Laboratory And Lysimeter Experimentation And Transport Modeling Of Neptunium And Strontium In Savannah River Site Sediments
The Savannah River Site (SRS) conducts performance assessment (PA) calculations to determine the appropriate amount of low-level radiological waste that can be safely disposed on site. Parameters are included in these calculations that account for the interaction between the immobile solid phase and the mobile aqueous phase. These parameters are either the distribution coefficient (K{sub d} value) or the apparent solubility value (K{sub sp}). These parameters are readily found in the literature and are used throughout the DOE complex. One shortcoming of K{sub d} values is that they are only applicable to a given set of solid and aqueous phase conditions. Therefore, a given radionuclide may have several K{sub d} values as it moves between formations and comes into contact with different solids and different aqueous phases. It is expected that the K{sub d} construct will be appropriate to use for a majority of the PA and for a majority of the radionuclides. However, semi-mechanistic models would be more representative in isolated cases where the chemistry is especially transitory or the radionuclide chemistry is especially complex, bringing to bear multiple species of varying sorption tendencies to the sediment. Semi-mechanistic models explicitly accommodate the dependency of K{sub d} values, or other sorption parameters, on contaminant concentration, competing ion concentrations, pH-dependent surface charge on the adsorbent, and solute species distribution. Incorporating semi-mechanistic concepts into geochemical models is desirable to make the models more robust and technically defensible. Furthermore, these alternative models could be used to augment or validate a Kd?based DOE Order 435.1 Performance Assessment. The objectives of this study were to: 1) develop a quantitative thermodynamically-based model for neptunium sorption to SRS sediments, and 2) determine a sorption constant from an SRS 11-year lysimeter study. The modeling studies were conducted with existing data sets. The first data set used laboratory generated Np sorption data as a function of concentration (three orders of magnitude) and as a function of pH (four orders of magnitude of proton concentration). In this modeling exercise, a very simple solution was identified by assuming that all sorption occurred only to the iron oxides in the sediment and that all the added NpO{sub 4}{sup -} remained in the oxidized state and was not reduced to the Np(IV) state (as occurs rapidly with Pu(V)). With rather limited input data, very good agreement between experimental and modeling results was observed. This modeling approach would be easy to add to the PA with little additional data requirements. This model would be useful in a system where pH is expected to change greatly, such as directly beneath a grout or concrete structure. The second model discussed in the report was to derive strontium K{sub d} values from data collected in an 11-year-old field transport study. In this controlled lysimeter study, a sensitivity analysis was conducted of hydrological and chemical processes that influence contaminant transport, including diffusion coefficients, seepage velocity, and K{sub d} value. The best overall K{sub d} derived from the model fit to the data was 32 L kg{sup -1}, which was the same value that was previously measured in traditional laboratory batch sorption studies. This was an unexpected result given the differences in experimental conditions between the batch test and the lysimeter flow through test, in particular the differences between strontium adsorption and desorption processes occurring in the latter test and not in the former. There were some trends in the lysimeter strontium data that were not predicted by the K{sub d} model, which suggest that other geochemical processes are likely also controlling strontium transport. Strontium release and cation exchange are being evaluated. These results suggest that future modeling efforts (e.g., PAs) could be improved by employing a more robust semi-empirical modeling approach to transient or complex conditions.
- Research Organization:
- Savannah River Site (SRS), Aiken, SC (United States)
- Sponsoring Organization:
- USDOE (United States)
- DOE Contract Number:
- DE-AC09-08SR22470
- OSTI ID:
- 1051765
- Report Number(s):
- SRNL-STI-2012-00052; TRN: US1300231
- Country of Publication:
- United States
- Language:
- English
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