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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.
Svein Sunde, Øivind Berg, Lennart Dahlberg, Nils-Olof Fridqvist
Nuclear Technology | Volume 143 | Number 2 | August 2003 | Pages 103-124
Technical Paper | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies | doi.org/10.13182/NT03-A3401
Articles are hosted by Taylor and Francis Online.
A mathematical model for a boiling water reactor steam-turbine cycle was assembled by means of a configurable, steady-state modeling tool TEMPO. The model was connected to live plant data and intermittently fitted to these by minimization of a weighted least-squares object function. The improvement in precision achieved by this reconciliation was assessed from quantities calculated from the model equations linearized around the minimum and from Monte Carlo simulations. It was found that the inclusion of the flow-passing characteristics of the turbines in the model equations significantly improved the precision as compared to simple mass and energy balances, whereas heat transfer calculations in feedwater heaters did not. Under the assumption of linear model equations, the quality of the fit can also be expressed as a goodness-of-fit Q. Typical values for Q were in the order of 0.9. For a validated model Q may be used as a fault detection indicator, and Q dropped to very low values in known cases of disagreement between the model and the plant state. The sensitivity of Q toward measurement faults is discussed in relation to redundancy. The results of the linearized theory and Monte Carlo simulations differed somewhat, and if a more accurate analysis is required, this is better based on the latter. In practical application of the presently employed techniques, however, assessment of uncertainties in raw data is an important prerequisite.