Cultivation Optimization and Modeling for Microalgae to Produce Biodiesel

Transcription

1 Cultivation Optimization and Modeling for Microalgae to Produce Biodiesel Item Type text; Electronic Dissertation Authors Ren, Ming Publisher The University of Arizona. Rights Copyright is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 01/07/ :18:15 Link to Item

2 CULTIVATION OPTIMIZATION AND MODELING FOR MICROALGAE TO PRODUCE BIODIESEL By Ming Ren A Dissertation Submitted to the Faculty of the DEPARTMENT OF CHEMICAL AND ENVIRONMENTAL ENGINEERING In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY WITH A MAJOR IN CHEMICAL ENGINEERING In the Graduate College THE UNIVERSITY OF ARIZONA 2012

3 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Ming Ren entitled: Cultivation Optimization and Modeling for Microalgae to Produce Biodiesel and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy Date: 05/17/2012 Kimberly Ogden Date: 05/17/2012 Farhang Shadman Date: 05/17/2012 Eduardo Saez Date: 05/17/2012 Wendell Ela Final approval and acceptance of this dissertation is contingent upon the candidate s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. Date: 05/17/2012 Dissertation Director: Kimberly Ogden

4 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted. SIGNED: Ming Ren

5 4 ACKNOWLEDGEMENTS I would first like to give thanks to Dr. Kim Ogden for her assistance and guidance in my research over the last 5 years. She has played a very vital role in transforming me into a better researcher and most importantly a better person. A special thanks to my committee members, Dr. Farhang Shadman, Dr. Eduardo Saez, and Dr. Wendell Ela, and to all the faculty and staff in the Department of Chemical and Environmental Engineering who have assisted in my education. I am also very appreciative of the time and effort of Anthony Montoya, Cameron Tomala and all others in our lab involved in this project. Moreover, I want to thank Greg Ogden, Brian Barbaris and Armando Durazo for their help in setting up experiments. I want to express my deepest thanks and love to my parents, Xin Ren and Xiaoping Shen. Without their support and love, I would not have been able to accomplish all that I have. I also want to thank Xinfeng Lian and Guifeng Hu for being wonderful and supportive father and mother in-law. Finally, I also want to thank Bo, my beloved husband, for his endless love and support.

6 5 DEDICATION To my family, My dad and mom, Mr Xin Ren and Mrs Xiaoping Shen Bo and Derek Lian

7 6 TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES ABSTRACT INTRODUCTION MATERIALS AND EXPERIMENTAL METHODS Culture conditions Determination of biomass density Lipid extraction Measurement of nitrate concentration Fatty acids methyl esters (FAME) preparation and GC-FID analysis MICROALGAE STRAINS SCREEN AND CULTURE CONDITIONS Introduction Results Microalgae growth and lipid content Microalgae lipid composition profile Light intensity and continuous lightening effect on algae growth Light path and flow rate effect on algae growth Two stages cultivation Discussion... 58

8 7 TABLE OF CONTENTS - Continued 3.4 Conclusion CULTIVATION CONDITIONS OPTIMIZATION FOR MICROALGAE NANNOCHLOROPSIS GADITANA Introduction Results Effect of tris-hcl buffer Air vs. 5% CO2 enriched air bubbling Effect of nitrogen and carbon sources on growth rate and biomass production Effect of nitrogen and carbon sources on lipid yield Groundwater cultivation Effect of carbon and nitrogen sources on fatty acid profile Discussion Conclusions MODELING OF MICROALGAE NANNOCHLOROPSIS GADITANA S GROWTH, NITRATE CONSUMPTION AND LIPID PRODUCTION UNDER NITROGEN-LIMITED CONDITIONS Introduction Mathematical modeling... 81

9 8 TABLE OF CONTENTS - Continued Nitrate consumption Lipid production Parameters estimation and sensitivity analysis Results Effect of nitrogen limitation on microalgae growth kinetics, nitrate consumption and lipid yield Model validation and sensitivity analysis Discussion Conclusions EFFECT OF NITRATE AND UREA REPLETION ON THE MICROALGAE GROWTH AND LIPID ACCUMULATION OF NANNOCHLOROPSIS GADITANA Introduction Results Effect of replete nitrate on growth and final lipid content Effect of urea as nitrogen source on growth and final lipid content Fatty acid profiles for nitrate and urea as nitrogen sources Discussions Conclusions CONCLUSIONS AND FUTURE WORKS

10 9 TABLE OF CONTENTS - Continued 7.1 Conclusions Future works APPENDIX A MATLAB CODE FOR MODELING APPENDIX B ROOT MEAN SQUARE ERROR (RMSE), MEAN ABSOLUTE ERROR (MAE) AND CORRELATION COEFFICIENT REFERENCES

11 10 LIST OF TABLES Table 1 Lipid content of some microalgae [3] Table 2 Summary of productivity of various photobioreactor designs Table 3 Stains name and cell length Table 4 f2-si medium recipe Table 5 Trace metal solution recipe Table 6 Vitamin solution recipe Table 7 Growth summary of adding different 10 mm carbon and nitrogen sources Table 8 Kinetic parameters for microalgae growth, nitrate consumption and lipid production Table 9 Model validation for biomass density, nitrate consumption and lipid yield Table 10 Growth summary of different nitrate concentration Table 11 Growth summary of different urea concentration

12 11 LIST OF FIGURES Figure 1 Effect of light intensity on growth rate of microalgae [3] Figure 2 Monod fit of ave. dissolved CO 2 concentration vs. growth rate in batch reactor for Chlorella vulgaris species [17] Figure 3 Productivity affected by light path and cell density. [23] Figure 4 Schematic presentation of an ALR for culture of microalgae cells Figure 5 Experimental setup for flask cultivation Figure 6 Tubular photobioreator setup Figure 7 Cell growth and ph profile for Tetraselmis sp (CCMP 908) Figure 8 Cell growth and ph profile for Dunaliella tertiolecta (CCMP364) Figure 9 Cells growth and ph profile for Nannochloropsis gaditana (CCMP527) Figure 10 Cells growth and ph profile for Nannochloropsis salina (CCMP1776) Figure 11 Lipid content comparison for fours strains mixed using air without CO Figure 12 Percentage of individual fatty acid composition profile for different strains. Values shown are averages of four replicates Figure 13 Light intensity effect on microalgae ccmp 527 growth, 80 µmol m -2 s -1 ( ); 122 µmol m -2 s -1 ( ); 220 µmol m -2 s -1 ( ) Figure 14 Microalgae CCMP 527 cell growth and lipid accumulation curve under continuous light. Dry cell density ( ); Lipid content ( ) Figure 15 Microalgae CCMP 527 cell growth and lipid accumulation curve under 12h/12h light cycle. Dry cell density ( ); Lipid content ( ) Figure 16 Light path effect on microalgae growth, 10mm ( ); 35mm ( ); 50mm ( )

13 12 LIST OF FIGURES - Continued Figure 17 Maximum aerial vs. volumetric productivity of cell mass as a function of the light path. Volumetric productivity ( ); Aerial productivity ( ) Figure 18 Flow rate effect on microalgae CCMP 527 growth in 50mm thickness photobioreactor. 1LPM ( ); 2LPM ( ); 3 LPM ( ) Figure 19 Two stage cultivation (w or w/o N) for microalgae CCMP 527 in 5L photobioreactor. Cells in f/2-si medium ( ); cells in f/2-si medium w/o sodium nitrate ( ); Lipid content ( ) Figure 20 Effect of Tris-HCl buffer (40 mm) on cell growth rate with 5% CO 2 enriched air bubbling Figure 21 Lipid content comparison in f/2-si medium with or without tris-hcl buffer. Values shown are averages of four replicated ± standard deviation Figure 22 Effect of 5% CO 2 enriched air on cell growth rate Figure 23 Lipid content comparison in tris-hcl buffered f/2-si medium with or without 5% CO 2 enriched. Values shown are averages of four replicated ± standard deviation Figure 24 Lipid content comparison in f/2-si medium with or without same total amount of 10mM N and C (glucose) addition. Values shown are averages of four replicated ± standard deviation Figure 25 Cell growth rate of distilled water vs. groundwater to make artificial seawater... 72

14 13 LIST OF FIGURES - Continued Figure 26 Comparison of lipid content using groundwater and distilled water to make artificial sea water. Values shown are averages of four replicated ± standard deviation Figure 27 Percentage of individual fatty acid methyl ester (FAME) composition profile in f/2-si medium with or without same total amount of 10mM N and C (glucose) addition. Values shown are averages of four replicated ± standard deviation Figure 28 Simulation results of microalgae growth, nitrate consumption and lipid concentration (solid line) at 100% N versus time. Experimental data are shown as dry cell density (o), nitrate concentration (Δ) and lipid yield ( ). Values shown are averages of four replicates ± standard deviation Figure 29 Linear regression between lipid concentration and dry cell density Figure 30 Simulation results of microalgae growth, nitrate consumption and lipid concentration (solid line) at 50% N versus time when µ m = hr -1. Experimental data are shown as dry cell density ( ), nitrate concentration (Δ) and lipid yield ( ). Values shown are averages of four replicates ± standard deviation Figure 31 Simulation results of microalgae growth, nitrate consumption and lipid concentration (solid line) at 25% N versus time when q 0 =0.0013±0.0001g N g -1 dw. Experimental data are shown as dry cell density ( ), nitrate concentration (Δ) and lipid yield ( ). Values shown are averages of four replicates ± standard deviation Figure 32 Percentage of individual fatty acid methyl esters (FAME) composition profile with time course (25% N). Values shown are averages of four replicates

15 14 LIST OF FIGURES - Continued Figure 33 Cells growth curve and lipid content vs. time for normal media (nitrate as N source) Figure 34 Nitrate concentration change with time course by adding 10mM, 20mM and 30mM nitrate into normal media Figure 35 Cells growth curve and lipid content vs. time for modified media (urea as N source) Figure 36 Percentage of individual fatty acid composition with time course (normal media). Values shown are averages of four replicates Figure 37 Percentage of individual fatty acid composition with time course (urea as N source). Values shown are averages of four replicates

16 15 ABSTRACT Microalgae has shown to be an ideal choice for biofuel industry. Algae has high oil productivity, a short growth cycle and survives in a wide variety of water sources including high salinity and waste water. For this project, four different species of marine microalgae were screened based on oil content. They were Dunaliella tertiolecta(ccmp364), Nannochloropsis gaditana (CCMP527), Tetraselmis sp (CCMP 908) and Nannochloropsis salina (CCMP1776). Experimental results showed that CCMP 527 and 1776 strains had higher lipid content and better fatty acids profile than the other two. Further investigations were carried on CCMP 527 in order to maximize biomass productivity and lipid content. Nutrients, salinity, ph, temperature, light intensity and aging of the culture can all affect both lipid content and fatty acid profile and were investigated. Nutrient stress is the easiest way to manipulate lipid composition and increase lipid content. Hence, various carbon and nitrogen sources were investigated to determine the range and amount of substrates that may be feasible for cultivation. For supplying lipid for biodiesel production, the optimum culture conditions for strain Nannochloropsis gaditana are using CO 2 enriched air bubbling, f/2-si medium, ph control, and nitrate as the nitrogen source. Use of other fertilizers is feasible as well, however, the nitrogen source greatly affects lipid productivity, but trace amounts of organics in ground water do not. A model which predicts cell growth, nitrogen concentration, and lipid yield in batch systems is developed that is applicable for low nitrogen conditions. Plus, a sensitivity analysis of three major parameters was done to validate how variations in these key

17 16 parameters affect simulation results. The fatty acid profile as a function of time was shown not to vary from mid-exponential to stationary phase. The model describes reactor behavior well, therefore it can be applied to the genus of Nannochloropsis to predict biomass yield and lipid accumulation, and be a useful tool to optimize and compare bioreactor systems for the biofuel industry. In addition, effects of nitrate and urea under repletion condition on microalgae growth, lipid yield and fatty acids profile for microalgae Nannochloropsis gaditana were investigated. Replacing nitrate by urea didn t show positive influence on lipid content and yield compared to normal medium. The major fatty acids for these two mediums were palmitic acid (C16:0) and palmitioleic acid (C16:1). Nannochloropsis gaditana still shows to be ideal candidate for biodiesel production using urea or nitrate enriched agriculture wastewater.

