Evaluation of lake erie algae as bio fuel feedstock

The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2010 Evaluation of Lake Erie algae as bio-fuel feedstock Vasudev Gottumukala The University of Toledo Follow this and additional works at: http://utdr.utoledo.edu/theses-dissertations Recommended Citation Gottumukala, Vasudev, "Evaluation of Lake Erie algae as bio-fuel feedstock" (2010) Theses and Dissertations Paper 851 This Thesis is brought to you for free and open access by The University of Toledo Digital Repository It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository For more information, please contact arjun.sabharwal@utoledo.edu A Thesis entitled Evaluation of Lake Erie Algae as Bio-fuel Feedstock by Vasudev Gottumukala Submitted to the Graduate Faculty as partial fulfillment of the requirements for The Master of Science in Chemical Engineering _ Advisor: Constance A Schall Committee Member: Thomas Bridgeman Committee Member: Sridhar Viamajala Dean: Dr Patricia Komuniecki College of Graduate Studies The University of Toledo May 2010 An Abstract of Evaluation of Lake Erie Algae as Bio-fuel Feedstock by Vasudev Gottumukala Submitted to the Graduate Faculty in partial fulfillment of the requirements for the Master of Science in Chemical Engineering The University of Toledo May 2010 Currently, transportation fuels are produced from continuously depleting fossil fuel sources This calls for additional renewable sources that could be used for the production of high quality transportation fuel Bio-diesel is one such alternative Soybean, a food crop, has been used in the past as a source of lipids for the production of bio-diesel Algae are an alternative non-food source of lipids for bio-diesel and/or carbohydrates for bio-ethanol We have surveyed algae and phytoplankton in the western Lake Erie basin to identify the predominant algae species The lipid, carbohydrate and the protein content of lake species were determined Sampling at selected lake sites was performed at regular intervals of time in an attempt to correlate lake conditions (i.e temperature, phosphorus and nitrogen) with the selection and composition of species Based on the results of these analyses, native species were identified as candidates for bio-diesel or bio-ethanol production iii Few preliminary experiments were performed to process soybean oil using a batch reactor to convert the triacylglycerides to free fatty acids which would then be converted to fatty acid methyl esters (bio-diesel) through transesterification The optimized processing conditions can then be utilized to process algae iv This work is dedicated to my parents, sister & brother-in-law and to my best friends for their tremendous support and encouragement throughout Acknowledgements I wish to express my sincere gratitude to my advisor Dr Constance A Schall I would also like to thank Dr Thomas Bridgeman, Dr Sasidhar Varanasi and Dr Sridhar Viamajala for their guidance and support during my research I would like to thank Dr Cyndee Gruden, Dr Thomas Kina and Dr Pannee Burckel for their help with analytical equipments and useful suggestions I would like to thank Dr Glenn Lipscomb for giving me admission in University of Toledo I am grateful to the Department of Chemical and Environmental Engineering for financial assistantship throughout my course of study I would also like to thank the Center for Innovative Food technology for funding my project I would really like to thank my labmates and friends Indira Priya Samayam, Noureen Faizee, Thehazhnan Ponnaiyan, Christopher Barr, Amber Bosley, Brett Digman, Richard Hausman, Justin Chaffin, Olga Mileyeva-Biebesheimer, Ananth Dadi, Kripa Rao and Micheal Mayer for their help, support and encouragement Last but not the least I would like to thank my colleagues at my company, Midwest Bio Renewables, for believing in me and giving me complete freedom and flexibility in my job timings in order to complete my thesis vi Table of Contents Abstract iii Acknowledgement vi Table of Contents vii List of Tables xi List of Figures xiii Introduction Chapter - Characterization of Algae 1.1 Chapter Introduction 1.2 Dry weight analysis 1.3 1.2.1 Equipment 1.2.2 Materials 1.2.3 