Chordal measurement of phase fraction distribution in a static gas-liquid system using collimated gamma-ray densitometer and artificial neural networks

Medición cordal de distribución de fracción de fase de un sistema estático gas-líquido usando densitómetro de rayos-gama colimado y redes neurales artificiales

Main Article Content

Cristhian Enrique Álvarez-Pacheco
Carlos Mauricio Ruiz-Diaz
Oscar Mauricio Hernandez-Rodriguez
Abstract

Two-phase flow occurs in various industries, as in the production of oil and gas. A collimated gamma-ray densitometer is applied for the study of a static gas-liquid system that simulates a stratified flow pattern. It stands out for its non-intrusive measurement capacity, its high sensitivity to density variations and its good spatial resolution. Chordal phase fraction distributions are obtained in a tube containing water and air at room conditions, with the water level varied between 25%, 50% and 75%. The results obtained highlight the usefulness of the collimated gamma-ray densitometer to determine phase fraction distributions along the pipe’s cross section. Furthermore, this study suggests the use of an artificial neural network (ANN) model for predicting holdup in pipeline systems using a dataset of 110 experimental data points. The ANN model considers factors such as absorbed intensity, water cut percentage, and dimensionless h/D ratio. The adopted configuration includes the use of the Adam solver, Rectified Linear Unit (ReLU) activation function, a batch size of 3, two hidden layers (60 neurons each), and a learning rate of 0.001. The model achieves good accuracy, with a minimum mean square error (MSE) of 0.3% and a low mean absolute error (MAE) of 0.028.

Keywords

Downloads

Download data is not yet available.

Article Details

References

C. J. Noriega-Sánchez, "Una revisión de fluidos de trabajo de tipo mezclas para ciclos de potencia de baja temperatura y su modelado termodinámico", Revista Ingenio, vol. 18, n.º 1, pp. 62–69, ene. 2021 doi: https://doi.org/10.22463/2011642X.2340. DOI: https://doi.org/10.22463/2011642X.2340

E. Espinel-Blanco, E. N. Flórez-Solano, and J. E. Barbosa-Jaimes, “Estudio para la generación de energía por un sistema con paneles solares y baterías,” Revista Ingenio, vol. 17, no. 1, pp. 9–14, 2020, doi: https://doi.org/10.22463/2011642x.2392. DOI: https://doi.org/10.22463/2011642X.2392

Z. Dang, Z. Yang, X. Yang, and M. Ishii, “Experimental study of vertical and horizontal two-phase pipe flow through double 90 degree elbows,” International Journal of Heat and Mass Transfer, vol. 120, pp. 861–869, 2018, doi: 10.1016/j.ijheatmasstransfer.2017.11.089. DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2017.11.089

A. M. Quintino, R. da Fonseca Junior, and O. M. H. Rodriguez, “Experimental study of liquid/dense-gas pipe flow,” Geoenergy Science and Engineering , vol. 230, Nov. 2023, doi: 10.1016/j.geoen.2023.212179. DOI: https://doi.org/10.1016/j.geoen.2023.212179

R. Hanus, M. Zych, V. Mosorov, A. Golijanek-Jędrzejczyk, M. Jaszczur, and A. Andruszkiewicz, “Evaluation of liquid-gas flow in pipeline using gamma-ray absorption technique and advanced signal processing,” Metrology and Measurement Systems, 2021, doi: 10.24425/mms.2021.135997. DOI: https://doi.org/10.24425/mms.2021.135997

A. Shmueli, T. E. Unander, and O. J. Nydal, “Characteristics of gas/Water/Viscous oil in stratified-Annular horizontal pipe flows,” in Offshore Technology Conference, 2015, pp. 1085–1102, doi: 10.4043/26176-ms. DOI: https://doi.org/10.4043/26176-MS

S. H. Stavland, C. Satre, B. T. Hjertaker, S. A. Tjugum, A. Hallanger, and R. Maad, “Gas fraction measurements using single and dual beam gamma-densitometry for two phase gas-liquid pipe flow,” I2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, vol. 2019-May, pp. 1–6, 2019, doi: 10.1109/I2MTC.2019.8827056. DOI: https://doi.org/10.1109/I2MTC.2019.8827056

Y. Pan, C. Li, Y. Ma, S. Huang, and D. Wang, “Gas flow rate measurement in low-quality multiphase flows using Venturi and gamma ray,” Experimental Thermal and Fluid Science, vol. 100, no. September 2018, pp. 319–327, 2019, doi: 10.1016/j.expthermflusci.2018.09.017. DOI: https://doi.org/10.1016/j.expthermflusci.2018.09.017

G. H. Roshani, A. Karami, E. Nazemi, and C. M. Salgado, “Flow regimes classification and prediction of volume fractions of the gas- oil-water three-phase flow using Adaptive Neuro-fuzzy Inference System,” pp. 17–27, 2020.

