Modeling and Experimental Study of an Open Channel Raceway System to Improve the Performance of Nannochloropsis Salina Cultivati

Modeling and Experimental Study of an Open Channel Raceway System to Improve

the Performance of Nannochloropsis salina Cultivation

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Stephen Yoonbum Park, B.S.

Graduate Program in Food, Agricultural and Biological Engineering

The Ohio State University

2014

Dissertation Committee:

Dr. Yebo Li, Advisor

Dr. Jiyoung Lee

Dr. Jay Martin Copyright by

Stephen Yoonbum Park

2014

Abstract

Lipid-rich microalgae are a potentially favorable alternative source of liquid fuels, and the use of open channel raceways has been proposed as a potential method for their mass cultivation. Studies on energy return on investment (EROI) show that open pond raceways are not feasible enough to be commercialized, but can be considered as a future commodity if energy can be recovered from both lipids and biomass residue. Analyses also show that the energy input in the form of CO2, nutrients, and mixing account for approximately 85% of the total energy consumption for algal biofuel production.

Therefore, the conceptual framework of this study was mainly focused on the improvement of the open pond cultivation of the photosynthetic microalgae,

Nannochloropsis salina, to increase the EROI of the process. The preexisting open pond cultivation process was hypothesized to be procedurally improved through modifications such as the conversion of algae biomass residue (ABR) via anaerobic digestion (AD) and the addition of phase-change material (PCM) to the open pond surface. A numerical approach was also employed by modeling the demonstration-scale cultivation systems using computational fluid dynamics (CFD) integrated with a kinetic model.

The growth kinetics of N. salina was integrated into a 3-dimensional CFD model.

Validation in a 120-m3 open channel raceway showed a good fit for the change in

ii biomass, CO2, and nitrogen concentrations. The model also showed the characteristics of dead zones that tail off the bends and increase in biomass concentration. The light attenuation, which is dependent on pond depth and cell concentration, was also observed to drastically increase in the system as the biomass concentration increased. Sensitivity analysis showed that the model was particularly sensitive to the several species-specific parameters.

In an attempt to improve the low biomass productivity in the open channel raceways, supposedly caused by excessive water evaporation, susceptibility to contamination, and sensitivity to ambient influences, hexadecane was introduced as a phase change material (PCM) to cover the pond surface. The existing model was modified to accommodate an immiscible secondary phase that flowed in conjunction with the pond medium. Simulated results were compared with the 150-d data acquisition of light intensity, temperature, nutrient concentration, and algal biomass acquired from a demonstration scale raceway pond constructed for the growth of N. salina and were observed to be in good agreement with one another.

Additional energy can be generated from ABR by means of anaerobic digestion

(AD), but is inhibited by the byproducts of excessive protein degradation. Fat, oil, and grease waste (FOG) from a local municipal waste receiving facility was co-digested with

ABR to evaluate the effects on methane yield and degradation of carbohydrates, lipids, and proteins. Co-digestion of ABR and FOG allowed for an increased loading rate while increasing methane yield. Lipids were the key contributor to methane yields.

iii

The above results suggest that the production of N. salina in an open environment was substantially improved through the modeling and experimental studies. The knowledge gained from this study may serve as a valuable resource in understanding the issues in the scale up of algal biofuel production and may be applied to other fuel feedstock candidates.

This dissertation is dedicated to Youji and Abigail, to whom I devote the rest of my life.

Acknowledgements

My deepest appreciation goes my academic advisor, Dr. Yebo Li, who put his faith and motivation into me from our very first meeting. He encouraged my strengths, and helped me find my weaknesses. Even in the most difficult and frustrating times of my graduate career, he did not cease to encourage me and tell me that I can change to become a better person. Many thanks go to the members of my dissertation committee:

Dr. Jiyoung Lee and Dr. Jay Martin for their insightful advice and efforts in reviewing this document. I would like to thank the valuable members of the Department of Food,

Agricultural and Biological Engineering, including Mrs. Mary Wicks, for the great amount of time and effort to review my publications, Mrs. Candy McBride and Mrs.

Peggy Christman, for their administrative support, and Mr. Michael Klingman, for his knowledge and effort in modifying and repairing our laboratory’s experimental apparata.

I would also like to acknowledge the numerous colleagues that I had a chance to work with, Dr. Caixia Wan, Dr. Jiying Zhu, Dr. Yuguang Zhou, Dr. Zhifang Cui, Dr. Shengjun

Hu, Dr. Xiaolan Luo, Dr. Xumeng Ge, Mrs. Ting Cai, Ms. Fuqing Xu, Mr. Johnathon

Sheets, Mr. Ratanachat Racharaks Mr. Wee Fong Lee, and many more, for their wisdom, advice and friendship. Special thanks go to my parents and siblings, for their love and support, regardless of how far we are apart. Lastly, and most importantly, my utmost

vi gratitude goes to my wife Youji Kim and daughter Abigail, as they are the ultimate driving force of my life.

vii

Vita

2008...... B.S. Biosystems Engineering, Virginia

Polytechnic Institute and State University

2008 to 2009 ...... Engineer and Lab Technician, Novozymes

Biologicals, Inc.

2009 to present ...... Graduate Research Fellow, Food

Agricultural and Biological Engineering,

The Ohio State University

Publications

Park. S., Li, Y. 2014. Integrated computational fluid dynamics model for open pond cultivation of Nannochloropsis salina using phase change material. Bioresour. Technol. (in preparation)

Park, S., Li, Y. 2014. Integration of biological kinetics and computational fluid dynamics to model the growth of Nannochloropsis salina in an open channel raceway. Biotechnol. Bioeng. (submitted)

Park, S., Li, Y. 2012. Evaluation of methane production and macronutrient degradation in the anaerobic co-digestion of algae biomass residue and lipid waste. Bioresour. Technol. 111: 42-48.

Cai, T.*, Park, S.*, Li, Y. 2013. Nutrient recovery from wastewater streams by microalgae: status and prospects. Renew. & Sustain. Energy Rev.19: 360-369. (* - equal contribution).

viii

Sheets, J. P., Ge, X., Park, S., Li, Y. 2013. Effect of outdoor conditions on Nannochloropsis salina cultivation in artificial seawater using nutrients from anaerobic digestion effluent. Bioresour. Technol. 152: 154-161.

Cai, T., Ge, X.,Park, S., Li, Y. 2013. Comparison of Synechocystis sp. PCC6803 and Nannochloropsis salina for lipid production using artificial seawater and nutrients from anaerobic digestion effluent. Bioresour. Technol. 144: 255-260.

Cai, T. Park, S., Racharaks, R., Li, Y. 2013. Cultivation of Nannochloropsis salina in anaerobic digestion effluent for nutrient removal and lipid production. Appl. Energy. 108: 486-492.

Li, Y., Zhu, J., Wan, C., Park, S. 2011. Solid-state anaerobic digestion of corn stover for biogas production. Trans. ASABE. 54(4): 1415-1421.

Li, Y., Park, S., Zhu, J. 2010. Solid-state anaerobic digestion for methane production from organic waste. Renew. & Sustain. Energy Rev. 15(1): 821-826.

Fields of Study

Major Field: Food, Agricultural, and Biological Engineering

Study in: Biological Engineering

Table of Contents

Abstract ...... ii

Acknowledgements ...... vi

Vita ...... viii

Table of Contents ...... x

List of Tables ...... xv

List of Figures ...... xvi

Chapter 1: Introduction ...... 1

1.1. Background ...... 1

1.2. Research Objectives ...... 5

1.3. Contribution of the Dissertation ...... 6

Chapter 2: Literature Review ...... 7

2.1. Introduction ...... 7

2.2. Microalgae Growth Factors ...... 10

2.2.1. Irradiance ...... 10 2.2.2. Temperature ...... 11 2.2.3. Carbon ...... 12 2.2.4. Dissolved Nutrients ...... 12 2.2.5. Salinity ...... 14 2.2.6. Mixing ...... 15 2.2.7. Contamination ...... 15

2.3. Cultivation Systems ...... 16 x

2.3.1. Open Systems ...... 16 2.3.2. Closed Systems ...... 17

2.4. Utilization of Algae Biomass...... 17

2.4.1. Anaerobic Digestion ...... 17 2.4.2. Alternative Uses ...... 19

2.5. Computational Fluid Dynamics ...... 20

2.5.1. General Concept ...... 20 2.5.2. CFD in Microalgae Cultivation ...... 21

2.6. Energy Return on Investment ...... 22

2.6.1. Overall Concept of EROI ...... 22 2.6.2. Employment of EROI in Energy System Analysis ...... 23 2.6.3. Impact of Current Research on the EROI of Algal Energy Production Systems ...... 25

2.7. Concluding Remarks ...... 27

Chapter 3: Use of Phase-Change Material (PCM) to Improve the Performance of the Demonstration Scale Cultivation of Nannochloropsis salina in an Open Channel Raceway ...... 31

3.1. Introduction ...... 31

3.2. Materials and Methods ...... 33

3.2.1. Culture Preparation ...... 33 3.2.2. Cultivation of Algae in Outdoor Open Channel Raceways ...... 35 3.2.3. Data Acquisition ...... 36 3.2.4. Biomass ...... 37 3.2.5. Nitrates, Ammonium and Phosphates ...... 37 3.2.6. Lipid Content and Composition ...... 38 3.2.7. Statistical Analysis ...... 40

3.3. Results and Discussion ...... 40

3.3.1. Temperature ...... 40 3.3.2. Biomass and CO2 ...... 41 3.3.3. Nitrogen and Phosphorus Removal ...... 42 3.3.4. Lipid Content and Composition ...... 43

3.4. Conclusions...... 44 xi

Chapter 4: Integration of Biological Kinetics and Computational Fluid Dynamics (CFD) to Model the Growth of Nannochloropsis salina in an Open Channel Raceway ...... 51

4.1. Introduction ...... 51

4.2. Materials and Methods ...... 55

4.2.1. Cultivation Environment ...... 55 4.2.2. CFD Methodology ...... 55 4.2.2.1. Geometry ...... 55 4.2.2.2. Mesh ...... 56 4.2.2.3. Solver ...... 56 4.2.2.4. Boundary Conditions ...... 57 4.2.3. Kinetic Equations ...... 57 4.2.3.1. Mass Balance of Biomass (X) ...... 57 4.2.3.2. Mass Balance of Carbon Dioxide (C) ...... 60 4.2.3.3. Mass Balance of Nitrogen (N) ...... 61 4.2.3.4. Mass Balance of Oxygen (O) ...... 61 4.2.3.5. Total Mass Balance ...... 62 4.2.4. Analytical Procedures ...... 62 4.2.5. Statistical Analysis ...... 63

4.3. Results and Discussion ...... 63

4.3.1. Validation of the Integrated Model ...... 63 4.3.2. Observations in the CFD Model ...... 65 4.3.3.1. Changes in X with Horizontal Location ...... 65 4.3.3.2. Impact of Increasing Biomass Concentration on Light Penetration ...... 65 4.3.3. Effects of paddlewheel velocity and CO2 loading ...... 65 4.3.4. Sensitivity Analysis of the CFD Model ...... 67

4.4. Conclusions...... 68

Chapter 5: Integrated Computational Fluid Dynamics (CFD) Model for Open Pond Cultivation of Nannochloropsis salina using Phase Change Material (PCM) ...... 76

5.1. Introduction ...... 76

5.2. Materials and Methods ...... 78

5.2.1. Cultivation Environment ...... 78 5.2.2. CFD Methodology ...... 78 5.2.2.1. Geometry ...... 78 5.2.2.2. Mesh ...... 78 xii

5.2.2.3. Solver ...... 79 5.2.2.4. Boundary Conditions ...... 80 5.2.3. Kinetic Equations ...... 80 5.2.4. Analytical Procedures ...... 81 5.2.4.1. Data Acquisition and Sample Analysis ...... 81 5.2.4.2. Statistical Analysis ...... 82

5.3. Results and Discussion ...... 82

5.3.1. Validation of the Integrated Model ...... 82 5.3.2. Effects of PCM Thickness and CO2 Loading ...... 83 5.3.3. Limitations of the Modified Model ...... 84

5.4. Conclusions...... 85

Chapter 6: Evaluation of Methane Production and Macronutrient Degradation in the Anaerobic Co-digestion of Algae Biomass Residue and Lipid Waste ...... 90

6.1. Introduction ...... 90

6.2. Materials and Methods ...... 94

6.2.1. Substrates and Inoculum ...... 94 6.2.2. Operational Procedures ...... 95 6.2.3. Total Solids, Volatile Solids, pH, Total Carbon and Nitrogen ...... 96 6.2.4. Total Carbohydrates ...... 97 6.2.5. Ammonia-Nitrogen and Total Crude Protein ...... 97 6.2.6. Total Crude Lipids ...... 98 6.2.7. Biogas Volume and Composition ...... 99 6.2.8. Statistical Analysis ...... 99

6.3. Results and Discussion ...... 100

6.3.1. Effects of Co-digestion on Methane Yield and Content ...... 100 6.3.2. Effects of Co-digestion on Nutrient Reduction ...... 102 6.3.3. Assessment of Co-digestion Performance with Theoretical Methane Potential ...... 105

6.4. Conclusions...... 107

Chapter 7: Improvement of the Energy Return on Investment of Microalgal Cultivation in an Open Channel Raceway ...... 115

7.1. Introduction ...... 115

7.2. Methods ...... 117 xiii

7.2.1. Model Description ...... 117 7.2.2. Assumptions ...... 118 7.2.3. Calculation of EROI ...... 120

7.3. Results and Discussion ...... 122

7.3.1. Impact of PCM on the Energy Balance ...... 122 7.3.2. Impact of Algae Cultivation with PCM on EROI ...... 123 7.3.3. Improvement of the EROI in Algal Energy Production Systems ...... 125 7.3.4. Impact of Anaerobic Digestion of ABR ...... 125

7.4. Conclusions...... 126

Chapter 8: Conclusions and Suggestions for Future Research ...... 131

8.1. Conclusions...... 131

8.2. Suggestions for Future Research ...... 134

References ...... 137

Appendix: User-Defined Function, Written In C-Language to Integrate Basic Kinetic Equations to the CFD Model...... 154

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List of Tables

Table 2.1. Growth rates and lipid contents of representative autotrophic microalgae. .... 28

Table 2.2. EROI estimates of various energy production systems...... 29

Table 3.1. Properties of various paraffin waxes as PCMs...... 45

Table 3.2. Effect of PCM on lipid content and profile...... 46

Table 4.1. Parameters used in simulation of algae growth in pond segment...... 69

Table 6.1. Characteristics of substrates and inoculant...... 108

Table 7.1. EROI estimation of N. salina cultivation in an open channel raceway with and without PCM treatment...... 127

List of Figures

Figure 2.1. General schematic of an energy system (Deng and Tynan, 2011)...... 30

Figure 3.1. (a) Entire view of open channel raceway and (b) close-up view of the paddlewheel and CO2-mixing sump...... 47

Figure 3.2. Temporal changes in (a) temperature and (b) daily temperature fluctuation. 48

Figure 3.3. Temporal changes in (a) biomass concentration and (b) CO2 concentration. 49

Figure 3.4. Mean removal rates of nitrate-based nitrogen (NO3-N), ammonium-based nitrogen (NH4-N), and phosphate-based phosphorus (PO4-P)...... 50

Figure 4.1. Geometry and mesh used for the open channel raceway...... 70

Figure 4.2. Changes in concentration of (a) biomass, (b) CO2, and (c) Total N...... 71

Figure 4.3. Contours of microalgal biomass (g m-3) from 35 d to 50 d...... 72

Figure 4.4. Vertical light distribution in mixing sump area of cultivation system from 0 d to 50 d...... 73

Figure 4.5. Simulated biomass productivities under varying paddlewheel velocities and CO2 loading...... 74

Figure 4.6. Sensitivity of model inputs presented in tornado plot format...... 75

Figure 5.1. Close-up view of geometry and mesh used for the two-phase open channel raceway...... 87

Figure 5.2. Changes in concentration of (a) biomass, (b) CO2, and (c) Total N...... 88

(* denotes time periods where data could not be collected) ...... 88

Figure 5.3. Simulated biomass productivities under varying PCM thicknesses (t) and CO2 loading...... 89 xvi

Figure 6.1. 1-L reactor for the semi-continuous digestion of ABR and FOG...... 109

Figure 6.2. Steady state (a) SMY and (b) VRP at varying ABR loading fractions and OLR...... 110

Figure 6.3. Steady state methane content at varying ABR loading fractions and OLR. 111

Figure 6.4. Steady state (a) carbohydrate reduction and (b) lipid reduction profiles at varying ABR loading fractions and OLR...... 112

Figure 6.5. NH3-N in the digester effluent per unit mass of protein fed at varying ABR loading fractions and OLR...... 113

Figure 6.6. Contributions of carbohydrates, protein and lipids on the theoretical methane potentials of various feed formulas...... 114

Figure 7.1. Integrated process schematic of microalgae cultivation, lipid extraction, biodiesel production, and anaerobic digestion...... 128

Figure 7.2. Simulated EROI values for varying paddlewheel rotational velocities (ω), partial pressures of supplied CO2, and PCM layer thicknesses in open channel raceways (a) without PCM and (b) with PCM...... 129

Figure 7.3. Energy balances of the anaerobic digestion of algae biomass residue with varying organic loading rates and loading fractions...... 130

xvii

Chapter 1: Introduction

1.1. Background

The increased consumption of conventional fossil fuels and the continuing decrease of existing fuel reserves has brought forth the approach to peak oil production

(Hall et al., 2008). The growing demand for energy has led to an increased interest in renewable energy sources, particularly in the transportation sector, where liquid fuels account for up to 94% of the total energy consumption (EIA, 2013). Biodiesel derived from various oils, both from oil crops and animal fats, displays fuel properties similar to that of petroleum-derived diesel while being considered to be carbon neutral, and thus is a popular candidate as an alternative transportation fuel. However, the current production scale of biodiesel is not sufficient to meet the demands to displace the use of conventional fuels (Chisti, 2007). In an effort to find an abundant and sustainable source of biofuel, the United States (US) launched the Aquatic Species Program in 1978, in which many species of microalgae were identified to be capable of producing large amounts of oil, which could be further processed into fuel (Sheehan et al., 1998).

The knowledge to cultivate microalgae in controlled environments has been well- accumulated over the years, but investment in microalgae-derived fuels has been dependent on the conventional fuel market (Chisti and Yan, 2011). Funding for the

1 concept has fluctuated similarly to the rising and falling of crude oil prices in the US, and interests in other alternative fuels resulted in the phasing out of the 19 year-long

Department of Energy’s Aquatic Species Program in 1996 (Aitken and Antizar-Ladislao,

2013; Sheehan et al., 1998). Efforts to commercialize microalgae production have once again resurged, as indicated in the Energy Independence and Security Act of 2007, in which the US has set a lofty goal to produce at least 79 million m3 (21 billion gallons) of advanced biofuels (i.e., those that are not derived from corn) by 2022.

The capability to efficiently convert solar energy and carbon dioxide (CO2) into biomass and lipids makes photosynthetic microalgae a strong candidate as a renewable fuel source. With high lipid contents, rapid growth rates, and significantly lower land mass requirement than most terrestrial crops, microalgae carry high potential as an agricultural commodity (Chisti, 2007). Many genera of microalgae are capable of being exceedingly rich in lipids, which can be converted into fatty acid alkyl esters through transesterification processes. Nannochloropsis sp. has a relatively high biomass and lipid productivity (Huerlimann et al., 2010), and hence is good candidate for biodiesel production.

Industrial designs for algae production are generally categorized as either open or closed systems. Open systems, typically in the form of raceway ponds where a constant flow keeps the cells suspended, are less expensive to build and operate than closed photobioreactors (Mata et al., 2010). However, the simplistic design of the open pond raceway system allows little control over culture conditions and with the additional susceptibility to contamination and water evaporation, often results in poor productivity

(Sheehan et al., 1998). Problems including in-pond settling, weather conditions, contamination, evaporation, and light distribution frequently occur (Weissman et al.,

1988), and close monitoring of a biological system with dynamic changes not only dependent on time, but also on location is extremely difficult. Moreover, even with successful commercial production much of the algal biomass still remains after lipid extraction, thus requiring a suitable method to utilize this organic material.

Thus, bringing the yet immature open pond raceway system to reality requires an integrated approach in a multitude of aspects. The problematic areas in the development of open raceway ponds can be categorized into three groups: (1) lack of overall system control, (2) issues occurring during the growth process, e.g. water evaporation and contamination, and (3) issues occurring before or after the growth process, e.g. economic allocation of nutrients and effective treatment of byproducts. Solutions in each of these groups can ultimately improve the viability of the open pond raceway system for commercialization.

The first area for possible system improvement is to fully understand its dynamics and be able to predict its behavior. Many existing growth models of microalgae

(Camacho Rubio et al., 2003) are empirically derived in laboratory environments, and cannot be directly applied to commercial scale environments without an assumption of constant temperature, illumination, and cell and nutrient distribution throughout the system. Without these assumptions, the outputs of commercial growth systems would greatly diverge from those obtained from the bench scale counterparts, and accurate design calculations would be difficult. Computational fluid dynamic (CFD) modeling is a

3 numerical technique that combines physical, hydrological, geometrical, and dynamic variables to predict the behavior of the open pond raceway system. By integrating biological growth models into a known fluid flow, large scale production scenarios can be simulated more accurately.

The second area for improvement involves low-cost, procedural manipulations to make the growth environment more stable. To address the instability of the open pond raceway system caused by water evaporation, contamination, and temperature fluctuation, a clear and immiscible phase can be placed above the growth medium.

The third and final area for the improvement of microalgal energy production involves the treatment of algal biomass, after the growth and lipid extraction process.

Anaerobic digestion (AD) uses a variety of microorganisms that favor oxygen-free conditions and convert microalgal biomass into biogas, comprised of 40–70 % methane and 30–60% CO2, and liquid effluent. Already established as a reliable technology in

Europe, AD is responsible for treating for more than 10% of organic waste in several countries (De Baere, 2000). The effluent that comes out of the digester is rich in nutrients and can be reused as a nutrient source for microalgal growth. The integration of AD and algae cultivation can mediate the disadvantages of each process and create an economically feasible and environmentally sustainable biofuel production system. The

4 joint processing of AD and microalgae holds a great potential to cut costs for biofuel and bioenergy production, while reducing the volume of greenhouse gas emissions.

1.2. Research Objectives

The central hypothesis of the current study is that the production of microalgae N. salina in an open channel raceway can be scaled up to a feasible process. The ultimate goal of this study is to develop a novel, controlled open pond raceway system that can be effectively scaled up for the mass production of microalgal biomass. The specific objectives for obtaining this goal were to:

(1) Investigate the effects of a PCM on an open channel raceway on the algal biomass

productivity, metabolism, and the lipid content and composition of N. salina

(Chapter 3).

