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Integrated Master in Bioengineering

Modelling and validation of daylight driven carbon partitioning in microalgae

biofilm

Dissertation for Master Degree in Bioengineering Major in Biological Engineering

by

Ana Rita Martins Pinto de Magalhães

Developed within the course of Dissertation

at

Bioprocess Engineering Chair Group, Wageningen University

Supervisor FEUP: Prof. Manuel Simões

Supervisors WUR: Prof. Marcel Janssen

Dr. Ward Blanken

January, 2016

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For my beloved Family

“We act as though comfort and luxury were the chief requirements of life, when all that we need to make us happy is something to be enthusiastic about”

Albert Einstein

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Acknowledgements

I would like to express my sincere gratitude to my supervisors in the Wageningen University, Prof. Marcel Janssen and Dr. Ward Blanken, for all the fruitful meetings and scientific advices. Your creativeness, support, criticism and trust were essential to me to get here. You believed in me and gave me the responsibility and space to “grow”! Of course, I would also like to deeply thank my supervisor in the University of Porto, Prof. Manuel Simões, for his guidance, availability and constructive suggestions.

I also acknowledge Prof. René Wijffels for allowing me to perform my thesis in the Bioprocess Engineering Chair Group (BPE). Thanks to him I was introduced to great researchers which helped and inspired me. I would also like to extend my gratitude to all the BPE technicians for their skilled technical support, which was essential for the development of this work. Special thanks for all the BPE (PhD) students for making easy my adaptation to the group and for the enjoyable moments during the coffee breaks.

I could not forget Prof. Filipa Lopes for introducing me to the great microalgae biotechnology research field during my first Erasmus experience in Paris. You’ve thrown the first rock in my enthusiasm for this research topic. Thanks for the expertise and teaching. You made me the researcher that I am today. Thank you!

I am very grateful to my Dutch friends, especially Kristian, Edgar, Camilo and Laura for all the laughs, support and kindness. You made me feel so at home. I will never forget you. You’re all in my heart!

To my dear friend Manú, for always carrying as only you can do!

Finally, I would like to deeply thank my Parents and my Sister for the love, unconditional support and for always believing in me. Without all your efforts, my dreams never would come to reality. You’re more than my reason to live, you’re my inspiration!

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“Our faith can move mountains”

Matthew 17:20

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Abstract

Modelling microalgae biofilm growth can give insights in the economic feasibility of using biofilms as a production platform for microalgae biomass production. In this study a simple kinetic model to predict microalgae growth under simulated outdoor conditions for both suspended and biofilm cultures was developed. The proposed model couples a light dependent sugar production model with an aerobic chemoheterotrophic sugar consumption for biomass production (growth-related respiration) model. Intracellular carbon was divided between a functional pool and a sugar storage pool to account for the daylight carbon partitioning. Differentiation was made between sugar mobilisation for biomass production during the day (described by a Droop function) and at night (a linear function was used instead).

Suspended chemostat day/night cycle experiments were performed in flat-panel photobioreactors to obtain data to calibrate kinetic model parameters (maximal specific sugar consumption rate and minimal sugar fraction). For this purpose, Chlorella sorokiniana was cultivated under a ´block´ day/night cycle (12 h/12 h) at two different dilution rates (0.22 h-1 and 0.04 h-1). However, an unexpected overnight biomass increase was observed. Due to this unexpected result the day/light carbon partitioning model could not be calibrated. Nevertheless, the experimental data obtained shown that both the biomass production and photosynthetic efficiencies are not affected by the dilution rate. However, when rapidly growing C. sorokiniana under day/night cycling internal sugar accumulation is faster.

To assess the viability of employing biofilm cultures for large-scale microalgal biomass production suspended and biofilm cultures cultivated under simulated outdoor conditions were compared. For that, C. sorokiniana biofilms were grown in an air tight rotating biological contractor (RBC) under various simulated day/night cycles (a ‘block’ 12/12, a ‘block’ 16/8 and a ‘sine’ 16/8 day/night cycles). For the various day/night cycles simulated high photosynthetic efficiencies were achieved, which indicate that the day/night cycling have no negative effect on the light efficiency use by C. sorokiniana biofilm cells. Similar efficiency of light use was found when C. sorokiniana was cultivated both in suspension and as biofilms under day/night cycles (12 h/12 h). However, lower productivities were obtained for the biofilm cultures compared to the suspended cultures. Therefore it seems that a different biofilm-photobioreactor design should be considered for outdoors microalgal biofilms cultivation.viii

Keywords: Chlorella sorokiniana, Microalgal biofilms, Day/night cycles, Carbon partitioning, Growth modelling.

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Resumo

A modelação matemática do crescimento de microalgas sob a forma de biofilmes pode fornecer informação sobre a viabilidade económica do uso de biofilmes como uma plataforma de produção de biomassa. Neste estudo foi desenvolvido um modelo cinético simples para prever o crescimento de microalgas sob condições simuladas de produção no exterior. Este modelo foi desenvolvido tanto para culturas em suspensão como para culturas em biofilme. O modelo proposto combina um modelo de produção de açúcar dependente da luz com um modelo de consumo aeróbico de açúcar para a produção de biomassa. No exterior, os ciclos naturais de luz conduzem a um processo de partição de carbono nas microalgas, que foi contabilizado no modelo proposto através da divisão do carbono intracelular numa secção funcional e numa secção de armazenamento de açúcar. Assim, foi efetuada uma diferenciação entre a mobilização de açúcar para a produção de biomassa durante o dia (descrita por uma função Droop) e à noite (descrita por uma função linear).

Foram realizadas experiências em suspensão em fotobioreatores planos, a operar em quimiostato sob condições de luz natural simuladas, para obter dados para calibrar os parâmetros cinéticos do modelo (taxa específica máxima de consumo de açúcar e fração mínima de açúcar na biomassa). Para este efeito, Chlorella sorokiniana foi cultivada sob um fotoperíodo de luz em forma quadrada (12h) e duas taxas de diluição (0.22 h-1 e 0.04 h-1). No entanto, foi observado um inesperado aumento da biomassa durante a noite, pelo que o modelo cinético desenvolvido não pôde ser calibrado. Contudo, os dados experimentais obtidos demonstram que a taxa de diluição parece não afetar a produtividade em biomassa nem a eficiência fotossintética das células. Porém, quando C. sorokiniana é cultivada sob condições de luz natural simuladas, quanto maior a taxa de diluição, mais rápida é a acumulação de açúcar interno.

Para avaliar a viabilidade de usar biofilmes para produção em larga escala de biomassa fotossintética, foram cultivadas culturas de microalgas em suspensão e em biofilme sob condições de luz natural simuladas. Neste sentido, biofilmes de C. sorokiniana foram cultivados num reator de disco rotativo fechado, sob varias condições de luz natural simuladas (fotoperíodo de luz em forma quadrada”: 12h e 16h; e em forma de seno: 16h). Quando C. sorokiniana foi cultivada em suspensão e em biofilmes sob um fotoperíodo de 12 horas, foram obtidas eficiências fotossintéticas idênticas. No entanto, foram obtidas produtividades mais baixas

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para as culturas em biofilme, pelo que um design diferente do reactor de biofilmes deve ser considerado para produção de biomassa no exterior.

Palavras-chave: Chlorella sorokiniana, Biofilmes de microalgas, Fotoperíodos, Partição de carbono, Modelagem de crescimento.

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Declaration

I declare, on my word of honour, that this work is original and that all non-original contributions were duly referenced with identifying the source.

(Ana Rita Martins Pinto de Magalhães)

January 29th 2016

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Table of contentsAcknowledgements................................................................................v

Abstract...............................................................................................vii

Resumo................................................................................................ix

Declaration...........................................................................................xi

Table of contents.................................................................................xiii

List of figures.....................................................................................xvii

List of tables.......................................................................................xxi

Nomenclature....................................................................................xxiii

Work outline..........................................................................................1

1.1 Background and project presentation..........................................3

1.2 Objectives..................................................................................4

1.3 Thesis organisation.....................................................................4

State of the art......................................................................................5

2.1 Microalgae.................................................................................7

2.2 Microalgae cultivation systems....................................................8

2.2.1 Suspended cultivation systems............................................................................8

2.2.2 Immobilised cultivation systems........................................................................12

2.3 Outdoor algae cultivation..........................................................15

2.4 Modelling photosynthetic biomass production............................16

Influence of day/night cycling on Chlorella sorokiniana suspended cultures...........................................................................................................19

3.1 Introduction.............................................................................21

3.2 Materials and methods..............................................................22

3.2.1 Microorganisms and culture medium.................................................................22

3.2.2 Photobioreactors set-up and experimental conditions........................................22

3.2.3 Analytical methods.............................................................................................24

3.2.4 Calculations........................................................................................................26xv

3.2.5 Statistical analysis..............................................................................................27

3.2.6 Daylight driven carbon partitioning model.........................................................27

3.3 Results and discussion..............................................................31

3.3.1 Biomass density.................................................................................................31

3.3.2 Biomass composition..........................................................................................33

3.3.3 Cell number and cell size...................................................................................35

3.3.4 Specific absorption coefficient............................................................................38

3.3.5 Daily biomass productivity.................................................................................38

3.3.6 Daily biomass yield on light energy....................................................................39

3.3.7 Daylight driven carbon partitioning model predictions.......................................40

3.4 Conclusions..............................................................................41

Effect of day/night cycles on Chlorella sorokiniana biofilm cultures.........43

4.1 Introduction.............................................................................45

4.2 Materials and methods..............................................................46

4.2.1 Microorganisms and culture medium.................................................................46

4.2.2 Reactor set-up and experimental conditions......................................................46

4.2.3 Analytical methods.............................................................................................49

4.2.4 Statistical analysis..............................................................................................52

4.2.5 Daylight driven carbon partitioning model.........................................................52

4.3 Results and discussion..............................................................54

4.3.1 Surface productivity...........................................................................................54

4.3.2 Biomass yield on light........................................................................................56

4.3.3 Chlorella sorokiniana specific absorption coefficient..........................................57

4.3.4 Daylight driven carbon partitioning model predictions.......................................58

4.3.5 Biofilms as a production platform for microalgae biomass.................................60

4.4 Conclusions..............................................................................61

General conclusions and research needs...............................................63

5.1 General conclusions..................................................................65

5.2 Research needs........................................................................65

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References...........................................................................................67

Appendix............................................................................................A.1

A.1 Microalgae products and applications..........................................A.1

A.2 Daylight driven carbon partitioning model MATLAB code for suspended cultures..........................................................................A.2

Master file....................................................................................................................A.2

ODE file........................................................................................................................A.4

A.3 Steady state...............................................................................A.6

A.4 Chlorella sorokiniana specific growth rate....................................A.8

Overnight evaporation calculations..............................................................................A.9

A.5 Absorption spectra over the 400-750 nm spectrum for planktonic cultures............................................................................................A.11

A.6 Daylight driven carbon partitioning model MATLAB code for biofilm cultures............................................................................................A.12

Master file..................................................................................................................A.12

ODE file......................................................................................................................A.15

A.7 Absorption spectra over the 400-750 nm spectrum for biofilm cultures........................................................................................................A.17

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List of figuresFigure 2.1 Schematic diagram of (A) vertical columns (B) tubular and (C) flat-panel PBRs (Gupta et al., 2015).................................................................................................................9

Figure 2.2 Tisochrysis lutea carbohydrates and lipids content during a simulated day/night cycle (12 hours of light and 12 hours of dark). Dashed line: carbohydrates; dotted line: lipids; grey line: light intensity. Adapted from Baroukh et al. (2014)...............................................15

Figure 3.1 (A) Flat-panel airlift PBR used for cultivation of rapidly and slowly growing C. sorokiniana in suspension under simulated outdoor conditions; and (B) Schematic overview of the PBRs set-up with medium inflow and overflow vessels on balances, and pH, temperature and antifoam control (adapted from de Mooij et al. (2014)).............................23

Figure 3.2 Daily variation in biomass density (g-DW L-1) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in triplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 h and resemble the average of 3 steady state days for the 0.22 h -1 reactor and 2 steady state days for the 0.04 h-1 reactor. Error bars represent standard deviations..32

Figure 3.3 Daily variation in total carbon biomass (Cb cmol m-3) (triangles), functional biomass (Cx cmol-x m-3) (squares) and starch content (Cs cmol-s m-3) (diamonds) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle at 0.22 h-1 dilution rate. Grey area represents the dark period. The values were determined in duplo. They are averages over 2 h and resemble the average of 3 steady state days. Error bars represent standard deviations.............................................................................................................................34

Figure 3.4 Daily variation in total carbon biomass (Cb cmol m-3) (triangles), functional biomass (Cx cmol-x m-3) (squares) and starch content (Cs cmol-s m-3) (diamonds) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle at 0.04 h-1 dilution rate. Grey area represents the dark period. The values were determined in duplicate. They are averages over 2 h and resemble the average of 2 steady state days. Error bars represent standard deviations.............................................................................................................................35

Figure 3.5 Daily variation in cell number of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in duplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 hours and resemble the average of 3 steady state days for the 0.22 h-1

reactor and 2 steady state days for the 0.04 h -1 reactor. Error bars represent standard deviations.............................................................................................................................36

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Figure 3.6 Daily variation in cell diameter of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in duplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 hours and resemble the average of 3 steady state days for the 0.22 h-1

reactor and 2 steady state days for the 0.04 h -1 reactor. Error bars represent standard deviations.............................................................................................................................36

Figure 3.7 Number of cells versus their cell diameter (µm) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 dilution rate. The values were measured in duplicate from cumulative overflow collected on ice for 2 h and resemble the average of 3 steady state days..................................................................................................................37

Figure 3.8 Number of cells versus their cell diameter (µm) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.04 h-1 dilution rate. The values were measured in duplicate from cumulative overflow collected on ice for 2 h and resemble the average of 2 steady state days..................................................................................................................37

Figure 3.9 Daily variation in average absorption coefficient over the PAR spectrum (m2

cmol-1) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h -1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in duplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 h and resemble the average of 3 steady state days for the 0.22 h-1 reactor and 2 steady state days for the 0.04 h-1 reactor. Error bars represent standard deviations..............................................................................39

Figure 3.10 Predicted diel oscillations during 15 days in sugars (green line, cmol-s m -3), functional biomass (blue line, cmol-x m-3) and total biomass (red line, cmol m-3) content of C. sorokiniana suspended cultures grown under a ‘block’ 12/12 day/night cycle in flat panel PBRs operated in chemostat mode. For every day, sunrise is represented by the whole numbers, while sunset is represented by half of the whole numbers....................................40

Figure 4.1 (A) RBC reactor used for cultivation of C. sorokiniana biofilms under various illumination schemes; and (B) Schematic overview of the RBC reactor................................47

Figure 4.2 Surface productivity (g-DW m-2 d-1) for C. sorokiniana grown as a biofilm under continuous light at 10% CO2 (grey, N=3) and 5% CO2 (diagonal lines, N=3) and under a ‘block’ 12/12 (dots, N=2), a ‘block’ 16/8 (squares, N=5) and a ‘sine’ 16/8 (horizontal lines, N=2) day/night cycles at 5% CO2. The values resemble the average of the two sides of the disk. Error bars indicate the standard deviation....................................................................54

Figure 4.3 Biomass yield on light energy (g-DW molph-1) for C. sorokiniana grown as a biofilm under continuous light at 10% CO2 (grey, N=3) and 5% CO2 (diagonal lines, N=3) and under a ‘block’ 12/12 (dots, N=2), a ‘block’ 16/8 (squares, N=5) and a ‘sine’ 16/8 (horizontal

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lines, N=2) day/night cycles at 5% CO2. The values resemble the average of the two sides of the disk. Error bars indicate the standard deviation..............................................................56

Figure 4.4 Average specific light absorption coefficient ax (m2 cmol-1) over the PAR spectrum for C. sorokiniana grown as a biofilm under continuous light at 10% CO2 (grey, N=3) and 5% CO2 (diagonal lines, N=3) and under a ‘block’ 12/12 (dots, N=2), a ‘block’ 16/8 (squares, N=5) and a ‘sine’ 16/8 (horizontal lines, N=2) day/night cycles at 5% CO2. Error bars indicate the standard deviation.....................................................................................57

Figure 4.5 Predicted diel oscillations in sugars (full lines, cmol-s m-3) and total biomass (dashed lines, cmol-s m-3) of C. sorokiniana biofilm cultures grown during 7 days in the lab scale rotating biological contractor reactor under (A) ‘block’ and (B) ‘sine’ 16/8 day/night cycles. For every day, sunrise is represented by the whole numbers, while sunset is represented by 0.7 of the whole numbers. Full lines refer to sugar content across the biofilm: upper full lines represent the surface biofilm layer and the bottom full lines represent the inner biofilm layer.................................................................................................................58

Figure 4.6 Predicted productivities (g m2) of C. sorokiniana biofilm cultures grown during 7 days under continuous light (light blue line), a ‘block’ 12/12 (red line), a ‘block’ 16/8 (dark blue line) and a ‘sine’ 16/8 (green line) in the lab scale rotating biological contractor reactor. In the day/night cycles, sun rises at the start of a new day (represented as whole numbers)............................................................................................................................................... 59

Figure A.1 Ratio between the optical density at 680 nm and 750 nm (OD680/OD750) of C. sorokiniana grown under a ´block´12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1

(squares) dilution rates (D). The values were measured in triplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively............................................................................................................................................. A.6

Figure A.2 Biomass density (g-DW L-1) of C. sorokiniana grown under a ´block´12:12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in triplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively....................................................A.6

Figure A.3 Cell number of C. sorokiniana grown under a ´block´12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in duplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively...................................................................................A.7

Figure A.4 Cell size of C. sorokiniana grown under a ´block´12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in duplicate from samples taken from the reactor every day at 11:00. Error bars show standard

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deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively...................................................................................A.7

Figure A.5 Daily variation in dilution rate (D, h-1) in steady state (in steady state the dilution rate equals the specific growth rate µ, h-1) in fast (0.22-h-1 dilution rate) and slow (0.04-h-1

dilution rate) growing cultures of C. sorokiniana cultivated under a ‘block’ 12:12 day/night cycle. Grey area represents the dark period. The values were estimated based on the amount of medium feed (triangles for the 0.22-h-1 reactor and crosses for the 0.04-h-1

reactor) and the amount of overflow produced (diamonds for the 0.22-h-1 reactor and squares for the 0.04-h-1 reactor). The values are averaged values over 2 hours and resemble the average of 3 and 2 steady state days for the 0.22-h -1 and the 0.04-h-1 reactor, respectively. Error bars represent standard deviations........................................................A.9

