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DOE OSTI2024

A Framework for Addressing Hydropower Modeling Gaps in Electric Grid Planning and Operational Studies [Slides]

Realistic representation of hydropower in power system planning and operation studies is extremely important as it helps in avoiding under/over-estimation of the services the hydro power units can provide. A framework and tools have been developed to account for hydrological conditions and modify power system models accordingly. For example, HyDat – data and AI driven platform, that provides that data required for editing the power system model files Collaborative effort with V&R Energy. Also, development of POM based tools have been performed, a) Tool1: editing .raw files with realistic current and maximum hydrogeneration and redispatch of other units, and b) Tool2: Editing .dyr files to represent water head, current and maximum generation. Finally the framework, HASP framework combines HyDat and V&R energy tools to produce modified power system model files. Key Take-away points from contingency analysis studies include a) the number of critical contingencies increases as the water levels are reduced (70%>75%>80%), and b) both the extent and the quality of dynamic response of the Hydropower unit is different with varying water head levels.

13 HYDRO ENERGY↗

DOE OSTI2025

Understanding the structure and mechanism of Na + diffusion in NASICON solid-state electrolytes and the effect of Sc- and Al/Y-substitution

NASICON (sodium superionic conductor) based ceramics are one of the most promising classes of solid-state electrolytes for all-solid-state batteries. However, the mechanism of sodium ion diffusion is not understood in great detail since there is still a discrepancy between reported average structure models, local structures, and the number and position of sodium sites. To close this gap, we investigate the underlying diffusion mechanism and structural changes governing the Na + transport in Na 3.4 Zr 2 Si 2.4 P 0.6 O 12 using quasielastic neutron scattering (QENS) and powder X-ray diffraction (XRD). In the temperature range from 298 K to 640 K, the correlations between structural changes of a monoclinic C2/c to rhombohedral R $\bar{3}$c phase transition and the result of ion diffusion are investigated. The analysis of the quasielastic neutron scattering data reveals two quasielastic components corresponding to the Chudley-Elliott jump-diffusion model. It clearly shows two different Na + diffusion processes, local and long-range, on two different time and length scales and allows calculations of their corresponding activation energies. Additionally, the effects of Sc 3+ and Al 3+ /Y 3+ aliovalent substitution of Zr 4+ ions on the crystal structure and Na + diffusion are also studied. We can distinguish a local, chain, and cross-chain diffusion mechanism based on correlated QENS and XRD comparison of relevant nearest crystallographic Na–Na distances. The results reveal that the Na + diffusion in these NASICONs is three-dimensional and can provide guidelines on how dopants and changes in the crystal structure can affect the Na + conductivity.

25 ENERGY STORAGE↗

DOE OSTI2022

Observational window effects on multi-object reverberation mapping

ABSTRACT Contemporary reverberation mapping campaigns are employing wide-area photometric data and high-multiplex spectroscopy to efficiently monitor hundreds of active galactic nuclei (AGNs). However, the interaction of the window function(s) imposed by the observation cadence with the reverberation lag and AGN variability time-scales (intrinsic to each source over a range of luminosities) impact our ability to recover these fundamental physical properties. Time dilation effects due to the sample source redshift distribution introduce added complexity. We present comprehensive analysis of the implications of observational cadence, seasonal gaps, and campaign baseline duration (i.e. the survey window function) for reverberation lag recovery. We find that the presence of a significant seasonal gap dominates the efficacy of any given campaign strategy for lag recovery across the parameter space, particularly for those sources with observed-frame lags above 100 d. Using the Australian Dark Energy Survey as a baseline, we consider the implications of this analysis for the 4MOST/Time-Domain Extragalactic Survey campaign providing concurrent follow-up of the Legacy Survey of Space and Time deep-drilling fields, as well as upcoming programmes. We conclude that the success of such surveys will be critically limited by the seasonal visibility of some potential field choices, but show significant improvement from extending the baseline. Optimizing the sample selection to fit the window function will improve survey efficacy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Mechanical Loads on Spent Nuclear Fuel in the General 30 cm Package Drop Scenario

