Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment

The present work describes the comparison of biodiesel yield prediction by Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The prediction models were developed based on three-level design of experiments conducted with waste cooking oil transesterified by varying four process parameters such as catalyst concentration, molar ratio, reaction time, and stirrer speed. The optimum reaction conditions were found to be 0.75% wt/wt catalyst concentration, 9:1 M ratio, 60 min reaction time and 500 rpm stirrer speed. For these optimum conditions, experimental fatty acid methyl ester (FAME) content of 95.05 +/- 0.26% was obtained, which was in good agreement with the predicted yield. The RSM model was developed using Box-Behnken design and the ANN predictive model was developed using a feed-forward backpropagation neural network algorithm with 14 neurons in the hidden layer. The mathematical models of RSM and developed ANN were compared for biodiesel yield. The higher value of correlation coefficient (R-2 = 0.99) and lower value of root mean square error (RMSE = 1.97) for ANN compared to RSM (R-2 = 0.95 and RMSE = 2.71) evidently proved that ANN model is far better in predicting FAME content compared to the RSM model.