(2) Develop an integrated CFD and kinetic model and simulate the growth of N.

salina in a demonstration-scale open channel raceway (Chapter 4).

(3) Develop a modified CFD and kinetic model and simulate the growth of N. salina

in a demonstration-scale open channel raceway with PCM (Chapter 5).

(4) Evaluate the effects of a lipid-rich co-digestate on the anaerobic digestion of N.

salina ABR (Chapter 6)

(5) Evaluate the feasibility of the cultivation of N. salina and the co-digestion of the

post-extraction residuals in terms of energy production efficiency (Chapter 7).

1.3. Contribution of the Dissertation

Currently, one peer-reviewed journal article has been published, which is included as Chapter 6 in the following dissertation. Three additional articles based on Chapters 3,

4, 5, and 7 are expected to be published in peer-reviewed journals.

Park, S., Li, Y. 2012. Evaluation of methane production and macronutrient degradation in the anaerobic co-digestion of algae biomass residue and lipid waste. Bioresour. Technol.

111: 42-48.

Chapter 2: Literature Review

2.1. Introduction

Algae is coarsely defined as “thallophytes (plants that lack roots, stems, and leaves) that have chlorophyll-a as their primary photosynthetic pigment and lack a sterile covering of cells around the reproductive cells (Lee, 2008).” The group of organisms encompasses both multicellular and unicellular forms. An estimated 72.500 species of algae exist globally, the majority being photosynthetic blue-green algae, diatoms, and green freshwater algae (Guiry, 2012). The oldest forms of algae date back to approximately 2700 million years ago, when photosynthesis from cyanobacteria formed the oxygen content (20%) of today’s atmosphere. Photosynthetic algae convert light energy, generally originating from the sun, into chemical energy, which is stored in the form of carbohydrate molecules synthesized from carbon dioxide (CO2) and water. Fatty acids are synthesized within the algal chloroplasts by means of fatty acid synthases, to become the essential building blocks of membrane lipids. The membrane lipids in algae can account for 5–20 % of the algal dry mass during growth under optimal conditions

(Hu et al., 2008). However, when algae are subject to unfavorable environmental or stress conditions, they respond by diverting their lipid synthesis pathways into accumulating neutral lipids, mainly in the form of triacylglycerides, to store carbon and energy

(Guschina and Harwood, 2006). Photo-oxidative stress or nutrient starvation was observed to increase total lipids by an average of 45.7% in oleaginous green microalgae

(Hu et al., 2008). In some cases, the lipid content in microalgae can reach more than 80% of its dry mass (Metting, 1996; Spolaore et al., 2006).

The triacylglycerides found in algae can be extracted and converted into biodiesel, or fatty acid methyl esters (FAMEs) by means of transesterification, in which fatty acids are separated from the glycerol backbone and subsequently methylated in the presence of an acid or base catalyst (Li and Watkins, 2001). The conversion of lipids to FAMEs is a well-established technology and the production of biodiesel from oil-rich plant matter – including soybeans, palm, corn, and rapeseed – and animal fats has been conducted for several decades (Ma and Hanna, 1999). However, the production scale of conventional biodiesel cannot displace the current petroleum demand, as most of the feedstocks for the transesterification process are primarily used in the food industry. Algae, especially unicellular microalgae, have great potential as a biofuel feedstock, considering that there is little demand for the biomass as a food source. In addition, there are several characteristics of microalgae that make it more favorable over other sources of biodiesel.

As microalgae are cultivated in aquatic suspensions, the required land mass to produce microalgal biodiesel is significantly lower compared to the equivalent biodiesel production from terrestrial crops (Chisti, 2008). Many species of microalgae display rapid growth rates and high lipid contents as well, implying high lipid productivities

(Table 2.1). Microalgae have high efficiencies in converting solar energy to biochemical

8 energy, with possible maximum efficiencies of 8.3%, compared to the estimated maximum efficiency of 4.6% in terrestrial plants (Chisti, 2013; Zhu et al., 2008).

The eustigmatophyte Nannochloropsis sp. is a marine microalga of particular interest for mass production. The genus’ characteristically high content of polyunsaturated fatty acids – mainly eicosapentaenoic acid (EPA) – and small cell size make it an ideal nutritional feed for marine fish hatcheries (Sukenik et al., 1993) and a potential source for value-added nutraceuticals. Nannochloropsis sp. cell are capable of structural modifications to quickly acclimate to changes in irradiance (Fisher et al.,

1998), and have tolerances to wide ranges of salinity (Renaud and Parry, 1994). The lipid content of the species can exceed 60% of its dry biomass when the flow of fixed carbon is diverted from protein to either lipid or carbohydrate synthesis under nitrogen-deficient conditions, hence making it a good candidate for the production of biodiesel (Boussiba et al., 1987; Rodolfi et al., 2009). However, large scale production of the species is not yet established, due to the issues with contamination and difficulties in controlling variable environmental conditions (e.g., temperature) for optimal growth (Hoffmann et al., 2010).

Thusly research in the growth behavior of Nannochloropsis is still ongoing.

Efforts towards the commercialization of microalgae-based fuels have been made since the 1950s (Meier, 1955), with the 1973 oil crisis fueling an extensive investigation known as the Aquatic Species Program. The 18-year program came to a halt with the conclusion that the current production technology for microalgal fuels was not competitive with the petroleum-based equivalents (Sheehan et al., 1998). Research in the sector has been recently funded through the Bioenergy Program for Advanced Biofuels in

2008, and the Biomass Research and Development Initiative in 2012 (Ziolkowska and

Simon, 2014). Current research is mainly focused on the increase of biomass productivity through strategies including the identification of species with high lipid content, cost minimization in the microalgae harvesting and lipid extraction processes, and development of co-products such as biomass-based polymers (Brennan and Owende,

2013). The following sections focus on the various aspects of the commercialization of microalgal production, namely, growth factors, system design, utilization of biomass, modeling techniques, and economics. The review serves as a basis for the research of the demonstration scale cultivation of N. salina.

2.2. Microalgae Growth Factors

2.2.1. Irradiance

Light is the essential source of energy that photosynthetic microalgae convert into the form of chemical energy, and is considered the most important factor affecting cell growth (Acien Fernández et al., 1997; Mata et al., 2010). Microalgal growth rates can be negatively affected not only in low exposure to light, but also excessive irradiation, where certain proteins can be degraded (Han et al., 2000). The effects of irradiance are typically modeled using a non-linear Monod relationship (Klasson et al., 1993):

where µ ≡ specific growth rate of algae (d-1),

-1 µmax ≡ maximum specific growth rate (d ),

I ≡ irradiance (µmol m2 s-1), and 10

2 - KI ≡ half-saturation constant for cell growth dependent on irradiance (µmol m s

1).

The fit of the model is dependent on external factors including species, temperature, and nutrient concentrations. For better model fits, other models have been proposed, such as a modified Monod relationship with irradiance factored by an exponent

(In) (Lee et al., 1987; Molina Grima et al., 1994), or a Poisson function (Jassby and Platt,

1976). Experimental studies show that the growth rate of N. salina increases with light up to a threshold value of 55 µmol m-2 s-1, and decreases thereon (Van Wagenen et al.,

2012). It should be also noted that the absorption of light in the microalgal chloroplasts tend to cause attenuation as light waves travel within cultivation systems, and thus the

Beer-Lambert law of absorption applies to determine the incident photons that reach a particular position (Garcia-Pichel, 1994).

2.2.2. Temperature

The temperature dependence of microalgae and most biological phenomena can be explained by the empirical Arrhenius equation. The optimal temperature for phytoplankton is generally between 20 and 30°C (Thornton et al., 2010). A variety of microalgae are known to tolerate temperatures up to 15°C lower than their optimal, while overheating by 2–4°C may cause system failures (Mata et al., 2010). The effect of temperature is reported to be influenced by light exposure; in the cultivation

Nannochloropsis oceanica the effect of temperature fluctuations from 14.5 to 35.7°C was observed to be attenuated when exposed to low light intensities (Sandnes et al., 2005).

Changes in temperature also affect the lipid metabolism of microalgae. A two-fold increase in lipid content of Nannochloropsis oculata was observed when the cultivation temperature was increased from 20 to 25°C, while overheating at 30°C caused a significant drop in the lipid content of Chlorella vulgaris (Converti et al., 2009).

2.2.3. Carbon

Carbon is the major building block of algae biomass and is generally introduced

2- to the organisms in the form of atmospheric CO2, dissolved carbonate (CO3 ) and

- bicarbonate (HCO3 ) by means of photosynthesis. The majority of photosynthetic

- microalgae are capable of utilizing CO2 and HCO3 substrates (Giordano et al., 2005;

2- Maberly et al., 2009), while CO3 is considered toxic for some species (Moss, 1973).

Algal biomass consists of approximately 50% carbon, and photosynthesis is limited when the local CO2 concentration falls below a critical value (Rubio et al., 1999). For example,

Chlorella sp. was found to require a CO2 concentration of at least 65 µM and a pH of 8.5.

In turn, excessive concentrations of CO2 was found to suppress photosynthesis and decrease pH (Kurano et al., 1995).

2.2.4. Dissolved Nutrients

Aside from carbon compounds, organic forms of nitrogen and phosphorus are the major components of microalgal biomass. The productivity of microalgae is regulated or limited when the availability of nitrogen or phosphorus is less than its biological demand

(Wetzel and Likens, 2000). Organic nitrogen is derived from inorganic sources including

- - + nitrate (NO3 ), nitrite (NO2 ), and ammonium (NH4 ) via assimilation (Cai et al., 2013a).

+ NH4 is thought to be the preferred form of nitrogen because a redox reaction is not involved in its assimilation and thus requires less energy. Studies have shown that, in

+ - - general, microalgae tend to prefer NH4 over NO3 , and NO3 consumption does not occur

+ until NH4 is almost completely consumed (Maestrini et al., 1986). Therefore,

+ wastewaters with high NH4 concentrations can be effectively used to rapidly grow

+ - microalgae. Although NH4 is preferred by algae, NO3 is the more highly oxidized form and the most thermodynamically stable in oxidized aquatic environments, and thus is

- predominant (Barsanti and Gualtieri, 2006). However, NO3 can also be an essential nitrogen source for microalgae as the presence of nitrate induces the activity of nitrate

+ reductase. In contrast, excess NH4 can have a repressive effect on the metabolism of

+ microalgae (Morris and Syrett, 1963). The NH4 tolerance of different algae species

+ -1 + -1 varies from 25 µmol NH4 –N L to 1000 µmol NH4 –N L (Collos and Berges, 2004).

+ In addition to the cell metabolism of NH4 , ammonia stripping in open systems causes the volatilization of the compound at increased pH and temperatures. Garcia et al. (2000)

+ observed the stripping of 32–47% of NH4 –N from the cultivation of a microalgal consortium in a high rate algal pond, mainly due to elevated pH.

2- Phosphorus, generally in the forms of hydrogen phosphates (HPO4 ) and

- dihydrogen phosphates (H2PO4 ), is incorporate into organic compounds through phosphorylation, much of which involves the generation of adenosine triphosphate (ATP) from adenosine diphosphate (ADP), accompanied by a form energy input (Martinez et al., 1999). Energy input can come from the oxidation of respiratory substrates, the

13 electron transport system of the mitochondria, or in the case of photosynthesis, from light. Phosphates are transferred by energized transport across the plasma membrane of the algal cell. Not only are inorganic forms of phosphorus utilized by microalgae, but some varieties of algae are able to use the phosphorus found in organic esters for growth

(Kuenzler, 1965).

2.2.5. Salinity

Changes in salinity normally affect marine phytoplankton through osmotic stress, ionic stress, and changes in cellular ionic ratios due to the selective ion permeability of the membrane (Kirst, 1989) The salinity of an outdoor cultivation can easily change due to the evaporation of water, particularly in arid regions. Although optimum salinities for microalgal growth vary among species, most microalgae are capable of surviving in a wide range of salt concentrations. Porphyra umbilicalis grew optimally in a range of 7–

52 g kg-1 and was even able to survive without cell division in a salinity 6 times that of seawater (Wiencke, 1984). The optimum salinity range for N. salina was reported to be

22–34 g kg-1 – slightly lower than the average salinity of seawater (35 g kg-1) – while most competing organisms including diatoms, cyanobacteria, ciliates, and rotifers could not survive in high salt concentrations (Bartley et al., 2013). In addition, the lipid content of N. salina was found to increase when the salinity was increased from 22 to 34 g kg-1.

2.2.6. Mixing

Mixing in microalgae cultivation environments can facilitate the transfer of gases and nutrients while preventing the microalgal cells from setting or floating. Particularly in larger scales of production, gentle turbulence is desired to promote homogeneity within the system and mitigate the self-shading effects that may occur in regions far away from the surface. However, high degrees or turbulence, facilitated by mechanical mixing or aeration, can inflict shear-induced damage to susceptible microalgal cells (Eriksen,

2008). The threshold value of shear stress at which the viability of Chaetoceros muelleri was compensated was 1.0–1.3 Pa (Michels et al., 2010).

2.2.7. Contamination

Contaminants are difficult to avoid in all unsterilized environments, and in open, outdoor cultivation systems exists a variety of foreign organisms including unwanted algae, mold, yeast, fungi, and bacteria. One strategy to mitigate the effects of contamination is to have an extreme change in an environmental factor (e.g., temperature, pH, light) which the intended culture organism is resistant to. Selective herbicides such as

3-(3:4-dichlorophenyl)-1:1-dimethyl urea was shown to be effective in suppressing the growth of a competing microalgae, Chaetoceros sp., while the growth of N. salina was unaffected (Gonen-Zurgil et al., 1996).

2.3. Cultivation Systems

2.3.1. Open Systems

Open systems are characterized by the exposure of the fluid surface to the atmosphere. Environmental constraints such as temperature and irradiance control are generally minimized in order to make full use to the surrounding environment, i.e., solar energy. Open systems are generally categorized into natural waters, e.g., lakes, lagoons, and ponds, and artificial ponds, e.g., tanks and raceways. The raceway design, which is the most commonly used artificial system (Jiménez et al., 2003), is a closed loop, generally limited to shallow depths of 0.2–0.5 m to maximize the penetration of solar irradiance. Sedimentation of the microalgal biomass is prevented by creating a flow along the raceway via paddlewheel or pump. The CO2 requirement is usually satisfied by the mass transfer from the atmosphere through the raceway surface, but submerged aerators are sometimes added to increase the concentration of dissolved CO2 (Terry and

Raymond, 1985). Open systems generally require less cost and energy to build and operate, compared to their closed counterparts, and thus currently are considered more favorable for scaling up (Rodolfi et al., 2009). However, the overall productivities of open systems are reported to be lower than those of closed systems, due to their susceptibility to contamination, evaporative water losses, and difficulties in maintaining optimal temperatures and light intensities during diurnal and seasonal fluctuations. It is estimated that the typical open system has an area productivity of 10–25 g m-2 d-1

(Brennan and Owende, 2010).

2.3.2. Closed Systems

Closed systems, or photobioreactors, are characterized by the isolation from their surrounding environment. Mechanisms for mixing, light distribution, nutrient loading and

CO2 aeration must be available to maximize the biomass productivity. Common geometries of photobioreactors are tubular, flat plate, and annular, and also in the form of polymer bags (Amaro et al., 2011; Borowitzka, 1999; Brennan and Owende, 2013).

Designs are optimized to minimize the light path length and subsequently increase the available light to each microalgal cell. Being isolated from daily and seasonal fluctuations in environmental factors such as irradiance, temperature, and CO2 input, photobioreactors are easier to control than open systems. In addition, high productivities of 25–50 g m-2 d-1 have been reported for the cultivation of microalgae in closed environments (Carlozzi, 2003, 2000). However, high capital costs and operational energy costs currently hinder the commercialization of photobioreactors, especially for low-cost products such as fuels. The application would be generally used for the culturing of microalgae for high value products such as pharmaceuticals.

2.4. Utilization of Algae Biomass

2.4.1. Anaerobic Digestion

Projections of the commercial production of microalgae suggested annual productivities of up to 263 tons ha-1 year-1 (Chisti, 2007), which led to estimates of the disposed, post-extraction algae biomass residue (ABR) of approximately 184 tons ha-1 year-1, which is equivalent to a nitrogen amendment of 8–16 tons ha-1 year-1 (Sialve et al.,

2009). By anaerobically digesting the ABR, the residual carbon can be converted into a biogas, while the mineralized nutrients can be used as fertilizer, or even returned to the microalgae cultivation system for reuse (Cai et al., 2013b; Sheets et al., 2014). The anaerobic digestion of algae was first suggested in the 1950s, upon the observation of methane produced in lagoons with large amounts of sedimentation from planktonic phagotrophs (Golueke et al., 1957). Attempts in the anaerobic digestion of microalgae

-1 resulted in biogas yields of 0.24–0.30 L CH4 g VS, less than that obtained from

-1 wastewater sludge (0.40 L CH4 g VS) (Ras et al., 2011; Salerno et al., 2009; Zamalloa et al., 2011). Several species of microalgae have high proportions of proteins (6–52%), and the carbon-to-nitrogen ratio (C/N) of an average of 10.2 is significantly lower than that of terrestrial plants, which is an average of 36 (Elser et al., 2000). The digestion of high amounts of protein result in the accumulation of ammonia which can be inhibitory to the anaerobic digestion process (Samson and LeDuy, 1986). In addition, the recalcitrant microalgal cell walls make it difficult for various microbes to access the substrate

(Golueke and Oswald, 1959). Marine microalgae cultivated in media with high sodium chloride contents (0.5–1.0 M) were reported to be toxic to anaerobic microflora, which were affected by sodium toxicity at concentrations greater than 0.4 M (Chen et al., 2008).

In order to increase the C/N of the anaerobic digestion substrate, the co-digestion of microalgal biomass with carbon-rich materials, including waste paper (Yen and Brune,

2007), peat hydrolyzate, and sewage sludge (Samson and LeDuy, 1983a), resulting in up to two-folds of productivity increase. The negative impact of low C/N on the anaerobic digestion process was confirmed when microalgal biomass was co-digested with a

18 protein-rich swine manure, resulting in decreased biogas yield with increasing microalgae additions (González-Fernández et al., 2011). The concept of coupling anaerobic digestion with microalgae cultivation showed that the selection of species for cultivation was important because the biogas yields from microalgae were species-dependent. Dunaliella

-1 salina and Chlamydomonas reinhardtii had biogas yields greater than 0.5 L CH4 g VS,

-1 while Scenedesmus obliquus and Chlorella vulgaris had low yields of 0.29 L CH4 g VS

(Mussgnug et al., 2010; Ras et al., 2011).

2.4.2. Alternative Uses

As of current, the main focus on the cultivation of microalgae is in the lipid- derived biodiesel production to displace liquid petroleum fuels. However, several concepts to use microalgae as a source of in alternative fuels or nutritional supplements have been explored. Novel strains of Chlorella vulgaris were identified to photoautotrophically produce hydrogen as a byproduct of their metabolisms (Hwang et al., 2014). Hydrothermal liquefaction of microalgal biomass has been proposed as an alternative route to produce bio-oils, the advantage coming from the process not requiring high oil yields in the biomass (López Barreiro et al., 2013). Botryococcus braunii has been identified to produce extracellular triterpene hydrocarbons, with yields up to 22.5% of its dry mass (Khatri et al., 2014). EPA, which is considered an essential element in human nutrition, has also been studied for increased production through microalgae

(Boelen et al., 2013). Although the aforementioned technologies are still in their infancy,

19 the potential use of microalgae is currently well demonstrated, thus urging the research in commercial biomass production to progress.

2.5. Computational Fluid Dynamics

2.5.1. General Concept

Fluid flows are governed by partial differential equations (PDEs) that represent conservation laws for mass, momentum, and energy. Computational fluid dynamics

(CFD) is a method that replaces these PDEs with algebraic equations that can be numerically solved with computers. By doing so various types of fluid flow phenomena can be modeled and simulated, and thus the costs in experimentation for large scale systems can be significantly reduced. CFD has been traditionally used in the fields of aerospace and mechanical engineering for the simulation of liquid and gas flows.

The fluid dynamics model is based upon the full three-dimensional form of the

Navier-Stokes equations. For each defined cell in the predefined mesh, the conservation of mass and momentum are considered (Chung, 2002). The conservation of mass is expressed as

⃗

where ρ ≡ amount of quantity q per unit time,

t ≡ time,

≡ divergence, and

⃗ ≡ velocity vector.

The conservation of momentum is expressed as 20

⃗ ⃗ ⃗ ⃗ ⃗

where p ≡ static pressure,

⃗ ≡ viscous stress tensor, and

⃗ ≡ gravitational body force.

A velocity field can be derived as a solution of the Navier-Stokes equations. For a reactive flow, e.g., biomass that accumulates by consuming CO2, the scalar transport equation is used:

( )

where ≡ arbitrary scalar, and

≡ diffusion coefficient.

2.5.2. CFD in Microalgae Cultivation

Research employing CFD in microalgae cultivation environments have focused on the physical behavior of mixing and aeration in photobioreactors. Turbulence modeling has been extensively performed in photobioreactors to simulate the effects of bubble size as well as various mixing patterns (Pruvost et al., 2006). A CFD code was used to model different raceway bend configurations and demonstrated that novel bend designs could cut energy losses by 87% (Liffman et al., 2013). To the author’s knowledge, there has not been an attempt to integrate the biological kinetics of microalgae with a physical CFD model. However, Wu (2012) simulated the anaerobic

21 digestion in a complete-mix digester using a series of kinetics equations and a CFD solver.

2.6. Energy Return on Investment

2.6.1. Overall Concept of EROI

Energy return on investment (EROI), also termed energy return on energy investment (EROEI), is a dimensionless value expressing how much of the useful energy delivered by an energy-gathering system must be diverted or otherwise used for operation

(Deng and Tynan, 2011). The ratio can be explained with a generic energy system schematic, shown below in Figure 2.1. When introduced to a primary energy input source

(Ein), an energy-gathering system will output energy in a useful form (E0), while some of the input energy is dissipated back to the environment (Ewaste), usually in the form of heat.

A portion of E0 needs to be diverted (Ediv) back to the system to relocate, extract, refine, or enrich the initial form of the energy. Thus, the net amount of useful energy output, less the energy used in the gathering process, is expressed as Enet.

