基于IBA-LightGBM的热轧带钢在线厚度预测  

Online thickness prediction of hot rolling strip based on IBA-LightGBM

作　　者：黄硕 张飞[1,2] 王丽君[2,3] 郭强[1] 肖雄[1] HUANG Shuo;ZHANG Fei;WANG Lijun;GUO Qiang;XIAO Xiong(National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education,University of Science and Technology Beijing,Beijing 100083,China;School of Automation&Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学高效轧制与智能制造国家工程研究中心,北京100083 [2]工业过程知识自动化教育部重点实验室,北京100083 [3]北京科技大学自动化学院,北京100083

出　　处：《冶金自动化》2024年第4期101-109,共9页Metallurgical Industry Automation

基　　金：国家重点研发计划重点专项(2022YFB3304002-02);广西重点研发计划(桂科AB21196025)。

摘　　要：针对热轧带钢的厚度数学模型耦合性强、精度低等问题,提出了一种带钢在线厚度预测算法。首先使用轧制数据,利用轻量级的梯度提升机(light gradient boosting machine, LightGBM)算法建立在线厚度预测模型;然后采用改进蝙蝠优化算法(improved bat algorithm, IBA)改善LightGBM的模型参数,并通过自学习系统优化结果;最后对比预测结果和真实厚度,验证预测模型准确性。实验结果表明,IBA-LightGBM模型能够快速高精度在线预测带钢厚度,在预测2 mm、3 mm、4 mm和5.65 mm规格的带钢时,均方根误差ERMS(root mean square error, RMSE)可以分别控制在11.0μm、11.5μm、11.6μm和16.4μm以内。结果可改善热轧带钢的厚度数学模型的精度,提高厚度控制系统的水平。An online thickness prediction algorithm for strip steel was proposed to address issues of strong coupling and low accuracy in the thickness mathematical model.Firstly,the rolling data is used to establish an online thickness prediction model based on light gradient boosting machine(LightGBM)model.Then improved bat algorithm(IBA)is applied to improve the LightGBM model parameters,and a self-learning system is deployed to optimize the results.Finally,the predicted results are compared with the actual thickness to verify the accuracy of the prediction model.The experimental results show that the online thickness prediction algorithm can quickly and accurately predict the strip thickness.When the IBA-LightGBM model was used to predict the 2 mm,3 mm,4 mm,and 5.65 mm strips,root mean square error ERMS(RMSE)can be controlled within 11.0μm,11.5μm,11.6μm and 16.4μm respectively.The results can improve the accuracy of the thickness mathematical model for hot rolling strip and enhance the level of the thickness control system.

关 键 词：热轧 机器学习 轻量级的梯度提升机 改进蝙蝠优化算法 在线厚度预测 

分 类 号：TG335.56[金属学及工艺—金属压力加工]