Results for “low radioactivity” — Page 10

DOE OSTI2020

Removal of Metal Oxides from Reactor Metals/Alloys by Laser Ablation

Storage of low and intermediate level radioactive waste in a deep geological repository (L and ILW DGR): - Large volumes of metallic waste = increased production of hydrogen gas. - Large fraction of the metallic wastes are out-of-core reactor components. - Carbon steel feeder pipes. - Radioactive contamination is concentrated within the surface oxides. - Removal of surface contaminants would lead to volume reduction of metallic waste that is stored in L and ILW DGR. - Current decontamination techniques generate large volumes of secondary waste. Objectives: Oxide removal from carbon steel using a ytterbium fiber laser (1064 nm) with a particular focus on identifying the key laser parameters that affect the removal efficiency. Advantages of laser ablation: Generate low volumes of waste, Flexibility of deployment, Remote operation. Grow an oxide film on carbon steel with the incorporation of cobalt. Optimize laser cleaning technology for the removal of surface oxides. Incorporate an oxide collection system for the ablated particles. Conclusions: Objective 1 - Simulated inactive oxides on carbon steel, using Co{sup 2+} as a surrogate for Co radionuclide. Objective 2 - Optimized laser parameters (200 MW/cm{sup 2}, 3 scans, 15 μm overlap); Complete removal of cobalt from surface and bulk metal with optimized cleaning settings. Objective 3 - Glass fiber media H14 HEPA filters had highest collection yield; High oxide collection yields (∼100%). Future Work: Further testing of the liquid trap will verify the efficacy of the HEPA filter and determine the amount of ablated oxides that pass through. Testing of the laser ablation system needs to be done on other reactor materials along with the incorporation of different transition metal cations. Improvements to the oxide collection system are required to improve collection yields.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Effect of supplementary cementitious materials on the degradation of cement-based barriers in radioactive waste repository: A case study in Korea

This study focuses on investigating the chemical degradation characteristics of cementitious barriers used in low- and intermediate-level radioactive waste repository by reactive transport modeling. The impact of the blending with supplementary cementitious materials (SCMs) in the barriers on the chemical degradation was evaluated to find the optimum barrier design. A number of different barrier designs were examined by replacing ordinary Portland cement (OPC) by SCMs (i.e., fly ash, silica fume, and blast-furnace slag). The simulation results showed that silica fume blended barrier has better durability against chemical degradation by rainwater compared to fly ash or blast-furnace slag blended barriers. In addition, the chemical durability of silica fume-based barrier increased with increasing replacement level up to about 20 %. It seems that the amount of formed calcium silicate hydrate (CSH) in the initial cement-based barrier highly affects the overall chemical durability. The newly developed reactive transport model demonstrated its capability for understanding the barrier performance and investigating the optimal design of the barrier system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Investigating radioactive negative ion production via double electron capture

The relative cross sections for radioactive negative ion production via double electron capture have been measured for collisions between a 40 keV projectile beam of uranium-238 and potassium vapor. This was performed at the collinear resonance ionization spectroscopy (CRIS) experiment at CERN-ISOLDE and is a step towards measuring the electron affinities (EAs) of elements that cannot be efficiently produced in negative ion sources at radioactive ion beam (RIB) facilities. This includes short-lived radioactive isotopes that have low production quantities and heavy and superheavy elements that systematically have smaller EAs than work functions of available ion source materials. Negative ions are particularly sensitive to electron–electron correlation effects, which make such studies ideal for benchmarking atomic structure models that go beyond the independent particle model. While the EAs of most light elements have been measured, experimental investigations on heavier elements, namely the actinides, remain scarce due to their radioactive nature and production difficulty. By developing negative ion production by charge exchange, we aim to make these studies feasible at RIB facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Development of Hopfield Artificial Neural Network for Anomaly Detection in Environmental Gamma Radiation Background: Consortium on Nuclear Security Technologies (CONNECT) (Q2 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore supervised machine learning (ML) algorithms for development of a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm for detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. In principle, gamma radiation sources can be detected and identified by their unique spectral lines. However, detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. In recent prior work, we have developed a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm to detect a weak signal anomaly hidden among the highly fluctuating background spectra. The objective of this work is to explore quantum computing methods to increase the speed of HNN. The approach is based on the Grover’s search algorithm in conjunction with a 3-SAT problem formalism. The Grover’s algorithm is implemented on a quantum computing simulator using Qiskit software. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign, where the anomaly is a subset of measurements containing a 137 Cs source. Results indicate that using Grover’s algorithm on a quantum simulator reduces runtime of HNN by two orders of magnitude.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Detritiation and Recovery of Heavy Water Moderator for Commercial Use