Figure A.6 The absorption spectra over the 400-750 nm for (A) fast growing and (B) slow growing planktonic C. sorokiniana cultures........................................................................A.11

Figure A.7 Average absorption spectra (m2 cmol-1) over the 400-750 nm spectrum for C. sorokiniana grown as a biofilm under continuous light at 10% CO2 (dark blue, N=3) and 5% CO2 (light blue, N=3) and under a ‘block’ 16/8 (orange, N=5), a ‘sine’ 16/8 (yellow, N=2) and ‘block’ 12/12 (green, N=2) day/night cycles at 5% CO2. The values resemble the average of the two sides of the disk..................................................................................A.17

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List of tablesTable 3.1 Overview of literature based model input parameters (based on Blanken et al. (2016)). Mx: molar mass of C. sorokiniana; ax: spectrally-averaged specific absorption coefficient; µmax: maximal specific growth rate; Yx/s: biomass yield on sugar; Ys/ph: sugar yield on photons; ms: maintenance-related specific sugar consumption rate........................31

Table 4.1 Overview of the experimental conditions used in the experiments.....................49

Table A.1 Commercial products from microalgae...............................................................A.1

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Nomenclature AbbreviationsCO2 Carbon dioxidePBRs PhotobioreactorsDSP Downstream processingO2 OxygenRBC Rotating biological contractorEPS Extracellular polymeric matrixRTE Radiative transfer equationPI Photosynthesis-irradiancePAR Photosynthetic Active RadiationOD Optical densityCOD Chemical oxygen demandDO Dissolved oxygenAA Arachidonic acidEPA Eicosapentaenoic acidDHA Docosahexaenoic acidGLA -linolenic acid

Measure unitsD Dilution rate h-1

DW Dry weight g-DW L-1

w Weight gV Volume LC Concentration mol m-3

a Specific light absorption coefficient m2 cmol-1l Optical light path mmr productivity g-DW m-2 h-1

r rate µmol m-2 s-1

F Flow rate L h-1

I Light intensity µmolph m-2 s-1

Y Yield g-DW mol-1Y Yield mol mol-1q rate cmol s-1

m Cell maintenance cmol-s cmol-x-1 s-1

D Dilution rate s-1

x fractiont time hM Molar mass g cmol-x-1

P productivity g m-2

Cs,Total Total starch content g g-DW -1

A Area m2

Greek lettersWavelenght nm-1

μ Specific growth rate h-1

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Subscriptsb Total biomass cmolx Functional biomass cmol-x starch Starch cmol-s s,total Total starch cmol-sx Areal biomass productivity g-DW m-2 h-1

OUT Overflow L h-1

I Illuminated area m2

ph Areal light absorption µmolph m-2 s-1

x/ph Biomass yield on light energy g-DW molph-1

s Internal sugar cmol-s m-3

x/s Biomass yield on sugar cmol-x cmol-s-1

s,c Specific sugar consumption rate cmol-s cmol-x-1 s-1

s,p Specific photosynthetic sugar production rate cmol-s cmol-x-1 s-1

s,p (z) Local specific sugar production rate cmol-s cmol-x-1 s-1

s,m,p Maximal specific sugar production rate cmol-s cmol-1 s-1

ph (z) Specific photon absorption rate molph cmol-1 s-1

s/ph Sugar yield on light cmol-s molph-1

s,c,day Specific sugar consumption rate during the day cmol-s cmol-x-1 s-1

s,m,c Maximal sugar consumption rate cmol-s cmol-x-1 s-1

s, min Minimal sugar concentration allowing growth cmol m-3

min Minimal sugar fraction inside the biomass cmol-s cmol-b-1

s,c,night specific sugar consumption rate at night cmol-s cmol-x-1 s-1

sunrise Time at which the lights were switch on hday Total day time hdI Illuminated fraction of the diskx/b Mass fraction of biomass to water g-DW g-1

l Total harvest dry weight of the liquid phase g-DW L-1

X,disks Disk surface productivity g-DW m-2 d-1

d Disk side total harvest dry weight g-DWl fraction of biomass in the liquid phased,1 Disk side 1d,2 Disk side 2b,1 Border side 1b,2 Border side 2IN Incident light intensity µmol m-2 s-1

df Illuminated fraction of the diskb,pred Biofilm productivity g m-2

R Reactor volume L

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CHAPTER 1Work outline

1

Chapter 1

2

Work outline

1.1 Background and project presentation

Interest in microalgae for diverse biotechnological uses is increasing, however, large-scale cultivation is not yet economically sustainable (Bosma et al., 2014). Drawbacks of the commonly suspended culture systems include low biomass density (Gross et al., 2013), high energy and water consumption (Ozkan et al., 2012) and costly downstream operations to harvest such a diluted biomass (Christenson & Sims, 2012). As biofilms are naturally concentrated and easily harvestable, they have been pointed out as promising candidates to make microalgae’s production economically feasible (Shen et al., 2015).

Although high productivities can be achieved with biofilm systems (Christenson & Sims, 2012, Naumann et al., 2012, Blanken et al., 2014, Gross & Wen, 2014), the access to light and nutrients, and the temperature and pH gradients over the biofilm can lead to biomass loss (Schnurr & Allen, 2015). To understand how microalgal biofilms can still reach high productivities, the biofilm growth can be mathematically simulated and modelled. However, modelling photosynthetic biofilms still stems for knowledge mainly due to the occurrence of such gradients that make the modelling effort much more complex (Murphy & Berberoglu, 2014). While some growth models have been formulated to describe the behaviour of phototrophic biofilms in production systems (Wolf et al., 2007, Cui & Yuan, 2013, Ozkan & Berberoglu, 2013, Cole et al., 2014, Munoz Sierra et al., 2014, Shen et al., 2014), a model to predict phototrophic biofilm productivity was constructed (Murphy & Berberoglu, 2014). This model coupled light and mass transport across the biofilm, and with algal growth kinetics to understand the influence of local light and nutrients uptake on the biofilm productivity. Lacking in the current phototrophic biofilm model is the effect of diurnal light fluctuations on biofilm productivity.

To economically cultivate microalgae at large-scale, cultivation should be done outdoors on sunlight (Blanken et al., 2013). However, the sun imposes a daily cycle of light and dark (photocycle) on microalgae, which affects its biochemical composition (de Winter et al., 2013). Indeed, it has long been recognised that the composition of microalgae changes with day/night cycles, with accumulation of carbon storage reserves (e.g. sugars and lipids) during the day, and its degradation at night to support growth and maintenance (Ogbonna & Tanaka, 1996). Various studies on modelling planktonic algae growth under day-night cycles are available (Bechet et al., 2013). However, most of these models do not include diurnal carbon partitioning between biomass and storage compound formation. Furthermore,

3

Chapter 1

studies on modelling biofilm growth under simulated outdoor conditions are still lacking. Nonetheless, such a model can play a crucial role to assess the feasibility of using biofilms as an algal production platform.

1.2 Objectives

The main aim of this study is to develop and validate a biofilm growth model that takes diurnal carbon partitioning into account to accurately predict microalgal biofilm growth under outdoor conditions. For this purpose, existing growth models will be combined to obtain a daylight driven carbon partitioning model that describes light dependent phototrophic sugar production and differential aerobic chemoheterotrophic sugar consumption for biomass production (growth-related respiration) during the day and at night.

To predict biofilm growth with a mathematical model, kinetic parameters are needed. In this sense, suspended chemostat day/night cycle experiments will be performed to obtain data to calibrate the daylight driven carbon partitioning model. Afterwards, the calibrated model will be applied on biofilm cultures. The kinetic biofilm growth model will be validated with biofilm day/night cycle experiments.

This study also aims to assess the viability of employing biofilm cultures for large-scale microalgal biomass production. Therefore, microalgal suspended and biofilm cultures will be grown under simulated outdoor conditions and productivities will be compared.

1.3 Thesis organisation

This thesis is divided in five chapters. Chapter 1 introduces the main aims of this study and its motivations. Chapter 2 provides a state of the art relevant to this study, including the scientific advances on microalgae cultivation technology (both in planktonic and benthic forms), outdoor cultivation and mathematical growth modelling. Chapter 3 focus on the effect of simulated day/night cycles on rapidly and slowly growing C. sorokiniana suspended cultures. Suspended chemostat day/night cycle experiments were carried out to obtain data to calibrate a daylight driven carbon partitioning growth model. In Chapter 4, C. sorokiniana biofilms were grown in an air tight rotating biological contactor under simulated day/night cycles, and productivities were compared with suspended cultures to assess the feasibility of using biofilms for mass microalgal production. Finally, Chapter 5 summarises the

4

Work outline

main conclusions made through this thesis, identifies its limitations and provides recommendations for future research.

5

Chapter 2

CHAPTER 2State of the art

6

State of the art

7

Chapter 2

2.1 Microalgae

Microalgae are a large and diverse group of unicellular eukaryotic microorganisms (between 2 and 140 µm), which can form colonies (e.g. Botryococcus brauni) or live as individual cells (e.g. Chlorella sorokiniana) (Ozkan & Berberoglu, 2013, Wijffels et al., 2013). They can be found in many different habitats ranging from terrestrial to aquatic ecosystems, either marine or freshwater environments (John et al., 2011).

Commercial interest in microalgae is related to their high growth rates and their potential as producers of a wide range of compounds, such as carotenoids and other vitamins and antioxidants, fatty acids (specifically poly-unsaturated fatty acids), proteins, carbohydrates (in the form of starch, sugars and other polysaccharides) and various special products (as hydrogen, enzymes and isotopes) (Spolaore et al., 2006, Singh et al., 2011, Parmar et al., 2011, Borowitzka, 2013, Wijffels et al., 2013). Among others, these molecules are of interest for human food and animal feed, cosmetics and biofuel (biodiesel, bioethanol and biomethanol) applications (Wijffels et al., 2010, Suali & Sarbatly, 2012, Bahadar & Khan, 2013). Due to its ability to fixate carbon dioxide (CO2) trough photosynthesis, microalgae are also an attractive source to mitigate CO2 emissions from the atmosphere (Ho et al., 2011, Tang et al., 2011). Moreover, because microalgae can remove inorganic nitrogen and phosphorus from wastewater effluents, microalgal biomass is suitable for wastewater treatment (Zippel et al., 2007, Ji et al., 2013b, Wu et al., 2014, Tuantet et al., 2014). A more in depth review about microalgae products and applications is presented in appendix A.1.

On a trophic level, microalgae are photoautotrophic organisms: they take up and accumulate nutrients using light as an energy source and carbon dioxide as an inorganic carbon source (Chisti, 2007). However, some microalgae species are able to grow using only organic compounds as carbon and energy sources. This metabolism is known as heterotrophy (Brennan & Owende, 2010, Mata et al., 2010). Although heterotrophy can enhance lipid productivity, phototrophic cultivation is the most frequently used method for large-scale microalgae production as it is cheap and easier to scale up (Oncel, 2013, Leite et al., 2013). For this reason, in this thesis, only photoautotrophic cultivation of microalgae is going to be discussed. To grow autographically, microalgae only require light energy, CO2 and other inorganic nutrients as nitrogen and phosphorus (Brennan & Owende, 2010).

8

State of the art

2.2 Microalgae cultivation systems

Due to the increased interest in microalgal biomass as a production platform for intermediate goods and commodities, microalgae growth systems have been extensively studied in the past years. Microalgae cultivation can be carried out either in suspended systems, where algae are grown in suspension in the water (planktonic algae), or in immobilised systems, where algae are attached to a surface (benthic algae). Suspended cultures, including open systems and closed reactors, and immobilised cultures, including matrix-immobilised systems and biofilm reactors, are addressed in the following sections.

2.2.1 Suspended cultivation systems

2.2.1.1 Open systems

Open systems for microalgae cultivation can be of two major types: natural and artificial ponds. The former includes shallow big ponds and tanks, while the latter comprises circular and raceway ponds (Hosikian et al., 2010, Ho et al., 2011, Parmar et al., 2011, Maity et al., 2014).

Because they are easy in operation and low in capital and operational costs, open systems are the most commonly used large-scale microalgae production devices, representing over 80% of the commercial-scale cultivation plants (Ho et al., 2011, Bilad et al., 2014, Bosma et al., 2014, de Vree et al., 2015). However, their major limitations include poor light utilisation by the cells, water loss trough evaporation, CO2 diffusion to the atmosphere, and temperature fluctuations within diurnal cycle as a consequence of the absence of a refrigeration system (cooling is achieved only by water evaporation). Moreover, owing to poor gas exchange, dark zones and inefficient use of CO2, they can only sustain low biomass productivities (ranging from 10-20 g m-2 day-1), which increases the biomass harvesting cost (Chisti, 2007, Christenson & Sims, 2011, Bilad et al., 2014, Maity et al., 2014). Additionally, due to their sensibility to contamination, production of high-value compounds (where monoalgal or even axenic cultures are often required) is not feasible using open systems. This lead to the development of enclosed photobioreactors (PBRs) where culture conditions can be strictly controlled (Chisti, 2007, Xu et al., 2009, Wijffels et al., 2010, Gupta et al., 2015).

9

Chapter 2

2.2.1.2 Closed photobioreactors

As in conventional heterotrophic cultivations, also in microalgae biotechnology high biomass productivities are required in order to reduce the size of the cultivation and thus production and downstream processing (DSP) costs (Gross & Wen, 2014). Because in photoautotrophic cultivation of microalgae light is usually the growth limiting substrate, only high efficiency of light utilisation can support high biomass productivities (Wang et al., 2014). Aiming at attaining a cost-effective process, several PBRs have been designed (Gupta et al., 2015). Generally, the PBRs’ narrow light path and large illuminating area results in a high area to volume ratio, which enables efficient light harvest by algal cells. Consequently, higher biomass productivities (up to a maximum of 40 g m-2 day-1) can be achieved with PBRs than with open systems (Xu et al., 2009, Hosikian et al., 2010, John et al., 2011, Liu et al., 2013). Furthermore, conversely to open systems, the closed design of PBRs prevents water loss by evaporation and facilitates sterility and control of culture conditions (such as pH, temperature, mixing and carbon dioxide and oxygen concentrations) (Posten, 2009, John et al., 2011, Maity et al., 2014). PBRs can be categorised based on the illuminated surface, as flat-panels, tubular or columns reactors, and the mode of liquid flow, as stirred, bubble column or airlift reactors (Gupta et al., 2015). The most popular configurations include vertical (bubble and airlift) columns, (horizontal and vertical) tubular and flat-panel PBRs (Hosikian et al., 2010, Kunjapur & Eldridge, 2010, de Vree et al., 2015, Gupta et al., 2015). A schematic representation of these designs is depicted in Figure 2.1.

10

State of the art

Figure 2.1 Schematic diagram of (A) vertical columns (B) tubular and (C) flat-panel PBRs (Gupta et al., 2015).

According to their liquid flow circulation mode, vertical column PBRs are grouped into two main types: bubble column and airlift reactors (Kunjapur & Eldridge, 2010, Gupta et al., 2015). Although aeration and mixing is achieved by gas sparging in both of them, conversely to bubble column in airlift PBRs there is a physical separation between the riser (up flowing) and the downcomer (down flowing) streams (Gupta et al., 2015). Therefore, culture flow in bubble columns is random (and algal cells may reside in high or low light intensities for a long periods without circulation), while airlift systems produce a circular mixing pattern in which the culture passes continuously through the dark (riser) to the light (downcomer) phases giving a flashing effect to algal cells (Xu et al., 2009, Monkonsit et al., 2011, Gupta et al., 2015). Enhanced photosynthetic efficiency under this flashing light effect has already been reported (Janssen et al., 2000, Wang et al., 2014). As light is more efficiently use by algae in airlift than in bubble column PBRs, higher biomass densities can be reached with airlift PBRs (Xu et al., 2009, Monkonsit et al., 2011).

Tubular PBRs are made of an array of transparent tubing (typically glass or plastic), through which algae are circulated by means of either airlift circulators or centrifugal pumps (Posten, 2009, Hosikian et al., 2010, Abdel-Raouf et al., 2012). Airlift systems are usually preferred because, aside providing both mixing and aeration, this technology also prevents potential cell damage associated with mechanical pumping (Ugwu et al., 2008, Xu et al., 2009). A dedicated degasser or

11

Chapter 2

stripper unit, where oxygen (O2) is removed by air injection, is connected to the tubing to avoid accumulation of O2 to inhibitory levels (O2 concentrations above air saturation generally inhibit photosynthesis in microalgae) (Bosma et al., 2014, de Vree et al., 2015). Tubular PBRs can be found in multiple orientations, but horizontal tubes arranged in a single plane and multiple planes of vertically stacked tubes (fence-like systems) are the most common. Fence-like systems pose the major advantage that mutual shading by the tubes allows for temperature control (Hosikian et al., 2010, Bosma et al., 2014, de Vree et al., 2015, Gupta et al., 2015). The high illumination area of tubular PBRs makes these systems particularly suitable for outdoor microalgae cultivation (Ugwu et al., 2008, Gupta et al., 2015). Nevertheless, these systems have some major drawbacks including the susceptibility to photo-inhibition (due to dissolve oxygen build along the tubes), formation of pH gradients algal biofilm growth on the surfaces (i.e. biofouling), which leads to lower light penetration, poor temperature regulation and, in the case of horizontal tubular PBRs, high land requirements (Ugwu et al., 2008, Kunjapur & Eldridge, 2010, Christenson & Sims, 2011, John et al., 2011, Abdel-Raouf et al., 2012, Gupta et al., 2015)

Flat-panel (or flat-plate) PBRs are transparent flat vessels, where the culture is sparged with CO2–enriched air to ensure both mixing and CO2 supply (Posten, 2009, de Vree et al., 2015). Similarly to tubular PBRs, also one of the main characteristics of flat panels is the large illumination area (Xu et al., 2009, Hosikian et al., 2010). However, oxygen build up in flat-panel PBRs is relatively low compared to tubular PBRs. Therefore, high photosynthetic efficiencies (and thus high biomass productivities) can be achieved in flat-panel PBRs (Ugwu et al., 2008, Kunjapur & Eldridge, 2010, Gupta et al., 2015). Furthermore, due to their simple geometry flat-panels are cost-effective (for instance, they have lower power consumption than tubular PBRs) (Xu et al., 2009, Kunjapur & Eldridge, 2010). Yet, the main limitations of flat-panel PBRs include difficult scale-up (as they require many compartments and support materials) and difficult temperature control (sprinkler systems are often used for evaporative cooling) (Ugwu et al., 2008, Xu et al., 2009, Hosikian et al., 2010, Gupta et al., 2015). Due to the inherent advantages of flat-panel PBRs, namely the high photosynthetic efficiencies and biomass productivities that can be achieved, this design was investigated in this work to grow microalgae suspended cultures.

