The construction of new nuclear reactors in the coming years will require an increase in fuel reprocessing capacity. This evolution requires scientific and technological developments to update process monitoring equipment. One of the parameters to be continuously monitored is the actinide content in solution, which is essential for process control and is currently measured using obsolete technologies. We therefore propose to develop LIBS (laser-induced breakdown spectroscopy) for this application, a technique well suited for quantitative online elemental analysis. As actinide spectra are particularly complex, we shall use multivariate data processing approaches, such as several artificial intelligence (AI) techniques, to extract quantitative information from LIBS data and characterize measurement uncertainty.
The aim of this thesis is therefore to evaluate the performance of online analysis of actinides in solution using LIBS and AI. In particular, we aim to improve the characterisation of uncertainties using machine learning techniques, in order to strongly reduce them and to meet the monitoring needs of the future reprocessing plant.
Experimental work will be carried out on non-radioactive actinide simulants, using a commercial LIBS equipment. The spectroscopic data will drive the data processing part of the thesis, and the determination of the uncertainty obtained by different quantification models.
The results obtained will enable publishing at least 2-3 articles in peer-reviewed journals, and even to file patents. The prospects of the thesis are to increase the maturity level of the method and instrumentation, and gradually move towards implementation on a pilot line representative of a reprocessing process.