A new application of radioactive particle tracking using MCNPX code and artificial neural network

Stirrers and mixers are highly used in chemical, food, pharmaceutical, cosmetic, concrete industries and others. During the fabrication process, the equipment may fail to appropriately stir or mix the solution. Besides that, it is also important to determine when the right homogeneity of the mixture is attained. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and maintain market competitiveness. Nuclear techniques, such as gamma densitometry, are widely used in industry to overcome a sort of difficulties, as they are minimally non-invasive techniques. This paper presents a method based on the principles of the radioactive particle tracking technique to predict the instantaneous position of a radioactive particle to monitor a concrete mixture inside an industrial unit by means of Monte Carlo method and artificial neural network. Counts obtained by an array of detectors properly positioned around the mixing canister will be correlated to each other, by means of an appropriate mathematical search location algorithm, in order to predict the instantaneous positions occupied by an inserted radioactive particle. The simulation consists of a detection geometry of eight NaI(Tl) scintillator detectors, a 662 keV 137Cs point source with isotropic emission of gamma-rays and a polyvinyl chloride tank. At first, the tank is air filled and, afterwards, filled with concrete made with Portland cement. The modeling of the detection system is performed using the MCNPX code. For both medium, the correlation coefficient was 0.99 for all coordinates, which indicates that this methodology could be a good tool to evaluate industrial mixers. •Radioactive Particle Tracking methodology developed using MCNPX code.•The detection system uses 137Cs (662 keV) gamma-ray source and eight NaI(Tl) detectors.•An artificial neural network gives the position of the radioactive particle.