Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models

Juan JIMENEZ; Javier MOCHON; Jesus Sainz de AYALA

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Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models

机译：基于神经网络的高炉铁水温度预测

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摘要

Blast furnace hot metal temperature prediction, by mean of mathematical models, plays an interesting role in blast furnace control, helping plant operators to give a faster and more accurate answer to changes in blast furnace state. In this work, the development of parametric models based on neural networks is shown. Time has been included as an implicit variable to improve consistency. The model has been developed departing from actual plant data supplied by Aceralia from its steel works located in Gijon.

机译：借助数学模型，高炉铁水温度预测在高炉控制中扮演着有趣的角色，帮助工厂操作员更快，更准确地回答高炉状态的变化。在这项工作中，显示了基于神经网络的参数模型的开发。时间已作为隐式变量包括在内，以提高一致性。该模型的开发偏离了Aceralia从其位于希洪的钢厂提供的实际工厂数据。

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来源
《ISIJ international》 |2004年第3期|p.573-580|共8页
作者
Juan JIMENEZ; Javier MOCHON; Jesus Sainz de AYALA;
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作者单位

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
原文格式 PDF
正文语种 eng
中图分类黑色金属材料;
关键词
ironmaking; blast furnace; neural networks; forecasting; simulation; hot metal temperature;

机译：炼铁;高炉;神经网络;预报;模拟;铁水温度;

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7. Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models [O] . Juan Jiménez, Javier Mochón, Jesús Sainz de Ayala, 2004

机译：基于神经网络的模型高炉热金属温度预测

Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models

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