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首页> 外文期刊>ISIJ international >Ensemble Prediction of Tundish Open Eyes Using Artificial Neural Networks
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Ensemble Prediction of Tundish Open Eyes Using Artificial Neural Networks

机译：中间包睁开眼的人工神经网络集成预测

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As global steelmakers are feeling the economical pinch, the need for improving quality and quantity using what is already readily available, increases. This gap in achievement can be bridged by innovation and perforation of already existing techniques and methodologies from other fields. Steel quality, an important issue, is often not associated with a phenomenon known as tundish open eyes. However, recently researchers have shown the detrimental effects of reoxidation and the deterioration of the final product (slabs/billets). Understanding the formation of this event, and mitigating the formation will be an important issue to solve. Current models investigating the former have existed largely in the computational fluid dynamics modelling domain. However, the solution for the former, can only provide static recommendations thus are less useful in a dynamic environment. Hence, development of a reliable model which has the ability to “learn on the fly” is very much needed. In the current study, artificial neural network models have been used to predict non-dimensional open eye sizes in the tundish. The dataset has been compiled from previous regression formulations. The performance of the models is determined based on the following metrics 1) coefficient of multiple determination (R~(2)), 2) and root mean square error (RMSE). The ANN based models, show significant promise, in particular the ensemble variants, which have shown increased accuracy and stability across all domain and range.
机译：随着全球钢铁制造商感受到经济上的压力，使用现有的产品来提高质量和数量的需求在增加。可以通过创新和突破其他领域已经存在的技术和方法来弥合成就上的差距。钢铁质量是一个重要问题，通常与所谓中间包睁开眼睛的现象无关。然而，最近的研究人员显示了再氧化的有害作用和最终产品（板/坯）的劣化。了解此事件的形成并减轻其形成将是要解决的重要问题。目前研究前者的模型主要存在于计算流体动力学建模领域。但是，前者的解决方案只能提供静态建议，因此在动态环境中的用处较小。因此，非常需要开发一种具有“动态学习”能力的可靠模型。在当前的研究中，人工神经网络模型已用于预测中间包中的无量纲睁眼尺寸。该数据集是根据以前的回归公式编制而成的。模型的性能基于以下指标1）多重确定系数（R〜（2）），2）和均方根误差（RMSE）确定。基于ANN的模型显示出巨大的希望，尤其是集成变体，它在所有领域和范围内均显示出更高的准确性和稳定性。

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