Study of radioactive particle tracking using MCNP-X code and artificial neural network
DOI:
https://doi.org/10.15392/bjrs.v9i2A.387Keywords:
radioactive particle tracking, artificial neural network, MCNP-X code, gamma densitometryAbstract
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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Bhusarapu, S., Al-Dahhan, M.H., Dudukovic, M.P., Trujillo, S., O’Hern, T.J., Experimental study of the solids velocity field in gas–solid risers. Industrial and Engineering Chemistry Research, 44 (25), 9739, 2005.
Blet, V., Berne, P., Legoupil, S. et al., Radioactive tracing as aid for diagnosing chemical reactors, Oil and Gas Science and Tecnholgy – Rev. IFP, v. 55, n.2, pp. 171-183, 2000.
Chaouki J., Larachi F., Dudukovic P., Non-Invasive Monitoring of Multiphase Flows. Elsevier Science B.V, Amsterdam, The Netherlands, 1997.
Devanathan, N., Moslemian, D., Dudukovic, M.P., Flow mapping in bubble columns using CARPT. Chemical Engineering Science, 45, pp. 2285–2291, 1990.
Doucet, J., Bertrand, F., Chaouki, J., An extended radioactive particle tracking method for systems with irregular moving boundaries, Powder Technology, 181 (2), pp. 195–204, 2008.
Godfroy L., Larachi F., Kennedy G., Grandjean B., Chaouki J., On-line Flow Visualization in Multiphase Reactors using Neural Networks, Appl. Rad. and Isotopes, 48 pp. 225 – 235, 1997.
Haykin S., Neural Networks, A Comprehensive Foundation, Prentice Hall, 2nd edition, 1999.
Larachi, F., Kennedy, G., Chaouki, J., A gamma-ray detection system for 3-D particle tracking in multiphase reactors, Nuclear Instruments and Meth. Phys. Research Sec. A: Accelerators, Spectrometers, Detectors and Associated Equipment, 338(2–3), pp. 568–576, 1994.
Luo, H., Kemoun, A., Al-Dahhan, M.H., Sevilla, J.M.F., Sanchez, J.L.G., Camacho, F.G., Grima, E.M., Analysis of photobioreactors for culturing high-value microalgae and cyanobacteria via an advanced diagnostic technique: CARPT, Chemical Engineering Science, 58 (12), 2519, 2003.
Mosorov, V. e Abdullah J., MCNP5 code in radioactive particle tracking, Applied Radiation and Isotopes, 69 pp. 1287–1293, 2011.
Otawara, K., Fan, L., Tsutsumi, A., Kuramoto, K., Yoshida, K., An artificial neural network as a model for chaotic behavior of a three-phase fluidized bed, Chaos Solitons & Fractals, 13, pp. 353 – 362, 2002.
Pelowitz D.B., MCNP-X TM User’s Manual, Version 2.5.0, LA-CP-05-0369, Los Alamos National Laboratory, 2005.
Roy, S., Larachi, F., Al-Dahhan, M.H., Dudukovic, M.H., Optimal design of radioactive particle tracking experiments for flow mapping in opaque multiphase reactors, Applied Radiation and Isotopes, 56 (3), pp. 485–503, 2002.
Salgado C.M., Brandão L.E.B., Nascimento C.M.N.A., Schirru R., Ramos R. e Silva A.X., Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network, Applied Radiation and Isotopes, 67, pp. 1812-1818, 2009.
Salgado C.M., Pereira C.M.N.A., Schirru R. e Brandão L.E.B., Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks, Progress in Nuclear Energy, Vol. 52(6), pp. 555-562, 2010.
Vieira, W., Brandão, L., Braz, D., An alternative method for tracking a radioactive particle inside a fluid, Applied Radiation and Isotopes, 85, pp. 139 – 146, 2014.
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