomass has the

potential to become a sustainable way to mitigate anthropogenic CO2 emission. Li et al. (2018)

investigated the relationship between the elevated level of CO2 concentration and dark/light

cycles in the case of Ulva prolifera. They found significantly higher growth rate and lower dark

respiration rate at 16:8 h light:dark treatment comparing to 12:12 h cycles regardless of the

CO2 treatment.

Photoautotrophic conditions are also affected by dissolved oxygen (DO) concen-

tration which is produced as a co-product via photosynthesis. Higher oxygen concentration

elevates photoinhibition due to the fact that oxygen radicals have damaging effects on pho-

tosynthetic structures (Ugwu et al., 2007). Increasing the DO concentration from 22±2 mg

L−1 to 60±19 mg L−1 decreases the chlorophyl (from 6.02±0.18 to 2.57±0.05 mg L−1 d−1) and

biomass productivity (from 570±28 to 380±18 mg L−1 d−1) (Richmond, 2007).

2.5 Microalgae biorefineries

There is a growing interest in the concept of algae biorefineries, which are able

to produce a diverse products in a wide range, similarly to oil refineries. Beside biofuels,

biorefineries can provide valuable components such as proteins, polysaccharides, pigments,

pharmaceutical, animal feed and fertilizers (Gikonyo, 2013). Microalgae biomass can be

converted into biofuels via biochemical and thermochemical conversion routes as it is detailed

in Figure 2.5.

8

2.6. Upstream technologies-Production and harvesting algae

Biorefineries or biofuels can be classified based on the converted type of feedstock

(Saladini et al., 2016; Salian and Strezov, 2017). First generation biofuels are produced mainly

from food-crops (e.g., sugar, starch, vegetable oils) and other sources such as sugarcane.

Lignocellulosic feedstocks that cannot be applied for food such as agricultural wastes and

forestry biomass are used for the production of second generation biofuels. Third generation

biofuels are made from aquatic biomass (i.e. algae).

A microalgae based biorefinery can be separated into 2 main parts: upstream and

downstream. The upstream section includes the cultivation and harvesting of algae biomass

while the downstream part combines several complex operation units according to the final

product(s) (Singh and Gu, 2010).

Figure 2.5 – Energy carriers and process units converting microalgae biomass (Amin, 2009)

2.6 Upstream technologies-Production and harvesting algae

2.6.1 Microalgae cultivation

Different cultivation systems can be used for commercial microalgae production.

These can be open ponds, raceway-ponds and closed photobioreactors (PBRs) in various

designs. The common in these cultivation systems is that they must be transparent to al-

low excellent illumination of the culture broth which is essential in the case of autotrophic

organisms.

9


2.6.1.1 Open system

Over 90% of the worldwide algae production is carried out in open ponds (Shuba and

Kifle, 2018). Open systems are favourable for large scale microalgae production because they

are less expensive to operate, durable and the oxygen what is produced in the process released

to the air and thus it does not decrease the biomass productivity. On the other hand, the

irradiation-volume ratio is lower compared to closed systems, the cultures can be infected by

other organisms and the control of the cultivation parameters is a challenging task. A raceway-

pond consists of an oval pond, a central baffle and a paddlewheel which is responsible for the

circulation of the culture broth. Various cultivation parameters, such as exposure to sunlight,

nutrient content, oxygen concentration, water temperature and algae settling, can affect the

algae growth rate in raceway ponds (James and Boriah, 2010; Xu et al., 2014). Sufficient mixing

of the culture broth is strictly linked to these factors and essential for efficient cultivation

because the algae cells are exposed to sunlight more frequently and the nutrient distribution

is becoming better in the cultivation system. Turbulent flow conditions prevent cell settling,

avoid shading and contribute to a homogeneous nutrient concentration. Dead zones decrease

the algae growth rate in open ponds. Therefore, the design of raceway-ponds has a key role

concerning energy efficient mixing and elimination of dead zones (Sompech et al., 2012). The

dead zone areas in a raceway pond can be reduced using baffles. Sompech et al. (2012) showed

that applying three end baffles with modified center wall result in a 21.5% circulation related

energy saving and decreasing dead zone area from 14.2% to 0.9%. Open raceway ponds are

used for the production of Dunaliella salina, Chlorella sp., Nannochloropsis and other species

as well (Radmann et al., 2007).

10


Figure 2.6 – A schematic figure of a raceway-pond unit with scaling. (a) Raceway-pond, (b)paddlewheel. (Sompech et al., 2012).

2.6.1.2 Closed photobioreactors

Photobioreactors are closed illuminated culture vessels where there is not direct

exchange between the environment (gases and contaminants) and the culture. Adjustable

closed cultivation systems have several advantages over open systems. The application of

photobioreactors (PBRs) minimize the contamination by invasive organisms and allow axenic

algal cultivation and thus the fermentation of monoculture becomes more productive. PBRs

prevent water evaporation, enable higher cell concentration, hinder CO2 loss, permit the

production of complex biopharmaceuticals and offer better control of cultivation conditions

such as pH, temperature, illumination, and CO2 concentration (Singh and Sharma, 2012). It

is easier to optimize closed systems which makes possible to cultivate certain species that

otherwise cannot be cultivated in open systems (Mata et al., 2010).

Different PBR design configurations are available for microalgae cultivation such as

tubular, airlift, stirred tank and flat plate. Horizontal tubular photobioreactor consists of two

main parts: transparent solar collector tubes and degassing unit (as it is illustrated in Figure

2.7). The shape of the tubes permits indoor and outdoor applications as well. The oxygen

that produced as a co-product of photosynthesis can reduce the photosynthetic efficiency

when it is accumulated in high concentration in the system. Thus degassing columns must

be applied for sufficient gas exchange. The major drawback of the configuration is the high

energy requirement paired with the culture broth circulation between the solar collector tube

11


and the degassing unit. The high energy input can be traced back to the required high velocity

of the suspension (20-50 cm s−1) which allows turbulent flow in the solar receiver tubes with

short light-dark cycles (Posten, 2009).

Figure 2.7 – A schematic figure of a tubular photobioreactor (Chisti, 2008a).

Airlift reactors (Fig. 2.8a) contain two different zones that are interconnected with

each other. Depending on the gas sparging point, one part of the vessel is called riser, the other

side is called downcomer which does not receive inlet air mixture. The flow characteristics

are different in the two section. The liquid phase in the downcomer flows in a laminar way.

Two main airlift designs are available: in the case of external loop the riser and downcomer

are separated by using different tubes while in the internal loop reactor regions are isolated

by a draft tube or a split cylinder (Loubière et al., 2009; Singh and Sharma, 2012; Ojha and Al-

Dahhan, 2018). The gas mixture is injected through a sparger than the bubbles move upward

in the riser providing sufficient mixing in the reactor. The gas bubbles leave the liquid culture

broth in the disengagement zone. High gas hold up in the downcomer can affect negatively

the fluid dynamics in the reactor which highlights the importance of using ideal operating

conditions and reactor design. The advantage of this design is the high photosysnthetic

efficiency but the scale-up is difficult.

12


(a)

(b)

(c)

Figure 2.8 – Other types of photobioreactors, (a) Airlift reactors, (b) Stirred tank photobioreac-tor, (c) Flat plate PBR (Singh and Sharma, 2012)

Stirred tank photobioreactors (Fig. 2.8b) can be used for algae cultivation by illumi-

nating them with external light sources. Mixing of the culture is provided mechanically by a

stirrer. The disadvantages of this system are the low surface area to volume ratio and the cell

wall of algae can break at the sharp edges of the stirrer.

Flat plate photobioreactors have cuboid structure as it is presented in Fig. 2.8c.

The advantage of this design is the high surface area to volume ratio and efficient gas disen-

13


gagement system. The mixing of the culture can be done by bubbling air and mechanical

rotation using a motor to provide a rocking motion as it is shown in the work of Gilbert et al.

(2011). The air or the air/CO2 mixture is injected at the bottom of the reactor through a sparger.

Tubular photobioreactors have better biomass productivity but flat plate systems offer low

oxygen build-up, low cost and good control of biofouling (Bergmann et al., 2013; Khadim

et al., 2018). The light path is very thin between the transparent parallel walls which prevents

the self-shadowing of cells and results the highest photosynthetic efficiency and growth rate

among PBRs (Tamburic et al., 2011). The scale up of flate plate PBRs is a difficult technological

challenge. Widening the light path decrease the photosynthetic efficiency while lengthening

of the reactor is also not recommended due to bulging and cracking. Vertical glass dividers

can be used in every 90 to 100 cm to hold together the plates (Cheng-Wu et al., 2001). The

scale-up can be done by placing several units over an area. Pilot-scale cultivation (≥80 L) is

already demonstrated using K. veneficum by López-Rosales et al. (2018).

2.6.2 Harvesting

Harvesting of microalgae biomass is a challenging task because of the small cell size

(with a diameter ranges between 3-30µm) and the dilute cultivation broth (Slater et al., 2015).

The harvesting process can involve mechanical, chemical, biological and electrical methods.

Thickening methods (e.g., coagulation, flocculation, bioflocculation, gravity sedimentation,

flotation and electrical based methods) can be applied to increase the solid concentration of

the produced broth. Following the thickening methods dewatering processes (filtration and

centrifugation) can be used to decrease further the water content of the algae stream (Barros

et al., 2015).

2.7 Downstream technologies-Processing algae biomass

2.7.1 Biochemical conversion

2.7.1.1 Fermentation

The high sugar and starch content of certain strains make the algae biomass suitable

to produce bioethanol via fermentation (El-Dalatony et al., 2016). Yeasts and bacteria are

used as fermentative organisms in the process. Yeasts such as Saccharomyces cerevisiae and

Schizosaccharomyces pombe are able to produce ethanol in the presence of oxygen based on

the Crabtree effect. Kluyveromyces lactis and Candida albicans are Crabtree-negative yeast and

degrade sugars to CO2 (Piškur et al., 2006; Mussatto et al., 2012; Azhar et al., 2017; Phwan et al.,

2018). Bacteria such as Zymomonas mobilis, Klebsiella oxytoca and Escherichia coli can be

used to produce bioethanol. Bacteria offer high growth rates, the utilization of several different

14

2.7. Downstream technologies-Processing algae biomass

carbon sources and economic operation. High ethanol yield (0.4 g ethanol (g algae)−1) was

achieved by E. coli SJL2526 in case of Chlorella vulgaris algae (Lee et al., 2011) at 37°C and

pH 7.0. Bacteria are able to ferment wider range of sugars to ethanol compared to yeast,

however, the ethanol yield is lower because of the formation of co-products throughout the

fermentation and more sensitive to pH change (Balat et al., 2008).

The fermentation of algae biomass to ethanol is a promising technology but the

conversion of sugar and starch containing feedstocks competes with the food market which

makes these sources less attractive for the production of energy carriers (Mata et al., 2010).

2.7.1.2 Transesterification of lipids

The oil content of biomass can be converted into biodiesel via transesterification.

Certain microalgae species can accumulate lipids in their cells up to 55 wt.% which make them

suitable to produce biodiesel (Chisti, 2007; Li et al., 2008). Following a lipid extraction step the

extracted triacylglycerols are reacted with alcohol to form fatty acid alkyl esters in the presence

of basic or acidic catalysts (NaOH or H2SO4) at 60-90°C (Shan et al., 2018). If the reagent

is methanol the reaction provides fatty acid methyl esters (FAMEs, biodiesel) and glycerol

as co-product (Eq. 2.1) (Torres et al., 2017). In scaled up industrial process the methanol is

added in excess to shift the reaction equilibrium into the direction of methyl ester production

(Yen et al., 2013). After the reaction phase the product stream is separated, the unconverted

methanol is regenerated through evaporation. The biodiesel is rinsed with water to remove

the remained glycerol, the pH neutralized and finally it is dried to meet the requirements of

alkyl ester-based biodiesel standards (ASTM D6751 and EN 14214) (Huang et al., 2010).

(2.1)

15


2.7.2 Thermochemical conversion

2.7.2.1 Pyrolysis

The pyrolysis of biomass is conducted at high temperature between 400-600°C in

inert atmospheric environment (Chiaramonti et al., 2015). Three types of the pyrolysis can be

differentiated: (1) slow pyrolysis is performed with long residence time (>30 minutes) and low

heating rate (8.3·10−2-1.7·10−1°C s−1), (2) fast pyrolyis is typically carried out with moderate

residence time (10-20 sec) and at high temperature (500°C) and heating rate (1-200°C s−1),

while (3) flash pyrolysis is conducted with short residence time (about 1 sec) and high heating

rate up to 1000°C s−1 (Marcilla et al., 2013). Flash pyrolysis provides liquid bio-oil (75%), solid

char (2%) and noncondensable pyrogas (13%), while in the case of slow pyrolysis these values

are 30%, 35%, 35%, respectively (Brennan and Owende, 2010).

2.7.2.2 Atmospheric gasification

Atmospheric gasification of dry biomass results in combustible gaseous product

containing mainly CH4, H2, CO2, CO and N2. The partial oxidation of biomass occurs at high

temperature range from 800-1000°C in air, oxygen and water (steam) media (Mathimani et al.,

2019). The process consists of 4 stages: (a) pre-dyring the feedstock to remove its moisture

content, (b) pyrolysis to break the biomass into smaller pieces, (c) oxidation of the smaller

fragments and (d) final gasification to produce small molecules with high energy content.

Gasifying algae biomass (Spirulina; Nannochloropsis sp.) result in high quality biogas (H2:

20-46%, CH4: 4-10%, CO: 6-35%, CO2:12-40%) as it is reported by Sanchez-Silva et al. (2013);

Yang et al. (2013); Duman et al. (2014).

2.7.3 Hydrothermal technologies

The hydrothermal treatment of biomass is a thermochemical process where the

feedstock is decomposed in hot compressed water. Depending on the reaction parameters

(temperature and pressure) biomass can be converted into solid (hydrochar), liquid (bio-oil)

and gaseous (fuel gas) energy carriers. The byproducts that are formed during the process are

useful nutrients that can be recovered and used in other processes. The main advantage of

hydrothermal treatment compared to conventional thermochemical processes is that high

moisture content biomass (up to even 90%) can be used as feedstock because the water next

to the dry matter are used as a reagent and a solvent at same time in the process. These

processes do not require the drying of the feedstock which allows the elimination of the drying

process resulting a significant amount of energy saving. The hydrothermal treatment becomes

favourable compared to conventional processes if the moisture content of the feedstock is

higher than 30% (Savage et al., 2010; Kumar et al., 2018).

16


The water acts as a solvent in hydrothermal processes and it can be found in its sub-

critical or super-critical states depending on the applied reaction conditions. The properties

of the water (ionic product, density, viscosity, dielectric constant, heat capacity, thermal

conductivity) changes above its critical point (Tc =374°C, pc =221 bar) as it is detailed in Table

2.2. Figure 2.9 shows that the dielectric constant decreasing significantly as the reaction

parameters get closer to the supercritical point of the water. The dielectric constant is around

only 5 above the supercritical point similarly to non-polar solvents. Thus, supercritical water

(SCW) acts as a non-polar solvent which provides an excellent media for organic reactions

(Kumar et al., 2018). The same downward tendency can be described in the case of density,

viscosity, and thermal conductivity, while the heat capacity and pKSU are increasing.

Figure 2.9 – Phase diagram of water and static dielectric constant of water in function oftemperature at 200 bars (Tran, 2016).

17


Table 2.2 – Water properties under different states (Kumar et al., 2018)

Parameters Normal water Sub-critical water Super-critical water

Temperature (°C) 25 250 400Pressure (MPa) 0.1 5 25Density (g cm−1) 0.997 0.800 0.170Viscosity (m Pa s) 0.89 0.11 0.03Dielectric constant 78.5 27.1 5.9Heat capacity(kJ kg−1 K−1)

4.22 4.86 13

pKsu 14.0 11.2 19.4Thermal conductivity(mW m−1 K−1)

608 620 160

Water has a prominent role in hydrothermal reactions. Free-radical reactions occur

at high temperature and low water density, while ionic reaction mechanism can be assumed at

high water density bellow the critical temperature or at supercritical condition and very high

pressures (Henrikson et al., 2003; Tran, 2016).

From technological point of view, there are various challenges that arise during

the hydrothermal treatment of wet biomass. These are related to corrosion, precipitation of

inorganic salts, char and coke formation, energy and conversion efficiency, product separation,

slurry feeding, biocrude stabilization, water management and process costs (Tekin et al., 2014;

Tran, 2016; Dãrãban et al., 2015).

2.7.3.1 Hydrothermal liquefaction (HTL)

The main product of hydrothermal liquefaction is liquid fuel also known as biocrude

or bio-oil. The process takes place at high temperature (300-374°C) and pressure (50-200 bar)

at subcritical water condition. Many complex reactions occur during the decomposition of

biomass. The degradation mechanisms include (Tekin et al., 2014; Toor et al., 2011):

• depolymerization of biomass,

• degradation of monomers (cleavage, dehydration, and decarboxylation reactions),

• recombination or polymerization of fragmented components.

Most of the HTL reactors are based on CSTR (continuous stirred tank reactor) batch

type design (Zhang et al., 2011; Jena et al., 2011; Brand et al., 2014) where the heat transfer is

18


carried out through the reactor wall using fluidized sand, molten tin, oil batches or external

electric heaters. Continuous operation can be carried out using tubular plug-flow reactors

though there are some requirements that must be taken care of: (i) short residence time

must be used to minimize coke formation and to prevent the decrease of heat transfer and

the fouling and plugging of the reactor, (ii) high heating rate should be used to prevent side

reactions, (iii) application of special alloys that can resist to high corrosion.

Catalysts can be used to improve component yields, process efficiency and to de-

crease the formation of char, coke and tar. In the case of NaOH the hydroxide ions prevent

polymerization and thus less char is produced (Sugano et al., 2008; Durak and Aysu, 2016).

Homogeneous and heterogeneous catalyst are also reported for the transformation of biomass.

Alkali salts (KOH, Na2CO3, KHCO3) are regarded as efficient catalysts in the process reducing

the char and tar formation and improving product yield. Karagöz et al. (2005) investigated low

temperature catalytic hydrothermal treatment of wood biomass and they ranked the catalytic

activity of alkali salts as follows: K2CO3>KOH>Na2CO3>NaOH. Alkali catalysts accelerate

water-gas shift reaction, increase the pH of the media decreasing dehydration reactions and

their utilization is considered to be economic. The general problem of applying homogeneous

catalyst is that the separation from the products are complicated and expensive.

Several heterogeneous catalyst also reported as efficient HTL catalysts such as ZrO2,

CeO2, La2O3, MnO, MgO, NiO, ZnO, ZnCl2, NiCl2 (Hammerschmidt et al., 2011; Yim et al., 2017;

Long et al., 2016; Christensen et al., 2014; Lee et al., 2016). The application of heterogeneous

catalyst in HTL process improves biocrude quality. Noble metals (Pt, Pd) can also be used

but their application is very expensive (Zhou et al., 2016). Ni/SiO2 and Co/CNTs catalyst were

used for the transformation of microalgae biomass, the catalysts improved the bio-oil yield

using low temperature (250°C) and improved the quality of bio-oil resulting low O/C ratios

(Saber et al., 2016; Chen et al., 2017).

2.7.3.2 Hydrothermal gasification (HTG)

Fuel gas (H2, CH4, CO2, CO, C2H4, C2H6) can be produced via supercritical water

gasification (SCWG) or hydrothermal gasification where water is at supercritical state. The

reaction temperature and pressure in the process are between 500-700°C and higher than

221 bar, respectively (Sert et al., 2018). The process has several advantages over atmospheric

gasification: (1) wet biomass with high moisture content (30-90%) can be applied as feedstock

without using any pre-drying step, (2) high quality syngas (H2/CO) can be produced, (3)

product stream contains less impurities and char/tar, (4) the biogas composition can be

controlled with the reaction parameters (temperature, pressure, feedstock concentration).

Figure 2.10 shows the conversion mechanisms of biomass to gaseous products.

19


The cellulose is hydrolyzed into glucose which is followed by isomerization to fructose and

mannose (Klingler and Vogel, 2010). After that the saccharides are dehydrated into furans and

furfural compounds (Watanabe et al., 2005). Furans (5-HMF and furfural) then undergoes

to hydration forming carboxylic acids. Lignin in the first step is hydrolyzed into p-coumaryl,

coniferyl and sinapy alcohols that hydrolyze further to cresols, catechol, phenol, syringol.

Above critical condition aldehydes, phenols and ketones are produced while bellow the water

supercritical point phenolics form coke and tar (Madenoglu et al., 2016). SCWG also includes

CO methanation (Eq. 2.2), Sabatier-reaction (Eq. 2.3), water-gas-shift reaction (Eq. 2.4) and

steam reforming (Eq. 2.5).

Figure 2.10 – The mechanism of biomass conversion via hydrothermal gasification (Madenogluet al., 2016).

CO+3H2 →CH4+H2O (2.2)

CO2+4H2 →CH4+2H2O (2.3)

CO+H2O →CO2+H2 (2.4)

20


C6H10O5+H2O → 6CO+6H2 (2.5)

High process temperature requires high energy input which decreases energy effi-

ciency. Catalytic HTG can be used to decrease the operational temperature and to improve

the produced biogas quality. Homogeneous and heterogeneous catalysts can also be used

similarly to HTL process (Peng et al., 2017; Onwudili and Williams, 2013; Zöhrer and Vogel,

2013; Elif and Nezihe, 2016).

