Processing Algal Biomass to Renewable : Oil Extraction And

PROCESSING ALGAL BIOMASS TO RENEWABLE : OIL EXTRACTION AND

FUEL

HYDROTHERMAL LIQUEFACTION

Thesis

Submitted to

The School of Engineering of the

UNIVERSITY OF DAYTON

In Partial Fulfillment of the Requirements for

The Degree of

Master of Science in Bioengineering

Sally Louis Homsy

Dayton, Ohio

August, 2012

PROCESSING ALGAL BIOMASS TO RENEWABLE FUEL: OIL EXTRACTION AND

HYDROTHERMAL LIQUEFACTION

Name: Homsy, Sally Louis

APPROVED BY:

______Sukh Sidhu, Ph.D. Donald Comfort, Ph.D. Advisory Committee Chairman Committee Member Division Head Assistant Professor Energy Technologies & Materials UDRI Department of Chemical and Materials Professor Engineering Department of Mechanical & Aerospace Engineering

______Matthew Lopper, Ph.D. Committee Member Assistant Professor Department of Chemistry

______John G. Weber, Ph.D. Tony E. Saliba, Ph.D. Associate Dean Dean, School of Engineering School of Engineering & Wilke Distinguished Professor

Sally Louis Homsy

2012

iii

ABSTRACT

PROCESSING ALGAL BIOMASS TO RENEWABLE FUEL: OIL EXTRACTION AND

HYDROTHERMAL LIQUEFACTION

Name: Homsy, Sally Louis University of Dayton

Advisor: Dr. Sukh S. Sidhu

Since the industrial revolution the world’s reliance on fossil fuels has been increasing at an accelerated rate. The negative environmental effects of burning fossil fuels and the demand for energy security have increased interest in renewable fuels technology.

Using biomass as a feedstock for energy generation has emerged as an area of interest, and the focus of this study is on the sustainable production of a crude oil from the algal species

Chlorella vulgaris. The derived crude oil is to serve as a feedstock for renewable diesel production. The constituents of this algae derived oil must be similar in structure and low in impurities, especially nitrogen and sulfur content, to allow for the economical upgrade of this oil to renewable diesel. Two methods for the generation of the crude oil were explored: direct oil extraction from the algal biomass and hydrothermal liquefaction of the algal biomass. Total algal lipid extraction from both dry and wet algal biomass was studied and multiple solvents, procedures and cell pretreatment methods were compared; this includes solvents at ambient conditions, supercritical carbon dioxide, liquefied dimethyl ether, ultrasonication, mechanical disruption and steaming. It was determined that pretreatment of the Chlorella vulgaris biomass is not necessary for total oil extraction, that total oil extraction from dry algae can be achieved by using a 95% ethanol solvent and that total oil

iv extraction from wet algae can be achieved by using a 6:77:17 w/w/w ratio of water to ethanol to hexane. The optimal oil extraction procedure was scaled up and a process was developed to fractionate the algal biomass and isolate the lipid fractions conducive to upgrading to renewable diesel. The crude oil produced through this method was analyzed and found to be suitable for economical upgrade to renewable diesel. However biomass conversion to oil was low; only about 13.5% of the biomass could be converted to oil due to the relatively low lipid content of the Chlorella vulgaris (about 18% lipids on a dry weight basis). The hydrothermal liquefaction of the Chlorella vulgaris biomass was capable of converting about 44% of the initial Chlorella vulgaris biomass to bio-crude. However, the quality of the oil produced was not ideal for upgrading to renewable diesel due to the high nitrogen and sulfur content of the oil and the diverse molecular structures of the oil constituents. In conclusion, it was recommended that a method to enhance Chlorella vulgaris lipid content, such as nitrogen starvation or the introduction of sugars in the growth media, should be adopted prior to harvest and that the developed oil extraction procedure should be used to produce a renewable upgradable crude oil.

ACKNOWLEDGEMENTS

My special thanks are in order to Dr. Sukh Sidhu, my advisor, for providing the time and equipment necessary for the work contained herein, and for directing this thesis and bringing it to its conclusion with patience and expertise. Special thanks to my committee members, Dr. Donald Comfort and Dr. Matthew Lopper, for reviewing my work and sharing their expertise, and thus allowing me to refine and finalize this thesis.

I would also like to express my appreciation for everyone who has helped me with this work. This includes Liliana Martinez, who introduced me to this project, provided her support and helped better focus and widen the scope of my work; Dr. Andres Fullana, whose approach to research inspired me and whose vision and expertise was fundamental to shaping my research; Dr. Moshan Kahandawala, who helped me acquire and set up equipment and provided his technical support; Dr. Jerry Servaites, who lent his expertise in algal biology and various lab techniques; Dr. Heinz Robota, the scope of this project was made possible with his support and expertise in fuel upgrading; Dr. Willie Steinecker who provided lab space, equipment, and support; Richard Striebich and Dr. Alex Morgan who helped with the analytical testing of my samples; Alex Lagounov, Nilesh Chavada, Sai Kumar,

Giacomo Flora and Jhoanna Alger all of whom provided their support and technical assistance. I would also like to thank the other members of the SET group, UDRI, and

IDCAST; without their support and hard work this thesis would not be feasible. Finally I would like to thank the AFRL/RXSC Energy and Environmental Quality Program Office, Air

Force Research Laboratory, Wright Patterson AFB.

TABLE OF CONTENTS

ABSTRACT...... iv ACKNOWLEDGEMENTS ...... vi LIST OF ILLUSTRATIONS ...... ix LIST OF TABLES ...... xi LIST OF SYMBOLS AND ABBREVIATIONS ...... xii CHAPTER 1: INTRODUCTION ...... 1 CHAPTER 2: BACKGROUND...... 12 2.1 Algae ...... 12 2.2 Oil Extraction ...... 13 2.2.1 Biomass Pretreatment ...... 15 2.2.2 Oil Extraction ...... 17 2.2.3 Oil Purification ...... 22 2.3 Hydrothermal Liquefaction ...... 25 CHAPTER 3: EXPERIMENTAL PROCEDURE ...... 27 3.1 Oil Extraction ...... 27 3.1.1 Floch’s Method ...... 28 3.1.2 Extractions from Freeze Dried Algae at Ambient Pressure ...... 29 3.1.3 Extractions from Wet Algae at Ambient Pressure ...... 33 3.1.4 Pressurized Extractions from Algae ...... 35 3.2 Oil Fractionation...... 43 3.2.1 Analytical Determination of Oil Extract Composition ...... 44 3.2.2 Modification of the Fractionation Method ...... 45 3.3 Large-Scale Extraction ...... 46 3.3.1 Unsaponifiable Fraction Analysis: ...... 49 3.3.2 Fatty Acid Fraction Analysis: ...... 50 3.3.3 Aqueous Fraction: ...... 51

vii

3.4 Hydrothermal Liquefaction ...... 53 CHAPTER 4: RESULTS AND DISCUSSION ...... 57 4.1 Oil Extraction Results ...... 57 4.2 Modification of the Oil Fractionation Method ...... 67 4.3 Large-Scale Oil Fractionation Results ...... 70 4.4 Hydrothermal Liquefaction Process ...... 85 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ...... 100 REFERENCES ...... 105 APPENDIX I: PHYSIOLOGY OF CHLORELLA VULGARIS ...... 114 APPENDIX II: STATISTICAL ANALYSES ...... 127

viii

LIST OF ILLUSTRATIONS

1. Figure 2.1-1: Photobioreactor systems at the University of Dayton Research Institute’s

Carbon Sequestration Laboratory...... 13

2. Figure 2.2.3-1: Examples of lipid structures abundant in algae (Vieler, et al. 2007) ...... 23

3. Figure 3.1.4-1: SFT-100 used for pressurized extractions ...... 36

4. Figure 3.1.4-2 : Impinger setup for extract collection ...... 38

5. Figure 3.1.4-3: SFT-100 flow diagram for supercritical carbon dioxide extractions ...... 40

6. Figure 3.1.4-4: SFT-100 flow diagram for liquid dimethyl ether extractions………………..42

7. Figure 3.2-1: Oil fractionation procedure as described by Ibanez Gonzalez et al., 1998…...

...... 43

8. Figure 3.3-1: Large-scale extraction procedure………………………………………………………….48

9. Figure 3.3.3-1: Experimental setup for the process water recycle experiment…………….52

10. Figure 3.4 -1: PPI Milton Roy pressure reactor…………………………………………………………..54

11. Figure 4.1-1 : Oil extraction yields for the various methods explored ...... 60

12. Figure 4.1-2: Relative extraction efficiencies of the various oil extraction methods

explored ...... 61

13. Figure 4.1-3: Supercritical carbon dioxide lipid extract pigmentation ...... 63

14. Figure 4.1-4 : Comparision of the distribution of the components of the extracts ...... 66

15. Figure 4.2-1: Oil fractionation using different acids and pH for protonation… ...... 69

16. Figure 4.3-1: Distribution of the initial algal dry weight post oil extraction and

fractionation………………………………………………………………………………………...………………… 70

17. Figure 4.3-2: GCxGC-MS chromatogram of the unsaponifiable fraction……………………….74

18. Figure 4.3-3: GCxGC-MS chromatogram of the fatty acid fraction……………………………….78

19. Figure 4.3-4: Comparison of algal growth in BBM and dilutions of the aqueous media

produced during the fractionation process………………………………………………………………..81

20. Figure 4.3-5: Pigmentation of algae grown in BBM and dilutions of the aqueous media

produced during the fractionation process………………………………………………………………..82

21. Figure 4.3-6: Elemental composition of the aqueous process water with respect to

BBM………………………………………………………………………………………………………………………...84

22. Figure 4.4-1: Distribution of the initial algal dry weight post hydrothermal

liquefaction……………………………………………………………………………………………………………...85

23. Figure 4.4-2: GC-TCD chromatogram of the isolated volatiles..…………………………………...86

24. Figure 4.4-3: GC-MS chromatogram of the isolated semi-volatiles………………………………88

25. Figure 4.4-4: GCxGC-MS Chromatogram of the HTL bio-crude……………………………………94

26. Figure 4.4-5: Elemental composition of the HTL aqueous phase compared to the

elemental composition of the fractionation process water and BBM…………………………..98

27. Figure 4.4-6: Nutrient recovery in the HTL aqueous phase compared to nutrient

recovery in the fractionation process water and the theoretical composition of Chlorella

vulgaris……………………………………………………………………………………………………………………99

28. Figure 5.1: Oil extraction and fractionation procedure with the isolation of

unsaponifiables……………………………………………………………………………………………………..102

29. Figure 5.2: Oil extraction and fractionation procedure without the isolation of

unsaponifiables……………………………………………………………………………………………………...103

30. Figure 5.3: Overall recommended process with process water recycle…………………..…104

LIST OF TABLES

1. Table 1-1: Methods used for the conversion of biomass to renewable diesel ...... 8

2. Table 2.2-1: Comparison of oil yields of common crops and Chlorella vulgaris ...... 15

3. Table 4.1-1: Lipid extraction results………………………………………………………………………….58

4. Table 4.3-1: Unsaponifiable fraction tocopherol, carotenoid, and sterol profile…………..71

5. Table 4.3-2: Nutraceutical uses of the unsaponifiable oil content……………………………….72

6. Table 4.3-3: Compounds in the unsaponifiable fraction as identified by GCxGC-MS……..75

7. Table 4.3-4: Fatty acid fraction elemental analysis results…………..……………………………..76

8. Table 4.3-5: Compounds in the fatty acid fraction as identified by GCxGC-MS……………..79

9. Table 4.4-1: Composition of the isolated volatiles…………………………...…………………………87

10. Table 4.4-2: Compounds in the isolated semi-volatiles as identified by GC-MS………..….88

11. Table 4.4-3: HTL bio-crude elemental analysis results………………………………..……………..91

12. Table 4.4-4: Compounds in the HTL bio-crude as identified by GCxGC-MS………………….95

LIST OF SYMBOLS AND ABBREVIATIONS

ANOVA Analysis of Variance

BBM Bold’s Basal Medium

GC Gas Chromatography

GMO Genetically modified organism

HTL Hydrothermal Liquefaction

MS Mass Spectrometry

TCD Thermal Conductivity Detector

xii

CHAPTER 1

INTRODUCTION

Based on 2011 estimates over 50% of the world’s proven oil reserves are centralized in four countries; about 18% are located in Saudi Arabia, 14% in Venezuela, 12% in Canada, and

9% in Iran. Over 50% of the world’s proven natural gas reserves are localized in three countries; about 24% are located in Russia, 16% in Iran and 14% in Qatar (CIA 2011). And based on 2008 estimates, 50% of the world’s proven coal reserves are located in three countries; 23% are located in the United States, 14% in Russia, and 13% in China (World

Energy Council 2010). Most nations are therefore energy dependent on foreign countries endowed with large fossil fuel reserves; this places these fuel rich countries at an advantage and creates a geopolitical imbalance.

The world’s dependence on these nonrenewable resources has grown significantly since the industrial revolution and the increased demand for fuels has led to raised fuel prices

(Hanagata, et al. 1992). In addition to the energy crisis, using fossil fuels for energy generation has also had a significant negative impact on the environment. Carbon dioxide levels have been increasing at an accelerated rate over the past couple of decades, and based on Paleoclimate data, scientists have correlated the increasing atmospheric carbon dioxide levels to eminent climate change, or global warming (Hansen, et al. 2008). The average atmospheric carbon dioxide level for March of 2012 was at 394.45 ppm according

1 to measurements made at the Mauna Loa Observatory, and this is significantly greater than the suggested upper safety limit of 350 ppm (Tans 2012).

Due to the demand for energy security, the energy crisis, and the negative environmental effects of burning fossil fuels, sustainable alternative energy technologies with low levels of carbon dioxide emissions are of interest. Clean sustainable energy sources include hydroelectricity, solar energy, wind energy, wave power, geothermal energy, tidal power, landfill biogas and bioelectricity. These technologies can be used to renewably generate clean electricity, generate clean burning hydrogen, improve energy efficiency, and power cities. For comparison, modern coal to electricity fuel chains emit an estimated 1,054 g

CO2/KWh of electricity generated, and natural gas fuel chains using combined cycle gas turbines emit 411 g CO2/KWh generated. It is estimated that bioelectricity generated from forest residues may emit as low as 8-16 g CO2/KWh generated (Bauen, Woods and Hailes

2004). These technologies, however, cannot effectively replace reliance on liquid hydrocarbon fuels.

Liquid hydrocarbon fuels such as gasoline and diesel are high energy density fuels on both a volumetric and a mass basis. Due to their relatively easy handling and use, liquid hydrocarbon fuels are currently the standard for use in light and heavy duty vehicles; light duty vehicles usually run on gasoline, while heavy duty vehicles generally run on diesel. In the United States about 8.9 million barrels of fuel per day (Mbbl/d) are used in light duty vehicles (Davis and Diegel 2009). By replacing internal combustion based light duty vehicles with plug-in hybrid electric vehicles containing batteries that provide a driving distance of about 64 km between charges, the gasoline requirement for light duty vehicles could be reduced to 3.4 Mbbl/d (Kintner-Meyer, Schneider and Pratt 2007). However the technology required to transition electricity and/or hydrogen for powering heavy duty

2 vehicles, such as trucks and military vehicles, is currently unavailable, and 8.3 Mbbl/d is required to fuel heavy duty vehicles in the United States (Davis and Diegel 2009). The reliance of heavy duty vehicles on liquid hydrocarbon fuels, mainly diesel, is both an environmental and a security concern. To remedy this, the conversion of biomass to renewable diesel is currently being investigated as a method for the production of an alternative sustainable liquid hydrocarbon fuel with a small carbon footprint.

Renewable diesel, or green diesel, is a hydrocarbon fuel that is produced from biomass and that is composed of alkanes with carbon chain lengths in the range of 10 to 18 carbons

(Knothe 2010). Renewable diesel has a high cetane rating, the properties of high grade diesel, and it can seamlessly substitute traditional diesel in the market (Holmgren, et al.

2007). Since biomass can be produced or is available globally, green diesel provides a renewable non-localized source of energy. Biomass from photoautotrophs can be used to produce renewable diesel with a near net-zero carbon footprint. Since photoautotrophs sequester carbon dioxide during growth and, due to the conservation of mass, cannot release more carbon dioxide than the amount they sequester, the emissions generated from renewable diesel derived from photoautotrophs are associated with the energy required to power feedstock production and the renewable diesel production process (Bauen, Woods and Hailes 2004).

Photosynthetic biomass sources that can be used for renewable diesel production include: woody biomass, grasses and other herbaceous plants, aquatic photoautotrophs such as algae, and agricultural crops such as corn, soybean, and sugarcane. Of these biomass sources, photosynthetic microalgae are the most appealing feedstock for renewable diesel production. In the recent decade the use of algae for environmental applications, including wastewater treatment, carbon dioxide sequestration and biomass generation for energy

3 production purposes, has emerged as an area of interest among researchers due to algae’s unique properties. Compared to higher plants, algal solar energy conversion efficiency and carbon dioxide fixation is superior (Badger, Kaplan and Berry 1980). Measured values of algal solar efficiency have ranged from 3-9% while higher plants can only achieve a theoretical maximum solar energy efficiency of 3.7% (Zittelli, et al. 1996; Zhu, Long and Ort

2008; Kebede and Ahlgren 1996). Due to their efficient use of solar energy and their advanced ability to sequester carbon dioxide, various microalgal species have a short life cycle and a high reproductive rate allowing for quick biomass accumulation (Schenk, et al.

2008). In addition, the cultivation of autotrophic algal species can be relatively low maintenance and low cost. Different microalgal species can proliferate under a vast array of conditions and in various climates and environments. Unlike terrestrial crop cultivation, algal cultivation does not require the use of herbicides and pesticides, and although algae grow in an aquatic medium, algae do not consume as much water as terrestrial crops

(Rodolfi, et al. 2009). Algae also do not require a fresh water source and can grow in certain wastewaters or salt water (Aslan and Kapdan 2006). Microalgae do not compete with conventional crops for resources, and microalgal cultivation does not affect market prices for agricultural crops (Rodolfi, et al. 2009). Some microalgal species also produce valuable biomolecules with high demand in the nutraceutical industry and/or have high oil content which can be advantageous for renewable diesel production (Spolaore, et al. 2006; Ceron, et al. 2008). The algal metabolic pathway is also not fixed; it is possible to control the composition of the algal biomass and enhance the production of certain valuable biomolecules by manipulating algal growth conditions (Mandalam and Palsson 1998;

Huang, Chen and Chen 2009). For this study, the biomass source used is the microalgae

Chlorella vulgaris. Chlorella vulgaris is a very resilient species of unicellular green algae classified as chlorophyta. Chlorella vulgaris biomass is typically 51%-58% protein 12%-

17% carbohydrate, and 14%-22% lipid (W. E. Becker 1994). A detailed review of Chlorella vulgaris biology and the advantages of using this alga as a biomass source for renewable diesel production is presented in Appendix I.

In general, biomass conversion to renewable diesel is accomplished through one of the following approaches:

 The biological, thermochemical or physiochemical production of a crude oil from the

biomass followed by the hydrotreatment, isomerization and/or cracking of this oil in

order to produce renewable diesel (Knothe 2010).

 The biological or thermochemical production of hydrocarbon building blocks such as

syn-gas or short chain alcohols that can be processed to renewable diesel through

Fischer-Tropsch synthesis or oligomerization respectfully (Harvey and Meylemans

2011; Unruh, Pabst and Schaub 2010).

Table 1-1 (on page 8) summarizes the primary methods that are used to produce renewable diesel from biomass. Since algae are aquatic organisms, methods requiring dry or mostly dry biomass feed are not ideal for algal processing; this includes pyrolysis and gasification.

Unless substantial waste heat is available, the energy required to dry the algal biomass could render the process inefficient. According to a study by Molina et al. algal harvesting costs, associated with dewatering algae to a 90% wet slurry, may contribute 20% – 30% of the total cost of algal biomass production, and the expense of drying the harvested biomass slurry to within 5%–15% moisture content can be a significant impediment to using microalgae as a feedstock (2003). On the other hand, biological methods for the conversion of algal biomass to fuel generally require the use of multiple pretreatment steps to partially digest the algal biomass, sterile environments, a relatively large footprint for digesters, and, for most efficient conversion, the genetic modification of microorganisms (Kapapinar and

Kargi 2006). Direct oil extraction from algae followed by hydrotreatment and hydrothermal liquefaction (HTL) of the algal biomass followed by hydrotreatment therefore appear to be the least demanding methods for the production of renewable diesel from algae, and hence these methods are the focus of this study. It must be noted, however, that although hydrotreatment is an established process, not all oil is conducive to effective and economical hydrotreatment.

