AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors
Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors and methods are developed to precisely measure volume fractions in multi-phase fluids. A significant challenge in multi-phase fluid pipe...
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| Format: | Article |
| Language: | English |
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Koya University
2024-11-01
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| Series: | ARO-The Scientific Journal of Koya University |
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| Online Access: | http://aro.koyauniversity.org/index.php/aro/article/view/1791 |
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| author | Abdulilah M. Mayet Salman A. Mohammed Shamimul Qamar Hassen Loukil Neeraj K. Shukla |
| author_facet | Abdulilah M. Mayet Salman A. Mohammed Shamimul Qamar Hassen Loukil Neeraj K. Shukla |
| author_sort | Abdulilah M. Mayet |
| collection | DOAJ |
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Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors and methods are developed to precisely measure volume fractions in multi-phase fluids. A significant challenge in multi-phase fluid pipelines is the formation of scale within the pipes. This issue is particularly problematic in the petroleum industry, leading to narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow measurement accuracy. This paper proposes a non-destructive metering system incorporating an artificial neural network with capacitive and photon attenuation sensors to address this challenge. The system simulates scale thicknesses from 0 mm to 10 mm using COMSOL multiphysics software and calculates counted rays through Beer Lambert equations. The simulation considers a 10% interval of volume variation in each phase, generating 726 data points. The proposed network, with two inputs—measured capacity and counted rays-and three outputs—volume fractions of gas, water, and oil—achieves mean absolute errors of 0.318, 1.531, and 1.614, respectively. These results demonstrate the system’s ability to accurately gauge volume proportions of a three-phase gas-water-oil fluid, regardless of pipeline scale thickness.
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| format | Article |
| id | doaj-art-06dfa6e9d1264ba08b527b8b8b4e6afc |
| institution | OA Journals |
| issn | 2410-9355 2307-549X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Koya University |
| record_format | Article |
| series | ARO-The Scientific Journal of Koya University |
| spelling | doaj-art-06dfa6e9d1264ba08b527b8b8b4e6afc2025-08-20T02:13:56ZengKoya UniversityARO-The Scientific Journal of Koya University2410-93552307-549X2024-11-0112210.14500/aro.11791AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon SensorsAbdulilah M. Mayet0Salman A. Mohammed1Shamimul Qamar2Hassen Loukil3Neeraj K. Shukla4Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Electrical Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Computer Science and Engineering, Applied College, Dhahran Al Janoub Campus, King Khalid University, Abha, Saudi ArabiaDepartment of Electrical Engineering, King Khalid University, Abha 61411, Saudi ArabiaDepartment of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors and methods are developed to precisely measure volume fractions in multi-phase fluids. A significant challenge in multi-phase fluid pipelines is the formation of scale within the pipes. This issue is particularly problematic in the petroleum industry, leading to narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow measurement accuracy. This paper proposes a non-destructive metering system incorporating an artificial neural network with capacitive and photon attenuation sensors to address this challenge. The system simulates scale thicknesses from 0 mm to 10 mm using COMSOL multiphysics software and calculates counted rays through Beer Lambert equations. The simulation considers a 10% interval of volume variation in each phase, generating 726 data points. The proposed network, with two inputs—measured capacity and counted rays-and three outputs—volume fractions of gas, water, and oil—achieves mean absolute errors of 0.318, 1.531, and 1.614, respectively. These results demonstrate the system’s ability to accurately gauge volume proportions of a three-phase gas-water-oil fluid, regardless of pipeline scale thickness. http://aro.koyauniversity.org/index.php/aro/article/view/1791Non-destructive meteringScale thickness in pipelinesMulti-phase fluidsArtificial neural networkCapacitive sensorsGamma-ray attenuation sensor |
| spellingShingle | Abdulilah M. Mayet Salman A. Mohammed Shamimul Qamar Hassen Loukil Neeraj K. Shukla AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors ARO-The Scientific Journal of Koya University Non-destructive metering Scale thickness in pipelines Multi-phase fluids Artificial neural network Capacitive sensors Gamma-ray attenuation sensor |
| title | AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors |
| title_full | AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors |
| title_fullStr | AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors |
| title_full_unstemmed | AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors |
| title_short | AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors |
| title_sort | ai based evaluation of homogeneous flow volume fractions independent of scale using capacitance and photon sensors |
| topic | Non-destructive metering Scale thickness in pipelines Multi-phase fluids Artificial neural network Capacitive sensors Gamma-ray attenuation sensor |
| url | http://aro.koyauniversity.org/index.php/aro/article/view/1791 |
| work_keys_str_mv | AT abdulilahmmayet aibasedevaluationofhomogeneousflowvolumefractionsindependentofscaleusingcapacitanceandphotonsensors AT salmanamohammed aibasedevaluationofhomogeneousflowvolumefractionsindependentofscaleusingcapacitanceandphotonsensors AT shamimulqamar aibasedevaluationofhomogeneousflowvolumefractionsindependentofscaleusingcapacitanceandphotonsensors AT hassenloukil aibasedevaluationofhomogeneousflowvolumefractionsindependentofscaleusingcapacitanceandphotonsensors AT neerajkshukla aibasedevaluationofhomogeneousflowvolumefractionsindependentofscaleusingcapacitanceandphotonsensors |