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Sistema especializado en identificación de patrones de flujo mediante redes neuronales artificiales

dc.contributor.authorGómez Camperos, July Andrea
dc.contributor.authorRuiz Diaz, Carlos Mauricio
dc.contributor.authorHernández Cely, Marlon Mauricio
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-27T12:18:26Z
dc.date.available2025-10-27T12:18:26Z
dc.date.issued2023-01-16
dc.identifier.issn1451-4117en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5946
dc.description.abstractEn este trabajo, se desarrolla una aplicación de inteligencia artificial en la industria del petróleo y gas para identificar patrones de flujo en tuberías horizontales y verticales de flujo bifásico de petróleo y agua, normalizando la información de las palabras y convirtiéndola en valores numéricos mediante el desarrollo de una red neuronal artificial, cuya capa de entrada se compone de las velocidades superficiales de cada fluido, la velocidad de la mezcla, la fracción volumétrica de las sustancias, el diámetro y la inclinación de las tuberías, y la viscosidad del petróleo. La Red Neuronal Artificial (RNA) cuenta con dos capas ocultas compuestas por 45 neuronas. La base de datos con la que se entrenó, validó y probó el modelo cuenta con 6993 filas de información correspondientes a las entradas del sistema inteligente y particularizadas para flujo anular en tuberías horizontales y DO/W en tuberías verticales. Cabe destacar que la información se obtuvo después de rediseñar la información presentada por 12 y 18 autores para tuberías horizontales y verticales, respectivamente. Finalmente, el error cuadrático medio obtenido por el modelo fue de alrededor de 1,38%, con un coeficiente de determinación máximo de 0,79.en_US
dc.language.isoen_USen_US
dc.relationhttps://scindeks.ceon.rs/Article.aspx?artid=1451-41172301285Gen_US
dc.titleSistema especializado en identificación de patrones de flujo mediante redes neuronales artificialesen_US
dc.title.translatedSpecialist system in flow pattern identification using artificial neural Networksen_US
dc.contributor.cvlacJuly Andrea Gómez Camperos [0001164287]en_US
dc.contributor.cvlacCarlos Mauricio Ruiz Diaz [0001772117]en_US
dc.contributor.cvlacMarlon Mauricio Hernández Cely [0001354455]en_US
dc.contributor.googlescholarCarlos Mauricio Ruiz Diaz [x10qI0UAAAAJ]en_US
dc.contributor.googlescholarMarlon Mauricio Hernández Cely [RQyKQW4AAAAJ]en_US
dc.contributor.orcidJuly Andrea Gómez Camperos [0000-0003-3894-144X]en_US
dc.contributor.orcidCarlos Mauricio Ruiz Diaz [0000-0003-3836-9332]en_US
dc.contributor.orcidMarlon Mauricio Hernández Cely [0000-0001-7564-4044]en_US
dc.contributor.researchgateCarlos Mauricio Ruiz Diaz [Carlos-Mauricio-Ruiz-Diaz]en_US
dc.contributor.researchgateMarlon Mauricio Hernández Cely [Marlon-Hernandez-Cely]en_US
dc.contributor.scopusJuly Andrea Gómez Camperos [57220550902]en_US
dc.contributor.scopusCarlos Mauricio Ruiz Diaz [57326268000]en_US
dc.contributor.scopusMarlon Mauricio Hernández Cely [57195293394]en_US
