The state of the art of cubic equations of state with temperature-dependent binary interaction coefficients - Université de Lorraine Accéder directement au contenu
Article Dans Une Revue Fluid Phase Equilibria Année : 2023

The state of the art of cubic equations of state with temperature-dependent binary interaction coefficients

Résumé

Cubic equations of state (CEoSs) are extensively used in academia and industry for their consistency, reliability, even under extrapolation conditions, and capacity to predict phase-equilibrium and energetic properties with acceptable accuracy. The optimal parametrization of a pure-component CEoS, a prerequisite for the development of any CEoS for mixtures, is discussed first. The binary interaction parameter (BIP) involved in the Van der Waals mixing rules of CEoSs is introduced with special attention to the temperature-dependent functions used to express it. The following questions are addressed: what is the physical meaning of the BIP? How does this parameter reveal deviations from ideal mixtures? What are the connections between Van der Waals and advanced (EoS/g E) mixing rules? What are the best methods to correlate this parameter? How can we predict it? In conclusion, the limitations and performances of models involving Van der Waals mixing rules, including BIPs, are examined and compared to the CEoSs based on advanced mixing rules.
Fichier principal
Vignette du fichier
P190_The state of the art of CEoS with T-dependent BIPS. From correlation to prediction.pdf (3.29 Mo) Télécharger le fichier
Origine : Publication financée par une institution
Licence : CC BY NC ND - Paternité - Pas d'utilisation commerciale - Pas de modification

Dates et versions

hal-03970737 , version 1 (02-02-2023)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Romain Privat, Jean-Noël Jaubert. The state of the art of cubic equations of state with temperature-dependent binary interaction coefficients: From correlation to prediction. Fluid Phase Equilibria, 2023, 567, pp.113697. ⟨10.1016/j.fluid.2022.113697⟩. ⟨hal-03970737⟩
15 Consultations
386 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More