Machine learning to predict refractory corrosion during nuclear waste vitrification

Date

2021-04-01

Author

Smith-Gray, Natalie J.
Sargın, Irmak
Beckman, Scott
McCloy, John

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The goal of this study was to determine the effects of model nuclear waste glass composition on the corrosion of Monofrax(R) K-3 refractory, using machine learning (ML) methods for data investigation and modeling of published borosilicate glass composition data and refractory corrosion performance. First, statistical methods were used for exploration of the data, and the list of features (model terms) was determined. Several model types were explored, and the Bayesian Ridge type was the most promising due to low mean average error and mean standard error as well as high R-2 value. Parameters and model results using previously identified model features and those from this study are compared. ML methods appear to give results at least as good as previously available models for describing the effects of glass composition on refractory corrosion.

Subject Keywords

Glass, Machine learning, Waste management, Corrosion, Nuclear materials

URI

https://hdl.handle.net/11511/96130

Journal

MRS ADVANCES

DOI

https://doi.org/10.1557/s43580-021-00031-2

Collections

Department of Metallurgical and Materials Engineering, Article

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Citation Formats

N. J. Smith-Gray, I. Sargın, S. Beckman, and J. McCloy, “Machine learning to predict refractory corrosion during nuclear waste vitrification,” MRS ADVANCES, vol. 6, no. 4-5, pp. 131–137, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96130.