One important step in zinc hydrometallurgy is the leaching process, which involves the dissolving of zinc-bearing material in dilute sulfuric acid to form a zinc sulfate solution. The key point in the control of the process is to determine the optimal pHs of the overflows of the continuous leach process and track them. This paper describes a model-based expert control system for the leaching process, which is being used in nonferrous metals smeltery. Specifically, steady-state mathematical models and rule models are first constructed based on the chemical reactions involved, the empirical knowledge of engineers and operators, and empirical data of the process. Then, a methodology is proposed for determining and tracking the optimal pHs with an expert control strategy based on a combination of mathematical models and rule models of the process. The results of actual runs show that the proposed control strategy is an effective way to control the leaching process.

This paper concerns a real-world application of an expert system to the automation of a zinc hydrometallurgy plant. The leaching process in zinc hydrometallurgy involves dissolving zinc-bearing material in dilute sulfuric acid to form a zinc sulfate solution. The key problems are to determine and track the optimal pHs of the overflows of the neutral and acid leaches, and to ensure the safe running of the process. This paper describes an expert control and fault diagnosis scheme that solves those problems. The expert control is based on a combination of steady-state mathematical models and rule models, and the fault diagnosis employs rule models with certainty factors and a Bayes representation. A real-world application of this scheme showed that it not only improved the control performance, but also correctly diagnosed faults.

The final step in zinc hydrometallurgy is the electrolytic process, which involves passing an electrical current through insoluble electrodes to cause the decomposition of an aqueous zinc sulfate electrolyte and the deposition of metallic zinc at the cathode. For the electrolytic process studied, the most important process parameters for control are the concentrations of zinc and sulfuric acid in the electrolyte. This paper describes an expert control system for determining and tracking the optimal concentrations of zinc and sulfuric acid, which uses neural networks, rule models and a single-loop control scheme. The system is now being used to control the electrolytic process in a hydrometallurgical zinc plant. In this paper, the system architecture, which features an expert controller and three single-loop controllers, is first explained. Next, neural networks and rule models are constructed based on the chemical reactions involved, empirical knowledge and statistical data on the process. Then, the expert controller for determining the optimal concentrations is designed using 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 system are presented. They showthat the system provides not only high-purity metallic zinc, but also significant economic benefits.

A distributed expert control system (DECSHZ) has been built for a hydrometallurgical zinc process, whose basic steps arc leaching, purification and electrolysis. It consists of a central computer management system and three local expert control systems, one for each of the basic steps. This paper deals with the design and application of DECSHZ, especially its distributed architecture and main functions: expert control strategies based on rule models and a combination of rule models and steady-state mathematical models: system implementation; and the results of actual runs. DECSHZ has been found to provide not only a very pure product, but also significant economic benefits.

(t)=Ax(t)+A1x(t −h1(t))+B1u(t)+B2u(t −h2(t)). In this paper, a new integral inequality for quadratic terms is first established. Then, it is used to obtain a new state- and input-delay-dependent criterion that ensures the stability of the closed-loop system with a memoryless state feedback controller. Finally, some numerical examples are presented to demonstrate that control systems designed based onthe criterionare effective, eventhough neither (A,B1) nor (A+A1,B1) is stabilizable.