Predicting radioactive waste glass dissolution with machine learning

Lillington, Joseph N.P. and Goût, Thomas L. and Harrison, Mike T. and Farnan, Ian (2020) Predicting radioactive waste glass dissolution with machine learning. Journal of Non-Crystalline Solids, 533. p. 119852. ISSN 0022-3093 DOI

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The vitrification of high-level nuclear waste within borosilicate glass and its disposition within a multi-barrier repository deep underground is accepted as the best form of disposal. Here, the ability of machine learning to predict both static and dynamic glass leaching behavior is analysed using large-scale unstructured multi-source data, covering a diverse range of experimental conditions and glass compositions. Machine learning can accurately predict leaching behavior, predict missing data, and time forecast. Accuracy depends upon the type of learning algorithm, model input variables, and diversity or size of the underlying dataset. For static leaching, the bagged random forest method predicts well, even when either pH or glass composition are neglected as input variables, additionally showing potential in predicting independent glass dissolution data. For dynamic leaching, accuracy improves if replacing final pH with a species dissolution rate as an input variable, although results show no preferred output species (Si, Na, or Al).

Item Type: Article
Uncontrolled Keywords: 2019AREP; 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: 533
Page Range: p. 119852
Identification Number:
Depositing User: Sarah Humbert
Date Deposited: 28 Feb 2020 14:16
Last Modified: 25 Jan 2021 01:01

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