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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.

Machine Learning↗
DOE OSTI2025

PyARC Status Report: New Integrations and Upgrades to the Fast Reactor Analysis Workflow Management Tool

PyARC was initially developed as an open source tool to support fast reactor analyses using the Argonne Reactor Computation (ARC) code suite as a part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench initiative in FY17. The goal of this initiative is to provide a common user interface for model generation, real-time validation, execution, output processing, and visualization for all integrated codes. This is accomplished through the reliance on tools available in the Workbench framework and runtime environment. While initially developed to support the ARC codes, PyARC was extended in FY22 to wrap other NEAMS and non-ARC codes, including Griffin and OpenMC, in the supported other neutronics workflows, and support users in the adoption of NEAMS-supported high fidelity analysis codes. Most recently, NUBOW-3D, a recently adopted ARC code, was integrated to support reactor bowing calculations as well. Integration of these codes into the NEAMS Workbench directly benefits the advanced reactor modeling community by: • Providing a set of controlled, maintained, documented and validated scripts to generate inputs, which promotes best practices, reduces the learning curve, and facilitates project collaboration. • Improving the user experience: the Workbench interface provides assistance for building an input through auto-completion, real-time validation, document navigation, and geometry and results visualization. • Automating complex calculations and workflows for reactor analysis. • Helping users transition to using high-fidelity NEAMS codes along-side the ARC codes. In FY22, a progress report was published that described the state of each of the tools integrated into PyARC. Since then, there have been many enhancements and upgrades to the existing integrations as well as entirely new code integrations as well. This report details all new integrations and major developments in PyARC since the version 2.0.0 release highlighted in the FY22 report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Electromagnetic Energy-Assisted Thermal Conversion of Fossil-Based Hydrocarbons to Low-Cost Hydrogen

The goal of this project was to develop and optimize catalysts for methane decomposition, particularly focusing on regeneration via an electromagnetic energy-assisted mechanism, to produce hydrogen more cost-effectively compared to electrolysis routes. To achieve this goal, the project pursued several key objectives. The project began with the preparation and testing of various catalysts. A nickel-silica based catalyst was identified as the most promising material for the pyrolysis of methane into carbon and hydrogen. Kinetic parameters for methane decomposition were determined, aiding in computational modeling efforts. Structured catalysts were investigated, highlighting the need for frequent cleaning or regeneration to maintain performance, with methane conversion rates exceeding 70% in tube furnace tests. Computational fluid dynamics modeling was employed to optimize reactor designs and electrode angles, leading the project team to propose a multi-compartment thermal conversion system for larger setups. This modeling work was important in understanding reaction characteristics, carbon deposition rates, and temperature profiles under various conditions. A bench-scale reactor system was assembled to evaluate catalyst regeneration using electromagnetic energy-assisted mechanisms. Experiments demonstrated the potential for carbon removal, though further optimization is needed. The carbon produced from the methane conversion process was evaluated for potential use in lithium-ion battery electrodes. The carbon exhibited properties similar to commercially available high-purity multi-walled carbon nanotubes and nanofibers, with a carbon content greater than 95%. Coin cell batteries assembled with this carbon showed that lower replacement levels (10% to 33%) outperformed the control group, improving specific capacity density and stability. However, higher replacement levels (100%) demonstrated poorer performance, suggesting that excessive carbon substitution negatively impacts battery performance. These findings indicate the potential marketability of the produced carbon as a component in lithium-ion batteries, though further testing is necessary to confirm long-term advantages and disadvantages associated with the use of the carbon product. These results however justified further technoeconomic assessments to determine if the process can provide low-cost hydrogen. The economic feasibility and technical performance of methane decomposition for hydrogen production were assessed, focusing on three plant configurations: 100E (electrically heated), 100C (combustion heated using produced hydrogen), and PE-Hybrid (a combination of pyrolysis (indicating decomposition) and electrolysis). The Levelized Cost of Hydrogen (LCOH) for the pyrolysis configurations was found to be approximately 25% lower than that of electrolysis. The 100E configuration had the lowest LCOH at $\$$3.12/kg. Including carbon product sales significantly improved the economics, with the 100C configuration achieving a negative LCOH of -$\$$0.35/kg. The PE-Hybrid configuration was not economically advantageous compared to pure pyrolysis plants due to its complexity and additional equipment requirements. Ultimately, methane pyrolysis presents a viable method for near carbon dioxide-free hydrogen production, with significant economic advantages over electrolysis, especially when considering the sale of carbon byproducts. The 100E and 100C configurations showed the most promise, with the choice between them ultimately depending on the prices of power and natural gas. In conclusion, this technology has the potential to lower hydrogen production costs by leveraging the methane decomposition process with the sale of valuable carbon byproducts. By optimizing catalyst performance and integrating electromagnetic energy-assisted regeneration, the process can achieve higher efficiency and economic viability, making it a competitive alternative to traditional hydrogen production methods.

