Solidifying sulfate-bearing wastes to make glass composite materials

Karlina, O. K.; Ozhovan, M. I.; Sobolev, I. A.; Овчинников, А. В.; Varlakova, G. A.; Polyakov, A. S.

doi:10.1007/bf01121091

Cited by 1 publication

(5 citation statements)

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“…As has been reported before, 1,3 sulfur can exist in glass melt in a wide range of oxidation states under various melting redox conditions, from sulfate (SO 4 2− ) under oxidizing conditions to sulfide (S 2− ) in more reducing atmosphere. Because the Hanford waste feeds are highly oxidizing in nature (due to the presence of nitrates), 15 sulfur is expected to be present primarily as SO 4 2− in the Hanford LAW and DF-HLW. 1,15,17 During vitrification, although, the reductants (for example, sucrose or oxalic acid) will be added to the waste feed to decompose nitrates, as described in the following reaction scheme, the amount added will be insufficient for the complete decomposition of nitrates or reduction of sulfur from +6 oxidation state in SO 4 2− .…”

Section: Consolidation and Evaluation Of Databasementioning

confidence: 99%

“…According to the literature, 1−3,43 Na 2 O content is one of the primary glass constituents driving the glasses' SO 3 solubility. This is because Na + can preferentially charge-compensate SO 4 2− in the glasses over other higher ionic field strength cations such as Li + and Ca 2+ . 2,43 Therefore, it is not surprising that the hybrid model predicted Na 2 O as the second most influential parameter (after melting time) governing SO 3 solubility in the nuclear waste glasses.…”

Section: Impact Of Glass Composition On So 3 Solubility In Nuclear Wastementioning

confidence: 99%

“…: long-term chemical durability is the other critical factor ]. Exceeding the solubility limit of SO 4 2– (incorporated and measured as SO 3 ) in borosilicate-based nuclear waste glass melt results in the formation of an immiscible sulfate-rich salt layer (known as “ gall ” or yellow phase, whose formation is facilitated by the presence of chromates in the waste) that floats on the top of the melt. − This low-viscosity, electrically conductive salt layer, on account of being rich in water-soluble alkali sulfates along with specific radionuclides (e.g., Tc, Sr, and Cs), can cause several problems for the melter, for example, corrosion of sidewalls and metallic components, thus severely impacting its performance and reduce its longevity. ,− In order to avoid the above-mentioned complicationsand for safe, long-term operation of the melterempirical (analytical) models have been developed to predict the practical limit of salt (SO 3 ) solubility in the melt as a function of feed composition. For example, Vienna et al, on the basis of 253 simulated Hanford LAW glass compositions, established an empirical model (shown in eq ) to predict SO 3 solubility in LAW glasses as a function of the concentrations of different components in the melter feed. Here, W SO 3 Pred is the predicted sulfur solubility (in the units of mass %); q represents the number of compositional components in the glass; and s i , s jk , n j , and n k are coefficients and normalized mass fractions, respectively, corresponding to each component ( i , or j , or k , or jk ).…”

Section: Introductionmentioning

confidence: 99%

“…Sulfur (in the form of sulfate, SO 4 2− )owing to its low solubility (<1 mass %) in borosilicate glassesis one of two critical factors impeding the loading of Hanford low-activity waste (LAW) in vitreous waste forms 1 [N.B. : long-term chemical durability is the other critical factor].…”

Section: Introductionmentioning

confidence: 99%

“…: long-term chemical durability is the other critical factor]. Exceeding the solubility limit of SO 4 2− (incorporated and measured as SO 3 ) in borosilicate-based nuclear waste glass melt results in the formation of an immiscible sulfate-rich salt layer (known as "gall" or yellow phase, whose formation is facilitated by the presence of chromates in the waste) that floats on the top of the melt. 2−5 This low-viscosity, electrically conductive salt layer, on account of being rich in water-soluble alkali sulfates along with specific radionuclides (e.g., Tc, Sr, and Cs), can cause several problems for the melter, for example, corrosion of sidewalls and metallic components, thus severely impacting its performance and reduce its longevity.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Enabled Models to Predict Sulfur Solubility in Nuclear Waste Glasses

Han

Huang

et al. 2021

ACS Appl. Mater. Interfaces

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The U.S. Department of Energy is considering implementing the direct feed approach for the vitrification of low-activity waste (LAW) and high-level waste (HLW) at the Hanford site in Washington state. If implemented, the nuclear waste with a higher concentration of alkali/alkaline-earth sulfates (than expected under the previously proposed vitrification scheme) will be sent to the vitrification facility. It will be difficult for the existing empirical models to predict sulfate solubility in these glasses or design glass formulations with enhanced sulfate loadings in such a scenario. Further, the existing models are unable to produce reliable predictions when applied to HLW glasses whose composition falls outside of the range encompassed by the database used to develop/calibrate the models. Accordingly, this study harnesses the power of artificial intelligence (machine learning, ML) with a goal to address the limitations of the existing models. Toward this, three ML models have been trained using a large database; comprising >1000 LAW and HLW glasses and encompassing a wide range of glass compositions and processing variables. Next, the ML model with the best prediction performance has been used to quantitatively assess and rank the influence (i.e., importance) of glasses’ compositional/processing variables on the SO3 solubility in the glasses. Finally, on the premise of such understanding of influential and inconsequential variables, two closed-form analytical modelswith disparate degrees of complexity (one highly parametrized and one with fewer input variables)have been developed. Results show that both analytical models produce predictions of SO3 solubility in LAW and HLW glasses with an accuracy analogous to ML models and substantially higher than the analytical models that represent the current state-of-the-art. Overall, this study’s outcomes present a roadmapinformed by data and channeled by artificial intelligencethat can be leveraged in the future to design nuclear waste glasses with unprecedented sulfur loadings.

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