Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators
The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace (BF) system. In this work, a prediction and feedback model of furnace heat indicators based on the fusion...
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| Published in: | International journal of minerals, metallurgy and materials Vol. 31; no. 6; pp. 1228 - 1240 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Beijing
University of Science and Technology Beijing
01.06.2024
Springer Nature B.V Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking (Ministry of Education),Shenyang 110819,China School of Metallurgy,Northeastern University,Shenyang 110819,China Institute for Frontier Technologies of Low-carbon Steelmaking,Northeastern University,Shenyang 110819,China |
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| ISSN: | 1674-4799, 1869-103X |
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| Abstract | The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace (BF) system. In this work, a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed. The data on raw and fuel materials, process operation, smelting state, and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed. A novel method for the delay analysis of furnace heat indicators was established. The extracted delay variables were found to play an important role in modeling. The method that combined the genetic algorithm and stacking efficiently improved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model. The hit ratio for predicting the temperature of hot metal in the error range of ±10°C was 92.4%, and that for the chemical heat of hot metal in the error range of ±0.1wt% was 93.3%. On the basis of the furnace heat prediction model and expert experience, a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels. These suggestions were highly accepted by BF operators. Finally, the comprehensive and dynamic model proposed in this work was successfully applied in a practical BF system. It improved the BF temperature level remarkably, increasing the furnace temperature stability rate from 54.9% to 84.9%. This improvement achieved considerable economic benefits. |
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| AbstractList | The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace (BF) system. In this work, a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed. The data on raw and fuel materials, process operation, smelting state, and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed. A novel method for the delay analysis of furnace heat indicators was established. The extracted delay variables were found to play an important role in modeling. The method that combined the genetic algorithm and stacking efficiently improved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model. The hit ratio for predicting the temperature of hot metal in the error range of ±10°C was 92.4%, and that for the chemical heat of hot metal in the error range of ±0.1wt% was 93.3%. On the basis of the furnace heat prediction model and expert experience, a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels. These suggestions were highly accepted by BF operators. Finally, the comprehensive and dynamic model proposed in this work was successfully applied in a practical BF system. It improved the BF temperature level remarkably, increasing the furnace temperature stability rate from 54.9% to 84.9%. This improvement achieved considerable economic benefits. The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace (BF) system. In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed. The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed. A novel method for the delay analysis of furnace heat indicators was established. The extracted delay variables were found to play an important role in modeling. The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model. The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4％,and that for the chemical heat of hot metal in the error range of±0.1wt％ was 93.3％. On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels. These sugges-tions were highly accepted by BF operators. Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system. It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9％ to 84.9％. This improvement achieved considerable economic benefits. |
| Author | Chu, Mansheng Tang, Jue Shi, Quan |
| AuthorAffiliation | School of Metallurgy,Northeastern University,Shenyang 110819,China;Institute for Frontier Technologies of Low-carbon Steelmaking,Northeastern University,Shenyang 110819,China;Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking (Ministry of Education),Shenyang 110819,China |
| AuthorAffiliation_xml | – name: School of Metallurgy,Northeastern University,Shenyang 110819,China;Institute for Frontier Technologies of Low-carbon Steelmaking,Northeastern University,Shenyang 110819,China;Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking (Ministry of Education),Shenyang 110819,China |
| Author_xml | – sequence: 1 givenname: Quan surname: Shi fullname: Shi, Quan organization: School of Metallurgy, Northeastern University, Institute for Frontier Technologies of Low-carbon Steelmaking, Northeastern University, Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking, Ministry of Education – sequence: 2 givenname: Jue surname: Tang fullname: Tang, Jue email: tangj@smm.neu.edu.cn organization: School of Metallurgy, Northeastern University, Institute for Frontier Technologies of Low-carbon Steelmaking, Northeastern University, Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking, Ministry of Education – sequence: 3 givenname: Mansheng surname: Chu fullname: Chu, Mansheng email: chums@smm.neu.edu.cn organization: School of Metallurgy, Northeastern University, Institute for Frontier Technologies of Low-carbon Steelmaking, Northeastern University, Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking, Ministry of Education |
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| Cites_doi | 10.1049/iet-cta.2016.1474 10.1109/TIM.2022.3185325 10.1007/s12613-020-2220-z 10.1007/s12613-023-2636-3 10.1016/j.fss.2020.08.012 10.1109/TIE.2020.3031525 10.1016/S1006-706X(15)30031-5 10.1007/s12613-022-2595-0 10.1016/j.ins.2015.07.002 10.1109/CAC.2017.8243432 10.1002/srin.202300385 10.1109/TFUZZ.2020.2983667 10.1007/s12613-023-2646-1 10.2355/isijinternational.ISIJINT-2019-545 10.1016/j.engappai.2021.104197 10.1515/htmp-2019-0049 10.3390/s18113792 10.2355/isijinternational.ISIJINT-2020-249 |
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| Copyright | University of Science and Technology Beijing 2024 University of Science and Technology Beijing 2024. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | genetic algorithm furnace heat blast furnace prediction and feedback stacking |
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| Publisher | University of Science and Technology Beijing Springer Nature B.V Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking (Ministry of Education),Shenyang 110819,China School of Metallurgy,Northeastern University,Shenyang 110819,China Institute for Frontier Technologies of Low-carbon Steelmaking,Northeastern University,Shenyang 110819,China |
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