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Modeling the Production Process of Fuel Gas, LPG, Propylene, and Polypropylene in a Petroleum Refinery Using Generalized Nets

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  • Danail D. Stratiev

    (Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria)

  • Angel Dimitriev

    (Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria)

  • Dicho Stratiev

    (Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria
    LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria)

  • Krassimir Atanassov

    (Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria
    Intelligent Systems Laboratory, Prof. Dr. Assen Zlatarov University, 1 “Prof. Yakimov” Blvd., 8010 Burgas, Bulgaria)

Abstract

The parallel processes involved in the production of refinery fuel gas, liquid petroleum gas (LPG), propylene, and polypropylene, occurring in thirteen refinery units, are modeled by the use of a Generalized Net (GN) apparatus. The modeling of the production of these products is important because they affect the energy balance of petroleum refinery and the associated emissions of greenhouse gases. For the first time, such a model is proposed and it is a continuation of the investigations of refinery process modelling by GNs. The model contains 17 transitions, 55 places, and 47 types of tokens, and considers the orders of fuel gas for the refinery power station, refinery process furnaces, LPG, liquid propylene, and 6 grades of polypropylene. This model is intended to be used as a more detailed lower-level GN model in a higher-level GN model that facilitates and optimizes the process of decision making in the petroleum refining industry.

Suggested Citation

  • Danail D. Stratiev & Angel Dimitriev & Dicho Stratiev & Krassimir Atanassov, 2023. "Modeling the Production Process of Fuel Gas, LPG, Propylene, and Polypropylene in a Petroleum Refinery Using Generalized Nets," Mathematics, MDPI, vol. 11(17), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3800-:d:1232897
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    References listed on IDEAS

    as
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    2. Pla, Benjamí n & Bares, Pau & Jiménez, Irina & Guardiola, Carlos & Zhang, Yahui & Shen, Tielong, 2020. "A fuzzy logic map-based knock control for spark ignition engines," Applied Energy, Elsevier, vol. 280(C).
    3. Danail Dichev Stratiev & Dicho Stratiev & Krassimir Atanassov, 2021. "Modelling the Process of Production of Diesel Fuels by the Use of Generalized Nets," Mathematics, MDPI, vol. 9(19), pages 1-10, September.
    4. Fahd Saeed Alakbari & Mysara Eissa Mohyaldinn & Mohammed Abdalla Ayoub & Ali Samer Muhsan & Ibnelwaleed A Hussein, 2021. "A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    5. 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.
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    Cited by:

    1. Danail Stratiev & Angel Dimitriev & Dicho Stratiev & Krassimir Atanassov, 2023. "Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery," Mathematics, MDPI, vol. 11(23), pages 1-19, November.

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