Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM
Abstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88486-3 |
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| author | Farooq Shaik D. Vinay Kumar N. Channa Keshava Naik G. Radha Krishna T. M. Yunus Khan Abdul Saddique Shaik Abdulrajak Buradi Addisu Frinjo Emma |
| author_facet | Farooq Shaik D. Vinay Kumar N. Channa Keshava Naik G. Radha Krishna T. M. Yunus Khan Abdul Saddique Shaik Abdulrajak Buradi Addisu Frinjo Emma |
| author_sort | Farooq Shaik |
| collection | DOAJ |
| description | Abstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore, Response Surface Methodology (RSM) was utilized to optimize the engine’s performance, identifying the optimal operating conditions of 2992.9 rpm engine speed and an E20-equivalent GEM blend. Under these conditions, the engine exhibited a brake thermal efficiency (B_The) of 34.63%, a brake specific fuel consumption (BSFC) of 243.7 g/kW-hr, and minimal emissions of 1.5% CO, 108.13 ppm HC, and 1211.8 ppm NOx, with an overall desirability of 0.820, indicating a highly favorable combination of performance and emissions characteristics. |
| format | Article |
| id | doaj-art-b6afe1fc9bc94caa9c0b3e64f2532fa3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b6afe1fc9bc94caa9c0b3e64f2532fa32025-02-09T12:29:17ZengNature PortfolioScientific Reports2045-23222025-02-0115112310.1038/s41598-025-88486-3Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSMFarooq Shaik0D. Vinay Kumar1N. Channa Keshava Naik2G. Radha Krishna3T. M. Yunus Khan4Abdul Saddique Shaik5Abdulrajak Buradi6Addisu Frinjo Emma7Department of Mechanical Engineering, Vignan’s Foundation for Science Technology and ResearchDepartment of Mechanical Engineering, Vignan’s Foundation for Science Technology and ResearchDepartment of Mechanical Engineering, BGS College of Engineering and TechnologyDepartment of Mechanical Engineering, School of Engineering, Presidency UniversityDepartment of Mechanical Engineering, College of Engineering, King Khalid UniversityDepartment of Mechanical Engineering, College of Engineering, King Khalid UniversityDepartment of Mechanical Engineering, Nitte Meenakshi Institute of TechnologyCollege of Engineering and Technology, School of Mechanical and Automotive Engineering, Dilla UniversityAbstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore, Response Surface Methodology (RSM) was utilized to optimize the engine’s performance, identifying the optimal operating conditions of 2992.9 rpm engine speed and an E20-equivalent GEM blend. Under these conditions, the engine exhibited a brake thermal efficiency (B_The) of 34.63%, a brake specific fuel consumption (BSFC) of 243.7 g/kW-hr, and minimal emissions of 1.5% CO, 108.13 ppm HC, and 1211.8 ppm NOx, with an overall desirability of 0.820, indicating a highly favorable combination of performance and emissions characteristics.https://doi.org/10.1038/s41598-025-88486-3PerformanceEmissionsEquivalent GEM blendsANNRSMPrediction |
| spellingShingle | Farooq Shaik D. Vinay Kumar N. Channa Keshava Naik G. Radha Krishna T. M. Yunus Khan Abdul Saddique Shaik Abdulrajak Buradi Addisu Frinjo Emma Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM Scientific Reports Performance Emissions Equivalent GEM blends ANN RSM Prediction |
| title | Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM |
| title_full | Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM |
| title_fullStr | Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM |
| title_full_unstemmed | Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM |
| title_short | Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM |
| title_sort | predictive modeling and optimization of si engine performance and emissions with gem blends using ann and rsm |
| topic | Performance Emissions Equivalent GEM blends ANN RSM Prediction |
| url | https://doi.org/10.1038/s41598-025-88486-3 |
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