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

One 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 inte...

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Published inApplied sciences Vol. 12; no. 3; p. 1336
Main Authors Alanazi, Abdullah K., Alizadeh, Seyed Mehdi, Nurgalieva, Karina Shamilyevna, Nesic, Slavko, Grimaldo Guerrero, John William, Abo-Dief, Hala M., Eftekhari-Zadeh, Ehsan, Nazemi, Ehsan, Narozhnyy, Igor M.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Artificial intelligence
Efficiency
Engineering
feature extraction
Linear algebra
Methods
MLP neural network
Neural networks
Radiation
scale thickness
Sensors
Simulation
Steel pipes
two-phase flow
Wavelet transforms
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Abstract One 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.
AbstractList One 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.
Author Alizadeh, Seyed Mehdi
Alanazi, Abdullah K.
Nazemi, Ehsan
Grimaldo Guerrero, John William
Abo-Dief, Hala M.
Narozhnyy, Igor M.
Nurgalieva, Karina Shamilyevna
Eftekhari-Zadeh, Ehsan
Nesic, Slavko
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  surname: Narozhnyy
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SSID ssj0000913810
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Snippet One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative,...
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SubjectTerms Artificial intelligence
Efficiency
Engineering
feature extraction
Linear algebra
Methods
MLP neural network
Neural networks
Radiation
scale thickness
Sensors
Simulation
Steel pipes
two-phase flow
Wavelet transforms
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Title 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
URI https://www.proquest.com/docview/2636122387
https://doaj.org/article/1d01061b1e6b44628298aa67b47cf3a2
Volume 12
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