dc.description.abstractenglishIn this work, an application of artificial intelligence in the oils & gas industry is developed to iden-tify flow patterns in horizontal and vertical pipes of two-phase flow of oil and water, normalizing the word information and converting it to numerical values through the development of an artifi-cial neural network, whose input layer is composed of the surface velocities of each fluid, the ve-locity of the mixture, the volumetric fraction of the substances, diameter and the inclination of pipelines and the oil viscosity. The Artificial Neural Networks (ANN) has two hidden layers composed of 45 neurons. The database with which the model was trained, validated, and tested has 6993 rows of information corresponding to the inputs of the intelligent system and particular-ized for annular flow in horizontal pipes and DO/W in vertical pipelines. Notice that the infor-mation was obtained after re-engineering the information presented by 12 and 18 authors for hor-izontal and vertical piping, respectively. Finally, the mean square error obtained by the model was around 1.38%, with a maximum coefficient of determination of 0.79.en_US
dc.subject.proposalRed neuronal artificialen_US
dc.subject.proposalPatrón de flujoen_US
dc.subject.proposalFlujo bifásicoen_US
dc.subject.proposalAprendizaje automáticoen_US
dc.subject.keywordsArtificial neural networken_US
dc.subject.keywordsFlow patternen_US
dc.subject.keywordsTwo-phase flowen_US
dc.subject.keywordsMachine learningen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
dc.relation.referencesH. F. Velasco Peña and O. M. H. Rodriguez, “Applications of wire-mesh sensors in multiphase flows,” Flow Meas. Instrum., vol. 45, pp. 255–273, 2015, doi: 10.1016/j.flowmeasinst.2015.06.024.en_US
dc.relation.referencesY. Mi, M. Ishii, and L. H. Tsoukalas, “Vertical two-phase flow identification using advanced instrumentation and neural networks,” Nucl. Eng. Des., vol. 184, no. 2–3, pp. 409–420, 1998, doi: 10.1016/S0029-5493(98)00212-X.en_US
dc.relation.referencesY. Mi, M. Ishii, and L. H. Tsoukalas, “Flow regime identification methodology with neural networks and two-phase flow models,” Nucl. Eng. Des., vol. 204, no. 1–3, pp. 87–100, 2001, doi: 10.1016/S0029-5493(00)00325-3.en_US
dc.relation.referencesJ. E. Juliá, Y. Liu, S. Paranjape, and M. Ishii, “Upward vertical two-phase flow local flow regime identification using neural network techniques,” Nucl. Eng. Des., vol. 238, no. 1, pp. 156–169, 2008, doi: 10.1016/j.nucengdes.2007.05.005.en_US
dc.relation.referencesC. Tan, F. Dong, and M. Wu, “Identification of gas/liquid two-phase flow regime through ERT-based measurement and feature extraction,” Flow Meas. Instrum., vol. 18, no. 5–6, pp. 255–261, 2007, doi: 10.1016/j.flowmeasinst.2007.08.003.en_US
dc.relation.referencesE. S. Rosa, R. M. 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,” Int. J. Multiph. Flow, vol. 36, no. 9, pp. 738–754, 2010, doi: 10.1016/j.ijmultiphaseflow.2010.05.001.en_US
dc.relation.referencesR. Banasiak et al., “Study on two-phase flow regime visualization and identification using 3D electrical capacitance tomography and fuzzy-logic classification,” Int. J. Multiph. Flow, vol. 58, no. January, pp. 1–14, 2014, doi: 10.1016/j.ijmultiphaseflow.2013.07.003.en_US
dc.relation.referencesH. Shaban and S. Tavoularis, “Identification of flow regime in vertical upward air-water pipe flow using differential pressure signals and elastic maps,” Int. J. Multiph. Flow, vol. 61, no. May, pp. 62–72, 2014, doi: 10.1016/j.ijmultiphaseflow.2014.01.009.en_US
