AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors DOI Creative Commons
Abdulilah Mohammad Mayet, Salman Arafath Mohammed, Shamimul Qamar

et al.

ARO-The Scientific Journal of Koya University, Journal Year: 2024, Volume and Issue: 12(2), P. 167 - 178

Published: Nov. 9, 2024

Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors methods are developed to precisely measure volume fractions multi-phase fluids. A significant challenge fluid pipelines the formation of scale within pipes. This issue particularly problematic petroleum industry, leading narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow paper proposes non-destructive metering system incorporating an artificial neural network with capacitive photon attenuation address this challenge. The simulates thicknesses from 0 mm 10 using COMSOL multiphysics software calculates counted rays through Beer Lambert equations. simulation considers 10% interval variation each phase, generating 726 data points. proposed network, two inputs—measured capacity rays-and three outputs—volume gas, water, oil—achieves mean absolute errors 0.318, 1.531, 1.614, respectively. These results demonstrate system’s ability accurately gauge proportions three-phase gas-water-oil fluid, regardless pipeline thickness.

Language: Английский

An optimised and novel capacitance-based sensor design for measuring void fraction in gas–oil two-phase flow systems DOI Creative Commons
Abdullah M. Iliyasu, Mohammad Hossein Shahsavari,

Abdullah S. Benselama

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: 39(8), P. 2450 - 2466

Published: Jan. 9, 2024

This study explores a new electrode configuration for measuring the void fraction of two-phase flows using capacitance-based sensors. The proposed method is considered 'skewed' because its unique geometric shape, and performance sensor was evaluated improved via multiple simulations COMSOL Multiphysics software. encompass three different flow patterns, stratified, annular homogeneous, whose themselves were verified in previous study. influences properties parameters on sensitivity to determine an optimal configuration. Furthermore, distribution fractions analysed various patterns. Additionally, also compared alongside double-ring concave sensors overall sensitivity. At 2.11 pF, significantly higher than that other It worth mentioning measurement precision multiphase meters high importance, particularly petroleum industry oil price amount transported products.

Language: Английский

Citations

6
Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors DOI Creative Commons
Abdulilah Mohammad Mayet, Farhad Fouladinia, Seyed Mehdi Alizadeh

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(5), P. e0301437 - e0301437

Published: May 16, 2024

Many different kind of fluids in a wide variety industries exist, such as two-phase and three-phase. Various combinations them can be expected gas-oil-water is one the most common flows. Measuring volume fraction phases without separation vital many aspects, which financial issues. methods are utilized to ascertain volumetric proportion each phase. Sensors based on measuring capacity so popular because this sensor operates seamlessly autonomously necessitating any form segregation or disruption for process. Besides, at present moment, Artificial intelligence (AI) nominated useful tool several fields, metering no exception. Also, three main type regimes found annular, stratified, homogeneous. In paper, fractions three-phase homogeneous regime measured. To accomplish objective, an Neural Network (ANN) capacitance-based utilized. train presented network, optimized was implemented COMSOL Multiphysics software after doing lot simulations, 231 data produced. Among all obtained results, 70 percent (161 data) awarded data, rest (70 considered test data. This investigation proposes new intelligent system Multilayer Perceptron network (MLP) that estimate water-oil-gas fluid’s water precisely with very low error. The Mean Absolute Error (MAE) equal 1.66. dedicates predicting method’s considerable accuracy. Moreover, study confined cannot measure void other fluid types future works. temperature pressure changes highly temper relative permittivity density liquid inside pipe another idea.

Language: Английский

Citations

6
A novel metering system consists of capacitance-based sensor, gamma-ray sensor and ANN for measuring volume fractions of three-phase homogeneous flows DOI
Farhad Fouladinia, Seyed Mehdi Alizadeh,

Evgeniya Ilyinichna Gorelkina

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 27

Published: July 7, 2024

Measuring the volume fraction of different types fluids with two or three phases is so vital. Among all available methods, them, capacitance-based and gamma-ray attenuation, are popular widely used. Moreover, nowadays, AI which stands for Artificial Intelligence can be seen almost in areas, measuring section no exception. In this paper, main goal to predict a three-phase homogeneous fluid contains water, oil, gas materials. To opt an optimised method, combination sensors, attenuation sensor Neural Networks (ANN) utilised. train proposed metering system MLP type, inputs considered. For first input, concave simulated COMSOL Multiphysics software combinations (different fractions) applied. Then through theoretical investigations sensor, Barium-133 radiates 0.356 MeV This way, second required input generated. Finally, implement new accurate system, number networks characteristics run MATLAB software. The best structure had Mean Absolute Error (MAE) equal 0.33, 3.68 3.75 oil phases, respectively. accuracy presented illustrated by received outcomes. novelty study proposing combined method that measure fluid's fractions containing precisely.

Language: Английский

Citations

4
MLP ANN Equipped Approach to Measuring Scale Layer in Oil-Gas-Water Homogeneous Fluid by Capacitive and Photon Attenuation Sensors DOI
Abdulilah Mohammad Mayet, Salman Arafath Mohammed,

Evgeniya Ilyinichna Gorelkina

et al.

