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Comparative assessment of various lipid extraction protocols and optimization of transesterification process for microalgal biodiesel production a

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Shovon Mandal , Reeza Patnaik , Amit Kumar Singh & Nirupama Mallick

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Agricultural and Food Engineering Department , Indian Institute of Technology Kharagpur , West Bengal , 721302 , India Published online: 08 Oct 2013.

To cite this article: Shovon Mandal , Reeza Patnaik , Amit Kumar Singh & Nirupama Mallick (2013) Comparative assessment of various lipid extraction protocols and optimization of transesterification process for microalgal biodiesel production, Environmental Technology, 34:13-14, 2009-2018, DOI: 10.1080/09593330.2013.827730 To link to this article: http://dx.doi.org/10.1080/09593330.2013.827730

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Environmental Technology, 2013 Vol. 34, Nos. 13–14, 2009–2018, http://dx.doi.org/10.1080/09593330.2013.827730

Comparative assessment of various lipid extraction protocols and optimization of transesterification process for microalgal biodiesel production Shovon Mandal, Reeza Patnaik, Amit Kumar Singh and Nirupama Mallick∗ Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal 721302, India

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(Received 10 January 2013; accepted 29 June 2013 ) Biodiesel, using microalgae as feedstocks, is being explored as the most potent form of alternative diesel fuel for sustainable economic development. A comparative assessment of various protocols for microalgal lipid extraction was carried out using five green algae, six blue-green algae and two diatom species treated with different single and binary solvents both at room temperature and using a soxhlet. Lipid recovery was maximum with chloroform–methanol in the soxhlet extractor. Pretreatments of biomass, such as sonication, homogenization, bead-beating, lyophilization, autoclaving, microwave treatment and osmotic shock did not register any significant rise in lipid recovery. As lipid recovery using chloroform–methanol at room temperature demonstrated a marginally lower value than that obtained under the soxhlet extractor, on economical point of view, the former is recommended for microalgal total lipid extraction. Transesterification process enhances the quality of biodiesel. Experiments were designed to determine the effects of catalyst type and quantity, methanol to oil ratio, reaction temperature and time on the transesterification process using response surface methodology. Fatty acid methyl ester yield reached up to 91% with methanol:HCl:oil molar ratio of 82:4:1 at 65◦ C for 6.4 h reaction time. The biodiesel yield relative to the weight of the oil was found to be 69%. Keywords: biodiesel; microalgae; lipids; transesterification; fatty acid methyl esters

1. Introduction Lipids, a diverse group of biological substances, are made up of polar (free fatty acids (FFA), phospholipids and sphingolipids) and non-polar compounds such as triglycerides, diglycerides, monoglycerides and sterols. They bind covalently to carbohydrates and proteins to form glycolipids and lipoproteins, respectively. The possibility of lipids to bind to other molecules and the ability of different solvents to solubilize different lipid classes have led to the concept of total lipid extraction. Thus, several methods have been developed for total lipid extraction,[1–5] where animal fats, fish tissue and aquatic invertebrates are used as source materials. For extraction of microalgal lipids, various methods are explored by different researchers, such as cell disruption by the bead-beater followed by extraction with chloroform–methanol,[6] lyophilized biomass followed by two-stage solvent extraction,[7] pre-treatment of biomass with propanol followed by sonication, oil extraction using n-hexane in a soxhlet apparatus,[8] etc. Lee et al. [9] compared various cell disruption techniques, such as autoclaving, bead-beating, microwave treatment, sonication and NaCl treatment followed by lipid extraction in chloroform– methanol; the microwave oven pre-treatment was reported to be the most efficient for lipid extraction from microalgae.

∗ Corresponding

author. Email: [email protected]

© 2013 Taylor & Francis

Chen et al. [10] applied freezing and bead-beating as pre-treatment processes followed by chloroform–methanol as solvents for lipid extraction from a green microalga Dunaliella tertiolecta. Moazami et al. [11,12] also used chloroform–methanol for lipid extraction from dried algal biomass following cell disruption using a sonication bath. This shows that a specific lipid extraction protocol for microalgae is yet to be identified. Like higher plants and animals, microalgae are able to biosynthesize triglycerides to store energy. Transesterification of triglycerides results in fatty acid methyl esters (FAMEs) or biodiesel with glycerol as a by-product. This process has now been widely used to reduce the high viscosity of oils. Various researchers have used different catalysts such as acids, bases or a combination of both to trigger the transesterification process; even the enzyme lipase has been tried.[13–16] In situ/direct transesterification, eliminating the lipid extraction step, has also been studied in recent years. Patil et al. [17] used wet algal biomass for conversion to biodiesel under supercritical methanolic conditions. This supercritical methanolic process includes pre-treatment of biomass at −80◦ C followed by direct transesterification at high temperature and pressure, which would certainly not be a cost-effective process for large-scale application.