The U.S. Department of Energy Office of Nuclear Energy (DOE-NE) Spent Fuel and Waste Science & Technology (SFWST) research program is guided by the high-level goal of closing prioritized knowledge gaps related to spent nuclear fuel (SNF) storage and transportation, which are summarized by Saltzstein et al. (2020). One of the high-priority knowledge gaps is the identification and quantification of mechanical loads that are expected to affect SNF during normal conditions of transportation and storage to inform the range of physical SNF test programs. This report uses modeling and analysis methods to estimate the mechanical loads on spent nuclear fuel (SNF) in the general 30 cm package drop scenario. The drop scenario assumes impact limiters are in place in the transportation configuration and the impact surface is perfectly rigid. The goal of this analysis is to consider the universe of potential mechanical loading conditions that can happen to SNF and present the results in a manner that is useful for materials testing, decision making, and regulatory rule making purposes. This study uses validated finite element models and methods to perform a broad parametric study of key variables that can affect the mechanical loads on SNF during a hypothetical package free drop scenario. Physical drop test data from a cask and fuel assembly drop test campaign is the basis for model validation. Additionally, the results of the parametric study are used to inform a damage model, which uses multiple nonlinear regression to estimate the relationships between input variables and output response. The parametric finite element analyses consider thousands of input variable combinations, while the damage model estimates millions of combinations. The breadth of this study provides confidence that the potential range of mechanical loads that SNF might experience during the general 30 cm package drop scenario are characterized well enough to consider this knowledge gap closed. While this report documents the overall peak values calculated in this study, the 95 th percentile values, the histograms, and the observed trends are equally important. This study covered a large range of SNF temperatures, room temperature to 300°C, and burnups, 10 GWd/MTU to 62 GWd/MTU. Each temperature and burnup combination has a different cladding yield strain, so it is more meaningful to summarize the calculated cladding strain response as its factor of safety, which is defined relative to the yield strain. The factor of safety is calculated as the yield strain divided by the peak cladding strain. A factor of safety greater than unity indicates that the cladding remains below yield, whereas a value less than unity is indicative of plastic deformation. In all cases of this study a safety factor over 1.0 was calculated, although in the most limiting case at 300°C the safety factor was only 1.01, which suggests that yielding could occur when additional loads like rod internal pressure are included. When the temperature is restricted to 200°C the limiting safety factor increases to 1.28, which has significant margin to accommodate internal pressure and potential local cladding defects that could cause a local stress concentration. An important trend in the calculated fuel rod mechanical loads is that the 2 nd highest loaded fuel rod in an assembly tends to be significantly lower than the highest loaded rod. The implication is that even if one rod in an assembly experiences a failure the loads would have to be significantly higher to cause two or more rods to fail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Overcoming persistent challenges in putting environmental flow policy into practice: a systematic review and bibliometric analysis

The implementation of environmental flows (e-flows) aims to reduce the negative impacts of hydrological alteration on freshwater ecosystems. Despite the growing attention to the importance of e-flows since the 1970s, actual implementation has lagged. Therefore, we explore the limitations in e-flows implementation, their systemic reasons, and solutions. We conducted a systematic review and a bibliometric analysis to identify peer-reviewed articles published on the topic of e-flows implementation research in the last two decades, resulting in 68 research and review papers. Co-occurrence of terms, and geographic and temporal trends were analyzed to identify the gaps in environmental water management and propose recommendations to address limitations on e-flows implementation. We identify the underlying causes and potential solutions to such challenges in environmental water management. The limitations to e-flow implementation identified were categorized into 21 classes. The most recognized limitation was the competing priorities of human uses of water (n = 29). Many secondary limitations, generally co-occurring in co-causation, were identified as limiting factors, especially for implementing more nuanced and sophisticated e-flows. The lack of adequate hydrological data (n = 24) and ecological data (n = 28) were among the most mentioned, and ultimately lead to difficulties in starting or continuing monitoring/adaptive management (n = 28) efforts. The lack of resource/capacity (n = 21), experimentation (n = 19), regulatory enforcement (n = 17), and differing authorities involved (n = 18) were also recurrent problems, driven by the deficiencies in the relative importance given to e-flows when facing other human priorities. In order to provide a clearer path for successful e-flow implementation, system mapping can be used as a starting point and general-purpose resource for understanding the sociohydrological problems, interactions, and inherited complexity of river systems. Secondly, we recommend a system analysis approach to address competing demands, especially with the use of coupled water-energy modeling tools to support decision-making when hydropower generation is involved. Such approaches can better assess the complex interactions among the hydrologic, ecological, socioeconomic, and engineering dimensions of water resource systems and their effective management. Lastly, given the complexities in environmental water allocation, implementation requires both scientific rigor and proven utility. Consequently, and where possible, we recommend a move from simplistic flow allocations to a more holistic approach informed by hydroecological principles. To ease conflicts between competing water demands, water managers can realize more 'pop per drop' by supporting key components of a flow regime that include functional attributes and processes that enhance biogeochemical cycling, structural habitat formation, and ecosystem maintenance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Modeling of the metal–insulator transition temperature in alio-valently doped VO 2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO 2 ) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO 2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

