Quasi-weighted least squares estimator for data reconciliation

Data reconciliation is important in chemical industries. Because of random and possibly gross errors in measurements, data reconciliation is needed to minimize the measurement errors. The most common estimator for data reconciliation is the weighted least squares, which is not robust. A robust estimator, quasi-weighted least squares, is proposed for data reconciliation. The properties of the estimator are analyzed, and the influence function is used to show that the estimator is robust. Two estimators, weighted least squares and quasi-weighted least squares, are used in atmospheric tower, ethylene separation and air separation process systems. Comparisons with other approaches are made on the steam metering process. The effectiveness of the robust estimator is demonstrated.