Investigation of different sources in order to optimize the nuclear metering system of gas–oil–water annular flows

  • A. Karami
  • G. H. Roshani
  • A. Khazaei
  • E. NazemiEmail author
  • M. Fallahi
Original Article


The used metering technique in this paper is based on the multienergy (at least dual) gamma-ray attenuation. The aim of the current study is investigation of different combinations of sources in order to find the best combination for precise metering gas, oil and water percentages in annular three-phase flows. The required data were generated numerically using Monte Carlo N Particle extended (MCNPX) code. As a matter of fact, the current investigation devotes to predict the volume fractions in the annular three-phase flow, on the basis of a multienergy metering system including different radiation sources and one sodium iodide detector, using the hybrid model. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must predict only two volume fractions. Six hybrid models associated with the number of applied radiation sources are employed. The models are applied to predict the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNPX code. The results show that the best prediction results are obtained for the oil and gas volume fractions of a system with the (241Am & 137Cs) radiation sources.


Annular regime Three-phase flow Volume fraction Intelligent integrated system Gray wolf optimization (GWO) algorithm 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

521_2018_3673_MOESM1_ESM.docx (31 kb)
Supplementary material 1 (DOCX 31 kb)


  1. 1.
    Abouelwafa MSA, Kendall EJM (1980) The measurement of component ratios in multiphase systems using alpha-ray attenuation. J Phys E Sci Instrum 13(3):341CrossRefGoogle Scholar
  2. 2.
    Nazemi E, Roshani GH, Feghhi SAH, Setayeshi S, Gholipour Peyvandi R (2015) A radiation-based hydrocarbon two-phase flow meter for estimating of phase fraction independent of liquid phase density in stratified regime. Flow Meas Instrum 46:25–32CrossRefGoogle Scholar
  3. 3.
    Roshani GH, Feghhi SAH, Mahmoudi-Aznaveh A, Nazemi E, Adineh-Vand A (2014) Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement 51:34–41CrossRefGoogle Scholar
  4. 4.
    Roshani GH, Nazemi E, Feghhi SAH, Setayeshi S (2015) Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 62:25–32CrossRefGoogle Scholar
  5. 5.
    Roshani GH, Nazemi E, Feghhi SAH (2016) Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas–liquid two-phase flows. Flow Meas Instrum 50:73–79CrossRefGoogle Scholar
  6. 6.
    Roshani GH, Nazemi E, Roshani MM (2017) Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Comput Appl 28(1):1265–1274CrossRefGoogle Scholar
  7. 7.
    Roshani GH, Nazemi E, Roshani MM (2017) Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement 100:122–130CrossRefGoogle Scholar
  8. 8.
    Roshani GH, Nazemi E (2017) A high performance gas–liquid two-phase flow meter based on gamma-ray attenuation and scattering. Nucl Sci Technol 28(11):169CrossRefGoogle Scholar
  9. 9.
    Roshani GH, Karami A, Nazemi E, Shama F (2018) Volume fraction determination of the annular three-phase flow of gas–oil–water using adaptive neuro-fuzzy inference system. Comput Appl Math. Google Scholar
  10. 10.
    Roshani GH, Hanus R, Khazaei A, Zych M, Nazemi E, Mosorov V (2018) Density and velocity determination for single-phase flow based on radiotracer technique and neural networks. Flow Meas Instrum 61:9–14CrossRefGoogle Scholar
  11. 11.
    Nazemi E, Feghhi SAH, Roshani GH (2014) Void fraction prediction in two-phase flows independent of the liquid phase density changes. Radiat Meas 68:49–54CrossRefGoogle Scholar
  12. 12.
    Nazemi E, Feghhi SAH, Roshani GH, Peyvandi RG, Setayeshi S (2016) Precise void fraction measurement in two-phase flows independent of the flow regime using gamma-ray attenuation. Nucl Eng Technol 48(1):64–71CrossRefGoogle Scholar
  13. 13.
    Zych M, Petryka L, Kępiński J, Hanus R, Bujak T, Puskarczyk E (2014) Radioisotope investigations of compound two-phase flows in an open channel. Flow Meas Instrum 35:11–15CrossRefGoogle Scholar
  14. 14.
    Hanus R, Petryka L, Zych M (2014) Velocity measurement of the liquid–solid flow in a vertical pipeline using gamma-ray absorption and weighted cross-correlation. Flow Meas Instrum 40:58–63CrossRefGoogle Scholar
  15. 15.
    Zych M, Hanus R, Vlasák P, Jaszczur M, Petryka L (2017) Radiometric methods in the measurement of particle-laden flows. Powder Technol 318:491–500CrossRefGoogle Scholar
  16. 16.
    Hanus R (2015) Application of the Hilbert transform to measurements of liquid–gas flow using gamma ray densitometry. Int J Multiph Flow 72:210–217CrossRefGoogle Scholar
  17. 17.
    Salgado CM, Brandao LEB, Schirru R, Pereira CMNA, Xavier da Silva A, Ramos R (2009) Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. Appl Radiat Isot 67:1812–1818CrossRefGoogle Scholar
  18. 18.
    Mosorov V, Zych M, Hanus R, Petryka L (2016) Modelling of dynamic experiments in MCNP5 environment. Appl Radiat Isot 112:136–140CrossRefGoogle Scholar
  19. 19.
    Cong T, Su G, Qiu S, Tian W (2013) Applications of ANNs in flow and heat transfer problems in nuclear engineering: a review work. Prog Nucl Energy 62:54–71CrossRefGoogle Scholar
