Optimization of Biodiesel Production by Response Surface Methodology and Genetic Algorithm

Abstract

Abstract The biodiesel production from alkali-catalyzed transesterification of karanja oil was investigated. In this study, the effect of three parameters, i.e., reaction temperature, catalyst concentration, and molar ratio of methanol to oil on biodiesel yield was studied. Central composite design (CCD) along with response surface methodology (RSM) was used for designing experiments and estimating the quadratic response surface. Catalyst concentration was found to have a negative effect on biodiesel yield, whereas molar ratio showed positive effect. Temperature and molar ratio showed significant interaction effect. The reaction conditions were optimized for maximum response, i.e., biodiesel yield from RSM. The program for the RSM model, coupled with genetic algorithm (GA), was developed for predicting the optimized process parameters for maximum biodiesel yield to obtain a global optimal solution. The results were found to be similar from both of the methods.