Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((133Ba-137Cs), (133Ba-60Co), (241Am-137Cs), (241Am-60Co), (133Ba-241Am) and (60Co-137Cs)) were investigated in order to optimize the three-phase flow meter. Three phases were water, oil and gas and the regime was considered annular. The required data was numerically generated using MCNP-X code which is a Monte-Carlo code. Indeed, the present study devotes to forecast the volume fractions in the annular three-phase flow, based on a multi energy metering system including various radiation sources and also one NaI detector, using a hybrid model of artificial neural network and Jaya Optimization algorithm. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must forecast only two volume fractions. Six hybrid models associated with the number of used radiation sources are designed. The models are employed to forecast the gas and water volume fractions. The next step is to train the hybrid models based on numerically obtained data. The results show that, the best forecast results are obtained for the gas and water volume fractions of the system including the (241Am-137Cs) as the radiation source.
