Research Article Received: 1 August 2009
Revised: 23 October 2009
Accepted: 27 October 2009
Published online in Wiley Interscience: 29 January 2010
(www.interscience.wiley.com) DOI 10.1002/jctb.2338
Microalgae as feedstock for biodiesel production: Carbon dioxide sequestration, lipid production and biofuel quality ´ ´ Erika C Francisco,a Debora B Neves,a Eduardo Jacob-Lopesb and Telma T Francoa∗ Abstract BACKGROUND: The novelty of this work is the estimation of the fuel properties of biodiesel, a comparison study with conventional sources of biodiesel commonly used as feedstock, and an investigation for meeting the requirements of the standard specifications for this fuel produced by six strains of microalgae (three cyanobacteria, two green algae and one diatom), cultivated photosynthetically in a bubble column photobioreactor. Lipid productivity and biofuel quality were the criteria for species selection. RESULTS: Chlorella vulgaris was found to be the best strain for use as a feedstock for biodiesel production and for this specie, a carbon dioxide sequestration rate of 17.8 mg L−1 min−1 , a biomass productivity of 20.1 mg L−1 h−1 , a lipid content of 27.0% and a lipid productivity of 5.3 mg L−1 h−1 were obtained. Qualitative analysis of the fatty acid methyl esters demonstrates the predominance of saturated (43.5%) and monounsaturated (41.9%) fatty acids. The quality properties of the biodiesel were an ester content of 99.8%, a cetane number of 56.7; an iodine value of 65.0 g I2 100 g−1 ; a degree of unsaturation of 74.1% and a cold filter plugging point of 4.5 ◦ C. CONCLUSION: The results indicate that among the fuel properties tested, the microalgal biodiesel complies with the US Standard (ASTM 6751), European Standard (EN 14214), Brazilian National Petroleum Agency (ANP 255) and Australian Standard for biodiesel. c 2010 Society of Chemical Industry Keywords: photobioreactor; microalgae; carbon dioxide sequestration; single cell oil (SCO); biodiesel
INTRODUCTION
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Process engineering to produce microalgae biodiesel is an emergent area for industrial practice with great promise for partially replacing the obtention of petrodiesel and biodiesel from oil crops.5,6 According to Sheehan et al.,7 if microalgal oil production could be scaled up industrially, fewer than 6 million hectares, amounting to less than 0.4% of arable land available, would be necessary worldwide to meet current fuel demands. However, limitations of organism survival, growth and lipid content, carbon dioxide enrichment, light penetration, seasonality, harvest and biosafety of transgenics are the main barriers to industrial production based on open pond technology.8 According to this author, the limiting factors of cost are predominantly biological, suggesting that great R&D efforts must still be taken to produce feedstock at competitive prices and the required quality.
∗
Correspondence to: Telma T Franco, School of Chemical Engineering, State University of Campinas, UNICAMP, P.O. Box 6066, 13083-970, CampinasSP, Brazil. E-mail:
[email protected]
a School of Chemical Engineering, State University of Campinas, UNICAMP, P.O. Box 6066, 13083-970, Campinas-SP, Brazil b School of Agricultural Engineering, Federal University of Pelotas, UFPel, 96010900, Pelotas-RS, Brazil
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A supply of secure, equitable, affordable and sustainable energy is vital to future prosperity,1 and according to the IPCC,2 approximately 30% of final consumer energy is used for transport and the CO2 emissions produced in meeting this demand mainly by fossil fuels account for substantial amounts of total global emissions. Assessments of the uptake of biofuels range between 20% and 25% of global transport road fuels by 2050. The World Energy Outlook reference scenario predicted that biofuels will supply 4% of road fuels by 2030 with a potential of up to 7% under the alternative policy scenario. Achieving twice this penetration, as envisaged under the alternative policy scenario, would save around 0.5 Gt CO2 year−1 , but is likely to require the large-scale introduction of second and third generation biofuels.3 Because of its environmental benefits and its renewable origin, biodiesel has become increasingly attractive. However, its high cost and the controversial matter of land competition for food are the main limitations to widespread commercialization. Sixty to 70% of the final price of biodiesel depends upon of the cost of the fats and oils used in its production, which could be minimized by using cheaper suitable oils from non-edible sources; however, its formulation must conform to a number of standards.4
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Table 1. Microalgal strains, origins and culture mediums used in the cultivations Strain Aphanothece microscopica N¨ageli (RSman92) Chlorella vulgaris (UTCC 90) Dunaliella tertiolecta (UTCC 420) Phaeodactylum tricornutum (UTCC 162) Phormidium sp.