Methods 1.2.4 Sample calculations Lipid analysis 10 vii 1.4 1.5 1.6 1.3.1 Equipment 10 1.3.2 Reagents 10 1.3.3 Materials 11 1.3.4 Methods 12 1.3.5 Sample calculations 19 Protein analysis 20 1.4.1 Equipment 20 1.4.2 Materials 20 1.4.3 Methods 20 1.4.4 Sample calculations 21 Structural carbohydrate & lignin analysis 22 1.5.1 Equipment 22 1.5.2 Reagents 22 1.5.3 Materials 23 1.5.4 Methods 23 Starch analysis 24 1.6.1 Equipment 24 1.6.2 Reagents 25 1.6.3 Materials 26 1.6.4 Methods 26 viii Chapter - Characterization of Aulacoseira granulata 29 2.1 Chapter Introduction 29 2.2 Results and Discussion 30 2.2.1 Dry weight analysis 30 2.2.2 Lipid analysis 31 2.2.3 Starch, structural carbohydrate and lignin analysis 38 2.2.4 Protein analysis 39 2.2.5 Conclusions 40 Chapter - Characterization of Cladophora glomerata 41 3.1 Chapter Introduction 41 3.2 Results and Discussion 43 3.2.1 Dry weight analysis 43 3.2.2 Lipid analysis 44 3.2.3 Starch, structural carbohydrate and lignin analysis 47 3.2.4 Protein analysis 49 3.2.5 Conclusions 51 Chapter - Characterization of Lyngbya wollei 52 4.1 Chapter Introduction 52 4.2 Results and Discussion 54 4.2.1 Dry weight analysis 54 4.2.2 Lipid analysis 55 ix Temperature, pressure and oil to water ratio was kept constant throughout all experiments Reaction time and use of catalyst for the reaction were the reaction variables tested in these experiments Pressure settings were selected based on reaction temperature (250 oC) and water properties at that temperature A pressure in excess of that of saturated steam at the operating temperature is selected so that there is no vaporization of water Oil to water ratio was selected to match the water content in algae The reaction temperature selection was made based on previous studies on hydrolysis in batch reactor (King J W., Holliday R L et al 1999) All these above mentioned settings were selected to prevent degradation that could be caused by high temperatures FID Soy top lyr 30 150 150 Linoleic acid Name Linolenic acid mVolts 100 Oleic acid 50 Stearic acid Palmitic acid mVolts 100 50 0 -50 -50 28 29 30 31 32 33 34 35 36 37 38 Minutes Figure 6.5 Chromatogram showing FFA sample run through GC Calculations of conversion to FFAs showed promising results An increase in the theoretical yield of triglycerides to fatty acids was observed with increasing reaction times An attempt was made to check if the yield would be any higher with the use of an acid catalyst No improvement was observed; instead the yield was found to be less 88 compared to the sample reacted without catalyst for the same duration of time These observations are detailed in Table 6.1 A significant improvement in the conversion was achieved as the reaction time was increased A conversion of approximately 85% was achieved using the following reaction conditions:  Reaction Temperature: 250 oC  Operating Pressure: 41.37 bar  Oil to Water ratio(v/v): 1:5  Reaction time: 60 Table 6.1: Theoretical yields obtained in soybean oil hydrolysis Reaction Time (min) Weight of 30 ml Soybean Oil (g) W/O W/ Catalyst Catalyst Weight of FFA formed - GC (g) W/O W/ Catalyst Catalyst Theoretical yield (%) W/O W/ Catalyst Catalyst 30 27.84 26.51 11.43 13.79 43 55 45 26.31 26.35 20.74 19.45 83 77 60 26.35 26.25 21.56 18.09 86 72 6.5 Sample Calculation: The theoretical yield of FFAs through hydrolysis of soybean oil was calculated based on the initial weight of fatty acids in the soybean oil, which primarily consists of TAGs It was found that TAGs, in soybean oil, contain approximately 95.3% (by weight) fatty acids This percentage was calculated assuming that the composition of the 89 feedstock is 10% - C16:0, 3% - C18:0, 24% - C18:1, 54% - C18:2 and 8% - C18:3 (see Table 6.2) The steps involved in the calculation of the theoretical yield are shown below:  Initial weight of soybean oil = U gm  Initial weight of fatty acids in soybean oil = U * 0.953 = V gm  Initial weight of water = W gm  Combined weight of FFAs obtained through GC = X gm  Theoretical yield, Y = X / V *100 FID Soy top lyr 60 FID Soy top lyr 45 FID Soy top lyr 30 30 45 60 150 150 50 50 0 mVolts 100 mVolts 100 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0 35.5 36.0 36.5 37.0 37.5 Minutes Figure 6.6 Chromatogram showing overlay of three sample (varying reaction times) runs through GC While the fatty acid distribution obtained through the hydrolysis of soybean oil without any catalyst coincides well with the data available in the literature (Van Gerpen J., Shanks B et al 2004), a slight deviation from the distribution of fatty acids was observed for the samples reacted with acid catalyst (Table 6.2), particularly at longer retention times 90 Table 6.2: FFA distribution at varying reaction times and catalyst loadings *(Van Gerpen J., Shanks B et al 2004) Reaction Time (min) FFA Wt.% of FFA (Literature*) 30 C16:0 C18:0 C18:1 C18:2 C18:3 Wt.% of FFA (Obtained) W/O Catalyst W/ Catalyst - 10 2-5 20 - 30 50 - 60 - 11 10 24 56 11 26 54 45 C16:0 C18:0 C18:1 C18:2 C18:3 - 10 2-5 20 - 30 50 - 60 - 11 22 56 10 12 29 51 60 C16:0 C18:0 C18:1 C18:2 C18:3 - 10 2-5 20 - 30 50 - 60 - 11 23 56 12 31 52 Higher yields were obtained in our batch studies compared to that reported in earlier studies using a flow reactor (King J W., Holliday R L et al 1999) Previous studies have shown that hydrolysis of soybean oil to FFAs using a flow reactor at 270 ºC and a residence time of 30 minutes resulted in an yield of 22% (King J W., Holliday R L et al 1999) An increase in yield, to 43%, was observed using a lower operating temperature (i.e., 250 oC) and the same reaction time (30 min) in the batch reactor (Table 6.2) These reaction conditions can be further optimized to achieve a conversion of greater than at least 95% After optimizing these reaction conditions, it could then be 91 tested with wet algal paste to convert the TAGs to FFAs and then converted to FAMEs through esterification The sensible heat input to heat water from 20 to 250 ºC at 41.37 bar is 683 kJ per kg of water This compares to 2257 kJ per kg of water for the latent heat of vaporization of water at atmospheric pressure This process could provide a less energy intensive way to handle the high water content in the algae samples than drying 92 CHAPTER Conclusions & Future Work Continuously increasing use of fossil fuels to fulfill our daily requirements of energy will lead to depletion in the availability of these fuels over a period time A clear indication of this situation can be observed with the increase in fuel prices in recent years The need to look for alternative fuel sources to meet the energy requirements has increased manifold Out of the many alternative energy sources known, bio-fuels have been attracting attention since biomass sources can provide a renewable carbon feedstock which can be converted to liquid transportation fuels An alga is one such source The focus of this thesis was to characterize the dominant species of algae available in western Lake Erie and to evaluate their use as biofuel feedstock Three different species of algae, Aulacoseira granulata (a diatom), Cladophora glomerata (green algae) and Lyngbya wollei (cyanobacteria), were sampled from Lake Erie Various characterization analyses were performed on each of these species, mainly to determine the lipid, carbohydrate, starch and protein content in them Of the three species, Aulacoseira granulata, as expected, was found to contain significant amounts of lipids (10-25%, dry weight) The lipid content measured using this procedure includes phospholipids, which are a part of the structural membrane of the 93 algae It would be interesting to determine the amount of neutral lipids (excluding the phospholipids) Cladophora glomerata species showed promising signs of being useful as a feedstock in the production of bio-ethanol and materials because of its high carbohydrate content (20-25%, dry weight) This species was also found to be rich in starch (5-10%, dry weight) compared to the other two species Extraction and processing of these carbohydrates from algae would be a challenging option to look at The protein content in the algae species was calculated based on the elemental composition (C, H and N) of the particular species The nitrogen content was taken as the basis for calculating the protein content All the species had significant quantities of proteins (20-35%, dry weight) Developing a protein extraction