Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks

Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma so...

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Autores:
Mayet, Abdulilah
Mehdi Alizadeh, Seyed
Azeez Kakarash, Zana
Al-Qahtani, Ali Awadh
Alanazi, Abdullah
Grimaldo Guerrero, John William
Alhashimi, Hala H.
Eftekhari-Zadeh, Ehsan
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9429
Acceso en línea:
https://hdl.handle.net/11323/9429
https://doi.org/10.3390/polym14142852
https://repositorio.cuc.edu.co/
Palabra clave:
Detection system
Feature extraction
RBF neural network
Oil and polymeric fluids
Dual-energy gamma source
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/9429
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dc.title.eng.fl_str_mv Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
title Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
spellingShingle Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
Detection system
Feature extraction
RBF neural network
Oil and polymeric fluids
Dual-energy gamma source
title_short Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
title_full Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
title_fullStr Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
title_full_unstemmed Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
title_sort Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks
dc.creator.fl_str_mv Mayet, Abdulilah
Mehdi Alizadeh, Seyed
Azeez Kakarash, Zana
Al-Qahtani, Ali Awadh
Alanazi, Abdullah
Grimaldo Guerrero, John William
Alhashimi, Hala H.
Eftekhari-Zadeh, Ehsan
dc.contributor.author.spa.fl_str_mv Mayet, Abdulilah
Mehdi Alizadeh, Seyed
Azeez Kakarash, Zana
Al-Qahtani, Ali Awadh
Alanazi, Abdullah
Grimaldo Guerrero, John William
Alhashimi, Hala H.
Eftekhari-Zadeh, Ehsan
dc.subject.proposal.eng.fl_str_mv Detection system
Feature extraction
RBF neural network
Oil and polymeric fluids
Dual-energy gamma source
topic Detection system
Feature extraction
RBF neural network
Oil and polymeric fluids
Dual-energy gamma source
description Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-08-04T14:24:50Z
dc.date.available.none.fl_str_mv 2022-08-04T14:24:50Z
dc.date.issued.none.fl_str_mv 2022-07-13
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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dc.identifier.citation.spa.fl_str_mv Mayet, A.M.; Alizadeh, S.M.; Kakarash, Z.A.; Al-Qahtani, A.A.; Alanazi, A.K.; Grimaldo Guerrero, J.W.; Alhashimi, H.H.; Eftekhari-Zadeh, E. Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks. Polymers 2022, 14, 2852. https://doi.org/10.3390/ polym14142852
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9429
dc.identifier.url.spa.fl_str_mv https://doi.org/10.3390/polym14142852
dc.identifier.doi.spa.fl_str_mv 10.3390/polym14142852
dc.identifier.eissn.spa.fl_str_mv 2073-4360
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Mayet, A.M.; Alizadeh, S.M.; Kakarash, Z.A.; Al-Qahtani, A.A.; Alanazi, A.K.; Grimaldo Guerrero, J.W.; Alhashimi, H.H.; Eftekhari-Zadeh, E. Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks. Polymers 2022, 14, 2852. https://doi.org/10.3390/ polym14142852
10.3390/polym14142852
2073-4360
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9429
https://doi.org/10.3390/polym14142852
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Polymers
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9. Roshani, M.; Sattari, M.A.; Ali PJ, M.; Roshani, G.H.; Nazemi, B.; Corniani, E.; Nazemi, E. Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter. Flow Meas. Instrum. 2020, 75, 101804. [CrossRef]
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19. Isaev, A.A.; Aliev, M.M.O.; Drozdov, A.N.; Gorbyleva, Y.A.; Nurgalieva, K.S. Improving the Efficiency of Curved Wells’ Operation by Means of Progressive Cavity Pumps. Energies 2022, 15, 4259. [CrossRef]
20. Lalbakhsh, A.; Mohamadpour, G.; Roshani, S.; Ami, M.; Roshani, S.; Sayem, A.S.M.; Alibakhshikenari, M.; Koziel, S. Design of a Compact Planar Transmission Line for Miniaturized Rat-Race Coupler With Harmonics Suppression. IEEE Access 2021, 9, 129207–129217. [CrossRef]
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22. Shukla, N.K.; Mayet, A.M.; Vats, A.; Aggarwal, M.; Raja, R.K.; Verma, R.; Muqeet, M.A. High speed integrated RF–VLC data communication system: Performance constraints and capac-ity considerations. Phys. Commun. 2022, 50, 101492. [CrossRef]
23. Hookari, M.; Roshani, S.; Roshani, S. High-efficiency balanced power amplifier using miniaturized harmonics suppressed coupler. Int. J. RF Microw. Comput.-Aided Eng. 2020, 30, e22252. [CrossRef]
24. Mayet, A.; Hussain, A.; Hussain, M. Three-terminal nanoelectromechanical switch based on tungsten nitride, an amor-phous metallic material. Nanotechnology 2016, 27, 035202. [CrossRef]
25. Lotfi, S.; Roshani, S.; Roshani, S.; Gilan, M.S. Wilkinson power divider with band-pass filtering response and harmonics suppression using open and short stubs. Frequenz 2020, 74, 169–176. [CrossRef]
26. Mayet, A.; Hussain, M. Amorphous WNx Metal For Accelerometers and Gyroscope. In Proceedings of the MRS Fall Meeting, Boston, MA, USA, 30 November–5 December 2014.
