Studies on Influence of Light and CO2 on Growth of Synechococcus BG0011

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Title:
Studies on Influence of Light and CO2 on Growth of Synechococcus BG0011
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1 online resource (53 p.)
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english
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Zhang, Yingxiu
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University of Florida
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Gainesville, Fla.
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Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Chemical Engineering
Committee Chair:
SVORONOS,SPYROS A
Committee Co-Chair:
PULLAMMANAPPALLIL,P C
Committee Members:
PHLIPS,EDWARD J

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Subjects / Keywords:
algae -- biofuels -- cyanobacteria -- modeling -- synechococcus
Chemical Engineering -- Dissertations, Academic -- UF
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Chemical Engineering thesis, M.S.
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theses   ( marcgt )
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Abstract:
As conventional fossil fuel sources are limited and concerns about their harmful effect on the environment, the demand for researching and utilizing renewable energy sources is increasing. Different from conventional fuels, algae as one of the renewable sources play an important role on energy research due to their less space needed, high biomass production rate and so on. A unicellular cyanobacterium, Synechococcus sp. BG0011 was chosen in this research because of its unique characteristics. BG0011 not only has the advantages of other microalgae, but it also can secrete polysaccharide, grow over a wide range of salinity and fix nitrogen. The polysaccharide can be used as feedstock for biofuel and bioproduct production or converted to methane together with the algal biomass through anaerobic digestion. Due to the tolerances for high salinity, fresh water resources can be conserved and moreover the risk of contamination from other microorganisms can be minimized. This research focused on cultivation of BG0011 and studied the influence of light and CO2 on growth. Comparing growth under low light and high light, air injection and mixture of air and 1% CO2 injection, it was observed that light and CO2 could significantly help to increase algae cell density. Cell density was monitored by measuring optical density (OD) of samples. A series of dry weight experiments were done to correlate OD to biomass dry weight (g/L). The correlation factor was 0.8865 g/L biomass/OD. Modeling of the algae and polysaccharide production rates was also done.
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In the series University of Florida Digital Collections.
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Includes vita.
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Statement of Responsibility:
by Yingxiu Zhang.
Thesis:
Thesis (M.S.)--University of Florida, 2014.
Local:
Adviser: SVORONOS,SPYROS A.
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Co-adviser: PULLAMMANAPPALLIL,P C.

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S TUDIES ON INFLUENCE OF LIGHT AND CO 2 ON GROWTH OF SYNECHOCOCCUS BG0011 By YINGXIU ZHANG A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2014

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2014 Yingxiu Zhang

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To my Parents

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4 ACKNOWLEDGMENTS Many people helped me during my research time. I would like to first express my sincere thanks to Cesar Moreira, who helped me and guided me almost everything when I started this experiment, Mingrui Zhao, who spent most time doing experim ent with me and al so helped me greatly, Nguyet Doan, who is very kind, helped me a lot and encouraged me a lot, Brian Wolfson, who has amazing practical skills, taught me a lot of useful skills about setti ng up the equipment. I would like to thank my advisor, Dr. Spyros Svo ronos, my co advisor, Dr. Pratap Pullammanappallil, and Dr. Edward Phl ips, for all the guidance they gave and all the Chemicals and places they support ed me during the entire period. I would also like to thank Qilong Ma, Raghavendran Murali, Yatin Behl, Bailey Trump and all o ther members in this big group. I would like to thank all the people in Department of Chemical Engineering, Department of Agriculture and Biological Engineering and Department of Fisheries and Aquatic Sciences for al l the help they ga ve me before. I would like to thank my friends for t heir encouragement and support s Finally, I would like to thank my parents for all the unconditional support s for my studies and my decisions.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ............................. 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 12 Using Algae a s New Energy Resource ................................ ................................ ... 12 Introduction t o A lgae ................................ ................................ ............................... 13 The Unique Characteristics o f BG0011 ................................ ................................ ... 14 Alage Cultivation Methods ................................ ................................ ....................... 1 5 Optimization o f Algae Growth ................................ ................................ .................. 16 Objectives ................................ ................................ ................................ ............... 1 8 2 MATERIALS AND METHODS ................................ ................................ ................ 1 9 Materials ................................ ................................ ................................ ................. 1 9 Medium Preparation ................................ ................................ ............................... 20 Reactor Setup ................................ ................................ ................................ ......... 2 2 Inoculation ................................ ................................ ................................ .............. 2 4 Light a nd CO 2 Control ................................ ................................ ............................. 2 5 Sampling ................................ ................................ ................................ ................. 2 5 pH Optical Density a nd Salinity Measurements ................................ ..................... 2 6 Correlat ion between Biomass Dry Weight a nd OD ................................ ................. 2 8 Correlation between P olysaccharide Dry Weight and OD ................................ ....... 2 9 3 RESULTS AND DISC USSION ................................ ................................ ............... 31 Relationship b etween OD and Biomass Dry Weight ................................ ............... 31 Relationship b etween OD and Polysaccharide Dry Weight ................................ .... 3 2 Influence of C O 2 and Light on Growth ................................ ................................ .... 3 3 Modeling for Algae Growth ................................ ................................ ..................... 3 6 Discussion ................................ ................................ ................................ .............. 42 4 CONCLUSIONS ................................ ................................ ................................ ..... 4 4 Research Conclusions ................................ ................................ ............................ 4 4

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6 Futu r e Works ................................ ................................ ................................ .......... 4 4 APPENDIX A MEDIUM FORMULA F OR BG0011 ................................ ................................ ........ 4 6 B pH TEST EXPERIMENT ................................ ................................ ......................... 49 LIST OF REFERENCES ................................ ................................ ............................... 51 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 53

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7 LIST OF TABLES Table page 2 1 D etailed conditions for three runs ................................ ................................ ....... 25 3 1 OD and Biomass Dry Weights of eight Samples with Different Dilutions ............ 3 2 3 2 Modeling p arameters for each reactor ................................ ................................ 40 B 1 pH Trends for Different Percent of CO 2 Injection ................................ ................ 4 9

