Application of Gamma Attenuation Technique and Artificial Intelligence to Detect Scale Thickness in Pipelines in Which Two-Phase Flows with Different Flow Regimes and Void Fractions Exist

Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique be used as a non-invasive effective tool for detecting scale petroleum pipelines. goal of this study is to propose dual-energy method with radial basis function neural network (RBFNN) determine thickness pipelines which two-phase flows different symmetrical flow regimes void fractions exist. detection system consists source, Ba-133 Cs-137 radioisotopes two 2.54-cm × sodium iodide (NaI) detectors record photons. first detector related transmitted photons, second one scattered transmission recorded signals, were counts under photopeak energy 356 keV 662 keV, respectively. signal scattering detector, total counts, was applied RBFNN inputs, assigned output.