Hydrothermal gasification of biomass were carried out on horse manure and model

compounds such as humic acid, and cellulose (Nanda et al., 2016; Gong et al., 2017). These

studies apply batch type reactors with high residence time (around 40-75 minutes). A few

studies deal with microalgae biomass where generally the main objectives are the evaluation

of different strains and catalysts to raise yields and decrease high reaction temperature (Deniz

et al., 2015; Graz et al., 2016; Safari et al., 2016; Cherad et al., 2016; Norouzi et al., 2017). Jiao

et al. (2017) processed Chlorella pyrenoidosa, S.platensis, Schizochytrium limacinum and

Nannochloropsis species where the highest hydrogen yield was found to be 6.17 mol kg−1

dry microalgae. Onwudili et al. (2013) used Spirulina platensis and Saccharina latissima as

feedstock for catalytic HTG, within their work the H2 yield was 15.1 mol kg−1 working with

NaOH homogeneous catalyst.

2.7.3.3 Hydrothermal carbonization (HTC)

Hydrothermal carbonization is performed at moderate reaction conditions (180-

250°C, 2-6 MPa, 5-240 min) compared to the other two hydrothermal process (Zhang et al.,

2015). The main product is solid hydrochar which has better properties comparing it to the

raw feedstock in terms of mass and energy density, combustion performance, volatile matter

and fixed carbon content (Tradler et al., 2018; Heidari et al., 2018). Based on the quality

of the solid product it can be utilized in various fields such as carbon sequestration as an

efficient adsorbent, soil amelioration, bioenergy production, functionalized carbon material

and wastewater pollution remediation. The yield and product quality (higher heating value,

energy densification, fuel ratio) are influenced by the reaction temperature, pressure, residence

time, feedstock concentration, feedwater pH and heating rate (Brand et al., 2014; Mäkelä et al.,

2016; Wang et al., 2018). Increased temperature levels decrease the product yield but at the

same time improve combustion properties (H/C, O/C, VM/FC ratios) (Fang and Xu, 2014). The

feedstock of the HTC process can be any wet biological raw material (e.g., food waste, algae,

cocconut fiber, empty fruit bunch, bark, bamboo, sewage sludge, etc.) which highlights the

attractiveness of the process (Liu et al., 2013; Novianti et al., 2014; Zhang et al., 2015; Peng

21


et al., 2016; Zhuang et al., 2018).

2.8 The energy efficiency of biorefineries

Sustainability of the energy sector and the need to mitigate the emission of green-

house gases require alternative energy resources and engineering solutions to produce renew-

able energy carriers. Biorefineries show a great potential in their application as they meet these

criteria, however, the efficiencies of these refineries must be investigated and upgraded to be

able to operate profitable plants at large scale. Several Life Cycle Assessments (LCAs) were

prepared to investigate the efficiencies of microalgae based refineries incorporating various

processes and conversion routes (Adesanya et al., 2014; Brentner et al., 2011; Collet et al.,

2014). Some of the former works surveyed hydrothermal technologies as well (de Boer et al.,

2012; Bennion et al., 2015). Bennion et al. (2015) investigated microalgae based biorefineries

conducting a well-to-pump life cycle and energetic analysis. Their investigation included the

growth phase of algae, dewatering, and thermochemical processing of algae. The calculated

net energy ratio (NER) of their system was 1.23. Jorquera et al. (2010) investigated the energy

efficiency of biodiesel production cultivation Nannochloropsis sp. in raceway ponds, flat plate

reactors and horizontal tubular reactor. Within their work they found that horizontal tPBRs

are not economically feasible (NER<1), but on the other hand NER values higher than 1 are

achievable in the case of raceway ponds and flat plate PBRs. Tredici et al. (2015) used flat panel

GWP reactors for the cultivation of Tetraselmis suecica and they determined a NER ratio of

0.6 for the investigated system. If photovolatics integrated into the refinery concept the NER

value is increased up to 1.7. These results suggest that positive energy balance is reachable

throughout the cultivation and transformation of microalgae biomass, however, the ideal

structure of biorefinery plants, conversion routes and expanded system bounderies need to

be determined and examined.

2.9 Carbon Capture and Storage (CCS)

Carbon dioxide is the primary greenhouse gas (GHG) and its uncontrolled emission

to the Earth atmosphere cause global warming, climate change and irreversible changes in

the nature. The increasing level of carbon dioxide (higher than 400 ppm in 2017) in the air

urges the spread of technological solutions that can prevent or at least mitigate the emission

of CO2. Carbon Capture and Storage (CCS) is a promising technology to lower the global CO2

emissions (Gohar and Shine, 2007; Boot-Handford et al., 2014). Power plants are responsible

for around the 30-40% of CO2 emissions and it is assumed that this value is going to increase

up to 60% by the end of this century (Alonso et al., 2017; Anwar et al., 2018). These point

carbon dioxide sources provide excellent opportunities for carbon capture.

22

2.9. Carbon Capture and Storage (CCS)

There are 3 different approaches to capture CO2, these are: (1) post combustion cap-

ture, (2) pre-combustion capture and (3) oxyfuel combustion capture (Fig. 2.11). Other sources

such as algae, biochar and charcoal show promising ways for carbon dioxide sequestration, as

well.

2.9.1 Carbon capture

During post combustion capture the CO2 content of the flue gas is separated using

various amine solutions like monoethanolamine, diethanolamine, potassium carbonate and

piperazine (Olajire, 2010). After the chemical reaction(s) (in the case of MEA absorbent: Eq. 2.6 -

Eq. 2.8) absorbent regenerated under high temperature (100-200°C depending on the absorber)

by steam (Kazemi and Mehrabani-Zeinabad, 2016). Other capturing technologies such as

membrane process (ceramic and polymeric) cryogenic distillation and algae cultivation can

be also applied in the case of post combustion capture. The post combustion technology can

be used in the case of flue gas with low CO2 content between 4 and 14 v/v % (Raza et al., 2018).

2RNH2+CO2+H2O → (RNH3)2CO3 (2.6)

(RNH3)2CO3+H2O+CO2 → 2RNH3HCO3 (2.7)

2RNH3HCO3 → 2RNH2+2H2O+2CO2 (2.8)

23


Figure 2.11 – Three different approaches to capture carbon dioxide (Rackley, 2017).

In the pre-combustion capture systems, the CO2 is captured before combustion.

The applied fuel is converted to H2 and CO2 containing fuel gas according to the following

equations (Eq. 2.9 - Eq. 2.11):

2C +3H2O → 3H2+CO+CO2 (2.9)

C +H2O →H2+CO (2.10)

CO+H2O →H2+CO2 (2.11)

In the first stage syngas is produced under low oxygen pressure then the CO/H2

mixture is passed through steam to form carbon dioxide. Finally, the CO2 is detached from H2

and sent to storage or used in other processes (Olajire, 2010).

In the case of oxyfuel combustion, pure oxygen is obtained from cryogenic air sep-

aration or membrane units. The produced flue gases contain mainly CO2 and water vapors.

The products are separated by condensing the water and the CO2 stream is dehydrated (Chen

et al., 2012).

24

2.9. Carbon Capture and Storage (CCS)

Biofixation of carbon dioxide can be carried out by microalgae biomass. Some

species are able to tolerate the toxic compounds (SO2, NOx, heavy metals) in the flue gas up to

a certain concentration. 1 kg of algae can fix 1.83-2.06 kg of CO2 (Pate et al., 2011; Zhou et al.,

2017) depending on the biological composition. Thus the direct injection of flue gas to the

cultivation medium can be used to mitigate GHG emission and to convert CO2 into valuable

compounds.

2.9.2 Carbon storage

The captured CO2 can be stored under the ground for thousands of years with low

leakage rate (only 0.1%) (van der Zwaan and Smekens, 2009). Efficient and long term storage

requires porous, permeable storage site with impermeable rock cap. Saline aquafiers, depleted

oil and gas reserves can also be used for the storage of CO2. (Underschultz et al., 2011; Anwar

et al., 2018)

25

3 Materials and Methods

3.1 Calculating the energy balance of an algae-based biorefinery

3.1.1 Energy requirements for the cultivation step

Three different cultivation systems were considered in the calculations: (1) open

raceway ponds (2) closed helical tubular photobioreactors (tPBRs) and (3) horizontal PBRs.

Open ponds were considered to be 0.3 m deep and have a total of 0.5 ha surface area. The

circulation energy demand can be decreased by applying three-end baffles which provide

more efficient circulation and decrease the dead-zone area to only 0.9%. The energy input

for a pond with buffles is 2.896 kW at 35 rpm paddle-wheel frequency (Sompech et al., 2012).

The energy requirements of the paddlewheels (Ep) in the ponds were calculated based on the

following equation (Eq. 3.1.):

Ep (MJ)=Pp ·NORP , (3.1)

where Pp is the energy requirement of a paddlewheel (MJ), NORP is the number of ponds.

As for closed systems helical and horizontal tubular photobioreactors (tPBRs) were

examined. The internal diameter was 0.053 m, the outer tube diameter was considered to be

0.060 m, the length of one PBR unit was 80 m. The total reactor volume was 0.2 m3. Within this

cultivation system, the volumetric productivity of the biomass can be as high as 1.535 g L−1 d−1,

with a biomass dry weight of 4 g L−1 and a dilution rate of 0.384 d−1 (Chisti, 2008b). In tubular

photobioreactors good mixing has to be maintained to achieve high biomass productivity and

avoid the creation of dark zones and cells shading. Turbulent flow regime and ideal mixing

can be provided by achieving high Reynolds number (>104), which can be attained at 0.5 m

s−1 liquid velocity (Molina et al., 2001). The energy requirement related to the circulation in

27

Chapter 3. Materials and Methods

closed tPRB was determined by the following equation (Eq. 3.2.) (Gómez-Pérez et al., 2015):

Ec (J s−1m−1)= (vπr2)4P, (3.2)

where 4P is the pressure drop (Pa m−1) (the pressure drop for straight tube is 58.6 Pa m−1), r

is the radius of the tube (m), and v is the velocity of the media (m s−1).

It is assumed that the tPBR units contain 90°elbows in the loop sections. These

elbows influence the pressure drop through the photobioreactor which was determined

calculating the equivalent pipe length (Eq. 3.3.) as follows:

le (m)= ξ ·df

, (3.3)

where le is the equivalent pipe length (m), ξ is the elbows minor loss coefficient (-), d is the

pipe diameter (m), and f is the Blasius friction factor (-).

The elbows minor loss coefficient (ξ) was determined based on equation 3.4.:

ξ (−)= kβ ·a√rm ·d−1 , (3.4)

where kβ is a correction factor that is determined by the elbow’s degree, a is the roughness. In

case of 90° the value of kβ is equal to 1.

If the Reynolds number (Re) (Eq. 3.5.) of the system is found to be between 2.32 x

103 and 105 then the friction factor can be determined by the Blasius equation (Eq. 3.6.) for

smooth pipes.

Re (−)= d ·v ·ρµ

, (3.5)

where d is the pipe diameter (m), v is the velocity (m s−1), ρ is the density (kg m−3) and µ is

the dynamic viscosity (Pa · s).

f (−)= 0.31644pRe

. (3.6)

28

3.1. Calculating the energy balance of an algae-based biorefinery

Further properties and calculated details of the open ORP and closed PBR systems

are listed in Table 3.1 and Table 3.2.

Table 3.1 – Properties of open Raceway-ponds. All data refer to one unit of raceway-pond.(Sompech et al., 2012; Jorquera et al., 2010)

Open Raceway-Pond (ORP)

Area (ha) 0.5

Depth (m) 0.3

Velocity (m s−1) 0.15

Rotational speed (rpm) 35

Energy requirement of a paddlewheel (kW) 2.852

Dilution rate (-) 0.1

Dry weight of microalgae (g L−1) 0.5

Biomass productivity (g m−2 d−1) 15

Water evaporation rate (% d−1) 0.29

Table 3.2 – Properties of helical tubular photobioreactor (tPBR). All data refer to one unit oftPBR. (Chisti, 2008b; Molina et al., 2001; Gómez-Pérez et al., 2015)

Tubular Photobioreactor (tPBR)

Reactor volume (m3) 0.2

Internal tube diameter (m) 0.053

Outer tube diameter (m) 0.060

Tube length (m) 80

Calculated equivalent pipe length (m) 3.31

Occupied area (m2) 12

Biomass productivity (g m−2 d−1) 35

Dry weight of microalgae (g L−1) 4

Dilution rate (-) 0.384

Gas hold-up (%) 23

Pressure drop for straight tube (Pa m−1) 58.6

Fluid velocity (m s−1) 0.5

Energy requirements of pumps (J m−1 s−1 ) 0.065

3.1.2 Nutrients and related energy requirements for the cultivation

Microalgae is a photoautotrophic biomass, which use light (photons), carbon dioxide

and nutrients in photosynthesis to build higher molecules such as proteins, carbohydrates

and lipids. Due to the great number of species and the rich biodiversity, the screening of

algae species allows us to find strains that can grow on either inorganic and/or organic media.

Some species (e.g., Chlorella vulgaris) are capable to grow mixotrophically, but this inves-

tigation focuses only on autotrophic cultivation and inorganic medias because this type of

cultivation ensures that the carbon content of algae is built using carbon dioxide and not from

29


other organic sources. This factor is important from the perspectives of carbon capture and

utilization.

The essential nutrients for autotroph microalgae cultivation are nitrogen and phos-

phorus containing compounds. Urea and diammonium phosphate were selected as nitrogen

and phosphorus source.

3.1.2.1 Energy requirements related to CO2

The carbon dioxide content of the flue gas can be used to provide inorganic carbon

sources to algae biomass. Some of the algae species are able to absorb the CO2 content of the

flue gas, though, the toxic compounds in high concentration (e.g., SO2, NOx above 100 ppm

and heavy metals) are lethal for the culture.

In order to determine the energy requirements related to carbon source, the required

amount of the CO2 has to be determined. The carbon requirement for the cultivation is

calculated based on Eq. 3.7. (Pate et al., 2011):

rC (kg CO2

kg algae)= Mass of CO2 input

Mass of dry weight algae= (44 ·C%)

1200, (3.7)

where rC shows the required amount of CO2 (kg) related to the amount of fermented microal-

gae biomass (kg), and C% is the weight percent of carbon in algae biomass (wt.%).

It is assumed that the algae biomass is cultivated continuously and based on the

dilution rate some of the produced algae is harvested daily. The required amount of CO2 for

the cultivation of algae biomass was calculated as follows (Eq. 3.8.):

m′CO2

(kg d−1)=malgae · rC +mharv · rC ·Nd , (3.8)

where m′CO2

is the required amount of CO2 (kg d−1), mharv is the amount of harvested algae

(kg), Nd is the number of days. The mass of algae (malgae , kg) is defined by equation 3.9.:

malgae (kg)=mharv · r−1harv100%−Lalgae

100%

, (3.9)

where rharv is the rate of harvesting (=dilution rate) (-), while Lalgae is the microalgae mass lost

during drying process (%).

If the flue gas contains high amount of toxic compounds it cannot be injected directly

to the algal culture broth, thus it is required to separate the CO2 content of the flue gas from

30


other compounds. For this purpose, absorption process with monoethanolamine (MEA)

absorbent was considered. The required energy for the carbon dioxide separation is calculated

based on Eq. 3.10.:

Epurif (MJ)=mCO2 · (msEs +Ev ), (3.10)

where Epurif is the required energy to purify the carbon dioxide stream (MJ), mCO2 is the mass

of carbon dioxide (kg), ms is the required mass of steam (kg kg CO−12 ), Es is the energy required

for the production of 1 kg of steam (MJ), while Ev is the required electric utility (MJ). MEA

based retrofit carbon capture plants have an energy demand of 3.2-4.5 MJ (kg CO2)−1 (Stec

et al., 2016; Wang et al., 2015a) which is used mainly for the regeneration of the absorbent.

This amount of energy can be saved if we can assume that the algae biomass can tolerate the

toxic compounds next to the carbon dioxide. The energy need for the injection of CO2 to the

culture broth was assumed to be 22.2 Wh (kg CO2)−1 (Lardon et al., 2009).

3.1.2.2 Energy requirements related to the nitrogen and phosphorus sources

To determine the required energy consumption which is related to the use of nitrogen

containing nutrient, the amount of required nitrogen was determined. The Redfield molar

ratio (C:N:P = 106:16:1) was used to calculate the required amount of nitrogen and phosphorus

nutrients. The required amount of nutrients were determined based on the work of Dassey

et al. (2014) (Equations 3.11 and 3.12):

N%= 224g ·C%

1272g, (3.11)

P%= 31g ·C%

1272g. (3.12)

Urea selected as nitrogen source, which can be produced reacting ammonia with

carbon dioxide in a two stage process, where the overall energy requirement of the process is

4.23 MJ (kg urea)−1. Diammonium phosphate (DAP) was considered as phosphorus source.

Liquid phosphoric acid is sprayed into a vessel with ammonia which result in DAP. The energy

requirement for the production of DAP was calculated based on Eq. 3.13 (Johnson et al., 2013):

EDAP (MJkg−1)= (Eelectr .+Enat.gas +ENH3 ·mNH3+EH3PO4 ·mH3PO4) ·mDAP , (3.13)

where EDAP is the energy requirement of the production of DAP (MJ kg−1), Eelectr . is the used

31


electricity (MJ kg−1), Enat.gas is the used natural gas, ENH3 and EH3PO4 are the energy required

(MJ kg−1) to produce 1 kg of ammonia and phosphoric acid, respectively. mNH3, mH3PO4 and

mDAP are the amount of ammonia, phosphoric acid and DAP (kg). The energy requirement

for the production of DAP was found to be 15.07 MJ (kg DAP)−1.

3.1.3 Harvesting and dewatering

Microalgae biomass can be cultivated in extremely dilute suspensions (the dry weight

content of the culture broth ranges typically between 0.5-4 g L−1 depending on the cultivation

system (Slater et al., 2015)) which brings difficulties in the downstream stage of the biorefinery.

Thus, dewatering of the fermented broth is necessary for further processing of the biomass.

Flocculation and sedimentation were considered as pre-concentration steps prior

the transformation of the biomass into energy carriers. Surendhiran and Vijay (2013) reported

that the efficiency of the flocculation of microalgae biomass can be as high as 82.27% using

Al2(SO4)3. Dong et al. (2014) reported that the optimal dosage of this type of chemical floccu-

lant is 20 mg L−1 at pH 5. Flocculation and sedimentation are suitable processes to achieve an

average final dry weight content of 30 g L−1. The required energy for the production of Al2SO4

was calculated based on Yao (2010) and it was found to be 9.26 MJ (kg Al2SO4)−1. Further

dewatering was considered to reach 200 g L−1 dry weight using centrifugation. The energy

requirement for the concentration of algae broth was calculated based on Zhang et al. (2013),

where the operational unit required 1 kWh (m−3 water) energy.

The separated water can be recycled and used again in the cultivation phase. The

energy need for the recycling was determined by using Equations 3.14 and 3.15 (Rogers et al.,

2014):

Erec (Wh)= (Vflocc +Vcentr ) ·ϕ ·h, (3.14)

ϕ (kg m−2 s−2)= γ ·ρwater ·g1000J kJ−1

, (3.15)

where Erec is the energy required for the recirculation (Wh), Vflocc and Vcentr are the volume

of the separated water by flocculation and centrifugation (m3), ϕ is a coefficient, h is the

differential head (m), γ is the efficiency of pumping (0.4-0.7), ρwater is the density of water (kg

m−3) and g is the gravitational constant (m2 s−1).

32


3.1.4 Pretreating for lipid extraction

Pretreatment of the cells are required for the further conversion of biomass. Pretreat-

ment of the biomass involves the disruption of algae cells, drying process and grinding. The

cell walls can be ruptured via several methods (e.g., ultrasonication, high-pressure homog-

enization, bead milling, chemical and enzymatic treatments (Slegers et al., 2014; Yap et al.,

2014)).

In this investigation it is assumed that the cells are disrupted by wet milling tech-

nology using bead mills. Dyno®-mill ECM Ultra type bead mill (WAB, 2018) was considered

for this process due to its high efficiency and robustness with a capacity of 0.5-6 m3 h−1 and

electricity consumption of 100-132 kWh.

Traditional solvent extraction of lipids can be carried out efficiently on feedstock

that contains moisture content less than 10 weight percent. In order to meet this criteria

drying step was considered to evaporate the excess water content of the biomass. The energy

requirement of the drying process Edryer (MJ) was calculated by Eq. 3.16.:

Edryer (MJ)= mw · (cw +cv ·4T )

1000 kJMJ ·ηdryer

, (3.16)

where mw is the amount of evaporated water (kg), cw is the latent heat of evaporation (kJ kg−1),

cv is the specific heat of water (kJ kg−1 °C−1), 4T is the temperature difference (°C) and ηdryer

is the efficiency of the dryer which was estimated to be 0.75.

After the drying process the dry bulk algae biomass was grinded into powder to

increase the efficiency of the extraction. The energy requirement of the grinder was calculated

based on Equation 3.17:

Egrinder (kWh)=mdryalgae ·Pgrinder , (3.17)

where Egrinder is the energy requirement of grinding (kWh), mdryalgae is the mass of grinded dry

algae (kg), Pgrinder is the performance of the grinder, which was considered to be 16 kWh (t dry

algae)−1 (Zhang et al., 2013).

33


3.1.5 Extraction of lipids

The extraction of lipids was considered to be done with traditional solvent extraction

using n-hexane as solvent. The efficiency of the solvent extraction was estimated to be 91.75%

which is an average value calculated based on the works of Sander and Murthy (2010) and

Brentner et al. (2011), while the mixing performance inside the extractor was assumed to be

30 kWh (t lipid)−1. The amount of solvent was calculated based on Xu et al. (2006) and it was

0.030 kg hexane (kg dry algae)−1.

