Machine Learning Prediction of Nitric Acid Extraction Behavior in PUREX Process

Plutonium uranium reduction extraction (PUREX) is a liquid-liquid extraction process used to recover plutonium (Pu) and uranium (U) from irradiated uranium fuel for various nuclear-related applications. Despite extensive efforts, quantitative prediction of liquid-liquid extraction parameters, i.e., distribution ratios and separation factors, of the process remains challenging. Existing thermodynamic models are difficult to develop and often have limited utility due to the complexity of the aqueous feed. Nitric acid is a critical component of the PUREX system, both as a driving force for dissolving irradiated fuels in preprocessing stages, as well as being efficiently extracted by tributyl phosphate (TBP). Models to understand nitric acid's distribution behavior is therefore a prerequisite to predict actinide extraction. In this work, we compiled a wealth of solvent extraction literature data and built machine learning (ML) models capable of predicting the organic phase nitric acid equilibrium concentration from initial acid and TBP concentrations across a variety of diluents. Our results demonstrate that ML is highly capable of predicting nitric acid extraction behavior in PUREX systems, and the resultant ML-aided response surfaces demonstrate promising progress as an in silico aid for optimizing the design of experiments for future work with the PUREX process.