GA BP prediction model for energy consumption of steel rolling reheating furnace
Abstract Energy consumption serves as a critical indicator of energy utilization efficiency and environmental sustainability in the steel production process. Accurately predicting the Heat energy consumption per ton (HEC, GJ/t) of steel billet in Steel Rolling Reheating Furnace (SRRF) presents a for...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-95134-3 |
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| author | Yi Duan Guang Chen Xiangjun Bao Jing Xu Lu Zhang Xiaojing Yang |
| author_facet | Yi Duan Guang Chen Xiangjun Bao Jing Xu Lu Zhang Xiaojing Yang |
| author_sort | Yi Duan |
| collection | DOAJ |
| description | Abstract Energy consumption serves as a critical indicator of energy utilization efficiency and environmental sustainability in the steel production process. Accurately predicting the Heat energy consumption per ton (HEC, GJ/t) of steel billet in Steel Rolling Reheating Furnace (SRRF) presents a formidable challenge owing to the complex interplay of factors such as production scheduling, raw material characteristics, process parameters, and equipment condition. This study proposes a novel approach to predict HEC (GJ/t) by utilizing actual production data from SRRF. A genetic algorithm (GA) optimized back-propagation neural network (BPNN) is developed and its performance is compared to that of a standard BP model. Experimental results reveal that the optimized GA-BP model, with a neural network structure of 17-10-1, achieves a prediction accuracy of 94.7% surpassing the 90.24% accuracy of the standard BP model. The proposed GA-BP model demonstrates superior predictive capabilities and robustness, offering valuable insights for optimizing process parameters and improving energy efficiency in SRRF operations. |
| format | Article |
| id | doaj-art-cf7c0f4d0c124a28ab311daca57ecef7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cf7c0f4d0c124a28ab311daca57ecef72025-08-20T03:07:41ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-95134-3GA BP prediction model for energy consumption of steel rolling reheating furnaceYi Duan0Guang Chen1Xiangjun Bao2Jing Xu3Lu Zhang4Xiaojing Yang5School of Energy and Environment, Anhui University of TechnologySchool of Energy and Environment, Anhui University of TechnologySchool of Energy and Environment, Anhui University of TechnologySchool of Energy and Environment, Anhui University of TechnologySchool of Energy and Environment, Anhui University of TechnologySchool of Energy and Environment, Anhui University of TechnologyAbstract Energy consumption serves as a critical indicator of energy utilization efficiency and environmental sustainability in the steel production process. Accurately predicting the Heat energy consumption per ton (HEC, GJ/t) of steel billet in Steel Rolling Reheating Furnace (SRRF) presents a formidable challenge owing to the complex interplay of factors such as production scheduling, raw material characteristics, process parameters, and equipment condition. This study proposes a novel approach to predict HEC (GJ/t) by utilizing actual production data from SRRF. A genetic algorithm (GA) optimized back-propagation neural network (BPNN) is developed and its performance is compared to that of a standard BP model. Experimental results reveal that the optimized GA-BP model, with a neural network structure of 17-10-1, achieves a prediction accuracy of 94.7% surpassing the 90.24% accuracy of the standard BP model. The proposed GA-BP model demonstrates superior predictive capabilities and robustness, offering valuable insights for optimizing process parameters and improving energy efficiency in SRRF operations.https://doi.org/10.1038/s41598-025-95134-3Heat energy consumption per ton of steel billetPrediction modelGenetic algorithmSteel rolling reheating furnace |
| spellingShingle | Yi Duan Guang Chen Xiangjun Bao Jing Xu Lu Zhang Xiaojing Yang GA BP prediction model for energy consumption of steel rolling reheating furnace Scientific Reports Heat energy consumption per ton of steel billet Prediction model Genetic algorithm Steel rolling reheating furnace |
| title | GA BP prediction model for energy consumption of steel rolling reheating furnace |
| title_full | GA BP prediction model for energy consumption of steel rolling reheating furnace |
| title_fullStr | GA BP prediction model for energy consumption of steel rolling reheating furnace |
| title_full_unstemmed | GA BP prediction model for energy consumption of steel rolling reheating furnace |
| title_short | GA BP prediction model for energy consumption of steel rolling reheating furnace |
| title_sort | ga bp prediction model for energy consumption of steel rolling reheating furnace |
| topic | Heat energy consumption per ton of steel billet Prediction model Genetic algorithm Steel rolling reheating furnace |
| url | https://doi.org/10.1038/s41598-025-95134-3 |
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