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At least 289 records · Page 16

Summary of Gas Generation Behavior Observed in 3013 Surveillance and Monitoring Program Shelf-Life Experiments

Gas generation experiments have been conducted in small- and full-scale test containers at Los Alamos National Laboratory on samples of plutonium oxide material collected from plutonium processes across the DOE complex and tested at the bounding conditions for the Department of Energy 3013 Standard. The gas composition and pressures in the sealed experimental containers were measured over periods of months to years. These experiments have provided results for the formation and consumption of hydrogen and other gases. The conditions supporting the formation of flammable gas mixtures of hydrogen and oxygen in flammable gas mixtures were also determined. Different behaviors were observed between the materials tested based on their compositions, the stabilization performed on the material, and the post stabilization handling of the material. Many of the experiments are still ongoing. This report summarizes the results obtained for the gas generation behavior for high-purity plutonium oxides and salt-bearing impure plutonium oxides in sealed containers.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Impact of Oxygen Storage Components in Prototype Pd-Based Three-Way Catalysts under Exhaust Conditions Relevant to Propane Engines

With increasing concerns about global warming, the push for sustainable and eco-friendly fuels is accelerating. Propane, recognized as liquefied petroleum gas or LPG, has garnered research interest as an alternative fuel due to its notable advantages, including a high-octane rating, reduced greenhouse gas emissions, and potential cost-effectiveness. However, to realize its full potential as an alternative fuel it is essential to develop catalysts that efficiently handle emissions at low temperatures. In our research, we investigated three distinct palladium (Pd)-based three-way catalyst (TWC) formulations (PdRh, Pd-only, and Pd-OSC) to investigate the influence of typical TWC components rhodium (Rh) and oxygen storage components (OSC) in exhaust scenarios relevant to propane-fueled engines. Among these, the formulation containing oxygen storage components (Pd-OSC) showed the highest reactivity for both NO and C 3 H 8 while minimizing performance degradation from hydrothermal aging (HTA). Notably, the temperature of 50% conversion (T50) for propane in the Pd-OSC fresh and HTA sample was lower by 30 °C and 13 °C, respectively, compared to the Pd-only sample, highlighting the role of oxygen storage materials in enhancing catalyst performance, even without dithering. Additionally, N 2 physisorption showed that the PdOSC sample has a higher surface area and increased pore volume. This underscores the idea that OSC materials not only augment the catalyst’s porosity but also optimize reactant accessibility to active sites, thus elevating catalytic efficiency. In addition to evaluating performance, we further explored the performance and characteristics of the catalysts using catalytic probe reactions, such as water–gas shift and steam reforming reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution Systems

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. In conclusion, the framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Multiscale Shear Properties and Flow Performance of Milled Woody Biomass

One dominant challenge facing the development of biorefineries is achieving consistent system throughput with highly variant biomass feedstock quality and handling performance. Current handling unit operations are adapted from other sectors (primarily agriculture), where some simplifying assumptions about granular mechanics and flow performance do not translate well to a highly compressible and anisotropic material with nonlinear time- and stress-dependent properties. This work explores the shear and frictional properties of loblolly pine at multiple experimental test apparatus and particle scales to elucidate a property window that defines the shear behavior over a range of material attributes (particle size, size distribution, moisture content, etc.). In general, it was observed that the bulk internal friction and apparent cohesion depend strongly on both the stress state of the sample in granular shear testers and the overall particle size and distribution span. For equipment designed to characterize the quasi-static shear stress failure of bulk materials ranging from 50 to 1,000 ml in test volume, similar test results were observed for finely milled particles (50% passing size of 1.4 mm) with a narrow size distribution (span between 10 and 90% passing size of 0.9 mm), while stress chaining and over-torque issues persisted for the bench-scale test apparatus for larger particle sizes or widely dispersed sample sizes. Measurement of the anisotropic particle–particle friction ranged from coefficients of approximately 0.20 to 0.45 and resulted in significantly higher and more variable friction measurements for larger particle sizes and in perpendicular alignment orientations. To supplement these laboratory-scale properties, this work explores the flow of loblolly pine and Douglas fir through a pilot-scale wedge-shaped hopper and a screw feeder. For the gravity-driven hopper flow, the critical arching distance and mass discharge rate ranged from approximately 10 to 30 mm and 2 to 16 tons/hour, respectively, for both materials, where the arching distance depends strongly on the overall particle size and depends less on the hopper inclination angle. Comparatively, the auger feeder was found to be much more impacted by the size of the particles, where smaller particles had a more consistent and stable flow while consuming less power.

