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Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method
Author(s) -
Liu Yi,
Liang Yu,
Gao Zengliang,
Yao Yuan
Publication year - 2018
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201600645
Subject(s) - principal component analysis , cluster analysis , data mining , flooding (psychology) , probabilistic logic , computer science , multivariate statistics , bayesian probability , component (thermodynamics) , engineering , artificial intelligence , machine learning , psychology , physics , psychotherapist , thermodynamics
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.