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Title
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Application of feature extraction and artificial intelligence techniques for increasing the accuracy of X-ray radiation based two phase flow meter
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Mathematics
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Publication
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2021
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ISSN
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2227-7390
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DOI
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10.3390/MATH9111227
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Volume/pages
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9
:11
(2021)
, 15 p.
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Article Reference
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1227
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ISI
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000660256900001
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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