Vapor–liquid phase equilibrium (VLE) plays a crucial role in chemical process design, process equipment control, and experimental process simulation. However, experimental acquisition of VLE data is a challenging and complex task. As an alternative to experimentation, VLE data prediction offers great convenience and utility. In this article, an artificial intelligence network is proposed to predict the temperature and the vapor phase composition of binary mixtures. We constructed a graph neural network (GNN) and designed an uncertainty‐aware learning and inference mechanism (UALF) in the prediction process. The model was tested on both a self‐constructed dataset and a publicly available dataset. The results demonstrate that the proposed method effectively reveals the phase equilibrium properties of the target data. This work presents a novel approach for predicting vapor–liquid phase equilibrium in binary systems and proposes innovative ideas for investigating phase equilibrium mechanisms and principles.

中文翻译：

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使用图神经网络预测二元系统混合物的气-液相平衡


气-液相平衡 （VLE） 在化学工艺设计、工艺设备控制和实验工艺模拟中起着至关重要的作用。但是，VLE 数据的实验采集是一项具有挑战性和复杂的任务。作为实验的替代方法，VLE 数据预测提供了极大的便利和实用性。在本文中，提出了一种人工智能网络来预测二元混合物的温度和气相组成。我们构建了一个图神经网络 （GNN），并在预测过程中设计了一个不确定性感知学习和推理机制 （UALF）。该模型在自建数据集和公开可用的数据集上进行了测试。结果表明，所提方法有效地揭示了目标数据的相平衡性质。这项工作提出了一种预测二元系统中气-液平衡的新方法，并为研究相平衡机制和原理提出了创新思路。