18 17 1. INTRODUCTION Currently, in the United States, biodiesel is predominantly produced from soy oil, while other biodiesel plants use canola, sunflower, palm, and jatropha oils. Biodiesel production from these plants has been restrained by increases in food prices and competition for available farming land. In some Asian countries, tropical forests are destroyed to grow oil plants. More importantly, these agricultural crops have a low yield of biodiesel from the biomass harvest (e.g., less than 5% of total biomass), which does not satisfy the ever increasing demand [1]. According to some reports, petroleum reserves will be depleted in approximately 50 years, while total diesel demand is predicted to increase to 80 billion gallons per year by 2015 compared to 58 billion gallons in 2002[2]. Thus, it is necessary to find a renewable alternative oil source for traditional fossil fuel. Microalgae is an ideal choice. Using microalgae to produce biodiesel has many advantages some of which are provided here. 1) Many species of microalgae have a high oil content. The lipid content can be more than 50% of cell dry weight [3]. 2) The short growth cycle and high yields can meet the demand of biofuel. Sometimes algae biomass yield can double in 24 hrs, which is not feasible for higher order plants[3]. 3) Microalgae reactors and culturing have a low cost requirement. Algae need sunlight, water, carbon dioxide and a small amount of inorganic nutrients. The

19 18 reaction is photosynthesis and biomass is produced. Other oil crops require fertile land for growth, whereas algae can be grown wherever there is sufficient sunlight. 4) Microalgae have the ability to survive and grow in a variety of water sources, such as wastewater and brackish water. Also it s possible to recycle growth medium[4]. Microalgae can be cultured near a power plant to utilize carbon dioxide emitted from the boiler. It helps to alleviate greenhouse effects caused by fossil fuel burning. However, there are also some disadvantages of using microalgae to produce biofuel, such as high cost, large water consumption, specific nutrients demands (nitrate, phosphate, and etc.), and requiring relatively large land area. Biodiesel, a renewable and sustainable fuel, is derived from triglycerides and free fatty acid through a transesterification reaction (Eq1), where R1 R2 and R3 are long hydrocarbon chains, also called fatty acid chains. Transesterification is catalyzed by alkali, acid and lipase enzyme. CH 2OCOR 1 CH 2 OH R COOCH 1 3 CHOCR CATALYST 2 3CH3OH CHOH R2COOCH 3 (1) CH 2OCOR 3 CH 2 OH R 3 COOCH 3 Triglycerides + Methanol Glycerin + Methyl Esters (biodiesel) Equation 1: Transesterfication reaction.

20 19 For the alkali catalyzed reaction, using sodium hydroxide or potassium hydroxide as a catalyst, the optimal methanol to oil molar ratio is 6:1. The optimal temperature is around 60 C[5]. An oil conversion of 90-98% can be achieved in 90 min [6]. One drawback of alkali-catalyzed reaction is saponification. This side reaction is induced by the fatty acid reacting with alcohol (Eq 2). HOOC R KOH K OOC R H2O (2) Fatty acid + Potassium hydroxide Potassium soap + Water Saponification not only consumes the catalyst, but also produces by products. To avoid it, less than 5% free fatty acid, anhydrous alkali catalyst and anhydrous alcohol needs to be used [6, 7]. This inevitably leads to increased costs. Acid catalyzed transesterification doesn t have the problem of saponification, but it has a much slower reaction rate compared to the alkali catalyzed reaction. It takes 69 hours to reach 90% conversion at 65 C and a methanol to fatty acid molar ratio of 30:1, using sulfuric acid as catalyst[6]. If the temperature is increased to 100 C, 0.5 wt% sulfuric acid catalyst, and a methanol to fatty acid molar ratio 9:1, the conversion can be greater than 99 % in 8 hours [8]. Another alternative is enzymatic biodiesel production, catalyzed by different kinds of lipase enzyme. Lipase enzyme is derived from a microbial source. In general, the conversions of biodiesel from various vegetable and waste oils by using the enzyme immobilized ranges from 76 to 100%[9]. But the limitation of enzymatic synthesis is the relatively high cost.

21 20 However not all microalgae yield high biomass and oil production. The species of microalgae need to be screened for the ability to make large amounts of fatty acids and thus biodiesel. Table 1 lists examples of the lipid content of some microalgae. [10], and Nannochloropsis sp. is a potential candidate based on the lipid content. Rodolfi et al. [11] also investigated the lipid content of 30 microalgal strains including marine and freshwater strains. They found that Nannochloropsis sp. is among the best producers according to the biomass harvest, lipid content and culturing conditions. Table 1 Lipid content of some microalgae [3] Species Lipid content (% dry biomass wt) Botryococcus braunii Chlorella sp Crypthecodnium cohnii 20 Cylindrotheca sp Dunaliella primolecta 23 Isochrysis sp Monallanthus salina >20 Nannochloris sp Nannochloropsis sp Neochloris oleoabundans Nitzschia sp Phaeodactylum tricornutum Schizochytrium sp

22 21 Tetraselmis sueica Algae are photosynthetic organisms. The photosynthesis process is described by Eq (3). Therefore, to maintain microalgae growth, light, carbon dioxide, water and nutrients are required. Growth temperature is usually best under 30 C and the ph can t be too low or high. Each of these components will be discussed in more detail. 6CO 2 +12H 2 O Light C 6 H 12 O 6 +6O 2 (3) Light is a key factor for microalgae growth. Increasing light intensity can accelerate growth rate, but excessive light can also decline the growth rate, which is called photo-inhibition. Fig.1 illustrates the general relationship between light intensity and growth rate. Photo-inhibition is induced by high light intensity and causes reversible damage to the repair mechanisms and photo-protective processes [12]. The repair mechanisms are that the photosynthesizing organisms repair the damage caused by photo- inhibition. And photo-protective processes are protecting against adverse effects of strong light. Park and Lee [13] found that using alternating dark and light periods enhances growth rate and total biomass harvest. The amount of CO 2 can limit the photosynthetic activity when bicarbonate is the main source of dissolved inorganic carbon[14]. The slow CO 2 diffusion rate from the atmosphere to seawater at ph 8.2 cannot satisfy the photosynthesis reaction demand. In order to improve mass transport of CO 2 inside the system, CO 2 enriched air is supplied, causing a drop in ph. It has been proven that simple air aeration can t satisfy the demand of intense algal growth [15]. Fig.2 shows that the relationship of

23 22 dissolved CO 2 concentration and growth rate fits the Monod model. While increasing CO 2 concentration can increase growth rate, at some point the reaction rate will attain a maximum value.co 2 can also be used to control ph. The ph increases as microalgae uptake inorganic carbon by photosynthesis. Meanwhile, mixing CO 2 with air bubbling causes a ph drop. Thus, strict ph control helps microalgae maintain a high growth rate [16]. Figure 1 Effect of light intensity on growth rate of microalgae [3]

24 23 Figure 2 Monod fit of ave. dissolved CO 2 concentration vs. growth rate in batch reactor for Chlorella vulgaris species [17] In addition, nitrogen and/or carbon sources are typically limited in a medium, therefore supplementing with either increases the growth rate, and determines the type of growth: autotrophic, mixotrophic or heterotrophic [16]. If a nitrogen source is added, growth is autotrophic. When glucose is present in the medium, mixotrophic growth is observed autotrophic when light is provided and heterotrophic without light. The type of growth also affects the growth rate, pigment composition and lipid content of Nannochloropsis genus [18, 19]. However the cost of biofuel from algae biomass is still expensive, around $3000 ton -1, and needs to drop significantly to be comparable to the cost of traditional fuel [1]. To achieve this, there are several bottlenecks that must be overcome. The first is strain selection. Oil content and productivity greatly rely on the strain and the environment [10]. It is imperative to find naturally occurring strains or naturally adapted strains

25 24 with high oil content and biomass yield that grow rapidly. The second area is large scale cultivation. There are three common ways to culture algae, which include open ponds, raceways and scaled-up photobioreactors. Open systems are easily contaminated, but low cost to build and maintain, while closed system are harder to scale up, because of light path and mass transfer limitations [20]. The third area is harvesting and extraction. Sedimentation, flocculation, floatation, filtration and centrifugation are typical ways to harvest algae [21]. However centrifugation requires large amounts of energy and filtration can be costly and has the disadvantage of membrane fouling. Flocculation and flotation require the use of other chemicals, which might cause further problems in oil extraction or separation processes. Sedimentation is time and space consuming which is challenging for biodiesel production. To recover the oil, large scale extraction from microalgae is usually accomplished with mechanical cell disruption followed by solvent extraction. The drawback of using a solvent is that the process requires an additional energy input to recover the solvent [22]. In order to provide favorable environment for microalgae growth and lipid accumulation and eliminate bacterial competition for nutrients, it is better to culture them in a closed system, such as photobioreactor. Photobioreactors can be categorized as outdoor culturing systems and indoor culturing systems. Also, the reactor is classified according to its configuration. Examples of photobioreactors include a tubular reactor, flat panel, conical helical reactor, and an air lift system. Table 2 summaries and compares the performance of these bioreactor designs. For photobioreactor design, several factors need to be considered.

26 25 a. Light path and distribution. In a massive culture system, cells are very easily shaded by each other, which is key problem that affects photosynthetic efficiency. Fig.3 shows that light path is important in determining productivity. Light path decreases Figure 3 Productivity affected by light path and cell density. [23] b. Mixing. Mixing not only determines the frequency of cells exposure to light in dense cultures, but also causes hydrodynamic stress on the cell surface. Since mechanical mixing always induces high shear stress on cells, usually aeration is used in a photobioreactor to generate turbulent flow. The highest feasible aeration rate in the limitation of cell shear stress tolerance is the optimum condition to operate, since shortest light/dark cycle time is achieved. c. Mass transfer. Carbon dioxide diffusion in the system controls the photosynthesis reaction rate. Similarly, oxygen accumulation in the culture system will inhibit algae growth. Thus, to get a high mass transfer rate and oxygen removal rate, optimization of the sparger and degas system design is very important. d. ph and temperature control or other environmental factors. Temperature and ph

27 26 affect microalgae growth rate. Especially when photobioreactor system are scaled up, ph and temperature control devices need to be included. Temperature is particularly costly to control in outdoor environments during the summer. Large amounts of cooling may be required thus causing an overall increase in reactor operation costs. However, a sterile culture system is advantageous and moderates the growth of contaminant algae found in the surrounding environment.

28 27 Table 2 Summary of productivity of various photobioreactor designs. Types Tubular Culturing Species A Chlorella sp. And three heterotrophic bacteria Total Illuminated area/total Productivity Max Biomass References volume (L) volume (m 2 /m 3 ) (g/l day) harvest (g/l) [24] Conical Helical Chlorella sp [25] Tubular Flat Inclined Spirulina platensis [23] Flat plate Spirulina platensis [26] Flat plate Nannochloropsis sp [27] Flat panel Nannochloropsis sp [28]

29 28 Green wall panel Internal loop air lift Medusa Internal loop air lift External loop tubular air-lift External loop air lift External loop tubular air lift Nannochloropsis sp [11] Haematococcus pluvialis [29] Chlorella salina [30] Phaeodactylum [31] tricornutum Arthrospira platensis (S [32] platensis) Phaeodactylum [33] tricornutum

30 29 The ultimate design goal is to maximize biomass productivity in the photobioreactor. But there is no existing model that could combine algae growth and fluid dynamics together, since we know fluid field will definitely affect cells growth. Hence, it is necessary to improve current photobioreactor models. In a continuous culturing system, biomass productivity is a function of dilution rate (D; inverse of the retention time) and cell concentration (C b ). At steady state, the dilution rate is equal to specific growth rate (μ). Thus the volumetric productivity P is: P=μC b (4) There are numerous models used to describe the specific growth rate μ (day -1 ) in the literature. The rate is controlled by light intensity (I). Table 3 summarizes several models, the majority of which are empirical. Table 3 Models of describing the specific growth rate as a function of light intensity Models References max I I max (5) [34] Imax ( 1 e (6) [35] ) max I I max max I e I (1 ) Imax (7) [36]

31 30 I ( K m I ) max 1 i m m (8) [37] maxi 2 I K s I K i (9) [38] I max n k I I n av n av (10) [39] I Ik (1 ( ) K i c ( b ) I0 maxiav c ( b ) 0 a I0 ) I c ( b ) I0 av (11) [40] max I m K s I (12) [41] However, all these models just consider light intensity as the only variable affecting growth rate. Actually, temperature and nutrient concentration will influence growth rate too. In addition, Eqs. (5-7, 10) in table 3 do not consider photo- inhibition, even we know excessive light inhibits the cells growth. On contrast, Eqs. (8-9, 11) are better models for estimating the specific growth rate, which takes light inhibition into account. Especially Eq. (11) considers the relationship between average light intensity and incident irradiance level (I 0 ). Merchuk et al. uses the concept of a Photosynthetic factory (PSF), which integrates light history using the modified Eilers & Peeters model. They developed a realistic model including fluid dynamics for bubble column [42].

