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.

中文翻译：

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自适应探索流程图的特征空间

化工流程的模拟和优化依赖于大量非线性方程的求解。可以通过构建基于机器学习的代理模型、通过简单、显式函数将特征和输出关联起来来支持找到此类解决方案。为了有效地为这些代理模型生成训练数据，必须采用自适应采样特征空间的方案。在本文中，我们提出了一系列新颖的效用函数，以支持对特征空间进行自适应贝叶斯探索，从而识别收敛区域并满足定制的不平等约束。此外，可以识别相对于冲突目标接近帕累托最优域的点，作为流程图的多标准优化的良好起始值。通过小型玩具示例以及工业相关的化学流程图说明了其好处。