dc.relation.referencesH. Shaban and S. Tavoularis, “Measurement of gas and liquid flow rates in two-phase pipe flows by the application of machine learning techniques to differential pressure signals,” Int. J. Multiph. Flow, vol. 67, no. December, pp. 106–117, 2014, doi: 10.1016/j.ijmultiphaseflow.2014.08.012.en_US
dc.relation.referencesL. 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 Trans. Instrum. Meas., vol. 66, no. 5, pp. 852–868, 2017, doi: 10.1109/TIM.2016.2634630.en_US
dc.relation.referencesA. 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,” J. Fluids Eng., pp. 1–12, 2020, doi: 10.1115/1.4048876.en_US
dc.relation.referencesA. Van Der Spek and A. Thomas, “Neural-net identification of flow regime with band spectra of flow-generated sound,” SPE Reserv. Eval. Eng., vol. 2, no. 6, pp. 489–498, 1999, doi: 10.2118/59067-PA.en_US
dc.relation.referencesR. 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 Meas. Instrum., vol. 60, no. April, pp. 17–23, 2018, doi: 10.1016/j.flowmeasinst.2018.02.008.en_US
dc.relation.referencesL. Zhang and H. Wang, “Identification of oil-gas two-phase flow pattern based on SVM and electrical capacitance tomography technique,” Flow Meas. Instrum., vol. 21, no. 1, pp. 20–24, 2010, doi: 10.1016/j.flowmeasinst.2009.08.006.en_US
dc.relation.referencesM. Meribout, N. Al-Rawahi, A. Al-Naamany, A. Al-Bimani, K. Al-Busaidi, and A. Meribout, “Integration of impedance measurements with acoustic measurements for accurate two phase flow metering in case of high water-cut,” Flow Meas. Instrum., vol. 21, no. 1, pp. 8–19, 2010, doi: 10.1016/j.flowmeasinst.2009.09.002.en_US
dc.relation.referencesS. Azizi, M. M. Awad, and E. Ahmadloo, “Prediction of water holdup in vertical and inclined oil-water two-phase flow using artificial neural network,” Int. J. Multiph. Flow, vol. 80, no. April, pp. 181–187, 2016, doi: 10.1016/j.ijmultiphaseflow.2015.12.010.en_US
dc.relation.referencesR. Shirley, D. P. Chakrabarti, and G. Das, “LIQUID-LIQUID TWO-PHASE FLOW,” vol. 199, no. 12, pp. 20–22, 2012.en_US
dc.relation.referencesC. M. R. Diaz, “Caracterización hidrodinámica de flujos multifase empleando técnicas de inteligencia artificial,” Universidad Industrial de Santander Facultad, 2021.en_US
dc.relation.referencesD. P. Chakrabarti, G. Das, and S. Ray, “Pressure Drop in Liquid-liquid Two Phase Horizontal Flow: Experiment and Prediction D.,” Chem. Eng. …, pp. 20–22, 2005.en_US
dc.relation.referencesD. P. Chakrabarti, G. Das, and P. K. Das, “Identification of stratified liquid-liquid flow through horizontal pipes by a non-intrusive optical probe,” Chem. Eng. Sci., vol. 62, no. 7, pp. 1861–1876, 2007, doi: 10.1016/j.ces.2006.11.056.en_US
dc.relation.referencesH. L. Hu, J. Dong, J. Zhang, Y. J. Cheng, and T. M. Xu, “Identification of gas/solid two-phase flow regimes using electrostatic sensors and neural-network techniques,” Flow Meas. Instrum., vol. 22, no. 5, pp. 482–487, 2011, doi: 10.1016/j.flowmeasinst.2011.07.004.en_US
dc.relation.referencesG. H. Roshani, E. Nazemi, and 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. April, pp. 39–45, 2017, doi: 10.1016/j.flowmeasinst.2016.10.001.en_US
dc.relation.referencesA. Karami, G. H. Roshani, E. Nazemi, and 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, no. December, pp. 164–172, 2018, doi: 10.1016/j.flowmeasinst.2018.10.015.en_US
dc.relation.referencesC. M. Salgado, C. M. N. A. Pereira, R. Schirru, and 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: 10.1016/j.pnucene.2010.02.001.en_US
