Analysis of Data on Vapor–Liquid Equilibrium in Multicomponent Systems Using Artificial Neural Networks

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Analysis of Data on Vapor–Liquid Equilibrium in Multicomponent Systems Using Artificial Neural Networks. / Toikka, A. M.; Misikov, G.H.; Petrov, A. V.

In: Theoretical Foundations of Chemical Engineering, Vol. 55, No. 3, 01.05.2021, p. 403-409.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{7d312534846342939f13e413aed6d637,

title = "Analysis of Data on Vapor–Liquid Equilibrium in Multicomponent Systems Using Artificial Neural Networks",

abstract = "A brief analysis of the possibilities of using the method of artificial neural networks (ANNs) for assessing and correlating data on vapor-liquid equilibrium is presented. The advantages of the Focke method are considered in the case of a limited amount of data, in this case, the parameters of vapor-liquid equilibrium. Six binary and four ternary systems are considered using a modified Argatov-Kocherbitov technique. The estimation of the correctness of the ANN method is presented for the values of excess Gibbs energy calculated from the data on the vapor-liquid equilibrium.",

keywords = "artificial neural networks, binary systems, Gibbs energy, modeling, ternary systems, vapor–liquid equilibrium, MIXTURES, ENERGY, vapor-liquid equilibrium, EXPRESSION",

author = "Toikka, {A. M.} and G.H. Misikov and Petrov, {A. V.}",

note = "Publisher Copyright: {\textcopyright} 2021, Pleiades Publishing, Ltd.",

year = "2021",

month = may,

day = "1",

doi = "10.1134/s004057952103026x",

language = "English",

volume = "55",

pages = "403--409",

journal = "Theoretical Foundations of Chemical Engineering",

issn = "0040-5795",

publisher = "МАИК {"}Наука/Интерпериодика{"}",

number = "3",

}

RIS

TY - JOUR

T1 - Analysis of Data on Vapor–Liquid Equilibrium in Multicomponent Systems Using Artificial Neural Networks

AU - Toikka, A. M.

AU - Misikov, G.H.

AU - Petrov, A. V.

PY - 2021/5/1

Y1 - 2021/5/1

N2 - A brief analysis of the possibilities of using the method of artificial neural networks (ANNs) for assessing and correlating data on vapor-liquid equilibrium is presented. The advantages of the Focke method are considered in the case of a limited amount of data, in this case, the parameters of vapor-liquid equilibrium. Six binary and four ternary systems are considered using a modified Argatov-Kocherbitov technique. The estimation of the correctness of the ANN method is presented for the values of excess Gibbs energy calculated from the data on the vapor-liquid equilibrium.

AB - A brief analysis of the possibilities of using the method of artificial neural networks (ANNs) for assessing and correlating data on vapor-liquid equilibrium is presented. The advantages of the Focke method are considered in the case of a limited amount of data, in this case, the parameters of vapor-liquid equilibrium. Six binary and four ternary systems are considered using a modified Argatov-Kocherbitov technique. The estimation of the correctness of the ANN method is presented for the values of excess Gibbs energy calculated from the data on the vapor-liquid equilibrium.

KW - artificial neural networks

KW - binary systems

KW - Gibbs energy

KW - modeling

KW - ternary systems

KW - vapor–liquid equilibrium

KW - MIXTURES

KW - ENERGY

KW - vapor-liquid equilibrium

KW - EXPRESSION

UR - http://www.scopus.com/inward/record.url?scp=85111626872&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/9192add0-2ec5-3be7-b252-c52f773fcc8b/

U2 - 10.1134/s004057952103026x

DO - 10.1134/s004057952103026x

M3 - Article

AN - SCOPUS:85111626872

VL - 55

SP - 403

EP - 409

JO - Theoretical Foundations of Chemical Engineering

JF - Theoretical Foundations of Chemical Engineering

SN - 0040-5795

IS - 3

ER -

ID: 87678401