Recent approaches for nonlinear and dynamic data reconciliation suffer from inapplicability and infeasibility for large systems. Because these systems are expressed by differential and algebraic equations, the complete problem definition requires a considerable number of equations that need to be solved simultaneously during the solution of the nonlinear programming problem. One way in avoiding this is to use a commercial software package to model a process and to reduce the size of the model by generating an input-output model from the simulation results. In this research two different approaches are presented to describe dynamics of the system and reduce the size of the model by model identification techniques.