The extraction step is followed by the regeneration of the solvent and the separation

of the extract and the solid residue. Solid-liquid separation should be applied to separate

the cell debris and the solvent containing extracted lipids, then in liquid-liquid separation

the lipid fraction should be separated from the organic solvent. Distillation selected for the

regeneration of hexane with an estimated overall solvent loss of 0.05 wt.% (Pokoo-Aikins et al.,

2010). The energy requirement of the solvent recovery was determined using equation :

Erec,hex (kJ h−1)= cp,hex ·whex · (Tb −T0), (3.18)

where Erec,hex is the energy needed for the separation of hexane and lipids (kJ h−1), cp,hex is

the specific heat capacity of hexane (2.26 kJ kg−1 °C−1), whex is the amount of hexane entering

the column (kg h−1), Tb is the boiling point of hexane (69°C), T0 is the temperature of hexane

right before entering the column (25°C).

3.1.6 Transesterification of lipids

Transesterification reaction is a suitable way to convert the separated lipids into

liquid energy carriers, biodiesel. During the reaction triacylglycerols are reacted with alcohol

(MeOH) in the presence of acidic or basic compounds (sulfuric acid or sodium hydroxid). The

main product of the reaction is fatty acid methyl ester (FAME) and glycerol as a co-product.

Yen et al. (2013) reported that methanol is added in excess in case of industrial scale

(twice more which is required stoichiometrically), the surplus methanol shifts the reaction

equilibrium to the formation of FAMEs. In this calculation NaOH was selected as for catalyst

of the reaction which was carried out at 60°C. The required heat was calculated based on

Equation 3.19:

Qtrans (kJ)= (mMeOH ·cp,MeOH +mlipid ·cp,lipid) ·4T , (3.19)

34


where Qtrans is the required heat of transesterification (kJ), mMeOH and mlipid are the mass

of methanol and lipids, while cp,MeOH and cp,lipid are the specific heat capacity of methanol

(2.528 kJ kg−1 °C−1) and lipids (1.991 kJ kg−1 °C−1 which was calculated based on the empirical

equation provided by Morad et al. (1995)). The energy requirement of the production of NaOH

(21.30 MJ (kg NaOH)−1) was calculated based on Kent (2007).

The product stream contains unconverted lipids, excess methanol, glycerol and

biodiesel. The methanol recovery through distillation process was simulated using ChemCAD

6.3.1.4168 software. The NRTL (Non-Random Two-Liquid) model was used in the simulation

to calculate phase equilibria. The methanol recovery efficiency was 96%, while the overall

energy investment (condensers, reboilers, pumps) was found to be 1.69 MJ (kg biodiesel)−1. It

is considered that the recovered methanol was feeded back to the transesterification reactor

with additional make up MeOH. The distillation residue contains glycerol, remaining catalyst

and FAMEs where the glycerol and the catalyst were separated in a hot water (50°C) washing

tank from the biodiesel. Subsequently, the dehydration of the biodiesel stream was considered

in the final purification step of biodiesel to meet the requirements of the EN 14214 (2008)

standard.

3.1.7 Thermochemical conversion of algae cake

The algae cake which is the extraction residue can be utilized for the production of

energy carriers through atmospheric thermochemical conversion involving processes such as

pyrolysis and gasification and hydrothermal conversion technologies such as hydrothermal

carbonization, liquefaction and gasification.

Gaseous energy carriers (hydrogen and methane rich biogas) can be combusted

in combined cycle gas turbines (CCGT) to produce heat and electricity with high efficiency

(ηelectricity >62%, GE Power (2018)), though the CO2 content of the biogas has to be removed

or upgraded prior combustion (Keche et al., 2014). Bio-oil can be used as liquid energy

carrier, though its high viscosity and N and O heteroatom containing compounds limit their

utilizations. Biochar is a co-product of liquefaction and gasification technologies but it can be

formed purposely through carbonization process. Biochar or hydrochar can be used as a solid

combustible or as a catalyst due to its properties is similar to active carbon (Cha et al., 2016;

Jafri et al., 2018).

Gasification was selected to transform the algae cake into energy carriers. Khoo et al.

(2013) showed experimentally that through the atmospheric gasification of lipid-depleted

biomass biochar, bio-oil and biogas can be formed in the following concentration: 58.18, 13.74

and 28.08 wt.%, respectively. It is considered that the higher heating values (HHVs) of the

product were 17.5, 34.1 and 32.9 MJ kg−1, respectively (Khoo et al., 2013). The higher heating

35


value is the amount of heat released by a specified mass of a fuel once it is combusted and the

resulted products have returned to 25°C. The energy requirement of the gasification process

was reported to be 8.26 MJ (kg feedstock)−1 with a conversion efficiency of 0.9 (Khoo et al.,

2013). The required energy investment of the gasification process (E′gasif ) for Nd days was

calculated according to equation 3.20:

E′gasif (MJ)= mac ·8.26MJkg−1 ·Nd

ηgasif, (3.20)

where mac is the mass of the lipid-depleted algae cake (kg), Nd is the number of days and

ηgasif is the conversion efficiency of gasificiation (-).

3.1.8 Hydrotermal conversion of wet biomass

The benefit of using hydrothermal technologies is that they are able to use wet

biomass as feedstock, and the moisture content of the biomass can be as high as 95 wt.%,

which value is far better compared to atmospheric thermochemical technologies, where the

acceptable maximum moisture content ranges between 15-20% (Blasi, 2008; Mohan et al.,

2006). The moisture content of the biomass is used as a process parameter, water acts as a

solvent and a reagent above its supercritical point (>374°C and 221 bar) (Barreiro et al., 2013;

Chaudry et al., 2015). Therefore, the hydrothermal liquefaction (HTL) process does not require

the pre-treating (cells rupture, drying, grinding) of biomass which results in a significant

energy saving potential.

The HTL unit requires an energy investment of 6.51 MJ (kg microalgae)−1 (Bennion

et al., 2015). This value can be decreased using energy recovery, thus the overall energy input

of the process was considered to be 5.90 MJ (kg microalgae)−1 (Bennion et al., 2015). The

energy need of the HTL process (EHTL, MJ) was calculated according to Eq. 3.21:

EHTL (MJ)=malgae ·5.90MJ kg−1, (3.21)

where malgae is the mass of algae (kg).

The produced HTL bio-oil is not suitable for direct combustion application because

it contains compounds that cause (1) high viscosity and (2) instability. The non-desirable

compounds can be extracted with near-supercritical state liquid propane (Agblevor et al.,

36


2014). The energy requirement of this process is calculated based on Equation 3.22:

Estab (MJ)=malgae ·Ybio−oil ·Eprp,extr , (3.22)

where Estab is the energy requirement for the stabilization of the bio-oil (MJ), malgae is the

mass of algae (kg), Ybio−oil is the yield of bio-oil (0.37 kgkg ) and Eprp,extr is the specific energy

demand of stabilization process (0.77 MJ (kg bio-oil)−1) (Bennion et al., 2015).

After the stabilization, the stabilized bio-oil still contains N and O atoms in excess.

Hydroprocessing can be used to remove nitrogen and oxygen heteroatoms. The energy re-

quirement of hydrogenation was calculated based on the work of Bennion et al. (2015) with

equations 3.23 and 3.24:

EH2 (MJ)=mstab−oil ·mH2 ·EH2,production, (3.23)

Ehydropr (MJ)=mstab−oil ·Phydropr , (3.24)

where EH2 is the energy required to produce H2 for hydrogenation (MJ), mstab−oil is the mass

of stabilized bio-oil (kg), mH2 is the mass of hydrogen (0.0488 kg H2 (kg stable bio-oil)−1),

EH2,production is the energy requirement to produce hydrogen (56.95 MJ (kg H2)−1), Ehydropr

is the energy need for hydroprocessing (MJ) and Phydropr is the specific energy need for the

hydrogenation process (0.8381 MJ kg−1).

3.1.9 The calculation of energy efficiency

The Net Energy Ratio (NER) was calculated to measure the efficiency of different

biorefinery alternatives. The NER is defined as follows (Eq. 3.25):

NER (−)=∑energy output∑energy input

, (3.25)

where∑

energy output is the total energy gained throughout the transformation of algae

biomass (MJ), while∑

energy input is the total invested amount of energy (MJ) which is

required by the processes in the upstream and downstream parts of a refinery.

37


3.2 Life Cycle Analysis (LCA)

Life cycle analyses of the investigated Carbon Capture and Storage alternatives were

carried out using SimaPro 8.0.1 software.

3.2.1 Goal & Scope

The goals of the LCAs were (i) the investigation of the CCS process from environmen-

tal point of view, (ii) identification of possible bottlenecks that affect the environmental factors

the most and (iii) finding solutions that can decrease the negative environmental effects of the

CCS technology.

The system boundary can be seen in Fig. 3.1 which includes the CO2 capture, the

regeneration of the absorbent, compression of the separated CO2, transportation via pipelines

and injection into geological reservoir.

The functional unit of the evaluation was the emitted amount of CO2 during the

production of 1 MWh electricity by burning coal, which was 774.5 kg CO2 MWh−1e .

Cradle-to-gate type life cycle analyses were conducted in case of all alternatives due

to the fact that the analyses were focused on the capture processes, the storage of the carbon

dioxide were not considered in the analyses.

Figure 3.1 – The flowsheet of the investigated Carbon Capture and Storage technology. MEA:monoethanolamine

38

3.2. Life Cycle Analysis (LCA)

3.2.2 Life Cycle Inventory (LCI)

The life cycle inventory was built based on the (1) Ecoinvent 3.1 database, (2) scien-

tific publications and (3) calculations detailed in this dissertation.

3.2.3 Life Cycle Impact Assessment (LCIA)

After collecting and calculating the required input and output data the environmen-

tal impacts were determined in the LCIA step. 4 different LCIA methods were used, namely the

IPCC 2007 (100a), Eco-indicator 99, IMPACT 2002+ and EPS 2000.

3.2.3.1 IPCC 2007 (100a)

Intergovernmental Panel on Climate Change (IPCC) 2007 method was used to eval-

uate the Global Warming Potential (GWP) of the CCS technology. GWP is expressed in CO2-

equivalent emission (kg CO2,eq) which is the amount of carbon dioxide that has the same

environmental effect as the emitted GHGs and heat that accumulate in the atmosphere. The

overall effects were obtained by multiplying the emitted amount of GHGs by their GWP’s value,

where the GWP of CO2 is standardized to 1.

3.2.3.2 Eco-indicator 99 (EI 99)

The damage oriented Eco-indicator 99 method was applied to screen environmental

effects in separate fields such as (i) Human Health, (ii) Ecosystem Quality, (iii) Resources.

Human Health consists of subcategories such as: the number and duration of dis-

eases, lost years due to permature death from environmental causes. The main effects of this

category were: climate change potential (DAILY, Disability-Adjusted Life Year), Ozone layer

depletion (DAILY), ionizing (nuclear) radiation (DAILY) and respiratory effects (organics and

inorganics) (DAILY).

Ecosystem quality incorporates effects related on species diversity. The subcate-

gories were ecotoxicity (Potentially Affected Fraction, PAF m2 yr), acidification (Potentially

Disappeared Fraction, PDF m2 yr), eutrophication (PDF m2 yr), land use (PDF m2 yr).

Resources category includes the surplus energy need that is required to extract

minerals (MJ surplus) and fossil resources (MJ surplus).

The Eco-indicator scores are calculated based on a three steps methodology (MHSPE,

2000): (i) collection of emission, resource extraction and land-use data, (ii) calculation of

damages and (iii) weighting damage categories (Human Health, Ecosystem Quality, Resources).

39


Impact indicators and weighting factors related to the damage assessment are collected in Pré

(2001) manual. Weighting is carried out based on Eq. 3.26:

EI99 (EI99Pt)=j∑

i=1(wi ·Dk), (3.26)

where EI99 is the Eco-indicator 99 point related to the applied functional unit (FU),

wi is the weighting factor of the ith damage category (-) and Dk is the normalised impact

indicator of the ith damage category (-). The representation of the damage model is illustrated

in Figure 3.2. Three types of characterization are included in the method (Table 3.3): (1)

Hierarchist (H), (2) Individualist (I) and (3) Egalitarian (E).

Table 3.3 – Characterization in the case of Eco-indicator 99 method.

Time Manageability Required level of evidence

Hierarchist (H) balanced between long

and short term

policy can avoid prob-

lemsinclusion based on consensus

Individualist (I) short timetechnology can avoid

many problemsonly proven effects

Egalitarian (E) very long termproblems can lead to

catastrophyall possible effects

3.2.3.3 IMPACT 2002+

IMPACT 2002+ method combines midpoint and damage based approaches (as it

is illustrated in Figure 3.3). IMPACT 2002+ incorporates several midpoint categories such as:

respiratory effects organics (kg C2H4eq) and inorganics (kg PM2.5,eq , where PM2.5,eq stands for

particulate matter with a diameter equivalent or less than 2.5µm), human toxicity carcinogenic

effect (kg C2H3Cleq), human toxicity non-carcinogenic effects (kg C2H3Cleq), ozone layer

depletion (kg CFC-11eq), ionizing radiation (Bq C-14eq), aquatic ecotoxicity (kg TEG water),

terrastrial ecotoxicity (kg TEG soil), aquatic acidification (kg SO2eq), terrestrial acidification (kg

SO2eq), land occupation (m2 org.arable), global warming (kg CO2eq), non-renewable energy

consumption (MJ primary) and mineral extraction (MJ surplus). The normalization factors

for the four damage categories are collected in the IMPACT 2002+ user manual prepared by

Humbert et al. (2012).

40

3.2. Life Cycle Analysis (LCA)

Figure 3.2 – Detailed representation of damage model of Eco-indicator 99 LCIA method(MHSPE, 2000).

41


Figure 3.3 – Overall scheme of IMPACT 2002+ LCIA method (Humbert et al., 2012).

3.2.3.4 EPS 2000

The EPS 2000 (Environmental Priority Strategies) method is based on 5 different

environmental classes that are the followings: (1) Human Health, (2) Ecosystem Production

Capacity, (3) Abiotic Stock Resource, (4) Bio-diversity and (5) Cultural and Recreational values.

Human health effects includes 5 different impact categories: Life expectancy (Person

year), severe morbidity and suffering (Person year), morbidity (Person year), severe nuisance

and nuisance (Person year).

Ecosystem production capacity consists of categories such as crop production capac-

ity (kg), wood production capacity (kg), fish & meat production capacity (kg), soil acidification

(mole H+ equivalents), production capacity of water (irrigation and drinking water) (kg).

Abiotic Stock Resource incorporates 5 elements: depletion of element reserves (kg of

element), depletion of fossil reserves: a.) natural gas (kg), b.) oil (kg), c.) coal (kg), depletion of

mineral reserves (kg of minerals).

42

3.3. PESTLE analysis

Bio-diversity is described with extinction of species impact category (no dimension

for this category).

3.3 PESTLE analysis

PESTLE (Political, Economic, Social, Technological, Legal and Environmental) analy-

sis was applied as a framework to identify and determine external and internal factors that

can affect the Carbon Capture and Storage process. The results of the PESTLE analysis was

evaluated with Multi-Criteria Decision Analysis which requires numerical input values, there-

fore, the main focus of the PESTLE analysis was the examination of numerical quantitative

impact categories.

3.3.1 Political & Legal factor

In some cases it is not possible to describe factors with quantitative values because

of the cross-complexity of the examined system makes difficult such examination. This was

the situation concerning political and legal categories, therefore these factors were merged

into one main factor which was analyzed generally based on literature data and industrial

forecasts.

3.3.2 Economic factor

A cost analysis was carried out to evaluate the CCS alternatives from economic point

of view. Two main economic subfactors were considered and evaluated: (i) the relativized

operational cost (-) and (ii) the specific capital cost (-). All of the calculations through the

economic assessment was related to the functional unit (FU) (774.5 kg CO2 MWh−1e ) of the

LCA.

3.3.3 Social factor

For the evaluation of possible social factors, impact categories of the EPS 2000 LCIA

and EI99 methods were used. These were: (i) Severe morbidity (PersonYr), (ii) Life expectancy

(PersonYr), (iii) Nuisance (PersonYr) and (iv) Human Health (EI99 Pt).

3.3.4 Technological factor

The technological aspect of the CCS process was investigated by determining the

Technical Readiness Level (TRL) and the Resource Consumption (GJ FU−1).

43


3.3.5 Environmental factor

The conducted life cycle analyses was used for the environmental evaluation of

effects related to the application of the CCS process chain.

3.4 MCDA analysis

Multi-Criteria Decision Analysis (MCDA) was used for ranking and selecting the

investigated alternatives. The CCS process alternatives were evaluated based on the identified

and determined numerical PESTLE factors. DECERNS (Decision Evaluation in Complex Risk

Network System) software was applied to conduct MCDA.

The Multi Attribute Value Theory (MAVT) was used in the investigation, where the

objective is the generation of value function V(x) as it is shown in Eq. 3.27.:

V (x)=F (V1(x1), ...,Vm(xm)), (3.27)

where x is an alternative that is defined as a vector x=(x1,...,xm). The final score of the alterna-

tives Vi (xi ) were calculated depending on their performance on criterion i, where

Vi (x)= 0≤Vi (x)≤ 1. (3.28)

The goal of the MAVT method is the identification of the x alternative which maxi-

mizes the value of V(x). This model is additive as it is described by Equations 3.29 and 3.30.:

V (x)=w1V1(x1)+ ...+wmVm(xm), (3.29)

wj > 0,∑

wi = 1, j = 1,...,m, (3.30)

where wj is the criterion weight.

44

3.5. Microalgae cultivation

3.5 Microalgae cultivation

3.5.1 Organism

Chlorella vulgaris MACC555 microalgae was used in the cultivation phase (Fig.

3.4). The strain was obtained from the Mosonmagyaróvár Algal Culture Collection (MACC)

(Széchényi István University). BG11 medium (CCAP, 2017) was used for the cultivation with

the following composition (in g L−1): NaNO3, 1.500; K2HPO4, 0.040; MgSO4 ·7H2O, 0.075;

CaCl2 ·2H2O, 0.036; Citric acid, 0.006; FeNH4SO4, 0.006; EDTANa2, 0.001; Na2CO3, 0.020

and 1.0 ml of A5 Solution (Sigma-Aldrich, 2018): (H3BO3, 286 mg; MnSO4 ·7H2O, 250 mg;

ZnSO4 ·7H2O, 22.2 mg; CuSO4 ·5H2O, 7.9 mg; Na2MoO4 ·2H2O, 2.1 mg; ion-exchanged water,

100 mL).

Figure 3.4 – Microscopic image of Chlorella v. microalgae isolate with oil immersion lens(1000x zoom, Bel).

3.5.2 Microtiter plate (MTP) and RGB-LED panel set up and operation

A 24 well microtiter plate (Polistyrene GPPS microplate, Porvairr) was used for

screening the effects of light parameters on biomass productivity. The well volume of the

microplate was 3.1 ml. The bottom of the wells was transparent, the sidewalls of the plate

were white which prevented the cross-illumination between the wells. The illumination was

provided at the bottom of the microplate with an RGB-LED panel. The design of the microplate

is presented in Figure 3.5b.

The light emitting diode panel was designed to be able to illuminate individually

all of the microplate wells. The structure of the RGB-LED panel is illustrated in Fig. 3.5a.

45


The RGB-LED panel consists of 24 light emitting diodes. The design was coherent with the

microplate structure. The LED intensity could be controlled on potmeters, while the light

wavelengths could be adjusted by jumpers. The design of the LED panel made available one

adjustment in one row, thus the configuration allowed to set up 6 different examinations of

light parameters (wavelength and intensity).

A timer was used to provide 16:8 hours light and dark period for the culture broth.

The total volume of each micro fermentation was set to 1.5 ml. The inoculation rate was

∼7% (0.1 ml) whereof the initial optical density (OD) was set to between 0.1-0.3. A shaking

incubator (Innova 40, New Brunswick Scientificr) was used to provide sufficient mixing of the

MTP cells, where the temperature and rotational speed were set to constant at 25°C and 250

rpm, respectively. The microplate module was fixed into the shaker with Enzyscreen’s (NL)

Clamp system.

The microplate fermentations were evaluated with the RGB color model. The trans-

parent bottom side of the MTP was scanned with a scanner (Scanworks 60a, LG) to determine

the green color code in each well which is correlated to the amount of pigments (e.g., chloro-

phylls). Calibration line was set up to determine the relation between dry-weight (g L−1),

optical density (-) and cell number (cell ml−1). The green values of each wells were determined

at 5 points (Adobe Photoshop CS6) as it is illustrated in Fig. 3.5b.

The sterilization of the MTP was carried out in 2 steps: (1) the plate was placed into

an ultrasonic bath (UC 006 DM1,TESLA) for 25 minutes, then (2) it was put into a biosafety

cabinet (Biobase) and treated under UV light for 20 minutes. The media was sterilized with an

autoclave (3870 ELV, Tauttnauer) at 121°C with 1 bar overpressure for 20 minutes.

3.5.3 Photobioreactors (PBRs) set up and operation

Stirred tank laboratory photobioreactors (PBR) were used for scaled up fermenta-

tions. The schematic illustration of the fermentors are presented in Fig. 3.6.

The height and diameter of the cylindrical PBR units were 26 and 15.5 cm, respec-

tively, with a total volume of 4.25 L. The surface of the fermenter was illuminated by LED-strips

that was fixed on a LED-stand. The RGB-LED lighting platform is provided by UTEX Culture

Collection of Algae at The University of Texas at Austin with the following colors: red (626 nm),

green (525 nm) and blue (470 nm). 16:8 hours of light and dark illumination cycle was provided

with a timer, similarly to the MTP fermentations. B.Braun (Melsungen, Germany) control

unit was used for the aeration of the culture broth where the aeration rate was controlled by

a rotameter. The inlet air was filtered with a sterile filter (0.2 µm, PTFE, Sartorius Midisart

2000). The culture was mixed continuously with a magnetic stirrer (IKA). The gas transfer was

increased by placing a sparger at the end of the inlet air manifold.

