Adaptively exploring the feature space of flowsheets

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, Bayesian exploration of the feature space in order to identify regions that are convergent and fulfill customized inequality constraints. Moreover, points close to the Pareto-optimal domain with respect to conflicting objectives can be identified, serving as good start values for a multicriteria optimization of the flowsheet. The benefit is illustrated by small toy-examples as well as by industrially relevant chemical flowsheets.