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.

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.

This paper concerns the problem of the delay-dependent robust stability of neutral systems with mixed delays and time-varying structured uncertainties. A new method based on linear matrix inequalities is presented that makes it easy to calculate both the upper stability bounds on the delays and the free weighting matrices. Since the criteria take the sizes of the neutral-and discrete-delays into account, it is less conservative than previous methods. Numerical examples illustrate both the improvement this approach provides over previous methods and the reciprocal in; uences between the neutraland discrete-delays.

(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.

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.