09 BIOMASS FUELS↗

SDSS-IV MaNGA: pyPipe3D Analysis Release for 10,000 Galaxies

We present here the analysis performed using the pyPipe3D pipeline for the final MaNGA data set included in the Sloan Digital Sky Survey data release 17. This data set comprises more than 10,000 individual data cubes, being the integral field spectroscopic (IFS) galaxy survey with the largest number of galaxies. pyPipe3D processes the IFS data cubes to extract spatially resolved spectroscopic properties of both the stellar population and the ionized gas emission lines. A brief summary of the properties of the sample and the characteristics of the analyzed data are included. The article provides details of: (i) the analysis performed; (ii) a description of the pipeline; (iii) the adopted stellar population library; (iv) the morphological and photometric analysis; (v) the adopted data model for the spatially resolved properties derived; and (vi) the individual integrated and characteristic galaxy properties included in the final catalog. Comparisons with the results from a previous version of the pipeline for earlier data releases and from other tools using this data set are included. A practical example of how to use the full data set and the final catalog illustrates how to handle the delivered product. Our full analysis can be accessed and downloaded from our web page.

79 ASTRONOMY AND ASTROPHYSICS↗

Novel Preprocessing Techniques for Enhancing Flowability of Miscanthus

Purpose-grown energy crops possess significant potential as promising feedstocks for biofuel and biochemical production. However, similar to other biomass feedstocks, their utilization has been hindered by handling and feeding challenges such as clogging and segregation, which prevent biorefineries from operating at full capacity. These challenges arise primarily due to particle attributes including irregular shape, high flexibility, and high compressibility, leading to interlocking, high compaction, and significant friction under stress consolidation. To enhance operational reliability, it is crucial to focus on preprocessing techniques to address these issues, alongside accurately characterizing the mechanical and physical properties and flow performance of these feedstocks. This study investigates novel preprocessing techniques, including pelletization and torrefaction, to fundamentally alter these particle attributes and improve flowability. Miscanthus, an herbaceous energy crop, was the primary focus of this study. Samples were processed using a Forest Concepts Crumbler rotary shear system and an oscillating multi-stack screen bed to achieve two nominal (baseline) particle sizes: 6mm and 2mm. The study evaluated the efficiency and energy consumption of size reduction, revealing that while 2mm particles required more energy, they exhibited better flowability and higher yield strength compared to 6mm particles. Shear and compression tests assessed the material's shear strength, internal friction, and elastic modulus, while wedge-shaped hopper tests measured the critical arching distance and mass flow rate. The results provide valuable insights into the mechanical properties and processing efficiency of Miscanthus, contributing to the development of a preprocessing framework that enhances energy efficiency and material flowability in bioenergy production. However, further preprocessing and development are required to optimize this framework fully.

09 - BIOMASS FUELS↗

High Solids Loading Aqueous Slurry Formation of Corn Stover Before Pretreatment in a Fed-batch Bioreactor

