Prediction of water holdup in vertical and inclined oil-water two-phase flow using artificial neural network

This paper presents the application of artificial neural network (ANN) in prediction of water holdup of oil-water two-phase flow in a vertical and an inclined pipe (90 degrees, 75 degrees, 60 degrees, and 45 degrees from horizontal) without knowing the type of flow pattern. For this purpose, superficial velocity of water and oil and the inclination angles of the pipe were used as input parameters, while water holdup values of two-phase flow were used as output parameters in training and testing of the multi-layer, feed-forward, back-propagation neural networks. Experimental data (468 data points) were taken from literature and used for developing of the ANN model. The obtained results showed that the network predictions have very good agreement with the experimental water holdup data. The accuracy between the neural network predictions and experimental data was achieved with low average absolute percent error (AAPE) and high coefficient of determination (R-2) for both training data (AAPE = 2.34% and R-2 = 0.999) and testing data (AAPE = 2.89% and R-2 = 0.997) sets. In addition, a comparison of the prediction results of the proposed ANN model with Mukherjee et al. (1981) correlation (AAPE = 9.83% and R-2 = 0.961) revealed that the correlation had more deviations.(C) 2015 Elsevier Ltd. All rights reserved.