End-point Temperature Preset of Molten Steel in the Final Refining Unit Based on an Integration of Deep Neural Network and Multi-process Operation Simulation

DOI Web Site 被引用文献2件 参考文献26件
  • Yang Jianping
    State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing
  • Zhang Jiangshan
    State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing
  • Guo Weida
    Laiwu Iron and Steel Group Yinshan Section Steel Co., Ltd.
  • Gao Shan
    Laiwu Iron and Steel Group Yinshan Section Steel Co., Ltd.
  • Liu Qing
    State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing Engineering Research Center of MES Technology for Iron & Steel Production, Ministry of Education

説明

<p>End-point temperature preset of molten steel in the final refining unit is as important as its prediction for casting temperature control. However, it has not been given sufficient concern yet, and the proposed preset models in the literature usually cannot be used as practical tools due to their inherent shortcomings, e.g., oversimplifications made to a real environment during modelling. In this study, a novel preset approach was developed by integrating deep neural network (DNN) and multi-process operation simulation (MOS). By using MOS, the accurate transfer times of heats between the final refining unit and continuous caster can be solved before their actual scheduling, which is very significant for availability of the preset model based on DNN in practice. The DNN preset model was trained and tested with varying the values of hyper-parameters based on vast data points collected from a real steelmaking plant. Furthermore, preset models based on extreme learning machine (ELM) and multivariate polynomial regression (MVPR) were also established for comparison. The testing results indicate the DNN preset model with 3 hidden layers which contain 8, 4 and 2 neurons in sequence shows an advantage over other alternatives because of its evident improvement in preset accuracy and robustness. Meanwhile, a fine classification of data points considering metallurgical expertise can improve the generalization performance of the DNN preset model. The integrated approach has been applying in the studied steelmaking plant, and the ratio of qualified heats increases by 9.5% than before using it.</p>

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