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 recently added feature is the ability to detect measurement bias. Each of these features were developed and tested using computer simulation. In this paper we report the successful application of NDDR to reconcile actual plant data from an Exxon Chemicals process.