A critical assessment on two predictive models of binary vapor–liquid equilibrium

Literature Information

Publication Date 2004-06-10
DOI 10.1039/B400322E
Impact Factor 3.676
Authors

Hansong Cheng, Virpul Parekh, Brian Peterson, Kamil Klier


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Abstract

A critical assessment on two predictive theoretical models for vapor–liquid equilibrium (VLE) is presented. The first model utilizes molecular dynamics to evaluate interaction energies in liquids. The energy terms are then used as input parameters in activity coefficient models to calculate the vapor–liquid equilibrium and activity coefficients. The second model is based on the recently proposed COSMO-RS to derive VLE diagrams. The assessment was made with 16 binary mixture systems. We show that the hybrid model that combines a predictive method based on molecular dynamics with a correlated method based on activity coefficient methods in general gives qualitatively incorrect results due to the fact that the energy terms used in activity coefficient models do not represent the true intermolecular interaction energies. COSMO-RS on the other hand often predicts VLE behavior with qualitative or semi-quantitative accuracy. However, the current implementations of the method do not allow for a description of non-ideal vapor phases or partially miscible mixtures.

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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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