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Growth optimization of Synechococcus sp. PCC7002 in laboratory photobioreactors Wu, Tong 2014

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    Growth Optimization of Synechococcus sp. PCC7002 in Laboratory Photobioreactors  by Tong Wu   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF APPLIED SCIENCE  in  The Faculty of Graduate and Postdoctoral Studies (Chemical and Biological Engineering)  The University of British Columbia (Vancouver)  March 2014  ? Tong Wu, 2014    ii  Abstract  Microalgae have the potential to be a significant source of renewable energy. Microalgae reduce CO2 emissions by consuming it via photosynthesis, and provide a cheap option to produce high value biological products. Synechococcus sp. PCC7002 is a microalga strain that possesses all of these potentials, and can be easily genetically modified. To utilize these potentials of Synechococcus sp. PCC7002, a method to optimize its growth in terms of a high biomass concentration and a high growth rate needs to be implemented.    To achieve this objective, shake flask scale experiments, as well as reactor scale experiments were designed and conducted. 250 mL shake flasks with 100 mL of medium were used for the flask experiments. In the first experiment, the A+ medium was investigated. The optimal concentrations of the three important nutrient components, NaNO3, FeCl3, and KH2PO4 to achieve highest Xmax were determined to be 23.5 mM, 0.028 mM, and 0.72 mM respectively. The optimal concentrations to achieve highest ?max were 5.88 mM, 0.007 mM, and 0.18 mM respectively. Another factorial experiment regarding the effects of temperature and light intensity was carried out next. The optimal conditions within the tested range were determined to be 35?C, 250?E/m2/s for maximum biomass concentration, and 35?C, 150 ?E/m2/s for maximum specific growth rate. The effects of inlet CO2 concentrations were studied in the large scale continuously aerated reactor. The optimal concentration of CO2 was found to be 8% by volume, and 3.1 g/L biomass concentration and 0.0186 hr-1 maximum specific growth rate were achieved under this condition. Further increase in inlet CO2 concentration led to a decrease in biomass concentration due to the lower pH associated with the higher carbonic acid concentration in the medium. Lastly, an experiment was completed using the recombinant strain, with a very good growth rate obtained at 33?C, 300 ?E/m2/s, and 0.5 L/min inlet gas with 10% CO2. Under this condition, a maximum biomass concentration of 3.1 g/L, and a maximum specific growth rate of 0.0180 hr-1 were achieved.       iii  Preface  The microalgae strains used in this project are provided by Dr. Francis Nano and his research team at University of Victoria.   The shake flask experiments in this project were performed and analyzed by Dr. Swati Yewalkar and I.   I performed the continuously aerated reactor experiments with Di Li.     iv  Table of Contents  Abstract ......................................................................................................................................................... ii Preface ......................................................................................................................................................... iii Table of Contents ......................................................................................................................................... iv List of Tables ................................................................................................................................................ vi List of Figures .............................................................................................................................................. vii List of Abbreviations .................................................................................................................................... ix Nomenclature ............................................................................................................................................... x Acknowledgements ...................................................................................................................................... xi Dedication ................................................................................................................................................... xii 1 Introduction ............................................................................................................................................... 1 1.1 Background ......................................................................................................................................... 1 1.2 Literature Review ................................................................................................................................ 5 1.2.1 Synechococcus sp. PCC7002 ......................................................................................................... 6 1.2.1 Factors Affect Microalgae Growth ............................................................................................... 7 1.2.2 Growth Models .......................................................................................................................... 12 1.2.3 Photobioreactor Designs ............................................................................................................ 14 2 Objectives ................................................................................................................................................. 18 3 Materials and Methods ............................................................................................................................ 19 3.1 Subculturing Synechococcus sp. PCC7002 ........................................................................................ 19 3.1.1 Liquid-Liquid Subculturing ......................................................................................................... 20 3.1.2 Agar Plate Subculturing .............................................................................................................. 20 3.2 Biomass Concentration Measurement ............................................................................................. 21 3.3 Flask Experiments ............................................................................................................................. 22 3.3.1 A+ Medium Screening ................................................................................................................ 22 3.3.2 A+ Medium Optimization ........................................................................................................... 23 3.3.3 Effects of Temperature and Light Intensity on Growth Rate and Final Biomass Concentration ............................................................................................................................................................ 25 3.4 Culturing Synechococcus sp. PCC7002 in Laboratory-Scale Bioreactors .......................................... 26 v  3.4.1 Acrylic Bubble Column Photobioreactor .................................................................................... 26 3.2.2 Glass Bubble Column Photobioreactor ...................................................................................... 28 3.2.3 2-Dimensional Flat Plate Internal Airlift Photobioreactor ......................................................... 30 3.2.4 Continuously Aerated Photobioreactor ..................................................................................... 33 3.2.5 CO2 Optimization Experiment .................................................................................................... 34 3.2.6 Recombinant Synechococcus sp. PCC7002 Experiment ............................................................. 34 4 Results and Discussion ............................................................................................................................. 35 4.1 Flask Experiments ............................................................................................................................. 35 4.1.1 A+ Medium Screening ................................................................................................................ 35 4.1.2 A+ Medium Optimization ........................................................................................................... 39 4.1.3 Temperature & Light Intensity Optimization ............................................................................. 43 4.2 Exploratory Experiments in an Acrylic Bubble Column Photobioreactor ......................................... 46 4.3 Experiments in a Glass Bubble Column Photobioreactor ................................................................. 51 4.4 Experiments in a 2-Dimensional Flat Plate Internal Airlift Photobioreactor .................................... 56 4.5 Experiments in a Continuously Aerated Photobioreactors .............................................................. 58 4.5.1 Effects of Inlet Gas CO2 Concentration ...................................................................................... 58 4.5.2 Experiments with the Mutant Synechococcus sp. PCC7002 ...................................................... 63 5 Discussion ................................................................................................................................................. 66 5.1 Comparison of flask results and photobioreactor results ................................................................ 66 5.2 Comparison of result with other published data .............................................................................. 68 6 Conclusions .............................................................................................................................................. 69 7 Future work .............................................................................................................................................. 71 Bibliography ................................................................................................................................................ 72 Appendix Appendix A: ................................................................................................................................. 79    vi  List of Tables  Table 1: Oil contents of microalgae (Chisti, 2007)......................................................................... 2 Table 2: Typical oil yields from various biomass sources in ascending order (Chisti, 2007) ........ 2 Table 3: Proteins and carbohydrates contents from various species of microalgae (Becker, 1994)......................................................................................................................................................... 3 Table 4 General description of methods for light supply in photobioreactors (Carvalho, et al., 2006) ............................................................................................................................................... 9 Table 5: Prospects and limitations of various culture systems for algae (Ugwu, et al., 2008) ..... 15 Table 6: A+ medium recipe .......................................................................................................... 19 Table 7: Full factorial nutrient screening experiment design ....................................................... 22 Table 8: Fractional factorial design for A+ medium optimization ............................................... 24 Table 9: Parameter estimates for screening experiment 2 using Xmax (R2=0.93).......................... 38 Table 10: Parameter estimates for screening experiment 2 using ?max log. (R2=0.98) ................. 38 Table 11: Parameter estimates for screening experiment 2 using ?max exp. (R2=0.18) ................ 38 Table 12: A+ medium optimization results .................................................................................. 39 Table 13: Parameter estimates for A+ optimization using Xmax (R2=0.96) .................................. 40 Table 14: Parameter estimates for A+ optimization using ?max exp. (R2=0.33) ........................... 41 Table 15: Parameter estimates for A+ optimization using ?max log. (R2=0.91) ............................ 41 Table 16: Temperature and light intensity experiment results...................................................... 43 Table 17: Parameter estimation for temperature-light intensity experiment using Xmax (R2=0.66)....................................................................................................................................................... 44 Table 18: Growth performance data from literature and current study ........................................ 68 Table 19: A+ screening run 2 results ............................................................................................ 79 Table 20: A+ optimization results 2 ............................................................................................. 81    vii  List of Figures  Figure 1: Proposed schematic flow sheet for a microalgae biorefinery (Singh & Gu, 2010)......... 1 Figure 2: An integrated microalgae biofuel process (Knoshaug, 2011) ......................................... 5 Figure 3: Synechococcus sp. PCC 7002 (Bryant & Kennedy, 2011) .............................................. 6 Figure 4: Effect of light intensity on specific growth rate of microalgae (Chisti, 2007) .............. 10 Figure 5: Five growth phases of microalgae cultures ................................................................... 12 Figure 6: Exponential growth (Divya, 2009) ................................................................................ 13 Figure 7: Logistic growth (Yang, et al., 2011) ............................................................................. 14 Figure 8: a) Bubble column reactor, b) Internal-loop airlift reactor, c) External-loop airlift reactor (Jones, 2007) ................................................................................................................................. 15 Figure 9: Liquid subculturing process .......................................................................................... 20 Figure 10: Agar plate subcultures ................................................................................................. 21 Figure 11: Biomass concentration vs. optical density calibration curve ...................................... 22 Figure 12: Initial attempts to scale-up growth of Synechococcus sp. PCC 7002 were carried out in a 10 L acrylic photobioreactor .................................................................................................. 26 Figure 13: 8 L glass photobioreactor ............................................................................................ 28 Figure 14: Parts of the glass photobioreactor, the lid (a), the column (b), and the sparger (c) .... 29 Figure 15: Foam bubbles in the glass photobioreactor ................................................................. 30 Figure 16: 2-Dimensional flat plate internal airlift photobioreactor ............................................. 31 Figure 17: 2-dimensional reactor dimensions (Kuan, 2013) ........................................................ 32 Figure 18: Continuously aerated reactors in operation ................................................................. 33 Figure 19: Logistic growth model applied to experiment data (flask #7) ..................................... 42 Figure 20: Exponential growth model vs. experiment data (flask #7) .......................................... 42 Figure 21: Temperature-light intensity response surface to Xmax ................................................. 45 Figure 22: Temperature-light intensity response surface to ?max log. .......................................... 46 Figure 23: Acrylic bubble column photobioreactor in operation.................................................. 47 Figure 24: Experimental run in unsterilized reactor ..................................................................... 47 Figure 25: Experimental run in sterilized reactor by 10% bleach solution................................... 48 Figure 26: Repeat run in the sterilized reactor by 10% bleach ..................................................... 48 Figure 27: Sterilized run at gas inlet flow of 10 L/min and 5% CO2............................................ 49 viii  Figure 28: Sterilized run at 35?C, 8 L/min, 5% CO2 with a 54 W fluorescent lamp .................... 49 Figure 29: Sterilized run at 35?C, 8 L/min, 5% CO2 with 54 W light and a 12-12 L-D cycle ..... 50 Figure 30: Sterilized run at room temperature, 8 L/min, 0.04% CO2, and 54 W light ................. 50 Figure 31: Room temperature experiment run 2 ........................................................................... 51 Figure 32: Room temperature experiment run 3 ........................................................................... 51 Figure 33: Performance in a glass reactor with 4.5 L/min air flowrate, 33?C at 148 hr ............... 52 Figure 34: Growth curve of sterilized run at 35?C, 4.5 L/min, 0.04% CO2 .................................. 52 Figure 35: Microalgae blown to the surface of the reactor ........................................................... 53 Figure 36: Growth curve of the second run at 35?C, 8 L/min, 5% CO2 ....................................... 53 Figure 37: pH curves of the first two runs in the glass reactor ..................................................... 54 Figure 38: Effect of 3x Tris buffer on A+ medium in shake flasks .............................................. 55 Figure 39: 4 L/min, 2.5% CO2, 35?C growth curve and pH curve ............................................... 55 Figure 40: 2-dimensional photobioreactor setup (Kuan, 2013) .................................................... 56 Figure 41: Results of the first 2-D reactor run (35?C, 150 ?E/m2/s, 0.25 L/min, 5% CO2) ......... 57 Figure 42: Results of the second 2-D reactor run (35?C, 150 ?E/m2/s, 0.25 L/min, 5% CO2) ..... 57 Figure 43: Results of the corresponding shake flask (35?C, 150 ?E/m2/s, 0.25 L/min, 5% CO2) 58 Figure 44: Experimental set up for continuously aerated photobioreactors ................................. 59 Figure 45: Growth curves at various CO2 inlet concentrations in CARs at 33?C, 200?E/m2/s and 0.5 L/min gas flow rate ................................................................................................................. 59 Figure 46: pH curves for experiments at different levels of CO2 ................................................. 60 Figure 47: Photobioreactor results fitted to a logistic model (8% CO2 experiment) .................... 61 Figure 48: Photobioreactor results fitted to an exponential model (8% CO2 experiment) ........... 61 Figure 49: CO2 concentration effect on Xmax ................................................................................ 62 Figure 50: CO2 concentration effect on ?max log. ....................................................................... 63 Figure 51: Growth curves of mutant Synechococcus sp. PCC7002 strains in photobioreactor .... 63 Figure 52: Wild and mutant strains growth curves for 10% CO2 gas inlet................................... 64 Figure 53: pH curves comparisons between wild and recombinant strain ................................... 65 Figure 54: Vitamin b12 effects on biomass concentration ........................................................... 80 Figure 55: Vitamin b12 effects on specific growth rate ............................................................... 80 Figure 56: Dissolved CO2 meter calibration curve for 2-dimensional reactor experiments ......... 82   ix  List of Abbreviations   Abbreviation   Description ALR    Airlift reactor ATP    Adenosine triphosphate  ADP    Adenosine diphosphate BCR    Bubble column reactor CAR    Continuous aerated reactor CUR    Carbon uptake rate GHG    Greenhouse Gas LED    Light emitting diode  MW    Molecular weight NADP+   Nicotinamide adenine dinucleotide phosphate  NADPH   Reduced form of nicotinamide adenine dinucleotide phosphate OD    Optical density  PBR    Photobioreactor  ppmv    Parts per million by volume RPM    Revolutions per minute  vvm    Volume of inflow per volume of culture volume per minute     x  Nomenclature  Symbol  Unit   Description ?   hr-1 or day-1  Specific growth rate X   g/L   Biomass concentration  t   s or min or hr  Time     xi  Acknowledgements  I would like to express my gratitude towards National Science and Engineering Research Council (NSERC) for funding this project.   I would also like to offer my most sincere gratitude to my supervisors Dr. Xiaotao Bi, Dr. Sheldon Duff, and Dr. Dusko Posarac for their constant guidance, and support. I have experienced some difficulties during this project, inside and outside of school, and you showed me your concerns and helped me overcome the difficulties. You also helped me improve as an academic, and an engineer. For all these things, I will be forever thankful.   I would like to thank Dr. Swati Yewalkar for her assistance on my microbiology techniques and knowledge, as well as carrying out some of the experiments. Thanks to Dr. Francis Nano and his team at University of Victoria for providing the microalgae strains and molecular biology works they have done for this project. I also owe a big thank you to my colleagues, especially David Kuan, Di Li, Yun Duan, and Sam Li for being enjoyable and helpful.   Last but not least, I need to thank my parents and friends for their continuous support and help throughout my life.     xii  Dedication     To my parents    1  1 Introduction 1.1 Background Microalgae have been one of the focused topics of chemical engineering research, including cultivation of microalgae for the production of bio-diesel, bio-hydrogen, bio-methane, bio-ethanol, biochemicals and pharmaceuticals (Kumar & Das, 2012) (Loubiere, et al., 2009) (Chader, et al., 2009). With many possible conversion routes to bio-energy production, an integrated microalgae biorefinery has been proposed by Singh and Gu (2010) (Figure 1).   Figure 1: Proposed schematic flow sheet for a microalgae biorefinery (Singh & Gu, 2010) *PUFAs: Polyunsaturated fatty acids  Microalgae are composed of proteins, carbohydrates, lipids and other valuable components such as pigment, anti-oxidants, fatty acids, vitamins etc. Microalgae may contain up to 77% oil content by weight (Table 1), and there is a possibility that higher oil content can be achieved with genetically modified strains (Oilgae, 2007). The lipid content can be used for biodiesel production, while the carbohydrates can be used for bioethanol production (Singh & Gu, 2010).   2  Table 1: Oil contents of microalgae (Chisti, 2007) Microalgae Oil content (%dwt) Botryococcus braunii 25-75 Chlorella sp. 28-32 Crypthecodinium cohnii 20 Cylindrotheca sp. 16-37 Dunaliella primolecta 23 Isochrysis sp. 25-33 Monallanthus salina >20 Nannochloris sp.  20-35 Nannochloropsis sp. 31-68 Neochloris oleoabundans 35-54 Nitzschia sp. 45-47 Phaeodactylum tricornutum 20-30 Schizochytrium sp.  50-77 Tetraelmis suecica 15-23 B. braunii 25-75  Microalgae do not directly compete with food crops, because they require less land area due to a growth rate 20-30 times faster than other biomass sources and can be cultivated on marginal land where normal food crops cannot be grown. As shown in Table 2 (Chisti, 2007), the oil yield from microalgae is also much higher than that from the first and second generation biofuel biomass sources (Singh & Gu, 2010), which are derived from arable food crops and cellulosic crops respectively (Mohr & Raman, 2013). With these advantages mentioned above, microalgae have become an attractive biomass source for biodiesel production.  Table 2: Typical oil yields from various biomass sources in ascending order (Chisti, 2007) S.N. Crop Oil yield (1/ha) 1 Corn 172 2 Soybean 446 3 Peanut 1059 4 Canola 1190 5 Rapeseed 1190 3  S.N. Crop Oil yield (1/ha) 6 Jatropha 1892 7 Karanj (Pongamia pinnata) 2590 8 Coconut 2689 9 Oil palm 5950 10 Microalgae (70% oil by wt.) 136900 11 Microalgae (30% oil by wt.) 58700  Microalgae can also be used as biomass feedstock for bioethanol fermentation, because of their rich carbohydrate contents (Table 3). The CO2 released during fermentation can also be recycled for microalgae cultivation (Singh & Gu, 2010).   Table 3: Proteins and carbohydrates contents from various species of microalgae (Becker, 1994) S.N. Algae strain Protein (%dwt) Carbohydrate (%dwt) 1 Scenedesmus obliquus 50-56 10-17 2 Scenedesmus quadricauda 47 - 3 Scenedesmus dimorphus8 8-18 21-52 4 Chlamydomonas rheinhardii 48 17 5 Chlorella vulgaris 51-58 12-17 6 Chlorella pyrenoidosa 57 26 7 Spirogyra sp. 6-20 33-64 8 Dunaliella bioculata 49 4 9 Dunaliella salina 57 32 10 Euglena gracilis 39-61 14-18 11 Prymnesium parvum 28-45 25-33 12 Tetraselmis maculate 52 15 13 Porphyridium cruentum 28-45 40-57 14 Spirulina platensis 52 8-14 15 Spirulina maxima 28-39 13-16 16 Synechococcus sp.  46-63 15 17 Anabaena cylindrical 43-56 25-30  4  Some of the microalgae-to-bio-ethanol conversion processes also produce methane, which can be used for further electricity generation or as fuel gas. Methane yields of up to 0.5 m3/kg biomass has been reported using algae strains (Morand & Briand, 1999).    Aside from all the fuel potentials, microalgae have other applications as well. Omega-3 fatty acids (Harun, et al., 2010), eicosapentanoic acid (Zittelli, et al., 1999), decosahexaenoic acid (Patil, et al., 2004) and chlorophyll (Nakanishi, 2001) are important food supplements and medicinal biomaterials derived from microalgae. Microalgae cultures can also be used as feed for farm animals and fish (Dhargalkar & Verlecar, 2009).   Not only can microalgae be used for the products mentioned previously, microalgae can also be genetically modified to produce valuable proteins for the food and pharmaceutical industries. Traditionally, recombinant protein production has used hosts such as mammalian cells, bacterial cells, and yeast cells. However microalgae cultures have shown promise in this industry with their fast growth and low cost while maintaining the production free of pathogens (Specht 2010). Synechococcus is one of the microalgae species that can be easily modified genetically, and this is the main reason for this species being selected in this project.   What makes microalgae even more valuable to the global energy industry is their potential for CO2 mitigation. Based on measurements conducted at Mauna Loa Observatory (Hawaii, US) in December, 2011, the concentration of CO2 in air was nearly 391 ppmv (Kumar & Das, 2012), and continues to increase. Microalgae perform photosynthesis and consume atmospheric CO2 in the presence of a light source, which will help in sequestrating CO2 from the environment (Yoshihara, et al., 1996) (Hu, et al., 1008). This photosynthetic ability coupled with their energy production potential, makes microalgae a promising source of renewable energy and various biomaterials that simultaneously mitigate the greenhouse gas emissions on this planet.  A major issue or barrier for deployment of microalgae technologies is the high cost associated with lighting, mixing, nutrient supply, and product separation. In order to make microalgae more economically feasible, two major approaches have been investigated; one is to reduce the production cost, and the other is to increase the value of products. CO2 is a major nutrient 5  required for microalgae growth, and it is also present in the flue gases from many industries. If flue gases from power plants, composting or other facilities can be utilized, not only the will the operation cost for microalgae production be reduced, CO2 emissions into the atmosphere will be decreased as well. Many researchers have also been looking into the option of using wastewater from wastewater treatment plants to provide nutrients, such as nitrogen and phosphorus, for microalgae cultivation. The idea of an integrated system is proposed by Knoshaug, see Figure 2,  Figure 2: An integrated microalgae biofuel process (Knoshaug, 2011) Another major cost associated with algae cultivation is the electrical usage for artificial lighting. The use of more energy efficient lights such as various fluorescent lights, LED lights and improved reactor design can provide the same algae growth with less electricity consumption. To increase the profit margins of algae products, many microalgae strains have been genetically-modified to increase the lipid content for better biodiesel production, and to produce valuable recombinant proteins. By combining the reduction in production costs using cheaper CO2 and nutrient sources and the increase in product value using genetically modified microalgae strains, a sustainable and economically viable algae process can be achieved.  1.2 Literature Review There are many factors that affect the growth rate of microalgae, such as the design of photobioreactors, type of algae strains, reactor temperature, growth media constituents, pH, light intensity, flue gas concentrations of CO2, SOx, NOx, and CO2 absorption rate etc (Kumar, et al., 2011), which will be investigated in this section.  6   1.2.1 Synechococcus sp. PCC7002 Synechococcus sp. PCC7002 was the microorganism chosen for this project. It is a marine blue-green cyanobacteria, and it is approximately 1 ?m in size (Bryant & Kennedy, 2011), shown in Figure 3.   Figure 3: Synechococcus sp. PCC 7002 (Bryant & Kennedy, 2011)   A+ is the most widely used medium for the cultivation of Synechococcus sp. PCC7002 (Table 6 in section 3). Like other cyanobacteria, Synechococcus sp. PCC7002 undergoes photosynthesis, meaning it utilizes CO2 for the production of energy. Unlike many other cyanobacteria, Synechococcus sp. PCC7002 is robust, and genetically traceable, and it can tolerate high light intensities as well as other environmental stresses such as high salinity (Zhu, et al., 2010). These qualities have made Synechococcus sp. PCC7002 a popular cyanobacterium for various genetic modifications. Despite the amount of research done on Synechococcus sp. PCC7002, most of them are regarding various genetic modifications (Xu, et al., 2013) (Dong, et al., 2009) (Zhu, et al., 2010), little to none of the work has been focused on optimizing the growth of the microalgae itself, in flask or photobioreactor scale.   The recombinant strain used in this research produces an enzyme called laminarinase, which breaks down polysaccharides found in brown algae called laminarins (Chesters & Bull, 1963).   7  1.2.1 Factors Affect Microalgae Growth 1.2.1.1 CO2 Effects CO2 is required by microalgae for photosynthesis, but the optimal amount of CO2 required by different microalgae, as well as the tolerable CO2 range for different microalgae are often not the same. Euglena gracilis has the highest specific growth rate with 4% flue gas CO2 (Kitaya, 2005), whereas Nannochloropsis sp. achieves high specific growth rate with 15% flue gas CO2 (Jiang, et al., 2011), and Chlorella sorokiniana grows fastest with 5% flue gas CO2 aeration (Kumar & Das, 2012).   Since CO2 solubility varies with culture media, a more accurate way to quantify the available CO2 to microalgae is the concentration of dissolved CO2 in the medium. Some researchers already used liquid CO2 values instead of just inlet gas CO2 concentrations, e.g., in a study on Chlorella vulgaris, it was found that 150 mg/L was the optimal dissolved CO2 concentration (Powell, et al., 2009).   To keep track of the amount of CO2 being utilized by microalgae, the following equation was proposed by Kajiwara et al. (1997)1,                                         (1) Where CUR is the CO2 uptake rate, dX/dt is the biomass growth rate, 0.465 is the amount of carbon in grams per gram of dry cell mass, MWco2 is the molecular weight of CO2, 44g/mol, and MWcarbon is the molecular weight of carbon, 12g/mol.   1.2.1.2 Temperature Effects  Most commonly cultured microalgae tolerate temperatures between 16?C and 27?C. Media temperatures lower than 16?C usually slow down algae growth significantly, whereas temperatures higher than 35?C are lethal to a number of species (Lavens & Sorgeloos, 1996). For the strain used in this research, Synechococcus sp. PCC7002 grows optimally at 38?C, as it was isolated from warm ocean water near Puerto Rico (Sakamoto, et al., 1997)                                                  1 This 0.465 represents 0.465 g of Carbon per g of dried cell mass, but only applicable to Synechococcus sp. PCC7942, a fresh water strain.  8  1.2.1.3 pH Effects on the Growth of Microalgae and Its Control Methods Most of the microalgae cultures have a pH range between 7 and 9 (Lavens & Sorgeloos, 1996).  There are exceptions though, for example, Euglena gracilis (Kitaya, 2005) thrive in acidic conditions. It is important to maintain pH of the culture at the optimal level for the specific species that is being studied, because a failure to do so may cause disruption in many cellular processes, and hinder microalgae growth.   In most lab reactors, a buffer in the media is used to control the pH of the culture. However, in larger reactors, a continuous addition of pH buffer is required to maintain the pH at the optimal level.   In a paper by Huo et al. (2012), CO2 regulated pH control was compared with acetic acid regulated pH control. The CO2 regulation was able to control the pH steadily at 6.5 to 8, whereas the acetic acid regulation had spikes between pH 6 and pH 9. CO2 regulation also showed a slightly better growth rate as well as final cell concentration than acetic acid regulation. In another study done by Janssen et al (2002), the pH was adequately maintained at 7.0 by automatic addition of 1N sodium hydroxide solution.   Aside from being an important factor for maintaining proper algae growth, pH control can also be used in downstream processing of algae. For example, pH can be adjusted to induce algae flocculation as to facilitate microalgae harvesting more efficiently (Wu, 2012). In some genetically modified species, pH change could also be used to induce expression of recombinant gene in microalgae.  1.2.1.4 Lighting Effects on the Growth of Microalgae Microalgae are photoautotroph microorganisms, and they undergo photosynthesis, which is a physio-chemical process in which light energy is used to drive the synthesis of organic compounds (Whitmarsh & Govindjee, 1999). The overall reaction of photosynthesis is:   6CO2 + 6H2O + Light ? C6H12O6 + 6O2     (2)  9  This photosynthesis reaction takes place in two steps known as the light-dependent reaction and the light-independent reaction (Masojidek, et al., 2004). As the name suggests, light dependent reaction involves light energy as below:   H2O + NADP+ + Pi + ADP + Light ? Oxygen + NADPH + H+ + ATP   (3)  In this reaction, light energy is captured and converted in to energy storing molecules ATP and NADPH. Oxygen is produced as hydrogen atoms from water are used to reduce the NADP+. The light independent reaction on the other hand is (Raven, et al., 2005):   CO2 + NADPH + H+ + ATP ? 1/6 Glucose + NADP+ + ADP + Pi   (4)  In this reaction, energy compounds produced previously, NADPH and ATP are used to convert CO2 to glucose (Shuler & Kargi, 2002).   Both sunlight and artificial light can provide the light energy used by microalgae during photosynthesis (Table 4), but the majority of the photobioreactor research use artificial lights such as compact fluorescent lamps, tube fluorescent lamps, and light emitting diodes (LEDs).   Table 4 General description of methods for light supply in photobioreactors (Carvalho, et al., 2006)   By using artificial lights, the conditions of the culture can be controlled in a precise and accurate manner. The major advantage of using LEDs is the low heat emission, which contributes to stabilizing testing conditions. However, the cost of LEDs considerably exceeds that of tube fluorescent lamps (Michel & Eisentraeger, 2004). As a result, LEDs are more suited for very small scale reactors. It has been reported that tube fluorescent lamps emitting in either blue or 10  red light spectrum are preferred in most microalgae cultures, as these are the most active portions of the light spectrum for photosynthesis (Lavens & Sorgeloos, 1996).   Light intensity is another variable that could dictate how well microalgae cultures grow. At low light intensities, the growth would be limited by the rate of photosynthesis due to the lack of light exposure for the cultures. As the light intensities ramp up, the growth rate would increase. However there is a limit to how much light the microalgae cultures should be exposed to, as photoinhibition occurs at high light intensities (Chisti, 2007), as illustrated in Figure 4. This upper limit of light intensity varies among different species of microalgae.   Figure 4: Effect of light intensity on specific growth rate of microalgae (Chisti, 2007) For a batch culture, the average light received by the microalgae in the photobioreactor decreases over time. As the microalgae biomass concentration increases in a photobioreactor, the effect of shading becomes important; some regions of the photobioreactor would receive less light than preferred due to the light penetration path being blocked by the biomass in the culture. There have been several approaches taken to address this problem. One of them is to improve the mixing in the reactor so all the microalgae could receive the lighting needed to perform photosynthesis. However since the cells receive too much shear damage at high mixing rate, an optimization is required. Another solution is to increase the number of lights; so that the light can be more uniformly distributed as the dense culture (Suh & Lee, 2001).   11  Light availability does not just affect microalgae growth; it also influences the biomass composition of microalgae. When cultivated under limited light conditions, amino acids and other essential cell constituents are prioritized, but under saturated light conditions, sugar and starch are formed instead (Fay, 1983). Therefore lighting conditions should be adjusted appropriately, based on the objectives of different projects.   The other important effect of lighting is the light-dark cycle. The presence of light and dark cycles can influence the growth of microalgae significantly. Many experiments used the 12h-12h cycle scheme to mimic day light in nature, which does not correspond to the optimal light-dark cycle for various species. Some reports have shown microalgae thriving in continuous lighting (Jacob-Lopes, et al., 2009), and in cycles such as 16h-8h (Barsanti & Gualtieri, 2006). Several studies have also shown that chlorophyll-a contents were twice as high under intermittent illumination conditions (i.e. light-dark cycles of 13 seconds ? 87 seconds) in comparison to continuous illumination (Janssen, et al., 1999).  1.2.1.5 Mixing Effects on the Growth of Microalgae Mixing during microalgae growth improves homogeneity in the photobioreactor, with respect to media temperature, cell mass distribution, nutrient distribution, and light distribution. Maintaining a good homogeneity in the photobioreactor is essential to obtaining high microalgae growth rate as well as high maximum biomass concentration. As mentioned previously, in the late exponential growth phase, the lack of light availability for parts of the culture due to the shading effect and photic zone2 (Janssen, et al., 1999) causes cells to stop multiplying at a rapid rate. Another possibility for limiting substrate is the dissolved CO2. As mentioned earlier, CO2 is a critical reactant during photosynthesis, and its improved transfer from gas to liquid phase could potentially improve microalgae growths. A well-mixed reactor is the answer to those limitations.   Mixing in photobioreactors is accomplished through aeration rather than mechanical agitation, because mechanical mixing creates very high shear stress on cells, causing cell damage and eventually cell death (Contreras, et al., 1999). To avoid damaging the cells during mixing, aeration is commonly used to induce mixing in many reactor configurations, such as the bubble                                                  2 The depth of water that receives sufficient light for photosynthesis to occur.  12  column photobioreactor, internal-loop airlift photobioreactor and the external-loop airlift photobioreactor (Kumar & Das, 2012).   1.2.2 Growth Models The growth of microalgae in batch mode reactors follows several phases similar to that of other microorganisms, as shown in Figure 5.   Figure 5: Five growth phases of microalgae cultures The lag phase is a phase where minimal increase in biomass occurs, due to the time taken by cells to adapt to the new environment, such as pH, temperature, nutrient composition, mixing, and other factors that affect cell growth. In general, this phase is quite lengthy for liquid cultures inoculated by plate colonies, but is relatively short for liquid-to-liquid inoculations provided that the inoculum is in the exponential growth phase. Exponential growth phase, also known as the logarithmic growth phase follows the lag phase. In this phase, cells have adjusted to the new environment and are multiplying at a fast rate. During this phase, balanced growth is achieved, wherein all the cells are dividing regularly by binary fission and are growing by geometric progression. This means that the cell composition remains constant, therefore specific growth rate3, ?, determined from either cell mass or cell number would be the same. Also important in this phase is the production of both primary metabolites and secondary metabolites, which are growth-related and non-growth-related products, respectively. Before reaching the stationary phase, the culture goes through a declining growth phase, where the growth rate of microalgae slows down due to various reasons, such as depletion of nutrients, and toxic build-up from dead                                                  3 Specific growth rate is the increase in cell mass per unit time. 13  cells or other by-products. It is important to note that cell concentration continues to increase in this phase, just at a slower rate. Stationary phase follows, and this is when net growth rate of the micro-algae culture becomes zero. During this phase only secondary metabolites are produced. The last phase in microalgae cultivation is the death phase, where cells break, and the total number of cells as well as number of viable cells begins to decline. (Shuler & Kargi, 2002).   Two growth models are commonly used for microalgae cultures, exponential growth model and the logistic growth model.   The exponential growth model focuses on the exponential growth phase on the growth curve, with an example shown in Figure 6. In many researches, keeping culture growing exponentially is the main objective, because this would allow a maximum production of biomass and primary metabolites.    Figure 6: Exponential growth (Divya, 2009)4 Only the data for the exponential growth phase were considered in fitting to the growth kinetics, as shown by the straight sections of a semi-log graph in Figure 6.                               (5) where ? is the specific growth rate, Xt is the final biomass concentration, X0 is the initial biomass concentration, and finally, t and t0 are their respective times.                                                    4 Growth curve for C. vulgaris in the circulating loop photobioreactor with 322 mW photosynthetic active radiation (PAR), 10% CO2 in inlet gas and 8 h dark phase.  14  The logistic growth model is another popular model for microalgae growth (Yang, et al., 2011), (Rao, et al., 2008) as it could capture almost the entire growth phase of microalgae, including the lag phase, exponential growth phase as well as the stationary phase.                               (6)                     (7) where X is the biomass concentration, Xmax is the maximum biomass concentration, X0 is the initial biomass concentration, ?max is the maximum specific growth rate, and t is time.   Figure 7: Logistic growth (Yang, et al., 2011)5  The logistic growth model can include the data from lag phase as well as stationary phase as seen in Figure 7 (black circles line). The logistic growth data shows a sigmoid shape, which indicates that substrate concentration is not a constraint (Rao, et al., 2008).  1.2.3 Photobioreactor Designs There are a large number of variables that may affect microalgae growth, so a well-controlled environment is required to maintain a high growth rate or to produce a specific metabolite. A photobioreactor 6  is thus widely used to provide accurate and precise control of culturing conditions (Rocha, et al., 2003). Various photobioreactors and their prospects and limitations are listed in Table 5.                                                   5 Growth curve of Chlorella minutissima UTEX2341 agitated at 100 rpm, under 35 ?Em-2s-1 light intensity at 22?C.  6 Bioreactor is an unit for the production of microorganisms, and the prefix ?photo? refers to the exposure to light, which is required for phototrophic organisms such as algae. 15  Table 5: Prospects and limitations of various culture systems for algae (Ugwu, et al., 2008)  Some main features of an ideal photobioreactor would include ease of operation, scalability, low space requirement, high biomass productivity and low operating cost (Kumar, et al., 2011) (Janssen, et al., 1999). Also the supply of pure carbon dioxide can constitute up to 30% of the overall microalgae production cost (Fernandez, et al., 2012), and carbon utilization efficiency should be improved by proper design and operation of the photobioreactor. The proper design of photobioreactors is thus critical for the use of microalgae for CO2 mitigation.  With these requirements in mind, the most widely adapted and experimented reactors have ranged from tubular photobioreactors (e.g. Pirt, 1983), thin panel photobioreactors (e.g. Tredici, 1992), and flat plate photobioreactors (e.g. Ratchford, 1992) to the more recent bubble column photobioreactors (e.g. Kumar, 2012), internal (e.g. Chiu, 2009) and external loop airlift photobioreactors (e.g. Sasi, 2009, Fernandez, 2001), see Figure 8.    Figure 8: a) Bubble column reactor, b) Internal-loop airlift reactor, c) External-loop airlift reactor (Jones, 2007) 16  Bubble column reactors (BCR) are usually aligned vertically and agitated pneumatically by gas bubbles. BCR varies greatly in shape and size; most of them are in a cylindrical shape with a height-to-diameter ratio of around 2:1. The liquid mixing is provided by randomly formed bubbles; the degree of mixing is mainly determined by the sparger design and the operating parameters (Jones, 2007). The main advantages of bubble columns are their high energy efficiency, suitability for low viscosity media, low shear mixing, great gas exchange rates, small land use, and high photosynthetic efficiency (Kunjapur & Eldridge, 2010). Even though bubble columns are relatively inexpensive to build and their operation is quite simple compared to other pneumatically agitated reactors (Jones, 2007), their application is also quite limited for microbial growth for a few reasons. First, BCRs have a narrow working range of gas flow rates because of bubble coalescence and broth foaming problems (Shuler & Kargi, 2002), and this narrow range of gas flow also limits the BCR mixing and mass transfer (Joshi, et al., 1990). Also, zones in the BCRs with inadequate mass transfer to maintain microalgae growth may exist (Chisti & Moo-Young, 1989).   Airlift reactors (ALRs) are another configuration of the vertical column reactors. Similar to BCRs, ALRs are agitated pneumatically, and have a variety of designs. Unlike BCRs, mixing patterns in ALRs are determined more dominantly by the reactor design rather than the sparging rates (Jones, 2007), and the bubble holdup is significantly lower in ALRs as opposed to BCRs. The ALRs are represented by the presence of two separate zones of fluid, with gas present in one zone and none in the other zone. This creates a difference in the density of the two fluid zones, driving the liquid circulation in the reactor. The advantages of using ALRs include very high mass transfer rates, well-defined mixing patterns, a broad range of gas flow rates, as well as the ability to handle more viscous fluids. All ALRs have 4 distinct sections, namely the riser (bubble presence), downcomer (bubble absence), base, and gas separator. Key parameters for the ALRs include superficial gas velocity, gas holdup, slip velocity, mixing time, superficial liquid velocity, area ratio between riser and downcomer, and gas-liquid mass transfer coefficients (Chisti & Moo-Young, 1993). The ALRs can be divided into internal and external configurations based on their structure (Van't Reit & Tramper, 1991), and these configurations can be further modified by the use of porous concentric tubes (Chiu, et al., 2009), baffles, draft tubes (Suh & Lee, 2001) etc. There are disadvantages to these reactors as well. ALRs require a minimum 17  volume that must be maintained to ensure proper liquid circulation, and the designs of ALRs are very application specific, giving it very little control using operating parameters (Siegel & Robinson, 1992).   In this study, the bubble column reactor was originally selected, because of its simple construction and operation, as well as its ease for scaling up.    18  2 Objectives  ? The objective of this research is to determine the effects of several factors on the growth of Synechococcus sp. PCC7002 in both a shake flask scale and a larger reactor scale.   ? Design and perform factorial experiments in shake flasks to determine the effects that medium composition, light intensity and temperature have on the biomass concentration and specific growth rate. Determine the optimal condition for the growth of Synechococcus sp. PCC7002 with respect to the factors being studied. Also, develop or adopt a model to describe the growth of this microalgae strain.   ? Conduct experiments in a photobioreactor to determine the effects that CO2 concentration the gas inlet have on the biomass concentration and specific growth rate. Optimize these factors for the best growth of Synechococcus sp. PCC7002.   ? Cultivate the Synechococcus sp. PCC7002 mutant strain in a photobioreactor, and compare its growth with the wild strain.        19  3 Materials and Methods 3.1 Subculturing Synechococcus sp. PCC7002   Synechococcus sp. PCC7002 was the microalgae used in this project; it was received from Dr. Francis Nano of University of Victoria. The medium used for this specific strain was a University of Texas (UTEX) recipe known as A+ medium, at pH 8.2. The components of A+ medium and their respective concentrations are shown in Table 6.   Table 6: A+ medium recipe Component Stock Solution Concentration Final Concentration NaCl  308 mM KCl  8.05 mM Tris-Base 100 g/L 8.26 mM MgSO4  20.29 mM CaCl2 37 g/L 1.81 mM NaNO3  11.8 mM KH2PO4 50 g/L 0.36 mM FeCl3 3.89 g/L 0.014 mM Vitamin B12 0.14 g/L 0.14 mg/L  Trace Metal Solution: H3BO3  34.3 mg/L MnCl2-4H2O  4.3 mg/L ZnCl  0.32 mg/L MoO3 3 g/L 0.03 mg/L CuSO4-5H2O 0.3 g/L 0.003 mg/L CoCl2-6H2O 1.22 g/L 0.012 mg/L  The Synechococcus cultures received from UVic group were in agar plates, from which more agar plates and liquid cultures were prepared. A single colony from the original plate was taken to inoculate another agar plate culture, while another single colony from the original plate was inoculated in a 10 mL A+ medium. A week later, this 10 mL culture is then used as inoculum for the first 100 mL liquid culture. The microalgae cultures had to be constantly maintained fresh and healthy by regular subculturing of the microalgae in the form of agar plates as well as liquid cultures.     20  3.1.1 Liquid-Liquid Subculturing Liquid subcultures were done weekly, so fresh inoculum would be available at all times. To prepare for a liquid-liquid subculture, in a 250 mL Erlenmeyer flask, 90 mL of A+ medium was prepared according to the recipe in Table 6. Prior to autoclaving, NaCl, KCl, NaNO3, and MgSO4 were added in solid form (weighed on a balance and added directly), and CaCl2, Tris-base buffer, FeCl3 were added from their respective stock solutions. To avoid precipitations in the media, KH2PO4, trace metal solution and vitamin B12 were filtered sterilized and added after the autoclave process. Once the 90 mL media were cooled after autoclaving, 10 mL of previous liquid culture in growth phase would be added to it to complete the subculture. Figure 9 shows the final step of this process, when inoculum was being added to fresh A+ media.   Figure 9: Liquid subculturing process  3.1.2 Agar Plate Subculturing Agar plates can keep cultures healthy for a longer period of time in its solid form, subculturing on these plates were done once every month. To prepare fresh agar plates, two 500 mL solutions had to be made and autoclaved. One of them was regular A+ medium at 2x the regular concentration, and the other was a solution with 15 g of Bacto agar, and 3 g Na2S2O3?5H2O. After autoclaving, the solutions were mixed together once they reach 60?C, and the final solution wa poured into several sterile petri plates for cooling. Then a Nichrome wire loop was used to 21  transfer a single colony from one of the older plates to one of the fresh agar plates. Figure 10 shows a picture the agar plate cultures.   Figure 10: Agar plate subcultures  3.2 Biomass Concentration Measurement  Optical density reading using a spectrophotometer is the most widely used way to determine the biomass concentration; these OD readings (at 600 nm wavelength) can be easily converted to biomass concentration using a calibration curve. This calibration curve was prepared by collecting a 50 mL sample that is approximately at OD reading of 1. Serial dilutions of this sample were made to form several samples that had OD readings between 0 ? 1. For each of these samples, 5 mL were collected weighed on a balance using a beaker of known weight, and then transferred in to a vial and placed to into an oven at 80?C for 24 hours. After 24 hours, these samples were weighed again to obtain the dry weight, which determines the biomass concentrations of the original samples. Lastly the biomass concentrations were plotted against OD readings to obtain the calibration curve (see Figure 11).  22   Figure 11: Biomass concentration vs. optical density calibration curve  3.3 Flask Experiments 3.3.1 A+ Medium Screening Based on the functionalities of the components in the A+ medium, MgSO4, an important co-factor, FeCl3, a compound that helps microalgae utilize CO2, KH2PO4, the phosphorus source and a buffer, CaCl2, another important co-factor and NaNO3, the nitrogen source were chosen for a screening experiment. These 5 chemicals had great effects on microalgae growth, and a 2-level full factorial experiment was designed to (1) determine which or if any of these chemicals can be eliminated without compromising the growth of Synechococcus sp. PCC7002, and (2) further investigate any components that had a relatively bigger effect on the microalgae growth. The species and concentrations tested are shown in Table 7.  Table 7: Full factorial nutrient screening experiment design Component Concentrations (mM)  Level 1 Level 2 MgSO4 0.00 20.29 CaCl2 0.00 1.81 NaNO3 0.00 11.76 KH2PO4 0.00 0.36 FeCl3 0.00 0.014  A total of 32 flasks were prepared, and to prepare these cultures, 50 mL of dH2O was added to a 250 mL Erlenmeyer flask, then using an analytical balance (Shimadzu AUW120), NaCl (1.8 g), KCl (0.06 g), NaNO3 (0.1 g), and MgSO4 (0.5 g) were added to the flask. Then liquid stock solutions of CaCl2 (37 g/L), FeCl3 (3.89 g/L 0.1 N HCl), and Tris Base (100 g/L pH 8.2) were y = 247.98x R? = 0.9927 0 50 100 150 200 250 0 0.5 1 Biomass Concentration (mg/L) Optical Density (600 nm) 23  added to the flask (except for KH2PO4, and trace metal solutions, because these components tend to precipitate during the autoclave process). Some of these were in liquid phase as the amount added to 100 mL of A+ media would be too small to measure accurately on the balance. Last step before autoclave is adding dH2O to make the total volume 90 mL for each flask. Finally flasks were sterilized using an autoclave which operated at 121?C, and 15 psi; this process eliminates any contaminations that could potentially lead to faulty results, and lasted 90 minutes in total. Once the sterilization was completed and the flasks were cooled, the remaining nutrients were added aseptically (These stock solutions were previously filter sterilized using a 0.22 ?m Millex GP filter). Finally, the flasks were inoculated with 10 mL of microalgae culture at biomass concentration of 250 mg/L obtained from the weekly liquid subcultures.   The flask experiments were conducted in New Brunswick Scientific Innova 4230 incubator shakers, controlled at 35?C and 125 RPM. The lights used to illuminate the cultures were compact fluorescent lamps with a light intensity of 100 ?E/m2/s (measured at the surface of the cultures using an Apogee MQ-200 quantum sensor).   2 mL samples were taken daily from each flask aseptically. Optical density (OD) readings at 600 nm were taken using Shimadzu UV-1800 spectrophotometer and pH readings were also recorded.   Dilutions were needed as the cultures became more concentrated; the diluted samples should always have an OD reading between 0 ? 1, because the calibration was done for this range of OD readings. During all of these procedures, aseptic techniques were used to avoid contamination.   3.3.2 A+ Medium Optimization Results from the screening experiment showed that FeCl3, NaNO3, and KH2PO4 had the most significant effects on the growth of Synechococcus sp. PCC7002, and a fractional factorial experiment was designed to further improve the A+ recipe. This fractional factorial experiment consisted of 3 factors and 5 levels, and a total of 20 flasks, this design is shown in Table 8.   24  Table 8: Fractional factorial design for A+ medium optimization  Symbol NaNO3 (mM) KH2PO4 (mM) FeCl3 (mM) ??? 5.88 0.18 0.007 ??+ 5.88 0.18 0.028 ?+? 5.88 0.72 0.007 ?++ 5.88 0.72 0.028 +?? 23.52 0.18 0.007 +?+ 23.52 0.18 0.028 ++? 23.52 0.72 0.007 +++ 23.52 0.72 0.028 a00 3.50 0.36 0.014 A00 39.51 0.36 0.014 0a0 11.76 0.11 0.014 0A0 11.76 1.21 0.014 00a 11.76 0.36 0.0041 00A 11.76 0.36 0.047 0 11.76 0.36 0.014 0 11.76 0.36 0.014 0 11.76 0.36 0.014 0 11.76 0.36 0.014 0 11.76 0.36 0.014 0 11.76 0.36 0.014  It was assumed that the component concentrations in the UTEX A+ medium recipe was well-tested, thus, the concentrations tested in this experiment are within a factor of 1.68 of the concentrations in the UTEX recipe. (This factor was chosen by JMP7?s experiment central composite design).   The incubating shakers were operated at 35?C and 125 RPM for this experiment. The light intensity for all the flasks were at 100 ?E/m2/s. exactly same as for the screening experiment.   The same media preparation steps and sample collection procedures were used for this experiment as the previous flask experiments.                                                    7 JMP is a statistical software 25  3.3.3 Effects of Temperature and Light Intensity on Growth Rate and Final Biomass Concentration Temperature and light intensity are two important environment factors when culturing microalgae, thus a full factorial experiment was designed (Table 9), with 5 levels of temperature and 3 levels of light intensity. The goal of this experiment was to study the effects of these two factors on ?max, the maximum specific growth rate, and Xmax, the maximum biomass concentration.    Table 9: Fractional factorial temperature and light intensity experiment design Flask Number Pattern X1 (Temp) X2 (Light Intensity)   ?C ?E/m2/s 1 11 23 100 2 12 23 150 3 13 23 250 4 21 30 100 5 22 30 150 6 23 30 250 7 31 35 100 8 32 35 150 9 33 35 250 10 41 38 100 11 42 38 150 12 43 38 250 13 51 40 100 14 52 40 150 15 53 40 250   This experiment was completed in a heated incubating shaker, the temperature and shaking speed can be controlled using the shaker controls, and the light intensity was controlled by the distance between the flasks and the lights.   The temperature range was chosen based on the temperatures used for various microalgae cultures. This is the range where growth is observed for most common microorganisms.   26  The light intensity range was chosen based on the capability of the equipment available. In this experiment, since light intensity was controlled by the distance between the culture and the light source, the light intensities available were limited by the height of the incubating shaker.   Preparation and data collection procedures were same as the previous flask experiments.  3.4 Culturing Synechococcus sp. PCC7002 in Laboratory-Scale Bioreactors 3.4.1 Acrylic Bubble Column Photobioreactor The first reactor experiments were attempted in an acrylic bubble column photobioreactor, shown in Figure 12.    Figure 12: Initial attempts to scale-up growth of Synechococcus sp. PCC 7002 were carried out in a 10 L acrylic photobioreactor This reactor was selected because it had high productivities and photosynthetic efficiency in lab scale, and it also caused the least amount of shear damage to cells. The reactor had an internal diameter of 12.7cm, a height of 91.4 cm and a working volume of 10 L. This dimension gave the reactor a high surface area to volume ratio, thus allowed the microalgae culture to receive light efficiently.   The aerator used for this reactor was a plate consisted of 48 holes, each with an inner diameter of 1 mm.  The temperature of the reactor was controlled using a HDL00019 1000 W, 120 VAC 27  cartridge immersion heater, connected to an Omega CN76000 feedback temperature controller. Two different gases were used to aerate the reactor, industrial grade CO2 and compressed air. The CO2 gas line had a flow meter that controlled the flow rate between 100-500 mL/min, and the compressed air line had a flow meter that varied the flow rate between 2-10 L/min. These lines were combined together before going through a 0.22 ?m filter, which sterilized the inlet gas. The light was supplied using a SYLVANIA 54 W 48" T5 fluorescent high output economy light bulb, positioned centrally inside the reactor. This reduced the light travelling distance and provided a more efficient light distribution. A gas outlet was put in place at the top of the reactor, and a tube was connected to lead the exhaust gas to a fume food. This reactor was operated under atmosphere pressure, but in case there was pressure build-up in the reactor, a U-tube manometer was connected to the reactor to monitor the pressure within the reactor, and to act as a pressure relief valve if required.   Acrylic is not autoclavable. Therefore the method of sterilization must be applied. The sterilization techniques considered were 70% ethanol wipe-down, 10% bleach sterilization with sterilized distilled water rinses, and 1 M sodium hydroxide sterilization with sterilized distilled water rinses.  For each experiment, 9 L of A+ medium was prepared and autoclaved, it was then combined with the 1 L Synechococcus sp. PCC7002 inoculums, and pumped into the reactor using a peristaltic pump.   Similar to flask experiments, samples were taken daily from two sample ports, one was 7.5 cm above the gas distributor, and the other was 60.9 cm above the gas distributor. When taking the sample from the reactors, 5 mL was drawn first using a syringe and discarded, and then another 5 mL was drawn and used for analysis. This was because there was a 3 mL dead space between the reactor and the outlet of the sample port. The pH and optical density of each sample were measured and recorded daily.   A problem observed during the operation of this reactor was the large amount of evaporation, which changed the concentrations of the medium, and decreased the water level inside the 28  reactor. As a result, the submersible heater could be exposed to air, and this raised a safety concern. To solve this problem, a LV-10 Series level switch from Omega was used in conjunction with the heater control. When the water level was more than 25.4 cm below the top of the column, the power is cut off to the heater, and sterile water would be added manually on a regular basis to continue the operation.   3.2.2 Glass Bubble Column Photobioreactor As a result of a number of persistent difficulties to operate the acrylic reactor, a glass bubble column photobioreactor was designed, as shown in Figure 13. Cansci Glass Products Ltd was contracted for the construction of this reactor.   Figure 13: 8 L glass photobioreactor This reactor had an internal diameter of 11.4 cm, a height of 81.3 cm, and a working volume of 8 L. The main improvements made in this design were (1) a temperature control system that prevented both hot spots and potential safety concerns associated with the immersion heater and (2) the reactor can be autoclaved.      29      a          b            c  Figure 14: Parts of the glass photobioreactor, the lid (a), the column (b), and the sparger (c) The reactor came in 3 pieces, the lid with several ports, the main body of the column, and the bottom which contained the air inlet and the sparger, shown in Figure 14. There were 7 ports on the lid of the reactor, these were used for gas outlet, U-tube manometer, dCO2 probe, dO2 probe, pH probe, thermocouple, and the tube fluorescent lamp. Online data recording was included in this set up, which was another improvement over the previous set up. This reactor had a 2.5 cm thick water jacket, and by connecting it to a circulating thermo-bath, the temperature of the reactor was controlled and monitored. The SYLVANIA 54 W 48" T5 fluorescent high output economy light bulb was positioned at the center of the reactor to provide illumination. The sparger was a glass frit with medium sized pores (10-15 ?m), positioned at the bottom of the reactor. The bubbles produced by this frit were very fine, especially in the A+ medium. This was explained by surface tension of the A+ medium, which under vigorous agitation, formed foam-like bubbles, much like the sea foam one would find at the beach, this phenomenon is shown in Figure 15.  30   Figure 15: Foam bubbles in the glass photobioreactor  A+ media for these experiments were prepared in the same way as it was for the acrylic reactor. 7.2 L of A+ medium was prepared, autoclaved before combined with the 0.8 L microalgae inoculums, and pumped into the reactor using a peristaltic pump.   Samples for pH and optical density were taken daily, except in this reactor the dead-space between the reactor and the outlet of the sample port was 5 mL, therefore approximately 7 mL would be discarded before the actual sample was taken. The sample point was approximately 30 cm below the top of the reactor.   3.2.3 2-Dimensional Flat Plate Internal Airlift Photobioreactor The third photobioreactor used in this project was a 2-dimensional reactor (Figure 16), it was designed and experimented on by a colleague, David Kuan, who was working with Synechococcus elongates PCC7942, a fresh water microalgae strain.   31   Figure 16: 2-Dimensional flat plate internal airlift photobioreactor This reactor was a flat-plate photobioreactor, and it had the advantages of short light-path length, high surface area to volume ratio, and simple operation (Hu & Kurano, 1998). The main purpose of a 2-dimensional reactor was that it was easier to obtain its hydrodynamic information such as liquid velocity, bubble size and gas holdup. This reactor had an internal height of 28 cm, an internal width of 16 cm, an internal depth of 2.54 cm, and a working volume of 1 L. Two 6? baffles were used to provide an internal liquid circulation, in order to improve aeration in the reactor (Kuan, 2013).  More detailed reactor dimensions are shown in Figure 17.  32   Figure 17: 2-dimensional reactor dimensions (Kuan, 2013) The reactor was made of acrylic, like the first bubble column reactor. Temperature of the system was controlled by a 2 cm water jacket connected to a circulating water bath. Four 60 W daylight compact fluorescent lamps were secured on a panel to provide the lighting for the microalgae culture. It had an acrylic block at the bottom of the reactor designed to support the sparger.  The sparger is a glass frit, with an average pore size of 4.0 ? 5.5 ?m, and it was secured on an acrylic block added to the bottom of the reactor. The gas sources used were compressed air and industrial grade CO2 from Praxair. Two ports were available at the top of the reactor, for the insertions of a dissolved CO2 probe and a dissolved O2 probe.   0.9 L A+ medium was prepared for every experiment using the UTEX recipe. This medium was then sterilized in an autoclave, and then inoculated with 0.1 L of Synechococcus sp. PCC7002.   Every day, a sample of 2 mL would be collected for the pH and optical density measurements. The sample point was approximately 13 cm below the top of the reactor.  33  3.2.4 Continuously Aerated Photobioreactor The last reactors used for this project were the continuously aerated photobioreactors (CARs). There were 4 of these CARs reactors in the lab, which have been used by other colleagues for some culturing work with Synechococcus strains. The reactor operation was very simple, and the reactors themselves were made of glass (see Figure 18), which made the sterilization much easier, by being able to use an autoclave.   Figure 18: Continuously aerated reactors in operation  These reactors were made with 2 mm thick glass, each with an internal diameter of 12 cm, and a working volume of 1 L. Each of these reactors had a 1 cm thick water jacket, connected to a water bath to have their temperatures controlled. These water jackets were connected in parallel to ensure all 4 reactors being operated at the same temperature throughout the experiments. The lighting was provided by 60 W daylight compact fluorescent lamps, secured behind the reactors. The light intensity can be adjusted using the panel on the top right of each compartment, as well as slight distance adjustment between the light and the reactors. Glass frit spargers were used for aeration, with compressed air as the main gas, blended with industrial grade CO2 from compressed gas cylinders.   34  0.9 L of A+ medium was prepared for each reactor, and each reactor was sterilized in an autoclave. Afterwards, 0.1 L of Synechococcus sp. PCC7002 was used to inoculate the culture in the reactor.   Every day, a sample of 2 mL was collected from each reactor for the pH and optical density measurements. The sample point was approximately 8 cm below the upper surface of the reactor.  3.2.5 CO2 Optimization Experiment  As mentioned previously, CO2 is a crucial nutrient for the microalgae cultures, and determining the optimal inlet gas CO2 concentration for Synechococcus sp. PCC7002 was the goal for the reactor experiments. 4 different concentrations of inlet CO2 (0.5 L/min total gas flow rate) were tested, namely 0.04%, 5%, 8% and 10%. The optimal concentration was chosen based on the maximum biomass concentration (Xmax), and maximum specific growth rate (?max) achieved in the culture.   The CO2 concentration used for aeration is adjusted by changing the flows of compressed air and industrial grade compressed CO2.   3.2.6 Recombinant Synechococcus sp. PCC7002 Experiment  Due to time constrains on the project, only 2 experiments were completed for the cultivation of the recombinant Synechococcus sp. PCC7002 strain, at 10% and 20% CO2, respectively. These were chosen based on the experimental design of Di Li, a PhD student who has been working with the recombinant strain exclusively.       35  4 Results and Discussion  4.1 Flask Experiments 4.1.1 A+ Medium Screening  An initial full factorial screening experiment was designed to identify the relatively more growth-influential components in the media.   Two levels of each component were tested, 0 mM and its original concentration from the A+ recipe, because the absence and presence of these components would be the simplest way to determine how critical they are to the microalgae growth. The rest of the A+ medium composition remained unchanged.   Three performance indicators were used to analyze this result, maximum specific growth rate for the logistic model, ?max log., maximum specific growth rate, ?max exp., and maximum biomass concentration, Xmax.  ?max is an important parameter in biotechnology, because at a commercial scale, continuous operation would often be maintained at the stage of maximum growth rate. Two different ?max are shown here, because these represent the ?max for two of the most used models for microalgae cultures, logistic growth model and exponential growth model. Xmax is also an important growth parameter, because most of the products from microalgae culture depend on the amount of biomass produced.   The design and their respective Xmax ?max log., and ?max exp. are listed in Table 10.  Table 10: A+ medium screening results   NaNO3 (mM) KH2PO4 (mM) FeCl3 (mM) CaCl2 (mM) MgSO4 (mM) Vitamin B12 (mM) Xmax (g/L) ?max exp (hr-1) ?max log (hr-1) Culture Duration (hr) 1 11.76 0.36 0.014 0.0000 0.00 2.95E-06 2.33 0.022 0.0086 860 2 11.76 0.36 0.000 0.0000 20.29 2.95E-06 2.21 0.022 0.0079 860 3 0.00 0.00 0.014 0.0000 20.29 2.95E-06 0.68 0.022 0.0173 409 4 0.00 0.00 0.014 0.0018 20.29 2.95E-06 0.67 0.021 0.0170 313 5 11.76 0.36 0.000 0.0000 0.00 2.95E-06 2.37 0.030 0.0080 860 6 0.00 0.36 0.000 0.0000 20.29 2.95E-06 0.57 0.022 0.0165 409 7 0.00 0.00 0.000 0.0000 20.29 2.95E-06 0.68 0.022 0.0173 360 8 11.76 0.00 0.014 0.0000 0.00 2.95E-06 2.56 0.022 0.0087 860 36    NaNO3 (mM) KH2PO4 (mM) FeCl3 (mM) CaCl2 (mM) MgSO4 (mM) Vitamin B12 (mM) Xmax (g/L) ?max exp (hr-1) ?max log (hr-1) Culture Duration (hr) 9 11.76 0.00 0.014 0.0000 20.29 2.95E-06 2.13 0.025 0.0096 716 10 0.00 0.00 0.014 0.0000 0.00 2.95E-06 0.71 0.022 0.0172 409 11 11.76 0.00 0.014 0.0018 20.29 2.95E-06 2.29 0.021 0.0093 860 12 11.76 0.36 0.000 0.0018 20.29 2.95E-06 2.94 0.021 0.0087 860 13 11.76 0.00 0.000 0.0000 20.29 2.95E-06 1.87 0.022 0.0088 860 14 0.00 0.36 0.000 0.0018 0.00 2.95E-06 0.68 0.022 0.0174 409 15 0.00 0.00 0.000 0.0000 0.00 2.95E-06 0.73 0.023 0.0163 409 16 11.76 0.36 0.014 0.0018 0.00 2.95E-06 2.88 0.023 0.0087 740 17 0.00 0.00 0.014 0.0018 0.00 2.95E-06 0.74 0.022 0.0172 409 18 11.76 0.00 0.014 0.0018 0.00 2.95E-06 2.21 0.023 0.0109 716 19 11.76 0.36 0.014 0.0018 20.29 2.95E-06 2.67 0.022 0.0087 812 20 0.00 0.36 0.014 0.0000 0.00 2.95E-06 0.61 0.021 0.0170 409 21 0.00 0.36 0.014 0.0000 20.29 2.95E-06 0.59 0.022 0.0164 409 22 11.76 0.36 0.014 0.0000 20.29 2.95E-06 2.88 0.029 0.0076 860 23 0.00 0.00 0.000 1.8100 20.29 2.95E-06 0.66 0.022 0.0168 409 24 11.76 0.00 0.000 1.8100 20.29 2.95E-06 1.78 0.021 0.0094 716 25 0.00 0.36 0.000 1.8100 20.29 2.95E-06 0.65 0.022 0.0178 313 26 0.00 0.00 0.000 1.8100 0.00 2.95E-06 0.71 0.021 0.0158 409 27 11.76 0.00 0.000 1.8100 0.00 2.95E-06 1.62 0.031 0.0102 716 28 11.76 0.00 0.000 0.0000 0.00 2.95E-06 1.90 0.021 0.0090 716 29 0.00 0.36 0.014 1.8100 0.00 2.95E-06 0.60 0.021 0.0166 481 30 11.76 0.36 0.000 1.8100 0.00 2.95E-06 2.46 0.023 0.0084 860 31 0.00 0.36 0.014 1.8100 20.29 2.95E-06 0.64 0.021 0.0174 409 32 0.00 0.36 0.000 0.0000 0.00 2.95E-06 0.68 0.022 0.0177 409  Xmax of the flasks were directly converted from the optical density readings, as the highest biomass concentration achieved. ?max exp. of the flasks were calculated using Equation 5, isolating the maximum instantaneous slopes of the individual ln(X/X0) vs. time graphs. Lastly, ?max log. of the flasks were determined using solver in Microsoft Excel, on the basis of Equation 6.    First thing of note is that every flask showed some resemblance of growth, and the data are divided into two big groups, one of which reached the stationary phase within around 400 hrs, and the other reached stationary phase at around 860 hrs. It is doubtful that some of these combinations of nutrients would support growth at all, as some of them are essential for a 37  healthy culture. The explanation for this is some of the nutrient from the 10 mL inoculums would support growth in the flasks that showed less growth, whereas the flasks that went on to grow for more than 400 hrs had their inoculums nutrients exhausted and had to consume the nutrient from fresh medium. Xmax results varied among the flasks, this was expected due to the different combinations of chemicals in these flasks. Similar to Xmax, ?max log. for these flasks varied for different combinations as well, this was not a surprise as ?max log. was determined through calculations involving Xmax. Both of these parameters had a strong correlation with how long the experiment lasted, the longer the cultures lasted, the higher Xmax was achieved, but at a slower growth rate. Unlike the previous 2 parameters, ?max exp. did not vary greatly between different flasks, most of these growth rates occurred between first to the second day of the culture, and were all within proximity of one another. This indicated that the growth rate at the beginning phase for all flasks were relatively constant.    Two growth curves from this experiment are shown in Figure 19. Flask 7 and flask 13 had similar medium compositions, only differ in NaNO3 concentration. However, this small difference resulted in very different growth. This indicated that the presence of NaNO3 had a strong effect on microalgae growth, dividing the flasks into the long culture duration group and the short culture duration group.   Figure 19: Screening experiment growth curves  0 500 1000 1500 2000 2500 0 200 400 600 800 1000 Biomass Concentration (mg/L) Time (hrs) Flask 13 Flask 7 38  Xmax, ?max log., and ?max exp., for all the flasks were analyzed by JMP, a statistic software using a standard least square method.   Table 11 shows the significance of all the nutrients? effects on Xmax. Statistically, only NaNO3 and KH2PO4 significantly affected Xmax at a 95% confidence interval, and both of these components positively affected Xmax.  Table 9: Parameter estimates for screening experiment 2 using Xmax (R2=0.93) Term Estimate Std Error t Ratio Prob>|t| NaNO3 0.1392 0.0077 18.07 <.0001 KH2PO4 0.6632 0.2488 2.67 0.0130 FeCl3 9.2660 6.6894 1.39 0.1778 CaCl2 -0.0836 0.0604 -1.38 0.1782 MgSO4 0.0004 0.0044 0.08 0.9339  Table 12 shows the significance of all the nutrients? effects on ?max log. Statistically only NaNO3 significantly affected ?max log at a 95% confidence interval, however, the effect was negative.  Table 10: Parameter estimates for screening experiment 2 using ?max log. (R2=0.98) Term Estimate Std Error t Ratio Prob>|t| NaNO3 -0.0007 0.0000 -31.91 <.0001 KH2PO4 -0.0013 0.0007 -1.85 0.0751 FeCl3 0.0133 0.0186 0.71 0.4810 CaCl2 0.0001 0.0002 0.64 0.5256 MgSO4 -0.0000 0.0000 -0.30 0.7660  Table 13 shows the significance of all the components effects on ?max exp. This analysis had a very poor fit, and statistically, none of the components affected ?max exp. significantly. This most likely indicated that exponential model does not describe Synechococcus sp. PCC7002?s growth accurately.  Table 11: Parameter estimates for screening experiment 2 using ?max exp. (R2=0.18) Term Estimate Std Error t Ratio Prob>|t| NaNO3 0.0002 0.0001 2.11 0.0443 KH2PO4 -0.0000 0.0000 -0.84 0.4080 FeCl3 -0.0312 0.0666 -0.47 0.6436 CaCl2 0.0007 0.0025 0.28 0.7814 MgSO4 0.0001 0.0001 0.23 0.8169 39   Even though based on these designs, only NaNO3, and KH2PO4 showed enough confident effects to be investigated further, FeCl3 was decided to be added to the next step of nutrient optimization as well. This was because FeCl3 showed large effects (shown in the JMP results) on all aspects of growth, but it just did not quite reach the same confidence level as NaNO3 and KH2PO4.   4.1.2 A+ Medium Optimization A fractional factorial design, more specifically, a central composite design, was used for the A+ medium optimization. A total of 20 flasks were prepared and the microalgae growth in each flask was monitored for 2 to 3 months, until the stationary phase was reached. The details of the combinations and their respective Xmax, ?max log., and ?max exp. are shown in Table 14.  Table 12: A+ medium optimization results   NaNO3  (mM) KH2PO4  (mM) FeCl3  (mM) Xmax  (g/L) ?max exp. (hr-1) ?max log. (hr-1) Culture Duration (hr) 1 5.88 0.18 0.007 2.45 0.034 0.0099 888 2 5.88 0.18 0.028 2.50 0.033 0.0097 888 3 5.88 0.72 0.007 2.38 0.031 0.0097 984 4 5.88 0.72 0.028 2.67 0.031 0.0092 888 5 23.52 0.18 0.007 6.13 0.033 0.0055 1632 6 23.52 0.18 0.028 6.77 0.034 0.0051 1824 7 23.52 0.72 0.007 6.44 0.032 0.0054 1608 8 23.52 0.72 0.028 6.37 0.032 0.0055 1632 9 3.5 0.36 0.014 1.60 0.034 0.0123 624 10 39.51 0.36 0.014 8.42 0.033 0.0052 1752 11 11.76 0.11 0.014 4.25 0.031 0.0071 1320 12 11.76 1.21 0.014 4.63 0.031 0.0074 1272 13 11.76 0.36 0.0041 3.65 0.034 0.0079 1320 14 11.76 0.36 0.047 4.39 0.032 0.0076 1272 15 11.76 0.36 0.014 4.02 0.031 0.0078 1272 16 11.76 0.36 0.014 4.21 0.031 0.0076 1200 17 11.76 0.36 0.014 4.68 0.034 0.0063 1464 18 11.76 0.36 0.014 4.18 0.035 0.0079 1152 19 11.76 0.36 0.014 4.09 0.033 0.0076 1200 20 11.76 0.36 0.014 5.37 0.034 0.0063 1464  40  The same performance indicators were chosen for this experiment. Like the screening experiment, the Xmax of the flasks varied greatly, spread between 1.6 g/L to 8.42 g/L. Based on the findings of the previous experiments, NaNO3 had a significant impact on microalgae growth, and it was once again demonstrated here, as the level of Xmax correlate almost perfectly linearly with the level of NaNO3 concentration. ?max log. also behaved in the same manner, once again this is because Xmax is a parameter used in the calculation of ?max log. For this experiment, ?max log. varied from 0.0051 hr-1 to 0.0123 hr-1, which was in general lower than the screening experiment by a factor of 2. Conversely, culture duration was longer by a factor of 2, this was not unexpected, as culture duration was the only other variable that dictated the calculations of ?max log., shown in Equation 6. ?max exp. for this experiment ranged from 0.031 hr-1 to 0.035 hr-1, slightly higher than the screening experiment, but remained consistent within all flasks. The increase in ?max exp. was most likely caused by a decrease in initial concentration of the cultures. In the previous experiment, the cultures started at 45 mg/L, whereas this experiment had the cultures started at 32 mg/L.   Again, Xmax, ?max log., and ?max exp. for each flask were analyzed with JMP using a standard least square method.      Table 15 shows the significance of all the components effects on Xmax, and only NaNO3?s effects were significant at a 95% confidence interval, and it positively affected Xmax.  Table 13: Parameter estimates for A+ optimization using Xmax (R2=0.96) Term Estimate Std Error t Ratio Prob>|t| NaNO3 0.4044 0.0731 5.53 0.0003* FeCl3 97.9643 61.3294 1.60 0.1413 KH2PO4 2.6326 2.3935 1.10 0.2971  Table 16 shows the significance of all the component effects on ?max exp. Similar to results obtained from the screening experiment, the exponential growth model did not fit the data very well, and none of the components showed a significant effect.  41  Table 14: Parameter estimates for A+ optimization using ?max exp. (R2=0.33) Term Estimate Std Error t Ratio Prob>|t| NaNO3 -0.0001 0.0002 -0.58 0.5746 FeCl3 -0.0786 0.1766 -0.44 0.6658 KH2PO4 -0.0015 0.0069 -0.22 0.8341  Table 17 shows the significance of each component?s effects on ?max log, and once again NaNO3 was the only component that had a significant effect at a 95% confidence interval, but it negatively affected ?max log.  Table 15: Parameter estimates for A+ optimization using ?max log. (R2=0.91) Term Estimate Std Error t Ratio Prob>|t| NaNO3 -0.0006 0.0001 -5.45 0.0003 FeCl3 -0.0749 0.0958 -0.78 0.4521 KH2PO4 -0.0014 0.0037 -0.37 0.7165  These results indicate that an optimization problem exists for the components? concentrations, because in the concentration ranges tested for this experiment, higher concentrations of all components improved Xmax, but hampered ?max. In conclusion, the best concentrations of NaNO3, KH2PO4 and FeCl3 for supporting Xmax were 23.5 mM, 0.72 mM, and 0.028 mM respectively, and 6.37 g/L biomass was produced with this composition. For supporting ?max log., the best concentrations of NaNO3 KH2PO4 and FeCl3 were 5.88 mM, 0.18 mM, and 0.007 mM respectively, and a specific growth rate of 0.0099 hr-1 was achieved with this composition.   An advantage of using JMP for analysis was that model equations can be derived to predict Xmax and ?max log. based on the estimated effects of each significant component and their respective concentrations.                             (5)                              (6)  where X1 is the concentration of NaNO3 in mM. Based on the experiments conducted, these two equations are only applicable for NaNO3 concentrations between 3.5 and 39.5 mM.   42  After the JMP analysis, experiment data were fitted to two growth models, the logistic growth model, and the exponential growth model. Figure 20 show the result of the logistic growth model fit.   Figure 20: Logistic growth model applied to experiment data (flask #7) The experiment data did not display any type of curvature that was seen in the logistic growth model.  Next, the exponential growth model was investigated in Figure 21.     Figure 21: Exponential growth model vs. experiment data (flask #7) Figure 21 is a semi log. graph, hence the legend says ?Linear experiment data?. The R2 value of 0.09 indicates that the experimental data are nowhere close to an exponential growth model. However, a closer look shows that the graph may be dissected into two sections, breaking at around the 15 hr mark. This indicated that a nutrient or condition becomes a limiting factor at this point of the culture, and was preventing it from flourishing. The possible constraints were 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 0 500 1000 1500 2000 Biomass Concentration (g/L) Time (hr) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 0 500 1000 1500 2000 Biomass Concentration (g/L) Time (hr) Experiment Data 43  temperature, light intensity, and CO2 supplied, and the former two constraints were investigated in the next experiment.   4.1.3 Temperature & Light Intensity Optimization Temperature and light intensity are two of the most important culture conditions for microalgae growth. A full factorial experiment with 3 different light intensities and 5 different temperatures was designed and performed to determine the optimal culture conditions for Synechococcus sp. PCC7002  The growth data from each experiment by the 21st day were used for analysis, even though the stationary phase had not been reached in all the flasks yet. This decision was made because beyond this period, due to the difference in incubation temperature, the evaporation reached a critical level and changed the results significantly.    Table 16: Temperature and light intensity experiment results Flask Number Pattern X1 (Temp) X2 (Light Intensity) Xmax ?max log.  ?max exp.   ?C ?E/m2/s g/L hr-1 hr-1 1 1-1 23 100 1.01 0.035 0.0066 2 1-2 23 150 1.17 0.036 0.0066 3 1-3 23 250 1.22 0.037 0.0070 4 2-1 30 100 1.26 0.039 0.0075 5 2-2 30 150 1.28 0.045 0.0078 6 2-3 30 250 1.53 0.046 0.0097 7 3-1 35 100 1.43 0.052 0.0085 8 3-2 35 150 1.94 0.042 0.0103 9 3-3 35 250 2.05 0.039 0.0099 10 4-1 38 100 0.98 0.452 0.0079 11 4-2 38 150 0.92 0.043 0.0081 12 4-3 38 250 1.01 0.050 0.0083 13 5-1 40 100 0.56 0.004 0.0083 14 5-2 40 150 0.63 0.036 0.0085 15 5-3 40 250 0.36 0.039 0.0083  The data above were put into JMP for analysis.   44  Table 19 shows the significance of temperature, light intensity and their secondary effects on Xmax of the cultures.  Temperature?s first and secondary effects were both significant at a 95% confidence interval.  Table 17: Parameter estimation for temperature-light intensity experiment using Xmax (R2=0.66) Term Estimate Std Error t Ratio Prob>|t| Intercept -233.488 58.172 -4.01 0.0025* Temperature -243.430 60.866 -4.00 0.0025* Temperature*temperature -63.111 15.895 -3.97 0.0026* Light intensity 0.047 0.0900 0.52 0.6117 Light intensity*light intensity -0.001 0.0026 -0.42 0.6835  Table 20 shows the significance of temperature, light intensity and their secondary effects on ?max exp. of the cultures. Similar to all the other times when exponential model is applied to the culture, this was a very poor fit, and none of the effects shown were significant.  Table 20: Parameter estimation for temperature-light intensity experiment using ?max exp. (R2=0.16) Term Estimate Std Error t Ratio Prob>|t| Intercept -1.367 21.194 -0.06 0.9498 Temperature -1.996 22.176 -0.09 0.9300 Temperature*temperature -0.588 5.791 -0.10 0.9212 Light intensity -0.029 0.033 -0.89 0.3944 Light intensity*light intensity 0.001 0.001 0.79 0.4494  Table 21 shows the significance of temperature, light intensity and their secondary effects on ?max log., and the result was the same as their effects on Xmax. Temperature and its secondary effects were the only significant factors.  Table 21: Parameter estimation for temperature-light intensity experiment using ?max log. (R2=0.70) Term Estimate Std Error t Ratio Prob>|t| Intercept -0.385 0.129 -2.98 0.0138* Temperature -0.417 0.135 -3.08 0.0116* Temperature*temperature -0.111 0.035 -3.13 0.0107* Light intensity 0.000 0.000 0.79 0.4475 Light intensity*light intensity -0.000 0.000 -0.56 0.5868  45  As a result, two equations generated by JMP to obtain Xmax and ?max, these equations were based on the factors investigated, light intensity and temperature.                                       (7)                                      (8)  where X1 is the temperature of the experiment in Celsius. These equations are only valid for temperatures between 23?C and 40?C.   Sigmaplot was used to create the response surfaces such as Figures 22 and 23 The first response surface showed that light intensity had very little effect on Xmax, especially when light intensity was higher than 150 ?E/m2/s. Temperature, on the other hand, had a much more dominant effect on the Xmax. Below 35?C, the Xmax decreased at a very fast rate, and for temperatures above 35?C, Xmax still decreased, but at a slower rate. The interaction effect between temperature and light intensity seemed to be minimal as well. A conclusion can be drawn from this response surface, the optimal condition to produce the highest amount of biomass was 35?C, and 250 ?E/m2/s. At this condition, 2.05 g/L of biomass was produced 21 days after inoculation in a 100 mL Synechococcus sp. PCC7002 culture.   Figure 22: Temperature-light intensity response surface to Xmax 46  Figure 23 shows how temperature and light intensity affected the ?max log. Light intensity had some small positive effects on ?max log, but these effects were negligible when compared to the temperature effects. ?max log. slowly increased as temperature increased from 23?C to 35?C, and decreased quickly once temperature went past 35?C. From this response surface, it can be concluded that the optimal conditions to achieve the highest ?max log. is at 35?C, and 150 ?E/m2/s. At this condition, a ?max log. of 0.0103 hr-1 was reached.   Figure 23: Temperature-light intensity response surface to ?max log.  For the response surfaces in Figures 22 and 23, at 35?C the differences in Xmax and ?max were minimal between 150 ?E/m2/s and 250 ?E/m2/s, which indicated a limited impact of light intensity on microalgae growth at 35?C. The most likely explanation is that the microalgae growth was limited in those flasks by the CO2 available for photosynthesis, since no gas was injected in these tests. This leads us to the investigation of CO2 effects on microalgae growth in photobioreactors with continuous injection of air or gas mixtures of different CO2 concentrations.  4.2 Exploratory Experiments in an Acrylic Bubble Column Photobioreactor An acrylic bubble column photobioreactor was used as the first attempt at culturing microalgae at a larger scale. Figure 24 shows this reactor in operation.  47   Figure 24: Acrylic bubble column photobioreactor in operation Due to the fact that sterilization methods for this reactor were very time consuming and material consuming, it would be beneficial if the sterilization would not be required. To test this theory, an unsterile run was conducted first (Figure 25), used for comparison with sterile runs (Figure 26).   Figure 25: Experimental run in unsterilized reactor  0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc (mg/L) Time (hr) High Port Low Port 48   Figure 26: Experimental run in sterilized reactor by 10% bleach solution  Both runs were conducted at an air aeration rate of 5 L/min, while maintained at 35?C, but the unsterilized run had a slightly longer culturing period, and reached a slightly higher Xmax. Two possible explanations for the difference in these results were proposed. First, the sterilization could have eliminated other microorganisms, reducing the amount of biomass detected. The second explanation is that there were mistakes during the sterilization process, which caused the slow growth. To further investigate the difference, a repeat sterilization run was conducted, with the results shown in Figure 27.   Figure 27: Repeat run in the sterilized reactor by 10% bleach The results are consistent with the other sterilized run; however the biomass concentration is well below the levels reached in the first run. To improve growth, experiments were conducted at 0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 49  higher inlet gas CO2 concentrations, as well as higher total inlet flow rate, with the results shown in Figure 28.   Figure 28: Sterilized run at gas inlet flow of 10 L/min and 5% CO2  There was no improved growth at all, despite the increased gas flow rate, improved liquid mixing, and CO2 concentration. This indicated that the cell growth was not limited by CO2 supply and cell mixing. Several factors were suspected for the low growth, these include too low a light intensity, lack of light-dark cycle, too strong a mixing, which damaged the cells, and too low a pH due to the dissolved CO2. All of these factors were considered and examined in following experimental runs with results shown in Figures 29, 30 and 31.   Figure 29: Sterilized run at 35?C, 8 L/min, 5% CO2 with a 54 W fluorescent lamp 0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 50   Figure 30: Sterilized run at 35?C, 8 L/min, 5% CO2 with 54 W light and a 12-12 L-D cycle  Figure 31: Sterilized run at room temperature, 8 L/min, 0.04% CO2, and 54 W light None of these results presented above showed significant improvement in the growth of Synechococcus. At this point, the lack of promising result and the operational difficulties (evaporation, poor temperature control, uneven bubbling due to poor gas distributor etc.) put a question mark on whether experiments with this reactor should be continued. Some rather abnormal results were obtained and the overall accuracy and reliability was questioned. Two repeat runs for the room temperature experiment were performed, as a final check to see if experiments with the acrylic reactor are worth of further pursuit, with the results shown in Figures 32 and 33.  