Precise Volume Fraction Percentage Measurement in Three-Phase Flows Using Gamma-Ray Technique and Artificial Neural Network

Document Type : Research Article

Authors

1 Assistant Professor, Department of physics, Faculty of Science, University of Qom, Qom, I.R. IRAN

2 Department of physics & Nuclear Engineering, Shahrood University of Technology, Shahrood, I.R. IRAN

Abstract

Precise volume fraction percentage prediction in water-gasoil-air three-phase flows in unstable operational conditions is an important parameter in the oil and petroleum industry. In this research, the volume fraction percentage was measured precisely in water-gasoil-air three-phase flows by using single energy gamma ray attenuation technique and neural network, for the first time. The volume fraction percentage determination in three-phase flows requires least two gamma radioactive sources with different energies while in this study, we used just a 137Cs source (with the single energy of 662 keV) and a NaI detector. Also, the multilayer perceptron (MLP) neural network was implemented to predict the volume fraction percentage. The acquired results from an experimental setup provides the required data for training and testing the network. The inputs of ANN have registered spectra in the transmitted detector as the dataset matrix for ANN consisted of a (Y118×42). In this ANN, the number of neurons in the input, hidden and output layers are 118, 10 and 3, respectively. Using this proposed method, the volume fraction was predicted in water-gasoil-air three-phase flows with Mean Relative Error percentage (MRE%) less than 6.95%. Also, the Root Mean Square Error (RMSE) was calculated simultaneous 2.60. The set-up used is simpler than other proposed methods and cost, radiation safety and shielding requirements are minimized.

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References

[1] Tjugum SA., Frieling J., Johansen G.A., A Compact Low Energy Multibeam Gamma-Ray Densitometer for Pipe-Flow Measurements, Nucl. Inst. Meth B, 197: 301-309 (2002).
[2] Johansen GA., Jackson P., Salinity Independent Measurement of Gas Volume Fraction in Oil/Gas/Water Pipe Flows, Appl. Radiat. Isot, 53: 595-601(2000).
[3] Abro E., Johansen GA., Improved Void Fraction Determination by Means of Multibeam Gamma-Ray Attenuation Measurements, Flow Meas. Instrum, 10: 99-108 (1999).
[4] Chu IC., Song CH., Development and Performance Evaluation of 32-Channel Gamma Densitometer for the Measurement of Flow Pattern and Void Fraction in the Downcomer of MIDAS Test Facility, KAERI/TR-2045/2002, KAERI (2002).
[5] Chang SK., Park HS., Chung CH., Analysis of the Test Results for the Two-Phase Critical Flow with Non-Condensable Gas. KAERI/TR-2242/2002, KAERI (2002).
[6] Salgado CM., Brandao LEB., Pereira CMNA., Salgado WL., Salinity Independent Volume Fraction Prediction in Annular and Stratified (Water-Gas-Oil) Multiphase Flows Using Artificial Neural Networks, Prog. Nucl. Energy, 76: 17-23 (2014).
[7] Salgado CM., Brandao LB., Pereira CMNA., Xavier da Silva A., Ramos R., Prediction of Volume Fractions in Three-Phase Flows Using Nuclear Technique and Artificial Neural Network, Appl.Radiat. Isot, 67: 1812–1818 (2009).
[8] Abro E., Khoryakov VA., Johansen GA., Determination of Void Fraction and Flow Regime Using a Neural Network Trained on Simulated Data Based on Gamma-Ray Densitometry, Meas. Sci. Tech, 10: 619-630 (1999).
[9] Roshani GH., Feghhi SAH., Mahmoudi-Aznaveh A., Nazemi E., Adineh vand A., Precise Volume Fraction Prediction in Oil-Water-Gas Multiphase Flows by Means of Gamma-Ray Attenuation and Artificial Neural Networks Using one Detector, Measurement, 51: 34-41 (2014).
[10] IAEA-TECDOC 1459., “Technical Data on Nucleonic Gauges”, IAEA., Vienna (2005).
[11] Taylor JG., “Neural Networks and Their Applications”, John Wiley & Sons, Inc., Brighton (1996).
[12] Gallant A.R., White H., On Learning the Derivatives of an Unknown Mapping with Multilayer Feed Forward Networks, Neural Networks, 5: 129-138 (1992).
[13] Hagan MT., Menhaj M., Training Feed Forward Networks with the Marquardt Algorithm, IEEE Trans. Neural Networks, 5: 989-993 (1994).
[14] Demuth H., Beale M., Hagan M., “Neural Network Toolbox TM 6, User’s Guide, The Math Works”, Massachusetts (2008).
[15] Zaknich A., “Neural Networks for Intelligent Signal Processing”, World Scientific Pub Co. Inc., Toh Tuck Link (2003).
[16] Bisht DCS., Jangid A., Discharge Modeling Using Adaptive Neuro-Fuzzy Inference System. Int. J. Adv.Sci. Tech, 31: 99-114 (2011).
Nashrieh Shimi va Mohandesi Shimi Iran
Volume 37, Issue 3 - Serial Number 89
October 2018
Pages 173-182

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