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8 LIST OF FIGURES Figure page 2 1 Photograph for different cell densities of Synechococcus sp. BG0011 .............. 1 9 2 2 Scanning electron microscope photograph of Synechococcus sp. BG0011 ....... 20 2 3 Schematic diagram of the algae cultivation apparatus ................................ ...... 2 3 2 4 Photograph of algae growing reactors ................................ ................................ 2 4 2 5 A Milton Roy Spectronic 401 instrument was used to measure OD ................... 2 7 2 6 An IEC Centra M Centrifuge was used for centrifugation in this research .......... 2 8 2 7 Alg ae samples at different dilutions f or correlating biomass dry weight with OD ................................ ................................ ................................ ...................... 2 9 3 1 Relationship between biomass dry weight and OD ................................ ............ 3 2 3 2 Supernatant dry weight vs. OD ................................ ................................ ........... 3 3 3 3 Biomass dry weight vs Time (low light condition) ................................ .............. 3 5 3 4 Biomass dry weight vs Time (hight light condition) ................................ ............ 3 5 3 5 Biomass dry wei ght vs Time (high light condition) ................................ ............. 3 5 3 6 Polysaccharide dry weight vs. Time (hight light condition) ................................ 3 6 3 7 The m ain flowsheet of simulation S sat in Apsen Plus ................................ .......... 3 8 3 8 Model for Reactor 1 (injection with air) ................................ ............................... 41 3 9 Model for Reactor 3 (injection with 1% CO 2 ) ................................ ...................... 41 3 10 Model for Reactor 4 (injection with 1% CO 2 ) ................................ ...................... 42 3 1 1 Model for CO 2 trends of R3 (injection with 1% CO 2 ) ................................ ........... 42 B 1 pH trends for different percent of CO 2 injection ................................ .................. 50

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9 LIST OF ABBREVIATIONS DI Water Deionized Water OD Optical Density. I n spectroscopy, the absorbance (also called optical density) of a material is a logarithmic ratio of the radiation falling upon a material, to the radiation transmitted through a material. psu Practical Salinity Units. Numbers of grams of dissolved substances for each kilogram of water.

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for th e Degree of Master of Science STUDIES ON INFLUENCE OF LIGHT AND CO 2 ON GROWTH OF SYNECHOCOCCUS BG0011 By Yingxiu Zhang May 2014 Chair: Spyros Svoronos Cochair: Pratap Pullammanappallil Major: Chemical Engineering As conventional fossil fuel sources are limited and concerns about their harmful effect on the environment, t he demand for researching and utilizing rene wable energy sources i s increasing Different from con ventional fuels, algae as one of the renewable sources play an important role on energy r esearch due to their less space needed, high biomass production rate and so on. A unicellular c yanobacterium, Synechococcus sp. BG0011 was chosen in this research because of its unique characteristics. BG0011 not onl y has the advantages of other micro algae, but it also can secrete polysaccharide grow over a wide range of salinity and fix nitrogen Th e polysaccharide can be used as feedstock for biofuel and bioproduct production or converted to methane together with the algal biomass through anaerobic digestion Due to t he tolerance s for high salinity fresh water resources can be conserved and moreover the risk of contamination from o th er micro organisms can be minimized. This research focus ed on cultivation of BG0011 and studied the influence of light and CO 2 on growth. Co mparing growth under low light and high light, air injection and

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11 mixture of air and 1% CO 2 injection, it was observed that light and CO 2 could significant ly help t o increase algae cell density C ell density was monitored by measuring optical density (OD) of samples. A series of d ry weigh t experiments were done to correlate OD to biomass dry weight (g/L) T he correlation factor was 0.8865 g/L bioma s s/OD. Modeling of the algae and polysaccharide production rate s was also done

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12 CHAPTER 1 INTRODUCTION Using Algae a s New Energy Resource T he demand for energy in the world is always increasing B ut the availability of conventional energy res ources is limited and the total energy available from these fossil resources is decreasing every year (Murali 2013) A lthough fossil fuel is the most widely used energy r esource today, it will finally be depleted and run out A dditionally, the problems caused by using fossi l fuels, such as global warming and environmental polluti on, also need to be considered. S o research into developing renewable, clean energy re sources is becoming very important D ifferent types of renewable energy resources have been developed, such as solar energy, wind energy, geothermal en ergy, bioenergy, a nd so on. B ioenergy (that is energy from plant or animal biomass resources ) not only meets the requirement for renewable energy, but also can reduce the net emission of CO 2 Even though considerable resources are availab le and can be used, currently due to cost and technology readiness level s problems, these resources do not compete favo rably with fossil fuels (Singh et at. 2010). Currently, traditional food crops such as corn and sugarcane, are used to produce biofuels. Biofuels produced from cornstar ch and sugarcane juice are referred to as first generation biofuels ( for example ethanol and butanol produced from these feedstocks ). T hese liquids are clean and environmental ly friendly, but they are accompanied by a number of problems. The starch and sugar component s of these crops are also used as food (and feed) resources, so diverting these for biofuel production leads to increase s in food price s. A dditionally, the planting of large arable

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13 land areas of crops for biofuel reduces land available for food c rops and reduces plant diversity. T he second generation biofuels are those produced from waste ligno cellulosic feedstocks or lignocellulosic energy crops Waste lignocellulosic feedstocks are residues of food and feed crops and forestry residues and waste energy crops such as herbaceous grasses and short rotation woody biomass. T hese biorenewable resources have higher yields and lower land requirements than first generation feedstocks The third generation biofuels are those produced from algae ( D aroch et al. 2012 ) U nlike traditional crops, algae do es not compete with food resources. A lgae can be cultivated on lands which are marginal or unsuitable for traditio nal crops S ome a lgae can be cultivated in waste water to r emove nitrogen, phosphorus and some p ollutants from it (Sturm et al ., 2012) If sa line algae are cultivated then fresh water resources are conserved. Fine chemicals can also be produced from algae as byproduct s Introduction t o Algae A lgae include both prokar yotic and eukaryotic species ( Moestrup 2001) O ver 3.5 billion years of evolutionary history have yielded a great diversity of algae species. Procaryotic microalgae include Cyanophyta and Prochlorophyta. Eucaryotes include Glaucophyta, Rhodophyta, Prasinophyta, Chlorophyta, Eugenophyt a, Phaeophyta, Cryptophyta, Raphidophyta, Xanthophyta, Chrysophyta, Bacillariophyta, Hapto phyta and Dinophyta (Reynold s, 2006 ) S ome common physical and chemical features used to define algae groups include: pigments, internal cell structure, flagellar structure, cellular or ganization, reproductive strate gies. I n this research, a unicellular cyanobacterium, Synechococcus sp. BG0011 was studied as a potential bioenergy resource. Cyanobacteria, also known as Cyanophyta,

