Modelo predictivo para la identificación de la fracción volumétrica en flujo bifásico

A predictive model for the identification of the volume fraction in two-phase flow

Contenido principal del artículo

C M Ruiz-Diaz
M. M Hernández-Cely
O. A González-Estrada

Resumen




Este trabajo presenta el uso de inteligencia artificial en flujos multifásicos, implementando una red neuronal artificial de perceptrón multicapa con retropropagación, y utilizando la función de activación tangente sigmoidea, para generar un modelo predictivo capaz de obtener la fracción volumétrica de un flujo bifásico compuesto por agua y aceite mineral en una tubería horizontal de 12 m. La red neuronal artificial se desarrolla a partir de una capa de entrada, formada por el diferencial de presión en la línea y las velocidades superficiales de los fluidos de trabajo, además, tiene dos capas ocultas y una capa de salida, que está formada por las fracciones volumétricas de los fluidos. El modelo predictivo de mejor rendimiento muestra un error medio porcentual absoluto del 3,07 % y un coeficiente de determinación R2 de 0,985 utilizando 15 neuronas en las dos capas ocultas de la red neuronal. Los 56 datos experimentales utilizados en el estudio se obtuvieron en el laboratorio LEMI EESC-USP (Brasil).




Palabras clave:

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Referencias (VER)

M. Süßer, “Flow Measurement Handbook: Industrial Designs, Operating Principles, Performance and Ap- plications,” Cryogenics, vol. 40, no. 6, p. 421, jan 2000. [Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0011227500000515

F.Romero,L.Velásquez,andE.Chica,“Consideraciones de diseño de una turbina Michell-Banki,” Revista UIS Ingenierías, vol. 20, no. 1, pp. 23–46, oct 2020. [Online]. Available: https://revistas.uis.edu.co/index.php/ revistauisingenierias/article/view/10906/11025

D. M. Rocha, C. H. de Carvalho, V. Estevam, and O. M. Rodriguez, “Effects of water and gas injection and viscosity on volumetric fraction, pressure gradient and phase inversion in upward-vertical three-phase pipe flow,” Journal of Petroleum Science and Engineering, vol. 157, no. March, pp. 519–529, aug 2017. [Online]. Available: http://dx.doi.org/10.1016/j.petrol. 2017.07.055https://linkinghub.elsevier.com/retrieve/pii/ S0920410517306010

V. S. Chalgeri and J. H. Jeong, “Flow regime identification and classification based on void fraction and differential pressure of vertical two-phase flow in rectangular channel,” International Journal of Heat and Mass Transfer, vol. 132, pp. 802–816, apr 2019. [Online]. Available: https://doi.org/10.1016/j.ijheatmasstransfer. 2018.12.015https://linkinghub.elsevier.com/retrieve/pii/ S0017931018344016

M. M. Hernández-Cely and C. M. Ruiz-Diaz, “Estudio de los fluidos aceite-agua a través del sensor basado en la permitividad eléctrica del patrón de fluido,” Revista UIS Ingenierías, vol. 19, no. 3, pp. 177–186, apr 2020. [Online]. Available: https://revistas.uis.edu.co/index.php/ revistauisingenierias/article/view/10570/10686

Y. Mi, M. Ishii, and L. Tsoukalas, “Vertical two-phase flow identification using advanced instrumentation and neural networks,” Nuclear Engineering and Design, vol. 184, no. 2-3, pp. 409–420, aug 1998. [Onli- ne]. Available: https://linkinghub.elsevier.com/retrieve/pii/ S002954939800212X

J. E. Juliá, Y. Liu, S. Paranjape, and M. Ishii, “Upward vertical two-phase flow local flow regime identification using neural network techniques,” Nuclear Engineering and Design, vol. 238, no. 1, pp. 156–169, jan 2008. [Online]. Available: https://linkinghub.elsevier.com/

retrieve/pii/S0029549307003822

C. Tan, F. Dong, and M. Wu, “Identification of gas/liquid two-phase flow regime through ERT-based measurement and feature extraction,” Flow Measurement and Instrumentation, vol. 18, no. 5-6, pp. 255–261, oct 2007. [Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0955598607000660

E. Rosa, R. Salgado, T. Ohishi, and N. Mastelari, “Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows,” International