process could be very useful The extracted protein can then be characterized (amino acid distribution) and its value as animal feed evaluated An attempt was made to process soybean oil by hydrolyzing the TAGs to FFA This system was chosen as a model system to evaluate processing of TAG feedstock with high water content Algal feeds that are filtered or centrifuged contain low solids and high water content The FFAs could then be transesterified to form FAMEs This approach was selected to deal with the water content in algae without having to dry the biomass completely Complete drying requires higher energy inputs The ratio of oil to water was selected to match the average water content in algae Experimental runs were performed by varying the reaction time and also by introducing catalyst into the reaction mixture Positive results were obtained through these preliminary experiments A theoretical yield of close to 85% (dry weight basis) was obtained Further testing on the 94 reaction conditions is needed to improve the yield Variables such as temperature, reaction time etc can be varied to improve yields of FFAs The optimized reaction conditions on soy bean oil can then be tested with algae An overall 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Freshwater Biology 38: 571-579 Werner D (1977) Silicate metabolism In Werner, D.[Ed.] The Biology of the Diatoms Berkeley, California, University of California Press: 110-149 99 Appendices Appendix A – Lake sampling data for Aulacoseira granulata (Sources: Dr Thomas Bridgeman, Environmental Sciences Department, Univeristy of Toledo) Table A-1: Available nutrients near the sampling location of Aulacoseira granulata in 2008 TKN – Total Kjeldahl nitrogen SRP – Soluble reactive phosphorus TN – Total nitrogen TP – Total phosphorus Table A-2: Lake conditions near the sampling location of Aulacoseira granulata in 2008 100 Sp Cond – Specific conductivity TDS – Total dissolved solids DO Conc – Dissolved oxygen concentration Table A-3: Available nutrients near the sampling location of Aulacoseira granulata in 2009 TKN – Total Kjeldahl nitrogen SRP – Soluble reactive phosphorus TN – Total nitrogen TP – Total phosphorus Table A-4: Lake conditions near the sampling location of Aulacoseira granulata in 2008 Sp Cond – Specific conductivity TDS – Total dissolved solids DO Conc – Dissolved oxygen concentration 101 Appendix B – Sample calculations: B-1 Structural carbohydrate analysis: A sample calculations of only one carbohydrate breakdown sugar (glucose) is described here Similar approach is taken for the other breakdown products Initial dry weight of sample, A = 300 mg Concentration, calculated based on the calibration curves of standards = B mg/ml Weight of glucose in the reaction mixture = B (mg/ml) * 87 (ml) = C mg % Glucose = {[(C / A) * (162/180)] / 300} *100 = D % To convert the measured glucan to glucose a conversion factor, F1 = (162/180) is used in the calculations for glucose, galactose and mannose To measure the exact conversion from xylan to xylose a conversion factor F2 = (132/150) is used in the calculation for xylose and arabinose B-2 Starch analysis: The following steps are used for calculating glucose content contributed by starch in the biomass sample Initial dry weight of sample, J = 100 mg Concentration, calculated based on the calibration curves of standards = K mg/ml Weight of glucose in the reaction mixture = K (mg/ml) * 10 (ml) = L mg % Glucose = {[(L / J) * (162/180)] / 100} *100 = N % To convert the measured amylose to glucose a conversion factor, F1 = (162/180) is used in the calculations 102 ... Thesis entitled Evaluation of Lake Erie Algae as Bio- fuel Feedstock by Vasudev Gottumukala Submitted to the Graduate Faculty as partial fulfillment of the requirements for The Master of Science in... Abstract of Evaluation of Lake Erie Algae as Bio- fuel Feedstock by Vasudev Gottumukala Submitted to the Graduate Faculty in partial fulfillment of the requirements for the Master of Science in Chemical... production of high quality transportation fuel Bio- diesel is one such alternative Soybean, a food crop, has been used in the past as a source of lipids for the production of bio- diesel Algae are