Scenedesmus obliquus (UTCC 5)
Culture medium
Origin Atlantic Ocean (32◦ 01 S, 52◦ 05 W – Brazil) UTCC UTCC
BBM50 ESAW51
UTCC
Ukeles52
Cuatro Ci´enegas Desert (26◦ 59 N, 102◦ 03 W – Mexico) UTCC
BGN49
BGN49
BBM50
In terms of quality of microalgal biodiesel, little information is available in the literature. Density, viscosity, flash point, solidifying point, heating value and cold filter plugging point were determined in previous work.9,10 However, important fuel properties of biodiesel from microalgal oil, such as ester content, cetane number, iodine value and degree of unsaturation have not been extensively described. So, for the inclusion of this feedstock source in the world energy matrix, several essential issues still need to be address. The aim of this study was to evaluate microalgae as a feedstock for biodiesel production; the focus was on carbon dioxide sequestration, lipid production and biofuel quality.
MATERIAL AND METHODS Microorganisms and culture media Six microalgal strains (Table 1) along with their origin and culture medium were studied. Stock cultures were propagated and maintained in a synthetic medium under incubation conditions of 25 ◦ C, a photon flux density of 15 µmol m−2 s−1 and a photoperiod of 12 h. Photobioreactor Measurements were made in a bubble column photobioreactor. The system was built of 4 mm thick glass and had an internal diameter of 7.5 cm, a height of 75 cm and a nominal working volume of 3.0 L. The dispersion system for the reactor consisted of a 1.5 cm diameter air diffuser located in the center of the column. The reactor was continuously illuminated with sixteen 20-W fluorescent lamps (daylight-type), connected in parallel, located in a photoperiod chamber. The duration of the light cycle was controlled by a timer. Airflow into the photobioreactor was provided via filtered air and pure CO2 cylinders through Teflon tubing. The CO2 –air mixture was adjusted to achieve the desired concentration of carbon dioxide in the air stream through three rotameters, which measured the flow rates of the carbon dioxide, the air and the mixture of gases, respectively. More details of the system are described by Jacob-Lopes et al.11
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Obtaining of kinetic data in an experimental photobioreactor The experiments were carried out in bioreactors operating in an intermittent regime, fed with 3.0 L of culture medium (Table 1).
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The experimental conditions were as follows: an initial cell concentration of 0.1 g L−1 , an isothermal reactor operating at a temperature of 30 ◦ C, a photon flux density of 150 µmol m−2 s−1 , and continuous aeration of 1 VVM (volume of air per volume of culture per minute) with the injection of air enriched with 15% carbon dioxide. The light cycle evaluated was 24 : 0 (day : night).12 Residence times of up to 168 h were considered for all the experiments. The cell density, carbon dioxide concentration and pH were monitored every 12 h during the growth phase of the microorganisms. The tests were carried out in duplicate and the kinetic data referred to the mean of four repetitions. Harvesting and drying The biomass was separated from the culture medium by decantation and centrifugation. It was then freeze dried at a temperature of −40 ◦ C and a pressure of 50 mmHg. Analytical methods Biomass concentration The cell concentration was gravimetrically evaluated by filtering a known volume of culture medium through a 0.45 µm filter and drying at 60 ◦ C for 24 h. Carbon dioxide sequestration The concentration of carbon dioxide dissolved in the liquid phase was evaluated by a dynamic method, using a polarographic probe (Mettler Toledo lnPro5000 series; Uster, Switzerland) by which the CO2 transfer was interrupted every 12 h of cultivation and the concentration of free carbon dioxide was measured as a function of time for 4 min, taking readings every 15 s. Estimates of carbon dioxide desorption were carried out by way of control experiments in the absence of the microorganism for each experimental condition as a function of the CO2 concentration, pH, temperature and stirring involved in the system, following the measurement procedures cited above. The two series of experimental data (absorption and desorption) obtained were fitted to a first-order kinetics model to estimate the kinetic variables of the absorption and desorption of carbon dioxide in the system, in accordance with the methodology proposed by Jacob-Lopes et al.13 Extraction and determination of total lipids The lipid fraction was extracted from the biomass by the Bligh and Dyer method,14 obtaining an immiscible system consisting of the sample water content and a mixture of chloroform and water. The total lipid concentration was determined gravimetrically from the chloroform extract by evaporating the chloroform in an atmosphere of nitrogen and subsequently drying to constant weight in a vacuum oven. Preparation of the fatty acid methyl esters The method of Hartman and Lago15 was used to saponify and esterify (methylation reaction) the dried lipid extract to obtain the fatty acid methyl esters (biodiesel). An amount of 250 mg of oil was added to 5.0 mL of NaOH 0.50 mol L−1 in methanol. The mixture was then heated under reflux for 5 min. After adding 15.0 mL of the esterification reagent (prepared from a mixture of 2.0 g of ammonia chloride, 60.0 mL of methanol, and 3.0 mL of concentrated sulfuric acid for about 15 min), the mixture was heated under reflux for another 3 min and subsequently transferred to a separation funnel