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S. Mandal et al.

A study conducted by Sanchez et al. [18] with two marine microalgae showed that the best reaction condition was 300:1 methanol:oil, 1% catalyst (NaOH), 60◦ C reaction temperature and reaction time of 11 h for in situ transesterification. An alkaline in situ transesterification has also been explored with Chlorella vulgaris with a methanol:oil ratio of 600:1.[19] Single-step transesterification with C. vulgaris resulted in significantly lower ester yield than the conventional two-step process, which includes extraction of lipids followed by transesterification.[20] Thus, in situ transesterification though seems to be technically feasible, the mammoth-scale solvent requirement and low ester yield make the process economically unattractive. Therefore, in this report, our efforts have been to identify a suitable lipid extraction protocol for microalgae by comparing various existing single and binary solvent extraction protocols with 13 different species, and optimized a transesterification protocol using response surface methodology (RSM) with the species identified as the best lipid accumulator among the test microalgae.

2. Materials and methods 2.1. Test organisms and growth conditions The microalgal species/strains collected from the rice fields of Agricultural and Food Engineering Department, IIT Kharagpur and freshwater bodies of West Midnapore District, West Bengal, India, and its adjacent regions were isolated using the standard dilution technique, and were identified following the standard keys/literatures.[21–25] Axenic cultures of five green microalgae, Scenedesmus obliquus (Trup.) Kutz. (SAG 276-3a), C. vulgaris (courtesy: Prof. L.C. Rai, Banaras Hindu University, Varanasi, India), Scenedesmus acuminatus (courtesy: Prof. S.P. Adhikary, Visva-Bharati, Santiniketan, India), Chlorella sp. and Chlamydomonas sp. were grown in 150-ml Erlenmeyer flasks containing 50 ml of N 11 medium [26] at pH 6.8. Six blue-green algae (Anabaena cylindrica, Anacystis nidulans, Nostoc muscorum, Spirulina maxima, Spirulina platensis and Synechocystis sp. PCC 6803), and two diatoms (Cyclotella sp. and Pinnularia sp.) were cultured using BG 11 [27] and WC [28] media, respectively. The cultures were maintained in a temperature-controlled culture room at 25 ± 2◦ C under a 14 h light : 10 h dark photoperiod at a light intensity of 75 μmol photon m−2 s−1 PAR without sparging with air or CO2 . The cultures were hand-shaken two–three times daily for 2 min to avoid settling. Dry cell weight (dcw) was determined gravimetrically according to Rai et al.[29] A known volume of algal culture was centrifuged at 5000 rpm for 10 min and the harvested biomass was dried at 60◦ C in a hot air oven, transferred to a desiccator to cool down to room temperature. After taking the dry weight, the vials were subjected to various solvents, as discussed below, for lipid extraction. The biomass content usually ranged between 0.9 and 1.4 g l−1 .

2.2. Extraction of lipids 2.2.1. Single and binary solvent(s) lipid extraction at room temperature and using the soxhlet apparatus Various lipid extraction protocols with different solvents were chosen for this study following the available literature. Lipids were extracted in four different single solvents, namely diethyl ether, hexane, chloroform and petroleum ether (Merck, India),[30,31] at room temperature as well as using the soxhlet apparatus from the dry biomass of a single batch stationary phase cultures of all the test organisms. Binary solvents such as chloroform– methanol, cyclohexane–2-propanol, acetone–hexane and dichloromethane–hexane (Merck, India) were also used for lipid extraction. For chloroform–methanol, the Bligh and Dyer [1] method was followed. In the modified Bligh and Dyer method, methanol is replaced by 2-propanol and chloroform by cyclohexane, respectively.[2] Similarly, lipid extraction was done with acetone–hexane (1:4) and dichloromethane–hexane (1:4) as given by Manirakiza et al.[32] The extracted lipid was then filtered through a Whatman filter paper for the separation of other cellular components. Lipid content was quantified gravimetrically following evaporation of the solvents, and was expressed as % dry cell wt. (dcw).

2.2.2. Pre-treatment of biomass Pre-treatments such as sonication, homogenization, beadbeating, lyophilization, autoclaving, microwave treatment and osmotic shock treatments were conducted, and were compared with the lipid recovery of direct extraction from the biomass of a single batch stationary phase cultures. The solvent system giving maximum lipid recovery in the previous experiments was used.

