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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Novel Charfuel® Coal Refining Process 18 TPD Pilot Plant Project for Co- Producing an Upgraded Coal Product, and Commercially Valuable Co- Products: Area of Interest #3 – Coal Beneficiation Pilot Plant Testing (Final Report)

Operation of Carbon Fuels, LLC’s (“CF”) existing, permitted 18 TPD pilot plant located in Golden, Colorado using two individually ranked (ASTM D 388) coal types (two campaigns), employing the novel Charfuel® coal refining process to produce an upgraded coal product and a number of high-valued organic and inorganic coproducts (for which there presently exists large commercial markets) in order to produce engineering and product data which will then be utilized toward the design of a commercial scale integrated facility (pre-feed document). Carbon Fuels, LLC has developed the Charfuel® Coal Refining Process which refines domestically abundant, raw coal (in the same manner as crude oil is refined) to produce the identical, high value co-products that are refined from crude oil. Thus, gasoline, jet fuel, “green diesel”, fuel oil, and marine fuels, as well as petrochemicals such as benzene, toluene, xylene, and methanol are refined from raw coal using this process. The Charfuel® Coal Refining Process is not a coal conversion process, like pyrolysis, or indirect liquefaction. Nor is it an alternative energy system. Rather it is a coal refining process that has the ability to economically produce products traditionally associated with the refining of crude oil but using only abundant, raw coal as the refinery feed stock. The Charfuel® Coal Refining Process is more economical than crude oil refining and is environmentally benign. Therefore, this value added process yields a return on investment well above 50% for a commercial facility. Furthermore, the Charfuel® process, unlike alternatives such as ethanol and hydrogen, can utilize the existing transportation, delivery, and other petroleum based systems. Hence, there is no need for new engines, pipelines, tankers, or product acceptance. As a result, the profitability of the process is increased. Objectives: (1) Operation of the integrated 18 tpd pilot plant, using two coal types (ranks); (2) Demonstration of process flexibility in being able to produce different products (gas, liquid, and char), as well as determination of operating parameters for identifying scale up criteria for two coal types (ranks); (3) Generation of engineering and design information (process specifications) for use in designing a commercial scale plant (scale-up); (4) Determination of important environmental issues surrounding the process and the products such as fate of trace elements (mercury and other heavy metals) and distributions of SO2, NOx, and CO 2 by analysis of effluent streams; (5) Production of sufficient product to allow reliable commercial economic evaluation of both the refined coal product and the coproducts; and, (6) Assessment of longer-term reliability of unit operations. Period 1: reconfiguration of the 18 TPD plant to meet specific FOA requirements and to qualify the facility for operation; and, Period 2: operation of the 18 TPD plant for two campaigns using two coals types (ranks) which are widely commercially used and abundant - the first being a subbituminous (Powder River Basin (“PRB”)) coal, and the second a bituminous (Illinois #6) coal.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Solidification of SRPPF Aqueous Recovery Liquid: Process Disruptions

Savannah River National Laboratory has identified a magnesium oxysulfate grout formulation to solidify the liquid effluent from the Savannah River Plutonium Processing Facility (SRPPF) Aqueous Recovery System (ARS). The formulation uses reactive, light burnt MgO, anhydrous MgSO 4 , and dead burnt MgO and provides good mixability, similar density to the original Portland Cement-based mix, and a leachate pH of 9.39, within the assumed Waste Isolation Pilot Plant (WIPP) brine pH range. The formulation will solidify the effluent using a solid first, lost paddle design, within a 55-gallon drum. A series of seven different process disruptions, or irregular processing during the expected ARS liquid effluent solidification process, were completed to assess the severity of the impact of the process disruption and how the formulation behaves under these conditions. Seven process disruptions were investigated including: No Mixing, Partial Mixing, Compacted/Stratified, Sealed, Overfilled, Underfilled, and Undermixed. The impact on solidification, leachate pH, Er (a Pu/Am surrogate) distribution, and density of the resulting grout were established. Overall, four of the process disruptions, No Mixing, Partial Mixing, Compacted/Stratified, and Overfilled, resulted in basic, unabsorbed liquid that would require additional solidification to be acceptable for WIPP. Most solids generated in this testing met the expected WIPP leachate pH requirements. Solids from the Compacted/Stratified process disruption test, however, produced a leachate pH slightly above the assumed range in the WIPP performance assessment. The distribution of Er for most of the tests was relatively consistent. The No Mixing process disruption test, however, resulted in non-homogeneous Er distribution, indicating that radioactive material would not be evenly distributed throughout this solid’s matrix. The Overfilled test is the only mix with a density slightly below the acceptable value. The Sealed and Overmixed tests produced a solid closest to the standard mix with no unabsorbed liquid, expected leachate pH levels, consistent Er concentrations, and expected density, indicating these process disruptions do not have a negative impact on the final grout form. Based on the other process disruptions, it is critical to have premixed dry materials and a well-defined mixing process to produce a solid, homogeneous magnesium oxysulfate cement, with no unabsorbed liquid. These process disruption tests show that the lack of adequate mixing is fundamental to creating an improperly solidified material that may require remediation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Assessment of Advanced Coal Gasification Processes

