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North American construction is back—smaller and faster—at OPG’s Darlington
“The nuclear renaissance is real here,” said Ontario Power Generation’s Subo Sinnathamby on May 8, one year to the day after OPG secured a final investment decision to build the first of four planned BWRX-300 reactors at its Darlington nuclear power plant, and shortly after the new reactor’s foundation was lifted into place. “We got our license to construct in April and our [final investment decision] in May, and we’ve been off to the races since.”
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.