Neural Network Model To Predict Slug Frequency of Low-Viscosity Two-Phase Flow

Abstract

Summary In this work, the artificial neural network (ANN) is implemented for prediction slug frequency FS for low viscosity μL flow (μL ≤ 1.6 mPa·s) in vertical, horizontal, and inclined pipes. To the authors' knowledge, no ANN model in the literature exists for predicting FS. The input parameters for the suggested ANN are superficial liquid velocity VSL, pipe diameter D, superficial gas velocity VSG, and pipe inclination ϕ. Measured data (450 data points) are gathered from five different resources for developing the ANN model. The ranges of FS, VSL, D, VSG, and ϕ covered by the data set, are (0.03 to 3.167 1/s), (0.05 to 2.073 m/s), (0.01905 to 0.0779 m), (0.133 to 11.84 m/s), and (0 to 90°), respectively. The most popular transfer functions of tangent sigmoid and linear are used in the hidden and output layers, respectively, whereas the Levenberg and Marquardt back propagation algorithm is conducted to train ANN. The experimental data set is divided into 70% for training, 15% for validation, and 15% for testing processes. Due to the absence of a systematic way to find the optimal structure of ANN, an exhaustive search method has been suggested and implemented to find the optimal topology, which is (4-16-1); four neurons in input layer, 16 neurons in the hidden layer, and one neuron in output layer. The proposed ANN predicts correctly the effect of each of the previously mentioned parameters on FS, and it yields a satisfactory prediction and clearly outperforms all the existing models, with a mean square error (MSE) and R2 of 0.0097 and 0.977, respectively.