Multivariate statistical monitoring of two-dimensional dynamic batch processes utilizing non-Gaussian information
Yao, Y, Chen, T and Gao, F (2010) Multivariate statistical monitoring of two-dimensional dynamic batch processes utilizing non-Gaussian information Journal of Process Control, 20 (10). pp. 1188-1197.
yyao2010_jpc.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
Dynamics are inherent characteristics of batchprocesses, and they may exist not only within a particular batch, but also from batch to batch. To model and monitor such two-dimensional (2D) batch dynamics, two-dimensionaldynamic principal component analysis (2D-DPCA) has been developed. However, the original 2D-DPCA calculates the monitoring control limits based on the multivariateGaussian distribution assumption which may be invalid because of the existence of 2D dynamics. Moreover, the multiphase features of many batchprocesses may lead to more significant non-Gaussianity. In this paper, Gaussian mixture model (GMM) is integrated with 2D-DPCA to address the non-Gaussian issue in 2D dynamicbatchprocessmonitoring. Joint probability density functions (pdf) are estimated to summarize the information contained in 2D-DPCA subspaces. Consequently, for online monitoring, control limits can be calculated based on the joint pdf. A two-phase fed-batch fermentation process for penicillin production is used to verify the effectiveness of the proposed method.
|Divisions :||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Identification Number :||10.1016/j.jprocont.2010.07.002|
|Additional Information :||NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Process Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Process Control, 20 (10), December 2010, DOI 10.1016/j.jprocont.2010.07.002.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||11 Jun 2012 15:57|
|Last Modified :||23 Sep 2013 19:28|
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