Machine learning surrogates for surface complexation model of uranium sorption to oxides

Journal Article · 19 March 2024 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-024-57026-w · OSTI ID:2325450

Li, Chunhui ^[1]; Adeniyi, Elijah O. ^[2]; Zarzycki, Piotr ^[1]

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1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
2. Montana State Univ., Bozeman, MT (United States)

The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Nuclear Energy (NE); USDOE
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2325450
• Alternate ID(s): OSTI ID: 2426889
• Journal Information: Scientific Reports, Vol. 14, Issue 1; ISSN 2045-2322
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English