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14 is a phylum of Procaryotes. Cyanob acteria include unicellular, colonial and filamentous species. They are major features in a wide range of habitats, including fresh water, marine or t errestrial ecosystems. They produce oxygen by photosynthesis and were largely responsible for the early de velopment of anaerobic atmosphere. Synecococcus is one of th e oldest groups of autotrophic prokary ote s. I t reproduce s by binary fission and has cylindrical to ovoid cells. The cells can exist singly, in pairs or in short chains and lack sheaths (Waterbury et al ., 1979) T he main pigment in Synechococcus is chlorophyl l a, and phycobiliproteins are i t s major accessory pigments (Stanier, 1977) The Unique Characteristics o f BG0011 Synechococcus sp. BG0011 was isolated fro m epiphytic samples collected from the Florida Keys ( Phlips et al ., 1989) The reason for choosing this species for bioenergy research is because it offers unique advantages: 1) It can grow over a wide range of salinities (10 70psu). T his characteristic solves problem s associated with fresh water requirement for cultivating other species of algae that are being studied for biofuel production. Using a high sa linity medium for BG0011 cultivation can also reduce the probability of contamination by other species. 2) It can fix nitrogen an d grow at near maximum rates in medi um lacking nitrogen (Phlips et al ., 1989) So no nitrogen nutrients are required which reduces cost of cultivating BG0011 3 ) It can secrete energy dense exopolysaccharide. T his polysaccharide being a byproduct of BG00 11, can either be converted to methane together with algae or recovered for use in ethanol and bioproduct production. A lso, this polysaccharide separate s naturally from the culture solution when left undisturbed and exposed to light

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15 (13 hours light/11 hour s dark cycle) (Behl 2013) T his characteristic can make the separation of polysaccharide easier to achieve. Algae Cultivation Method s There are two main methods for algae cult ivation: open culture systems and closed systems (photobioreactors). Commercial cultivation o f algae for health food industry started in 1970 (Ugwu et al ., 200 7 ) A lgae was cultivated in open ponds. O pen raceway ponds are the mos t basic cultivation systems (Slegers et al ., 2013) T he advantages of open pond are obvious. It is cheap t o build and easy to operate and clean A lso, it is suitable for mass production. B ut the re are drawbacks as well I t is hard to control contamination. It s operation is influenced by weather so it is hard to m aintain a constant environment, su ch as temperature and light (Scott et al., 2010) S o cultivation of algae in open pond s usually results in lower cell densities than cultivation in closed system s B ut this can be compensated by its reduced infrastructure requirements (like buildings to house reactors, reactors) and lower operational costs and high value of products. The closed systems (photobioreactors) cultivation method has more advantages as it maintains a controlled en vironment and higher cell densities are attained than open ponds culti vation method. I t has higher light utilization than open pond method. A lso, contamination can be avoided more easily. B ut the drawbacks of photobioreactors can not be ignored. Infrastructure and operational costs are higher and more energy needed for mixin g and maintaining sterile conditions for gas sparging. Also it becomes hard to maintain control led conditions as the scale is increased T hree types of photobioreactors are usually used for algae cultivation: vertical column photobioreactors, flat plate photobioreactors and tubular photobioreactors (Ugwu et al ., 200 7 )

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16 U sually, the methods of algae cultivation include batch, fed batch, semi continuous and continuous. I n this research, algae was cultured in batch mode in a small scale photobioreactor. A ut oclaved deionized water (DI water) was added to each reactor to make up water lost by evaporation and to mai ntain a constant salinity A lgae growth in batch culture follows four phases: lag phase, log phase or exponential growth phase, stationary phase and death phase. I n this research, algae was harvested during exponential phase or stationary phase Optimization o f Algae Growth Many factors can affect algae growth such as light, CO 2 concentration, nutrients, temperature, pH, salinity and so on. S o, it is important to optimize all the factors that influence algae growth First, light is one of the most important factors that should be considered. A utotrophi c algae, without light, can not success fully carry out photosynthesis. P hotosynthesis is the en e rgy source for algae growth T he algae grow th rate increases when the light intensity is increased over a certain range. A bove that range, net photosynthesis would decrease due to photorespiration and inhibition of gross photosynthesis rate decreasing the algae growth rate T here are several ways to optimize the incident light. One meth od is to increase the incidence area of light. F or example, for the same light intensity, using arrays of vertical annular columns (with the light source plac ed along the annulus) would have more light efficiency than using horizontal open ponds. O ther methods include reducing the antenna size either by growth at high light intensity or by mutation of genes response (Scott et al., 2010) Antenna is a complex of chlorophyll a and other pigments, such as carotenoids, and proteins that can trap light energy for photosynthesis. Reducing the antenna size can

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17 help more cells to get light so that it can improve the effective utilization of light. Increasing light int ensity can reduce the antenna size, but the change is reversible. The antenna size can increase again under low light intensity. Wavelength also affects algae growth. D ifferent algae species prefer different wavelength. S econd, CO 2 is also an important f actor for autotrophic algae. A lot of studies suggest that algal biomass can be enhanced by increasing CO 2 over normal atmospheric concentration (Sayadil et al ., 2011) S ince CO 2 is a limitation factor for photosynthesis under normal atmospheric concentration. Increasing CO 2 concentration helps to improve photosynthesis. CO 2 for algae growth can be supplied from biogas, which is a mixture of methane and CO 2 produced by anaerobic digestion. W hen injecting CO 2 with differe nt percentage, pH should be contro l l ed since large amount of CO 2 decrease the pH drop T here is also a range over which growth is increased. Beyond this range growth may be inhibited. T he comp osition of nutrients should also b e considered. A lgae growth needs several different elements. Generally, nitrogen and phosphorus are most often limiting for algae growth in aquatic ecosystems. U sually, onset of stationary phase indicates nutrient limitation. S ome elements are required in trace amounts, for example, iron and man ganese. A lgae w ould not grow well without the s upport of these trace elements. A dditionally, the optimum temperature, pH and salinity are not the same for different algae species. To achieve maximum specific growth rate of algae, these factors should be controlled carefully. Also, during cultivation, it is important to mix algae wel l s ince it can help to improve the utilization of light and enable better absorption of nutrients.

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18 Objectives The objectives in this research include two ma in parts. F irst, studies on influence of light and CO 2 on growth of Synechococcus BG0011. Comparing maximum specific growth rate, biomass and polysaccharide dry weight under different cultivation conditions (light intensity and CO 2 concentration). S econd, modeling algae growth and polysaccharide production based on three kinetics equations (algae growth rate equation, CO 2 consumption rate equation and polysaccharide production rate equation). D etermining each p arameter in these equations to make sure the mo dels fit for experimental data.

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19 CHAPTER 2 MATERIALS AND METHODS Materials The strain of unicellular cyanobacteria, Synechococcus sp. u sed in this project designated BG0011, was provided by Dr. Edward Phlips, Department of Fisheries and Aquatic Sciences, University of Florida. This cyanobacterium was isolated from epiphytic samples collected in the Florida Keys, 25 00 N 81 30 W (Phlips et al ., 1989). Figure 2 1 is a photo graph of cultures of BG0011. Figure 2 2 is a scanning electron mic roscope photograph of BG0011. Figure 2 1 Photograph for different cell densities of Synechococcus sp. BG0011.

