Vol. 21 Núm. 2 (2022): Revista UIS Ingenierías
Artículos

Modelo predictivo para el cálculo de la fracción volumétrica de un flujo bifásico agua-aceite en la horizontal utilizando una red neuronal artificial

Marlon Mauricio Hernández-Cely
Universidad de São Paulo
Carlos Mauricio Ruiz-Díaz
Universidad de São Paulo
Octavio Andrés González-Estrada
Universidad Industrial de Santander
Biografía

Publicado 2022-05-12

Palabras clave

  • flujo multifásico,
  • fracción volumétrica,
  • red neuronal artificial,
  • velocidad superficial,
  • industria 4.0

Cómo citar

Hernández-Cely , M. M. ., Ruiz-Díaz , C. M., & González-Estrada, O. A. (2022). Modelo predictivo para el cálculo de la fracción volumétrica de un flujo bifásico agua-aceite en la horizontal utilizando una red neuronal artificial. Revista UIS Ingenierías, 21(2), 155–164. https://doi.org/10.18273/revuin.v21n2-2022013

Resumen

Este artículo presenta la aplicación de una red neuronal artificial (RNA) para el desarrollo de un modelo capaz de predecir la fracción volumétrica de un flujo bifásico compuesto por agua y aceite mineral en una tubería horizontal. Se utilizan las velocidades superficiales de cada fluido y el diferencial de presión en la tubería como parámetros de entrada de la red neuronal artificial multicapa con retropropagación, mientras que la fracción volumétrica de los fluidos se utiliza como parámetro de salida en el entrenamiento de la misma. Los 56 datos experimentales con los que se trabajó se obtuvieron en el laboratorio LabPetro - CEPETRO-UNICAMP. Los resultados que arrojó el modelo predictivo con mejor rendimiento presentan un error absoluto medio porcentual (AAPE) de 3,01 % y un coeficiente de determinación  de 0,9964 utilizando 15 neuronas en la capa oculta de la red y la función de transferencia TanSig.

Descargas

Los datos de descargas todavía no están disponibles.