Algae derived oil may contain oxygen, nitrogen, sulfur and other elemental impurities.

Hydrotreatment, or contacting the oil with hydrogen at high temperatures and pressures, is used to eliminate elemental impurities from the oil, hydrogenate the oil, and generate alkanes (Jones, et al. 2009). The alkanes are then cracked and isomerized to produce carbon chain lengths and physical properties suitable for a diesel substitute (Knothe 2010). The amount of impurities present in the algal derived oil and the initial chemical structure of the oil can be an impediment to the economical hydrotreatment of the oil. High oxygen content in the algal derived oil causes water accumulation in the reaction vessel which has a slight negative effect on the hydrotreatment process (Knothe 2010). High sulfur and nitrogen content cause the accumulation of hydrogen sulfide and ammonia respectively, both of which have detrimental effects on hydrotreatment catalyst activity (Thakkar, et al. 2008).

With added impurities the cost of the hydrotreatment process increases; impurities increase the hydrogen requirement, necessitate the introduction of dedicated scrubbers in the hydrogen recycle line, and require the introduction of different catalysts to treat the different impurities (Thakkar, et al. 2008; National Advanced Biofuels Consortium 2011;

Knothe 2010). In addition, different hydrotreatment processes have been found to compete; for example, desulfurization and deoxygenation reactions are competitive reactions. The presence of both oxygen and sulfur in the algal derived oil would hence necessitate the use of a multistep hydrotreatment process (Kalnes, et al. 2009). In summary, the more diverse

6 the constituents of the oil the more complex, multistep, and expensive the hydrotreatment process becomes. Ideally, the algae derived oil should be composed of similar long chain hydrocarbons bonded with little impurities, preferably oxygen based, to allow for the economical upgrade of the oil to renewable diesel (Baldiraghi, et al. 2009; Knothe 2010).

The objective of this study is the efficient and sustainable production of algae based oil that can be efficiently upgraded to renewable diesel. The methods explored include 1) oil extraction and 2) hydrothermal liquefaction. In the following section the algal biomass production process will be presented along with a literature review on published work with regards to algal oil extraction and hydrothermal liquefaction. The experimental approach will then be delineated, and the results discussed.

Table 1-1: Methods used for the conversion of biomass to renewable diesel

Process Process Details

Pyrolysis is the catalytic thermochemical decomposition of virtually dry biomass (<5% by

weight moisture content) at moderate temperatures (450-500°C) in the absence of oxygen.

This process yields a mixture of bio-char (comprising about 10% of the yield on a weight

basis), pyrolysis gas (comprising about 30% of the yield) and pyro-oil (comprising about 60%

of the yield) (Mohan, Pittman and Steele 2006). Pyrolysis gas is a mixture of carbon monoxide,

hydrogen, carbon dioxide and other inorganic gases and light hydrocarbons. The solid Pyrolysis product, called bio-char, contains mineral matter and reduced carbon. Bio-char has various (Thermochemical) applications including use as soil amendment and a carbon sequestration agent (Laird, et al.

2009). The liquid product, pyro-oil is a high viscosity, dark-brown liquid composed of

numerous organic components along with up to 15-20% water. Pyro-oil can be upgraded to

green diesel through hydrotreatment and catalytic cracking (Mohan, Pittman and Steele

2006). One study on algal pyrolysis reported 57.9% yield of pyro-oil with properties

comparable to fossil fuel (Miao and Wu 2004).

Gasification is thermochemical decomposition of biomass (with less than 35% moisture

content on a weight basis) at temperatures greater than 700°C in the presence of oxygen (Ni,

et al. 2006). Through this process the biomass is reduced to syngas, mostly a mixture of Gasification hydrogen and carbon monoxide. Syngas can be subjected to Fischer-Tropsch synthesis to (Thermochemical) produce synthetic liquid hydrocarbon fuels. Biomass-to-liquid fuel conversion may be

expected in the range of 30−50% for chemical energy and 25−45% for carbon recovered in

hydrocarbon products (Unruh, Pabst and Schaub 2010).

Hydrothermal liquefaction is the reaction of water with biomass at high temperatures (250-

375°C) (Akhtar and Aishah 2011). During this process the biomass is decomposed into

reactive molecules that can re-polymerize to form bio-crude (Zhang, Xu and Champagne

Hydrothermal Liquefaction 2010). This process yields an aqueous fraction, a bio-crude fraction, a gaseous fraction and a

(Thermochemical) solid residue. Bio-crude yields have been found to depend on feedstock composition; high

lipid and protein yield have been correlated with high bio-crude yield (Savage, Levine and

Huelsman 2009). Bio-crude, like pyro-oil, can be upgraded to green diesel through

hydroprocessing and catalytic cracking.

Cultivating lipid rich biomass, extracting the lipid content and upgrading it to fuel is an

approach that has been explored for renewable diesel production (Robles Medina, González

Moreno, et al. 2009). Biomass is composed of protein, carbohydrates and lipids. Certain long

Oil Extraction chain lipids and lipid derivatives are ideal precursors to green diesel (Baldiraghi, et al. 2009).

(Physiochemical) These compounds have long hydrocarbon chains (14-22 carbon chain length) with an alcohol

or carboxylic acid end group. Once the oil is extracted, it is purified, hydrogenated,

deoxygenated, decarboxylated, cracked and subjected to isomerization to produce a stable

clean burning diesel with a very high cetane rating (Shonnard, et al. 2010).

Under certain conditions some strict anaerobic microalgae and bacteria (Clostrida species)

can ferment/digest carbohydrate rich substrates to produce hydrogen and/or methane gas, Fermentation/ Anaerobic butanol and/or ethanol (Ni, et al. 2006). Generally organic matter is first converted to organic Digestion acids (acidogenic phase) then acids are used to produced hydrogen and/or energy rich (Biological) organic alcohols (Kapapinar and Kargi 2006). Renewable diesel can be produced through the

dehydration of butanol and oligomerization of butene (Harvey and Meylemans 2011).

Genetic engineers have successfully developed heterotrophic microorganism that are capable

Genetically Modified of the conversion of biomass to hydrocarbons and autotrophic microorganisms capable of

Organisms production of hydrocarbons. These GMOs then secrete the hydrocarbons allowing for their

(Biological) easy collection and use as fuel. Joule Unlimited and LS9 are two biotechnology companies that

are implementing these GMOs for fuel production.

CHAPTER 2

BACKGROUND

2.1 Algae

Algae can be cultivated in large open air pools in areas where the climate is conducive to algal growth or in closed system photobioreactors where temperature, pH and other factors affecting algal growth can be monitored and controlled to optimize growth rate and/or oil production. Once the algal growth cycle is complete, the algal biomass is harvested from the growth media by means of membrane filtration, gravity sedimentation, centrifugation, ultrasonic separation, foam fractionation, and/or flocculation. Algal harvesting results in a

50 to 200 fold concentration of the algal biomass; post-harvest the algal biomass usually comprises about 10% of a thick slurry (Molina Grima, Belarbi, et al. 2003). If required, the biomass can then be further dehydrated for storage or for downstream processing by means of spray drying, drum drying, freeze drying, oven drying, waste heat and sun drying.

A strain of Chlorella vulgaris obtained from the UTEX Culture Collection of Algae at the

University of Texas is cultivated photoautotrophically in indoor 3,800 L closed system tubular photobioreactors at the University of Dayton Research Institute’s (UDRI) Carbon

Sequestration Laboratory (see Figure 2.1-1). The Chlorella vulgaris is grown in Bold’s Basal

Medium (BBM), a nutrient rich solution containing nitrates, phosphates and trace metals required for growth, with constant introduction of carbon dioxide at a rate of 5 L/min

(Bischoff and Bold 1963). The Chlorella vulgaris is grown at room temperature with

12 continuous illumination at 338 μmol∙m-2s-1, and it is aerated to enhance gas exchange by pumping 25 L/min of air through the reactor. UV-Vis spectrophotometry is used to monitor the algal growth rate, and the algal biomass is harvested by means of centrifugation about three times a week, or when the algal growth rate begins to decline. The dewatered algal biomass is then freeze dried, and the dried algae, with a remaining 5% to 10% moisture content, is stored at room temperature.

Figure 2.1-1: Photobioreactor systems at the University of Dayton Research Institute’s

Carbon Sequestration Laboratory

2.2 Oil Extraction

Direct oil extraction from algae for processing to renewable diesel has multiple advantages.

As mentioned earlier, unlike biological processing methods, direct oil extraction does not require sterile environments, extensive biomass digestion prior to processing and/or a

13 large footprint. Unlike themochemical processing methods, oil extraction is possible at moderate conditions and is therefore less energy intensive than gasification (which occurs at 700°C), pyrolysis (450°C) and hydrothermal liquefaction (350°C) (Mohan, Pittman and

Steele 2006; Biller and Ross 2011a). Oil extraction from wet algal biomass is also possible eliminating the drying step and associated additional energy requirement for both gasification and pyrolysis. And in addition, extracted oil is ‘clean’ and easier to process to diesel since it contains little nitrogenous compounds and/or other hetero-organics which are found in pyro-oils and HTL bio-crude (Shuping, et al. 2010; Ross, et al. 2010; W. E.

Becker 1994). Oil extraction for processing to renewable diesel, however, does have its challenges and limitations. The most obvious limitation is that conversion efficiency to diesel is limited by the oil content of the biomass used.

Microalgae have much greater oil yields than most terrestrial crops in terms of oil production per year per cultivated hectare. A comparison of terrestrial crop oil yields and

Chlorella vulgaris oil yield is presented in Table 2.2-1 on page 15 (Chisti 2007; Guschina and

Harwood 2006). Chlorella vulgaris typically produces between 14% and 22% lipids on a dry weight basis but may produce up to 40% lipids when nitrogen starved (Illman, Scragg and

Shales 2000). Chlorella vulgaris’ accelerated growth rate when compared to terrestrial crops and other algal species also allows for substantial oil production on a per day basis

(W. E. Becker 1994). The high rate of oil production and possibly high oil content of the

Chlorella vulgaris renders Chlorella vulgaris biomass a reasonable feedstock for the oil extraction process.

Oil extraction from algal biomass has however proven to be a challenge. In general, either mechanical oil presses or grinding, steaming followed by hexane based solvent extraction are employed to extract oil from terrestrial crops (Popoola and Yangomodou 2008).

However algal physiology differs from plant physiology and hence oil extraction from algae

14 presents different challenges. Chlorella vulgaris is unicellular with an average cell diameter between 3 µm to 10 µm (de Grooth, Geerken and Greve 1985). It is energetically unfavorable to use mechanical presses to extract oil from algal cells since a large amount of energy would be lost to frictional forces between the press surfaces to ensure cell wall sheering and oil extraction from this unicellular organism. Solvent based extraction is therefore preferred although the use of hexane as a solvent may not be ideal. Solvent based oil extraction from algae can be divided into three main steps: 1) biomass pretreatment, 2) oil extraction, and 3) oil purification.

Table 2.2-1: Comparison of oil yields of common crops and Chlorella vulgaris

Crop Oil yield (L/ha year)

Corn 172

Soybean 446

Canola 1,190

Jatropha 1,892

Coconut 2,689

Oil palm 5,950

Chlorella vulgaris 27,300 to 78,200*

*Estimated for 14%-40% lipid content

2.2.1 Biomass Pretreatment

Algal cells’ highly structured glycoprotein cell wall renders direct solvent based oil extraction from algal biomass difficult (Ender, et al. 2002). To increase extraction efficiency, researchers have studied algal cell wall lysing procedures that may be employed prior to

15 extraction. A study done by Lee et al. compared multiple pretreatment methods including subjecting the algal cells to microwaves, sonication, bead beating, autoclaving, and osmotic shock (2009). The study concluded that the subjection of Chlorella vulgaris to microwaves, capable of shattering cells by means of high-frequency shock waves, and autoclaving, which subjects the cells to 125°C and 1.5 MPa, are the most effective cell disruption methods. The study also found that subjecting the biomass to a 10% NaCl solution, which causes osmotic shock, and mechanical disruption by bead beating are the second most effective methods.

Sonication at 10 KHz was found to be the least effective method for cell disruption although oil extraction post-sonication was more effective than direct extraction.

A study done by Cravatto et al. indicated that low frequency ultrasonication, at 19 kHz, cracks cell walls and membranes due to a cavitation effect and facilitates lipid extraction more effectively than microwave assisted extraction (2008). And a third study by Ceron et al. indicated that mechanical cell disruption by means of ball milling and grinding with alumina is more effective at algal cell disruption than ultrasonication (2008).

Using an appropriate pretreatment method, one with scale-up feasibility and a low environmental impact, could save time and required solvent volume and increase extraction efficiency (Cravotto, et al. 2008). However it should be recognized that pretreatment methods can be energy intensive and can have negative effects on the stability of certain biomolecules, especially carotenoids (Ceron, et al. 2008; Zhao, et al. 2006). Pretreatment method, duration and intensity must therefore be tailored to the specific algal strain and trade-offs should be weighed. In this study biomass steaming, bead beating, ultrasonication, and mechanical grinding with alumina are explored to determine a feasible pretreatment method for large-scale algal processing.

2.2.2 Oil Extraction

Algae lipid content is not homogenous. Algal lipids generally comprise mixtures of nonpolar components such as mono- di- and triglycerides, carotenoids, waxes and sterols, as well as slightly polar free fatty acids and xanthophylls, and more polar phospholipids, sphingolipids, and glycolipids (W. E. Becker 1994). For total lipid extraction from algae, ideally the solvent or solvent mixture used must be adequately polar to extract polar lipids and disrupt lipid associations with cell membranes and cell components but also not too polar so as to ensure that the solvent readily dissolves nonpolar lipids (Johnson 1983). In addition to solvent polarity the following considerations must be made when selecting a solvent: 1) separation of the solvent from the oil must be relatively easy, and solvent recoverability for recycle must be high, 2) the solvent should be characterized by low specific heat, heat of vaporization and density in order to reduce costs associated with the energy requirement for solvent recycle and transport through piping, 3) the solvent should be environmentally friendly and preferably renewable so as not to render the renewable fuel production process futile, and 4) the solvent must be safe for human handling (Johnson

1983).

Generally solvents used for lipid extraction can be classified as one of the following: chlorinated hydrocarbons, petroleum hydrocarbons, or alcohols. Chlorinated hydrocarbons such as dichloromethane and chloroform are effective lipid solvents; however they are expensive and highly toxic (Johnson 1983). Hexane, the most commonly used solvent for large scale lipid extraction, is a petroleum hydrocarbon. The use of a solvent that is a petroleum distillate for the production of renewable diesel may seem unreasonable, but renewable hexane can be produced as a light distillate byproduct of the algal oil hydrotreatment and cracking process (Singh 2010). The advantages of using hexane include its low latent heat of vaporization and hence recoverability, its high stability, and its

17 noncorrosive nature (Johnson 1983). Hexane has been found to be very effective at extracting nonpolar lipids, but it only partially extracts polar lipids (Johnson 1983).

Depending on the conditions under which the Chlorella vulgaris was cultivated and the time of harvest, Chlorella vulgaris lipid content may be predominately polar, and hence depending on the composition of the Chlorella vulgaris used, hexane may or may not be a suitable solvent for total oil extraction from the algal biomass used in this study (W. E.

Becker 1994). Alcohols such as ethanol, isopropanol, n-propanol, n-butanol, and isobutanol are also effective solvents for oil extraction. Compared to other alcohols, ethanol has a relatively low latent heat of vaporization and ethanol’s polarity is most suitable for the uniform extraction of both polar and nonpolar lipids (Johnson 1983). Extraction with ethanol is an attractive option since ethanol is renewable; it is readily produced from agricultural residues. Another advantage of using ethanol over hexane is that biomass remaining post ethanol based lipid extraction, a protein rich cake known as the remnant, is higher quality than remnant remaining post hexane based lipid extraction. Remnant that has been extracted with ethanol is even suitable for use in the food industry (Johnson

1983). In a study by Ramirez Fajardo et al. a two-step ethanol based extraction process was developed to extract oil from the algal species Phaeodactylum tricornutum, and the process resulted in 96.1% lipid recovery (2007). However, the higher latent heat of vaporization of ethanol in comparison to hexane makes ethanol solvent recovery through distillation an expensive process. Non-distillation methods for solvent recovery such as chilling and recovering the extracted miscella from the alcohol may be used to reduce costs and energy requirement for ethanol recycle; by chilling the solvent up to 30% less energy is required for ethanol recovery than would be for hexane recovery by distillation (Johnson 1983).

Although common, lipid extraction using single component solvents at ambient pressures is

18 not the only option; solvent mixtures and pressurized extractions are also options for lipid extraction.

Over the years researchers have experimented with using mixtures of solvents in various ratios to fine tune solvent polarity and enhance oil extraction properties. The current standard protocol for total oil extraction from biomass for quantitative purposes is known as the “Bligh and Dyer method” and involves utilizing a 1:2 v/v chloroform to methanol solution for oil extraction (Smedes and Askland 1999; Bligh and Dyer 1959). However a study done by Iverson et al. revealed that lipid content in samples containing more than 2% lipids were greatly underestimated using the Bligh and Dyer method (2001). Iverson et al. concluded that using the “Floch method” for oil extraction from samples containing relatively high lipid content provides a more representative quantification. The Floch method involves using a 2:1 v/v chloroform to methanol solvent ratio for oil extraction

(Folch, Lees and Sloane-Stanley 1957). Multiple other modified analytical extraction procedures have been introduced over the years, and in preliminary work in the

Sustainable Environmental Technologies Laboratories multiple analytical methods for oil extraction were compared. It was concluded that the Floch method is most suitable for the extraction of representative total lipids. For this study, the Floch method was assumed to extract total lipids.

Multiple component solvents tend to be required for lipid extraction from wet samples.

Lipid extraction from wet biomass is a challenge due to the high polarity of water. The immiscibility of strictly nonpolar solvents such as hexane with the aqueous suspension disrupts the extraction capabilities of nonpolar solvents. On the other hand, the interaction of more polar solvents, such as ethanol, with water molecules enhances the extraction of polar lipids but reduces the solvent’s ability to extract nonpolar lipids. Multiple component

19 solvents can be used to fine tune solvent polarity and enhance extraction capability in aqueous media, and multiple researchers have developed appropriate mixed solvents.

Smedes and Askland introduced a procedure for oil extraction from wet aquatic biomass that utilizes a 11:8:10 v/v/v ratio of water to isopropanol to hexane, and Molina Grima et al. introduced a procedure for lipid extraction from algae that utilizes a 6:77:17 w/w/w ratio of water to ethanol to hexane (Smedes and Askland 1999; Molina Grima, Acien Fernandez, et al. 2009). At the ratios specified by Smedes and Askland and Molina Grima et al., these alcohol hexane mixtures form a single phase with the aqueous media and exhibit the necessary degree of polarity to extract lipids from the suspended biomass.

In pressurized extractors, room temperature gases such as propane, butane, carbon dioxide and dimethyl ether have been successfully used as solvents for oil extraction purposes

(Johnson 1983; Catchpole, Tallon, et al. 2007). The principle advantage of utilizing this class of solvents is that they are easily recoverable from the extracted oil by reducing pressure or applying slight heat. At high pressures these solvents are either in their liquid or supercritical fluid state. Supercritical fluids are fluids that are maintained at temperatures and pressures above their critical point, they have properties characteristic of both gases and liquids, and their density varies depending on the specific pressure and temperature conditions. Supercritical carbon dioxide is the most widely used pressurized solvent for lipid extraction.

Carbon dioxide is of interest because it has a relatively mild critical point (31°C and 7.4

MPa), and it is nontoxic, inexpensive, available in high purity, and nonflammable

(Valderrama, Perrut and Majewski 2003). Using carbon dioxide for lipid extraction also does not introduce residual organics in the remnant, which is an advantage if the remnant is to be further processed into animal feed or into synthetic fuels through thermochemical

20 means (Valderrama, Perrut and Majewski 2003). Supercritical carbon dioxide can be used to selectively extract particular compounds of interest. The solvation properties of carbon dioxide can be controlled by manipulating operating pressure and temperature during extraction; increased pressure and decreased temperature lead to decreased solvent diffusivity within the biomass matrix but increased solvent density or oil solvating power and vice versa (Marcias Sanchez, et al. 2010). Supercritical fluid extraction does not require prior cell wall disruption due to the high operating pressures, and energy costs associated with reaching the supercritical state for carbon dioxide have been shown to be less than the energy costs associated with solvent distillation (Kioschwitz and Howe-Grant 1991).