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20 Figure 2 2 Scanning electron microscope photograph of Synechococcus sp. BG0011. U bar indicates scale in microns ( m ). [ adapted from Phlips et al ., 1989 Growth, photosynthesis, nitrogen fixation and carbohydrate production by a unicellular cyanobacterium, Synechococcus sp. (Cyanophyta) (Page 139, Figure 1). Journal of Applied Phycology, 1, 137 145.] Medium Preparation The medium used for culturing BG0011 contains Na 2 EDTA, KCl, CaCl 2 2H 2 O, K 2 HPO 4 MgSO 4 7H 2 O, Stock Solution 1 (trace elements), Stock Solution 2 (iro n), Stock Solution 3 (vitamins), Stock Solution 4 (molybdenum) NaCl, Hepes T he specific composition of the medium for BG0011 can be found in Appendix A.

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21 The method to prepare 1 liter of A N ( the medium used for algae growth is N stands for Allen minus nitrogen. Since BG0011 can fix nitrogen from air so the medium does not contain nitro gen. ) Concentrate Stock Solution is as follows : 1. Autoclave all glassware, stirring bar and other transfer equipment before used. T he solution is prepared in a laminar flow cabinet. 2. Prepare an open bucket style container, the volume of this container is around 2 liters. Put a stirring bar in it. T hen place the container on a magnetic stirrer. 3. Pour 0.7 liters deionized water into this open bucket style container and turn on the magnetic stirrer. 4. M easure the exact weight of each chemical needed (see Appendix A to find the required weight). Add them slowly and make sure all the components are dissolved and mixed uniformly. Note that the stock solution 2 (iron) should be added last to prevent precipitation. 5. Fill with dist illed water to exactly 1 liter. T he method to prepare a liter of m edium for Synechococcus sp. BG00 11 is as follows : 1. Prepare an open bucket style container, able to hold all the medium to be created. P lace a stirring bar in it. T hen place the container o n a magnetic stirrer. 2. Pour 200 mL A N concentrate stock solution into the container a nd turn on the magnetic stirrer. 3. A dd 22.5 g NaCl and 5 g Hepes slowly into the container. 4. F ill to exactly 1 liter distilled water in the container and make sure all the components ar e dissolved and mixed uniformly. 5. Adjust pH to 7.5 (with NaOH). Then remove the medium into a container with screw on lid. S crew on the lid half of the way tight and cover it by aluminum foil. F ix the foil with tape and autoclave the cont ainer for 30 minutes at 121 After finishing autoclaving, take the container out and le t it return to room temperature. 6. A djust pH to 7.5 and 8.2 with NaOH in laminar hood before using.

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22 Reactor Setup The system for growing algae consisted of three mai n sections : an air intake section reactor section and off gas purge section E ach piece was connected to others by plastic tubes. A ll the m aterials used in this system were autoclaved before use T he schematic diagram for the w hole system is shown in F igure 2 3 and F igure 2 4 is a photograph of algae growth reactors. F or the air intake section a ir was pump ed into a reservoir. A rotameter that connected to the reservoir was used to adjust the flow rate of the air out of the reservoir I n this experimen t the flow rate was set to 2.0 L/min, so that each of th e four reactors rece i ved 0.5 L/min air T hen air was wetted by bubbling through the humidifier and split equally to two branches. E ach part passed throu gh a vertical expanding glass column that could condense water vapor to avoid flooding the rotameter. T he rotameter was set at 1.0 L/min ( 2.12 SCFH ) A n air filter was connected after each rotameter. Each filter was then connected to another chamber O ne chamber was used to mix air with CO 2 from a 100% pure compressed CO 2 gas c ylinder T he other one only had air The gas from each chamber was split using a tee connector. T wo branches from only air chamber were used to sparge reactor 1 and reactor 2, the two branches from the air CO 2 mixt ure chamber were used to sparge reactor 3 and reactor 4. The reactor section consisted of four 500 mL glass bottles (labelled Reactor 1, 2, 3 and 4) that were used for algae growth. A ll the bottles were sealed with a rubber stopper E ach rubber stopper had t hree holes through each a glass rod was inserted. One for gas inlet second one for gas o utlet and the third one for sampling. O ff gas from

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23 the reactors was passed through an expanding plastic U t ube to condense moisture Figure 2 4 is the photograph of these four reactors. In the off gas purge section, all the four gas streams exiting the four reactors was merged and bubbled through a flas k containing NaOH solution whose pH was 12. T his set up could prevent back contamination and consume the remaining CO 2 The humidifi er and exhaust flask were placed in a big black box with a UV lamp in it. T he UV lamp turned on intermittently to ensure sterilization of gases. T he four reactors were placed in a temperature controlled cabinet. The reactors were illuminated with four LED lamps. T emperatu re inside the chamber was maintained at 30 C 2 C using four small fans on the top of the chamber. A thermocouple was used to measure the temperature inside the chamber. Figure 2 3 Schematic diagram of the algae cultivation apparatus

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24 Figure 2 4 Photograph of algae growth reactors. I noculation In e ach reactor 250 mL medium was used To keep approx imately the same pH in all four reactors, the initial pH was different. A trial using medium and 1% CO 2 sparging showed that medium with initial pH 8.2 w ould drop t o 7.5 after 25 minutes and remain stable at that value. T he detailed information of this pH trial can be found in Appendix B. So the initially pH of medium in r eacto r 1 and reactor 2 with air sparging was adjusted to pH 7.5, whereas pH of medium used in reactor 3 and reactor 4 was adjusted to pH 8.2. Then each reactor was inoculated with 10 mL of exponentially growing inoculum. Inoculation was done in a laminar hood. A fter inoculation, all the reactors were shaken gently to make sure the inoculum was mixed uniformly with the medium. A fter inoculation, the four reactors were placed in the cabinet and all tubings connected. T he air pump was turned on. It was ensured tha t all gas flow rates were set at the values mentioned above T hese included the flow rate of air and air CO 2 mixture.

