Lillington, Joseph N.P. and Goût, Thomas L. and Harrison, Mike T. and Farnan, Ian (2020) Assessing static glass leaching predictions from large datasets using machine learning. Journal of Non-Crystalline Solids, 546. p. 120276. ISSN 00223093 DOI https://doi.org/10.1016/j.jnoncrysol.2020.120276
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Abstract
Radioactive waste vitrified within glass is planned to be ultimately disposed of within a geological disposal facility. This study has applied machine learning to predict static glass leaching using an international experimental database of approximately 450 glasses to train/test various algorithms. Machine learning can accurately predict B, Li, Na, and Si releases for this complex database with Tree-based algorithms (notably ‘BaggingRegressor’ and ‘RandomForestRegressor’ in Python). This is provided that leaching experiment results, including elemental releases, are incorporated within the algorithm training variables, given that this study finds inaccurate prediction solely using initial test parameters as features. The trained algorithms underwent additional testing using an external database with prediction showing worse performance, likely due to substantial MgO and Na2O pristine glass oxide compositional variations across databases, with B releases generally being overestimated and Na underestimated. The use of molar oxide content performed significantly better than weight-fraction oxide for learning.
Item Type: | Article |
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Uncontrolled Keywords: | 2020AREP; IA76 |
Subjects: | 03 - Mineral Sciences |
Divisions: | 03 - Mineral Sciences 08 - Green Open Access 12 - PhD |
Journal or Publication Title: | Journal of Non-Crystalline Solids |
Volume: | 546 |
Page Range: | p. 120276 |
Identification Number: | https://doi.org/10.1016/j.jnoncrysol.2020.120276 |
Depositing User: | Sarah Humbert |
Date Deposited: | 25 Aug 2020 16:24 |
Last Modified: | 08 Jul 2021 00:01 |
URI: | http://eprints.esc.cam.ac.uk/id/eprint/4866 |
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