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.

最终精炼装置中钢水的终点温度预设与其预测对于铸造温度控制一样重要。然而，目前还没有得到足够的关注，文献中提出的预设模型由于其固有的缺点，通常不能作为实用工具，例如，在建模过程中对真实环境进行了过度简化。在这项研究中，通过集成深度神经网络 (DNN) 和多进程操作模拟 (MOS)，开发了一种新颖的预设方法。通过使用 MOS，可以在最终精炼装置和连铸机之间的准确热量传递时间在实际调度之前解决，这对于基于 DNN 的预设模型在实践中的可用性非常重要。基于从真实炼钢厂收集的大量数据点，使用不同的超参数值对 DNN 预设模型进行了训练和测试。此外，还建立了基于极限学习机（ELM）和多元多项式回归（MVPR）的预设模型进行比较。测试结果表明 DNN 预设模型具有 3 个隐藏层，其中包含 8 个，序列中的 4 和 2 个神经元显示出优于其他替代方案的优势，因为其在预设准确性和鲁棒性方面的明显改进。同时，考虑冶金专业知识的数据点精细分类可以提高 DNN 预设模型的泛化性能。综合方法已在所研究的炼钢厂应用，合格炉次率比使用前提高了9.5%。