Weighted-coupling CSTR modeling and model predictive control with parameter adaptive correction for the goethite process

The goethite process is a complicated process with multiple interactive chemical reactions in zinc hydrometallurgy. The use of a dynamic model plays an important role in predicting the key indicator on-line and in process control and optimization. However, because of the coupling influences among the chemical reactions, the conventional continuous stirred tank reactor (CCSTR) model is not adequate to describe this process. In this paper, we develop a weighted-coupling CSTR (WCCSTR) model for the goethite process by introducing weighted parameters. A parameter identification method is proposed to determine the unknown parameters. Then, a model predicted control (MPC) scheme is designed to achieve the process performance goals and minimize the process cost. To overcome the impact of frequent fluctuations in production conditions on the control performance, a novel parameter adaptive correction approach is proposed. The convergence of the adaptive correction approach is proved based on Lyapunov stability theory. Simulation results verify that the WCCSTR model has a higher prediction accuracy than the CCSTR model. The experimental results demonstrate that the MPC scheme performs better in controlling the process and reducing the process costs.