Prediction model of hot rolling gap leveling based on incremental analysis and PSO-LSSVM
ZHANG Wei1, LI Tian-lun2, HOU Qing-long2, HE An-rui2, SHAO Jian2
1. Technology Center, Xinyu Iron and Steel Group Co., Ltd., Xinyu 338001, Jiangxi, China; 2. National Engineering Technology Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, China
Abstract：Roll gap leveling plays a key role in shape control and rolling stability of hot-rolled strip. At present, the operator's experience adjustment after visual inspection is the main method, which cannot meet the demand of less human and intelligent rolling technology in the future. Incremental factor extraction of process parameters is realized based on incremental analysis method, which effectively solves the problem of partial information loss in traditional discrete data prediction. At the same time, the particle swarm optimization algorithm is used to optimize the parameters of LSSVM model, which makes the parameter selection more scientific. The production data of a 1 580 mm hot rolling line are used for verification,results show that the prediction model based on incremental analysis and PSO-LSSVM can better predict the leveling value and leveling curve trend, and the F5-F7 leveling prediction accuracy of downstream in finishing rolling is about 95%, which provides assistance for setting of leveling strategy and provides key theoretical support for the development of unmanned rolling technology in the future.
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ZHANG Wei, LI Tian-lun, HOU Qing-long, HE An-rui, SHAO Jian. Prediction model of hot rolling gap leveling based on incremental analysis and PSO-LSSVM[J]. China Metallurgy, 2021, 31(3): 122-128.
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2021, Vol. 31 
