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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Latest News
NNSA awards BWXT $1.5B defense fuels contract
The Department of Energy’s National Nuclear Security Administration has awarded BWX Technologies a contract valued at $1.5 billion to build a Domestic Uranium Enrichment Centrifuge Experiment (DUECE) pilot plant in Tennessee in support of the administration’s efforts to build out a domestic supply of unobligated enriched uranium for defense-related nuclear fuel.
Robert O. Hoover, Supathorn Phongikaroon, Michael F. Simpson, Shelly X. Li, Tae-Sic Yoo
Nuclear Technology | Volume 171 | Number 3 | September 2010 | Pages 276-284
Technical Paper | Pyro 08 Special / Reprocessing | doi.org/10.13182/NT10-2A
Articles are hosted by Taylor and Francis Online.
The electrochemical processing of spent metallic nuclear fuel has been demonstrated by and is currently in operation at the Idaho National Laboratory (INL). At the heart of this process is the Mark-IV electrorefiner (ER). This process involves the anodic dissolution of spent nuclear fuel into a molten salt electrolyte along with a simultaneous deposition of pure uranium on a solid cathode. This allows the fission products to be separated from the fuel and processed into an engineered waste form. A one-dimensional model of the Mark-IV ER has begun to be developed. The computations thus far have modeled the dissolution of the spent nuclear fuel at the anode taking into account uranium (U3+), plutonium (Pu3+), and zirconium (Zr4+). Uranium and plutonium are the two most important elements in the system, whereas zirconium is the most active of the noble metals. The model shows that plutonium is quickly exhausted from the anode, followed by dissolution of primarily uranium, along with small amounts of zirconium. The total anode potential as calculated by the model has been compared to experimental data sets provided by INL. The anode potential has been shown to match the experimental values quite well with root-mean-square (rms) values of 2.27 and 3.83% for two different data sets, where rms values closer to zero denote better fit.