2.3. Analysis of algal oil The acid value was determined by the titrimetry method following the European standard EN14104,[33] which indicates the amount of FFA present in the oil. The saponification value represents the amount of KOH required to saponify 1 g of oil or fat. This was also determined by the titrimetry method.[34] The molecular weight of the oil was calculated from the saponification and acid values following Xu et al.[16]

2.4.

Standardization of transesterification process

About 1 g of freshly extracted microalgal oil was used for transesterification study. The transesterification was carried out in 50 cc glass vials with airtight caps (Schott Duran, Germany). The vials were kept in a temperaturecontrolled incubator at 60◦ C. The mixture was stirred using a cyclomixture for 2 min at every 30 min interval.

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Environmental Technology Comparative randomized trials were designed to provide preliminary evidences on the effects of catalyst type and quantity, methanol to oil ratio, reaction temperature and time on the transesterification process. Different levels of molar ratio of methanol to oil (30:1, 45:1, 60:1, 75:1 and 90:1); two types of acidic catalysts (HCl and H2 SO4 ); seven levels of catalyst concentrations (2.5, 3.0, 3.5, 4.0, 4.5, 5.0 and 5.5 M), four different temperatures (50◦ C, 60◦ C, 70◦ C and 80◦ C) were used in the experiments to find out the range best suited for the transesterification reaction. To prevent scorching of oil by acid, the latter was dissolved in methanol and the resulting solution was added to the reaction vials. Based on the results of the above experiments, optimization of the transesterification process for methanol and catalyst concentration, reaction temperature and time were conducted with the help of RSM to maximize the ester conversion from algal oil. A five-level-four-factor central composite rotary design (CCRD) of the above four variables was obtained using the commercial statistical package, Design Expert-version 7.1.1 (Stat-Ease, Minneapolis, MN, USA). Table 1 presents the experimental levels of four variables, namely molar ratio of methanol to oil (A), molar ratio of hydrochloric acid to oil (B), temperature (C) and reaction time (D) for response surface analysis. The ‘point optimization’ technique was employed to find out the level of each variable for maximum response, i.e. maximum FAME yield. 2.5. Purification of biodiesel After the transesterification process, the reaction mixture was allowed to cool down to room temperature. Five millilitre of ultrapure deionized water obtained through the Millipore Water Purification system and 5 ml of hexane were added to the reaction mixture. The content was agitated in a vortex and kept undisturbed for phase separation. The top organic phase, which contained the FAME, was pipetted out, while the bottom aqueous phase containing residual methanol, glycerol, catalyst, pigments and other sediments was discarded. The upper layer was then washed several times with warm ultrapure deionized water (50◦ C) until washing water became neutral.[35] The biodiesel was obtained by evaporating the hexane and residual water at 105◦ C in an open container.

Table 1.

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2.6. Analytical methods 2.6.1. Thin layer chromatography (TLC) Thin layer chromatography (TLC) was carried on with 0.25 mm thick silica gel plates developed with hexane: diethyl ether (9:1 v/v). To detect the FAME spots, plates were sprayed with 10% phosphomolybdic acid in ethanol and heated at 105◦ C.[36] 2.6.2. Gas chromatography–mass spectroscopy (GC-MS) About 0.2 μl aliquot of this derivative was injected into a Perkin-Elmer (Autosystem XL) Gas Chromatograph with a Turbomass Gold Mass Spectrometer (PerkinElmer, Shelton, CT, USA) equipped with PE-5® phenyl, methylpolysiloxane capillary column (30 m × 0.25 mm × 0.25 μm) and the electron impact mass spectra of the sample were collected. The FAMEs were confirmed by comparing their fragmentation pattern with the NIST mass spectral reference library. 2.7. Statistical analysis The commercial statistical package, Design Expert-version 7.1.1 (Stat-Ease, Minneapolis, MN, USA) was used for designing the experiments, regression analysis and drawing the response surface graphs. Duncan’s new multiple range test was performed using a window-based software MSTAT-C to analyse the difference between various treatments.

3. Results 3.1. Selection of solvent(s) for lipid extraction Figure 1 demonstrates the lipid content of S. obliquus extracted using single and binary solvents at room temperature and by the soxhlet extractor. Among the single solvents, maximum lipid recovery was recorded in chloroform (6.4% dcw), whereas among the binary solvents, chloroform– methanol (12.9% dcw) reported maximum lipid recovery at room temperature. A similar trend was observed for the other four green algae (Table 2). The lipid content of blue-green algae and the two diatoms species also showed higher values with chloroform–methanol as solvents. For

Variables and their levels used in the response surface experiment. Level

Independent variable Molar ratio of methanol to oil Molar ratio of HCl to oil Temperature (◦ C) Reaction time (h)

