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The methodology presented in this study is based on a 149.5 keV X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions in a three-phase system. Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. A mathematical algorithm based on an artificial neural network was used to correlate the energy spectra from both detectors with the fluids volume fractions. The pulse height distributions obtained by the detectors are used as input data of the network that outputs the volume fractions of gas and water. The mean relative error, using the procedure presented here, for all data, was below 2.5% for both phases investigated. These results show that the methodology based on an X-ray beam has the potential to be used with flow meters. • An artificial neural network was used to predict volume fraction of gas, water, and oil. • X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions. • Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. • The spectra obtained by the detectors are used as input data of the network that outputs gas and water volume fractions. [ABSTRACT FROM AUTHOR]

A recent investigation proposed a simulated radioactive particle tracking (RPT) system using eight scintillator detectors in order to predict instantaneous positions of a radioactive particle inside a concrete mixer using an artificial neural network as a location algorithm. In the context of RPT, the aim of the present study is to propose an optimization in the number of detectors in a single-phase flow RPT system. The new detection geometry consists of an array of six NaI(Tl) detectors, a 137Cs point source with isotropic emission of gamma-rays (radioactive particle) and a polyvinyl chloride mixer filled with concrete made with Portland cement as a homogenous flow regime. Another feature of this study is the use of MCNP6 code, which is based on Monte Carlo Method. In addition, three feed-forward multilayer perceptron networks with different configuration are tested as a location algorithm. All three networks showed good statistical results and the root mean square error is 1.18 in the worst scenario. The results also showed an agreement with previous study, which indicates that this methodology reducing two detectors works satisfactorily and maintain a good accuracy in position prediction. • Optimization of the detection system reducing two detectors. • Radioactive Particle Tracking methodology developed using MCNP6 code. • An artificial neural network predicts the position of the radioactive particle. • System based on RPT to evaluate industrial agitators in a homogeneous flow regime. [ABSTRACT FROM AUTHOR]

• The proposed geometry composes gamma-rays and two NaI(Tl) detectors. • Different thicknesses and relative positions of scale for the annular flow regime were modeled using the MCNP code. • A Backpropagation 5-layer perceptron network was used for scale prediction. • The gamma-ray scattering was used to quantify the maximum thickness of eccentric scales (BaSO 4). • The maximum scale was predicted independently of its position inside the tube and the presence of the fluids. This study investigates a methodology to study the deposition of barium sulfate scales (BaSO 4) commonly found in the oil industry; it causes an internal diameter decrease, making it difficult for the flow. A measurement procedure was elaborated on gamma-ray scattering with three NaI(Tl) detectors and a 137Cs gamma-ray source to detect and quantify the maximum thickness of eccentric scale. The detectors data were used to train the artificial neural network for the prediction of the maximum scale thickness values regardless of oil, saltwater, gas and scale inside the tube. A data subset for training and evaluation of the artificial neural network generalization capability was generated using the MCNP6 code. Different thicknesses and positions of the maximum scale value were considered. The results show that more than 90% of the patterns presented relative errors lower than ±10%. [ABSTRACT FROM AUTHOR]