Work

Machine Learning‐Driven Energy Efficiency Enhancement and Emission Reduction in Diesel Engines Using Pumpkin Seed Biodiesel Blends and CeO 2 Nanoparticles

Year: 2025

Type: article

Abstract: 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.

Source: International Journal of Energy Research

DOI: 10.1155/er/2329925

Authors: Shaisundaram V. S., Saravanakumar Sengottaiyan, Gunasekaran Raji, Kumaravel S., Chandrasekaran M.

Institutions: Vels University, Villa College

Language: English

Topic: Biodiesel Production and Applications

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Responsible consumption and production, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,700