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containing 25.0 mL of petroleum ether and 50.0 mL of deionized water. After stirring the mixture and separating the phases, the aqueous phase was discarded. Then 25.0 mL of deionized water was added to the organic phase. This mixture was stirred and, after phase separation, the aqueous phase was discarded. This procedure was repeated. The organic phase was collected, the solvent was evaporated in a rotary evaporator and the residue was removed under nitrogen flow. The methyl esters were solubilized in n-heptane before injection in the gas chromatograph. Determination of the fatty acid composition by GC–MS The fatty acid composition was determined using a VARIAN 3600 CX gas chromatograph (Varian, Palo Alto, CA, USA) equipped with a flame ionization detector and a DB-FFAP model with a megabore column (stationary phase of nitroterphthalic acid modified by polyethyleneglycol). A column of 30 m length and with a diameter of 0.25 mm and a 0.25 µm thick film was used. The stripping gas was nitrogen at a flow rate of 2 mL min−1 , and the injector and detector temperatures were 250 ◦ C and 270 ◦ C, respectively. The initial column temperature was 120 ◦ C for 1 min; then increased from 120 to 170 ◦ C at 5 ◦ C min−1 , remaining at 170 ◦ C for 1 min; increased again from 170 to 190 ◦ C at 2 ◦ C min−1 , remaining at 190 ◦ C for 2 min; and finally increased from 190 to 220 ◦ C at 5 ◦ C min−1 , remaining at 220 ◦ C for 20 min, giving a total run time of 32 min. The amount of sample injected was 1 µL. Most of the fatty acid methyl esters (FAMEs) were identified by comparison of the retention times with those of the standard (Supelco 37 component FAME mix, St. Louis, MO, USA) and quantified by area normalization using Varian Star 4.51 software. The identity of unknown FAMEs was confirmed by GC–MS. Injections of 1 µL were made under the same conditions of carrier gas and column of FID. Peaks were detected using a GCQ mass spectrometer (MS Saturn 2000 Varian) with electron impact ionization at 70 kV. Mass spectra of individual peaks were examined using Workstation 6.6 software (Palo Alto, CA, USA). Identities were made based on the similarity of spectra between standard and candidate peaks. Quality of biodiesel The ester content (EC) was determined experimentally, and the cetane number (CN), saponification value (SV), iodine value (IV), degree of unsaturation (DU), long-chain saturated factor (LCSF) and cold filter plugging point (CFPP) were determined by empirical equations. Data from the literature were also used.16 The ester content (EC) was determined in accordance with the methodology described in the EN 14103 standard method.17 The cetane number of the mixture was estimated by the empirical equations proposed by Krisnangkura.18 The cetane number, saponification value and iodine value were calculated in accordance with Equations 1–3: 5458 − (0.225 × IV), CN = 46.3 + SV (560 × N) SV = M
IV =
(254 × D × N) M
(1) (2)
(3)
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where DU is the unsaturation degree (%), MUFA is the weight percentage of the monounsaturated fatty acids (wt%) and PUFA is the weight percentage of the polyunsaturated fatty acids (wt%). The long-chain saturated factor was obtained from empirical Equation 5, taking into account the composition of fatty acids and assigning more weight to the composition of fatty acids with a long chain. This parameter was correlated with cold filter plugging point, using Equation 6:19 LCSF = (0.1×C16)+(0.5×C18)+(1×C20)+(1.5×C22)+(2×C24) (5) CFPP = (3.1417 × LCSF) − 16.477 (6) where LCSF is the long-chain saturated factor; C16, C18, C20, C22 and C24 are the weight percentage of each of the fatty acids (wt%) and CFPP is the cold filter plugging point. Statistical analysis The multivariate cluster analysis was used for determination of the similarity between biodiesel sources. The analyses were performed using Statistica 7.0 software (Tulsa, OK, USA).20
RESULTS AND DISCUSSION Biomass and oil production and CO2 sequestration The number of microorganisms capable of accumulating oil weighing more than about 20% of their biomass is relatively small in comparison with the total number of species. Some microalgae are known to produce fairly high amounts of lipids and can be applied in bioprocess to produce alternative oils for biofuel manufacture.21 The strains of microalgae used in this work, Chorella vulgaris, Dunaliella tertiolecta, Phormidium sp., Phaeodactylum tricornutum and Scenedesmus obliquus, were selected because they are widely employed in biochemical engineering studies,22 – 26 although not applied in biodiesel commercial-scale production. In addition, Aphanothece microscopica N¨ageli is a cyanobacteria that has high growth rates in photosynthetic