However carbon dioxide cannot extract complex lipids without the use of an organic co- solvent such as acetone, methanol or hexane, and carbon dioxide is immiscible in water making it a poor solvent for lipid extraction from aqueous media (Catchpole, Tallon, et al.

2007).

Liquefied dimethyl ether has emerged as an alternative to supercritical carbon dioxide for pressurized oil extraction from aqueous media. Dimethyl ether is a non-toxic environmentally friendly solvent with a boiling point of -25°C. Dimethyl ether is partially miscible in water and has been previously used to dry coal, sediment and various porous media (Catchpole, J, et al. 2008; Oshitaa, et al. 2010; Kanda, Makino and Miyahara 2008). In a study by the Central Research Institute of Electric Power Industry (CRIEPI) in Japan, liquefied dimethyl ether was successfully used to extract oil from cyanobacteria at moderate conditions, 20°C and 0.5MPa (2010).

In this study, the efficiency of oil extraction from both wet and dry Chlorella vulgaris will be compared for different solvents to determine a feasible solvent for large-scale extraction.

For freeze dried Chlorella vulgaris feedstock the extraction efficiencies of hexane, ethanol,

21 liquid dimethyl ether, supercritical carbon dioxide and supercritical carbon dioxide with acetone, methanol and hexane co-solvents will be compared. For wet Chlorella vulgaris feedstock the extraction efficiencies of dimethyl ether, ethanol, a water to isopropanol to hexane mixture (11:8:10 v/v/v), and an ethanol to hexane to water mixture (77:17:6 w/w/w) will be compared.

2.2.3 Oil Purification

Post total lipid extraction, the extract needs to be processed to eliminate lipid components that cannot be upgraded to green diesel and that may disrupt the hydrotreating process. If total oil extraction is achieved, the crude algal oil extract contains mono- di- and triglycerides, free fatty acids, waxes, sterols, tocopherols, pigments such as carotenoids and chlorophyll, phospholipids, glycolipids, and sphingolipids (Robles Medina, Molina Grima, et al. 1998). Long chain hydrocarbons, alcohols, and fatty acids are ideal structures for hydrotreatment and upgrade to renewable diesel, and of these, chlorella vulgaris produces fatty acids in greatest quantities (W. E. Becker 1994). Phosphate functional groups present in phospholipids, amino groups and polar nitrogenous head groups in sphingolipids, metal ligand containing nitrogenous chlorophyll chlorin rings, carbohydrate groups in glycoproteins, and complex sterols are all oil components that are counterproductive to renewable diesel production. Most of these structures, however, are generally bonded to fatty acid groups that are valuable for renewable diesel production (see Figure 2.2.3-1).

Note: R1 & R2 represent varying acyl residues (which are of interest); DGDG is digalactosyl- diacylglycerol; DGTS is l,2-diacylglyceryl-3-O-4_-(N,N,N-trimethyl)-homoserine; MGDG is monogalactosyl-diacylglycerol; PtdCho is phosphatidylcholine; PtdEtn is phosphatidylethanolamine; PtdGro is phosphatidylglycerol; and SQDG is sulfoquinovosyldiacylglycerol.

Figure 2.2.3-1: Examples of lipid structures abundant in algae (Vieler, et al. 2007)

A study by Ibnez Gonzalez et al. presents a three step method for the isolation of fatty acids from their linkages with these various groups. Ibnez Gonzalez et al. were interested in the isolation of eicosapentaenoic acid, a pharmaceutically valuable 20 carbon chain length fatty acid, from the algae Phaeodactylum tricornutum (1998). It is proposed that the process developed by Ibnez Gonzalez et al. can be adapted for the isolation of the fatty acids present in the Chlorella vulgaris oil extract; the purified fatty acids can then be processed to renewable diesel. The first step of the process proposed by Ibnez Gonzalez et al. is saponification. Saponification hydrolyzes the fatty acids freeing them from their bonds with the various lipid structures and reducing them to water soluble fatty acid salts.

Unsaponifiables, or lipids that are not hydrolyzed during saponification, remain nonpolar, insoluble in water, and soluble in hexane. Performing a hexane wash post-saponification isolates the unsaponifiables. Unsaponifiables include carotenoids, sterols, and tocopherols which are all very valuable in the nutraceutical industry (Ceron, et al. 2008; Robles Medina,

Molina Grima, et al. 1998). These oil components can be isolated and processed separately as value added products to render the renewable diesel production process more economically viable. To purify the fatty acids from the aqueous phase, which also contains polar amino groups, phosphates, etc., the media is acidified to protonate the fatty acid salts.

Post-protonation, fatty acids are insoluble in water and can be separated from the aqueous impurities by means of a hexane wash. Purified fatty acids are hence isolated through this method. In this study, the oil fractionation process developed by Ibanez Gonzalez et al. is adopted to fractionate oil extracted using the different solvents described in the previous section to determine how effective these solvents are at extracting fatty acid rich lipid components. Modifications of the process developed by Ibanez Gonzalez et al. are tested to render the process more feasible for use on a large-scale basis. It was also hypothesized that the aqueous waste stream produced through this process can be recycled as algal growth

24 medium since it should contain nutrients isolated from algae, including nitrogenous groups, phosphates and metals. This hypothesis is also tested.

2.3 Hydrothermal Liquefaction

Hydrothermal liquefaction is a thermochemical process during which biomass is decomposed into reactive molecules and re-polymerize to form bio-crude (Zhang, Xu and

Champagne 2010). It involves the reaction of biomass in water at high temperatures and pressures, or under supercritical water conditions. This process can be carried out with or without an added catalyst (Biller and Ross 2011a). The advantages of hydrothermal liquefaction include that wet algae can be directly processed, lipids are extracted and the remaining biomass is simultaneously thermochemically processed to a crude oil, the bio- crude yield is not solely reliant on the algal’s lipid content and higher yield than that from oil extraction is possible, and the aqueous phase produced during the hydrothermal liquefaction of algae is nutrient rich and has been successfully diluted and recycled as algal growth media (Jena, et al. 2011; Levine, et al. 2011; P. Biller, et al. 2011; Biller, Riley and

Ross 2011b).

HTL derived bio-crude composition is dependent on the biomass feedstock composition, the reaction conditions and duration, added catalyst or solvent, and the presence of a reducing gas (Akhtar and Aishah 2011). Based on a study by Ross et al., HTL bio-crude from Chlorella vulgaris may be composed of pyrrolidine, piperidine, phenol, amide, hydrocarbons, indole, alcohols, fatty acids, and derivatives of these compounds depending on the hydrothermal liquefaction procedure used (2010). Bio-crude from Chlorella vulgaris was also found to be

70%-75% C, 8%-11% H, 4%-7% N, 0%-0.6% S and 8%-16% oxygen (Biller and Ross 2011a;

Ross, et al. 2010). The oxygen content in HTL bio-crude is significantly lower than that in

25 pyro-oil, which is advantageous for downstream bio-crude upgrade to renewable diesel

(National Advanced Biofuels Consortium 2011). However, the possible diversity in the structure of the oil components and presence of elemental impurities in this oil could render this oil unfavorable for hydrotreatment.

The biochemical composition of Chlorella vulgaris biomass is variable, and it is dependent on growth conditions and the harvest cycle (W. E. Becker 1994). In this study, UDRI grown

Chlorella vulgaris is subjected to hydrothermal liquefaction without the presence of a catalyst. The purpose of this study is to determine the feasibility of hydrothermal liquefaction of this algal biomass for the production of an upgradable bio-crude.

Hydrothermal liquefaction of algae is a novel approach and little work in this area has been published. Biller and Ross are responsible for most of the work published on the hydrothermal liquefaction of Chlorella vulgaris. In this study hydrothermal liquefaction of

Chlorella vulgaris will be performed on a larger scale than that performed by Biller and

Ross, and the results will be compared.

The hydrothermal liquefaction of algal biomass for oil production and the oil extraction process will be compared. Based the experimental results from this study, an integrated process for oil production from algal biomass will be proposed.

CHAPTER 3

EXPERIMENTAL PROCEDURE

3.1 Oil Extraction

A batch of freeze dried Chlorella vulgaris (with 6% moisture content)1 was obtained from

UDRI’s Carbon Sequestration Laboratory. Using this batch of Chlorella vulgaris multiple oil extraction procedures aimed at total oil extraction from the freeze dried algae and from wetted algae were performed and evaluated. The affectivity of ambient pressure oil extractions, pressurized extractions, and multiple pretreatment procedures were compared.

Two repeats were performed for each method, and extracts from each run were compared gravimetrically. SAS software was used to perform an ANOVA statistical analysis and determine the statistical significance (α = 0.05) of the results obtained. Floch’s method was used as a baseline to gravimetrically determine total lipid content. The Chlorella vulgaris was pulverized to a fine powder prior to extraction using a mortar and pestle.

1 Based on results from the Center for Sustainable Environmental Technologies at Iowa State University

3.1.1 Floch’s Method

This procedure is an analytical gravimetric technique developed for total lipid quantification. The procedure presented is based on the original method published by Floch et al. (1957).

1. A Mettler Toledo PB602-S/FACT balance with 0.01 g readability was used to weigh 0.10

g of pulverized Chlorella vulgaris.

2. The Chlorella vulgaris was placed in a 5 mL cone based glass vial.

3. 2 mL of a 2:1 v/v chloroform to methanol mixture were added to the glass vial.

4. The vial was capped, and the mixture was vortexed for 30 seconds using a Denville

Scientific Inc. Vortexer 590a set to its highest setting.

5. The homogenate was then centrifuged for 10 minutes using a Clay Adams Dynac

Centrifuge set to ‘100’, or 1,425xg.

6. Using a glass Pasteur pipette, the liquid was collected in a 20 mL glass vial and the vial

was capped.

7. Steps 3 through 6 were repeated.

8. 0.8 mL of DI water was added to the 20 mL glass vial, and the vial was capped and

vortexed for 30 seconds.

9. The vial was then centrifuged for 5 minutes to achieve a phase separation.

10. The top aqueous phase was siphoned using a Pasteur pipet.

11. The bottom chloroform phase was collected in a pre-weighed 20 mL glass vial using a

clean glass Pasteur pipette.

12. The chloroform was dried under nitrogen.

13. The vial was weighed three times using a Mettler Toledo AB204-S/Fact micro balance

with a readability of 0.1 mg. The average weight was reported.

3.1.2 Extractions from Freeze Dried Algae at Ambient Pressure

Pretreatment by Alumina Grinding:

In the procedure outlined below, the ratio of alumina to Chlorella vulgaris used and the grinding time is based on results from a study by Ceron et al. where multiple algal pretreatment methods were optimized (2008).

1. 0.50 g of pulverized Chlorella vulgaris was weighed.

2. 0.50 g of alumina was weighed.

3. Using a mortar and pestle the mixture was ground for 5 minutes.

4. The Chlorella vulgaris and alumina mixture was placed in a 20 mL round bottom glass

vial.

5. 10 mL of a 2:1 chloroform to methanol mixture was added to the glass vial, the vial was

capped, and the mixture was vortexed on high for 30 seconds.

6. The homogenate was then centrifuged (1,425xg) for 10 minutes.

7. Using a glass Pasteur pipette, the liquid was collected in a 30 mL glass vial and the vial

was capped.

8. Steps 5 through 7 were repeated.

9. 4 mL of DI water was added to the 30 mL vial, and the vial was capped and vortexed on

high for 30 seconds.

10. The vial was then centrifuged (1,425xg) for 5 minutes to achieve a phase separation.

11. The top aqueous phase was siphoned using a Pasteur pipet.

12. The bottom chloroform phase was collected in a pre-weighed 20 mL glass vial using a

clean glass Pasteur pipette.

13. The chloroform was dried under nitrogen.

14. The vial was weighed three times using the micro balance (average weight reported).

Pretreatment by Ultrasonication:

The procedure described below is based on a study by Cravatto et al. where ultrasound assisted and microwave assisted extractions are compared (2008). The frequency used and the duration of the ultrasonication is based on the optimal conditions that Cravatto et al. observed.

1. 0.10 g of pulverized Chlorella vulgaris was weighed and placed in a 5 mL cone based

glass vial.

2. 2 mL of a 2:1 chloroform to methanol mixture were added to the glass vial.

3. The glass vial was placed in an ice bath and the contents were ultrasonicated at 19 kHz

for 5 second intervals for a minute. A Misonix Microson Ultrasonic Cell Disrupter XL was

used.

4. The vial was capped, and the mixture was vortexed on high for 30 seconds.

5. The homogenate was then centrifuged for 10 minutes.

6. Using a glass Pasteur pipette, the liquid was collected in a 20 mL glass vial and the vial

was capped.

7. 2 mL of the 2:1 chloroform to methanol mixture was added to the 5 mL cone based glass

vial and steps 4 through 6 were repeated.

8. 0.8 mL of DI water was added to the 20 mL glass vial, and the vial was capped and

vortexed on high for 30 seconds.

9. The vial was then centrifuged (1,425xg) for 5 minutes to achieve a phase separation.

10. The top aqueous phase was siphoned using a Pasteur pipet and the bottom chloroform

phase was collected in a pre-weighed 20 mL glass vial.

11. The chloroform was dried under nitrogen.

12. The vial was weighed three times using the micro balance (average weight reported).

For Ethanol Based Extraction:

The procedure described below is based on a study by Ramirez Fajardo et al. where multiple extraction times were compared for ethanol based lipid extraction from algae

(2007). The two step extraction process that, based on the study by Ramirez Fajardo et al., would yield 96.1% of the total lipids was adopted for this procedure (2007). Some aspects of the procedure that were suggested by Ramirez Fajardo et al. were, however, modified.

The ratio of ethanol to algae suggested by Ramirez Fajardo et al. was not used because at a

5:1 ratio the slurry formed was too thick for mixing. The suggested 96% ethanol was not used and the more widely available 190 proof ethanol was substituted. The ‘crude oil’ purification step was not employed since total lipid extraction was of interest, and hexane only partially extracts polar lipids (Johnson 1983).

1. 0.10 g of pulverized Chlorella vulgaris was weighed and placed in a 5 mL cone based

glass vial along with a magnetic stir bar.

2. 1 mL of ethanol (190 proof) was added to the glass vial.

3. The vial was capped and placed on a stir plate.

4. The stirring speed was adjusted to ensure constant agitation of the total slurry, and the

slurry was allowed to mix for ten hours.

5. The homogenate was then centrifuged (1,425xg) for 10 minutes.

6. Using a glass Pasteur pipette, the liquid was collected in a pre-weighed 20 mL glass vial.

7. Steps 2 through 6 were repeated, but the slurry was only mixed for 1.25 hours.

8. The ethanol was dried under nitrogen.

9. The vial was weighed three times using the micro balance, and the average weight was

reported.

For Hexane Based Extraction:

The procedure described below is based on the standard method used for industrial scale oil extraction from terrestrial crops. Hexane extraction and steam pretreatment is the standard protocol (Johnson 1983) (Popoola and Yangomodou 2008) (EPA 1995).

1. 0.10 g of pulverized Chlorella vulgaris was weighed and placed in a 5 mL cone based

glass vial.

2. 3 mL of technical grade hexanes was added to the glass vial, the vial was capped, and the

mixture was vortexed on high for 30 seconds.

3. The homogenate was then centrifuged (1,425xg) for 10 minutes.

4. Using a glass Pasteur pipette, the liquid was collected in a pre-weighed 20 mL glass vial.

5. Steps 2 through 4 were repeated.

6. The hexane was dried under nitrogen.

7. The vial was weighed three times using the micro balance, and the average weight was

reported.

8. 0.10 g of pulverized Chlorella vulgaris was weighed.

9. The Chlorella vulgaris was steamed for 5 minutes. This was achieved by suspending the

Chlorella vulgaris in a covered beaker of rapidly boiling water.

10. The steamed Chlorella vulgaris was placed in a 5 mL cone based glass vial.

11. Steps 2 through 7 were repeated.

12. Steps 8 through 11 were repeated for a 10 minute steam time.

3.1.3 Extractions from Wet Algae at Ambient Pressure

To simulate centrifuge harvested wet algae, water was added to the freeze dried algal biomass. A ratio of 1:9 w/w algae to water was used since centrifuged extracted algae is generally about 90% wet.

Modified Smedes and Askland’s Method:

The solvent ratios used and the procedure described below is based on a study by Smedes and Askland in which the affectivity of various multiple component solvents at extracting oil from wet aquatic biomass is compared (1999). Industrial quality mixed hexanes was however substituted for the recommended pure cyclohexane, and nitrogen drying was substituted for oven drying.

1. 0.10 g of pulverized Chlorella vulgaris was weighed and placed in a 5 mL cone based

glass vial.

2. 0.9 mL of DI water, 0.655 mL of isopropanol, and 0.82 mL of hexanes was added to the

vial.

3. The vial was capped, and the mixture was vortexed on high for 1 minute.

4. The homogenate was then centrifuged (1,425xg) for 10 minutes.

5. Using a glass Pasteur pipette, the top organic phase was collected in a 20 mL pre-

weighed glass vial.

6. 1 mL of hexanes and 0.11 mL of isopropanol were added to the 5 mL cone based glass

vial.

7. Steps 3 through 5 were repeated with vortexing for 2 minutes.

8. The solvent was dried under nitrogen.

9. The pre-weighed vial was weighed three times using the micro balance.

10. The average weight was reported.

Modified Molina et al.’s Method:

The solvent ratios and the procedure described below is based on a study by Molina et al.

where a multiple component solvent is optimized for oil extraction from wet algae (Molina

Grima, Acien Fernandez, et al. 2009). However, 190 proof ethanol was substituted for the

96% ethanol recommended.

1. 0.10 g of pulverized Chlorella vulgaris was weighed and placed in a 25 mL glass flask.

2. 0.9 mL of DI water, 14.64 mL 190 proof ethanol, and 4.0 mL of hexane and a stir bar was

added to the flask.

3. The flask was capped, and the mixture was stirred for 24 hours.

4. The homogenate was then decanted into two 20 mL glass centrifuge tubes and 2 mL of a

77:17:6 w/w /w ethanol to hexane to water mixture were used to wash the flask.

5. The homogenate was centrifuged (1,425xg) for 10 minutes.

6. Using a glass Pasteur pipette the liquid was collected in a 20 mL pre-weighed glass vial

and dried under nitrogen.

7. The pre-weighed vial was weighed three times using the micro balance.

8. The average weight was reported.

For 70% Ethanol Based Extraction:

The same procedure described for ethanol extraction from freeze dried algae was used, but

water was added to the pulverized algal biomass to simulate wet algae. A 70% ethanol

solution was used, which is common for commercial applications

1. 0.10 g of pulverized Chlorella vulgaris was weighed and placed in a 5 mL cone based glass

vial along with a magnetic stir bar.

2. 2.1 mL of ethanol (190 proof) and 0.9 mL of water were added to the glass vial.

3. The vial was capped and placed on a stir plate.

4. The stirring speed was adjusted to ensure constant agitation of the total slurry, and the

slurry was allowed to mix for ten hours.

5. The homogenate was then centrifuged (1,425xg) for 10 minutes.

6. Using a glass Pasteur pipette, the liquid was collected in a pre-weighed 20 mL glass vial.

7. Steps 2 through 6 were repeated, but the slurry was only mixed for 1.25 hours and no

water was added.

8. The solvent was dried under nitrogen, the vial was weighed three times using the micro

balance, and the average weight was reported.

3.1.4 Pressurized Extractions from Algae

An SFT-100 supercritical fluid extractor was acquired, setup and used to perform the pressurized extractions (see Figure 3.1.4-1). The SFT-100 is a supercritical carbon dioxide extractor manufactured by Supercritical Technologies Inc. The SFT-100 components include a dual piston pump fitted with a Peltier cooler, a 100 mL stainless steel cylindrical extraction vessel housed within an oven chamber, a restriction block to control the extraction vessel output, and the necessary controls to maintain set extraction vessel pressure and temperature.

The SFT-100 is rated for a maximum allowed working pressure of 10,000 psi (about 69

MPa) and a maximum allowed working temperature of 150°C. This extractor is a constant pressure extractor, whereby the pressure of the extraction vessel is set by the user, and built-in actuators are used to maintain that pressure by controlling carbon dioxide inlet flowrate. The maximum achievable carbon dioxide inlet flowrate is 24 mL/min.