The EROI of the energy system shown in Figure 1 can be calculated as the ratio of E0 and Ediv. Complications arise when different boundaries are defined for the numerator and denominator. Mulder and Hagens (2008) proposed a theoretical framework for EROI analysis that took various methodologies in literature under consideration.

EROI is beneficial in evaluating energy production systems because it serves as a combined, numerical representation of resource quality, availability, and acquisition efficiency. When used in conjunction with standard measures of the magnitude of energy,

EROI can provide additional insight about the net energy gain from an energy resource

(Murphy et al., 2010). For a particular energy source, if the EROI is high, only a small fraction of the energy produced is required to maintain production, and most of the energy can be used to run the general economy. On the other hand, if the EROI is low, very little net energy is available for useful, economic work (Gagnon et al., 2009). For an economic system to properly function, the aggregate EROI must be greater than 1, and to grow the EROI should be much greater (Cleveland et al., 1984; Hammerschlag, 2006).

Hall and Klitgaard (Hall and Klitgaard, 2012) suggested that the minimum EROImm for a fuel to deliver a given service to a consumer is approximately 3.3.

2.6.2. Employment of EROI in Energy System Analysis

EROI was initially derived from the concept of net energy, which is the difference between the amount of energy produced and the energy spent to obtain and concentrate the produced energy (Odum, 1973). One of the earliest publications using EROI as a formal term was by Cleveland et al. (1984). Initial research on the EROI was scarce, due to the relatively low fuel costs during 1984-2005, but the number of publications recently increased with the increasing concern in fuel economy. Analyses from past and recent literature employ EROI by (1) correlating it with various energy input parameters, (2)

23 comparing EROIs among various systems, and also (3) comparing EROIs among different time periods.

First, the effect of various energy input parameters on EROI can be evaluated by through correlation. Guilford et al. (2011) reported an inverse relation between drilling intensity and the EROI of oil and gas production in the United States (US). It would appear that securing a larger resource supply through more intense drilling may increase the energy output and in turn increase the EROI, but the study found otherwise.

Moerschbaecher and Day (2011) assessed the EROI of ultra-deepwater oil and gas production in the Gulf of Mexico and found that the EROI had an inverse correlation with the number of rigs, showing that technological advancement without the consideration of net energy use can have a detrimental effect in the long run.

Second, the relative quality of different energy production systems can be compared and evaluated with the EROI. Table 7.1 includes the various EROI estimates found in literature. For example, the EROI of petroleum is currently between 10 and 20, while that for corn-based ethanol is less than 2. It should be noted that most EROI of renewable energy, excluding wind and hydropower, are significantly less than those of fossil fuels and thus are not as competitive.

Third, EROIs can be compared at different points of time in history, so that socioeconomic effects or impacts from depletion can also be observed. As shown in

Table 1, significant drops in the average EROI for the discovery and production of oil and gas in the US are a consequence of the decreasing energy returns from increasingly

24 depleting oil reservoirs and shifts to deeper and further drilling (Cleveland, 2005;

Cleveland et al., 1984).

2.6.3. Impact of Current Research on the EROI of Algal Energy Production Systems

The proposed research addresses three problematic areas in the development of open raceway ponds: (1) lack of overall system control, (2) issues occurring during the growth process, e.g. water evaporation and contamination, and (3) issues occurring before or after the growth process, e.g. economic allocation of nutrients and effective treatment of byproducts. Solutions in each of these groups can ultimately improve the viability and the EROI of the open pond raceway system.

The first area for possible system improvement is to fully understand its dynamics and be able to predict it behavior. Many existing growth models of photosynthetic microalgae (Camacho Rubio et al., 2003) are empirically derived in laboratory environments, and cannot be directly applied to commercial scale environments without an assumption of constant temperature, illumination, and cell/nutrient distribution throughout the system. Mixing is a critical component that determines the EROI of the algal growth system. Computational fluid dynamics can be used to study factors in open pond raceways such as liquid currents as well as pond and paddle wheel geometry in order to find optimal mixing with minimal power input.

25 fluctuation, a clear, immiscible phase can be placed above the growth medium.

Hexadecane, which not only is capable of transmitting light in its liquid state, can also liquefy and solidify in ambient temperature ranges, thus providing latent heat at low temperatures and removing excess heat at high temperatures. By employing this technique any potential heat input can be minimized, and also the overall system productivity is projected to increase as well.

The third and final area for improvement involves the treatment of algal biomass, after the growth and lipid extraction process. Anaerobic digestion (AD) uses a variety of microorganisms that favor oxygen-free conditions and convert microalgal biomass into biogas, comprised of 40–70% methane and 30–60% CO2, and liquid effluent. The effluent that comes out of the digester is rich in nutrients and can be reused as a nutrient source for microalgal growth. The integration of AD and algae cultivation can mediate the disadvantages of each process and create an economically feasible and environmentally sustainable biofuel production system. The effluent and CO2 from AD can be effectively used to facilitate algal growth, while in turn the residual algae biomass remaining after lipid extraction can be used as a substrate in AD. The joint processing of

AD and microalgae holds a great potential to cut costs for biofuel and bioenergy production, while reducing the volume of greenhouse gas emissions.

Successful integration of anaerobic digestion with algae cultivation will increase the energy output by converting the biomass residue into a useful energy form, thus increasing the EROI of the system. In addition, the reuse of digester effluent as a

26 potential nutrient source may be beneficial to the EROI by decreasing the nutrient energy input.

2.7. Concluding Remarks

Dwindling fuel reserves and the continuously increasing global demand for energy sources have stimulated the research in microalgae as a potential feedstock for biofuel production. Several species of microalgae have been characterized with high biomass and lipid productivities, and various aspects of photosynthetic microalgae cultivation have been explored in an effort to understand the metabolic behavior of the organisms. The knowledge base for the commercialization of microalgae cultivation is continuously growing; yet a feasible solution is still in the works. Growth systems can be strategically engineered to generate higher productivities than current claimed, while the co-products of the biodiesel process can be effectively used to generate more energy and value. Although a relatively new topic in the biological sector, CFD can be applied to depict the hydrodynamic behavior commercial scale production. The effective use of modeling and simulation may provide a new perspective in improving the overall feasibility of the microalgal production process.

Table 2.1. Growth rates and lipid contents of representative autotrophic microalgae.

Species Growth rate Lipid content Reference(s)

(d-1) (% dry wt)

Botryococcus braunii 0.24 20–55 (Banerjee et al., 2002; Metzger and Largeau, 2005; Ruangsomboon, 2012)

Chlorella sp. 0.58–0.87 18–66 (Chiu et al., 2008; Hsieh and Wu, 2009; Rodolfi et al., 2009)

Dunaliella viridis. 0.22–0.80 17–32 (Gordillo et al., 1998)

Isochrysis sp. 0.10–0.60 6–50 (Rodolfi et al., 2009; Roopnarain et al., 2014; Yoshioka et al., 2012)

Nannochloropsis sp. 0.04–0.72 15–70 (Boussiba et al., 1987; Chiu et al., 2009; Rodolfi et al., 2009; Sheets et al., 2014)

Neochloris oleoabundans 1.06–1.36 35–54 (Sousa et al., 2012; Tornabene, 1983)

Scenedemus 0.54–0.82 30–53 (Xin et al., 2010a, 2010b)

Table 2.2. EROI estimates of various energy production systems.

Category Resource Area Year EROI Reference(s) Fossil fuels Oil and gas US 1930 250 (Cleveland, 2005; (discovery) Guilford et al., 2011) Oil and gas US 1970 8 (Grandell et al., 2011) (discovery) Oil and gas US 2000 5 (Guilford et al., 2011) (discovery) Oil and gas US 1970 30 (Cleveland et al., 1984) Oil and gas US 2005 8–11 (Cleveland, 2005) Oil and gas World 1999 35 (Gagnon et al., 2009) Oil and gas World 2005 19 (Hall et al., 2008) Natural gas US 2005 10–80 (Hall and Klitgaard, 2012) Coal (mine mouth) US 1930 80 (Cleveland et al., 1984) Coal (mine mouth) US 1970 30 (Cleveland et al., 1984) Bitumen from tar 2–4 (Hall and Klitgaard, sands 2012) Shale oil 5 (Hall and Klitgaard, 2012) Nuclear Nuclear 2–50 (Hall and Klitgaard, 2012) Renewables Hydropower >100 (Hall and Klitgaard, 2012) Wind 18 (Hall and Klitgaard, 2012) Solar 2–8 (Hall and Klitgaard, 2012) Ethanol (sugarcane) 0.8–1.7 (Hall and Klitgaard, 2012) Ethanol (corn) 0.8–1.7 (Hall and Klitgaard, 2012; Hammerschlag, 2006) Ethanol (cellulose) 0.7–6.6 (Hammerschlag, 2006) Biodiesel 1.3 (Hall and Klitgaard, 2012)

Ediv

Ein Energy-gathering system E0 Enet

E waste

Figure 2.1. General schematic of an energy system (Deng and Tynan, 2011).

Chapter 3: Use of Phase-Change Material (PCM) to Improve the Performance of the Demonstration Scale Cultivation of Nannochloropsis salina in an Open Channel

Raceway

Summary

Cultivation of N. salina in an open channel raceway with a layer of hexadecane resulted in increased microalgae biomass concentrations up to 1.14 g L-1. The elevated carbon dioxide (CO2) concentration in the culture medium was a major factor that contributed to the increased biomass concentrations. Subsequently, the phase change material (PCM)-covered raceway system displayed significantly increased removal rates of nitrate, while the reduction rates of ammonium and phosphate were not impacted.

Microalgal biomass from the PCM-covered raceway system had a 40.8% increase in lipid content, and decreased concentrations of eicosapentaenoic acid were observed while those for its precursors increased.

3.1. Introduction

Open pond systems for phototrophic algae cultivation have been tested since the

1950s (Borowitzka, 1999). The most commonly used artificial form of open ponds is the open channel raceway (Jiménez et al., 2003), in which the algae biomass is continuously

31 circulated with a paddlewheel to minimize sedimentation and adhesion. Any added nutrients are also mixed using the paddlewheel. Although the carbon dioxide (CO2) requirement may be satisfied through the surface contact with air, submerged aerators can also be installed to supplement CO2 (Terry and Raymond, 1985). Compared to closed photobioreactors, open-channel raceways are considered to be the more feasible design, due to lower capital costs, lower energy input requirements (Rodolfi et al., 2009), and easier maintenance (Ugwu et al., 2008).

However, several obstacles hinder the commercialization of open-channel raceways. For monocultures, the inherent threat of contamination from other algae species and microbes limit the system from achieving maximum productivity

(Borowitzka, 1999). Evaporation can disrupt the ionic composition of the medium and negatively impact microalgal growth (Rawat et al., 2013). Temperature fluctuation, particularly in regions with large variations in climate, can be difficult to control and can also impact CO2 solubility. Attempts to cover the pond surface with plastic covers or greenhouses showed improvements in biomass productivities, but problems including capital costs, maintenance and overheating made it difficult to scale up such systems

(Chaumont, 1993).

In an effort to find a more suitable barrier for the interphase between the outer environment and cultivation system, the use of phase change material (PCM) was considered in the present study. A PCM is a substance that has a high latent heat of fusion, and is able to store and release substantial amounts of energy when melting and solidifying at its point of fusion (Raoux, 2009). A PCM with a fusion point at a suitable

32 temperature would be able to absorb latent heat during the day and release it at night, thus mitigating the temperature fluctuation due to diurnal cycles. In addition, a PCM that is transparent in its liquid phase and has a smaller density than water may serve as a protective surface that keeps out invasive species while allowing light penetration and gas transfer. Paraffin waxes are non-toxic, non-corrosive, and chemically inert material that have been used for various heat storage applications (Bo et al., 1999). Hexadecane (CH3-

(CH2)14-CH3), was identified as a suitable candidate to cover an microalgae cultivation system (Table 3.1), mainly due to its point of fusion being slightly lower than the ideal growth temperature of microalgae (Van Wagenen et al., 2012).

In the current study, the commercial-scale cultivation of the marine microalgae

Nannochloropsis salina was performed in open channel raceway ponds with and without the presence of a hexadecane layer covering the surface. The proposed cultivation system is the first of its kind to utilize the properties of PCM in the cultivation of microalgae.

The effects of the hexadecane treatment on microalgal growth were studied during a seasonal transition. In addition, the impact of PCM on lipid content and profile was also reported.

3.2. Materials and Methods

3.2.1. Culture Preparation

Seed cultures of the marine microalgae N. salina (Culture Collection of Algae and

Protozoa, Oban, Scotland) were grown in a simulated marine environment using f/2 medium (Guillard and Ryther, 1962) and sea salt. The f/2 medium was formulated using

33 the commercially available Proline f/2 Algae Feed (Pentair Aquatic Eco-Systems,

-1 Sanford, NC, USA) which contained the following ingredients: 0.075 g L NaNO3,

-1 -1 -1 0.00565 g L NaH2PO4·H2O, 0.001 g L trace element stock solution, and 0.001 g L vitamin mix stock solution. The minor ingredients in the trace element stock solution included Na2EDTA, FeCl3·6H2O, CuSO4·5H2O, ZnSO4·7H2O, CoCl2·6H2O,

MnCl2·4H2O, NaMoO4·2H2O, and biotin. The vitamin stock solution contained cyanocobalamin (vitamin B12) and thiamine HCl (vitamin B1). The local groundwater

(Wooster, OH, USA) used for medium preparation contained moderate levels of Mg2+

+ + (31.21 ppm), (317.3 ppm), and low levels of (77.9 ppm), K (2.39 ppm), Na

(6.78 ppm), NH4–N (0.06 ppm), NO3–N (0.02 ppm), (4.81 ppm), and (6.42 ppm). Instant Ocean® (Spectrum Brands, Madison, WI, USA) sea salt was added to each reactor to maintain a salinity of 25 g kg-1.

Seed cultures were prepared in 1-L flasks and cultivated at 20°C under constant illumination using 32-W F32T8-SPX50 fluorescent lamps (GE Lighting, Ravenna, OH,

USA) until the optical densities of the cultures reached 5.0 at 440 nm, which was equivalent to an algal biomass concentration of 2.0 g L-1 based on ash-free dry weight

(AFDW). The cultures were subsequently transferred and diluted into 20-L indoor photobioreactors, where the cultivation process was continued in the simulated marine environment until the biomass concentrations reached 2.0 g L-1. The cultivation and dilution process was performed again in eight 760-L outdoor photobioreactors, each containing local groundwater with sea salt added to obtain a salinity of 25 g kg-1. The cultures were acclimated to the diurnal cycle of sunlight while the biomass concentrations

34 were continuously increased. In the outdoor reactors, NaNO3 was intermittently added to

-3 the system to bring nitrate-based nitrogen (NO3–N) 35 g m when concentrations were

-3 below 10 g m . Similarly, NaHPO4 was intermittently added to the system to bring and

-3 phosphate-based phosphorus (PO4–P) concentrations up to10 g m when concentrations were below 2 g m-3. Once the algal biomass concentrations reached 2.0 g L-1, the total volume of 6,080 L of seed culture was diluted into an outdoor raceway with an operating volume of 120 m3. The initial algal biomass concentration of the outdoor raceway was approximately 0.2 g L-1. Half of the raceway volume was transferred to a second 120-m3 raceway once the concentration reached 0.6 g L-1, and henceforth the two raceways were monitored while intermittently supplementing NaNO3 and NaHPO4.

3.2.2. Cultivation of Algae in Outdoor Open Channel Raceways

Each of the two open channel raceways used for the continuous cultivation was

57 m in length, 0.25 m deep, with 4.1 m-wide channels (Figure 3.1). The surface area and volume of the raceway was 460 m2 and 120 m3, respectively. A single, 1.2 m-diameter paddlewheel rotating at 5 rad/s created a surface flow of approximately 0.2 m/s along the raceway. An array of six diffusers was installed at the floor of a mixing sump in front of the paddlewheel in order to supply CO2 to the system. The CO2 was sourced from the flue gas of natural gas combustion in an on-site furnace, in which the partial pressure of

CO2 was an average of 0.05 atm. Approximately 9,200 L of hexadecane was added to one of the raceways to form a 0.02-m layer on top of its surface, while the other system was used as a control. Microalgae cultivation was monitored for 61 d from August to October,

35 when the ambient temperatures were expected to be around the fusion point of the PCM.

Groundwater was added intermittently to compensate for evaporation and maintain the water depth at 0.25 m. NaNO3 was intermittently added to the system to bring nitrate-

-3 -3 based nitrogen (NO3–N) 35 g m when concentrations were below 10 g m . Similarly,

NaHPO4 was intermittently added to the system to bring and phosphate-based

-3 phosphorus (PO4–P) concentrations up to10 g m when concentrations were below 2 g m-3.

3.2.3. Data Acquisition

For each raceway system, CO2 concentration, irradiance, and temperature were measured and recorded every 10 minutes with a CR3000 datalogger (Campbell Scientific,

Logan, UT, USA). The dissolved CO2 concentration was monitored with a partial pressure sensor and transmitter (OxyGuard International, Birkerød, Denmark). An SQ-

110 quantum sensor (Apogee Instruments, Logan, UT, USA) was used to measure the incident irradiance, while two SQ-316 quantum sensors (Apogee Instruments, Logan,

UT, USA) were used to measure the irradiance at 0.10-m and 0.20-m depths, respectively. The pond temperature was taken at two different locations – 1 m in front and 1 m behind the paddlewheel – each with a K-type thermocouple, submerged at a

0.10-m depth.

3.2.4. Biomass

Approximate 500-mL samples of the culture medium were taken behind the paddlewheel, in triplicates, once per day. The dry biomass content in the pond samples were determined and reported in terms of AFDW according to Zhu and Lee (1997).

Aliquots of 20 mL suspended biomass were filtered through pre-weighed, pre-combusted,

42.5-mm diameter, P6-grade filters (Fisher Scientific, Hampton, NH, USA) under vacuum, and an equal amount of 0.5 M ammonium bicarbonate was filtered in order to wash out any excess salts on the cell surface or intercellular water. The biomass was put in a Thelco Model 18 oven (Precision Scientific, Chennai, India) at 105°C for 4 h to dry each sample to constant weight. Afterwards it was cooled in a desiccator and weighed to obtain dry weight. The ash weight was obtained by igniting the sample in an Isotemp muffle furnace (Fisher Scientific, Dubuque, IA, USA) at 500°C for 4 h, then cooled and weighed. The AFDW was determined as the difference between the dry weight and ash weight.

3.2.5. Nitrates, Ammonium and Phosphates

The residual filtrate after filtering the biomass in the previous section was analyzed for nitrate, ammonium and phosphate content, using colorimetric methods performed by Condori et al. (2010). The total nitrogen content was determined as the sum of nitrate-based nitrogen (NO3–N) and ammonium-based nitrogen (NH4–N). In order to measure nitrate content, 100 µL of sample filtrate was mixed with 1 mL of 0.5%

Szechrome NAS reagent (Polysciences, Washington, PA, USA). The combination was

37 held at room temperature for 15 min before measuring absorbance at 570 nm. The nitrate concentration was then determined by comparing the absorbance to that from a calibration curve produced from the dilution of potassium nitrate (0–35 mg L-1).

Ammonium was measured by mixing 100 µL of sample filtrate with 200 µL of nitroprusside solution, 200 µL of alkaline hypochloride, and 700 µL of deionized water.

The combination was held at room temperature for 20 min before measuring absorbance at 600 nm. Ammonium concentration was determined by comparing the absorbance to that from a calibration curve produced from the dilution of ammonium nitrate (0–50 mg

L-1). The reagent for measuring phosphate concentration was prepared by mixing 118 mL

-1 of 8% (v/v) H2SO4 solution, 0.472 g of ammonium molybdate, 118 mL of 20 g L polyvinylpyrrolidone, and 096 g of ferrous ammonium sulfate (Towler et al., 2007).

Sample filtrate measured at 300 µL was mixed with 900 µL of reagent, and the combination was held for 10 min at room temperature before measuring absorbance at

650 nm. Phosphate concentration was determined by comparing the absorbance to that from a calibration curve produced from the dilution of monopotassium phosphate (0–250 mg L-1).

3.2.6. Lipid Content and Composition

Lipid content of the microalgal biomass was determined by a modified solvent extraction (Folch et al., 1957). Samples for lipid analysis were collected separately in 5-L aliquots during the exponential growth phase. The collected sample was concentrated ) to a solid concentration of 10–20% by weight, using a Sorvall RC 6+ centrifuge (Thermo

Scientific, Waltham, MA, USA) with a F12S-6x500 LEX fixed angle rotor (Thermo

Scientific, Waltham, MA, USA) rotating at 10,000 rpm for 10 min. The concentrate was subsequently stored at –20°C or directly used, in which 1.0–2.0 g of the slurry was initially mixed with 7 mL of methanol. Cells were disrupted with a UP400S ultrasonic processor (Hielscher Ultrasonics, Teltow, Germany) equipped with an H22 titanium sonotrode. The mixture was exposed 30 s to ultrasound with a frequency of 24 kHz and power output of 100 W. The sonication was repeated after adding 14 mL of chloroform to the mixture. In order to adjust the solvent ratio of chloroform:methanol:water to 8:4:3, 12 ml of chloroform-methanol (2:1 v/v) was added after the two cycles of cell disruption followed by 0.88% KCl solution, in which the amount added was calculated using the

AFDW of the original concentrate. The resulting mixture was manually shaken for 10 min in a tightly sealed glass tube. The organic phase and aqueous phase were separated using a Sorvall Legend T+ centrifuge (Thermo Scientific, Waltham, MA, USA) with a swinging bucket rotor rotating at 1,000 g for 5 min, and the aqueous layer was decanted from the top with a pipette. The organic phase, along with the residual biomass, was filtered into a tared round-bottom flask through a Whatman No. 1 filter paper (GE

Healthcare, Maidstone, UK), followed by a wash with 20 mL chloroform. The resulting crude lipid was obtained by removing the chloroform with a Laborota 4001 rotary evaporator (Heidolph Instruments, Schwabach, Germany).

Lipids were converted to fatty acid methyl esters (FAMEs) via transesterification

(Li and Watkins, 2001). Lipid samples were saponified with 0.5 N sodium hydroxide in methanol at 100°C for 5 min. The liberated fatty acids (FAs) were subsequently

39 methylated in 14% boron trifluoride in methanol at 100°C for 5 min. The resulting

FAMEs were extracted with hexane and quantified using a QP-2010 SE gas chromatograph-mass spectrophotometer (Shimadzu, Columbia, MD, USA), equipped with a ZB-FFAP polar capillary column (Phenomenex, Torrence, CA, USA). Helium was used as the mobile phase at a flow rate of 1 mL min-1. The oven temperature was raised from 50°C to 190°C at a rate of 25°C min-1, then to 220°C at a rate of 3°C min-1 and held for 18 min. The injector, interface, and ion source temperatures were set at 250°C, 250°C, and 200°C, respectively. FAME samples of 1 µL each were injected with a split ratio of

50:1. The compounds were identified in the National Institute of Standards and

Technology mass spectral database and quantified by comparing the peak area with 100

µg mL-1 methyl heptadecanoate as an internal standard. The cetane number of total FAs was calculated using a multiple regression model (Piloto-Rodríguez et al., 2013).

