An expert control strategy using neural networks for the electrolytic process in zinc hydrometallurgy

The final step in zinc hydrometallurgy is the electrolytic process. The most important parameters to control the the concentrations of zinc and sulfuric acid in the electrolyte. This paper proposes an expert control strategy for determining and tracking the optimal concentrations, which uses neural networks, rule models and a single-loop control scheme. First, the process is described and the strategy that features an expert controller and three single-loop controllers is explained. Next, neural networks and rule models are constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations is designed through a combination of the neural networks and rule models. The three single-loop controllers use the PI algorithm to track the optimal concentrations. Finally, the results of actual runs using the strategy are presented. They show that the strategy provides not only high-purity metallic zinc, but also significant economic benefits.