With the increase on population the world will depend on renewable sources to meet the increasingly energy needs. Use of lignocellulosic biomass as a renewable source has been proven efficient for conversion to cellulosic ethanol and capable of contributing to achieve the threshold on energy demand while reducing greenhouse gases in 90% when compared with fossil fuels (Wang et al., 2007, 2012). However, the processing of biomass encounters limitations in feeding and flow within biorefineries due to the system plugging by compaction and slurry high yield stress, preventing transport of biomass materials and in some cases unexpected plant shutdowns that results in high operational costs (dos Santos et al., 2021; Ximenes et al., 2021). Different solutions for biomass handling and slurry formation from densified materials like pellets have been studied. Cellulosic biomass residues are typically processed in pretreatment reactors to which acid or base is added (Humbird et al., 2002). Other pretreatments such as liquid hot water (LHW) and steam explosion that act without the addition of chemicals use pressures above the saturation vapor of water, to disrupt biomass structure (Ruiz et al., 2021). Although these methods are widely known, the use of pretreatments like acid digestion and LHW present challenges like waste disposal and could be energy inefficient (Mosier et al., 2005). Alternatively other approaches like enzymatic liquefaction have been applied for biomass transformation into slurries with promising results and without the use of pretreatments (dos Santos et al., 2021). Nevertheless, enzymatic liquefaction implementation faces difficulties due to the recalcitrant properties of biomass, its variability, and the release of enzyme inhibitors. While it has been proven that chemical composition properties in biomass have effects on enzyme inhibition (Huang et al., 2022; Kim et al., 2011; Zhai et al., 2018), hindering the efficiency of the liquefaction, not ample research has been conducted in understanding the physical properties as particle size, porosity, water adsorption retention and their effect in the ability to form a slurry at high solids concentration (Yan et al., 2020). In order to improve lignocellulosic biomass handling and formation of biomass slurries, enzyme assisted liquefaction for slurry creation from corn stover at solids loadings up to 30% is reported in this work. Two different kinds of biomass (pelleted corn stover and cobs) were liquefied in a fed-batch process using commercial enzymes Celluclast 1.5L or Ctec-2 at 1FPU or 3 FPU per gram of dry solids in 10 mM sodium citrate buffer solution (pH 4.8). Pellets were fed into a 1 L stirred bioreactor according to a predefined fed-batch protocol over the first 5 hours until reaching 30% of solids loading. After 6, 24 and 96 hours, samples were taken and characterized with respect to their sugar composition, rheology and water absorption. Successful slurry creation with dramatically reduced yield stress was achieved for corn stover for both assessed enzymes. Yield stresses of 178±7 Pa (3 FPU, Celluclast 1.5L) and 79±6 Pa (3 FPU, Ctec-2) were measured for corn stover at 24 hours, compared to 6,000 Pa for samples without enzyme. Yield stress was 155± 29 Pa (3FPU, Ctec-2) and 257 ± 72 Pa (1 FPU, Celluclast 1.5L) for corn cobs at 24 hours. Yield stress decreased when residence time increased with an enhanced fluidity noted for higher enzyme concentrations. A profile for 6, 24 and 96h of yield stress measurements is presented.

Gutierrez, Diana↗

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

frequentist statistics↗

A Rapid Sequential Separation Method for Determination of Actinides and TENORM in Fracking Wastes - 20223

With recent advances in unconventional drilling technology in the US and around the world, the risks of environmental contamination and exposure due to technologically enhanced naturally occurring radioactive materials (TENORM) from fracking wastes have also surfaced. Although the Permian Basin (southeastern New Mexico, western Texas) has long been a modest producer of oil, the advent of fracking technology a decade ago turned it into hot property for producers. A 2018 assessment of undiscovered, technically recoverable continuous oil and gas resources by the U.S. Geological Survey estimates an average 46.3 billion barrels of oil and 281 trillion cubic feet of gas. This is likely to increase the use of unconventional drilling and TENORM generation. TENORM concentrations in oil and gas exploration and production waste can be several hundred to several thousand times higher than background TENORM concentrations. Both the thorium and uranium decay chains contribute to airborne radionuclides arising from the radon gas escaping the ground and subsequently decaying as airborne particulates. Additionally, radon decay products {sup 210}Pb and {sup 210}Po can build up in scale on the internal surfaces of oil and gas handling pipes and in sludge in refineries, becoming potential inhalation and ingestion hazards for workers. Furthermore, southeastern NM is also home to world's only licensed and operating transuranic nuclear waste repository, the DoE's Waste Isolation Pilot Plant (WIPP). Plutonium isotopes ({sup 239+240}Pu) and {sup 241}Am, are expected to account for more than 99% of the total radioactivity scheduled for disposal in the WIPP repository. Thus, an improved understanding of the environmental fate and transport of TENORM, liberated by unconventional drilling, is essential to assess how best to protect individuals and the environment. In this context, accurate measurement of TENORM and actinides in environmental and biological samples is essential. In this presentation, a new sequential method for the separation and pre-concentration of actinides (Pu, Am, Np) and TENORM (Po, U and Th) derived from oil and gas exploration is proposed. The TEVA method involves a rapid co-precipitation step to remove matrix interferences from the samples, followed by plutonium oxidation state adjustment to Pu (IV) and an incubation period of ∼ 1 hour at 50-60 deg. C to allow the resultant Po (II) to oxidize into Po(IV). The polonium, neptunium, thorium and plutonium are then separated on a TEVA column, while americium and uranium are separated on a TRU column. After separation, the alpha counting source was prepared by micro-precipitation with copper sulfide (CuS) for polonium and neodymium fluoride (NdF{sub 3}) for actinides. The efficiency and reliability of the procedures were tested by analyzing filter, drinking water and frack sand samples. The method is simple, robust and can be performed quickly with excellent removal of interferences, high chemical recovery and very good alpha peak resolution. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

