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AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison

Author

Listed:
  • Vlad Mureșan

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Mihaela-Ligia Ungureșan

    (Physics and Chemistry Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Mihail Abrudean

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Honoriu Vălean

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Iulia Clitan

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Roxana Motorga

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Emilian Ceuca

    (Informatics, Mathematics and Electronics Department, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania)

  • Marius Fișcă

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

Abstract

In the paper, the comparison between the efficiency of using artificial intelligence methods and the efficiency of using classical methods in modelling the industrial processes is made, considering as a case study the separation process of the 18 O isotope. Firstly, the behavior of the considered isotopic separation process is learned using neural networks. The comparison between the efficiency of these methods is highlighted by the simulations of the process model, using the mentioned modelling techniques. In this context, the final part of the paper presents the proposed model being simulated in different scenarios that can occur in practice, thus resulting in some interesting interpretations and conclusions. The paper proves the feasibility of using artificial intelligence methods for industrial processes modeling; the obtained models being intended for use in designing automatic control systems.

Suggested Citation

  • Vlad Mureșan & Mihaela-Ligia Ungureșan & Mihail Abrudean & Honoriu Vălean & Iulia Clitan & Roxana Motorga & Emilian Ceuca & Marius Fișcă, 2021. "AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison," Mathematics, MDPI, vol. 9(23), pages 1-31, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3088-:d:691725
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    References listed on IDEAS

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    1. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
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    3. Abdelaziz, E.A. & Saidur, R. & Mekhilef, S., 2011. "A review on energy saving strategies in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 150-168, January.
    4. Nazir, Shareq Mohd & Cloete, Jan Hendrik & Cloete, Schalk & Amini, Shahriar, 2019. "Efficient hydrogen production with CO2 capture using gas switching reforming," Energy, Elsevier, vol. 185(C), pages 372-385.
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    Cited by:

    1. Marius Fișcă & Mihail Abrudean & Vlad Mureșan & Iulia Clitan & Mihaela-Ligia Ungureșan & Roxana Motorga & Emilian Ceuca, 2022. "Modeling and Simulation of High Voltage Power Lines under Transient and Persistent Faults," Mathematics, MDPI, vol. 11(1), pages 1-28, December.

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