AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance  and Photon Sensors

Abdulilah M. Mayet; Salman A. Mohammed; Shamimul Qamar; Hassen Loukil; Neeraj K. Shukla

doi:10.14500/aro.11791

Abdulilah M. Mayet Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia https://orcid.org/0000-0001-7739-0105
Salman A. Mohammed Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia https://orcid.org/0000-0003-3598-7610
Shamimul Qamar Department of Computer Science and Engineering, Applied College, Dhahran Al Janoub Campus, King Khalid University, Abha, Saudi Arabia https://orcid.org/0000-0002-1091-7715
Hassen Loukil Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia https://orcid.org/0000-0002-2028-3517
Neeraj K. Shukla Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia https://orcid.org/0000-0002-7093-3805

Keywords: Non-destructive metering, Scale thickness in pipelines, Multi-phase fluids, Artificial neural network, Capacitive sensors, Gamma-ray attenuation sensor

Abstract

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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Author Biographies

Abdulilah M. Mayet, Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia

Abdulilah M. Mayet, Ph.D., is an Associate Professor at the Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. He got the B.Sc. degree in Electrical Engineering, the M.Sc. degree in Electrical Engineering, and the Ph.D. degree in Electrical Engineering, field of microelectronics design and fabrication. He has extensive industry experience in microelectronics and N/MEMS fabrication. His research interests are in N/MEMS, sensors, actuators, artificial intelligence, and advanced materials. Dr. Mayet is a life member of IEEE, MEMS.

Salman A. Mohammed, Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia

Salman Arafath Mohammed is an Assistant Prof. at the Department of Electrical Engineering, Computer Engineering section, College of Engineering, King Khalid University. He got the B.Tech. degree in Computer Science; Information Technology, the M.Tech degree in Computer Science Engineering, and the Ph.D. degree in Computer Science and Engineering. His research interests are in security in wireless sensor networks, routing protocols, ambient intelligence, machine learning and neural work, and implementation/design of the mentioned technologies in healthcare.

Shamimul Qamar, Department of Computer Science and Engineering, Applied College, Dhahran Al Janoub Campus, King Khalid University, Abha, Saudi Arabia

Shamimul Qamar is a Professor at the Department of Computer Science and Engineering, Applied College, Dharan Al Janoub Campus, King Khalid University. He got the B.Tech degree in Electronics and Communication Engineering, the M.Tech degree in Communication; Information Systems, and the PhD degree in Computer Science and Engineering. His research interests are artificial intelligence, computer networks, and digital image processing. He is a lifetime member of the International Association of Engineers and a life member of the Indian Society of Technical Education.

Hassen Loukil, Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia

Hassen Loukil is an Assistant Professor at the Department of Electrical Engineering, College of Engineering, King Khalid University. He got the B.Sc. degree in electrical engineering, the M.Sc. degree in electronics, and the Ph.D. degree in electronics. His research interests are in image and video processing, embedded systems, and algorithm-architecture matching.

Neeraj K. Shukla, Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia

Neeraj K. Shukla, is an Associate Professor at the Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. He got the B.Tech. degree in Electronics and Telecommunication Engineering, the M.Tech. degree in Electronics Engineering, and the Ph.D. degree in Digital VLSI Design (Electronics and Communication Engineering). His research interests are in Digital VLSI Design, Low-Power SRAM Characterization, Machine Learning, and Advanced Materials. Dr. Shukla is a life member of IE, IETE, CSI, and ISTE.

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