and heterotrophic cultivation.11,27 Therefore, the lipid production potential of each microalgae was carefully monitored in batch photobioreactors (Table 2). The best biomass producer was the cyanobacterium A. microscopica N¨ageli (PX = 31.4 mg L−1 h−1 and Xmax = 5000 mg L−1 ) (where PX is the biomass productivity and Xmax is the maximum cellular concentration); however, its lipid content was low (8.0% of the dry biomass) and thus its lipid productivity (PL ) was also low (2.5 mg L−1 h−1 ). The lipid content of strains tested varied from 6.3% to 27% and the best producer, C. vulgaris (27.0%), also had the highest lipid productivity (5.3 mg L−1 h−1 ), since it had intermediate cell growth (PX = 20.1 mg L−1 h−1 and Xmax = 3240 mg L−1 ). Comparatively, the PL result obtained in this study for C. vulgaris was two times higher than the value reported by Rodolfi et al.,28 who tested 30 microalgae species and obtained a maximum PL of 2.5 mg L−1 h−1 for Nannochloropsis sp. F&M-M26. Additionally,
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where CN is the cetane number, SV is the saponification value, IV is the iodine value, D is the number of double bonds, M is
the molecular mass and N is the percentage of each fatty acid component. The degree of unsaturation was calculated from empirical Equation 4, taking into account the amount of monounsaturated and polyunsaturated methyl ester (wt%) present in the microalgae oil:19 DU = MUFA + (2 × PUFA) (4)
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Table 2. Kinetic parameters for different microalgae species Microalgae Aphanothece Chlorella Dunaliella Phaeodactylum Phormidium Scenedesmus
PX (mg L−1 h−1 )
Xmax (mg L−1 )
pHmax
rmax (mg L−1 min−1 )
Lipid (%)
PL (mg L−1 h−1 )
k1 /k2 (average)
31.4 20.1 15.3 0.3 17.3 27.3
5000 3240 2480 150 2800 4360
8.7 8.9 8.7 8.6 9.1 8.8
28.0 17.8 14.8 1.5 18.8 11.7
8.0 27.0 17.1 6.3 11.7 14.1
2.5 5.3 2.6 0.2 2.1 3.8
1.3 1.1 1.2 0.9 1.1 1.0
PX , biomass productivity; Xmax , maximum cellular concentration; pHmax , maximum pH value obtained in cultivation (TDH = 168 h); rmax , maximum rate of carbon dioxide removal; Lipid, lipid content; PL , lipid productivity; k1 /k2 , ratio between the rate constants for the absorption and desorption of CO2 ; TDH, hydraulic detention time (h).
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in a semicontinuous system, Chiu et al.29 reported PL values of 5.9 mg L−1 h−1 for the microalgae N. oculata. In general, productivity and lipid content were inversely related, a fact explained by the high metabolic cost of lipid biosynthesis;30 in this present work lipid-rich species showed lower biomass productivity, confirming that high biomass productivity and high lipid content (the desired traits for single cell oil production from microalgae) are mutually exclusive.7 The best lipid producer is the strain showing the best combination of biomass productivity and lipid content. Concerning the rates of removal of the carbon dioxide dissolved in the aqueous phase of the photobioreactor, it was observed that the values varied from 1.5 mg L−1 min−1 for the diatom P. tricornutum to 28.0 mg L−1 min−1 for the cyanobacterium A. microscopica N¨ageli. These removal rates do not represent the values corresponding exclusively to the biological assimilation of CO2 , since physicochemical and biological processes are involved in the removal of this gas from the liquid phase of the system.13,31 It was also shown that the highest ratios between the constant for the rate of carbon dioxide absorption and the constant for the rate of carbon dioxide desorption (k1 /k2 ) occurred with the cyanobacterium A. microscopica N¨ageli, suggesting that this strain is quite efficient in carbon dioxide sequestration, with a large uptake of CO2 . Fridlyand et al.32 found ratios between the rates of CO2 absorbed and CO2 desorbed of 0.66–4.0 as a function of the enzyme activity involved in photosynthetic metabolism with losses in efficiency occurring in the process as the concentration of inorganic carbon increased in the external environment. There was a gradual increase in pH as a function of time accompanied by an increase in cell mass in the photobioreactor, with maximum values between 8.6 and 9.1. The pH variation in the culture medium could be attributed to one of the two main mechanisms. The first was the transport of hydroxide ions to outside the cell occurs through a reaction catalyzed by the enzyme carbon anhydrase during the conversion of bicarbonate ions inside the cell to provide CO2 for the photosynthetic reaction, raising the pH of the culture medium. A second potential mechanism was the increase in pH due to activity of the enzyme ribulose 1,5-bisphosphate carboxylase, whose activity is considerably dependent on pH, increasing at higher pH. This enzyme is present in the photosynthetic apparatus of the microalgae, where the H+ ions are sequestered to the inside of the tylacoid membrane. These light-induced energy fluxes result in an increase in pH, activating the rubisco enzyme and resulting in efficient CO2 fixation.33 Both the mechanisms should have higher rates than the rate of acidification of the liquid phase due to CO2 solubilization.