This report represents a technical assessment of the following advanced coal gasification processes: AVCO High Throughput Gasification (HTG) Process; Bell Single-Stage High Mass Flux (HMF) Process; Cities Service/Rockwell (CS/R) Hydrogasification Process; Exxon Catalytic Coal Gasification (CCG) Process. Each process is evaluated for its potential to produce SNG from a bituminous coal. In addition to identifying the new technology these processes represent, key similarities/differences, strengths/weaknesses, and potential improvements to each process are identified. The AVCO HTG and the Bell HMF gasifiers share similarities with respect to: short residence time (SRT), high throughput rate, slagging and syngas as the initial raw product gas. The CS/R Hydrogasifier is also SRT but is non-slagging and produces a raw gas high in methane content. The Exxon CCG gasifier is a long residence time, catalytic, fluidbed reactor producing all of the raw product methane in the gasifier. The report makes the following assessments: 1) while each process has significant potential as coal gasifiers, the CS/R and Exxon processes are better suited for SNG production; 2) the Exxon process is the closest to a commercial level for near-term SNG production; and 3) the SRT processes require significant development including scale-up and turndown demonstration, char processing and/or utilization demonstration, and reactor control and safety features development.

coal gasification↗
NASA NTRSDate not supplied

Introduction of an Agile Systems Engineering Process to the NASA Armstrong Flight Research Center

This paper will provide background on the National Aeronautics and Space Administration (NASA) Aeronautics Research Mission Directorate (ARMD) ARMD Test Data Portal (ATDP) effort along with the rationale that motivated the ATDP team to consider implementing an agile systems engineering (SE) process. The primary motivation for using this new SE process was based on the desire to mitigate a major risk to project success. Since the agile SE process had never been used before at the NASA Armstrong Flight Research Center (AFRC), utilizing this new SE process required a trailblazing effort to introduce it to NASA AFRC leadership. As a result, the ATDP team chose to adhere to the agile SE process in addition to the trusted classical waterfall SE process on ATDP. Through successful execution of this hybrid SE process during Phase I, along with demonstrating the value and rigor of the agile SE process, NASA AFRC leadership ultimately permitted the ATDP project to solely use the agile SE process for developments beyond Phase I. While it is the opinion of the authors that the classical SE process is more effective for most projects at NASA AFRC, some projects may deem that the agile SE process is more advantageous. Although this paper is focused on introducing the agile SE process to NASA AFRC, lessons learned will be valuable to other organizations as well. Details of how the ATDP team implemented the agile SE process during Phase I will be summarized, along with how the team met all required classical SE milestones. Lessons learned throughout Phase I of this effort will also be highlighted.

Daniel S. Jones↗
DOE OSTI2023

CyTRICS Impact-Based Prioritization Process

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS prioritization process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS prioritization process was premised largely upon the impact which could result to an energy sector industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. CyTRICS has termed this process the “CyTRICS Impact-based Prioritization Process.” This paper describes the factors identified for use in the Impact-based Prioritization process and identifies the rationale for inclusion. During development, three National Laboratories piloted this prioritization process and generated prioritization scores for seven systems. Following the piloting of the process, laboratory subject matter experts (SME) validated that the numerical scores generated by the prioritization process were consistent with their knowledge of the impact that may occur should any of these systems be disrupted. The following document explains how to perform the prioritization process to generate prioritization scores for energy sector systems. After outlining assumptions required to conduct the process, it describes how to identify and elicit data which can be leveraged to evaluate a system and assign numerical values for each factor. The prioritization process uses different weights on different factors; rationale for each weight is included within the paper. Additionally, the paper includes some recommendations for future enhancements to prioritization, including lessons learned from developing and piloting the process. Finally, a comprehensive appendix includes example documents to be leveraged by those looking to execute the prioritization process.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

