Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network
Abstract The increasing global cost of fossil fuels and growing environmental concerns have accelerated the search for sustainable energy alternatives, positioning bioethanol as a promising renewable fuel for spark-ignition (SI) engines. This study uniquely integrates ethanol–petrol blends (E0, E10,...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-07964-w |
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| Summary: | Abstract The increasing global cost of fossil fuels and growing environmental concerns have accelerated the search for sustainable energy alternatives, positioning bioethanol as a promising renewable fuel for spark-ignition (SI) engines. This study uniquely integrates ethanol–petrol blends (E0, E10, E20, and E30) with Artificial Neural Networks (ANNs) to address critical gaps in predictive modeling and fuel optimization. Experimental tests were conducted on a single-cylinder, four-stroke SI engine under constant load conditions, capturing data on engine speed, mass flow rate, combustion efficiency, peak cylinder pressure, brake-specific fuel consumption (BSFC), and exhaust gas temperature. A feed-forward backpropagation ANN model was developed using 75% of the collected data for training and 25% for validation, achieving high predictive accuracy with R2 values exceeding 0.98 for most parameters. Results showed that E30 improved combustion efficiency by 12.5% compared to E0 at 1500 RPM and reduced BSFC by 22% in the 2000–2500 RPM range, while maximum cylinder pressure increased with RPM but remained slightly lower for higher ethanol blends due to ethanol’s cooling effect. By effectively predicting performance metrics across a broad RPM range (1500–3500), the ANN model reduces reliance on extensive experimental testing and offers a scalable approach for optimizing fuel-blending strategies, thereby supporting the transition to cleaner, more efficient energy systems. |
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| ISSN: | 2045-2322 |