Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

被引：20

作者：

Dian-yao G. ^{[1
]}

Jian-zhong X. ^{[1
]}

Liang-gui P. ^{[1
]}

Guo-dong W. ^{[1
]}

Xiang-hua L. ^{[1
]}

机构：

[1] State Key Laboratory of Rolling Technology and Automation, Northeastern University, Shenyang

来源：

Journal of Iron and Steel Research International | 2007年 / 14卷 / 4期

基金：

中国国家自然科学基金;

关键词：

hot rolled strip; laminar cooling; process control model; self-learning;

D O I：

10.1016/S1006-706X(07)60050-8

中图分类号：

学科分类号：

摘要：

Abstract: The mathematical model for online controlling hot rolled steel cooling on run-out table (ROT for abbreviation) was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control (CTC) model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective. © 2007 Central Iron and Steel Research Institute.

引用

页码：11 / 14

页数：3

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