36 MATERIALS SCIENCE↗

DOE OSTI2021

An effective QWBA/UHPLC-MS/tissue punch approach: solving a pharmacokinetic issue via quantitative Met-ID

Methods to provide absolute quantitation of the administered drug and corresponding metabolites in tissue in a spatially resolved manner is a challenging but much needed necessity in pharmaceutical research. Quantitative whole-body autoradiography (QWBA) after a single-dose intravenous (3 mg/kg) and extravascular (30 mg/kg) administrations of an in vitro metabolically stable test compound (structure not reported here) indicated quick tissue distribution and excretion. Good bioavailability and short in vivo half-lives were determined formerly for the same test compound. For closing gaps in the understanding of pharmacokinetic data and in vitro results, radioactive hot spots on whole-body tissue sections had been profiled. Punches from selected tissue regions containing high radioactivity in the tissue sections previously analyzed by QWBA were extracted by a highly organic solvent and analyzed without any consecutive sample preparation step, applying ultra high performance liquid chromatography-mass spectrometry (UHPLC-MS) and off-line radioanalysis to maximize signal levels for metabolite identification and profiling. The analysis revealed that the test compound was metabolized intensively by phase I reactions in vivo and the metabolites formed were excreted in bile and in urine. The predominant metabolites showed abundant signal intensities both by MS and by radioanalysis but the MS signal intensities generally underestimated the real abundances of metabolites relative to the unchanged drug. In this work we illustrate that maximizing the sensitivity of tissue punch radioanalysis and the combination with UHPLC-MS leads to a better insight of pharmacokinetic processes by providing quantitative data with high molecular selectivity.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Data for Application of Time-Domain 1H NMR for Investigating Dynamics of Vegetative Lipids in Bioenergy Crops at Different Developmental Stages

Sweet sorghum is typically cultivated for the food and fodder market. Recently, sweet sorghum varieties are being metabolically transitioned to enhance energy density by accumulating oil droplets in their vegetative tissues for bioenergy applications. Owing to the high biomass yield of sorghum, the transgenic lines can compete with oil-seed crops for biodiesel yield per unit area. In the initial phase of transgenic development, a high-throughput phenotyping method can bridge the gap between the production pipeline and analysis to improve the efficiency of the process. To meet the requirement, the present study extends the application of time-domain 1H-NMR spectroscopy for rapid quantification and characterization of the total in-situ lipids of sweet sorghum ‘ramada’ to lay the groundwork for analyzing the upcoming large quantity of transgenic samples. NMR technology has been successfully established for analyzing lipid contents of vegetative tissues of non-transgenic variety. The multiexponential analysis of spin-lattice (T1) relaxation spectra obtained from TD-NMR aided the investigation of the dynamics of the free and bound lipid fraction with plant development. The total lipid concentration of bagasse and leaves of non-transgenic sweet sorghum remained unchanged throughout the plant development. Leaves displayed a higher percentage of bound lipids as compared to bagasse. A significant variation in the lipid concentration of juice was observed at the different growth stages with a maximum lipid accumulation of 1.21 ± 0.04% w/w at the boot stage that decreased with further maturity of the plant.

Biomass Analytics↗

DOE OSTI2024

From roads to roofs: How urban and rural mobility influence building energy consumption

