Use of ab initio interaction energies for the prediction of phase equilibria in the system nitrogen–ethane

Literature Information

Publication Date 2002-01-14
DOI 10.1039/B108641N
Impact Factor 3.676
Authors

Gabriele Raabe, Juergen Koehler


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Abstract

Ab initio molecular orbital methods have been used for calculations of interaction parameters of the UNIQUAC and NRTL activity coefficient model to predict phase equilibria in the system nitrogen–ethane. The UNIQUAC and NRTL model with ab initio parameters, calculated on the MP4 and QCISD(T) theory level, have been used in different gE-mixing rules to predict high-pressure vapor–liquid- (VLE) and vapor–liquid–liquid-equilibria (VLLE) by the Peng–Robinson and the Soave–Redlich–Kwong equation of state. The results have been compared to predictions based on UNIFAC with the PSRK-mixing rule. The results using ab initio-UNIQUAC are poor. However, ab initio-NRTL gives good VLE-predictions with all gE-mixing rules and with both equations of state. Only in the temperature range below 133 K, where the VLLE occur, is ab initio-NRTL inferior to the predictions by UNIFAC.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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