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NASA NTRS2016

Demonstration of Robustness and Integrated Operation of a Series-Bosch System

Manned missions beyond low Earth orbit will require highly robust, reliable, and maintainable life support systems that maximize recycling of water and oxygen. Bosch technology is one option to maximize oxygen recovery, in the form of water, from metabolically-produced carbon dioxide (CO2). A two stage approach to Bosch, called Series-Bosch, reduces metabolic CO2 with hydrogen (H2) to produce water and solid carbon using two reactors: a Reverse Water-Gas Shift (RWGS) reactor and a carbon formation (CF) reactor. Previous development efforts demonstrated the stand-alone performance of a RWGS reactor containing Incofoam(TradeMark) catalyst and designed for robustness against carbon formation, two membrane separators intended to maximize single pass conversion of reactants, and a batch CF reactor with both transit and surface catalysts. In the past year, Precision Combustion, Inc. (PCI) developed and delivered a RWGS reactor for testing at NASA. The reactor design was based on their patented Microlith(TradeMark) technology and was first evaluated under a Phase I Small Business Innovative Research (SBIR) effort in 2010. The Microlith(TradeMark) RWGS reactor was recently evaluated at NASA to compare its performance and operating conditions with the Incofoam(TradeMark) RWGS reactor. Separately, in 2015, a fully integrated demonstration of an S-Bosch system was conducted. In an effort to mitigate risk, a second integrated test was conducted to evaluate the effect of membrane failure on a closed-loop Bosch system. Here, we report and discuss the performance and robustness to carbon formation of both RWGS reactors. We report the results of the integrated operation of a Series-Bosch system and we discuss the technology readiness level. 1

Abney, Morgan B.↗

NASA NTRS2016

Demonstration of Robustness and Integrated Operation of a Series-Bosch System

Manned missions beyond low Earth orbit will require highly robust, reliable, and maintainable life support systems that maximize recycling of water and oxygen. Bosch technology is one option to maximize oxygen recovery, in the form of water, from metabolically-produced carbon dioxide (CO2). A two stage approach to Bosch, called Series-Bosch, reduces metabolic CO2 with hydrogen (H2) to produce water and solid carbon using two reactors: a Reverse Water-Gas Shift (RWGS) reactor and a carbon formation (CF) reactor. Previous development efforts demonstrated the stand-alone performance of a NASA-designed RWGS reactor designed for robustness against carbon formation, two membrane separators intended to maximize single pass conversion of reactants, and a batch CF reactor with both transit and surface catalysts. In the past year, Precision Combustion, Inc. (PCI) developed and delivered a RWGS reactor for testing at NASA. The reactor design was based on their patented Microlith® technology and was first evaluated under a Phase I Small Business Innovative Research (SBIR) effort in 2010. The RWGS reactor was recently evaluated at NASA to compare its performance and operating conditions with NASA's RWGS reactor. The test results will be provided in this paper. Separately, in 2015, a semi-continuous CF reactor was designed and fabricated at NASA based on the results from batch CF reactor testing. The batch CF reactor and the semi-continuous CF reactor were individually integrated with an upstream RWGS reactor to demonstrate the system operation and to evaluate performance. Here, we compare the performance and robustness to carbon formation of both RWGS reactors. We report the results of the integrated operation of a Series-Bosch system and we discuss the technology readiness level.

Abney, Morgan B.↗

NASA NTRS2018

Assessment and Verification of SLS Block 1-B Exploration Upper Stage State and Stage Disposal Performance

