Dynamic bayesian approach to gross error detection and compensation with application toward an oil sands process

Chemical Engineering Science

Volumn 67, Issue 1, 2012, Pages 44-56

  Gonzalez, Ruben ^a   Huang, Biao ^a   Xu, Fangwei ^b   Espejo, Aris ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b SYNCRUDE CANADA LTD   (Canada)

Author keywords

Augmented state estimation; Bayesian diagnosis; Dynamic Bayesian networks; Dynamic data reconciliation; Gross error detection; Kalman filter

Indexed keywords

AUGMENTED STATE ESTIMATION; BAYESIAN APPROACHES; BAYESIAN INFERENCE; CONVENTIONAL TECHNIQUES; DYNAMIC BAYESIAN NETWORKS; DYNAMIC DATA RECONCILIATION; GROSS ERROR DETECTION; GROSS ERRORS; KALMAN-FILTERING; MASS AND ENERGY BALANCE; MEASUREMENT DATA; OFF-LINE APPLICATIONS; ON-LINE ALGORITHMS; ONLINE METHODS; REAL TIME; SATISFACTORY PREDICTIONS; SIMULATION EXAMPLE; STATE VARIABLES;

BAYESIAN NETWORKS; ERROR COMPENSATION; ERROR CORRECTION; ESTIMATION; INDUSTRIAL APPLICATIONS; INFERENCE ENGINES; INSTRUMENT ERRORS; KALMAN FILTERS; OIL SANDS; REAL VARIABLES; SYSTEMATIC ERRORS;

ERROR DETECTION;

EID: 81155132533     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2011.07.025     Document Type: Article

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