pp 1–8 | Cite as

Utilizing Features Extracted from Registered 60Co Gamma-Ray Spectrum in One Detector as Inputs of Artificial Neural Network for Independent Flow Regime Void Fraction Prediction

  • G. H. Roshani
  • E. NazemiEmail author
  • F. Shama
Original Paper


In this paper, we demonstrate that void fraction could be predicted independent of type of flow regime in two-phase flows using 60Co source and one scintillator NaI detector. For this purpose, firstly three features (Feature No. 1: counts under Compton continuum; Feature No. 2: counts under full energy peak of 1173 keV; Feature No. 3: counts under full energy peak of 1333 keV) were extracted from registered gamma-ray spectrum in detector. Secondly, these three features were utilized as the inputs of artificial neural network model of multilayer perceptron (MLP) in order to achieve the best structure for predicting the void fraction. In each structure, void fraction was considered constantly as the output of MLP network. Using the optimum MLP network structure, void fraction was predicted independent of type of flow regime in gas–liquid two-phase flow with MRE of less than 2.5%. Although obtained error using one detector for predicting the void fraction is more than when two or more detectors are utilized, using fewer detectors has advantages such as making the detection system simpler and reducing economical expenses.


60Co source Multilayer perceptron Two-phase flow Regime independent Feature extraction 


