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Argonne research aims to improve nuclear fuel recycling and metal recovery
Servis
Scientists at Argonne National Laboratory are investigating a used nuclear fuel recycling technology that could lead to a scaled-down and more efficient approach to metal recovery, according to a recent news article from the lab. The research, led by Argonne radiochemist Anna Servis with funding from the Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E), could have an impact beyond the nuclear fuel cycle and improve other high-value metal processing, such as rare earth recovery, according to Argonne.
The research: Servis’s work is being carried out under ARPA-E’s CURIE (Converting UNF Radioisotopes Into Energy) program. The specific project—Radioisotope Capture Intensification Using Rotating Packed Bed Contactors—started in 2023 and is scheduled to end in January 2026.
C. Rea, K. J. Montes, A. Pau, R. S. Granetz, O. Sauter
Fusion Science and Technology | Volume 76 | Number 8 | November 2020 | Pages 912-924
Technical Paper | doi.org/10.1080/15361055.2020.1798589
Articles are hosted by Taylor and Francis Online.
In this paper we lay the groundwork for a robust cross-device comparison of data-driven disruption prediction algorithms on DIII-D and JET tokamaks. In order to consistently carry on a comparative analysis, we define physics-based indicators of disruption precursors based on temperature, density, and radiation profiles that are currently not used in many other machine learning predictors for DIII-D data. These profile-based indicators are shown to well-describe impurity accumulation events in both DIII-D and JET discharges that eventually disrupt. The univariate analysis of the features used as input signals in the data-driven algorithms applied on the data of both tokamaks statistically highlights the differences in the dominant disruption precursors. JET with its ITER-like wall is more prone to impurity accumulation events, while DIII-D is more subject to edge-cooling mechanisms that destabilize dangerous magnetohydrodynamic modes. Even though the analyzed data sets are characterized by such intrinsic differences, we show through a few examples that the inclusion of physics-based disruption markers in data-driven algorithms is a promising path toward the realization of a uniform framework to predict and interpret disruptive scenarios across different tokamaks. As long as the destabilizing precursors are diagnosed in a device-independent way, the knowledge that data-driven algorithms learn on one device can be re-used to explain a disruptive behavior on another device.