A predictive model for the identification of the volume fraction in two-phase flow
This work presents the use of artificial intelligence in multiphase flows, implementing a multilayer perceptron artificial neural network with back-propagation, and using the sigmoid tangent activation function, to generate a predictive model capable of obtaining the holdup of a two-phase flow co...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2021
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| Online Access: | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/13417 |
| _version_ | 1801706351393505280 |
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| author | Ruiz-Diaz, C M Hernández-Cely, M. M González-Estrada, O. A |
| author_facet | Ruiz-Diaz, C M Hernández-Cely, M. M González-Estrada, O. A |
| author_sort | Ruiz-Diaz, C M |
| collection | OJS |
| description |
This work presents the use of artificial intelligence in multiphase flows, implementing a multilayer perceptron artificial neural network with back-propagation, and using the sigmoid tangent activation function, to generate a predictive model capable of obtaining the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe of 12 m. The artificial neural network is developed using an input layer, formed by the pressure differential in the line and the superficial velocities of the working fluids, also, it has two hidden layers and an outlet layer, which is made up of the volumetric fractions of the fluids. The best-performing predictive model shows a mean percentage absolute error of 3.07 % and a coefficient of determination R2 of 0.985 using 15 neurons in the two hidden layers of the neural network. The 56 experimental data used in the study were obtained in the laboratory LEMI EESC-USP (Brazil).
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| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-13417 |
| institution | Revista Ciencia en Desarrollo |
| language | spa |
| publishDate | 2021 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-134172022-06-15T16:53:08Z A predictive model for the identification of the volume fraction in two-phase flow Modelo predictivo para la identificación de la fracción volumétrica en flujo bifásico Ruiz-Diaz, C M Hernández-Cely, M. M González-Estrada, O. A Flujo multifásico, Fracción volumétrica, Red Neuronal Artificial, Presión diferencial, Velocidad superficial Multiphase flow, Volumetric fraction, Artificial Neural Network, Differential pressure, Surface speed This work presents the use of artificial intelligence in multiphase flows, implementing a multilayer perceptron artificial neural network with back-propagation, and using the sigmoid tangent activation function, to generate a predictive model capable of obtaining the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe of 12 m. The artificial neural network is developed using an input layer, formed by the pressure differential in the line and the superficial velocities of the working fluids, also, it has two hidden layers and an outlet layer, which is made up of the volumetric fractions of the fluids. The best-performing predictive model shows a mean percentage absolute error of 3.07 % and a coefficient of determination R2 of 0.985 using 15 neurons in the two hidden layers of the neural network. The 56 experimental data used in the study were obtained in the laboratory LEMI EESC-USP (Brazil). Este trabajo presenta el uso de inteligencia artificial en flujos multifásicos, implementando una red neuronal artificial de perceptrón multicapa con retropropagación, y utilizando la función de activación tangente sigmoidea, para generar un modelo predictivo capaz de obtener la fracción volumétrica de un flujo bifásico compuesto por agua y aceite mineral en una tubería horizontal de 12 m. La red neuronal artificial se desarrolla a partir de una capa de entrada, formada por el diferencial de presión en la línea y las velocidades superficiales de los fluidos de trabajo, además, tiene dos capas ocultas y una capa de salida, que está formada por las fracciones volumétricas de los fluidos. El modelo predictivo de mejor rendimiento muestra un error medio porcentual absoluto del 3,07 % y un coeficiente de determinación R2 de 0,985 utilizando 15 neuronas en las dos capas ocultas de la red neuronal. Los 56 datos experimentales utilizados en el estudio se obtuvieron en el laboratorio LEMI EESC-USP (Brasil). Universidad Pedagógica y Tecnológica de Colombia 2021-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/13417 10.19053/01217488.v12.n2.2021.13417 Ciencia En Desarrollo; Vol. 12 No. 2 (2021): Vol 12, Núm.2 (2021): Julio-Diciembre Ciencia en Desarrollo; Vol. 12 Núm. 2 (2021): Vol 12, Núm.2 (2021): Julio-Diciembre 2462-7658 0121-7488 spa https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/13417/11162 |
| spellingShingle | Flujo multifásico, Fracción volumétrica, Red Neuronal Artificial, Presión diferencial, Velocidad superficial Multiphase flow, Volumetric fraction, Artificial Neural Network, Differential pressure, Surface speed Ruiz-Diaz, C M Hernández-Cely, M. M González-Estrada, O. A A predictive model for the identification of the volume fraction in two-phase flow |
| title | A predictive model for the identification of the volume fraction in two-phase flow |
| title_alt | Modelo predictivo para la identificación de la fracción volumétrica en flujo bifásico |
| title_full | A predictive model for the identification of the volume fraction in two-phase flow |
| title_fullStr | A predictive model for the identification of the volume fraction in two-phase flow |
| title_full_unstemmed | A predictive model for the identification of the volume fraction in two-phase flow |
| title_short | A predictive model for the identification of the volume fraction in two-phase flow |
| title_sort | predictive model for the identification of the volume fraction in two phase flow |
| topic | Flujo multifásico, Fracción volumétrica, Red Neuronal Artificial, Presión diferencial, Velocidad superficial Multiphase flow, Volumetric fraction, Artificial Neural Network, Differential pressure, Surface speed |
| topic_facet | Flujo multifásico, Fracción volumétrica, Red Neuronal Artificial, Presión diferencial, Velocidad superficial Multiphase flow, Volumetric fraction, Artificial Neural Network, Differential pressure, Surface speed |
| url | https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/13417 |
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