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Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods

Author

Listed:
  • Radel Sultanbekov

    (Department of Oil and Gas Transport and Storage, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

  • Ilia Beloglazov

    (Department of Automation of Technological Processes and Production, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

  • Shamil Islamov

    (Department of Petroleum Engineering, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

  • Muk Chen Ong

    (Faculty of Science and Technology, University of Stavanger, 4036 Stavanger, Norway)

Abstract

Providing quality fuel to ships with reduced SOx content is a priority task. Marine residual fuels are one of the main sources of atmospheric pollution during the operation of ships and sea tankers. Hence, the International Maritime Organization (IMO) has established strict regulations for the sulfur content of marine fuels. One of the possible technological solutions allowing for adherence to the sulfur content limits is use of mixed fuels. However, it carries with it risks of ingredient incompatibilities. This article explores a new approach to the study of active sedimentation of residual and mixed fuels. An assessment of the sedimentation process during mixing, storage, and transportation of marine fuels is made based on estimation three-dimensional diagrams developed by the authors. In an effort to find the optimal solution, studies have been carried out to determine the influence of marine residual fuel compositions on sediment formation via machine learning algorithms. Thus, a model which can be used to predict incompatibilities in fuel compositions as well as sedimentation processes is proposed. The model can be used to determine the sediment content of mixed marine residual fuels with the desired sulfur concentration.

Suggested Citation

  • Radel Sultanbekov & Ilia Beloglazov & Shamil Islamov & Muk Chen Ong, 2021. "Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods," Energies, MDPI, vol. 14(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8422-:d:701792
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    References listed on IDEAS

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    1. Lindstad, Elizabeth & Rehn, Carl Fredrik & Eskeland, Gunnar S., 2017. "Sulphur Abatement Globally in Maritime Shipping," Discussion Papers 2017/8, Norwegian School of Economics, Department of Business and Management Science.
    2. Egnonnumi Lorraine Codjo & Bashir Bakhshideh Zad & Jean-François Toubeau & Bruno François & François Vallée, 2021. "Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation," Energies, MDPI, vol. 14(10), pages 1-20, May.
    3. Valentin Morenov & Ekaterina Leusheva & George Buslaev & Ove T. Gudmestad, 2020. "System of Comprehensive Energy-Efficient Utilization of Associated Petroleum Gas with Reduced Carbon Footprint in the Field Conditions," Energies, MDPI, vol. 13(18), pages 1-14, September.
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