  1. 1.
    Y. Jiang, and K.S. Rezkallah, An experimental study of the suitability of using a gamma densitometer for void fraction measurements in gas-liquid flow in a small diameter tube, Meas. Sci. Technol., 4 (1993) 496–505.ADSCrossRefGoogle Scholar
  2. 2.
    Y. Murai, S. Ohta and A. Shigetomi, et al., Development of an ultrasonic void fraction profiler. Meas Sci Technol, 20 (2009).
  3. 3.
    V. Mosorov, Flow pattern tracing for mass flow rate measurement in pneumatic conveying using twin plane electrical capacitance tomography. Part Syst Charact, 25 (2008) 259–265. Scholar
  4. 4.
    G. Rojas and M.R. Loewen, Fiber-optic probe measurements of void fraction and bubble size distributions beneath breaking waves, Exp Fluids, 43 (2007) 895–906. Scholar
  5. 5.
    E. Abro, and G.A. Johansen, Improved void fraction determination by means of multibeam gamma-ray attenuation measurements. Flow Measurement and Instrumentation, 10(2) (1999) 99–108.CrossRefGoogle Scholar
  6. 6.
    C.M. Salgado, L.E.B. Brandao, R. Schirru, C.M.N.A. Pereira, A. Xavier da Silva and R. Ramos, Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. Applied Radiation and Isotopes, 67 (2009) 1812–1818.CrossRefGoogle Scholar
  7. 7.
    C. Sætre, G.A. Johansen and S.A. Tjugum, Salinity and flow regime independent multiphase flow measurements, Flow Measurement and Instrumentation, 21 (2010) 454–461.CrossRefGoogle Scholar
  8. 8.
    E. Nazemi, S.A.H. Feghhi and G.H. Roshani, Void fraction prediction in two-phase flows independent of the liquid phase density changes. Radiation Measurements, 68 (2014), 49–54.ADSCrossRefGoogle Scholar
  9. 9.
    E. Nazemi, G.H. Roshani, S.A.H. Feghhi, R. Gholipour Peyvandi and S. Setayeshi, Precise void fraction measurement in two-phase flows independent of the flow regime using gamma-ray attenuation. Nuclear Engineering and Technology, 48 (2016) 64–71.CrossRefGoogle Scholar
  10. 10.
    G.H. Roshani, E. Nazemi, S.A.H. Feghhi and S. Setayeshi, Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 62 (2015) 25–32.CrossRefGoogle Scholar
  11. 11.
    S.-H. Jung, J.-S. Kim, J.-B. Kim and T.-Y. Kwon, Flow-rate measurements of a dual-phase pipe flow by cross-correlation technique of transmitted radiation signals. Applied Radiation and Isotopes, 67 (2009) 1254–1258.CrossRefGoogle Scholar
  12. 12.
    G.H. Roshani, E. Nazemi and M.M. Roshani, Identification of flow regime and estimation of volume fraction independent of liquid phase density in gas-liquid two-phase flow. Progress in Nuclear Energy, 98 (2017) 29–37.CrossRefGoogle Scholar
  13. 13.
    G.H. Roshani, E. Nazemi, F. Shama, M.A. Imani and S. Mohammadi, Designing a simple radiometric system to predict void fraction percentage independent of flow pattern using radial basis function. Metrology and Measurement Systems (2018).Google Scholar
  14. 14.
    G.H. Roshani, S.A.H. Feghhi, A. Mahmoudi-Aznaveh, E. Nazemi and A. Adineh-Vand, 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 (2014) 34–41.CrossRefGoogle Scholar
  15. 15.
    R. Hanus, L. Petryka and M. Zych, Velocity measurement of the liquid–solid flow in a vertical pipeline using gamma-ray absorption and weighted cross-correlation. Flow Measurement and Instrumentation, 40 (2014) 58–63.CrossRefGoogle Scholar
  16. 16.
    C.M. Salgado, L.E.B. Brandao, C.M.N.A. Pereira and W.L. Salgado, Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks. Progress in Nuclear Energy, 76 (2014) 17–23.CrossRefGoogle Scholar
  17. 17.
    E. Nazemi, G.H. Roshani, S.A.H. Feghhi, S. Setayeshi and R. Gholipour Peyvandi, A radiation-based hydrocarbon two-phase flow meter for estimating of phase fraction independent of liquid phase density in stratified regime. Flow Measurement and Instrumentations, 46 (2015) 25–32.CrossRefGoogle Scholar
  18. 18.
    E. Nazemi, G.H. Roshani, S.A.H. Feghhi, S. Setayeshi, E. Eftekhari Zadeh and A. Fatehi, Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. International Journal of Hydrogen Energy, 41, 7438–7444.Google Scholar
  19. 19.
    E. Abro, V.A. Khoryakov and G.A. Johansen, Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry. Measurement Science and Technology, 10(7) (1999) 619–630.ADSCrossRefGoogle Scholar
  20. 20.
    G.H. Roshani, A. Karami, E. Nazemi and F. Shama, Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system. Computational and Applied Mathematics (2018).
  21. 21.
    A. Karami, G.H. Roshani, A. Salehizadeh and E. Nazemi, The fuzzy logic application in volume fractions prediction of the annular three-phase flows, Journal of Nondestructive Evaluation (2017).Google Scholar
  22. 22.
    G.H. Roshani, A. Karami, A. Khazaei, A. Olfateh, E. Nazemi and M. Omidi, Optimization of radioactive sources to achieve the highest precision in three-phase flow meters using Jaya algorithm. Applied Radiation and Isotopes, 139 (2018) 256–265.CrossRefGoogle Scholar
  23. 23.
    G.H. Roshani, R. Hanus, A. Khazaei, M. Zych, E. Nazemi and V. Mosorov, Density and velocity determination for single-phase flow based on radiotracer technique and neural networks. Flow Measurement and Instrumentation, 61 (2018) 9–14.CrossRefGoogle Scholar
