Machine Learning-Driven Energy Efficiency Enhancement and Emission Reduction in Diesel Engines Using Pumpkin Seed Biodiesel Blends and CeO2 Nanoparticles

Abstract

The rising dependence on fossil fuels, depleting renewable resources, and increasing oil costs necessitate alternative energy sources. Biofuels, such as pumpkin seed biodiesel, offer environmentally friendly solutions with lower greenhouse gas emissions. This is the first study to integrate pumpkin seed oil–based biodiesel blended with cerium oxide (CeO₂) nanoparticles and machine learning (ML) models for optimizing diesel engine performance and emission characteristics. The study uses response surface methodology (RSM) and XGBoost ML to maximize engine performance and predict emissions of carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NO_x), and smoke opacity. The optimal blend, achieving a brake thermal efficiency (BTE) of 24.99% with minimal emissions, was 18.32% biodiesel, 63.84% engine load (operating at 75% of maximum capacity), and 48.55 ppm CeO₂. This study demonstrates the effectiveness of combining RSM and ML, providing new insights into the sustainable optimization of biodiesel blends for compression ignition engines.