08 HYDROGEN↗
DOE OSTI2022

Selective hydrodeoxygenation of 5-hydroxymethylfurfural (HMF) to 2,5-dimethylfuran (DMF) over carbon supported copper catalysts using isopropyl alcohol as a hydrogen donor

Selective hydrodeoxygenation (HDO) of 5-hydroxymethylfurfural to 2,5-dimethylfuran is of great importance. Here, we reveal a simple pathway for green and efficient HDO using readily available copper with in-situ hydrogen generation. A highly dispersed Cu/PBSAC catalyst consisting of small metallic Cu 0 nanoparticles carries out isopropyl alcohol (IPA) dehydrogenation and subsequent HDO of HMF. Density functional theory calculations reveal that the dehydrogenation of IPA is more favorable on Cu(211) with a lower energy barrier of ~0.6 eV. This facet exists in a higher ratio on nanosized catalysts. Batch reactions using Cu/PBSAC at 190 °C exhibited 91.9% HMF conversion and 71.7% DMF selectivity in 6 hr, and > 96% DMF yield in 10 hr. The mechanical strength of the carbon support is ideal for continuous processing for increased productivity; we demonstrate a > 90% DMF yield at 1/WHSV of 2.4 hr. The process demonstrated here can be integrated with upstream HMF separation utilizing carbon adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Fractional Multistage Hydrothermal Liquefaction of Biomass and Catalytic Conversion into Hydrocarbons

The objective of this project was to develop an improved multistage process for the hydrothermal liquefaction (HTL) of biomass to serve as a new front-end, deconstruction process ideally suited to feed Virent’s well-proven catalytic technology, which is already being scaled up. This process produced water soluble, partially de-oxygenated intermediates that are ideally suited for catalytic finishing to fungible distillate hydrocarbons. Through this project, Virent, with its partners, demonstrated the conversion of pine wood chips to drop-in hydrocarbon distillate fuels using a multi-stage fractional conversion system that is integrated with Virent’s BioForming® process. The majority of work was in the liquefaction task and included temperature scoping, solvent optimization, and separations.

09 BIOMASS FUELS↗
DOE OSTI2024

Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Methanol Synthesis Plant

Efforts continue to identify the most-economic methods to decarbonize several sectors of the United States (U.S.) economy. Industrial processes such as synfuel synthesis and high value commodity chemicals rely heavily on energy-dense and easily stored and transported fossil fuels, which power and feed their operations. Steam methane reforming (SMR) is a widely used process for producing methanol. In this process, methane (CH 4 ) from natural gas (NG) reacts with steam (H 2 O) over a catalyst at high temperatures (700-1,000°C) to produce syngas, a mixture of hydrogen (H 2 ) and carbon monoxide (CO). The syngas is then converted into methanol (CH 3 OH) through a second catalytic reaction. This method is known for being an efficient and commonly employed pathway for industrial methanol production. The high-temperature heat needed for SMR, which is currently used in the natural-gas-to-methanol process, cannot be supplied by small modular nuclear reactor (SMNR) direct heating; the temperatures required for the SMR process exceed those of the main steam produced by near-market high-temperature gas reactors (HTGRs). For the conventional methanol process, this leaves possible nuclear-integration opportunities that include: (1) blending nuclear hydrogen into the SMR NG fuel, or (2) assessing alternative synthesis routes leveraging nuclear capabilities and steam electrolysis outputs. In the reference methanol plant, SMR provides the methanol-synthesis reactor with H 2 and co. In Case (2), the state-of-the-art reverse water gas shift (RWGS) pathway achieves the same, sourcing carbon from an industrial CO 2 source.

