Modelling and Optimisation of Biodiesel Production from Margarine Waste Oil Using a Three-Dimensional Machine Learning Approach

Abstract

This work presents the use of three-dimensional machine learning approaches, namely the response surface methodology (RSM), the artificial neural network (ANN), and the adaptive neuro-fuzzy inference system (ANFIS), to optimise and model the biodiesel yield from waste margarine oil. The effect of the process parameters methanol-to-oil ratio (3–15 mole), catalyst ratio (0.3–1.5 wt. %), reaction time (30–90 min), and reaction temperature (30–70 °C) were studied. The performance metric results for the RSM, ANN, and ANFIS were 0.991, 996, and 0.998 for regression (R2); 0.924, 0.566, and 0.324 for root mean square error (RMSE); 0.568, 0.267, and 0.202 for mean absolute error (MAE); 0.746, 0.333, and 0.226 for mean absolute percentage error (MAPE); 0.008, 0.004, and 0.003 for average relative error (ARE); and 4.503, 2.114, and 1.828 for mean percentage standard deviation (MPSD). The developed three-dimensional machine learning approach—the RSM, ANN, and ANFIS models—is a potential method for optimising and modelling biodiesel yield. The study results may be used to create sustainable, efficient, and economical solutions for recycling waste margarine oil.