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Thermodynamic Modeling with Equations of State: Present Challenges with Established Methods
Author(s) -
Øivind Wilhelmsen,
Ailo Aasen,
Geir Skaugen,
Peder Aursand,
Anders Austegard,
Eskil Aursand,
Magnus Aa. Gjennestad,
Halvor Lund,
Gaute Linga,
Morten Hammer
Publication year - 2017
Publication title -
industrial and engineering chemistry research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.878
H-Index - 221
eISSN - 1520-5045
pISSN - 0888-5885
DOI - 10.1021/acs.iecr.7b00317
Subject(s) - robustness (evolution) , consistency (knowledge bases) , work (physics) , computer science , range (aeronautics) , equation of state , statistical physics , thermodynamics , chemistry , physics , materials science , artificial intelligence , biochemistry , composite material , gene
Equations of state (EoS) are essential in the modeling of a wide range of industrial and natural processes. Desired qualities of EoS are accuracy, consistency, computational speed, robustness, and predictive ability outside of the domain where they have been fitted. In this work, we review present challenges associated with established models, and give suggestions on how to overcome them in the future. The most accurate EoS available, multiparameter EoS, have a second artificial Maxwell loop in the two-phase region that gives problems in phase-equilibrium calculations and excludes them from important applications such as treatment of interfacial phenomena with mass-based density functional theory. Suggestions are provided on how this can be improved. Cubic EoS are among the most computationally efficient EoS, but they often lack sufficient accuracy. We show that extended corresponding state EoS are capable of providing significantly more accurate single-phase predictions than cubic EoS with only a doublin...

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