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• W2069620465 abstract "A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine the Gaussian mixture model (GMM) with hybrid unfolding of a multiway data matrix to partition all the sampling points into different clusters. Then, two sequential cluster alignments are used to adjust clusters so that each of them only contains consecutive sampling instants, and all the training batches at the same sampling time belong to the same cluster. The identified multiple clusters correspond to different operation phases in the batch process. Further, a localized probability index is defined to examine each sampling point of a monitored batch relative to its corresponding operation phase. Subsequently, the occurrence and duration of process faults can be detected in this way. The proposed batch monitoring approach is applied to a simulated penicillin fermentation process and compared with the conventional multiway principal component analysis (MPCA). The comparison of monitoring results demonstrates that the multiphase based approach is superior to the global MPCA method in detecting different types of faults in batch processes with a much higher detection rate and fault sensitivity." @default.
• W2069620465 created "2016-06-24" @default.
• W2069620465 creator A5056937548 @default.
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• W2069620465 date "2009-08-03" @default.
• W2069620465 modified "2023-10-18" @default.
• W2069620465 title "Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring" @default.
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• W2069620465 doi "https://doi.org/10.1021/ie900479g" @default.
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