08 HYDROGEN↗
DOE OSTI2025

Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Methanol Synthesis Plant

Efforts continue to identify the most-economic methods to decarbonize several sectors of the United States (U.S.) economy. Industrial processes such as synfuel synthesis and high value commodity chemicals rely heavily on energy-dense and easily stored and transported fossil fuels, which power and feed their operations. Steam methane reforming (SMR) is a widely used process for producing methanol. In this process, methane (CH 4 ) from natural gas (NG) reacts with steam (H 2 O) over a catalyst at high temperatures (700°1,000°C) to produce syngas, a mixture of hydrogen (H 2 ) and carbon monoxide (CO). The syngas is then converted into methanol (CH 3 OH) through a second catalytic reaction. This method is known for being an efficient and commonly employed pathway for industrial methanol production. The high-temperature heat needed for SMR, which is currently used in the natural-gas-to-methanol process, cannot be supplied by small modular nuclear reactor (SMNR) direct heating; the temperatures required for the SMR process exceed those of the main steam produced by near-market high-temperature gas reactors (HTGRs). For the conventional methanol process, this leaves possible nuclear-integration opportunities that include: (1) blending nuclear hydrogen into the SMR NG fuel, or (2) assessing alternative synthesis routes leveraging nuclear capabilities and steam electrolysis outputs. In the reference methanol plant, SMR provides the methanol-synthesis reactor with H 2 and co. In Case (2), the state-of-the-art reverse water gas shift (RWGS) pathway achieves the same, sourcing carbon from an industrial CO 2 source.

08 - HYDROGEN↗
DOE OSTI2012

Advanced regulatory control and coordinated plant-wide control strategies for IGCC targeted towards improving power ramp-rates

As part of ongoing R&D activities at the National Energy Technology Laboratory's (NETL) Advanced Virtual Energy Simulation Training & Research (AVESTAR™) Center, this paper highlights strategies for enhancing low-level regulatory control and system-wide coordinated control strategies implemented in a high-fidelity dynamic simulator for an Integrated Gasification Combined Cycle (IGCC) power plant with carbon capture. The underlying IGCC plant dynamic model contains 20 major process areas, each of which is tightly integrated with the rest of the power plant, making individual functionally-independent processes prone to routine disturbances. Single-loop feedback control although adequate to meet the primary control objective for most processes, does not take into account in advance the effect of these disturbances, making the entire power plant undergo large offshoots and/or oscillations before the feedback action has an opportunity to impact control performance. In this paper, controller enhancements ranging from retuning feedback control loops, multiplicative feed-forward control and other control techniques such as split-range control, feedback trim and dynamic compensation, applicable on various subsections of the integrated IGCC plant, have been highlighted and improvements in control responses have been given. Compared to using classical feedback-based control structure, the enhanced IGCC regulatory control architecture reduces plant settling time and peak offshoots, achieves faster disturbance rejection, and promotes higher power ramp-rates. In addition, improvements in IGCC coordinated plant-wide control strategies for “Gasifier-Lead”, “GT-Lead” and “Plantwide” operation modes have been proposed and their responses compared. The paper is concluded with a brief discussion on the potential IGCC controller improvements resulting from using advanced process control, including model predictive control (MPC), as a supervisory control layer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Enhanced IGCC regulatory control and coordinated plant-wide control strategies for improving power ramp rates

As part of ongoing R&D activities at the National Energy Technology Laboratory’s (NETL) Advanced Virtual Energy Simulation Training & Research (AVESTAR™) Center, this paper highlights strategies for enhancing low-level regulatory control and system-wide coordinated control strategies implemented in a high-fidelity dynamic simulator for an Integrated Gasification Combined Cycle (IGCC) power plant with carbon capture. The underlying IGCC plant dynamic model contains 20 major process areas, each of which is tightly integrated with the rest of the power plant, making individual functionally-independent processes prone to routine disturbances. Single-loop feedback control although adequate to meet the primary control objective for most processes, does not take into account in advance the effect of these disturbances, making the entire power plant undergo large offshoots and/or oscillations before the feedback action has an opportunity to impact control performance. In this paper, controller enhancements ranging from retuning feedback control loops, multiplicative feed-forward control and other control techniques such as split-range control, feedback trim and dynamic compensation, applicable on various subsections of the integrated IGCC plant, have been highlighted and improvements in control responses have been given. Compared to using classical feedback-based control structure, the enhanced IGCC regulatory control architecture reduces plant settling time and peak offshoots, achieves faster disturbance rejection, and promotes higher power ramp-rates. In addition, improvements in IGCC coordinated plant-wide control strategies for “Gasifier-Lead”, “GT-Lead” and “Plantwide” operation modes have been proposed and their responses compared. The paper is concluded with a brief discussion on the potential IGCC controller improvements resulting from using advanced process control, including model predictive control (MPC), as a supervisory control layer.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2012

State estimation of an acid gas removal (AGR) plant as part of an integrated gasification combined cycle (IGCC) plant with CO2 capture

