Sustainable evaluation of 2-ethylhexyl nitrate in mahua biodiesel blends: Performance, emissions, thermal stability via RSM & ML

The demand for renewable fuels as alternatives to conventional diesel has intensified due to environmental concerns and energy security. However, biodiesel blends often exhibit increased nitrogen oxide emissions, necessitating innovative additive-based solutions. This study investigates the effect of 2-ethylhexyl nitrate (2-EHN) as an oxygenated additive in biodiesel blends on engine performance and emissions using a single-cylinder, variable compression ratio diesel engine. The study utilizes Response Surface Methodology (RSM) and Machine Learning for multi-objective optimization of performance and emissions characteristics. Experimental findings demonstrate that incorporating 1000 ppm 2-EHN into BD30 (70% diesel + 30% mahua methyl ester) reduced brake-specific fuel consumption by 13.6% and improved brake thermal efficiency by 18.34% at lower loads. NOx emissions were reduced by 11.89% at lower loads and 1.86% at higher loads, aligning with the exhaust gas temperature trends. ML models, including Random Forest Regression and Decision Trees, outperformed conventional RSM models, achieving an R 2 accuracy of 0.99. This study establishes 2-EHN as a promising additive for NO x reduction and efficiency enhancement, presenting an optimized approach to biodiesel utilization.