Artificial Neural Network Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow

Abstract

Summary This paper presents two artificial neural network (ANN) models to identify the flow regime and calculate the liquid holdup in horizontal multiphase flow. These models are developed with 199 experimental data sets and with three-layer back-propagation neural networks (BPNs). Superficial gas and liquid velocities, pressure, temperature, and fluid properties are used as inputs to the model. Data were divided into three portions: training, cross validation, and testing. The results show that the developed models provide better predictions and higher accuracy than the empirical correlations developed specifically for these data groups. The developed flow-regime model predicts correctly for more than 97% of the data points. The liquid-holdup model outperforms the published models; it provides holdup predictions with an average absolute percent error of 9.407, a standard deviation of 8.544, and a correlation coefficient of 0.9896.