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DOE OSTI2022

Standardization of Smart Contracts for Energy Markets and Operation

This work presents a formal review of smart contracts, including definitions, technical requirements, and potential power and energy-related use cases. This includes in-depth discussions covering cybersecurity, legality and interoperability goals that must be taken into consideration by potential end-users. The paper presents a first attempt towards the standardization of smart contracts (SCs) within the field of power and energy as a work in progress activity under the IEEE Standards Association (IEEE SA) P2418.5 Working Group. This work also proposes a holistic, language-agnostic reference model that is intended to accelerate the adoption of Distributed Ledger Technology (DLT) by industry stakeholders by providing standardized processes. Finally, the paper discusses key takeaways that must continue to be developed to increase SC usage within the energy industry.

Blockchain, smart contracts, DLT↗

DOE OSTI2023

Evolution of Energy Efficiency Programs Over Time: The Case of Standby Power

Issued in 2001, Presidential Executive Order 13221 directed federal agencies to purchase products with low standby power, with the goal of 1) reducing energy consumption in federal facilities, and 2) drawing attention to the problem of high standby power consumption, with guidance provided by the Federal Energy Management Program (FEMP). At that time, standby power was newly recognized as an increasing building energy load. Since then, procurement of products with low standby power have been set in place in acquisition processes, and the purchasing power of the federal government continues to influence manufacturers’ design decisions related to standby power. In recent years, FEMP has shifted effort from direct manufacturer outreach for data collection, to integrating low standby requirement into broader acquisition programs including Energy Star and Electronic Product Environmental Assessment Tool (EPEAT). Another milestone has been the labeling of low standby products on the GSA Advantage website to simplify and enhance compliance. Looking forward into the program’s future, this question arises “How do we design programs over time to reflect market and technology changes, by adjusting programmatic requirements while maintaining effectiveness?” This paper discusses that question for the case of standby power, which transitioned from covering a single to multiple environmental attributes, both in the context of the program’s past and future.

Payne, CT↗

DOE OSTI2020

Reveal of Uranium Bioremediation Mechanisms by Bacillus Species through Proteomics Studies

Radionuclides, such as Uranium (U) and heavy metals continue to pose threats to the ecosystem health and processes at the Department of Energy (DOE) managed, Savannah River Site (SRS), located along the Savannah River near Aiken, SC. Such co-contaminated environments are difficult to remediate using conventional excavation and disposal or pump-and-treat approaches. Globally, more than 109 tons of uranium contaminated areas pose a long term threat to human and ecological health. Even with presented low concentrations in the brain, central nervous effects are still observed. Uranium and depleted uranium (DU) have long term effects on the kidneys. Some small general health effects include severe headaches and breathing problems. Uranus ions are toxic to living cells because they inhibit metabolism of carbohydrates by blocking ATP binding sites. Bioremediation by microorganisms represents an alternative solution, which is advantageous because of the possibility of biosorbent regeneration, cost-effectiveness, increased metal removal and easy recovery of some valuable metals. Bacillus sp. bacterium was previously used in the bioremediation of heavy metals in coal mine run off waters of SRS. However, its ability to bioremediate uranium was unknown so far. Hence, in the present study, uranium bioremediation by Bacillus sp. bacterium was investigated. The mechanism of bioremediation was also revealed through proteomics studies. Heavy metals contamination poses a serious threat to water, soil and human health. Soil and water are contaminated due to excessive exploitation of uranium mines for generation of nuclear energy and weaponry. It is not degradable easily and persist in soil and water for a long period of time due to its long half- life. Savannah river site (SRS) is one of the uranium contaminated sites. The physical or chemical remediation techniques are costly and complex. Microbial system approaches with competent bacteria has received increased attention due to its adaptability in various environmental matrices and cost effectiveness. However, even though there are multiple suggested pathways (F1), the specific mechanisms that drive this behavior are still unclear, especially with popular microorganisms such as Bacillus species. In a previous research (Ibeanusi et al, 2003) Bacillus sp. was shown to detoxify and precipitate a variety of heavy metals in coal pile runoff waters of SRS site. Additionally, genomic analysis demonstrated that this microorganism posses multiple attributes for chemical transport regulation and metabolic pathways. U remediation occurs during the first 20 hours of exposure. During this time period, Bacillus sp. may work primarily under two mechanisms - sorption and accumulation. These two mechanisms simultaneously work to protect the microorganism from high concentrations. Figure 10 demonstrates that certain proteins are up regulated and down regulated under extreme stressful conditions. Membrane fraction proteins were up regulated. Cytosolic fraction proteins were down regulated. This protein information coincides with the adsorption behavior. Bacillus sp. is a good candidate for U remediation at various concentrations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Continuous manufacturing of carbon foam structures at atmospheric pressure

A method of manufacturing essentially crack-free carbon foam structures and carbon foam structures made in single-piece flow through a continuous kiln under inert atmosphere at atmospheric pressure. This process greatly reduces the cost to manufacture by reducing manufacturing time and increasing the ability to integrate automation equipment.

