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

dc.contributor.authorRuiz Diaz, Carlos Mauricio
dc.contributor.authorHernández Cely, Marlon Mauricio
dc.contributor.authorGonzález Estrada, Octavio Andrés
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-27T20:08:03Z
dc.date.available2025-10-27T20:08:03Z
dc.date.issued2022-05-12
dc.identifier.issn1657 -4583en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5955
dc.description.abstractEste 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 𝑅2de 0,9964 utilizando 15 neuronas en la capa oculta de la red y la función de transferencia TanSig.en_US
dc.language.isospaen_US
dc.relationhttps://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/12954en_US
dc.titleModelo predictivo para el cálculo de la fracción volumétrica de un flujo bifásico agua-aceite en la horizontal utilizando una red neuronal artificialen_US
dc.title.translatedPredictive model for calculating the volumetric fraction of a horizontal two-phase water-oil flow using an artificial neural networken_US
dc.contributor.cvlacCarlos Mauricio Ruiz Diaz [0001772117]en_US
dc.contributor.cvlacMarlon Mauricio Hernández Cely [0001354455]en_US
dc.contributor.cvlacOctavio Andrés González Estrada [0001406575]en_US
dc.contributor.googlescholarCarlos Mauricio Ruiz Diaz [x10qI0UAAAAJ]en_US
dc.contributor.googlescholarMarlon Mauricio Hernández Cely [RQyKQW4AAAAJ]en_US
dc.contributor.googlescholarOctavio Andrés González Estrada [Z4YyFpgAAAAJ]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.orcidOctavio Andrés González Estrada [0000-0002-2778-3389]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.researchgateOctavio Andrés González Estrada [Octavio-Gonzalez-Estrada]en_US
dc.contributor.scopusCarlos Mauricio Ruiz Diaz [57326268000]en_US
dc.contributor.scopusMarlon Mauricio Hernández Cely [57195293394]en_US
dc.contributor.scopusOctavio Andrés González Estrada [25927116600]en_US
dc.description.abstractenglishthis paper presentsthe application of an artificial neural network (ANN) to develop a model able to predict the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe. The surface velocities of each fluid and the pressure differential in the pipe are used as input parameters of the multi-layer artificial neural network with back-propagation, while the volumetric fraction of the fluids is used as an output parameter in the training. The 56 experimental data were obtained in the laboratory LabPetro-CEPETRO-UNICAMP. The results of the predictive model with the best performance show a mean absolute error (AAPE) of 3.01 % and a coefficient of determination R^2 of 0.9964, using 15 neurons in the hidden layer of the network and the TanSig transfer function.en_US
dc.subject.proposalFlujo multifásicoen_US
dc.subject.proposalFracción volumétricaen_US
dc.subject.proposalRed neuronal artificialen_US
dc.subject.proposalVelocidad superficialen_US
dc.subject.proposalIndustria 4.0en_US
dc.subject.keywordsArtificial neural networksen_US
dc.subject.keywordsholdupen_US
dc.subject.keywordsMultiphase flowen_US
dc.subject.keywordsSurface velocityen_US
dc.subject.keywordsIndustry 4.0.en_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
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dc.identifier.DOIhttps://doi.org/10.18273/revuin.v21n2-2022013en_US


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