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

This paper is concerned with developing an online algorithm for detecting and estimating systematic errors (gross errors) in mass and energy balances from measurement data. This method has its application in diagnosing problems in an oil sands process. Conventional techniques for detecting gross errors presently exist for offline application. The proposed online method entitled Dynamic Bayesian Gross Error Detection (DBGED) is a dynamic Bayesian analogue of traditional gross error detection, and can be considered as a type of Switching Kalman Filter. As such, related topics such as Kalman Filtering, observability and Dynamic Bayesian Inference are discussed. In addition to detecting gross errors, the DBGED also estimates detected gross error magnitudes in real time (as an augmented state variable) so that future measurements can be corrected. When the estimate converges to yield satisfactory prediction errors, gross error estimation is stopped and instruments are corrected with a constant gross error correction term. DBGED performance is demonstrated through a simulation example and an example of an industrial application.

► DBGED was proposed for gross error detection and data reconciliation. ► A random walk Kalman Filter was proposed for dynamic data reconciliation. ► Gross errors were detected using Dynamic Bayesian Networks. ► Methods for tuning DBGED were discussed in detail. ► Practical reliability measures for applying DBGED were also discussed.