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| Study on Self-Learning Method for Laminar Flow Cooling Based on Case-Based Reasoning Technology |
| PENG Liang-gui1,LIU En-yang1,ZHANG Dian-hua1,YANG Gui-ling2,GUO Hong-wei2,WANG Feng-xiang2 |
1. State Key Lab of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China
2. Hot Rolling Mill, Jinan Iron and Steel Co., Ltd., Jinan 250101, Shandong, China |
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Abstract Decision method for long-term coefficient used for coiling temperature control(CTC) model were studied by case-based reasoning(CBR) technology which experience and knowledge can be effectively reused based on mass production data. In the course of the study, firstly a lot of case consisted of typical laminar flow cooling conditions and self-learning coefficient adopted by CTC model were constructed, secondly case retrieval was done with the absolute and/or relative methods of filtering, then self-leaning coefficient belong to related case can be directly reused or modified according to the similarity between current conditions and historical case conditions, finally the new self-leaning coefficient was adopted into CTC model. Application show that this method can effectively avoid strip head end much more lower temperature than strip body, and can significantly improve the precision of coiling temperature control for strip head end, especially when rolling conditions or specifications changed.
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Received: 05 April 2011
Published: 15 December 2011
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2011, Vol. 46 