Coded symbol

−2(−α)

−1

0

+1

+2(+α)

A B C D

60:1 3.5:1 50 4.0

67.5:1 4:1 55 5.5

75:1 4.5:1 60 7.0

82.5:1 5:1 65 8.5

90:1 5.5:1 70 10.0

Lipid content (% dcw)

2012 16 14 12 10 8 6 4 2 0

S. Mandal et al. Room temperature Soxhlet extractor

f

d d

d e a

blue-green algae, lipid recovery was 6.2% for A. cylindrica, 11.1% for A. nidulans, 7.2% for N. muscorum and S. maxima, 7.4% for S. platensis and 2.8% for Synechocystis sp. PCC 6803. The diatoms, Cyclotella sp. and Pinnularia sp. showed lipid content of 7.9 and 7.5% (dcw), respectively, in chloroform–methanol treatment (Table 2). As observed at room temperature, in the soxhlet extractor maximum lipid recovery was observed in the chloroform–methanol solvent system, i.e. 13.8% (dcw), from S. obliquus biomass (Figure 1, Table 2). The value for C. vulgaris was 10% (dcw), for S. acuminatus was 11.7% (dcw), for Chlorella sp. was 10.9% (dcw) and for Chlamydomonas sp. was 12% (dcw, Table 2). The lipid content of the blue-green algae and the diatoms also showed marginally higher values with the soxhlet extractor using binary solvents (Table 2). The lipid content of S. obliquus was found to be relatively higher than that of the other species studied under control conditions, so was selected for further experimentation.

f

c c

a

b

c c

c c b

Table 2. Lipid yield of 13 species studied using chloroform: methanol as binary solvents. Lipid content (% dcw) Name of the species

Room temperature

Soxhlet extraction

Scenedesmus obliquus Chlorella vulgaris Scenedesmus acuminatus Chlorella sp. Chlamydomonas sp. Anabaena cylindrica A. nidulans N. muscorum S. maxima S. platensis Synechosystis sp. PCC 6803 Cyclotella sp. Pinnularia sp.

12.9 ± 0.2 9.2 ± 0.1 10.5 ± 0.3 9.8 ± 0.2 11.1 ± 0.6 6.2 ± 0.3 11.1 ± 0.1 7.2 ± 0.2 7.2 ± 0.1 7.4 ± 0.1 2.8 ± 0.3 7.9 ± 0.2 7.5 ± 0.4

13.8 ± 0.2 10.0 ± 0.3 11.7 ± 0.2 10.9 ± 0.1 12.0 ± 0.1 7.4 ± 0.2 11.7 ± 0.1 7.9 ± 0.2 8.1 ± 0.3 8.0 ± 0.2 3.7 ± 0.2 8.3 ± 0.3 8.3 ± 0.1

3.2. Effects of pre-treatment The various cell disruption methods studied showed maximum lipid recovery of 11.7% (dcw), when S. obliquus was pre-treated with the microwave oven followed by sonication, bead-beating, autoclaving, homogenization, lyophilization and osmotic shock treatments with lipid recoveries of 11.2%, 10.7%, 8.5%, 8.0%, 7.7% and 6.5% using chloroform–methanol as solvents (Figure 2). The amount of lipid recovered with direct extraction from S. obliquus biomass at room temperature was 12.9% (dcw), which was not found to have any significant difference after pre-treatment with sonication, bead-beating and microwave methods at 5% level of significance. As the cell disruption techniques did not effectively increase the lipid recovery, direct extraction being less tedious was considered to be the most suitable method.

25

Lipid content (% dcw)

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Figure 1. Lipid extraction from S. obliquus biomass using various solvents (single and binary) at room temperature and soxhlet extractor. The bar graphs superscripted by different alphabets (a–f) are significantly different from each other at P < 0.05 (Duncan’s new multiple range test).

20 15

b b

10

a

a

b

b

a c

5 0

Figure 2. Effects of various pre-treatments on the lipid recovery from S. obliquus biomass. The bar graphs superscripted by different alphabets (a–c) are significantly different from each other at P < 0.05 (Duncan’s new multiple range test).

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3.3. Analysis of S. obliquus oil The acid and saponification values of the oil extracted from S. obliquus were 48.1 ± 2.7 and 231.7 ± 9.4 mg KOH g−1 , respectively. The average molecular weight of the oil was 917 ± 21. The acid value of S. obliquus oil was far above the 2 mg KOH g−1 limit for satisfactory transesterification reaction using a base catalyst. Therefore, two acid catalysts, i.e. H2 SO4 and HCl, were selected for this study.

