Multivariate statistical process monitoring of an industrial polypropylene catalyzer reactor with component analysis and kernel density estimatione

Data-driven tools, such as principal component analysis (PCA) and independent component analysis (ICA) have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latent variables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To extend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation (KDE) is rather a good choice. Though ICA is based on non-Gaussian distribution information, KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA with KDE (KPCA), and ICA with KDE (KICA), are demonstrated and compared by applying them to a practical industrial Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.