32 31 dx dt 1 Ix x 1 x x ) (13) 1 2 ( 1 2 dx 2 Ix Ix 1 x2 2 (14) dt k x Me (15) 2 Where I= Illuminance, or photon flux density, µe m -2 s -1 ; x 1 = Fraction of PSF in open state, dimension less; x 2 = Fraction of PSF in close state, dimension less; x 3 = Fraction of PSF in inhibited state, dimension less; α = Rate constant of photon utilization to transfer x 1 x 2, (µe m -2 ) -1 ; β = Rate constant of photon utilization to transfer x 2 x 3, (µe m -2 ) -1 ; δ = Rate constant of transfer x 3 x 1, s -1 ; γ = Rate constant of transfer x 2 x 1, s -1 ; µ = Specific growth rate, h -1 ; Me=Maintenance, s -1.

33 32 Liquid flow Light Riser (draft tube) Bubbly (turbulent) flow High gas holdup Downcomer Low gas holdup Bubble entrapment air/co2 Figure 4 Schematic presentation of an ALR for culture of microalgae cells Unlike the bubble column in which fluid movement is quasi chaotic, the air lift reactor provides an overall ordered flow in the duct [43]. A schematic presentation of an air lift reactor (ALR) with concentric tubes is shown in Figure 4. Christi establishes the relationship between superficial liquid velocity (U L, m/s) in the riser and height of the riser (h r, m) in the column [44]. U L K T 2g( r d ) hr 2 /(1 ) K ( A / A r B r d 2 2 ) /(1 ) d (16) Where h r = height of riser, m; ε r = Gas holdup in the riser; ε d =Gas holdup in the downcomer; K T = Frictional loss coefficient for the top zone of the air lift;

34 33 K B = Frictional loss coefficient for the bottom zone of the air lift; A r = Cross sectional area of riser, m 2 ; A d = Cross sectional area of downcomer, m 2. In eq. (14), since K T is much smaller than K B, K T can be ignored. They found that A K B A d b (17) And U gr r (18) ( U U ) 0 gr L (19) d r Where A b = Cross sectional area for flow under the baffle or the draft tube, m 2 ; U gr = Superficial velocity in the riser, m/s. K B was also determined by Fernandez et al. [33] to be: K 4C B f L eq (20) C Re (21) f U L Re L (22) Where C f = Fanning friction fator; φ= Tube diameter, m; L eq = Equivalent length of loop, m; μ L = Viscosity of the liquid, kg/m s.

35 34 The model developed by Fernandez et al. [33] has been used to successfully model air lift bioreactor behavior but does not include a specific term for light intensity. When we design and scale up a photobioreator, good gas-liquid mass transfer is also necessary. As mentioned before, carbon dioxide transfer in the liquid will determine carbon source uptake rate, and accumulation of oxygen will inhibit the photosynthesis reaction. Here we consider overall gas-liquid mass transfer coefficient k L. The relationship among mass transfer coefficient (k L ), the mean bubble diameter (d B ) and gas holdup is established by Christi [44]. k d L B k La L( 1 r ) 6 r (23) Where k L = Gas liquid mass transfer coefficient, m s -1 ; k L a L = Overall volumetric gas-liquid mass transfer coeffienct, s -1 ; a L = Gas-liquid interfacial area per unit volume of liquid (m -1 ) The k L /d B is found to be a constant for a bubble column and air lift photobioreactor for a given liquid. For a suspension with a water like liquid and air water dispersions, there is an empirical equation to calculate k L /d B ratio within±10% [44]. k d L B gd (24) L Cs ( ) e 3 L Where σ= Interfacial tension, N m -1 C s = Concentration of suspended solid, g/l

36 35 Finally, the specific power input for aeration can be calculated using eq. (25) to calculate [44]. P V G L gu L G (25) Where P G = Power input for aeration, w; V L = Culture system volume, m 3 ; U G = Superficial gas velocity, m/s. However, there is no reliable method to design and scale up a photobioreactor. The above provides a general idea on the specific growth models and mass transfer models, which can be used as a basis for further design. To scale up a photobioreactor is complicated. When we increase the volume, it will be inevitable change diameter or width, and height which will change light distribution or mass transfer rate. So we cannot just change parameters according to lab scale. Usually, the goal for scaling up a tubular photobioreactor is to ensure that the biomass and gas concentration are the same as the lab scale photobioreactor [45]. Now the cell quota is widely applied in the models of microalgae growth under nitrogen limitation conditions [46, 47]. The term cell quota is first introduced by Droop[48]. Cell quota (q) is defined as the content of a chemical element in the cells, and it can be like per unit of cell, biomass, or volume. So it considers the nutrient concentration in the cells instead of in the medium. How much nutrient disappearing from medium is the amount consumed by cells. Using this concept, it successfully explains why microalgae

37 36 can still grow even under nutrient starvation condition. It is because microalgae has nutrient in their cells to support them to keep growing. For my research, four marine algae were screened according to their growth rate, biomass production and lipid content. They were Dunaliella tertiolecta(ccmp364), Nannochloropsis gaditana (CCMP527), Tetraselmis sp (CCMP 908) and Nannochloropsis salina (CCMP1776). And Nannochloropsis gaditana (CCMP527) was selected as target strain to carry out further experiments on cultivation optimization, since it had fast growth rate and high oil content. Different cultivation parameters including ph control, CO 2 enrichment, light intensity, light cycle, light path, air flow rate and two stage cultivation were investigated. And various nitrogen and carbon sources were added into medium to determine the range and amount of substrates that may be feasible for cultivation. Especially under abundant nitrogen sources available (nitrate and urea) conditions, the microalgae growth, biomass production and lipid yield was monitored to see their impacts. Finally, a model describing microalgae growth, nitrate consumption and lipid concentration under nitrogen limitation condition was developed and validated. The overall goal of this research is to find a good candidate of microalgae to produce biofuel, optimize its cultivation conditions to maximize biomass production and oil content, and develop a model to predict the growth curve and final lipid yield.

38 37 2. MATERIALS AND EXPERIMENTAL METHODS In this chapter, cultivation conditions and setup for all experiments are provided in details. The biomass density determination and lipid extraction procedure is also described. In the end, analytical methods of measuring nitrate concentration and fatty acids profile are presented. 2.1 Culture conditions Four different marine microalgae stains were selected and their detailed information is listed in the table 3. They were obtained from Bigelow Laboratory for Ocean Sciences. All species were cultured in artificial sea water enriched with a f/2-si medium as shown in Table 4-6. Silica was omitted from the recipe because it precipitated during heat sterilization. Artificial sea water was prepared from Instant Ocean sea salt and distilled water. Salinity was controlled at 32 g/l. Table 3 Stains name and cell length Name Cell Length Tetraselmis sp. var. ( ccmp 908) µm Dunaliella tertiolecta Butcher (ccmp 364) 5-8 µm Nannochloropsis gaditana Lubian (ccmp 527) 2-3 µm Nannochloropsis salina Hibberd (ccmp 1776) 2-4 µm

39 38 Table 4 f2-si medium recipe Component Stock Solution Quantity Molar Concentration in Final Medium NaNO 3 75 g/l dh 2 O 1 ml 8.82 x 10-4 M NaH 2 PO 4 H 2 O 5 g/l dh 2 O 1 ml 3.62 x 10-5 M Na 2 SiO 3 9H 2 O 30 g/l dh 2 O 1 ml 1.06 x 10-4 M trace metal solution 1 ml --- vitamin solution 0.5 ml --- Table 5 Trace metal solution recipe Component Primary Stock Solution Quantity Molar Concentration in Final Medium FeCl 3 6H 2 O g 1.17 x 10-5 M Na 2 EDTA 2H 2 O g 1.17 x 10-5 M CuSO 4 5H 2 O 9.8 g/l dh 2 O 1 ml 3.93 x 10-8 M Na2MoO 4 2H 2 O 6.3 g/l dh 2 O 1 ml 2.60 x 10-8 M ZnSO 4 7H 2 O 22.0 g/l dh 2 O 1 ml 7.65 x 10-8 M CoCl 2 6H 2 O 10.0 g/l dh 2 O 1 ml 4.20 x 10-8 M MnCl 2 4H 2 O g/l dh 2 O 1 ml 9.10 x 10-7 M

40 39 Table 6 Vitamin solution recipe Component Primary Stock Solution Quantity Molar Concentration in Final Medium thiamine HCl (vit. B1) mg 2.96 x 10-7 M biotin (vit. H) 1.0 g/l dh 2 O 1 ml 2.05 x 10-9 M Cyanocobalamin (vit. B12) 1.0 g/l dh 2 O 1 ml 3.69 x M For bench top cultivation, strains were cultured in the 1L Erlenmeyer flasks with a 1 LPM flow rate of a sterile CO 2 /air mixture (5/95, v/v) at room temperature (24 C). In order to decrease water evaporation, the gas was saturated with water prior to entering the flask. The artificial light source was fluorescent light tubes illuminated on the top of the flasks, with a light intensity of 220 μmol/m 2 s at the media/air interface. Light intensity was measured using a LI-COR 250A light meter and an LI-COR 190SA quantum sensor. The light cycle was 12 hours on and 12 hours off. Fifteen ml of inoculum were added to 435 ml of medium. A Tris-HCl buffer (40 mm) was used to maintain ph at 7. The experiment setup is shown in Figure 5.

41 40 Figure 5 Experimental setup for flask cultivation For flat panel photobioreactor cultivation, three glass bioreactors with different light path depths were used. The light path depth was 10 mm, 35 mm and 50 mm respectively. The light source was on the side of bioreactors with 80 μmol/m 2 s light intensity. For tubular photobioreactor cultivation, the reactor was set up near the window in the small room on the top of the Civil Engineering Building at room temperature as shown in figure 6. The light source was sunlight through window. The light intensity at noon was around 800 μmol/m 2 s. To start cultivation experiments in this system, 200 ml of inoculum was mixed with 4.6 L of f2-si medium. Air at a flow rate of 10 LPM was continuously bubbled at the bottom of bioreactor.

42 41 Figure 6 Tubular photobioreator setup 2.2 Determination of biomass density Microalgae growth was monitored by cell absorbance using a Spectronic Genesys spectrophotometer at a wavelength of 540 nm or 750 nm in a cuvette with a 1-cm light path. Cell growth was only measured during the daylight hours. Each sample was diluted with distilled water to the range of when the optical density (OD) was greater than 0.4 to keep the measurements in the linear range of Beer-Lambert s law. The relationship between optical density (OD) and biomass density (BD, g/l) was Stain ccmp908 BC= OD 750 dilution factor (R 2 =0.96). (1) Stain ccmp364 BC= OD 750 dilution factor (R 2 =0.96). (2) Stain ccmp527 BC= OD 540 dilution factor (R 2 =0.96). (3) Stain ccmp1776 BC= OD 540 dilution factor (R 2 =0.96). (4)

43 42 The spectrophotometric determinations of dry biomass concentration were validated periodically. Biomass weight was estimated according to Rocha et al. [16]. 2.3 Lipid extraction Dry biomass from a 450 ml culture solution was harvested by centrifugation (3,000 g, 15 min), washed once, dried in an oven at 37 C and cooled to room temperature in a desiccator. The biomass was ground using a mortar and pestle. The lipid was extracted from approximately 0.1 g of dry biomass using 3 ml of a chloroform: methanol: water(10:5:4, v/v) solution. The extraction was repeated three times [49]. The final extract was dried under nitrogen evaporator (Model: N-EVAP 112; Organomation Associates, Inc., Berlin, MA). The total lipid content was determined by the weight. 2.4 Measurement of nitrate concentration A one ml sample was collected daily during cultivation. It was centrifuged at 6000 rpm for 20 minutes, and the liquid phase was diluted 10 fold to achieve a concentration in the range of 0-25 ppm. A 0.5 ml sample was taken and analyzed for nitrate using an ion chromatograph (DIONEX-500). The column was an IonPac AS19 anionic column. The eluent used was a 10 mm NaOH solution at a flow rate of 1 ml/min. 2.5 Fatty acids methyl esters (FAME) preparation and GC-FID analysis Lipid was collected and prepared for lipid composition analysis. Prior to analysis, the lipids were reacted via transesterfication. This was accomplished by adding the lipid extract to 3 ml of 3M methanolic-hcl for 20 minutes at 50 C [50]. Reaction products

44 43 were cooled to room temperature and the organic phase was analyzed in a gas chromatograph (GC HP5890) equipped with an injector (split 1:100) and a flame ionization detector, using a SIGMA-ALDRICH SP 2380 column (30 m 0.25 mm 0.20 µm). The sample size was 1μL. Oven temperature was increased from 90 to 220 C at a rate of 3 C min -1. The carrier gas was nitrogen at a flow rate of 1.5 ml min -1. FAME were identified and quantified. The internal standard FAME mix was RM-6(SUPELCO). All the experiments were repeated. Arithmetic means of the results and their standard deviation are presented.