One of the SLS Navigation System's key performance requirements is a constraint on the payload system's delta-v allocation to correct for insertion errors due to vehicle state uncertainty at payload separation. The SLS navigation team has developed a Delta-Delta-V analysis approach to assess the effect on trajectory correction maneuver (TCM) design needed to correct for navigation errors. This approach differs from traditional covariance analysis based methods and makes no assumptions with regard to the propagation of the state dynamics. This allows for consideration of non-linearity in the propagation of state uncertainties. The Delta-Delta-V analysis approach re-optimizes perturbed SLS mission trajectories by varying key mission states in accordance with an assumed state error. The state error is developed from detailed vehicle 6-DOF Monte Carlo analysis or generated using covariance analysis. These perturbed trajectories are compared to a nominal trajectory to determine necessary TCM design. To implement this analysis approach, a tool set was developed which combines the functionality of a 3-DOF trajectory optimization tool, Copernicus, and a detailed 6-DOF vehicle simulation tool, Marshall Aerospace Vehicle Representation in C (MAVERIC). In addition to delta-v allocation constraints on SLS navigation performance, SLS mission requirement dictate successful upper stage disposal. Due to engine and propellant constraints, the SLS Exploration Upper Stage (EUS) must dispose into heliocentric space by means of a lunar fly-by maneuver. As with payload delta-v allocation, upper stage disposal maneuvers must place the EUS on a trajectory that maximizes the probability of achieving a heliocentric orbit post Lunar fly-by considering all sources of vehicle state uncertainty prior to the maneuver. To ensure disposal, the SLS navigation team has developed an analysis approach to derive optimal disposal guidance targets. This approach maximizes the state error covariance prior to the maneuver to develop and re-optimize a nominal disposal maneuver (DM) target that, if achieved, would maximize the potential for successful upper stage disposal. For EUS disposal analysis, a set of two tools was developed. The first considers only the nominal pre-disposal maneuver state, vehicle constraints, and an a priori estimate of the state error covariance. In the analysis, the optimal nominal disposal target is determined. This is performed by re-formulating the trajectory optimization to consider constraints on the eigenvectors of the error ellipse applied to the nominal trajectory. A bisection search methodology is implemented in the tool to refine these dispersions resulting in the maximum dispersion feasible for successful disposal via lunar fly-by. Success is defined based on the probability that the vehicle will not impact the lunar surface and will achieve a characteristic energy (C3) relative to the Earth such that it is no longer in the Earth-Moon system. The second tool propagates post-disposal maneuver states to determine the success of disposal for provided trajectory achieved states. This is performed using the optimized nominal target within the 6-DOF vehicle simulation. This paper will discuss the application of the Delta-Delta-V analysis approach for performance evaluation as well as trajectory re-optimization so as to demonstrate the system's capability in meeting performance constraints. Additionally, further discussion of the implementation of assessing disposal analysis will be provided.

Patrick, Sean↗

NASA NTRS2018

Reliability of the Danish Aerospace Corporation Portable Pulmonary Function System

Metabolic gas analysis is a critical component of investigations that measure cardio-pulmonary exercise responses during and after long-duration spaceflight. The primary purpose of the current study was to determine the reliability and intra-subject repeatability of a metabolic gas analysis device, the Portable Pulmonary Function System (PPFS), designed for use on the International Space Station (ISS). The second objective of this study was to directly compare PPFS measurements of expired oxygen and carbon dioxide (FEO2 and FECO2) to values obtained from a well-validated clinical metabolic gas analysis system (ParvoMedics TrueOne (c) [PM]). Eight subjects performed four peak cycle tests to maximal exertion. The first test was used to prescribe work rates for the subsequent test sessions. Metabolic gas analysis for this test was performed by the PM, but samples of FEO2 and FECO2 also were simultaneously collected for analysis by the PPFS. Subjects then performed three additional peak cycle tests, consisting of three 5-min stages designed to elicit 25%, 50%, and 75% maximal oxygen consumption (VO2max) followed by stepwise increases of 25 W/min until subjects reached volitional exhaustion. Metabolic gas analysis was performed using the PPFS for these tests. Intraclass correlation coefficients (ICC), within-subject standard deviations (WS SD), and coefficients of variation (CV%) were calculated for the repeated exercise tests. Mixed model regression analysis was used to compare paired FEO2 and FECO2 values obtained from the PPFS and the PM during the initial test. The ICC values for oxygen consumption (VO2), carbon dioxide production (VCO2), and ventilation (VE) indicate that the PPFS is highly reliable (0.79 to 0.99) for all exercise levels tested; however, ICCs for respiratory exchange ratio (RER) were low ( 0.11 - 0.51), indicating poor agreement between trials during submaximal and maximal exercise. Overall, CVs ranged from 1.6% to 6.7% for all measurements, a finding consistent with reported values that were obtained using other metabolic gas analysis techniques. The PPFS and PM produced comparable FEO2 data; however, there was less agreement between measures of FECO2 obtained from the two devices, particularly at lower CO2 concentrations. The PPFS appears, in practically all respects, to yield highly reliable metabolic gas analysis data. Lower reliability of RER measurements reported in the literature and likely is not a function of the PPFS device. Further examination of PPFS CO2 data is warranted to better understand the limitations of these PPFS measurements. Overall, the PPFS when used for repeated measures of cardio-pulmonary exercise should provide accurate and reliable data for studies of human adaptation to spaceflight.

