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首页> 外文期刊>Journal of Achievements in Materials and Manufacturing Engineering >The use of artificial neural networks for the prediction of sulphur content in hot metal produced in blast furnace
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The use of artificial neural networks for the prediction of sulphur content in hot metal produced in blast furnace

机译：人工神经网络预测高炉铁水中硫含量

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摘要

Purpose: The paper presents the possibilities of using artificial intelligence for the prediction of sulphur content in hot metal produced in blast furnace. Design/methodology/approach: Three blast furnaces in ArcelorMittal, Unit in Dabrowa Gornicza, provided the data for the model construction. The data reflect a number of variables, which describe the blast furnace process. Findings:: Materials research performed with the use of data mining and neural networks is consistent with the results obtained during the real research in a real laboratory. The obtained results show that the construction of such neural networks is practical. There is a strong correlation between predicted value and real value. Practical implications: The presented model can be used in the industrial practice as an additional tool for blast furnace and steel plant operators. Originality/value: Prediction of sulphur content in hot metal at the stage of adjusting hot metal process parameters.
机译：目的：本文介绍了利用人工智能预测高炉生产的铁水中硫含量的可能性。设计/方法/方法：位于Dabrowa Gornicza的ArcelorMittal的三座高炉为模型构建提供了数据。数据反映了许多变量，这些变量描述了高炉过程。发现：使用数据挖掘和神经网络进行的材料研究与在真实实验室中进行的真实研究结果一致。获得的结果表明，这种神经网络的构建是可行的。预测值和实际值之间存在很强的相关性。实际意义：提出的模型可以在工业实践中用作高炉和钢厂操作员的附加工具。独创性/价值：在调整铁水工艺参数阶段预测铁水中的硫含量。

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