100 mL Extraction vessel

Restrictor block temperature Oven controller and display

Static & Oven temperature dynamic controller and Restrictor valve display block Restrictor valve

Carbon dioxide inlet Outlet

Carbon dioxide dual piston pump and pressure and inlet flow display and controller

Figure 3.1.4-1: SFT-100 used for pressurized extractions

The cylindrical extraction vessel is pre-filled with the material to be extracted and sealed. A stainless steel line leading from the carbon dioxide dual piston pump to the bottom of the extraction vessel acts as the carbon dioxide inlet. A line leading out from the top of the vessel and into the restrictor valve assembly acts as the extract outlet. The restrictor valve assembly is a heated block with two manual valves connected in series. The restrictor block temperature is maintained at 60°C to prevent the line from freezing due to rapid gas expansion at the outlet. The first valve in series is the static/dynamic valve, which is an on/off valve that is used to alternate between the static and dynamic conditions. The second valve is the restrictor valve and it is used to control the outlet flow rate.

The outlet line, a 1/16’’ stainless steel tube, was connected to a 20 mL glass impinger by means of a reducing union with compression fitting end connectors (see Figure 3.1.4-2). A graphite gasket was used to tighten the compression fitting against the glass impinger inlet.

As the carbon dioxide carrier gas enters the impinger, which is unpressurized and maintained at room temperature, it is vaporized and exits through the impinger outlet and the extract is deposited at the base of the impinger. The impinger outlet was fitter with rubber tubing, and a flowmeter was kept on hand to check for leaks for when the SFT was run under static conditions.

The SFT-100 can be operated in both a static and a dynamic fashion. Static, or batch process, extraction is achieved by tightening the static/dynamic valve and ‘soaking’ the contents of the extraction vessel with carbon dioxide over a period of time. The inlet carbon dioxide flow is then shut off and the static/dynamic valve is loosened to allow the extract to leave the vessel through the outlet line. Dynamic, or continuous, extraction is achieved by loosening the static/dynamic valve and operating the reactor with a continuous carbon dioxide inlet and outlet flow rate while maintaining vessel pressure conditions. Preliminary experiments showed that soaking the algae in carbon dioxide for 30 minutes at 40°C and

8,800 psi (about 61 MPa), and then loosening the restrictor valves and allowing the vessel to depressurize led to maximum extraction. These conditions were used to perform the supercritical carbon dioxide extractions in this study.

Figure 3.1.4-2 : Impinger setup for extract collection

Supercritical Carbon Dioxide Extractions

Figure 3.1.4-3 present a flow diagram of the SFT-100 setup for supercritical carbon dioxide extraction. Bone dry carbon dioxide (99.995% purity) supplied by Airgas was used for the supercritical carbon dioxide extractions. The extraction vessel was filled with 10.00 g of pulverized freeze dried Chlorella vulgaris for each of the runs and each setup was run twice.

Prior to running the extractions, the extractor was purged with carbon dioxide to ensure that only carbon dioxide was present in the extractor lines and vessel headspace.

 Setup 1: pure supercritical carbon dioxide was used to extract the algae.

 Setup 2: glass beads were added the vessel containing the freeze dried algae to simulate

bead beating during the extraction process.

 Setup 3: 10 mL of acetone were added to the algal biomass before sealing the vessel.

 Setup 4: 10 mL of hexane were added to the algal biomass before sealing the vessel.

 Setup 5: 10 mL of methanol were added to the algal biomass before sealing the vessel.

 Setup 6: 10 mL of acetone were added to the algal biomass before sealing the vessel. The

vessel was opened post extraction and 10 mL of hexane was added, and the extraction

was rerun. The vessel was opened post extraction and 10 mL of methanol was added

and the extraction was rerun.

For each of the runs, after allowing the extraction vessel to depressurize and cool, the extraction vessel was disconnected and the vessel outlet lines were washed with methylene chloride to ensure that the total extract was collected within the impinger. The extract collected within the impinger was then transferred to pre-weighed vials using methylene chloride to wash the walls of the impinger. The methylene chloride solvent was dried under nitrogen, and the pre-weighed vials were reweighed.

Heated Variable Oven

E-3 V-1 V-2

I-1 TAH TIC TIC F

TAH E-2 E-4

FIC PAH

E-1 FIO FAH

PIC

Carbon Dioxide

Figure 3.1.4-3: SFT-100 flow diagram for supercritical carbon dioxide extractions

Pressurized Dimethyl Ether Extractions

For the pressurized dimethyl ether extractions, a tank of chemically pure liquid dimethyl ether was obtained from Matheson Trigas. This tank was fitted with a pressure gauge and a check valve and connected directly to the extraction vessel (see Figure 3.1.4-4). The tank pressure, 529 kPa at 21°C, was the operating pressure of the extractions; the extraction vessel temperature was set to 21°C. Three 10 minute soak times followed by extract purging was found to maximize extraction, and this procedure was hence used for this study.

Two different setups were used for liquefied dimethyl ether extraction and each of the setups was run twice:

 Setup 1: 10.00 g of pulverized freeze dried algae was placed in the vessel and the

extraction was run.

 Setup 2: 40.75 g of centrifuge harvested wet algae was placed in the vessel and the

extraction was run.

Heated Variable Oven

E-3 V-1 V-2

I-1 TAH TIC TIC F

TAH E-2 E-4

Dimethyl Ether

Figure 3.1.4-4: SFT-100 flow diagram for liquid dimethyl ether extractions

3.2 Oil Fractionation

The fractionation method employed was based on a study by Ibanez Gonzalez et al. where an oil fractionation process was optimized to enhance eicopentaneoic acid isolation (1998).

The initial and optimized processes developed by Ibanez Gonzalez et al. are presented in

Figure 3.2-1. Since a total lipid extraction was performed prior to fractionation in this study, a modification of this procedure was utilized and is described in the following sections.

Figure 3.2-1: Oil fractionation procedure as described by Ibanez Gonzalez et al., 1998

3.2.1 Analytical Determination of Oil Extract Composition

For oil extraction procedures that performed well gravimetrically, oil fractionation was employed to ensure that the extract contained a high proportion of fatty acids and that the results of the gravimetric analysis were not skewed by impurities. The solvent ratios used are based on those cited by Ibanez Gonzalez et al. to achieve 100% eicosapentaenoic acid isolation and about 72% β-carotene isolation in the unsaponifiable fraction (1998). For this section of the study, hydrochloric acid (HCl) is used to bring the pH of the aqueous media post saponification to 1, to ensure total protonation of all the fatty acids and hence isolation from the aqueous media.

1. The oil extract was transferred to a 25 mL vial, and 0.48 mL of DI water, 1 mL of ethanol,

and 0.04 g of KOH were added.

2. The vial was capped and placed in a 60°C water bath for 1 hour.

3. The vial was allowed to cool and 0.21 mL of DI water and 25 mL of hexane were added

to the vial.

4. The vial capped and the mixture was vortexed for 30 seconds.

5. The homogenate was then centrifuged for 10 minutes.

6. Using a glass Pasteur pipette, the top organic phase was collected in a 20 mL pre-

weighed glass vial.

7. The hexane was evaporated under nitrogen.

8. The pre-weighed vial was weighed three times using the micro balance, and the average

weight was reported.

9. 3 Drops of HCl were added to the aqueous phase to bring the pH of the mixture to 1.

10. 25 mL of hexane were added to the vial, the vial was capped and the mixture was

vortexed for 30 seconds.

11. The homogenate was then centrifuged for 10 minutes.

12. Using a glass Pasteur pipette, the top organic phase was collected in a 20 mL pre-

weighed glass vial.

13. The hexane was evaporated under nitrogen.

14. The pre-weighed vial was weighed three times using the micro balance, and the average

weight was reported.

3.2.2 Modification of the Fractionation Method

Using hydrochloric acid could lead to the introduction of chlorine impurities in the extracted oil, and working with a pH of 1 for large-scale processing is not ideal. Ibanez

Gonzalez et al. concluded that total eicosapentaenoic acid could be protonated and hence isolated from the aqueous media by bringing the pH to 6 using HCl. In this section of the study the efficacy of fatty acid isolation when using the non-mineral organic acid acetic acid was tested. Three runs were performed. Prior to each run, oil was extracted from 0.10 g of freeze dried pulverized Chlorella vulgaris using ethanol. The extracted oil was then fractionated using the basic method described in the previous section while substituting a different protonation acid/pH level per run:

 Run 1: 3 drops of HCl were added to bring the pH down close to 1.

 Run 2: 2 drops of HCl were added to bring the pH down close to 6.

 Run 3: 5 drops of glacial acetic acid were added to bring the pH down close to 6

The fatty acid fractions from these runs were weighed and compared.

3.3 Large-Scale Extraction

Based on the results from the previous experiments, an effective procedure for large-scale oil extraction from Chlorella vulgaris was determined. A modified oil fractionation procedure was proposed and it was hypothesized that this procedure would allow for the efficient use of the total oil extract; unsaponifiables can be isolated and used as value added products, purified fatty acids can be directly upgraded to renewable diesel, and it was hypothesized that the aqueous phase that results from the fractionation procedure contains algal nutrients which may be recycled as algal growth medium. The proposed method for large-scale extraction, outlined in Figure 3.3-1, was used to extract and fractionate total oil from 100 g of freeze dried Chlorella vulgaris. Samples from the different extract fractions were used to identify and quantify the constituents of the various fractions.

1. 100 g of freeze dried algae was weighed and placed in a 2 L flask.

2. 1 L of ethanol and a magnetic stir bar were added and the flask was stoppered.

3. The flask was placed on a stir plate, the stirring speed was adjusted to ensure constant

agitation of the total slurry, and the slurry was allowed to mix for ten hours.

4. The homogenate was decanted into 1 L polyethylene centrifuge bottles and centrifuged

at 3000 rpm for 20 minutes. A IEC Centra-7R refrigerated centrifuge from the

International Equipment Company was used.

5. The extract was decanted in a 2 L glass bottle.

6. The remnant was collected in the 2 L flask.

7. Steps 2 through 5 were repeated, but the slurry was only mixed for 1.25 hours.

8. The ethanol was evaporated using a Heidolph Instruments Laborota 4003

rotoevaporator.

9. The extracted oil, 1 L of ethanol, 40 g of KOH and 480 mL of water were placed in a

round bottom flask.

10. The flask was placed in a water bath set to 60°C for 1 hour.

15. The vial was allowed to cool and 210 mL of DI water were added to the flask.

16. The slurry divided between two 2 L separator funnels.

17. The contents of each of the funnels was washed with 500 mL of hexane three times.

18. The hexane phase was collected and the hexane was evaporated by means of a

rotoevaporator.

19. 120 mL of acetic acid were added to the aqueous phase to bring the pH of the aqueous

phase to 5.83.

20. Steps 16 through 18 were repeated.

21. The ethanol was evaporated from the aqueous phase by means of a rotoevaporator.

The resulting extracted fractions were analyzed and processed separately.

1 L Ethanol

1 L Ethanol Remnant Mixing at ambient conditions (1.25 hours)

Extraction: Centrifuge 100 g Algae Mixing at ambient 3000 rpm Centrifuge conditions (20 mins) 3000 rpm RemnantRemnant (10 hours) (20 mins)

Extract + Ethanol

Extract + Ethanol Rotoevaporate 1 L Ethanol

1.5 L Hexane 210 mL H2O (X3) 0.5 L Hexane 1 L Ethanol

Saponification Biphasic Rotoevaporate Hexane Phase at 60°C 40 g KOH Separation (1 hour) 480 mL water

Aqueous Phase UnsaponifiablesUnsaponifiables

120 mL Acidification 1 L Ethanol Acetic Acid

Biphasic (X3) 0.5 L Hexane Rotoevaporate Separation Aqueous Phase

Organic Phase AqueousAqueous PhasePhase

Rotoevaporate 1.5 L Hexane

FattyFatty AcidsAcids

Figure 3.3-1: Large-scale extraction procedure

3.3.1 Unsaponifiable Fraction Analysis

A portion of the unsaponifiable fraction was sent to Craft Technologies Inc. to determine the tocopherol, sterol and carotenoid profile of this oil.

A GCxGC-MS analysis was run to identify the chemical constituents of this extract. An

Agilent 7890 GCxGC system equipped with two columns in series and two ovens was used.

The primary column, located in the primary oven, was a nonpolar 100% dimethylpolysiloxane column (DB-1) 30 m in length with a 250 μm internal diameter and

0.25 μm film thickness. The secondary column, located between the modulator, secondary oven and detector transfer line, was a polar Rxi-17silMS with 50% phenylmethyl polysiloxane and 50% dimethyl polysiloxane stationary phase (1.25 m in length with a 150

μm internal diameter and a 0.15 μm film thickness).

An Agilent auto-sampler with a 10 μL syringe was used was used to inject a 5 μL sample of the oil diluted in methylene chloride. Hydrogen carrier gas was used, and a constant column flow of 1.30 mL/min was maintained. An inlet split ratio of 50 (65 mL/min) was set such that a total flow of 66.3 mL/min was used. The inlet temperature was set to 325°C. The primary oven temperature was maintained at 35°C for 3 minutes, and then the temperature was increased to 265°C at a rate of 5°C/min and held for 45 minutes. The secondary oven temperature was maintained at a +5°C offset from the primary oven temperature and the temperature of the modulator was maintained at a +20°C offset from the primary oven. A 5 second modulation period with a 0.60 second hot pulse time and a 1.90 second cool time was maintained for the first 1020 seconds of operation, followed by a 5 second modulation period with a 0.80 second hot pulse and a 1.70 second cool time for the remainder of the run. The transfer line temperature between the secondary column and the MS was maintained at 300°C.

A 200 second acquisition delay was set for the MS. The lowest mass read by the MS was set at 30 u and the highest at 350 u. An acquisition rate of 200 spectra/second was set. The voltage of the MS detector used was 1650 V, with a 200°C ion source.

Blanks and multiple samples were run using this method and the clearest chromatogram was presented.

3.3.2 Fatty Acid Fraction Analysis

A portion of the fatty acid fraction was sent to Galbraith Laboratories Inc. for an elemental analysis of the carbon, hydrogen, oxygen, nitrogen, phosphorus, sulfur and chlorine content of the sample.

A GCxGC-MS analysis was run to identify the chemical constituents of this extract. The same

GCxGC-MS, columns, MS settings, carrier gas, column flow rate and injection volume used to analyze the unsaponifiable s was used to analyze the fatty acid fraction. The program was however modified to obtain better peak separation. An inlet split ratio of 10 (13 mL/min) was set such that a total flow of 14.3 mL/min was used. The inlet temperature was set to

325°C. The primary oven temperature was maintained at 35°C for 3 minutes, the temperature was increased to 185°C at a rate of 5°C/min, then increased to 200°C at a rate of 0.5°C/min, and finally increased to 265°C at a rate of 5°C/min and held for 10 minutes.

The secondary oven temperature was maintained at a +5°C offset from the primary oven temperature and the temperature of the modulator was maintained at a +20°C offset from the primary oven.

Blanks and multiple samples were run using this method and the clearest chromatogram was presented.

3.3.3 Aqueous Fraction

ICP-MS was used to quantify the metal content of the aqueous fraction. A sample of the process water was also sent to Galbraith Laboratories Inc. in order to quantify total nitrogen and phosphorus content.

Chlorella vulgaris growth in dilutions of the process water was also tested. To quantify biomass accumulation optical density measurements were taken at 550nm on a daily basis

(Patino, Janssen and Stockar 2007). Light intensity, pH, water level, carbon dioxide supply and other environmental factors effecting algal growth and accumulation were kept constant in all of the setups. The following procedure was used:

1. The process water, originally 690 mL, was diluted to 750 mL using DI water.

2. Ten 250 mL Erlenmeyer flasks were obtained and ten setups were prepared and

autoclaved:

 Setup 1: 100 mL of Bold’s Basal Medium

 Setup 2: 100 mL of DI water

 Setup 3: 0.5 mL process water diluted in DI to 100 mL

 Setup 4: 1 mL process water diluted in DI to 100 mL

 Setup 5: 2 mL process water diluted in DI to 100 mL

 Setup 6: 3 mL process water diluted in DI to 100 mL

 Setup 7: 4 mL process water diluted in DI to 100 mL

 Setup 8: 5 mL process water diluted in DI to 100 mL

 Setup 9: 6 mL process water diluted in DI to 100 mL

 Setup 10: 10 mL process water diluted in DI to 100 mL

3. The pH of the media within the flasks was measured and adjusted to 7.

4. 100 mL of Chlorella vulgaris, suspended in its growth medium, was obtained from one of

UDRI’s Carbon Sequestration Laboratory photobioreactors.

5. The Chlorella vulgaris was concentrated by centrifugation, and re-suspended in 10 mL

of DI water.

6. 1 mL of the Chlorella vulgaris suspension was added to each of the ten setups.

7. The vials were subjected to constant illumination and a constant stream of air was

bubbled through them (see Figure 3.3.3-1) for a period of 7days.

8. The pH of the setups was measured and adjusted to 7 daily.

9. On a daily basis the contents of the flasks were mixed and 1 mL was taken from each of

the setups and was used to measure the absorbance at a wavelength of 550 nm (a DI

water blank was used).

10. The water level in the flasks was adjusted to 100 mL every other day.

Figure 3.3.3-1: Experimental setup for the process water recycle experiment

3.4 Hydrothermal Liquefaction

Hydrothermal liquefaction of a sample from the same batch of Chlorella vulgaris that was used for the oil extraction and fractionation procedures was performed. The hydrothermal liquefaction process was not optimized, and this process was only investigated to gauge the potential of utilizing this process for the development of an algal crude oil from the available Chlorella vulgaris biomass on a large-scale basis.

A pressure reactor manufactured by Pressure Products Industries, Milton Roy was utilized.

The components of this reactor include a 1 L SS-316 reaction vessel that is 260 mm deep with a 67 mm internal diameter. The reaction vessel is encased in a heating element and is fitted with a thermocouple thermowell. The reactor is rated for a maximum operating pressure of 6000 psi (41.4 MPa) at the maximum operating temperature of 350°C. The reactor has seven 1/4” inlet/outlet lines located on around the reactor cover. Of these seven, one was fitted with a rupture disk, one was fitted with a 0.2 micron tee-filter from

Swagelock, a needle valve rated for use at high temperature and pressure, a pressure gauge and an outlet for gas collection, and the remaining five lines were blinded.

For the hydrothermal liquefaction of algae, 40 g of pulverized freeze dried Chlorella vulgaris was mixed with 360 mL of DI water to obtain a 90% aqueous algal solution (comparable to the water content in centrifuge harvested algae). The slurry was placed in the reactor vessel and the vessel was sealed. A helium leak test was performed to ensure the vessel was air tight; helium was pumped into the reaction vessel and a Restek helium leak detector was used to test for leaks. The vessel was heated to 350°C and the pressure was maintained at this temperature for 1 hour.

Figure 3.4 -1: PPI Milton Roy pressure reactor

Post hydrothermal liquefaction, the reaction vessel was allowed to cool overnight. A 1 L

FlexFoil gas collection bag from SKC Inc. was washed with helium; the bag was filled with high purity helium and emptied using a vacuum pump five times. The gas collection bag was connected to the reactor outlet line, and the needle valve was loosened allowing the gas that was formed in the reaction vessel to be collected in the gas collection bag. To isolate semi- volatiles from the volatiles and eliminate water vapor in the gas phase, a peristaltic pump was used to pump the gas from the gas collection bag through a 3/8’’ glass column packed with anhydrous sodium sulfate and XAD-2 sorbent resins and into a secondary washed pre- weighed FlexFoil gas collection bag. The semi-volatiles were trapped in the sorbent resin and the sodium sulfate trapped water vapor. The secondary bag was re-weighed.

The gas phase constituents were identified and quantified by means of GC-TCD. A Varian

CP-3800 gas chromatograph was utilized. A 2 mL sample of the collected gas was injected in the GC-TCD. Helium carrier gas was used, and the flow was set to 30 mL/min for 29.50 minutes. The injector temperature was maintained at 225°C, and the column oven was initially set to 40°C and this temperature was held for 5 minutes. The oven temperature was then raised at a rate of 20°C/min until a temperature of 225°C was achieved, and this temperature was held for 15 minutes. The TCD detector temperature was set to 230°C with a filament temperature of 300°C. Available calibration curves were utilized to quantify the gases.

