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

被引：8
|
作者：
Argatov, Ivan [1 ,3 ]
Kocherbitov, Vitaly [2 ,3 ]
机构：
[1] Malmo Univ, Fac Technol & Soc, SE-20506 Malmo, Sweden
[2] Malmo Univ, Fac Hlth & Soc, SE-20506 Malmo, Sweden
[3] Malmo Univ, Biofilms Res Ctr Biointerfaces, SE-20506 Malmo, Sweden
来源：
FLUID PHASE EQUILIBRIA | 2019年 / 502卷
关键词：
Vapor-liquid equilibrium; Ternary system; Excess gibbs energy; Activity coefficients; Artificial neural network; PREDICTION; SYSTEMS; COEFFICIENT;
D O I：
10.1016/j.fluid.2019.112282
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
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.
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页数：8
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