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New Developments in Field Studies of Low Activity Waste Glass Corrosion and Contaminant Transport

  • B. P. McGrail (a1), D. H. Bacon (a1), P. D. Meyer (a1), M. I. Ojovan (a2), D. M. Strachan (a1), N. V. Ojovan (a3) and I. V. Startceva (a3)...


Performance assessment calculations for low-activity waste glass to be disposed at the Hanford site depend on simulations of long-term glass corrosion behavior and contaminant transport that are being performed via reactive chemical transport modeling. Confidence in the underlying physical and chemical processes that are being approximated by the computer model could be significantly enhanced through carefully-controlled field testing, which includes studies of buried ancient glasses. Field tests with simulated low-activity waste glasses have been initiated on the Hanford site and at the Ballidon site in the United Kingdom. In addition, a joint PNNL – SIA RADON research project has been initiated to analyze a unique data set collected over 12+ years during a Russian in-situ testing program with actual low-activity waste glass. The glasses buried at Hanford are scaled down cylinders (45 kg mass) and include a representative glass composition for Hanford low-activity waste and a glass designed to be highly reactive. Each glass was doped with chemical analog tracers (Re, Se, and Mo). Design of the lysimeter test facility and sampling locations were done with aid of predictive calculations performed with the STORM code. These same glasses were also buried at Ballidon but in the form of small glass coupons. The coupons will be retrieved over a period of several years for detailed analysis along with core samples of the surrounding limestone, which will be analyzed for contaminant transport profiles. Analysis of porewater from underneath the Russian burial site ha s been compared with modeling calculations. Good agreement between the model and the field data has been obtained so far using estimated parameters for the glass corrosion. Independent laboratory tests are in progress to parameterize the STORM model for quantitative comparisons.



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