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Recognition of Converter Steelmaking State Based on Convolutional Recurrent Neural Networks

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Metallurgical and Materials Transactions B Aims and scope Submit manuscript

Abstract

The converter steelmaking process is an important part of metallurgical production, and the flame characteristics at the furnace mouth indirectly reflect the smelting conditions inside the furnace. Effectively recognizing and predicting the smelting conditions of converter steelmaking is a challenging and critical issue in industrial production. However, traditional image-based methods using a single static flame image as input have low recognition accuracy and cannot accurately reflect changes in smelting conditions. To address this problem, a new recognition model is proposed in this study, which first preprocesses the flame video sequences at the furnace opening, and then applies a convolutional recurrent neural network (CRNN) to further learn the spatio-temporal features and obtain recognition results. In addition, in order to further improve the accuracy of the model, we introduced the channel attention mechanism and verified the effectiveness of the model through the feature layer visualization technique. In addition we quantitatively evaluate the model performance by accuracy, precision, recall, and F1-score, and plot the confusion matrix with AUC–ROC curves. The experimental results show that the method is not only effective but also robust and has a large potential for industrial applications.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (52104318, 52074030).

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Authors and Affiliations

  1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing, 100083, P.R. China

    Chengyong Huang, Zhangjie Dai, Ye Sun, Wei Liu, Shufeng Yang & Jingshe Li

  2. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing, 100083, P.R. China

    Zijiao Wang & Shufeng Yang

Authors
  1. Chengyong Huang
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  2. Zhangjie Dai
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  3. Ye Sun
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  4. Zijiao Wang
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  5. Wei Liu
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  7. Jingshe Li
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Correspondence to Wei Liu or Shufeng Yang.

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Huang, C., Dai, Z., Sun, Y. et al. Recognition of Converter Steelmaking State Based on Convolutional Recurrent Neural Networks. Metall Mater Trans B (2024). https://doi.org/10.1007/s11663-024-03071-9

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