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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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)
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)
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)
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)
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)
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)
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)
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)
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)
Mosorov, et al., Modelling of dynamic experiments in MCNP5 environment. Appl. Radiat. Isot. 112, 136–140 (2016)
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)
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
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)
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)
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)
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)
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)
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)
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
About this article
Cite this article
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