Determination of the uranium elemental concentration in molten salt fuel using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid models

Abstract Accurate uranium (U) composition analysis of the molten salt nuclear fuel mixture is a necessary step in nuclear fuel quality control. Laser-induced breakdown spectroscopy (LIBS) is an optical emission spectrometry technique for elemental composition. The non-linear phenomena in LIBS signal are the drawback of this technique, especially for high atomic number (Z) element in a complex matrix. To overcome this limitation, a hybrid chemometrics model of the partial least squares-artificial neural network (PLS-ANN) was used to quantify the U composition in the salt fuel mixture. This work shows the optimisation of several parameters, viz., spectra pre-treatment procedure, factor number of PLS for ANN input, number of hidden neurons and layers in ANN, activation function, and number of ANN iterations. The collective advantages of data dimension reduction from PLS and the nonlinear processing ability from ANN, improved the accuracy of LIBS quantitative analysis for U from ≈7% using simple PLS to ≈4% precision using a hybrid model.