Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS

Abstract

Abstract Multivariate statistical procedures for the analysis and monitoring of batch processes have recently been proposed. These methods are based on multiway principal component analysis (PCA) and partial least squares (PLS), and the only information needed to exploit them is a historical database of past batches. In this paper, these procedures are extended to allow one to use not only the measured trajectory data on all the process variables and information on measured final quality variables but also information on initial conditions for the batch such as raw material properties, initial ingredient charges and discrete operating conditions. Multiblock multiway projection methods are used to extract the information in the batch set-up data and in the multivariate trajectory data, by projecting them onto low dimensional spaces defined by the latent variables or principal components. This leads to simple monitoring charts, consistent with the philosophy of SPC, which are capable of tracking the progress of new batch runs and detecting the occurrence of observable upsets. Powerful procedures for diagnosing assignable causes for the occurrence of a fault by interrogating the underlying latent variable model for the contributions of the variables to the observed deviation are also presented. The approach is illustrated with databases from two industrial batch polymerization processes.