Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification

Control of blast furnace (BF) ironmaking process has always been a hot and difficult issue in metallurgic engineering and automation. In this paper, a novel data-driven inverse system identification based predictive control method is proposed for multivariate molten iron quality (MIQ) indices in BF ironmaking process. First, since the widely used least-square support regression (LS-SVR) algorithm cannot cope with the multi-output problem directly, this paper uses multi-task transfer learning technology to construct a novel multi-output LS-SVR (M-LS-SVR) for multivariable nonlinear systems. Then, this M-LS-SVR is adopted to identify the inverse system model of the controlled BF ironmaking process with the help of the presented modeling performance comprehensive evaluation and NSGA II based multi-objective parameter optimization. In order to better perform the control of the MIQ indices, the identified inverse system model is used to compensate the controlled nonlinear BF system to a compounded pseudo linear system with linear transitive relation. Such an inverse system based data-driven predictive control can effectively improve the control performance of the conventional nonlinear predictive control. Data experiments using actual industrial data from a large BF show that the proposed methods are effective, advanced and practical, and provide a solution to the operational control and optimization of the BF ironmaking process.