Historical Inventory Savannah River Site hosted five tritium production reactors from the 1950s through 1990s • Pressurized heavy water reactor • D2O moderator • Approximately 1900 m3 of legacy moderator water is in storage • Ranges from 1% D2O to 99.8% D2O • Varying amounts of tritium, relatively low amounts of other radioactive elements • Due to shortage in D2O production, US Department of Energy is investigating detritiating the heavy water for commercial uses

Angelette, Lucas M. [Savannah River National Labor↗

DOE OSTI2021

The Broadband Counterpart of the Short GRB 200522A at z = 0.5536: A Luminous Kilonova or a Collimated Outflow with a Reverse Shock?

In this work, we present the discovery of the radio afterglow and near-infrared (NIR) counterpart of the Swift short gamma-ray burst (GRB) GRB 200522A, located at a small projected offset of ≈1 kpc from the center of a young, star-forming host galaxy at z = 0.5536. The radio and X-ray luminosities of the afterglow are consistent with those of on-axis cosmological short GRBs. The NIR counterpart, revealed by our Hubble Space Telescope observations at a rest-frame time of ≈2.3 days, has a luminosity of ≈(1.3–1.7) × 10 42 erg s -1 . This is substantially lower than on-axis short GRB afterglow detections but is a factor of ≈8–17 more luminous than the kilonova of GW170817 and significantly more luminous than any kilonova candidate for which comparable observations exist. The combination of the counterpart's color (i - y = -0.08 ± 0.21; rest frame) and luminosity cannot be explained by standard radioactive heating alone. Additionally, we present two scenarios to interpret the broadband behavior of GRB 200522A: a synchrotron forward shock with a luminous kilonova (potentially boosted by magnetar energy deposition), or forward and reverse shocks from a ≈14°, relativistic (Γ0 ≳ 10) jet. Models that include a combination of enhanced radioactive heating rates, low-lanthanide mass fractions, or additional sources of heating from late-time central engine activity may provide viable alternate explanations. If a stable magnetar was indeed produced in GRB 200522A, we predict that late-time radio emission will be detectable starting ≈0.3–6 yr after the burst for a deposited energy of ≈10 53 erg. Counterparts of similar luminosity to GRB 200522A associated with gravitational wave events will be detectable with current optical searches to ≈250 Mpc.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 1. Characterizing Thermal–Hydrological–Mechanical Processes

