A note on artificial neural network modeling of vapor-liquid equilibrium in multicomponent mixtures

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A note on artificial neural network modeling of vapor-liquid equilibrium in multicomponent mixtures

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Publication Article, peer reviewed scientific
Title A note on artificial neural network modeling of vapor-liquid equilibrium in multicomponent mixtures
Author Argatov, Ivan ; Kocherbitov, Vitaly
Research Centre Biofilms - Research Center for Biointerfaces
Date 2019
English abstract
Application of artificial neural networks (ANNs) for modeling of vapor-liquid equilibrium in multicomponent mixtures is considered. Two novel ANN-based models are introduced, which can be seen as generalizations of the Wilson model and the NRTL model. A unique feature of the proposed approach is that an ANN approximation for the molar excess Gibbs energy generates approximations for the activity coefficients. A special case of the ternary acetic acid-n-propyl alcohol-water system (at 313.15 K) is used to illustrate the efficiency of the different models, including Wilson's model, Focke's model, and the introduced generalized degree-1 homogeneous neural network model. Also, the latter one-level NN model is compared to the Wilson model on 10 binary systems. The efficiency of the two-level NN model is assessed by a comparison with the NRTL model. (C) 2019 Elsevier B.V. All rights reserved.
DOI https://doi.org/10.1016/j.fluid.2019.112282 (link to publisher's fulltext.)
Publisher Elsevier Bv
Host/Issue Fluid Phase Equilibria;
Volume 502
ISSN 0378-3812
Language eng (iso)
Subject Vapor-liquid equilibrium
Ternary system
Excess gibbs energy
Activity coefficients
Artificial neural network
Sciences
Research Subject Categories::NATURAL SCIENCES
Handle http://hdl.handle.net/2043/30525 Permalink to this page
Link to publication in DiVA Find this research publication in DiVA (n/a for student publ.)
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