Performance monitoring of processes with multiple operating modes through multiple PLS models

Many industrial processes possess multiple operating modes in virtue of different manufacturing strategies or varying feedstock. Direct application of many of the current multivariate statistical process monitoring (MSPM) techniques such as PCA (principal component analysis) and PLS (projection to latent structures) to such a process tends to produce inferior performance. This can most be attributed to the adopted assumption by most MSPM methodologies of only one nominal operating region for the underlying process. It is therefore reasonable to develop separate models for different operating modes. In this paper, based on metrics in the form of principal angles to measure the similarities of any two models, a multiple PLS model based process monitoring scheme is proposed. Popular multivariate statistics such as SPE (squared prediction error) and T2 can be incorporated in this framework straightforwardly. The proposed technique is assessed through application to the monitoring of an industrial pyrolysis furnace.

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