Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method

Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring... The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data‐driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c‐means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab‐scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Chemical Engineering & Technology (Cet) Wiley

Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
ISSN
0930-7516
eISSN
1521-4125
D.O.I.
10.1002/ceat.201600645
Publisher site
See Article on Publisher Site

Abstract

The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data‐driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c‐means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab‐scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods.

Journal

Chemical Engineering & Technology (Cet)Wiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

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