Application of artificial neural networks for predicting the isotopic composition of high burn-up solid plutonium sample using the 90–105 keV gamma-spectrum region

Arnab Sarkar

doi:10.1515/ract-2021-1129

Article

Application of artificial neural networks for predicting the isotopic composition of high burn-up solid plutonium sample using the 90–105 keV gamma-spectrum region

Arnab Sarkar

Published/Copyright: April 8, 2022

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From the journal Radiochimica Acta Volume 110 Issue 5

Abstract

An artificial neural network (ANN) algorithm was developed to predict isotopic composition of five Pu isotopes (²³⁸Pu, ²³⁹Pu, ²⁴⁰Pu, ²⁴¹Pu, and ²⁴²Pu) of high burn-up Pu samples. The study was carried out using the most complex but informative gamma energy region of Pu gamma spectra, 90–106 keV. This region has remained futile, due to the overlapping nature of the gamma emission lines and X-rays emitted by U, Pu, and Np. A backpropagation neural network algorithm based ANN with error minimization using the steepest gradient method was built with the help of normalized gamma spectra for ∼800 samples. The paper discusses the optimization of hidden neuron number and the layer design for best prediction. With the exception of ²⁴²Pu, the prediction accuracy and precision of the proposed technique was found to be ∼3% for all other isotopes of Pu.

Keywords: artificial neural network; gamma spectroscopy; high burn-up; plutonium

Corresponding author: Arnab Sarkar, Fuel Chemistry Division, Bhabha Atomic Research Center, Mumbai 400085, India; and Homi Bhabha National Institute, Anushaktinagar, Mumbai 400094, India, E-mail: arnab@barc.gov.in, asarkar@ymail.com

Funding source: Department of Atomic Energy, India

Award Identifier / Grant number: Project R&D Sector

Acknowledgments

The author is thankful to Dr. P.G. Jaison for his valuable suggestion during manuscript preparation. The author gratefully acknowledges Dr. S. Kannan, Head, Fuel Chemistry Division, B.A.R.C. for his constant support and encouragement.

Author contributions: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: Department of Atomic Energy, India, Project R&D Sector.
Conflict of interest statement: The author declare no conflicts of interest regarding this article.

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Received: 2021-11-24

Accepted: 2022-03-15

Published Online: 2022-04-08

Published in Print: 2022-05-25

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Articles in the same Issue

https://doi.org/10.1515/ract-2021-1129

Keywords for this article

artificial neural network; gamma spectroscopy; high burn-up; plutonium