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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

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Abstract

Background

Understanding the volume fraction of water-oil-gas three-phase flow is of significant importance in oil and gas industry.

Purpose

The current research attempts to indicate the ability of adaptive network-based fuzzy inference system (ANFIS) to forecast the volume fractions in a water-oil-gas three-phase flow system.

Method

The current investigation devotes to measure the volume fractions in the stratified three-phase flow, on the basis of a dual-energy metering system consisting of the 152Eu and 137Cs and one NaI detector using ANFIS. The summation of volume fractions is equal to 100% and is also a constant, and this is enough for the ANFIS just to forecast two volume fractions. In the paper, three ANFIS models are employed. The first network is applied to forecast the oil and water volume fractions. The next to forecast the water and gas volume fractions, and the last to forecast the gas and oil volume fractions. For the next step, ANFIS networks are trained based on numerical simulation data from MCNP-X code.

Results

The accuracy of the nets is evaluated through the calculation of average testing error. The average errors are then compared. The model in which predictions has the most consistency with the numerical simulation results is selected as the most accurate predictor model. Based on the results, the best ANFIS net forecasts the water and gas volume fractions with the mean error of less than 0.8%.

Conclusion

The proposed methodology indicates that ANFIS can precisely forecast the volume fractions in a water-oil-gas three-phase flow system.

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References

  1. I.M.M. Babelli, Development of the multiphase meter using gamma densitometer concept, in Proceedings of the International Nuclear Conference (1997), pp. 371–389

  2. C.M. Salgado, L.E.B. Brandao, R. Schirru, C.M.N.A. Pereira, A. Xavier da Silva, R. Ramos, Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network. Appl. Radiat. Isot. 67, 1812–1818 (2009)

    Article  Google Scholar 

  3. C.M. Salgado, C.M.N.A. Pereira, R. Schirru, L.E.B. Brandao, Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks. Prog. Nucl. Energy 52, 555–562 (2010)

    Article  Google Scholar 

  4. C.M. Salgado, L.E.B. Brandao, C.M.N.A. Pereira, W.L. Salgado, 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)

    Article  Google Scholar 

  5. G.H. Roshani, E. Nazemi, M.M. Roshani, Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Comput. Appl. 28, 1265 (2017)

    Article  Google Scholar 

  6. G.H. Roshani, E. Nazemi, M.M. Roshani, Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement 100, 122–130 (2017)

    Article  Google Scholar 

  7. G.H. Roshani, E. Nazemi, S.A.H. Feghhi, S. Setayeshi, Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 62, 25–32 (2015)

    Article  Google Scholar 

  8. E. Nazemi, G.H. Roshani, S.A.H. Feghhi, S. Setayeshi, E.E. Zadeh, A. Fatehi, Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. Int. J. Hydrogen Energy 41, 7438–7444 (2016)

    Article  Google Scholar 

  9. E. Nazemi, S.A.H. Feghhi, G.H. Roshani, void fraction prediction in two-phase flows independent of the liquid phase density changes. Radiat. Meas. 68, 49–54 (2014)

    Article  Google Scholar 

  10. E. Nazemi, G.H. Roshani, S.A.H. Feghhi, S. Setayeshi, 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 Meas. Instrum. 46, 25–32 (2015)

    Article  Google Scholar 

  11. R. Hanus, L. Petryka, M. Zych, Velocity measurement of the liquid–solid flow in a vertical pipeline using gamma-ray absorption and weighted cross-correlation. Flow Meas. Instrum. 40, 58–63 (2014)

    Article  Google Scholar 

  12. V. Mosorov, M. Zych, R. Hanus, L. Petryka, Modelling of dynamic experiments in MCNP5 environment. Appl. Radiat. Isot. 112, 136–140 (2016)

    Article  Google Scholar 

  13. R. Hanus, M. Zych, L. Petryka, D. Swisulski, Time delay estimation in two-phase flow investigation using the \(\gamma \)-ray attenuation technique. Math. Probl. Eng. (2014). https://doi.org/10.1155/2014/475735 (Article ID 475735)

    Article  MathSciNet  Google Scholar 

  14. M. Zych, L. Petryka, J. Kępinński, R. Hanus, T. Bujak, E. Puskarczyk, Radioisotope investigations of compound two-phase flows in an open channel. Flow Meas. Instrum. 35, 11–15 (2014)

