V. S., Shaisundaram and Sengottaiyan, Saravanakumar and Raji, Gunasekaran and S., Kumaravel and M., Chandrasekaran and Basumatary, Sanjay (2025) Machine Learning‐Driven Energy Efficiency Enhancement and Emission Reduction in Diesel Engines Using Pumpkin Seed Biodiesel Blends and CeO 2 Nanoparticles. International Journal of Energy Research, 2025 (1). ISSN 0363-907X
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Machine Learning‐Driven Energy Efficiency Enhancement and Emission Reduction in Diesel Engines Using Pumpkin Seed Biodiesel Blends and CeO 2 Nanoparticles Shaisundaram V. S. https://orcid.org/0000-0001-8642-0986 Saravanakumar Sengottaiyan https://orcid.org/0000-0001-6655-0011 Gunasekaran Raji https://orcid.org/0009-0006-8262-2859 Kumaravel S. https://orcid.org/0000-0003-4902-7864 Chandrasekaran M. https://orcid.org/0000-0002-5163-2513 Sanjay Basumatary
The rising dependence on fossil fuels, depleting renewable resources, and increasing oil costs necessitate alternative energy sources. Biofuels, such as pumpkin seed biodiesel, offer environmentally friendly solutions with lower greenhouse gas emissions. This is the first study to integrate pumpkin seed oil–based biodiesel blended with cerium oxide (CeO 2 ) nanoparticles and machine learning (ML) models for optimizing diesel engine performance and emission characteristics. The study uses response surface methodology (RSM) and XGBoost ML to maximize engine performance and predict emissions of carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NO x ), and smoke opacity. The optimal blend, achieving a brake thermal efficiency (BTE) of 24.99% with minimal emissions, was 18.32% biodiesel, 63.84% engine load (operating at 75% of maximum capacity), and 48.55 ppm CeO 2 . This study demonstrates the effectiveness of combining RSM and ML, providing new insights into the sustainable optimization of biodiesel blends for compression ignition engines.
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Item Type: | Article |
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Subjects: | Mechanical Engineering > Manufacturing Technology Mechanical Engineering > Engineering Drawing |
Domains: | Mechanical Engineering |
Depositing User: | Mr IR Admin |
Date Deposited: | 21 Aug 2025 11:39 |
Last Modified: | 21 Aug 2025 11:39 |
URI: | https://ir.vistas.ac.in/id/eprint/10284 |