Development of fuzzy rule-based systems for industrial flotation plants by use of inductive techniques and genetic algorithms

Flotation processes are difficult to model at a fundamental level and at present automatic monitoring and control of industrial plants have met with limited success. In practice these processes are most often controlled by human operators who tend to assess the performance of the plant based on their own experience and other heuristic rules. As a result plants are usually not controlled optimally, owing to lack of experience on the part of the operators, human error, etc. Indeed, considerable variation is sometimes observed between different shifts, or during different times of the day. These operational instabilities are considered to play a significant role in the cost-effective operation of flotation plants. For these reasons, some attempts at the development of decision support systems that would aid the operator controlling the plant have recently been made1,2. Although these systems have met with varying degrees of success, it was only with the advent of on-line sensors such as those based on digital image analysis, that effective data-driven development of expert systems could be initiated. For example, Moolman et al.3 have shown that by making use of computer vision systems, it is possible to characterize the performance of flotation plants with a high degree of accuracy. Other investigations have since followed4–7 and several commercial systems have been developed in the last few years. These systems typically make use of features extracted from digitized images of the structure of the flotation froths. Although these features are representative of the behaviour of the process, the data still have to be interpreted by an expert prior to incorporation into a decision support system. Unfortunately experts such as these are in short supply and even when available, it is doubtful whether they would be able to interpret all the subtle nuances that complex plants can exhibit. Moreover, different experts also exhibit cognitive biases such as overconfidence and over-simplification8,9. It could therefore take months, if not years, to develop an accurate, comprehensive expert system for a flotation plant and could require a large capital investment. This paper describes the design of a fuzzy logic decision support system for use in controlling industrial flotation plants. Rules obtained through probabilistic induction, using both froth features and physical plant parameters as input attributes, and grade of the floated minerals as the classification output, are fuzzified and incorporated into a fuzzy expert system shell, Fuzzy-CLIPS, together with heuristic rules obtained from a flotation domain expert. A description of the induction algorithm, used to obtain the classification rules, is given. The methodology of rule fuzzification and optimization is discussed. Thereafter the techniques are Development of fuzzy rule-based systems for industrial flotation plants by use of inductive techniques and genetic algorithms