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AIChE Journal
Article
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AIChE Journal
Article . 2018 . Peer-reviewed
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Predicting NRTL binary interaction parameters from molecular simulations

Authors: Ashwin Ravichandran; Rajesh Khare; Chau‐Chyun Chen;

Predicting NRTL binary interaction parameters from molecular simulations

Abstract

A predictive approach for calculating the binary interaction parameters ( ) of the nonrandom two liquid (NRTL) local composition model is developed, combining molecular simulations with the two‐fluid theory. The binary interaction parameters are determined for the following three sets of model binary mixtures: water + methanol, methanol + methyl acrylate, and water + methyl acrylate. For each binary mixture, the interaction parameters are expressed in terms of molecular size and strength of interactions, which are in turn, calculated from molecular simulations. We show that the binary interaction parameters determined from simulations are in qualitative agreement with those estimated from regressing experimental data. The major factors that determine the binary interaction parameters are outlined based on simple thermodynamic arguments for each mixture. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2758–2769, 2018

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Top 10%
Top 10%
hybrid