Association Rules Mining Approach to Mineral Processing Control

Mineral processes are multi-variable and power-intensive, strongly coupled with large delay and nonlinearities. The properties of controllability, observability and theory of minimal realization for linear systems are well understood and have been very useful in analyzing such systems. This paper deals with analogous questions for nonlinear systems with application to mineral processing. In many practical situations, standard asymptotic theory provides poor approximations, such as when the number of parameters is large or when the parameters are weakly identified. In this paper, a method that can control and provide accurate prediction of optimum milling condition and power consumption, water and chemical additive requirement is developed for mineral plants operation. A fuzzy mining algorithm is proposed for extracting implicit generalized knowledge on grading process performance as qualitative values. It integrates fuzzy-set concepts and generalized data mining technologies to achieve this purpose. Using a generalized similarity transformation for the error dynamics, simulation results show that under boundedness conditions the proposed approach guarantees the global exponential convergence of the error estimation. Although the nominal performance of the process is improved, the robust stability still is not guaranteed to fully avoid the mill plugging.