IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1459-d802834.html
   My bibliography  Save this article

Artificial Intelligence in Fractional-Order Systems Approximation with High Performances: Application in Modelling of an Isotopic Separation Process

Author

Listed:
  • Roxana Motorga

    (Automation Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania)

  • Vlad Mureșan

    (Automation Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania)

  • Mihaela-Ligia Ungureșan

    (Physics and Chemistry Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania)

  • Mihail Abrudean

    (Automation Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania)

  • Honoriu Vălean

    (Automation Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania)

  • Iulia Clitan

    (Automation Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj-Napoca, Romania)

Abstract

This paper presents a solution for the modelling, implementation and simulation of the fractional-order process of producing the enriched 13 C isotope, through the chemical exchange between carbamate and carbon dioxide. To achieve the goal of implementation and simulation of the considered process, an original solution for the approximation of fractional-order systems at the variation of the system’s differentiation order is proposed, based on artificial intelligence methods. The separation process has the property of being strongly non-linear and also having fractional-order behaviour. Consequently, in the implementation of the mathematical model of the process, the theory associated with the fractional-order system’s domain has to be considered and applied. For learning the dynamics of the structure parameters of the fractional-order part of the model, neural networks, which are associated with the artificial intelligence domain, are used. Using these types of approximations, the simulation and the prediction of the produced 13 C isotope concentration dynamics are made with high accuracy. In order to prove the efficiency of the proposed solutions, a comparation between the responses of the determined model and the experimental responses is made. The proposed model implementation is made based on using four trained neural networks. Moreover, in the final part of the paper, an original method for the online identification of the separation process model is proposed. This original method can identify the process of fractional differentiation order variation in relation to time, a phenomenon which is quite frequent in the operation of the real separation plant. In the last section of the paper, it is proven that artificial intelligence methods can successfully sustain the system model in all the scenarios, resulting in the feasible premise of designing an automatic control system for the 13 C isotope concentration, a method which can be applied in the case of other industrial applications too.

Suggested Citation

  • Roxana Motorga & Vlad Mureșan & Mihaela-Ligia Ungureșan & Mihail Abrudean & Honoriu Vălean & Iulia Clitan, 2022. "Artificial Intelligence in Fractional-Order Systems Approximation with High Performances: Application in Modelling of an Isotopic Separation Process," Mathematics, MDPI, vol. 10(9), pages 1-32, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1459-:d:802834
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1459/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1459/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haefner, Naomi & Wincent, Joakim & Parida, Vinit & Gassmann, Oliver, 2021. "Artificial intelligence and innovation management: A review, framework, and research agenda✰," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    3. Zhu, Zhen & Lu, Jun-Guo, 2021. "Robust stability and stabilization of hybrid fractional-order multi-dimensional systems with interval uncertainties: An LMI approach," Applied Mathematics and Computation, Elsevier, vol. 401(C).
    4. Akgül, Akif & Rajagopal, Karthikeyan & Durdu, Ali & Pala, Muhammed Ali & Boyraz, Ömer Faruk & Yildiz, Mustafa Zahid, 2021. "A simple fractional-order chaotic system based on memristor and memcapacitor and its synchronization application," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    5. Jun Shen & James Lam, 2014. "State feedback control of commensurate fractional-order systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 363-372.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emad A. Mohamed & Mokhtar Aly & Masayuki Watanabe, 2022. "New Tilt Fractional-Order Integral Derivative with Fractional Filter (TFOIDFF) Controller with Artificial Hummingbird Optimizer for LFC in Renewable Energy Power Grids," Mathematics, MDPI, vol. 10(16), pages 1-33, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    3. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    4. Du, Chuanhong & Liu, Licai & Zhang, Zhengping & Yu, Shixing, 2022. "A mem-element Wien-Bridge circuit with amplitude modulation and three kinds of offset boosting," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    5. De Cicco, Roberta & Iacobucci, Serena & Cannito, Loreta & Onesti, Gianni & Ceccato, Irene & Palumbo, Riccardo, 2024. "Virtual vs. human influencer: Effects on users’ perceptions and brand outcomes," Technology in Society, Elsevier, vol. 77(C).
    6. Fredström, Ashkan & Parida, Vinit & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak, 2022. "What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    7. Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2022. "Fuzzy-weighted differential evolution computing paradigm for fractional order nonlinear wiener systems," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    8. Ertugrul Uysal & Sascha Alavi & Valéry Bezençon, 2022. "Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1153-1175, November.
    9. Nordin, Fredrik & Ravald, Annika, 2023. "The making of marketing decisions in modern marketing environments," Journal of Business Research, Elsevier, vol. 162(C).
    10. Yanhao Ren & Qiang Luo & Wenlian Lu, 2023. "Synchronization Analysis of Linearly Coupled Systems with Signal-Dependent Noises," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    11. Ding, Bin & Li, Yameng & Miah, Shah & Liu, Wei, 2024. "Customer acceptance of frontline social robots—Human-robot interaction as boundary condition," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    12. Noble, Stephanie M. & Mende, Martin & Grewal, Dhruv & Parasuraman, A., 2022. "The Fifth Industrial Revolution: How Harmonious Human–Machine Collaboration is Triggering a Retail and Service [R]evolution," Journal of Retailing, Elsevier, vol. 98(2), pages 199-208.
    13. Jeon, Yongwoog Andrew, 2022. "Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes," Journal of Business Research, Elsevier, vol. 145(C), pages 892-904.
    14. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    15. Erik Hermann, 2022. "Anthropomorphized artificial intelligence, attachment, and consumer behavior," Marketing Letters, Springer, vol. 33(1), pages 157-162, March.
    16. Sullivan, Yulia & Fosso Wamba, Samuel, 2024. "Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation," Journal of Business Research, Elsevier, vol. 174(C).
    17. Poushneh, Atieh & Vasquez-Parraga, Arturo & Gearhart, Richard S., 2024. "The effect of empathetic response and consumers’ narcissism in voice-based artificial intelligence," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    18. Corbo, Leonardo & Kraus, Sascha & Vlačić, Božidar & Dabić, Marina & Caputo, Andrea & Pellegrini, Massimiliano M., 2023. "Coopetition and innovation: A review and research agenda," Technovation, Elsevier, vol. 122(C).
    19. Trocin, Cristina & Hovland, Ingrid Våge & Mikalef, Patrick & Dremel, Christian, 2021. "How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    20. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1459-:d:802834. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.