摘要
热轧带钢成品的宽度精度直接影响产品成材率,是产品性能提升的关键,而精轧区带钢出口宽度的精准预测可以为粗轧区宽度控制模型参数提供及时的优化调整指导。传统机理模型与实际情况往往存在较大差异,现有的数据驱动模型大多采用神经网络方法,但没有考虑轧制数据的时序性以及数据剪枝带来的信息损失。为了进一步提升精轧带钢宽度预测精度,提出一种基于轧制机理的混合神经网络宽度预测模型,利用精轧宽展的机理模型计算宽度基准值,结合卷积神经网络(CNN)和门控循环单元(GRU)输出宽度预测纠偏值。利用2250mm热连轧钢厂数据集试验,结果表明本文提出的热连轧带钢宽度预测模型训练效率较高,98.7%带钢宽度的预测精度在4mm内,较传统BP神经网络模型和其他单一结构网络有大幅提升,且模型在线测试速度满足工业现场应用需求。
The width accuracy of hot-rolled strip products directly affects the yield of products and is the key to improving product performance.The accurate prediction of the strip exit width in the finish rolling area can provide timely optimization and adjustment guidance for the width control model parameters in the rough rolling area.The traditional mechanism model is often different from the actual situation.Most of the existing data-driven models use neural network method,but do not consider the timing of rolling data and the information loss caused by data pruning.In order to further improve the precision of width prediction for finish rolling strip,a hybrid neural network width prediction model based on rolling mechanism was proposed.The width reference value was calculated using the mechanism model of finish rolling spread,and the width prediction correction value was output by combining convolutional neural network(CNN)and gated recurrent unit(GRU).The data set test of a 2250mm hot continuous rolling mill shows that the training efficiency of the proposed model is high,and deviations for 98.7%testing samples are within 4mm,which is greatly improved compared with the traditional BP neural network model and other single structure networks,and the on-line test speed of the model meets the industrial application requirements.
作者
王晓雯
张勇军
郭强
张飞
裴红平
WANG Xiao-wen;ZHANG Yong-jun;GUO Qiang;ZHANG Fei;PEI Hong-ping(Institute of Engineering Technology,University of Science and Technology Beijing,Beijing 100083,China;National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing,University of Science and Technology Beijing,Beijing 100083,China)
出处
《中国冶金》
CAS
CSCD
北大核心
2023年第2期114-120,共7页
China Metallurgy
基金
国家自然科学基金资助项目(U21A20483)。
关键词
热连轧
精轧
宽度预测
宽展机理模型
卷积神经网络
门控循环单元
hot continuous rolling
finish rolling
width prediction
width spread mechanism model
convolutional neural network
gated recurrent unit