Risk Assessment in a Chemical Laboratory Following an Explosive Incident Involving a Novel Diazonium Compound: Retrospective Analysis and Lessons Learned

Diazonium compounds are synthetically useful in the production of dyes and textiles, however they are highly explosive under dry conditions. Explosion prevention becomes more difficult when new diazonium compounds are synthesized, because while some syntheses include a counterion to increase their stability, this is not always a reliable method to prevent an explosive incident. Due to the uncertainty surrounding the explosiveness of different diazonium compounds, it is important to understand how to safely clean up after an incident and how to determine when it is safe to return a laboratory to typical operational use, particularly when the incident involves a novel compound where a standard does not exist for instrument calibration. Here, an explosive event is discussed involving the synthesis of 4-bromo-benzenediazonium-2-carboxylate. Following the explosive incident and 3-step cleanup, which involved a precautionary neutralization step, samples were collected from the fume hood where the incident occurred. Because the incident involved an unstable, novel compound that is not commercially available and was deemed unsafe to resynthesize for instrument calibration, we assessed the risk of further explosion by analyzing for the stable decomposition products. Mass spectrometry analysis confirmed that the residue in the fume hood contained 5-bromosalicylic acid, a decomposition product of 4-bromo-benzenediazonium-2-carboxylate. Samples were taken from multiple points in the fume hood and analyzed to estimate the spatial distribution of the decomposition product. Based on this analysis, we inferred that the primary decomposition product was far more abundant than residual energetic, indicating the energetic had been consumed or neutralized to a trace quantity where the risk of further explosion was low. Furthermore, the steps presented here─specifically, initial neutralization and then analyzing the spatial distribution of expected decomposition products to assess risk when a novel explosive material is detonated in a confined space─were our approach to assess further risk following an explosion due to a novel diazonium compound without the need for any further handling or resynthesis of the energetic. Here, we present our approach and critically analyze these steps by discussing retrospective lessons learned and alternative analytical approaches.

Computer simulations↗

The Twins Embedding of Type Ia Supernovae. I. The Diversity of Spectra at Maximum Light

In this work, we study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We decompose the diversity of these spectra into different extrinsic and intrinsic components, and we construct a nonlinear parameterization of the intrinsic diversity of SNe Ia that preserves pairings of "twin"SNe Ia. We call this parameterization the "Twins Embedding."Our methodology naturally handles highly nonlinear variability in spectra, such as changes in the photosphere expansion velocity, and uses the full spectrum rather than being limited to specific spectral line strengths, ratios, or velocities. We find that the time evolution of SNe Ia near maximum light is remarkably similar, with 84.6% of the variance in common to all SNe Ia. After correcting for brightness and color, the intrinsic variability of SNe Ia is mostly restricted to specific spectral lines, and we find intrinsic dispersions as low as ~0.02 mag between 6600 and 7200 Å. With a nonlinear three-dimensional model plus one dimension for color, we can explain 89.2% of the intrinsic diversity in our sample of SNe Ia, which includes several different kinds of "peculiar"SNe Ia. A linear model requires seven dimensions to explain a comparable fraction of the intrinsic diversity. We show how a wide range of previously established indicators of diversity in SNe Ia can be recovered from the Twins Embedding. In a companion article, we discuss how these results can be applied to the standardization of SNe Ia for cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗

Limited-angle computed tomography with deep image and physics priors

Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach.

47 OTHER INSTRUMENTATION↗

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.

