This study proposes a simple model for viscosities, based on entropy scaling, for real substances and mixtures. The residual entropy is calculated with the perturbed chain polar statistical associating fluid theory (PCP-SAFT). The model requires two or three pure component parameters, noting, however, that an entirely predictive group contribution approach as proposed in our previous work [Loetgering-Lin O.; Gross J. Ind. Eng. Chem. Res. 2015, 54, 7942−7952] gives also very good results. Overall, 140 real substances are considered with relative mean deviations from experimental data of about 5% (without excluding "outliers"). We performed molecular simulations for mixtures of simple model fluid in order to determine a suitable mixture model. A completely predictive approach for viscosities of real mixtures is thereby obtained. The model is evaluated for 566 mixtures with about 34,500 experimental data points of various complexity (i.e., nearly ideal systems as well as highly asymmetric mixtures). Mixtures of nonpolar substances and mixtures with at least one polar, but nonhydrogen-bonding component, are predicted very accurately with relative mean deviations of on average 6.2% (173 mixtures considered) and 5.3% (126 mixtures considered), respectively. Limitations of the model are found for mixtures with hydrogen-bonding (associating) components such as amines and alcohols, where deviations are systematically higher. Lastly, we present results of mixture viscosities using the purely predictive group contribution framework and find similar results for the predictive approach.
Entropy scaling is an intriguingly simple approach for correlating and predicting transport properties of real substances and mixtures. It is convincingly documented in the literature that entropy scaling is indeed a firm concept for the shear viscosity of real substances, including hydrogen bonding species and strongly nonspherical species. We investigate whether entropy scaling is applicable for thermal conductivity. It is shown that the dimensionless thermal conductivity (thermal conductivity divided by a reference thermal conductivity) does not show a single-variable dependence on residual entropy, for obvious choices of a reference thermal conductivity. We perform a detailed analysis of experimental data and propose a reference thermal conductivity that is itself a simple function of the residual entropy. We then obtain good scaling behavior for the entire fluid region for water and 147 organic substances from various chemical families: linear and branched alkanes, alkenes, aldehydes, aromatics, ethers, esters, ketones, alcohols, and acids. The residual entropy is calculated from the Perturbed Chain Polar Statistical Associating Fluid Theory equation of state. The correlation of experimental data requires two parameters for pure substances with scarce experimental data and up to five parameters for experimentally well-characterized species. The correlation results for all substances lead to average relative deviations of 4.2% to experimental data. To further assess the approach, we analyze extrapolations to states not covered by experimental data and find very satisfying results.
This study proposes a model for self-diffusion coefficients of pure substances from entropy scaling using the perturbed-chain polar statistical associating fluid theory (PCP-SAFT) equation of state. In accordance with the entropy scaling approach proposed by Y. Rosenfeld [RosenfeldY. Rosenfeld, Y. Phys. Rev. A1977152545–2549], we observe that the self-diffusion coefficient of real substances, once made dimensionless with an appropriate expression, only depends on residual entropy. The proposed model requires 3 parameters for each pure substance. For substances with scarce experimental data, however, a scheme is proposed to estimate one or two of these parameters. We study 133 substances from more than 14 different chemical families and find the average absolute deviation of 8.2% between the proposed model and experimental data (9992 data points). The model shows satisfying robustness for extrapolating self-diffusion coefficients to conditions rather distant from the state points where experimental data are available.
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Entropy scaling has proven to be a powerful method for calculating transport properties. The applicability of the entropy scaling approach to predict the viscosity, thermal conductivity, and self-diffusion coefficients of pure substances based on substance-specific parameters over the last years was convincingly demonstrated in the literature. In this work, we derive a predictive method for the thermal conductivity based on entropy scaling. The model is developed as a group-contribution approach where substances are considered to be composed of chemical (functional) groups. The excess entropy is calculated using the group-contribution PCP-SAFT equation of state. The model is applicable for gaseous phases and for liquid-phase conditions covering wide ranges of temperature and pressure. We consider pure fluids from various chemical families, namely, alkanes, branched alkanes, cyclic alkanes, alkenes, aldehydes, aromatics, esters, ethers, ketones and alcohols, and some individual substances, such as water, carbon dioxide, and the like. We propose parameters of 29 chemical groups by considering 231 substances with more than 50,000 experimental data points. The group-contribution method for the thermal conductivity proposed in this work is shown to be in convincing agreement with experimental data with 6.17% average absolute deviation for all considered data points.
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