Performance and Emission Optimization of Palm Biodiesel Fuels with Dual Nanoparticle Additives Using Gaussian Process Regression and Multi-Criteria Decision Analysis

This study employed a Gaussian Process Regression model combined with the Combinative Distance-Based Assessment method to analyze and optimize the performance and emission characteristics of a diesel engine operating with different fuel blends under various load conditions. The results indicated that increasing engine load generally improved brake thermal efficiency while reducing fuel consumption. Compared with conventional diesel fuel, palm biodiesel blends exhibited relatively higher fuel consumption and increased exhaust emissions under certain operating conditions. The incorporation of nanoparticle additives enhanced the combustion process, resulting in improved engine performance and reduced emissions. Among the tested fuels, the blend containing magnesium oxide nanoparticles exhibited the best overall performance across the investigated load range and showed greater potential for reducing incomplete-combustion emissions. The developed machine learning model accurately predicted engine performance and emission parameters and demonstrated strong generalization capability. Furthermore, the multi-criteria decision-making analysis enabled the identification of promising fuel–operating condition combinations based on multiple performance and emission indicators. Experimental validation demonstrated good agreement between the predicted and measured results, confirming the reliability of the proposed approach. The findings suggest that integrating machine learning techniques with multi-criteria decision-making methods provides an effective framework for fuel formulation optimization, engine performance enhancement, and emission reduction.