A review of separation processes proposed for advanced fuel cycles based on technology readiness level assessments

The results of an international review of separation processes for spent nuclear fuel (SNF) recycling in future closed fuel cycles are reported. This study was made by the Expert Group on Fuel Recycling Chemistry (EGFRC) organised by the Nuclear Energy Agency (NEA) of the Organisation for Economic Co-operation and Development (OECD). A unique feature of this study was that processes were classified according to a hierarchy of separations aimed at different elements within spent fuel (uranium; uranium-plutonium co-recovery; minor actinides; high heat generating radionuclides) and also the Head-end processes, used to prepare the SNF for chemical separation, were included. Separation processes covered both wet (hydrometallurgical) and dry (pyro-chemical) processes. A bespoke methodology suitable for assessing the technological maturities of separation processes for SNF recycling was then designed based on the well-established Technology Readiness Level (TRL) scale but adapted for spent nuclear fuel separations. The innovative feature of this was the use of a matrix approach that enabled information on both the scale of testing and materials used in testing to be combined in the evaluation of the TRL. TRLs for the Head-end, hydrometallurgical and pyrochemical processes that had been reviewed by the study have been evaluated. It is clear that, whilst more R&D is needed in most cases to raise TRLs, suitable options exist to enable the recovery of all actinide elements plus the HHRs by wet or dry processes, even up to the extraordinarily difficult isolation of americium alone. The technology readiness levels (TRL) for most processes fall in the ‘proof of principle’ range with TRLs between 4 and 6, although this is an evaluation at the system level and some gaps in the individual process steps may still exist that strictly would reduce the overall TRL. That is, flowsheets have been proposed for processes and tested with active materials under increasingly relevant conditions, through to initial hot tests with small quantities of SNF. The results of this work were shown to be comparable to a parallel European study that evaluated TRLs for minor actinide partitioning processes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Novel techniques for constraining neutron-capture rates relevant for r -process heavy-element nucleosynthesis

The rapid-neutron capture process (r process) is identified as the producer of about 50% of elements heavier than iron. This process requires an astrophysical environment with an extremely high neutron flux over a short amount of time ( seconds), creating very neutron-rich nuclei that are subsequently transformed to stable nuclei via beta decay. In 2017, one site for the r process was confiimed: the advanced LIGO and advanced Virgo detectors observed two neutron stars merging, and immediate follow-up measurements of the electromagnetic transients demonstrated an "afterglow" over a broad range of frequencies fully consistent with the expected signal of an r process taking place. Although neutronstar mergers are now known to be r-process element factories, contributions from other sites are still possible, and a comprehensive understanding and description of the r process is still lacking. One key ingredient to large-scale r-process reaction networks is radiative neutron-capture ( n , γ ) rates, for which there exist virtually no data for extremely neutron rich nuclei involved in the r process. Due to the current status of nuclear-reaction theory and our poor understanding of basic nuclear properties such as level densities and average g-decay strengths, theoretically estimated ( n , γ ) rates may vary by orders of magnitude and represent a major source of uncertainty in any nuclear-reaction network calculation of r-process abundances. Here in this review, we discuss new approaches to provide information on neutron-capture cross sections and reaction rates relevant to the r process. In particular, we focus on indirect, experimental techniques to measure radiative neutron-capture rates. While direct measurements are not available at present, but could possibly be realized in the future, the indirect approaches present a first step towards constraining neutron-capture rates of importance to the r process.

(n, γ,) cross sections↗
DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Solar Heat for Industrial Processes: Integration with Chemical Reactors

