ARTIFICIAL NEURAL NETWORKS IN LIQUID-LIQUID TWO-PHASE FLOW

Abstract

Abstract This research investigates the use of neural networks for identification of oil-water two-phase flow pattern in a horizontal conduit. The investigation seeks to find out if neural networks are useful tools for this application. In order to find a suitable network, all of the various types of networks available are studied and appropriate networks are selected using MATLAB as a computational tool. Four networks are selected for this investigation: feed-forward back propagation (FFBP), radial based function (RBF), probabilistic neural network (PNN), and learning vector quantization (LVQ) Networks. These various networks are created and tested to evaluate the relationships between key variables and performance. Once the networks are optimized, various architectures are compared based on mean square error (MSE), construction time, complexity, and the ability to identify transition regions. PNN is found to be the best network for this particular application, followed by FFBP, RBF, and LVQ. It is observed that making a network larger or more powerful does not necessarily improve the performance. Moreover, doing so causes a network to lose its ability to generalize by eventually "over-fitting" the training data. Keywords: Artificial neural networkHorizontal pipeLiquid-liquid flowMean square errorProbabilistic neural network