Blast furnace hot metal temperature prediction through neural networks-based models

被引：66
|
作者：
Jiménez, J
Mochón, J
de Ayala, JS
Obeso, F
机构：
[1] Ctr Nacl Invest Met, E-28040 Madrid, Spain
[2] Aceralia Verina, Gijon 33208, Spain
来源：
ISIJ INTERNATIONAL | 2004年 / 44卷 / 03期
关键词：
ironmaking; blast furnace; neural networks; forecasting; simulation; hot metal temperature;
D O I：
10.2355/isijinternational.44.573
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
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.
引用
收藏
页码：573 / 580
页数：8
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