Olson, III, Rudolph Andrew↗

DOE OSTI2022

Controlled compositional gradients of electroformed gold and silver

Motivated by an interest in high-pressure research, thick (>1 mm) compositionally graded gold/silver (Au/Ag) films were electroformed. Systematic studies were performed to understand the role of processing temperature on the nucleation and growth kinetics and associated microstructure. Furthermore, as the electrolyte composition is continuously changing during the experiment, systematic studies were performed to correlate electrochemical parameters with film morphology and composition. In this work, the results showed that the nucleation pathways and relative deposition rate of Au and Ag are heavily dependent on the processing temperature. A procedure to ramp the temperature while continuously pumping an Au electrolyte into an initial Ag electrolyte to produce the graded film is presented. The obtained film is comprised of a segment of pure Au followed by a complete compositional gradient and ending with a segment of pure Ag across 1.5 mm. The results presented here describe a general framework to fabricate gradients of other materials through electrodeposition.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Hybrid Analytics Solution to Improve Coal Power Plant Operations

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

PVAnalytics: A Python Package for Automated Processing of Solar Time Series Data

Multiple publicly available software packages exist that analyze solar time series data, including RdTools and Solar Data Tools, among others. Several of these packages contain their own unique quality assurance (QA) and feature recognition algorithms. The python PVAnalytics package was developed to offer an internally consistent source for these analysis tools, making it easier for the end user to deploy these routines on his or her solar data. The PVAnalytics package currently contains routines for outlier detection, inverter clipping detection, irradiance and temperature checks, orientation checks, and data shift detection, among other functions. These functions have been aggregated from various sources including Solar Forecast Arbiter, RdTools, and the QA process developed by NREL's PV Fleets Initiative. We are continuously adding new functionality to the package, including documentation, examples and algorithms. By bundling QA functionality into a single software package, we hope to make PVAnalytics a comprehensive software library to support analysis of solar metadata and time series data.

data cleaning↗

DOE OSTI2023

Ammonia generation in Ns pulse and Ns pulse/RF discharges over a catalytic surface

Plasma-catalytic ammonia synthesis in a ns pulse discharge and a 'hybrid' ns pulse/RF discharge in plane-to-plane geometry is studied by Fourier Transform infrared absorption spectroscopy. The data are taken in a preheated H 2 –N 2 mixture, with and without Ni/γ-Al 2 O 3 or Co/γ-Al 2 O 3 catalyst placed in the discharge section. The measurement results are taken using two different approaches. The first is a 'single-stage' process, where a ns pulse discharge in the H 2 –N 2 mixture is sustained continuously. In this case, the ammonia yield increases slowly, over a period of tens of minutes. The second is a 'two-stage' process, where the catalyst is first activated by the ns pulse discharge sustained in pure nitrogen, and then the activated catalyst is exposed to the H 2 –N 2 flow, with or without the discharge. In this case, a strong overshoot of the NH 3 number density at the reactor exit is detected, by over a factor of two compared to the single-stage process. After the initial overshoot, the ammonia yield gradually decreases to the 'single stage' value (with the discharge on), or to near zero (with the discharge off). The results demonstrate that the ammonia yield in the plasma-catalytic reactor is controlled by the N atom accumulation on the catalyst surface, which reacts with H atoms thermally dissociated on the catalyst or generated in the plasma. The results also show that the plasma-catalytic ammonia yield is significantly higher compared to that in the ns pulse discharge without the catalyst. The accumulation of H atoms on the catalyst, with their subsequent reactions with N atoms generated in the plasma, is of relatively minor importance at the present conditions. An additional series of measurements was made with a sub-breakdown RF waveform overlapped with the ns pulse discharge train, to enhance the vibrational excitation of nitrogen. The ammonia yield measured with the RF waveform added is approximately 20% higher compared to that at the baseline ns pulse discharge conditions, both with and without the catalyst. This effect is weaker compared to that of the catalyst activation by N atoms. Additional data are necessary to isolate the possible effect of the vibrationally excited N 2 molecules on the ammonia synthesis in the plasma catalytic reactions.

