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

A. M. Toikka, G.H. Misikov, A. V. Petrov

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)403-409
Number of pages7
JournalTheoretical Foundations of Chemical Engineering
Volume55
Issue number3
DOIs
StatePublished - May 2021

Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)

Keywords

  • artificial neural networks
  • binary systems
  • Gibbs energy
  • modeling
  • ternary systems
  • vapor–liquid equilibrium
  • MIXTURES
  • ENERGY
  • vapor-liquid equilibrium
  • EXPRESSION

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