46


86mm

126m

m

Potmeters Jumpers

Plan view

Front view

Power

conncetors

Positioning pins

The structure of RGB-panel

(a) RGB-LED panel

(b) Monitoring the microplate fermenta-tions (the red dots indicate the readingpoints on scanned photos).

Figure 3.5 – The structure of the RGB-panel and LED module. (a) The schematic figure of theRGB-LED panel, (b) Monitoring biomass growth in microplate cells.

The fermentations were carried out with a working volume of 2 L, while the inocula-

tion rate was about 7% (100-150 ml) - similarly to the scaled-down fermentations - which was

suitable to achieve an optical density of 0.15. The magnetic stirrer was held at 250 rpm, the

temperature was 25°C. The fermentations took approximately 240 hours, the cultures were

monitored daily by measuring the optical density. The light intensity and wavelength were

adjusted with a controller.

The sterilization of the media and fermenters were carried out in an autoclave (3870

ELV, Tauttnauer) at 121°C with 1 bar overpressure for 20 minutes.

47


Figure 3.6 – Laboratory scaled stirred tank photobioreactor. (1) Photobioreactor, (2) RGB-LED stand, (3) LED strip, (4) magnetic stirrer, (5) magnet, (6) sampling manifold, (7) inlet airmanifold, (8) sparger, (9) clucking, (10) sampling glass, (11) air filter, (12) air filter, (13) B.Brauncontrol unit, (14) rotameter

3.5.4 Analytical methods

3.5.4.1 Optical Density

The optical density (OD) of the algae suspensions was determined by an UV-Vis

spectrophotometer (Pharmacia LKB · Ultraspec Plus Spectrophotometer) at 560 nm. The OD

measurements were conducted with 3 replicates, where the blank sample was distilled water.

48


3.5.4.2 Cell number

The cell number in one ml was determined with a Bürker counting chamber. The

chamber was consisted of 2 different areas of square: (i) 1/25 mm2, (ii) 1/400 mm2. The depth

of the squares were 0.1 mm. A few drop of the suspension and a glass plate was placed to the

chamber, then the cell was counted in the smaller squares through a microscope (Bel) with a

1000x zoom rate. The cell number was determined based on Equation 3.31:

Cellnumber(cell ml−1)=CN1/25 ·4 ·106, (3.31)

where CN1/25 is the average number of the cells in the smaller squares across the diagonals

(2x13 piece of squares).

3.5.4.3 Dry weight content and biomass productivity

The dry weight content of microalgae was determined by gravimetric method. The

algae suspensions were filtered through 0.22 µm nitrocellulose membrane (MILLIPORE). The

membrane and microalgae were dried at 103-105°C in a drying cabinet (Heraous) for constant

weight, then the samples were cooled to room temperature in a dessicator. The dry weight

was calculated with the following equation (Eq. 3.32):

DW (g L−1)= (A−B) ·1000SV

, (3.32)

where DW is the dry weight (g L−1), A is the weight of the filter and microalgae on it (g), B is

the weight of the filter and SV is the volume of the sample (ml).

The biomass productivity was calculated based on Eq. 3.33:

P (g L−1d−1)= DWi −DW0

ti − t0, (3.33)

where P is the biomass productivity (g L−1 d−1), DWi and DW0 are the biomass concentration

at times ti and t0.

3.5.4.4 Calibration curves

Calibrations are carried out between dry weight and 255-Green code (Fig. B.1a) and

cell number and 255-Green code (Fig. B.1b) and optical density and 255-Green code (Fig.

B.1c) in case of the MTP device. The calibration curves for the laboratory scale stirred tank

photobioreactors are presented in Figure B.2.

49


3.5.4.5 Light intensity

The PPFD (Photosynthetic Photon Flux Density) is defined as the number of photons

in the PAR (Photosynhtetically Active Radiation) region emitted per m2 s1 (Matsuda et al.,

2016). The intensity of the light emitting diodes were measured by a lux meter (IEC 6 LF 22,

Cosilux Tungsram). The measured lx values were converted to µmol photon m−2 s−1 based on

Eq. 3.34 - Eq 3.36. (Ashdown, 2014):

I′RED [µmol photonm−2s−1]= IRED [lx ]

26.80, (3.34)

I′GREEN [µmol photonm−2s−1]= IGREEN [lx ]

8.77, (3.35)

I′BLUE [µmol photonm−2s−1]= IBLUE [lx ]

61.70. (3.36)

where I′RED , I

′GREEN , I

′BLUE are the light intensities in µmol photon m−2 s−1, while IRED , IGREEN ,

IBLUE are the light intensities in lx.

3.5.4.6 Ultimate analysis

Elemental composition (C,H,N content) was determined by combustion techniques.

Liebig-method (Usselman, 2003) was used to determine the C and H content of the biomass.

A long glass tube was used for the estimation of carbon and hydrogen content. The tube

was packed with copper oxide and coarse copper oxide. Oxygen atmosphere was supplied

throughout the measurements. 2 absorption tubes were connected to the combustion glass

which were filled with anhydrous calcium chloride and potassium hydroxide for the absorption

of water and CO2, respectively. The weight percent of the carbon and hydrogen were calculated

based on Eq. 3.37. and Eq. 3.38.:

wC (wt.%)=1244 ·mCO2

malgae, (3.37)

wH (wt.%)=218 ·mH2O

malgae, (3.38)

50

3.6. Biological composition of the algal biomass

where wC and wH are the weight percent (wt.%) of the carbon and hydrogen in the biomass,

mCO2 is the mass of carbon dioxide (g), mH2O is the mass of water (g), malgae is the mass of

algae biomass (g).

The nitrogen content of the biomass was determined based on Dumas-method

(Sainju, 2017) using FP-528, LECO device. Known weight of algae samples were placed into

sample holders then into the loading head of the FP-528. The rapid combustion was carried

out dropping the samples into a hot furnace. The nitrogen content was measured with a

thermal conductivity cell.

3.5.4.7 Proximate analyis

The proximate analyis (Volatile Matter (VM), Fixed Carbon (FC) and Ash content)

was determined based on the standards of American Society for Testing Materials (ASTM):

D3175 for volatile matter, D3172 for fixed carbon and D3174 for ash content. The samples

were burned in a Pt crucible by an electric furnace (1.4/1000, DENKAL). The weight of the

samples were determined before and after the combustion using analytical balance.

3.6 Biological composition of the algal biomass

The protein content of the biomass was determined by Eq. 3.39.:

wprotein (wt.%)=wN ·6.25, (3.39)

where wprotein is the weight percent (wt.%) of the protein in the biomass, wN is the weight

percent (wt.%) of the nitrogen in the biomass. The 6.25 is a conversion factor which assumes

that 1 kg of plant or animal protein contains 160 g N (1000g/160g=6.25). This 6.25 factor does

not relate to any specific feedstock (Sriperm et al., 2011). The lipid content of the biomass was

determined by extracting the lipids with hexane:methanol (2:1) solvent mixture. The resulted

two phase extract was separated in a shaking separating funnel. Then the hexane rich phase

was distilled with a rotadest (Laborota 4000, SIMEX), the residue, which contained the lipids,

was weighted. The carbohydrate content of the biomass was calculated based on Eq. 3.40.:

wcarbohydrate (wt.%)= 100%−wprotein −wlipid −wash, (3.40)

where wcarbohydrate , wprotein, wlipid , wash are the weight percent (wt.%) of the carbohydrate,

protein, lipid and ash in the biomass, respectively.

51


3.7 Hydrothermal gasification (HTG)

3.7.1 Design of equipment

Hydrothermal gasification (HTG) of microalgae biomass was carried out in a con-

tinuous plug flow tubular reactor system. The P&I diagram of the HTG process is shown in

Fig. 3.7. The algae feedstock was concentrated with a centrifuge (Hettich ROTINA 380) prior

loading the gasification reactor. The outer diameter of the stainless steel 316 tubular reactor

was 1/8 inch (=3.175 mm), while the wall thickness was 0.7 mm. An electric oven was used to

provide the required temperature for the process. The length of the reactor in the oven was

2 m. The algae suspension and water stream were transferred with HPLC pumps (PU-980,

Jasco; Model 303, Gilson). The algae and water streams were mixed at the inlet of the reactor to

obtain the required temperature value at the beginning of the measurements. The volumetric

flow was set to 2.5 ml min−1 thus the average residence time was 118.8 sec. The design of the

reactor system provides turbulent flow across the length of the reactor, the Reynolds number

was higher than 105. Two K-type thermocouples were installed directly at the beginning and

at the end of the reactor.

A phase separator was installed after the reactor section where the liquid and gaseous

products were separated. The produced gas was collected in a calibrated gas burette with a

volume of 425 ml. A 3 cm long glass tube attachment with a septum was installed on the gas

burette for sampling the gaseous product.

52

3.7. Hydrothermal gasification (HTG)

Figure 3.7 – The P&I diagram of microalgae cultivation and hydrothermal gasification. PBR:photobioreactor, P-1,2,3,4,5,6,7,8,9,10,11,12,13,14: pipe sections, v-1,2,3,4,5,6: needle valves,PUMP-1,2,3: pumps, HX-1,2,3: heat exchangers, R-1: HTG reactor section, T-I2-I3: thermocou-ples.

3.7.2 Operation

The concentration of the algae suspensions was set prior to the HTG measurements.

The oven was heating up to achieve the required reaction temperature. Ion-exchanged water

was used in the measurements (Simplicity UV, MILLIPORE). Pressure test was conducted

before every measurements. The gas burette was washed with argon, which was the carrier

gas in the following biogas analysis (see Section 3.7.3.1.). First, P-6 pipe section was filled with

the algae suspension, while V-4 neddle valve was closed. When the suspension was loaded,

the V-3 valve has been closed. The pressurization started with PUMP-2 and PUMP-3, when the

system pressure has reached the required value, the V-4 was opened and the algae feedstock

was transferred to the HTG reactor section. Depressurization of the system was carried out

when the gas production has stopped.

53


The biogas yield was determined based on Eq. 3.41.:

YGAS (molkg−1)= nbiogas

malgae, (3.41)

where YGAS is the total gas yield (mol kg−1), nbiogas is the mole number of the produced biogas,

malgae is the mass of the dry feedstock (kg).

The carbon gasification efficiency was calculated using Eq. 3.42.:

GEC (%)= mC ,gas

mC ,feed −mTC ,liq·100%, (3.42)

where GEC (%) is the carbon gasification efficiency, mC ,gas is the carbon content of the biogas

(g), mC ,feed is the carbon content of the feed (g), mTC ,liq is the total carbon (TC) content of

residue (g) which was measured with Shimadzu TOC-VCSH (Section 3.7.3.2.).

3.7.3 Analitical methods

3.7.3.1 Biogas Analysis

The produced biogas was analyzed with a HP5890SII/TCD/FID gas chromatograph.

The thermal conductivity and flame ionization detectors were connected simultaneously, thus

every gas components could be analyzed with one injection. Packed stainless steel column

(1.9 m, 1/8" OD) was installed and filled with Porapak Q (80/100 mesh) load. The Porapak Q

was appropriate to separate the biogas components. Argon was selected as carrier gas because

of the presence of H2 in the biogas. The column head pressure of the carrier flow was set to

150 kPa. The initial temperature was 50°C and held for 0.5 min, the final temperature was

150°C for 2 minutes, which was reached with 20°C min−1 heating rate. Measurements were

carried out in 4 replicates.

3.7.3.2 Total Carbon (TC) determination

Shimadzu TOC-VCSH was used to determine the total carbon content of the pro-

duced liquid product. The catalytic oxidation of the samples were carried out at 680°C, where

nondispersive infrared sensor (NDIR) was used as a gas detector. The measurements were

conducted with 3 replicates.

54

3.8. Experimental design and statistical analysis

3.8 Experimental design and statistical analysis

Design of experiments (DoEs) and statistical evaluation of the experimental results

were carried out using Dell™Statistica™13.1 software.

3.8.1 DoE for Light wavelength

The ideal illumination of wavelength was determined with One-way Analysis of

Variance (ANOVA). The effects of six different levels of settings were investigated based on

RGB-color mixing: red (R:626 nm, G:-, B-), green (R:-, G: 525 nm, B:-), blue (R:-, G:-, B:470 nm),

yellow (R: 626 nm, G: 525 nm, B:-), aquamarine (R: -, G: 525 nm, B: 470 nm) and purple (R: 626

nm, G: -, B: 470 nm) on biomass productivity (mg L−1 d−1).

3.8.2 DoE for Light intensity

The optimization of light intensity was carried out based on a Central Composite

Design (CCD). Red (X1) and Blue (X2) LED intensities were investigated with 5 levels as inde-

pendent variables, while the dependent variable was selected to be the biomass productivity

(Y1). Response Surface Methodology (RSM) was used for the evaluation of the experimental

results. The polinomical quadratic response model was defined by Eq. 3.43.:

Yp =β0+k∑

i=1βi Xi +

k∑i=1

βii X2i +

k∑i=1

k∑j=1

βij Xiβii Xj +ε, (3.43)

where Yp is the predicted response variable (biomass productivity), Xi , Xj are independent

variables (red and blue intensities), β0,βi ,βii ,βij are the regression coefficients and ε is the

random error.

3.8.3 DoE for the investigation of light intensity and aeration rate in PBR

A 22 full factorial design was applied where the investigated factors were RGB LED

intensity (RED: 178.9-256.9 µmol m−2 s−1; BLUE: 64.8-102.1 µmol m−2 s−1) and PBR aeration

rate (0.50-0.75 vvm) on two levels. The dependent variables were biomass productivity (mg

L−1 d−1), protein, carbohydrate and lipid content (wt.%).

55

4 Results and Discussion

4.1 Energy balance of a third generation biorefinery

The energy balances of the algae based biorefinery alternatives were calculated and

compared to each other. The results of the energy demand calculations by processes are listed

in Table 4.1. The functional unit (FU) of the calculations was 1.00 kg of the highest HHV’s

liquid energy carrier (biodiesel in case of the transesterification pathway and upgraded bio-oil

for the hydrothermal process). Two downstream processing routes were investigated based

on the moisture content of the biomass as it is detailed in Fig. 4.1: (1) traditional dry route

(where the dry weight content of algae is higher than 90 wt.%), (2) hydrothermal pathway

(where the dry weight content of algae can be as low as 5 wt.%). The dry route consists of

the following processes: (1.) cell disruption, (2.) drying of wet biomass, (3.) grinding, (4.)

extraction of lipids, (5.) transesterification of extracted lipids, (6.) separation of biodiesel and

glycerol, (7.) purification of biodiesel, and (8.) atmospheric gasification of lipid depleted cell

debris. The hydrothermal wet route involves (1.) hydrothermal liquefaction, (2.) stabilization

of the produced bio-oil, and (3.) hydroprocessing. The product yields of the dry route were

the following: 1.00 kg of biodiesel, 0.10 kg of glycerol, 1.70 kg of biochar, 0.40 kg of bio-oil and

0.82 kg of biogas. While, in case of the hydrothermal - wet - route 1.00 kg of upgraded bio-oil,

0.46 kg of biochar and 0.86 kg of biogas were produced transforming microalgae biomass. The

higher heating value of the products was given in Table 4.2.

57

Ch

apter

4.R

esults

and

Discu

ssion

Table 4.1 – Energy demand by operational units

Process Relative energy demandEnergy demand [ORP],[tPBR]

MJ (kg biodiesela)−1Energy demand [ORP],[tPBR]

MJ (kg upgraded bio-oilb)−1Source

UPSTREAMc

CO2 absorption 3.2 MJ (kg CO2)−1 50.31, 31.85 31.39, 27.82 Wang et al. (2015b)

CO2 transportation 0.022 kWh (kg CO2)−1 1.25, 0.79 0.78, 0.69 Zaimes and Khanna (2013)

CO2 injection 22 Wh (kg CO2)−1 1.25, 0.79 0.78, 0.69 Gikonyo (2013)

DAP production 15.07 MJ (kg DAP)−1 5.04, 4.25 3.14, 3.71 Current estimation

Urea production 4.23 MJ (kg Urea)−1 4.54, 3.83 2.83, 3.34 Johnson et al. (2013)

ORP mixing 2.852 kW (pond)−1 19.21, - 11.98, - Sompech et al. (2012)

tPBR mixing 6.46·10−2 J (m·s)−1 -, 13.00 -, 11.35 Current estimation

Al2SO4 production 9.26 MJ (kg Al2SO4)−1 2.17, 0.25 1.35, 0.22 Yao (2010)

Clarifier & mixer 5.96 kWh 0.13,0.13 0.08, 0.12 Rogers et al. (2014)

Pumping to centrifuge 0.414 kJ s−1 0.01, 0.00 0.01, 0.00 Current estimation

Dewatering with centrifuge 1 kWh m−3 0.70, 0.53 0.44, 0.46 Zhang et al. (2013)

Water recycling 27.5 kWh 0.62, 0.07 0.39, 0.06 Current estimation

Traditional DRY routed

Bead milling 20.83 kW m−3 2.81, 2.81 - WAB (2018)

Drying 3.43 MJ (kg evaporated water)−1 98.76, 56.00 - Current estimation

Grinding 16 kWh (t algae)−1 0.25, 0.18 - Zhang et al. (2013)

Hexane regeneration 26.99 Wh (kg C6H14)−1 0.01, 0.01 - Current estimation

Mixing during extraction 30 kWh (t lipid)−1 0.11,0.11 - Zhang et al. (2013)

Hexane production 0.48 MJ (kg dry algae)−1 2.08,1.49 -Wang (1999); Shirvani et al.

(2011)

MeOH production 58.89 MJ (kg MeOH)−1 2.55, 0.25 - Barrañon (2006)

NaOH production 21.30 MJ (kg NaOH)−1 0.42, 0.42 - Kent (2007)

Heat demand for transesterifica-

tion

87.55 kJ (kg biodiesel)−1 0.09, 0.09 - Current estimation

MeOH regeneration 1.69 GJ (t biodiesel)−1 1.69, 1.69 - Current estimation

Heating distilled water 53.29 MJ (t biodiesel)−1 0.05,0.05 - Current estimation

Biodiesel purification 330 MJ (t biodiesel)−1 0.33,0.33 - Current estimation

Atmospheric gasification 8.26 MJ (kg algae cake)−1 26.89, 15.82 Khoo et al. (2013)

Hydrothermal liquefaction - WET routee

HTL unit 5.9 MJ (kg microalgae)−1 - 16.82 Bennion et al. (2015)

Na2CO3 production 16.67 MJ (kg Na2CO3−1) - 1.90 ITP Mining (1997)

Bio-oil stabilization 0.77 MJ (kg bio-oil)−1 - 0.81 Bennion et al. (2015)

Propane production 51.80 MJ (kg propane)−1 - 1.09 Bennion et al. (2015)

Hydrogen production 56.95 MJ (kg H2)−1 - 2.93 Bennion et al. (2015)

Hydroprocessing 0.8381 MJ (kg stabilized oil)−1 - 0.88 Bennion et al. (2015)a Biodiesel is the liquid energy carrier product that produced through transesterification of algae lipids.bUpgraded bio-oil is the liquid energy carrier that produced through hydrothermal liquefaction and upgraded via bio-oil stabilization and hydroprocessing.c The UPSTREAM section of the biorefinery is responsible for the production of microalgae biomass.d The aim of the Traditional DRY route is the transformation of algae biomass to biodiesel through the transesterification of extracted lipids.eThe aim of the WET route is the transformation of wet algae biomass to upgraded bio-oil through hydrothermal liquefaction, bio-oil stabilization and hydroprocessing.

58

4.1. Energy balance of a third generation biorefinery

Figure 4.1 – Flowsheet of the investigated microalgae-based biofuel plant

Figure 4.2 shows the energy demand distribution of the traditional dry processing

route of algae using two different cultivation methods (Open Raceway Ponds, ORPs; and

tubular photobioreactor , tPBRs). It is found that the highest energy requirements belong to

only 4 processes. These are the drying step (ORP: 98.76 MJ (kg biodiesel)−1, tPBR: 56.00 MJ (kg

biodiesel)−1), CO2 absorption (ORP: 50.31 MJ (kg biodiesel)−1, tPBR: 31.85 MJ (kg biodiesel)−1),

gasification (ORP: 26.89 MJ (kg biodiesel)−1, tPBR: 15.82 MJ (kg biodiesel)−1) and mixing of

the culture broth (ORP: 19.21 MJ (kg biodiesel)−1, tPBR: 13.00 MJ (kg biodiesel)−1). Based on

the Pareto principle these processes are identified as energy demand bottlenecks.

Table 4.2 – Higher Heating Values (HHVs) of the produced energy carriers

Process Product HHV (MJ kg−1) Source

Transesterification Biodiesel 41 Brennan and Owende (2010)

Glycerol 19-25a de Greyt (2011)

Gasification Biochar 17.5 Khoo et al. (2013)

Bio-oil 34.1 Khoo et al. (2013)

Biogas 32.9 Khoo et al. (2013)

HTL Biochar 19.35 Bennion et al. (2015)

Bio-oil 34 Anastasakis and Ross (2015)

Biogas 1.1 Bennion et al. (2015)

Upgraded bio-oil 41.5 Biller et al. (2015)a Depends on purities

59

Chapter 4. Results and Discussion

Figure 4.2 – Energy demand distribution in the case of the dry route

The share of microalgae drying process in the total energy demand is almost 45%,

which highlights (i) the role of the dewatering step where a considerable amount of energy

can be saved if the feedstock contains less water, and (ii) the significance of hydrothermal

technologies where the drying step can be eliminated resulting in considerable energy savings.

