| dc.contributor.author | Álvarez Pacheco, Cristhian Enrique | |
| dc.contributor.author | Ruiz Diaz, Carlos Mauricio | |
| dc.contributor.author | Hernández Rodríguez, Oscar Mauricio | |
| dc.coverage.spatial | Colombia | en_US |
| dc.date.accessioned | 2025-10-27T15:32:48Z | |
| dc.date.available | 2025-10-27T15:32:48Z | |
| dc.date.issued | 2024-01-01 | |
| dc.identifier.issn | 2011-642X | en_US |
| dc.identifier.uri | https://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5953 | |
| dc.description.abstract | Flujos bifásicos ocurren en diversas industrias, destacando la de petróleo y gas. Un densitómetro de rayos-gamma colimado es utilizado para estudiar un sistema estático gas-líquido que simula un flujo bifásico en el patrón estratificado. El equipo se destaca por su capacidad de medición no intrusiva, su alta sensibilidad y su buena resolución espacial. Distribuciones cordales de fracción de fase en un tubo conteniendo agua y aire son presentadas y se varió el nivel de agua entre 25%, 50% y 75%. Se determino a distribución de fracción de fase a lo largo de la sección transversal. Además, este estudio presenta un modelo de red neuronal artificial (ANN) para predecir fracción volumétrica en sistemas de tuberías utilizando un conjunto de datos de 110 puntos experimentales. El modelo ANN considera factores como la intensidad absorbida, el porcentaje de corte de agua y la relación h/D. La configuración incluye el uso del solucionador Adam, la función de activación de Unidad lineal rectificada (ReLU), tamaño de lote de 3, dos capas ocultas (60 neuronas cada una) y una tasa de aprendizaje de 0,001. El modelo logra un error cuadrático medio (MSE) mínimo del 0,3 % y un error absoluto medio (MAE) bajo de 0,028. | en_US |
| dc.language.iso | en_US | en_US |
| dc.relation | https://revistas.ufps.edu.co/index.php/ingenio/article/view/4098 | en_US |
| dc.title | Medición cordal de distribución de fracción de fase de un sistema estático gas-líquido usando densitómetro de rayos-gama colimado y redes neurales artificiales | en_US |
| dc.title.translated | Chordal measurement of phase fraction distribution in a static gas-liquid system using collimated gamma-ray densitometer and artificial neural networks | en_US |
| dc.contributor.cvlac | Cristhian Enrique Álvarez Pacheco [0000138453] | en_US |
| dc.contributor.cvlac | Carlos Mauricio Ruiz Diaz [0001772117] | en_US |
| dc.contributor.googlescholar | Cristhian Enrique Álvarez Pacheco [sKtc1OgAAAAJ] | en_US |
| dc.contributor.googlescholar | Carlos Mauricio Ruiz Diaz [x10qI0UAAAAJ] | en_US |
| dc.contributor.googlescholar | Oscar Mauricio Hernández Rodríguez [i48N_sEAAAAJ] | en_US |
| dc.contributor.orcid | Cristhian Enrique Álvarez Pacheco [0000-0003-4062-0311] | en_US |
| dc.contributor.orcid | Carlos Mauricio Ruiz Diaz [0000-0003-3836-9332] | en_US |
| dc.contributor.orcid | Oscar Mauricio Hernández Rodríguez [0000-0003-1578-7996] | en_US |
| dc.contributor.researchgate | Cristhian Enrique Álvarez Pacheco [Cristhian-Alvarez-Pacheco] | en_US |
| dc.contributor.researchgate | Carlos Mauricio Ruiz Diaz [Carlos-Mauricio-Ruiz-Diaz] | en_US |
| dc.contributor.scopus | Carlos Mauricio Ruiz Diaz [57326268000] | en_US |
| dc.contributor.scopus | Oscar Mauricio Hernández Rodríguez [7103218937] | en_US |
| dc.description.abstractenglish | Two-phase flow occurs in various industries, as in the production of oil and gas. A collimated gamma-ray densitometer is applied for the study of a static gas-liquid system that simulates a stratified flow pattern. It stands out for its non-intrusive measurement capacity, its high sensitivity to density variations and its good spatial resolution. Chordal phase fraction distributions are obtained in a tube containing water and air at room conditions, with the water level varied between 25%, 50% and 75%. The results obtained highlight the usefulness of the collimated gamma-ray densitometer to determine phase fraction distributions along the pipe’s cross section. Furthermore, this study suggests the use of an artificial neural network (ANN) model for predicting holdup in pipeline systems using a dataset of 110 experimental data points. The ANN model considers factors such as absorbed intensity, water cut percentage, and dimensionless h/D ratio. The adopted configuration includes the use of the Adam solver, Rectified Linear Unit (ReLU) activation function, a batch size of 3, two hidden layers (60 neurons each), and a learning rate of 0.001. The model achieves good accuracy, with a minimum mean square error (MSE) of 0.3% and a low mean absolute error (MAE) of 0.028. | en_US |
| dc.subject.proposal | Densitómetro de rayos-gamma colimados | en_US |
| dc.subject.proposal | Distribución de fracción de fase | en_US |
| dc.subject.proposal | Flujo bifásico | en_US |
| dc.subject.proposal | Red neuronal artificial | en_US |
| dc.subject.keywords | Collimated gamma-ray densitometer | en_US |
| dc.subject.keywords | Phase fraction distribution | en_US |
| dc.subject.keywords | Two-phase flow | en_US |
| dc.subject.keywords | Artificial neural networks | 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 | https://doi.org/10.22463/2011642X.4098 | en_US |
