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

Vlad Mureșan, Mihaela-Ligia Ungureșan, Mihail Abrudean, Honoriu Vălean, Iulia Clitan, Roxana Motorga, Emilian Ceuca and Marius Fișcă
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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

Mathematics, 2021, vol. 9, issue 23, 1-31

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.

Keywords: separation cascade; modeling; efficiency; AI (artificial intelligence); neural network; 18 O isotope (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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