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Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns
Author(s) -
Liu Yi,
Hseuh Bo-Fan,
Gao Zengliang,
Wong David Shan Hill,
Yao Yuan
Publication year - 2019
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.201800435
Subject(s) - flooding (psychology) , nonparametric statistics , heteroscedasticity , autoregressive model , pressure drop , drop (telecommunication) , control chart , chart , parametric statistics , engineering , computer science , mathematics , process (computing) , mechanics , statistics , physics , mechanical engineering , psychology , psychotherapist , operating system
Abstract In the chemical industry, real‐time flooding prognosis is a necessity for packed‐column operation because the flooding phenomenon interferes with the performance of production systems. In this work, the profile monitoring technique is utilized to capture the dynamic behavior of pressure drop, which is an important indicator for the flooding phenomenon. In each moving window, the pressure drop signals are described by using an exponential generalized autoregressive conditional heteroskedastic model. The onset of the flooding phenomenon is then indicated by changes in model parameters. Moreover, to efficiently capture the process change, a nonparametric approach is utilized to establish a statistical control chart. Experimental and comparison results show the advantages of the proposed method.