IDENTIFICATION OF LIQUID-LIQUID FLOW PATTERN IN A HORIZONTAL PIPE USING ARTIFICIAL NEURAL NETWORKS

Abstract

Identification of flow pattern during the simultaneous flow of two immiscible liquids requires knowledge of the flow rate of each fluid as well as knowledge of other physical parameters like conduit inclination, pipe material, pipe diameter, viscosity of the oil, wetting characteristics of the pipe, design of the entry mixer, and fluid-fluid interfacial tension. This article presents an artificial neural network (ANN)-based novel technique to determine the liquid-liquid flow regime. This approach uses phase superficial velocities as input parameters, which are obtained from a specific set of data obtained from experimental investigations. Both experimental and ANN-based determinations of liquid-liquid flow pattern have been undertaken for a common data set and the results are compared to prove the effectiveness of ANNs in pattern recognition. A unique ANN architecture is identified with three hidden layers, and the inputs and outputs are modeled into binary form. Levenberg-Marquardt (LM) learning algorithm is used for training this neural network. The design details of the ANN, parameter modeling, and training aspects are presented.