27. Jamshidi, M.; Siahkamari, H.; Roshani, S.; Roshani, S. A compact Gysel power divider design using U-shaped and T-shaped resonators with harmonics suppression. Electromagnetics 2019, 39, 491–504. [CrossRef]
28. Mayet, A.; Smith, C.E.; Hussain, M.M. Energy reversible switching from amorphous metal based nanoelectromechanical switch. In Proceedings of the Nanotechnology (IEEE-NANO), 2013 13th IEEE Conference, Beijing, China, 5–8 August 2013; pp. 366–369.
29. Roshani, S.; Roshani, S. Two-section impedance transformer design and modeling for power amplifier applications. Appl. Comput. Electromagn. Soc. J. 2017, 32, 1042–1047.
30. Khaibullina, K.S.; Sagirova, L.R.; Sandyga, M.S. Substantiation and selection of an inhibitor for preventing the formation of asphaltresin-paraffin deposits. [Substanciação e seleção de um inibidor para evitar a formação de depósitos de asfalto-resina-parafina]. Period. Tche Quim. 2020, 17, 541–551.
31. Jamshidi, M.B.; Roshani, S.; Talla, J.; Roshani, S.; Peroutka, Z. Size reduction and performance improvement of a microstrip Wil-kinson power divider using a hybrid design technique. Sci. Rep. 2021, 11, 7773. [CrossRef]
32. Mayet, A.; Smith, C.; Hussain, M.M.; Smith, C. Amorphous metal based nanoelectromechanical switch. In Proceedings of the 2013 Saudi International Electronics, Communications and Photonics Conference, Riyadh, Saudi Arabia, 27–30 April 2013; pp. 1–5. [CrossRef]
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34. Khaibullina, K.S.; Korobov, G.Y.; Lekomtsev, A.V. Development of an asphalt-resin-paraffin deposits inhibitor and substantiation of the technological parameters of its injection into the bottom-hole formation zone. [Desenvolvimento de um inibidor de depósito de asfalto-resinaparafina e subscantiação dos parâmetros tecnológicos de sua injeção na zona de formação de furo inferior]. Period. Tche Quim. 2020, 17, 769–781.
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spelling Mayet, AbdulilahMehdi Alizadeh, SeyedAzeez Kakarash, ZanaAl-Qahtani, Ali AwadhAlanazi, AbdullahGrimaldo Guerrero, John WilliamAlhashimi, Hala H.Eftekhari-Zadeh, Ehsan2022-08-04T14:24:50Z2022-08-04T14:24:50Z2022-07-13Mayet, A.M.; Alizadeh, S.M.; Kakarash, Z.A.; Al-Qahtani, A.A.; Alanazi, A.K.; Grimaldo Guerrero, J.W.; Alhashimi, H.H.; Eftekhari-Zadeh, E. Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks. Polymers 2022, 14, 2852. https://doi.org/10.3390/ polym14142852https://hdl.handle.net/11323/9429https://doi.org/10.3390/polym1414285210.3390/polym141428522073-4360Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.16 páginasapplication/pdfengMDPI AGSwitzerlandAtribución 4.0 Internacional (CC BY 4.0)© 2022 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networksArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.mdpi.com/2073-4360/14/14/2852Polymers1. Hosseini, S.; Taylan, O.; Abusurrah, M.; Akilan, T.; Nazemi, E.; Eftekhari-Zadeh, E.; Bano, F.; Roshani, G.H. Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries. Polymers 2021, 13, 3647. [CrossRef] [PubMed]2. 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Three-terminal nanoelectromechanical switch based on tungsten nitride, an amor-phous metallic material. Nanotechnology 2016, 27, 035202. [CrossRef]25. Lotfi, S.; Roshani, S.; Roshani, S.; Gilan, M.S. Wilkinson power divider with band-pass filtering response and harmonics suppression using open and short stubs. Frequenz 2020, 74, 169–176. [CrossRef]26. Mayet, A.; Hussain, M. Amorphous WNx Metal For Accelerometers and Gyroscope. In Proceedings of the MRS Fall Meeting, Boston, MA, USA, 30 November–5 December 2014.27. Jamshidi, M.; Siahkamari, H.; Roshani, S.; Roshani, S. A