Engineering optimization approaches of nonferrous metallurgical processes

Abstract

The engineering optimization approaches arising in nonferrous metallurgical processes are developed to deal with the challenges in current nonferrous metallurgical industry including resource shortage, energy crisis and environmental pollution. The great difficulties in engineering optimization for nonferrous metallurgical process operation lie in variety of mineral resources, complexity of reactions, strong coupling and measurement disadvantages. Some engineering optimization approaches are discussed, including operational-pattern optimization, satisfactory optimization with soft constraints adjustment and multi-objective intelligent satisfactory optimization. As an engineering optimization case, an intelligent sequential operating method for a practical Imperial Smelting Process is illustrated. Considering the complex operating optimization for the Imperial Smelting Process, with the operating stability concerned, an intelligent sequential operating strategy is proposed on the basis of genetic programming (GP) adaptively designed, implemented as a multi-step state transferring procedure. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution with compact individuals. The optimal solution gained by evolution is a sequential operating program of process control, which not only ensures the tendency to optimization but also avoids violent variation by operating the parameters in ordered sequences. Industrial application data are given as verifications.