Data driven methodology for model selection in flow pattern prediction
The determination of multiphase flow parameters such as flow pattern, pressure drop and liquid holdup, is a very challenging and valuable problem in chemical industry, especially during petroleum transport. Recently, many approaches have been made for establishing a Unified Flow Model to predict acc...
- Autores:
-
Hernández Gómez, Juan Sebastián
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/34367
- Acceso en línea:
- http://hdl.handle.net/1992/34367
- Palabra clave:
- Flujo bifásico - Investigaciones - Métodos de simulación
Flujo multifásico - Investigaciones - Métodos de simulación
Arboles de decisión - Investigaciones - Estudio de casos
Transporte del petróleo - Investigaciones
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | The determination of multiphase flow parameters such as flow pattern, pressure drop and liquid holdup, is a very challenging and valuable problem in chemical industry, especially during petroleum transport. Recently, many approaches have been made for establishing a Unified Flow Model to predict accurately the mentioned properties with an outstanding advance made by Zhang et al. (2003b). This paper proposes a novel methodol-ogy for selecting closure relationships from the models included in the Unified Flow Model developed by the previously cited authors. A tree based model is built based on a data driven methodology developed from a 27670 points data set and later tested for flow pattern prediction in a set made of 9224 observations. The clo-sure relationship selection model achieved a 74% accuracy in classifying flow regimes for a wide range of two-phase flow conditions. |
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