2024
DOI: 10.1002/aic.18404
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Adaptively exploring the feature space of flowsheets

Johannes Höller,
Martin Bubel,
Raoul Heese
et al.

Abstract: Simulation and optimization of chemical flowsheets rely on the solution of a large number of nonlinear equations. Finding such solutions can be supported by constructing machine learning‐based surrogate models, relating features and outputs by simple, explicit functions. In order to generate training data for those surrogate models computationally efficiently, schemes to adaptively sample the feature space are mandatory. In this article, we present a novel family of utility functions to favor an adaptive, Baye… Show more

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