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Temperature distribution prediction in control cooling process with recurrent neural network for variable-velocity hot rolling strips

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Abstract

Control cooling is an essential method for microstructure and mechanical property control in hot rolling strip making. Therefore, it is vital to realize high-precision temperature distribution prediction and control in cooling process to ensure the industrial production. In this paper, a traditional mechanism model based on finite-difference method combined with online cycle velocity calculation strategy was introduced as one of the baseline methods estimating temperature distribution. However, considering calculation time, variable-velocity rolling makes it difficult to rapidly realize temperature and modifying water distribution of all segments in cooling zone. Herein, a temperature distribution prediction method based on recurrent neural network was proposed, by fully considering the variable-velocity rolling dynamic characteristics. And the temperature distribution prediction performance of the model with different recurrent cell and time steps was evaluated. The results indicated that the proposed model could realize temperature distribution prediction, and the model based on bi-LSTM and 48 timesteps has the highest determination coefficient value of 0.976, the lowest root mean square error of 8.03, and a mean absolute error of 5.7. Furthermore, compared with baseline model, the proposed model retained lower computational cost, making it applicable in industrial application by providing real-time temperature distribution prediction.

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Data Availability

All the data that supports the findings of study is available from the corresponding author upon reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (51804074), Project funded by China Postdoctoral Science Foundation (2020M680964), Fundamental Research Funds for the Central Universities (N2107004), and Northeastern University Postdoctoral Foundation (20200323).

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Authors and Affiliations

  1. State Key Laboratory of Rolling and Automation, Northeastern University, No. 11, Lane 3, Wenhua Road, HePing District, Shenyang, 110819, China

    Dong Chen, Rui Zhang, Zhenlei Li, Yunjie Li & Guo Yuan

Authors
  1. Dong Chen
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  2. Rui Zhang
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  3. Zhenlei Li
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  4. Yunjie Li
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  5. Guo Yuan
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Contributions

Dong Chen: methodology, data curation, visualization, investigation, writing — original draft. Rui Zhang data curation, software, visualization. Zhenlei Li: conceptualization, methodology, supervision, validation, writing — review and editing. Yunjie Li: visualization. Guo Yuan: conceptualization, supervision, validation, writing — review and editing.

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Correspondence to Guo Yuan.

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Chen, D., Zhang, R., Li, Z. et al. Temperature distribution prediction in control cooling process with recurrent neural network for variable-velocity hot rolling strips. Int J Adv Manuf Technol 120, 7533–7546 (2022). https://doi.org/10.1007/s00170-022-09065-8

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