Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow mete...

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Autores principales:	Siavash Hosseini, Osman Taylan, Mona Abusurrah, Thangarajah Akilan, Ehsan Nazemi, Ehsan Eftekhari-Zadeh, Farheen Bano, Gholam Hossein Roshani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wavelet feature extraction two-phase flow measurement Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/6840531cead147eeb65ab2de9046f280
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spelling	oai:doaj.org-article:6840531cead147eeb65ab2de9046f2802021-11-11T18:42:35ZApplication of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries10.3390/polym132136472073-4360https://doaj.org/article/6840531cead147eeb65ab2de9046f2802021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4360/13/21/3647https://doaj.org/toc/2073-4360Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.Siavash HosseiniOsman TaylanMona AbusurrahThangarajah AkilanEhsan NazemiEhsan Eftekhari-ZadehFarheen BanoGholam Hossein RoshaniMDPI AGarticlewaveletfeature extractiontwo-phaseflow measurementOrganic chemistryQD241-441ENPolymers, Vol 13, Iss 3647, p 3647 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wavelet feature extraction two-phase flow measurement Organic chemistry QD241-441
spellingShingle	wavelet feature extraction two-phase flow measurement Organic chemistry QD241-441 Siavash Hosseini Osman Taylan Mona Abusurrah Thangarajah Akilan Ehsan Nazemi Ehsan Eftekhari-Zadeh Farheen Bano Gholam Hossein Roshani Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries
description	Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
format	article
author	Siavash Hosseini Osman Taylan Mona Abusurrah Thangarajah Akilan Ehsan Nazemi Ehsan Eftekhari-Zadeh Farheen Bano Gholam Hossein Roshani
author_facet	Siavash Hosseini Osman Taylan Mona Abusurrah Thangarajah Akilan Ehsan Nazemi Ehsan Eftekhari-Zadeh Farheen Bano Gholam Hossein Roshani
author_sort	Siavash Hosseini
title	Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries
title_short	Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries
title_full	Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries
title_fullStr	Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries
title_full_unstemmed	Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries
title_sort	application of wavelet feature extraction and artificial neural networks for improving the performance of gas–liquid two-phase flow meters used in oil and petrochemical industries
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6840531cead147eeb65ab2de9046f280
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Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

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