Modeling of Gas Holdup and Pressure Drop Using ANN for Gas-Non-Newtonian Liquid Flow in Vertical Pipe

Abstract

This paper is an attempt to compare the the performance of the three different Multilayer Perceptron training algorithms namely Backpropagation, Scaled Conjugate Gradient and Levenberg-Marquardt for the prediction of the gas hold up and frictional pressure drop across the vertical pipe for gas non-Newtonian liquid flow from our earlier experimental data. The Multilayer Perceptron consists of a single hidden layer. Four different transfer functions were used in the hidden layer. All three algorithms were useful to predict the gas holdup and frictional pressure drop across the vertical pipe. Statistical analysis using Chi-square test (χ2) confirms that the Backpropagation training algorithm gives the best predictability for both cases.