3.2.7. Statistical Analysis

Comparisons for steady state values were performed using the Tukey–Kramer method, while analysis of variance tests were performed with a significance level of 0.05 using JMP 10.0.2 (SAS Institute Inc., Cary, NC, USA).

3.3. Results and Discussion

3.3.1. Temperature

Temporal changes in the mean pond temperature, as well as the daily temperature fluctuation ranges are shown in Figure 3.2. Over the 61-d growth period, the temperature

40 of the PCM-covered raceway was an average 5.1°C higher than that without the PCM treatment (Figure 3.2a), implying that the PCM had a significant insulating effect on the system. The mean ambient temperature during the study period was 20.3°C, with mean daily fluctuations of 18.6°C. The daily minimum temperatures of the PCM-covered raceway were higher than the maximum temperatures of the control raceways in most of the days. Figure 3.2b shows that the daily temperature fluctuation decreased when using

PCM. Therefore, it was implied that the use of PCM to cover the raceway surface enabled better temperature control and modulation of environmental extremes.

3.3.2. Biomass and CO2

Microalgal growth in both the treatment and control had no significant difference

(p > 0.05) during the initial 24 d of cultivation (Figure 3.3a). Differences between the biomass concentrations in the treatment and control became more evident as the growth progressed, as the increase in biomass concentration became less significant (p > 0.05) in the control, while the PCM-treated system continued its exponential phase for a prolonged period of time. The maximum biomass concentration in the PCM-treatment was 1.14 g L-1, which was comparable to the concentrations of 0.94 g L-1 achieved by growing N. salina in similar growth conditions (Crowe et al., 2012). The PCM covered raceway had a mean productivity of 7.0 g AFDW m-2 d-1, a two-fold difference compared to the control (3.5 g AFDW m-2 d-1). The productivity in an open channel raceway is

-2 -1 suggested to be limited to approximately 3.0 g m d when the CO2 is only supplied by diffusion from the atmosphere, but can increase about ten-fold when pure (100%) CO2 is

41 effectively incorporated (Benemann, 2013). Similar to what is observed in accumulated biomass, the differences in CO2 concentration between the PCM treatment and control also was evident after the 24-d mark (Figure 3.3b).

3.3.3. Nitrogen and Phosphorus Removal

Results from Figure 3.4 show that the presence of PCM in the system did not have any significant effect (p > 0.05) on the removal rate of NH4–N or phosphate-based phosphorus (PO4–P). However, the NO3–N removal rate in the PCM-covered raceway

-3 -3 was almost three folds of that of the control. NO3–N decreased from 35 g m to 10 g m in approximately 7.4 d, while it took 20.9 d in the control for NO3–N to decrease by the same degree. On the other hand, the amount of NH4–N found in both systems was found to be minimal for the entire cultivation period. Maximum NH4–N concentrations found in the control and PCM-covered raceway were 0.3 and 0.4 g m-3, respectively. Microalgae in general prefer to assimilate nitrogen in the form of ammonium than nitrate, and nitrate consumption does not occur until ammonium is depleted (Maestrini et al., 1986).

However, nitrate is more highly oxidized and thermodynamically stable than ammonium, and thus is predominant in the aquaculture environment (Barsanti and Gualtieri, 2006).

The active removal of NO3–N in low NH4–N concentration was indeed observed in the

-3 current study. Decreases in PO4–P from 10 to 2 g m were observed to take approximately 36 and 37 d in the control and PCM-covered raceway, respectively.

3.3.4. Lipid Content and Composition

The lipid contents and FA concentrations of N. salina cultured in both PCM- treated and untreated systems are shown in Table 3.2. Employment of PCM resulted in a

40.8% increase in lipid content. Considering that the lipid contents measured in the current study were from the exponential phase and in environments with superfluous nutrients, the lipid contents have potential to be further increased, especially when introduced to more stressful environments, e.g., nutrient-deprived conditions (Xin et al.,

2010b). In addition, the proportion of saturated FAs relative to unsaturated FAs increased in the PCM-covered systems. Based on a multiple regression model to calculate the cetane number (Piloto-Rodríguez et al., 2013), there was no significant change in the quality of the transesterified FAMEs.

Compared to the control, the PCM covered raceway showed significant increases in linoleic and linolenic acids, while the proportion of palmitoleic and eicosapentaenoic acids (EPA) decreased by 51.4% and 47.7%, respectively. EPA is derived from either the desaturation of linoleic and linolenic acids (Hu and Gao, 2003), and the accumulation of the precursors to EPA indicates that the desaturase was impacted by the physical changes in the system, namely, temperature, light availability, and CO2 concentration. The EPA content was observed to decrease under higher biomass concentrations and lower light availability in N. salina (Van Wagenen et al., 2012).

3.4. Conclusions

The PCM hexadecane could mitigate temperature fluctuation and water loss in the cultivation of N. salina in a demonstration scale open channel raceway. The proposed treatment proved to be able to effectively control the temperatures in an environment with substantial diurnal temperature fluctuations. The increased CO2 concentration in the presence of PCM is a major factor contributed to the increased microalgal biomass concentration and subsequent NO3-N removal. The lipid content of N. salina increased along with a shift in the fatty acid profile from EPA back to its precursors. Overall, a significant increase in microalgal productivity showed promise in the application of PCM in the open channel raceway for microalgae cultivation.

Table 3.1. Properties of various paraffin waxes as PCMs.

(Bo et al., 1999; Griesbaum et al., 2005; Schaerer et al., 1955)

Name Tetradecane Pentadecane Hexadecane Heptadecane Oxadecane

Empirical formula C14H30 C15H32 C16H34 C17H36 C18H38

Point of fusion 5.8 9.9 18.1 21.7 28.2

(°C)

Density (kg m-3)* 762.8 768.3 773.4 777.0 777.0

Viscosity (cP)* 2.3 2.9 3.5 † †

Flash point (°C) 99.0 132.0 135.0 302.0 317.0

* At standard conditions (20°C)

† Solid state under standard conditions

Table 3.2. Effect of PCM on lipid content and profile.

Control PCM Lipid content (g g-1 AFDW) 0.174 ± 0.005 0.245 ± 0.032 Total fatty acids (TFA) (g g-1 lipids) 0.707 ± 0.009 0.566 ± 0.006 Total saturated FAs (g g-1 TFA) 0.305 ± 0.005 0.324 ± 0.006 Total unsaturated FA (g g-1 TFA) 0.695 ± 0.007 0.676 ± 0.009 Fatty acid proportion (g g-1 TFA) Myristic acid (C 14:0) 0.051 ± 0.001 0.046 ± 0.000 Myristoleic acid (C 14:1) 0.003 ± 0.000 0 Palmitic acid (C 16:0) 0.236 ± 0.007 0.278 ± 0.006 Palmitoleic acid (C 16:1) 0.245 ± 0.005 0.119 ± 0.002 Stearic acid (C 18:0) 0.017 ± 0.000 0 Oleic acid (C 18:1) 0.047 ± 0.000 0.045 ± 0.002 Vaccenic acid (C 18:1 trans-11) 0.012 ± 0.000 0 Linoleic acid (C 18:2) 0.022 ± 0.000 0.178 ± 0.006 Linolenic acid (C 18:3) 0.008 ± 0.000 0.141 ± 0.005 Eicosanoic acid (C 20:0) 0 0 Eicosaenoic acid (C 20:1) 0 0 Eicosadienoic acid (C 20:2) 0 0 Eicosatrienoic acid (C 20:3) 0 0 Eicosatetraenoic acid (C 20:4) 0.043 ± 0.002 0.027 ± 0.001 Eicosapentaenoic acid (C 20:5) 0.316 ± 0.008 0.165 ± 0.006 Cetane number 56.148 ± 0.022 56.102 ± 0.016 Data presented as mean ± standard deviation.

(a)

57 m 0.25 m

(b)

Ø 1.2 m

Figure 3.1. (a) Entire view of open channel raceway and (b) close-up view of the paddlewheel and CO2-mixing sump.

3-dimensional representation created with ANSYS 14.5 DesignModeler™.

40 PCM 35

30 Control

C) ° 25 20

15 Temperature ( Temperature 10 5 0 0 10 20 30 40 50 60 Time (d)

5.0 C) ° 4.5 PCM 4.0 Control 3.5 3.0 2.5 2.0 1.5 1.0

Daily Daily FluctuationTemperature( 0.5 0.0 0 10 20 30 40 50 60 Time (d)

Figure 3.2. Temporal changes in (a) temperature and (b) daily temperature fluctuation.

1.2

) 1 - 1.0

0.8

0.6

0.4

0.2 PCM Biomass concentration (g (g concentration Biomass L Control 0.0 0 10 20 30 40 50 60 Time (d)

PCM

)

3 60 - Control

concentrationm (g

20 CO

0 0 10 20 30 40 50 60 Time (d)

Figure 3.3. Temporal changes in (a) biomass concentration and (b) CO2 concentration.

5.0 4.5

Control

)

1 -

d 4.0

- PCM 3.5 3.0 2.5 2.0

Removalrate m (g 1.5 1.0 0.5 0.0 NO3-N NH4-N PO4-P Nutrient type

Figure 3.4. Mean removal rates of nitrate-based nitrogen (NO3-N), ammonium-based nitrogen (NH4-N), and phosphate-based phosphorus (PO4-P).

Chapter 4: Integration of Biological Kinetics and Computational Fluid Dynamics

(CFD) to Model the Growth of Nannochloropsis salina in an Open Channel

Raceway

Summary

The aim of this study was to integrate physical and biological models to predict the location- and time- dependent algal growth in demonstration scale open channel raceways. A commercial computational fluid dynamics (CFD) software was used to solve the proposed model in regards to fluid flow and nutrient balance. User-defined functions written in C language were used to incorporate the kinetic equations into a three- dimensional standard k-ε turbulence model of an open-channel raceway system driven by a single paddlewheel. Simulated results were compared with the 150-day data acquisition of light intensity, temperature, nutrient concentration, and algal biomass acquired from a demonstration scale open channel raceway constructed for the growth of

Nannochloropsis salina and were observed to be in good agreement with each other.

4.1. Introduction

For over a half-century, interest in photosynthetic microalgae as a source of biofuels, including biogas (Golueke et al., 1957) and biodiesel (Chisti, 2007), has

51 prompted a plethora of research directed to feasibly producing the biomass in mass quantities. High biomass productivities and lipid contents highlight the advantages of microalgae as a potential commodity, while the organisms’ ability to sequester atmospheric carbon dioxide (CO2) and flourish in environments that do not require arable land (e.g., brackish water or systems built on non-arable land) make algal fuels environmentally favorable over fossil fuels. In order for microalgae-derived fuel to be economically competitive to its conventional counterparts, further improvements in criteria including operational costs, energy consumption, production stability, and overall productivity are in high demand.

Numerous design concepts for mass-producing microalgae can be generally categorized into two groups by their exposure to the surrounding environment: open ponds and closed photobioreactors. The open-channel raceway has been hailed as one of the most economically feasible systems, mainly due to their low energy requirements and low environmental impact (Jorquera et al., 2010). A number of startup companies including Aurora Algae, Inc. (Hayward, CA, USA), Sapphire Energy, Inc. (San Diego,

CA, USA), and Solix Biosystems, Inc. (Fort Collins, CO, USA) have employed the open pond raceway design in an attempt to commercialize microalgal fuels (Chisti and Yan,

2011). The main issue in operating these open ponds with natural light and air exposure is that the systems are not only affected by nutrient availability and predation, but also temperature and irradiance that differ on a daily basis. In addition, pond geometry, dimensions, and mixing conditions can also come into play during scale-up, and thus effective control and subsequently constant productivity is difficult to achieve. An

52 effective model that can precisely emulate and predict the behavior in the aforementioned industrial systems can help design and optimize the large scale system.

The biological process of microalgal growth and the impact of various parameters have been thoroughly studied and modeled since the 1940s. One of the earlier studies statistically correlated phytoplankton populations with six ecological variables – solar radiation, water transparency, phosphate concentration, mixed layer depth, surface temperature, and zooplankton quantity (Riley, 1946). The Monod model (Monod, 1949) and the cell-quota model (Droop, 1983, 1968) have both been established as fundamental methods to describe the nutrient limitation of phytoplankton reproductive rates, and have further evolved into more complex forms (Klausmeier et al., 2004). Although the cell- quota model has been reported to depict nutrient-limited growth more accurately than the

Monod model (Sommer, 1991), difficulties in measuring the independent variables in the former has prompted model developments that utilize the Monod equation (Buhr and

Miller, 1983; Yang, 2011). The effects of irradiance and its dynamic change dependent to cell concentration and suspension depth have been modeled as well (Baly, 1935; Cornet et al., 1995; Huisman and Weissing, 1994; Peeters and Eilers, 1978), and has led to combined studies of both nutrient and light (Bernard, 2011; Geider, 1998; Pahlow, 2005;

Quinn et al., 2011).

The aforementioned studies assume spatial homogeneity in physical parameters such as turbulence and heat transfer. This assumption may have negligible impact in bench-scale operations, but may not be able to capture larger changes that occur with increased production scale. The use of computational fluid dynamics (CFD) can help in

53 the prediction of such phenomena as well as the optimization of cultivation system design. Compared to the biological process, the application of fluid dynamics in microalgae cultivation systems is a lesser-explored concept. Mixing and mass transfer were modeled in a number of studies on photobioreactors (Babcock et al., 2002; Sato et al., 2010), but research in the hydrodynamic effects on microalgal growth in open ponds has been limited (James and Boriah, 2010).

A key issue in using CFD in biological systems is that the time step used in solving a biological model is several orders of magnitude greater than that used in a physical model. When combining the two models, the integrated version can be solved simultaneously in short time increments, given the typically available computing power that most research institutions have. However, it would be difficult to concurrently solve the biological and physical interactions with small time steps over extended periods of time. A practical two-step approach to integrate the two processes was suggested by Wu

(2012), where the temporal biological process was predicted using a large time step based on steady fluid flow and heat transfer, while each computational cell was physically treated as an individual bioreactor with its own residence time and temperature. The objective of the current study is to (1) take a similar approach to integrate the biological process of microalgal growth with the physical flow model of an open pond raceway system using existing kinetic mass balance equations and CFD, and (2) validate the constructed model with actual data collected from a commercial-scale system.

4.2. Materials and Methods

4.2.1. Cultivation Environment

The cultivation of N. salina was performed in a demonstration scale raceway with an open surface, as described in Section 3.2.2. Cultivation was monitored for a total of 56 d, during the months of July and August.

4.2.2. CFD Methodology

4.2.2.1. Geometry

A two-stage simulation method was developed based on the commercial CFD software ANSYS Fluent 14.5 (ANSYS, Cecil Township, PA, USA). The partial differential equations for the physical model were solved using the finite volume method under steady-state conditions. The time dependent ordinary differential equations for the biological model were solved using the fourth-order Runge-Kutta method (Desale, 2013), and written in C-language as a user-defined function. The 3-dimensional model of the open-channel raceway was developed using ANSYS 14.5 DesignModeler™ (Figure 3.1).

The geometry entailed a single body that contained the entire fluid region for the CFD analysis. The raceway modeled by extruding the solid perimeter walls and mixing sump, and subsequently using a fill feature to extract the fluid volume. The interface between the paddlewheel and fluid was created through a Boolean operation in which a cylinder with the same diameter as the paddlewheel was subtracted from the fluid body. All simulations were executed on a Windows 7 operating system installed on a custom-built

55 workstation equipped with a Xeon® E31220 quadruple-core processor and 16 GB of memory.

4.2.2.2. Mesh

A tetrahedral mesh was created using the ANSYS 14.5 meshing software (Figure

4.1). An automatic patch conforming method was used based on the CFD physics preference. The advanced size function was based on curvature, with the relevance center set to coarse. Smoothing, transition, and span angle center was set to medium, slow, and fine, respectively. Default settings were retained for the curvature normal angle, minimum element size, and growth rate, while the maximum face size and element size were modified to be 0.5 m and 1.5 m, respectively. The resulting mesh was composed of

23,422 nodes and 89,447 elements. The mean skewness of the mesh was 0.27 ± 0.14, with minimum and maximum values of 1.3 × 10-10 and 0.92, respectively. There were three named selections created in the meshing software to define the boundary conditions used in the solver: the stationary wall, stationary floor, and paddlewheel interface.

4.2.2.3. Solver

A transient simulation was chosen over the steady-state simulation, due to the difficulty of achieving a steady-state solution in multiphase flow with a free surface.

Gravitational acceleration was set to 9.81 m s-2 in the negative y-direction. The high

Reynolds number of 2.4 × 104 indicated that the flow was turbulent, and thus a turbulent

56 viscous model was used. The standard k-ε turbulence model was chosen, it being the most common CFD model with a wide range of applications.

4.2.2.4. Boundary Conditions

The free surface was characterized as a velocity outlet, while the walls and floor of the system was considered to be stationary walls with a no-slip shear condition. The walls were treated as smooth, since the system lining was made of coated polyester. The interface between the paddlewheel and fluid was treated as a no-slip moving wall with a constant rotational velocity of 5 rad s-1 along its axis, thus creating the flow.

4.2.3. Kinetic Equations

4.2.3.1. Mass Balance of Biomass (X)

Each element of the photosynthetic microalgae growth system can be assumed to be a chemostat with variable inflow and outflow of pond medium. The flux of biomass,

CO2 concentration, and nitrogen concentration can be expressed in the form of ordinary differential equations, and thus the kinetic balance of the system can be determined by solving the array of equations. The mass balance of algae in the photobioreactor can be expressed as shown in Equation 4.1 (Yang, 2011). The term for the exponential growth rate of algae ( ) is based on the Malthusian growth model:

where F ≡ volumetric flowrate (m3 d-1),

V ≡ volume of element (m3), 57

-3 X0 ≡ influent mass concentration of algae (g m ),

X ≡ effluent mass concentration of algae (g m-3),

μ ≡ specific growth rate of algae (d-1), and

D ≡ death coefficient (d-1).

The specific growth rate can be expressed as the combined product of the effects of CO2 concentration (C), and nitrogen concentration (N), irradiance (I), and temperature (T)

(Yang, 2011). The effects of CO2, nitrogen, and irradiance can be expressed in the form of Monod-type functions (Buhr and Miller, 1983; Cerco and Cole, 1995; Muñoz-Tamayo et al., 2013). Although other irradiance models such as the Poisson-chance model

(Peeters and Eilers, 1978) and the exponential model (Yang, 2011) were also available, the Monod model was chosen for simplicity.

( ) ( ) ( )

-1 where μmax ≡ maximum specific growth rate (d ),

-3 C ≡ effluent concentration of dissolved CO2 (g m ),

-3 KC ≡ half-saturation constant for cell growth dependent on CO2 (g m ),

-3 N ≡ concentration of inorganic nitrogen (g m ),

-3 KN ≡ half-saturation constant for cell growth dependent on N (g m ),

I ≡ irradiance at a specified pond depth (µmol m2 s-1),

KI ≡ half-saturation constant for cell growth dependent on irradiance

(µmol m2 s-1), and

g(T) ≡ effect of temperature.

The irradiance (I) at a specified depth in the pond can be calculated following Beer-

Lambert’s law (Bernard, 2011), which predicts an exponential decline of light intensity with culture depth:

2 -1 where I0 ≡ irradiance at the surface (µmol m s ),

ξ ≡ light attenuation rate (m-1), and

z ≡ vertical location (m).

The light attenuation rate (ξ) can be assumed to be dependent on biomass, and can be expressed as:

where a ≡ specific light attenuation coefficient (m2 g-1), and

b ≡ background turbidity (m-1).

The effect of temperature (g(T)) can be expressed by assuming an exponential variation due to non-optimal temperature (James and Boriah, 2010):

( ) where m ≡ empirical constant for non-optimal temperature,

T ≡ temperature (K), and

Topt ≡ optimal temperature for autotrophic growth (K).

Cerco and Cole (1995) suggest the use of different constants for conditions above and below the optimal temperature, but James and Boriah (2010) use a single value to reduce the number of variables. Equation 4.1 can be combined with Equations 4.2, 4.6, and 4.7 to be consolidated into a final expression for the mass balance of biomass:

̂ ( ) ( ) ( ) ( )

4.2.3.2. Mass Balance of Carbon Dioxide (C)

The mass balance of CO2 can be expressed with additional terms for gas flux and mass transfer through the gas-liquid interphase (Yang, 2011). The diffusion of CO2 through water is described by convective mass transfer.

-1 where kL ≡ mass transfer coefficient of CO2 in water (m d ),

α ≡ specific mass transfer area, i.e., bubble surface area per unit gas volume (m-1),

* -3 C ≡ saturation concentration of dissolved CO2 (g m ),

-3 C0 ≡ influent concentration of dissolved CO2 (g m ), and

YC/X ≡ mass of CO2 consumed per unit mass of microalgae.

* The saturation concentration of dissolved CO2 (C ) can be estimated by Henry’s law:

-1 -3 where hC ≡ Henry’s constant for CO2 (g atm m ), and

pC ≡ partial pressure of saturated CO2 (atm).

The temperature-dependent value of Henry’s constant for CO2 (hC) can be estimated by the correlation suggested by Carroll et al. (1991):

( )

where h'C ≡ Henry’s constant (Mpa/mole fraction of CO2 in H2O).

The units for h'C can be converted to that for hC using the following equation:

[ ]

( ) ( ) ( ) ( ) [ ]

4.2.3.3. Mass Balance of Nitrogen (N)

The mass balance of nitrogen is similar to that of CO2, only without a generation term:

-3 where N0 ≡ influent concentration of nitrogen (g m ), and

YN/X ≡ mass of nitrogen consumed per unit mass of microalgae.

4.2.3.4. Mass Balance of Oxygen (O)

The mass balance of oxygen is shown in the following equation:

-3 where O0 ≡ influent concentration of oxygen (g m ),

* -3 O ≡ saturation concentration of dissolved O2 (g m ), and

YO/X ≡ mass of oxygen produced per unit mass of microalgae.

