Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production

The attributes of renewability and environmental friendliness have made ethanol a preferable alternative to methanol in the production of biodiesel from lipid feedstocks. For the first time, this study adopted Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to model coconut oil ethyl ester (CNOEE) yield. Transesterification parameters such as reaction temperature and ethanol/coconut oil molar ratio and catalyst dosage were varied. Maximum CNOEE yield of 96.70% was attained at 73 °C reaction temperature, 11.9:1 molar ratio, and catalyst dosage of 1.25 wt. %. The experimental yield was in agreement with the predicted yield. Central Composite Design was adopted to develop the RSM while feed-forward back propagation neural network algorithm was employed for the ANN model. Statistical indices were employed to compare the models. The computed coefficient of determination (R²) of 0.9564, root-mean-squarce-error (RMSE) of 0.72739, standard error of prediction (SEP) of 0.008021, mean average error (MAE) of 0.612, and average absolute deviation (AAD) of 0.674901 for RSM model compared to those of R² (0.9980), RMSE (0.68615), SEP (0.007567), MAE (0.325), and AAD (0.3877) for ANN indicated the superiority of the ANN model over the RSM model. The key fuel properties of the CNOEE met with those of biodiesel international standards.