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Real-time dynamic estimation of carbon content in converter melt pool based on smelting mix data.

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Title: Real-time dynamic estimation of carbon content in converter melt pool based on smelting mix data.
Authors: Liu, Hai1 (AUTHOR), Li, Ailian1 (AUTHOR) 15647222559@163.com, Xie, Shaofeng2 (AUTHOR), Wu, GuoChao1 (AUTHOR), Zhang, Xiong1 (AUTHOR)
Source: Metallurgical Research & Technology. 2025, Vol. 122 Issue 5, p1-11. 11p.
Subjects: Carbon, Convolutional neural networks, Genetic algorithms, Steel manufacture, Real-time computing, Data analysis
Abstract: The carbon content in converter molten steel is crucial to the impact of steel, and in view of the current situation that it is difficult to realize the real-time dynamic estimation of molten steel carbon content throughout the blowing process in the converter smelting process, this paper proposes a real-time dynamic estimation of carbon content in converter molten pool based on smelting mixing data. This method integrates scalar and temporal data from converter steelmaking via a cosine similarity metric strategy, constructing an attention-enhanced temporal convolutional network. It employs a genetic optimization algorithm to determine the optimal model parameters and introduces a sliding window mechanism to address the challenge of training variable-length sequences. The proposed model, named the Cosine Similarity-based Sliding Window Temporal Convolutional Network with Self-Attention (CS-TCN-Attention), achieves real-time dynamic estimation of carbon content in the molten steel bath. The results of ablation and comparison experiments show that under the same parameter settings, the model performs well in terms of prediction accuracy and goodness of fit, with the mean absolute error (MAE) reduced to 0.0736, the coefficient of determination (R2) improved to 0.9963, and the root mean square error (RMSE) reduced to 0.0879, which significantly improves the accuracy of real-time prediction of molten steel carbon content in the process of converter blowing and provides a reference for the time-series prediction in process industries. [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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DbLabel: Engineering Source
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  Data: Real-time dynamic estimation of carbon content in converter melt pool based on smelting mix data.
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  Data: <searchLink fieldCode="JN" term="%22Metallurgical+Research+%26+Technology%22">Metallurgical Research & Technology</searchLink>. 2025, Vol. 122 Issue 5, p1-11. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Carbon%22">Carbon</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Steel+manufacture%22">Steel manufacture</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The carbon content in converter molten steel is crucial to the impact of steel, and in view of the current situation that it is difficult to realize the real-time dynamic estimation of molten steel carbon content throughout the blowing process in the converter smelting process, this paper proposes a real-time dynamic estimation of carbon content in converter molten pool based on smelting mixing data. This method integrates scalar and temporal data from converter steelmaking via a cosine similarity metric strategy, constructing an attention-enhanced temporal convolutional network. It employs a genetic optimization algorithm to determine the optimal model parameters and introduces a sliding window mechanism to address the challenge of training variable-length sequences. The proposed model, named the Cosine Similarity-based Sliding Window Temporal Convolutional Network with Self-Attention (CS-TCN-Attention), achieves real-time dynamic estimation of carbon content in the molten steel bath. The results of ablation and comparison experiments show that under the same parameter settings, the model performs well in terms of prediction accuracy and goodness of fit, with the mean absolute error (MAE) reduced to 0.0736, the coefficient of determination (R2) improved to 0.9963, and the root mean square error (RMSE) reduced to 0.0879, which significantly improves the accuracy of real-time prediction of molten steel carbon content in the process of converter blowing and provides a reference for the time-series prediction in process industries. [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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        Value: 10.1051/metal/2025073
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      – Code: eng
        Text: English
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        PageCount: 11
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      – SubjectFull: Carbon
        Type: general
      – SubjectFull: Convolutional neural networks
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      – SubjectFull: Genetic algorithms
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      – SubjectFull: Steel manufacture
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      – SubjectFull: Real-time computing
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      – SubjectFull: Data analysis
        Type: general
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      – TitleFull: Real-time dynamic estimation of carbon content in converter melt pool based on smelting mix data.
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            NameFull: Liu, Hai
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            NameFull: Li, Ailian
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            NameFull: Xie, Shaofeng
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            – D: 01
              M: 09
              Text: 2025
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