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The System of Models and Optimization of Operating Modes of a Catalytic Reforming Unit Using Initial Fuzzy Information

Author

Listed:
  • Batyr Orazbayev

    (Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Nur-Sultan 010000, Kazakhstan)

  • Ainur Zhumadillayeva

    (Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Nur-Sultan 010000, Kazakhstan)

  • Kulman Orazbayeva

    (Department of Management, Kazakh University of Economics, Finance and International Trade, Nur-Sultan 020000, Kazakhstan)

  • Sandugash Iskakova

    (Faculty of Information Technologies, Atyrau Oil and Gas University, Atyrau 060027, Kazakhstan)

  • Balbupe Utenova

    (Faculty of Information Technologies, Atyrau Oil and Gas University, Atyrau 060027, Kazakhstan)

  • Farit Gazizov

    (Department of Economics and Organization of Production, Kazan State Power Engineering University, 420066 Kazan, Russia)

  • Svetlana Ilyashenko

    (Basic Department of Trade Policy, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

  • Olga Afanaseva

    (Institute of Energy, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

Abstract

The study aims to develop a system of models and a method for optimizing the operating modes of a catalytic reforming unit using fuzzy information, which makes it possible to effectively control the reforming process of the object under study. The object of study of this work is a catalytic reforming unit that has been operating for more than half a century and is characterized by the lack of clarity of some part of the initial information. The research methods are methods of system analysis, mathematical modeling, multicriteria optimization, and expert assessments, as well as methods of theories of fuzzy set theories, which allows formalizing and using fuzzy information, as well as experimental-statistical methods. As a result of the conducted research, the following main results were obtained. Based on a systematic approach, an effective methodology has been developed for developing a system of models of interconnected plant units using various types of available information, including fuzzy information. Using the proposed method, hybrid models have been developed to determine the volume of the produced catalyzate and its quality indicators. A scheme has been constructed for combining the developed models of the main units of the catalytic reforming unit into a single package of models. The built system of models makes it possible to systematically simulate the operation of the plant under study and improve the efficiency of the facility by increasing the volume of target products produced and improving its quality indicators. A statement of the problem of multicriteria optimization is obtained, taking into account the partial fuzziness of the initial information, and a heuristic method for its solution is developed, which is based on the use of knowledge, experience, and intuition of the decision-maker. The results of modeling and optimization show the effectiveness of the proposed fuzzy approach.

Suggested Citation

  • Batyr Orazbayev & Ainur Zhumadillayeva & Kulman Orazbayeva & Sandugash Iskakova & Balbupe Utenova & Farit Gazizov & Svetlana Ilyashenko & Olga Afanaseva, 2022. "The System of Models and Optimization of Operating Modes of a Catalytic Reforming Unit Using Initial Fuzzy Information," Energies, MDPI, vol. 15(4), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1573-:d:754453
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    References listed on IDEAS

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    1. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    2. Batyr Orazbayev & Dinara Kozhakhmetova & Ryszard Wójtowicz & Janusz Krawczyk, 2020. "Modeling of a Catalytic Cracking in the Gasoline Production Installation with a Fuzzy Environment," Energies, MDPI, vol. 13(18), pages 1-13, September.
    3. Meng Xia & Fujun Zhang, 2020. "Application of Multi-Parameter Fuzzy Optimization to Enhance Performance of a Regulated Two-Stage Turbocharged Diesel Engine Operating at High Altitude," Energies, MDPI, vol. 13(17), pages 1-12, August.
    4. Derek Wang & Tianchi Li, 2018. "Carbon Emission Performance of Independent Oil and Natural Gas Producers in the United States," Sustainability, MDPI, vol. 10(1), pages 1-18, January.
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    Cited by:

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    2. Batyr Orazbayev & Yerbol Ospanov & Valentina Makhatova & Lazzat Salybek & Zhanat Abdugulova & Zhumazhan Kulmagambetova & Salamat Suleimenova & Kulman Orazbayeva, 2023. "Methods of Fuzzy Multi-Criteria Decision Making for Controlling the Operating Modes of the Stabilization Column of the Primary Oil-Refining Unit," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
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    4. Mehdi Toloo & Rouhollah Khodabandelou & Amar Oukil, 2022. "A Comprehensive Bibliometric Analysis of Fractional Programming (1965–2020)," Mathematics, MDPI, vol. 10(11), pages 1-21, May.
    5. Jian Chen & Jiajun Zhu & Xu Qin & Wenxiang Xie, 2023. "Reducing Octane Number Loss in Gasoline Refining Process by Using the Improved Sparrow Search Algorithm," Sustainability, MDPI, vol. 15(8), pages 1-21, April.

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