dc.relation.referencesH. Wu, “International Journal of Multiphase Flow Intelligent identification system of flow regime of oil – gas – water multiphase flow,” vol. 27, no. 3, pp. 1–7, 2001.en_US
dc.relation.referencesC. M. Ruiz-Diaz, M. M. Hernández-Cely, and 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, 2021, doi: 10.19053/01217488.v12.n2.2021.13417.en_US
dc.relation.referencesS. Fu, Z. Li, K. Liu, S. Din, M. Imran, and X. Yang, “Model Compression for IoT Applications in Industry 4.0 via Multiscale Knowledge Transfer,” IEEE Trans. Ind. Informatics, vol. 16, no. 9, pp. 6013–6022, 2020, doi: 10.1109/TII.2019.2953106.en_US
dc.relation.referencesJ. Dalzochio et al., “Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges,” Comput. Ind., vol. 123, p. 103298, 2020, doi: 10.1016/j.compind.2020.103298.en_US
dc.relation.referencesP. K. Malik et al., “Industrial Internet of Things and its Applications in Industry 4.0: State of The Art,” Comput. Commun., vol. 166, no. November 2020, pp. 125–139, 2021, doi: 10.1016/j.comcom.2020.11.016.en_US
dc.relation.referencesS. Cai, H. Toral, J. Qiu, and J. S. Archer, “Neural network based objective flow regime identification in air‐water two phase flow,” Can. J. Chem. Eng., vol. 72, no. 3, pp. 440–445, 1994, doi: 10.1002/cjce.5450720308.en_US
dc.relation.referencesH. Yan, Y. H. Liu, and C. T. Liu, “Identification of flow regimes using back-propagation networks trained on simulated data based on a capacitance tomography sensor,” Meas. Sci. Technol., vol. 15, no. 2, pp. 432–436, 2004, doi: 10.1088/0957-0233/15/2/017.en_US
dc.relation.referencesP. Wiedemann, A. Döß, E. Schleicher, and U. Hampel, “Fuzzy flow pattern identification in horizontal air-water two-phase flow based on wire-mesh sensor data,” Int. J. Multiph. Flow, vol. 117, pp. 153–162, 2019, doi: 10.1016/j.ijmultiphaseflow.2019.05.004.en_US
dc.relation.referencesM. M. F. Figueiredo, J. L. Goncalves, A. M. V. Nakashima, A. M. F. Fileti, and 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, Jan. 2016, doi: 10.1016/j.expthermflusci.2015.08.010.en_US
dc.relation.referencesT. Sunder Raj, D. P. Chakrabarti, and G. Das, “Liquid-liquid stratified flow through horizontal conduits,” Chem. Eng. Technol., vol. 28, no. 8, pp. 899–907, 2005, doi: 10.1002/ceat.200500067.en_US
dc.relation.referencesB. Grassi, D. Strazza, and P. Poesio, “Experimental validation of theoretical models in two-phase high-viscosity ratio liquid–liquid flows in horizontal and slightly inclined pipes,” Int. J. Multiph. Flow, vol. 34, no. 10, pp. 950–965, Oct. 2008, doi: 10.1016/j.ijmultiphaseflow.2008.03.006.en_US
dc.relation.referencesP. Abduvayt, R. Manabe, T. Watanabe, and N. Arihara, “Analisis of oil-water flow tests in horizontal, hilly-terrain, and vertical pipes,” Proc. - SPE Annu. Tech. Conf. Exhib., pp. 1335–1347, 2004, doi: 10.2523/90096-ms.en_US
dc.relation.referencesJ. Cai, C. Li, X. Tang, F. Ayello, S. Richter, and S. Nesic, “Experimental study of water wetting in oil-water two phase Flow-Horizontal flow of model oil,” Chem. Eng. Sci., vol. 73, pp. 334–344, 2012, doi: 10.1016/j.ces.2012.01.014.en_US
dc.relation.referencesA. Al-Sarkhi, E. Pereyra, I. Mantilla, and C. Avila, “Dimensionless oil-water stratified to non-stratified flow pattern transition,” J. Pet. Sci. Eng., vol. 151, no. September 2016, pp. 284–291, Mar. 2017, doi: 10.1016/j.petrol.2017.01.016.en_US
