Application of artificial neural networks for the prediction of volume fraction using spectra of gamma rays backscattered by three-phase flows

Abstract

The determination of the volume fraction percentage of the different phases flowing in vessels using transmission gamma rays is a conventional method in petroleum and oil industries. In some cases, with access only to the one side of the vessels, attention was drawn toward backscattered gamma rays as a desirable choice. In this research, the volume fraction percentage was measured precisely in water-gasoil-air three-phase flows by using the backscatter gamma ray technique andthe multilayer perceptron (MLP) neural network. The volume fraction determination in three-phase flows requires two gamma radioactive sources or a dual-energy source (with different energies) while in this study, we used just a 137Cs source (with the single energy) and a NaI detector to analyze backscattered gamma rays. The experimental set-up provides the required data for training and testing the network. Using the presented method, the volume fraction was predicted with a mean relative error percentage less than 6.47%. Also, the root mean square error was calculated as 1.60. The presented set-up is applicable in some industries with limited access. Also, using this technique, the cost, radiation safety and shielding requirements are minimized toward the other proposed methods.