45 44 3. MICROALGAE STRAINS SCREEN AND CULTURE CONDITIONS 3.1 Introduction The interest of biofuel from microalgae is due to high lipid content, especially nonpolar triglycerides which are the best source for biodiesel production. However, as mentioned in Chapter 1, lipid content is majorly determined by microalgae species. And the ideal fatty acids profile for biodiesel is suggested as 100% monounsaturated fatty acids C16:1, C18:1, C20:1 or C22:1 [2]. Thus it is important to find a promising candidate for biofuel production, which has high aerial lipid productivity and an ideal fatty acids profile. In this research, four different species of marine microalgae were selected to investigate their growth rate, lipid productivity and fatty acids profile, and they were Dunaliella tertiolecta(ccmp364), Nannochloropsis gaditana (CCMP527), Tetraselmis sp (CCMP 908) and Nannochloropsis salina (CCMP1776), respectively. These strains are in the category of promising microalgae for biofuel production, especially for Nannochloropsis sp. All four strains have been cultivated outdoors in open systems. Tornabene et al.[51] reported that Dunaliella salina contains 45-55% lipid content with dominant nonpolar TAGs. But for strain Dunaliella, nitrogen deprivation doesn t stimulate lipid accumulation[52]. Rodolfi et al. [11]investigated four species of Tetraselmis and found that they produce higher biomass even though the lipid content is relatively low between %. On the other hand, Nannochloropsis sp. is among the best producers because

46 45 it grows to a high cell density and has a high lipid content and is relatively easy to cultivate [11]. Cultivation conditions, like light intensity and path, light cycle and air flow rate, can also influence microalgae growth rate, biomass production and final lipid yield. Increasing light intensity can make microalgae grow faster, but excessive light intensity will cause light inhibition[3]. Light intensity also impacts microalgae fatty acid composition. For Nannochloropsis oculate, the ratio of total C16 unsaturated fatty acids to saturated 16: 0 fatty acid decreases with higher light intensity[53]. Another important factor is light path (i.e. width or thickness), especially for flat panel photobioreactor design and scale up. Zou and Richmond [54] reported that 100 mm is optimal light path ( mm) for Nannochloropsis sp in their vertical bioreactors. Meanwhile, 7.5 mm is proved to be best in range of mm light path for microalgae Spirulina platensis according to aerial productivity[55]. Light cycle determines how long the microalgae are exposed to light and definitely affects biomass output. Biomass productivity is achieved highest for continuous lightning (24:0), then reduced with decreasing light cycles (day:night) except for the 12:12 cycle for cyanobacterium Aphanothece microscopica Nageli [56]. In addition, more neutral lipids including triglycerides are accumulated during the light period, but consumed for cellular maintenance in the dark for Nannochloropsis sp[57]. Pneumatic bubbling is a typical mixing method in microalgae cultivation, since it has much better mass transfer. However, the air flow rate needs to be carefully optimized. If the flow rate is too low, exposure to light is limited. If it is too high, it will cause cell

47 46 damage and death. Hu and Richmond [58] investigated aeration rate effect on microalgae Spirulina platensis biomass production and the optimal aeration rate was 4 LPM under their conditions. Nitrogen deficiency in media can induce rapid TAGs accumulation in microalgae [11, 59]. However, microalgae cultivation under stress results in low biomass yield, and consequently, low final lipid production. There is a trade-off between biomass yield and lipid yield that is directly related to the amount of nitrogen for many species. Hence, a two stage cultivation strategy is one method that can be used. Such systems have a separate growth phase and lipid accumulation phase to achieve maximization of overall lipid productivity. This chapter contains growth information for 4 different marine microalgae and compares their productivity. It also contains experimental results for determining the effects of varying light intensity, light path, light cycle, and gas flow rate on microalgae Nannochloropsis gaditana growth. In addition, since nitrogen starvation increases lipid content while biomass productivity is decreased [11, 60], we designed two stage cultivation strategy to see how much lipid could be increased under nitrogen starvation conditions for microalgae Nannochloropsis gaditana. 3.2 Results Microalgae growth and lipid content The productivity of four microalgae strains was compared under the same cultivation conditions. The parameters evaluated for each strain were air versus CO 2 enriched air, as

48 47 well as buffered versus non-buffered media. Microalgae CCMP 908 in tris-hcl buffered f/2-si medium with 1 LPM 5% CO 2 bubbling achieved highest growth rate and biomass production (Figure 7). The same was observed for the other strains, CCMP 364, 527 and 1776 (Figures 8-10). The maximum biomass production was attained at 0.78 g/l, 0.57 g/l, 0.71 g/l and 0.64 g/l for CCMP 908, 364, 527 and 1776, respectively. The ph increased during the cultivation period without ph control for all four strains. While using tris-hcl buffer, the ph was maintained at around 8.2 with continuous air bubbling and around 7 with continuous 5% CO 2 enriched air bubbling. However, using tris-hcl buffered medium didn t show a significant positive effect on all microalgae growth with air bubbling. As expected, additional CO 2 is required for higher productivity. Lipid content was analyzed and compared in Figure 11 under 1 LPM air bubbling conditions. It is obvious that CCMP 527 and 1776, both of which are from the genus of Nannochloropsis, attained higher lipid content at 20% and 22% respectively, or approximately the same lipid content with in a deviation. Meanwhile, the lipid content for microalgae CCMP 908 and 364was relatively low, around 7% and 10%.

49 Dry Cell Density (g/l) ph Cell Dry Density (g/l) ph time (h) Figure 7 Cell growth and ph profile for Tetraselmis sp (CCMP 908). Tris-buffered with 5% CO2 bubbling ( ); tris-buffered with air ( ); f2/si with air ( ); ph (solid line) time (h) Figure 8 Cell growth and ph profile for Dunaliella tertiolecta (CCMP364)

50 Dry Cell Density (g/l) ph 49 Tris-buffered with 5% CO2 bubbling ( ); tris-buffered with air ( ); f2/si with air ( ); ph (solid line) time (h) Figure 9 Cells growth and ph profile for Nannochloropsis gaditana (CCMP527) Tris-buffered with 5% CO2 bubbling ( ); tris-buffered with air ( ); f2/si with air ( ); ph (solid line).

51 lipid content % Dry Cell Density (g/l) ph time (h) 10 Figure 10 Cells growth and ph profile for Nannochloropsis salina (CCMP1776). Tris-buffered with 5% CO2 bubbling ( ); tris-buffered with air ( ); f2/si with air ( ); ph (solid line) #908 #364 #527 #1776 Figure 11 Lipid content comparison for fours strains mixed using air without CO2.

52 Fatty Acids Composition (% wt/wt total lipid ) % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% C18:0 C18:3n9c C18:2n6c C18:1n9c C14:0 C16:1 C16: % 0.00% Figure 12 Percentage of individual fatty acid composition profile for different strains. Values shown are averages of four replicates Microalgae lipid composition profile The fatty acids compositions were analyzed for these four strains of microalgae. The most synthesized fatty acid chain length ranged from C16-18, and the dominant fatty acid was palmitic acid (C16:0). C16:0 compositions were 45.97%, 51.53%, 65.36% and 46%, respectively, for CCMP 908, 364, 1776 and 527. CCMP 908 and 364 were also rich in oleic acid (C18:1n9), 23.84% and 9.43%. Meanwhile, CCMP 1776 and 527 contained more palmitoleic acid (C16:1), 6.66% and 27%, respectively. Myristic acid (C14:0) composition was 5.33% and 6% in strain CCMP 1776 and 527, and was not found in the others. In addition, CCMP 908 and 364 was abundant in unsaturated linoleic acid

53 52 (C18:2n6) and linolenic acid (C18:3n9), which accounted for 8.61% and 7.09%, 5.65% and 5.22% respectively. Stearic acid (C18:0) composition was 3.56%, 3.03% and 3.00% for CCMP 364, 1776 and 527, and none for CCMP 908. Experimental results showed that CCMP 527 and 1776 strains had higher lipid content and better fatty acids profiles than the other two. CCMP 908 and 364 contained high percentages of unsaturated fatty acids C18:2 and C18:3, which have low oxidation stability. Thus, in later research, all cultivation investigations and optimizations were carried out for CCMP 527, which shows great potential for biofuel production Light intensity and continuous lightening effect on algae growth Light intensity effect on microalgae growth was investigated on CCMP 527. Three different light intensities of 80, 122 and 220 µmol m -2 were tested. The experimental result is shown in Figure 13. It is noted that while increasing the light intensity, the growth rate increased. The highest biomass yield (0.85g/L) was achieved under 220 µmol m -2 light intensity. The maximum biomass production for 122 and 80 µmol m -2 light intensity were 0.51g/L and 0.48 g/l respectively. Supplying light continuously was compared to a 12h/12h light cycle (Figure 14 and 15). There was no increase in microalgae s growth rate, biomass production or lipid content when light was provided continuously. The maximum lipid content was 38% under continuous lighting, and 45% for a 12h/12h light/dark cycle. The final biomass yield for continuous lighting was 29% less than the one for 12h/12h light cycle cultivation.

54 Dry Cell Density (g/l) Time (h) Figure 13 Light intensity effect on microalgae ccmp 527 growth, 80 µmol m -2 s -1 ( ); 122 µmol m -2 s -1 ( ); 220 µmol m -2 s -1 ( ).

55 Dry cell weight (g/l) Dry Cell Density (g/l) Lipid Content % time (h) Figure 14 Microalgae CCMP 527 cell growth and lipid accumulation curve under continuous light. Dry cell density ( ); Lipid content ( ) time (h) Lipid Content Figure 15 Microalgae CCMP 527 cell growth and lipid accumulation curve under 12h/12h light cycle. Dry cell density ( ); Lipid content ( ).

56 Dry cell density (g/l) Light path and flow rate effect on algae growth The length of the light path had an impact on algae growth (Figure 16). The longer the light path, the higher the biomass production was g/l dry cell density was achieved for 50mm light path, which was 1.32 and 1.75 times more than maximum dry cell density of 35mm and 10mm light path respectively. In addition, the maximum volumetric productivity and aerial productivity (dry weight per day per m 2 of irradiated surface area) were further compared in Figure 17. Volumetric productivity increased 2 fold with a 5 fold increase of light path (from 10mm to 50 mm). Meanwhile, aerial productivity of 50 mm light path increased 10 times compared to the 10 mm light path time (h) Figure 16 Light path effect on microalgae growth, 10mm ( ); 35mm ( ); 50mm ( ).

57 Dry Cell Density (g/l) Max Volumetric Productivity (g L -1 d -1 ) Max Areal Productivity (g dm -2 d -1 ) Light path (mm) 0 Figure 17 Maximum aerial vs. volumetric productivity of cell mass as a function of the light path. Volumetric productivity ( ); Aerial productivity ( ) time (h) Figure 18 Flow rate effect on microalgae CCMP 527 growth in 50mm thickness photobioreactor. 1LPM ( ); 2LPM ( ); 3 LPM ( ).