12

State of the art

Regardless their specific designs, it is often assumed that PBRs have higher construction and maintenance costs than open systems, which eventually increases the biomass production cost (Ugwu et al., 2008, Hosikian et al., 2010, John et al., 2011, Maity et al., 2014, Gupta et al., 2015). Moreover, even though higher biomass densities (ranging from 2-8 g-DW L-1) (Ozkan et al., 2012) can be achieved with PBRs compared to open systems (typically around 0.5 g-DW L -1) (Gross & Wen, 2014), water is still the main component of these cultures (over 99%). Therefore, costly harvesting, dewatering and DSP processes (which can account for up to 30% of total production costs) are required for microalgal biomass recovery (Berner et al., 2014, Gross & Wen, 2014, Schnurr & Allen, 2015, Shen et al., 2015). By growing algae in immobilised systems the biomass is highly concentrated (culture densities up to 70 g-DW L-1 have been reported for biofilm systems) (Christenson & Sims, 2011). These higher densities provide for a more cost-efficient harvesting with lower DSP costs (Berner et al., 2014, Schnurr & Allen, 2015). As such, there have been a growing interest in the use of immobilised systems for microalgal biomass production.

2.2.2 Immobilised cultivation systems

2.2.2.1 Matrix-immobilised systems

Industrial use of immobilised cell reactors suggests the possibility that this approach also could have applications in microalgae biotechnology. Indeed, previous studies on the application of immobilisation technology to algal cells showed the potential of using immobilised cell reactors (particularly the packed and the fluidised bed reactor designs) in a diverse number of bioprocesses, including production of high-value compounds (lipids, photopigments and hydrogen) and wastewater treatment (both for nutrients and heavy metal ion removal) (Mallick, 2002, Christenson & Sims, 2011, Eroglu et al., 2015).

In immobilised cell reactors, firstly algal cells are encapsulated in hydrophilic polymeric matrices to form small beads that trap the cells; later these beads are added to the reactor itself. The packed and the fluidised bed PBRs are similar in operation: the immobilised algal cells are placed along the reactor length and the medium is pumped through the bed in an upward flow to counteract compression by gravity. The main difference between these designs, however, lies on the flow

13

Chapter 2

velocity that is imposed: in packed bed PBRs the beads remain static as the medium passes through the bed because lower flow velocities are applied; conversely in fluidised bed reactors higher flow velocities are used and the beads reach a fluidised state when the force of the fluid on the solids balances their weight. As such, superior mixing (and thus mass transfer) levels are achieved with fluidised than packed bed PBRs, resulting in higher overall performances (Mallick, 2002, de-Bashan & Bashan, 2010). Several synthetic (polyacrylamide, polyurethane, polyvinyl and polypropylene) and natural (agar, alginate, carrageenan and chitosan) polymers have been used to immobilise algae. However, natural polymers (especially alginate and carrageenan) are most commonly employed because they have higher nutrient/product diffusion rates. Regardless the polymer used, the matrix must be hydrophilic to allow medium diffusion into the bead (de Godos et al., 2009, de-Bashan & Bashan, 2010, Eroglu et al., 2015).

Although higher biomass densities can be achieved with these matrix-immobilised systems when compared with conventional suspended systems, they are unsuitable for large-scale application because the encapsulation process is complex and cost-intensive. Indeed, the high cost of the polymeric matrices, together with their structural weakness during long operation periods, as well as potential algal growth limitation due to poor CO2 and O2 transfer from the liquid phase through the immobilisation matrix, have promoted the development of a new generation of biofilm PBRs (de-Bashan & Bashan, 2010, Christenson & Sims, 2011, Posadas et al., 2013, Bilad et al., 2014, Shi et al., 2014).

2.2.2.2 Biofilm photobioreactors

Biofilms are communities of microorganisms that are attached to each other and/or attached to a surface (solid growth substratum), and are encased in a matrix of extracellular polymeric substances (EPS) produced by the organisms themselves (Flemming & Wingender, 2010). Microalgal biofilms are those dominated by microalgae, although other microorganisms (as cyanobacteria and heterotrophic bacteria) can be present in non-axenic cultures (Berner et al., 2014). A full description of the microalgal biofilms formation process is given by Schnurr and Allen (2015).

Research focused on the symbiotic relationship between microalgae and bacteria in biofilms have shown that bacteria can be highly beneficial for the

14

State of the art

recruitment and overall growth of microalgal biofilms, mainly because in algal-bacterial biofilms the substratum is first colonised with bacterial cells that excrete the initial EPS matrix together with carbohydrates, vitamins and other inorganic compounds that become nutrients for algae (Posadas et al., 2014, Kouzuma & Watanabe, 2015, Schnurr & Allen, 2015). Aside from this biotic factor, key growth factors, as light (both intensity and regimen) and CO2 concentration, affect significantly algae growth kinetics (either planktonic or benthic algae) and thus biomass productivities (Berner et al., 2014, Schnurr & Allen, 2015). The effect of light on algae growth is outlined in section 2.4. The most significant amount of information on the effect of CO2 on algae growth regards to the planktonic state. According to Clement-Larosière (2012), the optimal CO2 concentration for microalgae growth in suspension ranges between 5 and 10% (v/v). In this range, algal growth is stimulated by an increase in CO2 concentration; a CO2

concentration higher than this optimal range negatively affects growth. A similar trend is likely to occur in the benthic state. Yet, as biofilms are very dense cultures it is likely that higher CO2 concentrations will be favourable for mass transport over the biofilm (Schnurr & Allen, 2015). Other abiotic factors, such as the type of surface material, pH, temperature, nutrient concentrations and shear stress (i.e. hydrodynamic conditions) are also prone to affect biofilm growth and they are extensively covered by Berner et al. (2014) and Schnurr and Allen (2015).

The innovative microalgal biofilm PBRs are based on the self-adhesive attachment of algal cells onto the surfaces of solid-supports (Posadas et al., 2013). Therefore, biofilm PBRs pose several advantages over matrix-immobilised systems, including much higher biomass densities (and without the associated cost of the matrix), easier to harvest the biomass and the harvest of high dry solid content which significantly reduces the working water volume and associated energy input requirements. Moreover, very high biomass productivities (up to a maximum of 80 g-DW m-2 day-1) have been reported for biofilm PBRs (Christenson & Sims, 2011, Ozkan et al., 2012, Boelee et al., 2013, Berner et al., 2014, Blanken et al., 2014, Gross & Wen, 2014). Drawbacks of biofilm PBRs comprise the formation of diffusional gradients over the biofilm (Boelee et al., 2013, Blanken et al., 2014, Schnurr & Allen, 2015) and biomass detachment due to high hydrodynamic forces that occur at interphase of the bulk liquid and the surface of the biofilm (Shi et al., 2014).

Based on the relative position of the cultivation medium and the microalgae on the support material, biofilm PBRs can be of three main types: constantly

15

Chapter 2

submerged, intermittently submerged, and porous substrate (or perfused) PBRs. While in submerged PBRs algae are directly submerged under a layer of medium, either the entire time (constantly submerged) or some of the time (intermittently submerged), in porous substrate PBRs a porous substrate is used to supply the medium to the microalgae which grow on the outside, exposed to a gas phase (Berner et al., 2014). Constantly submerged PBRs designs include the flow lane incubator (Zippel et al., 2007), vertical sheets (Boelee et al., 2011), and the algae biofilm PBR (Ozkan et al., 2012). The main disadvantages that these systems share are the limited control of microalgae cultivation and low productivities. In order to grow microalgae biofilms in a more controllable way, several intermittently submerged and porous substrate PBRs designs were developed. Most relevant intermittently submerged PBRs designs include the rotating spool system (Christenson & Sims, 2012), the rotating algal biofilm cultivation system (Gross et al., 2013, Gross & Wen, 2014) and the Algadisk system (Blanken et al., 2014). Reported porous substrate PBRs are the twin layer system (Nowack et al., 2005, Shi et al., 2007, Shi et al., 2014, Naumann et al., 2012, Schultze et al., 2015), and a similar design referred to as an attached PBR (Cheng et al., 2013, Liu et al., 2013, Cheng et al., 2014, Ji et al., 2013a).

In this study, a design based on the Algadisk system described by Blanken et al. (2014) was tested to grow microalgae biofilms. To avoid contamination, an air tight rotating biological contactor (RBC), instead of the RBC-based open container design, was employed. The RBC is a large disk where microalgae can grow on as a biofilm. The disk is partly submerged in the growth medium, and due to its rotation the biofilm changes frequently between the air and the liquid phase (Patwardhan, 2003). In this way, a large biofilm area is exposed to the gas phase and the diffusion paths from gas to the biofilm are shorter. Therefore, the main advantage of this system is the efficient gas-biofilm mass transfer that can save energy since both energy intensive sparging and mixing of the culture broth are not needed (Blanken et al., 2014).

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State of the art

2.3 Outdoor algae cultivation

For profitable microalgae production, process costs should decrease tenfold (Bosma et al., 2014). In this sense, is important to keep raw material costs low and decrease operational expenses. Due to their photoautotrophic metabolism, algae require energy in the form of photons that can be provided naturally (sun) or artificially (lamps). Artificial light can be used to produce small amounts of high-value compounds, but large-scale microalgae cultivation for bulk products should be done outdoors on sunlight (Blanken et al., 2013). However, outdoors the algae are continuously exposed to daily irradiance cycles of light and dark (photocycle), which resulted in the evolution of a circadian clock (de Winter et al., 2013). The circadian clock is an endogenous biochemical oscillator that helps microalgae to schedule their activities at an appropriate timeframe during this diel oscillations. In this way, the circadian clock imposes rhythms in microalgae’s metabolism. These rhythms are known as circadian rhythms and have a duration of approximately 24 hours (Mittag et al., 2005, de Winter et al., 2013). Photosynthesis and cell division are examples of processes under control of circadian rhythms in microalgae. Cell division, for example, is often scheduled in the night (de Winter et al., 2014).

Because of their photoautotrophic metabolism and the synchronisation of their circadian cycle on the daily light, microalgae store energy and carbon during the day to support growth and maintenance during the night. Therefore, carbon storage reserves, such as sugars and lipids, accumulate during the day and are metabolised during the night (Figure 2.2) (Ogbonna & Tanaka, 1996, Alderkamp et al., 2006, Radakovits et al., 2010, Baroukh et al., 2014). In this way, daylight energy is partitioned between biomass and storage compound formation, while at night storage compounds are consumed to produce new biomass. As such, daylight driven carbon

17

Figure 2.2 Tisochrysis lutea carbohydrates and lipids content during a simulated day/night cycle (12 hours of light and 12 hours of dark). Dashed line: carbohydrates; dotted line: lipids; grey line: light intensity. Adapted from Baroukh et al. (2014).

Chapter 2

partitioning significantly affects algae growth kinetics, which in turn will influence biomass productivity.

18

Chapter 2

2.4 Modelling photosynthetic biomass production

During photoautotrophic growth, light energy is absorbed by the microalgae’s photosynthetic machinery and water and carbon dioxide are converted into sugars, the building blocks for biomass formation (Kliphuis et al., 2010). As microalgae absorb light, the light intensity will decrease with depth inside microalgae cultures. Typically, in suspended PBRs light exponentially decreases over the light path (Wang et al., 2014). Research focused on biofilm PBRs showed that in photosynthetic biofilms light attenuation also results in an exponential decrease in available light with depth into the biofilm (Wolf et al., 2007, Murphy & Berberoglu, 2014, Schnurr & Allen, 2015). Microalgae growth, in both suspended and biofilm PBRs, can thus be described by a combination of a model describing light attenuation within the culture, and with a growth model, which couples photosynthetic sugar production in the chloroplast and sugar respiration in the mitochondria (Bernard, 2011, Bechet et al., 2013).

Light attenuation models include the Lambert-Beer law and radiative transfer equation (RTE) based models as the two flux model (Bechet et al., 2013, Wang et al., 2014). Although Lambert-Beer law only accounts for light absorption neglecting the effect of light scattering (i.e. both the reflected and the refracted light by algae cells), according to Blanken et al. (2016), the light gradient can be accurately described with the Lambert-Beer law as the increased light absorption due to scattering can be considered by modifying the attenuation coefficient.

Photosynthesis, and thus biomass productivity, depends on the photonic flux (Carvalho et al., 2011). The relationship between light intensity and cell photosynthetic activity is usually represented by a photosynthesis-irradiance (PI) curve, which shows that at low light intensities photosynthetic activity increases linearly with light intensity and reaches a maximum at high light intensities (Bechet et al., 2013). Several kinetic models have been proposed to describe PI relationships, starting from the hyperbolic expression (Baly, 1935) up to more complex representations including photoinhibition by light excess as the Steele relationship (Steele, 1962), the exponential model of Web (Webb et al., 1974), the hyperbolic tangent function (Jassby & Platt, 1976) or either the Eilers-Peeters expression (Eilers & Peeters, 1988).

Photosynthesis generates sugars that are then aerobically respired in the mitochondrion to support biomass growth. This process is generally referred as

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Chapter 2

aerobic heterotrophic growth and comprises two main processes: new biomass production and cellular maintenance. A model was proposed by Blanken et al. (2016) to describe microalgae growth by partitioning of sugar between aerobic respiration (catabolism reactions) and anabolic reactions needed for cell growth. This partitioning was described by the biomass yield on sugar (Yx/s). Additionally, the authors used the model of Pirt to describe the energy requirements for maintenance and growth. A similar approach was also reported by Kliphuis et al. (2012), but they measured the maintenance coefficient (mF) and the yield of biomass on light (YG) to fit the growth rate (µ) of Chlamydomonas reinhardtii to Pirt’s model. The maintenance coefficient can instead be modelled by means of first-order kinetics regarding to cell concentration (Bechet et al., 2013).

A number of models, with a broad complexity range, have been formulated to predict phototrophic biomass productivity in the planktonic stage (Slegers et al., 2011, Slegers et al., 2013, Klok et al., 2013, Lee et al., 2014, Barbera et al., 2015). Modelling biofilm productivity is challenging due to interactions between cells and surface, persistent gradients and hydrodynamic effects (Murphy & Berberoglu, 2014). Various growth models were designed to describe the behaviour of phototrophic biofilms in production systems, with focus on the inter species interaction, the interaction between the cells and the substratum and cell detachment (Wolf et al., 2007, Cui & Yuan, 2013, Ozkan & Berberoglu, 2013, Cole et al., 2014, Munoz Sierra et al., 2014, Shen et al., 2014). However, currently only the model proposed by Murphy and Berberoglu (2014) has focused on predicting phototrophic biofilm productivity.

When targeting outdoor cultivation of microalgae, the periodic cycle of day and night makes modelling PBRs productivity even more complex. Studies on modelling biofilm productivity under day/night cycles are lacking but are available for suspended cultures (see Bechet et al. (2013) for a full review). However, most of these models ignore diurnal carbon partitioning. To describe the diurnal carbon partitioning on planktonic microalgae, Mairet et al. (2011) designed a model where organic carbon was split into a functional (protein-rich biomass) and storage pools. As under periodic light conditions functional biomass and storage compounds are produced at lower rates than in continuous light, the diel dynamics of these pools were described based on the Droop approach. A number of more complex metabolic models have also been suggested to describe daylight driven carbon partitioning in suspended microalgae cultures (Dillschneider & Posten, 2013, Guest et al., 2013, Knoop et al., 2013, Baroukh et al., 2014). For instance, the work done by Baroukh et

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State of the art

al. (2014) indicate that storage compounds are consumed in a near-linear manner during the night. Indeed, previous research with Arabidopsis plants grown under simulated day/night cycles showed that starch accumulate almost linearly during the day and is also linearly degraded during the night (Lu et al., 2005, Stitt & Zeeman, 2012, Scialdone et al., 2013).

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Chapter 2

CHAPTER 3Influence of day/night cycling on Chlorella sorokiniana

suspended cultures

22

Influence of day/night cycling on Chlorella sorokiniana suspended cultures

23

Chapter 3

24


3.1 Introduction

The greatest amount of information on microalgae cultivation concerns to suspended algae systems (Gupta et al., 2015). However, most of this research has been carried out indoors, with artificial lights and under constant illumination conditions, which is not representative of outdoor conditions. Outdoors, cultivation systems are much more complex to characterise due to the dynamics imposed by the photocycle on microalgae. Indeed, at outdoor conditions the available light varies over the day and this will affect the system performance (both in terms of biomass productivity and photosynthetic efficiency), as well as the quality of the algae (in terms of biomass composition) (Bosma et al., 2014, Michels et al., 2014, Barbera et al., 2015). Previous researchers already tried to understand the influence of the diurnal light fluctuations on microalgae by mathematically modelling the planktonic algae growth under day/night cycles (Bechet et al., 2013). However, only a few included diurnal carbon partitioning between biomass and storage compound formation. An example is the model proposed by Mairet et al. (2011), already introduced in Chapter 2. Although it successfully describes the carbohydrates storage, it lacks to describe the decrease in the sugar quota at night due to its consumption to new biomass production (growth-associated respiration). In this context, a number of more complex metabolic models have been proposed (Dillschneider & Posten, 2013, Guest et al., 2013, Knoop et al., 2013, Baroukh et al., 2014). However, to be a suitable tool for PBRs design, the model should be kept as simple as possible.

Therefore, this study aims to develop and validate a simple engineering light limited microalgae growth model that incorporates both diurnal carbon partitioning and growth-related respiration, to accurately predict microalgae growth under outdoor conditions. The model was constructed for C. sorokiniana (due to its high growth rate and robustness) (Blanken et al., 2014), and thus biological and kinetic parameters are needed. While the former can be obtained from literature (Blanken et al., 2014), the later needed to be obtained by dedicated experiments. For this reason, the effects of a simulated day/night cycle on C. sorokiniana composition, biomass productivity and biomass yield on light supplied were assessed. These effects where investigated by monitoring the biomass density, cell number, cell size, starch content, functional biomass and microalgae specific absorption coefficient. Additionally, two dilution rates (D) were applied and compared for differences between rapidly and slowly growing microalgae in their response to the

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Chapter 3

changing light intensity over the light period. For this purpose C. sorokiniana was cultivated in flat-panel airlift PBRs under a ´block´ day/night cycle (12 h/12 h) simulating outdoor cultivation with a daily light input of 460 and 430 μmolph m-2 s-1, respectively for a 0.22 and 0.04-h-1 dilution rate.

