Using statistical features and a neural network to predict gas volume fractions independent of flow regime changes

Jeshvaghani, P. Aarabi; Rezaee, Kh.; Feghhi, S.A.H.; Jafari, Ahmad

doi:10.1016/j.flowmeasinst.2023.102430

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Problems can arise with the use of radioisotopes as a constant power source, including those related to transportation and the requirement for personnel to wear protective gear. Therefore, X-ray tube research into measuring multiphase flow properties has gained traction of late [8][9][10][11][12]. In the study [8], the researchers used an X-ray tube and a NaI detector so that they could identify the volumetric percentage and regime type of two-phase flows.…”

Section: Introductionmentioning

confidence: 99%

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Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

Mayet,

Guerrero,

Ijyas

et al. 2023

Separations

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With the passage of time, scale gradually forms inside the oil pipeline. The produced scale, which has a high density, strongly attenuates photons, which lowers the measurement accuracy of three-phase flow meters based on gamma radiation. It is worth mentioning that the need for multiphase flow metering arises when it is necessary or desirable to meter well stream(s) upstream of inlet separation and/or commingling. In this investigation, a novel technique based on artificial intelligence is presented to overcome the issue mentioned earlier. Initially, a detection system was comprised of two NaI detectors and a dual-energy gamma source (241 Am and 133 Ba radioisotopes) using Monte Carlo N particle (MCNP) code. A stratified flow regime with varying volume percentages of oil, water, and gas was modeled inside a pipe that included a scale layer with varying thicknesses. Two detectors record the attenuated photons that could travel through the pipe. Four characteristics with the names of the amplitude of the first and second dominant signal frequencies were extracted from the received signals by both detectors. The aforementioned obtained characteristics were used to train two Radial Basis Function (RBF) neural networks to forecast the volumetric percentages of each component. The RMSE value of the gas and oil prediction neural networks are equal to 0.27 and 0.29, respectively. By measuring two phases of fluids in the pipe, the volume of the third phase can be calculated by subtracting the volume of two phases from the total volume of the pipe. Extraction and introduction of suitable characteristics to determine the volume percentages, reducing the computational burden of the detection system, considering the scale value thickness the pipe, and increasing the accuracy in determining the volume percentages of oil pipes are some of the advantages of the current research, which has increased the usability of the proposed system as a reliable measuring system in the oil and petrochemical industry.

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Section: Introductionmentioning

confidence: 99%

“…The timing features of the detected signals were used to train two MLP neural networks. In [9], two-phase flows were studied by modeling them in various regimes at different volume fractions. In addition, artificial neural networks were educated by feeding them the statistical features of the incoming signals.…”

Section: Introductionmentioning

confidence: 99%

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

Mayet,

Guerrero,

Ijyas

et al. 2023

Separations

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Application of the convolutional neural network to recognition of the two-phase water-air flow regime in a pipeline using histograms of radiometric signals^*

Hanus,

Zych,

Ochał

et al. 2024

2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)

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Identification of the Structure of Liquid–Gas Flow in a Horizontal Pipeline Using the Gamma-Ray Absorption and a Convolutional Neural Network

Hanus,

Zych,

Ochał

et al. 2024

Applied Sciences

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Knowledge of the liquid–gas flow regime is important for the proper control of many industrial processes (e.g., in the mining, nuclear, petrochemical, and environmental industries). The latest publications in this field concern the use of computational intelligence methods for flow structure recognition, which include, for example, expert systems and artificial neural networks. Generally, machine learning methods exploit various characteristics of sensors signals in the value, time, frequency, and time–frequency domain. In this work, the convolutional neural network (CNN) VGG-16 is applied for analysis of histogram images of signals obtained for water–air flow by using gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with a radiometric measurement system. The essential part of the hydraulic installation is a horizontal pipeline made of metalplex, 4.5 m long, with an internal diameter of 30 mm. The radiometric measurement set used in the investigation consists of a linear Am-241 radiation source with an energy of 59.5 keV and a scintillation detector with a NaI(Tl) crystal. In this work, four types of water–air flow regimes (plug, slug, bubble, and transitional plug–bubble) were studied. MATLAB 2022a software was used to analyze the measurement signal obtained from the detector. It was found that the CNN network correctly recognizes the flow regime in more than 90% of the cases.

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Using statistical features and a neural network to predict gas volume fractions independent of flow regime changes

Cited by 4 publications

References 34 publications

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

Application of the convolutional neural network to recognition of the two-phase water-air flow regime in a pipeline using histograms of radiometric signals^*

Identification of the Structure of Liquid–Gas Flow in a Horizontal Pipeline Using the Gamma-Ray Absorption and a Convolutional Neural Network

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Using statistical features and a neural network to predict gas volume fractions independent of flow regime changes

Cited by 4 publications

References 34 publications

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness

Application of the convolutional neural network to recognition of the two-phase water-air flow regime in a pipeline using histograms of radiometric signals*

Identification of the Structure of Liquid–Gas Flow in a Horizontal Pipeline Using the Gamma-Ray Absorption and a Convolutional Neural Network

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Application of the convolutional neural network to recognition of the two-phase water-air flow regime in a pipeline using histograms of radiometric signals^*