Group Contribution Based Graph Convolution Network: Predicting Vapor–Liquid Equilibrium with COSMO-SAC-ML

Ryu, Beom Chan; Hwang, Seon Yoo; Kang, Sung Sin; Kang, Jeong Won; Yang, Dongsoo

doi:10.1007/s10765-022-03141-7

Int J Thermophys

2023

DOI: 10.1007/s10765-022-03141-7

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Group Contribution Based Graph Convolution Network: Predicting Vapor–Liquid Equilibrium with COSMO-SAC-ML

Beom Chan Ryu

Seon Yoo Hwang

Sung Sin Kang

et al.

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Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

Sun,

Xue,

Yang

et al. 2024

AIChE Journal

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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.

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Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

Sun,

Xue,

Yang

et al. 2024

AIChE Journal

View full text Add to dashboard Cite

show abstract

Binary Isobaric Vapor–Liquid Equilibrium for Methyl Benzoate with Benzyl Alcohol and Benzaldehyde

Baek,

Seo,

Kim

et al. 2024

Int J Thermophys

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Group Contribution Based Graph Convolution Network: Predicting Vapor–Liquid Equilibrium with COSMO-SAC-ML

Cited by 2 publications

References 30 publications

Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

Binary Isobaric Vapor–Liquid Equilibrium for Methyl Benzoate with Benzyl Alcohol and Benzaldehyde

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