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For economic nonlinear model predictive control and dynamic real-timeoptimization fast and accurate models are necessary. Consequently, the use ofdynamic surrogate models to mimic complex rigorous models is increasinglycoming into focus. For dynamic systems, the focus so far had been onidentifying a system's behavior surrounding a steady-state operation point. Inthis contribution, we propose a novel methodology to adaptively sample rigorousdynamic process models to generate a dataset for building dynamic surrogatemodels. The goal of the developed algorithm is to cover an as large as possiblearea of the feasible region of the original model. To demonstrate theperformance of the presented framework it is applied on a dynamic model of achlor-alkali electrolysis.

Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models