Prediction of void fraction for gas-liquid flow in horizontal, upward and downward inclined pipes using artificial neural network

In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas-liquid two-phase flow in horizontal and inclined pipes was investigated. For this purpose, an ANN model was designed and trained using a total of 301 experimental data points reported in the literature for inclination angles between -20 degrees and +20 degrees. Pipe inclination angle as well as superficial Reynolds number of gas (Re-sg) and liquid (Rest) were chosen as input parameters of different structures of multilayer perceptron (MLP) neural networks, while the corresponding void fraction was selected as their output parameter. A hyperbolic tangent sigmoid and a linear function were employed as transfer functions of hidden and output layers, respectively, and Levenberg-Marquardt back propagation algorithm was used to train the networks. By trial-and-error method, a three-layer network with 10 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the void fraction with a high accuracy. Mean absolute percent error (MAPE) of 1.81% and coefficient of determination (R-2) of 0.9976 for training data and MAPE of 1.52% and R-2 value of 0.9948 for testing data were obtained. Also for all data, MAPE of 1.95% and R-2 value of 0.9972 were calculated, and 96% data were within +/- 5% error band. In addition, the accuracy of the proposed ANN model was compared with the predictions from 17 void fraction correlations available in the literature for different flow patterns and horizontal and inclined flows. For all cases, the proposed ANN model gave better performance than all of the studied correlations. The results confirm the very good capability of the ANNs to predict the void fractions of gas-liquid flow in inclined pipes, regardless of flow pattern. Finally, by performing interpolation using the trained network, the void fraction values for some other conditions were predicted. (C) 2016 Elsevier Ltd. All rights reserved.