The simulation and identification of flotation processes by use of a knowledge based model

Abstract This paper illustrates the application of a knowledge based system to perform fault-diagnosis, dynamic simulation and the optimization of batch and continuous flotation processes. The proposed approach is essentially useful to simulate ill-defined dynamic processes where existing fundamental and empirical models fail owing to their lack of generality. A generalized kinetic model is defined and linked to a knowledge base via adjustment objects in order to perform flotation process simulation. The kinetic model and the accompanying knowledge base are also applied to produce the bounds to a generalized linear programming model of a generalized flotation plant to assist in its optimal design. It is shown that the proposed approach could be used to simulate flotation data for batch and continuous operations. In contrast with most existing models, no curve-fitting is required, as the kinetic model utilises experimental data directly. Also, adjustment factors are defined in an intelligent way in the knowledge-based system so as to relate the operating conditions to the kinetics of flotation. In this way, a generalised description of kinetic behaviour is effected. It is shown how the generalized kinetic model is applied to perform fault diagnosis i.e. to identify the process conditions within a flotation cell or bank, and to recommend process conditions which could improve flotation behaviour.