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.

This paper deals with the problem of delay-dependent robust stability for systems with time-varying structured uncertainties and time-varying delays. Some new delay-dependent stability criteria are devised by taking the relationship between the terms in the Leibniz-Newton formula into account. Since free weighting matrices are used to express this relationship and since appropriate ones are selected by means of linear matrix inequalities, the new criteria are less conservative than existing ones. Numerical examples suggest that the proposed criteria are e;ective and are an improvement over previous ones.

Two important aspects of the control of the coal blending process in the iron and steel industry are computation of the target percentage of each type of coal to be blended and the blending of the different types in the target percentages. This paper proposes an expert control strategy to compute and track the target percentages accurately. First, neural networks, mathematical models and rule models are constructed based on statistical data and empirical knowledge on the process. Then a methodology is proposed for computing the target percentages that combines the neural networks, mathematical models and rule models and uses forward chaining and model-based reasoning. Finally, the tracking control of the target percentages is carried out by a distributed PI control scheme. The expert control strategy proposed is implemented in an expert control system that contains an expert controller and a distributed controller. The results of actual runs show that the proposed expert control strategy is an effective way to control the coal blending process.

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.

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.