On-Line Identification and Classification of Grinding Mill Behaviour and Optimising Trajectories (a8358ca6-74b2-41ef-a3eb-33764a872971)

"To remain competitive in an open market, mining companies are more and more requested to optimise their complex and highly multivariate processes. The increasing amount of process data, brought about by intensive use of instrumentation and the progress in computer data acquisition, enhances the need for more and more powerful and robust tools for the analysis and modelling of the process data. Now, with the emergence of multivariate data analysis (MDA) techniques, on-line clustering of mineral process behaviour is possible and could help in the extraction of valuable process operation indicators. MDA can be used in different mineral processes such as flotation, grinding, smelting etc.In this paper, we focus on grinding process considered as the most costly and power consuming node of the mineral processing scheme. Different objective-oriented levels of data clustering will be addressed.First, the evaluation of grinding operation taking into account data and constraints from all processes before (e.g. blasting, crushing) and after (e.g. flotation, leaching, etc.) the grinding node. The second level deals with process diagnosis by differentiating operation level and identifying behaviour drifts. Control and optimisation are two other levels concerned here with the selection of optimum trajectories in the drift correct.Industrial plant data are processed in order to illustrate the feasibility and the potentiality of MDA applications in addressing the clustering and interpretation of grinding mill behaviour."