58 Cell dry weight (g/l) Lipid Content (%) 57 Three different flow rates of 1, 2 and 3 LPM were investigated in 50 mm thickness rectangular photobioreactor. There was no big difference in growth rate for 1 and 2 LPM flow rate. However, when the flow rate increased to 3 LPM, the growth rate dropped and final biomass was 29.2% less than the biomass for 2 LPM flow rate (Figure 18) Two stages cultivation time (h) Figure 19 Two stage cultivation (w or w/o N) for microalgae CCMP 527 in 5L photobioreactor. Cells in f/2-si medium ( ); cells in f/2-si medium w/o sodium nitrate ( ); Lipid content ( ). Considering there are a significant number of reports in the literature that state that lipid content increased under nitrogen starvation, two-stage cultivation in the tubular

59 58 photobioreactor was carried out. In the first stage, microalgae was cultured in normal f/2- Si medium until stationary phase was reached. After a 1 day lag phase, the cells entered exponential growth phase in the first stage and the final biomass density was achieved at 0.39 g/l. Prior to starting the second stage, microalgae biomass was collected by centrifugation. The cells were transferred into fresh f/2-si medium without sodium nitrate to begin the second stage of cultivation. Since we collected around 0.2 g of biomass for lipid extraction, the biomass density dropped at the start of the second stage. 400 ml of liquid culture were harvested every day during the second stage to monitor the lipid content change. In the second stage, without nitrate addition, cells grew slowly and the maximum biomass density was 0.71 g/l. The maximum lipid content was observed around the 22 nd day at 45%, which was 1.2 times more than the lipid content at the final point of first stage cultivation. However, within statistical error, a substantial increase in the percentage of lipid was not observed. 3.3 Discussion There are thousands of microalgal species on earth, and many freshwater or marine strains might be feasible for scale up biomass cultivation and biofuel production. Genetic modification on microalgae may increase the biomass and lipid production by changing metabolism and nitrogen flux, but it requires long term research and funding. Furthermore, it is unclear what regulations will be required to culture genetically modified algae outdoors. Thus finding natural strains that produce large amounts of lipid is the suggested method for the short term.

60 59 In our experiments, four strains were selected and investigated, which were Dunaliella tertiolecta(ccmp364), Nannochloropsis gaditana (CCMP527), Tetraselmis sp (CCMP 908) and Nannochloropsis salina (CCMP1776). The genus of Nannochloropsis was shown to be an ideal candidate for biodiesel production in terms of growth rate, biomass production and lipid content. Rodolfi et al. [11] also reported a similar result, that Nannochloropsis sp. strain is among the best producers. The majority of our research focused on Nannochloropsis gaditana (CCMP527). As expected, it was shown that using tris-hcl buffer for ph control was very effective [16]. It stabilized ph during the entire cultivation period. However, using tris-hcl buffer and only air bubbling did not increase the microalgal growth rate for any of the four strains. Whereas the addition of 5% CO 2 enriched air almost doubled microalgal biomass production. This observation suggests that only air bubbling might not satisfy the demand of microalgae growth, additional carbon source like CO 2, HCO 3-1 or organic carbon could help increase the biomass productivity. Thus the optimal cultivation condition is to use tris-hcl buffer with 5% CO 2 enriched air bubbling. Increasing light irradiance can increase volumetric biomass output, but too strong irradiance can also induce light inhibition [55]. In this study, three difference light intensities were investigated for Nannochloropsis gaditana (CCMP527). The experimental results show the obvious trend that the biomass production was increased by increasing light intensity over the range investigated. In this experiment, the light intensity did not achieve light saturation point. To reach saturation, experiments would

61 60 have to be done with varying amounts of shading outdoors. This observation is in agreement with most publications [3, 55]. Theighest growth rate and biomass yield was found under continuous lighting for cyanobacterium Aphanothece microscopica Nageli [56]. However, for Nannochloropsis gaditana (CCMP527), we didn t observe an increase in growth rate, biomass productivity or lipid content. It suggests that there is no need for continuous lighting, which requires more energy input. Also a 12h/12h light/dark cycle is similar to the natural day/night cycle, so it provides sound evidence that the natural light cycle is sufficient for future outdoor cultivation. The light path greatly affected cell mass productivity, and also is the key impact factor for flat panel photobioreactor design. In this study, when the light path increased from 10 mm to 50 mm, aerial and volumetric productivity increased dramatically. For the short light path reactor, photoinhibition for CCMP527 is induced which causes a decrease in cell growth rate. This light stress could be reduced or eliminated by increasing cell density or light path. For future study, longer light path could be tested in order to find the optimum light path for Nannochloropsis gaditana (CCMP527). 100 mm light path is found to be the optimal light path for Nannochloropsis sp [54]. Under the same light intensity level, the maximum biomass productivity and photosynthetic efficiency can be achieved by optimizing the mixing rate. The mixing rate determines the time that microalgae are exposed to light and not shaded by other algal cells. It was reported that increasing the speed of aeration in certain ranges enhances the biomass productivity, but increasing too much causes a decrease in biomass output [58].

62 61 The experimental data shows similar results. When the flow rate was further increased to 3 LPM, the biomass production and growth rate dropped significantly. The shear stress caused by high flow rate might result in the low growth rate of microalgae. According to current experimental results, the optimum flow rate for Nannochloropsis gaditana s cultivation is 1 LPM. The experimental result of the two stage cultivation strategy shows that the lipid content was not increased as expected. Under nitrogen deficiency condition, microalgae s growth rate decreases, and others have observed that the metabolic pathway of lipid accumulation is triggered. However, the lipid content during second stage was not increased dramatically as we initial expected. It was reported that lipid content increased threefold using the two-stage cultivation for Nannochloropsis oculata [61]. This might be attributed to the nitrogen source availability in the Instant Ocean we used for this experiment. The solution made with Instant Ocean was analyzed by IC and around 0.2 mm nitrate was found. Consequently, for second stage, it is unclear if a totally nitrogen free media was used since nitrate was not monitored. In the future, this two stage cultivation strategy will be repeated, and nitrogen free sources in Instant Ocean need to be guaranteed. 3.4 Conclusion Four different species of marine microalgae, Dunaliella tertiolecta(ccmp 364), Nannochloropsis gaditana (CCMP 527), Tetraselmis sp (CCMP 908) and Nannochloropsis salina (CCMP1776), were screened based on oil content and fatty acid

63 62 compositions. Experimental results show that CCMP 527 and 1776 strains contained higher lipid content and better fatty acids profile than the other two. Thus later, culture condition optimization was focused on microalgae Nannochloropsis Gaditana (CCMP 527), which had more than 40% content and a growth rate of 0.02hr -1. Different cultivation conditions were investigated for CCMP527. Increasing light intensity shows a positive effect on microalgae growth. In contrast, continuous lighting vs. a 12h/12h light/dark cycle, does not affect productivity. With a 50 mm light path, cells aerial and volumetric productivity was maximized. Increasing air flow rate induces higher shear stress on cells surface. A two stage cultivation strategy was tested in a tubular photobioreactor. Since Instant Ocean contained a nitrogen source, the experimental result did not show a sharp increase in lipid content during the second stage.

64 63 4. CULTIVATION CONDITIONS OPTIMIZATION FOR MICROALGAE NANNOCHLOROPSIS GADITANA 4.1 Introduction In order to make production of biodiesel from algae economically feasible, one key step is increasing reactor productivity. This involves maximizing the final biomass yield and lipid content while minimizing the growth period. Consequently, it is crucial to optimize culture conditions. Extensive studies have been done demonstrating that ph, CO 2 uptake, temperature, nitrogen concentration, light intensity and cycle, salinity, and heavy metals affect the growth rate, lipid accumulation, and lipid profile [45, 54, 62, 63]. The amount of CO 2 can limit the photosynthetic activity when bicarbonate is the main source of dissolved inorganic carbon[14]. The slow CO 2 diffusion rate from the atmosphere to seawater at ph 8.2 cannot satisfy the photosynthesis reaction demand. In order to improve mass transport of CO 2 inside the system, CO 2 enriched air is supplied, causing a drop in ph. CO 2 can also be used to control ph. In addition, nitrogen and/or carbon sources are typically limited in a medium, therefore supplementing with either increases the growth rate, and determines the type of growth: autotrophic, mixotrophic or heterotrophic [16]. If a nitrogen source is added, growth is autotrophic. When glucose is present in the medium, mixotrophic growth is observed autotrophic when light is provided and heterotrophic without light. The type of growth also affects the growth rate, pigment composition and lipid content of Nannochloropsis genus[18, 19].

65 64 Biodiesel is derived from the transesterification of triglycerides and free fatty acids. The ideal fatty acid profile for biodiesel is 100% monounsaturated fatty acids C16:1, C18:1, C20:1 or C22:1 [2]. Minor other compounds can significantly affect biodiesel properties, including freezing point, oxidative stability, cetane number and lubricity [64]. In our research, Nannochloropsis gaditana was selected as the target strain. This particular strain has not been thoroughly studied for production of biodiesel; however Nannochloropsis sp., which is categorized in the same class Eustigmatophyceae and the same genus Nannochloropsis, have high lipid contents [3, 11]. The research objective is to investigate the effects of different culture conditions on the growth rate, biomass yield, lipid production and fatty acid profile. Several experimental schemes were compared and contrasted: 1) tris-hcl buffer vs. w/o tris-hcl buffer; 2) air bubbling vs. CO2 enriched air; 3)) variation in nitrogen and carbon sources; and 4) the effect of trace organics in ground water. 4.2 Results Effect of tris-hcl buffer Microalgae growth rate is affected by ph. When microalgae uptake CO 2 by photosynthesis metabolism, the ph increases. Control of ph is achieved by adding inorganic acids/bases or CO 2 or by using buffer solutions. Using a buffer solution to control ph has been proven to be effective to accelerate microalgae growth rate with air bubbling [16]. Even though it is not feasible for large scale experiments, it is convenient

66 Dry cell density (g/l) 65 for bench top experiments, eliminating the need to control ph by acid or CO 2 addition. When a tris-hcl buffer solution was applied in the medium, ph was kept constant at 7 during the entire algae growth period. Experimental data showed that microalgae maintained a high growth rate (0.48 day -1 ) with a tris-hcl buffer, compared to a growth rate (0.19 day -1 ) without a tris-hcl buffer (Figure 20).The final biomass density was 0.71 g/l with the tris-hcl buffered medium, 1.7 times of amount without tris-hcl buffered medium. More importantly, the lipid content with the tris-hcl buffer was 43% as shown in Figure 21, 2.4 times more than without tris-hcl buffer (18%). Since Nannochloropsis gaditina was more productive in terms of growth rate, biomass yield and lipid content when the media was buffered, all subsequent experiments discussed in this paper were performed at constant ph w tris buffer w/o tris buffer Time (days) Figure 20 Effect of Tris-HCl buffer (40 mm) on cell growth rate with 5% CO 2 enriched air bubbling.

67 Lipid Content (% of Dry weight) f/2-si medium f/2-si medium with tris-hcl buffer Figure 21 Lipid content comparison in f/2-si medium with or without tris-hcl buffer. Values shown are averages of four replicated ± standard deviation Air vs. 5% CO2 enriched air bubbling To further investigate the effect of CO 2 on growth rate, biomass and lipid yield, Nannochlroposis gaditiana were grown with and without 5% CO 2 enrichment (Figure 22). The overall gas flow rate was constant. The growth rate was 0.36 day -1 without 5% CO 2 enrichment, 25% less than with 5% CO 2 enrichment. The final dry cell density reached 0.71 g/l with 5% CO 2 enrichment, almost two times of that obtained with air alone. In addition, the lipid content of the biomass was raised to 43% with CO 2 enrichment in Figure 23, which was an 87% increase compared to that with air alone (23%). All further experiments were carried out with 5% CO 2 enriched aeration.

68 Lipid Content (% of Dry Weight) Dry cell density (g/l) % CO2 enriched air bubbling air bubbling Time (days) Figure 22 Effect of 5% CO 2 enriched air on cell growth rate Air bubbling 5% CO 2 enriched air bubbling Figure 23 Lipid content comparison in tris-hcl buffered f/2-si medium with or without 5% CO 2 enriched. Values shown are averages of four replicated ± standard deviation.

