Prediction models of coating mass per unit area for hot-dip galvanized strip based on artificial neural network
QIN Da-wei1,2, LIU Hong-min1, ZHANG Dong2, WANG Jun-sheng2
1. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University, Qinhuangdao 066004, Hebei, China; 2. Beijing Research Institute Co., Ltd., Ansteel Group, Beijing 102211, China
Abstract：In order to solve the problems of large mass per unit area deviation,long adjustment time,and waste of zinc raw materials for continuous hot-dip galvanizing of steel strip,an air knife pressure preset model and a coating mass per unit area prediction model were established using historical data of the production process. Based on the production practice of the hot-dip galvanizing of steel strip,the influence factors and the control strategy of the coating mass per unit area were analyzed. The static sample data was collected,and the multi-variable partial correlation analysis was carried out,which showed that the coating mass per unit area was related to the air knife pressure,the air knife distance and the steel strip speed. According to the sample data,taking the coating mass per unit area,the distance of air knife and the speed of strip steel as input variables,BP neural network was used to establish the preset model of air knife pressure,and the preset precision reached 3 000 Pa. The sample data of time series during the hot-dip galvanizing process was collected and a prediction model of coating mass per unit area was established using NARX dynamic neural network. The prediction accuracy reached 6 g/m2. The foundation for closed-loop control of the coating mass per unit area for hot-dip galvanized strip was provided.
秦大伟, 刘宏民, 张栋, 王军生. 带钢热镀锌镀层单位面积质量神经网络预测模型[J]. 钢铁, 2020, 55(5): 68-72.
QIN Da-wei, LIU Hong-min, ZHANG Dong, WANG Jun-sheng. Prediction models of coating mass per unit area for hot-dip galvanized strip based on artificial neural network. Iron and Steel, 2020, 55(5): 68-72.
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2020, Vol. 55 
