Optimization of biodiesel production from Thevetia peruviana seed oil by adaptive neuro-fuzzy inference system coupled with genetic algorithm. and response surface methodology

This work focused on the application of adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) as predictive tools for production of fatty acid methyl esters (FAME) from yellow oleander (Thevetia peruviana) seed oil. Two-step transesterification method was adopted, in the first step, the high free fatty acid (FFA) content of the oil was reduced to <1% by treating it with ferric sulfate in the presence of methanol. While in the second step, the pretreated oil was converted to FAME by reacting it with methanol using sodium methoxide as catalyst. To model the second step, central composite design was employed to study the effect of catalyst loading (1-2 wt,%), methanol/oil molar ratio (6:1-12:1) and time (20-60 min) on the T. peruviana methyl esters (TPME) yield. The reduction of FFA of the oil to 0.65 +/- 0.05 wt.% was realized using ferric sulfate of 3 wt,%, methanol/FFA molar ratio of 9:1 and reaction time of 40 min. The model developed for the transesterification process by ANFIS (coefficient of determination, R-2 = 0.9999, standard error of prediction, SEP = 0.07 and mean absolute percentage deviation, MAPD = 0.05%) was significantly better than that of RSM, (R-2 = 0.9670, SEP = 1.55 and MAPD = 0.84%) in terms of accuracy of the predicted TPME yield. For maximum TPME yield, the transesterification process input variables were optimized using genetic algorithm (GA) coupled with the ANFIS model and RSM optimization tool. TPME yield of 99.8 wt.% could be obtained with the combination of 0.79 w/v catalyst loading, 12.5:1 methanol/oil molar ratio and time of 58.2 min using ANFIS-GA in comparison to TPME yield of 98.8 wt.% using RSM. The TPME structure was characterized using Fourier transform infra-red (FT-IR) spectroscopy. The results of this work established the superiority of predictive capability of ANFIS over RSM. (C) 2016 Elsevier Ltd. All rights reserved.