69 Effect of nitrogen and carbon sources on growth rate and biomass production Previous work with this strain has shown that the nitrogen and carbon sources affect microalgae growth, however these experiments were performed with air not CO 2 enriched air and a detailed comparison of yields, rates and lipid composition was not done[16]. Here a detailed analysis is presented (Table 7). Although the conventional nitrogen source for Nannochloropsis gaditana is nitrate, a variety of other nitrogen sources like ammonia, nitrite and urea can also be used to culture microalgae [65]. Pure nitrogen sources as well as mixtures of nitrogen sources that are commonly found in fertilizers are investigated here. The amount of additional nitrogen was kept constant at 10 mm N regardless of the source; this amount is 12 fold higher than the f/2-si media. The growth rate with 10 mm N of NaNO 3 or urea resulted in a slight increase from 0.48 to 0.52 day -1 compared to the rate on f/2-si medium, but the rates were not statistically different within a standard deviation. The final biomass yield was 3% higher and 30% lower respectively. With a 10 mm N of (NH 4 ) 2 SO 4 addition, the growth rate was close to f/2-si medium, but the final dry cell density decreased 38%. Glycine addition was not favorable for microalgae growth. The growth rate and final biomass density significantly declined to 0.33 day -1 and 0.25 g/l. Hence, the experimental results suggest that NaNO 3 and urea are better choices of nitrogen sources considering the observed faster growth rate and higher biomass yields. The addition of more carbon in the form of glucose (Table 7) resulted in an accelerated growth rate by mixotrophic growth compared to f/2-si medium. Final dry cell density

70 69 declined 53%, while the growth rate increased from 0.48 day -1 to 0.63 day -1. During the experiments, it was also observed that microalgae cells color was yellow for the first few days and then gradually turned green after exponential growth. This observation indicates that cells experienced heterotrophic growth first, and then grew via a photosynthetic mechanism when glucose was depleted. Usually glucose is not recommended for addition into a medium because it provides a beneficial environment for bacterial growth. Little bacterial growth was observed here based on microscopic evaluation of the culture. Table 7 Growth summary of adding different 10 mm carbon and nitrogen sources Nitrogen Sources Nitrogen (carbon) Concentration (mm) Exponential Growth Rate (day -1 ) Final Dry Cell Density (g/l) Lipid yield (g/l) NaNO 3 (f/2-si) ± ± ±0.005 NaNO ± ± ±0.013 NaNO 3 +(NH 4 ) 2 SO ± ± ±0.001 NaNO 3 +Urea ± ± ±0.003 NaNO 3 +Glycine ± ± ± NaNO 3 +Glucose (C) 0.63± ± ± *Values shown are averages of two replicated ± standard deviation.

71 Lipid Content (% of Dry Weight) Effect of nitrogen and carbon sources on lipid yield Table 7 and Figure 24 show lipid final yield and content. It is obvious that with 5% CO 2 enriched air bubbling the normal f/2-si medium obtained the highest lipid content (43%) and lipid yield (0.319 g/l) compared to other combinations and amounts of nitrogen sources. When 10 mm N of nitrate or urea was added to the media, the lipid yield was 16% and 44% less than with f/2-si medium. Addition of 10 mm N ammonium sulfate resulted in a lipid yield decline of 74%, which means that ammonium is not a favorable nitrogen source for lipid production for this algal species. Addition of glycine decreased the lipid yield even more. With 10 mm glucose as carbon source, the final lipid yield was 0.04 g/l, which means mixotrophic growth did no benefit lipid accumulation f/2-si medium (NH 4 ) 2 SO 4 Urea Glycine Glucose Figure 24 Lipid content comparison in f/2-si medium with or without same total amount of 10mM N and C (glucose) addition. Values shown are averages of four replicated ± standard deviation.

72 Groundwater cultivation Since, additional carbon in the form of glucose was shown to greatly influence lipid productivity, Nannochloropsis gaditina was cultured in f/2-si media using Tucson groundwater near a service station. It contained less than 0.1 mm nitrate. But this site was polluted by a petroleum release, and the concentration of methyl tert-butyl ether was approximately M. Figure 25 and 26 compare cell growth and final lipid content when using groundwater versus distilled water to make media. The results indicate that groundwater can be used to grow microalgae. The growth kinetics was almost same. Growth rate and final biomass yield was 0.46 day -1 and 0.70 g/l respectively. The lipid content when using groundwater for cultivation was 42%, in the same range within a standard deviation as the lipid content obtained using distilled water. These results demonstrate that microalgae can be cultured on groundwater with trace amounts of methyl tert-butyl ether with little loss in productivity.

73 Lipid Content (% of Dry Weight) Dry cell density (g/l) Distilled water Groundwater Time (days) Figure 25 Cell growth rate of distilled water vs. groundwater to make artificial seawater Groundwater Distilled water Figure 26 Comparison of lipid content using groundwater and distilled water to make artificial sea water. Values shown are averages of four replicated ± standard deviation.

74 Effect of carbon and nitrogen sources on fatty acid profile In addition to determining the effect of carbon and nitrogen sources on growth rate, biomass yield and lipid content, the influence on fatty acid profile was studied. The fatty acid profiles were compared in Figure 27. The predominant compounds were palmitic (C16:0) and palmitioleic (C16:1) for all of the carbon and nitrogen sources investigated. However, different combinations of nitrogen and carbon caused the lipid composition to vary. For example, the C16:0 percentage was 47%, 37%, 42%, 38% and 47% for f/2-si medium, 10 mm N ammonia, 10 mm N urea, 10 mm N glycine and 10 mm C glucose addition, respectively. The C16:1 percentage was 29%, 28%, 29%, 17% and 27% respectively. Although the fatty acids compositions varied, the major carbon chain length remained in the range of C14-20, which is favorable for conversion to biodiesel.

75 Fatty Acids Composition (% w/w total lipids) 74 60% 50% C16:0 C16:1 C14:0 C18:0 C18:1n9c C18:2n6c C18:3n9c 40% 30% 20% 10% 0% f/2-si medium (NH 4 ) 2 SO 4 Urea Glycine Glucose Figure 27 Percentage of individual fatty acid methyl ester (FAME) composition profile in f/2-si medium with or without same total amount of 10mM N and C (glucose) addition. Values shown are averages of four replicated ± standard deviation. 4.3 Discussion In this work, we investigated key culture conditions effect on microalgae Nannochloropsis gaditana s growth and lipid yield, including tris-hcl buffer, 5% CO2 enriched air bubbling vs. air bubbling, polluted groundwater cultivation and nutrients. The results clearly show that using a constant ph results in statistically significant

76 75 increases in growth rate, biomass yield and lipid content. This result is in agreement with the positive effect of a Tris-HCl buffer on cell growth demonstrated previously [16]. It has also been reported that for microalgae Nannochloropsis sp. biomass yield and lipid content was enhanced under CO 2 enriched aeration [18]. In this present work, a similar conclusion was obtained. Microalgae grown with 5% CO 2 enriched air bubbling had an increased growth rate, and also the biomass yield was doubled and lipid accumulation was increased. Uptake of dissolved inorganic nitrogen (NO - 3 and NH + 4 ) and organic nitrogen (urea and glycine) by cells varies with species and is related to their own biosynthetic mechanism. Ellipsoidion sp. achieved a higher growth rate and lipid content when ammonium was the nitrogen source compared to using urea or nitrate in the same growth phase [66]. From experimental results, microalgae Nannochloropsis gaditana s growth rate, biomass yield and final lipid content are also affected by nitrogen and carbon sources availability and amount. For this species however, nitrate as a nitrogen source is the best choice, especially mm NaNO 3. Use of urea, glycine, and ammonia as the primary nitrogen source resulted in decreased biomass and lipid productivity. Increasing the nitrate concentration to mm in the medium caused the cell growth rate and biomass production to slightly increase while lipid yield decreased. The decline in lipid content might be because the nitrogen source was not depleted. Similar studies using Neochloris oleoabundans showed that increasing the nitrate concentration to 10 mm promoted cell growth and biomass yield, but decreased lipid productivity[63].

77 76 Microalgae assimilate organic nitrogen under low inorganic nitrogen concentrations to satisfy total N demand. Urea is converted to NH + 4 and CO 2 by urease activity. Although urea addition did not achieve the highest biomass production and lipid yield, culture productivity was reasonable which is important since agriculture groundwater contains abundant urea and nitrate. Hence, the results imply that Nannochlroposis gaditina microalgae can be cultivated using agricultural water runoff and utilize the nitrogen present. In addition, some fertilizers readily available on the market that contain mixtures of nitrogen sources, may serve as inexpensive sources of nitrogen for large scale production. The amino acid (glycine) uptake mechanism is relatively complicated. Glycine tends to be decarboxylated before uptake and then assimilated [67].With a nitrogen combination of glycine and NaNO 3, the growth rate, final biomass and lipid yield was lowest. Tyler et al. [67] have reported that glycine uptake rates by fertilized NO -1 3 were lower than that by N-starved or NH + 4 fertilized macroalgae Ulva lactuca (Chlorophyta) and Gracilaria vermiculophylla (Rhodophyta). Some microalgae, like Chlorella sp., are reported to grow mixotrophically with high lipid yield, using light energy and sugar[68]. Cells can use sugar and oxygen to produce biomass through the heterotrophic mechanism and fix CO 2 through the photosynthesis mechanism. However, for this strain, lipid yield was low during mixotrophic growth, g/l, even though a higher growth rate was observed. Cells adsorbed glucose first. Once the glucose was depleted, photoautotrophic growth began. Hu and Gao [18] also

78 77 reported that Nannochloropsis sp. mixtrophic growth resulted in a lower lipid content than that of photoautotrophic growth under enriched CO 2 aeration. Moreover, the heterotrophic growth consumes sugar and releases CO 2, which does not achieve the goal of neutral carbon. Even though simple sugars such as glucose have a negative effect on Nannochlroposis gaditina productivity, trace amounts of organic contaminants commonly found in ground water near gas stations do not affect productivity. In these experiments, methyl tert-butyl ether polluted groundwater was used as the water source instead of distilled water to make the media. These results confirm that a variety of water sources can be used to cultivate microalgae. The experimental results also reveal that adding different nitrogen and carbon sources affected the fatty acid profile. Palmitic (C16:0) and palmitioleic (C16:1) were the major compounds regardless of carbon and nitrogen sources. This observation agrees with the lipid profiles for reported in the literature for other Nannochloropsis sp. [18, 69, 70]. Biodiesel physical properties, including cetane number, viscosity, oxidative stability, and lubricity, are all affected by the fatty acids profile. Minor components can cause a noticeble change of physical properties. For biodiesel, the following FAMEs are highly desirable: C16:1, C18:1, C20:1 and C22:1. Saturated fatty acids like C16:0 can be converted to unsaturated acid C16:1 by a dehydrogenation reaction to satisfy this profile. And the final fatty ester composition for biodiesel can be achieved by a fractionation (winterization) process [71]. However, this work has demonstrated that the fatty acid

79 78 profile can be adjusted by simply changing the nitrogen source to achieve a more desirable profile for the end product. For biodiesel production, using a small amount of ammonia may decrease the amount of saturated fatty acids. On the other hand, if alkanes are desirable, use of nitrate alone is a better choice. 4.4 Conclusions In conclusion, the Nannochloropsis gaditana strain (CCMP 527), which has a fast growth rate, high biomass yield and over 40% lipid content, is an excellent potential candidate for biofuel production. This strain is versatile since it can grow in mixtures of nitrogen sources, such as urea and nitrate and maintain productivity as well as grow in slightly contaminated groundwater. This work also clearly demonstrates the importance of culture conditions on algal cultivation strategies.

