18 Search Results

The vitrification of high-level waste (HLW) by heating a mixture of glass-forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimizationmore » algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first-order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.« less

Iron phosphate glasses, known for their exceptional chemical durability and potential applicability in nuclear waste management, have gained significant attention over the years. The structures of these glasses are complicated by the coexistence of Fe³⁺ and Fe²⁺, which plays a crucial role in determining their structures and properties. Here, this work uses molecular dynamics simulations to study the structural changes in Na₂O–Fe₂O₃–P₂O₅ glasses with varying glass composition and Fe²⁺/Fe³⁺ redox ratio. It was found that the redox ratio and modifier contents significantly affected the short-range and medium-range orders in the glasses. Significant changes in the local environments around P⁵⁺ andmore » Fe³⁺ were observed, as reflected by the bond distances and coordination numbers. Na⁺ cations are found to preferentially associate with Fe³⁺ (rather than Fe²⁺), whereas Fe²⁺ has stronger association with P⁵⁺ than Na⁺, confirming the structural role of Fe²⁺ as a glass modifier. The disruptions in P–O–P linkages upon increasing FeO suggest that FeO causes glass depolymerization. These glasses achieved higher connectivity with increasing Fe³⁺ / (Fe³⁺ + Fe²⁺) ratios, conerting phosphorous Q₂ to Q₃ units and iron Q₅ units to Q₄ units. The decrease of nonbridging oxygen fractions with increasing Fe³⁺ / (Fe³⁺ + Fe²⁺) ratios, through creating P–O–Fe linkages, is the main reason of enhanced network connectivity. Quantitative structure–property relationship analyses with different structural descriptors were used to correlate with measured properties. The analyses provided valuable insights into structure–property relationships, emphasizing the importance of choosing relevant energy parameters and defining glass network connectivity, particularly in F_net descriptors. It was found the Fe–O–P linkage density exhibits strong correlations to measured dissolution rates, supporting the importance of these linkages in improving the chemical durability in iron phosphate glasses.« less

Abstract Ensuring the long-term chemical durability of glasses is critical for nuclear waste immobilization operations. Durable glasses usually undergo qualification for disposal based on their response to standardized tests such as the product consistency test or the vapor hydration test (VHT). The VHT uses elevated temperature and water vapor to accelerate glass alteration and the formation of secondary phases. Understanding the relationship between glass composition and VHT response is of fundamental and practical interest. However, this relationship is complex, non-linear, and sometimes fairly variable, posing challenges in identifying the distinct effect of individual oxides on VHT response. Here, we leveragemore » a dataset comprising 654 Hanford low-activity waste (LAW) glasses across a wide compositional envelope and employ various machine learning techniques to explore this relationship. We find that Gaussian process regression (GPR), a nonparametric regression method, yields the highest predictive accuracy. By utilizing the trained model, we discern the influence of each oxide on the glasses’ VHT response. Moreover, we discuss the trade-off between underfitting and overfitting for extrapolating the material performance in the context of sparse and heterogeneous datasets.« less

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifyingmore » such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.« less

Glasses were designed for processing a nuclear waste from aqueous polishing of high-assay low-enriched uranium using either In-Can Melter (ICM) or GeoMelt® In-Container Vitrification™ (ICV) technologies, which operate at temperatures of T_p ≤ 1100 °C and T_p ≤ 1450 °C, respectively. Due to the different operating conditions, the melt and glass properties were optimized differently for each technology. Each glass was designed to optimize for maximum waste loading while simultaneously satisfying processing (e.g., crystallization, viscosity, and conductivity) and product quality (e.g., durability, hazard characteristic, and crystal content) constraints. Here, the raffinate waste contains high nitric acid (4 M) and lowmore » total solids (9 g·L^-1) concentrations. Feed preparation processes were tested to facilitate concentration and nitrate destruction/removal while controlling redox of the melter feed. A successful feed process including sugar addition and spray-drying was performed to generate an adequate melter feed for ICV processing.« less

Abstract Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–propertymore » relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.« less

