An evaluation of maximizing production and usage of biofuel by machine learning and experimental approach.

This work explores a novel integration of experimental conversion of waste cooking oil (WCO) into biodiesel with advanced machine learning modeling to optimize transesterification outcomes. A reusable CaO catalyst derived from egg shells was employed, delivering a more affordable and sustainable option compared to typical homogeneous catalysts. A total of 16 experimental runs were conducted to investigate the effects of catalyst concentration (CC), reaction temperature (RT), and methanol-to-oil molar ratio (MOR) on biodiesel yield. Four boosted ML algorithms XGBoost, AdaBoost, Gradient Boosting Machine (GBM), and CatBoost were applied to model the process, with hyperparameter tuning via grid search and validation through k-fold cross-validation (k = 5) and residual plots to ensure reliability and mitigate overfitting. CatBoost emerged as the best-performing model (R² = 0.955, RMSE = 0.83, MSE = 0.68, MAE = 0.52), predicting a maximum biodiesel yield of 95% at 3% CC, 80 °C RT, and a 6:1 MOR. Feature importance and partial dependence plots identified MOR and CC as the most influential parameters. Engine performance tests further validated the practical viability of CaO-based biodiesel, showing 26% lower CO emissions and 13% lower smoke emissions compared to diesel, resulting in a marginal 2.83% decline in brake thermal efficiency alongside a 4.31% rise in fuel consumption. This interdisciplinary approach combining green catalyst development with interpretable machine learning demonstrates a promising pathway for cleaner energy applications and data-driven optimization in biodiesel research.