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dc.contributor.authorRuiz Diaz, Carlos Mauricio
dc.contributor.authorGómez Camperos, July Andrea
dc.contributor.authorHernández Cely, Marlon Mauricio
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-27T21:29:44Z
dc.date.available2025-10-27T21:29:44Z
dc.date.issued2022-10-08
dc.identifier.issn1742-6596en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5957
dc.description.abstractDada la importancia del control de procesos en la industria petroquímica, existe la necesidad de determinar el comportamiento de los fluidos al interior de las tuberías. En este trabajo se desarrolla una metodología para la identificación de patrones de flujo en tuberías verticales con diámetros entre 0,01 m y 0,10 m, a partir de la implementación de técnicas de inteligencia artificial, para una combinación líquida de dos fases compuesta por petróleo con viscosidad en el rango de 792 Kg/m3 a 1823 Kg/m3 y agua a temperatura ambiente. Los modelos predictivos generados en la estructuración de la metodología se entrenaron con un 70% de datos basados ​​en parámetros de viscosidad, diámetro de la tubería, fracción de volumen y velocidades superficiales de los fluidos de trabajo almacenados en una base de datos. La información restante, equivalente al 30% del total, se utilizó para desarrollar la validación automática del modelo. Los patrones de flujo identificados por el sistema inteligente para el flujo de petróleo y agua, sin considerar la sustancia predominante, son agitado, disperso, dispersión muy fina, flujo de transición, intermitente y anular.en_US
dc.language.isoen_USen_US
dc.relationhttps://iopscience.iop.org/article/10.1088/1742-6596/2163/1/012001en_US
dc.titleIdentificación del patrón de flujo de líquidos (petróleo y agua) en tuberías verticales mediante técnicas de aprendizaje automáticoen_US
dc.title.translatedFlow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniquesen_US
dc.contributor.cvlacCarlos Mauricio Ruiz Diaz [0001772117]en_US
dc.contributor.cvlacJuly Andrea Gómez Camperos [0001164287]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.orcidCarlos Mauricio Ruiz Diaz [0000-0003-3836-9332]en_US
dc.contributor.orcidJuly Andrea Gómez Camperos [0000-0003-3894-144X]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.researchgateMarlon Mauricio Hernández Cely [Marlon-Hernandez-Cely]en_US
dc.contributor.scopusCarlos Mauricio Ruiz Diaz [57326268000]en_US
dc.contributor.scopusJuly Andrea Gómez Camperos [57220550902]en_US
dc.description.abstractenglishGiven the importance of process control in the petrochemical industry, there is a need to determine the behavior of the fluids inside the pipes. In this work a methodology is developed for the identification of flow patterns in vertical pipes with diameters between 0.01 m and 0.10 m, from the implementation of artificial intelligence techniques, for a liquid combination of two phases composed of oil with viscosity in the range of 792 Kg/m3 to 1823 Kg/m3 and water at room temperature. The predictive models generated in the structuring of the methodology were trained with 70% of data based on viscosity parameters, pipe diameter, volume fraction and surface velocities of the working fluids stored in a database. The remaining information, equivalent to 30% of the total, was used to develop the automatic model validation. The flow patterns identified by the intelligent system for oil and water flow, without considering the predominant substance, are churning, dispersed, very fine dispersion, transition flow, intermittent, and annular.en_US
dc.subject.proposalTuberías verticalesen_US
dc.subject.proposalTécnicas de aprendizaje automáticoen_US
dc.subject.proposalFlujo de líquidosen_US
dc.subject.keywordsVertical Pipelinesen_US
dc.subject.keywordsMachine Learning Techniquesen_US
dc.subject.keywordsLiquid Flowen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
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dc.identifier.DOI10.1088/1742-6596/2163/1/012001en_US


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