Study of radioactive particle tracking using MCNP-X code and artificial neural network

Roos Sophia de Freitas Dam; César Marques Salgado

doi:10.15392/bjrs.v9i2A.387

Autores/as

Roos Sophia de Freitas Dam Nuclear Engineering Institute
César Marques Salgado Nuclear Engineering Institute

DOI:

https://doi.org/10.15392/bjrs.v9i2A.387

Palabras clave:

radioactive particle tracking, artificial neural network, MCNP-X code, gamma densitometry

Resumen

Agitators or mixers are highly used in the chemical, food, pharmaceutical and cosmetic industries. During the fabrication process, the equipment may fail and compromise the appropriate stirring or mixing procedure. Besides that, it is also important to determine the right point of homogeneity of the mixture. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and to keep the market competitiveness. The radioactive particle tracking (RPT) technique is widely used in the nuclear field. In this paper, a method based on the principles of RPT is presented. Counts obtained by an array of detectors properly positioned around the unit will be correlated to predict the instantaneous positions occupied by the radioactive particle by means of an appropriate mathematical search location algorithm. Detection geometry developed employs eight NaI(Tl) scintillator detectors and a Cs-137 (662 keV) source with isotropic emission of gamma-rays. The modeling of the detection system is performed using the Monte Carlo Method, by means of the MCNP-X code. In this work, a methodology is presented to predict the position of a radioactive particle to evaluate the performance of agitators in industrial units by means of an Artificial Neural Network.

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