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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.
I. Pázsit, N. S. Garis, O. Glöckler
Nuclear Science and Engineering | Volume 124 | Number 1 | September 1996 | Pages 167-177
Technical Paper | doi.org/10.13182/NSE96-A24232
Articles are hosted by Taylor and Francis Online.
A neutron noise-based technique for the localization of excessively vibrating control rods is elaborated upon in the previous three papers of this series. The method is based on the inversion of a formula that expresses the auto- and cross spectra of three neutron detector signals through the parameters of the vibrating rod, i.e., equilibrium position and displacement components. Successful tests of the algorithm with both simulated and real data were reported in the previous papers. The algorithm had nevertheless certain drawbacks, namely, that its use requires expert knowledge, the redundancy of extra detectors cannot be utilized, and with realistic transfer functions the calculations are rather lengthy. The use of neural networks offers an alternative way of performing the inversion procedure. This possibility was investigated by constructing a network that was trained to determine the rod position from the detector spectra. It was found that all shortcomings of the traditional localization method can be eliminated. The neural network-based identification was also tested with success.