2004
DOI: 10.1002/aic.10147
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Multivariate monitoring of batch processes using batch‐to‐batch information

Abstract: Multiway principal component analysis (MPCA) and multiway partial-least squares (MPLS) are well-established methods for the analysis of historical data from batch processes, and for monitoring the progress of new batches. Direct measurements made on prior batches can also be incorporated into the analysis by monitoring with multiblock methods. An extension of the multiblock MPCA/MPLS approach is introduced to explicitly incorporate batch-to-batch trajectory information summarized by the scores of previous batc… Show more

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Cited by 52 publications
(24 citation statements)
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“…As an example, consider the case where common source of raw materials is being used for successive batches, and the materials from this source have some characteristics (for example, impurity concentrations, surface chemistry properties, and so on) which change slowly with time. Details on this approach can be found in (62).…”
Section: Batch To Batch Monitoringmentioning
confidence: 99%
“…As an example, consider the case where common source of raw materials is being used for successive batches, and the materials from this source have some characteristics (for example, impurity concentrations, surface chemistry properties, and so on) which change slowly with time. Details on this approach can be found in (62).…”
Section: Batch To Batch Monitoringmentioning
confidence: 99%
“…Martin et al581 reviewed the M 2 statistic (an alternative to the Hotelling T 2 statistic) and applied it to a batch MMA polymerization process. Other studies that have used multivariate statistical approaches were presented by Rannar et al,582 who used a multiblock PCA and PLS algorithm; Flores‐Cerrillo and MacGregor,583–586 who proposed multivariate approaches to predict particle size in emulsion polymerization reactors; Kumar et al587 for high‐pressure polymerization reactors; Albazzaz and Wang588 in the semi‐batch polymerization of polyol; Chen and Liu,589 who used dynamic PCA and PLS models; Duchesne et al590 who used PCA and PLS for the analysis and monitoring of process transitions (process start‐ups and restarts), and Sharmin et al544 who used PLS to develop a soft sensor to predict MFI.…”
Section: Other Topics Related To Sensorsmentioning
confidence: 99%
“…[20][21][22][23][24] A successful industrial application is presented in Yabuki et al 25 Given these computed optimal score adjustments, the manipulated variables (single variables or trajectories) for the remainder of the batch can then be calculated as given in Equations (2)-(4) in Section 2.10.4.…”
Section: Control Of Batch Processesmentioning
confidence: 99%