Group Contribution Method for the Residual Entropy Scaling Model for Viscosities of Branched Alkanes

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Abstract

In this work it is shown how the entropy scaling paradigm introduced by Rosenfeld (Phys Rev A 15:2545–2549, 1977, https://doi.org/10.1103/PhysRevA.15.2545) can be extended to calculate the viscosities of branched alkanes by group contribution methods (GCM), making the technique more predictive. Two equations of state (EoS) requiring only a few adjustable parameters (Lee–Kesler–Plöcker and PC-SAFT) were used to calculate the thermodynamic properties of linear and branched alkanes. These EOS models were combined with first-order and second-order group contribution methods to obtain the fluid-specific scaling factor allowing the scaled viscosity values to be mapped onto the generalized correlation developed by Yang et al. (J Chem Eng Data 66:1385–1398, 2021, https://doi.org/10.1021/acs.jced.0c01009) The second-order scheme offers a more accurate estimation of the fluid-specific scaling factor, and overall the method yields an AARD of 10 % versus 8.8 % when the fluid-specific scaling factor is fit directly to the experimental data. More accurate results are obtained when using the PC-SAFT EoS, and the GCM generally out-performs other estimation schemes proposed in the literature for the fluid-specific scaling factor.

Details

Original languageEnglish
Article number176
Number of pages43
Journal International journal of thermophysics
Volume44(2023)
Issue number12
Publication statusPublished - 16 Nov 2023
Peer-reviewedYes

External IDs

ORCID /0000-0001-7908-4160/work/168204401

Keywords

ASJC Scopus subject areas

Keywords

  • Group contribution method, Lee–Kesler–Plöcker equation of state, Machine learning, PC-SAFT equation of state, Residual entropy scaling, Viscosity

Library keywords