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

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
Utilizing a Four-Concave Capacitance Sensor and ANN to Forecast Void Fraction in Two-Phase Stratified Flow Independent of Liquid Type DOI
Mohammad Hossein Shahsavari, Seyed Mehdi Alizadeh,

Evgeniya Ilyinichna Gorelkina

et al.

Journal of Nondestructive Evaluation, Journal Year: 2025, Volume and Issue: 44(1)

Published: Feb. 9, 2025

Language: Английский

Citations

0
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
Feasibility Study on the Use of the Coplanar Capacitive Sensing Technique for Underwater Non-Destructive Evaluation DOI Creative Commons
Martin Mwelango, Xiaokang Yin, M. Zhao

et al.

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

Published: March 29, 2024

Abstract Recent advancements in Non-Destructive Evaluation (NDE) techniques have demonstrated potential assessing underwater structural integrity. However, evolving maritime structures demand more efficient, user-friendly, and technologically advanced NDE methods. Building on successful applications air as a medium, this paper explores the feasibility of utilizing coplanar capacitive sensors to gauge integrity environments, drawing assertions made by pioneering scholars. The study employs simulations, complemented experimental validation, assess its viability. With artificial surface defects both non-conducting conducting specimens, conducts comprehensive comparison performance between bare-electrode insulated-electrode Coplanar Capacitive Sensor (CCS). outcomes affirm viability technique for NDE. Notably, reveals that electrical conductivity is significantly influential factor, there are discernible differences response two sensor configurations. nature materials intricately tied dominant sensitivity value region. detecting poses challenge some instances. Overall, results show defect detection, characterisation imaging under water feasible, thereby emphasizing This broadens knowledge offers viable alternative inspecting equipment environments.

Language: Английский

Citations

0
Feasibility Study on the Use of the Coplanar Capacitive Sensing Technique for Underwater Non-Destructive Evaluation DOI
Martin Mwelango, Xiaokang Yin, Mingzhen Zhao

et al.

Journal of Nondestructive Evaluation, Journal Year: 2024, Volume and Issue: 43(3)

Published: July 28, 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