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Generating a Machine-Learned Equation of State for Fluid Properties.
The Journal of Physical Chemistry B ( IF 3.3 ) Pub Date : 2020-09-01 , DOI: 10.1021/acs.jpcb.0c05806
Kezheng Zhu 1 , Erich A Müller 1
Affiliation  

Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental data. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine-learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids.

中文翻译:

生成机器学习的流体特性状态方程。

流体的状态方程(EoS)在一个多世纪以来一直是工程设计和实践的主要内容。可用的EoS基于封闭形式的分析表达式与合适的实验数据的拟合。数学结构和基础物理模型极大地限制了所得EoS的适用性和准确性。这项贡献探讨了围绕用机器学习模型代替分析EoS的问题。作为概念的证明,特别是,我们描述了机器学习模型复制纯流体的统计关联流体理论(SAFT-VR Mie)EoS的有效性。为了量化机器学习技术的有效性,从EoS获得了大量伪数据,并将其用于训练机器学习模型。我们使用人工神经网络和高斯过程回归来关联和预测热力学特性,例如临界压力和温度,蒸气压以及纯模型流体的密度。这些是基于分子描述符进行的。机器学习的EoS和替代数据集之间的比较表明,所提出的方法显示出有望作为一种可行的技术来关联和预测流体的热物理性质。
更新日期:2020-10-02
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