* The saturation concentration of dissolved O2 (O ) can be estimated by Henry’s law, as shown in Equation 4.8. The temperature-dependent value of Henry’s constant for O2 (hO) can be estimated by the correlation suggested by Kavanaugh and Trussell (1980):

( ) ( )

-1 -1 where h'O ≡ Henry’s constant (mol bar kg ).

The units for h' can be converted to that for h using the following equation:

[ ] ( ) ( ) ( ) [ ]

4.2.3.5. Total Mass Balance

Equations 4.6, 4.7, 4.11, and 4.12 can be numerically solved to observe the change in biomass, carbon dioxide concentration, and nitrogen concentration.

̂ ( ) ( ) ( ) ( )

{

4.2.4. Analytical Procedures

Data acquisition was performed as described in Section 3.2.3. The biomass concentration in the system was expressed in terms of ash-free dry weight (AFDW), which was measured following the procedure as described in Section 3.2.4. The nitrogen content in each sample was determined as the sum of nitrate-based nitrogen (NO3–N) and ammonium-based nitrogen (NH4–N), which were measured following the procedures as described in Section 3.2.5.

4.2.5. Statistical Analysis

Comparisons for all steady state values were performed using the Tukey–Kramer method, while analysis of variance tests were performed with a significance level of 0.05 using JMP 10.0.2 (SAS Institute Inc., Cary, NC, USA).

4.3. Results and Discussion

4.3.1. Validation of the Integrated Model

The integrated model was validated by comparing it to the experimentally measured changes in biomass concentration, CO2, and nitrogen in the raceway pond, while using the initial concentrations, time, and temperature as independent variables.

Figure 4.2a shows the measured and predicted biomass concentration over the period of

56 days. The biomass concentration was shown to increase during the day and decrease when there was a lack of irradiance. In general, there was close agreement between the predicted and measured biomass growth curves, demonstrating the overall validity of the modeling approach. The model showed a steady linear increase of the biomass concentration for the initial 10 d, where the volumetric biomass productivity was calculated to be 0.15 g AFDW L-1 d-1 (R2 = 0.88), equivalent to an area productivity of

3.5 g m-2 d-1. There was some variability observed in the experimental data during the 20- d and 40-d mark, where the integrated model tended to underestimate the biomass concentration by 20–30%. The dynamic trend in biomass concentration from Figure 4.2a appeared to be in sync with the change in irradiance, temperature, and CO2, whereas the

63 changes in N content in the system seemed to affect the biomass content relatively less dramatically.

The dissolved CO2 content was measured for a 21-d interval, the absence of data occurring due to the membrane fouling of the partial pressure sensor for CO2 (Figure

4.2b). During this period, the integrated model was able to predict the variability CO2 on a daily basis. The cyclical change in CO2 content every day was also observed. The CO2 content was heavily impacted by the dependence of its solubility on temperature, which appeared to negate the changes caused by photosynthesis. The photosynthetic consumption of carbon dioxide by microalgae is governed by the stoichiometric ratio between CO2 and biomass. The resulting CO2 production is significant enough to create a change in pH, but is overpowered by the CO2 solubility caused by temperature, particularly in temperature-variable settings.

By using user-defined functions, the integrated model was capable of showing the irregular input of nitrogen into the pond. Regardless of its initial concentration, there was a steady decrease in nitrogen, with the depletion rate being 2.90 g m-3 d-1 (R2 = 0.94).

Nitrogen assimilation occurs from both nitrates and ammonium, and thus the experimental nitrogen content was presented as the sum of NO3–N and NH4–N. It should be noted that the ammonium content in the collected samples were relatively steady compared to the nitrate content, which decreased frequently, indicating the possibility that NO3–N consumption was actively occurring in constantly low NH4–N conditions

(Maestrini et al., 1986).

4.3.2. Observations in the CFD Model

4.3.3.1. Changes in X with Horizontal Location

The CFD model shows, although very slight, that X increases along the inside of the hairpin bend, shown in Figure 4.3. Slightly higher biomass contents were observed at the interior of the hairpin bends. Although the variance in biomass concentration within the system at a given point in time was not statistically significant (p > 0.05) enough to be deemed different, the characteristic shape of dead zones that tail off the bend could be seen once the biomass concentration reached 0.42 g L-1 (approximately 35 d).

4.3.3.2. Impact of Increasing Biomass Concentration on Light Penetration

Figure 4.4 shows the transient change in light attenuation as the biomass content increases. While the dark areas that were unreachable by sunlight only accounted for approximately 5% of the cross-sectional area at 0 d, the inaccessible area accounted for nearly 40% of the cross sectional area towards the end of the experimental period. The presence the aforementioned zones likely contribute to the decrease in biomass accumulation rate (Figure 1), which eventually reaches an asymptotic value, or steady state.

4.3.3. Effects of paddlewheel velocity and CO2 loading

The rotational velocity of the paddlewheel and CO2 concentration in the incoming flue gas were easily controlled and modified. The biomass output of the system was obtained via simulation of the proposed model. As a result, the area productivity of the

65 system was observed to increase with the increases in both paddlewheel velocity, from

-1 -1 2.5 rad s to 10.0 rad s , and incoming CO2 partial pressure of the flue gas, from 0.0004 atm to 0.100 atm (Figure 4.5). The CO2 partial pressure of 0.0004 atm was representative of the CO2 concentration in air, which is typically 0.035–0.039% by volume. The productivity of 3.3 g m-2 d-1 was slightly greater than the proposed productivity of an open channel raceway without any incorporation of additional CO2 other than the atmosphere (Benemann, 2013). There was up to a 17.6% increase in productivity when the incoming CO2 partial pressure was increased to 0.100 atm. The simulated results were similar to the reported growth rate increase of Chlorella kessleri when the CO2 injection concentrations were increased from 0.038% to 18% by volume in open bioreactors (Rosa et al., 2011).

Compared to the effect of incoming CO2 concentration on the biomass productivity of N. salina, there was no significant difference when increasing the paddlewheel velocity (p > 0.05). With an incoming CO2 partial pressure of 0.100 atm, the maximum increase in area productivity was 2.7%. The presence of turbulent mixing is essential for the prevention of sedimentation and subsequent production of biomass.

However, the rotational velocities of 2.5–10.0 rad s-1 all result in Reynolds numbers greater than 1.0 × 104 implying that the turbulent conditions were well maintained, even when the rotational velocity was decreased.

4.3.4. Sensitivity Analysis of the CFD Model

Sensitivity analysis was performed on the model using the variability in 15 predetermined constants (Quinn et al., 2011). Each input parameter was perturbed, both positively and negatively, by 20% and the resulting biomass concentration was evaluated at 100 h. An analysis of variance was performed to estimate the t-ratios for each input parameter. Factors with a t-ratio greater than the t-ratio at the 95% confidence interval were considered to have a more profound effect on the model’s output. Figure 4.6 shows that the model is very sensitive to values such as µmax, a, Topt, KI, and D. The outcome of the model is heavily governed by parameters specific to the species of microalgae, but not all of them affect the model significantly; the model appears to be insensitive to pC, m2, b, KN, KC, YC/X, YN/X, m1, kla, and YO/X. The analysis further proves that the growth of

N. salina is more significantly impacted by irradiance than dissolved nutrients.

Huesemann et al. (2013) states that as a rule of thumb, a 1% error in a species-specific parameter results in a 1% change in the predicted biomass growth. A 10% positive or negative change in µmax resulted in a maximum over- or under-prediction of 6.5% in biomass concentration, respectively. On the other hand, increasing or decreasing the specific light attenuation coefficient (a) by 10% resulted in an under- or over-prediction of up to 8.4%. The impact that a has on the model is greater than reported by Bosma et al.

(2007), who report a maximum error of 4.2% from doubling or halving a. However, the parameters that were deemed to be significant to the integrated model coincided with those reported by Quinn et al. (2011). Results show that the applicability of the proposed

67 model depends on the assumption that the values of µmax, a, Topt, KI, and D are accurately known for the culture of interest.

4.4. Conclusions

The combination of CFD and algal kinetics enabled the successful prediction of the overall growth of algal biomass while depicting its hydrodynamic behavior in an open pond raceway cultivation system. The model was relatively simple with minimal input parameters, and user-defined functions in the CFD software enabled the user to input actual variable irradiance data. Validation with experimentally obtained data showed a good fit for the change in biomass, CO2, and nitrogen concentrations. The model also showed the variability of biomass at different locations within the system, as well as the light attenuation dependent on depth and cell concentration. The biomass productivity was significantly affected by changes in the incoming CO2 concentration, and not significantly affected by the paddlewheel velocity under turbulent conditions. Sensitivity analysis showed that the model was particularly sensitive to the species-specific maximum growth rate, light attenuation coefficient, optimal growth temperature, half- saturation constant for growth based on irradiance, and death coefficient.

Table 4.1. Parameters used in simulation of algae growth in pond segment.

Symbol Definition Unit Value Reference 1. Independent variables -3 C0 influent concentration of dissolved CO2 g m - - 2 -1 I0 irradiance at the surface µmol m s - - -3 N0 influent concentration of nitrogen g m - - -3 O0 influent concentration of oxygen g m - - t time d - - T temperature K - - -3 X0 influent mass concentration of algae g m - - z vertical location m - - 2. Dependent variables -3 C effluent concentration of dissolved CO2 g m - - I irradiance at a specified pond depth µmol m2 s-1 - - N concentration of inorganic nitrogen g m-3 - - -3 O effluent concentration of dissolved O2 g m - - X effluent mass concentration of algae g m-3 - - 3. Calculated values * -3 C saturation concentration of dissolved CO2 g m - - F volumetric flowrate of influent and effluent m3 d-1 - - g(T) effect of temperature - - - h Henry’s constant g atm-1 m-3 - - h' Henry’s constant MPa mol mol-1 - - * -3 O Saturation concentration of dissolved O2 g m - - V volume of element m3 - - µ specific growth rate of algae d-1 - - ξ light attenuation rate m-1 - - 4. Constants a specific light attenuation coefficient m2 g-1 0.050 (Jupsin et al., 2003) b background turbidity m-1 0.320 (Jupsin et al., 2003) D death coefficient d-1 0.050 (Ambrose et al., 2006) -3 KC half-saturation constant for cell growth g m 0.044 dependent on CO2 (Yang, 2011) 2 -1 KI half-saturation constant for cell growth µmol m s 1000.000 dependent on irradiance Current study (Geider et al., 1997) -1 KLa mass transfer coefficient of bubbling CO2 d 10.080 (Carvalho and Malcata, 2005) -3 KN half-saturation constant for cell growth g m 0.014 dependent on N (Yang, 2011) -2 m1 effect of temperature below Topt on growth K 0.004 (Cerco and Cole, 1995) -2 m2 effect of temperature above Topt on growth K 0.006 (Cerco and Cole, 1995) pC partial pressure of saturated CO2 atm 0.300 Current study Topt optimal temperature for autotrophic growth K 296.150 (Van Wagenen et al., 2012) YC/X mass of CO2 consumed per unit mass of - 2.182 microalgae (Yang, 2011) YN/X mass of nitrogen consumed per unit mass of - 0.091 microalgae (Yang, 2011) YO/X mass of oxygen consumed per unit mass of - 1.587 microalgae (Yang, 2011) -1 µmax maximum specific growth rate d 1.300 (Van Wagenen et al., 2012)

Figure 4.1. Geometry and mesh used for the open channel raceway.

3-dimensional representation created with ANSYS 14.5 DesignModeler™ and .ANSYS

14.5 meshing software.

0.7

) 1 - (a) 0.6 0.5 0.4 0.3 0.2 Experimental 0.1 Biomassconcentration (g L * CFD Model 0.0 0 10 20 30 40 50 Time (d)

90 Experimental

) (b) 3

- CFD Model 70 60 50 40

30 concentrationm (g

20 2 10 CO * * 0 0 10 20 30 40 50 Time (d) 45 Experimental

) 3 - CFD Model 35 (c) 30 25 20 15 10 5 TotalN concentration (g m 0 0 10 20 30 40 50 Time (d)

Figure 4.2. Changes in concentration of (a) biomass, (b) CO2, and (c) Total N.

(* denotes time periods where data could not be collected). 71

Figure 4.3. Contours of microalgal biomass (g m-3) from 35 d to 50 d.

Figure 4.4. Vertical light distribution in mixing sump area of cultivation system from 0 d to 50 d.

4.5 ω = 2.5 rad/s ω = 5.0 rad/s ω = 10.0 rad/s

4.0

)

1 -

d 3.5

2 - 3.0

2.5

2.0

1.5

1.0 Biomassproductivity m (g 0.5

0.0 0.0004 0.050 0.100

Partial pressure of supplied CO2 (atm) Figure 4.5. Simulated biomass productivities under varying paddlewheel velocities and

CO2 loading.

µmax a Topt KI

pC m2 b KN K

Modelparameter C YC/X YN/X m1 kla YO/X -8 -6 -4 -2 0 2 4 6 8 t-ratio

Figure 4.6. Sensitivity of model inputs presented in tornado plot format.

Model inputs altered by ±20% with predicted biomass concentration after 100 h compared to baseline scenario. Dotted lines indicate 95% confidence limits.

Chapter 5: Integrated Computational Fluid Dynamics (CFD) Model for Open Pond

Cultivation of Nannochloropsis salina using Phase Change Material (PCM)

Summary

Physical and environmental effects were integrated to predict the space- and time- dependent algal growth in a demonstration scale raceway pond treated with a layer of hexadecane on the raceway surface. Equations depicting microalgal growth kinetics and mass transfer were combined with a two-phase fluid dynamics model. User-defined functions written in C language were used to incorporate the kinetic equations and mass transfer equations into a three-dimensional standard k-ε turbulence model of an open- channel raceway system driven by a single paddlewheel. The simulated results were able to show higher concentrations of carbon dioxide (CO2) within the aqueous layer when treated with hexadecane. Increase in microalgae biomass concentrations was observed in the simulations with increasing CO2 input as well as increase in the thickness of phase change material (PCM) layer.

5.1. Introduction

Open channel raceways are the most common cultivation system used in the cultivation of microalgae (Brennan and Owende, 2010), due to their ease in construction

76 maintenance. However, environmental factors including evaporative losses, diurnal and seasonal changes in temperature and incident irradiance, and limited carbon dioxide

(CO2) transfer result in open systems generally having relatively lower productivities compared to their closed counterparts (Rawat et al., 2013). In order to mitigate the operational issues in an open channel raceway, the use of a phase change material (PCM) to cover the raceway surface has been proposed. Hexadecane is an immiscible, inert,

PCM with a high latent heat that help stabilize the operating temperature of an open channel while blocking out foreign contaminants that can be inhibitory to algal growth.

In order to better understand the use of PCM in the microalgal cultivation process and reduce the costs in testing a commercial scale facility, the algae culture in raceways with PCM can be numerically modeled by integrating the kinetic equations associated with microalgae metabolism with a multiphase fluid dynamic model. The concept of utilizing a multiphase environment with gas transfer is commonly used in the conventional oil recovery process (Bijeljic et al., 2003) and has been also introduced in fermentation as emulsified vectors that facilitate gas transfer (Rols et al., 1990).

The current study modified the integrated model constructed in the previous chapter to simulate the cultivation of Nannochloropsis salina in an open channel raceway with a hexadecane layer on its surface. The simulated results were compared with actual data collected from a demonstration scale raceway system. The effects of varying PCM layer thickness and CO2 loading into the system on the algal growth were also studied.

5.2. Materials and Methods

5.2.1. Cultivation Environment

The cultivation of N. salina was performed in a demonstration scale open channel raceway with a 0.02 m layer of hexadecane on top of its surface, as described in Section

3.2.2. Cultivation was monitored for a total of 61 d, during the months from August to

October.

5.2.2. CFD Methodology

5.2.2.1. Geometry

The system’s geometry was divided into two main components: the lower aqueous phase which included the water and suspended culture of N. salina, and the upper organic phase which consisted of the hexadecane layer. The geometry of the aqueous phase was modeled with ANSYS 14.5 DesignModeler™, as depicted in Section

4.2.2.1. In order to create a second fluid body, the existing raceway walls were extruded an additional 0.02 m and a fill feature was used to extract the fluid volume.

5.2.2.2. Mesh

Meshing was performed with the ANSYS 14.5 meshing software (Figure 5.1).

Due to the constraint of having an extremely thin fluid layer in the model, several modifications were made to the default settings described in Section 4.2.2.2. The advanced size function was based on curvature, with the relevance center set to medium.

Smoothing, transition, and span angle center was set to high, slow, and fine, respectively.

Default settings were retained for the curvature normal angle and growth rate, while the minimum element size, maximum face size, and maximum element size were set to be

0.015 m, 1.0 m, and 2.0 m, respectively. In order to prevent an excessively fine mesh, an additional body sizing feature was applied to the organic phase with an element size of

0.1 m. The resulting mesh consisted of 110,573 nodes and 503,328 elements. The mean skewness of the mesh was 0.40 ± 0.27, with minimum and maximum values of 1.4 × 10-4 and 0.96, respectively. There were ten named selections created in the meshing software to define the boundary conditions used in the solver: two for the solid raceway body

(interphases with the surrounding air and ground), five for the aqueous phase (flue gas inlet, floor, side wall, paddlewheel interphase, and hexadecane interphase), and three for the organic phase (top surface, side wall, and paddlewheel interphase).

5.2.2.3. Solver

A transient, explicit-scheme, volume of fluid (VOF) multiphase model was used to depict the interactions between the aqueous and organic phases in the system. The

VOF model assumes that the interacting liquids are immiscible, and thus was appropriate for the following application. Although the inclusion of a third air phase is recommended for a more precise simulation (Amini and Schleiss, 2009), the addition of a tertiary phase required a significant amount of added computations and was much more time- consuming. Therefore, the VOF consisted of only two phases, and the flue gas that was bubbled from the mixing sump was modeled as a user-defined scalar, instead of being included in the multiphase model. The density of the hexadecane was set to 770 kg m-3 to

79 let float upon the surface of the aqueous phase. Constant values were set for the mass transfer rates of CO2 in water and hexadecane. Gravitational acceleration was set to 9.81 m s-2 in the negative y-direction. The standard k-ε turbulence model was chosen, it being the most common CFD model with a wide range of applications.

5.2.2.4. Boundary Conditions

The free surface on the organic phase was characterized using the open channel boundary condition included in the VOF model, while the walls and floor of the system was considered to be stationary walls with a no-slip shear condition. The walls were treated as smooth, since the system lining was made of coated polyester. The interfaces between the paddlewheel and both fluids were treated as no-slip moving walls with a constant rotational velocity of 5 rad s-1 along its axis, thus creating the flow.

5.2.3. Kinetic Equations

The kinetic equations for the rates of biomass concentration, nitrogen, and oxygen were as described in Section 4.2.3. The mass balance for CO2 described in Equation 4.7 was modified to take account for the mass transfer of CO2 through a hexadecane film of known length. The mass transfer rate of CO2 is given by Fick’s first law of diffusion

(Bijeljic et al., 2003):

-3 -1 where ≡ mass transfer rate of CO2 (g m s ),

A ≡ specific area of hexadecane perpendicular to the direction of mass transfer (m-

1),

2 -1 ≡ diffusion coefficient of CO2 from the aqueous phase (m s ),

-1 S ≡ solubility of CO2 in hexadecane from the aqueous phase (mol CO2 mol solution),

-3 Catm ≡ concentration of CO2 in the atmosphere (g m ),

-3 C ≡ concentration of dissolved CO2 in the aqueous phase (g m ), and

d ≡ hexadecane layer thickness (m).

The modified rate of change of CO2 in the systems results as:

-1 where kL ≡ mass transfer coefficient of CO2 in water (m d ),

α ≡ specific mass transfer area, i.e., bubble surface area per unit gas volume (m-1),

* -3 C ≡ saturation concentration of dissolved CO2 (g m ),

-3 C0 ≡ influent concentration of dissolved CO2 (g m ), and

YC/X ≡ mass of CO2 consumed per unit mass of microalgae.

5.2.4. Analytical Procedures

5.2.4.1. Data Acquisition and Sample Analysis

Data acquisition was performed as described in Section 3.2.3. The biomass concentration in the system was expressed in terms of ash-free dry weight (AFDW), which was measured following the procedure as described in Section 3.2.4. The nitrogen content in each sample was determined as the sum of nitrate-based nitrogen (NO3–N) and 81 ammonium-based nitrogen (NH4–N), which were measured following the procedures as described in Section 3.2.5.

5.2.4.2. Statistical Analysis

5.3. Results and Discussion

5.3.1. Validation of the Integrated Model

The proposed model was validated by comparing it to the experimentally measured changes in biomass concentration, CO2, and nitrogen in the raceway pond, while using the initial concentrations, time, and temperature as independent variables

(Figure 5.2). The model showed an overprediction of the microalgal growth of up to 22% during the initial 20 d of growth, and did not display a lag phase which was observed in the experimental data. Both the simulation and experimental did however display similar exponential phases that lasted approximately 7 d with approximate specific growth rates of 0.06 d-1, significantly lower than the reported growth rates of N. salina in photobioreactors (Sheets et al., 2014). The lack of a lag phase, which is attributed to the physiological adaptation of the cell metabolism to growth, in the proposed model implies the absence of a factor that represents the acclimation of microalgae to a new growth environment, i.e., the addition of a PCM layer upon the cultivation system. Additional

82 factors other than those included in the specific growth rate expression in Equation 4.2

(Section 4.2.3.1), such as the presence of micronutrients (Moreno-Garrido et al., 2000) may also affect the algae growth. There was no significant difference between the experimental and simulated data during the stationary phase (p > 0.05), the mean steady state concentration reaching up to 0.81 g L-1. At steady state, the simulation was able to show that there was a two-fold increase when compared to the system without the PCM treatment (Figure 4.2a).

The diurnal trend of CO2 concentration observed in the experimental data was well represented in the simulation (Figure 5.2b). The positive effect of the hexadecane layer on biomass and CO2 concentrations can be attributed to the physical properties of the added PCM. Hexadecane has a lower surface tension compared to water, and the presence of this inert liquid can increase the specific gaseous interfacial area (Rols et al.,

1990). In addition, the solubility of CO2 in hexadecane is greater than that in water, and thus the added layer facilitates increased mass transport of CO2 (Campos et al., 2009).

The addition of the CO2 mass transfer term in Equation 5.2 showed that this phenomenon could be successfully simulated. The nitrogen consumption rate was observed to be 3.56 g m-3 d-1 (R2 = 0.82), a 23% increase from that without the PCM treatment.

