Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting
Industrial development and active development of resources in the territory of the fragile Arctic ecosystem requires proper control of technological processes at enterprises located in these areas. Analysis and subsequent modelling of oil and oil products spills are performed in order to elaborate p...
Published in: | E3S Web of Conferences |
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Format: | Article in Journal/Newspaper |
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EDP Sciences
2021
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Online Access: | https://doi.org/10.1051/e3sconf/202132001012 https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/96/e3sconf_esei2021_01012.pdf https://doaj.org/article/e559d7e0242f4404b53b8d9563b04135 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:e559d7e0242f4404b53b8d9563b04135 2023-05-15T14:46:39+02:00 Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting Moskalev Aleksander Grebnev Yaroslav 2021-01-01 https://doi.org/10.1051/e3sconf/202132001012 https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/96/e3sconf_esei2021_01012.pdf https://doaj.org/article/e559d7e0242f4404b53b8d9563b04135 en fr eng fre EDP Sciences 2267-1242 doi:10.1051/e3sconf/202132001012 https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/96/e3sconf_esei2021_01012.pdf https://doaj.org/article/e559d7e0242f4404b53b8d9563b04135 undefined E3S Web of Conferences, Vol 320, p 01012 (2021) geo manag Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.1051/e3sconf/202132001012 2023-01-22T18:10:29Z Industrial development and active development of resources in the territory of the fragile Arctic ecosystem requires proper control of technological processes at enterprises located in these areas. Analysis and subsequent modelling of oil and oil products spills are performed in order to elaborate preventive measures concerning emergencies, means and methods of their liquidation being constantly ready to ensure safety of people and territories, as well as to reduce damage and losses as much as possible in case of their occurrence. The methods of oil product spill area assessment used at present, especially in the Arctic zone, have a number of limitations. This article presents modelling of the process of oil products spill to calculate pollutants concentration distribution and prediction of pollution area up to the moment of its localization with the application of neural network methods. The empirical results were got with NeuroPro neural network simulator and the PHOENICS software product were chosen. The simulation results were correlated with the data obtained in the analysis of an accident caused by depressurization of an aircraft fuel transfer pipeline from on an oil-loading pier on a river in the Arctic zone of Krasnoyarsk Krai. Article in Journal/Newspaper Arctic Krasnoyarsk Krai Unknown Arctic E3S Web of Conferences 320 01012 |
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Open Polar |
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op_collection_id |
fttriple |
language |
English French |
topic |
geo manag |
spellingShingle |
geo manag Moskalev Aleksander Grebnev Yaroslav Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting |
topic_facet |
geo manag |
description |
Industrial development and active development of resources in the territory of the fragile Arctic ecosystem requires proper control of technological processes at enterprises located in these areas. Analysis and subsequent modelling of oil and oil products spills are performed in order to elaborate preventive measures concerning emergencies, means and methods of their liquidation being constantly ready to ensure safety of people and territories, as well as to reduce damage and losses as much as possible in case of their occurrence. The methods of oil product spill area assessment used at present, especially in the Arctic zone, have a number of limitations. This article presents modelling of the process of oil products spill to calculate pollutants concentration distribution and prediction of pollution area up to the moment of its localization with the application of neural network methods. The empirical results were got with NeuroPro neural network simulator and the PHOENICS software product were chosen. The simulation results were correlated with the data obtained in the analysis of an accident caused by depressurization of an aircraft fuel transfer pipeline from on an oil-loading pier on a river in the Arctic zone of Krasnoyarsk Krai. |
format |
Article in Journal/Newspaper |
author |
Moskalev Aleksander Grebnev Yaroslav |
author_facet |
Moskalev Aleksander Grebnev Yaroslav |
author_sort |
Moskalev Aleksander |
title |
Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting |
title_short |
Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting |
title_full |
Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting |
title_fullStr |
Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting |
title_full_unstemmed |
Petroleum Bottling Model in the Arctic Zone of Krasnoyarsk Region by Neural Network Forecasting |
title_sort |
petroleum bottling model in the arctic zone of krasnoyarsk region by neural network forecasting |
publisher |
EDP Sciences |
publishDate |
2021 |
url |
https://doi.org/10.1051/e3sconf/202132001012 https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/96/e3sconf_esei2021_01012.pdf https://doaj.org/article/e559d7e0242f4404b53b8d9563b04135 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Krasnoyarsk Krai |
genre_facet |
Arctic Krasnoyarsk Krai |
op_source |
E3S Web of Conferences, Vol 320, p 01012 (2021) |
op_relation |
2267-1242 doi:10.1051/e3sconf/202132001012 https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/96/e3sconf_esei2021_01012.pdf https://doaj.org/article/e559d7e0242f4404b53b8d9563b04135 |
op_rights |
undefined |
op_doi |
https://doi.org/10.1051/e3sconf/202132001012 |
container_title |
E3S Web of Conferences |
container_volume |
320 |
container_start_page |
01012 |
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1766317854588665856 |