Modeling Dipolar\nMolecules with PCP-SAFT: A Vector\nGroup-Contribution Method

Predicting thermodynamic equilibrium properties is essential\nto\ndevelop chemical and energy conversion processes in the absence of\nexperimental data. For the modeling of thermodynamic properties, statistical\nassociating fluid theory (SAFT)-based equations of state, such as\nperturbed-chain polar (PCP)-SAFT, have been proven powerful and found\nbroad application. The PCP-SAFT parameters can be predicted by group-contribution\n(GC) methods. However, their application to the dipole term is substantially\nlimited: current GC methods neglect the dipole term or only allow\nfor a single dipolar group per substance to avoid handling the molecular\ndipole moment’s symmetry effects. Still, substances with multiple\ndipolar groups are highly relevant, and their description substantially\nimproves by including the dipole term in SAFT models. To overcome\nthese limitations, this work proposes a vector-addition-based (Vector-)GC\nmethod for the dipole term of PCP-SAFT that accounts for molecular\nsymmetry. The Vector-GC employs information on the substance’s\nmolecular 3D structure to predict the molecular dipole moment through\na vector addition of bond contributions. Combining the proposed sum\nrule for dipole moments with established sum rules for the remaining\nparameters yields a consistent GC method for PCP-SAFT for dipolar\nsubstances. The prediction capabilities of the Vector-GC method are\nanalyzed against experimental data for two substance classes: nonassociating\noxygenated and halogenated substances. We demonstrate that the Vector-GC\nmethod improves vapor pressure and liquid density predictions compared\nto neglecting the dipole term. Moreover, we show that the Vector-GC\nmethod enables differentiation between cis- and trans-isomers. The\nVector-GC method, hence, substantially increases the predictive capabilities\nand applicability domain of GC methods. All parameters are provided\nas JSON and CSV files, and the Vector-GC method is available through\nan open-source python package. Additionally, the developed regression\nframework for GC methods for PCP-SAFT is openly available. The regression\nframework can be employed to regress the Vector-GC method to other\nsubstance classes and is easily adaptable to other sum rules for PCP-SAFT.