dc.relation.referencesJ. Shi, L. Lao, and H. Yeung, “Water-lubricated transport of high-viscosity oil in horizontal pipes: The water holdup and pressure gradient,” Int. J. Multiph. Flow, vol. 96, pp. 70–85, 2017, doi: 10.1016/j.ijmultiphaseflow.2017.07.005.en_US
dc.relation.referencesT. Al-Wahaibi et al., “Experimental investigation on flow patterns and pressure gradient through two pipe diameters in horizontal oil–water flows,” J. Pet. Sci. Eng., vol. 122, pp. 266–273, Oct. 2014, doi: 10.1016/j.petrol.2014.07.019.en_US
dc.relation.referencesA. Dasari, A. B. Desamala, A. K. Dasmahapatra, and T. K. Mandal, “Experimental studies and probabilistic neural network prediction on flow pattern of viscous oil-water flow through a circular horizontal pipe,” Ind. Eng. Chem. Res., vol. 52, no. 23, pp. 7975–7985, 2013, doi: 10.1021/ie301430m.en_US
dc.relation.referencesR. Ibarra, I. Zadrazil, C. N. Markides, and O. K. Matar, “Towards a Universal Dimensionless Map of Flow Regime,” 11th Int. Conf. Heat Transf. Fluid Mech. Thermodyn., no. July, 2015.en_US
dc.relation.referencesM. Nädler and D. Mewes, “Flow induced emulsification in the flow of two immiscible liquids in horizontal pipes,” Int. J. Multiph. Flow, vol. 23, no. 1, pp. 55–68, 1997, doi: 10.1016/s0301-9322(96)00055-9.en_US
dc.relation.referencesO. M. H. Rodriguez and R. V. A. Oliemans, “Experimental study on oil–water flow in horizontal and slightly inclined pipes,” Int. J. Multiph. Flow, vol. 32, no. 3, pp. 323–343, Mar. 2006, doi: 10.1016/j.ijmultiphaseflow.2005.11.001.en_US
dc.relation.referencesJ. Shi and H. Yeung, “Characterization of liquid-liquid flows in horizontal pipes,” AIChE J., vol. 63, no. 3, pp. 1132–1143, 2017, doi: 10.1002/aic.15452.en_US
dc.relation.referencesA. Wegmann and P. Rudolf von Rohr, “Two phase liquid-liquid flows in pipes of small diameters,” Int. J. Multiph. Flow, vol. 32, no. 8, pp. 1017–1028, 2006, doi: 10.1016/j.ijmultiphaseflow.2006.04.001.en_US
dc.relation.referencesA. C. Bannwart, O. M. H. Rodriguez, C. H. M. de Carvalho, I. S. Wang, and R. M. O. Vara, “Flow Patterns in Heavy Crude Oil-Water Flow,” J. Energy Resour. Technol., vol. 126, no. 3, pp. 184–189, Sep. 2004, doi: 10.1115/1.1789520.en_US
dc.relation.referencesM. Du, N.-D. Jin, Z.-K. Gao, Z.-Y. Wang, and L.-S. Zhai, “Flow pattern and water holdup measurements of vertical upward oil–water two-phase flow in small diameter pipes,” Int. J. Multiph. Flow, vol. 41, pp. 91–105, May 2012, doi: 10.1016/j.ijmultiphaseflow.2012.01.007.en_US
dc.relation.referencesJ. Flores, X. Chen, and J. Brill, “Characterization of Oil-Water Flow Patterns in Vertical and Deviated Wells,” in Proceedings of SPE Annual Technical Conference and Exhibition, Oct. 1997, vol. 1, pp. 55–64, doi: 10.2523/38810-MS.en_US
dc.relation.referencesT. Ganat, S. Ridha, M. Hairir, J. Arisa, and R. Gholami, “Experimental investigation of high-viscosity oil–water flow in vertical pipes: flow patterns and pressure gradient,” J. Pet. Explor. Prod. Technol., vol. 9, no. 4, pp. 2911–2918, Dec. 2019, doi: 10.1007/s13202-019-0677-y.en_US
dc.relation.referencesG. W. Govier, G. A. Sullivan, and R. K. Wood, “The upward vertical flow of oil-water mixtures,” Can. J. Chem. Eng., vol. 39, no. 2, pp. 67–75, Apr. 1961, doi: 10.1002/cjce.5450390204.en_US
