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Ozone measurements monitoring using data-based approach

Article dans une revue avec comité de lecture
Author
HARROU, Fouzi
104346 King Abdullah University of Science and Technology [Saudi Arabia] [KAUST]
KADRI, Farid
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
KHADRAOUI, Sofiane
350478 American University of Sharjah
SUN, Ying
104346 King Abdullah University of Science and Technology [Saudi Arabia] [KAUST]

URI
http://hdl.handle.net/10985/16789
DOI
10.1016/j.psep.2016.01.015
Date
2016
Journal
Process Safety and Environmental Protection

Abstract

The complexity of ozone (O 3 ) formation mechanisms in the troposphere makes the fast and accurate modeling of ozone very challenging. In the absence of a process model, principal component analysis (PCA) has been extensively used as a data-based monitoring technique for highly correlated process variables; however, conventional PCA-based detection indices often fail to detect small or moderate anomalies. In this work, we propose an innovative method for detecting small anomalies in highly correlated multivariate data. The developed method combines the multivariate exponentially weighted moving average (MEWMA) monitoring scheme with PCA modeling in order to enhance anomaly detection performance. Such a choice is mainly motivated by the greater ability of the MEWMA monitoring scheme to detect small changes in the process mean. The proposed PCA-based MEWMA monitoring scheme is successfully applied to ozone measurements data collected from Upper Normandy region, France, via the network of air quality monitoring stations. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.

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