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Scale-up projections for these values in the present scenario are limited by the large-scale application of closed photobioreactors. However, in comparison with the productivity that may be achieved with soybean, the commonly Brazilian feedstock industrially used for biodiesel, a scale-down projections analysis indicate that for the Brazilian harvest of 2008, the average lipid productivity of soybean was 0.46 g lipid m−2 day−1 , considering a production cycle of 120 days.34 Each hectare of arable Brazilian soil produces an average of 2700 kg of soya, containing up to 20% oil. The data obtained for C. vulgaris indicate that a continuous photobioreactor with 0.85 L m−2 of working volume, operating in a cycle of 120 days year−1 , would yield the same amount of lipids as that produced by soybean. This comparison indicates that the lipid productivity of C. vulgaris can be increased by several fold by associating photobioreactor optimal design to a continuous operation during the year. Many regions in Brazil have photoperiods of 11–13 h for 300 days in a year. A bioconversion of the 3.07 kg CO2 L−1 cycle−1 is estimated under this condition. Quality properties of biodiesel Triglycerides are made up of 1 mol of glycerol and 3 mol of fatty acids (FAs), which have different lengths of carbon chain as the number of unsaturated bonds.35 Table 3 shows the FA composition of the oil extracts from the six strains evaluated here. Thirty-nine FAs were identified and, as expected, variability in the FA composition was observed for the different microalgae classes, since the species include cyanobacteria, green algae and diatoms. A representative chromatogram of the FAMEs of C. vulgaris UTCC 90 is shown (Fig. 1). The predominant FAs for A. microscopica N¨ageli and C. vulgaris were cis-10-heptadecenoic (C17 : 1) and pentadecanoic (C15 : 0), with dry weight percentages of 27.34% and 23.55%, and 31.64% and 31.81%, respectively. For D. tertiolecta, Phormidium sp. and P. tricornutum these were the linolelaidic (C18 : 2n6t) (27.13%), oleic (C18 : 1n9c) (25.92%) and myristoleic (C14 : 1) (50.97%), respectively. S. obliquus was predominant in myristoleic (C14 : 1) (21.71%), heptadecanoic (C17 : 0) (20.41%) and stearic (C18 : 0) (19.54%). The values obtained for the concentrations of saturated, monounsaturated and polyunsaturated fatty acids (Table 3) showed that A. microscopica N¨ageli, C. vulgaris and P. tricornutum were predominant in saturated and mono-unsaturated fatty acids, with values (SFA+MUFA) of 85.03%, 95.37% and 99.39%, respectively. D. tertiolecta shows equilibrium between SFA (33.33%), MUFA (34.94%) and PUFA (31.55%). Phormidium sp. was predominant in MUFA (63.87%), while S. obliquus was the
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Table 3. Fatty acid profiles of the six microalgae Methyl esters Butyric (C4 : 0) Caproic (C6 : 0) Caprylic (C8 : 0) Pelargonic (C9 : 0) Capric (C10 : 0) Undecanoic (C11 : 0) Undecenoic (C11 : 1) Lauric (12 : 0) Dodecaenoic (C12 : 1) Tridecanoic (C13 : 0) Tridecenoic (C13 : 1) Tridecaenoic (C13 : 2) Myristic (C14 : 0) Myristoleic (C14 : 1) Pentadecanoic (C15 : 0) cis-10-Pentadecenoic (C15 : 1) 2,4-Pentadienoic (C15 : 2) Pentatrienoic (C15 : 3) Palmitic (C16 : 0) Palmitoleic (C16 : 1) Hexadienoic (C16 : 2) Heptadecanoic (C17 : 0) cis-10-Heptadecenoic (C17 : 1) Stearic (C18 : 0) Octadecenoic (C18 : 1) Elaidic (C18 : 1n9t) Oleic (C18 : 1n9c) Linolelaidic (C18 : 2n6t) Linoleic (C18 : 2n6c) Arachidic (C20 : 0) γ -Linolenic (C18 : 3n6) cis-11-Eicosenoic (C20 : 1) Linolenic (C18 : 3n3) cis-11,14-Eicosadienoic (C20 : 2) Erucic (C22 : 1n9) Arachidonic (C20 : 4n6) Lignoceric (C24 : 0) cis-5,8,11,14,17-Eicosapentaenoic (C20 : 5n3) Nervonic (C24 : 1) cis-4,7,10,13,16,19-Docosahexaenoic (C22 : 6n3) SFAs (%) MUFAs (%) PUFAs (%) Total (%)
Aphanothece
Chlorella
Dunaliella
ND ND 1.83% 5.3% 1.74% 1.21% ND ND ND 3.51% ND ND ND ND 23.55% 2.30% ND ND 1.58% 1.02% ND 3.22% 27.34% 2.04% ND 8.05% ND 11.54% 3.13% 0.17% ND 2.17% 0.18% ND ND ND ND ND ND ND 44.15 40.88 14.85 99.88
0.13% 0.15% 0.63% 0.31% 0.54% ND ND 0.27% ND 0.65% ND ND 1.19% 0.94% 31.81% 2.38% 0.76% 1.22% 2.22% 1.36% ND 3.9% 31.64% 1.06% 4.3% 0,03% 7.98% 1.32% 0.1% 2.87% 0.01% ND ND ND ND ND 0.54% 0.53% 0.47% 0.54% 46.27 49.1 4.48 99.85
ND 0.06 ND ND 0.35% 0.98% 1.65% 3.35% 0.96% 1.83% 0.89% 1.47% 1.47% 1.2% 17.84% 2.44% 1.96% ND 1.16% 11.71% ND 1.37% 4.13% 4.91% 8.75% 3.03% 0.18% 27.13% 0.67% 0.01% ND ND ND ND ND 0.32% ND ND ND ND 33.33 34.94 31.55 99.82
Phormidium
Phaeodactylum
Scenedesmus
ND ND ND ND 24.59% 7.01% ND ND ND ND ND ND ND 50.97% 14.9% ND ND ND ND ND ND ND ND ND ND ND ND 0.58% ND 1.32% ND 0.60% ND ND ND ND ND ND ND ND 47.82 51.57 0.58 99.97
ND ND ND ND 0.99% 0.55% 0.20% 0.47% ND 0.19% ND ND 0.28% 21.71% 2.29% 6.24 ND ND 1.4% 5.16% 2.36% 20.41% 1.17% 19.54% ND 1.19% ND 13.25% 0.01% 1.97% 0.07% ND ND 0.40% ND ND ND ND ND ND 48.09 35.67 16.09 99.85
ND ND ND ND 0.36% 0.22% 1.53% 2.2% 2.06% 0.55% 0.25% ND 0.73% 16.61% 1.89% 2.42% ND ND 1.53% 12.29% 1.28% 9.8% 0.52% 12.25% ND 1.85% 25.92% 0.006% 4.53% ND ND 0.23% ND 0.16% 0.19% ND 0.19% ND ND 0.25% 29.72 63.87 6.22 99.81
ND, not detected; SFAs, saturated fatty acids; MUFAs, monounsaturated fatty acids; PUFAs, polyunsaturated fatty acids.