To identify and quantify the semi-volatiles formed, 5 μL of a deuterated polycyclic aromatic hydrocarbon (PAH) standard was injected into the 3/8’’ glass column packed with anhydrous sodium sulfate and XAD-2 sorbent resins. After injecting the standards, the column was extracted with 60 mL of methylene chloride. Most of the methylene chloride was dried under nitrogen concentrating the extract in a few milliliters. The PAH standard used contains 2 μg/μL of each of the following deuterated PAHs: acenaphthene-D10, chrysene-D12, 1,4-dichlorobenzene-D4, naphthalene-d8, and phenanthrene-d10 suspended in methylene chloride.

GC-MS was used to quantify the semi-volatile components. An Agilent Technologies 6890N

GC system equipped with a 29 m by 250 μm by 0.1 μm nominal capillary column with a cross-linked/surface bonded, 5% phenyl, 95% dimethylpolysiloxane (Agilent DB5HT column) was utilized. An automated injector using a 10 μL syringe was used to inject 1 μL of the semi-volatile suspension. Splitless injection was employed, and the injector temperature was set at 300°C. Helium carrier gas was used and total flow was set at 71 mL/min. The oven temperature was held at 40°C for 4 minutes and then increased at a rate of 10°C/min

55 to 350°C and maintained for 5 minutes. The solvent delay was set to 5 minutes, and the injector and detector temperatures were both set to 250°C.

The reactor vessel was opened and its contents were collected. The contents of the vessel were vacuum filtered, using a Buchner funnel fitted with a pre-weighed 0.2 micron

Whatman glass microfiber filter. The filter was allowed to dry and was then reweighed to determine the solid mass fraction. Methylene chloride (500 mL) was added to the collected liquid phase, and a 2 L separatory funnel was used to perform a phase separation.

The methylene chloride phase was collected and the methylene chloride was evaporated by means of rotoevaporation to isolate the bio-crude. The bio-crude constituents were identified by means of GCxGC-MS (see section 3.3.1 for program details). A sample of the oil was also sent to Galbraith Laboratories Inc for a carbon, hydrogen, oxygen, nitrogen, phosphorus, sulfur and chlorine content analysis.

ICP-MS was used to quantify the metal content of the aqueous phase. A sample of the aqueous phase was also sent to Galbraith Laboratories Inc. in order to quantify total nitrogen and phosphorus content.

CHAPTER 4

RESULTS AND DISCUSSION

4.1 Oil Extraction Results

The lipid content of the Chlorella vulgaris used comprises 17.94%±1.31% of the dry algal mass; the Floch method was used to determine this value. The detailed results of the various extraction methods tested are presented in Table 4.1-1, and the average percent extracted by each of these methods is compared in Figure 4.1-1. Note that standard deviation and the method of Kline and McClintock were used to determine experimental uncertainty and propagated uncertainty due to calculations, respectively.

Assuming that the total lipid content of the Chlorella vulgaris was in fact 17.94%±1.31% of the algal dry mass, the average relative extraction efficiencies of the various extraction methods used are presented in Figure 4.1-2. It is clear from these results that hexane, supercritical carbon dioxide, dimethyl ether, and the modified Smedes and Askland solvent are not very effective solvents for lipid extraction from the Chlorella vulgaris biomass; these methods extracted less than 50% of the total algal lipid content.

Table 4.1-1: Lipid extraction results

Dry Weight Total Extract Standard Percent Percent Method (g)* Mass (mg) deviation σ (mg) Extracted Uncertainty 0.09 17.40 18.51% ±1.90% Floch ±0.76 0.09 16.33 17.37% ±1.80% Floch Post Alumina 0.47 79.88 17.00% ±0.67% ±2.96 Pretreatment 0.47 75.69 16.10% ±0.67% 0.09 17.02 18.10% ±1.71% Floch Post Ultrasonication ±0.12 0.09 17.18 18.28% ±1.72% 0.09 20.16 21.45% ±2.26% 95% Ethanol ±1.02 0.09 21.60 22.98% ±2.39% 0.47 20.00 4.26% ±0.13% Hexane ±0.52 0.47 19.27 4.10% ±0.13% Hexane Post Steaming 0.09 4.20 4.46% ±0.47% ±0.21 (5min) 0.09 4.50 4.79% ±0.50% Hexane Post Steaming 0.09 2.65 2.82% ±0.35% ±0.23 Extraction from (10min) 0.09 2.33 2.48% ±0.32% Freeze Dried Algae 9.40 436.50 4.64% ±0.00% Dimethyl Ether ±0.18 9.40 436.24 4.64% ±0.00% 9.40 80.00 0.85% ±0.00% Supercritical CO ±0.00 2 9.40 80.00 0.85% ±0.00% 9.40 50.00 0.53% ±0.00% Supercritical CO & Beads ±0.00 2 9.40 50.00 0.53% ±0.00% 9.40 70.00 0.74% ±0.07% Supercritical CO & Acetone ±7.07 2 9.40 60.00 0.64% ±0.07% 9.40 60.00 0.64% ±0.00% Supercritical CO & Hexane ±0.00 2 9.40 60.00 0.64% ±0.00% Supercritical CO & 9.40 140.00 1.49% ±0.07% 2 ±7.07 Methanol 9.40 150.00 1.60% ±0.07% Supercritical CO & Acetone, 9.40 350.00 3.72% ±0.07% 2 ±7.07 Hexane, and Methanol 9.40 340.00 3.62% ±0.07%

3.83 38.94 1.02% ±0.01% Dimethyl Ether ±0.50 3.83 38.23 1.00% ±0.01% 0.09 19.50 20.74% ±2.13% 70% Ethanol ±0.85 Extraction from 0.09 20.70 22.02% ±2.24% Wet Algae Modified Smedes and 0.09 6.40 6.81% ±0.98% ±0.74 Askland Method 0.09 7.44 7.92% ±1.05% 0.09 21.50 22.87% ±2.19% Modified Molina Method ±0.42 0.09 20.90 22.23% ±2.13% *The listed dry weight is equivalent to 94% of the initial algal weight used (assuming 6% moisture content).

25.00%

Freeze dried algae, ambient conditions

20.00% Freeze dried algae, pressurized extractions Wet algae

15.00%

10.00% Percentof Dry Weight ExtractedWeight Dry Percentof

5.00%

0.00% A B C D E F G H I J K L M N O P Q R

A- Floch; B- Floch post alumina pretreatment; C- Floch post ultraconication; D- 95% ethanol; E- hexane; F- hexane post steaming (5 minutes); G- hexane post steaming (10 minutes); H- dimethyl ether; I- supercritical carbon dioxide; J- supercritical carbon dioxide with beads; K- supercritical carbon dioxide with 10 mL acetone; L - supercritical carbon dioxide with 10 mL of hexane; M- supercritical carbon dioxide with 10 mL of methanol; N- supercritical carbon dioxide, sequential extraction with the addition of 10 mL of each of acetone, hexane, and methanol; O- dimethyl ether; P- 70% ethanol; Q- modified Smedes and Askland method; R- modified Molina method.

Figure 4.1-1 : Oil extraction yields for the various methods explored

140%

120% Freeze dried algae, ambient conditions Freeze dried algae, pressurized extractions Wet algae 100%

80%

60%

Percent of Total Lipids Extracted Lipids Total of Percent 40%

20%

0% A B C D E F G H I J K L M N O P Q R

Figure 4.1-2: Relative extraction efficiencies of the various oil extraction methods explored

Liquefied dimethyl ether was the most effective solvent for pressurized oil extraction from freeze dried Chlorella vulgaris; however it was only capable of extracting about 26%±2% of the total algal lipid content. Oil extraction from wet algae by means of dimethyl ether, however, did not perform well and was only capable of extracting about 6% of the total lipid content. Using supercritical carbon dioxide for total lipid extraction performed very poorly, and adding glass beads to the extraction vessel to simulate bead beating during the extraction process and enhance cell wall disruption seemed to negatively impact extraction efficiency. This observation is in line with a study by Ceron et al. where it was indicated that excessive bead beating could have negative effects on lipid recovery due to the degradation of carotenoids (2008). Supercritical carbon dioxide, however, seemed to be capable of the selective extraction of specific lipids.

The pigmentation of the extracted lipids varied depending on the co-solvent used during supercritical carbon dioxide lipid extraction. With no co-solvent added the extract was a red/orange color, with added acetone the extract was a dark green color, with added hexane the extract was an olive green color, and with added methanol the extract was a darker green color (see Figure 4.1-3). Carotenoids, which are nonpolar lipids, have a characteristic orange or red color, while chlorophylls, which are relatively polar, have a characteristic green color (Boussiba 2000). Based on the colors of the extracted fractions, it can be concluded that pure carbon dioxide tends to select for and extract nonpolar lipids.

The addition of hexane, acetone or methanol co-solvents on the other hand allow for the extraction of polar lipids.

From left to right: supercritical carbon dioxide; supercritical carbon dioxide with beads; supercritical carbon dioxide with 10mL acetone; supercritical carbon dioxide with 10mL of hexane; supercritical carbon dioxide with 10mL of methanol

Figure 4.1-3: Supercritical carbon dioxide lipid extract pigmentation

About 5% of the Chlorella vulgaris total lipid content was extracted by means of supercritical carbon dioxide without an added co-solvent. Both with added acetone and hexane co-solvents 4% of the total lipid content was extracted, and with added methanol

9%±1% of the total lipid content was extracted. In total this adds up to about 21%±1%.

Sequentially extracting the Chlorella vulgaris with supercritical carbon dioxide with acetone co-solvent followed by supercritical carbon dioxide with hexane co-solvent and supercritical carbon dioxide with methanol co-solvent yielded about 20%±2% of the

Chlorella vulgaris total lipid content. It is therefore suggested that the oil extracted when no co-solvent is added was probably co-extracted when the different co-solvents were used and that the behavior of the different co-solvents is additive. This indicates that lipid solubility is largely influenced by the added co-solvent, and depending on the co-solvent added, selectivity for specific lipids is enhanced. It is concluded that utilizing supercritical carbon dioxide for lipid extraction from the Chlorella vulgaris is useful for the selective extraction of specific lipids, possibly for nutraceutical purposes, but not for total lipid extraction. It is also noteworthy that polar lipid extraction capability necessitates the use of

63 organic co-solvents; this defeats the purpose of utilizing a clean supercritical carbon dioxide extraction that does not introduce residual solvents in the remnant and does not require solvent distillation.

The use of alumina or ultrasonication pretreatments was found to have no statistically significant effect on the amount of oil extracted by the Floch method. This result was unexpected since many papers have been dedicated to the optimization of algal pretreatment methods prior to oil extraction. It was postulated that lyophilizing, or freeze drying, the Chlorella vulgaris prior to extraction had sufficiently disrupted the algal cell wall.

However in the study by Ceron et al. lyophilized algae was also used, and pretreatment methods still had an effect on oil extraction efficiency (2008). It is therefore concluded that the efficiency of the Floch method was probably not affected by cell wall disruption techniques since the solvent used was adequately polar to disrupt the cell wall biomolecule interactions and adequately nonpolar to extract the algal oil. The ambient condition methods that performed well all contained ethanol, a polar solvent capable of hydrogen bonding and hence disrupting the water balance in cells and possibly inducing osmotic shock. Ethanol polarity also allows this solvent to denature cell wall proteins and hence ‘pit’ the algal cell wall allowing for solvent penetration. Hexane and the modified Smedes and

Askland solvent were probably not sufficiently polar to accomplish this, which could have led to the low oil extraction yields observed when these solvents were utilized.

Hexane was found to be capable of extracting about 23%±2% of the total Chlorella vulgaris lipid content when no steam pretreatment was utilized. Although the effect of pre-steaming the algae for 5 minutes was found to be statistically insignificant, steaming the algae for 10 minutes led to significantly less oil extraction (only about 15%±2% of the total oil content was extracted). Post steaming for 10 minutes the Chlorella vulgaris biomass was observed

64 to turn a brown color and clump together and harden. The reduced lipid recovery could be attributed to lipid degradation due to subjecting the algae to high temperatures; carotenoids and chlorophyll are subject to degradation at temperatures above 60°C

(Fernández Sevilla, Acién Fernández and Molina Gima 2010). The reduced lipid recovery may have also been caused by the polymerization of the biomolecules of the algal cell walls which could have rendered the algal cells less penetrable to hexane.

The modified Smedes and Askland method was only capable of extracting 41%±5% of the total Chlorella vulgaris lipid content. This low yield could be attributed to the modifications made to the procedure, the relatively low polarity of this solvent, or the fact that this procedure was developed for lipid extraction from aquatic organisms, such as mussels, that do not have as high a lipid content as algae (Smedes and Askland 1999).

The solvents that performed best gravimetrically were Molina’s modified solvent and 70% ethanol for oil extraction from wet algae and 95% ethanol for oil extraction from freeze dried algae. It was concluded with 95% confidence that total lipids were extracted from the freeze dried biomass using ethanol, and total lipids were extracted from the wet algae using

70% ethanol or the ethanol/hexane solution. To compare the composition and hence the quality of extracts isolated using these different solvents, the extracts isolated using 95% ethanol, 70% ethanol, the modified Molina solvent, and the Floch method were fractionated.

The results are presented in Figure 4.1-4.

25%

20%

8.30% 7.86% 9.54% 15% 4.62%

10% 9.35% 9.83%

Percent of Dry Weight Dry of Percent 8.41% 7.87% 5%

4.34% 4.23% 3.33% 4.42% 0% Floch 95% Ethanol 70% Ethanol Ethanol:Hexane:H2O (77:17:6)

Unsaponifiable Lipids Free Fatty Acids Aqueous Extract Components

Figure 4.1-4 : Comparision of the distribution of the components of the extracts

It is evident from these results that utilizing 70% ethanol to extract oil from wet algae is not quite as effective as utilizing the modified Molina ethanol/hexane solvent. This observation is in line with a study by Ibanez Gonzalez et al. that indicates that increasing the volume ratio of water to ethanol decreases the fatty acid extraction efficiency of ethanol (1998). Of the oil extracted by ethanol from wet biomass only about 54%±7% was ‘useful’ oil, while the remaining was aqueous components. Of the oil extracted by Molina’s modified solvent and by 95% ethanol 63%±8% was ‘useful’ oil. Both the modified Molina solvent and 95% ethanol extracted more aqueous components than the Floch method (73%±9% of oil extracted by the Floch method was ‘useful’ oil). Assuming that the Floch method only extracts lipids, it can be deduced that some ash, carbohydrate or other polar impurities were extracted along with the lipids when the modified Molina solvent and 95% ethanol was used. This can be attributed to the high polarity of these solvents. It was therefore hypothesized that the aqueous fraction of the extract, when the modified Molina solvent and

95% ethanol is used, contains polar lipid fractions and possibly some metals and nutrients that can be recycled in algal growth medium.

Based on the results of this experiment it is recommended that 95% ethanol is used for lipid extraction from dry algae and that a hexane/ethanol mixture (6:77:17 w/w/w ratio of water to ethanol to hexane) is used to extract oil from wet biomass.

4.2 Modification of the Oil Fractionation Method

In previous work, elemental analysis of fatty acids extracted from Chlorella vulgaris and fractionated using the method outlined by Ibanez Gonzalez et al. indicated relatively high chlorine content in this fraction (350 ppm). It was hypothesized that the chlorine content can be reduced by replacing hydrochloric acid with a non-mineral acid such as acetic acid.

The distributions of extract components in the various extract fractions when different acids and pH levels are utilized during the fractionation process are presented in Figure

4.2-1. All three of the methods used performed similarly; the proportion of fatty acids that was isolated from the oil extracts for all of the setups were within ±2% of each other which is within the range of uncertainty.

Short chain carboxylic acids typically have a pKa value of about 4.8, but the pKa value for fatty acids with sufficiently long carbon chain lengths (8 carbons or more) is greater

(Kanicky and Shah 2003). Pure octanoic acid and octadecanoic acid tend to have a pKa of about 6.5 and 10 respectively, and the pKa of fatty acids has been observed to increase linearly with respect to carbon chain length for long carbon chain length fatty acids

(Kanicky and Shah 2003). This observation has been attributed to an increase in van der

Waals interactions caused by an increase in carbon chain length which in turn leads to hydrogen atom shielding and consequently a higher pKa (Kanicky and Shah 2002). In mixed solutions containing various components and fatty acids with various carbon chain lengths the van der Waals interactions are disrupted and the actual pKa of the fatty acids present in the solution is about 1-2 pH units less than the expected pKa values in the pure samples

(Kanicky and Shah 2002). Since Chlorella vulgaris tends to produce fatty acids with carbon chain lengths between 12 and 22 carbons, it is plausible that the fatty acids contained within the algal oil extract were in fact protonated at a pH of 6 (W. E. Becker 1994).

An elemental analysis indicated that the chlorine content of the fatty acids protonated by means of acetic acid was 98ppm; this represents is a 72% reduction in chlorine content. It is therefore concluded that using acetic acid to acidify the saponifiable media and protonate fatty acids is preferred, and it is sufficient to reduce the pH to 6 to achieve total fatty acid protonation.

100%

80% 36.67% 37.83% 38.09%

60%

Percent of Oil of Percent 40% 43.61% 43.29% 42.08%

20%

19.72% 18.88% 19.83%

0% pH = 1 (Using HCl) pH = 6 (Using HCl) pH= 6 (Using Acetic Acid)

Unsaponifiable Lipids Free Fatty Acids Aqueous Extract Components

Figure 4.2-1: Oil fractionation using different acids and pH for protonation

4.3 Large-Scale Oil Fractionation Results

The large-scale extraction and fractionation procedure yielded four fractions: the remnant, a copper tinted aqueous phase, a brown colored fatty acid fraction, and an orange/red colored unsaponifiable fraction. Figure 4.3-1 presents the mass distribution of the initial algal biomass between these four fractions on a dry weight basis. The various fractions were processed and analyzed separately. The results are presented below.

4% 9% Unsaponifiable Lipids

8% Free Fatty Acids

Aqueous Extract Components Remnant 79%

Figure 4.3-1: Distribution of the initial algal dry weight post oil extraction and fractionation

The Unsaponifiable Fraction

Analyses of the tocopherol content, carotenoid content and sterol content of this oil fraction were performed. The results are presented in Table 4.3-1. The nutracuetical uses of these various components are listed in Table 4.3-2. The tocopherols, carotenoids and sterols that were detected account for about 2.5% of the unsaponifiable fraction; about 1.06 mg of nutracuetically valuable molecules was isolated per gram of dry algae processed. It is noteworthy that the amount of hexane used to wash the saponified media and extract the unsaponifiables was only sufficient for the isolation of about 60% of the β-carotene content;

70 based on a study by Ibanez Gonzalez et al. a 40:1 v/v ratio of hexane to aqueous media is required for 99.7% recovery of β-carotene for a single extraction (1998). Although total carotenoid recovery was not feasible at lab scale, using a multistep counter current extractor for large-scale extractions could decrease hexane requirement, improve the economy of this process and increase carotenoid recovery. Assuming that only 60% of the total carotenoids were isolated through this process, it can be estimated that a total of about

1.1 mg of carotenoids can potentially be isolated per gram of dry algae processed, or a total of about 1.5 mg of value added products can be isolated per gram of dry algae processed.

Table 4.3-1: Unsaponifiable fraction tocopherol, carotenoid, and sterol profile

Detected (μg/g Isolated (μg/g Dry Unsaponifiable Oil) Algae) α-Tocopherol 1374 58.12 β-Tocopherol 43 1.82 γ-Tocopherol < 5 < 0.21 δ-Tocopherol < 5 < 0.22 Total Tocopherols: 1.42 mg/g 0.06 mg/g Lutein 5428 229.60 Zeaxanthin 273 11.55 cis-Lutein/Zeaxanthin 1276 53.97 α-Cryptoxanthin 131 5.54 β-Cryptoxanthin 152 6.43 Lycopene < 3 < 0.13 cis-Lycopene 710 30.03 α-Carotene 1170 49.49 β-Carotene 4132 174.78 cis-β-Carotene 2035 86.08 Total Carotenoids: 15.31 mg/g 0.65 mg/g Cholesterol < 20 < 0.85 Brassicasterol 370 15.65 Campesterol 279 11.80 Stigmasterol 6520 275.80 β-Sitosterol 1090 46.11 Total Plant Sterols: 8.26 mg/g 0.35 mg/g Total: 24.98 mg/g 1.06 mg/g

Table 4.3-2: Nutraceutical uses of the unsaponifiable oil content

Uses α-Tocopherol Vitamin E β-Tocopherol Lutein Antioxidant Supplement Zeaxanthin (Beneficial for Eyes) cis-Lutein/Zeaxanthin α-Cryptoxanthin Provitamin A β-Cryptoxanthin cis-Lycopene Powerful Antioxidant α-Carotene β-Carotene Provitamin A cis-β-Carotene Brassicasterol Food Additive Campesterol Veterinary Medicine Stigmasterol Progesterone and Provitamin D3 β-Sitosterol Cholesterol Treatment

To identify the composition of the remaining 97.5% of the unsaponifiable fraction, a GCxGC-

MS analysis of this oil was performed. The resulting chromatogram is presented in Figure

4.3-2. Table 4.3-3 lists the primary constituents of the oil and some of the minor constituents as identified by the GCxGC-MS. Note that the percent area listed is not the percent area relative to the total area of all of the peaks but the percent area relative to the total area of the peaks listed. The major constituents of this fraction were identified as phytol, heptadecane, and hexadecanoic acid. Phytol is a 20 carbon chain length alcohol that is generally found bonded to the chlorophyll chlorin ring, and hexadecanoic acid is a common algal fatty acid (Vieler, et al. 2007). Multiple alcohols, ketones, aldehydes, hydrocarbons, cyclic hydrocarbons and complex sterols were also detected with much smaller signals indicating minor concentrations.

