  For economic nonlinear model predictive control and dynamic real-time
optimization fast and accurate models are necessary. Consequently, the use of
dynamic surrogate models to mimic complex rigorous models is increasingly
coming into focus. For dynamic systems, the focus so far had been on
identifying a system's behavior surrounding a steady-state operation point. In
this contribution, we propose a novel methodology to adaptively sample rigorous
dynamic process models to generate a dataset for building dynamic surrogate
models. The goal of the developed algorithm is to cover an as large as possible
area of the feasible region of the original model. To demonstrate the
performance of the presented framework it is applied on a dynamic model of a
chlor-alkali electrolysis.