Abstract Bentonite clay is widely used in engineered barrier systems for the permanent disposal of high‐level radioactive waste due to its low permeability, high swelling capacity, and thermal stability. However, the complex thermal‐hydrological‐mechanical (THM) processes induced by heating from decaying radioactive waste and hydration from surrounding rock can lead to heterogeneous changes that are difficult to measure and predict. This study develops an Electrical Resistivity Tomography (ERT)‐based framework for monitoring THM processes, progressing from sample‐scale to bench‐scale tests, to inform field‐scale applications. Sample‐scale tests analyzed small bentonite samples under controlled variations in water content, temperature, and porosity to establish fundamental resistivity relationships. Bench‐scale tests involved larger bentonite columns subjected to heating (up to 200°C) and hydration under controlled pressure, simulating repository conditions. ERT measurements, complemented by X‐ray CT imaging, temperature monitoring, and tracing sensors, revealed coupled THM processes, such as hydration‐induced compression, swelling, and thermal gradients, leading to complex resistivity patterns. The results demonstrate the potential of ERT for capturing THM‐induced resistivity changes, though challenges remain in upscaling and quantitative analysis. This study evaluates laboratory test capabilities and proposes future improvements for understanding THM‐induced resistivity responses. A conceptual framework for ERT implementation in field‐scale monitoring is presented, synthesizing findings from both scales and exploring how ERT data can inform long‐term modeling and reduce prediction uncertainties. Overall, this ERT‐based framework offers a robust method for monitoring bentonite buffers, aiding in early issue detection and supporting the safe long‐term disposal of radioactive waste in geological repositories, while highlighting the need for future development. Plain Language Summary Bentonite clay is crucial in engineered barrier systems (EBS) for containing high‐level radioactive waste due to its ability to absorb water, swell, seal and remain stable under high temperatures. When bentonite absorbs water and heats up from radioactive decay, it experiences complex changes in its physical and mechanical properties. Understanding these changes is important for ensuring the long‐term safety and effectiveness of EBS. This study used Electrical Resistivity Tomography (ERT), a non‐invasive method that measures electrical conductivity to monitor these changes during laboratory experiments. The ERT data revealed significant variations in resistivity corresponding to changes in water content, temperature, and density, providing detailed spatial and temporal insights into the behavior of bentonite. These findings enhance our ability to predict the long‐term performance of bentonite barriers, ensuring the safe containment of radioactive waste. By improving our understanding of bentonite's behavior, this research supports the development of more reliable and effective barrier systems for radioactive waste disposal, protecting the environment and public health. Key Points ERT monitoring was employed to capture resistivity changes in bentonite during controlled heating and hydration experiments, providing insights into THM processes ERT data reveal significant resistivity changes correlated with water content, temperature, and mechanical effects, enhancing the understanding of THM dynamics in bentonite This study explores the potential of the framework for application in field‐scale EBS monitoring, emphasizing the need for integrating additional geophysical methods for comprehensive subsurface imaging

Chen, Hang↗

DOE OSTI2025

Demonstration Test Report: Vacuum Thermal Desorption (VTD) Testing to Support West Area Waste Feed Test 2

Approximately 54 million gallons of radioactive and hazardous waste are stored in underground storage tanks at the U.S. Department of Energy’s (DOE’s) Hanford Site in southeastern Washington State. The Hanford Waste Treatment and Immobilization Plant (WTP) is being constructed to immobilize the waste in glass waste forms. The baseline configuration of the WTP includes a Pretreatment Facility to separate the wastes into a small volume of high-level waste (HLW) containing most of the radioactivity and a larger volume of low-activity waste (LAW) containing most of the non-radioactive chemicals. The HLW will be converted to glass in the HLW vitrification facility for ultimate disposal at a federal repository.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1992

Radioactivities of Long Duration Exposure Facility (LDEF) materials: Baggage and bonanzas

Radioactivities in materials onboard the returned Long Duration Exposure Facility (LDEF) satellite were studied by a variety of techniques. Among the most powerful is low-background Ge-semiconductor detector gamma-ray spectrometry, illustrated here by results obtained at the Lawrence Berkeley Laboratory's (LBL) Low Background Facilities. The observed radioactivities are of two origins: those radionuclides produced by nuclear reactions with the radiation field in orbit, and radionuclides present initially as 'contaminants' in materials used for construction of the spacecraft and experimental assemblies. In the first category are experiment-related monitor foils and tomato seeds, and such spacecraft materials as aluminum, stainless steel, and titanium. In the second category are aluminum, beryllium, titanium, vanadium, and some special glasses.

Smith, Alan R.↗

DOE OSTI2020

The Influence of Aqueous Dispersivity on Transport of Radiological Material at LANL MDA G - 20450