Carotenoids and tocopherols are long chain hydrocarbons with cyclic hydrocarbon end groups and possibly some bonded oxygen groups. The cyclic and noncyclic hydrocarbons and oxygenated hydrocarbons identified by the GCxGC-MS resemble tocopherol and

72 carotenoid fractions. It is postulated that the carotenoids and tocopherols degraded within the GCxGC-MS due to exposure to high temperatures, and their presence in the unsaponifiable fraction is indicated by the chromatogram peaks representing the cyclic and noncyclic hydrocarbons and oxygenated hydrocarbons.

If the carotenoids, sterols and tocopherols are to be isolated as value added products the remainder of this oil fraction, mostly composed of phytol, heptadecane, and hexadecanoic acid, is structurally similar to fatty acids and can be easily processed along with the fatty acid extract to produce renewable diesel. The total oil fraction that can be upgraded to renewable diesel is thus about 13.5% of the dry algal biomass. If the isolation of value added products is not of interest, using a hexane wash to isolate these components is not required.

Sterols are complex molecules containing clustered cyclic hydrocarbon rings and are thus structurally dissimilar to fatty acids and cannot be easily hydrotreated with the fatty acid fraction. These heavier components can be isolated from the fatty acids prior to hydrotreatment through distillation. During distillation, if high temperatures are employed, it is hypothesized that the carotenoids and tocopherols will be degraded to cyclic and noncyclic fractions similar to those detected by the GCxGC-MS. The long chain possibly slightly oxygenated hydrocarbon fractions of the carotenoids and tocopherols are structurally similar to fatty acids and can be hydrotreated with the fatty acid fraction to produce renewable diesel. The cyclic ends are generally lighter than the other components of this fraction and can be isolated from this fraction through the distillation process.

Through this process about 13.6% of the initial algal biomass can be converted to renewable diesel.

Phytol Cyclic Sterols hydrocarbons

n-Heptadecane

Solvent n-Hexadecanoic Fatty acids, alcohols, acid ketones & aldehydes

Noncyclic hydrocarbons

Column bleed

Figure 4.3-2: GCxGC-MS chromatogram of the unsaponifiable fraction

Table 4.3-3: Compounds in the unsaponifiable fraction as identified by GCxGC-MS Compound Formula Peak Area Relative %Area Retention Time Phytol C20H40O 300641520 81.99% 2265 , 1.410 Heptadecane C17H36 16579011 4.52% 1785 , 1.075 2-Pentadecanone, 6,10,14-trimethyl- C18H36O 7826904 2.13% 1945 , 1.350 1-Docosene C22H44 4530228 1.24% 2815 , 1.210 Geranylgeraniol C20H34O 4170357 1.14% 2915 , 1.480 1,19-Eicosadiene C20H38 3080767 0.84% 2800 , 1.245 Tetradecane, 6,9-dimethyl- C16H34 2767706 0.75% 1785 , 1.060 Pregnenolone C21H32O2 2860827 0.78% 3365 , 4.810 Androstan-17-one, 3-ethyl-3-hydroxy-, (5à)- C21H34O2 2681028 0.73% 3435 , 0.250 2,10-Dodecadien-1-ol, 3,7,11-trimethyl-, (Z)- C15H28O 1373516 0.37% 2290 , 1.490 Androstan-3-one, 17-hydroxy-1,17-dimethyl-, (1à,5à,17á)- C21H34O2 1279378 0.35% 3345 , 4.270 Dodecane, 3-methyl- C13H28 1269780 0.35% 2495 , 1.050 1-Docosene C22H44 1096449 0.30% 2640 , 1.180 n-Hexadecanoic acid C16H32O2 1036552 0.28% 2085 , 1.475 16-Pregnen-3,20-dione C21H30O2 1020033 0.28% 3450 , 4.965 p-Xylene C8H10 1015748 0.28% 405 , 1.320 1-Nonadecene C19H38 1009878 0.28% 2020 , 1.115 Androstan-17-one, 3-ethyl-3-hydroxy-, (5à)- C21H34O2 993149 0.27% 3440 , 0.075 Isophytol C20H40O 987334 0.27% 2075 , 1.235 8-Heptadecene C17H34 986749 0.27% 1755 , 1.100 Benz[b]dihydropyran-6-ol, 2,2,5,7,8-pentamethyl- C14H20O2 927988 0.25% 3190 , 3.070 2-Benzofuranmethanol, 2,4,5,6,7,7a-hexahydro-4,4,7a-trimethyl-, cis- C12H20O2 836152 0.23% 3185 , 3.065 Tetradecane C14H30 893080 0.24% 1515 , 1.005 D-Limonene C10H16 802536 0.22% 735 , 1.140 3-Methyl-2-(3,7,11-trimethyldodecyl) furan C20H36O 796852 0.22% 2045 , 1.255 2(4H)-Benzofuranone, 5,6,7,7a-tetrahydro-4,4,7a-trimethyl- C11H16O2 786159 0.21% 1475 , 3.180 Hexadecane C16H34 778570 0.21% 1655 , 1.020 18-Nonadecen-1-ol C19H38O 763199 0.21% 2260 , 1.715 Heptadecane, 2,6,10,14-tetramethyl- C21H44 754112 0.21% 2030 , 1.065 3-Eicosene, (E)- C20H40 742655 0.20% 2195 , 1.110

The Fatty Acid Fraction

An elemental analysis of the fatty acid fraction of the oil extract was performed and the results are presented in Table 4.3-4. Little elemental impurities which could interfere with the downstream upgrade of the oil to renewable diesel are present in the fatty acid fraction.

This is an indication of high quality feed for upgrading. However, the structure of the compounds also plays a role in the upgrading process; it is preferred that the oxygen groups present are located at the ends of noncyclic hydrocarbons (Knothe 2010).

Table 4.3-4: Fatty acid fraction elemental analysis results

Element Quantity

Carbon 77.32%

Hydrogen 12.96%

Oxygen 7.22%

Nitrogen 0.0358%

Phosphorus 66 ppm

Chlorine 98 ppm

Sulfur 30 ppm

A GCxGC-MS analysis was run to determine the molecular composition of the fatty acid fraction. The resulting chromatogram is presented in Figure 4.3-3, and Table 4.3-5 lists the primary constituents of the oil and some of the minor constituents as identified by the

GCxGC-MS. Note that the percent area listed is not the percent area relative to the total area of all of the peaks but the percent area relative to the total area of the peaks listed. The

76 major constituents of this fraction were identified as hexadecanoic acids, doconexent, and octadecanoic acids. Eicosanoic, tetradecanoic, and pentadecanoic acids, alcohols, ketones, aldehydes, methyl esters, and cyclic hydrocarbons were also detected with smaller signals indicating lesser concentrations. It is postulated that a portion of the alcohols, ketones, aldehydes, hydrocarbons, and cyclic hydrocarbons detected corresponds to the estimated

40% of the total algal carotenoids that were not isolated in the unsaponifiable fraction due to the insufficient volume of hexane used.

In general, the constituents of this oil fraction are all conducive to upgrading to renewable diesel; the oxygen groups present are hydrocarbon end groups and the molecules are similarly structured. It is noteworthy that many of the compounds in this fraction of the oil

(including doconexent) have conjugated double bonds, which may be responsible for the observed dark brown pigmentation of the oil (Ladygin 2000).

Noncyclic hydrocarbons Solvent

Cyclic hydrocarbons

Shorter chain fatty acids, alcohols, ketones & aldehydes

Hexadecanoic acids Doconexent

Octadecanoic and Eicosanoic acids

Figure 4.3-3: GCxGC-MS chromatogram of the fatty acid fraction

Table 4.3-5: Compounds in the fatty acid fraction as identified by GCxGC-MS Compound Formula Peak Area Relative %Area Retention Time n-Hexadecanoic acid C16H32O2 69361667 30.73% 2115 , 2.130 Doconexent C22H32O2 66641874 29.52% 2060 , 2.360 9,12,15-Octadecatrienoic acid, (Z,Z,Z)- C18H30O2 44011690 19.50% 2445 , 3.880 11,14,17-Eicosatrienoic acid, methyl ester C21H36O2 12840915 5.69% 2070 , 2.280 Octadecanoic acid C18H36O2 5647065 2.50% 2500 , 2.825 cis-9-Hexadecenoic acid C16H30O2 5567913 2.47% 2075 , 2.045 Oleic Acid C18H34O2 3061104 1.36% 2460 , 3.005 Gamolenic Acid C18H30O2 2949082 1.31% 2370 , 3.175 Tetradecanoic acid C14H28O2 1873751 0.83% 1845 , 1.470 9,12-Octadecadienoic acid (Z,Z)- C18H32O2 1129967 0.50% 2395 , 3.135 2-Heptadecanone C17H34O 1073593 0.48% 2005 , 1.505 Pentadecanoic acid C15H30O2 977216 0.43% 1920 , 1.455 p-Xylene C8H10 946640 0.42% 410 , 1.345 Hexadecanal C16H32O 940854 0.42% 1905 , 1.415 7-Pentadecen-5-yne, (Z)- C15H26 857762 0.38% 2160 , 2.035 Tetradecane C14H30 790969 0.35% 1365 , 0.985 Hexadecane C16H34 742267 0.33% 1650 , 1.020 Dodecane C12H26 711917 0.32% 1045 , 0.910 9,12,15-Octadecatrienal C18H30O 631301 0.28% 2585 , 3.090 Tetradecanoic acid, 2-hydroxy-, methyl ester C15H30O3 547432 0.24% 1930 , 1.455 Octadecane C18H38 528403 0.23% 1910 , 1.040 1,5,9,11-Tridecatetraene, 12-methyl-, (E,E)- C14H22 479741 0.21% 2380 , 3.470 4,8,12,16-Tetramethylheptadecan-4-olide C21H40O2 456534 0.20% 2960 , 4.960 2-Pentadecanone, 6,10,14-trimethyl- C18H36O 433158 0.19% 1940 , 1.330 trans-13-Octadecenoic acid C18H34O2 404465 0.18% 2445 , 3.135 13-Tetradece-11-yn-1-ol C14H24O 398184 0.18% 2510 , 2.960 trans-2-Undecen-1-ol C11H22O 372765 0.17% 2110 , 2.115 Pentadecanoic acid C15H30O2 361794 0.16% 1965 , 1.465 Undecanoic acid C11H22O2 354527 0.16% 2270 , 2.225 Cyclohexene, 1-methyl-4-(1-methylethenyl)-, (S)- C10H16 312866 0.14% 720 , 1.175

The Aqueous Fraction

Chlorella vulgaris was cultivated in dilutions of the aqueous media that was produced by the fractionation process to determine the potential for recycling this fraction. The growth curves of the algae cultivated in the various growth media are presented in Figure 4.3-4.

Although algal growth was monitored for seven days, only the first five days are presented; by the sixth day the algae began to colonize, flocculate and collect on the walls and the base of the flasks rendering optical density measurements inconclusive. By the fifth day, the optical density measured for Chlorella vulgaris cultivated in some dilutions of the process water (0.46%, 0.92%, and 1.84% process water) was greater than that measured for algae cultivated in Bold’s Basal Medium (BBM). Since the optical density at 550 nm is a measurement of turbidity, this indicates that more algal cells were accumulated in these setups, or more growth was achieved in these setups than was achieved in the setup where

BBM was utilized. At higher concentrations of process water algal biomass accumulation was lower than algal biomass accumulation in BBM, but algal growth was still observed. At a concentration of 5.52% process water a negative effect on algal growth was observed; algal accumulation in DI water, containing no nutrients, was greater. And at a process water concentration of 9.20% virtually no algal growth was observed.

1.000

BBM 0.900 DI 0.800 0.46% Process Water 0.92% Process Water 0.700 1.84% Process Water 0.600 2.76% Process Water 3.68% Process Water

A(550) 0.500 4.60% Process Water

0.400 5.52% Process Water 9.20% Process Water 0.300

0.200

0.100

0.000 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Time (Hours)

Figure 4.3-4: Comparison of algal growth in BBM and dilutions of the aqueous media produced during the fractionation process

The algae grown in the various growth media exhibited different pigmentation (see Figure

4.3-5). Algal bleaching, or de-pigmentation, has been associated with changes in the algal metabolic pathway. In the presence of a sugar source Chlorella vulgaris is capable of heterotrophic growth and hence does not produce large quantities of chlorophyll, a biomolecule that is necessary for autotrophic growth and which lends algae its green pigmentation (Xu, Miao and Wu 2006). It is therefore concluded that algal growth in the dilutions of the aqueous process media was in part due to algal metabolism of sugars present in the media – especially in the setups using higher concentrations of process water.

By performing a mass balance it was estimated that the aqueous process media contains about 106 g of aqueous algal biomass extract per liter and 168 g of acetic acid per liter.

Upon neutralization of the acetic acid a high concentration of acetate is present in the media, and this acetate was probably metabolized by the algae. A later study by Dr.

Servaites, from UDRI, indicated that Chlorella vulgaris does in fact metabolize acetate.

Growth media utilized, from left to right: BBM; DI water; 0.46% process water; 0.92% process water; 1.84% process water; 2.76% process water; 3.68% process water; 4.60% process water; 5.52% process water; 9.20% process water.

Figure 4.3-5: Pigmentation of algae grown in BBM and dilutions of the aqueous media

produced during the fractionation process

An ICP-MS analysis of the process water was performed to determine the metal content of the media, and a total phosphorus and Kjeldahl nitrogen analysis were also performed.

Figure 4.3-6 presents the magnesium, Mg, calcium, Ca, manganese, Mn, copper, Cu, phosphorus, P, and nitrogen, N, content of the process water and how this relates to the content of these elements in BBM. Although copper levels were low, the general elemental composition of the aqueous process water was comparable to that of BBM. This supports the theory that the algal growth observed in the dilutions of the process water was due to acetate fueled heterotrophic growth; to match the nutrient concentrations available in BBM, which are optimal for autotrophic growth, the Chlorella vulgaris would need to be cultivated in non-dilute process water.

It is noteworthy that studies have indicated that lipid accumulation within algal cells is greater when algae are cultivated under heterotrophic conditions than when algae are cultivated under autotrophic conditions (Wu 2009; Miao and Wu 2006). The lipid content of algae grown heterotrophically was also found to contain less oxygen than the lipid content of autotrophically grown algae (Miao and Wu 2006). It is therefore proposed that a secondary heterotrophic algal cultivation step should be introduced post autotrophic cultivation of the Chlorella vulgaris and prior to biomass harvest. During this secondary cultivation step dilutions of the acetate rich fractionation process water can be used as growth media. This process could be used to enhance algal lipid accumulation and lipid quality and to increase the overall conversion efficiency of the oil extraction process.

100

1 Content (mg/L) Content

0.1

0.01 Mg Ca Mn Cu N P Fractionation Process Water BBM

Figure 4.3-6: Elemental composition of the aqueous process water with respect to BBM

4.4 Hydrothermal Liquefaction Process

The hydrothermal liquefaction of the algal biomass yielded four phases (see Figure 4.4-1).

The isolated solid phase resembled a fine bio-char, the bio-crude was a thick viscous dark brown liquid, and the aqueous phase was a copper tinted liquid. Each of the gas and liquid phases was collected and analyzed separately.

2.81%

Organic Phase 44.17% Solid Phase 45.82% Aqeuous Phase Gas Phase

7.20%

Figure 4.4-1: Distribution of the initial algal dry weight post hydrothermal liquefaction

Gas Phase

A total of about 1 L of gas was released through this process. The collected volatiles formed were identified by means of GC-TCD (see Figure 4.4-2). Available calibration curves were used to quantify the gases formed, and the results of the quantification are presented in

Table 4.4-1. The vast majority of the gas formed was carbon dioxide; about 2.8% of the dry algal biomass was converted to carbon dioxide. Carbon monoxide, methane, ethylene and ethane were also formed, as was a minor amount of hydrogen gas.

Figure 4.4-2: GC-TCD chromatogram of the isolated volatiles

Table 4.4-1: Composition of the isolated volatiles

Retention Total Gas Released Gas Generated Area Time (min) (g/L) (μg/g of Dry Algae)

Hydrogen 1.353 0.0004 - -

Carbon monoxide 6.839 0.1570 0.0156 414.08

Methane 10.707 0.0079 0.0005 13.68

Carbon dioxide 12.627 6.9719 1.0417 27,667.53

Ethylene 18.514 0.0058 0.0003 7.64

Ethane 21.083 0.0043 0.0003 6.84

Total 1.0583 28.11 mg/g

The isolated semi-volatiles were analyzed by means of GC-MS. The resulting chromatogram is presented in Figure 4.4-3. Table 4.4-2 lists the constituents of the semi-volatile fraction as identified by the GC-MS. Note that the quantification is not accurate because the standards used had different structures than the compounds identified. The major constituents of the semi-volatile phase were short chain hydrocarbons, alcohols, ketones and aldehydes. Some of the major constituents of the HTL crude-oil fraction were also present in this volatile phase. Three sulfur containing semi-volatiles were identified by the GC-MS, however, they accounted for less than 10 μg in total or about 0.3 μg per gram of algae processed. In total, only about 5.5 μg of semi-volatiles are released per gram of algae processed.

