Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

Abdulrahman Basahel, Mohammad Amir Sattari, Osman Taylan and Ehsan Nazemi
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Abdulrahman Basahel: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Mohammad Amir Sattari: Friedrich Schiller University Jena, Fürstengraben 1, 07743 Jena, Germany
Osman Taylan: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Ehsan Nazemi: Imec-Vision Lab, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium

Mathematics, 2021, vol. 9, issue 11, 1-15

Abstract: The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works.

Keywords: radiation-based flowmeter; two-phase flow; feature extraction; artificial intelligence; time domain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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