DocumentCode :
3017561
Title :
Coiling Temperature Optimal Setting Control Model Based on Genetic Algorithms and Application in Hot Strip Rolling Mill
Author :
Dazhi, Zhang ; Haili, Ye ; Xiaofei, Xiang
Author_Institution :
Autom. Dept., Nat. Eng. Res. Center for Adv. Rolling, Beijing, China
fYear :
2010
fDate :
25-27 June 2010
Firstpage :
591
Lastpage :
594
Abstract :
Coiling temperature is one of the most important targets of hot strip rolling. The coiling temperature can be controlled by laminar cooling system. A lot of measured data were got by many experiments in considering of the characteristics of hot strip rolling and the strip temperature change characteristics in laminar cooling area. To overcome the defects of traditional model, a new coiling temperature setting control model based on mended genetic algorithms neural network is set up. The new model has been used and results of industrial application show that the model has high precision. The temperature control error within ±20°C (contract target) is 100% while ±10°C is 93%.
Keywords :
cooling; genetic algorithms; hot rolling; neurocontrollers; rolling mills; temperature control; coiling temperature optimal setting control model; genetic algorithm; hot strip rolling mill; laminar cooling system; neural network; Computational modeling; Cooling; Strips; Temperature; Temperature measurement; coiling temperature; genetic algorithms; hot strip rolling; neural networks; optimal setting; temperature control model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
Type :
conf
DOI :
10.1109/iCECE.2010.151
Filename :
5631808
Link To Document :
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