3.2 Materials and methods

3.2.1 Microorganisms and culture medium

The freshwater green algae Chlorella sorokiniana (CCAP 211/8k) was obtained from the Culture Collection of Algae and Protozoa (Ambleside, UK). The algae was cultivated in M8-a medium with the following composition (mmol L -1): KH2PO4 (5.44), Na2HPO4.2H2O (1.46), MgSO4.7H2O (1.62), CaCl2.2H2O (0.09), NaFeEDTA (0.32), Na2EDTA.2H2O (0.10), H3BO3 (1.00×10-3), MnCl2.4H2O (65.59×10-3), ZnSO4.7H2O (11.13×10-3), CuSo4.5H2O (7.33×10-3). The M8-a medium was supplemented with 30 mM urea as nitrogen source, pH was set to 6.7 and the medium was filter-sterilised (pore size 0.2 µm). The cultures were pre-cultivated in 250-mL shake flasks containing 100 mL of medium in a shaking incubator (Snijders Scientific, The Netherlands) at 120 rpm, with light provided continuously at an intensity of 190 μmol m-2 s-1. Temperature was kept at 37 ºC and the headspace of the incubator was enriched with 4% (v/v) CO2. The algae suspension was used to inoculate the flat-panel PBRs, as will be explained later.

3.2.2 Photobioreactors set-up and experimental conditions

The experiments were carried out in two flat-panel airlift PBRs (Algaemist, Technical Development Studio, Wageningen University, The Netherlands). One PBR (high-µ PBR) was used for the experiments at a 0.22-h-1 dilution rate, and another flat-panel PBR (low-µ PBR) with the same configuration as the high-µ PBR was operated at a dilution rate of 0.04-h-1. The two different dilution rates were applied to create different physiological states. A fast growing culture was cultivated in the high-µ PBR by setting the dilution rate to 0.22 h -1, i.e. 80% of the maximum specific growth rate of C. sorokiniana (0.27 h-1 according to Sorokin (1959)). A slow growing culture was cultivated in the low-µ PBR by setting the dilution rate to 0.04 h-1, i.e. 15% of the maximum specific growth rate of C. sorokiniana. These PBRs were previously described by de Mooij et al. (2014) and have a working volume of 0.4 L, a light path of 14 mm, and an illuminated area of 0.028 m2. The flat-panel airlift PBR and the PBR’s schematic overview is shown in Figure 3.1 A and B, respectively.

26


The PBRs were illuminated from one side by LED lamps (BXRA W1200, Bridgelux, USA) with a warm-white light spectrum. A black cover was placed on the back of the reactors to ensure no environmental light was able to enter the reactors. The average light intensity over the illuminated area was measured at the beginning and the end of the experiments with a LI-COR LI-250A 2π quantum sensor (PAR range 400-700 nm) (LI-COR, USA), according to the method described by de Mooij et al. (2014).

Figure 3.1 (A) Flat-panel airlift PBR used for cultivation of rapidly and slowly growing C. sorokiniana in suspension under simulated outdoor conditions; and (B) Schematic overview of the PBRs set-up with medium inflow and overflow vessels on balances, and pH, temperature and antifoam control (adapted from de Mooij et al. (2014)).

The average light intensity over the light-exposed surface was 460 and 430 μmolph

m-2 s-1 for the high-µ PBR and the low-µ PBR, respectively. This light intensity represents photons in the so-called PAR (Photosynthetic Active Radiation) ranging from 400 to 700 nm, the wavelength range which can be used by microalgae for photosynthesis.

In both PBRs, the 2× concentrated M8-a media was supplemented with 53 mM urea as nitrogen source, and pH was set to 6.7. An additional 5 mM NaHCO3 was included after setting the pH to increase the dissolved CO2 concentration. The

27

A B

Chapter 3

medium was added to each PBR by a diaphragm metering pump (STEPDOS 08, KNF Neuberger, Swithzerland). To prevent carbon limitation, the pH was controlled at 6.7 by suppling CO2 on-demand at a rate of 83 mL min-1 for the high-µ PBR and 89 mL min-1 for the low-µ PBR. For both PBRs, the cultures were mixed by aeration at a rate of 400 mL min-1. The temperature inside the PBRs was maintained at 37 ºC using the internal temperature control system of the Algaemist system that was connected to the water jacket of the PBRs. Tap water was supplied on demand through the use of an external cryostat to cool the PBRs. In both PBRs, foam formation was prevented by automatically supplying a heat-sterilised 1% (v/v) antifoam solution (Antifoam B, J.T.K., The Netherlands) through the Algaemist system dilution pump (type 400A1, Watson Marlow, UK) at a rate of 1.11 mL min-1. The antifoam solution was added automatically for 16 seconds every 60 minutes using a time switch.

The PBRs were heat-sterilised during 90 minutes at 121 ºC, and the diaphragm metering pumps were chemical sterilised with 0.04% (v/v) peracetic acid for 10 minutes. After chemical sterilisation the pumps were washed with 99% ethanol and sterilised with demineralised water. A microalgae suspension pre-cultivated in a shake flask was added to the medium to inoculate the reactors. Both PBRs were inoculated to an optical density at 750 nm (OD750) of about 0.3. The PBRS were operated in batch mode until an optical density at 750 nm of about 5.6 and 5.9 was reached for the high-µ PBR and the low-µ PBR, respectively. At this biomass densities, chemostat control was started and a block shaped day/night cycle (12 h/12 h) was applied to simulate outdoor light conditions. Chemostat control ensured dilution of the culture with fresh medium to keep the culture volume constant. In this way, by setting the rate with which the culture was diluted with fresh medium (i.e. by setting the dilution rate, D), the specific growth rate of microalgae (µ) was kept constant. The cultures were continuously diluted with fresh medium during the day (12 hours) at a fixed dilution rate of 0.22 h-1 and 0.04-h-1 for the high-µ PBR and the low-µ PBR, respectively. The fresh new medium was added to the PBRs trough the diaphragm metering pumps at a rate of 1.44 mL min -1 for the high-µ PBR and 0.36 mL min-1 for the low-µ PBR.

3.2.3 Analytical methods

The systems were allowed to reach steady state, which was defined as a constant biomass growth at regular sample time. Please note that the steady state is generally obtained after the time the reactor is diluted 5 reactor volume. Samples 28


were taken daily at the same time (11:00) from the reactors to monitor biomass growth and steady state by measuring the optical density at 750 nm and 680 nm, dry weight, cell number and cell size. When steady state was reached, overflow was collected on ice during the light period (12 h) at 2 h interval. From the overflow, samples analysis were performed to determine dry weight, cell number, cell size, biomass composition (total starch content) and microalgae specific absorption coefficient (also known as the optical cross section of microalgae). For the high-µ PBR, overflow samples were taken for a period of 3 steady state days. Reported values are average values of these 3 data sets. For the low-µ PBR, overflow samples were taken for a period of 2 steady state days. Reported values are average values from these 2 data sets.

3.2.3.1 Optical density measurements

The optical densities (ODs) at 750 nm (OD750) and 680 nm (OD680) were measured in triplicate on an UV-VIS spectrophotometer (DR 6000, Hach Lange, Germany) against demineralised water as a blank. At 750 nm, the algae hardly absorb any light and cellular light scattering is determining the OD. At 680 nm, the OD is determined by both scattering and light absorption by chlorophyll-a (the primary photosynthetic pigment of microalgae). The ratio between the optical density at 680 nm and 750 nm (OD680/OD750) was therefore used as a relative measure of the chlorophyll-a content of microalgae cells and as an indicator of bleaching of the cells due to photoinhibition (Cuaresma et al., 2009). This ratio should stay above 1.2 for healthy cells.

3.2.3.2 Dry weight measurements

To determine the biomass dry weight content, the samples were passed through glass microfiber filters as described by Kliphuis et al. (2012). The measurements were performed in triplicate.

3.2.3.3 Cell number and cell size measurements

Cell number and cell size were determined in duplicate with a Beckman Coulter Multisizer 3 (50 µm orifice, Beckman Coulter, USA).

29

Chapter 3

3.2.3.4 Total starch content determination

Total starch content was determined in duplicate using a Total Starch Kit (Megazyme, Ireland) as described by de Winter et al. (2013). Absorbance was measured at 510 nm on a microtiter plate spectrophotometer (Infinite M200, Tecan, Austria).

3.2.3.5 Light absorption measurements

Light absorption was measured in a double-beam spectrophotometer (UV-2600, Shimadzu, Japan) equipped with an integrating sphere (ISR-2600) to minimise the effect of light scattering. The samples were transferred to 2 mm light path-cuvettes (100.099-OS, Hellma, Germany) and absorbance was measured from 300 to 750 nm against a demineralised water blank. The measurements were performed in duplicate and the samples were re-suspended between the two measurements to avoid sedimentation effects. As the absorbance measured in the 740-750 nm region is mainly a result of residual backward and sideward scatter (de Mooij et al., 2014), the average absorption of this region was subtracted from all data points.

3.2.4 Calculations

3.2.4.1 Dry weight

The dry weight of the samples (DW, g-DW L-1) was determined by the difference in weight between the dry filters containing the samples (w2, g) and the empty filters weight (w1, g) over the sample volume (V, L) (equation 3.1).

(3.1)

3.2.4.2 Functional biomass and starch content

The total biomass (Cb, cmol m-3) was defined as the sum of the functional biomass (Cx, cmol-x m-3) and the starch content (Csartch, cmol-s m-3) (equation 3.2). The total starch content (Cstarch,total, cmol-s m-3) was defined as the ratio between the starch content and the total biomass (equation 3.3).

(3.2)

30


(3.3)

Therefore, the starch content is given by multiplying the total starch content by the biomass (equation 3.4). On the other hand, the functional biomass is the total biomass minus the starch content (equation 3.5).

(3.4)

(3.5)

3.2.4.3 Chlorella sorokiniana specific absorption coefficient

The wavelength-dependent specific absorption coefficient ( , m2 cmol-1) was calculated using equation 3.6.

(3.6)

where is the average absorbance measured at wavelength of the two duplicates, ln(10) is the conversion factor to convert a base 10 logarithm to the natural logarithm, Cb is the dry weight-dependent biomass concentration (cmol-x m -

3) in the cuvette, and l is the cuvette optical light path (mm).

3.2.4.4 Biomass productivity

The areal biomass productivity (rx, g-DW m-2 h-1) was calculated for each time point based on the rate of overflow produced (FOUT, L h-1), the dry weight (DW, g-DW L-1) and the illuminated area (AI, m2) of the PBRs (equation 3.7). The daily areal biomass productivity was obtained by summing up the areal biomass productivity obtained for each time point.

(3.7)

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3.2.4.5 Biomass yield on light energy

The areal light absorption rate (rph, µmolph m-2 s-1) was defined as the measured surface-averaged incident light intensity (Iph,IN, µmolph m-2 s-1) (equation 3.8).

(3.8)

Then, the biomass yield on light energy (Yx/ph, g-DW molph-1) was calculated for each time point based on the areal light absorption rate (rph, µmolph m-2 s-1), the areal biomass productivity (rx, g-DW m-2 h-1) and a factor to convert the amount of light in μmol per second to the amount in mol per hour (3.6×10 -3)(equation 3.9). The daily biomass yield on light energy was obtained by summing up the biomass yield on light energy obtained for each time point.

(3.9)

3.2.5 Statistical analysis

For both PBRs, the daily biomass density, functional biomass, starch content, cell number, cell diameter and absorption coefficient values (dependent variables) over the time (independent variable) were analysed by the one-way ANOVA to test if they differed significantly from each other (p<0.05). The statistical analysis was carried out by SPSS software (version 23.0, IMB Statistical Package for the Social Sciences).

3.2.6 Daylight driven carbon partitioning model

3.2.6.1 Model structure

Photoautotrophic growth of microalgae can be simplified as a combination of phototrophic sugar production in the chloroplast and aerobic chemoheterotrophic growth on sugar (that takes place in the mitochondria). Phototrophic growth, can therefore be described by coupling the kinetics of phototrophic sugar production in the chloroplast to overall microalgal growth by means of a simple sugar balance (Blanken et al., 2016).

To account for daylight carbon partitioning, intracellular carbon can be divided between a functional pool and a storage pool. The functional pool includes the

32


biosynthetic apparatus (proteins, nucleic acids and chlorophyll) and the structural material (membranes mainly made of glycolipids and phospholipids) (Mairet et al., 2011). Starch was assumed to be the major storage compound, a valid assumption as starch is the main storage metabolite in many plants (Stitt & Zeeman, 2012). Furthermore, is not likely that Chlorella sorokiniana (the model organism) accumulate lipids during photoautotrophic growth.

3.2.6.2 Sugar and functional biomass balances

To describe diurnal carbon partitioning between storage and biomass growth, the total biomass (Cb, cmol m-3) was divided in functional biomass (Cx, cmol-x m-3) and the accumulated sugar inside the total biomass (Cs, cmol-s m-3) (equation 3.9).

(3.9)

By diving the total biomass into functional biomass and sugar, two mass balances describe C. sorokiniana growth in suspension, equation 3.10 for functional biomass, and equation 3.11 for internal sugar. Biomass growth was described according to Pirt (1965), which states that a small amount of substrate is continuously consumed for cellular maintenance. The model used in this study considers that sugar is expended for cell maintenance, but when all the sugar is consumed functional biomass is used. As the PBRs were operated in chemostat mode, the dilution effect was taken into account in the material balances.

Biomass accumulation was calculated through a balance between biomass production based on sugar consumption, biomass consumption for cellular maintenance and biomass losses due to culture dilution. The functional biomass balance can thus be expressed by:

(3.10)

where qs,c (cmol-s cmol-x-1 s-1) is the specific sugar consumption rate, ms (cmol-s cmol-x-1 s-1) is the maintenance related sugar consumption rate, Yx/s (cmol-x cmol-s-

1) is the biomass yield on sugar, which describes the amount of sugar required for growth, Cx (cmol-x m-3) is the functional biomass concentration and D (s-1) is the dilution rate.

33

Chapter 3

Therefore, sugar accumulation was calculated through a balance between phototrophic sugar production, sugar consumption for biomass production and sugar losses due to culture dilution. The sugar balance can be thus expressed by:

(3.11)

where qs,p (cmol-s cmol-x-1 s-1) is the specific photosynthetic sugar production rate, qs,c (cmol-s cmol-x-1 s-1) is the specific sugar consumption rate, Cx (cmol-x m-3) is the functional biomass concentration, Cs (cmol-s m-3) is the accumulated sugar inside the total biomass and D (s-1) is the dilution rate.

Consequently, biomass growth is only possible when the internal sugar concentration is sufficient.

3.2.6.3 Sugar production

All sugar is produced photosynthetically, and sugar production is therefore limited by the amount of light the microalgae absorb (Wang et al., 2014). Previously, the kinetic model of Blanken et al. (2016), which combines Lambert-Beer law (to describe light attenuation) and the hyperbolic tangent model of Jassby and Platt (to predict the specific sugar production rate of microalgae), was successfully employed to predict light dependent sugar production by microalgae. The local specific sugar production rate (qs,p (z), cmol-s cmol-x-1 s-1) was thus calculated based on the maximal specific sugar production rate (qs,m,p cmol-s cmol-x-

1 s-1), the local specific photon absorption rate (qph (z), molph cmol-x-1 s-1) and the sugar yield on light (Ys/ph, cmol-s molph-1) (equation 3.12).

(3.12)

3.2.6.4 Sugar consumption

The specific sugar consumption rate is dependent on the internal sugar concentration and the kinetics are different for daytime and night time (Baroukh et al., 2014).

34


As the daylight carbon partitioning slows down the sugar consumption rate for biomass production, the specific sugar consumption rate during the day (q s,c,day, cmol-s cmol-x-1 s-1) was described based on the Droop approach (Droop, 1968):

(3.13)

where qs,m,c (cmol-s cmol-x-1 s-1) is the maximal sugar consumption rate and Cs,min

(cmol-s m-3) is the minimal sugar concentration allowing biomass growth, which is given by the product of the minimal sugar fraction inside the biomass (xmin, cmol-s cmol-b-1) and the total biomass concentration (Cb, cmol m-3) (equation 3.14). Consequently, the total biomass will always contain a small fraction of sugar which is not consumed for biomass production. When the sugar concentration is below the minimal sugar concentration this sugar can only be consumed to fuel maintenance related sugar consumption (ms).

(3.14)

On the other hand, the specific sugar consumption rate at night (qs,c night, cmol-s cmol-x-1 s-1) was calculated as a linear function of the functional biomass, such that all the available sugar is consumed at sunrise, according to experimentally derived kinetics determined for higher plants (Stitt & Zeeman, 2012, Scialdone et al., 2013):

(3.15)

where tsunrise is the moment the lights were switched on (h) and tday is the total day time (h).

3.2.6.5 Input parameters

Most daylight driven carbon partitioning model parameters were obtained from Blanken et al. (2016) and they are summarised in Table 3.1. The xmin and the qs,m c

parameters need to be calibrated with experimental data. However, as a first estimation, the xmin parameter was set as 0.08 (based on previous experimental observations) and the qs,m,c parameter was defined as half of the maximal specific sugar production rate (qs,m,p) (to represent sugar accumulation, the amount of sugar consumed needs to be lower than the amount of sugar consumed). Additionally, the daylight driven carbon partitioning model requires the incoming light intensity, light

35

Chapter 3

path and dilution rate. These values are indicated in the “Materials and Methods” section.

Table 3.1 Overview of literature based model input parameters (based on Blanken et al. (2016)). Mx: molar mass of C. sorokiniana; ax: spectrally-averaged specific absorption coefficient; µmax: maximal specific growth rate; Yx/s: biomass yield on sugar; Ys/ph: sugar yield on photons; ms: maintenance-related specific sugar consumption rate.

24 5.8 0.27 0.59 0.10

3.2.6.6 Computational methods

To predict the daily variations on the functional and sugar storage pools of C. sorokiniana, the discreet differential equations introduced in section 3.2.6.1 (equations 3.10 and 3.11) were discretised over 50 layers and solved employing a build in ODE solver of MATLAB R2013a (ODE15s). An if else function based on the total day time (tday) was used to differentiate sugar consumption during the day and at night. The MATLAB code can be found in appendix A.2.