3.4. Standardization of transesterification process 3.4.1. Effect of reaction time To study the effect of reaction time on transesterification process, acid-catalysed transesterification of algal oil was carried out with oil:methanol:acid (H2 SO4 ) molar ratios of 1:60:3.5 at 60◦ C. An aliquot of reaction mixture (100 μl) was withdrawn at 1 h interval during the course of reaction. TLC profile of FAME indicated that there was no discernible triglyceride or diglyceride left after 7 h of transesterification (data not shown). Hence, the reaction time was fixed at 7 h to study the effects of other variables. 3.4.2. Effects of catalyst type and molar ratio of methanol to oil To see the effects of catalyst type and molar ratio of methanol to oil on ester conversion, five different molar ratios of methanol to oil, i.e. 30:1, 45:1, 60:1, 75:1 and 90:1 were selected. Each reaction was run for 7 h with two types of acid catalysts (H2 SO4 and HCl) separately with a concentration of 3.5 M at 60◦ C. When the molar ratio of methanol to oil was 75:1, ester conversion was recorded to be 82.1% and 73.2%, respectively, under HCl and H2 SO4 catalytic reactions. With further increase in methanol to oil ratio, there was little improvement in ester conversion and the optimum methanol to oil ratio was fixed at 75:1. Among the catalysts, it was observed that HCl was superior to H2 SO4 , and was selected for further experimentation (data not shown). 3.4.3. Effects of catalyst concentration and temperature Seven different catalyst concentrations (2.5, 3.0, 3.5, 4.0, 4.5, 5.0 and 5.5 M HCl) were selected for this study. For each case, the reaction was continued for 7 h at 60◦ C with 75:1 molar ratio of methanol to oil. Maximum ester yield was obtained at 4.5 M of HCl, where the value reached up to 84.7%. It was also observed that further increasing the catalyst concentration had a negative effect on ester conversion (data not shown). Effect of temperature on ester conversion was also investigated by carrying out the experiments at four different levels of temperature ranging from 50◦ C to 80◦ C with an interval of 10◦ C, while the molar ratios of HCl:methanol:oil was specified at 4.5:75:1, respectively, for 7 h. Maximum

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FAME yield was obtained between 60◦ C and 70◦ C (data not shown). 3.4.4. Optimization of transesterification process by RSM 3.4.4.1. Experimental design and the actual and predicted responses Based on the results of the above experiments, a five-level-four-factor CCRD was employed to find out the interactive effects among the four variables (A, B, C and D, Table 1) on ester conversion of S. obliquus oil. Thirty experiments were set up by varying the levels, each variable as per the design matrix given in Table 3. The results of the CCRD experiments with 16 cubic points, 8 axial points and 6 centre points for studying the curve fitting of experimental data are presented in Table 3. The FAME yield obtained from the experiments varied between 65.4% and 93.3% at different combinations of the variables. The predicted values, calculated using the model were in the range of 66.3–94.2%. 3.4.4.2. Regression model of response Regression analysis of the response to fit a predicted set of values based on a quadratic model demonstrated that the linear model terms (A, B, C and D), quadratic model terms (A2 , B2 , C 2 and D2 ) and the two-factor model terms (AB, AC and BC) significantly fitted the model (P < 0.05), whereas the twofactor model terms AD, BD and CD did not significantly fit the model (P > 0.05). Applying multiple regression analysis, the results were fitted to a second-order polynomial equation, where the insignificant model terms were omitted. Thus, the mathematical regression model for FAME yield in terms of coded factors was as follows: Y (FAME yield %) = + 87.65 + 4.51A − 2.63B + 4.12C + 1.08D + 0.61AB + 0.61AC − 0.85BC − 1.01A2 − 1.65B2 − 3.28C 2 − 0.55D2 . From the analysis of variance (ANOVA) analysis, the F-value of 87.9 implied that the model fitted data with high significance (probability > F = 0.0001). The adequate precision value of 36.3 indicated high adequate signal which means this model can be used to navigate the design space and further the optimization process. The mathematical model was found to be highly reliable with an R2 value of 0.98 indicating good agreement between the experimental and predicted values of FAME yield and ‘adjusted R2 ’ value of 0.97. The ‘lack of fit’ was found to be insignificant (P > F = 0.82), thus indicating that the model was accurate for predicting the response. 3.4.4.3. Determination of optimized conditions for maximum FAME yield The 3D response surface plots obtained from the regression model illustrate the interactive effects of the variables on FAME yield (Figure 3(a–c)). The

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S. Mandal et al. Table 3.