80 79 5. MODELING OF MICROALGAE NANNOCHLOROPSIS GADITANA S GROWTH, NITRATE CONSUMPTION AND LIPID PRODUCTION UNDER NITROGEN-LIMITED CONDITIONS 5.1 Introduction The biomass productivity, lipid yield and composition determine the cultivation and downstream costs. The ideal cultivation strategy involves high biomass productivity and high lipid yield with fatty acids profiles that are easily converted into fuels using traditional methods. For most microalgae, lipid accumulation is enhanced under nitrogen deficient conditions. Under this stress, more neutral lipids, especially triacylglycereol (TAG), are produced while polar lipid synthesis is reduced [72-74]. However, there is trade off relationship between lipid content and microalgae growth. It has been reported that nitrogen limitation increases lipid content, while biomass productivity is decreased [11, 60]. Thus, it is important to identify an optimal point between lipid content and biomass yield to maximize biofuel productivity. A mathematical model of microalgae growth and lipid yield is a useful tool to optimize the cultivation process, scale-up the system, and design bioreactors that interface with downstream processes. Recently, models that focus on algae growth, substrate consumption and lipid production have been published demonstrating the importance of predicting algal productivity. Yang et al. [75] developed a mathematical model to

81 80 describe Chlorella minutissima s growth and lipid production under photoheterotrophic conditions. Modeling results and experimental data are in good agreement, however the use of glycerin may not be practical on a large scale since it creates beneficial environment for bacterial growth. Furthermore, not all species produce large quantities of lipid under heterotropic conditions. Mairet et al. [46] developed and validated another model for lipid production of microalgae Isochrysis aff. galbana in chemostats under nitrogen limitation. This model is based on both the Droop parameters (Droop, 1974) and intracellular carbon flow parameters that are more challenging to determine. Packer et al. [47] proposed a model to predict growth and lipid synthesis for a Pseudochlorococcum sp. green microalgae with respect to nitrogen concentration (using the Droop equation) and light intensity. This species was able to grow to high cell densities of up to 8 g dw/l. At these high cell density values, other media components may limit growth such as another micronutrient (iron or phosphorus) or light. Although light intensity was a variable, experimental data involving variations in light intensity were not used to validate the model. Agreement between experiments and model were achieved for nitrogen levels up to 0.06 g N/L. To demonstrate the predictive capability of these models it is useful to validate them with experimental data from other algal species. One salt water species that is being tested in a variety of indoor and outdoor settings is Nannochloropsis sp., since they grow rapidly and have high lipid yields (0.32 g/l) using nitrate as nitrogen source[11]. The model presented here combines the Droop equation [76]to predict growth and the Luedeking- Piret [77]equation for lipid production. All model parameters were determined

82 81 experimentally. Furthermore, a sensitivity analysis of the parameters was performed and the fatty acid composition change with time was analyzed. 5.2 Mathematical modeling A mathematical model was developed to describe microalgae growth, nitrate consumption and lipid production. It is built on mass balances for phytoplankton carbon (C) and phytoplankton nitrogen (N). This model has these following assumptions: 1. Microalgae grow in a homogeneous and well mixed batch reactor. 2. Light intensity and temperature is constant under cultivation conditions. 3. Lipid accumulation depends on microalgae growth and nitrogen concentration. Microalgae growth Nitrogen limited growth for microalgae follows the well-established cell-quota model (Droop equation), which shows that growth rate is more closely related to the intracellular nutrient concentration than to the external one [48]. Cell quota is defined as the content of a chemical element in the cells on a biomass or volume basis [76, 78]. In this model, cell quota is calculated from the proportion of N in the biomass. Moreover, there is a threshold intracellular quota (q 0 ) or subsistence quota, below which microalgae do not grow. So the microalgae growth equation is as follows: ( ) ( ) ( ) (1)

83 82 ( ) ( ( ) ) (2) Where A(t) is microalgae biomass density (g/l dw); µ(a,n) is algae growth rate (hr -1 ); is the maximum specific growth rate of microalgae (hr -1 ); and Q(t) is the cell quota or internal nitrate-nitrogen concentration per biomass density; (g N g -1 dw). Cell quota Q(t) can be calculated using the following equation: ( ) ( ) ( ) ( ) ( ) ( ) (3) Where N(t) is nitrate-nitrogen concentration in the solution(g/l); A(0), Q(0) and N(0) are the initial (t=0) microalgae biomass density, cell N quota and nitrate-nitrogen concentration Nitrate consumption Inorganic nitrogen assimilation is modeled using the Michaelis-Menten expression and the maximum nitrogen assimilation rate is regulated by the cell N quota [79]. The rate of change of N is a function of nitrogen assimilation only, and remineralization is neglected. The final nitrate consumption equation is: ( ) ( ) ( ) (4) ( ) ( ) ( ( ) ( ) ) (5)

84 83 where v(a,n) is nitrate-nitrogen uptake rate (g N (g dw) -1 hr -1 )); Q max is maximum cell N quota; and v m and K nit are the maximum nitrate-nitrogen uptake rate and half saturation constant, respectively Lipid production The equation used to model lipid production is derived from the Luedeking-Piret equation [77]. Lipid accumulation rate is regulated by the microalgae growth rate ( ) in a linear fashion and the instantaneous biomass density A(t): ( ) ( ) ( ) (6) Where L(t) is lipid concentration(g/l); α is the growth associated lipid formation coefficient; and β is a non-growth associated lipid formation correlation coefficient Parameters estimation and sensitivity analysis The model parameters were obtained from experimental data (Figure 28), and the values are summarized in Table 8. The subsistence nitrogen quota(q0) was calculated from the cell quota at the stationary phase of microalgae growth [80]. The maximum specific growth rate was calculated from the slope of ln (biomass density (g/l)) versus time (h) during the exponential growth phase with no nitrogen limitation. The initial cell N quota Q(0) was obtained by determining the minimal N requirements for microalgae growth using the approximate molecular formula CO 0.48 H 1.83 N 0.11 P 0.01 for Nannochloropsis sp.[10]. The maximum nitrogen quota Q max was obtained from nitrate concentration

85 84 measurements and biomass density using Equation (3). The maximum carbon specific nitrogen uptake rate v m and K nit were determined from a plot of v as a function of initial nitrate concentration. In order to determine coefficients α and β, a relationship between biomass density and lipid yield was determined (Figure 29). The data clearly indicate that there is a linear relationship between biomass density and lipid concentration and the corresponding relationship is: L(t) = 0.404A(t). The correlation coefficient was For this algae strain under these conditions, β is equal to zero. Hence, Equation (6) is simplified and integrated to yield the following expression for lipid yield as a function of time: L(t)=αA(t)+k (7) Where k is the integration constant. Table 8 Kinetic parameters for microalgae growth, nitrate consumption and lipid production Variable Definition Value Units µ m Maximum growth rate hr -1 q 0 Minimum/subsistence cell N quota g N g -1 dw Q max Maximum cell N quota 0.12 g N g -1 dw K nit Half saturation constant for N uptake g N L - 1

86 Dry cell weight (g/l) Lipid Concentration (g/l) 85 v m Maximum uptake rate of N g N -1 g -1 dw hr -1 α Lipid formation coefficient β Non-growth correlation coefficient 0 k Integration constant Time (h) Nitrate Concentration Figure 28 Simulation results of microalgae growth, nitrate consumption and lipid concentration (solid line) at 100% N versus time. Experimental data are shown as dry cell density (o), nitrate concentration (Δ) and lipid yield ( ). Values shown are averages of four replicates ± standard deviation.

87 Lipid Concentration (g/l) y = x R²= Dry Cell Density (g/l) 5.3 Results Figure 29 Linear regression between lipid concentration and dry cell density Effect of nitrogen limitation on microalgae growth kinetics, nitrate consumption and lipid yield To clarify and further understand the effects of initial nitrate concentration on microalgae growth kinetics and lipid yield for Nannochlroposis gaditana, experiments were performed under three different nitrogen conditions. The suggested growth media for this strain according to the Bigelow Laboratory is f/2-si medium, whose nitrate concentration is mm and will be denoted as 100% N. The microalgae growth curve, nitrate consumption and lipid concentration as a function of time are presented in Figure 28. A final biomass density of 0.84 g/l and a lipid concentration of 0.38 g/l were achieved at the end of exponential growth. Final lipid content of dry biomass was 45.0±0.2%. Nitrate

88 87 uptake was slow for the first 2 days during the lag phase, and then increased exponentially. Nitrate was almost depleted by the 7 th day. For 50% N (0.441 mm) in the medium, a maximum biomass density (0.58 g/l) and lipid concentration (0.29 g/l) was attained at the end of exponential growth (Figure 30). The final biomass density is roughly half of the value obtained for the 100 % N case showing that the culture is nitrogen limited in both cases. A maximum lipid content of 51±1% was achieved. Nitrate was consumed by the 7 th day. Figure 31 shows the growth curve, lipid and nitrate concentration changes with time for 25% N (0.221 mm). The final biomass density and lipid yield were 0.33g/L and 0.15 g/l respectively. The maximum lipid content was 45±3%. Nitrate was exhausted by the 6 th day. The initial nitrate concentration had little effect on the final lipid content. Fatty acid profile change with time was analyzed. Data is provided for the Here 25% NaNO 3 case (Figure 32). Since similar trends were observed for all initial nitrogen conditions. The major fatty acids observed had carbon chain lengths from 14 to 16 units. Once the algae entered stationary phase, the percentage of each of these compounds increased. More specifically, the increase for C16:0 was from 38.4% to 52.8%; C16:1 was from 10.93% to 19.7%; C18:1 was from 2.7% to 5.0%; and C14:0 was from 3.1% to 4.2%. There was no significant change for C18:0 composition (around 2.3%). Once the algae were in stationary phase, the fatty acid profile remained constant within experimental error.

89 Figure 30 Simulation results of microalgae growth, nitrate consumption and lipid concentration (solid line) at 50% N versus time when µ m = hr -1. Experimental data are shown as dry cell density ( ), nitrate concentration (Δ) and lipid yield ( ). Values shown are averages of four replicates ± standard deviation. 88

90 Dry Cell Density (g/l) Lipid Concentration (g/l) Nitrate Concentration (mm) Nitrate experim Time (h) Figure 31 Simulation results of microalgae growth, nitrate consumption and lipid concentration (solid line) at 25% N versus time when q 0 =0.0013±0.0001g N g -1 dw. Experimental data are shown as dry cell density ( ), nitrate concentration (Δ) and lipid yield ( ). Values shown are averages of four replicates ± standard deviation.

91 Fatty acids composition (% w/w total lipid) 90 90% 80% 70% 60% 50% 40% 30% C18:0 C14:0 C18:1n9c C16:1 20% C16:0 10% 0% Time (h) Figure 32 Percentage of individual fatty acid methyl esters (FAME) composition profile with time course (25% N). Values shown are averages of four replicates Model validation and sensitivity analysis Equations 1-5 and 7 were simulated in Matlab, and the absolute tolerance was set at 1E- 8. The proposed mathematical model results were compared with experimental data from Nannochloropsis gaditana cultivation experiments at various initial nitrogen concentrations. Figures 30 and 31 demonstrate the ability of the model to simulate batch reactor behavior under nitrogen limitation conditions of and mm NaNO 3 respectively. The results of microalgae growth and nitrate consumption agreed well with experimental results at both initial nitrate concentrations.

92 91 In order to further validate this model, simulation results were evaluated through rootmean-square errors (RMSE) and mean absolute errors (MAE) in Table 9. The values of RMSE and MAE for biomass density and nitrate consumption were small, suggesting that this model represented microalgae growth and nitrate consumption curves. Furthermore, the values of RMSE and MAE for lipid yield were 0.046, 0.058, g/l and 0.030, 0.050, g/l at 100%, 50% and 25% nitrogen concentrations, indicating that the model is able to predict lipid yield under nitrogen limitation once the parameters are determined at one initial nitrogen concentration. The R 2 values are also included in Table 2. Overall, the regression analysis shows good agreement between model and experimental data for the biomass and nitrate data at all concentrations. There are more steps involved in obtaining lipid experimental data and hence the regression analysis is not as good but data trends are predicted. A sensitivity analysis of three major parameters V max, µ m and q 0 determined experimentally was done to further demonstrate how variations in these key parameters affect simulation results. A 10% deviation in V max resulted in almost no effect on microalgae growth, nitrate consumption and lipid yield modeling predictions; hence the effect is not shown in the figures. The standard deviation of µ m is hr -1. The solid line in Figure 30 was generated using all of the parametric values shown in Table 8, while the dotted lines show ± the standard deviation in µ m. The majority of the biomass data points fall within the theoretical band that accounts for variation of this parameter. Similarly, Figure 31 shows how varying q 0 by 10% effects the modeling predictions. Changing this parameter affects modeling predictions related to final biomass and lipid

93 92 yield, demonstrating the importance of this parameter to the proposed model. The nitrate consumption curves were similar, which makes sense that more intracellular nitrogen was used for growing at lower subsistence quota q 0. Table 9 Model validation for biomass density, nitrate consumption and lipid yield Nitrate Concentration (mm) Root Mean Square Error (RMSE) Mean Absolute Error(MAE) Biomass Density (g/l) Correlation Coefficient (R 2 ) (100%) (50%) (25%) Nitrate Consumption (mm) (100%) (50%) (25%) Lipid Yield (g/l) (100%) (50%) (25%) Discussion Some studies demonstrate that nitrogen limitation or starvation increases lipid content in Nannochloropsis genus, while the growth rate and final biomass production decrease [11,

94 93 59, 74]. But there are also some contrasting results that show that there are not any measurable increases in the lipid content under nitrogen stress [52, 59, 81, 82]. Actually, a combination of environmental factors influence the increase in lipid content, such as salinity, light intensity, temperature, ph, age of the culture and CO 2 concentration[53, 83, 84]. For a culture of Nannochloropsis gaditana strain with ph control, the lipid content was 45.0±0.2% at mm N, 51±1% at mm N and 45±3% at mm N. Thus, there were no significant increases in lipid content at the end of exponential growth for the nitrogen concentrations investigated here. This experimental result is supported by the observation that lipid content does not increase when the ph of the culture is constant while cells are deprived of nitrogen [82, 85]. Biomass production decreased under nitrogen stress. The maximum biomass density was 0.84 g/l at mm N, which is 1.45 and 2.54 times of mm N and mm N. Correspondingly, lipid yield declined from 0.38 g/l to 0.29 g/l to 0.15 g/l. These experimental results were in agreement with most published data [11, 59, 82]. So for Nannochloropsis gaditana, final lipid content was not affected by initial N amount under nitrogen stress, but cell yield was. As to nitrate, it was depleted from the culture media about half way through the exponential growth phase. The cells continued dividing and accumulated lipid, almost doubling in cell mass as shown in Figures 1, 3, 4. Meanwhile, there were slight increases in nitrogen concentration in the medium for first two days; this might be because of cell secretion. Takagi et al. [59] reported the same observation for nitrate consumption curves.

