On the Choice of Regression Variables in Nonlinear Data Reconciliation

Abstract

Reconciliation of process data constrained by multicomponent material balance equations can be accomplished by a regression calculation in which either total flow rates and compositions (primary variables) or total and component flow rates (secondary variables) are used as the regression variables. It is shown by means of computer simulation experiments that the choice of secondary variables for regression can adversely affect the performance of an associated gross error detection algorithm. The principal effect is an increased tendency of the algorithm to make Type I errors.