Changes of fluid properties, especially density, strongly affect the performance of radiation-based multiphase flow meter and could cause error in volume fraction measuring. One solution in such situations is continuous recalibration of the system, which is a difficult and long time task. In this study, a new methodology is presented for identifying flow regime and estimating the void fraction in gas-liquid flows independent of liquid phase density changes. The approach is based on gamma-ray attenuation and scattering combined with artificial neural networks (ANNs). The detection system uses a fan beam geometry, comprised of one 137Cs source and three NaI(Tl) detectors. Two of these three detectors were implemented to measure transmitted photons and the third one was used to measure scattered photons. Also, four ANNs were used in this study, the first one for identifying the flow regime independent of liquid phase density changes and the other three ANNs for predicting void fraction independent of liquid phase density changes. Using this methodology, three flow regimes of annular, stratified and bubbly were correctly distinguished in liquid phase density changes range of 0.735e0.980 g/cm3 and void fraction was predicted with a mean relative error (MRE) of less than 4.3%.
