Optimization of Rubber Seed Oil Transesterification to Biodiesel Using Experimental Designs and Artificial Neural Networks

Abstract

The development of biofuels is driven both by concern about the greenhouse effect and by interest in the opportunities for exploitation of biomass of agricultural origin. In order to improve the yield and quality of biodiesel through modeling and optimization, several studies are in progress. In this paper, biodiesel produced from rubber seed oil in the homogeneous transesterification is studied using a Plackett-Burman experimental design, a full factorial design, a central composite design and an Artificial Neural Network (ANN) coupled with a Genetic Algorithm (GA).Variables such as temperature, stirring speed, reaction time, type of alcohol, and type of catalyst are studied to obtain the best specific gravity and kinematic viscosity. Type of alcohol and type of catalyst have the greatest effect on the two responses, with ethanol (alcohol) and sulphuric acid (catalyst) producing the best results. The specific gravity and kinematic viscosity changes recorded during the transesterification process followed the first and second order polynomial models, respectively. The ANN coupled with GA was used to optimize the two responses simultaneously. Global optimal values of specific gravity (0.883) and kinematic viscosity (6.76 cSt) were recorded when a temperature of 90°C, a stirring speed of 305 rpm, and a treatment time of 141 min were imposed.