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25 Light a nd CO 2 Control I n this research, two types of lights were used. F or the low light condition, two 20 Watts fluorescent lights were used which supplied 60 mol photon m 2 s 1 F or t he high light condition, four 18 Watts L ED projector lamps were used that supplied 1200 mol photon m 2 s 1 L ight and dark cycle was controlled automatically. The lamps were turned on for 13 hours and turne d off for 11 hours in a 24 hour period Algae growth under air and mixture of air with 1% CO 2 were also compared in this resear ch. Reactor 1 and reactor 2 were sparged with air and reactor 3 and reactor 4 with air and 1% CO 2 In this research, three runs were used for comparison and analysis. D etailed conditions for these runs are shown in Table 2 1. Table 2 1. Detailed conditions for three runs No. Cultivation Time Temperature ( C ) Light intensity ( mol photon m 2 s 1 ) 1 2 3 07/24/2013 08/04/2013 08/19/2013 08/31/2013 11/12/2013 12/18/2013 30 2 30 2 30 2 60 1200 1200 Sampling 1 mL of sa mple from each reactor was taken everyday. Before taking the samples, the air pump was turned off CO 2 valve was shut off and the pressure in the humidifier released Before sampling, each reactor was gently shaken to make sure the algae was uniformly mix ed. Then 1 mL of sample was withdrawn through the sampling port using a syringe After sampling, air pushed into the sampling t ube using a syringe connec ted to a filter to make sure there was no liquid hold up in the sampling tube. T hen the sampling port was close d by folding the tube and crimping with clamp The pump was t urn ed on CO 2 valve turned on and rotameters adjust ed to the desired flow rates

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26 pH, Optical Density a nd Salinity Measurement s pH paper was used to measure pH of samples T he optical density (OD) of resuspended biomass and supernatant was also measured using a Milton Roy Spectronic 401 instrument (F igure 2 5) as follows: 1. Switch on the spectrophotometer and leave it about 15 minutes to warm up. In the meantime, prepare a bottle of DI water and fill 1 mL DI water into the cuvette by pipette. Prepare another four cuvettes and transfer 1 mL of sample from each rea ctor into each cuvette. 2. S et the wavelength of the device to 540 nm. T his wavelength is used for cell biomass. 3. P lace the 1 mL DI water sample into the device and set it as blank. 4. T hen measure each algae sample. The sample s were vortexed before placing in the spectrophotometer. Measurement were made three times for each sample and use the mean value as record. 5. A fter measurement, the samples were transferred to the original sample tube. T he sample was centrifuge d for 15 minutes in an IEC Centra M Centrifuge (Figure 2 6) at a speed of 1200 RPM. 6. A fter centrifugation, the supernatant was decanted. T he biomass pellet was resuspended by addition of 1 mL DI water. 7. The OD of the decanted supernatant and the resuspended samples was measured. 8. Absorbance measurements are not linear above a value of 0.6. If OD measured is greater than 0.6, the samples are diluted appropriately and absorbance measured again. 9. Measurements were taken and r ecord ed everyday. M easure the salinity of the supernata nt of each sample. I f the value is higher than 35 psu, then autoclaved DI water is added into the reactor to bring salinity to 35 psu. T he equation to calculate the volume of water added is: V w = V r (S r 35)/35

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27 H ere V w represents volume of water to be added to the reactor, V r represents the volume of culture in the reactor, and S r represents the salinity measured. A fter finishing all the measurements, put each sampl e back to the original tube and store them in the refrigerator. Figure 2 5 A Milton Roy Spectronic 401 instrument was used to measure Optical Density.

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28 Figure 2 6 An IEC Centra M Centrifuge was used for centrifugation in this research. C orrelation b etween Biomass Dry Weight a nd OD T o correlate biomass dry weight with OD, 8 sample s each 20 mL but made at different dilution s were used (F igure 2 7) F or example, 1 part sample and 3 part water will be a dilution. S am ples were centrifuged at 15000 RPM for 1 hour T he superna tant was decanted. T he biomass pellet was washed with 20 mL DI water until the salinity of wash water became 0 T he pellet was re suspended ensuring that the biomass was uniformly dispersed in the suspension. T he OD of the resuspended biomass was measured. P repared 8 boats and measured their weight. E ach resus pended sample was poured into a separate boat. A ll the boats were placed in an oven at 60 C for drying. A fter 2 or 3 days, c hecked the weight of each boat and put back in the oven. T he boats were again w eighed the next day T he drying process was done until weight data did not change over two consecutive days. T he biomass dry weight was the difference of boat and dried sample and the boat s weight. D rew a gra ph of OD and biomass dry weight to determine the relationship between them

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29 Figure 2 7 Algae samples at different dilution s for correlating biomass dry weight with OD Correlation b etween Poly saccharide Dry Weight a nd OD S amples from the same run with different dilutions as well as the growth medium were used. S amples were c entrifuged at 15000 RPM for 1 hour. S upernatant was decanted. M easured each supernatant s OD and salinity P repare d boats and measured their weight s T hen poured the supernatant from each sample into a separate T he medium was also taken in a separate boat. T he boats were placed in the oven at 60 C for drying. A fter 2 or 3 days, checked the weight of each boat and put back in the oven. T he boats were again weighed the next day. T he drying process was done until weight data did not change over two consecutive days. T he dry weights were the difference of these data from the boats weight. F or the samples which had similar salinity of the medium, the salt content of the supernatant i s the dry weight of growth medium. T he difference is the weight of polysaccharide.

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30 S everal samples from different runs as well as the growth medium were used. F or run 3 ( Table 2 1 ), a 1 mL sample at the end of the run was measured supernatant OD, an d after drying the sample, a combined salt and polysaccharide weight was obtained. S ubtracting the salt from the medium, polysaccharide dry weight was obtained

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31 CHAPTER 3 RESULTS AND DISCUSSION B atch cultures were used to study the grow th of BG0011 and th e production of exopolysaccharide under different conditions B iomass dry weight and polysaccharide dry weight were correlated to o pti cal d ensity. I n the earl y research stage, the effect of air and mixture of 1% CO 2 and air bubbling were compared. I n the later period, low light and high light conditions were compared to see the influence of light intensity on growth and polysaccharide production A lgae growth and polysacchari de production were modeled using experimental results Relationship between OD a n d Biomass Dry Weight The data of OD and biomass dry weight s of 8 samples prepared using at different dilutions are shown in T able 3 1. D ue to the light shading effect, the precision of OD value decreased when cell density increasing. C onsidering both the p recision of OD value and accuracy of line in the graph, OD value over 0.8 was discarded. Error was around 10% for OD value under 0.8 which was acceptable. W hen drawing the biomass dry weight graph, 5 points were used which was also acceptable. F rom the graph in Figure 3 1, the relationship between OD and biomass dry weight is defined by : y =0. 8865 x x represents OD, y represents biomass dry weight (g/L).