surface reactions↗

DOE OSTI2020

What can we learn from N 2 O isotope data? – Analytics, processes and modelling

The isotopic composition of nitrous oxide (N 2 O) provides useful information for evaluating N 2 O sources and budgets. Due to the co–occurrence of multiple N 2 O transformation pathways, it is, however, challenging to use isotopic information to quantify the contribution of distinct processes across variable spatiotemporal scales. Here, we present an overview of recent progress in N 2 O isotopic studies and provide suggestions for future research, mainly focusing on: analytical techniques; production and consumption processes; and interpretation and modelling approaches. Comparing isotope–ratio mass spectrometry (IRMS) with laser absorption spectroscopy (LAS), we conclude that IRMS is a precise technique for laboratory analysis of N 2 O isotopes, while LAS is more suitable for in situ/inline studies and offers advantages for site–specific analyses. When reviewing the link between the N 2 O isotopic composition and underlying mechanisms/processes, we find that, at the molecular scale, the specific enzymes and mechanisms involved determine isotopic fractionation effects. In contrast, at plot–to–global scales, mixing of N 2 O derived from different processes and their isotopic variability must be considered. We also find that dual isotope plots are effective for semi–quantitative attribution of co–occurring N 2 O production and reduction processes. More recently, process–based N 2 O isotopic models have been developed for natural abundance and 15 N–tracing studies, and have been shown to be effective, particularly for data with adequate temporal resolution. Here, despite the significant progress made over the last decade, there is still great need and potential for future work, including development of analytical techniques, reference materials and inter–laboratory comparisons, further exploration of N 2 O formation and destruction mechanisms, more observations across scales, and design and validation of interpretation and modelling approaches. Synthesizing all these efforts, we are confident that the N 2 O isotope community will continue to advance our understanding of N 2 O transformation processes in all spheres of the Earth, and in turn to gain improved constraints on regional and global budgets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

High-fidelity simulations of the run-in process for a pebble-bed reactor

Pebble-bed reactors (PBRs) rely on a continual feed of fuel pebbles being cycled through the core. As a result, they require a “run-in” period in order to reach an equilibrium state. The run-in period for a PBR is a complex, time-dependent problem that requires the injection of new fuel, different types of fuel, and power increases. This complexity in the run-in makes it important to capture the physical processes in order to generate an accurate representation. The present work details the creation of a high-fidelity Monte Carlo methodology for analyzing the run-in and subsequent approach to equilibrium for PBRs. The methodology entails a Python module wrapped around Serpent so as to perform neutronics calculations, move pebbles, refuel the core, and discharge pebbles, thereby modeling the explicit behavior of the PBR run-in. Further, three run-in simulations (a constant temperature profile, a linear temperature profile, and a constant temperature profile using control rods) were examined in order to identify the key physical phenomena present in the run-in process. Utilizing kugelpy, we found the inclusion of a temperature profile to be important for accurately capturing a discharge burnup (around 141 MWd/kg), a consistent k-eff (around 1.005), and an average pebble power (around 2.5 kW/pebble) that all fall within acceptable limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Cycloalkane-rich sustainable aviation fuel production via hydrotreating lignocellulosic biomass-derived catalytic fast pyrolysis oils

Sustainable aviation fuel (SAF) produced from lignocellulosic biomass is emerging as an ideal alternative to conventional jet fuel for aviation sector decarbonization. Catalytic fast pyrolysis (CFP) can convert lignocellulosic biomass into relatively stable bio-oil that can be selectively transformed to various transportation fuels through hydroprocessing under conditions of different severities. In this contribution, two CFP oils produced from pine-based feedstocks over different types of catalysts (i.e., ZSM-5 and Pt/TiO 2 catalysts) were hydrotreated at 125 bar in a non-isothermal process with a maximum temperature of 385 °C over a sulfided NiMo/Al 2 O 3 catalyst to produce SAF with high cycloalkane concentrations of 89–92 wt%. Cycloalkanes are an important component of jet fuel with advantageous fuel properties, such as high energy density, low sooting, and potential for replacing aromatic hydrocarbons to provide good seal swelling properties. The hydrotreating process successfully converted 91–92% of the biogenic carbon in the CFP oil intermediates to liquid-phase hydrotreated products. Through distillation, 39–40 wt% of the hydrotreated oils were collected in the jet-fuel range as SAF fractions. The rest of the hydrotreated product could be valorized as fuels (e.g., diesel) or chemicals. The SAF fractions with oxygen contents below the detection limit (<0.01 wt%) met ASTM D7566 finished fuel blend and D4054 Tier 1 specifications with respect to density, lower heating value (LHV), volatility, flash point, and freeze point. These results indicate hydrotreating lignocellulosic biomass-derived CFP oil as a promising pathway to produce high-quality SAF rich in cycloalkanes. Continued research is required to increase the SAF yield by process improvements, such as increased CFP oil yields, and an enhanced production of SAF-range molecules via e.g., cracking of high-molecular weight compounds either during CFP or hydrotreating, as well as evaluation of a broader range of jet fuel properties and performance requirements.

