Data reconciliation: A robust approach using a contaminated distribution

Abstract

On-line optimization provides a means for maintaining a process around its optimum operating range. This optimization heavily relies on process measurements and accurate process models. However, these measurements often contain random and possibly gross errors as a result of miscalibration or failure of the measuring instruments. This paper proposes a data reconciliation strategy that deals with the presence of such gross errors. Instead of constructing the objective function to be minimized on the basis of random errors only, the proposed method takes into account both contributions from random and gross errors using a so-called contaminated Gaussian distribution. It is shown that this approach introduces less bias in the estimation due to its natural property to reject gross errors.