  20. 20.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  21. 21.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. In: IEEE Transactions on Systems, Man, and Cybernetics SMC-15, pp 116–132Google Scholar
  22. 22.
    Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing, vol 19. Prentice Hall, Upper Saddle River, pp 510–514Google Scholar
  23. 23.
    Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control, special issue on fuzzy logic in engineering applications. In: Proceedings of the IEEE, vol 83, pp 378–406Google Scholar
  24. 24.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefzbMATHGoogle Scholar
  25. 25.
    Amiri A, Karami A, Yousefi T, Zanjani M (2012) Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders. Pol J Chem Technol 14:46–52CrossRefGoogle Scholar
  26. 26.
    Karami A, Rezaei E, Shahhosseni M, Aghakhani M (2012) Fuzzy logic to predict the heat transfer in an air cooler equipped with different tube inserts. Int J Therm Sci 53:141–147CrossRefGoogle Scholar
  27. 27.
    Yousefi T, Karami A, Rezaei E, Ebrahimi S (2012) Fuzzy modeling of the forced convection heat transfer from a V-shaped plate exposed to an air impingement slot jet. Heat Transf Asian Res 41:5Google Scholar
  28. 28.
    Karami A, Yousefi T, Rezaei E, Amiri A (2012) Modeling of the free convection heat transfer from an isothermal horizontal cylinder in a vertical channel via the fuzzy logic. Int J Multiphys 6:7–16CrossRefGoogle Scholar
  29. 29.
    Aghakhani M, Jalilian MM, Karami A (2012) Prediction of weld bead dilution in GMAW process using fuzzy logic. Appl Mech Mater 110–116:3171–3175Google Scholar
  30. 30.
    Aghakhani M, Ghaderi MR, Karami A, Derakhshan AA (2014) Combined effect of TiO2 nanoparticles and input welding parameters on the weld bead penetration in submerged arc welding process using fuzzy logic. Int J Adv Manuf Technol 70:63–72CrossRefGoogle Scholar
  31. 31.
    Amza CG, Cicic DT (2015) Industrial image processing using fuzzy-logic. Proc Eng 100:492–498CrossRefGoogle Scholar
  32. 32.
    Karami A, Roshani GH, Salehizadeh A, Nazemi E (2017) The fuzzy logic application in volume fractions prediction of the annular three-phase flows. J Nondestruct Eval 36(35):1–9. Google Scholar
  33. 33.
    Karami A, Akbari E, Rezaei E, Ashjaee M (2013) Neuro-fuzzy modeling of the free convection from vertical arrays of isothermal cylinders. J Thermophys Heat Transf 27:588–592CrossRefGoogle Scholar
  34. 34.
    Karami A, Rezaei E, Rahimi M, Khani S (2013) Modeling of heat transfer in an air cooler equipped with classic twisted tape inserts using adaptive neuro-fuzzy inference system. Chem Eng Commun 200:532–542CrossRefGoogle Scholar
  35. 35.
    Karami A, Yousefi T, Mohebbi S, Aghanajafi C (2014) Prediction of free convection from vertical and inclined rows of horizontal isothermal cylinders using ANFIS. Arab J Sci Eng 39:4201–4209CrossRefGoogle Scholar
  36. 36.
    Karami A, Yousefi T, Harsini I, Maleki E, Mahmoudinezhad S (2015) Neuro-fuzzy modeling of the free convection heat transfer from a wavy surface. Heat Transf Eng 36:847–855CrossRefGoogle Scholar
  37. 37.
    Karami A, Yousefi T, Ebrahimi S, Rezaei E, Mahmoudinezhad S (2013) Adaptive neuro-fuzzy inference system (ANFIS) to predict the forced convection heat transfer from a v-shaped plate. Heat Mass Transf 49:789–798CrossRefGoogle Scholar
  38. 38.
    Catalao JPS, Pousinho HMI, Mendes VMF (2011) Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans Power Syst 26(1):137–144CrossRefGoogle Scholar
  39. 39.
    Jiang H, Kwong CK, Ip WH, Wong TC (2012) Modeling customer satisfaction for new product development using a PSO-based ANFIS approach. Appl Soft Comput 12(2):726–734CrossRefGoogle Scholar
  40. 40.
    Pousinho HMI, Mendes VMF, Catalão JPS (2011) A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal. Energy Convers Manag 52(1):397–402CrossRefGoogle Scholar
  41. 41.
    Pousinho HMI, Mendes VMF, Catalão JPS (2012) Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach. Int J Electr Power Energy Syst 39(1):29–35CrossRefGoogle Scholar
  42. 42.
    Turki M, Bouzaida S, Sakly A, M’Sahli F (2012) Adaptive control of nonlinear system using neuro-fuzzy learning by PSO algorithm. In: 16th IEEE Mediterranean electrotechnical conference (MELECON)Google Scholar
  43. 43.
    Rini DP, Shamsuddin SM, Yuhaniz SS (2013) Balanced the trade-offs problem of ANFIS using particle swarm optimisation. TELKOMNIKA 11(3):611–616CrossRefGoogle Scholar
  44. 44.
    Kuppusamy V, Paramasivam I (2017) Grey fuzzy neural network-based hybrid model for missing data imputation in mixed database. Int J Intell Eng Syst 10:146–155CrossRefGoogle Scholar
  45. 45.
    Sargolzaei A, Faez K, Sargolzaei S (2011) A new method for Foetal Electrocardiogram extraction using adaptive nero-fuzzy interference system trained with PSO algorithm. In: 2011 IEEE international conference on electro/information technology (EIT)Google Scholar
  46. 46.
    Mirjalili S (2015) How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell 43:150–161CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentRazi UniversityKermanshahIran
  2. 2.Electrical Engineering DepartmentKermanshah University of TechnologyKermanshahIran
  3. 3.Young Researchers and Elite Club, Kermanshah BranchIslamic Azad UniversityKermanshahIran

Personalised recommendations