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.creatorMayet, Abdulilah
dc.creatorMehdi Alizadeh, Seyed
dc.creatorAzeez Kakarash, Zana
dc.creatorAl-Qahtani, Ali Awadh
dc.creatorAlanazi, Abdullah
dc.creatorGrimaldo Guerrero, John William
dc.creatorAlhashimi, Hala H.
dc.creatorEftekhari-Zadeh, Ehsan
dc.date2022-08-04T14:24:50Z
dc.date2022-08-04T14:24:50Z
dc.date2022-07-13
dc.date.accessioned2023-10-03T19:50:01Z
dc.date.available2023-10-03T19:50:01Z
dc.identifierMayet, 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.identifierhttps://hdl.handle.net/11323/9429
dc.identifierhttps://doi.org/10.3390/polym14142852
dc.identifier10.3390/polym14142852
dc.identifier2073-4360
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9172459
dc.descriptionInstantaneously 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.
dc.format16 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMDPI AG
dc.publisherSwitzerland
dc.relationPolymers
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dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.mdpi.com/2073-4360/14/14/2852
dc.subjectDetection system
dc.subjectFeature extraction
dc.subjectRBF neural network
dc.subjectOil and polymeric fluids
dc.subjectDual-energy gamma source
dc.titleIncreasing 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.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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