0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Concentration (mg/L) Time (hr) High Port Low Port 0 200 400 600 800 1000 1200 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 51    Figure 32: Room temperature experiment run 2   Figure 33: Room temperature experiment run 3  As seen in these figures, under the same operating conditions, different runs gave very different results. Thus, seriously raising questions over the results obtained until now.   Since this acrylic reactor was difficult to sterilize, and the unreliable data it produced, it was decided that a new reactor would be constructed to perform the reactor scale experiments.  4.3 Experiments in a Glass Bubble Column Photobioreactor To improve the performance in cultivation of microalgae, a glass reactor was designed and commissioned (Figure 34). The sterilization was accomplished by autoclaving the reactor and the medium.  0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 0 100 200 300 400 500 600 700 800 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) High Port Low Port 52   Figure 34: Performance in a glass reactor with 4.5 L/min air flowrate, 33?C at 148 hr The first run was conducted with gas aeration at 4.5 L/min (Figure 35), with the reactor temperature maintained at 35?C. These conditions were chosen because theoretically they should not hamper the microalgae growth in any way.   Figure 35: Growth curve of sterilized run at 35?C, 4.5 L/min, 0.04% CO2   As seen above, the growth was much more promising than that of the acrylic reactor. Biomass concentration reached levels of 1.22 g/L with a specific growth rate of 0.0304 hr-1.   A problem with this photobioreactor was observed in the first run. A significant amount of microalgae was blown to the top of the reactor during the beginning stage (first 8 hrs of the operation). This phenomenon is shown in Figure 36.  0 200 400 600 800 1000 1200 1400 0 100 200 300 400 Biomass Conc. (mg/L) Time (hr) 53   Figure 36: Microalgae blown to the surface of the reactor As seen in this picture, the top of the reactor was clearly greener than rest of the reactor, this indicated a great amount of microalgae was blown to the surface of the liquid. Inlet gas flow rates were reduced to help with this problem without much success. This problem was most likely caused by the foam in the medium. However, since the growth did not seem affected, the experiments continued without addressing this problem.   In the next experiment, the inlet gas flow rate, and CO2 concentration were increased to examine their effect on the growth of microalgae, and the 5% CO2 run is shown in Figure 37.   Figure 37: Growth curve of the second run at 35?C, 8 L/min, 5% CO2  0 50 100 150 200 250 300 350 0 20 40 60 80 100 Biomass Conc. (mg/L) Time (hr) 5%CO2 8L/min 54  The short growth phase in Figure 36 was unexpected, as CO2 was the growth constraint in the flasks, and the increase in CO2 should have increased microalgae growth. Upon looking at some other data, it was discovered that the pH of the culture might have caused this lack of growth, as shown in Figure 38.   Figure 38: pH curves of the first two runs in the glass reactor As seen above, the initial pH for this run was 7.78, as the A+ medium has a pH of 8.2 prior to the introduction of gas. The spike in pH was caused by the cut-off of CO2 flow due to unstable pressure, but it soon returned to normal values. At around 68 hr, pH drastically decreased, and around the same time, the growth of microalgae instantaneously stopped. Two possible explanations were developed for this result. One of them was the buffer in the medium reached its capacity and pH started to drop due to continuous introduction of CO2. The other explanation was that the culture was contaminated with an organism that lowers the pH.   A liquid sample was taken and observed under the microscope to check for contaminations, but the result was inconclusive. Therefore, to increase the strength of the buffer, a quick experiment in the flasks was conducted to investigate if increasing the Tris buffer concentration in the A+ medium could improve its buffering capacity without hampering the microalgae growth. The results are shown in Figure 39.  4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 0 100 200 300 400 pH Time (hr) Air 4L/min 5% CO2 8L/min 55   Figure 39: Effect of 3x Tris buffer on A+ medium in shake flasks The biomass concentrations from both flasks were matched throughout the 312 hour testing period, while the pH increase for the 3x Tris flask was half of the 1x Tris flask, this indicated that buffering capacity can be increased by increasing the Tris concentration without hampering the microalgae growth.   Based on this result, the next experiment, at 4 L/min, 2.5% CO2 and 35?C, was designed with less CO2 being introduced to the system to minimize the pH drop it could cause, while the Tris concentration in the A+ medium was increased by 3 fold.    Figure 40: 4 L/min, 2.5% CO2, 35?C growth curve and pH curve As shown in Figure 40, a similar trend to the second run (Figure 36) was observed, with a slightly delayed response of growth to the pH change in the medium. When the pH medium dropped below the optimal pH range for Synechococcus, 240mL Tris buffer was added to adjust 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6 0 200 400 600 800 1000 1200 0 100 200 300 400 pH Biomass Conc. (mg/L) Time (hr) 1x Biomass Conc. 3x Biomass Conc. 1x pH 3x pH 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 0 50 100 150 200 250 300 350 400 0 50 100 150 200 pH Biomass Conc. (mg/L) Time (hr) Biomass Conc. pH 56  the pH at 74 hr. However the effect only lasted for 50 hours, as the pH as well as the biomass concentration started dropping again. A few further experiments were conducted without success in sustaining the microalgae growth by properly controlling the pH value. As a result, the project was moved onto a 2-dimensional reactor previously used for culturing other microalgae strains.    4.4 Experiments in a 2-Dimensional Flat Plate Internal Airlift Photobioreactor The next set of experiments in the 2-D reactor, shown in Figure 41.   Figure 41: 2-dimensional photobioreactor setup (Kuan, 2013) The first experiment was at 35?C, 150 ?E/m2/s, 0.5 L/min gas inlet flow, with 5% CO2. 24 hours after the inoculation, the microalgae died with the reactor at a pH of 6. It was thought that the CO2 level was too high, leading to quick drop in the pH at the early growth phase, causing cell deaths. Another main reason for this lack of growth was that the total inlet gas flow rate might have been too high, because a large amount of microalgae was blown to the upper surface of the liquid by the foam. With these possible causes in mind, in the next experiment, the reactor was not aerated for the first 24 hours, and then it was aerated with air at 0.25 L/min for another 24 hours, finally on the 3rd day, the inlet flow was adjusted to 5% CO2 at a flow rate of 0.25 L/min. Another adjustment made was to increase the inoculums amount, so it could help get the growth started as smoothly as possible. As seen in Figure 42, the growth seemed to be underway by 50 hr, but soon after, the biomass concentration leveled off and started decreasing.  57   Figure 42: Results of the first 2-D reactor run (35?C, 150 ?E/m2/s, 0.25 L/min, 5% CO2) There was a pH drop at 50 hr, indicating the increase in the dissolved CO2 level. A second run at similar conditions was completed, this time not only was more attention paid to the preparation of the reactor, also another culture in a shake flask was inoculated with the same inoculum as the photobioreactor, to make sure the lack of growth was not due to an unhealthy batch of microalgae inoculum.  The only difference for this run was that the gas inflow remained at 0.25 L/min with 5% CO2 for the entire experiment, since the varied gas flow regime did not show to have any significant positive effect on the growth (See Figure 43).   Figure 43: Results of the second 2-D reactor run (35?C, 150 ?E/m2/s, 0.25 L/min, 5% CO2)  The growth for this run was very similar to the first one. The culture showed a little bit of growth for the first 60 hrs, and started decreasing. The decrease in biomass concentration corresponded to a drop in pH. The growth curve for the flask showed a different trend, as seen in Figure 44.  6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 0 20 40 60 80 100 120 140 0 50 100 150 200 pH Biomass Conc. (mg/L) Time (Hr) Biomass Conc. pH 6.7 6.8 6.9 7.0 7.1 7.2 7.3 0 10 20 30 40 50 60 70 0 50 100 150 pH Biomass Conc. (mg/L) Time (hr) biomass reactor ph reactor 58   Figure 44: Results of the corresponding shake flask (35?C, 150 ?E/m2/s, 0.25 L/min, 5% CO2) Over the same period, the shake flask culture grew normally, with a steady pH increase, consistent with previous flask experiments. Again, due to the time constraint, instead of keep working with this photobioreactor, it was decided to use the continuously aerated reactors for the investigation of CO2 effects, since this has been proven to work well with Synechococcus.   4.5 Experiments in a Continuously Aerated Photobioreactors 4.5.1 Effects of Inlet Gas CO2 Concentration This set of experiments was completed under 33?C, and 200 ?E/m2/s. The lights for this set up is placed on one side of the reactor, as shown in Figure 45, with the light intensity being measured on the side that is closest to the light sources.  7.3 7.4 7.5 7.6 7.7 7.8 7.9 0 50 100 150 200 250 0 50 100 150 pH Biomass Conc. (mg/L) Time (hr) Biomass pH 59   Figure 45: Experimental set up for continuously aerated photobioreactors    One of the conclusions from the flask experiments is that CO2 available to the microalgae culture was one of the possible constraints on the growth. Therefore in this photobioreactor, several CO2 concentrations in the inlet gas stream were tested, namely 0.04%, 5%, 8% and 10%. The growth curves for these runs are shown in Figure 46.    Figure 46: Growth curves at various CO2 inlet concentrations in CARs at 33?C, 200?E/m2/s and 0.5 L/min gas flow rate 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 200 400 600 800 Biomass Conc. (g/L) Time (hr) 0.04% 5% 10% 8% 60  At the beginning of every run, there was a slight decrease in the biomass concentration due to some of the microalgae being blown to the top of the reactor by the sparging gas. However, after this phase, the growth would elevate at a much faster pace. All the runs lasted for around 650 hrs.   To investigate why the microalgae begin to show a decrease in growth at 10% CO2, the pH curves of all cultures (see Figure 47) were examined.    Figure 47: pH curves for experiments at different levels of CO2 The optimal pH level for Synechococcus sp. PCC7002 is between 7 and 9. At 10% CO2, the initial pH was around 6, which definitely slowed down the microalgae growth, as shown with an extra long lag phase in Figure 45. The growth never recovered to match the levels that were reached with 0.04%, 5% and 8% CO2.   Like in the flask experiments, the data obtained in the photobioreactor were fitted to both the exponential growth model and the logistic growth model, as shown in Figures 48 and 49.   6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 0 200 400 600 800 pH Time (hr) Air pH 5% 10% 8% 61   Figure 48: Photobioreactor results fitted to a logistic model (8% CO2 experiment)   Figure 49: Photobioreactor results fitted to an exponential model (8% CO2 experiment)  Compared to the fitting of flask experimental data, the photobioreactor results were fitted much better to the logistic growth model, although it was still far from ideal. Both the experimental data and logistic model displayed a lag phase, and a declining phase, the difference came from the middle of the graph. In this section the experiment data were very linear, it did not display the same curvatures shown in the logistic model. Even though overall the growth improved with CO2, there might be yet another constraint that prevented the culture to grow at a higher pace during this phase. This constraint could have been the varying pH, which was not investigated in this research. Similar to the flask experiments, the experimental data still do not show any 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 200 400 600 800 Biomass Concentration (g/L) Time (hr) 0 2 4 6 8 10 12 0 200 400 600 800 Biomass Concentration (g/L) Time (hr) Experiment Data Expon. (Experiment Data) 62  resemblance to an exponential growth model, and further analysis using this model was abandoned.   Increasing the CO2 inlet concentration had a significant effect on the maximum biomass concentration. From 0.04% CO2 (air) to 5% CO2, the maximum biomass concentration increased from 2.55 g/L to 3.07 g/L, which was not a surprise. As shown in flask experiments before, under aeration with regular air, the microalgae growth was limited, most likely by the amount of CO2 available. As a result, the significant increase in CO2 available to growth significantly increased the microalgae growth. From 5% to 8%, the maximum biomass concentration only slightly improved from 3.07 g/L to 3.09 g/L. From 8% to 10%, the maximum biomass concentration decreased from 3.09 g/L to 1.75 g/L. This suggested that the optimal CO2 level is around 8%, as shown graphically in Figure 50.    Figure 50: CO2 concentration effect on Xmax Figure 51 showed the variation of logistic specific growth rate of these runs, which followed the very same trend as the maximum biomass concentration. From 0.04% to 5% CO2, ?max log. increased only slightly from 0.0136 hr-1 to 0.0148 hr-1, although a bigger jump was expected over this CO2 range. More significant increase was observed when CO2 increased from 5% to 8%, ?max log. increased from 0.0148 hr-1 to 0.0186 hr-1. From 8% to 10% CO2, ?max log. decreased from 0.0186 hr-1 to 0.0164 hr-1, which was also a surprise, because even though 10% CO2 gave the lowest biomass concentration in this experiment, it did have the 2nd highest ?max log. This might partly result from a slightly lower initial biomass concentration in the test with 10% CO2, 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% Maximum Biomass Concentration (g/L) CO2 Concentration 63  because it was shown in the flask experiments that a lower initial biomass concentration could lead to a higher ?max.    Figure 51: CO2 concentration effect on ?max log.  The estimated optimal gas inlet CO2 concentration was 8%, as it produced the highest biomass concentration as well as it created a culture with the highest specific growth rate.   4.5.2 Experiments with the Mutant Synechococcus sp. PCC7002 Two runs with the recombinant strain were completed in this part of the project. The conditions used were 33?C, 300 ?E/m2/s, and 0.5 L/min gas flow rate at 10% and 20% CO2 gas inlet concentration. The results are shown in Figure 52.   Figure 52: Growth curves of mutant Synechococcus sp. PCC7002 strains in photobioreactor 0.000 0.005 0.010 0.015 0.020 0.00% 5.00% 10.00% 15.00% ?max log (hr-1 ) CO2 Concentraion 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 0 200 400 600 Biomass Concentration (g/L) Time (hr) 20% CO2 10% CO2 64  There was a slightly longer lag phase compared to other experiments, but unlike the wild strain, the recombinant strain was able to flourish under 10% CO2. This happened despite the fact that no targeted modification was made to help the microalgae survive lower pH conditions. When the CO2 gas inlet concentration was further increased to 20%, the culture performance decreased. An event to note here is for the 20% CO2 experiment, on day 4, the CO2 was turned off due to the lack of CO2 supply, and was put back online at 20% CO2 24 hrs later. This event seemed to kick start the growth, although the performance was still lower than that of the 10% CO2 run.   