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

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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.

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ACKNOWLEDGMENTS

The authors are grateful to V. Kocherbitov (University of Malmö, Sweden) for useful advice. The research was carried out using the computing resources of the Resource Center “Computer Center of SPbU”.

Funding

The study was financially supported by the Russian Foundation for Basic Research (project no. 19-03-00375).

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  1. Institute of Chemistry, Saint Petersburg State University, St. Petersburg, 199034, Russia

    A. M. Toikka, G. Kh. Misikov & A. V. Petrov

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  1. A. M. Toikka
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  2. G. Kh. Misikov
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  3. A. V. Petrov
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Correspondence to A. M. Toikka.

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Toikka, A.M., Misikov, G.K. & Petrov, A.V. Analysis of Data on Vapor–Liquid Equilibrium in Multicomponent Systems Using Artificial Neural Networks. Theor Found Chem Eng 55, 403–409 (2021). https://doi.org/10.1134/S004057952103026X

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