5.3.2. Effects of PCM Thickness and CO2 Loading

As shown in Figure 5.3, the area productivity of the simulated system was observed to increase with the increases in both the thickness of the PCM layer, from 1 cm to 3 cm, and incoming partial pressure of the flue gas, from 0.0004 atm to 0.100 atm

(Figure 5.3). The CO2 partial pressure of 0.0004 atm was representative of the CO2 concentration in air, which is typically 0.035–0.039% by volume. The productivity increased by up to 46.1% when the partial pressure of the incoming CO2 was increased to

0.100 atm, which was significantly greater than the productivity increase caused by the increase in CO2 concentration observed in the system without a PCM layer (Figure 4.5).

The results implied that the high solubility of the PCM layer helped in facilitating mass transfer from the atmosphere into the pond.

The effect of the PCM layer thickness was not as evident as that of the incoming

CO2 partial pressure, but nonetheless a significant difference was observed (p < 0.05).

With an incoming CO2 partial pressure of 0.004 atm, the maximum increase in algae productivity was 5.7%. Equation 5.1 shows a positive relationship between the rate of mass transfer and boundary layer thickness. In other words, a larger thickness of hexadecane results in a greater rate of mass transfer of CO2 from the atmosphere into the system. The factor being in the denominator, however, would result in a threshold value where the mass transfer rate converges.

5.3.3. Limitations of the Modified Model

The model in the current study was able to illustrate the general trends in microalgal growth, as well as the changes in nutrients in the open channel cultivation system, with a minimal amount of input. However, there were a number of elements that the model was not able to simulate accurately, including the dynamic changes of the dependent variables in differing physical locations and the detailed interactions between

84 the incoming gas phase and two liquid phases. The main liming factor of the numerical simulation was the available amount of computing power, which restricted the number of mesh elements in the model as well as the number of calculations that could be performed for each iteration. Compared to the scenario in Chapter 4, the solution for multiphase system required a greater number of mesh elements to accurately simulate the thin organic phase, and thus the time required to perform the same number of iterations was significantly greater. Using a three-phase model would have more accurately simulated the interactions between the incoming gas phase and two liquid phases. In addition, creating a sliding mesh to depict the paddlewheel movement and simulate the shear stress applied on the culture would have resulted in a more robust model. The proposed model yet shows promise in that such a simulation is possible, and with the current advances in computing technology, the model has potential to be further improved.

5.4. Conclusions

An integrated model combining microalgal growth kinetics, mass transfer equations, and a VOF model of a two-phase PCM-water system was successfully constructed and validated with a commercial scale open channel raceway for the cultivation of N. salina. Validation with experimentally obtained data showed a good fit for the change in biomass, CO2, and nitrogen concentrations. The proposed model was able to show the improvement of CO2 mass transfer through the PCM interphase, which was attributed to the low surface tension and high solubility of hexadecane. The PCM-

85 implemented system was positively impacted by increasing CO2 input and the thickness of PCM layers. By demonstrating a satisfactory simulation, the model shows potential to be more robust, given increased computing capacity.

Figure 5.1. Close-up view of geometry and mesh used for the two-phase open channel raceway.

(3-dimensional representation created with ANSYS 14.5 DesignModeler™ and .ANSYS

14.5 meshing software)

1.2 Experimental

(a)

) 1 - 1.0 CFD Model

0.8

0.6

0.4

0.2 * Biomassconcentration (g L 0.0 0 10 20 30 40 50 60 Time (d)

Experimental

) (b)

3 60 - CFD Model 50 40 30

concentrationm (g

10 CO 0 * * 0 10 20 30 40 50 Time (d)

50 Experimental

) (c) 3 - 40 CFD Model 35 30 25 20 15 10 5

TotalN concentration (g m 0 0 10 20 30 40 50 Time (d)

Figure 5.2. Changes in concentration of (a) biomass, (b) CO2, and (c) Total N.

(* denotes time periods where data could not be collected) 88

9.0 t = 1 cm t = 2 cm t = 3 cm

8.0

)

- d

7.0

2 - 6.0

5.0

4.0

3.0

2.0 Biomassproductivity m (g 1.0

0.0 0.0004 0.050 0.100 Partial pressure of supplied CO2 (atm)

Figure 5.3. Simulated biomass productivities under varying PCM thicknesses (t) and CO2 loading.

Chapter 6: Evaluation of Methane Production and Macronutrient Degradation in

the Anaerobic Co-digestion of Algae Biomass Residue and Lipid Waste

Summary

Algae biomass residue was co-digested with lipid-rich fat, oil, and grease waste

(FOG) to evaluate the effect on methane yield and macronutrient degradation. Co- digestion of algae biomass residue and FOG, each at 50% of the organic loading, allowed for an increased loading rate up to 3 g VS L-1 d-1, resulting in a specific methane yield of

-1 -1 -1 -1 0.54 L CH4 g VS d and a volumetric reactor productivity of 1.62 L CH4 L d . Lipids were the key contributor to methane yields, accounting for 68–83% of the total methane potential. Co-digestion with algae biomass residue fractions of 33%, 50%, and 67% all maintained lipid degradations of at least 60% when the organic loading rate was increased to 3 g VS L-1 d-1, while synergetic effects on carbohydrate and protein degradation were less evident with increased loading.

6.1. Introduction

As microalgal lipid-derived fuel appears to be a promising alternative to petroleum-based liquid fuel (Singh et al., 2011), the utilization or disposal of post- extraction microalgal biomass, or algae biomass residue (ABR), is a critical issue when

90 considering the commercialization of the fuel production process. ABR, which accounts for approximately 65% of the harvested biomass (Subhadra and Edwards, 2011), can generate additional energy through anaerobic digestion. By integrating anaerobic digestion as an on-site operation with algal lipid production, the efficiency of converting photosynthetically captured energy into more accessible forms can be increased. In addition, the residual effluent from the anaerobic digester can be used as a nitrogen and phosphorus source for microalgal growth, thus reducing the costs for nutrients as well as treating the waste stream. Accordingly, the synergetic integration of anaerobic digestion with microalgae production has been previously explored to improve the commercial potentials of both technologies (Golueke and Oswald, 1959; Mussgnug et al., 2010; Ras et al., 2011; Sialve et al., 2009; Zamalloa et al., 2011). Species-dependent nutrient proportions of carbohydrates (5–23%), proteins (6–52%), and lipids (7–23%) (Brown et al., 1997) affect the potential of microalgae as a substrate for anaerobic digestion.

Based on species and nutrient composition, the theoretical methane potential of

-1 -1 unprocessed whole algae is estimated to be approximately 0.47–0.80 L CH4 g VS d

(Sialve et al., 2009). However, studies on the semi-continuous digestion of whole algae

- typically report lower specific methane yields (SMY) ranging from 0.09 to 0.65 L CH4 g

1 VS d-1, and suggest inhibitory factors including high cell wall resistance to bacterial attack and ammonia toxicity derived from high concentrations of protein (Golueke and

Oswald, 1959; Golueke et al., 1957; Yen and Brune, 2007). The recalcitrance of the algal cell wall structure can be overcome by adding a pretreatment step prior to subjecting the substrate to hydrolytic, acetogenic, and methanogenic microorganisms. Thermal

91 pretreatment of microalgae at 100°C for 8 hours was proposed to improve the methane production efficiency by 33% (Chen and Oswald, 1998). Samson and LeDuy (1983b) also showed an improvement with pretreatments including ultrasonic lysis, disintegration, and thermochemical treatments with acid and alkali. However, excessive energy input to maximize the methane conversion might negatively impact the economic feasibility of this technology.

Issues that stem from high protein concentrations in the substrate can be moderated through co-digestion, a less energy-demanding alternative that can improve the anaerobic digestion performance by adding a secondary substrate that supplies additional nutrients which the initial substrate lacks. The combination of two or more substrates creates a synergistic effect by alleviating the preexisting nutrient imbalance and, in turn, attenuating the inhibition that would otherwise occur during digestion of the individual substrate. In the anaerobic digestion of microalgal biomass, which generally contains superfluous nitrogen, the addition of a carbon-rich co-substrate may facilitate the methane conversion process. For example, the addition of carbon-rich waste paper to a mixture of Scenedesmus spp. and Chlorella spp. resulted in an improved methane yield induced by a balance between carbon and nitrogen in the feed, as well as increased cellulase activity (Yen and Brune, 2007). Soybean oil and glycerin, both rich in carbon, also proved to have positive effects on biogas yield when added to algae harvested from wastewater treatment ponds (Salerno et al., 2009). Similar studies have been performed with cyanobacteria, which are not classified in the same eukaryotic domain as algae, but share similar traits such as photosynthetic metabolism and high protein content (Samson

92 and LeDuy, 1986). Co-digestion of Spirulina maxima with a carbon-rich sewage sludge at a 50% ratio increased the SMY by over two-fold (Samson and LeDuy, 1983a). In contrast, combining swine manure and algal biomass, both with high nitrogen contents, did not result in significant performance improvement (González-Fernández et al., 2011).

When selecting the substrates to be used in co-digestion, it is critical that the co- substrates be mutually propitious. An ideal co-substrate for ABR should have a high carbon-to-nitrogen ratio to minimize the inhibitory effects of ammonia, and should synchronously benefit from the ancillary nutrients as well. Reducing ammonia inhibition is a key issue in the digestion of ABR, because ABR may experience more severe ammonia inhibition than whole algae, due to higher protein content. Materials that contain significant lipid proportions can be such an ideal co-substrate. Compared to carbohydrates and proteins, lipids have high methane potential (Sialve et al., 2009), but their low alkalinity and buffering capacity make lipids more susceptible to inhibition caused by intermediate products such as long chain fatty acids (LCFAs) and volatile fatty acids (VFAs). LCFAs, which are enzymatically detached from the glycerol backbone of triglycerides during anaerobic digestion, are reported to retard microbial activity by disorienting essential groups on the cell membrane (Galbraith and Miller, 1973). VFAs, in high concentrations, can be detrimental to anaerobic digestion and thus are often used as process indicators (Ahring et al., 1995). Therefore, the co-digestion of ABR and a lipid-rich material would concurrently offset the carbon and nitrogen imbalance found in

ABR as well as the lack of alkalinity in the lipids, creating a synergistic effect (Boubaker and Cheikh Ridha, 2007; Yen and Brune, 2007). The well-maintained nutrient balance of

93 the digester inflow could also allow a higher loading capacity, improving the economic feasibility of the system.

In this study, lipid-rich fat, oil, and grease waste (FOG) was hypothesized to be an effective co-substrate in the semi-continuous anaerobic digestion of the residue of

Nannochloropsis salina, an algal species known for its potential as a biodiesel feedstock due to its high lipid content and biomass productivity (Gouveia and Oliveira, 2009). The objectives of this study were to (1) evaluate the effects of ABR loading fraction and organic loading rate (OLR) on methane production, expressed in both SMY, volumetric reactor productivity (VRP), and methane content; and (2) identify the synergetic effects of co-digestion through the degradation of carbohydrates, protein, and lipids.

6.2. Materials and Methods

6.2.1. Substrates and Inoculum

The ABR used in this study was derived from N. salina, grown in photobioreactors and harvested by centrifugation (Touchstone Research Laboratory,

Triadelphia, WV, USA). The continuous centrifuge was operated under a relative centrifugal force of 12,000 g and a volumetric throughput of 0.19 L s-1. Lipids were extracted from the harvested algal biomass by means of electric pulsation, acid hydrolysis, and solvent recovery using hexane (SRS Energy, Dexter, MI, USA) and the resulting ABR slurry was directly used as an anaerobic digestion substrate. FOG was collected from a local oil receiving facility (Recycling and Treatment Technologies of

Ohio, Painesville, OH, USA). Both substrates were stored in tightly sealed plastic 19-L

94 buckets at 4°C prior to use. Effluent from a commercial scale digester fed with wastewater solids (Schmack BioEnergy, Akron, OH, USA) was used as an inoculant, providing the microbial consortia to degrade the organic compounds to biogas. The acetotrophic methanogen content was found to be approximately 2.1 × 108 cells g-1 VS, using the most-probable-number method (Balch et al., 1979). The characteristics of the materials used in the anaerobic co-digestion of algal biomass residue and FOG are listed in Table 6.1.

6.2.2. Operational Procedures

Semi-continuous anaerobic digestion was performed in 1-L glass bottles (Figure

6.1.), which were capped with rubber stoppers having two outlet ports – one connected to a polyvinyl fluoride gas bag (CEL Scientific Corp., Santa Fe Springs, CA, USA) and the other having a Luer taper cap for the anaerobic transfer of feed and effluent. The reactors were constantly agitated in an Innova 43R incubated shaker (New Brunswick, Edison,

NJ, USA) at 150 rpm and 37°C. To create an anaerobic environment, each reactor was initially filled with 0.70 L of inoculant and subsequently flushed with nitrogen gas for 2 min. The reactors were agitated and incubated at 37°C for 3 days to acclimate the microbes to the environment before adding any substrate.

Once the microbes were acclimated and steady daily gas production was confirmed, the digesters were unloaded and loaded on a daily basis. A fixed amount of effluent as designed was removed daily from each reactor and stored at -20°C for further analysis. Each reactor was then fed with an equal amount of substrate using a 50-mL

95 polypropylene syringe with a Luer taper (BD, Franklin Lakes, NJ, USA). Twenty different feeding formulas were prepared, each corresponding to one of four OLRs (2, 3,

4 and 6 g VS L-1·d-1) and one of five ABR loading fractions (0, 33, 50, 67, and 100% of the total organic load). The hydraulic retention times (HRT) of the reactors with OLRs of

2, 3, 4, and 6 g VS L-1·d-1 were 40, 27, 20, and 13 d, respectively. Each treatment had two replicates. In order to ensure sufficient mixing in the reactor, the syringes were purged five times before reactor unloading and after feeding. The polyvinyl fluoride bags connected to the reactors were removed daily for biogas volume measurement and composition analysis, and the emptied bags were reconnected to the reactors afterwards.

6.2.3. Total Solids, Volatile Solids, pH, Total Carbon and Nitrogen

Total solids (TS) content, volatile solids (VS) content, and pH were measured according to the Standard Methods for the Examination of Water and Wastewater

(APHA, 2005). One gram of each sample was placed in a porcelain crucible and dried in a Thelco Model 18 oven (Precision Scientific, Chennai, India) at 105°C for 4 hours. After each sample was brought to constant weight, it was cooled in a desiccator and weighed to obtain TS content. The ash weight was obtained by igniting the sample in an Isotemp muffle furnace (Fisher Scientific, Dubuque, IA, USA) at 500°C for 4 hours, then cooled and weighed. VS content was determined as the difference between the dry weight and ash weight. The pH of each reactor was measured using an AP110 portable pH meter, equipped with an Ag/AgCl reference electrode probe (Accumet, Singapore). A Vario

MAX CNS elemental analyzer (Elementar Analyseneyeteme GmbH, Hanau, Germany)

96 was used for the dry combustion assay of total carbon (ISO, 1995) and total nitrogen

(Sweeny, 1989).

6.2.4. Total Carbohydrates

Total carbohydrate content was measured according to ASTM standard method

(ASTM, 2007), where the available carbohydrates were converted to their monomeric counterparts by acid hydrolysis and then quantified by high performance liquid chromatography (HPLC). An LC-20AB HPLC unit (Shimadzu, Columbia, MD, USA) equipped with an Aminex® HPX-87P column (Bio-Rad Laboratories, Hercules, CA,

USA) was used with a refractive index detector. Deionized water was used as the mobile phase at a flow rate of 0.6 mL min-1. The temperature of the column and detector were maintained at 80°C and 55°C, respectively.

6.2.5. Ammonia-Nitrogen and Total Crude Protein

Ammonia-nitrogen (NH3-N) was determined by a procedure adapted from EPA

Method 350.2 (EPA, 1974) and AOAC International Method 973.49 (Horowitz and

Latimer, 2005). In order to liberate the ammonia, 50 mL of 6.0 N sodium hydroxide was added to each sample diluted with deionized water. The ammonia was distilled into 4% boric acid solution within a B-324 distillation unit (Büchi Labortechnik AG, Flawil,

Switzerland) and titrated with 0.01 N hydrochloric acid, using a DL 22 titrator (Mettler-

Toledo Inc., Columbus, OH, USA). Total Kjeldahl nitrogen (TKN) was obtained through digestion with 98% sulfuric acid in a Tecator digester (FOSS, Eden Prairie, MN, USA),

97 followed by distillation and titration. Total crude protein was determined with AOAC

International Method 2001.11 (Horowitz and Latimer, 2005), in which the TKN was multiplied by a conversion factor of 6.25.

6.2.6. Total Crude Lipids

Solvent extractives were analyzed with a modified Bligh and Dyer (1959) method

(White et al., 1979). A liquid sample with a volume of 6.5 mL was mixed with 8 mL of chloroform and 16 mL of anhydrous methanol. If the original sample was solid, 6.5 mL

-1 of phosphate buffer (prepared with 8.7 g K2HPO4 L neutralized with 1 N HCl to pH 7.4) was added to 1.5 g of sample prior to mixing with chloroform and methanol. After vortex mixing for 5 minutes, each sample was allowed to partition for a minimum of 2 hours. An additional 8 mL of chloroform and 8 mL of deionized water were added to the suspension, mixed and allowed to separate for 24 hours. The upper aqueous phase was removed by suction, and the organic phase was recovered with a Gooch filter crucible, lined with filter paper. The proportions of water:methanol:chloroform were maintained to be 0.8:2:1 (v/v) for the single phase extraction and 0.9:1:1 (v/v) after separation into the second phase. Solvents were removed from the lipids with an Isotemp 282A vacuum oven (Fisher Scientific, Marietta, OH, USA) at 40°C and recovered with a condensing apparatus.

6.2.7. Biogas Volume and Composition

Gas volume was measured daily by liquid displacement. Using a Dyna-Pump

Model #3 vacuum pump (Neptune Products Inc., Dover, NJ, USA), gas from each polyvinyl fluoride bag was transferred to a custom-made, graduated glass tube (Adria

Scientific Glassworks Inc., Geneva, OH). The glass tube was filled with a mixed solution of 3% v/v H2SO4 and 20% w/v Na2SO4·10H2O to prevent the solubilization of carbon dioxide or methane. Proportions of methane, carbon dioxide, oxygen, and nitrogen in the collected biogas were measured with a HP 6890 gas chromatograph (Agilent

Technologies, Santa Clara, CA) equipped with a 3.05 m, stainless steel, 45/60-mesh 13X molecular sieve column and a thermal conductivity detector. Helium was used as the carrier gas, with a flowrate of 5.2 mL min-1. The temperatures of the injector and detector were set to 150 and 200°C, respectively. For each sample injection, the column temperature was held at 40°C for 2 min, increased to 60°C at a rate of 20°C min-1, and held for 5 min.

6.2.8. Statistical Analysis

Using a method suggested by Yum and Pierce (1997), steady state values for all response variables were determined by locating a time point where the slope of the response versus time curve did not statistically differ from zero. Comparisons for all steady state values were performed using the Tukey-Kramer method, while analysis of variance tests were performed with a significance level of 0.05 using JMP® 9.0.0 (SAS

Institute Inc., Cary, NC, USA).

6.3. Results and Discussion

6.3.1. Effects of Co-digestion on Methane Yield and Content

-1 -1 SMY, expressed in L CH4 g VS d , is used to express how effectively the

-1 -1 substrate is converted to methane; while VRP, expressed in L CH4 L d , shows how much methane can be produced per unit reactor working volume. The optimization of both response parameters is important in maximizing the efficiency of the anaerobic digestion process. The comparison of the steady state SMY and VRP resulting from varying ABR loading fractions and OLRs is shown in Figure 6.2. All steady state values were achieved between one and two HRTs after initial feeding. Among all feed formulas,

-1 -1 -1 -1 100% FOG had the highest SMY of 0.69 L CH4 g VS d at an OLR of 2 g VS L d .

-1 -1 The SMY of 100% ABR was no higher than 0.13 L CH4 g VS d , implying FOG as a more readily digestible substrate with a higher methane potential. It was noted that the

SMY of 100% ABR was on the lower end of the spectrum when compared to those reported for whole microalgae (Golueke and Oswald, 1959; Golueke et al., 1957; Yen and Brune, 2007), mainly due to the decreased energy content after removing lipids from the cells. For both substrates, increasing the OLR to higher than 2 g VS L-1 d-1 resulted in digester failure – characterized by a steady state SMY value of zero – indicating possible inhibition of the methanogenic communities when subject to overloading.

In comparison to the digestion of 100% FOG and 100% ABR, co-digestion allowed an increase in OLR up to 3 g VS L-1 d-1. At an OLR of 3 g VS L-1 d-1, the co-

-1 -1 digestion of 50% ABR and 50% FOG resulted in a SMY of 0.54 L CH4 g VS d , a

100 significant improvement in digestion performance compared to 100% FOG or 100%

ABR at the same loading rate (p < 0.05). However, when the OLR was greater than 3 g

VS L-1 d-1, the same inhibitory behavior as seen in the digestion of 100% FOG or 100%

ABR was observed. The SMY of 50% ABR at an OLR of 3 g VS L-1 d-1 was 22% less than that of 100% FOG at 2 g VS L-1 d-1, but the VRP displayed a 17% increase from

-1 -1 1.38 to 1.62 L CH4 L d . These results suggest that scale-up of co-digestion may allow an increased organic feed throughput and biogas productivity without an additional increase in processing capacity. As shown in Figure 6.2, the ABR loading fraction of

50% resulted in optimal responses when increasing the OLR. When the OLR was increased from 2 to 3 g VS L-1 d-1 for a feed formula with more FOG (67%) than ABR

(33%), there was a 76% and 64% decrease in SMY and VRP, respectively, similar to what was observed for 100% FOG. The increase in ABR loading fraction from 0% to

50% appeared to ameliorate the adverse impact of overloading lipids. On the other hand, when the OLR was increased from 2 to 3 g VS L-1 d-1 for a feed formula with more ABR

(67%) than FOG (33%), there was a 26% decrease in SMY and a 8% increase in VRP.

The ameliorative effect of ABR addition diminished when there was excess ABR. Thus, it was determined that the optimal condition for the co-digestion of ABR and FOG with maximum throughput was in the vicinity of a 50% ABR loading fraction.

In addition to SMY and VRP, the methane content in the biogas should be sufficiently high to ensure high process efficiency and minimize purification needs. High methane content in an anaerobic digester implies a steady balance of methane and carbon dioxide, which are products of methanogenesis and acetogenesis, respectively. On the

101 contrary, low methane content implies some form of inhibition that diminishes the methanogenic activity within the microbial consortium. It is shown in Figure 6.3 that there was an optimal ABR loading fraction of 50% at a given OLR that resulted in greater methane content than other feeding formulas. Co-digestion at 50% ABR may have promoted an optimal balance between methanogenesis and acetogenesis so that the production of methane relative to that of carbon dioxide could be maximized. Overall, methane contents ranged from 33% to 69%. Digesters with methane contents of 60% or greater had ABR loading fractions less than 100% and OLRs less than 4 g VS L-1 d-1.

