title: Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.
abstract: The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.
authors.affiliation: Control Engineering and Intelligent Systems Group - eXiT, Department of Electronics, Computer Science and Automatic Control, University of Girona, Campus Montilivi CP 17071 Building PIV, Girona, Spain. magda.ruiz@upc.edu
authors.forename: Magda
authors.initials: M
authors.lastname: Ruiz
authors.forename: Gürkan
authors.initials: G
authors.lastname: Sin
authors.forename: Xavier
authors.initials: X
authors.lastname: Berjaga
authors.forename: Jesús
authors.initials: J
authors.lastname: Colprim
authors.forename: Sebastià
authors.initials: S
authors.lastname: Puig
authors.forename: Joan
authors.initials: J
authors.lastname: Colomer
mesh_terms: D019149:Bioreactors; D003657:Decision Making; D008962:Models, Theoretical; D025341:Principal Component Analysis; D014865:Waste Disposal, Fluid / Q000379:methods*; D018508:Water Purification / Q000379:methods*