Generating a machine-learned equation of state for fluid properties
| Autor: | Kezheng Zhu, Erich A. Müller |
|---|---|
| Přispěvatelé: | Engineering & Physical Science Research Council (EPSRC) |
| Rok vydání: | 2020 |
| Předmět: |
Equation of state
Computer science FOS: Physical sciences 010402 general chemistry 01 natural sciences 09 Engineering Surrogate data Set (abstract data type) Kriging 0103 physical sciences Materials Chemistry Statistical physics Physical and Theoretical Chemistry cond-mat.stat-mech Condensed Matter - Statistical Mechanics 02 Physical Sciences Statistical Mechanics (cond-mat.stat-mech) 010304 chemical physics Basis (linear algebra) Artificial neural network Experimental data Computational Physics (physics.comp-ph) 0104 chemical sciences Surfaces Coatings and Films physics.comp-ph Mathematical structure 03 Chemical Sciences Physics - Computational Physics |
| Popis: | 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. Comment: Submitted to J. Phys. Chem. B |
| Databáze: | OpenAIRE |
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