Revista de pulvimetalurgia y minería

Abstracto

A neural Network-Based Expert Control System for the Electrolytic Process in Zinc Hydrometallurgy

Nakano Michi

The electrolytic process, which involves passing an electrical current through insoluble electrodes to cause the breakdown of an aqueous zinc sulfate electrolyte and the deposition of metallic zinc at the cathode, is the final step in zinc hydrometallurgy. The electrolyte concentrations of zinc and sulfuric acid are the most critical control parameters for the investigated electrolytic process. Using neural networks, rule models, and a single-loop control scheme, this paper describes an expert control system for determining and tracking the ideal concentrations of zinc and sulfuric acid. In a hydrometallurgical zinc plant, the system is currently being used to control the electrolytic process. In this paper, the framework design, which includes a specialist regulator and three single-circle regulators, is first made sense of. The chemical reactions involved, empirical knowledge, and statistical data on the procedure are then used to construct neural networks and rule models. Then, at that point, the master regulator for deciding the ideal focuses is planned utilizing the brain organizations and rule models. The three single-circle regulators utilize the PI calculation to follow the ideal focuses. At last, the aftereffects of real runs utilizing the framework are introduced. They demonstrate that the system provides significant economic advantages in addition to high-purity metallic zinc.