Statistical and machine learning analysis of diesel engines fueled with Moringa oleifera biodiesel doped with 1-hexanol and Zr2O3 nanoparticles

Abstract

This experimental study based on DOE (Design of experiments) explores the performance and emission characteristics of Moringa oleifera-based biodiesel blends enhanced with zirconium oxide (ZrO2) and 1-hexanol as boosting agents in a slow-speed diesel engine operating at 1500 rpm. The novelty lies in the synergistic use of these additives for improving fuel efficiency and reducing emissions, combined with advanced statistical and machine learning models for optimization and prediction. Four test blends were analyzed: 90D5MO5H + 25 ppm ZrO2, 80D10MO10H + 50 ppm ZrO2, 70D15MO15H + 75 ppm ZrO2, and 100MO + 100 ppm ZrO2. A comprehensive methodology involving experimental testing and statistical modelling using Gradient Boosting (GBoost), Extreme Learning Machine (ELM), and Response Surface Methodology (RSM) was employed. Key findings include a brake thermal efficiency (BTE) of 8.63% higher than diesel and a fuel consumption reduction of 46.13% (0.14 kg/kWh) for the 90D5MO5H + 25 ppm ZrO2 blend. This blend also demonstrated superior combustion characteristics, including a peak cylinder pressure of 70 bar and a heat release rate (HRR) of 45 J/°CA. Emission analysis revealed significantly reduced hydrocarbon emissions (0.020%) for 100MO + 100 ppm ZrO2 and the lowest carbon monoxide emissions (10.1%) for 90D5MO5H + 25 ppm ZrO2. Among predictive models, ELM exhibited the highest accuracy with an R2 value of 0.9604, outperforming other approaches. The findings suggest that optimized moringa oleifera blends with zirconium oxide and 1-hexanol offer a promising solution for sustainable and cleaner diesel engine operation, with potential applications in transportation and energy sectors aiming for reduced environmental impact.