Determination of the volume fraction in (water-gasoil-air) multiphase flows using a simple and low-cost technique: Artificial neural networks

Abstract

The precise prediction of the volume fraction in three-phase flows plays an important role in the petroleum and process industries. In this study, attenuation gamma rays (single pencil beam) and multilayer perceptron neural networks were used to precisely predict the volume fraction percentage in water-gasoil-air three-phase flows. The detection system uses just one 137Cs source (single energy of 662 keV) and one NaI(Tl) detector in order to calculate the transmitted beams. The experimental setup was simulated using the MCNPX code to provide the required data for the neural network. The volume fraction percentage was measured with a root mean square error of 2.48 and a mean relative error percentage of less than 7.08%. The proposed setup is the best and simplest design for reducing radiation hazards and cost.