At 10% CO2, a Xmax of 3.1 g/L and ?max of 0.0180 hr-1 was achieved, and this was prior to the growth reaching stationary phase.   Even though the light intensity of this experiment was at 300 ?E/m2/s, where as the wild strain photobioreactors were operated at 200 ?E/m2/s, the growths of both strains were compared for reference purposes, for 10% gas inlet CO2 in Figures 53, and 54.   Figure 53: Wild and mutant strains growth curves for 10% CO2 gas inlet 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0 200 400 600 800 Biomass Conc. (g/L) Time (hr) 10% Wild 10% Rec. 65   Figure 54: pH curves comparisons between wild and recombinant strain Even though no definite conclusions can be made from these figures, due to the difference in light intensity for the two microalgae strains, it can be speculated that the recombinant strain is able to thrive more under a gas inlet CO2 concentration of 10%. Another argument can be made that light intensity effect was much greater in this set up, and the difference in growth was thus mainly due to the extra light intensity for the recombinant strain. The pH curves were very similar between the two runs, which ruled out any difference in medium condition that might have caused the better growth in the recombinant strain. Further tests are needed before any firm conclusions can be drawn regarding the growth difference between the wild and recombinant strains.    6.00 6.20 6.40 6.60 6.80 7.00 7.20 7.40 0 200 400 600 800 pH Time (hr) 10% Wild 10% Rec. 66  5 Discussion  5.1 Comparison of flask results and photobioreactor results   Synechococcus sp. PCC7002 showed good growth in shake flasks, but the growth was quite linear rather than exponential or logistic. This indicated that the growth in flask experiments was limited, most likely by available CO2. In the photobioreactors, the growth curves were also quite linear, but somehow followed the logistic growth with short duration lag phases, and declining growth phases. Based on the results obtained, pH was believed to be the most likely limiting factor for algae growth in photobioreactors. The growth differences between flasks and photobioreactors are illustrated in Figure 55.   Figure 55: Flask growth vs. photobioreactor growth Besides the difference in the shape of the growth curves, there is also a major difference in culture duration between the growth curves for the two types of growths. In flask cultures, the growth lasted between 35 to 72 days, whereas the growth in photobioreactor cultures lasted for up to 25 days only. Part of this difference in the culture duration was due to the fact that water evaporation was not accounted for in the flask, which might have boosted the biomass concentration even though the growth could have stopped. In contrary, the culture volume in photobioreactors was kept constant by daily addition of sterilized distilled water.   0 500 1000 1500 2000 2500 3000 3500 0 200 400 600 800 1000 Biomass Concentration (mg/L) Time (hr) Photobioreactor growth 67  The pH curves also showed some differences between flask experiments and photobioreactor experiments, as shown in Figure 56.   Figure 56: Flasks pH curves vs. photobioreactor pH curves pH generally increased as the biomass concentration increased, and this was most likely due to the generation of a growth related product, which neutralized H+ ions. In flasks, pH reached a maximum, and then decreased even though biomass concentration was still increasing (comparing Figure 55, and 56). However microalgae in photobioreactor cultures reached the stationary phase without experiencing the decrease in pH. Commonly in flasks, when the maximum pH was reached, the culture colour became green, and would then turn yellowish green as the pH began to decrease. The pH results seemed to suggest that the microalgae cells began to die at around 550 hr in the tested flasks. However such trend was not observed in the growth curves until 850 hr. This supports our speculation that evaporation taking place in flasks played an important part in the measured increase in cell concentration, even though the culture seemed to be dying according to the colour and the pH of the cultures. Since optical density could not catch this phenomenon, various staining techniques were designed and attempted by Dr. Swati Yewalkar in our lab to monitor the live and dead cells, which will not be discussed in this work.       7.0 8.0 9.0 10.0 11.0 12.0 0 200 400 600 800 1000 pH Time (hr) Photobioreactor pH curve Flask pH curve 68  5.2 Comparison of result with other published data  Table 18 compiled some typical Xmax and ?max data from open literature obtained using various types of reactors and microalgae strains. It is seen that the Xmax and ?max values obtained n this study fall well within the range as most other published results.   Table 18: Growth performance data from literature and current study Sources Reactor Strain Xmax (g/L) ?max (hr-1) Current study CAR Synechococcus wild 3.1 0.0186 CAR Synechococcus mutant 3.1 0.0180 Kumar & Das (2012) BC Chlorella 3.1 0.0181 IAL Chlorella 4.4 0.0178 Suh & Lee (2001) IAL Synechococcus 2.9 0.0081 Divya (2009) EAL Chlorella 0.42 0.0257 Chiu et al. (2009) BC Chlorella 2.37 0.0092 Jacob-Lopes et al. (2009) BC Aphanothece 5.1 0.0267 Kulk et al. (2011) Flask Synechococcus   0.017 Flask Synechococcus   0.011 Kajiwara et al. (1997) EAL Synechococcus 1.12 0.0551  Because of a lack of reported experiments data for Synechococcus sp. PCC7002, results from this study was compared to the growth of other microalgae strains. Among the reported Synechococcus strain experiments, the highest microalgae growth was achieved in the continuously aerated reactors. The best growth observed in this table was achieved by Jacob-Lopez, with Aphanothece, with 5.1 g/L microalgae being obtained at a ?max of 0.0267 hr-1, and the Xmax and ?max achieved in this study were on the same magnitude.     69  6 Conclusions  The first factor investigated in this research project was the nutrient medium. Upon reviewing the functions of each component in the A+ medium, NaNO3, KH2PO4, FeCl3, MgSO4, and CaCl2 were selected for a screening in shake flasks, as a result, NaNO3, KH2PO4, and FeCl3 were found statistically to be the 3 most important components, and were taken further for an optimization experiment. A fractional factorial experiment was designed, and response surface method was used to analyze the collected data, which showed that 23.5 mM, 0.72 mM, and 0.028 mM were the best concentrations for NaNO3, KH2PO4, and FeCl3 to support Xmax respectively, and 5.88 mM, 0.18 mM, and 0.007 mM were best concentrations for these components to support ?max.  In another factorial experiment, temperature and light intensity effects were investigated in the shake flasks. As a result, the optimal condition for best biomass concentration within the test range was identified as 35?C and 250 ?E/m2/s, and the optimal condition for best growth rate was 35?C and 150 ?E/m2/s.   Several reactor designs were explored in this research project. At first, it was a 10 L acrylic bubble column reactor, internally illuminated with a tubular fluorescent light. It had the temperature controlled through the use of a submerged electrical heater in conjunction with a liquid level detector. This reactor was equipped with a gas distributor with a pores size of 1 mm. Industrial grade CO2 and compressed air were used for aeration, and A+ recipe was used as the medium of the culture. However, despite of the amount of effort put into modifying this reactor to suit for a microalgae culture, the results were very inconsistent, and unreliable. Reasons for this include possible hotspots in the reactor, poor mixing due to poor gas distributor, as well as the fact that acrylic could not be autoclaved to provide reliable sterilization. With these deficiencies in mind, a new glass bubble column reactor was designed to culture microalgae. The dimensions of this reactor gave a high radiated area to volume ratio, which results in microalgae receiving the light more efficiently. The biggest advantage of this reactor though, was that it can now be autoclaved, while the major disadvantage of this reactor is the difficulty to install all probes/sensors. Experimentally, it was found that as soon as CO2 was introduced in the inlet gas, the pH of the culture would decrease after several days, to a level that microalgae could not 70  survive anymore. Even when fresh buffer was added to the culture, it would only delay but not prevent the pH drop, and this did not improve the cell growth. Due to the constraint of time for this project, a 2-dimensional flat-plate photobioreactor available in the lab was used. Unfortunately, once again, pH was dropping to below a healthy level for the microalgae over a period of few days, causing the growth never to flourish. Another possible explanation for the lack of growth in both the glass reactor and the 2-dimensional reactor was caused by the very fine bubbles produced from the glass frit spargers with pore opening ranging from 4.0 - 5.5 ?m, especially when a saline medium was used. The fine bubbles carried a large amount of microalgae to the upper surface of the reactor, decreased the biomass concentration in the bulk solution greatly.   At last, four continuously aerated reactors were used to investigate the CO2 gas inlet concentration effect using gases at 0.04%, 5%, 8% and 10% CO2 concentrations. The results showed that best growth was achieved at 8% CO2, with an Xmax of 3.1 g/L and an ?max of 0.0186 hr-1. It is suspected that at 10% CO2, the dissolved CO2 accumulated in the reactor, forming carbonic acid, which lowered the pH to below the critical level. Lastly, tests were conducted with a recombinant Synechococcus strain under 33?C, 300 ?E/m2/s, 0.5 L/min gas inlet flow with 10% and 20% CO2, respectively. The result for 10% CO2 culture was very promising, achieved an Xmax of 3.1 g/L and an ?max of 0.0180 hr-1.  Overall, this research demonstrated that both Synechococcus sp. PCC7002 wild and mutant strains could be grown adequately in laboratory photobioreactors. However, more work is required for both the wild and recombinant strains in order to grow them optimally in a laboratory photobioreactor before this process can be explored in a commercial scale.    71  7 Future work  Several aspects of the photobioreactor performance can be further investigated for both the wild and recombinant strains used in this project, which include pH control, different light sources, and lighting regime.   pH has introduced some problems in the reactors, even in the continuously stirred reactors, at higher concentrations of CO2 in the inlet, and pH seemed to be the main issue that prevented a better performance. Manual pH control by adding buffer or other pH adjusting solutions can be implemented readily. On-line automatic pH controllers can be used as well, but might be more complicated to implement.   As mentioned in the background section, lighting is essential to microalgae growth, and it can play a vital role on optimizing the microalgae growth. LED has been used for microalgae cultivation (Zhao, et al., 2013), and has the potential to provide similar growth performance at less electricity consumption, an improvement over fluorescent lights. There has been a study on using flashing lights, to synchronize the light radiation with photosynthetic reactions happening in the microalgae, hence saving energy and improving reactor efficiency (Lunka & Bayless, 2013). Another study on using a 13-87s light-dark cycle suggested this regime could increase chlorophyll-a content in microalgae, hence likely improving the photosynthetic efficiency (Janssen, et al., 1999).   Also, looking at this project as a whole, an economical analysis can be performed to see where this operation would stand from a finance point of view. On top of that, a CO2 balance analysis can be conducted by collecting data such as CO2 in the outlet and dissolved CO2 concentration in the reactor to demonstrate whether Synechococcus sp. 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J Biotechnol, pp. 299-312.    79  Appendix Appendix A:  Table 19: A+ screening run 2 results    NaNO3 (mM) KH2PO4 (mM) FeCl3 (mM) CaCl2 (mM) MgSO4 (mM) Vitamin B12 (mM) Xmax (g/L) ?max exp (hr-1) ?max log (hr-1) Culture Duration (hr) 1 11.76 0.36 0.014 0.0000 0.00 0.00 1.54 0.040 0.01365 602 2 11.76 0.36 0.000 0.0000 20.29 0.00 0.51 0.052 0.02422 362 3 0.00 0.00 0.014 0.0000 20.29 0.00 0.39 0.048 0.01790 435 4 0.00 0.00 0.014 0.0018 20.29 0.00 0.59 0.074 0.02550 338 5 11.76 0.36 0.000 0.0000 0.00 0.00 0.90 0.041 0.013352 603 6 0.00 0.36 0.000 0.0000 20.29 0.00 0.53 0.065 0.02540 266 7 0.00 0.00 0.000 0.0000 20.29 0.00 0.45 0.044 0.02477 315 8 11.76 0.00 0.014 0.0000 0.00 0.00 1.44 0.040 0.01300 603 9 11.76 0.00 0.014 0.0000 20.29 0.00 1.75 0.057 0.01344 602 10 0.00 0.00 0.014 0.0000 0.00 0.00 0.57 0.066 0.02809 315 11 11.76 0.00 0.014 0.0018 20.29 0.00 2.12 0.076 0.01371 603 12 11.76 0.36 0.000 0.0018 20.29 0.00 0.40 0.069 0.02636 315 13 11.76 0.00 0.000 0.0000 20.29 0.00 0.72 0.054 0.01887 484 14 0.00 0.36 0.000 0.0018 0.00 0.00 0.41 0.044 0.02629 266 15 0.00 0.00 0.000 0.0000 0.00 0.00 0.48 0.070 0.02628 315 16 11.76 0.36 0.014 0.0018 0.00 0.00 2.08 0.066 0.01598 603 17 0.00 0.00 0.014 0.0018 0.00 0.00 0.65 0.061 0.02522 315 18 11.76 0.00 0.014 0.0018 0.00 0.00 1.97 0.079 0.01479 603 19 11.76 0.36 0.014 0.0018 20.29 0.00 2.09 0.051 0.01578 649 20 0.00 0.36 0.014 0.0000 0.00 0.00 0.58 0.057 0.02475 315 21 0.00 0.36 0.014 0.0000 20.29 0.00 0.61 0.060 0.02747 338 22 11.76 0.36 0.014 0.0000 20.29 0.00 0.87 0.047 0.012987 603 23 0.00 0.00 0.000 1.8100 20.29 0.00 0.55 0.062 0.02485 315 24 11.76 0.00 0.000 1.8100 20.29 0.00 1.55 0.057 0.01296 649 25 0.00 0.36 0.000 1.8100 20.29 0.00 0.39 0.039 0.02486 315 26 0.00 0.00 0.000 1.8100 0.00 0.00 0.56 0.065 0.02487 288 27 11.76 0.00 0.000 1.8100 0.00 0.00 0.54 0.078 0.02532 435 28 11.76 0.00 0.000 0.0000 0.00 0.00 0.63 0.066 0.01946 603 29 0.00 0.36 0.014 1.8100 0.00 0.00 0.67 0.066 0.02306 362 30 11.76 0.36 0.000 1.8100 0.00 0.00 0.90 0.059 0.01683 603 31 0.00 0.36 0.014 1.8100 20.29 0.00 0.58 0.073 0.02333 383 32 0.00 0.36 0.000 0.0000 0.00 0.00 0.36 0.057 0.02045 338  80   Figure 57: Vitamin b12 effects on biomass concentration   Figure 58: Vitamin b12 effects on specific growth rate   0 500 1000 1500 2000 2500 3000 3500 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Biomass Conc. (mg/L) Flask Number without vit with vit 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 ?max (hr-1) Flask Number without vit with vit 81  Table 20: A+ optimization results 2   NaNO3  KH2PO4  FeCl3  Xmax  ?max exp. ?max log. Culture Duration (mM) (mM) (mM) (g/L) (hr-1) (hr-1) (hr) 1 5.88 0.18 0.007 2.49 0.024 0.0096 888 2 5.88 0.18 0.028 2.65 0.026 0.0096 888 3 5.88 0.72 0.007 1.73 0.022 0.0114 792 4 5.88 0.72 0.028 1.96 0.024 0.0117 792 5 23.52 0.18 0.007 4.26 0.026 0.0117 1488 6 23.52 0.18 0.028 10.42 0.023 0.0047 1848 7 23.52 0.72 0.007 5.84 0.022 0.0055 1632 8 23.52 0.72 0.028 6.25 0.024 0.0055 1632 9 3.5 0.36 0.014 1.39 0.022 0.0136 624 10 39.51 0.36 0.014 9.08 0.026 0.0042 2160 11 11.76 0.11 0.014 4.08 0.023 0.0059 1632 12 11.76 1.21 0.014 3.03 0.026 0.0075 1152 13 11.76 0.36 0.0041 3.37 0.024 0.0076 1320 14 11.76 0.36 0.047 4.12 0.023 0.0063 1392 15 11.76 0.36 0.014 4.13 0.022 0.0064 1320 16 11.76 0.36 0.014 3.98 0.027 0.0073 1128 17 11.76 0.36 0.014 4.54 0.026 0.0062 1488 18 11.76 0.36 0.014 4.76 0.025 0.0061 1368 19 11.76 0.36 0.014 4.92 0.022 0.0056 1368 20 11.76 0.36 0.014 3.96 0.026 0.0062 1368    82    Figure 59: Dissolved CO2 meter calibration curve for 2-dimensional reactor experiments   y = 13.621x + 10 R? = 0.9881 0 200 400 600 800 1000 1200 0 20 40 60 80 dCO2 Concentration (ppm) mV 

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