Abstract
In the process of hot strip rolling, the calculation accuracy of rolling force directly affects the actual thickness of strip steel, which is also the prerequisite of accurate online control. However, because the actual rolling process is affected by many factors, the prediction accuracy using the traditional model is often lower. In order to improve the prediction accuracy of rolling force, this paper proposes a parallel heterogeneous extreme learning machine (PELM) prediction model. Taking the actual production data of 2250 production line for a steel plant in Baotou as the example to predict the rolling force, the results show that the rolling force prediction model trained by the algorithm has good prediction accuracy and is suitable for the rolling force prediction of hot strip rolling process.
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Acknowledgements
This work was supported by Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region No.NJYT22057 and National Natural Science Foundation of China No. 62063027 and Inner Mongolia Autonomous Region Science and Technology Planning project No. 2020GG0048.
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Yang, J. et al. (2023). Rolling Force Prediction Based on PELM. In: S. Shmaliy, Y., Nayyar, A. (eds) 7th International Conference on Computing, Control and Industrial Engineering (CCIE 2023). CCIE 2023. Lecture Notes in Electrical Engineering, vol 1047. Springer, Singapore. https://doi.org/10.1007/978-981-99-2730-2_32
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DOI: https://doi.org/10.1007/978-981-99-2730-2_32
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