3.3 Results and discussion

3.3.1 Biomass density

Biomass density (in grams of dry weight per liter) of C. sorokiniana grown during the simulated day/night cycle was calculated from equation 3.1 for both the fast (0.22-h-1 dilution rate) and the slow (0.04-h-1 dilution rate) growing reactors, and is

36


depicted in Figure 3.2. During the light period (between 08:00 and 20:00), samples were taken from the overflow with 2 hours intervals. Error bars represent the standard deviations between the 3 data sets for the fast growing reactor and the 2 data sets for the slow growing reactor. Please note that based on daily measurements (data is shown in appendix A.3) the steady state was confirmed.

As expected, the low dilution rate of 0.04 h-1 gave higher densities (1.7 – 2.3 g-DW L-1) compared to the high dilution rate of 0.22 h-1 (0.4 – 0.5 g-DW L-1). In a light-limited chemostat, the imposed dilution rate dictates the steady-state biomass concentration achieved. Light availability is the growth-limiting factor, which in turn is limited by self-shading of the cells in dense cultures. Therefore, high dilution rates must be supported by fast-growing cells, i.e. cells with higher specific growth rate (µ), whose illumination requirements can only be met at low biomass concentrations. On the other hand, low dilution

Figure 3.2 Daily variation in biomass density (g-DW L-1) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in triplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 h and resemble the average of 3 steady state days for the 0.22 h-1 reactor and 2 steady state days for the 0.04 h-1 reactor. Error bars represent standard deviations.

rates give rise to high density cultures whose growth is severely limited by self-shading (Grima et al., 1996). Other studies (Cuaresma et al., 2009, Cuaresma et al.,

37

Chapter 3

2011b, Tuantet et al., 2015 ) also reported higher biomass densities at lower dilution rates.

Although in general a slow increase in biomass density over the day can be observed for the slow growing reactor (i.e. at 0.04-h -1 dilution rate), no significant differences in the daily biomass values were found (p=0.133). This is likely due to the high standard deviations associated to the measurements. Conversely, for the fast growing reactor (i.e. at 0.22-h-1 dilution rate) a significant difference in the daily biomass values was found (p=0.002). A slow decrease in biomass density over the day can be observed for this PBR, which was not expected. As during daylight microalgae use the photosynthetic machinery to produce carbon storage reserves like sugars, the building blocks for new biomass, an increase in biomass density over the day was expected. Moreover, as microalgae are photoautotrophic organisms (i.e. they reproduce themselves using photosynthesis), it is not plausible to observe an overnight increase in biomass (because no light is supplied during this period). Nevertheless, Cuaresma et al. (2011a) also found a similar trend when cultivating C. sorokiniana under simulated outdoor conditions, which demonstrates that the results obtained in this study were not due to experimental errors.

The biomass decrease observed during the day in the fast growing reactor can be a result of biomass wash-out, which occurs when the dilution rate is higher than the specific growth rate of microalgae. This result can also be due to the short fed-batch time (which leads to high cell densities) in the start of the day (as explained in appendix A.4, it was estimated that for this reactor, on average, 38.2 mL of culture evaporated overnight, leading to approximately 12 minutes of fed-batch growth after the lights were switched on at 8:00). Another possibility is that a volatile compound is produced during the day and it evaporates in the dry weight measurements, resulting in decreased biomass densities. Additionally, it can be that C. sorokiniana produces and export to the extracellular medium an organic compound (such as glucose) that is then consumed for other cells, also leading to an overall decrease in biomass density over the day. The last two hypothesis can be investigated by performing COD (chemical oxygen demand) measurements.

3.3.2 Biomass composition

The daylight sugar and the functional biomass concentrations needed to be determined to calibrate the daylight driven carbon partitioning model. Therefore, starch (in cmol-s m-3), functional (in cmol-x m-3) and total (in cmol m-3) biomass

38


levels of C. sorokiniana grown during the simulated day/night cycle were calculated from equation 3.4, 3.5 and 3.2, respectively. The results obtained for the fast growing reactor are presented in Figure 3.3.

Based on statistical analysis, significant differences in the daily starch (p=0.017) and functional biomass (p=0.024) values were found for this PBR. As expected, starch increases while functional biomass decreases during the day. As Figure 3.3 shows, starch increases in a near-linear manner and functional biomass decrease is also approximately linear during the day. These trends are in agreement with other studies (Lu et al., 2005, Stitt & Zeeman, 2012, Scialdone et al., 2013, Baroukh et al., 2014). As over the day, the amount of incoming carbons that goes to functional biomass decrease, while the starch content increase, it seems that during the day there is a higher carbon demand for storage compounds than for functional biomass synthesis. This result therefore suggest that, as expected, C. sorokiniana uses daylight energy to store reserve compounds such as sugars. According to Figure 3.3, when growing C. sorokiniana at a dilution rate of 0.22 h-1 about 25% of the cell dry weight corresponds to sugars.

Figure 3.3 Daily variation in total carbon biomass (Cb cmol m-3) (triangles), functional biomass (Cx

cmol-x m-3) (squares) and starch content (Cs cmol-s m-3) (diamonds) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle at 0.22 h-1 dilution rate. Grey area represents the dark period. The values were determined in duplo. They are averages over 2 h and resemble the average of 3 steady state days. Error bars represent standard deviations.

39

Chapter 3

However, as it can be seen in Figure 3.3, total biomass (obtained by summing up starch and functional biomass) decreases over the day. This result was unexpected and possible explanations were already discussed in the previous section. Due to this unexpected result, the high-µ PBR data cannot be used to calibrate the daylight driven carbon partitioning model.

The results obtained for the slow growing reactor are presented in Figure 3.4.

Similar daylight trends were found for this PBR. However, no significant differences in the daily starch (p=0.161) and functional biomass (p=0.313) values were found. Therefore, the low-µ PBR data also cannot be used to calibrate the diurnal carbon partitioning model. Nevertheless, as Figure 3.4 shows when C. sorokiniana is grown at a dilution rate of 0.04 h-1 only approximately 15% of the cell dry weight is starch. Therefore, it seems that if algae grow slower they also accumulate sugars slower.

Figure 3.4 Daily variation in total carbon biomass (Cb cmol m-3) (triangles), functional biomass (Cx

cmol-x m-3) (squares) and starch content (Cs cmol-s m-3) (diamonds) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle at 0.04 h-1 dilution rate. Grey area represents the dark period. The values were determined in duplicate. They are averages over 2 h and resemble the average of 2 steady state days. Error bars represent standard deviations.

40


3.3.3 Cell number and cell size

In order to get a clearer picture of the cell cycles of C. sorokiniana over the day, cell number and cell size can be evaluated. Figure 3.5 shows the daily evolution of cell number and figure 3.6 shows the daily evolution of cell size (in µm) of C. sorokiniana grown during the simulated day/night cycle for both the fast (0.22-h-1

dilution rate) and the slow (0.04-h-1 dilution rate) growing reactors.

Following the trend of the biomass density, a higher number of cells was reached for the lowest dilution rate of 0.04 h-1 (1.3×108 - 1.6×108 cells) than for the highest dilution rate of 0.22h-1 (1.0×107 - 4.6×107 cells) (Figure 3.5). Based on statistical analysis, no significant differences either in the daily cell number (p=0.566) and cell size (p=0.489) values were found for the slow growing reactor. Conversely, for the fast growing reactor significant differences in the daily cell number (p=0.015) and cell size (p=0.013) values were found.

As previously discussed, microalgae use daylight energy to produce storage compounds and new biomass. Therefore, in response to the onset of light availability an increase in cell diameter is expected, as microalgae cells absorb and use the light to perform photosynthesis.

41

Figure 3.5 Daily variation in cell number of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in duplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 hours and resemble the average of 3 steady state days for the 0.22 h-1 reactor and 2 steady state days for the 0.04 h-1 reactor. Error bars represent standard

Figure 3.6 Daily variation in cell diameter of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in duplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 hours and resemble the average of 3 steady state days for the 0.22 h-1 reactor and 2 steady state days for the 0.04 h-1 reactor. Error bars represent standard

Chapter 3

At some point, microalgae cells start dividing which results in an increase in cell number. However, is likely that these new daughter-cells will be smaller than the mother-cells, and thus a decrease in cell size can be expected. It is also expected that this trend is more pronounced in the fast growing reactor compared to the slow growing reactor, as probably not all cells in the slow growing reactor reach the point that they can divide every day. In this case, the average diameter will vary less in the slow growing reactor. Indeed, as Figure 3.6 shows the average cell diameter in this PBR remained nearly constant over the light period.

The moment of cell division was determined by analysing the changes in cell number and cell size during the experiments. For that, the number of cells was plotted against their cell diameter (in µm). The results are depicted in Figure 3.7 and 3.8 for the fast and the slow growing reactors, respectively.

42

Figure 3.7 Number of cells versus their cell diameter (µm) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 dilution rate. The values were measured in duplicate from cumulative overflow collected on ice for 2 h and resemble the average of 3 steady state days.

Figure 3.8 Number of cells versus their cell diameter (µm) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.04 h-1 dilution rate. The values were measured in duplicate from cumulative overflow collected on ice for 2 h and resemble the average of 2 steady state days.


For the fast growing reactor (Figure 3.7), the cells in the first sample (9:00h) had an average diameter of 3.0 µm. In the next sample (11:00h) average cell diameter increased to 3.2 µm, while cell number slightly decreased. The increase in cell diameter is a result of cell growth, while the decrease in cell number is probably due to biomass wash-out. The increase in cell size continued until 17:00, when a new peak with small daughter cells appear, indicating the start of cell division. In the subsequent sample (19:00h) this population of small daughter cells increased in both size and number, while the number of larger cells decreased. However, at this time point not all the mother-cells divided yet, which indicates that cell division goes on during the night. For the slow growing reactor (Figure 3.8), the cell number and cell size remained almost constant until 19:00, when a new peak with small daughter cells appear, indicating the beginning of cell division. For this reactor is clear that a smaller population is dividing.

Both the fast and the slow growing cultures started to divide when the light was still on. As cells divide in the same time frame, it seems that the circadian clock of C. sorokiniana was synchronised with the light/dark cycle supplied. Previous research done by de Winter et al. (2015) on the Neochloris oleoabundans circadian clock already showed synchronisation with simulated light/dark cycles.

3.3.4 Specific absorption coefficient

The wavelength-dependent optical cross section (in m2 cmol-1) of C. sorokiniana grown during the simulated day/night cycle was calculated from equation 3.6 for both the fast (0.22-h-1 dilution rate) and the slow (0.04-h-1 dilution rate) growing reactors, to compare the degree of pigmentation of low and high diluted cultures. As microalgae only absorb and use light from the PAR spectrum (400 to 700 nm), the values displayed in Figure 3.9 are average absorption levels over the PAR spectrum. The absorption spectra over the 400-750 nm spectrum for each sample point for both PBRs can be found in appendix A.5.

For the slow growing reactor, no significant differences in the daily absorption levels over the PAR spectrum were found (p=0.824). Conversely, for the fast growing reactor significant differences in the daily absorption levels over the PAR spectrum were found (p=0.036). For this PBR, a slow increase in the specific absorption values can be observed until 15:00, indicating a photo acclimation of the

43

Chapter 3

cells to light conditions during this period. The values obtained are in the range expect for planktonic C. sorokiniana cells (Blanken et al., 2016).

3.3.5 Daily biomass productivity

The areal biomass productivity was calculated for each time point from equation 3.7. These values were summed up to obtain the daily biomass productivity. A daily biomass productivity of about 15.47 and 16.75 g-DW m-2 h-1 were achieved for the fast and the slow growing reactors, respectively. These results show that the high dilution rate of 0.22 h-1 does not led to a higher biomass productivity. Indeed, at this dilution rate although the growth rate goes up also the biomass concentration goes down.

Figure 3.9 Daily variation in average absorption coefficient over the PAR spectrum (m2 cmol-1) of C. sorokiniana grown under a ‘block’ 12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). Grey area represents the dark period. The depicted values were measured in duplicate from cumulative overflow collected on ice for two hours. Therefore the values depicted are the averages over 2 h and resemble the average of 3 steady state days for the 0.22 h -1 reactor and 2 steady state days for the 0.04 h-1 reactor. Error bars represent standard deviations.

3.3.6 Daily biomass yield on light energy

To assess the performance of a PBR, i.e. its ability to convert sunlight into biomass, the photosynthetic light efficiency is of importance. As such, the efficiency 44


of light use in photosynthesis was evaluated by the yield of microalgae biomass (in gram dry weight) on light energy (in mole PAR photons). The biomass yield on light energy was calculated for each time point from equation 3.9 and the obtained values were summed up to calculate the daily biomass yield on light. For the fast growing reactor a biomass yield on light of approximately 0.83 g-DW molph-1 was reached. For the slow growing reactor a biomass yield on light of about 0.90 g-DW molph-1 was estimated. These results show that fast growing and slow growing algae use light in a similar efficiency. Nevertheless, these values are lower than the theoretical maximum biomass yield on light energy when using urea as nitrogen source (1.8 g-DW molph-1 - according to Cuaresma et al. (2009)). It seems that a significant amount of energy is wasted after photosynthesis, probably in dissipative processes known as non-photochemical quenching. It can also be related with not considering the-fed batch growth for the first hours.

3.3.7 Daylight driven carbon partitioning model predictions

To evaluate the effect of photocylce on biomass composition (in terms of internal accumulated sugar, functional and total biomass) of C. sorokiniana planktonic cultures grown under day/night cycling in flat panel PBRs operated in chemostat mode, the suspended daylight driven partitioning model was simulated for a ‘block’ 12/12 day/night cycle (with light supplied at 400 μmolph m-2 s-1). The predicted diel oscillations (during 15 days) in internal sugar, functional and total biomass levels are depicted in Figure 3.10.

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Figure 3.10 Predicted diel oscillations during 15 days in sugars (green line, cmol-s m -3), functional biomass (blue line, cmol-x m-3) and total biomass (red line, cmol m-3) content of C. sorokiniana suspended cultures grown under a ‘block’ 12/12 day/night cycle in flat panel PBRs operated in chemostat mode. For every day, sunrise is represented by the whole numbers, while sunset is represented by half of the whole numbers.

As Figure 3.10 shows, the model predicts an increase in internal sugar content over the day, and a decrease during the night, which is due to overnight sugar consumption for biomass production (growth-related respiration). A maximal sugar fraction of about 36% is predicted immediately before sunset, while a minimal sugar faction of approximately 7% is predicted immediately before sunrise. Conversely, the model predicts a decrease in functional biomass during the day, which is because of its dilution in the total biomass due to carbon storage. These results are in agreement with the experimental observations (see section 3.2.2). Also in line with the experimental results, the model predicts that over the day there is a higher carbon demand for internal sugars, than for functional biomass synthesis, and thus that C. sorokiniana uses daylight energy to store reserves compounds as sugars. Baroukh and co-workers (2014) reported similar diel dynamics in the microalgae Tisochrysis lutea grown under day/night cycling. This indicates that the simple kinetic approach used can correctly describe the diel dynamics on internal sugar and functional biomass pools of C. sorokiniana cultures grown under simulated outdoor conditions.

The model predicts that total biomass follows the sugar trend (i.e. increases during the day and decreases at night). This is because carbon is only incorporated 46


during the day through photosynthesis, and is lost during the night by respiration to meet energy demands necessary to support overnight biomass synthesis (growth-related respiration). Although this dynamic was expected (Baroukh et al., 2014), it is not in agreement with the experimental observations as experimentally it was observed an unexpected decrease in total biomass over the day, which, as previously discussed, cannot be physically explained (see section 3.2.2). Therefore, the experimental data could not be used to calibrate model parameters (x,min and the qs,m,c). As such, the daylight driven carbon partitioning model introduced in this study could not be validated with real suspended data for C. sorokiniana.

3.4 Conclusions

A simulated day/night cycle (12 h/12 h) was applied for C. sorokiniana grown in suspension in two different biological states (fast-growing microalgae in a 0.22-h -1 D PBR and slow-growing microalgae in a 0.04-h-1 D PBR) in order to obtain experimental data to calibrate and validate the daylight driven carbon partitioning model. An unexpected overnight increase in biomass was obtained, which cannot be explained physically. Therefore it should be investigated before fitting the model. Nevertheless, the experimental observations shown that both the biomass production and photosynthetic efficiencies are not affected by the dilution rate. However, when rapidly growing C. sorokiniana under day/night cycling, internal sugar accumulation is faster.

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48

Effect of day/night cycles on Chlorella sorokiniana biofilm cultures

CHAPTER 4Effect of day/night cycles on Chlorella sorokiniana biofilm

cultures

49

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50


51

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4.1 Introduction

Biofilm-PBRs are gaining more attention as a promising alternative to traditional suspension cultures because of the advantages discussed in Chapter 2. However, their potential has not been yet fully realised because, although various biofilm-PBRs have been developed in the recent years, experiments have been performed in a wide range of conditions, limiting the capability for quantitative comparisons with suspended systems (Berner et al., 2014, Schnurr & Allen, 2015). Mathematically modelling both planktonic and biofilm growth, with focus on biomass productivity, would allow to close this gap. Nevertheless, currently, only the model proposed by Murphy and Berberoglu (2014) has focused on predicting phototrophic biofilm productivity. However, this model does not take into account the effect of the diurnal light fluctuations. Indeed, even though models for planktonic growth under day/night cycles are available (as already discussed in Chapter 3), models for biofilm growth under day/night cycles are still lacking. Nonetheless, the growth models for suspended cultures can also be used to predict biofilm growth, where the key difference between a suspension and biofilm cultivation is that there is no spatial mixing in biofilms and therefore the microalgae are exposed to a local light intensity, which depend on the position within the biofilm.

The aim of this study was to compare suspended and biofilm C. sorokiniana cultures grown under simulated outdoor conditions. In this sense, a light limited biofilm growth model, with a similar structure to the one introduced in Chapter 3, was also designed to predict the diel oscillations imposed by the photocycle on C. sorokiniana biofilms. To validate the kinetic biofilm growth model, C. sorokiniana biofilms were grown in the air tight RBC under day/night cycling, and light absorption, biomass productivity and biomass yield on light energy were monitored. To ensure that light was the only growth-limiting factor, the carbon dioxide (CO2) replete concentration was assessed. For this purpose, continuous light was provided and two CO2 concentrations (5 and 10% v/v) were applied and compared for differences in productivity. Under replete nutrient concentrations (5% CO2), a 12/12 and a 16/8 day/night cycles were applied and results were compared with continuous light supply to investigate the influence of day/night cycles on biofilm productivity, biomass yield on light and light absorption. Additionally, the influence of ‘block’ and ‘sine’ lighting regimes was tested by suppling light as a ‘block’ and a ‘sine’ shape for biofilms grown under a 16/8 day/night cycle. Productivities obtained

52


for suspended and biofilm cultures of C. sorokiniana grown under a ‘block’ 12/12 day/night cycles were compared to assess the feasibility of use microalgae biofilms as a production platform for microalgae biomass.