Portable Pulmonary Function System (PPFS)↗

NASA NTRS2017

Evolution of Trajectory Design Requirement of NASA's Planned Europa Clipper Mission

Europa is one of the most scientifically intriguing targets in planetary science due to its potential suitability for extant life. As such, NASA has funded the California Institute of Technology Jet Propulsion Laboratory and the Johns Hopkins University Applied Physics Laboratory to jointly develop the planned Europa Clipper mission—a multiple Europa flyby mission architecture aimed to thoroughly investigate the habitability of Europa and provide reconnaissance data to determine a landing site that maximizes the probability of both a safe landing and high scientific value for a potential future Europa lander. The trajectory design—a major enabling component for this Europa Clipper mission concept—was developed to maximize science from a set of eight model payload instruments determined by a NASA-appointed Europa Science Definition Team (SDT) between 2011-2015. On May 26, 2015, NASA officially selected 10 instruments from 6 different U.S. research facilities and universities. With the selection of instruments have come the development of new science measurement requirements, as well as a rich set of requirements stemming from project policies, planetary protection, and the evolved capability and characteristics of the flight system and mission operations system. This paper will focus on the evolution of requirements levied on the trajectory design, discuss strategies and solutions to the multidimensional optimization problem of designing high fidelity end-to-end trajectories that maximize Europa science while mitigating mission risk, complexity and cost, and last, verification of candidate trajectories to meet the requirements on the trajectory design.

Buffington, Brent↗

NASA NTRS2021

The Peak Absorbance Wavelength of Photosynthetic Pigments Around Other Stars From Spectral Optimization

In the search for life on other planets, the presence of photosynthetic surface vegetation may be detectable from the colors of light it reflects, which on the modern Earth is a steep increase in reflectance between the red and near-infrared wavelengths, a “red edge.” This edge-like signature occurs at wavelengths of peak photon absorbance, which are the result of adaptations of the phototroph to their spectral environment. On planets orbiting different stellar types, red edge analogs may occur at other colors than red. Thus, knowing the wavelengths at which photosynthetic organisms preferentially absorb and reflect photons is necessary to detect red edge analogs on other planets. Using a numerical model that predicts the absorbance spectrum of extant photosynthetic pigments on Earth from Marosvolgyi & van Gorkom (2010), we calculate the absorbance spectrum for pigments on an Earth-like planet around F through late M type stars that are adapted for maximal energy production. In this model, cellular energy production is maximized when pigments are tuned to absorb at the wavelength that maximizes energy input from incident photons while minimizing energy losses due to thermal emission and building cellular photosynthetic apparatus. We find that peak photon absorption for photosynthetic organisms around F type stars tends to be in the blue while for G, K, and early M type stars, red or just beyond is preferred. Around the coolest M type stars, these organisms may preferentially absorb in the near-infrared, possibly past 1 micron. These predictions are consistent with previous, qualitative estimates of pigment absorptance. Our predicted absorbance spectra for photosynthetic surface organisms depend on both the stellar type and planetary atmospheric composition, especially atmospheric water vapor concentrations, which alter the availability of surface photons and thus the predicted pigment absorption. By constraining the absorbance spectra of alien, photosynthetic organisms, future observations may be better equipped to detect the weak spectral signal of red edge analogs.

