Radioactive particle tracking methodology to evaluate concrete mixer using MCNPX code. (July 2019)

• Record Type: Journal Article
• Authors: Dam, Roos Sophia; Barbosa, Caroline M.; Lopes, José M.; Thalhofer, Jardel L.; Silva, Leandro B.; Salgado, César M.; da Silva, Ademir X.
• Is Part Of: Radiation physics and chemistry. Volume 160(2019:Jul.)
• Page Start: 26
• Page End: 29
• Publication Date: 2019-07
• Subjects: AlAluminum -- ANNArtificial neural network -- MCNPXMonte Carlo N-Particle eXtended -- MgOMagnesium oxide -- NaI(Tl)Sodium iodide doped with thallium -- PHDPulse height distribution -- PVCPolyvinyl chloride -- PRRadioactive particle -- RPTRadioactive particle tracking; Gamma-ray -- Radioactive particle tracking -- MCNPX code -- Artificial neural network -- Industrial mixer; Radiation chemistry -- Periodicals; Radiometry -- Periodicals; Radiation -- Periodicals; Chimie sous rayonnement -- Périodiques; 539.2
• DOI: 10.1016/j.radphyschem.2019.03.027
• Languages: English
• ISSNs: 0969-806X

Abstract

Abstract: 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 198 Au 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 198 Au by neutron activation in reactors. The purpose of this study is to use an artificial neural network as a location algorithm of the 198 Au 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. Highlights: Radioactive Particle Tracking methodology to evaluate concrete mixer. Mathematical simulation developed using MCNPX code. Geometry employs 198 Au (411 keV) gamma-ray source and eight NaI(Tl) detectors. An artificial neural network calculates the radioactive particle position.