Multiobjective-Based Optimization and Control for Iron Removal Process Under Dynamic Environment

Abstract

Industrial processes often operate in the complex dynamic environment. They challenge the cost-effective and reliable operation of industrial processes. This article proposes a multiobjective optimization and control strategy for the iron removal process to be optimally operated in the dynamic environment. In the optimization layer, multiobjective optimization and local optimization problems, minimizing the process cost and maximizing economic index, are developed to obtain the optimal set-points of the outlet ferrous ion concentrations. In the control layer, model predictive control based on the system dynamic model is constructed to control the outlet ferrous ion concentrations to track the set-points. To reduce the influences from dynamic disturbances and improve the tracking performance, we use least squares support vector machine (LSSVM) to establish the correction model. Fuzzy-logic-based compensation is proposed to compensate the set-points according to the priori and posteriori information. Finally, simulations using real-world plant data are carried out to verify the effectiveness of the proposed control strategy. The simulation results demonstrate that the proposed control strategy achieves a satisfactory tracking performance with less process consumption. It improves the iron content in the goethite precipitate, which has more commercial value. The proposed control strategy can make the plant more profitable.