4.2 Materials and methods

4.2.1 Microorganisms and culture medium

The freshwater green algae Chlorella sorokiniana CCAP 211:8k (Culture Collection of Algae and Protozoa, Ambleside, UK) was cultivated in M8-a medium as described in chapter 3, section 3.2.1. The cultures were pre-cultivated in 250-mL shake flasks containing 100 mL of medium, in an illuminated climate room under standard conditions: 25ºC, atmospheric CO2, continuous shaking at 120 rpm, and a light regime of 16 hours per day at 40-50 μmolph m-2 s-1 and 8 hours of darkness. The algae suspension was used to inoculate the RBC reactor, as will be explained later. Four days prior to the biofilm reactor inoculation, the cultures were transferred to a shaking incubator (Snijders Scientific, The Netherlands) at 120 rpm, with light provided continuously at an intensity of 190 μmolph m-2 s-1. Temperature was kept at 37C and the headspace of the incubator was enriched with 4% (v/v) CO2.

4.2.2 Reactor set-up and experimental conditions

The experiments were carried out in an air tight RBC reactor (Algadisk, Technical Development Studio, Wageningen University, The Netherlands), a transparent gas sealed container that can be split up in three different parts: the rotating disk, the headspace and the liquid phase. The rotating disk was the support structure for biofilm growth. An airflow enriched with CO2 was continuously supplied to the headspace, which thus diffused to the liquid. Nitrogen and other nutrients in the M8-a medium were supplied in the liquid phase, which was kept dark. Thereby, only the upper portion of the disk was illuminated. This strategy created a selective pressure for biofilm growth on the disk (and minimised microalgae growth in suspension). Furthermore, a separator from black foam was placed between the gas and the liquid phase to prevent light from the headspace entering the liquid. The reactor had a liquid volume of approximately 4.1 L. The RBC reactor and a reactor’s schematic overview is shown in Figure 4.1 A and B, respectively.

The disk material used in this study was a stainless steel woven mesh (Twilled Dutch Weave type 80/700, GKD SolidWeave, Düren, Germany), with a tread

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thickness of 0.10/0.076 mm and a particle size of 47 µm. Each side of the disk had a cultivable area of about 0.055 m2 and the illuminated area of each disk side was approximately 0.021 m2. The disk was assembled with a metallic border and a plastic support with a diameter of 0.032 m and 0.048 m, respectively. As the algae grew on both sides of the disk and the disk borders, the total cultivable area was approximately 0.14 m2. Approximately 38% of the total cultivable area was in the light (fdI).

Figure 4.1 (A) RBC reactor used for cultivation of C. sorokiniana biofilms under various illumination schemes; and (B) Schematic overview of the RBC reactor.

The reactor was illuminated from both sides by LED lamps (BXRA W1200, Bridgelux, USA) with a warm-white light spectrum. A metallic cover was placed on the top of the reactor to ensure no environmental light was able to enter the reactor. The average light intensity over the illuminated disk surface was individually measured for each side of the disk at the beginning, once a month and at the end of the experiments with a LI-COR LI-250A 2∏ quantum sensor (PAR range 400-700 nm) (LI-COR, USA), according to the method described by Blanken et al. (2014). The average light intensity over the disk surfaces was 393 μmolph m-2 s-1 and it will be referred onwards as 400 μmolph m-2 s-1.

In the reactor, the M8-a media was supplemented with 60 mM NH4Cl as nitrogen source. The medium was circulated through a peristaltic pump (505-S, Watson Marlow, UK) at a rate of 0.54 L min-1. The temperature inside the reactor was monitored with a temperature sensor (6100, West, US) and maintained at 37 ºC by a water bath (FP40, Julabo, Germany) placed in the circulation loop. The culture dissolved oxygen (DO) and pH were monitored with a LHT (MXD73, LHT, UK) DO-pH monitor (Technical Development Studio, Wageningen University, The Netherlands).

54

A B


The culture pH was controlled at 6.7 by suppling a 1 M NaOH solution through a fixed-speed pump (L/S 7542-02, Masterflex, The Netherlands). Air enriched with CO2

(at different concentrations) was continuously supplied to the reactor headspace with a mass flow controller (0254, Brooks, The Netherlands) at a rate of 200 mL min -

1 to supply enough inorganic carbon for microalgae growth. Culture evaporation was prevented by connecting a condenser cooled to 4 ºC by a cryostat (F34, Julabo, Germany) to the gas outlet. The amount of CO2 and O2 in the outgoing gas flow was measured online by a two gas analysers (SERVOPRO 1400 4100, Servomex, The Netherlands) to monitor the algae growth.

The mass flow controller, the gas analysers and the pH and DO sensors were calibrated before the reactor start-up. The disk was heat-sterilised during 90 minutes at 121C and placed in the middle of the reactor. The reactor was filled with approximately 4 L of medium and a filter-sterilised (pore size 0.2 µm) microalgae suspension pre-cultivated in a shake flask was added to the medium to inoculate the reactor. The reactor was inoculated to an optical density at 750 nm of about 4.8 and closed air tight. The disk was turned at 9 rpm and light was provided continuously at an intensity of about 400 μmolph m-2 s-1. The reactor was then operated in batch mode and the selective pressure for biofilm growth was used to initiate biofilm development. The continuous rotation of the disk allowed the biofilm to change frequently between the gas and the liquid phase, which ensured a good contact of the algae with the bulk nutrients and the light in the headspace. Two weeks after inoculation a solid biofilm was formed on the disk. This biofilm was then harvested by scraping, according to the method described by Blanken et al. (2014). Due to the porosity of the disk, the biofilm could re-grow from the biomass that remained in the disk after the harvest. After the initial harvest, the experiments were started. During the experiments, the biofilm was harvest every 7 days. The disk was kept in tap water, during which the remained biomass in the tubbing was removed and the reactor was cleaned with boiled tap water. The pH sensor was calibrated occasionally and the tubbing was replaced once in a while. Fresh medium was added, the disk was again placed in the reactor and the reactor was closed air-tight. During all the experiments, the disk was turned the same direction at 9 rpm.

Table 4.1 shows a summary of the different experimental conditions that were tested. Please note that the maximum light intensity in experiment ‘5%CO2 Sine 16/8’ was chosen such, the total amount of light provided to the algae per day was the same as in experiment ‘5%CO2 Block 16/8’. Experiments ‘10%CO2 Cont. 24/0’ and ‘5%CO2 Cont. 24/0’ were performed in triplicate. Reported values are average

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values from these 3 data sets. Experiments ‘5%CO2 Block 12/12’ and ‘5%CO2 Sine 16/8’ were performed in duplicate. Reported values are average values from these 2 data sets. Experiment ‘5%CO2 Block 16/8’ was repeated 5 times and thus reported values are average values over these 5 data sets.

Table 4.1 Overview of the experimental conditions used in the experiments.

Experiment CO2 Photoperiod

Light supply

Maximum intensity

Light per day

% (v/v)

[h] [µmol m-2 s-1] [mol m-2]

10%CO2 Cont. 24/0

10 24 Continuous 400 35

5%CO2 Cont. 24/0

5 24 Continuous 400 35

5%CO2 Block 12/12

5 12 Block 400 17

5%CO2 Block 16/8

5 16 Block 400 23

5%CO2 Sine 16/8

5 16 Sine 623 23

4.2.3 Analytical methods

Samples were taken daily from the liquid phase to monitor algae attachment to the disk by measuring the liquid optical density at 750 nm and 680 nm. Additionally, these samples were observed under a microscope to check for invasive species. During the experiments, the biofilm was harvested weekly (after 7 days of growth). The biomass of each side of the disk was harvest separately and weighted 56


to determine the wet biofilm weight. Afterwards, three samples of each disk side were taken and dry weight and microalgae specific absorption coefficient measurements were performed. In order to close the carbon balance, the biomass from each side of the disk borders and from the liquid phase were also collected. The wet weight of the biomass harvested separately from each side of the disk borders and the biomass harvested from the liquid phase was measured. Afterwards, three samples of each disk border side and three samples of the liquid phase were taken for dry weight measurements.

4.2.3.1 Optical density measurements

The liquid optical densities (ODs) at 750 nm (OD750) and 680 nm (OD680) were measured in triplicate on an UV-VIS spectrophotometer (DR 6000, Hach Lange, Germany) against a demineralised water blank.

4.2.3.2 Microscope imaging

The liquid samples were examined under a culture microscope (CK40, Olympus, Japan). Observations were performed using a 10×/0.25 NA (A10PL, Olympus, Japan) and a 40×/0.55 NA (LWDCDPlan40FPL, Olympus, Japan) objectives to check for ciliates and bacteria, respectively.

4.2.3.3 Dry weight determination

To determine the dry weight of the disk and the disk borders samples, the biomass was dried at 95 C overnight, allowed to cool to room temperature in a desiccator and weighted. The dry weight of the liquid samples was determined as described by Kliphuis et al. (2012).

4.2.3.4 Light absorption measurements

The mass fraction of biomass to water in the wet biofilms of each disk side and disk borders (fx/b, g-DW g-1) was determined by diving the dry biomass weight (w1, g-DW) with the wet biofilm weight (w2, g-DW) (equation 4.1).

(4.1)

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4.2.3.5 Liquid total harvested dry weight

The total harvested dry weight of the liquid phase (DW l, g-DW L-1) was estimated by diving the average dry weight of the three liquid samples ( , g-DW) over the volume harvested (V, L) (equation 4.2).

(4.2)

4.2.3.6 Chlorella sorokiniana specific absorption coefficient

The wavelength-dependent absorption coefficient ( , m2 cmol-1) was calculated based on the average absorbance measured at wavelength of the two duplicates (

), the dilution factor (d), the dry weight-dependent biomass concentration in the cuvette (Cb, cmol m-3), the cuvette optical light path (l) and a factor to convert a 10 logarithm to the natural logarithm (ln10) (equation 4.3). The average absorption over the 740-750 nm region was subtracted from all data points because it is mainly background noise de Mooij et al. (2014).

(4.3)

4.2.3.7 Surface productivity

For each side of the disk, the surface productivity (rx,disks, g-DW m-2 d-1) was calculated based on the disk side total harvest dry weight (DWd, g-DW), the biofilm growth period (t, d) and the disk side cultivable area (Ad, m2) (equation 4.4). Reported values resemble the average over the two sides of the disk.

(4.4)

4.2.3.8 Mass fraction of biomass in the liquid phase

For each light scheme, the mass fraction of biomass in the liquid phase (fl) was calculated based on the total harvested dry weight of the liquid phase (DWl, g-DW L-

1), the total harvest dry weight for each side of the disk (DWd,1 and DWd,2, g-DW L-1) 58


and the total harvest dry weight for each side of the disk borders (DWb,1 and DWb,2, g-DW L-1) (equation 4.5). The value reported resemble the average of all light schemes tested.

(4.5)

4.2.3.9 Biomass yield on light energy

The areal light absorption rate (rph, molph m-2 d-1) was calculated based on the incident light intensity (IIN, μmolph m-2 s-1), the total light time during the biofilm growth period (t, d), the illuminated fraction of the disk (fdI) and a factor to convert the amount of light in μmol per second to the amount in mol per day (8.64×10 -2) (equation 4.6).

(4.6)

For each side of the disk, the biomass yield on light energy (Yx/ph, g-DW molph-1) was calculated based on the surface productivity (rx,disks, g-DW m-2 d-1) and the areal light absorption rate (rph, molph m-2 d-1) (equation 4.7). Reported values resemble the average over the two sides of the disk.

(4.7)

4.2.4 Statistical analysis

The absorption coefficient, the surface and total productivities and the surface and total biomass yields on light energy values (dependent variables) for the different experimental conditions (independent variable) were analysed with the ANOVA Post Hoc Tukey HSD test to test whether they differed significantly from each other (p<0.05). The statistical analysis was carried out by SPSS software (version 23.0, IMB Statistical Package for the Social Sciences).

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4.2.5 Daylight driven carbon partitioning model

4.2.5.1 Model structure

Microalgal growth in a biofilm is not in steady state. This means that the system variables depend on time and place, resulting in a system description by partial differential equations. To keep model complexity at a minimal level while still providing sufficient accuracy, a microalgal biofilm can also be represented by a large but finite number of layers with depth dz, resulting in a set of ordinary differential equations. Therefore, microalgal biofilm growth under day/night cycles can be mathematically described by a model with a similar structure as the model designed for planktonic microalgae growth (see section 3.2.6 for more details), by re-writing the mass balances.

4.2.5.2 Sugar and functional biomass balances

The functional biomass (equation 3.10) and the sugar (equation 3.11) balances were re-written into equation 4.8 and 4.9 such that they depend on the thickness of a biofilm layer dz. It must be assumed that the total biomass concentration (Cb) in a biofilm layer remains constant (equation 4.10). By assuming a constant total biomass concentration, growth is represented by increasing the thickness of the biofilm (Lf). Equation 4.11 is obtained to predict the thickness of a biofilm layer.

(4.8)

(4.9)

(4.10)

(4.11)

60


Employing equation 4.10 and the product rule, equation 4.12 and 4.13 were obtained to predict the functional biomass concentration change and the sugar concentration in a biofilm layer.

(4.12)

(4.13)

4.2.5.3 Sugar production

As the RBC was designed such as part of the rotating disk was kept dark to create a selective pressure for biofilm growth, a correction factor (fdI) was included in the local specific sugar production rate (q s,p (z)) calculation (equation 4.14).

(4.14)

4.2.5.4 Biofilm productivity

Biofilm productivity (Pb,pre, g m-2) was predicted based on the total biomass concentration (Cb, cmol m-3), molar mass of microalgae (Mx, g cmol-x-1) and predicted biofilm thickness (Lf,pre, m) (equation 4.15).

(4.15)

4.2.5.5 Input parameters

In addition to the input parameters required in the planktonic microalgae growth model (see section 3.2.6.5 for more details), the biofilm growth model also requires as input parameter the illuminated fraction of the disk (fdI).

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4.2.5.6 Computational methods

To predict the daily variations on the functional and sugar storage pools of C. sorokiniana biofilms, the discreet differential equations introduced in section 4.2.5.2 (equations 4.11-4.13) were discretised over 100 layers and solved employing a build in ODE solver of MATLAB R2013a (ODE15s). An if else function based on the total day time (tday) was used to differentiate sugar consumption during the day and at night. The changes in biofilm thickness were described with a matrix composed by the local biofilm thickness (Lf(z)) and the relative distance from biofilm surface ((dz)). The matrix was initiated by considering a 5x10 -6 m initial biofilm thickness (Lf(0)). The MATLAB code can be found in appendix A.6.

4.3 Results and discussion

4.3.1 Surface productivity

The effect of different illumination schemes where evaluated based on surface productivity. The surface productivity (in g-DW m2 d-1) was calculated from equation 4.4 to quantify C. sorokiniana biofilm growth under the different conditions tested. The results obtained are presented in Figure 4.2.

Figure 4.2 Surface productivity (g-DW m-2 d-1) for C. sorokiniana grown as a biofilm under continuous light at 10% CO2 (grey, N=3) and 5% CO2 (diagonal lines, N=3) and under a ‘block’ 12/12 (dots, N=2), 62


a ‘block’ 16/8 (squares, N=5) and a ‘sine’ 16/8 (horizontal lines, N=2) day/night cycles at 5% CO 2. The values resemble the average of the two sides of the disk. Error bars indicate the standard deviation.

Surface productivities of about 11 g-DW m-2 d-1 were obtained for both the 10 and 5% CO2 concentrations concentrations. Based on statistical analysis, no significant differences in the surface productivity values for the two CO2 concentrations were found (p>0.05). Therefore, it seems that the 5% CO2 concentration does not limit biofilm growth and thus correspond to the CO2 replete concentration. As such, it was validated that only light was limiting and the remaining light regimes where compared employing 5% CO2 in the incoming gas.

Under continuous light conditions, surface productivities of 3.51 g m -2 d-1 (Gross et al., 2013), 5.5 g m-2 d-1 (Christenson & Sims, 2012), 20.1 g m-2 d-1 (Blanken et al., 2014) and 31.2 g m-2 d-1 (Schultze et al., 2015) were reported for other lab-scale biofilm PBRs. Please note that the high productivity reported by Schultze and co-workers (2015) was obtained under a higher light intensity (1023 µmol m-2 s-1) than the one used in our study. Nevertheless, a higher productivity was obtained with the lab scale designed proposed by Blanken et al. (2014). Compared to this study (performed at about the same light intensity), Blanken et al. (2014) obtained a higher surface productivity, which could be associated to the design. Indeed, due to the large dark part of the biofilm there are losses of productivity. For all the conditions tested, it was obtained an average mass fraction of biomass in the liquid phase of about 0.3. This result indicates that algae also grow well in the liquid phase or, for instance, they can also fall from the adhesion surface.

At 5% CO2, the surface productivities in the day/night experiments were slighty lower than the surface productivity in continuous light (a surface productivity of about 6 g-DW m-2 d-1 was achieved for C. sorokiniana biofilms developed under a ‘block’ 12/12 day/night cycle and surface productivities of about 7 g-DW m -2 d-1 were reached for biofilm developed either under a ‘block’ or a ‘sine’ 16/8 day/night cycles). Indeed, the statistical analysis revealed that the surface productivities in the day/night experiments were significantly diferent than the surface productivity in continuous light (p<0.05). This was expected because under day/night cycling the total amount of light supplied to the biofilm is lower (see Table 4.1).

The statistical analysis also revealed that the surface productivities for the ‘block’ 12/12 day/night cycle were not significantly different from the ones obtained for the ‘block’ 16/8 day/night cycle (p>0.05). Indeed the experimental values obtained for these two conditions were similar. Altough the total amount of light supplied to the

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biofilm in the ‘block’ 12/12 day/night cycle is lower than in the ‘block’ 16/8 day/night cycle (see Table 4.1), it seems that is not sufficiently different to give a significant difference in productivity. This result suggest that additional photoperiods should be study to properly assess the influence of day/night cycles on biofilm productivity.

Moreover, for the biofilms grown under a 16/8 day/cycle, no significant differences in the surface productivities were observed when light was provided either in a ‘block’ or in a ‘sine’ form (p>0.05). This result is in agreement with the expectation as de Winter et al. (2015) also reported no clear differences in biomass productivity when light was provided as a ‘block’ or a ‘sine’ to suspended cultures of Neochloris oleaobundans grown under a 16/8 day/night cycle.