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32 Table 3 1. OD and biomass dry weights of eight samples with different dilutions No. Dilution OD (540nm) B iomass dry weight (g/20mL) Biomass dry weight (g/L) 1 2 3 4 5 1/1 1/ 3 1/ 3.5 1/ 4 1/ 5 1.547 0.949 0.836 0.723 0.611 0.0416 0.0166 0.0149 0.0127 0.0111 2.080 0.830 0.745 0.635 0.555 6 1/ 6 0.513 0.0083 0.415 7 8 1/ 8 1/ 10 0.408 0.342 0.0075 0.0067 0.375 0.335 Note: Samples were from reactor 4 (1% CO 2 +Air) cultured on 9/29/2013. T he biomass dry weight experiment was done on 10/18/2013. Figure 3 1 Relationship between biomass dry weight and OD. In the equation y=0.8865 x, x represents OD, y represents biomass dry weight (g/L). Relationship b etween OD a nd Polysaccharide Dry Weight T he results showed that t he relationship between OD and polysaccharide dry weight varies according to the length of time after the termination of a culture run (Figure 3 2 ) At present, this relationship was obtained with data from the end of the run F or run 3 after drying the sample, a combined salt and polysaccharide weight of 39.77 g/L was obtained. S ubtracting the salt from the medium (34.5 g/L ), 5.27 g /L of polysaccharide was obtained, which translated to 4.35 g (an average data for last seven days) polysaccharide/L (Figure3 6 )

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33 Figure 3 2 Supernatant dry weight vs. OD (combination of several dry weight experimental result) Influence of CO 2 and Light on Growth F or all culture runs R eactor s 1 and 2 were always sparge d with air, R eactor s 3 and 4 were sparged with a mixture of 1% CO 2 and air. To reduce algae adaptation time, reactor 1 and 2 were inoculat ed with samples previously cultivated with air reactor 3 and 4 were inoculated with samples previously cultivated with 1% CO 2 Low (60 mol photon m 2 s 1 ) and high ( 1200 mol photon m 2 s 1 ) light intensity treatment groups were compared. A lgae growth curves for all treatment groups are shown in F igure s 3 3 and 3 4 Figure 3 3 plo ts data from low light intensity and F igure 3 4 plots data from high light intensity The curves in each figure were the average data of two replicate reactors. S pecific growth rates we re calculated using the formula: Here X represents biomass dry weight (g/L), t is cultivation time that has elapsed (day). 1 represents starting point and 2 represents end point for algae growth From F igure 3 3 and F igure 3 4 it is seen that algae cultures sparged with 1% CO 2 (in air) grew faster than those sparged with air. So it can say that the growth of algae is affected by CO 2 concentration and enrichment of CO 2 can h elp to increase

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34 algae growing to some extent. But there is no difference in maximum specific growth rates under low light Comparing data in Figure 3 3 and Figure 3 4 it can be seen that algae under high light intensity grew faster than under low light intensity Under both air or 1% CO 2 sparging, the m aximum specific growth rates of high light intensity were higher than low light intensity B iomass dry weight accumulation under high light and 1% CO 2 conditions was over two times than under low light and 1% CO 2 conditions. B iomass dry weight with 1% CO 2 was just higher than air under low light intensity. T hat means under low light intensity, light limitation is the factor that influence algae growth. S o increasing light intensity can improve algae growth to some extent, and combination of increasing ligh t intensity and CO 2 enri chment can improve algae growth significantly. CO 2 can also help to increase polysaccharide production. Figure 3 5 and Figure 3 6 were extended run s for observing biomass and polysaccharide production. A fter 20 days when algae rea ched the stationary phase polysaccharide production started and sparging with 1% CO 2 helped increase polysaccharide production. W hen at the stationary phase, 4.18 g polysaccharide/L and 4.35 g polysaccharide/L could be obtained. F or 1% CO 2 samples, supernatant exhibited a high viscosity due to polysaccharide accumulate in the aqua tic phase

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35 Figure 3 3 Biomass dry weight vs. Time (low light condition ) Figure 3 4 Biomass dry weight vs. Time (high light condition) Figure 3 5 Biomass dry w eight vs. Time (high light condi tion)

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36 Figure 3 6 Polysaccharide dry weight vs. Time (high light condition) Modeling for Algae Growth The model equations for algae g rowth, CO 2 consumption and polysaccharide production were as follows: The first model equation is for algae growth rate. I t is a modified Monod equation with light inhibition. S ince different cell densities make different shading effect of light. T hat is the reason to add (1 X) term. T he second model equation is for CO 2 consumption rate. I t includes three terms, mass transfer term, consumption term for cell growth and consumpti on term for polysaccharide production term. T he third equation is for polysaccharide production rate. I t is a modified Leudeking Piret kinetics equation. I t includes two terms, nongrowth associated term and growth associated term. For

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37 nongrowth associated term, assuming that polysaccharide production influences by both cell and CO 2 concentration. F or growth associated term, polysaccharide production influences by cell growth rate. T he introduction of each parameters in the equations are as follows: Model ing Parameters: X: cell concentration (g/L) S: CO 2 +HCO 3 +CO 3 2 concentration (mole/L) P: EPS concentration (g/L) Fixed Parameters: S sat : saturated CO 2 concentration in water (mole/L). Obtained by simulation in Aspen Plus using the electrolyte NRTL model. Adjust pH of the solution to 7.5 by NaOH. No NaCl was added. S ince when adding NaCl into simulation in Aspen Plus, the result of pH is messed up. When adding or not adding Na Cl into simulation in Unisim, the result shows that NaCl concentration doesn t influence pH much. So a ssuming that NaCl could not change pH and influence saturation of CO 2 much in water. Simulate S sat for air injection and 1% CO 2 injection respectively. S sat was 1. 44 10 4 mole/L f or air injection and was 5. 02 10 3 mole/L for 1% CO 2 injection. T he main flowsheet of simulation was shown in F igure 3 7

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38 Figure 3 7 The main flowsheet of simulation S sat in Aspen Plus A ir, CO 2 water, NaOH are used for saturation of CO 2 simulation. A ll the compone nts are mixed together and go to a flask and separate into liquid and vapor. T he result of CO 2 concentration in liquid is the data used for S sat The banners above each component represent temperature ( C ), pres sure (bar) and vapor fraction respectively. : cell growth coefficient (grams of cell growth for per mole of CO 2 consumed) Assuming that 90% of CO 2 dissolved could be used for algae growth and polysaccharide production and 10% of CO 2 dissolve d could be used fo r respiration U sing an empirical formula (C 5 H 7 O 2 N) for algae cells one mole of CO 2 should yield 20.34 grams ( 1 0.9 12 113 60=20.34 ) dry weight per mole of CO 2 consumed) A ssuming the same percent of CO 2 consumption as above. Using C(H 2 O) for polysaccharide. So when consuming one mole of CO 2 polysaccharide could increase by 27 grams (1 0.9 12 30 12=27). E stimated Parameters:

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39 max : maximum specific growth rate (day 1 ) G etting from exponential fit of beginning growth dry biomass data. : inhibition coefficient of cell concentration (L/g) =1/average X at stationary phase. : influence coefficient of CO 2 concentration for cell gr owth : CO 2 mass transfer coefficient (day 1 ) : non growth associated coefficient (comes from Leudeking Piret kinetics) 2 concentration for polysaccharide production : growth associated coefficient (comes from Leudeking Piret kinetics) Some other assumptions were proposed before applying these equations. Temperature and pH were regarded as constants. E ffect of light and dark cycle was not considered. P ut the existing data into the formula and solve , , by using solver by minimizing sum of error squared. T he equation of error squared is: Here J represents for sum of models of biomass and polysaccharide error squared, X exp represents ex perimental biomass dry weight, X represents modeling biomass dry weight, EPS exp represents experimental polysaccharide dry weight, EPS re presents modeling polysaccharide dry weight. The parameters of each reactor are shown in table 3 2. Using these parameters, m odels for R1, R3 and R4 all fit well. M odels for these three reactors were shown in F igure s 3 8, 3 9 and 3 10 Figure 3 11 is enlarged graph of CO 2 trends. Comparing

