Experimental Study of Void Fraction Measurement Using a Capacitance-Based Sensor and ANN in Two-Phase Annular Regimes for Different Fluids

ORCID
0000-0002-4530-3916
Affiliation
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah 6715685420, Iran
Veisi, Aryan;
ORCID
0000-0003-2496-7294
Affiliation
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah 6715685420, Iran
Shahsavari, Mohammad Hossein;
ORCID
0000-0002-3328-1502
Affiliation
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah 6715685420, Iran
Roshani, Gholam Hossein;
GND
1231322179
ORCID
0000-0003-1480-1450
Affiliation
Institute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich Schiller University Jena, 07743 Jena, Germany
Eftekhari-Zadeh, Ehsan;
ORCID
0000-0001-5457-6943
Affiliation
Imec-Vision Laboratory, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium
Nazemi, Ehsan

One of the most severe problems in power plants, petroleum and petrochemical industries is the accurate determination of phase fractions in two-phase flows. In this paper, we carried out experimental investigations to validate the simulations for water–air, two-phase flow in an annular pattern. To this end, we performed finite element simulations with COMSOL Multiphysics, conducted experimental investigations in concave electrode shape and, finally, compared both results. Our experimental set-up was constructed for water–air, two-phase flow in a vertical tube. Afterwards, the simulated models in the water–air condition were validated against the measurements. Our results show a relatively low relative error between the simulation and experiment indicating the validation of our simulations. Finally, we designed an Artificial Neural Network (ANN) model in order to predict the void fractions in any two-phase flow consisting of petroleum products as the liquid phase in pipelines. In this regard, we simulated a range of various liquid–gas, two-phase flows including crude oil, oil, diesel fuel, gasoline and water using the validated simulation. We developed our ANN model by a multi-layer perceptron (MLP) neural network in MATLAB 9.12.0.188 software. The input parameters of the MLP model were set to the capacitance of the sensor and the liquid phase material, whereas the output parameter was set to the void fraction. The void fraction was predicted with an error of less than 2% for different liquids via our proposed methodology. Using the presented novel metering system, the void fraction of any annular two-phase flow with different liquids can be precisely measured.

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