Journal of Nondestructive Evaluation, Journal Year: 2025, Volume and Issue: 44(2)

Published: April 1, 2025

Language: Английский

Citations

0
Multiphase Flow’s Volume Fractions Intelligent Measurement by a Compound Method Employing Cesium-137, Photon Attenuation Sensor, and Capacitance-Based Sensor DOI Creative Commons
Abdulilah Mohammad Mayet, Farhad Fouladinia, Robert Hanus

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3519 - 3519

Published: July 18, 2024

Multiphase fluids are common in many industries, such as oil and petrochemical, volume fraction measurement of their phases is a vital subject. Hence, there lots scientists researchers who have introduced methods equipment this regard, for example, photon attenuation sensors, capacitance-based so on. These approaches non-invasive reason, very popular widely used. In addition, nowadays, artificial neural networks (ANN) attractive lot fields because accuracy. Therefore, paper, to estimate proportion three-phase homogeneous fluid, new system proposed that contains an MLP ANN, standing multilayer perceptron network, sensor, sensor. Through computational methods, capacities mass coefficients obtained, which act inputs the network. All these were divided randomly two main groups train test presented model. To opt suitable network with lowest rate mean absolute error (MAE), number architectures different factors tested MATLAB software R2023b. After receiving MAEs equal 0.29, 1.60, 1.67 water, gas, phases, respectively, was chosen be paper. based on outcomes, approach’s novelty being able predict all flow low error.

Language: Английский

Citations

1
Utilizing Artificial Neural Networks and Combined Capacitance-Based Sensors to Predict Void Fraction in Two-Phase Annular Fluids Regardless of Liquid Phase Type DOI Creative Commons
Mustafa Al‐Fayoumi, Hani Almimi, Aryan Veisi

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 143745 - 143756

Published: Jan. 1, 2023

Assessing the void fraction in diverse multiphase flows across industries, including petrochemical, oil, and chemical sectors, is crucial. There are multiple techniques available for this objective. The capacitive sensor has gained significant popularity among these methods been extensively utilized. Fluid properties have a substantial impact on performance of capacitance sensors. Factors such as density, pressure, temperature can introduce errors measurements. One approach to address issue meticulous laborious routine calibration process. In current study, an artificial neural network (ANN) was developed accurately Assess proportion gas biphasic fluid motion, irrespective variations phase form or variations, eliminating need frequent recalibration. To achieve objective, novel combined capacitance-based sensors were specifically designed. simulated by employing COMSOL Multiphysics application. simulation encompassed five distinct liquids: diesel fuel, gasoline, crude water. input training multilayer perceptron (MLP) came from data gathered through Multiphysics, simulations estimating Percentage content annular two-phase with specific liquid form. MATLAB software utilized construct model proposed network. utilization precise apparatus measuring intended MLP demonstrated ability prognosticate volume percentage mean absolute error (MAE) 0.004.

Language: Английский

Citations

3
Comparison of backscattered and transmitted gamma rays spectra for prediction of volume fraction of three-phase flows using machine learning model DOI Creative Commons
Seyedeh Zahra Islami rad,

R. Gholipour Peyvandi

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 14, 2024

Abstract Estimation of volume fraction percentage the multiple phases flowing in pipes with limited access is a challenge oil, gas, chemical processes, and petrochemical industries. In this research, gamma backscattered spectra together machine learning model were used to predict precise percentages water-gasoil-air three-phase flows solve aforementioned challenge. The detection system includes single energy 137Cs source NaI(Tl) detector measure rays. MCNPX code was simulate setup produce required data for artificial neural network. calculated mean relative error 13.60% root square 2.68, respectively. Then, results compared acquired transmitted gamma-ray spectra. proposed design suitable, safe, low-cost choice

Language: Английский

Citations

0
Comparison of Backscattered and Transmitted Gamma Rays Spectra for Prediction of Volume Fraction of Three-Phase Flows Using Machine Learning Model DOI

S. Z. Islami rad,

R. Gholipour Peyvandi

Journal of Nondestructive Evaluation, Journal Year: 2024, Volume and Issue: 43(4)

Published: Sept. 21, 2024

Language: Английский

Citations

0
AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors DOI Creative Commons
Abdulilah Mohammad Mayet, Salman Arafath Mohammed, Shamimul Qamar

et al.

ARO-The Scientific Journal of Koya University, Journal Year: 2024, Volume and Issue: 12(2), P. 167 - 178

Published: Nov. 9, 2024

Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors methods are developed to precisely measure volume fractions multi-phase fluids. A significant challenge fluid pipelines the formation of scale within pipes. This issue particularly problematic petroleum industry, leading narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow paper proposes non-destructive metering system incorporating an artificial neural network with capacitive photon attenuation address this challenge. The simulates thicknesses from 0 mm 10 using COMSOL multiphysics software calculates counted rays through Beer Lambert equations. simulation considers 10% interval variation each phase, generating 726 data points. proposed network, two inputs—measured capacity rays-and three outputs—volume gas, water, oil—achieves mean absolute errors 0.318, 1.531, 1.614, respectively. These results demonstrate system’s ability accurately gauge proportions three-phase gas-water-oil fluid, regardless pipeline thickness.

Language: Английский

Citations

0