Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks
Glycine max oil biofuel (GMOB) is a product of the transesterification of soybean oil. It contains a substantial amount of thermal energy. In this study, the result of varying fuel injection timings on the performance, ignition, and exhaust parameters of a research engine with single-cylinder, four-...
| Main Authors: | , , , , |
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| Format: | Article |
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Springer
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113930/ |
| _version_ | 1848866359772971008 |
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| author | Rajak, Upendra Panchal, Manoj Dasore, Abhishek Verma, Tikendra Nath Chaurasiya, Prem Kumar |
| author_facet | Rajak, Upendra Panchal, Manoj Dasore, Abhishek Verma, Tikendra Nath Chaurasiya, Prem Kumar |
| author_sort | Rajak, Upendra |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Glycine max oil biofuel (GMOB) is a product of the transesterification of soybean oil. It contains a substantial amount of thermal energy. In this study, the result of varying fuel injection timings on the performance, ignition, and exhaust parameters of a research engine with single-cylinder, four-stroke with direct injection (DI) diesel was experimentally investigated and optimised using artificial neural networks (ANN). The results demonstrated that a 20% fuel blend with 24.5° before top dead centre (b TDC) decreased brake thermal efficiency (BTE), NOx emissions, and exhaust cylinder temperature but improved fuel consumption, carbon dioxide emissions (CDE), and smoke emissions. With 26.5° b TDC, the BTE was found to be approximately 5.0% higher while the fuel consumption was approximately 2.0% lower than with the original injection timing of 24.5° b TDC. At 26.5° b TDC, the NOx emission was approximately 8.6% higher, and the smoke emission was approximately 4.07% lower than at the original injection timing (24.5° b TDC). |
| first_indexed | 2025-11-15T14:19:21Z |
| format | Article |
| id | upm-113930 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:19:21Z |
| publishDate | 2024 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1139302025-02-05T07:28:56Z http://psasir.upm.edu.my/id/eprint/113930/ Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks Rajak, Upendra Panchal, Manoj Dasore, Abhishek Verma, Tikendra Nath Chaurasiya, Prem Kumar Glycine max oil biofuel (GMOB) is a product of the transesterification of soybean oil. It contains a substantial amount of thermal energy. In this study, the result of varying fuel injection timings on the performance, ignition, and exhaust parameters of a research engine with single-cylinder, four-stroke with direct injection (DI) diesel was experimentally investigated and optimised using artificial neural networks (ANN). The results demonstrated that a 20% fuel blend with 24.5° before top dead centre (b TDC) decreased brake thermal efficiency (BTE), NOx emissions, and exhaust cylinder temperature but improved fuel consumption, carbon dioxide emissions (CDE), and smoke emissions. With 26.5° b TDC, the BTE was found to be approximately 5.0% higher while the fuel consumption was approximately 2.0% lower than with the original injection timing of 24.5° b TDC. At 26.5° b TDC, the NOx emission was approximately 8.6% higher, and the smoke emission was approximately 4.07% lower than at the original injection timing (24.5° b TDC). Springer 2024-07-25 Article PeerReviewed Rajak, Upendra and Panchal, Manoj and Dasore, Abhishek and Verma, Tikendra Nath and Chaurasiya, Prem Kumar (2024) Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks. Environmental Science and Pollution Research. ISSN 0944-1344; eISSN: 1614-7499 https://link.springer.com/article/10.1007/s11356-024-34429-w?error=cookies_not_supported&code=d1f4e543-d828-48bd-95fb-442e7212c504 10.1007/s11356-024-34429-w |
| spellingShingle | Rajak, Upendra Panchal, Manoj Dasore, Abhishek Verma, Tikendra Nath Chaurasiya, Prem Kumar Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks |
| title | Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks |
| title_full | Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks |
| title_fullStr | Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks |
| title_full_unstemmed | Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks |
| title_short | Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks |
| title_sort | predict the characteristics of the di engine with various injection timings by glycine max oil biofuel using artificial neural networks |
| url | http://psasir.upm.edu.my/id/eprint/113930/ http://psasir.upm.edu.my/id/eprint/113930/ http://psasir.upm.edu.my/id/eprint/113930/ |