Experimental and ML-assisted optimization of injection timing and EGR in a diesel engine fueled with palmyra biodiesel

Abstract

Growing environmental concerns and stricter emission regulations have intensified the need for cleaner combustion and sustainable energy solutions. In this pursuit, Palmyra Methyl Ester (POME) stands out as a promising biodiesel, offering renewable origin and desirable fuel characteristics for cleaner, more sustainable engine applications. This study presents an integrated experimental and computational investigation into the performance, combustion, and emission characteristics of a diesel engine operating on POME blends, with a focus on optimizing injection timing and exhaust gas recirculation (EGR). Using a desirability-based multi-objective optimization framework, engine tests were conducted under varied conditions, guided by Response Surface Methodology (RSM). The predictive capabilities of RSM were benchmarked against advanced machine learning models like Extreme Gradient Boosting (XGBoost) and Random Forest. The optimal setting, found as POME20 with 23°bTDC injection timing and EGR, further improved BTE and significantly lowered NO_x emissions. Among the predictive models, XGBoost outperformed RSM and Random Forest, yielding the highest test R² and lowest MSE and MAPE, demonstrating superior accuracy in predicting engine responses. These results highlight the synergistic potential of renewable fuel utilization and data-driven modeling in optimizing diesel engine operation. The findings provide a viable pathway toward cleaner, high-efficiency combustion systems, contributing to the broader goals of sustainable transportation and global energy transition.