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DOE OSTI2024

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Hybrid-Supported Bilayers Formed with Mixed-Charge Surfactants on C 18 -Functionalized Silica Surfaces

Mixtures of cationic-anionic surfactants have been demonstrated to spontaneously form ordered monolayers at hydrophobic-hydrophilic boundaries including air-water and oil-water interfaces. Here, confocal Raman microscopy is used to investigate the structure of hybrid-supported surfactant bilayers (HSSBs) formed by deposition of a distal leaflet of mixed cationic-anionic surfactants onto a proximal leaflet of n-alkane (C 18 ) chains on the interior surfaces of chromatographic silica particles. The surface coverage of the two surfactants in a hybrid bilayer was determined from carbon analysis and the relative Raman scattering of their respective head-groups. Within the measurement uncertainty, the stoichiometric ratio of the two surfactants is one-to-one, equivalent to mixed-charge-surfactant monolayers at air-water and oil-water interfaces and consistent with the role of the head-group electrostatic interactions in their formation. When self-assembled on the hydrophobic surface, pairs of oppositely-charged n-alkyl-chain surfactants resemble a phospholipid (phosphati¬dylcholine) molecule, with its zwitterionic head group and two hydrophobic acyl-chain tails. Indeed, the structure of these hybrid-supported surfactant bilayers on C 18 -modified silica surfaces is similar to that of hybrid-supported lipid bilayers (HSLBs) on the same supports, but with denser and more-ordered n-alkyl chains. Hybrid-supported surfactant bilayers exhibit a melting phase transition (gel to liquid-crystalline phase) with structural and energetic characteristics similar to hybrid-supported bilayers prepared from a zwitterionic phospholipid of the same alkyl chain length. These mixed-charge surfactants on n-alkane modified silica are stable in water over time (months), results that suggest the potential use of these hybrid bilayers for generating supported lipid bilayer-like surfaces or for separation applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A Scaling Analysis of Post-Detonation Mixing With Detailed Chemical Kinetics

This report documents a scaling study of hemispherical pentaerythritol tetranitrate explosives detonated in ambient air. The focus is on the mixing layer and evolution of the chemical species. The HyBurn code developed at the University of Florida is used to simulate center initiated 12, 38, 120, and 1200 mm diameter charges. The results show blast structures commonly found in the literature. Azimuthal averaging is used to radially average the flow variables to get mean values. The charges exhibit self-similar behavior at early times in a primarily gas dynamic regime, but discrepancies between the scales form at later times due to the finite rate chemistry. The turbulent mixing layer is calculated and used to define a region to conditionally average by mixture fraction. The results of the conditional averages imply a transition from a kinetic-limited regime to a mixing-limited regime with increasing scale. The scaling of the total heat release rate confirms an early time gas dynamic regime, and a later time regime where mixing and chemistry becomes impactful.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Quantification of Swelling in Hematite Pellets Reduced Using Hydrogen–Nitrogen Gas Mixture

Iron ore pellets are reduced in a 50%H 2 –50%N 2 1 atm gas mixture at 750, 800, 850, 900, and 950 °C while simultaneously documenting swelling (change in pellet radius) and weight change. Swelling increases with increasing temperature, with catastrophic swelling (>20% of reduction swelling index) observed at 850, 900, and 950 °C. As the pellet is reduced, the pellet radius increases until 40–50% reduction is achieved, followed by a decrease in diameter beyond 40–50% reduction at 750 and 850 °C. At 950 °C, the pellet radius continues to increase with additional pellet reduction without any subsequent decrease in diameter. Scanning electron microscopy (SEM) analysis shows that the neighboring grains inside the pellet sinter together at 750 and 850 °C, whereas the individual grains sinter internally at 950 °C. SEM analysis and observations suggest that the reduction process at 750 and 850 °C can be approximated as a topochemical reaction process, while the reduction process at 950 °C can no longer be approximated as a topochemical reaction process. In conclusion, an empirical equation for the radius of the pellet is derived with fitting parameters dependent on temperature and the degree of reduction of the pellet undergoing reduction based on the experimental data.