Methane contents of 60% or greater were also associated with the reactors with greater

-1 -1 methane production, having SMYs of at least 0.27 L CH4 g VS d and VRPs of at least

-1 -1 0.73 L CH4 L d . Although the data is not shown, it was noted that there was a moderately positive correlation (R2 = 0.60) between methane content and digester pH.

Similar to pH, methane content can be used as a means of indicating the health and stability of the anaerobic digestion process.

6.3.2. Effects of Co-digestion on Nutrient Reduction

As shown in Figure 6.4a, the co-digestion of ABR and FOG resulted in carbohydrate reductions ranging between 71% and 88%. When digesting 100% ABR, an average of 83% of the carbohydrates were converted to degradation products, whereas the digestion of 100% FOG had an average carbohydrate reduction of 53%. At a given loading rate, there was no evident increase (p > 0.05) in carbohydrate reduction when performing co-digestion; however, the decrease in reduction with increasing OLR was

102 less severe in the presence of ABR (p < 0.05). The carbohydrate reduction decreased by

27% when increasing the OLR of 100% FOG from 2 to 4 g VS L-1 d-1, while reactors that included some portion of ABR had reductions of 14% or less when the OLR was increased from 2 to 4 g VS L-1 d-1. In a mixed substrate environment, microbial growth will tend to occur more in communities that favor the dominant substrate (Hobson et al.,

1974). In the case of 100% FOG, the consumption of lipids may have been preferred over that of carbohydrates for the microbes that were able to metabolize both substrates.

Coupled with an increased removal of microbes due to a greater volumetric displacement at higher OLRs, the adverse impact on carbohydrate reduction was more evident in 100%

FOG than in other feed formulas.

The positive effect of co-digestion on the degradation of lipids was more evident than on carbohydrates. Figure 6.4b shows that the mean lipid degradation during co- digestion was greater than that for either 100% FOG or 100% ABR at OLRs of 3 and 4 g

VS L-1 d-1. At 100% FOG, 70% of lipids were reduced at an OLR of 2 g VS L-1 d-1, but the degree of reduction drastically decreased by 41% when the OLR was increased to 4 g

VS L-1 d-1. The lipid degradation from the digestion of 100% ABR was only as high as

42% at OLR of 2 g VS L-1 d-1, and was not significantly affected by OLR (p > 0.05). By combining the two substrates, high lipid degradations of at least 60% were maintained while simultaneously allowing increased OLRs of up to 3 g VS L-1 d-1. When the OLR was further increased to 4 g VS L-1 d-1, there were drastic decreases in lipid reduction at

ABR fractions of 33% and 67%, but the decrease was less in the digestion of 50% ABR, resulting in a lipid degradation of 59%. Considering that the FOG was comprised of 75%

103 lipids and most of the chemical energy in the feedstock was derived from the lipids, it could be concluded that maintaining the lipid degradation efficiency is the critical factor to allow increased OLR in the anaerobic digesters. By performing co-digestion, an optimal nutrient and alkalinity balance for lipolytic activity was obtained, hence increasing the lipid reduction.

In comparison with carbohydrates, proteins were a more significant factor in the nutrient composition, accounting for 48% of the total solids in ABR and 10% in that of

FOG. In the anaerobic digestion process, proteins in the substrate are broken down into amino acids, while proteins in the form of microbial biomass are constantly synthesized as a result of heterotrophic metabolism. Thus, the total crude protein content in the digester effluent cannot clearly explain the protein reduction from the substrate.

However, ammonia is a major byproduct of protein degradation, and can be used as an indicator to assess the amount of protein that was reduced in the digester. The amount of

NH3-N in the effluent per unit mass of protein fed was used to quantify the degree of protein degradation from the feed stream without taking into account the microbial generation. Although the total amount of ammonia released from 100% ABR was greater than that from 100% FOG, it is shown in Figure 6.5 that feed with 100% FOG produced up to 2.4 times more ammonia per unit mass of protein than that with 100% ABR, implying that protein degradation was relatively more active in FOG than in ABR. It was also noted that increasing the OLR of 100% FOG from 3 to 4 g VS L-1 d-1 decreased the amount of NH3-N in the effluent per unit mass of protein fed by 38%, while for 100%

ABR the increase in OLR did not result in any significant decrease in NH3-N (p > 0.05).

104

Similar to carbohydrates, the reduction of proteins in the substrate was less affected by

OLR when the proportion of FOG was less than 100%.

In addition to comparing the degree of protein degradation, it was also important to observe the ammonia concentration within the digester. Elevated NH3-N concentrations in the range of 4051–5734 mg L-1 resulted in a 56.5% loss of methanogenic activity in the semi-continuous anaerobic digestion of potato juice (Koster and Lettinga, 1988). The NH3-N concentration in the digester effluent from the current study was 1756–4968 mg L-1, suggesting the possibility of ammonia inhibition in some of the digesters. A high ammonia concentration in the digester medium not only has a toxic effect to methanogenic archaea, but also decreases the deamination activity of proteolytic bacteria (Gallert and Winter, 1997). The combination of FOG and ABR only appeared to create a dilution effect, rather than improving the degradation activity.

6.3.3. Assessment of Co-digestion Performance with Theoretical Methane Potential

The SMY of each feed formula was compared to its theoretical methane potential, calculated based on the yield estimates of carbohydrates, lipids, and proteins. Angelidaki and Sanders (2004) were able to obtain theoretical methane potentials of these substrate components based on the following equation adapted from Symons and Buswell (1933):

( )

→ ( ) ( )

105

Although the methane potentials from Equation 6.1 do not take into account the nutrients required for cell maintenance, the degree of possible conversion can be assessed from the calculated values. The steady state values of SMY from Figure 6.2 were compared to the corresponding methane potential for each feed configuration, shown in Figure 6.6. An

OLR of 2 g VS L-1 d-1 resulted in SMYs of at least 90% of the calculated methane potential, with the exception of 100% ABR, which had an SMY of 42% of the theoretical value. The low SMY values of 100% ABR could be explained by the resistance of residual compounds in the cell wall to bacterial attack, which may still be present after lysis for lipid extraction (Golueke et al., 1957). When the OLR was increased to 3 g VS

L-1 d-1, co-digestion with 33% and 67% ABR reached 22% and 68% of the methane potential, respectively, while the digestion of both 100% FOG and 100% ABR experienced a significant drop in SMY (p < 0.05). The feed with an ABR loading fraction of 50% showed an SMY that was 23% greater than the theoretical methane potential, indicating a synergetic effect caused by mixing ABR and FOG at an optimal ratio.

Using the experimentally acquired nutrient concentrations and the methane potentials from Equation 6.1, the contributions of carbohydrates, proteins, and lipids to the methane yield were calculated as shown in Figure 6.5. Lipids were responsible for

94% and 46% of the methane potential in FOG and ABR, respectively. Even though lipids accounted for only 15% of the total solids in ABR, they accounted for a significant portion of the methane potential (p < 0.05) due to their relatively high energy content compared to carbohydrates and proteins. For the co-digestion configurations, lipids accounted for 68–83% of the total methane potential. Maximizing the lipid content will

106 increase the potential methane yield, but excessive portions can induce LCFA and VFA inhibition, which can reduce the pH in the digester leading to decreased lipid degradation and methane production. On the other hand, increasing the protein content can help increase the lipid degradation stability with favorable alkalinity levels, but excessive amounts of ammonia from the protein can decrease the methanogenic activity.

6.4. Conclusions

Co-digestion of ABR and FOG resulted in improved methane yields at OLR of 3

-1 -1 -1 -1 g VS L d . VRPs of up to 1.62 L CH4 L d showed that co-digestion could increase reactor productivity while allowing for higher feed throughput. During co-digestion, the degradation of carbohydrates and proteins did not change significantly with increasing

OLR. Lipid degradation during co-digestion was greater than that in the digestion of

100% FOG or 100% ABR at increased OLRs. Additional alkalinity provided by the 50%

ABR helped retain the lipid degradation efficiency when the OLR was increased up to 4g

VS L-1 d-1.

107

Table 6.1. Characteristics of substrates and inoculant.

Characteristic Unit ABR FOG Inoculant

Total solids %, w/w 14.5 ± 0.0 9.3 ± 0.1 10.1 ± 0.0

Volatile solids % of TS, w/w 77.2 ± 0.2 85.3 ± 0.1 55.4 ± 0.1

pH - 5.9 ± 0.0 4.4 ± 0.0 8.1 ± 0.0

Total carbon % of TS, w/w 37.0 ± 0.0 58.1 ± 0.0 45.7 ± 0.0

Total nitrogen % of TS, w/w 8.4 ± 0.0 3.5 ± 0.0 6.6 ± 0.0

Carbon-to-nitrogen ratio - 4.4 ± 0.0 16.5 ± 0.0 7.0 ± 0.0

Total carbohydrates % of TS, w/w 4.1 ± 0.4 0.8 ± 0.0 1.3 ± 0.0

Total crude lipids % of TS, w/w 14.6 ± 0.1 75.4 ± 10.1 10.5 ± 4.8

Total crude protein % of TS, w/w 47.9 ± 1.2 10.2 ± 1.0 50.9 ± 1.1

+ -1 Ammonia (NH3, NH4 ) g L 15.2 ± 0.1 8.4 ± 2.1 -

108

Figure 6.1. 1-L reactor for the semi-continuous digestion of ABR and FOG.

109

(a) - 0.9 VS VS d

0.8

- g

4 4 0.7

0.6

) 0.5 1 0.4 0.3 0.2

0.1 Specific Specific methane CH yield (L 0.0 2 3 4 6 -1 -1

Organic loading rate (g VS L d )

)

1 1.8

(b) -

- 1.6

4 1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

2 3 4 6 Volumetric Volumetric reactor productivity CH (L Organic loading rate (g VS L-1 d-1)

0% ABR 33% ABR 50% ABR 67% ABR 100% ABR 100% FOG 67% FOG 50% FOG 33% FOG 0% FOG

Figure 6.2. Steady state (a) SMY and (b) VRP at varying ABR loading fractions and

OLR.

110

content (%) content

4 30 CH

0 0 33 50 67 100 ABR loading fraction (% of total organic load)

2 g VS/L·d 3 g VS/L·d 4 g VS/L·d 6 g VS/L·d

Figure 6.3. Steady state methane content at varying ABR loading fractions and OLR.

111

(a) 100

40 Carbohydrate Carbohydrate reduction (%) 20

0 2 3 4 Organic loading rate (g VS L-1 d-1)

100 (b)

40 Lipid Lipid reduction (%)

0 2 3 4 Organic loading rate (g VS L-1 d-1)

0% ABR 33% ABR 50% ABR 67% ABR 100% ABR 100% FOG 67% FOG 50% FOG 33% FOG 0% FOG

Figure 6.4. Steady state (a) carbohydrate reduction and (b) lipid reduction profiles at varying ABR loading fractions and OLR.

112

N in in N effluent/protein fed(%, w/w)

- 3

NH 4

0 2 3 4

Organic loading rate (g VS L-1 d-1)

0% ABR 33% ABR 50% ABR 67% ABR 100% ABR 100% FOG 67% FOG 50% FOG 33% FOG 0% FOG

Figure 6.5. NH3-N in the digester effluent per unit mass of protein fed at varying ABR loading fractions and OLR.

113

0.7

0.6

VS VS addedd)

4 0.5

0.4

0.3

0.2

0.1 Theoretical methane potential (LCH

0.0 0 33 50 67 100

ABR loading fraction (% of total organic load)

Carbohydrates Protein Lipids

Figure 6.6. Contributions of carbohydrates, protein and lipids on the theoretical methane potentials of various feed formulas.

114

Chapter 7: Improvement of the Energy Return on Investment of Microalgal

Cultivation in an Open Channel Raceway

Summary

Nannochloropsis salina cultivation in an open pond raceway was reported to have comparable energy return on investment (EROI) values to similar competing technologies, although significantly lower than those found in conventional technologies based on fossil fuels. The resulting EROI for an open pond raceway with and without phase change material (PCM) treatment were 1.13 and 0.82, respectively. The increased use of carbon dioxide (CO2) and mixing power showed negative impacts on the EROI.

The algae biomass residue (ABR) after lipid extraction accounted for 69.7–78.5% of the total energy output. Utilization of the ABR after lipid extraction may drastically increase the EROI. A maximum of approximately 10% of the energy (310 GJ year-1) produced from microalgae was returned through the anaerobic digestion of ABR.

7.1. Introduction

Energy return on investment (EROI) is an important term for assessing the feasibility of an energy production process. The feasibility of energy infrastructure is heavily dependent on the amount of energy into its production, including heating,

115 mechanical and chemical energy. EROI is defined as the ratio between the amount of energy produced and the diverted energy required for production. A fuel with a high

EROI requires a relatively small fraction of its produced energy to maintain production, thus having more net energy production (Gagnon et al., 2009). It is therefore imperative for a potential fuel source to have an EROI value that is comparable to that of currently mass-produced fuels, i.e., conventional fossil fuels. Conventional fuels have high EROI values, mainly due to the fact that the fuels are chemically ready to use, and the majority of the required energy is devoted to the acquisition and refining process (Cleveland,

2005). Newly emerging renewable fuels, on the other hand, have relatively lower EROI, since large fractions of energy must be used for the synthesis of the fuel (Murphy et al.,

2010).

Microalgae has gained interest as a means to photosynthetically convert solar energy into the form of biomass with high lipid concentrations, which can be subsequently converted to liquid transportation fuels in the form of biodiesel or jet fuel

(Rawat et al., 2013). In order for algal energy production systems to be beneficial and competitive in relation to currently established energy production systems, not only does the EROI need to exceed the threshold value of 1, the ratios should be somewhat comparable, if not greater than those of the established systems. Despite the extensive research in the mechanics and economics of microalgal biodiesel, there is currently no industrial facility for the commercial production of biodiesel from microalgae, and hence there is no commercial algal biofuel industry to serve as a reference. The EROI of the biodiesel derived from Chlorella has been estimated to be low, ranging from 9.2 × 10-5 to

116

0.36 and thus indicating that the large scale production of microalgae-based biodiesel is challenging (Beal et al., 2011). The EROI of the biomass production of Nannochloropsis salina in various cultivation environments was explored, to find that the use of horizontal tubular photobioreactors is not economically feasible (EROI < 1), while the use of flat- plate photobioreactors or open channel raceways have greater feasibilities, having EROI values of 1.65 and 3.05, respectively (Jorquera et al., 2010).

In the preceding chapters, techniques to improve the productivity of N. salina in open channel raceways were explored and the use of a phase change material PCM) was suggested to improve the mass transfer of carbon dioxide (CO2) and mitigate evaporative losses. The current study utilized the EROI concept in order to further evaluate the feasibility of the technology. In addition, the energy balance of the anaerobic digestion of the algae biomass residue (ABR) after lipid extraction was evaluated to determine if the conversion of biomass to biogas was beneficial to the overall process.

7.2. Methods

7.2.1. Model Description

Microalgae can be converted into various forms of energy, such as biodiesel from the transesterification of extracted lipids, and methane in biogas produced via anaerobic digestion. The schematic in Figure 7.1 shows the possible pathways taken into consideration in the current study. The goal of the proposed process is to effectively utilize the accumulated biomass to the fullest extent where biofuels are readily available.

The concept of coupling microalgal biodiesel production and anaerobic digestion has

117 been previously proposed as a means to make microalgae-derived biofuels more sustainable (Ehimen et al., 2011; Sialve et al., 2009). By utilizing the algae biomass residue (ABR) from the lipid extraction process as a substrate, the organic carbon that has not been converted to biodiesel can be converted to biogas. Methane, which constitutes

60–70% of the biogas volume, can be separated from the bulk volume to be used in a purer form as a transportation fuel, i.e., compressed natural gas. The remaining CO2, along with the residual nutrients from the digester effluent, can in turn be reused in the microalgae cultivation process – hence creating a closed-loop process for the production of bioenergy.

It should be noted that the performance of a biofuel and its production process cannot be entirely assessed by EROI alone, and other factors including environmental and socio-economic impacts should also be taken in consideration. However, the EROI as a representation of resource quality, availability, and acquisition efficiency, is a useful numerical tool in determining the net energy gain of a potential resource and comparing its feasibility in terms of energy with other conventional means of energy (Murphy et al.,

2010).

7.2.2. Assumptions

Nannochloropsis salina grown in a 120-m3 open channel raceway located in

Wooster, OH (40.8092°N, 81.9372°W) was used as the template for the current study.

Details of the system are specified in Section 3.2.2. The raceway was located outdoors and exposed to sunlight on a diurnal cycle. Thus the solar energy input was assumed to

118 be a free source and was not included in the calculations. The annual system biomass production was assumed to be 100,000 kg. The calorific value of N. salina was determined based the total solid (TS) and volatile solid (VS) content acquired from experimental assumed energy content of its protein, carbohydrate, and lipid components,

(Jorquera et al., 2010). The energy content of whole algae biomass was calculated to be

31.5 MJ/kg, while the energy content of algal lipids alone was 39.0 MJ kg-1 The presence of a PCM layer on the system’s surface was studied, as well as the effects of varying paddlewheel rotational velocities, partial pressures of supplied CO2, and PCM layer thicknesses.

Lipid extraction and the transesterification process to produce biodiesel in the form of fatty acid methyl esters (FAMEs) followed the solvent based, wet process proposed in Section 3.2.6. The processing yields and required facilities for the extraction and transesterification of algal lipids were assumed to be equivalent to those of soybean oils (Xu et al., 2011). Specifically, 0.24 MJ of electricity and 0.76 MJ of heat were assumed to be used for the lipid extraction of 1 kg of algal biomass, and 0.35 MJ of electricity and 1.75 MJ of heat were assumed for the conversion of crude algal lipids to

FAMEs and glycerol (Sheehan et al., 1998). The total efficiency for converting the lipids to biodiesel was assumed to be 90%.

The ABR after the lipid extraction process accounts for approximately 65% of the total harvested biomass (Subhadra and Edwards, 2011) and has potential to be converted into biogas via anaerobic digestion. However, the high protein content (6–52%) of microalgal biomass often results in excessive ammonia production, which may be

119 inhibitory to the digestion process and cause low methane yields (Brown et al., 1997;

Golueke et al., 1957). Thusly the anaerobic digestion of N. salina was assisted by the co- digestion of a lipid-rich waste material, as described in Section 6.2. Energy balance scenarios for the digestion under varying organic loading rates (2–6 g VS L-1·d-1) and

ABR loading fractions (33–100%) were compared, assuming the energy input of the lipid co-digestate to be 37.7 kJ g-1. Energy input for mixing in the digester was considered negligible in comparison to that for maintaining reactors at a constant temperature of

37°C (Lübken et al., 2007).

7.2.3. Calculation of EROI

Given the annual biomass production (Ytot) and average lipid content (coil), the annual lipid production (Yoil) can be calculated as:

In this analysis, the energy diverted back into the system (Ediv) was defined as:

-1 where annual energy spent for capital and repairs (GJ year ),

-1 annual energy spent for water supply (GJ year ),

-1 annual energy spent for evaporative losses (GJ year ),

-1 annual energy spent for mixing (GJ year ),

-1 annual energy spent for nutrient addition (GJ year ),

-1 annual energy spent for CO2 injection (GJ year ),

-1 annual energy spent for dewatering with a centrifuge (GJ year ), 120

-1 annual energy spent for lipid extraction (GJ year ), and

-1 annual energy spent for PCM (GJ year ).

-1 -1 Values of Ecap, Edew, and Eext were estimated to be 80.4 GJ year , 95.4 GJ year and

-1 240.1 GJ year (Xu et al., 2011). The energy equivalents for water and CO2 supply were

-1 -1 1.33 kJ L and 7.33 MJ kg , respectively (Beal et al., 2011). Emix was calculated as:

( )

3 where reactor volume (m ),

-3 mixing power requirement (W m ), and

daily volumetric productivity (kg m-3·d-1).

Enut was calculated as:

-3 -1 where rN ≡ mean removal rate of nitrogen (kg m year ),

-1 eN ≡ energy equivalent of nitrogen (GJ kg )

-3 -1 rP ≡ mean removal rate of phosphorus (kg m year ),

-1 eP ≡ energy equivalent of phosphorus (GJ kg )

Energy equivalents for nitrogen and phosphorus were 26.3 MJ kg-1 and 8.6 MJ kg-1, respectively (Beal et al., 2011). The resulting EROI was calculated as:

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7.3. Results and Discussion

7.3.1. Impact of PCM on the Energy Balance

Energy flows for processing 100,000 kg of N. salina were compared for cultivation scenarios in an open channel raceway with and without the treatment of PCM

(Figure 7.1). The resulting EROI for producing biodiesel from an open pond raceway with 90% efficiency was slightly lower than the reported value of 1.3 from Hall and

Klitgaard (2012). The low EROI was mainly attributed to the low volumetric productivity obtained from the experimental data. Algal biomass productivity is heavily dependent on the photosynthetic organism’s metabolism, ability to convert CO2 to biomass, and environmental conditions. It should be noted that the location and seasonal conditions were not optimal for the growth of N. salina. Higher productivities are observed in commonly commercialized cultures such as Chlorella (0.15–0.4 g L-1 d-1). There is potential for N. salina to have higher productivities, as seen in the case with a PCM layer on the raceway surface. The implementation of PCM resulted in a 37.9% increase in lipid content and 2-fold increase in volumetric productivity. In addition, the amount of water required to compensate for evaporative losses were 78.6% less than the control. The benefits from applying the PCM to the system outweighed the energy costs, resulting in a positive gain in EROI from 0.82 to 1.13. Nonetheless, the reported EROI values are far less than those of conventional fossil fuel, which typically are an order of magnitude greater (8–35) (Cleveland, 2005; Gagnon et al., 2009). Beal et al. (2011) also performed similar net energy analyses on open pond raceway systems and obtained EROI values which were several orders less than those seen in Table 7.1. This is mainly because the

122 productivities of the systems used in the study were extremely low, and many auxiliary energy inputs such as temperature control (greenhouse fans and propane heating) and pumps were added.

The key potential for increasing the EROI in the microalgal cultivation process can be found in its biomass residuals after lipid extraction. Although the algal lipids are key in producing a liquid fuel with high energy values, much of the chemical energy

(69.7–78.5%) remains in the biomass residuals. Thus the biorefinery approach is suggested to derive value-added chemicals from the biomass, such as ethanol from starch components (Nguyen and Hanh, 2012) and animal feed (Gatrell et al., 2014). The maximum EROI for using the microalgal biomass in its totality was 7.64–9.04, bringing it up to par with those of solar energy or shale-derived oil (Hall and Klitgaard, 2012).

