The application of nonlinear dynamic data reconciliation to plant data

We have extended a fairly comprehensive data reconciliation approach called nonlinear dynamic data reconciliation (NDDR) that was originally presented by Liebman et al. (1994, Comput. Chem. Engng, 16, 963-986). This approach is capable of reconciling data from both steady-state and dynamic processes as well as estimating parameters and unmeasured process variables. One rec ently added feature is the ability to detect measurement bias. Each of thes e features were developed and tested using computer simulation. In this pap er we report the successful application of NDDR to reconcile actual plant d ata from an Exxon Chemicals process. (C) 1998 Elsevier Science Ltd. All rig hts reserved.