Engineering topics

Gervasio, Vivianaluxa

Publications and source records attributed to Gervasio, Vivianaluxa.

9 records

DOE OSTI2026

Effects of Composition and Oxidation States on the Structures of Chromium-Containing Sodium Silicate Glasses: Molecular Dynamics Simulations using Machine Learning Interatomic Potentials

Chromium represents a significant challenge for the vitrification of high-level nuclear waste into silicate and borosilicate glasses due to its low solubility and variable oxidation states, which can limit the waste loading due to promotion of crystallization or phase separation during processing. In this study, we modeled chromium containing silicate glasses using molecular dynamics simulations with three machine learning interatomic potentials (MLIPs), MACE, CHGNet, and PFP were employed, to gain insights on glass composition and oxidation states on the structures of these glasses. One of the goals is to evaluate their ability of these MLIPs to accurately represent the general structure of silicate glasses and chromium local environments as a function of chromium oxidation states. Density Functional Theory (DFT) based calculations and experimental data such as neutron structure factors were used to validate the structural models. It was found that the foundation models of all three MLIPs are able to reproduce general structural features of the sodium silicate glass structure consistent with experimental and DFT data, but only CHGNet and PFP can accurately capture the oxidation states and local environment of chromium: tetrahedral for Cr6+ and octahedral for Cr3+. Furthermore, we studied the effect of varying Cr3+/ Cr6+ (Cr3+/Crtotal) ratio and total chromium content using PFP. Our results show that Cr6+ enhances network polymerization by reducing non-bridging oxygens through Na? charge compensation required due to the formation of chromate (CrO42-) species, while Cr³? acts as a network modifier that disrupts connectivity. System size effects on the structural characteristics and chromium environments were also tested using the PFP potential. This work highlights the importance of careful validation on the precision, transferability, and potential of MLIPs for modeling glasses containing transition metal elements that can exist in multiple oxidation states. It is also encouraging to see the foundational models are all three MLFFs are able to reproduce the basic sodium silicate glass structures, while suggesting additional training or refining is needed to improve the description of more complex systems containing transition metals.

Puga, Christina L.↗

DOE OSTI2026

Low Activity Waste Glass Optimization with Property Models from Machine Learning, Part 2: Experimental Validation and Active Learning

The United States Department of Energy is responsible for managing legacy nuclear waste stored in underground tanks at the Hanford Site. To treat the waste, it is planned as the current baseline to separately vitrify low-activity waste (LAW) and high-level waste fractions. Previously, machine learning (ML) based glass property models (e.g., chemical durability, viscosity, electrical conductivity and SO3 solubility) were developed with prediction uncertainties. A waste glass optimization approach was then established to enable the capability of using these ML models in LAW glass formulation. In this study, the previous ML models were first experimentally validated, and the results were incorporated back into the database to update the ML models. The updated models and formulations showed increased waste loading while reducing the failure rate, demonstrating improved predictive accuracy, reduced uncertainties, and the effectiveness of active learning in guiding high-dimensional, nonlinear LAW glass design. This represents the first experimental validation of ML based LAW glass formulation, with practical benefits such as higher waste loading, shorter mission duration, and lower operational risk.

Lu, Xiaonan (ORCID:0000000179708148)↗

DOE OSTI2024

Direct Feed High-Level Waste APPS Model Glass Testing (DFHLW APPS) Matrix

This report summarizes the data collected during the batching and melting of the Direct Feed High-Level Waste APPS Model Glass Matrix (DFHLW APPS) to serve as a quality-assured validation of the Aspen Process Performance Simulation (APPS) formulation method. Of 15 glasses tested, 12 satisfied all target property constraints. Two glasses, APPS-05 and -06, formed nepheline on canister centerline cooling heat-treatment and failed the Product Consistency Test response limits. Glass APPS-07-2 formed unacceptably high concentrations of crystals (primarily Na3Nd(PO4)2) when heat treated at 950 °C. All other glasses were found to be satisfactory. The measured property values were compared to predicted values from a set of current models. In many cases the current models were found to be inadequate for design of DFHLW glasses. These models are being adjusted to correct for mispredictions. Other models, e.g., density, toxicity characteristic leaching procedure, and sulfur solubility, are adequate for formulation of DFHLW glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗

