Applying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer

dc.creatorMayet, Abdulilah
dc.creatorSalama, Ahmed S.
dc.creatorAlizadeh, Mehdi
dc.creatorNesic, Slavko
dc.creatorGrimaldo Guerrero, John William
dc.creatorEftekhari-Zadeh, Ehsan
dc.creatornazemi, ehsan
dc.creatorIliyasu, Abdullah
dc.date2022-04-07T20:47:11Z
dc.date2022-04-07T20:47:11Z
dc.date2022
dc.date.accessioned2023-10-03T19:39:05Z
dc.date.available2023-10-03T19:39:05Z
dc.identifier2079-9292
dc.identifierhttps://hdl.handle.net/11323/9119
dc.identifierhttps://doi.org/10.3390/electronics11030459
dc.identifier10.3390/electronics11030459
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9171038
dc.descriptionScale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).
dc.format14 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMDPI Multidisciplinary Digital Publishing Institute
dc.publisherSwitzerland
dc.relationElectronics
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dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
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/2079-9292/11/3/459
dc.subjectPipeline’s scale
dc.subjectRBF neural network
dc.subjectTwo-phase flow
dc.subjectOil and gas
dc.subjectArtificial intelligence
dc.titleApplying data mining and artificial intelligence techniques for high precision measuring of the two-phase flow’s characteristics independent of the pipe’s scale layer
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/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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