Nonlinear dynamic data reconciliation via process simulation software and model identification tools

Abstract

Existing strategies for the solution of the nonlinear dynamic data reconciliation problem use the process model as a constraint which is expressed as a differential-algebraic equation system. Modeling a process using conservation laws may require a considerable number of equations to obtain an accurate representation of the system. It is possible to model a process using commercial dynamic simulation software. However, this also requires the solution of a large number of equations interfaced to reliable optimization software in order to perform data reconciliation. This paper focuses on two new approaches for dynamic data reconciliation using model identification tools and commercial dynamic simulation software. The first one is based on an analogy to the nonlinear dynamic data reconciliation method developed by Liebman et al).1 The second approach uses time series analysis to generate a simplified model of the plant. A simplified process model is generated by a model identification method to replace the simulation software. Several techniques including parametric and nonparametric methods can be applied to identify a local input-output model from simulation results. Data reconciliation constrained by this reduced model yields a more computationally efficient algorithm.