Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI
This study investigates experimental and artificial intelligence-based predictions of heterogeneous combustion performance in a diesel engine fueled with neat biodiesel. The combustion aspects, including cylinder pressures, heat energy developed and released, mass burnt fractions (MBF), mean gas tem...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Next Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949821X25001462 |
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| author | Aditya Kolakoti |
| author_facet | Aditya Kolakoti |
| author_sort | Aditya Kolakoti |
| collection | DOAJ |
| description | This study investigates experimental and artificial intelligence-based predictions of heterogeneous combustion performance in a diesel engine fueled with neat biodiesel. The combustion aspects, including cylinder pressures, heat energy developed and released, mass burnt fractions (MBF), mean gas temperatures (MGT), and the influence of combustion temperatures on NOx formation, are examined experimentally. The combustion results are trained in a feed-forward artificial neural network (ANN) algorithm for the predictions, and an error histogram with 20 bins helps identify the accuracy of the trained model. The prediction results of combustion parameters are recorded quite accurately for most instances, as the errors are centered around 0. The overall accuracy of the trained model is achieved with a high correlation coefficient (R = 0.99) and a low mean square error (MSE). In addition, the influence of combustion temperature on NOx emissions is highlighted, and a correlation is developed with errors of 2.22% and 1.96% at 75% and 100% loads, respectively. Finally, biodiesel exhibits controlled diffusion combustion, achieving more sustained combustion, with 6.19% and 6.18% lower NOx formation compared to diesel fuel at 75% and 100% loads. |
| format | Article |
| id | doaj-art-f5d3d02db9624fd3b9e83f455ed61ae3 |
| institution | DOAJ |
| issn | 2949-821X |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Next Energy |
| spelling | doaj-art-f5d3d02db9624fd3b9e83f455ed61ae32025-08-20T03:16:10ZengElsevierNext Energy2949-821X2025-10-01910038310.1016/j.nxener.2025.100383Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AIAditya Kolakoti0Corresponding author.; School of Marine Engineering and Technology, Indian Maritime University, Kolkata, West Bengal, 700088, IndiaThis study investigates experimental and artificial intelligence-based predictions of heterogeneous combustion performance in a diesel engine fueled with neat biodiesel. The combustion aspects, including cylinder pressures, heat energy developed and released, mass burnt fractions (MBF), mean gas temperatures (MGT), and the influence of combustion temperatures on NOx formation, are examined experimentally. The combustion results are trained in a feed-forward artificial neural network (ANN) algorithm for the predictions, and an error histogram with 20 bins helps identify the accuracy of the trained model. The prediction results of combustion parameters are recorded quite accurately for most instances, as the errors are centered around 0. The overall accuracy of the trained model is achieved with a high correlation coefficient (R = 0.99) and a low mean square error (MSE). In addition, the influence of combustion temperature on NOx emissions is highlighted, and a correlation is developed with errors of 2.22% and 1.96% at 75% and 100% loads, respectively. Finally, biodiesel exhibits controlled diffusion combustion, achieving more sustained combustion, with 6.19% and 6.18% lower NOx formation compared to diesel fuel at 75% and 100% loads.http://www.sciencedirect.com/science/article/pii/S2949821X25001462Heterogeneous combustionMean combustion temperaturesNitrogen oxide (NOx)Combustion performanceRenewable fuelsArtificial intelligence |
| spellingShingle | Aditya Kolakoti Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI Next Energy Heterogeneous combustion Mean combustion temperatures Nitrogen oxide (NOx) Combustion performance Renewable fuels Artificial intelligence |
| title | Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI |
| title_full | Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI |
| title_fullStr | Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI |
| title_full_unstemmed | Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI |
| title_short | Optimizing diesel engine heterogeneous combustion performance and NOx emissions: A next energy perspective with AI |
| title_sort | optimizing diesel engine heterogeneous combustion performance and nox emissions a next energy perspective with ai |
| topic | Heterogeneous combustion Mean combustion temperatures Nitrogen oxide (NOx) Combustion performance Renewable fuels Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2949821X25001462 |
| work_keys_str_mv | AT adityakolakoti optimizingdieselengineheterogeneouscombustionperformanceandnoxemissionsanextenergyperspectivewithai |