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40 Figure 3 8 f or air condition and Figure 3 11 for 1% CO 2 condition CO 2 trends were the same. Table 3 2 Modeling p arameters f or each reactor Parameters R 1 (Air) R3 (1% CO 2 ) R 4 (1% CO 2 ) max 0.5 18 0.528 6.95 E 06 34.20 20.34 0.5 1 8 0. 276 6.95E 06 2.09 20.34 0. 51 8 0 .282 6.95E 06 2.09 20.34 27 27 27 J 0.104 2.42E 05 1.14E 12 0.7217 0.104 2.42E 05 1.14E 12 2.2520 0.104 2.42E 05 1 .14 E 12 3.0243 Note: S ince reactor 2 had some contamination, it was not used for modeling. F rom T able 3 2 except for , S sat all the parameters are the same for R1, R3 and R4. i s different since different cell concentration at stationary phase causes different light shading effect. i s different since different CO 2 mass transfer force in R1, R3 and R4. S ince R3 and R4 produ ce more polysacchar ide than R1, which is a high viscosity product, it makes CO 2 harder to transfer in R3 and R4 than R1. Cells are surrounded by polysaccharides also make CO 2 harder to get into cells. S sat is different since different CO 2 concentration injection makes different saturation of CO 2 in the culture. is nearly zero means growth associated term doesn t help polysaccharide production much. S o this term in the equation can be ignored. F rom the figures of models, same trends of b iomass growth, polysaccharide production and CO 2 consumption could get. B ecause of the help of CO 2 biomass dry weight and polysaccharide dry weight under CO 2 injection were about two times than air injection condition T he remaining CO 2 concentration of a ir injection was close to zero at any stage. It confirmed that carbon limitation influenced the growth rate of air injection.

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41 Figure 3 8 Model f or Reactor 1 (injection with Air ) Figure 3 9 Model for Reactor 3 (injection with 1% CO 2 )

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42 Figure 3 10 Model for Reactor 4 (injection with 1% CO 2 ) Figure 3 1 1 Model for CO 2 trends of R3 (injection with 1% CO 2 ) Discussion I n this research, maximum biomass dry weight of 3.5g/L was obtained after cultivation for 36 days. C ultures were grown in 13 hours light/11 hours dark cycles at 1200 mol photon m 2 s 1 30 C and were bubbled with air and 1% CO 2 I n previous study of BG0011, 1.7g/L was obtained after cultivation for 39 days. C ultures were grown in continuous light at 12 0 mol photon m 2 s 1 30 C and we re bubbled with air and 0.5% CO 2 (Phlips et al ., 1989 ) The data proved once again that increase light intensity and CO 2 concentration help algae growth. A dditionally, algae growth under specific control

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43 in bioreactors is much better than it grows in nature. For example, t he Synechococcus biomass observed in the south ern Mid Atlantic Bight was 0.11 g/L in summer and 3 10 4 g/L in winter (Moisan et al. 2010) The yields of polysaccharide were substantial, in excess of 4.0 g/L/month, and the curves in Figure3 6 indicate the potential for more polysaccharide production capacity. I n previous study of BG0011, the yields of polysaccharide were in excess of 1.0 g/L/month. The observed yields were high compared to those observed in other cyanobacterial species (Phlips et al ., 1989 ). T he modeling in this research considering three factors: algae growth, CO 2 consumption and polysaccharide production. E xcept for these three factors, other factors, such as temperature, pH, salinity, were considered to be constant. M odified Monod equation and modified Le udeking Piret kinetics equation were used for the modeling. Other models such as Droop model, which has been proven to represent the effect of macronutrients on the growth rate of micro algae (Hartmann et al. 2013), could also be considered to be used in the future. B y r eferencing to other algae model, o ther influence factors, such as temperature, global irradiance phosphorus, should also be added into specific growth rate to make the m odel apply more widely (Haario et at ., 2009) Additionally algae decay term should also be considered and added in the m odel to make it more complete.

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44 CHAPTER 4 CONCLUSIONS Research Conclusions The unicellular cyanobacterium, Synechococcus sp. BG0011, due to its unique characteristic s, such as nitrogen fixation, tolerance for high salinity and polysaccharide secretion is an excellent candidate for biofuels production. At laboratory scale, increasing light intensity and CO 2 concentration can i mprove algae grow th I t was observed that the maximum specific growth rates of algae under low light conditions were the same (0.33 day 1 ). A nd the maximum specific growth rate of algae under high light and 1% CO 2 conditi ons was 0.55 day 1 which is almost the same as under high light and air conditions (0.56 day 1 ). I ncrease of light intensity made max increase a lot. According to biomass dry weight experiment, the relationship between biomass dry weight and OD was linear and the coefficient to translate OD to biomass dry weight was 0.8865 (g/L)/OD M aximum biomass dry weight of 3.6 g/L was obta ined. P olysaccharide started to accumulate during stationary phase and sparging with CO 2 improve d polysaccharide production. T he maximum polysaccharide concentratio n obtained in our experiment was 4.35 g/L. The model developed for algae and polysaccharide production fit the experimental data well and supported some assumptions of this research. Future Works A number of key issues need to be investigated to further the development of a Synechococcus BG0011 based on biofuels production system: (1) define the optimum CO 2 concentration for algae growth and polysaccharide production; (2) determine an efficient and accurate method to measure the dry weight of polysaccharide; (3)

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45 characterize carbohydrate and protein composition of Synechococcus BG0011; (4) determine optimum anaerobic digestion conditions with special emphasis on salinity tolerance.

