Developing A Digital Twin of a TRUEX Extraction Process that Enables Non-proliferation and Safeguards Monitoring through Optical Spectroscopy and Machine Learning [version 1; peer review: 1 approved, 2 approved with reservations] - Justin T. Cooper; Ramedy Flores; Addyson Barnes; Eduardo Trevino; Kathrine Jesse; Kolton Heaps; Adam J. Pluth; Ashley Shields; Jaren Brownlee (Nuclear Science and Technology Open Research)
Background The recycling of spent fuel can increase the longevity and decrease the waste footprint associated with the nuclear fuel cycle. However, nuclear fuel reprocessing introduces the risk of proliferation of fissile material. For example, the recycling of uranium (U) and plutonium (Pu) is generally accomplished via a solvent extraction process. The separation efficiency of the process is dependent on a variety of parameters such as acid concentration, organic to aqueous (O/A) volume ratio, number of separation stages, etc. A change in any of these parameters could result in different extraction conditions of fissile material and lead to a process upset with potential safeguards and proliferation concerns. Thus, the ability to monitor the solvent extraction process in real-time would enable the rapid identification and diagnosis of process upsets. Methods In this work, we discuss the development of a digital twin of a surrogate small-scale nuclear fuel reprocessing solvent extraction process utilizing online optical spectroscopic monitoring, machine learning (ML), and chemical modeling. A cascade of centrifugal contactors was employed with the TRUEX (TransUranic Extraction) solvent and surrogate minor actinides. Spectroscopic sensors were placed at strategic locations within the contactor bank and were used to predict TRUEX surrogate analyte concentrations at those locations. Results A solvent extraction digital twin consisting of the data acquisition, chemical model, and anomaly detection, provided expected concentrations at each contactor during normal operations. The digital twin demonstrated the ability to accurately simulate the extraction behavior under ideal conditions. Furthermore, the digital twin’s ML anomaly detection algorithm was shown to be successful in identifying several non-ideal process upset conditions.