Abstract
Mineral processing plants show a lot of difficulties as control objects. As they involve many variables, non-linearity and large disturbances, they can hardly be mathematically modeled, which is required for application of deterministic control theories. It is therefore hard to control those plants by the conventional, especially linear control algorithms. The authors have therefore conducted a series of studies on feasibility of the fuzzy logic control in mineral processing. As a result, the fuzzy logic control yielded sufficient control performances. Usually a trial and error method, however, must have been practiced in fuzzy logic control design to optimize the controller parameters, such as scaling factors, fuzzy rule tables and so on. The authors intended in this paper to develop a way to automate a part of the above procedure by applying the genetic algorithm to optimize the scaling factors. The genetic algorithm is a unique optimum-seeking method that comes from an evolution process, and has recently been highlighted in different engineering areas. It is the algorithm to search the optimal value by mapping the search space structure into the dynamics of the evolution of the living beings. The target control system for this study is concentration control system based on actual resources processing plant. From the results of the computer simulations, it has been demonstrated that this system can determine the fuzzy controller scaling factors automatically, and can be an effective method for a control system design of mineral processing plants.
