Restrictions on MMM Industrial Data to Build PCA Models

Mining, mineral and metal (MMM) processes present by its nature (multiphase, with solid particle properties distribution, high temperature) difficult scenarios to obtain high quality measurements of key variables. Furthermore, the characteristics of the feed are almost always changing over time, upsetting the process and giving a hard time to the stabilizing controllers. Then steady state operation condition is seldom met. Even more, some key measurements as metal grades and particle size are usually sparsely obtained with complex procedures involving sampling handling and correlations methods. All this characteristics put a lot of pressure on maintenance procedures of installed instrumentation. In summary, there are plenty of opportunities that sets of observations collected from a data base may contain all kind of pitfalls. Multivariate statistics can provide us with very powerful tools to analyze large set of data, and to efficiently extract the relevant information. However, the set of data must contain this information with the less degree of confusion as possible, for example as the result of a designed experiment.In this work, the application of these methods to smelters and flotation plants data are discussed. Special emphasis is put on how the previous work to assure the quality of the data, used in building such models, plays an important role on the success or failure of a powerful methodology.