Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Improvement in measurement of radiation based two-phase flowmeters independent of flow regime and scale thickness using ant colony optimization and GMDH

  • Received : 2024.04.07
  • Accepted : 2024.06.25
  • Published : 2024.11.25
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Abstract

The formation of scales in pipes is one element that has a major impact on the efficiency of machinery used in the oil and gas sector. With the help of artificial intelligence, this new, non-invasive device was able to figure out the volume fraction of a two-phase flow by taking into account the thickness of the scale in the tested pipeline. The proposed design uses an isotope pair of barium-133 and cesium-137 as a dual-energy gamma generator. One detector records photons that are transmitted, and another detector records photons that are scattered. The signals from the detectors were simulated using the Monte Carlo N-Particle (MCNP) code, and then ten frequency and wavelet characteristics were extracted. To choose the best inputs from the collected features for computing the volume fraction, an ant colony optimization (ACO)-based method is applied. Six attributes, representing the optimal combination, were developed using this method. In order to forecast the volume percentage of two-phase flows independently of flow regime and scale thickness, we fed the characteristics introduced by ACO into a group method of data handling (GMDH) neural network. Volume fraction calculations had a maximum RMSE of 0.056, which is quite little compared to previous research. By using the ACO to choose the best characteristics, the current work has significantly increased its accuracy in identifying volume fractions.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/191/45.

References

  1. A. Bahadori, G. Zahedi, S. Zendehboudi, Estimation of potential barium sulfate (barite) precipitation in oilfield brines using a simple predictive tool, Environ. Prog. Sustain. Energy 32 (2013) 860-865.  https://doi.org/10.1002/ep.11678
  2. A.B. BinMerdhah, Inhibition of barium sulfate scale at high-barium formation water, J. Pet. Sci. Eng. 90 (2012) 124-130.  https://doi.org/10.1016/j.petrol.2012.04.005
  3. M. Roshani, M.A. Sattari, P.J. 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 (2020) 101804. 
  4. M.A. Sattari, G.H. Roshani, R. Hanus, E. Nazemi, Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique, Measurement 168 (2021) 108474, https://doi.org/10.1016/j.measurement.2020.108474. 
  5. M.A. Sattari, G.H. Roshani, R. Hanus, Improving the structure of two-phase flow meter using feature extraction and GMDH neural network, Radiat. Phys. Chem. 171 (2020 Jun 1) 108725. 
  6. S. Hosseini, G.H. Roshani, S. Setayeshi, Precise gamma based two-phase flow meter using frequency feature extraction and only one detector, Flow Meas. Instrum. 72 (2020 Apr 1) 101693. 
  7. S. Hosseini, O. Taylan, M. Abusurrah, T. Akilan, E. Nazemi, E. Eftekhari-Zadeh, F. Bano, G.H. Roshani, Application of wavelet feature extraction and artificial neural networks for improving the performance of gas-liquid two-phase flow meters used in oil and petrochemical industries, Polymers 13 (21) (2021) 3647. 
  8. A. Basahel, M.A. Sattari, O. Taylan, E. Nazemi, Application of feature extraction and artificial intelligence techniques for increasing the accuracy of X-ray radiation based two phase flow meter, Mathematics 9 (11) (2021 Jan) 1227. 
  9. O. Taylan, M.A. Sattari, I. Elhachfi Essoussi, E. Nazemi, Frequency domain feature extraction investigation to increase the accuracy of an intelligent nondestructive system for volume fraction and regime determination of gas-water-oil three-phase flows, Mathematics 9 (17) (2021 Jan) 2091. 
  10. M. Balubaid, M.A. Sattari, O. Taylan, A.A. Bakhsh, E. Nazemi, Applications of discrete wavelet transform for feature extraction to increase the accuracy of monitoring systems of liquid petroleum products, Mathematics 9 (24) (2021 Jan) 3215. 
  11. M. Alamoudi, M.A. Sattari, M. Balubaid, E. Eftekhari-Zadeh, E. Nazemi, O. Taylan, E.M. Kalmoun, Application of gamma attenuation technique and artificial intelligence to detect scale thickness in pipelines in which two-phase flows with different flow regimes and void fractions exist, Symmetry 13 (7) (2021) 1198. 
