基于数据驱动的卷取温度关键因子研究  

Research on key factors of coiling temperature based on data-driven

作　　者：阎新杰 秦红波[1] 郑立康 陈彤 YAN Xinjie;QIN Hongbo;ZHENG Likang;CHEN Tong(Information and Automation Department,Tangshan Iron and Steel Co.,Ltd.,Tangshan 063500,China;Production Line Intelligent Center,Tangshan Huitang IoT Technology Co.,Ltd.,Tangshan 063500,China;Technology Center,Tangshan Iron and Steel Co.,Ltd.,Tangshan 063500,China)

机构地区：[1]唐山钢铁集团有限责任公司信息自动化部,河北唐山063500 [2]唐山惠唐物联科技有限公司产线智能中心,河北唐山063500 [3]唐山钢铁集团有限责任公司技术中心,河北唐山063500

出　　处：《轧钢》2024年第4期101-109,共9页Steel Rolling

基　　金：河北省“三三三人才工程”资助项目(C20221046)

摘　　要：在热连轧生产过程中,卷取温度控制精度是决定产品质量优劣的关键参数之一。以换热机理模型为基础,通过实际生产数据和过程参数的综合分析,在综合考虑终轧温度、带钢厚度等因素的基础上,深入研究了穿带速度、冷却水温以及季节变化等关键因子对卷取温度模型的影响并对模型进行了修正和优化。同时,采用机器学习算法构建了基于合金成分的卷取温度偏差补偿模型,并对不同算法进行对比分析。研究结果表明:随机森林预测模型在提高卷取温度控制精度方面表现优异。研究成果应用于实际生产厚度h≤6.0 mm、6.0 mm<h≤13.0 mm、h>13.0 mm带钢平均卷取温度合格率分别提升了3.07%、3.82%、4.68%,为进一步提升卷取温度控制精度提供了新的有效途径。In the production process of hot rolling,the control accuracy of coiling temperature is one of the key parameters that determine the quality of the product.Based on the heat transfer mechanism model and comprehensive analysis of actual production data and process parameters,taking into account factors such as finlshing rolling temperature and strip thickness,it is deeply studied the impact of key factors such as strip speed,cooling water temperature,and seasonal variations on the coiling temperature model,and the model is revised and optimized.At the same time,the compensation model based on alloy composition was constructed using machine learning algorithms,and comparative analysis was conducted on different algorithms.The research results show that the random forest prediction model performs well in improving the control accuracy of coiling temperature.The research results were applied to actual production,resulting in an increase in the average qualification rate of coiling temperature for strip with thickness h≤6.0 mm,6.0 mm<h≤13.0 mm,and h>13.0 mm by 3.07%,3.82%,and 4.68%respectively,providing a new and effective way to further improve the control accuracy of coiling temperature.

关 键 词：热连轧带钢 卷取温度 换热机理模型 数据驱动 机器学习算法 卷取温度模型 

分 类 号：TG333.24[金属学及工艺—金属压力加工]