Central composite design matrices with actual and predicted responses (FAME yield). Process variable

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Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Response (Y )

A

B

C

D

Actual

Predicted

67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 67.5 (−1) 82.5 (+1) 60.0 (−2) 90.0 (+2) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0) 75.0 (0)

4.0 (−1) 4.0 (−1) 5.0 (+1) 5.0 (+1) 4.0 (−1) 4.0 (−1) 5.0 (+1) 5.0 (+1) 4.0 (−1) 4.0 (−1) 5.0 (+1) 5.0 (+1) 4.0 (−1) 4.0 (−1) 5.0 (+1) 5.0 (+1) 4.5 (0) 4.5 (0) 3.5 (−2) 5.5 (+2) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0) 4.5 (0)

55 (−1) 55 (−1) 55 (−1) 55 (−1) 65 (+1) 65 (+1) 65 (+1) 65 (+1) 55 (−1) 55 (−1) 55 (−1) 55 (−1) 65 (+1) 65 (+1) 65 (+1) 65 (+1) 60 (0) 60 (0) 60 (0) 60 (0) 50 (−2) 70 (+2) 60 (0) 60 (0) 60 (0) 60 (0) 60 (0) 60 (0) 60 (0) 60 (0)

5.5 (−1) 5.5 (−1) 5.5 (−1) 5.5 (−1) 5.5 (−1) 5.5 (−1) 5.5 (−1) 5.5 (−1) 8.5 (+1) 8.5 (+1) 8.5 (+1) 8.5 (+1) 8.5 (+1) 8.5 (+1) 8.5 (+1) 8.5 (+1) 7.0 (0) 7.0 (0) 7.0 (0) 7.0 (0) 7.0 (0) 7.0 (0) 4.0 (−2) 10.0 (+2) 7.0 (0) 7.0 (0) 7.0 (0) 7.0 (0) 7.0 (0) 7.0 (0)

75.4 81.3 70.4 78.5 83.2 92.3 75.6 86.4 76.2 82.8 72.3 81.3 85.2 93.2 76.8 88.3 79.7 93.3 86.7 75.2 65.4 83.5 82.1 88.6 89.1 85.7 87.9 88.4 86.4 88.4

74.6 81.0 69.5 78.4 83.5 92.3 75.0 86.3 76.4 83.1 72.0 81.1 85.0 94.2 77.2 88.8 82.6 92.6 86.3 75.8 66.3 82.8 83.3 87.6 87.7 87.7 87.7 87.7 87.7 87.7

Values in parentheses denote coded level of the variables.

interaction of molar ratio of methanol to oil and acid to oil is shown in Figure 3(a), while the other two variables, temperature and reaction time were kept at a constant level. In this figure, the FAME yield increased with the increase in the level of methanol to oil ratio when acid to oil ratio was kept near to zero level, i.e. 4.5:1. The interaction of methanol to oil ratio and temperature at zero level of molar ratio of acid to oil (4.5:1) and reaction time (7 h) was significant as visualized from Figure 3(b), where the FAME yield increased with the increase in the level of methanol to oil at zero level of temperature (60◦ C). The optima could be seen near the boundary, thus suggesting that changing the range of the variables might result in increasing the yield further. Figure 3(c) depicts the interaction of acid to oil ratio and reaction temperature at zero level of methanol to oil ratio (75:1) and reaction time (7 h), where increase in the concentration of acid to oil ratio and temperature would contribute to the rise in the FAME yield. On the other hand, the interactive model terms AD (varying molar ratio of methanol to oil and reaction time), BD (varying molar ratio of acid to oil and reaction time) and CD (varying temperature and reaction time) are not shown graphically, since they did not illustrate

significant effects on the FAME yield, which was reflected from the flat response surface and more parallel contour lines. Applying the ‘Point Prediction’ technique, maximum FAME yield of 93.4% (a confidence interval of 95%) was predicted at 82:4:1 molar ratio of methanol: hydrochloric acid:oil at 65◦ C and a reaction time of 6.4 h. 3.4.4.4. Validation of the model Experiments were performed in triplicate and repeated thrice using the above optimized conditions to verify the model. It could be visualized from Table 4 that the predicted (93.4%) and experimental (91.1%) FAME yield, however, did not vary significantly. The overall biodiesel yield on the basis of the weight of the oil was 69.3%. 3.5. Fatty acid profile of S. obliquus biodiesel Biodiesel from the cultures grown in N 11 medium showed the presence of four major FAMEs, namely palmitic, oleic, linoleic and linolenic acids (peakarea < 0.1% were considered to be negligible). In the stationary phase cultures, major constituents were palmitic (38.8%) followed by oleic

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Environmental Technology

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Figure 3. 3D response surface for FAME yield. Notes: Interactive effects of (a) varied molar ratio of methanol to oil and molar ratio of acid to oil at zero level of reaction temperature (60◦ C) and time (7 h), (b) varied molar ratio of methanol to oil and temperature at zero level of molar ratio of acid to oil (4.5:1) and reaction time (7 h) and (c) varied molar ratio of acid to oil and reaction temperature at zero level of molar ratio of methanol to oil (75:1) and reaction time (7 h). Table 4.

