Development of direct transesterification (DT) method for accurate quantification of microalgal lipid content
4.3 Results and discussion
4.3.2 Optimization of process parameters for transesterification
transesterification in the single stage DT methods M2 and M3. When CH3ONa was used as catalyst in single stage transesterification, no FAME yield was detected in case of Nannochloropsis sp. and reported that two stages (CH3ONa /BF3) are necessary to obtain higher transesterification efficiency (Laurens et al., 2012). However, when H2SO4 was used as single catalyst in M4, a 2.4 fold increase in FAME yield was obtained in comparison to conventional method M1 and single stage DT methods M2 and M3 applied to lyophilized biomass. The two stage DT methods M5 and M7 with base and acid catalyzed treatments for first and second stage respectively showed higher FAME yield. Interestingly, when catalysts order was reversed in M6 and M8, the process resulted in lower FAME yield. In a recent study, it has been reported that the alkaline hydrolysis of algal biomass in the first stage of DT prior to methylation might have lead towards higher FAME yield (Griffiths et al., 2010). DT is effective transesterification method that can bypass the oil extraction step by merging it into transesterification step (Cheng et al., 2011) with subsequent reduction in the production cost of biodiesel from algal feedstock. The sequential two stages DT method M7 with lyophilized algal biomass was selected for further optimization using CCD on the basis of highest FAME yield obtained.
were found significant (Table 4.3) except interaction of MBR and RT in first stage of transesterification and interaction of CBR and MBR in second stage of transesterification.
F-values of 144.91 for first and 17.20 for second stage were greater than the critical values which show their significance and the low probability of getting errors in their value. The regression model provided the predicted FAME yield with R2 value of 0.99 and 0.96 for the first and second stage of transesterification respectively (Table 4.2). The signal to noise ratios of 41.83 and 13.23 were found greater than their critical value 4 for first and second stage of optimization respectively showing very less noise disturbance in the models.
Therefore, the model fit to the experimental system was adequate. The model equations for first and second stage are shown in the equations 4.2 and 4.3 respectively,
𝑌 = −134.1 + 183.42𝑋1+ 2.19𝑋2+ 6.48𝑋3− 141.54𝑋12− 0.026𝑋22− 0.15𝑋3 2 + 0.45𝑋1∗ 𝑋2− 0.97𝑋1∗ 𝑋3 + 0.002𝑋2∗ 𝑋3 (4.2)
𝑌 = −41.01 + 30.6𝑋1+ 1.43𝑋2+ 2.01𝑋3− 7.9𝑋12 − 0.012𝑋22− 0.026𝑋3 2 + 0.089𝑋1∗ 𝑋2 − 0.32𝑋1∗ 𝑋3− 0.014𝑋2∗ 𝑋3 (4.3)
Fig. 4.3 shows the response surface and contours for the effect of different combinations of transesterification parameters in both the stages. Each plot of FAME yield is a representation of different combinations of two parameters at one time while keeping the third parameter constant at its middle value (Fig. 4.3A-4.3F). NaOH to biomass ratio 0.67, MBR 49.51 and RT 19.33 min were found as optimal points of FAME yield in first stage modeling (Fig.
4.3A-4.3C). Likewise in second stage optimization, surface plots (Fig.4.3D-4.3F) revealed that optimum resides at H2SO4 to biomass ratio 2.07, MBR 61.07 and RT 10 min. The variations in parameter values from their optimal region resulted in negative effect on FAME yield.
