ArticleScientific reports2025
Machine learning predictions for enhancing engine performance and emission using aluminum oxide nano additives in castor biodiesel.
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 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning based prediction of diesel engine emissions and performance using hemp biodiesel enriched with nano additives.Scientific reports · 2026Article
- Exploring multi-feedstock biodiesel-hydrogen synergies for enhanced diesel engine performance using hybrid AI techniques.Scientific reports · 2026Article
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Authors and funding
7 authors.
Funding
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Abstract
This study investigates the role of aluminum oxide nano-additives in enhancing the performance and reducing the environmental footprint of a B30 castor biodiesel blend in a compression ignition (CI) engine, emphasizing principles of green and sustainable chemistry. With global concerns over emissions and the depletion of fossil fuels, there is an urgent need for cleaner and more efficient alternative fuels. Biodiesel derived from non-edible sources, such as castor oil, presents a sustainable solution, but improvements in its combustion efficiency and emissions profile are crucial for widespread adoption. In this research, aluminum oxide nanoparticles were incorporated into a B30 biodiesel blend to enhance combustion properties, reduce ignition delay, and significantly mitigate harmful emissions, including carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). The biodiesel was synthesized through the transesterification of castor oil, which is rich in ricinoleic acid, and tested on a Kirloskar diesel engine operating at a constant speed of 1500 rpm under varying load conditions. Results demonstrated that the nano-additive-infused biodiesel blend outperformed conventional diesel in terms of fuel economy, atomization, vaporization, and overall combustion efficiency. Additionally, the use of aluminum oxide nanoparticles reduced brake-specific fuel consumption (BSFC) and pollutant emissions. To further optimize engine performance and minimize emissions, a machine learning framework was applied, comparing algorithms such as Random Forest and XGBoost. The analysis identified XGBoost as the most accurate predictive tool, offering valuable insights for optimizing engine parameters. This work highlights the application of green and sustainable chemistry through the integration of nano-additives and advanced data analytics to develop cleaner, more efficient biodiesel fuels, contributing to the global shift toward environmentally friendly energy solutions.
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Registered trials
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