The share of the carbon dioxide absorption process is found to be 22.7%. This result shows

the importance of flue gas composition. If flue gas does not contain toxic compounds above

certain limits (NOx, SO2 < 100 ppm), then the flue gas can be directly injected into the cul-

tivation system without any purification process which leads to a significant energy saving.

From this point of view the diversity and resistance of certain algae strains becomes also an

important issue. The gasification process has the third highest energy requirement with a

share of 12.12%, but at the same time more energy can be obtained in this process compared

to the transesterification of lipids by burning energy carriers (biogas, biochar, bio-oil) which is

produced in this step (as it is illustrated in Fig 4.4). The rest of the processes are responsible

for the 12.96% of the total energy demand.

The Pareto principle prevails in the case of the hydrothermal route as well (Fig. 4.3),

where the highest energy demands are paired with CO2 absorption process (ORP: 31.39 MJ

(kg upgraded bio-oil)−1, tPBR: 27.82 MJ (kg upgraded bio-oil)−1), hydrothermal liquefaction

(HTL) unit (ORP&tPBR: 16.82 MJ (kg upgraded bio-oil)−1) and mixing related to the cultivation

stage (ORP: 11.98 MJ (kg upgraded bio-oil)−1; tPBR: 11.35 MJ (kg upgraded bio-oil)−1).

60

4.1. Energy balance of a third generation biorefinery

Figure 4.3 – Energy demand distribution in the case of the wet route

The CO2 absorption has the highest energy demand in the wet route with a share of

40.44% which increases the role of direct injection of CO2 rich flue gas. The carbon dioxide

absorption process has higher share regarding the hydrothermal pathway, however, the exact

values are equal for both processing chain if the upstream process is the same. The second

highest energy demands are paired with the hydrothermal liquefaction unit with a share of

21.67%. The third highest energy requirements are paired with the cultivation related mixing

(15.44%).

The energy gain distribution is presented by energy carriers in Figure 4.4. The type

of the cultivation system can affect the biomass composition (lipid, protein, carbohydrate

content) which ultimately influences the product yields of the downstream process chains.

For the comparison of different refinery scenarios, the produced amount of biodiesel was

the same for all dry route alternatives. Throughout the calculation of energy balances it is

found that in the case of the dry route, the highest energy gain can be obtained by producing

biodiesel. However, it is also found that more energy can be produced combining the energy

content of the rest of the carriers that were produced by the lipid depleted algae cake. In fact,

it is found that 1.68 times more energy can be produced by the atmospheric gasification of the

algae extraction residue than the transesterification of lipids.

Closed cultivation systems provide better opportunity to control cultivation parame-

61


ters and thus the biomass composition. Elevating the lipid content of the biomass result in

increasing biodiesel yield but at the same time less transformable protein and carbohydrate

rich lipid depleted feedstock can be obtained which decreases the amount of co-produced

energy carriers and conclusively the energy gain by burning them. It is found that, if the

functional unit is 1 kg of biodiesel, the overall energy gain is higher applying open raceway

ponds, however the energy balance is better using closed photobioreactor systems.

In the case of the wet route scenarios the highest energy gain is obtained from the

production of upgraded bio-oil which is followed by HTL biochar and HTL bio-gas, respec-

tively.

Figure 4.4 – Energy gaining by products. Biodiesel and glycerol are produced through trans-esterification; biochar, bio-oil and biogas are produced through atmospheric gasification,Upgraded bio-oil, HTL bio-char and HTL bio-gas are produced via hydrothermal liquefaction,bio-oil stabilization and hydroprocessing.

62

4.2. Energy Balance, Net Energy Ratio of alternatives

4.2 Energy Balance, Net Energy Ratio of alternatives

The net energy ratio - which is defined as the ratio of the total energy gain and the

total invested energy (Eq. 3.25) - is calculated for the investigated biorefinery alternatives

to measure their efficiency. It is found that through the transesterification of lipids and

gasification of algae cake 1.37 (ORP) and 1.43 (tPBR) times more net energy can be produced

compared to hydrothermal pathway, though, the energy balances of the dry route alternatives

were found unfavorable in all cases, with NER values less then 1.

All together 10 refinery alternatives were investigated as it is illustrated in Figure

4.5. 3 different cultivation systems were included: (1) open raceway-ponds, (2) helical tubular

photobioreactors and (3) horizontal tubular photobioreactors with 2 downstream processing

chains. Transesterification of lipids and gasification of algae cakes were considered as the dry

downstream route, while hydrothermal liquefaction was called as the wet route. Apart from the

cultivation system two operation methods were considered: (i) one where the CO2 absorption

process was included in the process chain, and (ii) one where it was excluded depending on

the puritiy of the flue gas. Since the flue gas purification considered as an optional process,

the NER values were determined including and excluding the monoethanolamine based CO2

absorption process.

Figure 4.5 – Energy demand and net energy ratio of the biorefinery alternatives. (NER valuesare highlighted above the columns) (a) Wet route, ORP; (b) Wet route, tPBR; (c) Dry route, tPBR;(d) Wet route, tPBR with flue gas purification; (e) Wet route, ORP with flue gas purification; (f )Dry route, ORP; (g) Dry route; tPBR with flue gas purification; (h) Dry route, ORP with flue gaspurification, (i) Dry route, horizontal tPBR with flue and (j) Wet route, horizontal tPBR withflue gas purification.

Figure 4.5 shows the calculated net energy ratios of alternatives. Positive energy

balances (NER>1) are found in two cases: (1) Using closed photobioreactors for the cultivation

63


of algae then processing the biomass via hydrothermal liquefaction, and (2) utilizing ORPs

and HTL process where the NER values are 1.137 and 1.109, respectively.

It is found that positive energy balance is not achievable with configurations that

contain flue gas purification process. The reason for that can be traced back to the regeneration

of MEA absorbent after the carbon capture. The regeneration of monoethanolamine requires

high temperature and thus it has an enormous energy demand. The NER of the wet route with

tPBR system is found to be 0.703 (d), wet route with ORPs with 0.661 (e), dry route & tPBR is

0.621 (g), dry route and ORPs with 0.510 (h). Therefore, resistant algae species and toxic-free

flue gas sources are playing a key role to achieve a net energy ratio above 1.

The wet route which contains the hydrothermal technology is obtaining more fa-

vorable net energy ratio compared to the dry route. The elimination of the drying process

result in significant energy saving: 80.3% in the case of ORP system and 39.7% in the case of

tPBR. The alternatives that imply tubular photobioreactors achieved better NER scores than

the ones with open raceway ponds although both the invested and gained energy values are

higher applying ORPs.

Horizontal type tubular photobioreactor option was also investigated in the overall

energy evaluation but it is found that this cultivation system has unfavorable energy demand

which makes it unsuitable to use for energy efficient algae cultivation. Negative energy bal-

ances are found both with dry and wet routes with NER values of 0.117 and 0.089, respectively.

Some NER values which determined in former studies using renewable and fossil

sources as feedstock are collected in Table 4.3. The highest score is paired with conventional

diesel where the energy gain is approximately 5-times higher compared to the herein presented

values. Among the renewable sources, the heterotrophic algae based refinery has a higher NER

score but the difference is not significant and organic carbon sources are not required in the

case of autotrophic algae sources.

As a conclusion, positive energy balances of third generation biorefineries are achiev-

able in present technological levels, however, the energy gain is not significant (NER is slightly

higher than 1) and all of the products has to be combusted in order to attain the positive

energy balance.

64

4.3. Identified refinery’s bottlenecks

Table 4.3 – Net energy ratios of biomass based refineries and energy fuels. ORP, Open Raceway-ponds, tPBR, tubular photobioreactor, HTL, Hydrothermal liquefaction.

Fuel NER Source

Biodiesel from autotrophic algae via ORP and dry route 0.51 Current estimation

Bioethanol production from corn 0.53 Mizsey and Racz (2010)

Biodiesel from heterotrophic microalgae fed with starch 0.96 Zhang et al. (2013)

Upgraded bio-oil from autotrophic algae, ORP, HTL 1.11 Current estimation

Upgraded bio-oil from autotrophic algae, tPBR, HTL 1.14 Current estimation

Biodiesel from soy 1.25 Bennion et al. (2015)

Heterotrophic microalgae fed with cellulose 1.46 Zhang et al. (2013)

Conventional diesel 5.55 Bennion et al. (2015)

4.3 Identified refinery’s bottlenecks

The concept of a complex integrated biorefinery has the potential to become eco-

nomically feasible but the efficiency of the refinery must be further developed. The cradle-

to-grave analysis focused on the high-volume biofuel and bioenergy production which can

mitigate climate change and global warming due to the closed carbon cycle.

Throughout the energy evaluation of different biorefinery configurations some pro-

cesses are identified as bottlenecks from an energetic point of view. These are the (1) drying

process or hydrothermal treatment of biomass, (2) carbon capture process, CO2 absorption,

(3) upstream part of the biorefinery, microalgae cultivation.

In order to be able to improve the efficiency of an algae based refinery the above

mentioned processes has to be upgraded. Based on the energetic evaluation the following

steps could improve the net energy ratio of a biorefinery:

• Targeted cultivation of algae biomass to obtain higher product yields and favourable

biological composition,

• The application of resistant algae strains to obtain better flue gas tolerance,

• Better cultivation systems with higher energy efficiency (design improvement for a less

energy intense mixing and media recirculation),

• Process optimization to minimize operational costs and maximize the overall efficiency

of biorefineries,

• Integration of other plants and/or technologies, energy integration,

• Application of wastewater and/or process wastewater sources as nutrient source to

reduce the relatively high energy demand of nutrients production,

65


• Decreasing the energy requirement of the carbon capture process.

The identified bottlenecks, carbon capture, microalgae cultivation and hydrothermal

treatment of the feedstock are investigated further in Section 4.4, 4.7 and 4.8, respectively. The

aim of these investigation was finding solutions that enable to improve these processes and

the rentability and the efficiency of the microalgae biorefinery chain.

4.4 Life Cycle Analysis of Carbon Capture and Storage process alter-

natives

The energetic analysis showed that MEA based carbon dioxide absorption is a tech-

nological bottleneck in the biorefineries configuration. In this section, the life cycle of Carbon

Capture and Storage (CCS) chain is investigated to improve its efficiency and to decrease the

possible environmental impacts that arises through the application of the CCS technology.

4.4.1 CCS system setup

The investigated Carbon Capture and Storage process chain is presented in Figure 3.1.

It is assumed that the flue gas comes from a fossil plant. The investigated system incorporates

the carbon capture process, where chemical absorption with monoethanolamine absorbent

is considered. The regeneration of the solvent is based on a temperature swing method. For

evaluation of the absorption and regeneration process additional elements (such as: make-

up MEA, NaOH, activated carbon, steam and electricity) are included. Korre et al. (2010)

presented input and output data for a fossil based power plant equipped with MEA-based

post combustion CO2 capture process, which data set is used in the herein presented life cycle

analysis as a base case. These data and additional input and output life cycle inventory data

are listed in Table 4.4. It is considered that the carbon depleted flue gas is emitted in the air,

while the separated CO2 rich stream is compressed, then it is transported through pipelines to

a geological reservoir, where it is injected into the storage system. The energy demand of the

compression, transportation and injection processes were determined. The output of the CO2

capture process is a purified flue gas stream which can be released into the atmosphere, and a

separated CO2 stream with high purity.

In the LCAs the flue gas composition is simplified and only the CO2 content of the

flue is considered in the environmental evaluation, thus the environmental effects of SOx,

NOx and CO are omitted when the carbon depleted flue gas is emitted into the air. The

separated carbon dioxide is compressed up to 100 bars because the transportation of CO2 is

more advantageous in its liquid form. The energy requirements of the compression step was

calculated based on House et al. (2009).

66

4.4. Life Cycle Analysis of Carbon Capture and Storage process alternatives

Table 4.4 – Life cycle inventory data of the investigated CCS process related to 774.5kgCO2/MWhe

Input/Output Quantity

Matter and energy consumption to capture

MEA 1.1 kg

Activated carbon 27 g

NaOH 47 g

Steam 4.14 GJ

Electricity 34 kWh

End products of capture

Separated CO2 735.7 kg

Waste 4.5 kg

Compression to 100 bar

Energy 229 MJ

Injection

Energy 35 MJ

Transport

Pipeline 100 km

The transportation of the purified CO2 is considered to be done via pipelines. Trans-

portation of the compressed CO2 is possible by several ways like via railway, on road with

transport tanker and on sea with tanker ships. However, these options have high risk of

accidents and cost more. Wei et al. (2016) expressed in a techno-economic analysis that

transportation through pipelines is a cost-effective method. The CAPEX and OPEX (capital

investment and operating cost) of CO2 transportation to 100 km are about between 0.83 and

11.7 USD t−1CO2depending on the CO2 mass flow rate (1-10 Mt a−1). Further input data about

the pipeline installation, material and energy requirements were collected from the Ecoinvent

(2013) database. At the end, the CO2 stream is injected into the geological reservoir.

In this study 4 different cases are investigated. The uncontrolled CO2 release is

compared to three CCS alternatives: (1) Basic CCS technology using fossil fuels ("CCS Fossil"),

(2) Technologically improved CCS process chain, using heat integration in order to decrease

the energy demand of the absorbent regeneration process ("CCS Improved") and (3) the basic

CCS technology using renewable energy for heat generation ("CCS Renewable").

67


4.4.2 CCS via fossil fuel based absorbent regeneration

The environmental impact assessment results of the CCS fossil alternative are illus-

trated in Fig. 4.6. The global warming potential (GWP) is investigated by IPCC 2007 method.

It is found that 278 kg CO2,eq emission can be prevented during the operation of a 1 MWhe

coal fueled power plant (Fig. 4.6a). Screening the environmental effects with multiperspective

impact assessment methods (such as Eco-indicator 99 and IMPACT 2002+) give contradictory

results as it is shown in Figures 4.6b and 4.6c. It turns out that the application of CCS technol-

ogy has higher environmental load than the direct release of the CO2 to the atmosphere. The

environmental impacts of the process chain are 1.8 and 5.8 times higher compared them to

the uncontrolled release in the case of IMPACT 2002+ and EI 99, respectively.

The effects of multi-perspective impact categories are shown in Figure 4.7. Three

impact categories are identified as significant elements in the case of the EI 99 method (Fig.

4.7a). These are the Respiratory inorganics (15.4 EI99 Pt), Climate change (4.9 EI99 Pt) and

Fossil fuels (20.1 EI99 Pt) categories. The highest environmental damage is paired with the

application of fossil fuels (e.g., natural gas, heavy fuel oil) during the operation of CCS process.

In the case of EI 99 methods it accounts to the 46.2% of the total environmental effects.

The same tendency is found in the case of IMPACT 2002+ LCIA method, where the highest

environmental damage is identified on human health factor. The significant subcategories

are found to be the inorganic air pollutants (Respiratory inorganics), Non-renewable energy

sources and Global warming, approximately with the same magnitude (Fig. 4.7b).

The multi-perspective LCIA methods (both EI 99 and IMPACT 2002+) give the similar

result if only the global warming potential impact category investigated, thus it can be stated

that the CCS process is beneficial in regards of the prevention of CO2 emission. However, when

broader environmental categories are examined the result shows that the CCS process is not

preferred over the direct release of CO2. The simulation results show that the environmental

impacts of the Carbon Capture and Storage technology must be upgraded further in order

to become a favourable technology regarding the emission prevention of greenhouse gases.

In the following sections two main solution are investigated to decrease the environmental

hazards of CCS technology: (1.) decreasing the energy requirement of MEA regeneration using

process improvements and (2.) replace fossil fuels to renewable ones because the application

of fossil fuels are found to be significant from environmental point of view (Figs. 4.7a and

4.7b).

68


CCS Uncontrolled release0

100

200

300

400

500

600

700

800

GW

P(k

gC

O2

eq)

Carbon capture CompressionTransportation Injection

CO2 release

(a) IPCC 2007


10

20

30

40

50

Eco

-in

dic

ato

r99

(EI9

9P

t)


CO2 release

(b) Eco-indicator 99


20

40

60

80

100

120

140

160

IMPA

CT

2002

+(m

Pt)


CO2 release

(c) IMPACT 2002+

Figure 4.6 – Total environmental impacts of the CCS Fossil alternative compared to the uncon-trolled CO2 release in case of (a) IPCC 2007, (b) Eco-indicator 99 and (c) IMPACT 2002+ LCIAmethods.

69


(a)

(b)

Figure 4.7 – The results of the multi-perspective impact assessment methods by impact cate-gories in case of CCS fossil LCA alternative. (a) Eco-indicator 99, (b) IMPACT 2002+

70


4.4.3 CCS via process improvement

Figure 4.6 illustrates that the highest environmental impact by processes is asso-

ciated with the carbon capture process, more precisely with the regeneration of the MEA

absorbent after the CO2 capture which has the highest energy input requirement among the

processes (Table 4.4). The works of Nagy and Mizsey (2015) and Yu et al. (2016) proved that the

MEA based carbon capture process can be significantly improved by the proper optimization

of absorbent/flue gas ratio and the utilization of energy integration option that recovers the

heat content of the regenerated hot absorbent. These process improvements can reduce the

energy requirement of the capture process to approximately the 60% of the presented base

case in Table 4.4.

Figure 4.8 shows the recalculated environmental damages applying process im-

provements. The results show that the environmental issues can be decreased using process

improvements compared to the base case. Based on the IPCC 2007 method the GWP is de-

creased by 550.4 kg CO2,eq (Fig. 4.8a) compared to the uncontrolled carbon dioxide release. In

the case of the IMPACT 2002+ multi-perspective method, the overall environmental damage is

decreased from 141.2 mPt to 63.3 mPt (Fig. 4.8c), which is a meaningful environmental effect

reducement. However, according to the results obtained with EI 99 method, the uncontrolled

carbon dioxide release remains still better from environmental viewpoint since this evaluation

option emphasizes the human health much higher than IMPACT 2002+ LCIA (Fig. 4.8b).

71



100

200

300

400

500

600

700

800

GW

P(k

gC

O2

eq)


CO2 release

(a) IPCC 2007


2

4

6

8

10

12

14

16

18

20

Eco

-in

dic

ato

r99

(EI9

9P

t)


CO2 release



20

40

60

80

100

IMPA

CT

2002

+(m

Pt)


CO2 release

(c) IMPACT 2002+

Figure 4.8 – Total environmental impacts of the CCS Improved alternative compared to theuncontrolled CO2 release in the case of (a) IPCC 2007, (b) Eco-indicator 99 and (c) IMPACT2002+ LCIA methods.

4.4.4 CCS via application of renewable energy

The application of fossil fuels for absorber regeneration and supplementary elec-

tricity generation throughout the CCS process result in a strange outcome, i.e., the direct flue

gas release becomes a favourable option from the viewpoint of the multi-perspective impact

assessment methods.

Figures 4.6 and 4.8 demonstrate that carbon capture has the highest environmental

72


impact in the CCS process chain. Other processes such as the CO2 transportation and the

injection to a storage place have marginal environmental impacts. Figures 4.7a and 4.7b show

that significant environmental issues are associated with the application of fossil fuels and

non-renewable energy sources in the process chain. Therefore, in order to make the CCS

technology environmentally much more beneficial, the utilization of fossil fuels should be

avoided and alternative energy sources should be applied. Based on the Ecoinvent (2013)

database different renewable energy sources (e.g., wood, wood pellets, biogas) are considered

as a substitute of fossil fuels for heat generation and to mitigate natural gas and heavy fuel

consumption.

a.

b.

c.

d.

e.

f.

g.

5.87

4.61

4.71

5.12

9.25

12.96

28.53

79.43

26.87

28.36

29.54

52.29

73.77

103.75

EI 99 [Pt] IMPACT 2002+ [mPt]

Figure 4.9 – The total environmental impacts of different renewable energy sources for MEA sol-vent regeneration. a.) Heavy fuel oil, burned in industrial furnace 1MW, non-modulating/CHU; b.) Heat, at cogeneration with biogas engine, agricultural covered, allocation exergy/CHU; c.) Heat, at cogeneration with biogas engine, allocation exergy/CH U; d.) Heat, at cogen-eration with ignition biogas engine, agricultural covered, alloc. energy/CH U; e.) Heat, atcogeneration ORC 1400 kWth, wood, emission control, allocation energy/CH U; f.) Heat, atcogeneration 6400kWth, wood, allocation energy/CH U; g.) Heat, central or small-scale, otherthan natural gas (Europe without Switzerland) heat production, wood pellet, at furnace AllocDef, U

Figure 4.9 shows the results of multi-perspective life cycle impact assessments for

each alternative renewable energy sources. The evaluation of the life cycle inventory with EI

99 and IMPACT 2002+ methods give the same tendency for different renewables. It is found

that among the investigated renewable energy sources the biogas based systems have the

lowest environmental impacts. Additionally, the CCS technology becomes a much preferable

way from environmental viewpoint comparing it to the uncontrolled release of CO2 in case of

73


all life cycle impact assessment methods including the multi-perspective ones as well. The

environmental effects of the carbon capture and regeneration process decrease immensely

so its environmental effects become comparable with other processes (e.g., compression,

transportation, injection).

Figure 4.10 illustrates the total environmental impacts of the Carbon Capture and

Storage chain when the most favourable renewable energy source (biogas from agricultural

waste) is applied for heat generation. It is demonstrated that according to each life cycle

impact assessment method (Figs. 4.10a-4.10c) the total environmental impacts of the CCS

chain is lower than that of the CO2 release.