08 HYDROGEN↗

DOE OSTI2022

Discrimination between individual dust and bioparticles using aerosol time-of-flight mass spectrometry

Ice nucleating particles (INPs) impact cloud properties and precipitation processes through their ability to trigger cloud glaciation. Dust and bioparticles are two important sources of INPs that have markedly different atmospheric loadings and ice nucleating efficiencies. In situ identification of the sources of INPs in clouds has been accomplished using single particle mass spectrometry (SPMS). However, external mixtures of dust and bioparticles present a unique challenge as they have overlapping mass spectral ion signatures, complicating their unambiguous identification. This study presents a detailed discussion of dust and bioparticle SPMS signatures, uniting data from a broad array of studies. As emphasized, the ion signals from both dust and bioparticles are highly sensitive to ionization conditions. To understand the observed variations, we characterize the mass spectral dependence of distinct dust and bioparticle samples using total positive ion intensity (TPII) as an indicator of the laser pulse energy each particle encountered. Through this analysis, a broad range of characteristic biogenic low intensity ion peaks that may be useful to distinguish bioparticles from dust became apparent and are highlighted. Insights informed by this analysis were utilized to identify bioparticles in ambient SPMS data. Ambient particles exhibiting both dust and characteristic biogenic spectral fingerprints were excluded from the bioparticle classification. Although bioparticles only made up 0.2% of all sampled particles, their abundance was moderately correlated with INP concentrations measured at 15 °C.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

DME-Propane Autoignition Measurements inside a Shock Tube

Dimethyl ether (DME) is a biofuel that has the potential to replace diesel in heavy-duty engines. A blend of propane (C3H8) and DME could reduce emissions when compared to diesel at heavy-duty compression engine-relevant conditions. Testing a potential new mixture in a compression engine requires a high-fidelity chemical kinetics model that accurately predicts autoignition delay times, which is especially necessary for a compression engine. In this work, autoignition data has been gathered at an equivalence ratio of 2.0 for pressures of 60 and 80 bar. DME and propane were combusted in synthetic air while excited hydroxyl (OH*) chemiluminescence was used to gather ignition delay times. Data was compared to recent chemical kinetic mechanisms for the two different pressures. Using the mechanism with the best fit of autoignition data, a sensitivity analysis was conducted to analyze the chemical kinetics of the DME/C3H8 combustion at elevated pressures.

Mohammed, Zuhayr Pasha↗

DOE OSTI2025

SpecDis: Value Added Distance Catalog for 4 Million Stars from DESI Year-1 Data

We present the SpecDis value-added stellar distance catalog accompanying DESI Data Release 1. SpecDis trains a feed-forward neural network (NN) with Gaia parallaxes and gets the distance estimates. To build up an unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead, we incorporate parallax error into the loss function. Moreover, we employ principal component analysis to reduce the noise and dimensionality of stellar spectra. Validated by independent external samples of member stars with precise distances from globular clusters, dwarf galaxies, stellar streams, combined with blue horizontal branch stars, we demonstrate that our distance measurements show no significant bias up to 100 kpc, and are much more precise than Gaia parallax beyond 7 kpc. The median distance uncertainties are 23%, 19%, 11%, and 7% for S/N < 20, 20 ≤ S/N < 60, 60 ≤ S/N < 100, and S/N ≥ 100. Selecting stars with ${\mathrm{log}}\,g\lt 3.8$ and distance uncertainties smaller than 25%, we have more than 74,000 giant candidates within 50 kpc of the Galactic center and 1500 candidates beyond this distance. Additionally, we develop a Gaussian mixture model to identify unresolvable equal-mass binaries by modeling the discrepancy between the NN-predicted and the geometric absolute magnitudes from Gaia parallaxes and identify 120,000 equal-mass binary candidates. Our final catalog provides distances and distance uncertainties for >4 million stars, offering a valuable resource for Galactic astronomy.

astronomy data analysis↗

DOE OSTI2023

Chemical composition, coordination, and stability of Ca–organic associations in the presence of dissolving calcite

