Performance comparison of capacitance-based flowmeter with gamma-ray attenuation-based two-phase flowmeter for determining volume fractions in an annular flow regime’s components

Abstract

Precise metering of the void fraction is one of the important problems in the oil, chemical and petrochemical industries. For void fraction measurement, there are different kinds of sensors with different configurations. In this regard, the capacitance-based sensor and gamma-ray attenuation-based sensor are very well known as two most accurate and widely used sensors. In this paper, we report, to the best of our knowledge, for the first time a comparison between these two sensors in an annular air–oil flow. Simulations were accomplished with benchmarked COMSOL Multiphysics software and MCNPX code. Results show that the general sensitivity of gamma-ray sensor is ~ 90% higher than the general sensitivity of capacitance-based sensor. For a more accurate comparison, momentary sensitivity factors for a variety void fractions in both sensors were obtained. In the low void fraction range, the gamma-ray sensor has much better performance; however, in the high void fraction range, the capacitance-based sensor has a better performance. In the range of 0.9–1 void fractions, the momentary sensitivity of capacitance-based sensor is ~ 67% higher than that of gamma-ray attenuation-based sensor.

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References

  1. 1.

    M.S.A. Abouelwafa, E.J.M. Kendall, The use of capacitance sensors for phase percentage determination in multiphase pipelines. IEEE Trans. Instrum. Meas. 29(1), 24–27 (1980)

    Article  Google Scholar 

  2. 2.

    M.S.A. Abouelwafa, E.J.M. Kendall, The measurement of component ratios in multiphase systems using alpha-ray attenuation. J. Phys. E Sci. Instrum. 13(3), 341 (1980)

    ADS  Article  Google Scholar 

  3. 3.

    H. Ahmed, Capacitance sensors for void-fraction measurements and flow-pattern identification in air–oil two-phase flow. IEEE Sens. J. 6(5), 1153–1163 (2006)

    ADS  Article  Google Scholar 

  4. 4.

    S.M. Salehi, H. Karimi, A.A. Dastranj, A capacitance sensor for gas/oil two-phase flow measurement: exciting frequency analysis and static experiment. IEEE Sens. J. 17(3), 679–686 (2016)

    ADS  Article  Google Scholar 

  5. 5.

    S.M. Salehi, H. Karimi, R. Moosavi, A.A. Dastranj, Different configurations of capacitance sensor for gas/oil two phase flow measurement: an experimental and numerical study. Exp. Therm. Fluid Sci. 82, 349–358 (2017)

    Article  Google Scholar 

  6. 6.

    S.M. Salehi, H. Karimi, A.A. Dastranj, R. Moosavi, Twin rectangular fork-like capacitance sensor to flow regime identification in horizontal co-current gas-liquid two-phase flow. IEEE Sens. J. 17(15), 4834–4842 (2017)

    ADS  Article  Google Scholar 

  7. 7.

    C.M. Salgado, C.M. Pereira, R. Schirru, L.E. Brandão, Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks. Prog. Nucl. Energy 52(6), 555–562 (2010)

    Article  Google Scholar 

  8. 8.

    C.M. Salgado, L.E. Brandão, C.M. 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 

  9. 9.

    C.M. Salgado, L.E.B. Brandão, C.C. Conti, W.L. Salgado, Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks. Appl. Radiat. Isot. 116, 143–149 (2016)

    Article  Google Scholar 

  10. 10.

    Mosorov, et al., Modelling of dynamic experiments in MCNP5 environment. Appl. Radiat. Isot. 112, 136–140 (2016)

    Article  Google Scholar 

  11. 11.

    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 

  12. 12.

    R. Hanus, M. Zych, L. Petryka, D. Świsulski, A. Strzępowicz, Application of ANN and PCA to two-phase flow evaluation using radioisotopes, in EPJ Web of Conferences, vol. 143 (EDP Sciences, 2017), p. 02033

  13. 13.

    R. Hanus, M. Zych, M. Kusy, M. Jaszczur, L. Petryka, Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods. Flow Meas. Instrum. 60, 17–23 (2018)

    Article  Google Scholar 

  14. 14.

    R.G. Peyvandi, A simple and inexpensive design for volume fraction prediction in three-phase flow meter: single source-single detector. Flow Meas. Instrum. 69, 101587 (2019)

    Article  Google Scholar 

  15. 15.

    R.G. Peyvandi, S.I. Rad, Application of artificial neural networks for the prediction of volume fraction using spectra of gamma rays backscattered by three-phase flows. Eur. Phys. J. Plus 132(12), 511 (2017)

    Article  Google Scholar 

  16. 16.

    M. Roshani, G. Phan, G.H. Roshani, R. Hanus, B. Nazemi, E. Corniani, E. Nazemi, Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas–oil–water three phase flows. Measurement 168, 108427 (2021)

    Article  Google Scholar 

  17. 17.

    M. Roshani, G.T. Phan, P.J.M. Ali, G.H. Roshani, R. Hanus, T. Duong, E. Corniani, E. Nazemi, E.M. Kalmoun, Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness. Alexandria Eng. J. 60, 1955–1966 (2021)

    Article  Google Scholar 

  18. 18.

    M. Roshani, M.A. Sattari, P.J.M. Ali, G.H. Roshani, B. Nazemi, E. Corniani, E. Nazemi, Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter. Flow Meas. Instrum. 75, 101804 (2020)

    Article  Google Scholar 

  19. 19.

    M. Roshani, G. Phan, R.H. Faraj, N.H. Phan, G.H. Roshani, B. Nazemi, E. Corniani, E. Nazemi, Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. Nucl. Eng. Technol. (2020). https://doi.org/10.1016/J.NET.2020.09.015

    Article  Google Scholar 

  20. 20.

    A. Karami, G.H. Roshani, A. Khazaei, E. Nazemi, M. Fallahi, Investigation of different sources in order to optimize the nuclear metering system of gas–oil–water annular flows. Neural Comput. Appl. 32(8), 3619–3631 (2020)

    Article  Google Scholar 

  21. 21.

    G.H. Roshani, S. Roshani, E. Nazemi, S. Roshani, Online measuring density of oil products in annular regime of gas–liquid two phase flows. Measurement 129, 296–301 (2018)

    ADS  Article  Google Scholar 

  22. 22.

    E. Nazemi, S.A.H. Feghhi, G.H. Roshani, 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(1), 64–71 (2016)

    Article  Google Scholar 

  23. 23.

    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 

  24. 24.

    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. Hydrog. Energy 41(18), 7438–7444 (2016)

    Article  Google Scholar 

  25. 25.

    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(1), 1265–1274 (2017)

    Article  Google Scholar 

  26. 26.

    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 

  27. 27.

    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 

  28. 28.

    G.H. Roshani, E. Nazemi, Intelligent densitometry of petroleum products in stratified regime of two phase flows using gamma ray and neural network. Flow Meas. Instrum. 58, 6–11 (2017)

    Article  Google Scholar 

  29. 29.

    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)

    ADS  Article  Google Scholar 

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Correspondence to Enrico Corniani.

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Roshani, M., Phan, G.T.T., Nazemi, E. et al. Performance comparison of capacitance-based flowmeter with gamma-ray attenuation-based two-phase flowmeter for determining volume fractions in an annular flow regime’s components. Eur. Phys. J. Plus 136, 176 (2021). https://doi.org/10.1140/epjp/s13360-021-01169-6

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