In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

4.2.1.31 Integrated Life Cycle Sustainability Analysis

This project provides the Department of Energy's Bioenergy Technologies Office (BETO) with strategic decision-support for the evaluation of its R&D portfolio by developing, validating, and applying a coherent methodology and consistent model framework to quantify the net effects of an expanding US bioeconomy. The framework fills an analysis gap previously identified by Peer Review and supports a related milestone in BETO's Multi-Year Program Plan. The framework was scoped with inputs from practitioners in academia, national laboratories, and federal agencies. The model is a top-down, economy-wide framework using a coherent methodology to compute environmental and socio-economic metrics. It is purposefully complementary to existing bottom-up, process-based techno-economic and life cycle assessment BETO tools and uses their data as inputs. Presently, the model covers several commercial and near-commercial biofuel routes and an emerging pathway for plastics upcycling. It covers temporal detail across four time-steps and is currently being expanded with a prospective modeling capability. The model has provided analyses for the Third Triennial Report to Congress (RtC3) on the environmental impacts of the Renewable Fuel Standard (RFS2), among others. As part of this project, NREL also provides scientific support to BETO in the International Energy Agency's Technology Collaboration Program on Bioenergy (IEA Bioenergy) Task 45 on Sustainability. Here, NREL evaluates and synthesizes activities that develop, compare, or apply metrics, methods, and tools to quantify sustainability effects of bioeconomy products. NREL also coordinates related national lab involvement and a BETO Working Group on Sustainable Land Management.

bioeconomy↗

DOE OSTI2025

GeoBridge: Unearthing Insights from Connecting Communities to Geothermal Information and Opportunities: Preprint

Knowledge is essential for overcoming obstacles in the development and adoption of geothermal technologies, and the geothermal community is home to numerous tools, events and organizations dedicated to sharing knowledge. However, many of these tools can be difficult to find, their resources undiscoverable by search engines, available only to members, or hidden away behind pay walls (Weers et al., 2024). The Department of Energy's (DOE) GeoBridge was developed by the National Renewable Energy Laboratory (NREL) to help bridge gaps in information and connect the geothermal community to the resources it needs. Launched in October 2024, GeoBridge aspires to expand the pool of geothermal stakeholders by providing in-roads to geothermal information, tools, and community resources. It helps to make these resources available to the broader geothermal community as well as those looking to join, such as entrepreneurs or innovators in adjacent industries looking to expand into geothermal energy. This paper explores a post-launch analysis of GeoBridge including data from analytics, feedback from GeoBridge users, the geothermal community, and the GeoBridge Advisory Group as well as an analysis of efficacy of various promotions for GeoBridge.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Ceramic Composite Experimental Testing Status

Over recent years, ceramic matrix materials such as SiC–SiC and C–C have been gaining interest for use in fusion reactors, light water reactors (LWRs), and high-temperature reactors (HTRs). These materials are good candidates to operate in very high temperature and moderate to high radiation environments. The evaluation of composite materials, in general, is challenging because of variations in precursor materials, variations in the fabrication process across fabricators, and the wide range of potential fiber architectures, to name a few. However, the need to evaluate neutron-irradiated properties adds another layer of complexity, which includes cost, timeline, and specimen size limitations (often associated with irradiation testing). A qualification methodology for the use of ceramic composites is provided in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code Section III-5-HHB. The methodology is supported by ASTM International (ASTM) guides, which provide a pathway to accomplish this effort. Part of the qualification strategy is for the designer to collect material property data on environmental conditions representative of its design envelope. These data include irradiation effects. This report presents an experimental study and test campaign developed to partially address this gap by providing initial mechanical and physical property data required for design. A variety of different materials using different manufacturing techniques are considered as part of this campaign. The test plan suggests performing a screening or partial irradiation study to assist the designer during the material selection process. The designer can then perform a more comprehensive qualification study if the material performance is promising. This work focuses on the status of the specimen preparations (machining of samples), the current test methods and failure analysis as well as the preparation of irradiation vehicles for the irradiation campaign. The irradiation will be performed at Oak Ridge National Laboratory (ORNL) in the High Flux Isotope Reactor (HFIR) and at Idaho National Laboratory (INL) in the Advanced Test Reactor (ATR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Here, we develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson’s rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS 2 /WSe 2 , MoS 2 /h-BN, and MoSe 2 /CrI 3 ), to compare the band-alignment description with the predictions from Anderson’s rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the JARVIS-HETEROSTRUCTURE website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts.

2-dimensional systems↗

DOE OSTI2022

Identification of beneficial and detrimental bacteria impacting sorghum responses to drought using multi-scale and multi-system microbiome comparisons