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The CN is the dimensionless descriptor of the ignition quality of a diesel fuel; it is a prime indicator of biodiesel quality. The primary reference fuel on this scale is hexadecane or cetane, with a CN value of 100. The minimum value on this scale is 15. The CN of a diesel fuel is determined by the ignition delay time. Most engine manufacturers designate a range of required CN, usually 40–50, for their engines.38 According to Clothier et al.,39 branching and chain length influence CN, with the number becoming smaller with decreasing chain length and increasing branching. With this in mind, long, unbranched chains of fatty acids similar to those of the
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microalgae strain with the highest fraction of SFA (48.09%). It should be considered, however, that these characteristics are primarily dependent on the microalgal strain and culture conditions employed.36 Techniques for preservation of the biomass are also important.27 The important fuel properties of biodiesel that are influenced by the FA profile, and consequently by the structural features of the various fatty esters, are the cetane number (CN), heat of combustion, iodine value, oxidative stability and cold flow properties as well as the exhaust emissions, viscosity and lubricity.37,38
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Figure 1. Gas chromatogram of biodiesel (Chlorella vulgaris UTCC 90).
n-alkanes produce a good conventional biodiesel fuel. Standards for CN indices have been established worldwide. Standard ASTM D675140 for biodiesel fuel requires a minimum CN of 47. In the European standard (EN 14214)17 and the Australian standard41 the value is 51; in Brazil, the National Petroleum Agency (ANP 255)42 requires a minimum of 45. The values obtained for different microalgae species range from 52.2 to 56.7 and are in accordance with all standards reported. One parameter which was not included in the ASTM, Australian and Brazilian standards, but might be necessary when defining general standards for FAMEs, is the iodine number, which describes the content of unsaturated FAs. According to Mittelbach,37 this parameter is only dependent on the origin of the oil (the mass of iodine, in grams, that is consumed by 100 g of a chemical substance). The European standard17 defines a maximum value of 120 g I2 100 g−1 , which may be necessary due to the fact that heating higher unsaturated fatty acids results in polymerization of glycerides, leading to the formation of deposits or to deterioration of the lubricating oil. This effect is increased with the number of double bonds in the FA chain. Therefore, it seems better to limit the content of higher unsaturated FAs than to limit the degree of unsaturation with the iodine number.37 In this work, the biodiesel samples produced by all species had lower iodine values than the maximum accepted by the European standards (a maximum value of 83.8 g I2 100 g−1 was found for D. tertiolecta). Besides chain length and chain branching, the degree of unsaturation is another structural feature that influences the
physical and fuel properties of the fatty ester molecule.43 In Table 4 the degree of unsaturation (DU) is shown for different microalgae species. The values obtained varied from 52.7% to 98.0%. P. tricornutum produced the biodiesel with the lowest degree of unsaturation, while D. tertiolecta produced the one with the highest degree of unsaturation, indicating the high PUFA content of this microalgae, since DU is a weighted sum of the masses of monounsaturated and polyunsaturated fatty acids. The influence of chemical structure of the FA, especially DU, is very important in the oxidative stability of biodiesel, influencing the sensitivity to long storage. The presence of air, heat, light, traces of metal, antioxidants and peroxides as well as the nature of the storage container determine the stability of the biofuel. The reason for autoxidation is the presence of double bonds in the chains of fatty compounds. The autoxidation of unsaturated fatty compounds proceeds at different rates, depending on the number and position of the double bonds. The positions allylic to double bonds are particularly more susceptible to oxidation. The bis-allylic positions, commonly found in polyunsaturated FAs such as linoleic and linolenic, are even more prone to autoxidation than allylic positions.44 D. tertiolecta, Phormidium sp. and C. vulgaris with high DU values and high concentrations of linoleic+linolenic (98.0% and 0.67%, 76.3% and 4.53% and 74.1% and 0.11%, respectively, tend to produce methyl ester fuels with poorer oxidation stability than other species tested. Additionally, NOx exhaust emissions increase with an increasing unsaturation degree.45 On the other hand a decrease in viscosity
Table 4. Properties of biodiesel from microalgal oil Source Aphanothece Chlorella Dunaliella Phaeodactylum Phormidium Scenedesmus
EC (wt %)
CN
SV
IV (g I2 100 g−1 )
DU (wt %)
LCSF (wt %)
CFPP (◦ C)
99.9 99.8 99.8 99.9 99.8 99.8
55.8 56.7 52.2 53.7 54.6 56.1
225.1 217.8 220.8 266.1 217.9 217.5
65.4 65.0 83.8 58.7 74.5 68.2
70.6 74.1 98.0 52.7 76.3 67.8
3.8 6.7 2.6 1.3 6.6 11.9
−4.6 4.5 −8.4 −12.3 4.4 20.8
400
EC, ester content; DU, degree of unsaturation; CN, cetane number; SV, saponification value; IV, iodine value; LCFS, long-chain saturated factor; CFPP, cold filter plugging point.
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Microalgae for biodiesel production
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Peanut (a)
Aphanothece
Phaeodactylum Phormidium
Dunaliella
Aphanothece Chlorella
Phormidium
Scenedesmus Phaeodactylum
Palm (a) Soybean (a)
Scenedesmus
Sunflower (a)
Dunaliella
Chlorella
Rapeseed (b) 0.5
1.0
1.5
2.0
2.5
0
3.0
4
2
6
10
8
12
Linkage Distance CN
Linkage Distance PL
Aphanothece
Aphanothece
Chlorella
Dunaliella
Phormidium
Phaeodactylum
Scenedesmus
Soybean (a)
Palm (a)
Sunflower (a)
Phaeodactylum
Rapeseed (b)
Dunaliella
Chlorella
Rapeseed (c)
Phormidium
Peanut (a)
Palm (a)
Soybean (a)
Scenedesmus
Sunflower (a)
Peanut (a) 0
5
10
15
20
25
1
2
3
4
5
6
7
8
9
Linkage Distance CFPP
Linkage Distance DU
Figure 2. Cluster dendograms for lipid productivity (PL ), cetane number (CN), degree of unsaturation (DU) and cold filter plugging point (CFPP). Sources: (a) Ramos et al.,19 (b) Knothe,38 (c) calculated from data in Ma and Hanna.53 .
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do not mention a low-temperature parameter in their lists of specifications. Knothe38 suggests that each country specify certain temperature limits for different times of the year, depending on climate conditions. According to our best knowledge, the literature does not describe important fuel properties of biodiesel of microalgae in photosynthetic conditions. However, Miao and Wu10 and Xu et al.48 have described some properties of biodiesel obtained by C. protothecoides produced in heterotrophic conditions. The parameters found for this feedstock were: a density of 0.864 kg L−1 , a viscosity of 5.2 mm2 s−1 ; cSt at 40 ◦ C, a flash point of 115 ◦ C, a solidifying point of −12 ◦ C, an acid value of 0.374 mg KOH g−1 , a heating value of 41 MJ kg−1 and a cold filter plugging point of −11 ◦ C. Comparisons: Multivariate analysis A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures, i.e. to develop taxonomies. Multivariate statistical cluster analysis is widely used for this purpose. In Fig. 2 the dendograms for one production parameter (PL ) and three biodiesel quality parameters (CN, DU and CFPP) are shown for the six microalgae species and five vegetable sources. Single linkage as an amalgamation rule and Euclidean linkage distances as a measure of similarity were used in the analysis. For lipid productivity (PL ), the dendogram shows four distinct sets of clusters: cluster 1 (C. vulgaris), cluster 2 (S. obliquus), cluster 3 (P. tricornutum) and cluster 4 (Phormidium sp., D. tertiolecta and A. microscopica N¨ageli).
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401
with an increasing degree of unsaturation favors the mechanical properties of biodiesel and diesel engine operation. Finally, the low temperature flow of the biodiesel is another important quality parameter when saturated molecules are present, crystallization may occur at temperatures within the normal engine operation range. Therefore, when temperatures are low enough, these crystals grow rapidly and agglomerate, clogging fuel lines and filters and causing major operational problems.38,46 In general, saturated FAs have significantly higher melting points than unsaturated fatty compounds, and in a mixture, crystallize at temperatures where unsaturated fatty acids remain liquid. The cold filter plugging point (CFPP) is a parameter usually used for the prediction of the flow performance of biodiesel at low temperatures. The CFPP obtained for different microalgae oils varied from −12.3 to 20.8 ◦ C. S. obliquus diesel had poor low-temperature flow properties (20.8 ◦ C of CFPP), only comparable to the published data on peanut biodiesel (17 ◦ C of CFPP).47 Peanut and S. obliquus biodiesel have the highest CFPP because they are composed of long-carbon-chain saturated fatty acids (expressed by high LCSF; Equation 5). The longer the carbon chains in the biodiesel, the worse their low-temperature properties47 . Mittelbach and Remschmidt16 reported that when a liquid biodiesel is cooled, the stearic and palmitic acid methyl esters are the first to precipitate and therefore typically constitute a major share of material recovered from clogged biodiesel fuel filters. S. obliquus biodiesel, rich in stearic acid (19.54%), had the highest CFPP. Additives can be used to inhibit the agglomeration of crystals, thus lowering the point at which fuel filter plugging occurs. The European, ASTM, Australian and Brazilian standards
´ Francisco et al. EC
www.soci.org Two sets of clusters are visibly apparent in the CN parameter: cluster 1 (rapeseed) and cluster 2 (D.tertiolecta, sunflower, soybean, palm, S. obliquus, C. vulgaris, A. microscopica N¨ageli, Phormidium sp., P. tricornutum and peanut). However, in cluster 2 it is possible to view the formation of four subclusters: subcluster 1 (D. tertiolecta, sunflower and soybean), subcluster 2 (palm), subcluster 3 (S. obliquus, C. vulgaris, A. microscopica N¨ageli and Phormidium sp.) and subcluster 4 (P. tricornutum and peanut). For the parameter DU, two clusters are also clearly shown: cluster 1 (sunflower, soybean, peanut, rapeseed and D. tertiolecta) and cluster 2 (P. tricornutum, palm, S. obliquus, Phormidium sp., C. vulgaris and A. microscopica N¨ageli). The subclusters formed are: subcluster 1 (soybean and sunflower), subcluster 2 (peanut, rapeseed and D. tertiolecta), subcluster 3 (P. tricornutum) and subcluster 4 (palm, S. obliquus, Phormidium sp., C. vulgaris and A. microscopica N¨ageli). Finally, two principal sets of clusters were formed for the parameter CFPP: cluster 1 (peanut, S. obliquus, palm, Phormidium sp. and C. vulgaris) and cluster 2 (rapeseed, sunflower, soybean, P. tricornutum, D. tertiolecta and A. microscopica N¨ageli), which could be divided into four subclusters: subcluster 1 (peanut and S. obliquus) and subcluster 2 (palm, Phormidium sp. and C. vulgaris), subcluster 3 (rapeseed, sunflower and soybean) and subcluster 4 (P. tricornutum, D. tertiolecta and A. microscopica N¨ageli). Therefore, it was found that in terms of lipid productivity, C. vulgaris was unique among the microalgae tested. The multivariate technique suggested that palm biodiesel (the highest cetane number) was similar to the all sources considered (with exception of rapeseed); however, when subclusters were compared, subcluster 3 (composed of microalgae S. obliquus, C. vulgaris, A. microscopica N¨ageli and Phormidium sp.) contained the sources most similar to palm in terms of cetane number. Soybean and sunflower had the poorest oxidative stability of sources considered (subcluster 1 for the DU parameter). Of the species tested, P. tricornutum biodiesel (subcluster 3) was the microbial biofuel most prone to autoxidation. As regards cold flow properties, biodiesel from peanut and S. obliquus has similar performance features at low temperatures.
CONCLUSIONS
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Microalgae have emerged as one the most promising feedstocks for biodiesel production. They have several key traits that make them a desirable energy source. They can be grown away from farmlands and forests and their yields of oil are orders of magnitude higher than those from traditional oilseeds. In addition, microalgae grow through bioconversion of the carbon dioxide from stationary industrial emissions. Sources of high purity CO2 emission at reduced temperatures should be identified and the photobioreactors adapted to these conditions. This strategy is environmental friendly, but several studies warn that there are still high hurdles to overcome before microalgal biofuels can compete economically with conventional fossil fuels. Current economical modeling of microalgae biodiesel places the price of production in the range of $6.5–8.0 per gallon. The target cost on commercial scale is estimated on a dollar per gallon basis, which requires a reduction cost of approximately 80%. Finally, the life cycle assessment (LCA) of the microalgal oil is a necessary step to the investigation and valuation of the environmental impacts of this biofuel. LCA method should be used to identify and quantify emissions and energy efficiency of the system throughout the whole life cycle.
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In this study, Chlorella vulgaris was the microalgae with the greatest potential for SCO exploitation among all the strains tested (lipid productivity of 5.3 mg L−1 h−1 ), producing biodiesel with the following quality characteristics: an ester content of 99.8%, a cetane number of 56.7, an iodine value of 65.0 g I2 100 g−1 , a degree of unsaturation of 74.1% and a cold filter plugging point of 4.5 ◦ C. All of these parameters comply with the limits established by the US Standard (ASTM 6751), European Standard (EN 14214), Brazilian National Petroleum Agency Standard (ANP 255) and Australian Standard for biodiesel quality.
ACKNOWLEDGEMENTS Funding for this research was provided by FAPESP, CAPES, CNPq and PETROBRAS (Brazil). The authors are grateful to Dr Maria Isabel Queiroz (Federal University of Rio Grande, Brazil) for providing the cyanobacterium A. microscopica N¨ageli.
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