SAFT-γ-Mie Cross-Interaction Parameters from Density Functional Theory-Predicted Multipoles of Molecular Fragments for Carbon Dioxide, Benzene, Alkanes, and Water

Abstract

Determining unlike-pair interaction parameters, whether for group contribution equation of state or molecular simulations, is a challenge for the prediction of thermodynamic properties. As the number of components and their respective complexity increase, it becomes impractical to fit all the unlike interactions. Lorentz−Berthelot combining rules work well for systems, where the main interactions are dispersion forces, but they do not account for electrostatics. In this work, we derive predictive combining rules within the SAFT-γ-Mie framework. In the resulting model, the unlike-pair interactions account for the effect of ionization energies, partial charges, dipole moments, and quadrupole moments. We then estimate these properties for molecular fragments using density functional theory calculations and demonstrate their use to obtain realistic cross-interaction energies without the need for experimental data. An open-source python package, Multipole Approach to Predictively Scale Cross-Interactions, is included to facilitate use of the methods presented in this work. A good qualitative agreement was obtained for all phase equilibria calculations of binary mixtures containing carbon dioxide with propane, hexane, benzene, and water, as well as mixtures of hexane and benzene. Finally, we discuss future improvements to our methodology, including the use of physical insights when fitting self-interaction parameters.

Improved vapor pressure prediction from PR + COSMOSAC EOS using normal boiling temperature

“…50,51 An EOS can be made predictive if its parameters are estimated via group contribution or quantum mechanics-based methods. [52][53][54] However, only a few research examines the predictability of pure substance vapor pressure. 51 Recently, the Conductor-like Screening MOdel (COSMO) method 55 based on quantum mechanical implicit solvation calculations emerges as a promising approach for describing the solvation free energy in the liquid phase.…”

“…These models contain three or more molecular parameters that are determined by regression to equilibrium properties of interest, such as vapor pressure and liquid densities 50,51 . An EOS can be made predictive if its parameters are estimated via group contribution or quantum mechanics‐based methods 52–54 . However, only a few research examines the predictability of pure substance vapor pressure 51 …”