Application of Machine Learning for Methanolysis of Waste Cooking Oil Using Kaolinite Geopolymer Heterogeneous Catalyst

Abstract

This work uses three machine learning techniques, response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) to optimise and model biodiesel production from waste cooking oil using process parameters such as methanol-to-oil ratio, catalyst loading, reaction temperature, and reaction time. RSM was used for process optimisation. Model construction of the ANN model used 70% of the data for training, 15% for testing, and 15% for validation. The network was trained using feed-forward propagation and the Levenberg–Marquardt algorithm. The ANFIS was generated using a grid partition and trained using a hybrid method. The effectiveness of the machine learning was assessed through error metrics such as regression (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and average relative error (ARE). The optimum yield was obtained at 15 wt.%, 4 wt.%, 120 °C, and 4 h, methanol-to-oil ratio, catalyst loading, temperature, and reaction time, respectively, yielding 93.486%.