数据驱动的热轧轧制力预报模型研究与应用
Study and application of data-driven rolling force prediction model for hot strip mills
王海玉 1方坤 1董立杰 2郭立伟 1孙力娟 2李亮举1
作者信息
- 1. 北京首钢自动化信息技术有限公司,北京 100041
- 2. 北京首钢股份有限公司,河北迁安 064400
- 折叠
摘要
当前热轧带钢轧制逐渐呈现品种多样性、工艺复杂性的特点,由于考虑因素较少,传统轧制力预报模型逐步显现一些缺陷和不足,已不能满足高精度、高性能产品控制精度的要求.为此,针对国内某2 250 mm热连轧精轧机组,利用数据挖掘技术和智能化算法,结合机理模型,开发机理模型+梯度提升决策树算法的智能化轧制力模型.该模型考虑因素全面,能够提前获取计划单数据,对将要轧制钢种进行针对性训练,提升小样本预报精度;开发自训练与闭环控制技术,能够在多种环境部署应用,实现自动闭环控制.将该模型在线应用后,轧制力长遗传预报误差控制为5％以内,单次计算耗时为10 ms以内.结果表明,该模型响应速度快、计算精度高、计算稳定性好,能够满足换钢种规格、换工况下的轧制力精度控制要求,从而提高带钢轧制稳定性和头部厚度控制精度,提升产品竞争力.
Abstract
The current hot strip rolling gradually presents the characteristics of variety diversity and process com-plexity.Due to the consideration of fewer factors,the traditional rolling force prediction models gradually reveal some defects and deficiencies,and cannot meet the requirements of high-precision and high-performance product con-trol accuracy.Therefore,for a 2 250 mm hot rolling finishing mill in China,an intelligent rolling force model based on mechanism model and gradient boosting decision tree algorithm was developed using data mining technology and intelligent algorithms,combined with mechanism models.The model had comprehensively considered various fac-tors,enabling the acquisition of plan sheet data in advance and targeted training for the steel grades to be rolled,thereby enhancing the prediction accuracy for small samples.Additionally,self-training and closed-loop control technologies had been developed,allowing for deployment and application in various environments and achieving au-tomatic closed-loop control.After applying the model on-line,the long genetic prediction accuracy error of rolling force was controlled within 5％,and the single calculation time took less than 10 ms.The results show that the model has fast response speed,high calculation accuracy and good calculation stability,which can meet the require-ments of rolling force accuracy control under changing steel grades and working conditions,thereby improving the stability of strip rolling and the control accuracy of head thickness,and enhancing product competitiveness.
关键词
热轧带钢/轧制力预测/机理模型/GBDT算法/模型应用Key words
hot rolled steel strip/rolling force prediction/mechanism model/GBDT algorithm/model application引用本文复制引用
出版年
2024
NETL
NSTL
万方数据