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Application of Artificial Neural Networks for the Analysis of Data on Liquid–Liquid Equilibrium in Three-Component Systems

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Abstract

The potential use of artificial neural networks to describe liquid–liquid phase equilibria in ternary systems under polythermal conditions is considered. The study was carried out on the example of ten ternary systems, including binary splitting subsystems of water–esters of carboxylic acids, which determines the phase splitting in ternary systems (the third component is alcohol or carboxylic acid). The features of the selected network architecture are presented, and the results, with a critical assessment of the accuracy of the calculations, are given in the tables. Approximations based on artificial neural networks are compared with calculations based on the non-random two-liquid (NRTL) model.

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ACKNOWLEDGMENTS

The authors are grateful to V. Kocherbitov (University of Malmö, Sweden) for useful consultations. The research was carried out on the computing resources of the Research Park of St. Petersburg State University, the Computing Centre of St. Petersburg State University.

Funding

The study was financed by the Russian Science Foundation (grant no. 21-13-00038).

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Authors and Affiliations

  1. St. Petersburg State University, Institute of Chemistry, Department of Chemical Thermodynamics and Kinetics, St. Petersburg, Russia

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

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

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Misikov, G.K., Petrov, A.V. & Toikka, A.M. Application of Artificial Neural Networks for the Analysis of Data on Liquid–Liquid Equilibrium in Three-Component Systems. Theor Found Chem Eng 56, 200–207 (2022). https://doi.org/10.1134/S0040579522020129

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