Modeling Dipolar Molecules with PCP-SAFT: A Vector Group-Contribution Method

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