Expert control of grinding and flotation plants has been successfully used in the minerals industry since the 1970’s.

The earliest of these systems were written in a hard-coded fashion in FORTRAN, BASIC or Pascal. Second generation systems were built using the first experimental expert system shells that were being developed in the artificial intelligence community. Later systems were deployed in systems designed for real-time processing plants that also include the ability to model the process with neural network models and optimize setpoint selection through the use of genetic algorithms.

Significant performance increases have been achieved using these systems but in general they suffer from the static nature of their rules and to a degree the process models. There is an opportunity to further increase system performance by systematically taking advantage of the tremendous amount of data produced by the expert system to improve the design, the heuristic rules, the model topologies and the use of the models.

“Data mining refers to extracting or mining knowledge from large amounts of data.” “The objective is to automatically analyze the data, automatically classify it, automatically summarize it to automatically discover and characterize trends in it and to automatically flag anomalies.” [1]

Clearly, modern process control systems used in the minerals industry are capable of collecting vast amounts of data. Without a doubt, these data contain important information on the operation of our plants and their ultimate optimization. Coupling the information mined from these data with Expert Control Systems should produce more effective control and greater knowledge of the grinding process and the flotation process.