Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Journal Article · 25 July 2021 · Journal of the American Ceramic Society

DOI: https://doi.org/10.1111/jace.17983 · OSTI ID:1810192

Lu, Xiaonan  ^[1]; Sargin, Irmak  ^[2]; Vienna, John D.  ^[1]

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1. Energy and Environment Directorate Pacific Northwest National Laboratory Richland WA USA
2. Mechanical and Materials Engineering Department Washington State University Pullman WA USA

Abstract Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high‐level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k‐nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols. This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitation.

• Sponsoring Organization: USDOE
• Grant/Contract Number: NE0008597
• OSTI ID: 1810192
• Journal Information: Journal of the American Ceramic Society, Journal Name: Journal of the American Ceramic Society Journal Issue: 11 Vol. 104; ISSN 0002-7820
• Publisher: Wiley-BlackwellCopyright Statement
• Country of Publication: United States
• Language: English