    Article  Google Scholar 

  15. R. Hanus, Application of the Hilbert transform to measurements of liquid–gas flow using gamma ray densitometry. Int. J. Multiph. Flow 72, 210–217 (2015)

    Article  Google Scholar 

  16. S.H. Jung, J.S. Kim, J.B. Kim, T.Y. Kwon, Flow-rate measurements of a dual-phase pipe flow by cross-correlation technique of transmitted radiation signals. Appl. Radiat. Isot. 67, 1254–1258 (2009)

    Article  Google Scholar 

  17. G.H. Roshani, E. Nazemi, M.M. Roshani, Identification of flow regime and estimation of volume fraction independent of liquid phase density in gas–liquid two-phase flow. Prog. Nucl. Energy 98, 29–37 (2017)

    Article  Google Scholar 

  18. G.H. Roshani, S.A.H. Feghhi, A. Mahmoudi-Aznaveh, E. Nazemi, 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, 34–41 (2014)

    Article  Google Scholar 

  19. G.H. Roshani, E. Nazemi, 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 Meas. Instrum. 50, 73–79 (2016)

    Article  Google Scholar 

  20. A. El Abd, Intercomparison of gamma ray scattering and transmission techniques for gas volume fraction measurements in two phase pipe flow. Nucl. Instrum. Methods Phys. Res. A 735, 260–266 (2014)

    Article  ADS  Google Scholar 

  21. E. Nazemi, G.H. Roshani, S.A.H. Feghhi, R.G. Peyvandi, S. Setayeshi, Precise void fraction measurement in two-phase flows independent of the flow regime using gamma-ray attenuation. Nucl. Eng. Technol. 48, 64–71 (2016)

    Article  Google Scholar 

  22. A. Yadollahi, E. Nazemi, A. Zolfaghari, A.M. Ajorloo, Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete. Prog. Nucl. Energy 89, 69–77 (2016)

    Article  Google Scholar 

  23. G.H. Roshani, S.A.H. Feghhi, F. Shama, A. Salehizadeh, E. Nazemi, Prediction of materials density according to number of scattered gamma photons using optimum artificial neural network. Comput. Methods Phys. 2014, 305345 (2014)

    Google Scholar 

  24. A. Yadollahi, E. Nazemi, A. Zolfaghari, A.M. Ajorloo, Optimization of thermal neutron shield concrete mixture using artificial neural network. Nucl. Eng. Design 305, 146–155 (2016)

    Article  Google Scholar 

  25. Yu. Zhao, Qincheng Bi, Richa Hu, Recognition and measurement in the flow pattern and void fraction of gas liquid two-phase flow in vertical upward pipe using the gamma densitometer. Appl. Therm. Eng. 60(12), 398–410 (2013)

    Article  Google Scholar 

  26. Y. Zhao, Q. Bi, Y. Yuan, H. Lv, Void fraction measurement in steam-water two phase flow using the gamma ray attenuation under high pressure and high temperature evaporating conditions. Flow Meas. Instrum. 49, 18–30 (2016)

    Article  Google Scholar 

  27. G.H. Roshani, E. Nazemi, M.M. Roshani, Intelligent recognition of gas–oil–water three-phase flow regime and determination of volume fraction using Radial Basis Function. Flow Meas. Instrum. 54, 39–45 (2017)

    Article  Google Scholar 

  28. G.H. Roshani, R. Hanus, A. Khazaei, M. Zych, E. Nazemi, V. Mosorov, Density and velocity determination for singlephase flow based on radiotracer technique and neural networks. Flow Meas. Instrum. 61, 9–14 (2018)

    Article  Google Scholar 

  29. A. Karami, G.H. Roshani, A. Salehizadeh, E. Nazemi, The fuzzy logic application in volume fractions prediction of the annular three-phase flows. J. Nondestruct. Eval. 36, 35 (2017)

    Article  Google Scholar 

  30. G.H. Roshani, E. Nazemi, M.M. Roshani, A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function. Appl. Radiat. Isot. 123, 60–68 (2017)

    Article  Google Scholar 

  31. G.H. Roshani, A. Karami, A. Salehizadeh, E. Nazemi, The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas–oil–water. Appl. Radiat. Isot. 129, 156–162 (2017)

