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AuthorRoshani, Mohammadmehdi
AuthorPhan, Giang T.T.
AuthorJammal Muhammad Ali, Peshawa
AuthorHossein Roshani, Gholam
AuthorHanus, Robert
AuthorDuong, Trung
AuthorCorniani, Enrico
AuthorNazemi, Ehsan
AuthorKalmoun, El Mostafa
Available date2024-07-21T06:24:19Z
Publication Date2021
Publication NameAlexandria Engineering Journal
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.aej.2020.11.043
ISSN11100168
URIhttp://hdl.handle.net/10576/56832
AbstractThe main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline's scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline's scale layer thickness.
Languageen
PublisherElsevier
SubjectFlow pattern
Multi-layer perceptron
Oil pipeline
Scale layer
Support vector machine
TitleEvaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline's scale layer thickness
TypeArticle
Pagination1955-1966
Issue Number1
Volume Number60
dc.accessType Open Access


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