FAME yield before and after optimization. FAME yield (%) After optimization

Variable Molar ratio of methanol to oil Molar ratio of HCl to oil Temperature (◦ C) Reaction time (h)

Before optimization

After optimization

Before optimization

Predicted

Experimental

75:1 4.5:1 60 7

82:1 4:1 65 6.4

84.7 ± 1.19

93.4

91.1 ± 1.36

(35.4%), linolenic (15%) and linoleic (10.8%) acid methyl esters. The saturated and mono-unsaturated fatty acids, i.e. palmitic and oleic acid constitute ∼75% of total fatty acid esters. In the logarithmic phase cultures, the linoleic and linolenic acids esters constituted ∼40% of the total FAME. 4. Discussion Various solvents were compared with 13 microalgal species to find out a suitable solvent system, which could be applicable for extraction of lipids from microalgae. Maximum lipid recovery was obtained with chloroform–methanol (Figure 1), which was significantly higher (P < 0.05, Duncan’s new multiple range test) than that was observed for

other single and binary solvents. A possible reason could be the solubility of lipids in the solvent system and the types of lipids present, i.e. the proportion of neutral lipids and polar lipids. Higher lipid yield using the Bligh and Dyer method,[1] could be attributed to the selectivity of microalgal lipids towards chloroform and methanol, which are more polar in nature.[32,37] In contrast to the report of Lee et al.,[6,9] pre-treatment of S. obliquus biomass by bead-beater or microwave did not depict any significant rise in lipid recovery (Figure 2). Moreover, the lipid recovery from S. obliquus (Figure 1) and C. vulgaris (Table 2) without any pre-treatment was quite comparable with the values recorded for C. vulgaris and Scenedesmus sp. with microwave oven pre-treatment.[9] This is, therefore, well in

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S. Mandal et al.

agreement with the report of Ryckebosch et al. [38] that cell disruption was not essential for microalgal lipid extraction. It is also noteworthy here that lipid recovery in the soxhlet extractor was only marginally higher than that obtained at room temperature with the chloroform–methanol solvent system. From economical point of view, Bligh and Dyer [1] lipid recovery at room temperature proved to be the most suitable method for lipid extraction from microalgae, although till date many researchers have been following various other solvent systems with higher cost and/or including various pre-treatments.[6–12] The acid value of S. obliquus lipids/oil was found to be 48.1 mg KOH g−1 corresponding to FFA content of about 24%, which is far above the 1% limit recommended for satisfactory transesterification reaction using base catalysts. FFA can react with an alkali catalyst to produce soap and water. Saponification not only consumes the alkali catalyst, but the resulting soap can form emulsion, which creates difficulties in separation of biodiesel from glycerine fraction.[39] Therefore, acid-catalysed transesterification was selected to produce biodiesel from S. obliquus oil. The ester conversion increases with increasing reaction time. TLC profile of the FAME samples indicated that there were no discernible triglycerides or diglycerides left after 7 h of transesterification. This is proximate to the findings of Vicente et al.,[40] where acid-catalysed transesterification was carried out with the fungus, Mucor circinelloides for 8 h. Since acid-catalysed transesterification is an equilibrium reaction, much more methanol than that given by the stoichiometric 3:1 mole ratio of methanol to triacylglycerol is required to drive the reaction to completion. Taher et al. [41] advocated the use of high amount of alcohol in acid-catalysed transesterification of microalgal oil. However, an excess of alcohol slows down the separation of esters from the by-products by increasing solubility of glycerol. Consequently, a part of the diluted glycerol remained in the ester phase, leading to foam formation and, therefore, apparent loss of the ester product.[42] The molar ratio of alcohol to oil depends on the type of feedstock, type of catalyst, temperature and reaction time.[41] A molar ratio of 56:1 methanol:oil was used by Miao and Wu [43] for Chlorella protothecoides. The ratio of 75:1 found in this study confirms that the requirement could vary with feedstock, reaction temperature, and type as well as concentration of the catalyst. Among the catalysts, HCl was found to be superior to H2 SO4 , which contradicts with the observation of Johnson and Wen [15] for microalga Schizochytrium limacinum, where both the acids showed similar efficiency. Transesterification can occur at different temperatures, depending on the properties of the oil. It could be at ambient temperature, or at a temperature close to the boiling temperature of methanol. The maximum yield of ester was obtained at 60◦ C; a further temperature increment decreased the conversion efficiency. It may be due to the loss/vaporization of methanol above its boiling point (65◦ C).