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46 APPENDIX A MEDIUM FORMULA FOR BG0011 Stock Solutions F or A N Concentrate Stock Solution S olution 1 (trace elements): F or 4 liters distilled H 2 O 1) H 3 BO 3 68.52 g 2) MnCl 2 4H 2 O 8.64 g 3) ZnCl 2 0.63 g 4) CuSO 4 0.006 g 5) CoCl 2 6H 2 O 0.0028 g S olution 2 (iron ): F or 1 liter distilled H 2 O 1) HCl (concentrated) 8.3 mL (in 1 liter H 2 O, makes 0.1 n HCl Solution) 2) FeSO 4 7H 2 O 3.89 g S olution 3 (vitamins): F or 1 liter distilled H 2 O 1) Thiamine 0.1 g M ake the following separate solutions in 1 liter H 2 O each (makes 100 concentration): 2) Cobalamin 0.1 g 3) Biotin 0.1 g E xtract 1 mL from each of the 2 solutions and add to 1 liter of distilled H 2 O containing Thiamine S olution 4 (molybdenum):

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47 F or 1 liter distilled H 2 O 1) Na 2 MoO 4 2H 2 O 0.5 g A N Concentrate Stock Solution All quantities for 1 liter distilled H 2 O 1) H 2 O 0.7 L 2) Na 2 EDTA 0.15 g 3) KCl 3.0 g 4) CaCl 2 2H 2 O 1.85 g 5) K 2 HPO 4 0.25 g 6) MgSO 4 7H 2 O 25 g (potential modification) 7) Stock Solu tion 1 (trace elements) 10 mL 8) Stock Solution 2 (iron) 5 mL 9) Stock Solution 3 (vitamins) 0.5 mL 10) Stock Solution 4 (molybdenum) 15 mL Fill with distilled H 2 O to e xactly 1 liter (or other desired final volume). For a low salinity stock solution, only add 5 g/L of MgSO 4 7H 2 O A N Medium (For SYNECHOCOCCUS sp. BG0011) All quantities for 1 liter distilled H 2 O 1) A N concentrate 200 mL 2) NaCl 22.5 g

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48 3) Hepes 5 g 4) Distilled H 2 O F ill to exactly 1 liter (or other desired final volume). 5) Adjust pH to 8.2 with N aOH (Last modified by Balley Trump on 0 5/01 /2013 )

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49 APPENDIX B pH TEST EXPERIMENT Experiment about i nfluences of different percent of CO 2 on pH was done, to make sure the reactors injected with air and the reactors injected with mixture of CO 2 and air could reach the same pH condition when cultivating. T wo reactors (R3 and R4) with initial pH 8.25 medium were used for this experiment Changing on pH of 1%, 5%, 10%, 15% CO 2 injection was tested. D ata of pH chang ing by time are shown in table B 1. Figure B 1 is a graph of pH trends for different percent of CO 2 injection. Table B 1. pH trends for different percent of CO 2 injection Time (minute) 1% CO 2 injection 5% CO 2 injection 10% CO 2 injection 15% CO 2 injection 0 25 40 55 70 8.25 7.66 7.52 7.52 7.49 7.52 7.30 7.16 7.08 7.01 7.69 6.88 6.88 6.88 6.88 7.56 6.90 6.86 6.89 6.86 85 7.50 7.06 6.90 6.87 105 120 135 150 165 A fter 1 day 7.42 7.50 7.50 7.50 7.49 7.54 7.02 7.04 7.03 7.04 7.02 7.16 6.92 6.88 6.89 6.90 6.91 6.98 6.85 6.85 6.89 __ __ __ Note: The data above was the average data of reactor 3 and reactor 4 T he flow rate for 1%, 5%, 10%, 15% CO 2 were 10, 50, 100, 150 mL/min respectively. T he pH test exp eriment was done o n 03/21 /2013.

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50 Figure B 1 pH trends for different percent of CO 2 injection

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51 LIST OF REFERENCES Behl, Y. (2013). Laboratory scale studies of Cyanobacteria, Synechococcus BG0011. University of Florida Digital Collections Daroch, M., Geng, S., Wang, G. (2012). Recent advances in liquid biofuel production from algal feedstocks. Applied Energy, 102 1371 1381 Haario, H., Kalachev, L., Laine, M. (2009). Reduced models of algae growth. Bulletin of Mathematical Biology, 71, 1626 1648 Hartmann, P., Bechet, Q., Bernard, O. (2013). T he effect of photosynthesis time scales on microalgae productivity. Bioprocess Biosyst Engineering, 37, 17 25 Moestrup, (2001). Algae: Phylogeny and Evolution. Encyclopedia of life sciences ( www.els.net ) Moisan, T. A., Blattner K. L., Makinen C. P. (2010). Influences of temperature and nutrients on Synechococcus abundance and biomass in the southern Mid Atlantic Bight. Continental Shelf Research 30, 1275 1282 Murali, R. (2013). Dynamic modeling and validation of growth of Synechococcus BG0011 using laboratory scale studies. University of Florida Digital Collections Phlips, E. J., Zeman, C., Hansen, P. (1989). Growth, photosynthesis, nitrogen fixation and carbo hydrate production by a unicellular cyanobacterium, Synechococcus sp. (Cyanophyta). Journal of Applied Phycology, 1 137 145 Reynolds, C. S. (2006). Ecology of phytoplankton. Cambridge, New York: Cambridge University Press Sayadil, M. H., Ghatnekar, S. D. Kavian, M. F. (2011). Algae a promising alternative for biofuel. Proceedings of the International Academy of Ecology and Environmental Sciences, 1(2) 112 124 S cott A. S., Davey M P ., De nnis J. S ., Horst, I. (2010). Biodiesel from algae: challenges a nd prospects. Current Opinion in Biotechnology, 21 277 286 Singh, O. V., Harvey, S. P. (2010). Sustainable Biotechnology: Sources of Renewable Energy. Dordrecht, London: Springer Slegers, P. M., Losing, M. B., Wijffels, R. H., van Straten, G., van Boxtel, A. J. B. (2013). Scenario evaluation of open pond microalgae production. Algal Research, 2 358 368 Stanier, R. Y., Bazire, G. C. (1977). Phototrophic prokaryotes: the cyanobacter ia. Annual Review of Microbiology, 31 255 274

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52 Sturm B. S. M., Peltier, E., Smith, V., deNoyelles, F (2012). Controls of Microalgal Biomass and Lipid Production in Municipal Wastewater Fed Bioreactors. Environmental Progress and Sustainable Energy, 31 ,10 16 Ugwu, C. U., Aoyagi, H., Uchiyama, H. (2007). Photobioreactors for mass cultivation of algae. Bioresource Technology, 99 4021 4028 Waterbury, J B. Watson, S W ., Guillard, R RL ., Brand, L E (1979). Widespread occurrence of a unicellular marine plankto nic cyanobacterium. Nature, 277 293 294

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53 BIOGRAPHICAL SKETCH Yingxiu Zhang was born in 1988 in Tianjin, China. She did her schooling in Baodi Middle School. She completed her Bachelor of Science in Applied Chemistry fro m Tianjin University in 20 10 Af ter graduation, she worked as a technician in Tianjin Samsung Electro Mechanics Co., LTD for one year. In 2012, s he came to Gainesville, Florida and joined into Dr. Spyros Svoronos s research group in the Department of Chemical Engineering at University of Florida She will get the Master of Science in Chemical Engineering from University of Florida in s pring 2014.