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Industrial IoT–enabled real-time prediction of strip cross-section shape for hot-rolling steel

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Abstract

Because strip cross sections cannot be obtained during hot rolling in advance, traditional automatic shape control systems can only rely on the measured shape at the exit of the final mill for feedback control, which causes a significant lag and poor adjustment effect. To accurately predict the cross-sectional shape, an industrial Internet of things platform for steel plants is developed to collect real-time production data. A novel real-time prediction model that can determine the cross sections of strips is proposed to address the drawbacks of traditional data-driven methods that perform offline predictions. This model is established by adopting a dynamic mode decomposition algorithm (DMD) to optimize the sparse identification of nonlinear dynamics (SINDy). A practical dataset of 81 variables from a 2250-mm hot-rolling production line is utilized to validate the proposed method, and this method is compared with SINDy, EMD-optimized SINDy, and VMD-optimized SINDy. The experimental results show that the proposed method can achieve higher prediction accuracy and more minor errors.

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Funding

The work would like to thank the National Natural Science Foundation of China (Grant No. 51975043) for their financial support.

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Authors and Affiliations

  1. National Engineering Technology Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing, 100083, China

    Youzhao Sun, Jingdong Li, Hongfan Li, Xiaochen Wang & Quan Yang

  2. Institute of Engineering Technology, University of Science and Technology Beijing, Beijing, 100083, China

    Youzhao Sun, Jingdong Li, Hongfan Li, Yamin Sun, Xiaochen Wang & Quan Yang

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  1. Youzhao Sun
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Contributions

YoS: conceptualization, writing—review and editing; JL: methodology, writing—original draft; HL: resources, software; YaS: formal analysis, conceptualization; XW: formal analysis, methodology; QY: project administration, supervision.

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Correspondence to Youzhao Sun.

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Sun, Y., Li, J., Li, H. et al. Industrial IoT–enabled real-time prediction of strip cross-section shape for hot-rolling steel. Int J Adv Manuf Technol 130, 961–972 (2024). https://doi.org/10.1007/s00170-023-12745-8

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