Hybrid modeling and distributed optimization control method for the iron removal process

Iron removal by goethite is a key procedure in zinc hydrometallurgy. Due to its complex chemical reaction mechanism, multiple reactors cascade, and high uncertainty, it is difficult to optimize and control in industrial process. In this paper, a distributed optimization control method which is based on a novel hybrid model of the iron removal process is proposed. By combining the mechanism model and a data-driven oxygen mass transfer coefficient model, a hybrid model is first established. Then, to overcome the influence of the former reactor on the latter reactor, the ratio of the status in each subsystem to the set point is taken as a new status, and a distributed optimization control problem is constructed. Considering the high dimensionality of this problem, it is necessary to reconstruct it by Virtual Motion Camouflage (VMC), so that the optimal control problem is transformed into a nonlinear constrained optimal trajectory planning problem. And a Legendre pseudo spectral method is used to solve the problem accurately to obtain the optimal trajectory of the ion concentration. Finally, simulation results show that the proposed method can effectively reflect the industrial process, and track the fluctuation of inlet ion concentrations with a nice real-time performance.