June 19, 2024
Gholam Hossein Roshani

Gholam Hossein Roshani

Academic rank: Associate professor
Education: Ph.D in Nuclear Engineering
Faculty: Faculty ofٍٍ Electrical Engineering


Flow regime independent volume fraction estimation in threephase flows using dual-energy broad beam technique and artificial neural network
Type Article
Researchers Gholam Hossein Roshani، Ehsan Nazemi، M. M. Roshani


In this paper, based on dual-energy broad beam gamma ray attenuation technique (using two transmission 1-inch NaI detectors and a dual-energy gamma ray source), an artificial neural network (ANN) model was used in order to predict the volume fraction of gas, oil and water in threephase flows independent of the flow regime. A multilayer perceptron (MLP) neural network was used for developing the ANN model in MATLAB software. The input parameters of the MLP model were registered counts under first and second full energy peaks of the both transmission NaI detectors, and the outputs were gas and oil percentage. The volume fractions were obtained precisely independent of flow regime using the presented model. Mean absolute error of the presented model was less than 2.24%.