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Glass strategy: Hanford’s enhanced waste glass program
The mission of the Department of Energy’s Office of River Protection (ORP) is to complete the safe cleanup of waste resulting from decades of nuclear weapons development. One of the most technologically challenging responsibilities is the safe disposition of approximately 56 million gallons of radioactive waste historically stored in 177 tanks at the Hanford Site in Washington state.
ORP has a clear incentive to reduce the overall mission duration and cost. One pathway is to develop and deploy innovative technical solutions that can advance baseline flow sheets toward higher efficiency operations while reducing identified risks without compromising safety. Vitrification is the baseline process that will convert both high-level and low-level radioactive waste at Hanford into a stable glass waste form for long-term storage and disposal.
Although vitrification is a mature technology, there are key areas where technology can further reduce operational risks, advance baseline processes to maximize waste throughput, and provide the underpinning to enhance operational flexibility; all steps in reducing mission duration and cost.
Luv Sharma, Tunc Aldemir, Robert Parker
Nuclear Technology | Volume 169 | Number 1 | January 2010 | Pages 18-33
Technical Paper | Reactor Safety | doi.org/10.13182/NT10-A9340
Articles are hosted by Taylor and Francis Online.
In the simulation of nuclear plant behavior through system codes, there are often uncertainties associated with the large number of model parameters required as code inputs. The use of the Taguchi method is investigated for the importance ranking of uncertainties when a single metric is used to characterize system performance. The proposed procedure is illustrated on a simplified boiling water reactor (BWR) model to determine the dominant parameters affecting the maximum limit cycle amplitude (MLCA) in BWRs. A reduced-order BWR model is used for the analysis. A regression model is also generated to predict the MLCA as a function of the parameter values in their assumed uncertainty regions. The results indicate that (a) 7 out of the 11 parameters (factors) under consideration have a significant impact on the MLCA, (b) a linear regression model can be constructed to predict the MLCA with 88% confidence, (c) higher-order effects of the control factors are negligible, and, (d) cross effects between the factors are negligible compared to their individual effects. The results also indicate that the use of the Taguchi method leads to a 99.4% reduction in the computational effort over a full factorial experiment design. The use of the Taguchi method is not proposed to replace the well-established conventional methods for sensitivity and uncertainty analysis but rather to assist them in the selection of the parameters that may require more detailed analysis.