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Machine learning to predict refractory corrosion during nuclear waste vitrification

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Abstract

The goal of this study was to determine the effects of model nuclear waste glass composition on the corrosion of Monofrax® 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 R2 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.

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Data availability

Data will be made available on reasonable request.

Notes

  1. Monofrax® is a trademark of the GmbH & Co. KG of Vienna, Austria.

  2. Python™ is a trademark of the Python Software Foundation Montreal, Quebec, Canada.

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Acknowledgements

The authors thank Will Eaton, Dong-Sang Kim, and Jorge Perez for useful discussions regarding this work. Comments from an anonymous reviewer greatly improved the manuscript. This research was supported by the Department of Energy Waste Treatment and Immobilization Plant Federal Project Office, contract number 89304017CEM000001, under the direction of Dr. Albert A. Kruger.

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Authors and Affiliations

  1. Material Science and Engineering Program, Washington State University, Pullman, WA, 99164, USA

    Natalie J. Smith-Gray, Scott Beckman & John McCloy

  2. School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 991654, USA

    Irmak Sargin, Scott Beckman & John McCloy

Authors
  1. Natalie J. Smith-Gray
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  2. Irmak Sargin
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  3. Scott Beckman
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  4. John McCloy
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Correspondence to John McCloy.

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John McCloy was an editor of this journal during the review and decision stage. For the MRS Advances policy on review and publication of manuscripts authored by editors, please refer to mrs.org/editor-manuscripts.

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Smith-Gray, N.J., Sargin, I., Beckman, S. et al. Machine learning to predict refractory corrosion during nuclear waste vitrification. MRS Advances 6, 131–137 (2021). https://doi.org/10.1557/s43580-021-00031-2

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