  24. 24.
    V. Mosorov, M. Zych, R. Hanus and L. Petryka, Modelling of dynamic experiments in MCNP5 environment. Applied Radiation and Isotopes, 112 (2016) 136–140.CrossRefGoogle Scholar
  25. 25.
    G.H. Roshani, A. Karami and E. Nazemi, An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas-oil-water. Computational and Applied Mathematics (2018).Google Scholar
  26. 26.
    T. Cong, R. Chen, G. Su, S. Qiu and W. Tian, Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm. Nuclear Engineering and Design, 9 (2011) 241. Scholar
  27. 27.
    G.H. Roshani, E. Nazemi and M.M. Roshani, Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using Radial Basis Function. Flow Measurement and Instrumentation.
  28. 28.
    A. Karami, G.H. Roshani, A. Khazaei, E. Nazemi and M. Fallahi, Investigation of different sources in order to optimize the nuclear metering system of gas–oil–water annular flows. Neural Computing and Applications. (2018).
  29. 29.
    R. Hanus, M. Zych, L. Petryka and D. Swisulski, Time delay estimation in two-phase flow investigation using the γ-ray attenuation technique. Mathematical Problems in Engineering, 2014 (2014), Article ID 475735.
  30. 30.
    G.H. Roshani and E. Nazemi, A novel dual-molality densitometer for gauging in annular two phase flows using radial basis function. Kerntechnik, 83(2) (2018) 145–151.CrossRefGoogle Scholar
  31. 31.
    G.H. Roshani, S. Roshani, E. Nazemi and S. Roshani, Online measuring density of oil products in annular regime of gas-liquid two phase flows. Measurement (2018).Google Scholar
  32. 32.
    G.H. Roshani, A. Karami and E. Nazemi, Combination of a gamma radiation-based system and the adaptive network-based fuzzy inference system (ANFIS) for calculating the volume fraction in stratified regime of a three-phase flow. Radiation Detection Technology and Methods, 2 (2018) 38.CrossRefGoogle Scholar
  33. 33.
    M. Zych, L. Petryka, J. Kępiński, R. Hanus, T. Bujak, and E. Puskarczyk, Radioisotope investigations of compound two-phase flows in an open channel. Flow Measurement and Instrumentation, 35 (2014) 11–15.CrossRefGoogle Scholar
  34. 34.
    G.H. Roshani, E. Nazemi and S.A.H. Feghhi, Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows. Flow Measurement and Instrumentation, 50 (2016) 73–79.CrossRefGoogle Scholar
  35. 35.
    D.B. Pelowitz, MCNP-X TM User’s Manual, Version 2.5.0. LA-CP-05e0369. Los Alamos National Laboratory (2005).Google Scholar
  36. 36.
    E. Nazemi, B. Rokrok, A. Movafeghi and M.C. Dastjerdi, Simulation of a complete X-ray digital radiographic system for industrial applications. Applied Radiation and Isotopes, 139 (2018) 294–303.CrossRefGoogle Scholar
  37. 37.
    E. Nazemi, A. Movafeghi, B. Rokrok and M.C. Dastjerdi, A novel method for predicting pixel value distribution non-uniformity due to heel effect of X-ray tube in industrial digital radiography using artificial neural network. Journal of Nondestructive Evaluation, 38 (2019) 1–10.CrossRefGoogle Scholar
  38. 38.
    Yadollahi A., Nazemi E., Zolfaghari A. and A.M. Ajorloo, Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete. Progress in Nuclear Energy, 89 (2016) 69–77.CrossRefGoogle Scholar
  39. 39.
    A. Yadollahi, E. Nazemi, A. Zolfaghari and A.M. Ajorloo, Optimization of thermal neutron shield concrete mixture using artificial neural network. Nuclear Engineering and Design, 305 (2016) 146–155.CrossRefGoogle Scholar
  40. 40.
    E. Eftekharizadeh, A. Sadighzadeh, A. Salehizadeh, E. Nazemi and G.H. Roshani, Neutron activation analysis for cement elements using an IECF device as a high energy neutron source. Analytical Methods, 8 (11) (2016) 2510–2514.CrossRefGoogle Scholar
  41. 41.
    A. Karami, G.H. Roshani, E. Nazemi, and S. Roshani, Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm. Flow Measurement and Instrumentation, 64 (2018) 164–172.CrossRefGoogle Scholar
  42. 42.
    C. Voyant, G. Notton, C. Darras, A. Fouilloy and F. Motte, Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case. Energy, 125 (2017) 248–257.CrossRefGoogle Scholar
  43. 43.
    J.M. Johns and D. Burkes, Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys. Journal of Nuclear Materials, 490 (2017) 155–166.ADSCrossRefGoogle Scholar
  44. 44.
    X. Shi, Y. Feng, J. Zeng and K. Chen, Chaos time-series prediction based on an improved recursive Levenberg–Marquardt algorithm. Chaos, Solitons & Fractals, 100 (2017) 5–61.ADSMathSciNetCrossRefGoogle Scholar
  45. 45.
    A. Sarabakha, N. Imanberdiyev, E. Kayacan, M.A. Khanesar and H. Hagras, Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles. Information Sciences, 417 (2017) 361–380.CrossRefGoogle Scholar

Copyright information

© Metrology Society of India 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentKermanshah University of TechnologyKermanshahIran
  2. 2.Nuclear Science and Technology Research InstituteTehranIran
  3. 3.Department of Electrical Engineering, Kermanshah BranchIslamic Azad UniversityKermanshahIran

Personalised recommendations