Recursive state estimation in nonlinear processes

Abstract

The task of improving the quality of the data so that it is consistent with material and energy balances is called reconciliation. Since chemical processes often operate dynamically in nonlinear regimes, techniques like extended Kalman filter (EKF) and nonlinear dynamic data reconciliation (NDDR) have been developed. There are various issues that arise with the use of either of these techniques: EKF cannot handle inequality or equality constraints, while the NDDR has high computational cost. In this paper, a recursive nonlinear dynamic data reconciliation (RNDDR) formulation is presented. The RNDDR formulation extends the capability of the EKF by allowing for incorporation of algebraic constraints and bounds. The RNDDR is evaluated with four case studies that have been previously studied by Haseltine and Rawlings. It has been shown that the EKF fails in constructing reliable state estimates in all the four cases due to the inability in handling algebraic constraints. Reliable state estimates are achieved by the RNDDR formulation in all the cases in presence of large initialization errors.