Isotopic composition analysis of dilute Pu solutions using 90-105keV region of gamma ray spectra

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

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.

Using the 90-105 keV gamma-rays for isotopic composition determination of plutonium in dilute solutions

Using the 90-105 keV gamma-rays for determining Pu isotopic composition is studied for dilute Pu solutions (0.0001-0.05 μg/mm³) as well as Pu-U mixed solutions. It is shown that for concentrations higher than 0.001 μg/mm³ Pu, results match well with those of mass spectrometric results. However, in mixed solutions, beyond 0.005 mg/mm³ U concentration, the errors on isotopic compositions of Pu increased as U content increased.