Rectification of process measurement data in the presence of gross errors

A systematic strategy is developed for the location of the source and the rectification of gross, biased measurement errors in a chemical process. The proposed strategy proceeds in three levels: (a) A structural analysis of the balance equations identifies subsets of balances with measurements which are suspected to possess gross errors. (b) A sequential analysis of the balance equations with suspect measurements further reduces the size of the problem. Statistical criteria are used in this step. (c) Finally, a sequential analysis of the suspect measurements appearing in the reduced set of balances leads to the identification of the source of the gross errors. The proposed strategy: (i) reduces the size of the data reconciliation problem significantly, even for large-scale chemical processes, (ii) is computationally simple and (iii) it conforms with the general process of variable monitoring in a chemical plant. Numerical examples are presented to clarify the elements of the procedure involved and demonstrate their value and effectiveness in dealing with realistic situations.