Optimization of Biodiesel Production from Mixed Jatropha curcas–Ceiba pentandra Using Artificial Neural Network- Genetic Algorithm: Evaluation of Reaction Kinetic Models

Abstract

Biodiesel production from non-edible vegetable oil is one effective way to anticipate the problems associated with fuel crisis and environmental issues. In this study, artificial neural network and genetic algorithm based Box Behnken experimental design used to optimize the parameters of the biodiesel production for mixed of Jatropha curcas?Ceiba pentandra oil such as methanol to oil ratio, agitation speed and catalyst concentration. Based on the results, the optimum operating parameters for the transesterification of the oil mixture J50C50 are as follows: methanol-to-oil ratio: 40 %v/v, agitation speed: 1,794 rpm and the catalyst concentration: 0.68 % wt. This process is carried out at constant temperature and time of 60 °C and 2 h. The theoretical yield predicted under this the highest yield for the J50C50 biodiesel with a value of 93.70 %. The model developed was validated by applying the optimum values to three independent experimental replicates with a 93.56 %. Comparison between the predicted values to the actual value with a small error percentage indicates that the regression model was reliable in predicting the conversion at any given conditions within the ranges studied. Moreover, the activation energy of 24.421 kJmol^-1 and frequency factor of 1.88 x 10² min^-1 was required for the transesterification process. The fuel properties of the biodiesel were measured according to ASTM D 6751 and EN14214 standards and found to be within the specifications.