A b u n d a n c e

TIC: SV_H TL_Saly_1.D

1 e + 0 7

9 0 0 0 0 0 0

8 0 0 0 0 0 0

7 0 0 0 0 0 0

6 0 0 0 0 0 0

5 0 0 0 0 0 0

4 0 0 0 0 0 0

3 0 0 0 0 0 0

2 0 0 0 0 0 0

1 0 0 0 0 0 0

6 . 0 0 8 . 0 0 1 0 . 0 0 1 2 . 0 0 1 4 . 0 0 1 6 . 0 0 1 8 . 0 0 2 0 . 0 0 2 2 . 0 0 2 4 . 0 0 2 6 . 0 0 T i m e - - >

Figure 4.4-3: GC-MS chromatogram of the isolated semi-volatiles

Table 4.4-2: Compounds in the isolated semi-volatiles as identified by GC-MS

Compound Retention Time (min) Peak Area Relative Area Quantification (μg) 2-Hexadecanal, 2-methyl 5.691 69794599 3.50% 8.63 2-Cyclopenten-1-one, 2-methyl- 6.366 32824779 1.65% 4.06 Pyrimidine, 4,6-dimethyl- 6.478 22714459 1.14% 2.81

Phenol, 2-methyl 6.513 33345066 1.67% 4.12 Benzaldehyde 7.406 15073693 0.76% 1.86 3-Hexene-2,5-dione 7.477 43541215 2.18% 5.38 1-Butanol-3methoxy 7.565 28474401 1.43% 3.52 Methyl 2-hydroxyethyl sulfoxide 7.777 20119461 1.01% 2.49 Benzoic acid, 2-(dimethylamino)ethyl ester 7.912 34586897 1.73% 4.27 Cyclooct-4-en-1-ol, 8-iodo-, acetate 7.982 20864910 1.05% 2.58 2,3,4,5-Tetrahydropyridazine 8.064 19121190 0.96% 2.36 2-Heptanone, 6-methyl- 8.176 18065634 0.91% 2.23 Pirizine-2methyl, 6ethyl 8.211 10908382 0.55% 1.35 Pirizine-2methyl, 5ethyl 8.252 9167034 0.46% 1.13 1,2-Dichlorobenzene D4 8.335 80913463 4.05% 10.00 2,5-Pyrrolidinedione, 1-hydroxy- 8.746 47406494 2.38% 4.37 Propane, 1,3-dichloro- 8.799 13415209 0.67% 1.24 2-Pentene-4-methyl 8.828 10812690 0.54% 1.00 2-Pentene-2-one (E) 8.899 21727447 1.09% 2.00 Sulfuric acid dimethyl ester 9.328 8868236 0.44% 0.82 1-Hydroxy-3-methyl-2-butanone 9.651 7774320 0.39% 0.72 Benzenamine, N-ethyl-3-methyl- 9.792 8865349 0.44% 0.82 Tetrahydropyrrole-3-amino-2,5-dione 9.968 8093291 0.41% 0.75 4-Nonanone, 7-ethyl- 10.310 10224076 0.52% 0.94 Benzenepropanol, 3-methoxy- 10.420 13087141 0.65% 1.21 2-Dodecanoic acid 10.773 5762425 0.29% 0.53 Benzaldehyde 4ethyl 10.973 8022695 0.40% 0.74 Naphthalene D8 11.226 136183797 6.82% 10.00 2-Propenoic acid, 2-methyl-, 1-methylethyl ester 12.089 8770428 0.44% 0.85 Hexane, 2,4-dimethyl- 12.148 5698206 0.29% 0.55 Tetrahydropyrrole-3-amino-2,5-dione 12.283 14316137 0.72% 1.38 1-Propene, 3-methoxy-2-methyl- 12.665 7615593 0.38% 0.73 1-Pentene, 3,3-dimethyl- 12.794 34751103 1.74% 3.35 1,5-Heptadiene, 3,4-dimethyl- 12.841 38208903 1.91% 3.68 2,6-Octadiene, 4,5-dimethyl- 12.965 24646123 1.24% 2.38

4-Methyl-1,5-Heptadiene 13.047 29327587 1.47% 2.83 2-Pentenoic acid, 2-methyl- 13.112 7550041 0.38% 0.73 2-Pentenoic acid, 2-methyl- methylethyl ester 13.423 6771693 0.34% 0.65 Cyclopentane, 1,1-dichloro- 13.705 15153758 0.76% 1.46 4-Pentenoic acid, 2-methoxy-, methyl ester 14.022 54214393 2.72% 5.23 Valeric acid, pent-2-en-4-ynyl ester 14.310 7470144 0.37% 0.72 Butane, 1-(methylsulfinyl)- 14.628 18234332 0.91% 1.76 Propanoic acid, 2-chloro-, 2-propenyl ester 14.904 16830312 0.84% 1.62 Acenaphthene-d10 15.291 71199810 3.57% 10.00 1-Ethyl-3-methylcyclohexane (c,t) 15.568 7310718 0.37% 0.60 1-Methyl-4-(1-methylethyl)-cyclohexane 15.632 6544600 0.33% 0.53 Unknown 15.673 12700770 0.64% 1.03 6,9,12-Octadecatrien-1-ol 15.773 10117173 0.51% 0.82 2,4-Cyclohexadiene-1-carboxylic acid, methyl ester 15.808 8067119 0.40% 0.66 1-Methyl-4-(1-methylethyl)-cyclohexane 15.932 6310505 0.32% 0.51 Hexanoic acid, 5-oxo-, ethyl ester 15.961 6660699 0.33% 0.54 2,4-Dimethyl 1,4-pentadiene 16.032 13597255 0.68% 1.11 Cyclopropane, trimethylmethylene- 16.161 19667600 0.99% 1.60 Cyclohexanemethanol, 4-methyl-, trans- 16.284 7914725 0.40% 0.64 Cyclohexane, 1-methyl-4-(1-methylbutyl)- 16.420 9812566 0.49% 0.80 Diethyl Phthalate 16.684 8801189 0.44% 0.72 Hexadecane 16.772 34982468 1.75% 2.85 Benzophenone 17.060 4415992 0.22% 0.36 1(2H)-Naphthalenone, 3,4-dihydro-6-methoxy- 17.324 20666292 1.04% 1.68 Cyclohexane, 1,1-dichloro- 17.377 4819077 0.24% 0.39 Heptadecane 17.883 56958697 2.85% 4.64 Phenanthrene-D10 18.635 174299085 8.73% 10.00 Octadecane 18.934 20797634 1.04% 1.16 Eicosane 19.939 5133261 0.26% 0.29 Methyl benzothiazole-6-carboxylate 20.039 10604047 0.53% 0.59 Chrysene-D12 24.745 183891270 9.21% 10.00

Organic Phase

An elemental analysis of the organic phase was performed. The results of this elemental analysis and, for comparison, the elemental composition of the fatty acid fraction isolated during the oil extraction process and the composition of bio-crude produced by Biller and

Ross from Chlorella vulgaris processed under similar conditions are presented in Table

4.4-3 (2011a). The HTL bio-crude contained slightly more oxygen and significantly more nitrogen and sulfur that the fatty acid oil. Compared to the composition of the bio-crude obtained by Biller and Ross, the oxygen content in the bio-crude obtained in this study was significantly less, but nitrogen and sulfur content was greater. Based on the elemental composition of the HTL bio-crude it can be concluded that this oil is less suited for hydrotreatment than the fatty acid oil fraction; multiple hydrotreatment steps would be required to treat the high content of the various impurities.

Table 4.4-3: HTL bio-crude elemental analysis results

Element HTL Bio-Crude Biller and Ross HTL Fatty Acid Oil

Bio-Crude Fraction

Carbon 74.18% 70.7% 77.32%

Hydrogen 9.16% 8.6% 12.96%

Oxygen 8.98% 14.8% 7.22%

Nitrogen 6.98% 5.9% 0.0358%

Phosphorus 8.3 ppm - 66 ppm

Chlorine - - 98 ppm

Sulfur 0.70% 0% 30 ppm

A GCxGC-MS analysis was run to determine the constituents of the HTL bio-crude. The resulting chromatogram is presented in Figure 4.4-4. Table 4.4-4 lists the primary constituents of the oil and some of the minor constituents as identified by the GCxGC-MS.

Note that the percent area listed is not the percent area relative to the total area of all of the peaks but the percent area relative to the total area of the peaks listed. Unlike the fatty acid oil fraction, no constituents dominated the HTL bio-crude. The area of the largest peak represented about 12% of the total peak areas, and the major constituents were not close derivatives of one another. The major constituents of the HTL bio-crude included hexadecanoic acid, pyrrols, dodecanamides, octadecanoic acid, and pyrazines. The HTL bio- crude also contained hydrocarbons, aldehydes, ketones, other fatty acids, alcohols, indoles, piperdines, and cyclic hydrocarbons. The diversity of the constituents of this oil and the presence of complex cyclic nitrogenous compounds such as pyrrols render this bio-crude difficult to upgrade to renewable diesel. Multiple upgrading treatment conditions, catalysts and dedicated scrubbers would be required to treat the various fractions, and this may not be cost effective.

The presence of nitrogenous compounds in the bio-crude is due to the conversion of the

Chlorella vulgaris proteins to bio-oil (Levine, et al. 2011). Based on a study by Biller and

Ross, algal lipid content is responsible for 80-55% of the bio-crude yield, protein content is responsible for 18-11% of the bio-crude yield and carbohydrate for 15-6% depending on the whether an alkali catalyst, acid catalyst or no catalyst is used and depending on the original algal biochemical composition (2011a). Biller and Ross also found that protein contribution to the bio-crude yield is greatest when no catalyst is used, and carbohydrate contribution is greatest when an alkali catalyst is used (2011a). Using an alkali catalyst can therefore reduce biomass protein content contribution to the HTL bio-oil and hence the nitrogen content in the bio-crude. However Biller et al. were only capable of reducing the

92 nitrogen content of the bio-oil by about 17% by using an alkali catalyst (from a nitrogen content of 5.9% of the total mass of the oil to 4.9%). In addition the overall biomass conversion to bio-crude was reduced by about 50% (Biller and Ross 2011a).

Bio-crude produced from protein tends to contain high levels of nitrogen. Since Chlorella vulgaris is very rich in protein and has little carbohydrate content, bio-crude derived through the hydrothermal liquefaction of Chlorella vulgaris will tend to be high in nitrogen.

Eliminating or reducing protein conversion to bio-crude reduces nitrogenous bio-crude components but also reduces the overall bio-crude yield; the bio-crude yield becomes more representative the algal lipid content. In other words, if protein contribution to the HTL bio- crude is to be eliminated, oil extraction yield would be similar to HTL yield. Since oil extraction does not require the energy intensive high temperature and pressure conditions required by hydrothermal liquefaction, oil extraction would be preferable over hydrothermal liquefaction.

Solvent

Hexadecanoic acids Octadecanoic acids

Cyclic Compounds

Column bleed Noncyclic hydrocarbons, alcohols etc… Amides and other nitrogen containing compounds

Figure 4.4-4: GCxGC-MS chromatogram of the HTL bio-crude

Table 4.4-4: Compounds in the HTL bio-crude as identified by GCxGC-MS

Compound Formula Peak Area Relative %Area Retention Time n-Hexadecanoic acid C16H32O2 5586395 12.06% 2075 , 1.475 Pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl)- C11H18N2O2 4595011 9.92% 1975 , 4.310 N-Methyldodecanamide C13H27NO 4357305 9.41% 2320 , 1.940 Dodecanamide C12H25NO 3583565 7.74% 2280 , 2.045 9,12-Octadecadienoic acid (Z,Z)- C18H32O2 3225324 6.96% 2250 , 1.680 Pyrazine, 2,5-dimethyl- C6H8N2 1977311 4.27% 635 , 1.235 Indole C8H7N 1677262 3.96% 1215 , 2.550 Hydantoin, 1-butyl- C7H12N2O2 1725410 3.73% 1850 , 3.590 Pyrazine, methyl- C5H6N2 1609594 3.48% 580 , 0.990 N,N-Dimethyldodecanamide C14H29NO 1502841 3.24% 2370 , 1.790 Phenol C6H6O 1351841 2.92% 820 , 1.275 9-Octadecenamide, (Z)- C18H35NO 1293438 2.79% 2460 , 2.150 n-Dodecanoylpyrrolidine C16H31NO 1276433 2.76% 2660 , 2.060 n-Nonanoylmorpholine C13H25NO2 1174282 2.54% 2605 , 1.705 9,12,15-Octadecatrienoic acid, methyl ester, (Z,Z,Z)- C19H32O2 1157478 2.50% 2270 , 1.780 Acetamide, N-(2-phenylethyl)- C10H13NO 1126673 2.43% 2950 , 2.445 Oleic Acid C18H34O2 1008305 2.18% 2260 , 1.590 Pyrrolidine, N-(4-methyl-3-pentenyl)- C10H19N 947227 2.05% 880 , 0.810 2,5-Piperazinedione, 3-benzyl-6-isopropyl- C14H18N2O2 921410 1.99% 2315 , 4.535 1-Dodecanol, 3,7,11-trimethyl- C15H32O 909546 1.96% 1960 , 1.050 2,5-Piperazinedione, 3,6-bis(2-methylpropyl)- C12H22N2O2 810280 1.75% 1970 , 3.345 2-Nonadecanone C19H38O 782167 1.69% 3165 , 2.115 Pyridine, 5-ethyl-2-methyl- C8H11N 729842 1.58% 680 , 1.800 2-Hexadecene, 3,7,11,15-tetramethyl-, [R-[R*,R*-(E)]]- C20H40 717492 1.55% 1945 , 1.040 N,N-Dimethyldecanamide C12H25NO 538514 1.16% 2535 , 1.905 3,6-Diisopropylpiperazin-2,5-dione C10H18N2O2 519540 1.12% 1720 , 3.845 Isophytol C20H40O 500784 1.08% 2065 , 1.220 Pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(phenylmethyl)- C14H16N2O2 430923 0.93% 2380 , 0.535 1-Heptene, 2-methyl- C8H16 404115 0.87% 895 , 2.490

Octadecanoic acid C18H36O2 353776 0.76% 2290 , 1.475 3,7,11,15-Tetramethyl-2-hexadecen-1-ol C20H40O 350244 0.76% 2020 , 1.155 Benzenamine, N-ethyl-3-methyl- C9H13N 334145 0.72% 795 , 1.745 Dodecanamide, N,N-diethyl- C16H33NO 313531 0.68% 2505 , 1.700 Benzenamine, N,N-dimethyl- C8H11N 29619 0.06% 1335 , 1.725 N-Ethyl-3-methoxyaniline C9H13NO 28441 0.06% 1080 , 1.855 Non-7-enoic acid, dimethylamide C11H21NO 28325 0.06% 2340 , 1.935 Ethyl-(1-naphthalen-2-yl-ethyl)-amine C14H17N 28216 0.06% 1720 , 2.655 Cyclo-(l-leucyl-l-phenylalanyl) C15H20N2O2 28104 0.06% 2375 , 4.305 Hexanoic acid, 3,4-dimethylphenyl ester C14H20O2 27906 0.06% 1275 , 1.625 p-Pentylaniline C11H17N 27369 0.06% 1485 , 1.545

Aqueous Phase

An ICP-MS analysis of the aqueous phase was performed to determine the metal content of the media, and a total phosphorus and Kjeldahl nitrogen analysis were also performed.

Figure 4.4-5 presents the magnesium, Mg, calcium, Ca, manganese, Mn, copper, Cu, phosphorus, P, and nitrogen, N, content of the aqueous phase and how this relates to the content of these elements in BBM. For comparison, the elemental composition of the process water produced through the fractionation procedure is also presented. Although copper levels were low, generally the HTL aqueous phase has higher levels of nutrients than

BBM. Very high dilution of the aqueous phase is thus required before algae can be cultivated in this media; this is in-line with previous observations made by other researchers (Jena, et al. 2011; Levine, et al. 2011; P. Biller, et al. 2011).

To determine the efficiency of nutrient recycle from algal biomass through hydrothermal liquefaction in comparison to nutrient recycle through the oil extraction process, the amount of Mg, Ca, Mn, Cu, P, and N isolated through each of these processes per kilogram of algae processed was compared with the theoretical elemental composition of Chlorella vulgaris (see Figure 4.4-6). Note that the theoretical composition of the Chlorella vulgaris is based on data published by Oh-Hama and Miyachi (1988). It is evident that through hydrothermal liquefaction algal nutrient recycle is more efficient, especially for nitrogen and phosphorus recycle. This is intuitive since the total algal biomass is degraded through hydrothermal liquefaction while through fractionation only nutrients extracted with the oil fraction are recovered.

10000

1000

100

10 Content (mg/L) Content 1

0.1

0.01 Mg Ca Mn Cu N P HTL Aqueous Phase Fractionation Process Water BBM

Figure 4.4-5: Elemental composition of the HTL aqueous phase compared to the elemental composition of the fractionation process water

and BBM

100000

10000

1000

100

10 Content (mg/kg) Content

0.1

0.01 Mg Ca Mn Cu N P

HTL Aqueous Phase Fractionation Process Water Algae

Figure 4.4-6: Nutrient recovery in the HTL aqueous phase compared to nutrient recovery in the fractionation process water and the

theoretical composition of Chlorella vulgaris

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

The oil extraction and fractionation procedure delineated in this study is an appealing option for the production of algae based oil that can be upgraded to renewable diesel. The quality of the oil isolated is suitable for hydrotreatment and contains little impurities that would disrupt the process. The similar chemical characteristics of the constituents of the oil, including structure, molecular weight, boiling point, solubility properties etc. make for straightforward handling of this oil for upgrade. The algal oil produced through this method can be relatively economically upgraded to renewable diesel. This process also allows for the effective use of valuable biomolecules, including carotenoids, sterols and tocopherols, produced by the algae. The aqueous phase produced through this process can be recycled as algal growth medium, and the solvents used in the process can be recycled. A source of water, acetic acid and KOH are the only non-recyclable process feeds required (see Figure

5.1 for total process including the isolation of unsaponifiables, and Figure 5.2 for the total process excluding unsaponifiable isolation). The disadvantage associated with this process is, however, that only about 13.5% of the algae (on a dry weight basis) can be processed to an oil that can be upgraded to renewable diesel. This low recovery is due to the low lipid content of the UDRI grown Chlorella vulgaris. It is recommended that prior to harvesting the biomass the Chlorella vulgaris is either stressed by nitrogen starvation or a sugar source

100 is added to the growth media to enhance the oil content of the algae (Illman, Scragg and

Shales 2000; Wu 2009). Dilutions of the acetate rich process water can be added to the algae as a sugar source to increase lipid content prior to harvesting (see Figure 5.3).

The hydrothermal liquefaction process, on the other hand, was capable of converting about

44% of the total Chlorella vulgaris biomass (on a dry weight basis) to bio-crude. However, the quality of the oil produced was not ideal for upgrading due to the high nitrogen and sulfur content of the oil and the complex molecular structures of some of the oil constituents. Previous studies have indicated that the addition of certain catalysts could enhance the quality of oil but will reduce the conversion of the algal biomass to oil (Biller and Ross 2011a; Biller, Riley and Ross 2011b; Ross, et al. 2010; Levine, et al. 2011). If lipid content within the Chlorella vulgaris cannot be increased, experimentation with different

HTL catalysts is recommended.

101

Algae (10% to 90% dry)

Extraction: Ethanol Mixing at ambient or conditions Ethanol:Hexane

Solvent RemnantRemnant Separate Remnant Extract

Saponification Ethanol, Water at 60°C for & KOH 1 hour

Ethanol Water & Hexane Hexane Biphasic Separate Separation Organic Phase

Aqueous Phase Phytol,Phytol, Heptadecane,Heptadecane, Carotenoids,TocopherolCarotenoids,Tocopherol Acetic Acid ss && SterolsSterols Acidification (adjust to pH 6)

Hexane

Aqueous Phase Separate Separate

Organic Phase AqueousAqueous phasephase Hexane

Separate

FattyFatty AcidsAcids

Figure 5.1: Oil extraction and fractionation procedure with the isolation of unsaponifiables

102

Algae (10% to 90% dry)

Extraction: Ethanol Mixing at ambient or conditions Ethanol:Hexane

Solvent RemnantRemnant Separate Remnant Extract

Saponification Ethanol, Water at 60oC for & KOH 1 hour

Ethanol Acetic Acid Acidification (adjust to pH 6)

Hexane

Aqueous Phase Separate Separate

Organic Phase Hexane AqueousAqueous phasephase Separate

LightLight CyclicsCyclics

FattyFatty Acids,Acids, Distil Hydrocarbons,Hydrocarbons, AlcoholsAlcohols andand SimilarSimilar

SterolsSterols

Figure 5.2: Oil extraction and fractionation procedure without the isolation of

unsaponifiables

103

Photobioreactors for Autotrophic Algal Cultivation

Chlorella vulgaris biomass

Vessels for Heterotrophic Algal Cultivation

Acetate Rich Process Water

Chlorella vulgaris biomass with high lipid content

Oil Extraction and Remnant Fractionation

Isolated Oil Fractions

Figure 5.3: Overall recommended process with process water recycle

104

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APPENDIX I: PHYSIOLOGY OF CHLORELLA VULGARIS

Algae are a very diverse group of aquatic eukaryotes. Blue-green algae is commonly misconstrued as an algal subgroup, however it is actually a prokaryotic bacterial phylum also known as cyanobacteria. Blue-green algae, nonetheless, did play a role in the evolution of algae. It is hypothesized that primitive green algal ancestors evolved from a eukaryote engulfing and retaining a blue-green algal cell. This single endosymbiotic event led to development of photosynthetic eukaryotes (Green, et al. 2000). The engulfment of cyanobacteria-like organisms by other eukaryotes led to diversity in the basic physiology and metabolic pathways observed amongst algae. Further secondary and even tertiary endosymbiotic relationships led to the further diversification of these organisms and hence the development of the existent variety of algae; see Figure 1 (Patron and Keeling 2005). It is also hypothesized that higher plants evolved from green algae. This theory lends its credence to the fact that among photosynthetic organisms, only plants and green algae are capable of storing organic molecules in the form of starch within a plastid (Patron and

Keeling 2005).

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Figure 1: Algal Evolution (Boussiba 2000)

Oil extraction and conversion to renewable diesel is one of the less energy intensive approaches for biomass utilization for fuel production, and one of the reasons green microalgae are currently being considered for biofuel production is because they are a very good oil source. Oil content in microalgae may exceed 80% by weight of the dry algal biomass, although commonly oil content for most algal strains is 20% to 50% on a dry weight basis (Spolaore, et al. 2006). In terms of oil production per year per cultivated hectare, microalgae provides much greater oil yields than most terrestrial crops – a comparison of oil yields is presented in Table 1 (Chisti 2007). The average oil contents of different algal species are listed in Table 2 (Guschina and Harwood 2006). Not all algal oil is conducive to diesel production; hence species selection plays an important role in the biodiesel production process. The focus of this study is on Chlorella vulgaris, photosynthetic unicellular green algae classified as chlorophyta. Chlorella vulgaris typically produces between 14% and 22% lipids on a dry weight basis, but may produce up to 40% lipids under certain conditions (W. E. Becker 1994; Illman, Scragg and Shales 2000).

