Data reconciliation with uncertain models
Data reconciliation has proven to be an effective technique for providing frequent, accurate and consistent "best estimates" of plant operation data. However, in almost all the proposed techniques until today, the mathematical model of the process has been considered as exact. In point of fact, this hypothesis is uncommon and frequently the models used are uncertain. This paper proposes a new technique of data reconciliation which is able to exploit the knowledge about the uncertainties of the model with regard to which the reconciliation is done. It leads to the solution of a classical quadratic optimisation problem subject to constraints. The originality of the proposed technique is to use penalty functions for solving this problem and to weight each constraint with regard to their uncertainties.