Multivariate Statistical Process Control of a Mineral Processing Industry

Multivariate Statitical Process Control procedures are becoming increasingly popular in process industries due to the need for monitoring a large number of process variables simultaneously. Although extensions to classical univariate control charts such as Shewhart, CUSUM and EWMA to multivariate situations are possible, more recently introduced statistical projection methods such as the Principal Component Analysis (PCA) and Partial Least Squares (PLS) seem to be more suitable for dynamic processes with input-output relationships. These methods not only utilize the product quality data (Y), but also the process variable data (X). This paper gives an overview of the PCA and the PLS methods and their use in monitoring operating performance of a crusher used in a mineral processing plant.