Hemprich Carl, Rehner Philipp, Esper Timm, Gross Joachim, Roskosch Dennis, Bardow André
Energy and Process Systems Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.
Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Stuttgart 70569, Germany.
ACS Omega. 2024 Sep 5;9(37):38809-38819. doi: 10.1021/acsomega.4c04867. eCollection 2024 Sep 17.
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.
在缺乏实验数据的情况下，预测热力学平衡性质对于开发化学和能量转换过程至关重要。对于热力学性质的建模，基于统计缔合流体理论（SAFT）的状态方程，如扰动链极性（PCP）-SAFT，已被证明是强大的，并得到了广泛应用。PCP-SAFT参数可以通过基团贡献（GC）方法预测。然而，它们在偶极项中的应用受到很大限制：当前的GC方法忽略了偶极项，或者仅允许每种物质有一个偶极基团，以避免处理分子偶极矩的对称效应。尽管如此，具有多个偶极基团的物质非常重要，通过在SAFT模型中包含偶极项，对它们的描述有了显著改进。为了克服这些限制，本文提出了一种基于向量加法的（Vector-）GC方法，用于PCP-SAFT的偶极项，该方法考虑了分子对称性。Vector-GC利用物质分子三维结构的信息，通过键贡献的向量加法来预测分子偶极矩。将提出的偶极矩求和规则与其余参数的既定求和规则相结合，得到了一种适用于偶极物质的PCP-SAFT的一致GC方法。针对两类物质的实验数据，分析了Vector-GC方法的预测能力：非缔合的含氧化合物和卤代化合物。我们证明，与忽略偶极项相比，Vector-GC方法改善了蒸气压和液体密度的预测。此外，我们表明Vector-GC方法能够区分顺式和反式异构体。因此，Vector-GC方法大大提高了GC方法的预测能力和适用范围。所有参数都以JSON和CSV文件形式提供，Vector-GC方法可通过一个开源Python包获得。此外，为PCP-SAFT开发的GC方法回归框架是公开可用的。该回归框架可用于将Vector-GC方法回归到其他物质类别，并且很容易适应PCP-SAFT的其他求和规则。
