Gas-non-Newtonian Liquid Flow Through Horizontal Pipe – Gas Holdup and Pressure Drop Prediction using Multilayer Perceptron

Abstract

Prediction of the gas holdup and pressure drop in a horizontal pipe for gas-non-Newtonian liquid flow using Artificial Neural Networks (ANN) methodology have been reported in this paper from the data acquired from our earlier experiment. The ANN prediction is done using Multilayer Perceptrons (MLP) trained with three different algorithms, namely: Backpropagation (BP), Scaled Conjugate gradient (SCG) and Levenberg-Marquardt (LM). Four different transfer functions were used in a single hidden layer for all algorithms. The Chi-square test confirms that the best network for prediction of gas holdup is when it is trained with Levenberg-Marquardt (LM) algorithm in the hidden and output layer with the transfer function 1 in hidden layer having 5 processing elements. The Chi-square test also confirms that the best network for prediction of pressure drop is when it is trained with Backpropagation (BP) algorithm in the hidden and output layer with the transfer function 4 in hidden layer having 15 processing elements.