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Table 1: Oil Yields of Common Crops

Crop Oil yield (L/ha year) Corn 172 Soybean 446 Canola 1,190 Jatropha 1,892 Coconut 2,689 Oil palm 5,950 Microalgae 136,900 to 58,700

Table 2: Oil Content of Common Algal Species

Species Oil content (dry wt. %) Botrycoccus brauni 25-75 Chlorella sp. 28-32 Chlorella vulgaris 14-40 Crypthecodinium cohnii 20 Cylindrotheca sp. 16-37 Dunaliella primolecta 23 Isochrysis sp. 25-33 Monallanthus salina >20 Nannochloris sp. 20-35 Nannochloropsis sp. 31-68 Neochloris oleoabundans 35-54 Nitzschia sp. 45-47 Phaeodactylum 20-30 tricornutum Schizochytrium sp. 50-77 Tetraselmis sueica 15-23

The use of algae as an oil source for diesel production is convenient due to the short microalgal lifecycle – the algae is usually ready for harvest 1 to 10 days after initial cultivation (Schenk, et al. 2008). With respect to higher plants, algal solar energy

116 conversion efficiency is also superior. Measured values of algal solar efficiency have ranged from 3-9% while higher plants can only achieve a theoretical maximum solar energy efficiency of 3.7% (Zittelli, et al. 1996; Zhu, Long and Ort 2008; Kebede and Ahlgren 1996).

Also, the cultivation of an autotrophic algal species is low maintenance and low cost. These species only require carbon dioxide, sunlight and fertilizers to proliferate. The elemental requirements for the photosynthetic growth of Chlorella vulgaris include: N, P, K, Mg, Ca, S,

Fe, Cu, Mn and Zn. Miniscule amounts of most of these elements are present in the elemental composition of the algae (see Table 4), but never the less they are required in the algal growth medium to ensure proper growth and development (Oh-Hama and Miyachi

1988).

Table 4: Elemental Composition of Chlorella vulgaris

Element Weight % Carbon 51.4 – 72.6 Oxygen 11.6 – 28.5 Hydrogen 7.0 – 10.0 Nitrogen 6.2 – 7.7 Phosphorus 1.0 – 2.0 Potassium 0.85 – 1.62 Magnesium 0.36 – 0.80 Sulfur 0.28 – 0.39 Iron 0.04 – 0.55 Calcium 0.005 – 0.08 Zinc 0.0006 – 0.005 Copper 0.001 – 0.004 Manganese 0.002 – 0.01

Unlike terrestrial crops algal cultivation does not require the use of herbicides and pesticides, and although algae grows in an aquatic medium it does not consume as much water as terrestrial crops. Algae do not require a fresh water source and can grow in wastewater, typically nutrient rich or salt water. Algae can be cultivated in large open air

117 pools in areas where the climate is conducive to algal growth, or in closed system photobioreactors where temperature, pH and other factors affecting algal growth can be monitored and controlled to optimize algal growth and oil production if needed. It is noteworthy that by slightly manipulating algal growth conditions the algal growth rate and the quantity and quality of algal oil produced can be regulated.

According to a study done by Mandalam in 1995, the green algae Chlorella vulgaris does not secrete growth autoinhibitory compounds. Therefore assuming otherwise optimal conditions it can be deduced that nutrient deficiency is what leads to the stationary phase in algal development. Depending on the growth media composition, it has also been reported that Chlorella vulgaris will have different elemental composition. Studies have shown that nitrogen starvation of algae slows overall biomass productivity, but it increases lipid and carbohydrate accumulation in the cell (Huang, Chen and Chen 2009; Rodolfi, et al. 2009). A state of nitrogen depletion has been found to trigger the accelerated depletion of chlorophyll levels in the cells hence stunting cell proliferation. This in turn has been observed to trigger the accumulation of carbohydrates initially and lipids afterwards

(Mandalam and Palsson 1998). Due to the environmental stress caused by lack of necessary nitrogen, cell proliferation seems to be put on hold, while lipids and carbohydrates, cell energy and carbon storage molecules, are produced (Huang, Chen and Chen 2009; Rodolfi, et al. 2009). This relatively easy manipulation of environmental factors that is capable of modifying cellular composition and ensuring maximum lipid production per batch is not observed in land crops, thus making the use of algae as an oil source a more attractive option.

Other factors that affect algal growth include growth medium pH, ambient temperature, and algal light exposure. Different algal strains exhibit optimal growth rates at different pH

118 levels and temperatures. The variation in optimal conditions amongst algal species is due to evolutionary trends that enhance algal growth in their natural habitats. Exposure to light at different intensities and for different durations of time also effects algal growth and cell composition. This effect has a more complex nature, and to better understand this phenomena algal photosynthesis must be studied.

Photosynthesis is a two-step process involving 1) the light-dependent reactions and 2) the

Calvin cycle. These reactions, which capture solar energy and produce energy storage compounds, take place in the algal cells’ chloroplasts. Figure 2 presents a diagram of the light dependent reactions – which take place at the thylakoid membrane. The first step is light capture. This is done by means of light harvesting antennae complexes (LHCs). These proteins bind the algal produced chlorophyll and carotenoid molecules which have high photon affinity and hence capture the light (Horton and Ruban 2005).

Figure 2: Light Reactions (Britannica online encyclopedia 2010)

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The algal chlorophyll to carotenoid ratio is not constant; studies have found that changes in irradiance affect the chlorophyll and carotenoid levels in algal cells which in turn affect the growth rate. At high irradiance β-carotene content was found to increase, while at low irradiance chlorophyll-a content was found to increase (Boussiba 2000). This phenomenon is due to the natural selection of algal strands that are capable of growing at a wide range of light intensity – on the surface of water bodies where light intensity is high and deep within bodies of water where light intensity is diminished. Therefore algae are capable of altering their light harvesting compounds to enhance efficient photon capture at various light intensities.

In general, subjecting algae to increased irradiance was found to increase algal growth rate to a degree; growth rate was found to plateau at mid to high light intensities and finally to deteriorate and very high light intensities (Sorokin and Krauss 1961). The limiting step in algal photosynthesis is the rate of transfer of electrons between the two photosystems

(Marsho and Kok 1970). Once the rate of photon capture by the photosynthetic pigments equals the rate of electron transfer between the photosystems the optimal light conditions are achieved. Since algae generally grow in deeper water bodies, the optimal growth conditions are not at high light intensities. This is due to algal deep-water-low-light- intensity survival adaptations that enhance photon capture rates with respect to electron transfer rates (Melis, Neidhardt and Benemann 1999). These adaptations include tightly stacked thylakoids and large light-harvesting antenna complexes – which are generally universal traits of the algal photosynthetic apparatus (Mussgnug, et al. 2007). At light intensities higher than optimal the pigments are saturated with photons. It is common practice to grow algae in outdoor vertical tubular photobioreactors or in shallow ponds, however the algae is only capable of converting about 10% of the solar energy captured to final product (Marsho and Kok 1970). The energy of the excess photons is lost as heat or

120 impacts water and oxygen molecules to form reactive oxygen species which can decrease photosynthetic efficiency and introduce a negative stress on algal growth (Long, Humphries and Falkowski 1994). The increased production of carotenoids at high irradiances protects the cells from much of the negative effects of high light intensity since carotenoids have antioxidant properties and can stabilize the reactive oxygen species (Marcias Sanchez, et al.

2010). Thus growth rate is seen to plateau, not diminish, at mid to high light intensities.

At even higher light intensities photoinhibition is observed. Carotenoids are incapable of sequestering all the reactive oxygen species that are formed, and in turn this causes a diminished growth rate and even the phenomenon of photooxidative death. Photooxidative death is the algal cell death observed when these cells are exposed to intense light in the presence of oxygen; the reactive oxygen species formed are responsible for the cells’ death

(Abeliovich and Shilo 1972). The deterioration phase may also be somewhat attributed to the temperature increase brought on by the intense light; this temperature change shifts the algae away from its optimal growth temperature and causes the growth rate to diminish.

Growth rates are not the only factors affected by irradiance intensity. At slightly elevated irradiances and light exposure times protein levels within the cells are at their highest, the production of saturated fatty acids increases with respect to the production of unsaturated fatty acids, and the ratio of triglycerides to polar lipids also increases (Roessler 1990;

Seyfabadi, Ramezanpour and Khoeyi 2010). Therefore not only is it possible to increase lipid production rates by means of nutrient management, it is also possible to control lipid quality – saturated fatty acids are higher quality feed for biofuel production.

The physiology of algae must be considered when designing photobioreactors for algal growth. By understanding the effect experienced by algae under different levels of irradiance at the biochemical level, photobioreactor design can be optimized to enhance

121 algal growth rate, to increase photosynthetic efficiency, to manipulate lipid content, and/or to increase algal carotenoid content if required (Qiang and Richmond 1996). Carotenoids such as β-carotene and lutein are valuable in the nutraceutical industry due to their antioxidant properties.

The Calvin cycle, which follows the light reactions is responsible for carbon dioxide fixation and hence the production of biomolecules. The rate of carbon fixation, and hence biomolecule accumulation and cell growth are dependent on the efficiency and rate of the light reactions. The photosynthetic light reactions produce ATP and NADH molecules which power the active transporters responsible for pumping carbon dioxide into the algal cells for processing (Huertas, Colman, et al. 2000). These molecules also power the endothermic

Calvin cycle carbon fixing reactions.

Rubisco or ribulose-1,5-bisphosphate carboxylase/oxygenase, is an enzyme capable of catalyzing both the carboxylation and the oxygenation of ribulose-1-5-bisphosphate. The carboxylation of ribulose-1-5-biphosphate is a necessary step in the Calvin cycle and hence is responsible for fixing CO2. It leads to the production of 3-phosphoglycerate which enters the metabolic pathway for essential biomolecule production. The oxygenation of ribulose-1-

5-biphosphate leads to deleterious photorespiration. This enzyme is present in concentrated amounts in the pyrenoid, a subsection within the chloroplast (Borkhsenious,

Mason and Moroney 1998). The concentration of the substrate found in the vicinity of the rubisco dictates whether respiration or carbon fixation occurs. Hence for carbon fixation to occur carbon dioxide must be concentrated within the pyrenoid (Shelp and Canvin 1981).

The algal carbon dioxide concentration mechanism (CCM) is used to ensure that the carbon dioxide concentration within the pyreniod is greater than the oxygen concentration under light conditions (see Figure 3). Carbon dioxide is actively pumped into the cell and across

122 the chloroplast membrane by means of active pumps. These pumps are activated by phosphoylation, photophosphorylation and NADPH oxidation (Hillrichs and Schmid 2001).

When carbon dioxide concentrations in the algal medium are low, carbon dioxide is hydrolyzed into bicarbonate. Bicarbonate can also be actively pumped into the cell, and in general carbon dioxide that is pumped into the cell is hydrolyzed to bicarbonate in the cytoplasm (Huertas, Espie, et al. 2000). However since CO2 is required for rubisco carboxylation, localized carbonic anahydrase in the pyrenoid reverts the bicarbonate to carbon dioxide for fixation (van Hunnik, et al. 2001). At low carbon dioxide levels algae is superior at CO2 fixation than higher plants (Badger, Kaplan and Berry 1980).

Figure 3: Carbon Dioxide Concentration Mechanism (Hillrichs and Schmid 2001)

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Using algae for fuel production can positively impact the environment. Carbon dioxide levels have been increasing at an accelerated rate over the past decade, and based on

Paleoclimate data, scientists have correlated the increasing atmospheric carbon dioxide levels to eminent climate change, or global warming (Hansen, et al. 2008). Average atmospheric carbon dioxide levels for March of 2012 were at 394.45ppm, according to measurements made at the Mauna Loa Observatory, and this is greater than the suggested upper safety limit of 350ppm (Tans 2012). The carbon footprint associated with burning diesel is eliminated by utilizing photosynthetic algae capable of sequestering atmospheric carbon dioxide and converting it to biomass; the process is carbon neutral. Carbon dioxide sequestration by algae is especially efficient – lab tests have shown that some algal species are capable of up to 99% carbon dioxide capture (Weissman and Tillett 1992). Coupling the ability of algae to sequester up to 4g CO2L-1Day-1, with the production of biodiesel, which burns cleaner than regular diesel, could mitigate the conditions currently leading to climate change (Kurano, Ikemoto and Miyashita 1997).

Algal cultivation also has applications in wastewater remediation. Chlorella vulgaris has a high affinity for and is hence capable of the biofiltration of polyvalent metals. Algae require a certain amount of metals in their growth medium since essential algal metabolic enzymes require metals at their active sites to function. Further biosorption of metals is possible because algae are capable of the uptake and storage of relatively large amounts of metals in cytoplasmic structures without toxic effects on the cells. Excess metals in the medium also form complexes with algal extracellular polymers and cell wall polymers. Microorganisms accumulate different metals with different affinity, but generally Chlorella vulgaris and

Scenedesmus actus are used for direct metal removal from wastewater due to their high affinity to and tolerance of most heavy metals. Chlorella vulgaris is capable of the removal of cadmium, chromium, copper, iron, lead, mercury, nickel, and zinc (Travieso, et al. 1999).

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Chlorella vulgaris is also capable of Biological Nutrient Removal (BNR), or the remediation of wastewater rich in phosphates and nitrates. Chlorella’s high tolerance to water rich in metals and its ability to grow in nutrient rich wastewater can be exploited to decrease algal biomass production costs while simultaneously remediating water (Shi, Podola and

Melkonian 2007).

Chlorella vulgaris is also capable of heterotrophic growth under appropriate conditions. Its ability to grow heterotrophically is due to the presence of sugar symporters in its cell membrane (Doebbe, et al. 2007). Algal species capable of heterotrophic growth obtain their energy and carbon building blocks from sugars, generally glucose, introduced in the medium. When placed in dark fermenters with an ample sugar source the chlorophyll in the algal cells degrade (‘algal bleaching’) and heterotrophic growth ensues. Industrial waste streams from food processing plants that are high in organic molecules can be used as an algal growth medium after some pretreatment.

Since the growth rate limiting light reactions do not take place during heterotrophic growth, heterotrophic growth of algae results in up to 3.4 times the cell biomass and up to 4.2 times the lipid content of algae grown autotrophically (Liang, Sarkany and Cui 2009). The lipid content of algae grown heterotrophically has been measured at 55 dry wt %. The lipid yield was also found to contain lower oxygen content than the lipid yield of autotrophically grown algae (Miao and Wu 2006). Low oxygen content is indicative of lipids with higher heating value, and lipids with low oxygen content require less downstream processing to produce renewable diesel (Miao and Wu 2004). However heterotrophic algal growth needs to be done in a controlled aseptic environment to prevent fermentative competition with bacteria that could lead to algal death, and oil produced through this method has a carbon

125 footprint – since carbon dioxide is not sequestered during heterotrophic growth. It is for these reasons that mixotrophic growth is currently being investigated.

Mixotrophic algal growth refers to subjecting the algae to different growth conditions that alter the cells’ metabolism in a cyclic manner. Initially the algae is cultivated phototrophically; it is grown in a photobioreactor and allowed to sequester carbon dioxide until algal growth reaches its stationary phase. The algae is then pumped from the photobioreactor to a fermenter and allowed to grow heterotrophically in a pretreated wastewater medium. A portion of the algae is then harvested for oil extraction while the rest is pumped back to the photobioreactor. This method has been found to yield more than a 50 dry wt % lipid content, and the algae was found to have an average growth rate equal to double the growth rate of hetrotrophically grown algae (Wu 2009). In conclusion this method is capable of coupling carbon dioxide sequestration and wastewater treatment while proving high yields of oil at high rates.

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APPENDIX II: STATISTICAL ANALYSES

For Dry Algae Extraction

SAS Program: data DryAlgae; input Procedure $ Extract; datalines; Floch 17.40 Floch 16.44 Fajardo 18.53 Fajardo 21.60 Hexane 4.00 Hexane 3.85 ; ods graphics on; proc glm data=DryAlgae PLOTS=(diagnostics(unpack)); class Procedure; model Extract=Procedure; means Procedure /hovtest=bartlett; lsmeans Procedure /adjust=tukey lines; run; quit; ods graphics off;

The following ANOVA table was generated:

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Since the P value is less than the assigned significance level of 0.05, there is a significant difference between extracts due to solvent.

A Tukey test is used to investigate the difference between the extraction solvents:

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As a solvent hexane extracts significantly less oil that Fajardo’s method or Floch’s method. There is no significant difference between Fajardo or Floch’s methods.

The normal probability plot (presented above) does not indicate any meaningful deviations from normality (the residuals plotted against the quantile practically fall along the diagonal). Therefore the assumption of normality seems to hold.

To verify that the assumption of constant variance holds, Bartlett’s test was carried out. Bartlette’s test is applicable since the normality assumption seems to hold. The results were as follows:

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The p-value for testing whether there are differences between the variances is 0.1568. This is greater than the assigned significance level of 0.05. Thus the null hypothesis that the variances for the different treatments are equal cannot be rejected.

For Hexane Extractions

SAS Program: data Hexane; input Steamed $ Extract; datalines; No 4 No 3.85 5min 4.2 5min 4.5 10min 2.65 10min 2.33 ; ods graphics on; proc glm data=Hexane PLOTS=(diagnostics(unpack)); class Steamed; model Extract=Steamed; means Steamed /hovtest=bartlett; lsmeans Steamed /adjust=tukey lines; run; quit; ods graphics off;

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The following ANOVA table was generated:

Since the P value is less than the assigned significance level of 0.05, there is a significant difference between extracts due to pre-steam time.

A Tukey test is used to investigate the difference between the extraction steam times:

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Steaming the algae for 10 minutes significantly decreases the amount of oil extracted from the algae. There is no significant difference between steaming the algae for 5 minutes of not steaming it at all.

The p-value for testing whether there are differences between the variances is 0.9748. This is greater than the assigned significance level of 0.05. Thus the null hypothesis that the variances for the different treatments are equal cannot be rejected.

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For Pretreatments

SAS Program: data Pretreatment; input Procedure $ Extract; datalines; Alumina 15.98 Alumina 15.14 Ultrasonication 17.02 Ultrasonication 17.18 Floch 17.40 Floch 16.44 ; ods graphics on; proc glm data=Pretreatment PLOTS=(diagnostics(unpack)); class Procedure; model Extract=Procedure; means Procedure /hovtest=bartlett; lsmeans Procedure /adjust=tukey lines; run; quit; ods graphics off;

The following ANOVA table was generated:

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Since the P value is greater than the assigned significance level of 0.05, there is no significant difference between extracts due to the pretreatment methods tested and using no pretreatment method at all.

The p-value for testing whether there are differences between the variances is 0.4500. This is greater than the assigned significance level of 0.05. Thus the null hypothesis that the variances for the different treatments are equal cannot be rejected.

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For Extractions from Wet Algae

SAS Program: data WetAlgae; input Procedure $ Extract; datalines; DME 0.96 DME 0.94 Ethanol 19.50 Ethanol 20.70 Smedes 6.40 Smedes 7.44 Molina 21.50 Molina 20.90 Floch 17.40 Floch 16.44 Fajardo 18.53 Fajardo 21.60 ; ods graphics on; proc glm data=WetAlgae PLOTS=(diagnostics(unpack)); class Procedure; model Extract=Procedure; means Procedure /hovtest=bartlett; lsmeans Procedure /adjust=tukey lines; run; quit; ods graphics off;

The following ANOVA table was generated:

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Since the P value is less than the assigned significance level of 0.05, there is a significant difference between extracts due to solvent.

A Tukey test is used to investigate the difference between the extraction solvents:

As a solvent DME extracts significantly less oil from wet algae than any of the other procedures. Smedes also performs significantly poorer than 70% Ethanol or Molina’s procedure. There is no significant difference between using 70% ethanol or Molina’s solvent, and the performance of these solvents is comparable to oil extraction from dry algae using Fajardo’s or Floch’s method.

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The p-value for testing whether there are differences between the variances is 0.1613. This is greater than the assigned significance level of 0.05. Thus the null hypothesis that the variances for the different treatments are equal cannot be rejected.

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