  12. D.F. Oliveira, J.R. Nascimento, C.A. Marinho, R.T. Lopes, Gamma transmission system for detection of scale in oil exploration pipelines, Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip. 784 (2015) 616-620.  https://doi.org/10.1016/j.nima.2014.11.030
  13. T.P. Teixeira, C.M. Salgado, R.S.D.F. Dam, W.L. Salgado, Inorganic scale thickness prediction in oil pipelines by gamma-ray attenuation and artificial neural network, Appl. Radiat. Isot. 141 (2018) 44-50.  https://doi.org/10.1016/j.apradiso.2018.08.008
  14. W.L. Salgado, R.S.D.F. Dam, T.P. Teixeira, C.C. Conti, C.M. Salgado, Application of artificial intelligence in scale thickness prediction on offshore petroleum using a gamma-ray densitometer, Radiat. Phys. Chem. 168 (2020) 108549. 
  15. D.B. Pelowitz, MCNP-X TM User's Manual, Version 2.5.0; LA-CP-05e0369; Los Alamos National Laboratory, New Mexico, NM, USA, 2005. 
  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 (2021) 108427, https://doi.org/10.1016/j.measurement.2020.108427. 
  17. C.M. Salgado, L.E.B. Brandao, C.C. Conti, W.L. Salgado, Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks, Appl. Radiat. Isot. 116 (2016) 143-149.  https://doi.org/10.1016/j.apradiso.2016.08.001
  18. M.A. Sattari, N. Korani, R. Hanus, G.H. Roshani, E. Nazemi, Improving the performance of gamma radiation based two phase flow meters using optimal time characteristics of the detector output signal extraction, Journal of Nuclear Science and Technology (JonSat) 41 (2) (2020) 42-54. 
  19. G.H. Roshani, P.J. Ali, S. Mohammed, R. Hanus, L. Abdulkareem, A.A. Alanezi, M. A. Sattari, S. Amiri, E. Nazemi, E. Eftekhari-Zadeh, E.M. Kalmoun, Simulation study of utilizing X-ray tube in monitoring systems of liquid petroleum products, Processes 9 (5) (2021 May) 828. 
  20. 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 (2018) 17-23.  https://doi.org/10.1016/j.flowmeasinst.2018.02.008
  21. E. Nazemi, G.H. Roshani, S.A.H. Feghhi, S. Setayeshi, E. Eftekhari 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 (18) (2016) 7438-7444.  https://doi.org/10.1016/j.ijhydene.2015.12.098
  22. Ilker Meric, Geir A. Johansen, J. Mattingly, R.P. Gardner, On the ill-conditioning of the multiphase flow measurement by prompt gamma-ray neutron activation analysis, Radiat. Phys. Chem. 95 (2014) 401-404.  https://doi.org/10.1016/j.radphyschem.2012.12.047
  23. M.B. Holstad, G.A. Johansen, Produced water characterization by dual modality gamma-ray measurements, Meas. Sci. Technol. 16 (2005) 1007-1013.  https://doi.org/10.1088/0957-0233/16/4/013
  24. H.J. Nussbaumer, The fast Fourier transforms, in: Fast Fourier Transform and Convolution Algorithms, Springer, Berlin, Heidelberg, 1981, pp. 80-111. 
  25. Ingrid Daubechies, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inf. Theor. 36 (5) (1990) 961-1005.  https://doi.org/10.1109/18.57199
  26. Skander Soltani, On the use of the wavelet decomposition for time series prediction, Neurocomputing 48 (1-4) (2002) 267-277.  https://doi.org/10.1016/S0925-2312(01)00648-8
  27. M. Dorigo, C. Blum, Ant colony optimization theory: a survey, Theor. Comput. Sci. 344 (2-3) (2005) 243-278.  https://doi.org/10.1016/j.tcs.2005.05.020
  28. S. Roshani, S. Roshani, Design of a compact LPF and a miniaturized Wilkinson power divider using aperiodic stubs with harmonic suppression for wireless applications, Wireless Network 26 (2) (2020 Feb) 1493-1501.  https://doi.org/10.1007/s11276-019-02214-0
  29. M. Hookari, S. Roshani, S. Roshani, Design of a low pass filter using rhombus-shaped resonators with an analyticallc equivalent circuit, Turk. J. Electr. Eng. Comput. Sci. 28 (2) (2020) 865-874.  https://doi.org/10.3906/elk-1905-153
  30. S. Roshani, J. Azizian, S. Roshani, M.B. Jamshidi, F. Parandin, Design of a miniaturized branch line microstrip coupler with a simple structure using artificial neural network, Frequenz 76 (5-6) (2022) 255-263.  https://doi.org/10.1515/freq-2021-0172
  31. Mohammed A Awadallah, Mohammed Azmi Al-Betar, Asaju La'aro Bolaji, Emad Mahmoud Alsukhni, Hassan Al-Zoubi, Natural selection methods for artificial bee colony with new versions of onlooker bee, Soft Comput. 23 (5) (2018). 
