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Free fatty acid (FFA) optimization consequent to transesterification of high viscous rubber seed oil by solid base catalyst studied is presented in this work. Presence of more FFA in the feedstock leads to soap formation in the case of base catalysis and hence, pre-treatment is required to reduce the high acid value of raw rubber seed oil. FFA of raw rubber seed oil is reduced to 1.64% by acid catalyzed esterification and the FFA of esterified oil is minimized to 0.115% after biodiesel formation using fluorite (CaF 2 ) derived from the calcination of fluorspar. Response surface methodology (RSM) equipped with central composite design (CCD) is used for FFA optimization. From the RSM analysis, the FFA of synthesized biodiesel is expressed as a quadratic model having a coefficient of determination, R 2 of value 0.8179. From the 3D-surface plots and 2D contour plots obtained from RSM analysis, it is observed that reaction time is the most significant process parameter that influences FFA optimization. Artificial Neural Network (ANN) modeling with the Levenberg-Marquardt algorithm is used for model network training in rubber seed oil FFA optimization. Mean square error (MSE) of 7.04 × 10 −4  and coefficient of determination R 2  value of 0.993 was observed at the best validation performance of 0.0019395 at epoch 5. On comparing the coefficient of determination of R 2  values observed for both RSM and ANN, it is observed that the value obtained from ANN model fits the data well with minimum error.

Free fatty acid optimization and modeling of biodiesel production from high viscous rubber seed oil– A comparative study of RSM and ANN