Referencias

  1. G. Valle-Tamayo, L. Valbuena-Luna, C. Rojas-Beltrán, M. Cabarcas-Simancas, “Modelo numérico para el análisis y el diseño de redes de tubería para flujo bifásico,” Rev. UIS Ing., vol. 17, no. 2, pp. 201-214, Oct. 2017, doi: https://doi.org/10.18273/revuin.v17n2-2018018
  2. M. Süßer, “Flow Measurement Handbook: Industrial Designs, Operating Principles, Performance and Applications,” Cryogenics (Guildf)., vol. 40, no. 6, p. 421, 2000, doi: https://doi.org/10.18273/10.1016/S0011-2275(00)00051-5
  3. M. de M. F. Figueiredo, F. de C. T. Carvalho, A. M. F. Fileti, A. L. Serpa, “Flow pattern classification in water-air vertical flows using a single ultrasonic transducer,” Exp. Therm. Fluid Sci., vol. 119, no. January, 2020, doi: https://doi.org/10.18273/10.1016/j.expthermflusci.2020.110189
  4. W. Tang, Z. chuan Sun, W. Li, “Visualization of flow patterns during condensation in dimpled surface tubes of different materials,” Int. J. Heat Mass Transf., vol. 161, 2020, doi: https://doi.org/10.18273/10.1016/j.ijheatmasstransfer.2020.120251
  5. Z. Lin, X. Liu, L. Lao, H. Liu, “Prediction of two-phase flow patterns in upward inclined pipes via deep learning,” Energy, vol. 210, p. 118541, 2020, doi: https://doi.org/10.18273/10.1016/j.energy.2020.118541
  6. S. Zeguai, S. Chikh, L. Tadrist, “Experimental study of air-water two-phase flow pattern evolution in a mini tube: Influence of tube orientation with respect to gravity,” Int. J. Multiph. Flow, vol. 132, 2020, doi: https://doi.org/10.18273/10.1016/j.ijmultiphaseflow.2020.103413
  7. M. Descamps, R. V. A. Oliemans, G. Ooms, R. F. Mudde, R. Kusters, “Influence of gas injection on phase inversion in an oil-water flow through a vertical tube,” Int. J. Multiph. Flow, vol. 32, no. 3, pp. 311-322, 2006, doi: https://doi.org/10.18273/10.1016/j.ijmultiphaseflow.2005.10.006
  8. D. M. Rocha, C. H. M. de Carvalho, V. Estevam, O. M. H. Rodríguez, “Effects of water and gas injection and viscosity on volumetric fraction, pressure gradient and phase inversion in upward-vertical three-phase pipe flow,” J. Pet. Sci. Eng., vol. 157, pp. 519-529, 2017, doi: https://doi.org/10.18273/10.1016/j.petrol.2017.07.055
  9. Y. Ma, W. Liu, H. Wu, Y. Liu, J. Lyu, Z. Cai, “Visualization experiment of gas–liquid flow pattern downstream of single-orifice plates in horizontal pipes under an intermittent upstream flow,” Exp. Therm. Fluid Sci., vol. 119, 2019, p. 110206, 2020, doi: https://doi.org/10.18273/10.1016/j.expthermflusci.2020.110206
  10. M. W. Yaqub, R. Rusli, R. Pendyala, “Experimental study on gas-liquid-liquid three-phase flow patterns and the resultant pressure drop in a horizontal pipe upstream of the 90° bend,” Chem. Eng. Sci., vol. 226, p. 115848, 2020, doi: https://doi.org/10.18273/10.1016/j.ces.2020.115848
  11. E. S. Rosa, R. M. Salgado, T. Ohishi, N. Mastelari, “Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows,” Int. J. Multiph. Flow, vol. 36, no. 9, pp. 738-754, 2010, doi: https://doi.org/10.18273/10.1016/j.ijmultiphaseflow.2010.05.001
  12. V. S. Chalgeri, J. H. Jeong, “Flow regime identification and classification based on void fraction and differential pressure of vertical two-phase flow in rectangular channel,” Int. J. Heat Mass Transf., vol. 132, pp. 802-816, 2019, doi: https://doi.org/10.18273/10.1016/j.ijheatmasstransfer.2018.12.015
  13. F. Liang, H. Zheng, H. Yu, Y. Sun, “Gas-liquid two-phase flow pattern identification by ultrasonic echoes reflected from the inner wall of a pipe,” Meas. Sci. Technol., vol. 27, no. 3, 2016, doi: https://doi.org/10.18273/10.1088/0957-0233/27/3/035304
  14. C. Sunde, S. Avdic, I. Pázsit, “Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach,” Prog. Nucl. Energy, vol. 46, no. 3-4, pp. 348–358, 2005, doi: https://doi.org/10.18273/10.1016/j.pnucene.2005.03.015
  15. L. S. Zhai, N. De Jin, Z. K. Gao, Z. Y. Wang, D. M. Li, “The ultrasonic measurement of high water volume fraction in dispersed oil-in-water flows,” Chem. Eng. Sci., vol. 94, pp. 271-283, 2013, doi: https://doi.org/10.18273/10.1016/j.ces.2013.02.049
  16. K. J. Albion, L. Briens, C. Briens, F. Berruti, “Multiphase Flow Measurement Techniques for Slurry Transport,” Int. J. Chem. React. Eng., vol. 9, no. 1, pp. 1-4, Sep. 2011, doi: https://doi.org/10.18273/10.2202/1542-6580.1726