astrobiology↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

open access↗

NASA NTRS2025

Assessing Commercialization Strategies for Evolving Network Demand (ASCEND) in the NASA Space Communications and Navigation (SCaN) Program

What will NASA’s future communications demand and expenditure look like, in a paradigm where space policy encourages maximum private sector involvement? In this paper we consider this question, as NASA moves towards commercial procurement for Direct-To-Earth and Space Relay communication services. We develop a new quantitative Techno-Economic Assessment (TEA) model capable of evaluating the incremental cost over time to NASA of utilizing commercial communication services, for different mission and market scenarios. We find that current and future NASA demand could be viably procured from commercial services, with the potential to reduce cost using non-exclusive networks and the sharing of fixed costs. However, there is a key trade-off identified between maximizing economies of scale benefits and ensuring sufficient competition between communication providers to avoid collusion and excessive pricing. For example, procuring from 1-2 providers would maximize scale economies, whereas procuring from 5-6 providers would maximize competition. Given this context, it would be prudent to seek to award service contracts for 3-4 providers to optimize economies of scale benefits, while mitigating possible market collusion. These recommendations enable NASA to successfully achieve its scientific mission over the next decade, while managing limited financial and networking resources.

NSN↗

NASA NTRS2024

Envisioning an Optimal Network of Space-Based Lasers for Orbital Debris Remediation

The rapid increase in resident space objects, including satellites and orbital debris, poses a significant threat to the safety and sustainability of space missions. This paper explores orbital debris remediation using a network of collaborative space-based lasers, leveraging laser ablation for momentum transfer on debris. A novel delta-v vector analysis framework quantifies the e↵ects of multiple simultaneous laser-to-debris (L2D) engagements by using vector composition of the imparted delta-v vectors. The paper introduces the Concurrent LocationScheduling Problem (CLSP), which optimizes the placement of laser platforms and the scheduling of L2D engagements to maximize debris remediation capacity. Due to the computational complexity of the CLSP, it is decomposed into two sequential subproblems: (1) optimal laser platform locations are determined using the Maximal Covering Location Problem, and (2) a novel integer linear programming-based approach schedules L2D engagements within the network configuration to maximize remediation capacity. Computational experiments are conducted to evaluate the proposed framework’s e↵ectiveness under various mission scenarios, demonstrating key network functions such as collaborative nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further examines how varying the number and distribution of laser platforms a↵ects debris remediation capacity, providing insights into optimizing the performance of space-based laser networks.

David O Williams Rogers↗

DOE OSTI2019

A Machine-Learning Decision-Support Tool for Travel-Demand Modeling

Utility maximization(UM) models are the lifeblood of virtually all travel demand models (TDM) in practice. Be it the traditional travel demand models or more advanced activity-based models, utility maximization models are used extensively to model and predict myriad travel choices such as location choice, mode choice, route choice etc. More recently machine learning (ML) models are being applied in a variety of contexts to predict choice patterns (product suggestions on Amazon, restaurant suggestions on Yelp etc.,). In the TDM arena, there has been interest in incorporating ML models where they can enhance prediction accuracy. Though there have been sporadic efforts at comparing specific utility maximization models to machine learning models, there is a need for a standard comparison tool which can evaluate ML models against UM models for a given choice context. Addressing this need, we present a tool for applying an array of models including logit, nested logit, neural network, Naive Bayes and decision tree classifiers. The tool is specifically tailored to aid in the deciding the best model for a given choice context and can be used to choose an appropriate model family or to construct a model ensemble to improve upon current modeling standards in travel demand modeling. We test our proposed system on household vehicle count and work schedule targets from the 2017 National Household Travel Survey. Results demonstrate that for some variables, logit are not the most effective models, and the proposed system can aid in selecting a better model.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Optimizing thermodynamic trajectories using evolutionary reinforcement learning

