论文标题
生成流体特性的机器学习状态方程
Generating a Machine-learned Equation of State for Fluid Properties
论文作者
论文摘要
一个多世纪以来，用于流体的状态方程（EOS）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与合适的实验数据的拟合。潜在的数学结构和基础物理模型显着限制了所得EOS的适用性和准确性。这项贡献探讨了围绕机器学习模型替代分析EOS的问题，特别是，我们将机器学习模型的有效性描述为复制统计统计的流体理论（SAFT-VR-MIE）EOS对纯流体的有效性。通过利用人工神经网络和高斯过程回归，基于分子描述符，对热力学特性（例如临界压力和温度，蒸气压和密度的密度）进行预测。为了量化机器学习技术的有效性，使用机器学习的EOS与替代数据集之间的比较来构建大型数据集，这表明所提出的方法表明了有望作为流体热物理特性的相关性，外推和预测的可行技术。
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 underlying 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 analytical EoS for machine-learned models, in particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate statistical associating fluid theory (SAFT-VR-Mie) EoS for pure fluids. By utilizing Artificial Neural Network and Gaussian Process Regression, predictions of thermodynamic properties such as critical pressure and temperature, vapor pressures and densities of pure model fluids are performed based on molecular descriptors. To quantify the effectiveness of the Machine Learning techniques, a large data set is constructed using 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, extrapolation and prediction of thermophysical properties of fluids.