Table 4.3 ANOVA for the quadratic regression model obtained from CCD-RSM employed in optimization of parameters involved in two stage sequential direct transesterification of lipid from Chlorella sp. FC2 IITG
Source
ANOVA for first stage (alkali catalyst) regression model
ANOVA for second stage (acid catalyst) regression model
Sum of
Squares DOF Mean
Square F value p-value* Sum of
Squares DOF Mean Square
F
Value p-value
Model 518.42 9 57.6 144.91 < 0.0001 863.29 9 95.92 17.2 0.0013
X1 1.5 1 1.5 3.77 0.1004 57.34 1 57.34 10.28 0.0184
X2 2.51 1 2.51 6.31 0.0458 31.05 1 31.05 5.57 0.0563
X3 13.54 1 13.54 34.06 0.0011 106.79 1 106.79 19.15 0.0047
X12 142.63 1 142.63 358.81 < 0.0001 523.22 1 523.22 93.83 < 0.0001
X22 469.16 1 469.16 1180.25 < 0.0001 103.66 1 103.66 18.59 0.005
X32 136.4 1 136.4 343.14 < 0.0001 63.86 1 63.86 11.45 0.0148
X1*X2 12.63 1 12.63 31.76 0.0013 16.76 1 16.76 3.01 0.1337
X1*X3 5.2 1 5.2 13.08 0.0111 79.63 1 79.63 14.28 0.0092
X2*X3 0.23 1 0.23 0.59 0.4715 43.8 1 43.8 7.86 0.0311
Residual 2.39 6 0.4 -- -- 33.46 6 5.58 -- --
Lack of fit 2.37 5 0.47 37.07 0.124 33.21 5 6.64 26.35 0.1468
Pure Error 0.013 1 0.013 -- -- 0.25 1 0.25 -- --
Total 520.8 15 -- -- -- 896.75 15 -- -- --
R2 = 0.9954 Signal to noise ratio = 41.83 R2 = 0.9627 Signal to noise ratio = 13.23
* - p value >0.05 is considered as insignificant
Fig. 4.3 Response surface plots representing the effect of various parameters and their interaction on FAME yield. (A) methanol to biomass ratio and NaOH to biomass ratio (B) reaction time and NaOH to biomass ratio (C) reaction time and methanol to biomass ratio (D) methanol to biomass ratio and H2SO4 to biomass ratio (E) reaction time and H2SO4 to biomass ratio (F) reaction time and methanol to biomass ratio.
In the first stage of transesterification, MBR up to 50 (v/w) was found to affect significantly the FAME yield in which the methanol acts not only as the reactant for transesterification but also as a solvent in cell lysis. Increased volumes of methanol used decreased the FAME yield which may be due to the dilution of NaOH catalyst concentration and/or lipid (Patil et al., 2011; Zhang et al., 2010) and use of such high volumes of solvent augments the cost of solvent recovery and purification. At lower volumes of methanol, the extraction and transesterification processes were hindered due to limited availability of the solvent (Zhang et al., 2010). Similar response for increasing MBR (61.07, v/w) was obtained in the second stage of transesterification. At a lower NaOH to biomass ratio, catalyst was insufficient to carry out complete reaction whereas in higher ratios, decrease in FAME yield was observed attributed to increased formation of fatty acid salts (Leung and Guo, 2006). Lower reaction time caused incomplete extraction and transesterification of intracellular lipid while higher reaction time encouraged degradation of FAME as well as soap formation (Eevera et al., 2009). The acid catalyst availability was low to carry out complete DT at lower ratio of H2SO4 to biomass while in higher ratio; excess acid caused loss of unsaturated esters leading to undesirable side reactions (Morrison and Smith, 1964). Validation of the predicted optimum through experiment resulted in similar FAME yields (44.3%, w/w DCW) as predicted by the model (44.15% w/w, DCW). In comparison to the un-optimized two stage DT method M7, the RSM optimized method showed 13% increase in FAME yield. The optimized method showed 462.6% increase in FAME yield when compared with conventional Bligh and Dyer method M1 (Fig. 4.4). The optimized DT method M7 was further tested with another microalgal strain Chlorella sorokiniana FC6 IITG and the FAME yield was compared with the corresponding value obtained from conventional method M1.
An increment of 445.4% FAME yield from the optimized DT in comparison to conventional Bligh and Dyer method for Chlorella sorokiniana FC6 (Fig. 4.4).
Fig. 4.4 Comparison of FAME yield (%, w/w DCW) obtained by Bligh Dyer method (BD) and optimized DT method (ODT). FC2-BD and FC2-ODT represents conventional Bligh Dyer method and optimized DT method respectively for Chlorella sp. FC2 IITG. FC6-BD and FC6-ODT represents conventional Bligh Dyer method and optimized DT method respectively for Chlorella sorokiniana FC6 IITG.
4.3.3 Effect of transesterification methods on hydrolysis and transesterification