ArticleScientific reports2025
Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Integrated experimental and machine learning investigation of green-synthesized ZnO nanoparticle-dispersed neem biodiesel: effects on diesel engine performance, combustion, and emissions.Scientific reports · 2026Article
- Machine learning based prediction of diesel engine emissions and performance using hemp biodiesel enriched with nano additives.Scientific reports · 2026Article
- Influence of CeO₂ nanoparticle addition on engine performance, combustion, and emissions of ethiopian podocarpus falcatus biodiesel.Scientific reports · 2026Article
- Performance, combustion, emission and optimization characteristics of biodiesel-n-butanol blends enriched with NiScientific reports · 2026Article
- Article
- Optimizing the factors affecting the quality condition of 4WD tractors' engine oil using the hybrid RSM-GRA method.Scientific reports · 2025Article
- Machine learning and response surface optimization to enhance diesel engine performance using milk scum biodiesel with alumina nanoparticles.Scientific reports · 2025Article
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9 authors.
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Abstract
The growing need for sustainable energy sources and stricter environmental regulations necessitate the development of alternative fuels with lower emissions and improved performance. This study addresses these challenges by optimizing the performance and emission characteristics of a single-cylinder diesel engine powered by neem oil biodiesel blends enhanced with alumina nanoparticlesusing the powerful desirability-based optimization. Neem oil, a non-edible feedstock, was selected to avoid competition with food resources, while alumina nanoparticles were utilized for their catalytic properties to enhance combustion efficiency. The process involved experimental evaluation of biodiesel blends (B10, B20, and B30) combined with alumina nanoparticles at concentrations of 100 ppm, 150 ppm, and 200 ppm using a design of experiments approach. With the engine running at maximum load of 100% and an aluminum oxide concentration of 100 parts per million, the optimal fuel mix comprises of 89.85% diesel and 30% biodiesel. The lowest brake-specific fuel consumption of 0.45 kg per kilowatt-hour that the optimization produced points to effective fuel use. With a little variance of 3.33%, the brake thermal efficiency was maximized at 38.18%, quite near to the validation result of 37.89%. The alumina nanoparticles enhanced combustion through improved fuel atomization and oxidation due to their high surface area and catalytic effects. To further validate the effectiveness of RSM, the results are compared with the performance of several advance machine learning algorithms, including linear regression, decision tree, and random forest. The random forest model demonstrated the highest predictive accuracy for performance (test R
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