Data-based methods for process analysis, monitoring and control

This paper gives an overview of methods for utilizing the massive amounts of highly correlated data available in most process databases. These data matrices are almost always of less than full statistical rank, and therefore latent variable methods are shown to be well suited to obtaining useful subspace models for treating a variety of important industrial problems. The following problems are discussed and illustrated with industrial examples: (i) the analysis of historical databases and trouble-shooting process problems; (ii) process monitoring and FDI; (iii) building soft sensors or inferential models; (iv) using of multivariate information from novel sensors; (v) subspace identification; and (vi) process control in reduced dimensional subspaces. In each of these problems latent variable models provide the framework on which solutions are based.