09 BIOMASS FUELS↗

DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Genesis of a novel high-rate composite manufacturing process using large-scale additive manufacturing – compression molding (AM-CM) system: Possibilities and limitations

Oak Ridge National Laboratory (ORNL) has developed a highly automated manufacturing process for thermoplastic composites that combines the benefits of Additive Manufacturing and Compression Molding (AM-CM) to produce high-performance functional composite structures at automotive production rates. Here, the AM-CM process creates highly precise preforms by additively placing extruded fiber-filled polymers (with controlled fiber orientations and multi-material configurations) in the desired mold location before undergoing a secondary compression molding process immediately before the preform cools down. Preforms can be in the form of short, long-chopped, or continuous fiber-filled thermoplastic polymers (e.g., CF/GF-filled ABS, PC, LM-PAEK, etc.). The AM-CM process combines the benefits of controlled fiber alignment, that is only achievable in AM-printed parts with the classical CM process, which eliminates porosity and good surface finish. A preform created using AM-CM can integrate various materials to enable additional architectural functionalities, including over-molding, selective stiffening, and the incorporation of electrically or thermally conductive channels. All these advantages come with a fast part production cycle time. The AM-CM process can manufacture multi-material, multi-functional parts in under 3 min, starting from raw material (pellets) to the final product. The novel AM-CM process offers superior microstructural control and enhanced multi-functionality previously unattainable with any other traditional high-rate thermoplastic composite manufacturing method. This work covers the manufacturing concept, system development, materials and applications of AM-CM process in detail.

Kumar, Vipin [Oak Ridge National Laboratory (ORNL)↗

DOE OSTI2025

Signal processing and spectral modeling for the BeEST experiment

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of 7 Be by precisely measuring the recoil energy of the 7 Li daughter. In Phase III, the BeEST experiment has been scaled from a singl superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. Here, we have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large datasets. Furthermore, this article presents the optimized procedures before unblinding the majority of the Phase III dataset to search for physics beyond the standard model.

6 ≤ A ≤ 19↗

DOE OSTI2026

Optimal Design and Techno-Economic Analysis of 3D-Printed, Intensified Packings for Absorbers and Strippers in Solvent-Based CO 2 Capture

A potential technology for the CO 2 absorption process is utilizing intensified structured packing with embedded cooling/heating channels for continuous heat exchange, which can overcome limitations of discrete methods, such as discrete intercooling and centralized reboilers, to aid in reducing energy consumption and decreasing costs. This work investigates the modeling of intensified packing (IP) for the stripper tower, extending on previous work for the absorber, which distributes heat internally within the column, improving the thermodynamics for the solvent regeneration process. The model includes submodels for steam turbine extraction to produce steam at various qualities as well as a surrogate model for calculating steam enthalpy. A cost model for a plant-scale absorption capture process was developed, allowing for the design of the plant to be optimized, subject to minimizing capture cost using two different power plant flue gas sources. In this optimization, the placement of IP in both towers is optimized to balance the trade-off between enhanced heat transfer and reduced mass transfer volume. For natural gas combined cycle flue gas, the standard process configuration had a minimum cost of $\$$65.40/tonne CO 2 , and considering IP, the minimum capture cost is reduced to $\$$62.73/tonne, with utilization in the stripper column, which reduces yearly costs by up to $\$$2.67 MM/yr. Cooling the absorber through IP, or intercoolers, was only found to be beneficial at higher capture rates, with IP in both towers having a cost of capture of $\$$68.08/tonne at 99.9% capture, a reduction of $\$$12.64/tonne when using only intercoolers at the same capture rate. When capturing from pulverized-coal power plants, the minimum cost of capture when using IP in both towers is $\$$44.18/tonne (at 97% capture), while the standard configuration with and without intercoolers was $\$$45.69 and $\$$47.22 per tonne, respectively. This results in a reduction in yearly costs of $\$$16.98 MM/yr from the base-case configuration. At this higher CO 2 concentration, cooling in the absorber from the IP becomes extremely beneficial, reducing energy consumption by up to 6%.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

