Statistical and machine learning analysis of diesel engines fueled with Moringa oleifera biodiesel doped with 1-hexanol and Zr2O3 nanoparticles DOI Creative Commons
K. Sunil Kumar, Abdul Razak, M. K. Ramis

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 1, 2025

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

Language: Английский

Statistical and machine learning analysis of diesel engines fueled with Moringa oleifera biodiesel doped with 1-hexanol and Zr2O3 nanoparticles DOI Creative Commons
K. Sunil Kumar, Abdul Razak, M. K. Ramis

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 1, 2025

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

Language: Английский

Citations

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