A variable relevant multi-local PCA modeling scheme to monitor a nonlinear chemical process

In chemical plants, operated processes require different conditions to produce various product grades and meet the time-to-market demand. Conventional multivariate statistical process control techniques based on a single global model are certainly poor. In the past, data were partitioned first without considering variable correlations before each local model was constructed. This may cause information loss. This study proposes a multi-local principal component analysis (ML-PCA) modeling strategy to monitor a non -linear process over a large operating region. ML-PCA can automatically enhance the data of the clustered model and weaken those data of the other local models. The operating data are collected from the whole range of operations to cluster and construct the corresponding PCA for each operating condition simul-taneously. Once ML-PCA models are constructed, two statistical indices are designed for process monitor -ing. The effectiveness and accuracy of the proposed ML-PCA are demonstrated through a numerical example and a real industrial process. (c) 2021 Published by Elsevier Ltd.

Automated summary

The study proposes a multi-local principal component analysis (ML-PCA) modeling strategy to monitor a non-linear process over a large operating region. ML-PCA automatically enhances the data of the clustered model and weakens those data of the other local models, demonstrating improved accuracy and effectiveness in process control.