Data reconciliation and gross error detection are traditional methods toward detecting mass balance inconsistency within process instrument data. These methods use a static approach for statistical evaluation. This thesis is concerned with using an alternative statistical approach (Bayesian statistics) to detect mass balance inconsistency in real time.
The proposed dynamic Baysian solution makes use of a state space process model which incorporates mass balance relationships so that a governing set of mass balance variables can be estimated using a Kalman filter. Due to the incorporation of mass balances, many model parameters are defined by first principles. However, some parameters, namely the observation and state covariance matrices, need to be estimated from process data before the dynamic Bayesian methods could be applied. This thesis makes use of Bayesian machine learning techniques to estimate these parameters, separating process disturbances from instrument measurement noise. / Process Control
|Contributors||Dr. Biao Huang, Chemical and Materials Engineering, Dr. Vinay Prasad, Chemical and Materials Engineering, Dr. Bob Koch, Mechanical Engineering|
|Source Sets||Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada|
|Format||2322349 bytes, application/pdf|
|Relation||Gonzalez, Huang, Expejo, Almaraj, Lam (2010). Computers and Chemical Engineering 38|
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