Abstract
Liquid-liquid extraction (LLE) systems used in spent nuclear fuel reprocessing require reliable, real-time monitoring methods to improve process control, operational efficiency, and material accountancy. Spectroscopic techniques combined with chemometric and machine learning approaches provide a promising pathway for rapid, non-destructive quantification in these complex biphasic environments. In this work, PUREX-relevant solvents, nitric acid solutions (≤ 5 M) and 30% v/v tributyl phosphate (TBP) in n-dodecane, were used as a model LLE system to develop a chemometric workflow for direct quantification of nitric acid extraction from mixed-phase Raman spectra without phase separation. In parallel, single-phase aqueous and organic measurements were collected using both Raman and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to evaluate sensor-specific chemometric performance across relevant concentration ranges. Different machine learning methods were applied to improve nitric acid quantification from Raman and ATR-FTIR spectra, and the performance of these approaches was compared across relevant concentration ranges. Data fusion strategies combining Raman and ATR-FTIR measurements were also explored to improve quantification in regions where one technique showed reduced sensitivity or higher prediction uncertainty. The results show that good quantification of nitric acid extraction can be obtained directly from mixed biphasic spectra using tailored algorithms. Also, integrating spectroscopy with machine learning and data fusion can improve inline monitoring capabilities in PUREX-relevant solvent extraction systems.