Gross error modeling and detection in plant linear dynamic reconciliation

This paper presents a method to identify and estimate gross errors in plant linear dynamic data reconciliation. An integral dynamic data reconciliation method presented in a previous paper (Bagajewicz and Jiang, 1997) is extended to allow multiple gross error estimation. The dynamic integral measurement test is extended to identify hold-up measurements as suspects of gross error. A series of theorems are used to show the equivalencies of gross errors and to discuss the issue of exact identification. A serial approach for gross error identification and estimation is then presented. Gross errors are identified without the need for measurement elimination. The strategy is capable of effectively identifying a large number of gross errors.