Surface Complexation/Ion Exchange Hybrid Model for Radionuclide Sorption to Clay Minerals (M4SF-23LL010301062)
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
This progress report (Level 4 Milestone Number M4SF-23LL010301062) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite International Collaborations Activity Number SF-23LL01030106. The activity is focused on our long-term commitment to engaging our partners in international nuclear waste repository research. The focus of this milestone is the establishment of international collaborations for surface complexation modeling and the associated impacts of unlocking larger, community-based datasets. More specifically, we are developing a database framework for Spent Fuel and Waste and Science Technology (SFWST) that is aligned with the Helmholtz Zentrum Dresden Rossendorf (HZDR) sorption database development group in support of the database needs of the SFWST program. In our FY22 effort, we described a detailed analysis of U(VI) sorption to quartz through both traditional surface complexation modeling and through a hybrid ML framework. In FY23, effort was placed on publication of these results and expansion of the LLNL surface complexation and ion exchange database (L-SCIE) in order to assess mineral-based radionuclide retardation under a wider variety of geochemical conditions (e.g., ionic strength, varying electrolyte compositions). Efforts were initiated to expand L-SCIE to include radionuclide surface complexation and ion exchange to clays that are relevant to subsurface geochemical processes occurring at nuclear waste repositories. In particular, a large source of sorption data for clays resides at the Paul Scherrer Institute (PSI) (work primarily by Bradbury and Baeyens) and we initiated discussions on how to retrieve those data and apply FAIR principles to those datasets. In addition to L-SCIE development, two hybrid models that incorporate AI/ML were investigated and compared to discern the most promising approaches for accurate and precise estimations of radionuclide retardation. Key considerations for future model development include (1) the ability to reduce computational burden on determining retardation coefficients for PA and (2) the ability to quantify and predict radionuclide-mineral partitioning at a more efficient, rapid pace due to automated workflows. Upon the careful consideration of the most effective modeling approaches, we are identifying ways to implement these approaches into PA. Ultimately, the data science-based workflows will provide a major incentive for other institutions to adopt a FAIR-formatted, interoperable database. LLNL will play a key role in disseminating sorption data and acting as good data stewards by updating the database in a consistent format and assessing the quality of the newly assimilated data in an organized fashion. To this end, all data and workflows are open access and made available on the LLNL Seaborg research website (https://seaborg.llnl.gov/resources/geochemical-databases-modeling-codes).
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Nuclear Energy (NE), Office of Spent Fuel and Waste Disposition. Office of Spent Fuel and Waste Science and Technology
- DOE Contract Number:
- AC52-07NA27344
- OSTI ID:
- 1994028
- Report Number(s):
- LLNL-TR-852474; 1079840; TRN: US2404637
- Country of Publication:
- United States
- Language:
- English
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