95 94 Fatty acids increased during exponential growth, but the profile did not change once N was depleted. Rodolfi et al.[11] observed similar results for Nannochloropsis sp. under nitrogen starvation conditions. For biodiesel, the following FAMEs are highly desirable: C16:1, C18:1, C20:1 and C22:1[2]. Saturated fatty acids like C16:0 can be converted to unsaturated acid C16:1 by a dehydrogenation reaction to satisfy this profile. This strain Nannochloropsis gaditana, which produces these fatty acids, is a potential candidate for biodiesel production. Continuous production can be carried out in late exponential or early stationary phase with very little change in productivity and lipid profile. Three well established models - the Droop equation for microalgae biomass, the Michaelis-Menten equation as modified by Geider et al. for nitrate simulation[79], and the Luedeking-Piret for product synthesis were applied to predict microalgae growth, nitrate consumption and lipid production as a function of time under nitrate limiting conditions. Application of Droop model on microalgae growth has been widely validated [80, 86, 87], in which cell quota is function of nitrogen concentration. Geider et al. [79] developed a dynamic regulatory model describing how the C, N and Chl content of phytoplankton change in response to changes in light and nutrient and temperature. For this current study, the initial nitrate concentration was the only variable to be evaluated. Therefore the model was modified and simplified to Equations (4) and (5) to simulate nitrate uptake rate. After obtaining all of the parameters required for the models from experimental data, these equations were solved numerically, and used to predict cultivation behavior in small batch reactors. Similarly to Yang et al.[75], a linear relationship between lipid concentration and microalgae biomass concentration was

96 95 observed (Fig. 2), suggesting that lipid accumulation is directly related to microalgae growth for this strain. The Luedeking-Piret equation (Eqn. 6) was applied to predict lipid yield. This equation is widely used for modeling product formation in a variety of bioreactors. The RMSE, MAE and R 2 analyses demonstrated that the parameters obtained are reproducible under the conditions evaluated. A sensitivity analysis for key model parameters - µ m q 0 and V max - was performed. The results show that µ m affected microalgae growth, nitrate consumption and lipid yield greatly whereas variations in V max have very little influence on model predictions. Growth rate is affected by many environmental parameters. But once known for a given set of conditions, the expression for exponential growth rate is easily incorporated into this model and used to predict reactor performance. Nitrate utilization is more sensitive to the value of the subsistence cell quota q 0. Variations in q 0 affect model predictions of total cell biomass and lipid percentage. Finally, it is necessary to know the optimum time to harvest microalgae in order to attain the highest lipid yield, especially in large scale systems. This model predicts the best time to harvest microalgae. More importantly, this is a simple model compared to other existing models for microalgae autotrophic growth, nitrate consumption and lipid production, requiring the least amount of parameters that can all be determined from experimental data.

97 Conclusions The model successfully described microalgae Nannochloropsis gaditana growth rate, nitrate consumption and lipid yield under nitrogen limitations. This is a simple mathematical model requiring less parameters and experiments, and is easy to understand and apply to other strains. It also can serve as a useful tool to optimize cultivation processes and design bioreactors for the biofuel industry.

98 97 6. EFFECT OF NITRATE AND UREA REPLETION ON THE MICROALGAE GROWTH AND LIPID ACCUMULATION OF NANNOCHLOROPSIS GADITANA 6.1 Introduction Microalgae, as the third generation feedstock of biofuel production, have more advantages than conventional biofuels, such as high growth rate and lipid content, carbon neutral and so on[88, 89]. However, high costs are still big problem for commercial production [3, 90]. Especially in arid land area, water resources are extremely valuable. It is applicable to use agriculture groundwater to culture microalgae. Recently a lot of studies focus on increasing biomass productivity and lipid yield by manipulating culture conditions, such as nitrogen starvation, salinity, light intensity and temperature[59, 89, 91-93]. But usually agriculture groundwater contains abundant nitrogen sources, like urea and nitrate. So it is important to know the effect of different nitrogen sources and concentrations on the growth characteristics, lipid yield and chemical compositions. For green alga Neochloris oleoabundans, using nitrate as nitrogen source achieves faster growth rate and higher lipid content than using urea [94]. Feng et al. [95] have reported that highest lipid content (53%) is attained at extremely high nitrate concentration (9g/L, 24h interval) for marine microalgae Isochrysis zhangjiangensis (Chrysophyta). Microalgae Nannochloropsis gaditana have been proved to be an ideal candidate for biodiesel production because of high lipid productivity and most of fatty acids in the

99 98 range of carbons in previous study. The conventional nitrogen sources for Nannochloropsis gaditana is nitrate. However, Rocha et al. [96] have shown that the presence of 10mM urea increases the growth rate and final biomass production. And the influence of repletion nitrate and urea as nitrogen sources on microalgae Nannochloropsis gaditana s lipid content and profile has rarely been reported. The goal of this research is to study the effect of nitrate and urea under repletion condition on microalgae growth, lipid yield and fatty acids profile for microalgae Nannochloropsis gaditana. 6.2 Results Effect of replete nitrate on growth and final lipid content Figure 33 shows the growth curve and lipid accumulation for microalgae Nannochloropsis gaditana with normal medium (f/2-si), whose nitrogen concentration was 0.882mM. The maximum dry cell density was achieved at 0.72g/L on the 8 th day. The lipid content increased dramatically at the middle of exponential growth and then kept around 38% from mid to stationary phase.

100 Dry Cell Density (g/l) Lipid Content (%) Time (h) Figure 33 Cells growth curve and lipid content vs. time for normal media (nitrate as N source). To investigate the effect of replete nitrate concentration on cell growth and lipid yield on Nannochloropsis gaditana, 10mM, 20mM and 30mM sodium nitrate was added into f/2- Si medium respectively, and then final nitrogen concentration was , and mM in modified medium. The growth summary was shown in Table 10, including the experimental data of normal medium. The specific growth rate was calculated by logistic regression fitting (p<0.01). As shown in Table 10, the growth rate increased in the range of 4-7% compared to normal medium by adding more nitrate from 10 to 30mM, while the lipid content dropped around 18-25%. The lag phase was just one day for all modified medium. Additional nitrate did not help in harvesting more biomass, which resulted in low final lipid yield, 0.19, 0.16 and 0.17 g/l with , and

101 mM nitrogen concentration. There was no obvious trend in experimental data when adding different amount of sodium nitrate. Figure 34 shows the nitrate consumption curve with initial , and mM N in the medium. After 9 days of cultivation, total nitrogen consumption compared to initial concentration was 45%, 44% and 40% respectively. Table 10 Growth summary of different nitrate concentration Nitrogen Sources Nitrogen Concentration (mm N) Exponential Growth Rate (hr -1 ) Lag Time (Days) Lipid Content % Lipid yield (g/l) NaNO NaNO NaNO NaNO *Values shown are averages of four replicates

102 Nitrate Concentration (mm) mmol mmol mmol Time (h) Figure 34 Nitrate concentration change with time course by adding 10mM, 20mM and 30mM nitrate into normal media Effect of urea as nitrogen source on growth and final lipid content The growth curve and lipid accumulation of Nannochloropsis gaditana in the modified medium using urea instead of sodium nitrate as nitrogen source is shown in Figure 35. Since each sodium nitrate molecule supplies one nitrogen atom while urea supplies two, 0.441mM urea was added into medium. The cells rarely grew during first three days, then entering the exponential growth phase once they adjusted into the new environment. After 9 days cultivation, the dry cell density was 0.47g/L, and lipid content gradually increased and finally attained 23%.

103 Dry Cell Density (g/l) Lipid Content (%) Time (h) Figure 35 Cells growth curve and lipid content vs. time for modified media (urea as N source). Then, 5, 10 and 15mM urea was added into growth medium to investigate the effect on growth rate, lag phase, lipid content and final lipid yield. All the experimental data was summarized in table 11. The growth rates were similar in the same deviation range. And it is obvious that adding more urea into medium, microalgae accumulated more lipids. The highest lipid content and yield occurred when urea concentration in the medium was 15mM, which was almost 2 and 2.5 times more than 0.441mM urea concentration. Even using 5mM and 10mM urea in the medium, the final lipid yield was 1.6 and 1.1 times more than 0.441mM urea concentration.

104 103 Table 11 Growth summary of different urea concentration Nitrogen Sources Nitrogen Concentration (mm N) Exponential Growth Rate (hr -1 ) Lag Time (Days) Lipid Content % Lipid yield (g/l) urea urea (from urea) urea (from urea) NaNO 3 +urea NaNO 3 +urea NaNO3+urea Values shown are averages of four replicates Because inoculums were cultured in the normal medium (nitrate as nitrogen source), ones using urea as nitrogen source were tested. The lag time was shortened into 1 day for microalgae cultured in modified medium with and 0.221mM urea. However, the lipid content and yield dropped 66% and 64% for 0.441mM urea concentration. Even with further half amount of N concentration, the lipid content and yield was only 6.45% and 0.02g/L with 0.221mM urea concentration whereas the growth rate decreased to hr -1. It indicates that while decreasing urea amount, it will not stimulate lipid accumulation.

105 Fatty acid profiles for nitrate and urea as nitrogen sources The main fatty acids of Nannochloropsis gaditana are myristic acid (C14:0), palmitic acid (C16:0), palmitioleic acid (C16:1), stearic acid (C18:0) and oleic acid (C18:1n9c). The fatty acid profiles of nitrate or urea as sole N source were shown in Figure 36 and 37 according to time course. Whether growing in nitrate or urea medium, the dominant fatty acids were similar. C14:0, C16:0, C16:1, C18:0 and C18:1n9c reached to about 6.08%, 45.58%, 27.48%, 2.53% and 5.81%, respectively, in the post-logarithmic phase of using normal medium. When urea was used as the sole nitrogen source, C 14:0, C16:1, C18:0 and C18:1n9c composition went down, composed to 4.38%, 20.52%, 1.67% and 3.36% of total lipid at final phase, respectively, and only C16:0 composition increased to 51.18%. The fatty acids profile was affected not only by nitrogen sources but also by growth phase. In the normal medium, the fatty acid compositions varied in the range of 6% from the middle to stationary growth phase. C18:1n9c increased from 4.29% to 7.02% at middle growth phase and then dropped to 2.53% at final stage (figure 36). The same thing happened to urea medium, the fatty acid compositions changed with time in the deviation range of 4%.C16:1 reached to 27.4% and then declined to 20.52% at stationary phase, in the same phase more saturated fatty acids of C16:0 and 14:0 were accumulated (figure 37).

106 Fatty acids composition (% w/w total lipid) % 90% 80% 70% 60% 50% 40% 30% 20% 10% C18:0 C18:1n9c C14:0 C16:1 C16:0 0% Time (h) Figure 36 Percentage of individual fatty acid composition with time course (normal media). Values shown are averages of four replicates.