Introducing the effective features using the particle swarm optimization algorithm to increase accuracy in determining the volume percentages of three-phase flows

dc.creatorChen, Tzu-Chia
dc.creatorAlizadeh, Mehdi
dc.creatorAlbahar, Marwan
dc.creatorThanoon, Mohammed
dc.creatorAlammari, Abdullah
dc.creatorGrimaldo Guerrero, John William
dc.creatornazemi, ehsan
dc.creatorEftekhari-Zadeh, Ehsan
dc.date2023-06-01T22:22:04Z
dc.date2023-06-01T22:22:04Z
dc.date2023-01-11
dc.date.accessioned2023-10-03T20:03:58Z
dc.date.available2023-10-03T20:03:58Z
dc.identifierChen, T.-C.; Alizadeh, S.M.; Albahar, M.A.; Thanoon, M.; Alammari, A.; Guerrero, J.W.G.; Nazemi, E.; Eftekhari-Zadeh, E. Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows. Processes 2023, 11, 236. https:// doi.org/10.3390/pr11010236
dc.identifierhttps://hdl.handle.net/11323/10218
dc.identifier10.3390/pr11010236
dc.identifier2227-9717
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174159
dc.descriptionWhat is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.
dc.format16 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMDPI AG
dc.publisherSwitzerland
dc.relationProcesses
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dc.rights© 2023 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/2227-9717/11/1/236
dc.subjectVolume fraction
dc.subjectPSO
dc.subjectMLP neural network
dc.subjectFeature extraction
dc.subjectWavelet
dc.subjectFrequency domain
dc.subjectArtificial intelligence
dc.titleIntroducing the effective features using the particle swarm optimization algorithm to increase accuracy in determining the volume percentages of three-phase flows
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


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