The integration of solar thermal systems with chemical reactors has been proposed as part of a larger effort to develop and deploy solar heat for industrial processes (SHIP) technologies. A strong motivation for SHIP processes and technologies is the potential for high thermal efficiency coupled with low-cost thermal energy storage (TES) which can enable commercial deployment of such systems. While there are different ways to categorize SHIP technologies, one important such distinction is between directly irradiated systems and indirect off-sun process driven by a SHIP system. While directly irradiated systems can provide high thermal efficiencies and high fluxes, they usually require complex engineering solutions due to the need for redesigning the established processes and unit operations. In most cases, it is also more challenging to couple such a process to a TES system, losing some of the benefits of SHIP. On the other hand, using a SHIP system to drive an industrial process off-sun can allow better integration with existing process chains, easier TES capabilities, and potential for more applications fitting a specific SHIP technology. However, the integration of SHIP systems with the industrial processes is not fully explored in detail, especially in the case of high-temperature processes such as reforming, cracking, cement manufacturing, and iron/steelmaking. Many of these systems require heating fluxes of >50 kW/m^2, supplied via combustion of hydrocarbons in a fire box and benefitting from radiative heat transfer between the flue gases and the reaction zones. As such, using SHIP systems for such processes is more complex than providing the same thermal input in the form of a heat transfer medium (HTM) entering the reactor, kiln, or furnace. Moreover, in case convective heat transfer using SHIP is envisioned, for example using supercritical CO2 as the HTM from a particle receiver, the thermal integration might be more challenging than initially envisioned: lower heat transfer coefficients and limited approach temperature might require large flow rates, causing a mismatch between the process thermal requirements and the thermal capacity of the SHIP system. In addition, even if the heat exchange between SHIP and reactor is effective, there is still a cold leg HTM at the reaction temperature or slightly below it. Chemical plants usually include a set of heat exchangers, heat recovery steam generators, and even power generation units - in a tightly integrated design - to recover the flue gases which are eventually vented. With SHIP systems mostly operating on a closed HTM loop, bottoming the cold leg is crucial. In this talk, we will present different modeling results for a variety of syngas production reactions, using catalytic and chemical looping processes, and discuss some of the challenges and design considerations for off-sun chemical reactors using SHIP systems.

14 SOLAR ENERGY↗
DOE OSTI2022

How Can Construction Process Simulation Modeling Aid the Integration of Lean Principles in the Factory-Built Housing Industry?

New and existing factories that produce and deliver factory-built housing can benefit from construction process simulation modeling to explore the integration of Lean principles in their operations. Construction process simulation modeling provides digital or virtual recreations of the real-world factory environments to visualize, quantify, analyze, and optimize their underlying behavior, including factory productivity, material flow, labor dynamics, bottlenecks, and work scope. One of the key benefits of process simulation modeling is the ability to create and compare "what-if" scenarios, including integrating Lean principles such as reducing waste (for example, transportation, waiting), line balancing, and just-in-time concepts. In general, three process simulation methods are widely used: discrete event simulation (DES), agentbased modeling (ABM), and system dynamics (SD). Myriad process simulation software also is available, but depending on the industry, complexity of the system, and purposes of the simulation, some software might be more appropriate. Similar to how computer-aided design (CAD) software such as AutoCAD and Rhinoceros enable building design of modular or factory-built housing, process simulation modeling software such as jStrobe, ProModel, and AnyLogic can enable factory design of new and existing factories to deliver modular affordable housing at scale, as opposed to traditional site-built construction. Software with DES capabilities can help generate a process model that is a logical representation of resources and activities in a factory. Software with CAD-DES integration can leverage product-process data integration to help spatially visualize a DES model of the factory in the CAD environment. Software with multimethod simulation capabilities, widely used in the manufacturing industry, brings together DES, ABM, and SD in a single platform that allows visualization, quantification, analyses, and optimization at varying data fidelities. Near-real-time data from an existing factory can be directly plugged into multimethod simulation software so that the construction process simulation model is a near-accurate representation of the real-world factory conditions. This report provides insights into the use of simulation as an aid to integrate Lean concepts in factories, including guidelines for selecting the appropriate process simulation modeling method and software. These insights have been developed as part of ongoing process simulation modeling research, development, and demonstration projects at the U.S. Department of Housing and Urban Development, the U.S. Department of Energy, and the National Renewable Energy Laboratory focused on how process simulation models can enable better integration of resilience, energy efficiency, and low-carbon design strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Techno-economic optimization of shale gas to dimethyl ether production processes via direct and indirect synthesis routes

