Multiphase Flow’s Volume Fractions Intelligent Measurement by a Compound Method Employing Cesium-137, Photon Attenuation Sensor, and Capacitance-Based Sensor

Abdulilah Mohammad Mayet, Farhad Fouladinia, Robert Hanus (), Muneer Parayangat, M. Ramkumar Raja, Mohammed Abdul Muqeet and Salman Arafath Mohammed
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Abdulilah Mohammad Mayet: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Farhad Fouladinia: Independent Researcher, Muscat 123, Oman
Robert Hanus: Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, PL-35-959 Rzeszów, Poland
Muneer Parayangat: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
M. Ramkumar Raja: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Mohammed Abdul Muqeet: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Salman Arafath Mohammed: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia

Energies, 2024, vol. 17, issue 14, 1-20

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

Keywords: capacitance-based sensor; photon attenuation sensor; ANN; volume fraction; measuring system; multiphase homogeneous fluid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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