  32. Javid Salimi MohsenKhaleghi, Visar Farhangi, Mohammad Javad Moradi, Moses Karakouzian, Evaluating the behaviour of centrally perforated unreinforced masonry walls: applications of numerical analysis, machine learning, and stochastic methods, Ain Shams Eng. J. 13 (3) (May 2022) 101631. 
  33. N. Moradi, M.H. Tavana, M.R. Habibi, M. Amiri, M.J. Moradi, V. Farhangi, Predicting the compressive strength of concrete containing binary supplementary cementitious material using machine learning approach, Materials 15 (2022) 5336, https://doi.org/10.3390/ma15155336. 
  34. R. Hanus, M. Zych, A. Golijanek-Jedrzejczyk, Measurements of dispersed phase velocity in two-phase flows in pipelines using gamma-absorption technique and phase of the cross-spectral density function, Energies 15 (24) (2022) 9526, https://doi.org/10.3390/en15249526. 
  35. R. Hanus, M. Zych, A. Golijanek-Jedrzejczyk, Investigation of liquid-gas flow in a horizontal pipeline using gamma-ray technique and modified cross-correlation, Energies 15 (16) (2022) 5848, https://doi.org/10.3390/en15165848. 
  36. A. Mayet, M. Hussain, Amorphous WNx metal for accelerometers and gyroscope, in: Proceedings of the MRS Fall Meeting, Boston, MA, USA, 2014. 
  37. A. Mayet, A. Hussain, M. Hussain, Three-terminal nanoelectromechanical switch based on tungsten nitride-an amorphous metallic material, Nanotechnology 27 (2016) 035202. 
  38. G.H. Roshani, R. Hanus, A. Khazaei, M. Zych, E. Nazemi, V. Mosorov, Density and velocity determination for single-phase flow based on radiotracer technique and neural networks, Flow Meas. Instrum. 61 (2018) 9-14.  https://doi.org/10.1016/j.flowmeasinst.2018.03.006
  39. 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 (2017) 39-45.  https://doi.org/10.1016/j.flowmeasinst.2016.10.001
  40. 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 (2018) 296-301.  https://doi.org/10.1016/j.measurement.2018.07.026
  41. 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) (2016) 64-71.  https://doi.org/10.1016/j.net.2015.09.005
  42. A.G. Ivakhnenko, Polynomial theory of complex systems, IEEE Trans. Syst. Man Cybern. SMC-1 (4) (1971) 364e378. 
  43. R. Gholipour Peyvandi, S.Z. Islami Rad, Application of artificial neural networks for the prediction of volume fraction using spectra of gamma rays backscattered by three-phase flows, The European Physical Journal Plus 132 (12) (2017) 1-8.  https://doi.org/10.1140/epjp/i2017-11280-8
  44. 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 (2015) 25-32.  https://doi.org/10.1016/j.measurement.2014.11.006
  45. A.M. Mayet, S.M. Alizadeh, Z.A. Kakarash, A.A. Al-Qahtani, A.K. Alanazi, H. H. Alhashimi, E. Nazemi, Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime, Mathematics 10 (10) (2022) 1770. 
  46. T.C. Chen, S.M. Alizadeh, M.A. Albahar, M. Thanoon, A. Alammari, J.W. G. Guerrero, E. Eftekhari-Zadeh, Introducing the effective features using the Particle swarm optimization algorithm to increase accuracy in determining the volume percentages of three-phase flows, Processes 11 (1) (2023) 236. 
  47. A.M. Iliyasu, D.K. Bagaudinovna, A.S. Salama, G.H. Roshani, K. Hirota, A methodology for analysis and prediction of volume fraction of two-phase flow using Particle swarm optimization and group method of data handling neural network, Mathematics 11 (4) (2023) 916.