The shale gas to dimethyl ether process via direct synthesis route with a syngas H2/CO=1 has a higher dimethyl ether yield than that via indirect synthesis route. However, the direct synthesis route involves various energy and capital-intensive processes, such as the complicated dimethyl ether separation process, and the CO2 recycle and comprssion process. In this study, a techno-economic analysis of the shale gas to dimethyl ether process via direct and indirect synthesis routes is performed using Aspen Process Economic Analyzer based on the rigorous process model developed in Aspen Plus. Effects of different CO2 removal technologies and dimethyl ether synthesis technologies from syngas are evaluated. Both, direct and indirect plants are optimized in Aspen Plus equation-oriented environment using the rigorous process models. The effects of key design parameters (i.e. H2/CO in the syngas) as well as investment parameters (i.e. prices of raw materials, products, and utilities, plant scale) on the process economics as been evaluated. The direct dimethyl ether synthesis route processing a syngas with H2/CO =1 and using the Rectisol process for CO2 capture has the highest overall thermal efficiency on the lower heat value basis is found to be much lower than the current U.S. market diesel price indicating the feasibility of the process.

Mevawala, Chirag↗
DOE OSTI2026

Opportunities and challenges in process modeling and simulation of electrochemical systems

Electrochemical technologies have garnered intense interest in both academic research and industrial applications due to their potential to increase energy efficiency and reduce carbon footprint. However, electrochemical process fundamentals have been absent from the chemical process simulators available to millions of chemical engineers worldwide. To expedite process research and development of electrochemical technologies in the chemical industry, it is imperative that essential electrochemical process fundamentals be incorporated into process simulators to support modeling and simulation of electrochemical processes. Here, this study examines three process fundamentals key to the research and development of electrochemical processes: electrochemical reaction kinetics, electrolyte thermodynamics, and heat and mass transfer. It further illustrates application of these process fundamentals with a case study modeling an electrochemical process for the conversion of acrylonitrile to adiponitrile. The modeling example highlights the roles of applied voltage on reaction rates, and electron flow rates on the performance of electrochemical processes. It suggests that the applied voltage and electron flow rates are two unique concepts that should be included in simulators for electrochemical processes.

09 BIOMASS FUELS↗
DOE OSTI2024

Barium stars as tracers of s -process nucleosynthesis in AGB stars

Barium (Ba) stars help to verify asymptotic giant branch (AGB) star nucleosynthesis models since they experienced pollution from an AGB binary companion and thus their spectra carry the signatures of the slow neutron capture process (s process). For a large number (180) of Ba stars, we searched for AGB stellar models that match the observed abundance patterns. We aim to uncover any systematic deviations of the sample abundances from the predictions of the nucleosynthesis models. We employed three machine learning algorithms as classifiers: a Random Forest method, developed for this work, and the two classifiers used in our previous study. Compared to that work, we also expanded our observational sample with 11 Ba stars available in the supersolar metallicity range. We studied the statistical behaviour of the different s-process elements in the observational sample to investigate if the AGB models systematically under- or overpredict the abundances observed in the Ba stars and show the results in the form of violin plots of the residuals between spectroscopic abundances and model predictions. We inspected the correlations between the observed [Fe/H], the s-process elemental abundances, and the residuals. We employed the [Zr/Fe] and [Nb/Fe] abundances as a thermometer to constrain the operational temperature that rules the production of these elements in the sample stars, assuming a steady-state s process. We also investigated the mass distribution of the identified polluter AGB stars and the behaviour of the δ parameter, which describes the fraction of accreted AGB material relative to the Ba star envelope. We find a significant trend in the residuals that implies an underproduction of the elements just after the first s-process peak (Nb, Mo, and Ru) in the models relative to the observations. This may originate from a neutron-capture process (e.g. the intermediate neutron-capture process, i process) not yet included in the AGB models of metallicity from solar to roughly 1/5 solar, corresponding to the range of the Ba stars. Correlations are found between the residuals of these peculiar elements, suggesting a common origin for the deviations from the models. In addition, there is a weak metallicity dependence of the residuals of these elements. The s-process temperatures derived with the [Zr/Fe] – [Nb/Fe] thermometer have an unrealistic value for the majority of our stars. The most likely explanation is that at least a fraction of these elements are not produced in a steady-state s process, and instead may be due to processes not included in the AGB models. The mass distribution of the identified models confirms that our sample of Ba stars was polluted by low-mass AGB stars (< 4 M ⊙ ). Most of the matching AGB models require low accreted mass, but a few systems with high accreted mass are needed to explain the observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The s process in massive stars, a benchmark for neutron capture reaction rates

