Abstract
Currently, in almost all segments of the production chain, automation is a requirement for productivity improvement. With respect to nuclear facilities, active online monitoring is one of best practices for nuclear security and safety maintenance, to prevent incidents that could compromise a particular installation. In this context, spectral signature monitoring automation can be explored, aiming at the rapid identification of adverse events, such as radiological accidents. The main objective of this work was an automated radionuclides classification technique establishment, using an Artificial Neural Networks (ANN) architecture. The methodology used consisted basically of simulating the geometry of an established experimental apparatus, using the MCNP5 code, obtaining the simulated gamma spectral signature for the studied nuclides. The simulated spectra were used to compose the ANN training and testing data set, while the experimental spectra were subjected to the artificial intelligence model classification, in order to allow the neural network quality assessment. The final developed architecture of ANN was correct to recognize the experimental spectra of 60Co, 137Cs and 152Eu. Therefore, the results were satisfactory and proved automation technique development viable.
Highlights
There are constant changes in labor relations and in the productivity increase in the industry and services sectors, based on artificial intelligence solutions
The main purpose of this work is to establish an automated classification model for radionuclides based on Artificial Neural Networks (ANN)
The comparison with the studies of VARLEY [3] and JHUNG et al.[4] shows that, like the aforementioned authors, this study shows the feasibility of using Artificial Neural Networks for the classifiers implementation within the nuclear area
Summary
There are constant changes in labor relations and in the productivity increase in the industry and services sectors, based on artificial intelligence solutions. Regarding the nuclear installations area, there are points that deserve attention, especially those dealing with these installations safety, as critical events such as leaks, radioactive elements dispersion in the atmosphere and even nuclear material theft may, compromise physical safety of a plant, and exposing the public to hazardous conditions. In this context, artificial intelligence, especially supervised machine learning, can offer an agile way of detecting security incidents, allowing for quick action by the team responsible for the installation. The model is validated with data obtained from the experimental survey of the spectra of some of these nuclides
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