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Title
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Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector
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Author
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Abstract
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| Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%. |
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Language
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English
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Source (journal)
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Measurement / International Measurement Confederation. - London
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Publication
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London
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2014
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ISSN
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0263-2241
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DOI
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10.1016/J.MEASUREMENT.2014.01.030
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Volume/pages
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51
(2014)
, p. 34-41
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ISI
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000336016400004
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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