An accurate estimation of process state variables not only can increase the effectiveness and reliability of process measurement technology, but can also enhance plant efficiency, improve control system performance, and increase plant availability. Future integrated gasification combined cycle (IGCC) power plants with CO2 capture will have to satisfy stricter operational and environmental constraints. To operate the IGCC plant without violating stringent environmental emission standards requires accurate estimation of the relevant process state variables, outputs, and disturbances. Unfortunately, a number of these process variables cannot be measured at all, while some of them can be measured, but with low precision, low reliability, or low signal-to-noise ratio. As a result, accurate estimation of the process variables is of great importance to avoid the inherent difficulties associated with the inaccuracy of the data. Motivated by this, the current paper focuses on the state estimation of an acid gas removal (AGR) process as part of an IGCC plant with CO2 capture. This process has extensive heat and mass integration and therefore is very suitable for testing the efficiency of the designed estimators in the presence of complex interactions between process variables. The traditional Kalman filter (KF) (Kalman, 1960) algorithm has been used as a state estimator which resembles that of a predictor-corrector algorithm for solving numerical problems. In traditional KF implementation, good guesses for the process noise covariance matrix (Q) and the measurement noise covariance matrix (R) are required to obtain satisfactory filter performance. However, in the real world, these matrices are unknown and it is difficult to generate good guesses for them. In this paper, use of an adaptive KF will be presented that adapts Q and R at every time step of the algorithm. Results show that very accurate estimations of the desired process states, outputs or disturbances can be achieved by using the adaptive KF.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Aggregation Dynamics of Colloidal Particles in Tin Perovskite Crystalline Film Formation

We present an approach to understanding the crystallization of tin-based perovskite films for photovoltaic applications, starting from precursor suspensions processed via spin-coating. By integrating colloidal theory with the fluid dynamics of suspensions, this approach elucidates the influence of both chemical variables and process parameters on the crystallization pathways of perovskite suspensions and the resulting microstructural features of the solid films. Specifically, the incorporation of SnCl 2 as an additive was found to accelerate crystallization, whereas tBP induces a slowdown of the process leading, however, to a marked improvement in film uniformity and microstructural quality.

Crystallization↗
DOE OSTI2013

Laser-assisted nanomaterial deposition, nanomanufacturing, in situ monitoring and associated apparatus

Laser-assisted apparatus and methods for performing nanoscale material processing, including nanodeposition of materials, can be controlled very precisely to yield both simple and complex structures with sizes less than 100 nm. Optical or thermal energy in the near field of a photon (laser) pulse is used to fabricate submicron and nanometer structures on a substrate. A wide variety of laser material processing techniques can be adapted for use including, subtractive (e.g., ablation, machining or chemical etching), additive (e.g., chemical vapor deposition, selective self-assembly), and modification (e.g., phase transformation, doping) processes. Additionally, the apparatus can be integrated into imaging instruments, such as SEM and TEM, to allow for real-time imaging of the material processing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Principles for Equitable Transmission Planning

Energy justice is gaining traction in the planning processes that govern the generation and supply of electric power in the U.S., with a growing number of federal and state authorities applying it as a framework to ensure that the benefits of a clean energy future extend to all levels of society. However, the principles of energy justice have not yet been integrated into electric transmission planning processes, which are designed to meet established reliability criteria and have not historically considered externalities like social inequities. This paper presents a review of transmission planning requirements in the U.S. and existing literature on energy justice, then synthesizes them to propose a framework for incorporating energy justice principles into transmission planning processes to yield more equitable outcomes. By applying procedural and recognition justice principles to create more extensive, inclusive, and diverse public participation in the planning process, planners can gain insight into—and begin to correct—the inequities created by previous planning decisions. Equipped with this understanding and informed by affected communities, planners can improve the distributional outcomes of the planning process and identify opportunities for energy system investments that will restore communities and place them on more equitable footing.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

A multi-scale metrics approach to forest fragmentation for Strategic Environmental Impact Assessment

