Model-Based Experimental Design: A Comparison between Sequential and Simultaneous Optimization approaches

Mathematical models are essential for chemical processes since they contribute to both identification and manipulation of process mechanisms, e.g. in reaction systems, separation processes and process controls. The methodologies of model-based experiment design aim at reducing the uncertainties of estimated model parameters, and thus, make the identification and use of these models possible. Up to now, sequential optimization approaches have been applied to solve the related extended optimal control problem. In this contribution, a substantial comparison between the sequential and the simultaneous optimization approach for optimal model-based experimental design is presented with respect to convergence behavior and computational load. Moreover, both approaches are compared regarding high order and continuous control trajectories as well as restrictions from the process. Process restrictions, especially regarding state variables and continuity of control trajectories have a tremendous importance with respect to the practical implementation, but are mostly just touched or totally neglected during the optimization steps so far. The results are discussed by the application on a CSTR example