Noise and error analysis and optimization in particle-based kinetic plasma simulations

In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix C(x,y) is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫C(x,y)dy = 0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, p(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. Next, we repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. We find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. Furthermore, we show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Development and Characterization of Cementitious Waste Forms for Immobilization of Granular Activated Carbon, Silver Mordenite, and HEPA Filter Media Solid Secondary Waste

At the Department of Energy’s Hanford site, over 53 million gallons of chemically complex and radioactive wastes have been stored in 177 underground tanks. The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is under construction and is designed to treat and immobilize these wastes. During operations of WTP, solid secondary wastes (SSWs) will be generated as a result of waste treatment, vitrification, off-gas management, and supporting process activities. SSW treatment processes and resulting disposal pathways for the final disposition form of the SSW are needed to support direct feed low activity waste (DFLAW) operations and facilitate continued operation of WTP. The SSWs produced through WTP operations are expected to include used process equipment, contaminated tools and instruments, decontamination wastes, high-efficiency particulate air (HEPA) filters, carbon absorption beds (granular activated carbon, GAC), silver mordenite (AgM) and spent ion-exchange resins. These waste streams are planned to be immobilized in a cementitious waste form and disposed of either as stabilized/blended (non-debris) or encapsulated (debris) in a cementitious waste form. Accordingly, cementitious waste forms from these streams were included in the 2017 Integrated Disposal Facility (IDF) Performance Assessment (PA). The input data used to represent these SSW forms in the 2017 IDF PA involved many assumptions and associated uncertainties. This data limitation was due to the lack of material- and site-specific data available for representative SSW materials in cementitious matrices. To verify the assumed values used in the IDF PA and fill this limitation in available data, Washington River Protection Solutions, LLC (WRPS), has initiated a program targeted toward gathering site specific data relevant to Hanford SSW disposal. The work within this report is a continuation of this ongoing program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

A New Process for Carbon Dioxide Conversion to Fuel

In this project, TDA developed a new mixed metal oxide-based sorbent that converts CO 2 (captured from coal fired power plant) to CO, which can then be combined with renewable H 2 generated by water electrolysis to produce different liquid fuels. TDA’s absorbent-based CO 2 conversion process uses a redox process, which splits the reverse water gas shift reaction steps into two stages: CO 2 reduction to CO and H 2 oxidation to H 2 O and eliminates the equilibrium limitations. The CO produced in the two-stage reactor system can then be further reacted with renewable H 2 to produce methanol, naphtha, diesel, or gasoline. We worked with the Gas Technology Institute (GTI) and Advanced Power & Energy Program (APEP) of University of California, Irvine (UCI) to design and develop the liquid fuel synthesis process that is built around this new material. We demonstrated the techno-economic viability of the new sorbent based redox process to convert CO 2 into synthesis gas by: 1) demonstrating continuous carbon dioxide reduction in a prototype test system for over 500 hours while converting up to 0.4 kg CO 2 /day. With the successful completion of the R&D effort, the technology is now ready for a larger pilot-scale demonstration and the technology readiness has been raised from TRL 3 to TRL 5. In collaboration UCI, we also completed a high-fidelity process design and economic analysis. The required selling price (RSP) for methanol (Case 1 H2-MeOH) is $\$$3.06/gal, naphtha and diesel (Case 2 H2-FT) are $\$$6.21/gal and $\$$8.91/gal, and for gasoline (Case 3 H2-MTG) is $\$$6.96/gal on a 2011 dollar basis. To put these costs in perspective, the recent (June 2020) U.S. price for methanol made from natural gas is about $\$$1.19/gal according to Methanex, a major methanol producer in North America. The recent (June 2, 2020) California prices for gasoline and diesel with California’s strict specifications are $\$$3.35/gal and $\$$3.73/gal according to the U.S. Energy Information Agency. On the other hand, it should be noted that the gasoline and diesel produced by the designs of Case 2 and Case 3 would be both nitrogen and sulfur free. These RSPs are based on a cost of imported electricity of $\$$64/MWh based on the low-end current wind generated electricity cost (Genevieve and Ramsden, Wind Electrolysis: Hydrogen Cost Optimization, Technical Report, June 2011, NREL/TP-5600-50408). This cost is by far the largest component of the variable costs used in computing the RSPs. The cost of the imported electricity has to approach zero for the RSP to be competitive for methanol produced in Case 1 and naphtha and diesel produced in Case 2 while the cost of electricity has to be less than $\$$10/MWh for gasoline produced in Case 3.

01 COAL, LIGNITE, AND PEAT↗