Journal Article

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

Abdullah, Mohd. Zulkifly, Yong, Xu, Shangzhi, Wei, Hasan, Nasim, Ramalingam, Krishnamoorthy, Elumalai, Perumal Venkatesan, Sangeetha, Allam

2025

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Algorithms / Alternative energy sources / Analysis / Biodiesel fuels / Biofuels / Biology and Life Sciences / Biomass / Catalysts / Computer and Information Sciences / Datasets / Decision trees / Ecology and Environmental Sciences / Efficiency / Energy consumption / Energy conversion / Energy demand / Energy development / Engineering and Technology / Environmental sustainability / Fatty acids / Fossil fuels / Household wastes / Hydrogen / Industrial wastes / Learning algorithms / Machine Learning / Models, Theoretical / Modernization / Moisture absorption / Moisture content / Municipal solid waste / Municipal waste management / Musa - chemistry / Optimization / Optimization techniques / Physical Sciences / Polynomials / Process parameters / Raw materials / Refuse as fuel / Regression analysis / Renewable energy / Root-mean-square errors / Solid waste management / Solid wastes / Statistical methods / Supply and demand / Sustainability / Sustainable development / Sustainable energy / Waste products as fuel / Waste to energy