Although the complete energy recovery from the residual biomass is unlikely, the high

EROI values serve as an indicator of the utilization potential of microalgal biomass.

7.3.2. Impact of Algae Cultivation with PCM on EROI

Even in the case of N. salina, the EROI can fluctuate depending on various operating conditions. In Chapter 4, the cultivation of the microalgae in an open channel raceway was simulated with changing paddlewheel rotational speeds (2.5–10.0 rad s-1) and inflowing CO2 partial pressures (0.004–0.1 atm). Although the increase in paddlewheel rotational speed and inflowing CO2 partial pressure positively impacted the biomass concentration in the system (Figure 4.5), a contrasting result was seen for both of the parameters (Figure 7.2a). The EROI for biodiesel production was as high as 1.05

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-1 when the CO2 partial pressure was 0.004 atm and paddlewheel speed was 2.5 rad s . The results imply that the feasibility of an energy production system cannot be assessed with the output yield alone. In the current case, the energy associated with mixing and CO2 supply accounted for 20.6% and 11.2% of the diverted energy. Similar EROI trends seen in the control simulation were observed the simulation with the PCM treatment when the

CO2 concentration was altered (Figure 7.2b); the maximum EROI was 1.18 when the

CO2 concentration was 0.004 atm. Changing the thickness of the PCM layer, however, did not have a significant effect on the energy balance (p > 0.05). The impact of the PCM treatment on the raceway surface showed that in the best-case scenario for EROI when the CO2 partial pressure was 0.004 atm and PCM thickness was 0.03 m. In all cases, the addition of PCM resulted in an increased EROI when compared to the control.

The resulting maxima of EROI found in this study were far less in comparison to those of conventional fossil fuels, which can be more than ten-fold greater with current recovery methods (Cleveland, 2005; Hall and Klitgaard, 2012). However, the current study proposes that through optimization, the energy input for producing microalgal biomass can be minimized, and hence the technology has potential for feasible commercial production. Considering that the continuous consumption and subsequent depletion of fossil fuels are affecting their respectable EROI to steadily decrease over time (Hall et al., 2008), it is possible that renewable energy technologies including microalgal biodiesel may be more competitive in the near future.

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7.3.3. Improvement of the EROI in Algal Energy Production Systems

As Equation 7.5 suggests, the EROI of an algal energy production system is directly governed by its numerator (E0) or denominator (Ediv). By increasing the productivity of the system (E0) or decreasing the energy expenses involved in production

(Ediv), the EROI can be improved. Algal biomass production capacity can be improved by increasing the productivity or the overall lipid content of the algal cells. This can be achieved by means including strain selection, environmental manipulation, or genetic modification. The system productivity can also be improved by maximizing the solar energy input, and so the geographical latitude of the production facility is also critical

(Chisti, 2008). Past efforts from the Aquatic Species Program (ASP), funded by the U.S.

Department of Energy, focused on algal strain isolation and characterization as well as analyses of outdoor demonstration-scale mass culture systems (Sheehan et al., 1998). As a result, current knowledge on the productivity of different species of microalgae is very broad.

7.3.4. Impact of Anaerobic Digestion of ABR

As mentioned in Section 7.3.1, at least 70% of the chemical energy in the microalgal biomass can be recovered with technologies other than lipid extraction and subsequent biodiesel conversion. Anaerobic digestion is an efficient method to convert organic carbon into methane, with conversion efficiencies above 75% (Charles et al.,

2011). The energy balance in Figure 7.3 serves as a measure to assess if the addition of the lipid-rich fat, oil and grease waste is beneficial in recovering the chemical energy

125 from the algae biomass. Due to the high energy content of the co-digestate that was added into the system, the resulting net energy balances were negative for many of the configurations. The configurations with the highest net energy of 310 GJ year-1 was 50%

ABR at 3 g VS L-1 d-1. This was equivalent to approximately 10% of the original chemical energy produced in the form of microalgae during the cultivation step.

7.4. Conclusions

The EROI analysis of N. salina production in an open channel raceway demonstrated that techniques such as the application of a PCM may be beneficial to the overall energy balance. The application of PCM layer on the raceway surface to attenuate evaporative losses and enhance mass transfer was found to have a positive effect on the

EROI. The increased use of energy intensive parameters such as mixing and CO2 input resulted in the decrease of EROI. The ABR from the biodiesel production process accounts for a significant amount of energy that can be recovered. With the thorough optimization of the fuel production process, and consideration of utilizing ABR, microalgae-based biodiesel shows potential for commercialization.

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Table 7.1. EROI estimation of N. salina cultivation in an open channel raceway with and without PCM treatment.

Variable Units Control With PCM

I. Energy output from algal biomass -3 -1 Daily volumetric productivity (p) kg m ·d 0.15 0.30 3 Required reactor volume (VR) m 1,826 913 -1 Annual biomass production (Ytot) kg year 100,000 100,000 -1 Energy output (E0,tot) GJ year 3,155 3,155 II. Energy output from lipids only

Average lipid content (coil) % 17.4 24.5 -1 Annual lipid production (Yoil) kg year 17,400 24,500 -1 Energy output (E0,oil) GJ year 679 956 III. Energy diverted back to system -1 Capital and repairs (Ecap) GJ year 80.40 80.40 -3 Mixing power requirement (Pmix) W m 3.11 3.11 -1 Water supply (EH2O) GJ year 2.43 1.21

Evaporation (Eevap) 4.86 1.04 -1 Mixing energy consumption (Emix) GJ year 134.35 67.18 -1 Growth nutrients (Enut) GJ year 22.27 30.31 -1 CO2 injection (ECO2) GJ year 73.30 73.30 -1 Dewatering, centrifugation (Edew) GJ year 95.40 95.40 -1 Lipid extraction (Eext) GJ year 240.12 240.12 -1 PCM (EPCM) GJ year 0 79.52 -1 Total diverted energy (Ediv) GJ year 653.13 668.48 -1 Transesterification (Etra) GJ year 92.00 92.00

EROI for total biomass (EROItot) - 7.64 9.04

EROI for oil production (EROIoil) - 1.04 1.43 EROI for biodiesel production (η=0.9) - 0.82 1.13

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CO2

Recycled water and nutrients

Sunlight Microalgae H O Centrifuge 2 Production CO2 Algae slurry Organic Cell waste disruption

Algae biomass residue Biogas Anaerobic Gas Extraction Methane digestion separator Recycled solvent Effluent Solid Solvent Centrifuge residuals removal

Lipids FAME Trans- (Biodiesel) esterification Glycerol

Nutrients (e.g., N, P)

Figure 7.1. Integrated process schematic of microalgae cultivation, lipid extraction, biodiesel production, and anaerobic digestion.

(Pathways used for EROI calculations shown in solid lines)

128

(a) 1.2

1.0

0.8 pCO2 = 0.004 0.6 pCO2 = 0.05

0.4 pCO2 = 0.10

0.2 EROI EROI forbiodiesel production

0.0 2.5 5.0 10.0 Rotational velocity of paddlewheel (rad s-1)

(b) 1.4

1.2

1.0 pCO2 = 0.004 0.8 pCO2 = 0.05 0.6 pCO2 = 0.10 0.4

EROI EROI forbiodiesel production 0.2

0.0 0.01 0.02 0.03 PCM layer thickness (m)

Figure 7.2. Simulated EROI values for varying paddlewheel rotational velocities (ω), partial pressures of supplied CO2, and PCM layer thicknesses in open channel raceways

(a) without PCM and (b) with PCM.

129

)

b 1 400 - 300 200 100 0 -100 -200

Net energy produced (GJ (GJ year produced energy Net -300 -400 -500 2 3 Organic loading rate (g VS L-1 d-1) 50% ABR 67% ABR 100% ABR 50% FOG 33% FOG 0% FOG

Figure 7.3. Energy balances of the anaerobic digestion of algae biomass residue with varying organic loading rates and loading fractions.

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Chapter 8: Conclusions and Suggestions for Future Research

8.1. Conclusions

Microalgae with high growth rates and lipid contents have considerable potential as a biodiesel feedstock. The aforementioned study explored various approaches to improving the biomass productivity of Nannochloropsis salina in a demonstration scale open channel raceway. The addition of a phase change material (PCM) was shown to double the microalgal biomass productivity. Algal biomass concentrations as high as 1.14 g L-1 were observed in the presence of PCM. PCM was able to mitigate the diurnal and seasonal temperature fluctuation, minimize of water losses that occurred due to evaporation, and also maintain elevated carbon dioxide (CO2) concentrations in the raceway medium. The removal rate of nitrate-based nitrogen (NO3–N) was increased almost three-fold in the PCM-applied raceway, while the rates of ammonium-based nitrogen (NH4–N) and phosphate-based phosphorus (PO4–P) were not impacted.

Microalgal biomass from the PCM-covered raceway system had 40.8% increase in lipid content, and decreased concentrations of EPA were observed while those for its precursors increased. The improved performance by applying PCM demonstrated the commercial potential of microalgae cultivation in an open channel raceway.

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In an attempt to better understand the microalgal growth mechanisms in a scaled up environment, the open channel raceway system was modeled using computational fluid dynamics (CFD). The biological kinetics of microalgae were combined with the conservation of mass and momentum based on the Navier-Stokes equations. The simulation of the integrated models provided adequate predictions of microalgal biomass,

CO2, and nitrogen concentrations through 56 d of operation. Biomass concentrations were observed to be variable depending on the physical position within the cultivation environment, and the formation of dead zones were observed at the hairpin bends at elevated biomass concentrations greater than 0.42 g L-1. A 17.6% increase in microalgal biomass productivity was shown when the partial pressure of CO2 in the aerated flue gas was increased from 0.05 atm to 0.10 atm, while changes in the paddlewheel’s rotational velocity did not result in any significant changes in the simulation. Sensitivity analysis showed that the model was particularly sensitive to the species-specific maximum growth rate, light attenuation coefficient, optimal growth temperature, half-saturation constant for growth based on irradiance, and death coefficient.

The aforementioned integrated model was further modified to become a two- phase volume of fluid model that included the presence of PCM on the surface of the open channel raceway for the cultivation of N. salina. Validation with experimentally obtained data showed a good fit for the change in biomass, CO2, and nitrogen concentrations. Improved mass transfer of CO2 through the PCM interphase was attributed to the low surface tension and high CO2 solubility. Under the influence of the

PCM, the microalgal biomass productivity was increased by up to 46.1% when the partial

132 pressure of CO2 in the aerated flue gas was increased from 0.05 atm to 0.10 atm.

Significant improvement of the productivity in the open channel raceway was also observed when the PCM layer was increased up to 0.03 m.

Algae biomass residue (ABR) from the lipid extraction of N. salina showed high potential to be converted into biogas via anaerobic digestion. However, the low carbon- to-nitrogen ratio of 4.4 raised concerns of ammonia-induced inhibition during anaerobic

-1 -1 digestion, shown by low methane (CH4) yields of less than 0.13 L CH4 g VS d when digesting solely ABR. Co-digestion of ABR and carbon-rich fat, oil, and grease waste

(FOG) resulted in improved methane yields at organic loading rates (OLRs) of 3 g VS L-1

-1 -1 -1 d . Volumetric reactor productivities of up to 1.62 L CH4 L d showed that co- digestion could increase reactor productivity while allowing for higher feed throughput.

During co-digestion, the degradation of carbohydrates and proteins did not change significantly with increasing OLR. Lipid degradation during co-digestion was greater than that in the digestion of 100% FOG or 100% ABR at increased OLRs. Additional alkalinity provided by the 50% ABR helped retain the lipid degradation efficiency when the OLR was increased up to 4g VS L-1 d-1.

The energy return on investment (EROI) analysis of N. salina production in an open channel raceway demonstrated that techniques such as the application of a PCM may be beneficial to the overall energy balance. The resulting EROI for an open pond raceway with and without PCM treatment were 1.13 and 0.82, respectively. Although the

EROI of the microalgae production was similar to other renewable fuel technologies, it was significantly lower than those found in current fossil fuel acquisition technologies.

133

The application of PCM layer on the raceway surface to attenuate evaporative losses and enhance mass transfer was found to have a positive effect on the EROI. The increased use of CO2 and mixing power showed negative impacts on the EROI. The algae biomass residue (ABR) after lipid extraction accounted for 69.7–78.5% of the total energy output.

Utilization of the algae biomass residue (ABR) after lipid extraction may drastically increase the EROI. Up to approximately 10% of the energy (310 GJ year-1) produced from microalgae was returned through the anaerobic digestion of ABR. With the thorough optimization of the fuel production process, and consideration of utilizing ABR, microalgae-based biodiesel shows potential for commercialization.

8.2. Suggestions for Future Research

Results from this dissertation provide strong ground to the possibility of commercializing the production of microalgal fuels. The use of PCM was shown to increase the mass transfer with minimal energy input, and simulation techniques using

CFD served as a valuable tool to perform experiments in a scaled up environment. The utilization of residual biomass in anaerobic digestion implies the possibility of integrated biorefineries to convert stored chemical energy in biomass with high efficiencies. Yet, microalgal biomass productivities are still significantly lower than the proposed values of at least 50 g m-2 d-1 by the U.S. Department of Energy (Sheehan et al., 1998). Therefore, additional efforts are required to bring commercialization of microalgae to fruition.

CO2 availability was assumed to be infinite in the aforementioned study. From an economic standpoint, algal culture is not feasible unless CO2 is available for free (Chisti,

134

2007). It was estimated that even if 10% of the CO2 emitted from coal-fired power stations in the US was diverted into algal biomass, the crude oil output from microalgae would only compensate for less than 3% of the total oil consumption (Chisti, 2013). Even though it can be assumed that microalgal fuel production can be established to divert the consumption of conventional fuels, the technology is still dependent on fossil fuels. If microalgae can efficiently sequester the CO2 in the atmosphere, there would be no need for petroleum-derived point sources. Accelerated carbon capture via genetic modifications (Savile and Lalonde, 2011) or low-energy physical-chemical strategies

(Metz et al., 2005) are some potential areas of exploration.

The utilization of CFD for the development is a concept that is still in its infancy.

Even with the continuous development of computers and digital tools as of current, the modeling of biological processes over long periods of time is still economically and computationally demanding. In particular, numerical simulations of multiphase systems are typically solved in transiently with small time steps and therefore are quite computationally expensive. Efforts in simplifying the phases within the models need to be made to accurately depict the biological phenomena of interest.

Extensive research in the prediction of mixing, heat and light transfer in bioreactors has been made in the recent years (Wu, 2012). However, almost all the studies have no direct link to the biological process such as anaerobic fermentation and algal growth. The current study is among the few attempts to integrate a biological process in a CFD model. There is still a great challenge in the integration of physical and biological processes in bioreactors. An integrated model would achieve the goal of

135 optimizing the bioenergy systems by maximizing energy output while minimizing energy input. Continued research in simplifying such models and incorporating details to encompass all aspects of the biological process of interest is necessary.

136

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Appendix: User-Defined Function, Written In C-Language to Integrate Basic

Kinetic Equations to the CFD Model.

#include "udf.h" //header file for user-defined functions

/*User-defined scalar (UDS) variables for cells to define non- constant source terms in UDS transport equations (mem.h)*/

#define X(c,t)C_UDSI(c,t,0) //biomass concentration (g m^-3) #define C(c,t)C_UDSI(c,t,1) //CO2 concentration (mol m^-3) #define N(c,t)C_UDSI(c,t,2) //NO3 concentration (mol m^-3) #define O(c,t)C_UDSI(c,t,3) //O2 concentration (mol m^-3) #define I(c,t)C_UDSI(c,t,4) //irradiance (M m^-2 d^-1)

//Definitions of constants

#define a 0.05 /spec. light atten. coeff. (m^2 g^-1) #define b 0.32 //background turbidity (m^-1) #define D 0.05 //death constant of algae (d^-1) #define kla 10.08 //mass transfer coeff. of CO2 (d^-1) #define KC 0.044 //half-sat. const. for CO2 (g m^-3) #define KN 0.014 //half-sat. const. for NO3 (g m^-3) #define KI 1000 //half-sat. light (umol m^-2 s^-1) #define mumax 1.3 //maximum specific growth rate (d^-1) #define Topt 23.0 //optimal growth temperature (deg C) #define Ycx 2.1824 //CO2 per algae produced (g CO2/g) #define Ynx 0.09128 //nitrate per algae produced (g N/g) #define Yox 1.5872 //oxygen per algae produced (g O2/g)

//Initialization of UDS (run once on initialization)

DEFINE_INIT(system_init, d) //set initial field values { Thread *t; //pointer to a thread cell_t c; //define cell real xyz[ND_ND]; thread_loop_c(t,d) //loop over all threads in domain { if(NULL != THREAD_STORAGE(t,SV_UDS_I(4)))

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/*check to make sure all 5 UDS variables have been allocated in the GUI (macro will return NULL if no memory space is allocated for UDS 4)*/

begin_c_loop(c,t) //loop over all cells { C_CENTROID(xyz,c,t); X(c,t) = 0.2; //initialize UDS0: biomass C(c,t) = 10; //initialize UDS1: CO2, (g m^-3) N(c,t) = 10; //initialize UDS2: NO3, (g m^-3) O(c,t) = 8; //initialize UDS3: O2, (g m^-3) I(c,t) = 0; //initialize UDS4: irradiance } end_c_loop(c,t) } }

//Adjust function (run once every iteration)

DEFINE_ADJUST(system_adj, d) //modify flow variables { FILE *file; //pointer to irradiance data file Thread *t; //pointer to a thread cell_t c; //define cell

//Definitions of non-constant variables

real time, h, iter, Io, T, gT, Nadd; real xyz[ND_ND]; //store cell’s centroid coordinates real xi, mu; real dX, k2X, k3X, k4X; real Csat, dC, k2C, k3C, k4C; real dN, k2N, k3N, k4N; real Osat, dO, k2O, k3O, k4O;

//Evaluation and storage of non-constant values

//Surface irradiance (Io) and system temperature (T)

time = CURRENT_TIME/86400; //current time (d) h = CURRENT_TIMESTEP/86400; //timestep size (d)

file = fopen("E:\\PAR1_T1_705.txt", "r");

//access data file with irradiance data

fscanf(file, "%f %f %f %f", &iter, &Io, &T, &Nadd);

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//scan first surface irradiance entry (umol m^-2 s^-1)

while (iter < time/h) //find current iteration { fscanf(file, "%f %f %f %f", &iter, &Io, &T, &Nadd); //scan current irradiance entry (umol m^-2 s^-1) }

fclose(file); //stop irradiance data access

if (T <= Topt) { gT = exp(-0.004*pow((T - Topt), 2)); //temperature effect factor at or below Topt } else { gT = exp(-0.006*pow((T - Topt), 2)); //temperature effect factor above Topt }

Csat = 1/(9.8692*18*exp(-6.8346 + 1.2817*10000/(T+273.15) - 3.7668*1000000/pow((T+273.15),2) + 2.997*100000000/pow((T+273.15),3))/4400000)-110;

//saturation concentration of dissolved CO2 (g m^-3)

Osat = 32*1.2*exp(1700*(1/(T + 273.15) - 1/(298.15)))/(0.98692);

//saturation concentration of dissolved O2 (g m^-3)

thread_loop_c(t,d) //loop over all threads in the domain { if(NULL != THREAD_STORAGE(t,SV_UDS_I(4)))

//check to make sure all 5 UDS variables have been allocated in the GUI

begin_c_loop(c,t) //loop over all cells in thread { C_CENTROID(xyz,c,t);

//store the current cell's centroid coordinates into 'xyz' (xyz[0]=x, xyz[1]=y, xyz[2]=z)

//Definition of cell-dependent variables

xi = 1000*a*X(c,t) + b; //extinct. coeff. (m^-1) 156

I(c,t) = Io*exp(-xi*(0.254 - xyz[1]));

//irradiance at depth (0.254 - xyz[1]) (m); surface is zero-depth

mu = mumax*(C(c,t)/(KC + C(c,t)))*(N(c,t)/(KN + N(c,t)))*(I(c,t)/(KI + I(c,t)))*gT;

//specific growth rate (d^-1)

//Solution of the 1st order non-linear ODE using the Runge-Kutta 4th order method

//Rate of change of each component: biomass (X), CO2 (C), nitrogen (N), and oxygen (O)

dX = (mu - D)*X(c,t); k2X = (mu - D)*(X(c,t) + h*dX/2); k3X = (mu - D)*(X(c,t) + h*k2X/2); k4X = (mu - D)*(X(c,t) + h*k3X);

X(c,t) += h*(dX + 2*k2X + 2*k3X + k4X)/6; if(X(c,t) < 0) { X(c,t) = 0; }

if(xyz[1] < -0.6) { C(c,t) = Csat; } else { dC = kla*(Csat - C(c,t)) - mu*1000*X(c,t)*Ycx; k2C = kla*(Csat-(C(c,t)+h*dC/2)) - mu*1000*(X(c,t) + h*dX/2)*Ycx; k3C = kla*(Csat-(C(c,t)+h*k2C/2)) -mu*1000*(X(c,t)+ h*k2X/2)*Ycx; k4C = kla*(Csat-(C(c,t)+h*k3C/2)) -mu*1000*(X(c,t) +h*k3X/2)*Ycx;

C(c,t) += h*(dC + 2*k2C + 2*k3C + k4C)/6; if(C(c,t) < 0) { C(c,t) = 0; } }

dN = -mu*1000*X(c,t)*Ynx; k2N = -mu*1000*(X(c,t) + h*dX/2)*Ynx; k3N = -mu*1000*(X(c,t) + h*k2X/2)*Ynx; 157

k4N = -mu*1000*(X(c,t) + h*k3X)*Ynx;

N(c,t) += h*(dN + 2*k2N + 2*k3N + k4N)/6 + Nadd;

if(N(c,t) < 0) { N(c,t) = 0; }

dO = mu*X(c,t)*Yox - kla*(O(c,t) - Osat); k2O = mu*(X(c,t) + h*dX/2)*Yox - kla*((O(c,t) + h*dO/2) - Osat); k3O = mu*(X(c,t) +h*k2X/2)*Yox - kla*((O(c,t) + h*k2O/2) - Osat); k4O = mu*(X(c,t) +h*k3X/2)*Yox - kla*((O(c,t) + h*k3O/2) - Osat); O(c,t) += h*(dO + 2*k2O + 2*k3O + k4O)/6;

if(O(c,t) < 0) { O(c,t) = 0; } }

end_c_loop(c,t) //end cell loop } }

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