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Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network

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Abstract

In this paper, based on dual-energy broad beam gamma ray attenuation technique (using two transmission 1-inch NaI detectors and a dual-energy gamma ray source), an artificial neural network (ANN) model was used in order to predict the volume fraction of gas, oil and water in three-phase flows independent of the flow regime. A multilayer perceptron (MLP) neural network was used for developing the ANN model in MATLAB 8.1.0.604 software. The input parameters of the MLP model were registered counts under first and second full energy peaks of the both transmission NaI detectors, and the outputs were gas and oil percentage. The volume fractions were obtained precisely independent of flow regime using the presented model. Mean absolute error of the presented model was less than 2.24%.

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Authors and Affiliations

  1. Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran

    G. H. Roshani

  2. Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    E. Nazemi & M. M. Roshani

Authors
  1. G. H. Roshani
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  2. E. Nazemi
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  3. M. M. Roshani
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Correspondence to E. Nazemi.

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Roshani, G.H., Nazemi, E. & Roshani, M.M. Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Comput & Applic 28 (Suppl 1), 1265–1274 (2017). https://doi.org/10.1007/s00521-016-2784-8

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  • DOI: https://doi.org/10.1007/s00521-016-2784-8

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