11 - Industrial Applications of Data Reconciliation and Gross Error Detection Technologies

Abstract

This chapter presents a review of major industrial applications of data reconciliation and gross error detection. The chapter analyzes various aspects, such as the context of the industrial application, the problems associated with each type of application, and the methods used to solve them. The interest in applying data reconciliation methods to industrial data started in the late 1980s, when plant management realized the benefits of using a data reconciliation system and commercial software for data reconciliation and gross error detection became available. Process unit reconciliation was the first type of application of data reconciliation and gross error detection. Steady-state detection is necessary in order to increase the accuracy in the reconciled values and to provide meaningful gross error detection. Most online implementations are for steady-state operations. Average data (usually for one-hour period) are used for steady-state reconciliation. A steady-state detection for process data is usually a part of the steady-state online data reconciliation. If the process is not operated at steady-state for a longer period of time, dynamic data reconciliation should be applied. Proper component and thermodynamic characterization and accurate compositions are very important for a successful data reconciliation and gross error detection. Rigorous model enables merging the data reconciliation and parameter estimation into one problem that can be solved simultaneously. Plant-wide material and utilities reconciliation is an important tool for production (or yield) accounting.