dc.relation.referencesY. F. Han, N. D. Jin, L. S. Zhai, H. X. Zhang, and Y. Y. Ren, “Flow pattern and holdup phenomena of low velocity oil-water flows in a vertical upward small diameter pipe,” J. Pet. Sci. Eng., vol. 159, no. May, pp. 387–408, Nov. 2017, doi: 10.1016/j.petrol.2017.09.052.en_US
dc.relation.referencesA. R. Hasan and C. S. Kabir, “A New Model for Two-Phase Oil/Water Flow: Production Log Interpretation nd Tubular Calculations,” SPE Prod. Eng., vol. 5, no. 02, pp. 193–199, May 1990, doi: 10.2118/18216-PA.en_US
dc.relation.referencesA. R. Hasan and C. S. Kabir, “A simplified model for oil/water flow in vertical and deviated wellbores,” SPE Prod. Facil., vol. 14, no. 1, pp. 56–62, 1999, doi: 10.2118/54131-PA.en_US
dc.relation.referencesA. K. Jana, G. Das, and P. K. Das, “Flow regime identification of two-phase liquid–liquid upflow through vertical pipe,” Chem. Eng. Sci., vol. 61, no. 5, pp. 1500–1515, Mar. 2006, doi: 10.1016/j.ces.2005.09.001.en_US
dc.relation.referencesA. K. Jana, P. Ghoshal, G. Das, and P. K. Das, “An Analysis of Pressure Drop and Holdup for Liquid-Liquid Upflow through Vertical Pipes,” Chem. Eng. Technol., vol. 30, no. 7, pp. 920–925, Jul. 2007, doi: 10.1002/ceat.200700033.en_US
dc.relation.referencesJ. Guo et al., “Heavy oil-water flow patterns in a small diameter vertical pipe under high temperature/pressure conditions,” J. Pet. Sci. Eng., vol. 171, pp. 1350–1365, Dec. 2018, doi: 10.1016/j.petrol.2018.08.021.R. A. Mazza and F. K. Suguimoto, “Experimental investigations of kerosene-water two-phase flow in vertical pipe,” J. Pet. Sci. Eng., vol. 184, p. 106580, Jan. 2020, doi: 10.1016/j.petrol.2019.106580.en_US
dc.relation.referencesK. Mydlarz-Gabryk, M. Pietrzak, and L. Troniewski, “Study on oil–water two-phase upflow in vertical pipes,” J. Pet. Sci. Eng., vol. 117, pp. 28–36, May 2014, doi: 10.1016/j.petrol.2014.03.007.en_US
dc.relation.referencesO. M. H. Rodriguez and A. C. Bannwart, “Experimental study on interfacial waves in vertical core flow,” J. Pet. Sci. Eng., vol. 54, no. 3–4, pp. 140–148, 2006, doi: 10.1016/j.petrol.2006.07.007.en_US
dc.relation.referencesJ. Xu, D. Li, J. Guo, and Y. Wu, “Investigations of phase inversion and frictional pressure gradients in upward and downward oil–water flow in vertical pipes,” Int. J. Multiph. Flow, vol. 36, no. 11–12, pp. 930–939, Nov. 2010, doi: 10.1016/j.ijmultiphaseflow.2010.08.007.en_US
dc.relation.referencesY. Yang et al., “Oil-Water flow patterns, holdups and frictional pressure gradients in a vertical pipe under high temperature/pressure conditions,” Exp. Therm. Fluid Sci., vol. 100, no. September 2018, pp. 271–291, Jan. 2019, doi: 10.1016/j.expthermflusci.2018.09.013.en_US
dc.relation.referencesD. Zhao, L. Guo, X. Hu, X. Zhang, and X. Wang, “Experimental study on local characteristics of oil-water dispersed flow in a vertical pipe,” Int. J. Multiph. Flow, vol. 32, no. 10–11, pp. 1254–1268, 2006, doi: 10.1016/j.ijmultiphaseflow.2006.06.004.en_US
dc.relation.referencesG. Jiang, H.-Q. Gao, C.-P. Wu, and G. Cybenko, “Convergence Analysis of Discretization Procedure in Q-Learning,” IFAC Proc. Vol., vol. 32, no. 2, pp. 8551–8556, Jul. 1999, doi: 10.1016/S1474-6670(17)57458-3.en_US
dc.relation.referencesJ. L. Trallero, C. Sarica, and J. P. Brill, “A Study of Oil-Water Flow Patterns in Horizontal Pipes,” SPE Prod. Facil., vol. 12, no. 03, pp. 165–172, Aug. 1997, doi: 10.2118/36609-PA.en_US
dc.identifier.DOIhttps://doi.org/10.5937/jaes0-40309en_US


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