Thus, from the ‘one-factor-at-a-time’ strategy, FAME yield reached up to 84.7% at a molar ratio of methanol:HCl:oil, 75:4.5:1 for 7 h reaction time at 60◦ C. The result has also drawn the conclusion with the approximate ranges of the factors where the optimum point can be searched. The interactive effects of these variables were further analysed using CCRD and RSM for maximum ester conversion. According to the second-order polynomial equation, the linear coefficient A, C and D showed positive effects on FAME yield, whereas B exhibited a negative impact. ANOVA of the predicted model showed the adequacy of the model, as visualized from the high ‘F’ value of 87.9 (F0.01(12,17)tabular = 3.4) and a low ‘P’ value ( F = 0.82) also advocated for reliability of the model. At the same time, a relatively lower value of coefficient of variation (1.3%) indicated a better precision and reliability of the experiments carried out. The contour plots in 3D response surface depicted the variation in FAME yield as a function of interaction of variables. The more prolate contour lines, as observed in Figure 3(c), depicted a highly significant interaction of molar ratio of acid to oil with temperature as compared with interaction of molar ratio of acid to oil with methanol to oil (Figure 3(a)). The interaction of temperature with molar ratio of acid to oil (Figure 4(c)) was also more significant than the interaction of temperature with molar ratio of methanol to oil as observed from the contour lines (Figure 3(b)). From Figures 3(a–c), it could be concluded that increasing methanol to oil ratio and temperature and decreasing acid to oil ratio to certain points resulted in increased FAME yield. After optimization, the FAME yield was increased up to 91% in S. obliquus biodiesel (Table 4). However, on the basis of the weight of microalgal total lipid, this process gave 69% recovery of biodiesel. This might be due to the processing of crude oil of S. obliquus for biodiesel conversion. The crude lipids include pigments, phospholipids, glycolipids, in addition to FFA and neutral lipids.[44,45] Neutral lipids such as triglycerides, diglycerides, monoglycerides and polar FFA are the components for making biodiesel. Nonetheless, the value is marginally higher in comparison with the report of Chinnasamy et al.,[13] where 64% recovery was obtained by a two-step process: an acidcatalysed followed by a base-catalysed transesterification. For microalga S. limacinum, FAME yield reached up to 66% with H2 SO4 -catalysed transesterification.[15] The properties of biodiesel are mainly determined by its fatty acid esters.[46] Poly-unsaturated fatty acids with

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Environmental Technology four or more double bonds are quite common in microalgal oil, which are susceptible to oxidation during storage, thus reducing the acceptability of microalgal oil for production of biodiesel.[47] The fatty acid composition of algae can vary both quantitatively and qualitatively with their physiological state. Analysis of fatty acid composition of S. obliquus revealed a marked increase in the levels of saturated and mono-unsaturated fatty acids, with a concomitant decrease in the levels of poly-unsaturated fatty acids at the stationary phase, thus ensuring an increase in the oxidative stability of the biodiesel produced from the latter stage of the cultures. It has been reported that biosynthesis of poly-unsaturated fatty acids takes place mainly during the phase of intense cellular activity, although the underlying regulatory mechanism is yet unknown.[48] The reduction in the level of poly-unsaturated fatty acid in microalgal oil with increasing culture age has been reported in previous studies.[48–50] Changes in fatty acid composition related to age of the culture may be associated with nutrient depletion during the stationary phase in batch cultures.

5. Summary and conclusions In this study, lipid recovery under soxhlet extraction was marginally higher when compared with the values at room temperature. However, on cost point of view, extraction of lipids from algal biomass with chloroform–methanol [1] is recommended at room temperature. By multifactor optimization using CCRD and RSM, the biodiesel yield was increased up to 91% with methanol:HCl:oil molar ratio of 82:4:1 at 65◦ C for 6.4 h reaction time. The biodiesel yield relative to the weight of the oil obtained from S. obliquus was 69%, thus indicating that separation of the non-esterifiable components, such as phospholipids, glycolipids, pigments, etc. from the crude algal oil is required before proceeding for transesterification. Further, a rise in the proportion of saturated and mono-unsaturated fatty acids methyl esters, i.e. from 60% to 75%, was evident in the biodiesel processed from the stationary phase cultures. Nevertheless, this is desirable for production of a good-quality biodiesel.

Funding The work was financially supported by Indian Institute of Technology Kharagpur, West Bengal; Indian Council of Agricultural Research, New Delhi, India.

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