The LCA scores for woods and woods pellets are found to be greater than the uncon-

trolled carbon dioxide release in case of the multi-perspective methods. Woods are considered

renewable energy sources but logging requires lots of energy and the time that is required

to grow up trees could take decades, consequently burning trees for heat generation is not

considered as an environmental friendly method.

74

4.5. PESTLE analysis of CCS process alternatives


100

200

300

400

500

600

700

800

GW

P(k

gC

O2

eq)


CO2 release

(a) IPCC 2007


1

2

3

4

5

6

7

Eco

-in

dic

ato

r99

(EI9

9P

t)


CO2 release



20

40

60

80

100

IMPA

CT

2002

+(m

Pt)


CO2 release

(c) IMPACT 2002+

Figure 4.10 – Total environmental impacts of the CCS Renewable alternative compared to theuncontrolled CO2 release in the case of (a) IPCC 2007, (b) Eco-indicator 99 and (c) IMPACT2002+ LCIA methods.

4.5 PESTLE analysis of CCS process alternatives

Life cycle analysis of the CCS technology gives insight about the environmental

effects of the processes though other external factors can also influence the CCS technology.

PESTLE analysis provide a broad scope of the impacts of processes and can be used to identify

external factors that have high effects on CCS technology alternatives. In the followings

LCA alternatives are investigated exhaustively using PESTLE analysis for screening them via

multiple criteria.

75


4.5.1 Political & Legal aspects

The political and legal aspects are examined together because of the complexity of

the factors. Environmental regulations (e.g., European Union Emission Trading Scheme (EU-

ETS, 2013)) were set up by the European Union and countries including Iceland, Liechtenstein

and Norway to reduce the anthropogenic greenhouse gas emission. The CO2 quota trading

market plays a key role as a political directive and an environmental regulation to meet the

Kyoto Protocol emission standards. The EU-ETS acts as a major driver of investment in

sustainable clean technologies and low carbon solutions, moreover, companies are allowed to

buy credits from emission saving projects around the world. The EU ETS is organized into three

major phases: (1) 1. January 2005. - 31. December 2007, (2) 1. January 2008 - 31. December

2012. (3) 1- January 2013. - 31. December 2020. The price of the quota is determined by actual

market conditions and the ratio of supply and demand. A variety of unexpected political and

other events, energy-, environmental policy decisions (e.g., introduction of the European 2020

objectives, Financial and Economic crises in 2007, weakening economic growth predictions)

can influence the quota price (de Perthuis and Trotignon, 2014; Koch et al., 2014; Sanin et al.,

2015) as it is illustrated in Fig. 4.11. The average price of the quota was 25 ACt−1CO2since the

introduction of European Union Allowance (EUA) until the financial and economic crisis of

2008. The economic crisis decreased the price to an average 18ACt−1CO2, and in the middle of

Phase III (in 2016), the quota price was 5-7ACt−1CO2(Luo and Wang, 2016; Wang and Du, 2016).

Figure 4.11 – CCS and quota trading cost

76


4.5.2 Economic aspect

The economic aspect of the CCS alternatives are evaluated via cost analysis. It is

considered that 1 MWhe electricity is produced by burning coal. Assuming uncontrolled 774.5

kg of CO2 emission costsAC19.4,AC13.9 andAC3.9 by purchasing EU ETS quota in different years

according to Fig 4.11. It is estimated that the efficiency of the carbon capture process isψ=95%,

thus the unseparated CO2 is charged with EU ETS quota prices of AC0.97, AC0.69 and AC0.19.

Table 4.5 summarizes the specific annual operation cost and full prices for each step in the

CCS chain. The total annual cost of the CCS chain is determined by applying a rule of thumb,

where the operational costs measures up to the 2/3 while the investment gives the 1/3 of the

costs.

Table 4.5 – Cost analysis for 1 MWhe production; a(Zauba, 2016), b(BAC, 2016), c (Wesnaes andWeidema, 2006), d (Eurostat, 2014), e(Metz et al., 2005)

Name Specific price Full price

Uncontrolled CO2

CO2 in flue gas AC25, 18, 5/t AC19.4,AC13.9,AC3.9

Matter and energy consumption to capture

MEA AC1000/ta AC1.1

Activated carbon AC20/kgb AC0.54

NaOH AC5/kg c AC0.22

Steam AC11/GJ AC48.30

Electricity AC0.05/kWhde AC1.47

End products of capture

Non-Separated CO2 AC25, 18, 5/t AC0.97,AC0.69,AC0.19

Compression to 100 bar

Energy AC0.05/kWhde AC2.76

Injection

Energy AC0.05/kWhde AC0.42

Transport

Pipeline AC1.094/te AC0.85

Operation cost AC55.85

Investment cost AC27.93

Total cost AC83.78

Figure 4.12 demonstrates that the market price of the steam has a crucial role to

achieve economic operation of CCS technology. The figure shows the comparison of the

77


fossil based CCS technology and the CO2 release charged with quota prices. The dashed

solid lines indicate the emitted 774.5 kg CO2 quota prices in different years. The results show

that the CCS becomes beneficial at low steam prices between 0ACGJ−1 to 1ACGJ−1, preferably

close to 0 ACGJ−1. The operational cost rises if the steam is produced via biogas engine. A

gas engine thermal efficiency is about 50% (ClarkeEnergy, 2013), and 1 GJ biogas costsAC8.3

(Probiopol, 2016) as a result the required steam generation costs aroundAC69 which is a higher

price compared to the fossil based steam production. The application of biogas increases the

total costs toAC108 t−1CO2including the carbon capture process, tax regulations, compression,

injection and transportation of the captured CO2 via pipelines. Significant cost reduction

can be achieved by applying process improvement (AC70.2 t−1CO2). The calculated costs are

comparable with literature data, where the total cost of the CCS process can be found between

$70-89 t−1CO2(Vaccarelli et al., 2014; Jeong et al., 2015).

Figure 4.12 – CO2 CCS and quota trading cost by three quota trading rate (AC5/t;AC18/t;AC25/t)producing 1 MWhe

Table 4.6 shows the economic subfactors (OPEX and specific capital cost) of each

CCS alternatives. The fossil based CCS process is selected as a base case and the other alter-

78


natives are compared to it. The calculated operational expenditure of each alternatives has

been divided by the OPEX of the base case. Li et al. (2016) found that MEA based process

improvement results in 5.3% saving on the specific capital cost while the OPEX also decreases

significantly.

Table 4.6 – Economic factors of each alternatives

Economic

factor

CCS

Fossil

CCS

Improved

CCS

Renewable

CO2

release

Operation cost 1 0.65 1.33 0.07

Specific capital cost 1 0.94 1 0

4.5.3 Social aspect

The EPS (Environmental Priority Strategies) 2000 impact assessment method and the

human health category of the EI 99 method were used for the evaluation of social factors e.g.,

severe morbidity, life expectancy, nuisance, human health in accordance with the suggestions

of Unicef (2016) for key social factors. Table 4.7 summarizes the simulated LCIA results of

CCS alternatives. It is found that the Improved alternative has better social scores in every

subcategory. The best alternative from social point of view is considered to be the renewable

based alternative, which outranks the others in severe morbidity, life expectancy and in the

overall score of EI 99 Human health category.

Table 4.7 – Social factors of each CCS alternatives

Social

factor

CCS

Fossil

CCS

Improved

CCS

Renewable

CO2

release

Result of the EPS 2000 Method

Severe morbidity (PersonYr) 1.65E-04 1.14E-04 4.50E-05 2.72E-04

Life expectancy (PersonYr) 5.64E-04 4.25E-04 1.46E-04 6.20E-04

Nuisance (PersonYr) 1.55E-02 8.29E-03 3.62E-03 1.00E-03

Result of the EI 99(H) Method

Human health (Pt) 1.169E+01 9.830E+00 2.971E+00 5.857E+00

4.5.4 Technological aspect

The technological aspect of CCS alternatives are represented by two subfactors,

namely the Technological Readiness Level (TRL) (ASDR&E, 2011), and Resources Consumption

79


(RC) (GJ energy need for absorbent regeneration). Table 4.8 represents the identified TRL

and RC values of each CCS alternatives. The uncontrolled CO2 release was not evaluated on

the TRL scale (1-the lowest technological readiness, 9-final form of the technology) because

of the lack of the usage of carbon capture technology. The most advanced CCS alternative

is the Fossil based which scored the highest in TRL scale (9) due to the already working

plants using this technology. The Improved CCS alternative is graded for 7. Renewable based

CCS alternative is scored only 5 for the reason that it is not a wide spread technology yet.

The required heat for absorbent regeneration requires the same energy input for the Fossil

and Renewable alternatives (4.14 GJ), while this value is only 2.48 GJ in the case of the CCS

Improved alternative (60% of the Fossil alternative).

Table 4.8 – Technological factors of each alternatives

Technological

factor

CCS

Fossil

CCS

Improved

CCS

Renewable

CO2

release

TRL (-) 9 7 5 -

RC (GJ) 4.14 2.48 4.14 0

4.5.5 Environental aspect

The results of the life cycle analysis of the CCS alternatives (Section 4.4) are consid-

ered as for environmental aspects in PESTLE analysis. The environmental subfactors and the

results are collected in Tables 4.9-4.13. Table 4.9 and 4.10 express the results of the comparative

life cycle impact assessment using the midpoint environmental impacts in the cases of EI

99 and IMPACT 2002+ multi-perspective LCIA methods. It is found that the utilization of

renewable energy source compared to fossil fuels decreases the midpoint impacts (such as

global warming (approx. to the forth from 4.950E+02 kg CO2eq to 1.213E+02 kg CO2eq) and

climate change potentials (from 1.060E-04 DALY to 2.774E-05 DALY), ozone layer depletion

(from 9.059E-05 kg CFC-11 eq to 1.640E-05 kg CFC-11 eq) and terrestrial acidification (from

6.315E+00 kg SO2eq to 2.723 kg SO2eq)) significantly. Table 4.11 shows that the Global Warm-

ing Potential (GWP) can be reduced by the utilization of CCS alternatives from 774 kg CO2eq

to 496, 224 and 132 kg CO2eq in the case of CCS Fossil, CCS Improved and CCS renewable,

respectively. It is found that through the CCS technology the GWP is decreasing independently

from the types of energy source. On the other hand, the overall environmental impacts are

disadvantageous if fossil based energy carriers are used in the process chain. The endpoint

environmental indicators are collected in Tables 4.12 and 4.13. The total environmental score

of the fossil based CCS alternative is 3.43-fold higher than the renewable based one in the case

of the IMPACT 2002+. Compared to the uncontrolled release of carbon dioxide this ratio is 2.96,

80


therefore, it can be stated that using renewables are considered to be a much more beneficial

option from environmental point of view compared to the others. The same tendency can

be drawn using the Eco-indicator 99 LCIA method: the total impact of fossil based CCS is

3.22-fold, the direct CO2 release is 1.27-fold higher in comparison with the application of

renewable energy carrier.

81

Ch

apter

4.R

esults

and

Discu

ssion

Table 4.9 – Midpoint impacts of CO2 release and CCS technology using IMPACT 2002+ method. Fossil fuel: Heavy fuel oil, burned in industrial furnace; Renewableenergy source: biogas from agricultural waste.

Impact category Unit CCS Fossil CCS Improved CCS Renewable CO2 Release

Carcinogens kg C2H3Cleq 3.078E+00 5.640E-01 3.338E-01 x

Non-carcinogens kg C2H3Cleq 1.405E+00 1.040E+00 2.577E-01 x

Respiratory inorganics kg PM2.5eq 3.207E-01 2.750E-01 7.639E-02 1.209E-02

Ionizing radiation Bq C – 14eq 4.028E+03 3.360E+03 3.337E+03 x

Ozone layer depletion kg CFC – 11eq 9.059E-05 5.970E-05 1.640E-05 x

Respiratory organics kg C2H4eq 2.128E-01 1.320E-01 1.224E-02 x

Aquatic ecotoxicity kg TEG water 1.143E+04 6.090E+03 5.542E+03 x

Terrestrial ecotoxicity kg TEG soil 3.135E+03 1.350e+03 3.774E+02 x

Terrestrial acid/nutri kg SO2eq 6.315E+00 5.170E+00 2.723E+00 1.550E-01

Land occupation m2org.arable 2.781E-01 7.860E-02 3.384E-01 x

Aquatic acidification kg SO2eq 2.619E+00 1.460E+00 7.044E-01 1.550E-01

Global warming kg CO2eq 4.950E+02 3.350E+02 1.213E+02 7.745E+03

Non-renewable energy MJ primary 9.522E+02 9.522E+02 9.522E+02 x

Mineral extraction MJ surplus 6.307E-02 5.050E-02 6.307E-02 x

82

4.5.P

EST

LE

analysis

ofC

CS

pro

cessaltern

atives

Table 4.10 – Midpoint impacts of CO2 release and CCS technology using Eco-indicator 99 (H) method. Fossil fuel: Heavy fuel oil, burned in industrial furnace;Renewable energy source: biogas from agricultural waste.


Carcinogens DALY 4.322E-06 1.480E-05 2.066E-06 x

Respiratory organics DALY 5.435E-07 3.670E-07 1.100E-07 x

Respiratory inorganics DALY 2.298E-04 2.000E-04 5.619E-05 8.463E-06

Climate change potential DALY 1.060E-04 7.170E-05 2.774E-05 1.627E-04

Radiation DALY 8.173E-07 6.830E-07 6.783E-07 x

Ozone layer DALY 9.410E-08 6.170E-08 1.622E-08 x

Ecotoxicity PAF m2 yr 1.807E+02 1.170E+02 7.220E+00 x

Acidification/Eutrophication PDF m2 yr 6.574E+00 5.380E+00 2.835E+00 1.614E-01

Land use PDF m2 yr 1.421E+00 1.030E-01 4.409E-01 x

Minerals MJ surplus 4.664E-02 3.790E-02 4.664E-02 x

Fossil fuels MJ surplus 3.430E+01 3.420E+01 3.423E+01 x

83

Ch

apter

4.R

esults

and

Discu

ssion

Table 4.11 – Summary of impact assessment results, IPCC 2007 GWP 100a method


Global Warming Potential kg CO2 eq 496 224 132 775

Table 4.12 – Summary of impact assessment results, weighting, IMPACT 2002+ method

Damage category Unit CCS Fossil CCS Improved CCS Renewable CO2 Release

Human health Pt 3.362E-02 (36.4%) 2.800E-02 (40.4%) 7.878E-03 (29.3%) 1.193E-03 (1.5%)

Ecosystem quality Pt 2.354E-03 (2.6%) 1.200E-03 (1.7%) 4.719E-04 (1.8%) 1.177E-05 (0.0%)

Climate change Pt 5.000E-02 (54.2%) 3.390E-02 (48.9%) 1.225E-02 (45.6%) 7.822E-02 (98.5%)

Resources Pt 6.266E-03 (6.8%) 6.260E-03 (9.0%) 6.266E-03 (23.3%) x

Total Pt 9.223E-02 (100.0%) 6.930E-02 (100%) 2.687E-02 (100.0%) 7.943E-02 (100.0%)

Table 4.13 – Summary of impact assessment results, weighting, Eco-indicator 99(H) method

Damage category Unit CCS Fossil CCS Improved CCS Renewable CO2 Release

Human health Pt 1.169E+01 (78.6%) 9.830E+00 (79.3%) 2.971E+00 (64.4%) 5.857E+00 (99.8%)

Ecosystem quality Pt 1.823E+00 (12.2%) 1.200E+00 (9.7%) 2.794E-01 (6.1%) 1.128E-02 (0.2%)

Resources Pt 1.365E+00 (9.2%) 1.360E+00 (11.0%) 1.363E+00 (29.5%) x

Total Pt 1.488E+01 (100.0%) 1.240E+01 (100%) 4.613E+00 (100.0%) 5.868E+00 (100.0%)

84

4.6. Multi-Criteria Decision Analysis of CCS alternatives

4.6 Multi-Criteria Decision Analysis of CCS alternatives

In Section 4.5, it is discussed that several external factors can influence the Carbon

Capture and Storage chain. The great numbers of determining factors make the selection of the

best alternative difficult, therefore Multi-Criteria Decision Analysis (MCDA) is used for decision

making. PESTLE Analysis can be used in conjunction with MCDA, where the numerical results

of the PESTLE analysis can be utilized as input for the MCDA. PESTLE analysis provides a

framework for screening the alternatives, while MCDA is applied for ranking and selecting the

best CCS alternative.

Figure 4.13 shows the MCDA results of the economic, social, technological and

environmental factors using Multi Attribute Value Theory (MAVT) for the evaluation. The

economic review demonstrates that the most economical process is the uncontrolled CO2

release. The uncontrolled release of industrial flue gas is charged by CO2 quota prices but on

the other hand it is characterized by the lack of operational costs and does not require any

investment which cannot be concluded in the case of CCS alternatives. Process improvement

cuts down the fuel demand resulting reduced operational costs but due to the required invest-

ments the overall cost of CCS Improved is higher compared to the CCS Fossil alternative. The

least economic option is the CCS process that utilizes renewable energy.

The renewable based CCS is identified as the most advantageous alternative in

regard of social aspects. The least favoured alternative is the basic fossil based CCS which can

be traced back to the application of fossil fuels and the high energy requirements of the MEA

absorption process.

From technological point of view the CCS Improved scores the highest among the

alternatives though there is not significant difference between them.

The environmental evaluation shows that the CCS Renewable is the most advanta-

geous choice over the others, while the second best is the CCS Improved.

Figure 4.14 shows the radial graph of the investigated alternatives in function of

external criteria. Compared to the basic CCS Fossil case it can be seen that the CCS Improved

outperforms the base case in every aspects. The uncontrolled CO2 release is the clear winner

in regard of economic aspect, while from social and environmental perspective, the CCS

Renewable is identified as the best Carbon Capture and Storage alternative.

85


Economic Social Technological Environmental0

0.2

0.4

0.6

0.8

1

0.07

0.15

0.5

0.30.26

0.45

0.580.54

0

0.95

0.44

0.87

1

0.42

0.5

0.3

MAV

TSc

ore

s(-

)

CCS Fossil CCS Improved CCS Renewable CO2 release

Figure 4.13 – MCDA result of the reduced PESTLE factors using Multi Attribute Value Theory(MAVT) for the evaluation.

For the final evaluation and selection of the best alternative, the ranking method is

applied for weighting the main factors. The overall results of the MCD Analysis is demonstrated

in Figure 4.15. The Carbon Capture and Storage chain addresses an environmental issue, the

main goal is the prevention or mitigation of GHG emission, therefore the environmental aspect

is ranked in the first place with a share of 40%. The sustainability and social aspects are also

important factors, thus they are considered in the second place with a share of 30%. The

technological factor is ranked at the third place (20%), while the economic factor in the fourth

with 10%. Applying the Multi Attribute Value Theory, the renewable based CCS technology

is detected as the best alternative considering several external influencing factors with an

MAVT score of 0.73. The second best alternative is the CCS Improved which highlights the

importance of developing the efficiencies of the carbon capture technology (MAVT Score=0.47).

The uncontrolled CO2 release is scored to a higher MAVT value (0.24) compared to the CCS

Fossil alternative (MAVT Score=0.07), thus the CCS technology becomes beneficial if process

improvements or renewables are used throughout the operation.

86

4.6. Multi-Criteria Decision Analysis of CCS alternatives

Economic

Social

Technological

Environmental

CCS Fossil

CCS Improved

CCS Renewable

CO2 Release

Figure 4.14 – Radial graph of the overall MCDA results

Alternatives0

0.2

0.4

0.6

0.8

1

0.73

0.47

0.24

0.07

MAV

TSc

ore

s(-

)

CCS RenewableCCS Improved

CO2 releaseCCS Fossil

Figure 4.15 – Overall result of the Multi-Criteria Decision Analysis

87


4.7 Investigation of illumination conditions to influence the biomass

productivity of Chlorella vulgaris

The advantages of cultivating microalgae biomass in closed systems are the pos-

sibility to prevent infections by other organisms and the ability of controlling cultivation

parameters such as illumination, CO2 content of inlet air, temperature, pH and gas transfer

rate. The proper illumination of the culture broth can greatly influence the biomass pro-

ductivity and biological composition and thus the overall efficiency of a biorefinery. The

technological challenge that related to efficient illumination of the reactor surfaces have been

mainly addressed by various design features, optimization of surface-to-volume ratio and

light-dark cycles (Tamburic et al., 2011). Therefore, one of the main focus of this section is the

investigation and determination of effects of light factors (wavelength and light intensity) on

growth rate of Chlorella vulgaris to (i) increase its biomass productivity and (ii) to influence

the biological composition of algae cells. The proximate and ultimate analysis of the selected

microalgae strain is presented in Table 4.14.

Table 4.14 – Proximate and ultimate analysis of Chlorella vulgaris

Microalgae biomass Proximate analysis (wt%) Ultimate analysis (wt%)Volatile matter Fixed carbon Ash C N H O

Chlorella vulgaris 75.36 21.53 3.10 59.77 10.07 4.36 25.80

4.7.1 Optimal wavelength

One of the main problem of microalgae based biorefineries is the low biomass

productivity at large scale thus the improvement of the upstream section of a biorefinery is

highly required. Closed indoor systems require artificial light sources which offer an efficient

way to influence the biomass yield by the optimization of light factors.

Photoautotroph organisms contain various pigments with different absorbent peaks.