54 ENVIRONMENTAL SCIENCES↗

Storage-Induced Collapse of Lignin Macromolecular Structure and Its Impacts on the Biorefinery

Lignin plays a vital role in the economics of biorefineries, serving as a source of process energy and a feedstock for sustainable fuels and chemical production. While understanding lignin’s chemical composition is crucial, emerging evidence suggests that a more comprehensive understanding of its macromolecular structure is critical to explaining its complex behavior in the biorefinery. This study investigated the partial collapse of the lignin network in corn stover feedstock after harvest and storage as a result of the microbial digestion of hemicellulose. Fluorescence microscopy was used to detect the collapse of lignin in terms of lignin’s inter-molecular interaction and the re-orientation of lignin’s chromophores, by the changes in lignin’s fluorescence lifetime, anisotropy, and the number of effective emitters. With minimal sample perturbation, our in-situ microscopic results revealed lignin's coil-globule transition phenomena, which was only previously predicted by molecular dynamics modeling extracted lignin in solvent. This collapse of lignin macromolecular structure was confirmed by results from NMR, IR, Raman, and powder X-ray diffraction. We also investigated the impact of this storage-induced collapse on the downstream biorefinery processes. Our study revealed that the two major approaches for lignin valorization in the lignin-first biorefinery model, namely monomer extraction and milled wood lignin extraction, were negatively impacted by the lignin collapse. As changes during storage are a source of feedstock variability, our study highlights the importance of understanding the effect of feedstock handling on biorefinery operations and economics.

09 BIOMASS FUELS↗

Cask RT-100 Type B(U) Self-Certification for DOT 7A, Type A Nuclear Waste Transportation - 20098

The purpose of this Type A certification is to diversify the use of the RT-100 cask by allowing the end user to transport either Type A or Type B radioactive waste in accordance with the handling procedures for each waste category. The project's scope included the development of a matrix that contains all applicable requirements for a Type A package from the Code of Federal Regulations (CFR), Title 49 for the Department of Transportation (DOT) with a justification for each requirement. Moreover, the scope included the writing of conditions and restrictions list for the transportation of Type A nuclear waste, the writing of a compliance statement, and the design and fabrication of a Type A placard to be permanently placed on the RT-100 cask. Type A packages are self-certified by the end user using one of three methods as permitted by 49 CFR 173.461, either by performance of tests with prototypes or samples of the specimens representing the packaging, or reference to a previous satisfactory demonstration of compliance of a sufficiently similar nature, or calculations or reasoned evaluation using reliable and conservative procedures and parameters. Since the RT-100 cask was licensed by the Nuclear Regulatory Commission (NRC) as a Type B cask, Robatel relied on the method of reference to a previous satisfactory demonstration of compliance of a sufficiently similar nature for Type A certification. This method was determined to be acceptable, since Type B packages are evaluated for more demanding transport conditions than those required for Type A packages. This paper presents Robatel's process for certifying the RT-100 cask for Type A shipments, an overview of the DOT regulations related to Type A packages, and the documents that were generated for the end user. This paper is concluded with a discussion on the advantages of having the option to transport both Type A and Type B nuclear waste, and possible future projects to further improve the operation of the cask when making Type A shipments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗

MapsTorch : automatic differentiation for X-ray fluorescence data analysis

X-ray fluorescence (XRF) is a popular spectroscopy technique for elemental analysis. Spectrum fitting and parameter tuning are at the core of XRF analysis and are conventionally manually intensive, especially for synchrotron experiments involving large amounts of diverse samples. This work introduces the automatic differentiation (AD) technique to XRF and an open-source package called MapsTorch. By transforming an analytical model of the XRF spectrum into a differentiable computation graph with AD, MapsTorch enables robust optimization of parameters and elemental intensities. We evaluate MapsTorch by conducting computational experiments on a large number of historical synchrotron XRF datasets and compare its performance with the currently practiced fitting tool NLopt. The results show that MapsTorch consistently achieves high-quality fits and often leads to better fitting quality than NLopt, particularly in tasks such as initial spectrum fitting and elemental intensity refinement. The robust performance of MapsTorch paves the way for developing automated and high-throughput XRF data analysis workflows to handle the increasing data volumes expected from next-generation synchrotron facilities.

X-ray fluorescence↗