| dc.contributor.author | Ruiz Diaz, Carlos Mauricio | |
| dc.contributor.author | Gómez Camperos, July Andrea | |
| dc.contributor.author | Hernández Cely, Marlon Mauricio | |
| dc.coverage.spatial | Colombia | en_US |
| dc.date.accessioned | 2025-10-27T21:29:44Z | |
| dc.date.available | 2025-10-27T21:29:44Z | |
| dc.date.issued | 2022-10-08 | |
| dc.identifier.issn | 1742-6596 | en_US |
| dc.identifier.uri | https://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5957 | |
| dc.description.abstract | Dada 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.iso | en_US | en_US |
| dc.relation | https://iopscience.iop.org/article/10.1088/1742-6596/2163/1/012001 | en_US |
| dc.title | Identificación del patrón de flujo de líquidos (petróleo y agua) en tuberías verticales mediante técnicas de aprendizaje automático | en_US |
| dc.title.translated | Flow pattern identification of liquid-liquid (oil and water) in vertical pipelines using machine learning techniques | en_US |
| dc.contributor.cvlac | Carlos Mauricio Ruiz Diaz [0001772117] | en_US |
| dc.contributor.cvlac | July Andrea Gómez Camperos [0001164287] | en_US |
| dc.contributor.cvlac | Marlon Mauricio Hernández Cely [0001354455] | en_US |
| dc.contributor.googlescholar | Carlos Mauricio Ruiz Diaz [x10qI0UAAAAJ] | en_US |
| dc.contributor.googlescholar | Marlon Mauricio Hernández Cely [RQyKQW4AAAAJ] | en_US |
| dc.contributor.orcid | Carlos Mauricio Ruiz Diaz [0000-0003-3836-9332] | en_US |
| dc.contributor.orcid | July Andrea Gómez Camperos [0000-0003-3894-144X] | en_US |
| dc.contributor.orcid | Marlon Mauricio Hernández Cely [0000-0001-7564-4044] | en_US |
| dc.contributor.researchgate | Carlos Mauricio Ruiz Diaz [Carlos-Mauricio-Ruiz-Diaz] | en_US |
| dc.contributor.researchgate | Marlon Mauricio Hernández Cely [Marlon-Hernandez-Cely] | en_US |
| dc.contributor.researchgate | Marlon Mauricio Hernández Cely [Marlon-Hernandez-Cely] | en_US |
| dc.contributor.scopus | Carlos Mauricio Ruiz Diaz [57326268000] | en_US |
| dc.contributor.scopus | July Andrea Gómez Camperos [57220550902] | en_US |
| dc.description.abstractenglish | Given 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.proposal | Tuberías verticales | en_US |
| dc.subject.proposal | Técnicas de aprendizaje automático | en_US |
| dc.subject.proposal | Flujo de líquidos | en_US |
| dc.subject.keywords | Vertical Pipelines | en_US |
| dc.subject.keywords | Machine Learning Techniques | en_US |
| dc.subject.keywords | Liquid Flow | en_US |
| dc.publisher.faculty | Facultad ingenierías | en_US |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | en_US |
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| dc.identifier.DOI | 10.1088/1742-6596/2163/1/012001 | en_US |