Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting
Abstract The intensification of the extraction of natural resources in the Arctic leads to an increase in the anthropogenic load on the environment. Due to wear and tear of equipment, corrosion of metals, mechanical damage, due to lack of proper maintenance and prolongation of the operating time in...
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Online Access: | http://dx.doi.org/10.1088/1757-899x/1155/1/012052 https://iopscience.iop.org/article/10.1088/1757-899X/1155/1/012052 https://iopscience.iop.org/article/10.1088/1757-899X/1155/1/012052/pdf |
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crioppubl:10.1088/1757-899x/1155/1/012052 2024-06-02T08:00:39+00:00 Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting Grebnev, Ya V Moskalev, A K 2021 http://dx.doi.org/10.1088/1757-899x/1155/1/012052 https://iopscience.iop.org/article/10.1088/1757-899X/1155/1/012052 https://iopscience.iop.org/article/10.1088/1757-899X/1155/1/012052/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining IOP Conference Series: Materials Science and Engineering volume 1155, issue 1, page 012052 ISSN 1757-8981 1757-899X journal-article 2021 crioppubl https://doi.org/10.1088/1757-899x/1155/1/012052 2024-05-07T14:03:18Z Abstract The intensification of the extraction of natural resources in the Arctic leads to an increase in the anthropogenic load on the environment. Due to wear and tear of equipment, corrosion of metals, mechanical damage, due to lack of proper maintenance and prolongation of the operating time in difficult climatic conditions, the operation of equipment used at enterprises leads to an increase in the risk of accidents. On the territory of the Russian Arctic, there are a number of large potentially dangerous objects, which have over 60 tanks for storing petroleum products. In 2020, there were technological accidents related to the bottling of petroleum products, which actualized the problem of taking prompt measures to prevent such emergencies. The methods for assessing the area of bottling of petroleum products currently used, especially in the Arctic zone, have a number of limitations. In the work with the use of the software product Toxy + risk, the risk of an emergency at one of the enterprises of the Russian Arctic was calculated. Neural network prediction of the area of pollution of the surface of the Kheta River was carried out using the NeuroPro neural network simulator. Comparison of the simulation results with the data obtained earlier in the analysis of the accident in the tank farm located beyond the Arctic Circle in the city of Norilsk. Article in Journal/Newspaper Arctic norilsk IOP Publishing Arctic Kheta ENVELOPE(151.327,151.327,61.117,61.117) Norilsk ENVELOPE(88.203,88.203,69.354,69.354) IOP Conference Series: Materials Science and Engineering 1155 1 012052 |
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Abstract The intensification of the extraction of natural resources in the Arctic leads to an increase in the anthropogenic load on the environment. Due to wear and tear of equipment, corrosion of metals, mechanical damage, due to lack of proper maintenance and prolongation of the operating time in difficult climatic conditions, the operation of equipment used at enterprises leads to an increase in the risk of accidents. On the territory of the Russian Arctic, there are a number of large potentially dangerous objects, which have over 60 tanks for storing petroleum products. In 2020, there were technological accidents related to the bottling of petroleum products, which actualized the problem of taking prompt measures to prevent such emergencies. The methods for assessing the area of bottling of petroleum products currently used, especially in the Arctic zone, have a number of limitations. In the work with the use of the software product Toxy + risk, the risk of an emergency at one of the enterprises of the Russian Arctic was calculated. Neural network prediction of the area of pollution of the surface of the Kheta River was carried out using the NeuroPro neural network simulator. Comparison of the simulation results with the data obtained earlier in the analysis of the accident in the tank farm located beyond the Arctic Circle in the city of Norilsk. |
format |
Article in Journal/Newspaper |
author |
Grebnev, Ya V Moskalev, A K |
spellingShingle |
Grebnev, Ya V Moskalev, A K Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
author_facet |
Grebnev, Ya V Moskalev, A K |
author_sort |
Grebnev, Ya V |
title |
Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
title_short |
Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
title_full |
Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
title_fullStr |
Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
title_full_unstemmed |
Simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
title_sort |
simulation modelling of the filling oil products process in the arctic zone using neural network forecasting |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1757-899x/1155/1/012052 https://iopscience.iop.org/article/10.1088/1757-899X/1155/1/012052 https://iopscience.iop.org/article/10.1088/1757-899X/1155/1/012052/pdf |
long_lat |
ENVELOPE(151.327,151.327,61.117,61.117) ENVELOPE(88.203,88.203,69.354,69.354) |
geographic |
Arctic Kheta Norilsk |
geographic_facet |
Arctic Kheta Norilsk |
genre |
Arctic norilsk |
genre_facet |
Arctic norilsk |
op_source |
IOP Conference Series: Materials Science and Engineering volume 1155, issue 1, page 012052 ISSN 1757-8981 1757-899X |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1757-899x/1155/1/012052 |
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IOP Conference Series: Materials Science and Engineering |
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1155 |
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1 |
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012052 |
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1800744733157359616 |