4.3.2 Biomass yield on light

To evaluate the efficiency of light use by C. sorokiniana biofilm cells for biomass conversion, the biomass yield on light energy (in g-DW molph-1) was calculated from equation 4.7 and the results are shown in Figure 4.3.

Figure 4.3 Biomass yield on light energy (g-DW molph-1) for C. sorokiniana grown as a biofilm under

continuous light at 10% CO2 (grey, N=3) and 5% CO2 (diagonal lines, N=3) and under a ‘block’ 12/12 (dots, N=2), a ‘block’ 16/8 (squares, N=5) and a ‘sine’ 16/8 (horizontal lines, N=2) day/night cycles at

64


5% CO2. The values resemble the average of the two sides of the disk. Error bars indicate the standard deviation.

The biomass yield on light of C. sorokiniana grown as a biofilm under the different conditions tested ranged between 0.8 and 0.9 g-DW molph-1. No significant differences in the biomass yields on light values for the different conditions were found (p=0.317). Another indication that 5% CO2 was not limiting is that sine and block gave the same yield on light. If 5% CO2 would be limiting this would be even more limiting for the sine as the peak light will be higher.

The results are in the same range as the ones obtained for suspended C. sorokiniana cultures which suggest a similar efficiency of light use for both the suspended and biofilm cultures.

4.3.3 Chlorella sorokiniana specific absorption coefficient

To get a clearer picture of the influence of the conditions tested on the cells pigmentation levels, the average absorbance levels (in m2 cmol-1) over the PAR spectrum (400-700 nm) for the different experimental conditions was calculated and the results are depicted in Figure 4.4. The absorption spectra over the 400-750 nm spectrum for light scheme can be found in appendix A.7.

Figure 4.4 Average specific light absorption coefficient ax (m2 cmol-1) over the PAR spectrum for C. sorokiniana grown as a biofilm under continuous light at 10% CO2 (grey, N=3) and 5% CO2 (diagonal

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lines, N=3) and under a ‘block’ 12/12 (dots, N=2), a ‘block’ 16/8 (squares, N=5) and a ‘sine’ 16/8 (horizontal lines, N=2) day/night cycles at 5% CO2. Error bars indicate the standard deviation.

The average specific light absorption coefficient of C. sorokiniana grown under the different conditions tested ranged between 5.0 and 5.7 m2 cmol-1. These vaues are in the range reported by Blanken et al. (2016) for C. sorokiniana planktonic cells, which suggest that the cells were in ideal suspension. No significant differences in the absorption levels over the PAR spectrum for the different conditions tested were found (p>0.05). A higher level of pigments can be expected when the photoperiod is higher (because cells have more light). Maybe the lack of difference between the conditions is due to the large dark part and the short light part of the biofilm. Therefore, a large part of the biomass experience actually the same light conditions although illumination schemes are different.

4.3.4 Daylight driven carbon partitioning model predictions

The photocycle effect on C. sorokiniana biofilm cultures grown under day/night cycles in the lab scale RBC was also determined by simulating the biofilm daylight driven carbon partition model for a ‘block’ and a ‘sine’ 16/8 day/night cycles. For the block lighting regime, light was set at a constant light intensity of 400 μmolph m-2

s-1. The sine function was chosen such as the total amount of light provided was the same as for the block form (23 molph m-2). The predicted diel oscillations in sugar and total biomass for a 7-days growth cycle are presented in Figure 4.5.

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Figure 4.5 Predicted diel oscillations in sugars (full lines, cmol-s m-3) and total biomass (dashed lines, cmol-s m-3) of C. sorokiniana biofilm cultures grown during 7 days in the lab scale rotating biological contractor reactor under (A) ‘block’ and (B) ‘sine’ 16/8 day/night cycles. For every day, sunrise is represented by the whole numbers, while sunset is represented by 0.7 of the whole numbers. Full lines refer to sugar content across the biofilm: upper full lines represent the surface biofilm layer and the bottom full lines represent the inner biofilm layer.

As can be seen in Figure 4.5, in line with the planktonic algae growth model, the biofilm growth model also predicts sugar accumulation during the day and its consumption at night. However, when the biofilm gets thicker less layers are in the direct light and therefore it was expected to see a decrease in sugar content over the biofilm due to light attenuation. Indeed, the model predicts a decrease in sugar content over the biofilm with a 2.5 times drop in sugar levels for the inner biofilm layer. Please note that total biomass does not change with biofilm thickness because it was assumed to be constant.

For both light regimes, the model predicts a maximal daylight sugar fraction of about 25% for the surface biofilm layer and a fraction of about 6% for the inner biofilm layer. However, the lighting regime seems to affect the diel sugar dynamics. As Figure 4.5 B shows, when light is supplied in a sine shape, sugar accumulation follows the light pattern and a sugar peak is reached when light intensity is at its maximum. A maximum sugar content seems to be reached faster when light is provided in a sine than in a block shape. Therefore, this result suggest that it might be interesting to investigate the light supply regime when using C. sorokiniana

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A

B

Chapter 4

biofilms as a production platform for sugars, as a different harvesting time should be considered according to the lighting regime.

In order to determine the effect of day/night cycles on C. sorokiniana biofilms productivity, the biofilm growth model was simulated for continuous light, a ‘block’ 12/12, a ‘block’ and a ‘sine’ 16/8 day/night cycles. A constant light intensity of 400 μmolph m-2 s-1 was used in both block shapes. The sine function was chosen as already explained in the planktonic growth model simulations (see section 3.3.7). The obtained results are shown in Figure 4.6.

Figure 4.6 Predicted productivities (g m2) of C. sorokiniana biofilm cultures grown during 7 days under continuous light (light blue line), a ‘block’ 12/12 (red line), a ‘block’ 16/8 (dark blue line) and a ‘sine’ 16/8 (green line) in the lab scale rotating biological contractor reactor. In the day/night cycles, sun rises at the start of a new day (represented as whole numbers).

As shown in Figure 4.6, biofilm productivity is slowed down by day/night cycling, as a decrease biofilm productivity is predicted with decrease day length. These results are in agreement with the experimental observations (see section 4.3.1).

During day/night cycling, a diurnal increase in biofilm productivity can be expected because during the day sugars (the building blocks for new biomass) are produced by photosynthesis. Furthermore, an overnight decrease in biofilm productivity is also expected, as during the night biomass is respired for maintenance. The model predictions show that these fluctuations are sharper when light is supplied as a block, while when light is supplied as a sine the daily variations have a rounded shape. Nevertheless, similar productivities are predicted for both

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light regimes, which is in agreement with the experimental observations (see section 4.3.1).

It is worth to note that the minimal sugar fraction inside the biomass (xmin) and the maximal sugar consumption rate (qs,m c) were common parameters for both the suspended and the biofilm growth models. Since they could not be obtained from literature, they needed to be calibrated with the data obtained from the suspended chemostat day/night cycle experiments performed in Chapter 3. However, as already explained, due to the observed decrease in total biomass over the day, the suspended experimental data could not be used to calibre these model parameters. Consequently, the biofilm growth model introduced in this study could not be validated.

4.3.5 Biofilms as a production platform for microalgae biomass

A surface productivity of about 6 g-DW m-2 d-1 was reached when C. sorokiniana was grown as a biofilm under a ‘block’ 12/12 day/cycle. This productivity is quite low compared to the average productivity of approximately 16 g-DW m -2 d-1

obtained for the suspended cultures under the same illumination scheme. Please note that this value resemble the average productivity obtained for fast and slow growing algae. As previously discussed, the lower productivity obtained for biofilm cultures can be associated with the reactor design. Therefore, this result suggest that perhaps the lab scale biofilm-PBR used is not the best design if outdoors microalgae cultivation is aimed. Nevertheless, similar biomass yields on light energy for both the suspended and the biofilm cultures were obtained which shows that light is used in a similar efficiency when algae are cultivated either in planktonic or in biofilm forms.

4.4 Conclusions

Various simulated day/night cycles (‘block’ 12 h/12 h, ‘block’ 16 h/8 h and ‘sine’ 16 h/8 h) were applied for C. sorokiniana grown as biofilms in order to compare cells and biofilms cultivated under simulated outdoor conditions.

Since the biofilm growth model shared kinetic parameters with the planktonic growth model, which could not be calibrated with the suspended culture data, the

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biofilm growth model could not be validated. However, the trends on biofilm productivity predicted by the model are in agreement with the experimental observations, showing the model potential to assess the effect of photocycle on biofilm cells.

As lower productivities were obtained for the biofilm cultures compared to those suspended, it seems that the lab-scale biofilm PBR used is apparently not the best design for outdoor production. However, similar photosynthetic efficiencies were obtained for the suspended and the biofilms cultures. This result shows that microalgae biofilms could be used as a production platform for microalgae biomass, has they have no negative effect on light efficiency.

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71

Chapter 2

CHAPTER 5General conclusions and research needs

72

General conclusions and research needs

73

Chapter 5

74

General conclusions and research needs

5.1 General conclusions

In this study, a simple engineering light limited microalgae growth model that incorporates both diurnal carbon partitioning and growth-related respiration was design to predict the diel dynamics of microalgal cells (Chapter 2) and biofilms (Chapter 3) cultures grown under outdoor conditions.

When rapidly and slowly growing C. sorokiniana in suspension under a simulated day/night cycle (12 h/12 h), net daily performances (in terms of biomass production and photosynthetic efficiency) are similar. However, when rapidly growing C. sorokiniana under day/night cycling, internal sugar accumulation is faster. Unexpectedly, there was an overnight biomass increase that cannot be explained physically. For this reason, the light limited microalgae growth model cannot be calibrated.

Under day/night cycling (12 h/12 h), a similar efficiency of light use was found for both cells and biofilms. Therefore, biofilms can be used as a viable production platform for large-scale microalgal biomass. However, productivity was lower for the biofilms in comparison to suspended cells, and it can be associated to the large dark part of the disk. As such, it appears that the biofilm-PBR design employed in this study is not the most suitable design for outdoors cultivation.

As the kinetic parameters could not be calibrated with the suspended chemostat day/night cycle experiments, the biofilm growth model could not be validated.

5.2 Research needs

The overnight biomass increase cannot be explained physically and it should therefore be investigated before fitting the daylight driven carbon partitioning model. It is suggested to perform COD experiments in order to monitor the total organic matter over the day. For instance, these experiments can be carried out in shake-flasks and the same light scheme used in the suspended cultures experiments should be used.

In the suspended cultures, culture overnight evaporation was unavoidable and that can be easily avoided by continuously dilute the culture day and night.

As lower productivities were obtained with the biofilm-PBR, studies should be carried out to try to maximise biofilm mass production. For example, it is interesting

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Chapter 5

to look into high light intensities, higher substrate concentrations, other disk speeds and support materials. In addition, other disk orientations should be studied (for example place the disk slightly inclined rather than vertically), to increase its illuminated area.

Dedicated experiments can be performed to obtain real data to calibrate the maintenance rate parameter, and thus improve model accuracy. This can be done by monitoring the respiration rate over a simulated day/night cycle.

Finally, the light limited growth model proposed in this study should be extended to account for other limitations such as cell decay and diffusion limitation of nutrients and gas.

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84

Appendix

A.1 Microalgae products and applications

Table A.1 Commercial products from microalgae.

Product Applications Algal strain Reference

Biomass Biomass Carbon sequestration, cosmetics , food and feed additives, wastewater treatment

Botryococus braunii, Chlamydomonas sp., Chlorella sp., Dunaliella sp., Haematococcus pluvialis, Isochrysis galbana, Monoraphidium minutum, Nannochloropsis sp., Nitzschia hantzchiana, Phaeodactylum tricornutum, Porphyridium sp., Scenedesmus sp.

Spolaore et al., 2006, Brennan & Owende, 2010, Mata et al., 2010, Ho et al., 2011, Ratha & Prasanna, 2012, Maity et al., 2014, Wu et al., 2014

Fatty acids (FA)

Arachidonic acid (AA), Eicosapentaenoic acid (EPA), Docosahexaenoic acid (DHA), - linolenic acid (GLA)

Food and feed additives, fuel production

Chlorella sp., Nannochloropsis sp., Nitzschia laevis, Parietochloris incisa, Porphyridium sp., Phaeodactylum tricornutum, Schizochytrium sp.

Spolaore et al., 2006, Brennan & Owende, 2010, Singh & Gu, 2010, Bahadar & Khan, 2013, Oncel, 2013

Polymers Polysaccharides, proteins Cosmetics, food additives, pharmaceuticals

Chlorella sp., Porphyridium sp. Ratha & Prasanna, 2012, Oncel, 2013

Pigments & Antioxidants

Carotenoids, phycobiliproteins

Cosmetics, food and feed additives

Chlorella sp., Dunaliella salina, Haematococcus pluvialis, Muriellopsis sp., Nannochloropsis sp., Prototheca moriformis,

Spolaore et al., 2006, Mata et al., 2010, Parmar et al., 2011, Oncel, 2013

A.1

Chapter 3

Scenedesmus sp.,

Special products

Enzymes, hydrogen, isotopes

Hydrogen production, research, medicine

Botryococus braunii, Chlamydomonas reinhardtii, Platymonas subcordiformis, Scenedesmus obliquus

Spolaore et al., 2006, Ghasemi et al., 2012, Suali & Sarbatly, 2012, Bahadar & Khan, 2013

2


A.2 Daylight driven carbon partitioning model MATLAB code for suspended cultures

Master file

% master file for the daylight driven carbon partitioning model for % suspended cultures% includes:% algae growth in suspension% day night clear allclose all global t_light D umax m_s Y_xs Y_sph a_x droop x_min q_smc q_smp E_phar par light data_A nl n z WF_SSE_Cx WF_SSE_Cs ODE_out res_out t0

%% Process conditionst_light=12;% hours lightR_l=0.014; % reaction thickness in m

% chemostat V=404 ;%volume of the reaction in mlF_set=1.455;% ml/minF=F_set*60*12; %flowrate going in and out of the reactor in ml/dD=F/V/24/3600;% Dilution rate per second %% Chlorella sorokiniana specific constantsMx = 24; % molecular weight biomass [g/c-molx] (Kliphuis 2010a)umaxh = 0.27; % maximum specific growth rate [h-1] (Sorokin 1959)umax = umaxh/3600; % max specific growth rate [s-1] m_s=2.5e-6; % maintenance rate [molo2/cmolx/s]Y_xs=0.59; % biomass yield on sugar [c-molx/mols]Y_sph=(1/10); % quantum yield [molo2/molph] % absorption coefficienta_x_m = importdata('input\abs_Chlor.txt').*(Mx/1000); % imported absorption in m2/kg converted to [m2/c-mol]a_x_set=5.8; % average absorption coefficient value m2/cmol-xa_x= (a_x_set/mean(a_x_m)).*a_x_m; % recaluculation fo the a_x imported to new a_x

%% Constants for sugar consumption ratesdroop = 1; % includes a power to the droop model to slow down sugar consumptionx_min=0.08; % minimal starch fraction inside the biomass in cmol s/cmol xq_sm=(umax/Y_xs)+m_s; % maximal sugar consumptoin rate rate mol sugar / c-mol / s - specific algae constants in separate fileq_smc=q_sm*0.5 ; % maximal sugar consumption rate mol sugar / c-mol / s - specific algae constants in separate fileq_smp=q_sm ; % maximal sugar production mol sugar / c-mol / s - specific algae constants in separate file

A.3


%% Light inputs E_phar = importdata('input\EparRL.txt'); % E n,PAR,l in [nm-1] - LL assumes same light properties: warm-white directional LED light source %par is the vector containing the parameters that Fit_A_fmins is alloud to changepar0_Q=[q_smc , x_min]; %starting values for par lb=[0.1*q_sm , 0]; %lower bound for parub=[q_smp , 0.2 ]; %upper bound for parpar=par0_Q % light file for day/night cyclelight=importdata('input\light_A_fast.txt');light(:,1)=light(:,1).*3600; % recalculation of the time to secondslight(:,2)=light(:,2).*1e-6; % recalculation of the light in umol/m2/s to mol/m2/s

%% Data Chemostatsdata_A=importdata('input\data_A_fast.txt');data_A(:,4)=data_A(:,2)+data_A(:,3);

%% Calculation Rasternl=50; % amount of layersn=nl+1; % amount of grid pointsz=linspace(0,R_l,n); % makes a liniar grid for light model distance from front of the reactor in m

%% Ode %weight factors SSE:WF_SSE_Cx=1;WF_SSE_Cs=1; par=par0_Q SSE=Fit_A_fmins(par); t=ODE_out(:,1);C=ODE_out(:,2:end);Cx_pred=res_out(:,1); Cs_pred=res_out(:,2);Cb_pred=Cx_pred+Cs_pred; %total predicted biomass in cmol/m3t_d=t/3600/24; % recalculation of t in s to t in dayst_h=t/3600; C_smin=x_min.*C(:,1);

%% Extracting data predicted from results% getting prediction data last dayrow_resday=find(t<=t0(2)-24*3600,1,'last');t_h_resday=(t(row_resday:end)-t(row_resday))/3600;C_resday=C(row_resday:end,:);C_resday(:,3)=C_resday(:,1)+C_resday(:,2);

%% Figuresfigure(2) % plots the fraction starch over time

A.4

Appendix

plot(t_d,C(:,1),t_d,C(:,2),t_d,C(:,1)+C(:,2))grid onlegend('cmol-x/m3','cmol-s/m3','cmol-total/m3')xlabel('time in days')ylabel('cmol/m3')

ODE file

function [dcdt]=Fit_A_Combi_dr_Li__odeDN_C(t,C,q_smc,x_min,D ) global I_i Y_xs droop Y_sph n E_phar a_x q_smp z light t_light m_s function [q_sp]=fq_sp(Cx,t_day) % function to predict the sugar production rate based on light % includes spectral light and wavelenght dependend absorption % coefficient % % calculatoin of the Cx used in light calculations C_xo=Cx(1); % only funcitonal biomass [row,col]=find((light(:,1)-t_day)<=0,1,'last'); %search for the closest lower value t in datafile for time=t(sec) I_i=light(row,col+1); %The I that belongs to that t % calculates the light intensity on the grid points Iz = sum(((I_i*E_phar)*ones(1,n)).*exp(-(a_x)*C_xo*z)); % in mol/m2/s % calculates the light absorption rates for evey layer r_ph=zeros(1,n-1); for j=2:n r_ph(1,j-1)= (Iz(1,j)-Iz(1,j-1))./((z(j)-z(j-1)).*C_xo); %create vector with photon uptake rate values for every layer, by substrating values of every point in z and I end % in mol-ph/cmol-x/s % Calculates the sugar production rate q_sp=mean(q_smp*tanh( Y_sph.*-r_ph./q_smp) ); % in cmol-s/cmol-x/s end function [D_t]=fD(D,t_day) % function to start and stop the dilution % start dilution with light intensity above 0 % stops dilution with light under 0 [row,col]=find((light(:,1)-t)<=0,1,'last'); %search for the closest lower value t in datafile for time=t(sec) I_i=light(row,col+1); %The I that belongs to that t if t_day>=t_light*3600 D_t=0; else D_t=D; end end

A.5


function [q_sc]=fq_sc(C_s,C_x,t_day,t_sunrise,C_smin) %sugar consumption rate in cmol-s/cmol-x/s if t_day>=t_light*3600 % linear consumption q_sc=(C_s-C_smin)/(t_sunrise-t_day)/C_x; %night else % droop q_sc=q_smc*(1-C_smin/C_s)^droop; %day end end

% definitions for C_sminC_smin=x_min.*(C(1)+C(2));C_x=C(1);C_s=C(2);t_day=(t/3600/24-floor(t/3600/24))*3600*24;t_sunrise=24.5*3600; % ode equationsdcdt(1)=Y_xs*(fq_sc(C_s,C_x,t_day,t_sunrise,C_smin)-m_s)*C_x - fD(D,t_day)*C_x;dcdt(2)=fq_sp(C_x,t_day)*C_x - fq_sc(C_s,C_x,t_day,t_sunrise,C_smin)*C_x- fD(D,t_day)*C_s; dcdt=dcdt'; end

A.3 Steady state

As described in the “Materials and methods” section of Chapter 3, the PBRs were allowed to reach steady state. Samples were taken from the reactors every

A.6

Appendix

day at the same time (11:00) to monitor biomass growth and steady state by measuring the optical density at 750 nm and 680 nm (OD750 and OD680) and dry weight (biomass density, Figure A.2) in triplicate, and cell number (Figure A.3) and cell size (Figure A.4) in duplicate. The ratio between the optical density at 680 nm and 750 nm (OD680/OD750) (Figure A.1) was used to indirectly assess the amount of chlorophyll-a per microalgae cell. When steady state was reached, samples were taken from the overflow vessels over the light period (12 hours) at 2 hour intervals, during 3 steady state days for the 0.22-h-1 reactor and 2 steady state days for the 0.04-h-1 reactor (indicated by the full and dashed arrows for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively).