Using a model heat engine we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We use an evolutionary learning algorithm to evolve a population of neural networks, subject to a directive to maximize the efficiency of a trajectory composed of a set of elementary thermodynamic processes; the resulting networks learn to carry out the maximally-efficient Carnot, Stirling, or Otto cycles. Given additional irreversible processes this evolutionary scheme learns a hitherto unknown thermodynamic cycle. Our results show how the reinforcement learning strategies developed for game playing can be applied to solve physical problems conditioned upon path-extensive order parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models

Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intractable but sampling is possible, conventional BED methods have difficulties in efficiently estimating the posterior distribution and maximizing the mutual information (MI) between data and parameters. Recent work proposed the use of gradient ascent to maximize a lower bound on MI to deal with these issues. However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models. In this paper, we propose a novel approach that leverages recent advances in stochastic approximate gradient ascent incorporated with a smoothed variational MI estimator for efficient and robust BED. Without the necessity of pathwise gradients, our approach allows the design process to be achieved through a unified procedure with an approximate gradient for implicit models. Several experiments show that our approach outperforms baseline methods, and significantly improves the scalability of BED in high-dimensional problems.

Zhang, Jiaxin↗

DOE OSTI2019

Implementation of Increased Salt and Decreased Sludge Volumes in the Defense Waste Processing Facility - 19597

The Savannah River Site (SRS) Liquid Waste System (LWS) safely stores and treats high-level radioactive waste. The LWS consists of 51 waste storage tanks (eight of which are operationally closed and filled with grout), waste evaporators, treatment facilities, and solidification facilities such as the Defense Waste Processing Facility (DWPF) and Saltstone Production Facility (SPF). The disposition of high-level waste (HLW) into a glass waste form poured into canisters at the DWPF began in 1996. DWPF processed a sludge only flowsheet until 2008 when the Actinide Removal Process (ARP) and Modular Caustic Side Solvent Extraction Unit (MCU) began treating the salt waste from the underground liquid storage tanks by striking with monosodium titanate (MST) to remove actinides, when necessary to meet downstream acceptance criteria, and removing the MST and entrained sludge solids (SS) by filtration before using an organic solvent to strip the cesium from the salt, creating two new concentrated HLW streams. This change caused the DWPF to begin processing under both a sludge-only and a coupled operations flowsheet where salt streams would only be added when available. This strategy allows for continuous glass canister production at DWPF. Recently, DWPF completed a 10-month melter replacement in December 2017 and the Concentrate, Storage, and Transfer Facility (CSTF) has completed repairs to the 3H Evaporator system. These facility outages have caused delays in preparing the next batch of sludge feed for DWPF. Concurrent with these facility outages, the Salt Waste Processing Facility (SWPF) has been undergoing system testing and commissioning, working toward startup. SWPF is designed to process salt waste in the same manner as ARP/MCU, but with much higher throughput volumes produced per unit time. In order to process the SWPF salt streams efficiently, DWPF will implement a coupled operation only flowsheet. Based on the facility outages mentioned above, efforts have been made to identify strategies to minimize sludge volumes and maximize salt volumes in the DWPF until the next sludge batch is ready for processing in DWPF. Savannah River Remediation (SRR) recently performed an evaluation that examines the effects of reducing the volume of sludge and maximizing the addition of the cesium-rich concentrated salt solution in each Sludge Receipt and Adjustment Tank (SRAT) batch in order to extend the existing sludge batch as long as possible until the next sludge batch is ready. These effects include a reduction of weight percent solids in the DWPF processing vessels, an increase in cycle time, a new canister heat generation value that remains within regulatory limits, and higher gamma source that remains within bulk facility gamma shielding. Balancing the above parameters resulted in a small reduction of sludge volume per SRAT batch under SWPF operation and maximizing the salt volumes for both ARP/MCU and SWPF operation. This slight reduction in sludge volume and increase in salt volumes allows SRR to mitigate some of the schedule impact incurred by the facility outages. This strategy was evaluated for both ARP/MCU and SWPF operation. This strategy has been successfully implemented under ARP/MCU operation using small step changes in the current process. This paper provides an overview of the evaluation performed for DWPF with ARP/MCU and DWPF with SWPF operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

