An assessment of optimizing biofuel yield percentage using K-fold integrated machine learning models for a sustainable future

Abstract

Accelerating population and modernization has triggered a steady rise in energy demand and a significant rise in household waste, particularly municipal solid waste. In this context, waste-to-energy conversion has emerged as a sustainable solution. This study aims to maximize biofuel production yield using biomass-based banana peel catalyst waste by optimizing process parameters through machine learning models integrated with k-fold cross-validation. The models employed include Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Linear Regression (LR). The three key input variables including reaction temperature (RT), catalyst concentration (CC), and methanol-to-oil molar ratio (MOR) were used to train and test the models, with biodiesel yield as the measured output. Among the models, PR emerged as the best-performing one for predicting biofuel yield, demonstrated by its high R2 value of 0.956 and low error metrics (RMSE = 1.54 MSE = 2.39 MAE = 1.43). The best model was determined through balancing bias and variance across k-fold validation iterations, where PR exhibited the highest average R2 value of 0.868. Furthermore, the optimized process parameters predicted by PR for maximum biofuel yield were a RT of 59°C, CC of 2.96%, and a MOR of 9.21, resulting in a yield of 95.38%. These findings contribute to advancing large-scale machine learning-driven biofuel optimization, supporting industrial waste-to-energy applications, and fostering sustainable energy development.