Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy
For complex, difficult‐to‐control, and hour‐delay blast furnace (BF) systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag a...
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| Published in: | Steel research international Vol. 95; no. 2 |
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| Abstract | For complex, difficult‐to‐control, and hour‐delay blast furnace (BF) systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag and iron discharge during the entire BF process are analyzed, with a total of 171 variables and 5033 groups of data. Based on the knowledge of BF technology, a comprehensive index of hearth activity is then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method is verified from the two aspects of furnace heat level and furnace status coincidence. Compared with the traditional single‐machine learning algorithm, the performance of the proposed method that combines genetic algorithm and stacking exhibits significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity are 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions are also simultaneously pushed. The model of BF hearth activity is successfully applied in practical online production. During the application period, the average furnace hearth activity increases by 10% compared to the historical value.
Herein, a blast furnace hearth activity evaluation, prediction, and feedback model is established by integrating data‐driven technology and blast furnace metallurgical process. It not only realizes the quantitative evaluation of blast furnace hearth activity and accurate prediction of future trends, but also significantly improves the level of blast furnace hearth activity by pushing the regulation strategy in advance. |
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| AbstractList | For complex, difficult‐to‐control, and hour‐delay blast furnace (BF) systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag and iron discharge during the entire BF process are analyzed, with a total of 171 variables and 5033 groups of data. Based on the knowledge of BF technology, a comprehensive index of hearth activity is then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method is verified from the two aspects of furnace heat level and furnace status coincidence. Compared with the traditional single‐machine learning algorithm, the performance of the proposed method that combines genetic algorithm and stacking exhibits significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity are 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions are also simultaneously pushed. The model of BF hearth activity is successfully applied in practical online production. During the application period, the average furnace hearth activity increases by 10% compared to the historical value. For complex, difficult‐to‐control, and hour‐delay blast furnace (BF) systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag and iron discharge during the entire BF process are analyzed, with a total of 171 variables and 5033 groups of data. Based on the knowledge of BF technology, a comprehensive index of hearth activity is then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method is verified from the two aspects of furnace heat level and furnace status coincidence. Compared with the traditional single‐machine learning algorithm, the performance of the proposed method that combines genetic algorithm and stacking exhibits significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity are 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions are also simultaneously pushed. The model of BF hearth activity is successfully applied in practical online production. During the application period, the average furnace hearth activity increases by 10% compared to the historical value. Herein, a blast furnace hearth activity evaluation, prediction, and feedback model is established by integrating data‐driven technology and blast furnace metallurgical process. It not only realizes the quantitative evaluation of blast furnace hearth activity and accurate prediction of future trends, but also significantly improves the level of blast furnace hearth activity by pushing the regulation strategy in advance. |
| Author | Chu, Mansheng Tang, Jue Shi, Quan |
| Author_xml | – sequence: 1 givenname: Quan orcidid: 0000-0002-3182-4630 surname: Shi fullname: Shi, Quan organization: Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking (Ministry of Education of China) – sequence: 2 givenname: Jue orcidid: 0000-0002-3765-277X surname: Tang fullname: Tang, Jue email: tangj@smm.neu.edu.cn organization: Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking (Ministry of Education of China) – sequence: 3 givenname: Mansheng surname: Chu fullname: Chu, Mansheng organization: Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking (Ministry of Education of China) |
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| Cites_doi | 10.1002/srin.202200215 10.1016/j.knosys.2017.02.013 10.2355/isijinternational.ISIJINT-2021-371 10.1007/s11704-019-8208-z 10.1002/srin.202200266 10.1051/metal/2023002 10.2355/isijinternational.ISIJINT-2018-005 10.1002/srin.202200680 10.1016/j.engappai.2021.104197 10.2355/isijinternational.42.504 10.1080/03019233.2017.1400732 10.1021/acsomega.2c05029 10.1016/j.eswa.2018.12.022 10.1080/03019233.2021.1907957 10.1016/j.physa.2011.12.004 10.1007/s12613-023-2636-3 10.1002/cjce.24790 10.1179/030192309X12506804200627 10.1007/s11663-021-02399-w 10.1007/s11042-020-10139-6 10.1051/metal/2020007 10.1002/widm.1249 10.3390/pr7080519 10.1631/FITEE.2200366 10.2355/isijinternational.ISIJINT-2020-249 |
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| SubjectTerms | data-driven analyses Genetic algorithms hearth activities Machine learning Metallurgical analysis prediction and feedbacks process metallurgies Process metallurgy time lags |
| Title | Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy |
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