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.
Published Version
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