“Max-tech FHR”: What is the maximum technically achievable water delivery capacity from a single 120 V plug?

Electrifying water heating will require affordable, direct drop-in replacements for existing gas water heaters that meet consumer expectations for hot water delivery. For many replacement scenarios, affordability requires a replacement option with like-for-like physical product dimensions, and existing consumer expectations presume a large delivery capacity from a small water heater. This work explores the technical potential for hot water delivery capacity for a heat pump water heater (HPWH) powered from a common 120 V plug. Delivery capability in this work is quantified by two metrics: the First Hour Rating (FHR) defined in the Code of Federal Regulations, and a real-world draw profile that resulted in hot water runout during field studies of 120 V HPWHs. The work focuses on the common “tall-and-slim” gas water heater product category, with external product dimensions of 20 inch diameter and 60 inch height. Using a simulation model in DOE/ORNL Heat Pump Design Model, validated by experimental baseline testing of a commercially available 120 V HPWH model, several measures were evaluated to maximize the delivery capacity of a 120 V HPWH. The 120 V HPWH was constrained by 1) external product dimensions of 20 in diameter and 60 inch height, 2) electrical power of 1.5 kW (85% of the available power from a dedicated 120 V, 15 A circuit), and 3) commercially-available components. The measures implemented included a large compressor, a pumped loop with plate heat exchanger to maximize tank stratification, enlarged heat exchangers, and relatively thin insulation to maximize the water storage volume.

