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Latest revision as of 17:43, 11 March 2021
Abstract
In this work, the application of an artificial neural network (ANN) is proposed to develop a predicting model for the holdup of a two-phase flow composed of water and mineral oil in a horizontal pipe. For this, the surface velocities of each fluid and the differential pressure in the pipeline are used as input parameters of the multilayer artificial neural network with backpropagation, while the holdup of the fluids is used as the output parameter for the training. A set of 56 experimental data was obtained in the LabPetroCEPETRO-UNICAMP laboratory. The best performing results for the predictive model show a mean absolute error (AAPE) of 3.01% and a coefficient of determination 2 of 0.9964 using 15 neurons in the hidden layer of the network and the TanSig transfer function.
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Published on 11/03/21
Submitted on 11/03/21
Volume 300 - Multiscale and Multiphysics Systems, 2021
DOI: 10.23967/wccm-eccomas.2020.283
Licence: CC BY-NC-SA license
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