Proposing a Nondestructive and Intelligent System for Simultaneous Determining Flow Regime and Void Fraction Percentage of Gas–Liquid Two Phase Flows Using Polychromatic X-Ray Transmission Spectra

Amiri, Sara; Ali, Peshawa J. Muhammad; Mohammed, Shivan; Hanus, Robert; Abdulkareem, Lokman A.; Alanezi, Adnan Alhathal; Eftekhari-Zadeh, Ehsan; Roshani, Gholam Hossein; Nazemi, Ehsan; Kalmoun, El Mostafa

doi:10.1007/s10921-021-00782-w

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…In present article, the detection system that is consisted of an X-ray tube and a NaI detector which are located on both sides of the Pyrex-glass pipe, was modeled using the MCNP code. This code has been employed for modeling measuring instruments based on ionizing radiation [10][11][12][13][14][15][16].…”

Section: X-ray Systemmentioning

confidence: 99%

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

et al. 2021

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The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works.

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Section: X-ray Systemmentioning

confidence: 99%

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

et al. 2021

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“…These challenges mainly arise from the complex and stochastic nature of gasliquid flow patterns. Researchers have conducted numerous studies investigating how to identify flow patterns in gas-liquid two-phase flows using photon radiation [5]. For example, Abro et al [6] proposed a multi-beam radiation technique, Faghihi et al [7] developed a scheme that uses polyethylene phantoms to simulate two-phase flow, Nazemi et al [8] introduced a broad beam gamma-ray transmission methodology, Khayat et al [9] designed a multi-energy gamma-ray absorption measurement system, and Hanus et al [10] proposed a gamma density method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Determining the Void Fraction in Gas-Liquid Multi-Phase Systems Using a Dynamic Conductivity Probe

Luo,

Qi,

Luo

et al. 2024

FDMP

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Conventional conductivity methods for measuring the void fraction in gas-liquid multiphase systems are typically affected by accuracy problems due to the presence of fluid flow and salinity. This study presents a novel approach for determining the void fraction based on a reciprocating dynamic conductivity probe used to measure the liquid film thickness under forced annular-flow conditions. The measurement system comprises a cyclone, a conductivity probe, a probe reciprocating device, and a data acquisition and processing system. This method ensures that the flow pattern is adjusted to a forced annular flow, thereby minimizing the influence of complex and variable gas-liquid flow patterns on the measurement results; Moreover, it determines the liquid film thickness solely according to circuit connectivity rather than specific conductivity values, thereby mitigating the impact of salinity. The reliability of the measurement system is demonstrated through laboratory experiments. The experimental results indicate that, in a range of gas phase superficial velocities 5-20 m/s and liquid phase superficial velocities 0.079-0.48 m/s, the maximum measurement deviation for the void fraction is 4.23%.

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“…Since the discovery of Roentgen rays in the 19th century, research on detection methods based on different types of rays has developed rapidly [4]. Although radiographic methods are highly accurate and can distinguish flow patterns from gas holdup without imaging, long-term measurements increase the radiation risk [5]. The optical method can detect the parameters of two-phase flow according to the difference of optical properties of light in different media, such as the scattering rate, the refractive index and absorbance.…”

Section: Introductionmentioning

confidence: 99%

Direct estimation of gas holdup in gas–liquid bubble column reactors using ultrasonic transmission tomography and artificial neural processing

Li¹,

Hu²,

Wang³

et al. 2022

Meas. Sci. Technol.

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Ultrasonic transmission tomography is an effective non-intrusive method for detecting gas–liquid two-phase flow patterns. A specific interest is the many processes whose reaction utilizes a bubble column, where the fast estimation of cross-sectional gas-holdup ratio is important for monitoring and control. In this study reference indirect image-based estimates were obtained from reconstructed tomographic data. Direct (non-image) estimation of the gas holdup ratio was also obtained using trained neural processing networks. Two forms were trialled: a generalized regression neural network; and a long short-term memory network. Comparison trials were carried out for single-bubble, dual-bubble, circulation and laminar flows. Relative cross-sectional gas holdup error was selected for evaluation. For the image-based indirect trials the Tikhonov regularization algorithm had the lowest error range: 2.15%–15.64%. For direct methods the long short-term memory network had the lowest error range: 0.41%–9.63%, giving better performance than the image-based methods. The experimental data were used to verify the effectiveness of the network. The root-mean-square error of the test metrics for generalized regression neural network and long short-term memory network were 6.4260 and 5.4282, respectively, indicating that long short-term memory network has higher performance in processing the data in this paper.

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Proposing a Nondestructive and Intelligent System for Simultaneous Determining Flow Regime and Void Fraction Percentage of Gas–Liquid Two Phase Flows Using Polychromatic X-Ray Transmission Spectra

Cited by 5 publications

References 37 publications

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

A Novel Method for Determining the Void Fraction in Gas-Liquid Multi-Phase Systems Using a Dynamic Conductivity Probe

Direct estimation of gas holdup in gas–liquid bubble column reactors using ultrasonic transmission tomography and artificial neural processing

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