Dynamic modeling and optimal control of goethite process based on the rate-controlling step

The iron removal process is an important technology in zinc hydrometallurgy. Due to its complicated reaction mechanism, it is difficult to highly control the performance of process solely relying on manual experience. Therefore, this paper focuses on the dynamic modeling for the goethite process and its optimal control method. In different reactors, different operating conditions will influence the rate-controlling step of process reactions, as well as the kinetic model. We determine the rate-controlling step of reactions in each reactor according to the reaction conditions, and subsequently the dynamic model has been developed based on the rate-controlling step. Then an optimal control method for the goethite process has been proposed, which satisfies the technical requirements with minimal process consumption. The proposed optimal control includes pre-setting of descent gradient of outlet ferrous ion concentration and optimal control of oxygen and zinc oxide. The simulation results demonstrate that the proposed dynamic model exhibits greater performance comparing with the process model without considering rate-controlling step, and the proposed control strategy has a higher satisfactory than the nonlinear model predictive control. Then, the proposed optimal control is validated by the industrial experiment, which the average oxygen and zinc oxide consumptions decreased by 568 m(3)/day (6.22%) and 3.09 t/day (5.16%) and the qualified rate of outlet ferrous ion concentrations increased by 5.5%, compared with the manual control.