Model Free Adaptive Control of Molten Iron Quality Based on Multi-parameter Sensitivity Analysis and GA Optimization

The silicon content (Chemical heat) and the molten iron temperature (Physical heat) are two important molten iron quality indices of blast furnace ironmaking process, whose modeling and control is of great importance to the operation and optimization of the whole blast furnace ironmaking process. Considering the extremely complicated dynamic characteristics and the puzzle of conventional mechanism modeling and control of the blast furnace ironmaking process, a multi-parameter sensitivity analysis (MPSA) and large-scale mutation genetic parameter optimization based blast furnace molten iron quality model free adaptive control method is proposed based on direct data-driven control theory. First, a multivariable data-driven controller structure for molten iron quality is determined based on compact form dynamic linearization (CFDL) based model free adaptive control (MFAC) technique. Then, considering that it is very time-consuming and less effective to adjust all the CFDL-MFAC adjustable parameters, which have a high influence on the controller performance, a CFDL-MFAC controller parameter tuning method based on large-scale mutation and elite local search genetic optimization is proposed. Finally, applied the parameter-tuned CFDL-MFAC controller into the control of multivariate molten iron quality in the blast furnace ironmaking process and compare it to data-driven predictive control based on recursive subspace identification to verify the effectiveness and advancement of the proposed control method. Copyright © 2021 Acta Automatica Sinica. All rights reserved.