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A hybrid approach combining data-driven and mathematical models to predict the endpoint carbon content and temperature in BOF.

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Title: A hybrid approach combining data-driven and mathematical models to predict the endpoint carbon content and temperature in BOF.
Authors: Hou, Yaobin1 (AUTHOR), Liu, Jianhua1 (AUTHOR) liujianhua@metall.ustb.edu.cn, He, Yang1 (AUTHOR), Yuan, Jing2 (AUTHOR), Shuai, Yong2 (AUTHOR), Luo, Renhui2 (AUTHOR), Zheng, Zhong3 (AUTHOR)
Source: Metallurgical Research & Technology. 2025, Vol. 122 Issue 5, p1-12. 12p.
Subjects: Basic oxygen furnaces, Mathematical models, Random forest algorithms, Forecasting, Temperature measurements, Empirical research
Abstract: Accurate endpoint prediction is critical in the basic oxygen furnace (BOF) steelmaking process. This paper proposes a hybrid approach that integrates data-driven methods with mathematical models to predict the endpoint carbon content and temperature in BOF. The proposed model is based on the Just-In-Time Learning (JITL) framework, which uses similarity functions to select historical heats that closely resemble the smelting conditions of the target sample, thereby constructing local models. The Trust Region Reflective (TRR) algorithm and Multiple Linear Regression (MLR) algorithm are independently used to calculate the decarburization parameters and heating parameters for these local models. These parameters are then applied in mathematical equations to predict the carbon content and temperature during the second blowing process of BOF. By integrating theoretical analysis with the Random Forest (RF) algorithm, it was determined that the key variables influencing both the decarburization and heating parameters were the temperature and carbon content measured by the TSC sub-lance, along with the slag weight. The study validated the JITL model with silicon steel, achieving an 85.83% hit rate for carbon content prediction (±0.02 wt%) and 83.42% for temperature prediction (±15 °C), demonstrating superior accuracy over the traditional sub-lance model. [ABSTRACT FROM AUTHOR]
Copyright of Metallurgical Research & Technology is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A hybrid approach combining data-driven and mathematical models to predict the endpoint carbon content and temperature in BOF.
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  Data: <searchLink fieldCode="AR" term="%22Hou%2C+Yaobin%22">Hou, Yaobin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jianhua%22">Liu, Jianhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liujianhua@metall.ustb.edu.cn</i><br /><searchLink fieldCode="AR" term="%22He%2C+Yang%22">He, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Jing%22">Yuan, Jing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shuai%2C+Yong%22">Shuai, Yong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Renhui%22">Luo, Renhui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Zhong%22">Zheng, Zhong</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Metallurgical+Research+%26+Technology%22">Metallurgical Research & Technology</searchLink>. 2025, Vol. 122 Issue 5, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Basic+oxygen+furnaces%22">Basic oxygen furnaces</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Temperature+measurements%22">Temperature measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink>
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  Data: Accurate endpoint prediction is critical in the basic oxygen furnace (BOF) steelmaking process. This paper proposes a hybrid approach that integrates data-driven methods with mathematical models to predict the endpoint carbon content and temperature in BOF. The proposed model is based on the Just-In-Time Learning (JITL) framework, which uses similarity functions to select historical heats that closely resemble the smelting conditions of the target sample, thereby constructing local models. The Trust Region Reflective (TRR) algorithm and Multiple Linear Regression (MLR) algorithm are independently used to calculate the decarburization parameters and heating parameters for these local models. These parameters are then applied in mathematical equations to predict the carbon content and temperature during the second blowing process of BOF. By integrating theoretical analysis with the Random Forest (RF) algorithm, it was determined that the key variables influencing both the decarburization and heating parameters were the temperature and carbon content measured by the TSC sub-lance, along with the slag weight. The study validated the JITL model with silicon steel, achieving an 85.83% hit rate for carbon content prediction (±0.02 wt%) and 83.42% for temperature prediction (±15 °C), demonstrating superior accuracy over the traditional sub-lance model. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Metallurgical Research & Technology is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1051/metal/2025059
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      – Code: eng
        Text: English
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        PageCount: 12
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      – SubjectFull: Basic oxygen furnaces
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Random forest algorithms
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      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Temperature measurements
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      – SubjectFull: Empirical research
        Type: general
    Titles:
      – TitleFull: A hybrid approach combining data-driven and mathematical models to predict the endpoint carbon content and temperature in BOF.
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            NameFull: Hou, Yaobin
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            NameFull: Liu, Jianhua
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            NameFull: He, Yang
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            NameFull: Yuan, Jing
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            NameFull: Shuai, Yong
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            NameFull: Luo, Renhui
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            – D: 01
              M: 09
              Text: 2025
              Type: published
              Y: 2025
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              Value: 122
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