Precise Volume Fraction Percentage Measurement in Three-Phase Flows Using Gamma-Ray Technique and Artificial Neural Network

Precise volume fraction percentage prediction in water-gasoil-air three-phase flows in unstable operational conditions is an important parameter in the oil and petroleum industry. In this research, the volume fraction percentage was measured precisely in water-gasoil-air three-phase flows by using single energy gamma ray attenuation technique and neural network, for the first time. The volume fraction percentage determination in three-phase flows requires least two gamma radioactive sources with different energies while in this study, we used just a 137Cs source (with the single energy of 662 keV) and a NaI detector. Also, the multilayer perceptron (MLP) neural network was implemented to predict the volume fraction percentage. The acquired results from an experimental setup provides the required data for training and testing the network. The inputs of ANN have registered spectra in the transmitted detector as the dataset matrix for ANN consisted of a (Y118×42). In this ANN, the number of neurons in the input, hidden and output layers are 118, 10 and 3, respectively. Using this proposed method, the volume fraction was predicted in water-gasoil-air three-phase flows with Mean Relative Error percentage (MRE%) less than 6.95%. Also, the Root Mean Square Error (RMSE) was calculated simultaneous 2.60. The set-up used is simpler than other proposed methods and cost, radiation safety and shielding requirements are minimized.