Journal of Multiphase Flow, vol. 36, no. 9, pp. 738–754, sep 2010. [Online]. Available: http://dx.doi.org/10.1016/j. ijmultiphaseflow.2010.05.001https://linkinghub.elsevier. com/retrieve/pii/S0301932210000911

R.Banasiak,R.Wajman,T.Jaworski,P.Fiderek,H.Fidos, J. Nowakowski, and D. Sankowski, “Study on two-phase flow regime visualization and identification using 3D electrical capacitance tomography and fuzzy-logic classifi- cation,” International Journal of Multiphase Flow, vol. 58, pp. 1–14, jan 2014. [Online]. Available: https://linkinghub. elsevier.com/retrieve/pii/S0301932213001080

H. Shaban and S. Tavoularis, “Identification of flow regime in vertical upward air-water pipe flow using diffe- rential pressure signals and elastic maps,” International Journal of Multiphase Flow, vol. 61, pp. 62–72, may 2014. [Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0301932214000159

——,“Measurementofgasandliquidflowratesintwo- phase pipe flows by the application of machine learning techniques to differential pressure signals,” International Journal of Multiphase Flow, vol. 67, pp. 106–117, dec 2014. [Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0301932214001608

L. Wang, J. Liu, Y. Yan, X. Wang, and T. Wang, “Gas- Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms,” IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 5, pp. 852–868, may 2017. [Online]. Available: http://ieeexplore.ieee.org/document/ 7790803/

A. van der Spek and A. Thomas, “Neural-Net Identi- fication of Flow Regime With Band Spectra of Flow- Generated Sound,” SPE Reservoir Evaluation & Enginee- ring, vol. 2, no. 06, pp. 489–498, dec 1999. [Online]. Avai-

lable: https://onepetro.org/REE/article/2/06/489/109008/ Neural-Net-Identification-of-Flow-Regime-With-Band

S. Cai, H. Toral, J. Qiu, and J. S. Archer, “Neural network based objective flow regime identification in air-water two phase flow,” The Canadian Journal of Chemical Engineering, vol. 72, no. 3, pp. 440–445, jun 1994. [Online]. Available: http://doi.wiley.com/10.1002/ cjce.5450720308

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, no. February, pp. 17–23, apr 2018. [Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0955598617303667

C. Díaz, O. A. González-Estrada, and M. Cely, “Predictive Modeling of Holdup in Horizontal Wateroil Flow Using a Neural Network Approach,” in 14th WCCM-ECCOMAS Congress, no. January. Paris, Francia: CIMNE, 2021, pp. 11–15. [Online]. Available: https://www.scipedia.com/public/Diaz_et_al_2021a

M. Meribout, N. Al-Rawahi, A. Al-Naamany, A. Al- Bimani, K. Al-Busaidi, and A. Meribout, “Integration of impedance measurements with acoustic measu- rements for accurate two phase flow metering in case of high water-cut,” Flow Measurement and Instrumentation, vol. 21, no. 1, pp. 8–19, mar 2010. [On- line]. Available: https://linkinghub.elsevier.com/retrieve/ pii/S0955598609000405

R.Shirley,D.P.Chakrabarti,andG.Das,“Artificialneural networks in liquid-liquid two-phase flow,” Chemical Engi- neering Communications, vol. 199, no. 12, pp. 1520–1542, dec 2012. [Online]. Available: http://www.tandfonline. com/doi/abs/10.1080/00986445.2012.682323

R. H. Ruschel, Proposição de modelo de fluxo de desliza- mento para escoamento líquido-líquido horizontal. Cam- pinas, Brasil: Universidade Estadual de Campinas, 2020.

E. Jorjani, S. Chehreh Chelgani, and S. Mesroghli, “Application of artificial neural networks to pre- dict chemical desulfurization of Tabas coal,” Fuel, vol. 87, no. 12, pp. 2727–2734, sep 2008. [Onli- ne]. Available: https://linkinghub.elsevier.com/retrieve/pii/

S0016236108000409

H. M. Al-Rikabi, M. A. Al-Ja’afari, A. H. Ali, and S. H. Abdulwahed, “Generic model implementation of deep neural network activation functions using GWO- optimized SCPWL model on FPGA,” Microprocessors and Microsystems, vol. 77, p. 103141, sep 2020. [Onli- ne]. Available: https://linkinghub.elsevier.com/retrieve/pii/ S0141933120303082

Citado por: