The group contribution SAFT-γ Mie EoS is based on the statistical associating fluid theory for fused heteronuclear molecules. While the chain term of the model has been modified to account for the new functional group-specific parameters, it does not impose a bonding order to these functional groups, only considering intergroup interactions in the monomer reference fluid. This leaves the model unable to account for the different physical properties of structural isomers and implicitly introducing modeling bias to species where the molecular structure mimics those used in the parameter regression. In this work, a simple but physically meaningful modification to the chain term in SAFT-γ Mie is proposed that accounts for the number of intergroup bonds, thereby encoding structural information in the model, without introducing an additional regressed parameter. The resulting structural SAFT-γ Mie (s-SAFT-γ Mie) requires reparameterization of the group parameters, which we present for linear and branched alkanes (CH3, CH2, CH, and C groups) here. Following an identical parameterization procedure to the original model, validation showed that the modification actually improves prediction accuracy for linear alkanes while addressing the original inability of the framework to distinguish between structural isomers. The good predictive performance seen in this work, for both pure component and mixture properties, lays a good foundation for expansion to other functional groups in future work.

Statistical associating fluid theory (SAFT) equations of state (EoSs) are powerful thermodynamic modeling tools that show promise in application to a wide range of different properties and systems. SAFT-γ Mie, the group-contribution variant of the state-of-the-art SAFT-VR Mie, can describe new systems using transferable functional-group parameters. There had been a void in the modeling of nonself-associating dipolar species prior to this work, in which groups were parametrized for 2-ketones, 3-ketones, and n-alkyl propanoates (viz. CH2CO, CH3CO, and COOpr., respectively). These components occur in a wide variety of industrial processes and modeling them with SAFT-γ Mie presented the opportunity to evaluate the model’s treatment of dipolar interactions without a fundamental dipolar term in the EoS. Our new groups provide reliable binary mixture phase-equilibrium, excess enthalpy, and speed of sound predictions for all of the considered components, despite the fact that 2-ketone pure-component predictions are slightly less accurate than what is expected from such a complex SAFT model. The latter observation suggests that very precise modeling of smaller, highly dipolar molecules is challenging with a first-order group-contribution model, even in the SAFT-VR Mie-based framework. Binary mixture VLE predictions of ketone + n-alkane and ketone +1-alkanol systems are in good agreement with experimental data, suggesting that the pseudo-association approach used to treat the dipolar interactions of ketones is adequate, and that its nonrigorous nature does not inherently produce an erroneous representation of the balance between different intermolecular interactions. Two successful high-pressure binary system VLE predictions were also generated, which may spur further research into using SAFT-γ Mie and the new dipolar groups for modeling high-pressure systems.