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首页> 外文期刊>ISIJ international >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
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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

机译：基于深度神经网络的整合和多过程操作模拟的集成，终点温度预设最终精炼单元。

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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.
机译：最终精炼单元中钢水的终点温度预设与其对铸造温度控制的预测同时重要。然而，它尚未获得足够的问题，并且由于其固有的缺点，文献中的拟议预设模型通常不能用作实用工具，而例如。 ，在建模期间对真实环境进行的过度简化。在本研究中，通过集成深神经网络（DNN）和多过程操作模拟（MOS）来开发一种新的预设方法。通过使用MOS，在实际调度之前可以解决最终精炼单元和连续脚轮之间的热量的准确转移时间，这对于实践中基于DNN的预设模型的可用性非常重要。 DNN预设模型培训并测试了基于从真实炼钢厂收集的广大数据点来改变超参数的值。此外，还建立了基于极端学习机（ELM）和多变量多项式回归（MVPR）的预设模型进行比较。测试结果表明，具有3个隐藏层的DNN预设模型，其含有8,4和2神经元，其序列显示出其他替代方案，因为其预设精度和鲁棒性的显而易见。同时，考虑冶金专业知识的数据点的精细分类可以提高DNN预设模型的泛化性能。综合方法一直在研究的炼钢厂申请，合格的热量的比例比使用前增加了9.5％。

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