A.7


A.8

Figure A.1 Ratio between the optical density at 680 nm and 750 nm (OD680/OD750) of C. sorokiniana grown under a ´block´12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in triplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1

reactor, respectively.

Figure A.2 Biomass density (g-DW L-1) of C. sorokiniana grown under a ´block´12:12 day/night cycle, at 0.22 h-1

(diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in triplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively.

Figure A.3 Cell number of C. sorokiniana grown under a ´block´12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in duplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively.

Figure A.4 Cell size of C. sorokiniana grown under a ´block´12/12 day/night cycle, at 0.22 h-1 (diamonds) and 0.04 h-1 (squares) dilution rates (D). The values were measured in duplicate from samples taken from the reactor every day at 11:00. Error bars show standard deviations. Full and dashed arrows refer to overflow sampling days for the 0.22-h-1 reactor and the 0.04-h-1 reactor, respectively.

Appendix

For the 0.04-h-1 reactor, dry weight was not measured in the daily sample at 11:00 for the first steady state day (Figure A.2) because a significant overnight evaporation occurred (overnight evaporation is explained in section A.4). Therefore, to do not disturb the steady state, less volume was taken for the daily samples as the dilution rate was not high enough to compensate for this volume. For the other overflow sampling days, although overnight evaporation occurred (as explained in section A.4), the dilution rates were high enough to compensate for the volume taken from the reactors.

Some fluctuations can be observed in the dry weight measurements (Figure A.2). Experimental errors (e.g. incorrectly pipetting) can lead to some oscillations. However, it is not likely that only experimental errors can cause the variations observed. As already explained, a volatile compound can be produced and therefore steady state can be more accurately assessed by performing COD measurements instead. Nevertheless, the OD (Figure A.1), cell number (Figure A.3) and cell size (Figure (A.4) measurements show that oscillations can be observed until the steady state was reached. However, when samples were taken from the overflow during the light period the measurements gave nearly constant values, showing that the steady state was maintained throughout the experiments.

A.4 Chlorella sorokiniana specific growth rate

C. sorokiniana was grown under a block shaped 12/12 day/night cycle and biomass growth was monitored until steady state was reached. In steady state, the growth rate (µ) is equal to the dilution rate (D), which was monitored by both logging the amount of medium fed (VIN) (equation A.1) and the amount of overflow produced (VOUT) (equation A.2) over the time (t), during 3 steady state days for the high-µ PBR and 2 steady state days for the low-µ PBR.

(A.1)

(A.2)

The volume taken from the reactors by sampling to monitor biomass growth was weighed and added to the overflow produced. The results are presented in Figure A.5 (in h-1) and are averaged values over 2 hours and resemble the average

A.9


of the 3 data sets for the 0.22-h-1 reactor and the 2 data sets for the 0.04-h-1 reactor. Error bars represent the standard deviations between the 3 and 2 steady state days for the 0.22-h-1 and the 0.04-h-1 reactor, respectively.

As Figure A.5 shows, both methods conduce to similar results.

The average dilution rate, and therefore the average specific growth rate over daylight (12 hours) was kept constant at 0.22-h-1 and 0.04-h-1 for the high-µ PBR and for the low-µ PBR, respectively. However, when monitoring the average dilution rate over shorter time intervals during daylight (2 hours), oscillations were observed.

For both reactors, at 9:00 the specific growth rate is slightly lower than the set value. This can be explained by culture evaporation overnight, which leads to a short period of fed-batch growth after the lights are switch on. It was estimated that on average 23.2 mL and 38.4 mL of culture evaporated overnight leading to 1 hour and 27 minutes and 12 minutes of fed-batch growth in the slow and in the fast growing reactor, respectively. These calculations are explained below. To avoid overnight evaporation, a condenser could be connected to the overflow (and therefore and additional pump to get the medium overflow is needed). Other option is continuously dilute the cultures day and night.

Slight changes in the dilution rates during the light period are possible due to instability of the medium pumps.

Figure A.1 Daily variation in dilution rate (D, h-1) in steady state (in steady state the dilution rate equals the specific growth rate µ, h-1) in fast (0.22-h-1 dilution rate) and slow (0.04-h-1 dilution rate) growing cultures of C. sorokiniana cultivated under a ‘block’ 12:12 day/night cycle. Grey area represents the dark period. The values were estimated based on the amount of medium feed (triangles

A.10

Appendix

for the 0.22-h-1 reactor and crosses for the 0.04-h-1 reactor) and the amount of overflow produced (diamonds for the 0.22-h-1 reactor and squares for the 0.04-h-1 reactor). The values are averaged values over 2 hours and resemble the average of 3 and 2 steady state days for the 0.22-h-1 and the 0.04-h-1 reactor, respectively. Error bars represent standard deviations.

Overnight evaporation calculations

In a chemostat, the dilution rate (D) is defined as the flow of medium over the volume of culture (i.e. the reactor working volume, VR). Thus, for the slow growing reactor the rate of medium pumped into the reactor (FIN) and the rate of overflow produced (FOUT) 2 hours after the starting light period at 8:00 can be determined by equation A.3 and A.4, respectively.

(A.3)

(A.4)

In this way, both the expected volume of medium feed to the reactor (Vexpected, IN) and overflow produced (Vexpected, OUT) over these 2 hours can be estimated by equation A.5 and A.6, respectively.

(A.5)

(A.6)

Therefore, the amount of culture evaporated overnight (Vevaporated) can be estimated by equation A.7.

(A.7)

Finally, the period of fed-batch growth (tfed-batch) 2 after the lights were switch on can be estimated by equation A.8.

(A.8)

A.11


Following the same approach for the fast growing reactor (and taking D In as 0.22 h-1 and DOUT as 0.172 h-1), the amount of culture evaporated overnight is equal to 38.4 mL and thus the fed-batch period is 12 minutes.

A.5 Absorption spectra over the 400-750 nm spectrum for

planktonic cultures

The absorption spectra over the wavelength 400-750 nm for each sample point for fast growing and slow growing planktonic C. sorokiniana cultures are depicted in Figure A.6.

A.12A

Appendix

Figure A.6 The absorption spectra over the 400-750 nm for (A) fast growing and (B) slow growing planktonic C. sorokiniana cultures.

A.6 Daylight driven carbon partitioning model MATLAB

code for biofilm cultures

Master file

% master file for the daylight driven carbon partitioning model for % biofilm cultures

A.13

B


% includes% biofilm growth% day night clear allclose all global m_s Y_xs Y_sph nl n E_phar a_x f_dI dz0 zl0 light_dn t_light C_b t_sunriseglobal x_min q_smp q_smc % to be added %%Process conditionsf_dI=0.45 ; % fraction of the disk in the light C_xk=200 ; % biomass concentratoin in g/lt0=[0 7*24*3600]; % starting time in seconds Important when using day night as it wil start with that intensity

%% Chlorella sorokiniana specific constants Mx = 24; % molecular weight biomass [g/c-molx] (Kliphuis 2010a)umaxh = 0.27; % maximum specific growth rate [h-1] (Sorokin 1959)umax = umaxh/3600; % max specific growth rate [s-1] m_s=2.5e-6; % maintenance rate [molo2/cmolx/s]Y_xs=0.59; % biomass yield on sugar [c-molx/mols]Y_sph=(1/10); % quantum yield [molo2/molph]C_b=C_xk/Mx*1000; % biomass conc [c-mol/m3] q_sm=(umax/Y_xs)+m_s; % maximal sugar production mol sugar / c-mol / s - specific algae constants in separate file

% absorption coefficienta_x_m = importdata('input\abs_Chlor.txt').*(Mx/1000); % imported absorption in m2/kg converted to [m2/c-mol]a_x_set=5.8; % average absorption coefficient value m2/cmol-xa_x= (a_x_set/mean(a_x_m)).*a_x_m; % recaluculation fo the a_x imported to new a_x E_phar = importdata('input\EparRL.txt'); % E n,PAR,l in [nm-1] - LL assumes same light properties: warm-white directional LED light source

%% Constants for sugar consumption ratesx_min=0.08; % minimal starch fraction inside the biomass in cmol s/cmol xq_smc=q_sm*0.5 ; % maximal sugar consumption rate mol sugar / c-mol / s - specific algae constants in separate fileq_smp=q_sm*1 ; % maximal sugar production mol sugar / c-mol / s - specific algae constants in separate filet_sunrise=24.1*3600;

%% Calculation Rasternl=100; % amount of layersn=nl+1; % amount of grid points % Creates a exponentional raster towards the biofilm surface

A.14

Appendix

dz0P=(nl:-1:1).^(5); % exponential decreasing grid,dz0=dz0P/sum(dz0P); % standardizing layer thickness zl0(1:(n))=0; % maks an empty vector for zl0 for i=1:(n) zl0(i)=sum(dz0(i:(n-1))); % relative distance from biofilm surface for every point end %% Light file for day/night cyclelight_file{1}=importdata('input/block16-8.txt');light_file{2}=importdata('input/sine16-8.txt');light_file{3}=importdata('input/block12-12.txt');light_file{4}=importdata('input/cont.txt'); for j=1:4 light_dn=0; light_dn(1:length(light_file{j}(:,1)),1)=light_file{j}(:,1).*3600; % reacalculation of the time to secondslight_dn(1:length(light_file{j}(:,1)),2)=light_file{j}(:,2).*1e-6; % reacalculation of the light in umol/m2/s to mol/m2/s t_light=light_dn(end,1);

%% Biofilm starting thicknessrho_bf=1000; % kg/m3 L_f0=50E-6;x0(1:nl)=C_b;s0(1:nl)=0.1.*(C_b);L0(1:nl)=fliplr(L_f0.* dz0);C0=[L0 x0 s0];

%% ODE options=odeset('NonNegative',1:length(C0),'stats','off','abstol',1e-9,'MaxStep',60*60);[sol]=ode15s(@sugar_ODE_Idn2,t0,C0,options);t=sol.x'; %to extract all time from the solution of the odeC=sol.y';%to extract all data from the solution of the ode

%% output%timetd{j}=(t-t(1,1))/60/60/24; % makes time factor start at zero and converts time from seconds to days % delta thickness layersC_L{j}=C(:,1:nl)*1e6;% biofilm thicknessL_F{j}=sum(C_L{j},2); % functional biomass concentration

A.15


C_x{j}=C(:,nl+1:nl*2);C_s{j}=C(:,1+nl*2:nl*3);Pb{j}=C_b.*L_F{j}*Mx*1e-6;tt{j}=t*ones(1,nl)./3600./24; clear i yyyfor i=1:nlyyy(:,i)=sum(C(:,1:i),2)-C(:,i)/2;end yy{j}=yyy; end %% figures figure(1) subplot(2,1,1) plot([td{1}(1,1); td{1}(end,1)],[C_b; C_b],'--',td{1},C_s{1}) title('Block 16-8') legend('cmol-s/m3','cmol-total/m3') xlabel('time in days') ylabel('cmol/m3') subplot(2,1,2) title('Sine 16-8') plot([td{1}(1,1); td{1}(end,1)],[C_b; C_b],'--',td{2},C_s{2}) legend('cmol-s/m3','cmol-total/m3') xlabel('time in days') ylabel('cmol/m3') figure(3) plot(td{1},Pb{1},td{2},Pb{2},td{3},Pb{3},td{4},Pb{4}) ylabel('biofilm productivity in g/m2') xlabel('time in days') legend('block 16-8','sine 16-8','block 12-12','continious')

ODE file

function [ dcdt ] = sugar_ODE_Idn2(t,C)%% Growth model (Marcel Janssen)% Growth rate calculated based on light limiting conditions with a % hyperbolic tangent model. In the hyperbolic tangent model gross % growth rates are calculated by means of oxygen production. Growth related maintenance and cell % maintenance are separated. Specific growth is then calculated by % Pirts law. global m_s Y_xs Y_sph nl E_phar a_x f_dI light_dn t_light C_b t_sunrise

A.16

Appendix

global x_min q_smp q_smc % to be added %% sugar productoin rate function [q_sp]=fq_sp(C_x,t,z_I,dz) % function to predict the sugar production rate based on light % includes spectral light and wavelenght dependend absorption % coefficient % % calculatoin of the Cx used in light calculations t_day=(t/3600/24-floor(t/3600/24))*3600*24; zl=[z_I z_I+dz]; [row,col]=find((light_dn(:,1)-t_day)<=0,1,'last'); %search for the closest lower value t in datafile for time=t(sec) I_i=light_dn(row,col+1); %The I that belongs to that t % calculates the light intensity on the grid points Iznm = (((I_i*E_phar)*ones(1,2)).*exp(-(a_x)*C_b*zl)); % in mol/m2/s Iz=sum(Iznm); % calculates the light absorption rates for evey layer r_ph= (Iz(1)-Iz(2))./((zl(1)-zl(2)).*C_b); %create vector with photon uptake rate values for every layer, by substrating values of every point in z and I % in mol-ph/cmol-x/s % Calculates the sugar production rate q_sp=q_smp*tanh( Y_sph.*-r_ph./q_smp)*f_dI ;% in cmol-s/cmol-x/s %q_sp/q_smp*100 end %% sugar consumptoin rate in cmol-s/cmol-x/s function [q_sc]=fq_sc(C_s,C_x) %sugar consumptoin rate in cmol-s/cmol-x/s t_day=(t/3600/24-floor(t/3600/24))*3600*24; C_smin=x_min.*C_b; if C_s<=C_smin q_sc=0; elseif t_day>=t_light q_sc=(C_s-C_smin)/(t_sunrise-t_day)/C_b; %night else q_sc=q_smc*(1-C_smin/C_s); %day end end %% diffential equation that describes the growth of the biofilm% dcdt is the change in biofilm thickness [m] dcdt=zeros(nl*3,1); % matrix to iniciate biofilm thickness L_f=sum(C(1:nl)); for L=1:nl Cx=C(L+nl);

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Cs=C(L+nl*2); dz=C(L); z_I=sum(C(1:L))-dz; dcdt(L)=Cx/(Cx+Cs)*dz*(Y_xs*(fq_sc(Cs,Cx)-m_s) + fq_sp(Cx,t,z_I,dz) - fq_sc(Cs,Cx));end for x=1+nl:nl*2 Cx=C(x); Cs=C(x+nl); dz=C(x-nl); z_I=sum(C(1:x-nl))-dz; dcdt(x)=Cx*(Y_xs*(fq_sc(Cs,Cx)-m_s) - Cx/(Cx+Cs)*(Y_xs*(fq_sc(Cs,Cx)-m_s)+fq_sp(Cx,t,z_I,dz)-fq_sc(Cs,Cx)));end for s=1+nl*2:nl*3 Cx=C(s-nl); Cs=C(s); dz=C(s-nl*2); z_I=sum(C(1:s-nl*2))-dz; dcdt(s)=Cx*(fq_sp(Cx,t,z_I,dz)-fq_sc(Cs,Cx))-Cs*Cx/(Cx+Cs)*(Y_xs*(fq_sc(Cs,Cx)-m_s)+fq_sp(Cx,t,z_I,dz)-fq_sc(Cs,Cx));end t/(7*3600*24)*100 end

A.7 Absorption spectra over the 400-750 nm spectrum for

biofilm cultures

The wavelength-dependent absorption coefficient ( m2 cmol-1) over the 400-750 nm for the different experimental conditions tested in the biofilm cultures is displayed in Figure A.7.

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Appendix

Figure A.7 Average absorption spectra (m2 cmol-1) over the 400-750 nm spectrum for C.

sorokiniana grown as a biofilm under continuous light at 10% CO2 (dark blue, N=3) and 5% CO2 (light blue, N=3) and under a ‘block’ 16/8 (orange, N=5), a ‘sine’ 16/8 (yellow, N=2) and ‘block’ 12/12 (green, N=2) day/night cycles at 5% CO2. The values resemble the average of the two sides of the disk.

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Guião de Relatório de Projecto / Estágio · Web viewFinally, the period of fed-batch growth...

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