Multivariate linear-regression variable parameter spatio-temporal zoning model for temperature prediction in steel rolling reheating furnace. (March 2023)

• Record Type: Journal Article
• Authors: Bao, Qingfeng; Zhang, Sen; Guo, Jin; Li, Zhiqiang; Zhang, Zhenquan
• Is Part Of: Journal of process control. Volume 123(2023)
• Page Start: 108
• Page End: 122
• Publication Date: 2023-03
• Subjects: Multivariate linear-regression -- Variable parameter spatio-temporal model -- Temperature prediction -- Least-square-matrix-block algorithm -- Steel rolling reheating furnace; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2023.01.013
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In the steel industry, temperature control and prediction of billets during heating in steel rolling reheating furnace (SRRF) can guarantee product performance and save energy consumption. However, the nonlinear time-varying (NTV) and distributed of SRRF are a great challenge for the accurate temperature control of billets. It is important to predict and regulate the temperature of the SRRF online and to identify the causal variables that may lead to temperature variations. In this paper, a new multivariate linear regression variable parameter spatio-temporal (MLR-VPST) zoning model approach is proposed to predict the temperature. Firstly, a MLR-VPST zoning model method is proposed to predict the temperature of the SRRF, which can handle the distribution characteristics of the temperature field and the NTV characteristics. Secondly, a new least squares matrix block (LSMB) algorithm is proposed to obtain batch parameter solutions, which can improve the dynamic and temperature prediction accuracy. Thirdly, a zoning model approach is proposed to describe the degree of fitting of the SRRF model in each zone, and the linear correlation is gradually increasing from the reheating zone to the soaking zone. It makes the temperature of the soaking zone more accurate. Simulation results of real-time data from steel company show that it meets the requirements of the rolling process. Highlights: The paper analyzes the causes of temperature change of SRRF. A MLR-VPST zoning model approach is proposed to predict the temperature of the SRRF. Its advantage is that it can deal with the distributed characteristics of temperature field and the characteristics of nonlinear time-varying (NTV). A new least-squares-matrix-block (LSMB) algorithm is proposed to obtain batch parameter solutions to enhance the dynamics and temperature prediction accuracy of the SRRF model. A zoning model method is proposed to describe the fitting degree of the SRRF model in each zones, and the linear correlation is gradually increasing from reheating furnace zone to soaking zone. It can make the temperature of soaking zoning more accurate in the SRRF. The simulation results of real-time data of iron and steel company demonstrate that it meets the requirements of rolling process.