A clear definition of the contribution from the slow neutron-capture process (s process) to the solar abundances between Fe and the Sr-Zr region is a crucial challenge for nuclear astrophysics. Robust s-process predictions are necessary to disentangle the contribution from other stellar processes producing elements in the same mass region. Nuclear uncertainties are affecting s-process calculations, but most of the needed nuclear input are accessible to present nuclear experiments or they will be in the near future. Neutron-capture rates have a great impact on the s process in massive stars, which is a fundamental source for the solar abundances of the lighter s-process elements heavier than Fe (weak s-process component). In this work we present a new nuclear sensitivity study to explore the impact on the s process in massive stars of 86 neutron-capture rates, including all the reactions between C and Si and between Fe and Zr. We derive the impact of the rates at the end of the He-burning core and at the end of the C-burning shell, where the 22 Ne(α,n) 25 Mg reaction is is the main neutron source. We confirm the relevance of the light isotopes capturing neutrons in competition with the Fe seeds as a crucial feature of the s process in massive stars. For heavy isotopes we study the propagation of the neutron-capture uncertainties, finding a clear difference of the impact of Fe and Co isotope rates with respect to the rates of heavier stable isotopes. The local uncertainty propagation due to the neutron-capture rates at the s-process branching points is also considered, discussing the example of 85 Kr. The complete results of our study for all the 86 neutron-capture rates are available online. Finally, we present the impact on the weak s process of the neutron-capture rates included in the new ASTRAL library (v0.2).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Manuscript Workflows from and Processed Organic Matter Composition of Experimentally Burned Open Air and Muffle Furnace Vegetation Chars across Differing Burn Severity and Feedstock Types from Pacific Northwest, USA (v3)

This dataset includes processed organic matter chemistry data from an experimental study designed to compare how the chemical composition of organic matter changes across different burn conditions and vegetation materials representative of major land cover types of the Pacific Northwest, USA. Chars were created in a closed muffle furnace or on an open burn table from four different feedstock species representing vegetation commonly impacted by fire regimes across the Pacific Northwest, USA. Source data and associated metadata (including methods and geospatial information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022). This dataset provides processing scripts and processed data for both solid and dissolved phase organic matter characterization data from experimentally generated chars. These processed data can be used to compare how different burn conditions may influence resultant organic matter chemistry and help further our understanding of potential biogeochemical impacts on river corridors post-fire. The processed data were subsequently analyzed; and the results and ecological implications of the findings were published in peer-reviewed manuscripts. The scripts and workflows used to develop the manuscripts are also included in this data package.This data package was originally published June 2024. It was updated September 2024 (new and modified files) and in January 2025 (modified files). See the change history section in the readme for more details.This dataset is comprised of one data package readme, one data dictionary (dd), one file level metadata (flmd), and folders containing (A) processed data; (B) general processing scripts; and (C) additional folders with specific manuscript analysis scripts and processed data. Step-by-step instructions to assist the user in recreating the workflow used to generate the results in the manuscripts is also provided. The processed data folder includes (1) a folder of processed Parallel Factor Analysis (PARAFAC) and spectra indices outputs from excitation emissions matrix (EEM) fluorescence and absorbance data; (2) a folder of processed solid state carbon-13 (13-C NMR) integrals; (3) folder of high resolution characterization of organic matter via 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) processed data outputs from Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude), blank corrections and data aggregation, and calculated molecular indices. All files are .pdf, .csv, .html, .Rmd, .R, or .RData.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Quantitative analysis of geomorphic processes using satellite image data at different scales

When aerial and satellite photographs and images are used in the quantitative analysis of geomorphic processes, either through direct observation of active processes or by analysis of landforms resulting from inferred active or dormant processes, a number of limitations in the use of such data must be considered. Active geomorphic processes work at different scales and rates. Therefore, the capability of imaging an active or dormant process depends primarily on the scale of the process and the spatial-resolution characteristic of the imaging system. Scale is an important factor in recording continuous and discontinuous active geomorphic processes, because what is not recorded will not be considered or even suspected in the analysis of orbital images. If the geomorphic process of landform change caused by the process is less than 200 m in x to y dimension, then it will not be recorded. Although the scale factor is critical, in the recording of discontinuous active geomorphic processes, the repeat interval of orbital-image acquisition of a planetary surface also is a consideration in order to capture a recurring short-lived geomorphic process or to record changes caused by either a continuous or a discontinuous geomorphic process.

Williams, R. S., Jr.↗