Revista UIS Ingenierías

Vol. 21 No. 2 (2022): Revista UIS Ingenierías
Articles

A predictive model to calculate the holdup in horizontal biphasic water-oil flow using an artificial neuronal network

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  • Carlos Mauricio Ruiz-Díaz ,
  • Marlon Mauricio Hernández-Cely ,
  • Octavio Andrés González-Estrada
Carlos Mauricio Ruiz-Díaz
Universidad de São Paulo
Marlon Mauricio Hernández-Cely
Universidad de São Paulo
Octavio Andrés González-Estrada
Universidad Industrial de Santander
Bio
DOI: https://doi.org/10.18273/revuin.v21n2-2022013

Published 2022-05-12

Keywords

  • artificial neural network,
  • holdup,
  • multiphase flow,
  • surface velocity,
  • Industry 4.0

How to Cite

Ruiz-Díaz , C. M., Hernández-Cely , M. M. ., & González-Estrada, O. A. (2022). A predictive model to calculate the holdup in horizontal biphasic water-oil flow using an artificial neuronal network. Revista UIS Ingenierías, 21(2), 155–164. https://doi.org/10.18273/revuin.v21n2-2022013

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Abstract

This paper presents the application of an artificial neural network (ANN) to develop a model able to predict the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe. The surface velocities of each fluid and the pressure differential in the pipe are used as input parameters of the multi-layer artificial neural network with back-propagation, while the volumetric fraction of the fluids is used as an output parameter in the training. The 56 experimental data were obtained in the laboratory LabPetro-CEPETRO-UNICAMP. The results of the predictive model with the best performance show a mean absolute error (AAPE) of 3.01 % and a coefficient of determination R^2 of 0.9964, using 15 neurons in the hidden layer of the network and the TanSig transfer function.

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