Multiphase flow meters are used to measure the water-liquid ratio (WLR) and void fraction in a multiphase fluid stream pipeline. In the present study, a system of multiphase flow measurement has been designed by application of three thallium-doped sodium iodide scintillators and a radioactive source of 133Ba simulated by Monte Carlo N-particle (MCNP) transport code. In order to capture radiations passing across the pipe, two direct detectors have been installed on opposite sides of the radioactive source. Another detector has been placed perpendicular to the transmission beam emitted from the 133Ba source to receive radiations scattered from the fluid flow. Simulation was done by the MCNP code for different volumetric fractions of water, oil, and gas phases for two types of flow regimes, namely, homogeneous and annular; training and validation data have been provided for the artificial neural network (ANN) to develop a computation model for pattern recognition. Depending on applications of the neural system, several structures of ANNs are used in the current paper to model the flow measurement relations, while the detector outputs are considered as the input parameters of the neural networks. The first, second, and third structures benefit from two, three, and five multilayer perceptron neural networks, respectively. Increasing the number of ANNs makes the system more complicated and decreases the available data; however, it increases the accuracy of estimation of WLR and gas void fraction. According to the results, the maximum relative difference was observed in the scattering detector. It was clear that transmission detectors would demonstrate the difference between the flow regimes as well. It is necessary to note that the error calculated by the MCNP simulator is <0.5% for the direct detectors (TR1 and TR2). Due to the difference between the data of the two flow regimes and the errors of data in the simulation codes of the MCNP, it was possible to separate these flow regimes. The effect of changing WLR on the efficiency for a constant void fraction confirms a considerable variance in the results of annular and homogeneous flows occurring in the scattering detector. There is a similar trend for the void fraction; hence, one can easily distinguish changes in efficiency due to the WLR. Analysis of the simulation results revealed that in the proposed structure of the multiphase flow meter and the computation model used for simulation, the two flow regimes are simply distinguishable.
1. Scheers, A. M. (2000). An oil/water/gas composition meter based on multiple energy gamma ray absorption (MEGRA) measurement. ETDEWEB ID:20061564.
2. Holstad, M. B. (2004). Gamma-ray scatter methods applied to industrial measurement systems. Unpublished Ph.D. thesis, Department of Physics and Technology, University of Bergen, Norway.
3. Scheers, A., & Letton, W. (1996). An oil/water/gas composition meter based on multiple energy gamma ray absorption (MEGRA) measurement. In Proceedings of 14th North Sea Flow Measurement Workshop.
4. Blaney, S. (2008). Gamma radiation methods for clamp-on multiphase flow metering. Unpublished PhD thesis, Cranfield University.
5. Salgado, C., Brandao, L. E. B., Conti, C. C., & Salgado, W. L. (2016). Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks. Appl. Radiat. Isot., 116, 143–149.
6. AL-Qutami, T. A., Ibrahim, R., Ismail, I., & Ishak, M. A. (2018). Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing. Expert Syst. Appl., 93, 72–85.
7. Roshani, G., Nazemi, E., & Roshani, M. (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(Suppl. 1), 1265–1274.
8. Nazemi, E., Roshani, G. H., Feghhi, S. A. H., Setayeshi, S., Eftekhari Zadeh, E., & Fatehi, A. (2016). 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.
9. Johansen, G., & Jackson, P. (2000). Salinity independent measurement of gas volume fraction in oil/gas/water pipe flows. Appl. Radiat. Isot., 53(4), 595–601.
10. Johansen, G. A., & Tjugum, S. -A. (2007). Fluid composition analysis by multiple gamma-ray beam and modality measurements. In 25th International North Sea Flow Measurement Workshop, Citeseer.
11. Åbro, E., Khoryakov, V. A., Johansen, G. A., & Koc-bach, L. (1999). Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry. Meas. Sci. Technol., 10(7), 619–630.
12. Roshani, G. H., Feghhi, S. A. H., & Setayeshi, S. (2015). Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network. Radiat. Meas., 82, 154–162.
13. Salgado, C. M., Brandão, L. E., Pereira, C. M., & Salgado, W. L. (2014). Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks. Prog. Nucl. Energy, 76, 17–23.
14. Salgado, C. M., Pereira, C. M., Schirru, R., & Brandão, L. E. (2010). 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.
15. Srinivasan, Ravichandran, Chan, Vidhya, & Ramakirishnan, Krishnan. (2002). Exponentiated backpropagation algorithm for multilayer feedforward neural networks. In Proceedings of the 9th International Conference on Neural Information Processing, pp. 227–331.
16. Kamarthi, S. V., & Pittner, S. (1999). Accelerating neural network training using weight extrapolation. Neural Netw., 12(9), 1285–1299.
17. More, J. J. (1978). The Levenberg-Marquardt algorithm: Implementation and theory. In G. A. Watson (Ed.), Numerical analysis. (Lecture Notes in Mathematics, Vol. 630). Berlin, Heidelberg: Springer.