The operational flexibility of a chemical process refers to its ability to maintain feasible operations despite uncertain deviations from the nominal conditions. It is an important characteristic that ensures the system’s adaptability and resilience in the face of changing operating conditions. To quantify the feasible region and evaluate the flexibility of a given process design, the volumetric flexibility index is used by calculating the ratio between the hypervolume of the feasible region and the hypervolume of the region that encompasses all possible combinations of expected uncertain parameters. To deal with general problems involving nonlinear constraints, nonconvex, nonsimply connected, or high-dimensional feasible regions, we introduce a novel method that utilizes a deep regression network and a classification network to achieve a reliable and efficient evaluation of the flexibility index. We demonstrate the effectiveness of the proposed method through multiple numerical illustrations and case studies.

中文翻译：

---

使用深度神经网络的新型可行集学习和过程灵活性分析方法


化学过程的操作灵活性是指尽管标称条件出现不确定的偏差，其仍能维持可行的操作。它是确保系统面对不断变化的运行条件时的适应性和弹性的重要特性。为了量化可行区域并评估给定过程设计的灵活性，通过计算可行区域的超体积与包含预期不确定参数的所有可能组合的区域的超体积之间的比率来使用体积灵活性指数。为了处理涉及非线性约束、非凸、非简单连通或高维可行区域的一般问题，我们引入了一种利用深度回归网络和分类网络来实现对灵活性指数的可靠和高效评估的新方法。我们通过多个数值说明和案例研究证明了所提出方法的有效性。