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...

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Bibliographic Details
Published in:E3S Web of Conferences
Main Authors: Moskalev Aleksander, Grebnev Yaroslav
Format: Article in Journal/Newspaper
Language:English
French
Published: EDP Sciences 2021
Subjects:
geo
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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spelling 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
institution Open Polar
collection Unknown
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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