Here, borosilicate waste glasses were successfully developed to immobilize two high-level waste raffinate streams via an in-can melter process with an Inconel 601 canister at 1050 °C. Measured viscosity and crystallinity thermal profiles were within the targeted processing constraints for the in-can melter process. Measured chemical durability of the glass by ASTM C1285–21 (Method A), ranged from normalized loss of boron, NL(B) = 1.44 – 2.65 g•m^-2, and NL(B) decreased with increased waste loading, accompanied by increased SnO₂ crystallinity. Measured corrosion of the in-can melter canister by a glass melt showed that Inconel 601 performed well at 1100 °C formore » up to 500 hr. Resistance polarization measurements versus time revealed that Inconel 601 corrosion rates in (and by) glass melts decreased from an initial rate of 63 mm•y^-1 down to 10.2 mm•y^-1 after 137 h with increased duration, which was attributed to formation of an oxide passivation layer (mainly Cr₂O₃) at the alloy-glass interface.« less

Predicting chemical durability of glass materials is important for various applications from daily life such as drink glass and kitchen ware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored prediction of initial dissolution rate through structural features from molecular dynamics (MD) simulations for a wide range of glass compositions (total 28) including borosilicate and aluminosilicate glasses, ZrO₂-containing and V₂O₅-containing boroaluminosilicate glasses. The initial dissolution rates (r₀) measured experimentally at 90 °C with varying solution conditions were correlated with structural features (e.g., polyhedral linkages and non-bridging oxygen species) obtained from MD simulations, either frommore » this study or from literature. Since hydrolysis of the glass network through breaking of the network former linkages (e.g., Si-O-Si, Si-O-Al, etc.) is a critical step of network glass dissolution, the statistics of these linkages obtained from MD were correlated to r0 through linear regression, where the coefficient of determination (R²) and root mean square error are found to be 0.949 and 0.681, respectively. This model was compared and discussed with existing models developed by various approaches including machine learning, the kinetic rate equation, topological constraint theory, and other descriptors from MD simulations. The discussion provides insights on future model improvements to predict glass dissolution. In addition, the impact of V₂O₅ on the glass dissolution was examined in detail, implicating that the impact is not the same across all glass compositions and test conditions.« less

The immobilization of used nuclear fuel (UNF) may be desirable for storage and permanent disposal. Ceramics are viable candidates for immobilization for an entire UNF assembly, as ceramic phases such as pyrochlore and fluorite incorporate target elements (i.e., U, Pu). In this work, titanate and zirconate ceramics were formulated to account for light water reactor UNF compositions. They were fabricated using Ce and Gd as analogues for U and the other actinides in nonradioactive formulations and U or U/Pu for radioactive formulations using similar processing conditions. Ceramics were characterized with powder X-ray diffraction, microscopy techniques (e.g., SEM-EDS, EBSD), and X-raymore » absorption near edge structure spectroscopy. For nonradioactive titanate ceramics, perovskite, rutile, and zirconolite were detected when Ce was used as an analogue, and pyrochlore and zirconolite were formed when Gd was used. For the radioactive titanate ceramics, pyrochlore, perovskite, and fluorite (including UO₂) phases formed. Only pyrochlore was formed for zirconates using Gd analogues but required high temperatures and long dwell times to produce. When Si was added as a sintering aid to lower temperatures and dwell times, fluorite and apatite phases formed on Gd zirconates. Fluorite, perovskite, and pyrochlore phases were observed in a U based zirconate using Si as a sintering aid. Altogether, the nonradioactive ceramics were more consolidated than the radioactive ceramics; future work should focus on improving processing conditions for radioactive formulations.« less

Lanthanide borosilicate glasses containing Pu and the analog element, Ce, were subjects of an X-ray absorption spectroscopy investigation to quantify the +3/+4 ratio of the redox-sensitive element. The data show that the dominant oxidation states are +4 for Pu and +3 for Ce. The data also indicate that the reductive potential of glasses can be quantified, although allowances must be made for glass composition, from a solution chemistry method adapted to glass chemistry. These data can therefore be used to formulate glass compositions and processing schedules that lead to knowledge of the oxidation state of Pu in melts. Furthermore, themore » data show that Ce is a poor analog for Pu behavior in melt and that the suitability of surrogates can be evaluated by this method. The methods demonstrated in this paper can be used to estimate the oxidation states of a range of multi-valent elements as functions of temperature and composition with data from only a single redox couple.« less