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 reconciliatio n method presented in a previous paper (Bagajewicz and Jiang, 1997) is exte nded to allow multiple gross error estimation. The dynamic integral measure ment 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 er rors and to discuss the issue of exact identification. A serial approach fo r 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. (C) 1 998 Published by Elsevier Science Ltd. All rights reserved.