Therefore, fermentations operating under ideal illumination regimes can boost the growth of

autotroph organisms. The effects of wavelength settings was investigated using a microtiter-

plate (MTP) screening device equipped with RGB light emitting diodes. The dry weight content

of the wells throughout the fermentations is presented in Fig. 4.16, while the overall biomass

productivity values are listed in Table 4.16.

The experimental results show that the dry weight content of the cells and biomass

productivity are influenced greatly by different wavelength regimes. It is found that dichro-

matic mixed colors are favoured to increase the dry weight content of the culture during the

cultivation time. The highest values are achieved with purple (626 nm & 470 nm), yellow (626

nm & 525 nm), bluegreen (525 nm & 470 nm) and white (626 nm & 525 nm & 470 nm) color

88

4.7. Investigation of illumination conditions to influence the biomass productivity ofChlorella vulgaris

settings. These findings are in agreement with the fact that the absorbent peaks of chlorophyll

pigments are in the red and blue intervals. Thus, higher photosynthetic efficiency can be

achieved using mixed wavelength settings compared to monochromatic illuminations.

0 2 4 6 8 10 12 14

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Time (Days)

Dry

wei

ght(

gL−

1)

Purple (626 nm & 470 nm)Yellow (626 nm & 525 nm)

Bluegreen (525 nm & 470 nm)White (626 nm & 525 nm & 470 nm)

Blue (470 nm)Red (626 nm)

Green (525 nm)

Figure 4.16 – Fermentations to determine ideal wavelength settings. Data are arithmetic meansof 3 replicates

One-way ANOVA was used for the evaluation of experimental data (Table 4.15) and

to determine the ideal illumination color settings. Wavelength is found to be a significant

factor (F=3.50, p<0.05) regarding the biomass productivity of Chlorella vulgaris. The highest

productivity in case of Chlorella vulgaris is obtained with purple illumination. It is found

that illuminating the culture at 626 nm & 470 nm result in 49.9% higher biomass productivity

compared to mixed white color combination (626 nm & 525 nm & 470 nm). Irradiating the

algae suspensions with monochromatic red, blue and green colors are found to be unsuitable

to increase further the biomass productivity of Chlorella vulgaris which is in agreement with

the result found with other strains such as Nannochloropsis salina and Nannochloropsis

oculata (Ra et al., 2016). It is determined that the illumination of the culture broth with

mixed dichromatic colors improve biomass productivity. The same conclusion was reported

by Schulze et al. (2016) in case of N.oculata and T.chuii. The highest biomass productivity

is achieved with dichromatic red (626 nm) and blue (470 nm) LEDs, though de Mooij et al.

(2016) found the lowest biomass productivity with this wavelength combination in case of

Chlamydomonas reinhardtii which suggests the strain specificity of ideal light conditions and

highlights the importance of screening light factors.

89


Table 4.15 – Analysis of variance (ANOVA) for the investigation of wavelength effect.

Effect Sum ofsquares

df Meansquare

F-value p-value

Intercept 27026.62 1 27026.62 363.06 <0.0001Wavelength 1302.03 5 260.41 3.498 0.0435

Error 744.41 10 74.44 - -

Table 4.16 – Biomass productivity under illumination by different wavelengths. Data arearithmetic means (±S.E.) of 3 different experiments.

Color(Wavelength)

Red(626 nm)

Green(525 nm)

Blue(470 nm)

Yellow(626 nm, 525 nm)

Purple(626 nm, 470 nm)

Bluegreen(525 nm, 470 nm)

White(626 nm, 525 nm, 470 nm)

Overallproductivity(mgL−1d−1)

31.7(±2.5) 29.9(±0.4) 33.4(±0.1) 41.2(±12.0) 60.4(±1.7) 50.0(±7.8) 40.3(±9.4)

4.7.2 The effect of light intensity

Photon flux density (PFD) is also an important cultivation factor. Low light intensities

can cause photolimitation, while excessive intensity levels lead to photoinhibition. There-

fore, economic microalgae cultivation demands optimized illumination conditions in closed

systems. The light intensity is playing a key role attaining elevated biomass productivity

levels. The RGB-MTP device was applied to investigate the effects of light intensity on biomass

productivity. The favourable red and blue dichromatic wavelength levels are selected for

the examination of light intensity. 5-level-2-factor Central Composite Design was applied to

examine the effects of intensity and to determine the optimal red (626 nm) and blue (470 nm)

PFD levels.

The experimental design and results are listed in Table 4.17. The highest biomass

productivity is achieved at 243.47 µmol m−2 s−2 and 96.76 µmol m−2 s−2 red and blue intensity

levels, respectively. The experimental data was evaluated by Response Surface Methodology

(RSM). The ANOVA results and estimated regression coefficients are listed in Table 4.18 and

Table B.1, respectively.

90


Table 4.17 – Central composite design. Data are arithmetic means (±S.E.) of 3 differentexperiments.

Run Independent variable levels Real values of variables Productivity

(mg L−1 d−1)X1 X2 Red (626 nm)

intensity

(µmol m−2 s−1)

Blue (470 nm)

intensity

(µmol m−2 s−1)

1 0 0 243.47 96.76 74.62(±9.66)

2 1.41 0 312.32 96.76 36.75(±5.97)

3 1 -1 292.16 58.05 40.69(±1.72)

4 -1 -1 194.78 58.05 25.50(±1.16)

5 -1 1 194.78 135.46 43.70(±4.59)

6 0 -1.41 243.47 42.02 20.50(±2.41)

7 -1.41 0 174.60 96.76 39.50(±2.75)

8 0 1.41 243.47 151.49 14.30(±1.30)

9 0 0 243.47 96.76 75.57(±1.93)

10 1 1 292.16 135.46 18.72(±4.37)

It is found that the biomass productivity is influenced most significantly by the

quadratic blue effect (F=2768.28, p=0.0121) which is followed by the quadratic red (F=7041.13,

p=0.0076) and the cross effect between the two main effects (F=807.62, p=0.0224). The model

fitting was examined by the test of lack-of-fit. The lack-of-fit is found to be insignificant

(F=30.66, p=0.132), thus it can be stated that the fitted model is appropriate. The RSM model

fits well on the experimental data with an R2 of 0.9889. The adequacy of the statistical model

was further investigated graphically on Fig. 4.17. The residues show normal distribution on

the normal probability plot (Fig. 4.17a), the predicted and observed values are close to each

other which indicates high model accuracy (Fig 4.17b). Plotting raw residuals against case

numbers shows no unique patterns (Fig 4.17c) thus the applied statistical model is considered

adequate.

The objective of the light intensity optimization was finding specific red and blue

intensity levels where biomass productivity can be increased. The applied response surface

plot is illustrated in Fig. 4.18, while the describing equation is given in Eq. 4.1.:

Yp(mg L−1 d−1)=−646.417+4.054·X1−0.007·X2+4.843·X 21 −0.019·X 2

2 −0.005·X1·X2, (4.1)

where Yp is the predicted value of biomass productivity (mg L−1 d−1), X1 is the intensity level

at 626 nm, while X2 is the intensity level at 470 nm.

91


Table 4.18 – Analysis of variance (ANOVA) for response surface model.

Factor Sum ofsquares

df Mean square F-value p-value

X1 23.93 1 23.93 47.86 0.0913X21 1384.14 1 1384.14 27668.28 0.0121

X2 19.71 1 19.71 39.42 0.1005X22 3520.57 1 3520.57 7041.13 0.0076

X12 403.81 1 403.81 807.62 0.0224Lack of fit 45.99 3 45.99 30.66 0.1318Pure Error 0.50 1 0.50 - -

Total 4184.08 9 - - -

The highest biomass productivity can be achieved at the critical point of the surface

which is identified at 241.34 and 95.97 µmol m−2 s−2 for red and blue intensities, respectively.

Two extrememum, namely the photolimitation and photoinhibation, are also observed during

the experimental section. Low light availability and excess external irradiation settings re-

sulted in decreased growth rate and low biomass productivity. These phenomena prevent the

propagation of microalgae cells which highlight the importance of light factorial optimization.

Figure 4.18 – Productivity under different intensity levels, surface plot

92


(a) (b)

(c)

Figure 4.17 – Testing adequacy of the fitted statistical model for investigation of light intensity.(a) Normal probability plot, (b) Predicted vs Observed values, (c) Raw residuals vs Case numberplot.

The verification of the statistical model was carried out using random high, middle

and low factor levels. Table B.2 summarizes the experimental results compared to the esti-

mated biomass productivites. The model based predicted biomass productivities are plotted

against the repeated experimental results as it is shown at Figure 4.19. It can be seen that the

estimated and experimental biomass productivites are not differing significantly thus the fitted

polynomial model considered to be adequate and describes well the biomass productivity in

function of 626 nm and 470 nm light intensity levels.

93


20 30 40 50 60 70

20

30

40

50

60

70

80

y = 0.0073x − 0.0016R2 = 0.9842

Experimental biomass productivity (mg L−1 d−1)

Pre

dic

ted

bio

mas

sp

rod

uct

ivit

y(m

gL−

1d−1

)

Figure 4.19 – Predicted versus experimental values, model verification

4.7.3 Scaled up fermentations

Following the light factorial optimization, scaled-up fermentations are carried out in

stirred tank photobioreactors to (i) validate the ideal illumination parameters, (ii) to investigate

the effects of photon flux density and aeration rate on the biological composition of algae cells

and (iii) to examine whether the biogas composition and yield can be influenced indirectly

already at the cultivation stage in case of hydrothermal gasification.

22 factorial design of experiment (DoE) is used to investigate the effects of light in-

tensity and aeration rate on biomass productivity and biological composition. The dry weight

contents illustrated in Figure 4.20 in function of the cultivation time. The selected factors are

investigated in two different levels (intensity: 256.9 (R) & 102.1 (B) and 178.9 (R) & 64.8 (B)

µmol m−2 s−1 and aeration rate: 0.75 and 0.50 vvm). Throughout the fermentations the highest

final dry weight content was 64.40 mg L−1 at 256.88 and 102.10 µmol m−2 s−1 red and blue

photon flux densities and 0.75 vvm aeration rate. The achieved biomass productivites shows

the same tendency as it is observed using the MTP device, that is, applying optimized intensity

levels result in elevated biomass productivity. However, the highest biomass productivity was

59.82 mg L−1 d−1 which is lower compared to the results of the MTP device. The difference

can be traced back to the differing geometrical design of the cultivation systems and to the

fact, that self-shadowing of the cells are increasing with the scaled-up cultivation volume.

94


0 50 100 150 2000

0.1

0.2

0.3

0.4

0.5

0.6

Time (h)

Dry

wei

ght(

gL−

1)

256.9(R)102.1(B) µmol m−2s−1, 0.75 vvm

256.9(R)102.1(B) µmol m−2s−1, 0.50 vvm

178.9(R)64.8(B) µmol m−2s−1, 0.75 vvm

178.9(R)64.8(B) µmol m−2s−1, 0.50 vvm

Figure 4.20 – Fermentations with the scaled up fermentors.

The biomass productivities and the final biological compositions of the scaled up

fermentations are collected in Table 4.19. It is found that the protein content of the biomass

remains roughly the same using different light intensity and aeration rate settings. However, it

turns out that the lipid and carbohydrate content can be influenced significantly. Increased

photon flux density and lower aeration levels result in higher carbohydrate content, while

elevated intensity and aeration levels provide higher lipid content and biomass productivity.

The cross-effect between the factors is investigated using bar charts (Fig. 4.22).

Noticeable cross-over interaction identified in case of biomass productivity (in Fig. 4.22a)

which means that the effect of intensity on biomass productivity changes depending on the

level of aeration. The same effect is observed in case of the protein content of the biomass

(Fig. 4.22b). An interaction can be also expected in case of carbohydrate content (Fig. 4.22c).

No interactions are found in case of lipid content between the examined factors (Fig 4.22d).

Throughout the experimental section it is found that the biomass productivity and

the biological composition can be influenced using different photon flux density and aeration

rates on the culture. The specific component (protein, carbohydrate, lipid and ash) produc-

tivities are illustrated on a radial graph (Fig. 4.21). Specific component productivities are

important indicators regarding the efficiency of the cultivation phase. The traditional down-

stream conversion route demands high amount of oil content for the production of biodiesel.

Fig. 4.21 demonstrates that the lipid productivity of the cells can be greatly influenced and

increased applying ideal illumination and aeration rate settings. The radial graph shows that

the protein production is influenced mainly by light intensity while the carbohydrate and lipid

production can be controlled by the illumination and the rate of aeration. Higher intensity lev-

95


els (256.88 (R) and 102.10 (B) µmol m−2 s−1) are favoured to increase the specific productivity

of lipids, carbohydrates and proteins. Higher intensity levels (256.88 (R) and 102.10 (B) µmol

m−2 s−1) result in a 3.27-fold lipid productivity increasement at constant 0.75 vvm aeration

rate. Decreasing the aeration rate cause a serious 1.99 times lipid productivity reduction while

on the other hand the carbohydrate productivity is increasing by 2.04 times.

Carbohydrate(mgL−1d−1)

Protein(mgL−1d−1)

Lipid(mgL−1d−1)

Ash(mgL−1d−1)

10

20

30

40

256.88(R),102.10(B); 0.50vvm256.88(R),102.10(B); 0.75vvm178.90(R),64.82(B); 0.50 vvm178.90(R),64.82(B); 0.75 vvm

Figure 1: Specific component production of biomass

1

Figure 4.21 – Specific component production of biomass

96


-1 10

10

20

30

40

50

60

Intensity levels (-)

Bio

mas

sp

rod

uct

ivit

y(m

gL−

1d−1

)

Aeration -1 Aeration 1

(a)

-1 150

55

60

65


Pro

tein

(wt%

)


(b)

-1 10

5

10

15

20


Car

bo

hyd

rate

(wt%

)


(c)

-1 10

5

10

15

20

25


Lip

id(w

t%)


(d)

Figure 4.22 – Detecting interaction between aeration and light intensity parameters in case of(a) biomass productivity, (b) protein, (c) carbohydrate and (d) lipid content. Intensity level-1: 178.90 (Red) & 64.82 (Blue) µmol m−2 s−1; 1: 256.88 (Red) & 102.10 (Blue) µmol m−2 s−1.Aeration level -1: 0.50 vvm; 1: 0.75 vvm.

97


Table 4.19 – Microalgae cultivation in stirred tank photobioreactor and final biological compo-sition of Chlorella vulgaris

Intensity levels

Run

Red (626 nm)

intensity

(µmol m−2 s−1)

Blue (470 nm)

intensity

(µmol m−2 s−1)

Aeration

(vvm)

Biomass

productivity

(mg L−1 d−1)

Protein

(wt%)

Carbohydrate

(wt%)

Lipid

(wt%)

Ash

(wt%)

1 256.88 (1) 102.10 (1) 0.50 (-1) 52.34 60.79 20.52 13.18 5.51

2 256.88 (1) 102.10 (1) 0.75 (1) 59.82 62.59 8.77 23.02 6.62

3 178.90 (-1) 64.82 (-1) 0.50 (-1) 25.64 65.31 17.70 9.60 7.38

4 178.90 (-1) 64.82 (-1) 0.75 (1) 22.96 63.07 14.47 18.39 4.07

4.8 Using targeted cultivation to increase the yield and composi-

tion of HTG biogas

The scaled-up fermentor batches are converted into biogas via hydrothermal gasi-

fication at 550°C, 30.0 MPa, average 120 sec residence time. The biogas composition and

yield are summarized in Table 4.20. It is found that the biological composition of Chlorella

vulgaris can determine the biogas quality that produced via the hydrothermal treatment. It is

found that targeted cultivation using specific illumination and aeration levels can affect the

lipid and carbohydrate content of biomass. Analyzing the biogas composition it is found that

higher methane yield can be achieved if the lipid content of the biomass increased during the

cultivation stage. When the biological composition of the cells were shifted through targeted

cultivation towards higher carbohydrate content, the HTG hydrogen yield is increased. The

H2, CH4, CO2 and CO yields are illustrated in Fig. 4.23.

Table 4.20 – Hydrothermal gasification of microalgae biomass at 550°C and 30.0 MPa.

Intensity levels

RunRed intensity(µmol m−2 s−1)

Blue intensity(µmol m−2 s−1)

Aeration(vvm)

H2

(mol%)CH4

(mol%)CO2

(mol%)CO

(mol%)GEC

(%)Ybiogas

(mol kg−1)

1 256.88 (1) 102.10 (1) 0.50 (-1) 37.50 1.41 8.70 52.40 67.56 24.912 256.88 (1) 102.10 (1) 0.75 (1) 23.78 7.59 7.51 60.77 69.60 22.073 178.90 (-1) 64.82 (-1) 0.50 (-1) 28.23 1.75 7.82 62.20 85.87 24.244 178.90 (-1) 64.82 (-1) 0.75 (1) 24.34 2.02 2.35 71.29 81.12 17.98

In the upstream section increased biomass productivities are paired with high lipid

content of biomass. The conversion of high lipid content biomass contribute elevating the

methane content of biogas. It is found that the methane yield is increased from 0.35 mol

kg−1 to 1.68 mol kg−1. The highest hydrogen yield is found to be 9.34 mol kg−1 which is an

important increasement compared to the lowest H2 yield (4.38 mol kg−1). This is significant

98

4.8. Using targeted cultivation to increase the yield and composition of HTG biogas

because the hydrogen yield increment is achieved through the optimization of cultivation

parameters operating hydrothermal gasification without any catalyst during the process. The

highest methane and hydrogen yields are obtained using different Chlorella vulgaris biological

compositions. The achieved highest hydrogen yield is comparable with previous studies where

the yields range between 5.97 and 15.10 mol kg−1 (Jiao et al., 2017; Onwudili et al., 2013), but in

these works the yields are elevated using various catalysts in the process and not investigated

the possible effects of targeted algae cultivation on the transformation of the biomass.

The effects of targeted cultivation is further investigated calculating specific gas

yields. Figure 4.24 shows the effects of different illumination and aeration regimes on biomass

productivity, total gas yield and total specific gas yield. Specific gas yields are estimated based

on achieved biomass productivities and total gas yields considering 200 h cultivation period.

The results show that the final specific gas yield elevated via light intensity optimizations, thus

cultivation parameters (such as photon flux density and aeration rate) are determinant factors

wich should be considered not only in the upstream technologies but in the downstream stage

as well. The experimental data and calculated results suggest that the overall efficiency of a

biorefinery can be upgraded further applying targeted cultivation to develop the specific yield

of downstream technologies.

a b c d

5

10

15

20

25

Yie

ld(m

olk

g−1

)

H2 CH4 CO2 CO

Figure 4.23 – Yields of H2, CH4, CO2 and CO biogas components at 550°C, 30.0 MPa andaverage 120 sec residence time. (a) 256.88(R) 102.10(B) µmol m−2 s−1, 0.50 vvm; (b) 256.88(R)102.10(B) µmol m−2 s−1, 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m−2 s−1, 0.50 vvm; (d) 178.90(R)64.82(B) µmol m−2 s−1, 0.75 vvm.

99


a b c d0

10

20

30

40

50

60

70P

rod

uct

ivit

y(m

gL−

1d−1

)

0

10

20

30

40

Tota

lgas

yiel

d(m

olk

g−1

)

0

2

4

6

8

10

12

Tota

lsp

ecifi

cga

syi

eld

(mm

olL

−1)

Figure 4.24 – Comparing biomass productivity, total gas and total specific yield of hydrothermalgasification. (a) 256.88(R) 102.10(B) µmol m−2 s−1, 0.50 vvm; (b) 256.88(R) 102.10(B) µmolm−2 s−1, 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m−2 s−1, 0.50 vvm; (d) 178.90(R) 64.82(B) µmolm−2 s−1, 0.75 vvm.

H2 CH4 CO2 CO0

1

2

3

4

5

6

7

8

Spec

ific

gas

yiel

d(m

mo

lL−1

)

a b c d

Figure 4.25 – Specific gas yield of biogas components. (a) 256.88(R) 102.10(B) µmol m−2 s−1,0.50 vvm; (b) 256.88(R) 102.10(B) µmol m−2 s−1, 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m−2

s−1, 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m−2 s−1, 0.75 vvm.

100

4.8. Using targeted cultivation to increase the yield and composition of HTG biogas

The specific gas component yields are presented in Figure 4.25 in function of dif-

ferent illumination and aeration settings. It is demonstrated that the targeted cultivation

indirectly influence the obtainable amount of fuel gases (CH4, synthesis gas (H2/CO)). As

a consequence, targeted cultivation plays key role elevating the overall efficiency of a third

generation’s biorefinery.

101

5 Conclusions

The cradle-to-grave energetic analysis shows that the net energy ratio of an algae

based biorefinery can be higher than 1, if the flue gas can be injected directly into the culture

broth and transformation of biomass involves the application of hydrothermal treatment. The

energetic bottlenecks of biorefinery alternatives are identified, these are the drying process

(44.6%), MEA based carbon dioxide absorption (22.7%), atmospheric gasification (12.1%)

and cultivation step (8.7%) in case of the traditional dry route while CO2 absorption (40.4%),

hydrothermal liquefaction unit (21.7%) and cultivation step (15.4%) are spotted in case of the

wet/hydrothermal route. The determined energy flows demonstrate that the dry downstream

pathway has significantly higher energy demand compared to the wet route. The highest net

energy ratio is found to be 1.137 in the case of tubular photobioreactor cultivation system and

through the transformation of wet microalgae biomass via hydrothermal conversion.

The life cycle analysis of the carbon capture and storage technology shows that

the environmental impacts of CCS using monoethanolamine absorbent are more beneficial

from the viewpoint of carbon footprint compared to the uncontrolled CO2 release if process

improvement (-550.4 kg CO2, eq) or renewable energy sources are applied (-642.0 kg CO2, eq).

