Open Access
Energies, volume 14, issue 24, pages 8422
Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods
Publication type: Journal Article
Publication date: 2021-12-14
Electrical and Electronic Engineering
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Optimization
Engineering (miscellaneous)
Energy (miscellaneous)
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.
Citations by journals
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Energies
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Energies
4 publications, 14.81%
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Journal of Marine Science and Engineering
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Journal of Marine Science and Engineering
3 publications, 11.11%
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Symmetry
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Symmetry
3 publications, 11.11%
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E3S Web of Conferences
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E3S Web of Conferences
3 publications, 11.11%
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Applied Sciences (Switzerland)
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Applied Sciences (Switzerland)
2 publications, 7.41%
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Journal of Applied Engineering Science
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Journal of Applied Engineering Science, 2, 7.41%
Journal of Applied Engineering Science
2 publications, 7.41%
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Egyptian Journal of Petroleum
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Egyptian Journal of Petroleum, 1, 3.7%
Egyptian Journal of Petroleum
1 publication, 3.7%
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Journal of Porous Materials
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Journal of Porous Materials
1 publication, 3.7%
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Fuel
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Fuel
1 publication, 3.7%
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Industrial & Engineering Chemistry Research
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Industrial & Engineering Chemistry Research
1 publication, 3.7%
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Fluids
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Fluids
1 publication, 3.7%
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Resources
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Resources
1 publication, 3.7%
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Journal of Petroleum Exploration and Production Technology
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Journal of Petroleum Exploration and Production Technology
1 publication, 3.7%
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Energy Science and Engineering
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Energy Science and Engineering
1 publication, 3.7%
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Izvestiya. Ferrous Metallurgy
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Izvestiya. Ferrous Metallurgy
1 publication, 3.7%
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Journal of Mining Institute
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Journal of Mining Institute
1 publication, 3.7%
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Citations by publishers
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14
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
14 publications, 51.85%
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EDP Sciences
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EDP Sciences
3 publications, 11.11%
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Springer Nature
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Springer Nature
2 publications, 7.41%
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Institut za Istrazivanja I Projektovanja u Privredi
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Institut za Istrazivanja I Projektovanja u Privredi, 2, 7.41%
Institut za Istrazivanja I Projektovanja u Privredi
2 publications, 7.41%
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Egyptian Petroleum Research Institute
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Egyptian Petroleum Research Institute, 1, 3.7%
Egyptian Petroleum Research Institute
1 publication, 3.7%
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Elsevier
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Elsevier
1 publication, 3.7%
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American Chemical Society (ACS)
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American Chemical Society (ACS)
1 publication, 3.7%
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Wiley
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Wiley
1 publication, 3.7%
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National University of Science & Technology (MISiS)
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National University of Science & Technology (MISiS)
1 publication, 3.7%
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Saint Petersburg Mining University
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Saint Petersburg Mining University
1 publication, 3.7%
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Sultanbekov R. et al. Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods // Energies. 2021. Vol. 14. No. 24. p. 8422.
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Sultanbekov R., Beloglazov I., Islamov S., Ong M. C. Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods // Energies. 2021. Vol. 14. No. 24. p. 8422.
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TY - JOUR
DO - 10.3390/en14248422
UR - https://doi.org/10.3390%2Fen14248422
TI - Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods
T2 - Energies
AU - Sultanbekov, Radel
AU - Beloglazov, Ilia
AU - Islamov, Shamil
AU - Ong, Muk Chen
PY - 2021
DA - 2021/12/14 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 8422
IS - 24
VL - 14
SN - 1996-1073
ER -
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@article{2021_Sultanbekov,
author = {Radel Sultanbekov and Ilia Beloglazov and Shamil Islamov and Muk Chen Ong},
title = {Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods},
journal = {Energies},
year = {2021},
volume = {14},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {dec},
url = {https://doi.org/10.3390%2Fen14248422},
number = {24},
pages = {8422},
doi = {10.3390/en14248422}
}
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Sultanbekov, Radel, et al. “Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods.” Energies, vol. 14, no. 24, Dec. 2021, p. 8422. https://doi.org/10.3390%2Fen14248422.