Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai
Abstract Intensified human activity in exploiting the natural resources leads to increasing technogenic load on Arctic’s fragile ecosystem. The boost in industrial facilities is associated with a growing number of stationary fuel reservoirs poorly monitored due to their considerable remoteness and e...
Published in: | IOP Conference Series: Earth and Environmental Science |
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2021
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Online Access: | http://dx.doi.org/10.1088/1755-1315/816/1/012007 https://iopscience.iop.org/article/10.1088/1755-1315/816/1/012007 https://iopscience.iop.org/article/10.1088/1755-1315/816/1/012007/pdf |
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crioppubl:10.1088/1755-1315/816/1/012007 2024-06-02T08:01:04+00:00 Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai Grebnev, Y Moskalev, A 2021 http://dx.doi.org/10.1088/1755-1315/816/1/012007 https://iopscience.iop.org/article/10.1088/1755-1315/816/1/012007 https://iopscience.iop.org/article/10.1088/1755-1315/816/1/012007/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining IOP Conference Series: Earth and Environmental Science volume 816, issue 1, page 012007 ISSN 1755-1307 1755-1315 journal-article 2021 crioppubl https://doi.org/10.1088/1755-1315/816/1/012007 2024-05-07T13:58:40Z Abstract Intensified human activity in exploiting the natural resources leads to increasing technogenic load on Arctic’s fragile ecosystem. The boost in industrial facilities is associated with a growing number of stationary fuel reservoirs poorly monitored due to their considerable remoteness and extreme weather conditions. The 2020 emergency in the Arctic zone of Krasnoyarsk Krai exposed the lack of adequate methods for risk assessment and behaviour in case of accidents at potentially hazardous facilities. The existing methodologies for assessing the area of spill following an accidental depressurisation present significant limitations. Most methodologies are based on analytical models not taking into account the physics of processes. This work uses modelling with neural networks of oil spill at the potentially hazardous object located in the Arctic territory of the Krasnoyarsk Krai. The software used was neural network simulator NeuroPro, developed in the Institute of Computational Modelling of Krasnoyarsk Scientific Centre of SB RAS. For training the neural network there were used daily operational data on fourteen main vectors affecting the propagation rate. The neural network modelling of the accidental oil spill during the depressurization of one of the fuel tanks at potentially hazardous facilities in the Arctic zone in 2020 correlated perfectly with the real data. Article in Journal/Newspaper Arctic Krasnoyarsk Krai IOP Publishing Arctic IOP Conference Series: Earth and Environmental Science 816 1 012007 |
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Abstract Intensified human activity in exploiting the natural resources leads to increasing technogenic load on Arctic’s fragile ecosystem. The boost in industrial facilities is associated with a growing number of stationary fuel reservoirs poorly monitored due to their considerable remoteness and extreme weather conditions. The 2020 emergency in the Arctic zone of Krasnoyarsk Krai exposed the lack of adequate methods for risk assessment and behaviour in case of accidents at potentially hazardous facilities. The existing methodologies for assessing the area of spill following an accidental depressurisation present significant limitations. Most methodologies are based on analytical models not taking into account the physics of processes. This work uses modelling with neural networks of oil spill at the potentially hazardous object located in the Arctic territory of the Krasnoyarsk Krai. The software used was neural network simulator NeuroPro, developed in the Institute of Computational Modelling of Krasnoyarsk Scientific Centre of SB RAS. For training the neural network there were used daily operational data on fourteen main vectors affecting the propagation rate. The neural network modelling of the accidental oil spill during the depressurization of one of the fuel tanks at potentially hazardous facilities in the Arctic zone in 2020 correlated perfectly with the real data. |
format |
Article in Journal/Newspaper |
author |
Grebnev, Y Moskalev, A |
spellingShingle |
Grebnev, Y Moskalev, A Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai |
author_facet |
Grebnev, Y Moskalev, A |
author_sort |
Grebnev, Y |
title |
Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai |
title_short |
Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai |
title_full |
Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai |
title_fullStr |
Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai |
title_full_unstemmed |
Modelling the accidental oil spills at potentially hazardous facilities in the Arctic zone of Krasnoyarsk Krai |
title_sort |
modelling the accidental oil spills at potentially hazardous facilities in the arctic zone of krasnoyarsk krai |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1755-1315/816/1/012007 https://iopscience.iop.org/article/10.1088/1755-1315/816/1/012007 https://iopscience.iop.org/article/10.1088/1755-1315/816/1/012007/pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Krasnoyarsk Krai |
genre_facet |
Arctic Krasnoyarsk Krai |
op_source |
IOP Conference Series: Earth and Environmental Science volume 816, issue 1, page 012007 ISSN 1755-1307 1755-1315 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1755-1315/816/1/012007 |
container_title |
IOP Conference Series: Earth and Environmental Science |
container_volume |
816 |
container_issue |
1 |
container_start_page |
012007 |
_version_ |
1800745321384378368 |