Industrial IoT-enabled real-time prediction of strip cross-section shape for hot-rolling steel

作     者：Sun, Youzhao Li, Jingdong Li, Hongfan Sun, Yamin Wang, Xiaochen Yang, Quan 

作者机构：Univ Sci & Technol Beijing Natl Engn Technol Res Ctr Flat Rolling Equipment Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY》 (国际先进制造技术杂志)

年 卷 期：2024年第130卷第1-2期

页      面：961-972页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China 

主　　题：Cross-section shape Hot rolling Industrial Internet of things platform Dynamic mode decomposition algorithm Sparse identification of nonlinear dynamics 

摘      要：Because strip cross sections cannot be obtained during hot rolling in advance, traditional automatic shape control systems can only rely on the measured shape at the exit of the final mill for feedback control, which causes a significant lag and poor adjustment effect. To accurately predict the cross-sectional shape, an industrial Internet of things platform for steel plants is developed to collect real-time production data. A novel real-time prediction model that can determine the cross sections of strips is proposed to address the drawbacks of traditional data-driven methods that perform offline predictions. This model is established by adopting a dynamic mode decomposition algorithm (DMD) to optimize the sparse identification of nonlinear dynamics (SINDy). A practical dataset of 81 variables from a 2250-mm hot-rolling production line is utilized to validate the proposed method, and this method is compared with SINDy, EMD-optimized SINDy, and VMD-optimized SINDy. The experimental results show that the proposed method can achieve higher prediction accuracy and more minor errors.