S. Vestøl, W. A. S. Kumara, and M. C. Melaaen, “Gamma densitometry measurements of gas/ liquid flow with low liquid fractions in horizontal and inclined pipes,” International Journal of Computational Methods and Experimental Measurements, vol. 6, no. 1, pp. 120–131, 2018, doi: 10.2495/CMEM-V6-N1-120-131. DOI: https://doi.org/10.2495/CMEM-V6-N1-120-131

N. M. A. Mohamed, “Dual displacer-gamma ray system for level measurement of fluids-interface in oil separator,” vol. 184, Jul. 2021, doi: 10.1016/j.radphyschem.2021.109453. DOI: https://doi.org/10.1016/j.radphyschem.2021.109453

C. Ferreira, “Detecção de alargamento de anular em dutos frexíveis usando A técnica de transmissão de radiação gama,” [Tese ( Doutorado) UFRJ], 2021.

L. O. Zampereti, A. M. Quintino, and O. M. H. Rodriguez, “Data-Driven Machine Learning Applied to Liquid-Liquid Flow Pattern Prediction,” 2022, pp. 123–129. DOI: https://doi.org/10.1007/978-3-030-93456-9_11

A. M. Quintino, D. L. L. N. da Rocha, R. Fonseca Jr., and O. M. H. Rodriguez, “Flow Pattern Transition in Pipes Using Data-Driven and Physics-Informed Machine Learning,”Journal of Fluids Engineering, vol. 143, no. 3, pp. 1–11, Oct. 2020, doi: 10.1115/1.4048876. DOI: https://doi.org/10.1115/1.4048876

P. B. Bazon, J. E. Castro-Bolivar, C. M. Ruiz-Diaz, M. M. Hernández-Cely, and O. M. H. Rodriguez, “Hybrid machine learning model applied to phase inversion prediction in liquid-liquid pipe flow,” Multiphase Science and Technology, vol. 35, no. 1, pp. 35–53, 2023, doi: 10.1615/MultScienTechn.2022046139. DOI: https://doi.org/10.1615/MultScienTechn.2022046139

R. Hanus, M. Zych, M. Kusy, M. Jaszczur, and L. Petryka, “Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods,” Flow Measurement and Instrumentation, vol. 60, pp. 17–23, Apr. 2018, doi: 10.1016/j.flowmeasinst.2018.02.008. DOI: https://doi.org/10.1016/j.flowmeasinst.2018.02.008

J. Ambrosio, A. Lazzaretti, D. Pipa, and M. da Silva, “Two-phase flow pattern classification based on void fraction time series and machine learning,” Flow Measurement and Instrumentation, vol. 83, no. December 2020, p. 102084, 2022, doi: 10.1016/j.flowmeasinst.2021.102084. DOI: https://doi.org/10.1016/j.flowmeasinst.2021.102084

C. Gomez, D. Ruiz, and M. Cely, “Specialist system in flow pattern identification using artificial neural Networks,” vol. 21, no. 2, pp. 285–299, Jan. 2023, doi: 10.5937/jaes0-40309. DOI: https://doi.org/10.5937/jaes0-40309

G. Elseth, “An Experimental Study of Oil / Water Flow in Horizontal Pipes,” [Thesis (Doctoral) The Norwegian University of Science and Technology], 2001.

W. A. S. Kumara, B. M. Halvorsen, and M. C. Melaaen, “Single-beam gamma densitometry measurements of oil-water flow in horizontal and slightly inclined pipes,”Jun. 2010, doi: 10.1016/j.ijmultiphaseflow.2010.02.003. DOI: https://doi.org/10.1016/j.ijmultiphaseflow.2010.02.003

K. Vijayaprabakaran and K. Sathiyamurthy, “Towards activation function search for long short-term model network : A differential evolution based approach,”Journal of King Saud University - Computer and Information Sciences, 2020, doi: 10.1016/j.jksuci.2020.04.015. DOI: https://doi.org/10.1016/j.jksuci.2020.04.015

OJS System - Metabiblioteca |