Aqueous dispersivity is a phenomenon by which a solute moving in groundwater mixes with water that does not contain the solute and becomes diluted. Solutes take paths of varying lengths through pore networks or experience slower velocities due to interactions with pore walls. Aqueous dispersivity leads to breakthrough curves with earlier arrival than expected in purely advective systems and persistent 'tails,' or late-arriving, dilute concentrations of the solute. Dispersivity can be longitudinal (in the direction of flow) or transverse (orthogonal to flow). LANL has disposed of radioactive material at Material Disposal Area (MDA) G since the late 1950's. Most of the disposed materials at MDA G are low-level waste (LLW), but other types of radioactive wastes are disposed or stored at the site. For example, there is a collection of remote-handled transuranic waste (RH-TRU) waste in 33 shafts, which were intended to be stored retrievably. Aqueous-phase radionuclides included in models of contaminant transport at MDA G are subject to dispersive processes in porous media of the vadose zone and saturated zone. To model dispersivity at MDA G probabilistically, we created site-specific distributions of aqueous dispersivity. First, we performed a comprehensive review of peer-reviewed publications, reports from other performance assessments of MDA G, and documentation from sites that were considered similar based on expert opinion, such as Yucca Mountain. Data were included from a wide range of sources, so that distributions developed based on the data reflect the current state of knowledge in properties of the subsurface environment. Data were grouped based on value type (e.g., determined via experiment or used in a different model), rock type, zone type (saturated or unsaturated), and site, and were weighted in the distributions according to their relevance to the site and zone type. We combined Monte Carlo simulation and bootstrapping techniques to produce approximate distributions of mean aqueous dispersivity. Distributions of dispersivity for saturated and unsaturated zone transport are differentiated by data weight and the length scales over which transport occurs. We find that underestimating or disregarding transverse dispersivity may lead to overstating simulated well capture in the saturated zone at MDA G. Neglecting transverse dispersivity is a common assumption due in part to use of one-dimensional transport models that do not consider solute spreading orthogonal to flow. Also, we find that variability in longitudinal dispersivity is associated with substantial travel time alterations in the MDA G unsaturated zone and in simulated contaminant breakthrough times at a downstream observation plane in the MDA G saturated zone. Finally, at large scales (>1000 m), a linear trend between the dispersivity coefficient and length over which transport occurs may overestimate the dispersivity coefficient. Future modeling work with application to solute spreading could include interacting advective and diffusive zones in the highly fractured basalt below MDA G. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Improving PCM Waste Management Through New Approaches to Screening for LLW Treatment and Disposal - 20145

A major step forward in the management of Plutonium Contaminated Material (PCM) Waste has been taken at the Sellafield site with the introduction of new capabilities to enable wastes previously conservatively designated as PCM Intermediate Level Waste (ILW) to be re-classified for treatment as Low Level Waste (LLW). The Sellafield site, located in the North West of England, within the United Kingdom (UK), has operated plutonium processing plants and waste facilities for almost 70 years. During this time management of the raw PCM waste produced has presented a variety of challenges. The waste is currently processed by high force waste compaction in the Waste Treatment Complex (WTC). The WTC converts 200 litre drums of raw PCM waste into a compacted, grout encapsulated monolith form within stainless steel containment, ready for emplacement within the UK's Geological Disposal Facility (GDF) when it becomes available. To help progress some of the wastes waiting for treatment, recent improvements have been made with the introduction of new waste assay instrumentation which enables the identification of a 'candidate' waste potentially suitable for disposal via the LLW treatment route. Opening a new LLW route for previously designated PCM has been a major advance in PCM waste management policy. It has required a range of technical assessment arrangements to be developed, tested and commissioned and involved the engagement of a wide range of stakeholders. In particular, very detailed technical analysis methods have been developed to ensure that there is a robust and conservative approach to the final sentencing of waste. Following completion of commissioning tests, the first 86 m{sup 3} of previously designated PCM waste was successfully treated through the LLW route and consigned to LLW disposal in June 2019. The direct benefit of sentencing waste to the correct waste route (LLW) is the significant cost savings across the complete range of waste management activities. This work is also strategically important for demonstrating to a range of stakeholders including workforce and regulators, that waste previously designated as PCM can be re-classified in a systematic and justifiable way without increasing risk to personnel or the wider environment. By ensuring that waste is being managed appropriately it will be possible to carry out more remediation work safely, in less time, and at a lower cost. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Calculating Potential Radiological Emissions for Waste Management Activities at INL - 20068

