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For on-line batch process monitoring, multiway principal component analysis (MPCA) is a useful tool. But the MPCA-based methods suffer two disadvantages: (i) it restricts itself to a linear setting, where high-order statistical information is discarded; (ii) all the measurement variables must follow Gaussian distribution and the objective of MPCA is only to decorrelate variables, but not to make them independent. To improve the ability of batch process monitoring, this paper proposes a monitoring method named multiway kernel independent component analysis (MKICA). By using kernel trick, the new monitoring indices are investigated, which have been mapped into high-dimensional feature space. On the benchmark simulator of fed-batch penicillin production, the presented method has been validated. [ABSTRACT FROM AUTHOR]

As a valid statistical tool, principal component analysis (PCA) has been widely used in industrial process monitoring. But due to its intrinsic linear character, it performs badly in nonlinear process monitoring. Kernel PCA (KPCA) can extract useful information in nonlinear data. However KPCA-based monitoring is not suitable for on-line monitoring because of large calculation and much memory occupation. The paper introduces an on-line monitoring method based on KPCA and neural network (NN), where KPCA is used to extract nonlinear principal components (PCs) and then NN approximates the relationship between process data and nonlinear PCs. We can obtain nonlinear PCs by NN to compute the monitoring indices and then achieve the on-line monitoring. The case study shows the validity of the method. [ABSTRACT FROM AUTHOR]

Conventional PCA-based monitoring method relies on the assumption that process data is normally distributed, which the actual industrial processes often don't satisfy. Instead, mixture probabilistic principal component analysis (MPPCA) models are suitable to process with any probability density function. But, it suffers a drawback that the needed charts are too many to be watched in practice while the number of sub-models in MPPCA is large. Different from existing MPPCA, this paper proposes a novel method, which integrates every monitoring chart of MPPCA models into only one chart via probability and field process monitoring can rely on just one chart. The application in real chemical separation process shows validity of the proposed method. [ABSTRACT FROM AUTHOR]