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

• Published in: Scientific reports Vol. 14; no. 1; pp. 6603 - 10
• Main Authors: Li, Chunhui, Adeniyi, Elijah O., Zarzycki, Piotr
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.03.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 704/172/169/896; 704/4111; Contaminants; Convergence; Environmental impact; GEOSCIENCES; Humanities and Social Sciences; Learning algorithms; Machine learning; MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES; Mobility; multidisciplinary; natural hazards; Neural networks; Nuclear accidents & safety; Nuclear fuels; pollution remediation; Radioactive wastes; Radioisotopes; Safety; Science; Science (multidisciplinary); Sorption; Uranium

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 h...