    Article  Google Scholar 

  32. J.F. Briesmeister, MCNP—a General Monte Carlo N-particle Transport Code, Version 4C. Report LA-13709-M (Los Alamos National Laboratory, April 2000)

  33. T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. 15, 116 (1985)

    MATH  Google Scholar 

  34. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, vol. 19 (Prentice Hall, Upper Saddle River, 1997), pp. 510–514

    Google Scholar 

  35. J.S.R. Jang, C.T. Sun, Neuro-fuzzy modeling and control. Proc. IEEE 83, 378–406 (1995)

    Article  Google Scholar 

  36. The MathWorks, Fuzzy Logic Toolbox User’s Guide, Inc., vol. 3 (Apple Hill Drive, Natick, 1995–2007), pp. 01760–02098

  37. A. Amiri, A. Karami, T. Yousefi, M. Zanjani, Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders. Polish J. Chem. Technol. 14, 46–52 (2012)

    Article  Google Scholar 

  38. A. Karami, E. Rezaei, M. Shahhosseni, M. Aghakhani, Fuzzy logic to predict the heat transfer in an air cooler equipped with different tube inserts. Int. J. Therm. Sci. 53, 141–147 (2012)

    Article  Google Scholar 

  39. T. Yousefi, A. Karami, E. Rezaei, S. Ebrahimi, 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, 5 (2012)

    Google Scholar 

  40. A. Karami, T. Yousefi, E. Rezaei, A. Amiri, Modeling of the free convection heat transfer from an isothermal horizontal cylinder in a vertical channel via the fuzzy logic. Int. J. Multiphys. 6, 7–16 (2012)

    Article  Google Scholar 

  41. M. Aghakhani, M.M. Jalilian, A. Karami, Prediction of weld bead dilution in GMAW process using fuzzy logic. Appl. Mech. Mater. 110—-116, 3171–3175 (2012)

    Google Scholar 

  42. M. Aghakhani, M.R. Ghaderi, A. Karami, A.A. Derakhshan, Combined effect of TiO\(_{2}\) nanoparticles and input welding parameters on the weld bead penetration in submerged arc welding process using fuzzy logic. Int. J. Adv. Manuf. Technol. 70, 63–72 (2014)

    Article  Google Scholar 

  43. E. Rezaei, A. Karami, T. Yousefi, S. Mahmoudinezhad, Modeling the free convection heat transfer in a partitioned cavity using ANFIS. Int. Commun. Heat Mass Transf. 39, 470–475 (2012)

    Article  Google Scholar 

  44. A. Karami, E. Akbari, E. Rezaei, M. Ashjaee, Neuro-fuzzy modeling of the free convection from vertical arrays of isothermal cylinders. J. Thermophys. Heat Transf. 27, 588–592 (2013)

    Article  Google Scholar 

  45. A. Karami, E. Rezaei, M. Rahimi, S. Khani, Modeling of heat transfer in an air cooler equipped with classic twisted tape inserts using adaptive neuro-fuzzy inference system. Chem. Eng. Commun. 200, 532–542 (2013)

    Article  Google Scholar 

  46. A. Karami, T. Yousefi, S. Mohebbi, C. Aghanajafi, Prediction of free convection from vertical and inclined rows of horizontal isothermal cylinders using ANFIS. Arab. J. Sci. Eng. 39, 4201 (2014)

    Article  Google Scholar 

  47. A. Karami, T. Yousefi, I. Harsini, E. Maleki, S. Mahmoudinezhad, Neuro-fuzzy modeling of the free convection heat transfer from a wavy surface. Heat Transf. Eng. 36, 847–855 (2015)

    Article  ADS  Google Scholar 

  48. A. Karami, T. Yousefi, S. Ebrahimi, E. Rezaei, S. Mahmoudinezhad, Adaptive neuro-fuzzy inference system (ANFIS) to predict the forced convection heat transfer from a v-shaped plate. Heat Mass Transf. 49, 789–798 (2013)

    Article  ADS  Google Scholar 

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Roshani, G.H., Karami, A. & Nazemi, E. 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. Radiat Detect Technol Methods 2, 38 (2018). https://doi.org/10.1007/s41605-018-0053-3

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  • DOI: https://doi.org/10.1007/s41605-018-0053-3

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