Abstract Drought is a major abiotic stress limiting agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere. How these changes ultimately affect plant health remains elusive. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key root-associated microbes that robustly affect drought-stressed plant phenotypes. Fifty-three Arabidopsis-associated bacteria were applied to sorghum seeds and their effect on root growth was monitored. Two Arthrobacter strains caused root growth inhibition (RGI) in Arabidopsis and sorghum. In the context of synthetic communities, Variovorax strains were able to protect plants from Arthrobacter-caused RGI. As a transitional system, high-throughput phenotyping was used to test the synthetic communities. During drought stress, plants colonized by Arthrobacter had reduced growth and leaf water content. Plants colonized by both Arthrobacter and Variovorax performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across drought conditions. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a novel method and were able to observe the negative correlation between the abundance of Arthrobacter and plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of bacteria with high confidence that positively associated with plant growth under drought stress. In conclusion, a three-tiered experimental system successfully spanned the lab-to-field gap and identified beneficial and deleterious bacterial strains for sorghum under drought.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Development and Characterization of Densified Biomass-plastic Blend for Entrained Flow Gasification (Final Technical Report)

Supported by the U.S. DOE NETL Award DE-FE0032043, this project was a collaborative effort. Project participants included the University of Kentucky Institute for Decarbonization and Energy Advancement (UK IDEA), Biosystems and Agricultural Engineering department (UK BAE), and Wabash Valley Resources, LLC. The goal of this final technical project report is to comprehensively summarize the work conducted on project DE-FE0032043. In accordance with the Statement of Project Objectives (SOPO), the University of Kentucky (UK) (Project Prime Recipient) has developed and studied a biomass/plastic fuel with a hydrophobic surface area less than 10 m 2 /m 3 that is suitable for oxygen-blown entrained flow gasification with slurry feed. The project involved the utilization of an existing thermogravimetric analysis (TGA)-mass spectrometer (MS), 1.5” drop tube furnace, 1 ton per day (TPD) coal gasifier, and high-pressure extruder operated at UK. The pilot-scale production of blended material was done at the Polymers Technology Center in Charlotte, North Carolina. Parametric testing and solid fuel blend slurry performance validation was completed using the UK entrained flow gasifier with multiple opposed burners to narrow the major near-term technical gaps that impede gasification of biomass and carbonaceous mixed wastes such as plastics in order to achieve net-negative CO 2 emissions. Project results validated the UK approach to address the major technical challenges on the biomass/plastic pretreatment and gasification. Previously, this has been limited in application to fluidized-type or moving bed-type gasifiers due to the high-water uptake of porous biomass containing hydroxyl groups during the conventional slurry preparation, resulting in a highly viscous, un-pumpable slurry. The biomass pretreatment with plastic developed for this project demonstrates advantages in cost and flexibility, which include: 1) the development of a blended solid fuel slurry with 55-60 wt% solids and comparable heating value to 100% coal-based water slurry; 2) the collection of gasification kinetic data and identification of preliminary operating conditions by performing thermogravimetric analysis, gasification experiments by using a 1.5” drop tube furnace; and finally 3) the demonstrated gasification of the blended solid fuel in the UK entrained flow gasifier with a long-lasting stable solid fuel blend slurry, dataset detailing operating conditions, and characterization of slag phase formation and solidification. The lab-scale data and experience obtained during this project encourages the development of technologies and commercial approaches to enable a hydrogen-based energy economy while achieving net-negative CO 2 emissions through gasification of coal, biomass, and carbonaceous mixed wastes such as plastics.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Biofuel Air Emissions Analysis

U.S. has goals to produce 3 billion gallons of sustainable aviation fuel (SAF) annually by 2030, increasing to 35 billion gallons by 2050 to decarbonize the aviation sector. Meeting these production targets will require large number of biorefineries to be set up. However, ability to comply with federal air quality standards is prerequisite to being issued a construction permit. Negotiating the permitting process for a new biorefinery can be quite onerous and cost the investors significant time and money. Although there can be numerous reasons for delayed biorefinery construction, air permitting is fraught with pitfalls because the permitting process relies on precedence, which the future SAF biorefineries lack. NREL's Biofuel Air Emissions Analysis project is unique and innovative in terms of the tools, approaches, and analyses provided. NREL is the only national laboratory that is actively working at the intersection of Federal air quality regulations, emissions and air quality analysis across the supply chain, and process design. This project is focused on providing much needed data and analyses that address biorefinery air permitting. This project develops models and quantitative analyses and measures progress towards meeting air quality regulatory requirements. These models and methods are applied to analyze air permitting related to wastewater sludge to biofuel conversion pathways using hydrothermal liquefaction (HTL), impacts of HTL pathway on local and regional air quality including an assessment of health and equity impacts. In addition to filling research gaps, this project also disseminates the findings to the relevant stakeholders at BETO, other national labs, and regulatory agencies.

air quality↗

DOE OSTI2021

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