The COSMO-SAC modeling approach has found wide application in science as well as in a range of industries due to its good predictive capabilities. While other models for liquid phases, as for example UNIFAC, are in general more accurate than COSMO-SAC, these models typically contain many adjustable parameters and can be limited in their applicability. In contrast, the COSMO-SAC model only contains a few universal parameters and subdivides the molecular surface area into charged segments that interact with each other. In recent years, additional improvements to the construction of the sigma profiles and evaluation of activity coefficients have been made. In this work, we present a comprehensive description how to postprocess the results of a COSMO calculation through to the evaluation of thermodynamic properties. We also assembled a large database of COSMO files, consisting of 2261 compounds, freely available to academic and noncommercial users. We especially focus on the documentation of the implementation and provide the optimized source code in C++, wrappers in Python, sample sigma profiles calculated from each approach, as well as tests and validation results. The misunderstandings in the literature relating to COSMO-SAC are described and corrected. The computational efficiency of the implementation is demonstrated.

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Using residual entropy scaling approaches, transport properties, such as viscosity and thermal conductivity, can be linked to a thermodynamic property, i.e., residual entropy. It has been demonstrated in the literature that these approaches can be successfully used to correlate transport properties in the gas phase as well as the liquid phase over large temperature and density ranges. Recently, Yang et al. [J. Chem. Eng. Data 2021, 66(3), 1385−1398 proposed a residual entropy scaling approach for the viscosity of 39 refrigerants and their mixtures, and they extended this approach in a subsequent work to 124 fluids [Int. J. Thermophys. 2022, 43(12), 183]. The method of Yang et al. requires a fluid-specific scaling factor for each fluid. Yang et al. proposed a method for estimating this parameter as being proportional to the residual entropy at the critical point. In this work, it is demonstrated that for hydrocarbons, the fluid-specific scaling factor can be better approximated as a linear function of the longest carbon chain. All linear and branched alkanes for which accurate multiparameter equations of state are available have been considered in this work. Furthermore, the performance of other predictive equations of state, i.e., the Peng− Robinson and Lee−Kesler−Plocker equations of state, is evaluated.

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