An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes
In manufacturing enterprises, the variety of heat treatment processes, insufficient data in the early stages of production, and the continuous increase of samples over time make it difficult to predict and optimize carbon emissions dynamically. Therefore, this paper proposes a low-carbon optimizatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25005805 |
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| author | Qian Yi Xin Wu Junkang Zhuo Congbo Li Chuanjiang Li Huajun Cao |
| author_facet | Qian Yi Xin Wu Junkang Zhuo Congbo Li Chuanjiang Li Huajun Cao |
| author_sort | Qian Yi |
| collection | DOAJ |
| description | In manufacturing enterprises, the variety of heat treatment processes, insufficient data in the early stages of production, and the continuous increase of samples over time make it difficult to predict and optimize carbon emissions dynamically. Therefore, this paper proposes a low-carbon optimization method for heat treatment processes based on incremental data-driven approaches. Using life cycle assessment (LCA) theory, carbon emission sources are accurately analyzed and quantified, and a full life cycle carbon emission model is established. The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. Then the model is solved by the Multi-objective Seagull Optimization Algorithm (MOSOA), and the result achieves a good balance between the carbon emission and hardness of the heat treatment process. Finally, using real data collected in enterprises for four consecutive years, it is verified that the predictive model can be effectively applied to the working conditions of multi-variety, small batch, and incremental heat treatment data. |
| format | Article |
| id | doaj-art-0f34de7d632a419aa8b1dd5c3a3ad5dc |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-0f34de7d632a419aa8b1dd5c3a3ad5dc2025-08-20T01:55:21ZengElsevierCase Studies in Thermal Engineering2214-157X2025-08-017210632010.1016/j.csite.2025.106320An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processesQian Yi0Xin Wu1Junkang Zhuo2Congbo Li3Chuanjiang Li4Huajun Cao5College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, PR China; Corresponding author. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, PR China.College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, PR ChinaCollege of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, PR ChinaCollege of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, PR ChinaState Key Laboratory of Public Big Data, Guizhou University, Guizhou, Guiyang, 550025, PR ChinaCollege of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, PR ChinaIn manufacturing enterprises, the variety of heat treatment processes, insufficient data in the early stages of production, and the continuous increase of samples over time make it difficult to predict and optimize carbon emissions dynamically. Therefore, this paper proposes a low-carbon optimization method for heat treatment processes based on incremental data-driven approaches. Using life cycle assessment (LCA) theory, carbon emission sources are accurately analyzed and quantified, and a full life cycle carbon emission model is established. The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. Then the model is solved by the Multi-objective Seagull Optimization Algorithm (MOSOA), and the result achieves a good balance between the carbon emission and hardness of the heat treatment process. Finally, using real data collected in enterprises for four consecutive years, it is verified that the predictive model can be effectively applied to the working conditions of multi-variety, small batch, and incremental heat treatment data.http://www.sciencedirect.com/science/article/pii/S2214157X25005805Incremental dataHeat treatmentCarbon consumption predictionProcess optimization |
| spellingShingle | Qian Yi Xin Wu Junkang Zhuo Congbo Li Chuanjiang Li Huajun Cao An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes Case Studies in Thermal Engineering Incremental data Heat treatment Carbon consumption prediction Process optimization |
| title | An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes |
| title_full | An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes |
| title_fullStr | An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes |
| title_full_unstemmed | An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes |
| title_short | An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes |
| title_sort | incremental data driven approach for carbon emission prediction and optimization of heat treatment processes |
| topic | Incremental data Heat treatment Carbon consumption prediction Process optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25005805 |
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