Gluesenkamp, Kyle↗

DOE OSTI2023

AN ACID BAKING APPROACH TO ENHANCE RARE EARTH ELEMENT RECOVERY FROM BITUMINOUS COAL SOURCES

Rare earth elements (REE) are a group of 17 elements typically classified as light and heavy rare earth elements, which play a crucial role in developing the latest technologies for energy, defense, and medical sectors. Even though REEs have been found in more than 200 minerals, only bastnaesite, monazite and xenotime are commercially exploited for REE extraction. However, the recent exponential increase in REE demand has spurred countries such as the United States into research for the extraction of REEs from secondary sources such as coal, acid mine drainage, and coal ash. Several coal sources (e.g., Fire Clay seam coal) across the United States have been identified to contain elevated concentrations of rare earth elements (>600 ppm), and various researchers have investigated the feasibility of both physical and hydrometallurgical extraction techniques for rare earth concentration and subsequent extraction. However, both physical and direct leaching were concluded to be inefficient for RE beneficiation and extraction due to low recoveries. Alternatively, thermal treatment provides promising means for RE recovery from bituminous coal sources. The positive impact of thermal treatment/calcination was established to be due to the decarbonization and dehydroxylation of the clays, which released entrapped rare earth elements within the dominant minerals and converted them into an acid-soluble form. Nonetheless, the improvement in recovery was limited to the light REEs (LREEs) with an insignificant increase in the heavy REEs (HREE). It was demonstrated that the light and heavy REEs in the material were associated with difficult-to-leach minerals such as monazite, xenotime, and zircon, which were not decomposed by simple calcination due to their high thermal stability. Hence, roasting in the presence of chemicals was necessary to ensure the decomposition of those REE-containing minerals. As such, this study was focused on the acid baking treatment of bituminous coals with an aim to enhance REE recovery, especially HREEs. Based on the presence of REE minerals like monazite and xenotime, three pre-leach treatment methods, i.e., 1) roasting, 2) direct acid baking, and 3) acid baking after roasting were investigated. Roasting tests at 600 ⁰C revealed that the recovery of light REEs (LREEs) was enhanced while the recovery of HREEs remained relatively unaffected. LREE and HREE recovery values of 38.3% and 21.3%, respectively, were achieved using a 50 g/L sulfuric acid solution at 5% solid concentration and a solution temperature of 75 ⁰C for 2 hours. Comparatively, direct acid baking at 250 °C provided substantial increased LREE and HREE recovery values to approximately 49.4% and 53.0%, respectively, using an equivalent acid dosage. Recoveries were maximized to 77.0% and 79.6% for LREE and HREE, respectively, by roasting followed by acid baking. Similar results were obtained from the treatment of a second bituminous coal source. Due to strong correlations between REE and Al recovery values, tests were performed on kaolinite and illite, which were prominent clay minerals within the source coals. These experiments revealed that the REE recovery improvements were likely a result of dehydroxylation of clays and subsequent release and decomposition of REE-bearing minerals such as monazite, xenotime and zircon. Subsequently, a parametric study was conducted to identify the impact of acid baking parameters on rare earth element recovery. The factors investigated using a three-level statistical experimental program were acid baking time, acid solution concentration, baking temperature, and acid solution-to-solids ratio which were found to significantly impact REE and contaminant element (Al, Fe, and Ca) recovery. An increase in baking temperature up to around 250 ⁰C improved the light and heavy REE recovery values by more than 50 absolute percentage points relative to performances achieved when direct leaching. As aforementioned, acid baking was needed to both decompose the clay minerals and liberate the REE minerals, which allowed access for the acid to solubilize the REEs. Acid concentration of the solution used for acid baking was studied as a means of minimizing the amount of acid needed to achieve a target REE recovery. However, thermo-gravimetric and differential scanning calorimetry analysis (TGA-DSC) of sulfuric acid under oxidizing atmosphere revealed that the addition of water decreased the evaporation temperature, which explains the lower REE recovery values obtained when using acid concentrations less than 100%. Using pure sulfuric acid at an acid-to-solid ratio of 0.8:1 resulted in recovery values of around 70% for both LREEs and HREEs. The decomposition reaction time was relatively quick with 65% of the TREEs recovered within the first 10 minutes. Following the identification of optimum operating conditions through the parametric study, a systematic leaching study was carried out to examine the impact of leaching parameters, such as solid-to-liquid (S/L) ratio, temperature, and time, on REE recovery using acid baking conditions of 1:1 acid to solids ratio at 250 °C for 30 minutes. The solid-to-liquid ratio was varied from 1-20% by weight at 25 °C, 50 °C, and 75 °C solution temperatures. The results indicated that reaction time and solution temperature considerably impacted the recovery of heavy and light REEs. Interestingly, LREE recovery reduced from 68% at 5% S/L to 58% at 20%S/L, whereas HREE recovery of 78% remained unaffected. The decrease in the LREE recovery was determined to be due to La and Ce precipitation, likely through isomorphic substitution with calcium in gypsum. The kinetic data indicated that 67% LREE and 77% HREE recovery could be obtained within the first 15 minutes of the reaction, suggesting fast reaction kinetics. Furthermore, raising the solution temperature from 25 °C to 75 °C increased the LREE and HREE recovery from 60% and 32% to 67% and 79%, respectively. The kinetic modeling results demonstrated that the rate-limiting step in the LREE dissolution was diffusion and chemical reaction, whereas the HREE extraction was controlled by only chemical reaction. The leaching study concluded that using 20% S/L at 75 °C for 15 minutes maximized LREE and HREE recovery. The lab-scale precipitation study showed that Fe and Al in solution could be removed at pH 4.5 followed by REE precipitation at pH 6.0 using 6 mol/L NaOH. Finally, the bench-scale data was used to develop a process flowsheet for REE recovery from low-grade bituminous coal sources using acid baking. Finally, based on the proposed flowsheet, a concentrated RE-cake obtained through selective precipitation at pH 6.5 was re-leached using HCl at pH 1.5. The resultant leachate was used to identify the impact of various operating parameters on REE recovery and purity with an aim to maximize REE precipitation efficiency while minimizing the oxalic acid dosage. The operating parameters for this investigation were oxalic acid dosage, iron (III) contamination, solution pH and temperature. The resultant model suggested that oxalic acid dosage and reaction pH are the most significant factors for the REE precipitation efficiency, followed by the interaction of oxalic dosage and Fe concentration. Test results indicated that increasing the oxalic acid concentration from 0g/L to 80g/L improved the REE precipitation efficiency from approximately 4.2% to 95.0%. Furthermore, raising the solution pH from 0.5 to 2.5 considerably enhanced the precipitation efficiency from 0.0% to 98.9%. A solution temperature elevation decreased REE recovery, which indicated an exothermic reaction between REEs and oxalate anions. Finally, a high level of Fe contamination adversely impacted REE precipitation efficiency. The speciation analysis revealed that the dominant iron species in the solution system were Fe-(C₂O₄)₃³⁻, Fe-(C₂O₄)²⁻, and Fe-(C₂O₄)⁺, which consumed the majority of the oxalate anions

