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Application of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thickness

dc.contributor.authorAlanazi, Abdullahspa
dc.contributor.authorAlizadeh, Seyed Mehdispa
dc.contributor.authorNurgalieva, Karinaspa
dc.contributor.authorNesic, Slavkospa
dc.contributor.authorGrimaldo Guerrero, John Williamspa
dc.contributor.authorAbo-Dief, Hala M.spa
dc.contributor.authorEftekhari-Zadeh, Ehsanspa
dc.contributor.authornazemi, ehsanspa
dc.contributor.authorIgor, Narozhnyyspa
dc.date.accessioned2022-04-07T20:50:29Z
dc.date.available2022-04-07T20:50:29Z
dc.date.issued2022-01-27
dc.identifier.citationAlanazi, A.K.; Alizadeh, S.M.; Nurgalieva, K.S.; Nesic, S.; Grimaldo Guerrero, J.W.; Abo-Dief, H.M.; Eftekhari-Zadeh, E.; Nazemi, E.; Narozhnyy, I.M. Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness. Appl. Sci. 2022, 12, 1336. https://doi.org/10.3390/ app12031336spa
dc.identifier.issn2076-3417spa
dc.identifier.urihttps://hdl.handle.net/11323/9121spa
dc.description.abstractOne of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.eng
dc.format.extent13 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherMDPI Multidisciplinary Digital Publishing Institutespa
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.spa
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.titleApplication of neural network and time-domain feature extraction techniques for determining volumetric percentages and the type of two phase flow regimes independent of scale layer thicknesseng
dc.typeArtículo de revistaspa
dc.identifier.urlhttps://doi.org/10.3390/app12031336spa
dc.source.urlhttps://www.mdpi.com/2076-3417/12/3/1336spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doi10.3390/app12031336spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.placeSwitzerlandspa
dc.relation.ispartofjournalApplied Sciencesspa
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dc.subject.proposalArtificial intelligenceeng
dc.subject.proposalFeature extractioneng
dc.subject.proposalScale thicknesseng
dc.subject.proposalTwo-phase floweng
dc.subject.proposalMLP neural networkeng
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