Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III
To reduce engine pollutant emissions, an emission modeling and optimization scheme based on a hybrid artificial intelligence scheme is proposed in this study to reduce pollutant emissions of methanol/diesel dual-fuel engines under low load. Firstly, a data cleaning method based on isolated forest an...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001320 |
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| author | Mingzhang Pan Xinxin Cao Changcheng Fu Shengyou Liao Xiaorong Zhou Wei Guan |
| author_facet | Mingzhang Pan Xinxin Cao Changcheng Fu Shengyou Liao Xiaorong Zhou Wei Guan |
| author_sort | Mingzhang Pan |
| collection | DOAJ |
| description | To reduce engine pollutant emissions, an emission modeling and optimization scheme based on a hybrid artificial intelligence scheme is proposed in this study to reduce pollutant emissions of methanol/diesel dual-fuel engines under low load. Firstly, a data cleaning method based on isolated forest and correlation analysis is designed to improve the stability of the system. Secondly, a hybrid emission prediction model based on improved Transformer (ITransformer) and Bidirectional Gated Recurrent Unit (BiGRU) is built to obtain an accurate mathematical model between control parameters and emissions. Finally, based on the obtained mathematical model, the 3rd Non-dominated Sorting Genetic Algorithm (NGSA-III) is used to adjust and optimize the control parameters. Using engine bench test data to evaluate the proposed hybrid emission prediction model, the R2 of CO, HC, and NOx prediction is 0.9969, 0.9973, and 0.9982, respectively, which is higher than the accuracy of the seven existing modeling methods. Compared with the unoptimized MESR46, the CO, HC, and NOx emissions of the optimized scheme are reduced by at least 45.17 %, 15.30 %, and 17.32 % respectively, which can significantly reduce the CO, HC, and NOx emissions, and comparison and analysis with the most advanced optimization technologies show a competitive optimization effect. |
| format | Article |
| id | doaj-art-d2015d1c0c7c496ea574a6a9c3f5d60f |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-d2015d1c0c7c496ea574a6a9c3f5d60f2025-01-27T04:22:22ZengElsevierEnergy and AI2666-54682025-01-0119100466Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-IIIMingzhang Pan0Xinxin Cao1Changcheng Fu2Shengyou Liao3Xiaorong Zhou4Wei Guan5College of Mechanical Engineering, Guangxi University, Nanning, 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning, 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning, 530004, ChinaResearch and Engineering Institute, Guangxi Yuchai Machinery CO. Ltd, Yulin,537000, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning, 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning, 530004, China; Corresponding author.To reduce engine pollutant emissions, an emission modeling and optimization scheme based on a hybrid artificial intelligence scheme is proposed in this study to reduce pollutant emissions of methanol/diesel dual-fuel engines under low load. Firstly, a data cleaning method based on isolated forest and correlation analysis is designed to improve the stability of the system. Secondly, a hybrid emission prediction model based on improved Transformer (ITransformer) and Bidirectional Gated Recurrent Unit (BiGRU) is built to obtain an accurate mathematical model between control parameters and emissions. Finally, based on the obtained mathematical model, the 3rd Non-dominated Sorting Genetic Algorithm (NGSA-III) is used to adjust and optimize the control parameters. Using engine bench test data to evaluate the proposed hybrid emission prediction model, the R2 of CO, HC, and NOx prediction is 0.9969, 0.9973, and 0.9982, respectively, which is higher than the accuracy of the seven existing modeling methods. Compared with the unoptimized MESR46, the CO, HC, and NOx emissions of the optimized scheme are reduced by at least 45.17 %, 15.30 %, and 17.32 % respectively, which can significantly reduce the CO, HC, and NOx emissions, and comparison and analysis with the most advanced optimization technologies show a competitive optimization effect.http://www.sciencedirect.com/science/article/pii/S2666546824001320Methanol/diesel dual-fuelEmissions modeling and optimizationImproved transformerBiGRU, NSGA-III |
| spellingShingle | Mingzhang Pan Xinxin Cao Changcheng Fu Shengyou Liao Xiaorong Zhou Wei Guan Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III Energy and AI Methanol/diesel dual-fuel Emissions modeling and optimization Improved transformer BiGRU, NSGA-III |
| title | Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III |
| title_full | Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III |
| title_fullStr | Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III |
| title_full_unstemmed | Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III |
| title_short | Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III |
| title_sort | emission prediction and optimization of methanol diesel dual fuel engines based on itransformer bigru and nsga iii |
| topic | Methanol/diesel dual-fuel Emissions modeling and optimization Improved transformer BiGRU, NSGA-III |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001320 |
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