‎Gholam Hossein Roshani - Electrical Engineering Department‎, ‎Kermanshah University of Technology‎, ‎Kermanshah‎, ‎Iran
‎Alimohammad Karami - Mechanical Engineering Department‎, ‎Razi University‎, ‎Kermanshah‎, ‎Iran
‎Ehsan Nazemi - Nuclear Science and Technology Research Institute‎, ‎Tehran‎, ‎Iran
‎Cesar Marques Salgado - Instituto de Engenharia Nuclear‎, ‎CNEN/IEN‎, ‎P.O‎. ‎Box ۶۸۵۵۰‎, ‎۲۱۹۴۵-۹۷۰ Rio de Janeiro‎, ‎Brazil‎

‎The used metering technique in this study is based on the dual energy (Am-241 and Cs-137) gamma ray attenuation‎. ‎Two transmitted NaI detectors in the best orientation were used and four features were extracted and applied to the model‎. ‎This paper highlights the application of Adaptive Neuro-fuzzy Inference System (ANFIS) for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems‎. ‎In fact‎, ‎the aim of the current study is to recognize the flow regimes based on dual energy broad-beam gamma-ray attenuation technique using ANFIS‎. ‎In this study‎, ‎ANFIS is used to classify the flow regimes (annular‎, ‎stratified‎, ‎and homogenous) and predict the value of volume fractions‎. ‎To start modeling‎, ‎sufficient data are gathered‎. ‎Here‎, ‎data are generated numerically using MCNPX code‎. ‎In the next step‎, ‎ANFIS must be trained‎. ‎According to the modeling results‎, ‎the proposed ANFIS can correctly recognize all the three different flow regimes‎, ‎and other ANFIS networks can determine volume fractions with MRE of less than 2% according to the recognized regime‎, ‎which shows that ANFIS can predict the results precisely‎.

‎Three-phase flow, ‎Pattern recognition, ‎Volume fraction, ‎Adaptive neuro-fuzzy inference system, ‎Monte Carlo simulation‎