  17. M. M. Hernández-Cely, C. Ruiz-Díaz, “Estudio de los fluidos aceite-agua a través del sensor basado en la permitividad eléctrica del patrón de fluido,” Rev. UIS Ing., vol. 19, no. 3, pp. 177-186, 2020, doi: https://doi.org/10.18273/revuin.v19n3-2020017
  18. M. M. F. Figueiredo, J. L. Goncalves, A. M. V. Nakashima, A. M. F. Fileti, R. D. M. Carvalho, “The use of an ultrasonic technique and neural networks for identification of the flow pattern and measurement of the gas volume fraction in multiphase flows,” Exp. Therm. Fluid Sci., vol. 70, pp. 29-50, 2016, doi: https://doi.org/10.18273/10.1016/j.expthermflusci.2015.08.010
  19. H. Wu, F. Zhou, Y. Wu, “Intelligent identification system of flow regime of oil–gas–water multiphase flow,” Int. J. Multiph. Flow, vol. 27, no. 3, pp. 459-475, 2001, doi: https://doi.org/10.18273/10.1016/S0301-9322(00)00022-7
  20. T. A. AL-Qutami, R. Ibrahim, I. Ismail, M. A. Ishak, “Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing,” Expert Syst. Appl., vol. 93, pp. 72-85, 2018, doi: https://doi.org/10.18273/10.1016/j.eswa.2017.10.014
  21. M. R. G. Meireles, P. E. M. Almeida, M. G. Simões, “A comprehensive review for industrial applicability of artificial neural networks,” IEEE Trans. Ind. Electron., vol. 50, no. 3, pp. 585-601, 2003, doi: https://doi.org/10.1109/TIE.2003.812470
  22. F. A. Gomide, “Redes neurais artificiais para engenharia e ciências aplicadas: curso prático,” Sba Control. Automação Soc. Bras. Autom., vol. 23, no. 5, pp. 649-652, 2012, doi: https://doi.org/10.1590/s0103-17592012000500011
  23. F. Rozo-García, “Revisión de las tecnologías presentes en la industria 4.0,” Rev. UIS Ing., vol. 19, no. 2, pp. 177-191, 2020, doi: https://doi.org/10.18273/revuin.v19n2-2020019
  24. C. M. Ruiz-Díaz, M. M. Hernández-Cely, O. A. González-Estrada, “Modelo predictivo para la identificación de la fracción volumétrica en flujo bifásico,” Cienc. en Desarro., vol. 12, no. 2, pp. 49-55, Sep. 2021, doi: https://doi.org/10.19053/01217488.v12.n2.2021.13417
  25. R. Quiroga, O. A. González-Estrada, G. González Silva, “Efecto de la temperatura en la fracción de vapor del crudo pesado en el reactor Vortex de cavitación hidrodinámica mediante CFD,” Cienc. en Desarro., vol. 12, no. 2, pp. 57-65, Sep. 2021, doi: https://doi.org/10.19053/01217488.v12.n2.2021.13418
  26. Y. Yan, L. Wang, T. Wang, X. Wang, Y. Hu, Q. Duan, “Application of soft computing techniques to multiphase flow measurement: A review,” Flow Meas. Instrum., vol. 60, pp. 30-43, 2018, doi: https://doi.org/10.1016/j.flowmeasinst.2018.02.017
  27. 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., vol. 54, no. October 2016, pp. 39-45, 2017, doi: https://doi.org/10.1016/j.flowmeasinst.2016.10.001
  28. C. M. Salgado, L. E. B. Brandão, C. M. N. A. 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, vol. 76, pp. 17-23, 2014, doi: https://doi.org/10.1016/j.pnucene.2014.05.004
  29. C. M. Salgado, C. M. N. A. Pereira, R. Schirru, L. E. B. Brandão, “Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks,” Prog. Nucl. Energy, vol. 52, no. 6, pp. 555-562, 2010, doi: https://doi.org/10.1016/j.pnucene.2010.02.001
  30. A. Karami, G. H. Roshani, E. Nazemi, S. Roshani, “Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm,” Flow Meas. Instrum., vol. 64, pp. 164-172, 2018, doi: https://doi.org/10.1016/j.flowmeasinst.2018.10.015
  31. 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., vol. 61, pp. 9-14, 2018, doi: https://doi.org/10.1016/j.flowmeasinst.2018.03.006
  32. R. H. Ruschel, “Proposição de modelo de fluxo de deslizamento para escoamento líquido-líquido horizontal,” University of Campinas, Campinas, Brasil, 2020.
  33. E. Jorjani, S. Chehreh Chelgani, S. Mesroghli, “Application of artificial neural networks to predict chemical desulfurization of Tabas coal,” Fuel, vol. 87, no. 12, pp. 2727–2734, 2008, doi: https://doi.org/10.1016/j.fuel.2008.01.029
  34. H. M. H. Al-rikabi, M. A. M. Al-ja, A. H. Ali, S. H. Abdulwahed, “Microprocessors and Microsystems Generic model implementation of deep neural network activation functions using GWO-optimized SCPWL model on FPGA,” Microprocessors and Microsystems, vol. 77, 2020, doi: https://doi.org/10.1016/j.micpro.2020.103141