Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

工业规模的化学过程的流程模拟需要求解大型非线性方程组，因此可解性成为一个实际问题。来自技术、经济、环境和安全考虑的额外约束可能会进一步限制超出收敛要求的可行解决方案空间。先验地，模拟收敛并满足强加约束的设计变量域通常是未知的，并且通过简单地为每个选择运行模拟来区分可行和不可行的设计变量选择会变得非常耗时。为了支持对此类场景的设计变量空间的探索，最近提出了一种基于机器学习模型的自适应采样技术。然而，这种方法只考虑对收敛域的探索，而忽略了额外的约束。在本文中，我们提出了一种改进，它特别考虑了约束的实现。我们成功地将所提出的算法应用于多达 20 维的玩具示例和工业相关的流程模拟。