Estimation of Pressure Drop of Two-Phase Flow in Horizontal Long Pipes Using Artificial Neural Networks

Abstract

Abstract Gas–liquid two-phase flows through long pipelines are one of the most common cases found in chemical, oil, and gas industries. In contrast to the gas/Newtonian liquid systems, the pressure drop has rarely been investigated for two-phase gas/non-Newtonian liquid systems in pipe flows. In this regard, an artificial neural networks (ANNs) model is presented by employing a large number of experimental data to predict the pressure drop for a wide range of operating conditions, pipe diameters, and fluid characteristics. Utilizing a multiple-layer perceptron neural network (MLPNN) model, the predicted pressure drop is in a good agreement with the experimental results. In most cases, the deviation of the predicted pressure drop from the experimental data does not exceed 5%. It is observed that the MLPNN provides more accurate results for horizontal pipelines in comparison with other empirical correlations that are commonly used in industrial applications.