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首页> 外文期刊>Journal of Materials Engineering and Performance >Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill
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Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

机译：热轧机入口温度预测的神经和中性灰箱建模

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摘要

In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.
机译：在热轧带钢轧机中，必须在钢筋进入轧机之前计算出初始控制器设定点。计算依赖于滚动变量的丰富知识。只有在钢筋进入轧机后才能进行测量，因此必须进行估算。工艺变量（尤其是温度）的估计对于钢筋前部满足质量要求至关重要，因此必须在尽可能短的时间内进行以保持热量。当前，温度估计是通过物理建模来进行的。但是，它会受到测量不确定性，进料条状态的变化以及最终产品变化的高度影响。为了克服这些问题，已经提出了诸如人工智能神经网络和模糊逻辑的人工智能技术。在本文中，鉴于其重要性，将基于神经网络的系统（包括基于神经网络的Gray-Box模型）用于估算除垢器入口温度，并将其性能与工厂中使用的物理模型进行比较。使用实际数据设计和测试了几种神经系统和几种基于神经的Gray-Box模型。利用神经网络进行输入合并的灵活性，还测试了一些对过程有影响的因素。事实证明，与当前工厂中使用的物理模型相比，本研究中提出的系统具有更好的性能指标，因此具有更好的预测能力。

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