Search

In Brazil, concrete and cement are highly used in construction, therefore mixers are widely used in this industry. During the fabrication process of concrete/cement, the equipment may fail and compromise the appropriate mixing procedure. Besides that, it is also important to determine the right point of homogeneity of the mixture. It is important to have a methodology to monitor the mixing process to ensure the quality of the product. This study presents a methodology based on the principles of the radioactive particle tracking technique to predict the instantaneous positions occupied by the radioactive particle inside an industrial mixer by means of a mathematical location algorithm. The detection geometry modeled by means of MCNPX code employs an array of eight NaI(Tl) scintillator detectors, a 198Au spherical gamma-rays source with isotropic emission and a test section filled with concrete that represents an industrial mixer. The choice of the radionuclide is due its well-characterized peak of 411 keV, its half-life of 2.7 days and the possibility to obtain 198Au by neutron activation in reactors. The purpose of this study is to use an artificial neural network as a location algorithm of the 198Au radioactive particle inside an industrial mixer. Results showed that over 56% of the cases were below 5% of relative error for all coordinates of the radioactive particle, which indicates that it is possible to track the radioactive particle trajectory inside the industrial mixer using the artificial neural network algorithm. • Radioactive Particle Tracking methodology to evaluate concrete mixer. • Mathematical simulation developed using MCNPX code. • Geometry employs 198Au (411 keV) gamma-ray source and eight NaI(Tl) detectors. • An artificial neural network calculates the radioactive particle position. [ABSTRACT FROM AUTHOR]

In Brazil, concrete and cement are highly used in construction, therefore mixers are widely used in this industry. During the fabrication process of concrete/cement, the equipment may fail and compromise the appropriate mixing procedure. Besides that, it is also important to determine the right point of homogeneity of the mixture. It is important to have a methodology to monitor the mixing process to ensure the quality of the product. This study presents a methodology based on the principles of the radioactive particle tracking technique to predict the instantaneous positions occupied by the radioactive particle inside an industrial mixer by means of a mathematical location algorithm. The detection geometry modeled by means of MCNPX code employs an array of eight NaI(Tl) scintillator detectors, a 198Au spherical gamma-rays source with isotropic emission and a test section filled with concrete that represents an industrial mixer. The choice of the radionuclide is due its well-characterized peak of 411 keV, its half-life of 2.7 days and the possibility to obtain 198Au by neutron activation in reactors. The purpose of this study is to use an artificial neural network as a location algorithm of the 198Au radioactive particle inside an industrial mixer. Results showed that over 56% of the cases were below 5% of relative error for all coordinates of the radioactive particle, which indicates that it is possible to track the radioactive particle trajectory inside the industrial mixer using the artificial neural network algorithm. • Radioactive Particle Tracking methodology to evaluate concrete mixer. • Mathematical simulation developed using MCNPX code. • Geometry employs 198Au (411 keV) gamma-ray source and eight NaI(Tl) detectors. • An artificial neural network calculates the radioactive particle position. [ABSTRACT FROM AUTHOR]

The extraction of oil is accompanied by water and sediments that, mixed with the oil, cause the formation of scale depositions in the pipelines walls promoting the reduction of the inner diameter of the pipes, making it difficult for the fluids to pass through interest. In this sense, there is a need to control the formation of these depositions to evaluate preventive and corrective measures regarding the waste management of these materials, as well as the optimization of oil extraction and transport processes. Noninvasive techniques such as gamma transmission and scattering can support the determination of the thickness of these deposits in pipes. This paper presents a novel methodology for prediction of scale with eccentric deposition in pipes used in the offshore oil industry and its approach is based on the principles of gamma densitometry and deep artificial neural networks (DNNs). To determine deposition thicknesses, a detection system has been developed that utilizes a 1 mm narrow beam geometry of collimation aperture comprising a source of 137Cs and three properly positioned 2″×2″ NaI(Tl) detectors around the system, pipe-scale-fluid. Crude oil was considered in the study, as well as eccentric deposits formed by barium sulfate, BaSO 4. The theoretical models adopted a static flow regime and were developed using the MCNPX mathematical code and, secondly, used for the training and testing of the developed DNN model, a 7-layers deep rectifier neural network (DRNN). In addition, the hyperparameters of the DRNN were defined using a Baysian optimization method and its performance was validated via 10 experiments based on the K-Fold cross-validation technique. Following the proposed methodology, the DRNN was able to achieve, for the test sets (untrained samples), an average mean absolute error of 0.01734, mean absolute relative error of 0.29803% and R2 Score of 0.9998813 for the scale thickness prediction and an average accuracy of 100% for the scale position prediction. Therefore, the results show that the 7-layers DRNN presents good generalization capacity and is able to predict scale thickness with great precision, regardless of its position inside the tube. • A method based on the principles of the gamma densitometry technique is presented. • Theoretical models have been developed using MCNPX code. • The detection system uses gamma-ray source and three NaI(Tl) detectors. • The location and thickness scale are calculated by means of DNN. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to precise identify the interface region, which is formed in the transport of petroleum by-products in polyducts, using gamma densitometry. The simulated geometry is compose for a collimated 137Cs source and a NaI(Tl) detector to measure the transmitted beam. The modeling was validated experimentally on stratified flow regime using water and oil. The different volume fractions were calculated using the MCNPX code in order to determine the region interface with an accuracy of 1%. [ABSTRACT FROM AUTHOR]

The Nuclear Engineering Department of the Military Institute of Engineering (SE/7-IME) is designing and simulation a neutron irradiator with 1 Ci of 241Am-9Be source. The objective of this irradiator is to generate a flux of neutrons to be used in research and teaching maintaining, for purposes of radiological protection, the rate of ambient dose equivalent, H*(10) , below 10 μSv/h at 30 cm from the surface. This paper presents the proposed irradiator, values of H*(10) at different distances from the irradiator and the neutron flux in different points of the beam irradiation, all calculated using the MCNPX code. • Modeled cilindrical irradiator using the MCNPX code. • Developing a neutron irradiator with 1 Ci of 241Am-9Be source. • The rate of ambient dose equivalent, H*(10) , below 10 μSv/h at 30 cm from the surface. • The shielding allowed the H*(10) rate to be below 10 μSv/h at 30 cm from the surface. • The neutron flux decreasing its intensity as a function of distance. [ABSTRACT FROM AUTHOR]