Simulation of Monitoring and Diagnosis of Flotation Columns Operation Using Projection Techniques

On line monitoring and diagnosis systems have become important part of industrial programs leading to improve process operation and therefore product quality over time. Today, great amount of process variables are routinely collected at high frequency by Distributed Control Systems (DCS). Highly correlated process input variables, low signal/noise ratios and missing data are some of the main difficulties found in modelling the process for monitoring and diagnosis purposes. Multivariate statistical projection methods, such as Principal Component Analysis (PCA) have been proposed to effectively deal with these situations. The development of a simulator to illustrate the application of these ideas to a flotation process is discussed here. In this work, the use of these projection tools in the detection of relevant variables presenting measurement problems, and in the identification of the set of variables responsible for driving the process outside its normal operating region, is demonstrated, giving considerable information for the correction of the operating problem.