On-line monitoring of batch processes using multiway independent component analysis
SCIE
SCOPUS
- Title
- On-line monitoring of batch processes using multiway independent component analysis
- Authors
- Yoo, CK; Lee, JM; Vanrolleghem, PA; Lee, IB
- Date Issued
- 2004-05-28
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- Batch processes play an important role in the production of low-volume, high-value products such as polymers, pharmaceuticals, and biochemical products. Multiway principal component analysis (MPCA), a multivariate projection method, has been widely used to monitor batch processes. But in-control data of non-stationary processes in fact contain inherent non-Gaussian distributed data due to ramp changes, step changes. and even weak levels of autocorrelation. Monitoring charts obtained by applying MPCA to such non-Gaussian data may contain nonrandom patterns corresponding to the data characteristics. To obtain better monitoring performance in a batch process with non-Gaussian data, on-line batch monitoring method with multiway independent component analysis (MICA) is developed in this paper. MICA is based on a recently developed feature extraction method, called independent component analysis (ICA), whereas PCA looks for Gaussian components. whereas ICA searches for non-Gaussian components. MICA projects the multivariate data into a low-dimensional space defined by independent components (ICs). When the measured variables have non-Guassian distributions, MICA provides more meaningful statistical analysis and on-line monitoring compared to MPCA because MICA assumes that the latent variables are not Gaussian distributed. The proposed method was applied to the on-line monitoring of a fed-batch penicillin production. The simulation results demonstrate the power and advantages of MICA. (C) 2004 Elsevier B.V. All rights reserved.
- Keywords
- fault detection and diagnosis; multiway independent component analysis (MICA); multiway principal component analysis (MPCA); on-line batch process monitoring; FERMENTATION; SUPERVISION; CHARTS
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/17916
- DOI
- 10.1016/j.chemolab.2004.02.002
- ISSN
- 0169-7439
- Article Type
- Article
- Citation
- CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, vol. 71, no. 2, page. 151 - 163, 2004-05-28
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