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Fault detection and diagnosis for distillation column using two‐tier model
Author(s) -
Tian WenDe,
Sun SuLi,
Guo QingJie
Publication year - 2013
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.21795
Subject(s) - distillation , fractionating column , fault detection and isolation , nonlinear system , fault (geology) , process (computing) , stripping (fiber) , linear model , computer science , engineering , chemistry , chromatography , machine learning , physics , quantum mechanics , seismology , geology , operating system , electrical engineering
In this paper, a two‐tier model‐based fault detection and diagnosis method for a distillation column is developed. It employs the nonlinear model developed earlier to monitor the distillation process and a corresponding linear model to identify an abnormal source when large deviations of measured values occur. The inner distillation fault parameters are estimated through linear least‐square method based on the linear model. The proposed method is applied to the stripping tower in the Tennessee Eastman process simulator. Case studies demonstrate that the two‐tier diagnosis structure effectively captures the variation of fault parameters and is more efficient than a pure nonlinear model‐based structure.