PCP-SAFT Parameters of Pure Substances Using Large Experimental Databases

Esper, Timm; Bauer, Gernot; Rehner, Philipp; Gross, Joachim

doi:10.1021/acs.iecr.3c02255

Cited by 14 publications

(16 citation statements)

References 66 publications

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“…We present results for solvation free energies from the PC-SAFT equation of state, which are obtained using the parameters from Esper et al As discussed in our previous work, for sufficiently large systems the solvation free energy is equal to the residual chemical potential in the canonical ensemble (constant number of solute molecules Ñ and solvent molecules N , volume V , and temperature T ) according to normalΔ Ω normals normalo normall normalv = μ normalr normale normals ( Ñ , N , V , T ) …”

Section: Resultsmentioning

confidence: 99%

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State

Bursik,

Eller,

Gross

2024

J. Phys. Chem. B

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We critically assess the capabilities of classical density functional theory (DFT) based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state to predict the solvation free energies of small molecules in various hydrocarbon solvents. We compare DFT results with experimental data from the Minnesota solvation database and utilize statistical methods to analyze the accuracy of our approach, as well as its weaknesses. The mean absolute error of the solvation free energies is 3.7 kJ mol −1 for n-alkane solvents, ranging from pentane to hexadecane, with 473 solute−solvent systems. For solvents consisting of cyclic hydrocarbons (cyclohexane, benzene, toluene, and ethylbenzene) with 245 solute−solvent systems, we report a slightly larger mean absolute error of 4.2 kJ mol −1 . We identify three possible sources of errors: (i) the neglect of solute−solvent and solvent−solvent Coulomb interactions, which limits the applicability of PC-SAFT DFT to nonpolar and weakly polar molecules; (ii) the solute's Lennard-Jones parameters supplied by the general AMBER force field, which are not parametrized toward solvation free energies; and (iii) the application of the Lorentz-Berthelot combining rules to the dispersive interactions between a segment of the PC-SAFT solvent and a Lennard-Jones interaction site of the solute. The approach is more accurate than standard implementations of phenomenological models in common chemistry software packages, which exhibit mean absolute errors larger than 9.12 kJ mol −1 , even though newer phenomenological models achieve a mean absolute error of about 2 kJ mol −1 . PC-SAFT DFT is more computationally efficient than state of the art explicit molecular simulations in combination with free energy perturbation methods. It is predictive with respect to solvation free energies, i.e., the input for the model is the (element-specific) molecular force field, the solute configuration from molecular dynamics simulations, and the (substance-specific) PC-SAFT parameters. The PC-SAFT parametrization uses pure-component data and does not require experimental solvation free energies. The PC-SAFT equation of state, without applying a DFT formalism, can also be used to calculate solvation free energies, provided that the PC-SAFT parameters for the solute are available. A large number of substances was recently parametrized by members of our group (Esper, T.; Bauer, G.; Rehner, P.; Gross, J. Ind. Eng. Chem. Res. 2023, 62), which enables a comparison to the DFT approach for 103 substances. Accurate results are obtained from the PC-SAFT equation of state with an MAE below 2.51 kJ mol −1 . The DFT approach does not require PC-SAFT parameters for the solute and can be applied to all solutes that can be represented by the molecular force field.

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Section: Resultsmentioning

confidence: 99%

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State

Bursik,

Eller,

Gross

2024

J. Phys. Chem. B

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“…For the parameters used in PC-SAFT, we adopted the values from the literature, the parameters of n -dodecane are m = 5.4950, σ = 3.8329, and ε/ k = 244.84, and the parameters of methyl laurate are m = 6.5833, σ = 3.7301, and ε/ k = 249.46. For the BIP, we only used the experimental data of speed of sound for fitting to provide k ij correlations that specialize in calculating the speed of sound and set the objective function as follows: F obj = prefix∑ i = 1 N true( c cal − c exp c exp true) 2 …”

Section: Resultsmentioning

confidence: 99%

Speed of Sound Measurements and Thermophysical Modeling of Binary Mixtures of Methyl Laurate and n-Dodecane

He,

Zhan,

Chen

et al. 2024

J. Chem. Eng. Data

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The speed of sound in binary mixtures of methyl laurate and n-dodecane can directly characterize fuel injection and NOx emissions from diesel engines. Currently, experimental data on the speed of sound of binary mixtures have not yet been reported. In this study, the speeds of sound of the binary system were measured by using Brillouin light scattering. The speeds of sound were measured in the range of 298.15−568.15 K and 0.1−9 MPa. The relative combined expanded uncertainty for the reported speed of sound is estimated to be 1.6% with the level of confidence is 0.95 (k = 2). The experimental data were correlated as a function of temperature and pressure resulting with absolute average percentage deviations of 0.19, 0.16, 0.20, 0.24, and 0.22% for the mole fractions of n-dodecane being 0.100, 0.300, 0.500, 0.700, and 0.900, respectively. Considering the effect of temperature on the binary interaction parameter k ij , k ij of the PC-SAFT equation of state (EoS) was fitted using experimental data for methyl laurate and n-dodecane. The speeds of sound of binary system were calculated using the PC-SAFT EoS, and the average absolute relative deviation is 2.7%.

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“…To analyze the correlation results of the developed GC method, we compare the GC results on the training data to the results from individual parametrization using the PC-SAFT equation of state as a thermodynamic model to calculate the densities (Figures and ). The PC-SAFT parameters are taken from Esper et al For the individual parametrization as a benchmark, only 206 substances are included, because PC-SAFT parameters are not available for all 256 substances. Apart from errors introduced by the GC method for the permittivity model, the total error also contains inaccuracies from the density prediction with the homosegmented GC method for PC-SAFT.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory

Rueben,

Schilling,

Rehner

et al. 2023

J. Chem. Eng. Data

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The computer-aided design of (bio)chemical processes requires models that predict thermodynamic properties with as little experimental effort as possible. For the important class of electrolyte systems, the relative static permittivity of the solvent is an important thermodynamic property that depends on the temperature, pressure, composition, and molecular structure of the solvent. This work presents a broadly applicable model for the temperature-dependent relative static permittivity of pure and mixed solvents based on perturbation theory, including a group contribution method. For this purpose, we extend our previous model for polar substances to nonpolar substances. The developed model is parametrized for 785 substances, where permittivity and density data are available in the Dortmund Data Bank and the ThermoML database. Subsequently, a group contribution method is developed to predict the permittivity parameters from the molecular structure. With a mean absolute deviation of 0.2 averaged over all 785 substances, the parametrized model accurately correlates the relative static permittivity over a wide range of permittivities and temperatures. Moreover, the group contribution method achieves a mean absolute deviation of 0.6 for the substances in the training set. A leave-one-out cross-validation shows that the group contribution method accurately predicts substances not included in the training set.

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PCP-SAFT Parameters of Pure Substances Using Large Experimental Databases

Cited by 14 publications

References 66 publications

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State

Speed of Sound Measurements and Thermophysical Modeling of Binary Mixtures of Methyl Laurate and n-Dodecane

Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory

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