The application of multi-perspective life cycle impact assessment methods demonstrate that

applying renewable energy sources reduce the environmental damages of CCS technology

by 3.22- and 3.43-times in the case of IMPACT 2002+ and Eco-indicator 99 LCIA methods,

respectively. The highest environmental impact is associated with the absorbent regeneration

process in case of every CCS alternatives. The performed PESTLE analysis shows that beyond

the environmental factors, economic, social and technological factors also affect the CCS

process chain. The Multi-Criteria Decision Analysis demonstrates that among the investigated

CCS alternatives the renewable based technology is the best carbon capture alternative in

regard of multiple aspects.

The light factorial optimization of Chlorella vulgaris cultivation results in a biomass

103

Chapter 5. Conclusions

productivity increment from 14.30 mg L−1 d−1 to 75.57 mg L−1 d−1 in the MTP system. The

highest microalgae productivity is attained using red (626 nm) and blue (470 nm) color illumi-

nations at 243.5 and 96.8 µmol m−2 s−1. Scaled up fermentations at stirred tank photobiore-

actors shows that the biological composition can be shifted by changing the levels of light

illumination and aeration rate. It is found that these parameters affect the overall efficiency of

a biorefinery.

It is demonstrated that the yields of hydrothermal gasification and the produced

biogas composition can be influenced by the illumination conditions throughout the culti-

vation stage. During the experimental section high H2 yield is achieved (4.38-9.34 mol kg−1)

which is comparable with literature data where the HTG of the feedstock was performed in the

presence of various heterogeneous and homogeneous catalysts.

104

6 Major New Results

Thesis 1 [I, IV, V, VII]

I determined the energy flow of autotroph microalgae based biorefineries containing

of different cultivation systems and downstream processing routes. I identified their bottle-

necks that are mainly responsible for the efficiency decrease. These elements of the refinery

are the drying, CO2 absorption, atmospheric gasification, hydrothermal treatment and hy-

drodinamics in cultivation systems. I determined that energy gain is achievable through the

utilization of tubular photobioreactors and hydrothermal conversion technologies.

Thesis 2 [II, VIII]

I developed the carbon capture related biorefineries. Applying life cycle analysis,

I detected that the Global Warming Potential of the CO2 capture and storage process chain

can be decreased by 71% if the MEA-Water absorbent regeneration is more efficient due to

heat integration. The CO2 capture shows reduced environmental effects by 67% in the case of

multiperspective Eco-indicator 99 method if renewables are applied and fossil energy carriers

are excluded.

Thesis 3 [II, VIII]

I developed an algorithm for the comprehensive study of Carbon Capture and Stor-

age technology. I expanded the environmental assessment of the CCS chain by external

factors using PESTLE analysis and I applied Multi-Criteria Decision Analysis for ranking and

selecting CCS alternatives. I identified the renewables based technology as the best carbon

capture process alternative comparing it to the fossil and improved CCS alternatives and to

the uncontrolled release of CO2.

105

Chapter 6. Major New Results

Thesis 4 [III, VI, IX]

I determined the best artificial illumination condition for Chlorella vulgaris MACC555

microalgae strain to increase its biomass productivity. This can be completed with the dichro-

matic light emitting diodes (LED) working at 626 nm and 470 nm using optimized light

intensity levels at 241.34 and 95.97 µmol photon m−2 s−1, respectively.

Thesis 5 [III, IX]

I determined that the lipid content of Chlorella vulgaris MACC555 can be favourably

influenced by the parameters of light intensity and aeration level in stirred tank photobioreac-

tor at 250 rpm stirring rate. Increasing the aeration level from 0.50 to 0.75 vvm elevates the

lipid content by 1.75-times but decrease the carbohydrate content by 2.34-times at optimized

light intensity levels.

Thesis 6 [III, IX, X]

I determined that the light factorial optimization and ideal aeration levels of Chlorella

vulgaris MACC555 cultivation can improve the composition and yield of biogas produced by

hydrothermal gasification of the feedstock. I determined that the hydrogen yield can be raised

by 4.96 mol kg−1 if the carbohydrate content of the cells increased by 2.34-times

106

Articles related to the thesis

[I] Fozer, D., Valentinyi, N., Racz, L., Mizsey, P., Evaluation of microalgae-based

biorefinery alternatives, Clean Technologies and Environmental Policy, 2017, 19(2) 501-515,

doi: 10.1007/s10098-016-1242-8

[II] Fozer, D., Sziraky, F.Z., Racz, L., Nagy, T., Tarjani, A.J., Toth, A. J., Haaz, E., Benko, T.,

Mizsey, P., Life cycle, PESTLE, and Multi-Criteria Decision Analysis of CCS process alternatives,

Journal of Cleaner Production, 2017, 147, 75-85, doi: 10.1016/j.jclepro.2017.01.056

[III] Fozer, D., Kiss, B., Lorincz, L., Szekely, E., Mizsey, P., Nemeth, A., Improve-

ment of microalgae biomass productivity and subsequent biogas yield of hydrothermal

gasification via optimization of illumination, Renewable Energy, 2019, 138:1262-1272, doi:

10.1016/j.renene.2018.12.122

[IV] Fozer, D., Valentinyi, N., Racz, L., Mizsey, P., Harmadik generációs biofinomítói

alternatívák értékelése, Ipari Ökológia, 2016, 3-22 (in Hungarian)

Oral presentations

[V] Fozer, D., Valentinyi, N., Racz, L., Mizsey, P., Harmadik generációs – mikroalgán

alapuló – biofinomítói alternatívák értékelése, XXXIX. Kémiai Eloadói Napok, 17.10.2016,

Szeged, Hungary (in Hungarian)

[VI] Fozer, D., Kiss, B., Nemeth, A., Determine optimal growth conditions for Chlorella

vulgaris, Proceedings of the 12th Fermentation Colloquium, 19-21.10.2016, Keszthely, Hungary

[VII] Fozer, D., Mizsey, P., Mikroalgák az energiaiparban, egy harmadik generációs

biofinomító energiamérlege, MTA Vegyipari Muveleti és Gépészeti Munkabizottság és az MKE

Muszaki Kémiai Szakosztály ülése, 08.06.2017 Budapest, Hungary, (in Hungarian)

[VIII] Fozer, D., Sziraky, F.Z., Racz, L., Nagy, T., Tarjani, A.J., Toth, A. J., Haaz, E., Benko,

T., Mizsey, P., Life cycle and PESTLE analysis of CCS alternatives, 7th European Young Engineers

Conference, 23-25.04.2018, Warsaw, Poland

[IX] Fozer, D., Farkas, C., Kiss B., Lorincz L., Toth AJ., Andre, A., Nagy, T., Tarjani AJ.,

Haaz, E., Valentinyi, N., Nemeth, A., Szekely, E., Mizsey, P., The investigation and improvement

of hydrothermal gasification parameters on microalgal biomass, 7th European Young Engineers

Conference, 23-25.04.2018, Warsaw, Poland

[X] Fozer, D., Mizsey, P., Szén-dioxid megkötés és hatékony hasznosítása mikroalga

alapú technológiával, Magyar Kémikusok Egyesülete BAZ megyei Területi Szervezete és a

Miskolci Akadémiai Bizottság Vegyészeti Szakbizottsága - 35. Borsodi Vegyipari Nap, 22.11.2018,

107

Chapter 6. Major New Results

Miskolc, Hungary (in Hungarian)

[XI] Fozer, D., Sztancs, G., Kiss, B., Toth, A.J., Nemeth, A., Nagy, T., Mizsey, P., Hy-

drothermal carbonization of Chlorella vulgaris for upgrading the yields of hydrothermal

gasification, Application of supercritical fluids 2018 conference, 17.05.2018, Budapest, Hun-

gary

[XII] Fozer, D., Sztancs, G., Kiss B., Toth A.J., Haaz E., Nemeth A., Mizsey P., Biogáz

eloállítás Chlorella vulgaris és Chlorella zofingiensis hidrotermális elgázosításával, III. Gazdálkodás

és Menedzsment Tudományos Konferencia 2018, 27.09.2018, Kecskemét, Hungary

[XIII] Fozer, D., Toth, A.J., Kiss, B., Nagy, T., Nemeth, A., Mizsey, P., Hydrothermal

Gasification of Chlorella vulgaris and Chlorella zofingiensis for the Production of Fuel Gases,

51th GOMA Fuels Symposium, 17-19.10.2018, Opatija, Croatia

[XIV] Fozer, D., Greenery - Közösségi alapú zöldhálózati információs rendszer, Klí-

makonferencia - Klímastratégia és éghajlatváltozási platform létrehozása Budapesten, 17.11.2017,

Budapest, Hungary (in Hungarian)

Other Articles

[XV] Cespi, D., Passarini, F., Cavani, F., Volanti, M., Neri, Mizsey, P., Fozer, D., Tereph-

thalic acid from renewable sources: early stage sustainability analysis of a bio-PET precursor,

Green Chemistry, 2019, doi: 10.1039/C8GC03666G

[XVI] Andre, A., Nagy, T., Toth, A.J., Haaz, E., Fozer, D., Tarjani, A.J., Mizsey, P., Dis-

tillation contra pervaporation: comprehensive investigation of isobutanol-water separation,

Journal of Cleaner Production, 2018, 187:804-818., doi: 10.1016/j.jclepro.2018.02.157

[XVII] Toth, A.J., Haaz, E., Valentinyi, N., Nagy, T., Tarjani, A.J., Fozer, D., Andre, A.,

Selim, A., Solti, Sz., Mizsey, P., Selection between separation alternatives: Membrane Flash

Index (MFLI), Industrial & Engineering Chemistry Research, 2018, 57(33):11366-11373, doi:

10.1021/acs.iecr.8b00430

[XVIII] Tarjani, A.J., Toth, A.J., Nagy, T., Haaz, E., Valentinyi, N., Andre, A., Fozer, D.,

Mizsey, P., Thermodynamic and Exergy Analysis of Energy-Integrated Distillation Technologies

Focusing on Dividing-Wall Columns with Upper and Lower Partitions, Industrial & Engineering

Chemistry Research, 2018, 57(10):3678-3684, doi: 10.1021/acs.iecr.7b04247

[XIX] Racz L., Fozer D., Nagy T., Toth A.J., Haaz E., Tarjani A.J., Andre A., Selim A.K.M.,

Valen-tinyi N., Mika L.T., Deak, Cs., Mizsey, P., Extensive comparison of biodiesel production

alternatives with Life Cycle, PESTLE and Multi-Criteria Decision Analyses, Clean Technologies

and Environmental Policy, 2018, 20(9):2013-2024, doi: 10.1007/s10098-018-1527-1

108

[XX] Haaz, E., Fozer, D., Nagy, T., Valentinyi, N. Andre, A. Matyasi, J. Balla J., Mizsey,

P., Toth, A.J., Vacuum evaporation and reverse osmosis treatment of process wastewaters

containing surfactant material: COD reduction and water reuse, Clean Technologies and

Environmental Policy, 2019, doi: 10.1007/s10098-019-01673-5

[XXI] Haaz E., Valentinyi N., Tarjani A.J., Fozer D., Andre A., Selim A.K.M., Rahimli F.,

Nagy, T., Deak Cs., Mizsey P., Toth A.J., Platform molecule removal from aqueous mixture with

organophilic pervaporation: experiments and modelling, Periodica Polytechnica-Chemical

Engineering, 2019, 63:138-146., doi: 10.3311/PPch.12151

[XXII] Valentinyi, N., Andre, A., Haaz, E., Fozer, D., Toth A.J., Nagy, T., Mizsey, P.,

Experi-mental investigation and modelling of the separation of ternary mixtures by hydrophilic

pervaporation, Separation Science and Technology, 2019, doi: 10.1080/01496395.2019.1569692

[XXIII] Selim, A., Valentinyi, N., Nagy, T., Toth, A.J., Fozer, D., Haaz, E., Mizsey, P., Effect

of Ag-nanoparticles generated in poly (vinyl alcohol) membranes on ethanol dehydration via

pervaporation, Chinese Journal of Chemical Engineering, 2018, doi: 10.1016/j.cjche.2018.11.002

[XXIV] Tarjani, A.J., Toth, A.J., Nagy, T., Haaz, E., Fozer, D., Andre, A., Mizsey, P.,

Controllability features of dividing-wall column, Chemical Engineering Transactions, 2018,

63:403-408, doi: 10.3303/CET1869068

[XXV] Toth, A.J., Haaz, E., Nagy, T., Tarjani, A.J., Fozer, D., Andre, A., Valentinyi, N.,

Mizsey, P., Novel method for the removal of organic halogens from process wastewaters en-

abling water reuse, Desalination and Water Treatment, 2018, 13:54-62, doi: 10.5004/dwt.2018.22987

[XXVI] Toth, A.J., Haaz, E., Solti, Sz., Valentinyi, N., Andre, A., Fozer, D., Nagy, T.,

Mizsey, P., Parameter estimation for modelling of organophilic pervaporation, Computer-Aided

Chemical Engineering, 2018, 43:1287-1292, doi: 10.1016/B978-0-444-64235-6.50226-6

[XXVII] Toth, A,J, Haaz, E., Nagy, T., Tarjani, A.J., Fozer, D., Andre, A., Valentinyi, N.,

Solti, Sz., Mizsey, P., Treatment of pharmaceutical process wastewater with hybrid separation

method: distillation and hydrophilic pervaporation, Liquid Waste Recovery, 2018, 3:8-13, doi:

10.1515/wtr-2018-0002

[XXVIII] Toth, A.J., Haaz, E., Szilagyi, B., Nagy, T., Tarjani, A.J., Fozer, D., Andre,

A., Valentinyi, N., Solti, Sz., Mizsey, P., COD reduction of process wastewater with vacuum

evapora-tion, Liquid Waste Recovery, 2018, 3:1-7, doi: 10.1515/wtr-2018-0001

[XXIX] Fozer, D., Kiss, B., Lorincz, L., Toth, A.J., Andre, A., Tarjani A.J., Nagy, T., Haaz

E., Valen-tinyi, N., Nemeth A., Szekely, E., Mizsey, P., Metán és hidrogén tartalmú biogáz eloál-

lításának vizsgálata mikroalga biomassza hidrotermális elgázosításával, Körforgásos Gazdaság

és Környezetvédelem/Circular Economy and Environmental Protection, 2017, 1(4):5-16, doi:-

109

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A List of symbols

a Roughness (-)

ASTM American Society for Testing Materials

C% Weight percentage of carbon in algae biomass (wt.%)

CC Climate change

CCD Central Composite Design

CCGT Combined Cycle Gas Turbine

CH U Switzerland, Unit

cp,hex Specific heat capacity of hexane (2.26 kJ kg−1 °C−1)

cp,lipid Specific heat capacity of lipid (kJ kg−1 °C−1)

cp,MeOH Specific heat capacity of methanol (kJ kg−1 °C−1)

cv Specific heat of water (kJ kg−1 °C−1)

cw Latent heat of evaporation (kJ kg−1)

CCS Carbon Capture and Storage

CCU Carbon Capture and Utilization

d Diameter of pipe (m)

DAILY Disability-Adjusted Life Year

DAP Diammonium phosphate

DECERNS Decision Evaluation in Complex Risk Network System

df degree of freedom

DoE Desing of Experiment

DW Dry weight (g L−1)

EDAP Energy requirement of the production of DAP (MJ kg−1)

EI 99 Eco-indicator 99

EP Energy requirement of the paddlewheels (MJ)

EPS Environmental Priority Strategies

EUA European Union Allowance

133

Appendix A. List of symbols

f Blasius friction factor (-)

FC Fixed carbon (wt.%)

GEC (%) Carbon gasification efficiency (-)

GHG Greenhouse gas

GWP Global warming potential

h Differential head (m)

HHV Higher Heating Value (MJ kg−1)

HS Hazard severity

HTC Hydrothermal carbonization

HTL Hydrothermal liquefaction

HTG Hydrothermal gasification

I′RED Red light intensity µmol photonm−2s−1

I′BLUE Blue light intensity µmol photonm−2s−1

I′GREEN Gren light intensity µmol photonm−2s−1

le Equivalent pipe length (m)

LCA Life Cycle Analysis

LCI Life Cycle Inventory

LCIA Life Cycle Impact Assessment

LED Light Emitting Diode

kβ Correction factor that is determined by the elbow’s degree (-)

MAVT Multi Attribute Value Theory

MCDA Multi-Criteria Decision Analysis

malgae Mass of algae (kg)

mCO2 Mass of CO2 (kg)

MEA Monoethanolamine

mPt 10−3 Point (-)

MTP Microtiter plate

µ Dynamic viscosity (Pa · s)

mw Amount of evaporated water (kg)

NER Net Energy Ratio

NGCC Natural gas combined cycle

NORP Number of ORP Units (-)

ηdryer Efficiency of the dryer

ηgasif Conversion efficiency of gasificiation (-)

OD Optical Density (-)

OP Ozone depletion

ORP Open Raceway-pond

P Biomass productivity (g L−1d−1)

PAF Potentially Affected Fraction (m2 yr)

134

PDF Potentially Disappeared Fraction (m2 yr)

PCC Post carbon capture

Pgrinder Performance of the grinder (kWh)

Pp Energy requirement of a paddlewheel (MJ)

Qtrans Required heat of transesterification (kJ)

r Radius of PBR tube (m)

rC Required amount of CO2 (kg)

rharv Rate of harvesting (=dilution rate) (-)

R Risk level

Re Reynolds number (-)

RC Resource Consumption

RGB Red, Green, Blue

RSM Response Surface Methodology

ρ Density (kg m−3)

TA Terrastrial acidification

TAG Triacylglycerol

tPBR Tubular photobioreactor

TDS Total dissolved solids

TRL Technical Readiness Level

ξ Elbows minor loss coefficient (-)

γ Efficiency of pumping (-)

Ybio−oil Yield of bio-oil (-)

YGAS Gas yield (mol kg−1)

VM Volatile matter (wt.%)

whex Amount of hexane entering the column (kg h−1)

135

Appendix B. Supplementary materials

B Supplementary materials

160 180 200 220 240

0

0.2

0.4

0.6

0.8

1

1.2

y = 0.0123x − 1.8986R2 = 0.9648

255-Green code (-)

Dry

Wei

ght(

gL−

1)

(a)

150 175 200 225 250

5.00

10.00

15.00

20.00·107y = 1.7696 ·106x − 2.6327 ·108

R2 = 0.9610

255-Green code (-)

Cel

lnu

mb

er(c

ells

mL−

1)

(b)

150 175 200 225 2500.2

1.2

2.2

3.2y = 0.0309x − 4.3928

R2 = 0.9963

255-Green code (-)

OD560

(-)

(c)

Figure B.1 – Calibration in the MTP device. (a) Chlorella vulgaris MACC555,Dry Weight (g L−1)in function of (255-Green) code, (b) Chlorella vulgaris MACC555, Cell number (cells mL−1)in function of 255-Green code, (c) Chlorella vulgaris MACC555, Optical Density (OD560) infunction of 255-Green code.

138

0.2 0.6 1 1.4 1.8 2.2 2.6 3

0

0.2

0.4

0.6

0.8

1

1.2 y = 0.4014x − 0.1471R2 = 0.9779

OD560 (-)

Dry

Wei

ght(

gL−1

)

(a)

0.2 1.2 2.2 3.20.00

5.00

10.00

15.00

20.00·107

y = 0.5760 ·108x − 1.2072 ·107R2 = 0.9748

OD560 (-)

Cel

lnu

mb

er(c

ells

mL−

1)

(b)

Figure B.2 – Calibration for the laboratory scaled stirred tank photobioreactors (a) Chlorellavulgaris MACC555, Dry Weight content (g L−1) in function of Optical density (OD560), (b)Chlorella vulgaris MACC555, Cell number (cells mL−1) in function of Optical density (OD560).

139

Appendix B. Supplementary materials

Table B.1 – Estimated regression coefficients of the fitted response surface model in case ofintensity optimization.

Source Coefficients p-value

β0 -646.417 0.0002

β1 (X1:Red) 4.054 0.0003

β2 (X2:Blue) -0.007 0.0004

β11 4.843 <0.0001

β22 -0.019 <0.0001

β12 -0.005 0.0042

Table B.2 – Experimental and predicted results for model verification. Data are arithmeticmeans (±S.E.) of 3 different experiments.

Run Red intensity

(µmol m−2 s−1)

Blue intensity

(µmol m−2 s−1)

Predicted

productivity

(mg L−1 d−1)

Experimental

productivity

(mg L−1 d−1)

1 198 141 34.20 33.35(±3.56)

2 202 101 64.38 64.30(±2.99)

3 313 96 37.46 37.73(±3.13)

4 291 136 16.77 16.82(±1.20)

5 276 131 37.13 36.49(±4.04)

6 250 92 74.50 72.43(±5.74)

7 194 91 56.99 56.42(±5.00)

8 235 135 47.95 48.99(±6.20)

140

DECLARATION

I, Daniel Fozer, the undersigned, hereby declare, that this Ph.D. dissertation is my

own work, and I have only used the sources listed in the reference list. I have

clearly indicated all parts that were taken from other sources and quoted literally

or paraphrase.

Budapest, 25.01.2019

Daniel Fozer

NYILATKOZAT

Alulírott Fózer Dániel kijelentem, hogy ezt a doktori értekezést magam

készítettem és abban csak a megadott forrásokat használtam fel. Minden olyan

részt, amelyet szó szerint, vagy azonos tartalomban, de átfogalmazva más

forrásból átvettem, egyértelműen, a forrás megadásával megjelöltem.

Budapest, 2019. 01. 25.

Fózer Dániel

development of microalgae based biorefinery

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