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Deep Neural Network Prediction Model of Hydrogen Content in VOD Process Based on Small Sample Dataset

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Abstract

This work combines metallurgical thermodynamics and kinetics to train a high-accuracy and low-cost deep neural network (DNN) prediction model for hydrogen content in vacuum oxygen decarburization (VOD) process. The mean absolute error of the trained DNN model was 0.0258 ppm, and the R2 and root mean square errors were 0.9973 and 0.0329, respectively. The sensitivity of the model to single-feature and multi-feature coupling is tested, and the current VOD process is optimized through the model. It was found that by increasing the consumption of argon to 100 m3, controlling the initial hydrogen content below 8 ppm, and reducing the vacuum to 70 Pa, the final hydrogen content will be reduced to 0.99 ppm.

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Acknowledgments

The authors are grateful for the financial support of this work from the National Natural Science Foundation of China (No.51922003, U1810207, and FRF-TP-19-004C1).

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. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 10083, P.R. China

    Wenjie Yang & Lijun Wang

  2. Taiyuan Iron and Steel (Group) Co., Ltd, Taiyuan, 030003, Shanxi, P.R. China

    Wei Zhang & Jianmin Li

Authors
  1. Wenjie Yang
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  2. Lijun Wang
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  3. Wei Zhang
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  4. Jianmin Li
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Correspondence to Lijun Wang.

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Yang, W., Wang, L., Zhang, W. et al. Deep Neural Network Prediction Model of Hydrogen Content in VOD Process Based on Small Sample Dataset. Metall Mater Trans B 53, 3124–3135 (2022). https://doi.org/10.1007/s11663-022-02592-5

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  • DOI: https://doi.org/10.1007/s11663-022-02592-5

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