At Idaho National Laboratory (INL), work involving radionuclides is evaluated for potential emissions from a project in order to comply with the National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations, 40 CFR 61 Subpart H. Emission calculations are documented in an Air Permitting Applicability Determination (APAD) to analyze unmitigated and mitigated emissions and determine if an Application to Construct (ATC) or continuous monitoring is required. To calculate the unmitigated and mitigated emissions, a spreadsheet was developed to provide ease in determining potential emissions by providing the maximum operating temperature and the material being used. The spreadsheet aids in determining the potential emissions for research projects and waste management activities at Materials and Fuels Complex (MFC) and other locations across the INL site. Furthermore, it can also be used for periodic confirmatory measurements (PCM) to justify low emissions. Elements that factor into the unmitigated and mitigated calculations include the amount of each radionuclide used (in curies or grams), specific activity (if amount is given in grams), the temperature the material is heated to in Celsius, the dose conversion factor which is derived from Clean Air Act Assessment Package - 1988 (CAP-88) modeling, and the number of HEPA filters used for mitigated measures. The main drivers for calculating the unmitigated emissions for a project are the amount used per radionuclide, the maximum operating temperature, and the location of the work. The maximum operating temperature determines the airborne release factor which is dependent on the physical state of the radionuclide. Prior to October 2017, if the radionuclide was heated to greater than 100 deg. C, the radionuclide was assumed to be a gas, which has the highest airborne release factor. This assumption would be overly conservative for radionuclides with high melting and boiling points, which provided a challenge to demonstrate low emissions. In October 2017, the Environmental Protection Agency (EPA) approved an alternative method for INL. This method allows the airborne release factor to be determined by using the melting point and 90% of the boiling point of the radionuclide. This methodology was included in the spreadsheet to allow unmitigated emission calculations for APADs to be completed more efficiently and effectively. Results show a reduction in time completing air emission calculations as well as lower total emissions across all facilities at INL. MFC annual emissions were reduced by 62% from the previous year and Research and Education Campus (REC) facilities were reduced by 38% due to implementation of the approved alternative method. Time spent on APADs, PCMs, and documentation for the annual NESHAP report was also reduced significantly. The spreadsheet provided in Table I provides the potential emission calculations for the 'Advanced Retrieval and Disposition Techniques for Remote Handled Mixed Low Level Waste (RH MLLW) at the Radioactive Scrap and Waste Facility (RSWF)' project. Calculations show the Potential Effective Dose Equivalent (PEDE) at RSWF to be 7.27 E-04 mrem/yr (7.27 E-09 Sv/yr) which is well below the 0.1 mrem/yr threshold. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Chemical degradation of fly ash blended concrete with the seasonal variation of rainwater in a radioactive waste repository: A thermodynamic modeling approach

Highlights: • The thermodynamic model for the cementitious system was successfully developed. • The chemical degradation of concrete was greatly affected by rainwater types. • Inorganic carbon species in rainwater reduced the chemical resistance of concrete. • The durability of concrete to rainwater decreased with increasing fly ash content. This study presents a long-term degradation behavior of fly ash blended concrete in a vault type low- and intermediate-level radioactive waste (LILW) repository by thermodynamic equilibrium calculations using PHREEQC combined with CEMDATA18 database. Since rainwater plays a predominant role as leachate for the concrete waste container in the repository, the effect of chemical properties of seasonal rainwater on the chemical degradation of concrete was analyzed. In addition, the impact of the blending with fly ash on the chemical degradation was evaluated through a gradual replacement of ordinary Portland cement (OPC) by fly ash. Regardless of the replacement level, autumn rainwater showed the greatest influence on the concrete degradation owing to the presence of inorganic carbon species, which induce the carbonation. The resistance of concrete to chemical degradation by rainwater was persistently reduced due to the decreasing formation of portlandite and calcium silicate hydrate (C-S-H) with increasing replacement level.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Boron nitride: Novel ceramic reductant for low‐activity waste vitrification

Abstract During vitrification of radioactive wastes, excessive foaming reduces processing rates within melters by hindering heat transfer from the molten glass pool to the reacting melter feed. Formulations of low‐activity waste (LAW) melter feeds, for vitrification at the Waste Treatment and Immobilization Plant at the Hanford Site, conventionally include the addition of sucrose to mitigate excessive foaming by hastening the denitration process. However, incomplete combustion of sucrose produces organics such as acetonitrile (C 2 H 3 N) that may exceed bounding limits of downstream effluent treatment facilities. Using boron nitride (BN) as an alternate reductant to sucrose, in a representative LAW melter feed reduced C 2 H 3 N production by 90% by preventing the low‐temperature sucrose–nitrate reactions. Furthermore, foaming was suppressed due to the higher decomposition temperature of BN than H 3 BO 3 meaning a delayed reaction of a large fraction of boron with the transient glass‐forming melt until above the foam onset temperature, thus reducing the quantity and viscosity of the connected melt and trapping less gas in the foam layer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