Forests are becoming severely fragmented as a result of land development. South Korea has responded to changing community concerns about environmental issues. The nation has developed and is extending a broad range of tools for use in environmental management. Although legally mandated environmental compliance requirements in South Korea have been implemented to predict and evaluate the impacts of land-development projects, these legal instruments are often insufficient to assess the subsequent impact of development on the surrounding forests. It is especially difficult to examine impacts on multiple (e.g., regional and local) scales in detail. Forest configuration and size, including forest fragmentation by land development, are considered on a regional scale. Moreover, forest structure and composition, including biodiversity, are considered on a local scale in the Environmental Impact Assessment process. Recently, the government amended the Environmental Impact Assessment Act, including the SEA, EIA, and small-scale EIA, to require an integrated approach. Therefore, the purpose of this study was to establish an impact assessment system that minimizes the impacts of land development using an approach that is integrated across multiple scales. This study focused on forest fragmentation due to residential development and road construction sites in selected Congestion Restraint Zones (CRZs) in the Greater Seoul Area of South Korea. Based on a review of multiple-scale impacts, this paper integrates models that assess the impacts of land development on forest ecosystems. The applicability of the integrated model for assessing impacts on forest ecosystems through the SEIA process is considered. On a regional scale, it is possible to evaluate the location and size of a land-development project by considering aspects of forest fragmentation, such as the stability of the forest structure and the degree of fragmentation. On a local scale, land-development projects should consider the distances at which impacts occur in the vicinity of the forest ecosystem, and these considerations should include the impacts on forest vegetation and bird species. Impacts can be mitigated by considering the distances at which these influences occur. In particular, this paper presents an integrated environmental impact assessment system to be applied in the SEIA process. The integrated assessment system permits the assessment of the cumulative impacts of land development on multiple scales. -- Highlights: • The model is to assess the impact of forest fragmentation across multiple scales. • The paper suggests the type of forest fragmentation on a regional scale. • The type can be used to evaluate the location and size of a land development. • The paper shows the influence distance of land development on a local scale. • The distance can be used to mitigate the impact at an EIA process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimization of distributed compute resources utilization in the CMS Global Pool

The CMS Submission Infrastructure is the primary system for managing computing resources for CMS workflows, including data processing, simulation, and analysis. It integrates geographically distributed resources from Grid, HPC, and cloud providers into federated pools managed by HTCondor and Glidein- WMS, for a total of around 500k CPU cores. This system dynamically manages workloads based on priorities defined by the collaboration. Additionally, CMS scheduling strategies must be flexible to handle multiple concurrent workloads while considering changing processing demands and resource availability from various providers.Efficient utilization of vast amounts of distributed compute resources is a key element for the success of the scientific programs of the LHC experiments. Optimizing the system is essential to maximize resource efficiency and fully utilize the distributed computing power. The CMS Submission Infrastructure team thus systematically investigates sources of inefficiency in workload scheduling to reduce their impact. In addition, a strategy of pilot overloading has been introduced to compensate for other inefficiency sources, thereby optimizing resource utilization and enhancing computational throughput.

Mascheroni, Marco [UC, San Diego (main)]↗
DOE OSTI2017

chapter 40 - Gasification Technologies, "Handbook of Climate Change Mitigation and Adaptation"

Gasification is an enabling technology for the clean-up of fossil and biomass fuels for energy production. A history of gasifier development is told from the perspective of a technology evolving to meet the ever changing market needs. The chemistry and physics of coal conversion includes pyrolysis, combustion, gasification, mineral transformations as well as a discussion of the materials developed to meet the most challenging gasifier applications. Integrated gasification and combined cycle processes are discussed with respect to electrical power production. The distinguishing features for different types of gasifiers are described including fixed bed, fluidized bed, entrained flow, transport gasifiers. The twelve major gasifiers being marketed today are described. The hydrodynamics and kinetics of each are reviewed along with salient differences in performance, such as gas composition, when using a variety of fuels under different conditions. Critical operational features that are discussed include oxidizing media, air or oxygen blown; the system pressure; fuel feed stock; and downstream clean-up. Thermal integration is discussed with respect to its impact on the gasifier performance. Gas clean-up is also considered with respect to the removal of potential pollutants and the shifting to environmentally benign transportation and process fuels.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Session 078 - Panel: Start-Up of Three Major DOE Treatment Facilities -SWPF, IWTU and DFLAW (2/2) (R2.4)

This panel focused on the successes and challenges of the DOE High-Level radioactive tank waste management program, with an emphasis on the completion of construction and startup of three major treatment facilities - the Salt Waste Processing Facility (SWPF) at Savannah River, the Integrated Waste Treatment Unit (IWTU) at Idaho, and Direct Feed Low-Activity Waste (DFLAW) processing at Hanford. As a companion to Session 23, DOE managers and federal project directors discussed the status of facility startup efforts from a federal management and oversight perspective, highlighting readiness planning, challenges and lessons learned. Panelists with presentations: Transitioning Oversight: A Project Becomes an Operating Facility (James Folk); Tools for SU/C (Tom Fletcher); Startup and Commissioning -Integrate Waste Treatment Unit-Federal Perspective (Joel Case)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