Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 1

Russell, Renee L.; Jin, Tongan; McCarthy, Benjamin P.; P, Darnell, Lori; Rinehart, Donald E.; Bonham, Charles C.; Gervasio, Vivianaluxa; Mayer, Jodi L.; Arendt, Christina L.; Lang, Jesse B.; Schweiger, Michael J.; Vienna, John D.

doi:10.2172/1854561

Cited by 3 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Powders were mixed, milled, melted in platinum crucibles at 1150°C, quenched, milled, melted again, and quenched. From the 249 glasses created in prior studies, 23–26 we selected 22 samples to maximize the spread of mass fractions of main oxide components (Al 2 O 3 , B 2 O 3 , CaO, Fe 2 O 3 , Li 2 O, MgO, Na 2 O, SiO 2 , ZnO, and ZrO 2 ).…”

Section: Methodsmentioning

confidence: 99%

“… Note : New‐OL‐* comes from [23], LP2‐* comes from [24], LP4‐* comes from [26], and LAWB99 comes from [28]. …”

Section: Methodsmentioning

confidence: 99%

“…Glass samples utilized in this study were prepared as part of larger studies designed to cover and extend LAW glass composition space and provide support for property modeling. [23][24][25][26] Glasses were prepared from carbonates, oxides, hydroxides, and salts of the main components. Powders were mixed, milled, melted in platinum crucibles at 1150 • C, quenched, milled, melted again, and quenched.…”

Section: Glass Transition Temperature Measurementsmentioning

confidence: 99%

“…Powders were mixed, milled, melted in platinum crucibles at 1150 • C, quenched, milled, melted again, and quenched. From the 249 glasses created in prior studies, [23][24][25][26] Glass transition temperatures were measured using a NETZSCH STA 449 F1 Jupiter simultaneous thermal analyzer in a combination thermogravimetric analysis−differential scanning calorimetry (TGA-DSC) mode. A 10 ± 2 mg quenched glass sample was loaded in platinum crucibles.…”

Section: Glass Transition Temperature Measurementsmentioning

confidence: 99%

“…Target glass composition and measured 𝑇 g values for the 22 samples are listed in Table 1. [23][24][25][26]28…”

Section: Glass Transition Temperature Measurementsmentioning

confidence: 99%

See 4 more Smart Citations

Glass transition temperature of low‐activity waste nuclear glasses

George

Ferkl

Marcial

et al. 2023

Int J of Appl Glass Sci

Self Cite

View full text Add to dashboard Cite

The glass transition temperature (𝑇 g ) is a parameter used in many glass melt viscosity models as it denotes a temperature around which liquid-glass transition occurs. In this work, 𝑇 g values were measured for a series of low-activity waste (LAW) glasses using differential scanning calorimetry. These data were combined with 𝑇 g data of other waste glasses available from literature. The combined dataset, consisting of 137 data points, was used for the development of several models to estimate 𝑇 g from glass composition. When testing the number of influential components and different supervised learning methods, we demonstrated that using more than 10 components or using non-linear methods brought marginal improvement to the model accuracy. The best model predicts 𝑇 g of both LAW and high-level waste glasses with reasonable accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

“… Note : New‐OL‐* comes from [23], LP2‐* comes from [24], LP4‐* comes from [26], and LAWB99 comes from [28]. …”

Section: Methodsmentioning

confidence: 99%

Section: Glass Transition Temperature Measurementsmentioning

confidence: 99%

Section: Glass Transition Temperature Measurementsmentioning

confidence: 99%

“…Target glass composition and measured 𝑇 g values for the 22 samples are listed in Table 1. [23][24][25][26]28…”

Section: Glass Transition Temperature Measurementsmentioning

confidence: 99%

See 3 more Smart Citations

Glass transition temperature of low‐activity waste nuclear glasses

George

Ferkl

Marcial

et al. 2023

Int J of Appl Glass Sci

Self Cite

View full text Add to dashboard Cite

show abstract

Parsimonious viscosity–composition relationships for high-temperature multicomponent glass melts

Ferkl

Hrma

Kruger

2022

Journal of Asian Ceramic Societies

View full text Add to dashboard Cite

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

Sargin

Vienna

2021

J. Am. Ceram. Soc.

View full text Add to dashboard Cite

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.

show abstract

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 1

Cited by 3 publications

References 0 publications

Glass transition temperature of low‐activity waste nuclear glasses

Glass transition temperature of low‐activity waste nuclear glasses

Parsimonious viscosity–composition relationships for high-temperature multicomponent glass melts

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

Contact Info

Product

Resources

About