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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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07964-w |
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| author | Menelik Walle Kumlachew Yeneneh Gadisa Sufe |
| author_facet | Menelik Walle Kumlachew Yeneneh Gadisa Sufe |
| author_sort | Menelik Walle |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-18915511d76b4c4780488f94bffc62df |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-18915511d76b4c4780488f94bffc62df2025-08-20T03:46:08ZengNature PortfolioScientific Reports2045-23222025-07-0115112510.1038/s41598-025-07964-wInvestigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural networkMenelik Walle0Kumlachew Yeneneh1Gadisa Sufe2Department of Motor Vehicle Engineering, College of Engineering, Ethiopian Defence UniversityDepartment of Motor Vehicle Engineering, College of Engineering, Ethiopian Defence UniversityFaculty of Mechanical Engineering, Wrocław University of Science and TechnologyAbstract 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.https://doi.org/10.1038/s41598-025-07964-wSpark-ignition engineEthanol–petrol blendsArtificial neural networks (ANNs)Engine performance predictionCombustion efficiencyBrake-specific fuel consumption (BSFC) |
| spellingShingle | Menelik Walle Kumlachew Yeneneh Gadisa Sufe Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network Scientific Reports Spark-ignition engine Ethanol–petrol blends Artificial neural networks (ANNs) Engine performance prediction Combustion efficiency Brake-specific fuel consumption (BSFC) |
| title | Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network |
| title_full | Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network |
| title_fullStr | Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network |
| title_full_unstemmed | Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network |
| title_short | Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network |
| title_sort | investigation of spark ignition engine performance in ethanol petrol blended fuels using artificial neural network |
| topic | Spark-ignition engine Ethanol–petrol blends Artificial neural networks (ANNs) Engine performance prediction Combustion efficiency Brake-specific fuel consumption (BSFC) |
| url | https://doi.org/10.1038/s41598-025-07964-w |
| work_keys_str_mv | AT menelikwalle investigationofsparkignitionengineperformanceinethanolpetrolblendedfuelsusingartificialneuralnetwork AT kumlachewyeneneh investigationofsparkignitionengineperformanceinethanolpetrolblendedfuelsusingartificialneuralnetwork AT gadisasufe investigationofsparkignitionengineperformanceinethanolpetrolblendedfuelsusingartificialneuralnetwork |