DOE OSTI2024

Glass formulation and lab-scale testing of glasses designed for in-can Melter and in-container Vitrification of high-assay low-enriched uranium aqueous polishing Raffinate waste

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 low 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.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 5 and Phase 6

This report summarizes the data collected on two test matrices of low-activity waste (LAW) glass compositions intended to expand the composition-property database: Low-Activity Waste (LAW) Phase 5: Expansion of LAW Glass Composition Boundaries and LAW Phase 6: High PCT and VHT Response Glass Matrix. Both matrix glass compositions were statistically designed to expand the LAW glass composition region. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, container centerline cooling with crystal identification, the product consistency test (PCT) response, the vapor hydration test (VHT) response, and sulfur solubility. Because of the slightly different scope of the two matrices, not all methods were applied to both matrices. Specifically, the following measurements were taken only on the LAW Phase 5: Expansion of LAW Glass Composition Boundaries glasses: crystal fraction as a function of temperature, density (ρ), viscosity (η), and electrical conductivity (EC, e). Combined, these data contribute a significant amount, 51 glasses, to the database for high LAW loaded enhanced waste glasses. Most of these data are focused near the boundaries of acceptable PCT and VHT responses, where prediction uncertainties are most impactful.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Hanford low-activity waste glass composition-temperature-melt viscosity relationships

This study developed a model for predicting viscosity of alkali-alumino-borosilicate glass melts as functions of composition and temperature. The model is based on a total of 3935 viscosity-temperature data from 574 glasses with viscosity values ranging from 2.53 to 7260 Poise (P) in the temperature range of 900–1260°C. Several different model forms were surveyed, including those based on Arrhenius, Vogel-Fulcher-Tammann, Avramov-Milchev, and Mauro-Yue-Ellison-Gupta-Allen. For each of these models, combinations of the temperature-independent parameters were fitted to composition. It was found that generally fitting more than one temperature-independent parameter as functions of composition resulted in overfitting. The Avramov-Milchev-based model was found to best represent the Hanford low-activity waste glass melt viscosity data based on model fit and validation statistics. A 21-term partial quadratic mixture model was recommended for use. This model predicts melt viscosity with a root-mean square error of .1736 ln(P), which is similar to the error in viscosity measurements from replicate glass analyses of .1383 ln(P). Viscosity was found to be most increased by SiO 2 > Al 2 O 3 > ZrO 2 > SnO 2 and most decreased by Li 2 O > Na 2 O > B 2 O 3 > CaO > K 2 O > MgO, at temperatures from 900 to 1260°C.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 4

This report summarizes and analyzes the data collected on a fourth test matrix of 25 low-activity waste glass compositions intended to expand the composition-property database and to validate the Vienna et al. (2020) property-composition models. The 25 low-activity waste glass compositions were statistically designed to be within the composition region of the 2020 models. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, canister centerline cooling with crystal identification, product consistency test response, vapor hydration test response, and sulfur solubility. This report discusses the results obtained from these tests.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 2

The U.S. Department of Energy’s (DOE) Office of River Protection (ORP) requested Pacific Northwest National Laboratory (PNNL) to support the River Protection Project vitrification in an effort to support operations upon completion of startup activities (DOE 2012). This work was performed under the PNNL project titled “ORP Glass Support Work.” One task of this project—Enhanced Hanford Waste Glass Models—is the subject of this report. A previous task focused on generating property-composition data and models for the Hanford site low-activity waste (LAW) glasses with lower waste loadings, which are relevant to the commissioning of the LAW vitrification facility. The current task has the long-term objective of expanding the Hanford site LAW glass database and property composition models for the balance of the Hanford site tank waste treatment and immobilization mission. During the balance of the mission, LAW glasses with higher waste loadings will be produced. This report presents the glass compositions and glass property data developed in Phase 2 of the enhanced Hanford LAW glass property data development effort. When this effort is complete, enhanced LAW glass property models will be developed. Section 1.1 summarizes the status of the LAW glass composition regions and waste loading constraints prior to the data development effort documented in this report. Section 1.2 summarizes the LAW Phase 2 glass composition region and test matrix. Section 1.3 documents the quality assurance program used in performing the work discussed in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