compact Gysel power divider design using U-shaped and T-shaped resonators with harmonics suppression. Electromagnetics 2019, 39, 491–504. [CrossRef]28. Mayet, A.; Smith, C.E.; Hussain, M.M. Energy reversible switching from amorphous metal based nanoelectromechanical switch. In Proceedings of the Nanotechnology (IEEE-NANO), 2013 13th IEEE Conference, Beijing, China, 5–8 August 2013; pp. 366–369.29. Roshani, S.; Roshani, S. Two-section impedance transformer design and modeling for power amplifier applications. Appl. Comput. Electromagn. Soc. J. 2017, 32, 1042–1047.30. Khaibullina, K.S.; Sagirova, L.R.; Sandyga, M.S. Substantiation and selection of an inhibitor for preventing the formation of asphaltresin-paraffin deposits. [Substanciação e seleção de um inibidor para evitar a formação de depósitos de asfalto-resina-parafina]. Period. Tche Quim. 2020, 17, 541–551.31. Jamshidi, M.B.; Roshani, S.; Talla, J.; Roshani, S.; Peroutka, Z. Size reduction and performance improvement of a microstrip Wil-kinson power divider using a hybrid design technique. Sci. Rep. 2021, 11, 7773. [CrossRef]32. Mayet, A.; Smith, C.; Hussain, M.M.; Smith, C. Amorphous metal based nanoelectromechanical switch. In Proceedings of the 2013 Saudi International Electronics, Communications and Photonics Conference, Riyadh, Saudi Arabia, 27–30 April 2013; pp. 1–5. [CrossRef]33. Hookari, M.; Roshani, S.; Roshani, S. Design of a low pass filter using rhombus-shaped resonators with an analytical LC equiv-alent circuit. Turk. J. Electr. Eng. Comput. Sci. 2020, 28, 865–874. [CrossRef]34. Khaibullina, K.S.; Korobov, G.Y.; Lekomtsev, A.V. Development of an asphalt-resin-paraffin deposits inhibitor and substantiation of the technological parameters of its injection into the bottom-hole formation zone. [Desenvolvimento de um inibidor de depósito de asfalto-resinaparafina e subscantiação dos parâmetros tecnológicos de sua injeção na zona de formação de furo inferior]. Period. Tche Quim. 2020, 17, 769–781.35. Pirasteh, A.; Roshani, S.; Roshani, S. Design of a miniaturized class F power amplifier using capacitor loaded transmission lines. Frequenz 2020, 74, 145–152. [CrossRef]36. Tikhomirova, E.A.; Sagirova, L.R.; Khaibullina, K.S. A review on methods of oil saturation modelling using IRAP RMS. IOP Conf. Ser. Earth Environ. Sci. 2019, 378, 012075. [CrossRef]37. Roshani, S.; Dehghani, K.; Roshani, S. 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[CrossRef]1611414Detection systemFeature extractionRBF neural networkOil and polymeric fluidsDual-energy gamma sourcePublicationORIGINALIncreasing the Efficiency of a Contro.pdfIncreasing the Efficiency of a Contro.pdfapplication/pdf2886088https://repositorio.cuc.edu.co/bitstreams/be1d1f4d-5a5e-4d24-be8b-f8f8da90f09c/download25db853bb043ad2d535bda39c7bf9d65MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/156be3c9-22f8-488d-a3b6-45b9c6e3dcfa/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTIncreasing the Efficiency of a Contro.pdf.txtIncreasing the Efficiency of a Contro.pdf.txttext/plain49489https://repositorio.cuc.edu.co/bitstreams/28d8bd1d-a576-4636-9978-0eed7df039a8/download3d41b09f4593f800d8379bce2daf0620MD53THUMBNAILIncreasing the Efficiency of a Contro.pdf.jpgIncreasing the Efficiency of a Contro.pdf.jpgimage/jpeg16820https://repositorio.cuc.edu.co/bitstreams/8ffc622e-5a71-4858-ba01-84acce40ac8d/download44566593d8959c5c05ef8cd8378030d4MD5411323/9429oai:repositorio.cuc.edu.co:11323/94292024-09-17 14:12:57.681https://creativecommons.org/licenses/by/4.0/Atribución 4.0 Internacional (CC BY 4.0)open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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