rare earth elements, acid baking, high-temperature↗

DOE OSTI2022

NIR Dataset and Models (Near-Infrared Spectroscopy Calibration Dataset for Butanediol Fermentation and Multivariate Calibration Models) [SWR-22-60]

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be used in a variety of chemical feedstocks, liquid fuels, and biosynthetic building blocks. While 2,3-BDO can be efficiently produced by fermentation, the fermentation requires continuous monitoring and control to maximize 2,3-BDO yields and minimize inhibitory coproducts. Because of the time required for sampling and at-line measurement of fermentation samples with high pressure liquid chromatography (HPLC), the ability for operators to perform real-time modification to fermentation conditions is limited. To overcome this challenge, researchers from the National Renewable Energy Laboratory (NREL) have developed a calibration model which can predict the concentration of several analytes in real-time using near-infrared (NIR) spectra of the filtered fermentation broth. While significantly reducing the need for off-line sampling, NREL expects this technology to play a critical role in maximizing 2,3-BDO production. 2,3-butanediol (2,3-BDO) is a useful chemical platform that can be used to create a variety of products. For instance, 2,3-BDO can be (1) dehydrated and converted into methyl ethyl ketone, a liquid fuel additive or (2) deoxydehydrated into 1,3-butadiene for synthetic rubber, which can also be oligomerized in high yields to gasoline, diesel, and jet fuel. In order to maximize 2,3-BDO production, frequent measurement of fermentation samples is needed, as small changes in oxygen concentration can drive the fermentation to undesired products. For example, oxygen-deficient conditions result in glycerol production, while excess oxygen concentrations result in acetoin production. This results in the need for measuring dissolved oxygen, glucose, and xylose concentrations in order to optimize the aeration rate of the fermentation. Traditional monitoring methods occurs off-line and can take up to 30 minutes per sample. With multiple fermenters and high-pressure liquid chromatography (HPLC) injectors, resulting in the need for multiple samples, the sampling process can take hours to complete. NREL’s calibration model can predict the glucose, xylose, 2,3-BDO, acetoin, and glycerol concentrations from NIR spectra of filtered fermentation liquor samples. Using a partial least-squares (PLS) calibration model, NREL’s model can monitor the concentration of these analytes during subsequent fermentations at bench- and pilot-scale, demonstrating the utility of NIR spectroscopy combined with chemometrics for real-time, at-line monitoring of 2,3-BDO fermentations.

Wolfrum, Edward↗