Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation

State estimation and detection of measurement systematic errors are critical components of plant monitoring and control procedures. Reliable estimations of the process variables are attained by Classic Dynamic Data Reconciliation procedures when measurements follow exactly a known distribution. Howe...

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Main Authors: Claudia E. Llanos, Mabel C. Sanchez, Ricardo A. Maronna
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2019-05-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/9886
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spelling doaj-14890f73905e4caf8d45a62d4b2781e12021-02-16T21:05:39ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162019-05-017410.3303/CET1974121Dynamic System State Estimation and Outlier Detection Using Robust Data ReconciliationClaudia E. LlanosMabel C. SanchezRicardo A. MaronnaState estimation and detection of measurement systematic errors are critical components of plant monitoring and control procedures. Reliable estimations of the process variables are attained by Classic Dynamic Data Reconciliation procedures when measurements follow exactly a known distribution. However, if this assumption happens approximately due to the presence of systematic errors, as outliers, classic dynamic data reconciliation provides biased results. In this work, a two-step methodology of Robust Dynamic Data Reconciliation and Systematic Error Detection is proposed. It takes advantages of a moving measurement window of fixed dimension and the features of the M-estimators. Furthermore, the presence of outliers is detected using a Robust Measurement Test. Two case studies are proposed, which work with the Huber and Biweigth M-estimators. A nonlinear benchmark extracted from the literature is considered, and performance measures are reported. The results obtained demonstrate the effectiveness of the proposed methodology.https://www.cetjournal.it/index.php/cet/article/view/9886
collection DOAJ
language English
format Article
sources DOAJ
author Claudia E. Llanos
Mabel C. Sanchez
Ricardo A. Maronna
spellingShingle Claudia E. Llanos
Mabel C. Sanchez
Ricardo A. Maronna
Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation
Chemical Engineering Transactions
author_facet Claudia E. Llanos
Mabel C. Sanchez
Ricardo A. Maronna
author_sort Claudia E. Llanos
title Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation
title_short Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation
title_full Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation
title_fullStr Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation
title_full_unstemmed Dynamic System State Estimation and Outlier Detection Using Robust Data Reconciliation
title_sort dynamic system state estimation and outlier detection using robust data reconciliation
publisher AIDIC Servizi S.r.l.
series Chemical Engineering Transactions
issn 2283-9216
publishDate 2019-05-01
description State estimation and detection of measurement systematic errors are critical components of plant monitoring and control procedures. Reliable estimations of the process variables are attained by Classic Dynamic Data Reconciliation procedures when measurements follow exactly a known distribution. However, if this assumption happens approximately due to the presence of systematic errors, as outliers, classic dynamic data reconciliation provides biased results. In this work, a two-step methodology of Robust Dynamic Data Reconciliation and Systematic Error Detection is proposed. It takes advantages of a moving measurement window of fixed dimension and the features of the M-estimators. Furthermore, the presence of outliers is detected using a Robust Measurement Test. Two case studies are proposed, which work with the Huber and Biweigth M-estimators. A nonlinear benchmark extracted from the literature is considered, and performance measures are reported. The results obtained demonstrate the effectiveness of the proposed methodology.
url https://www.cetjournal.it/index.php/cet/article/view/9886
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AT mabelcsanchez dynamicsystemstateestimationandoutlierdetectionusingrobustdatareconciliation
AT ricardoamaronna dynamicsystemstateestimationandoutlierdetectionusingrobustdatareconciliation
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