MULTIVARIATE STATISTICAL-ANALYSIS OF MINERAL PROCESSING PLANT-DATA

Because of the availability of powerful industrial computers which col lect huge amounts of real-time data in mineral processing plants, ther e is a need for efficient methods to extract relevant information from them. Multivariate statistical techniques, such as principal componen ts analysis (PCA) and projection to latent structures (PLS), are well suited to analyze these large sets of noisy and ill-conditioned data. The power of PCA and PLS is illustrated on historical data from a grin ding and flotation plant. Three hundred and fifty observations of fort y-four process variables are used to show the capacity of these techni ques for preliminary data analysis, classification of operating regime s, process monitoring, and process empirical modelling