Environmental biotic and abiotic factors and soil physical, mineralogical, and chemical properties control the chemical composition of soil organic matter (SOM). Particularly, soil mineralogy and the presence of multivalent cations affect SOM labile fraction composition, stability, and environmental persistence. The persistence of SOM in aridic or limestone deposit derived soils, i.e., calcareous soils, has been partially attributed to SOM stabilization through adsorption or inclusion into the calcite mineral structure. Recently, however, it was shown that Ca(aq) released during calcite dissolution formed aqueous Ca–organic associations with OM components, which were unbound to mineral surface sites. This study investigates the structure, composition, and coordination of these associations by characterizing lyophilized Ca–organic containing solutions with spectromicroscopy [Scanning Transmission X-ray Microscopy (STXM)] and with a nanoimaging chemical probe [Infrared scattering-Scanning Nearfield Optical Microscopy (IR s-SNOM)]. Chemical stability of Ca–organic associations is furthermore determined with pyrolysis mass spectrometry analysis. The results demonstrate that Ca–organic associations are formed in the presence of dissolving calcite and OM components relevant to soil chemistry, i.e., lignin and amino acids. This study further reveals a spatial homogeneity of solution-derived (bi) carbonate in Ca–organic associations indicating for the first time that an inorganic anion, such as (bi)carbonate, may be part of these associations. Most likely, Ca ions are bound to both the (bi)carbonate and the organic components. These Ca (bi)carbonate–organic associations seem to have greater chemical stability than the pristine organic mixtures and, possibly, a higher environmental stability and reduced mineralization rate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Direct Numerical Simulation of Partial Fuel Stratification Assisted Lean Premixed Combustion for Assessment of Hybrid G-Equation/Well-Stirred Reactor Model

Partial fuel stratification (PFS) is a promising fuel injection strategy to stabilize lean premixed combustion in spark-ignition (SI) engines. PFS creates a locally stratified mixture by injecting a fraction of the fuel, just before spark timing, into the engine cylinder containing homogeneous lean fuel/air mixture. Further, this locally stratified mixture, when ignited, results in complex flame structure and propagation modes similar to partially premixed flames and allows for faster and more stable flame propagation than a homogeneous lean mixture. This study focuses on understanding the detailed flame structures associated with PFS-assisted lean premixed combustion. First, a two-dimensional direct numerical simulation (DNS) is performed using detailed fuel chemistry, experimental pressure trace, and realistic initial conditions mapped from a prior engine large-eddy simulation (LES), replicating practical lean SI operating conditions. DNS results suggest that the conventional triple flame structure is prevalent during the initial stage of flame kernel growth. Both premixed and nonpremixed combustion modes are present with the premixed mode contributing dominantly to the total heat release. Detailed analysis further reveals the effects of flame stretch and fuel pyrolysis on flame displacement speed. Based on the DNS findings, the accuracy of a hybrid G-equation/well-stirred reactor (WSR) combustion model is assessed for the PFS-assisted lean operation in the LES context. It is found that the G-equation model qualitatively captures the premixed branches of the triple flame, while the WSR model predicts the nonpremixed branch of the triple flame. Finally, potential needs for improvements to the hybrid G-equation/WSR modeling approach are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Liquid–Solid Mixtures of Ga Metal Infused with Cu Microparticles and Nanoparticles for Microscale and Nanoscale Patterning of Solid Metals at Room Temperature

This paper demonstrates and characterizes a simple ink for nanopatterning of solid metallic structures under ambient conditions by taking advantage of the low melting point of gallium and its affinity to form intermetallics with other metals. Bare copper micro- and nanoparticles readily mix with liquid gallium near room temperature to form a paste that ultimately amalgamates into a dense solid when mixed at the appropriate concentration ratios. The paste has sufficient shelf life that can be extruded from a nozzle at modest temperatures to form solid three-dimensional (3D) shapes. Additionally, the paste can be molded at room temperature to replicate feature sizes ranging from a few millimeters down to hundreds of nanometers. In situ X-ray diffraction (XRD) and thermo-mechanical analysis (TMA) data show gallium and copper readily interdiffuse to form the thermodynamically expected intermetallic phase. We describe the capabilities and limitations of a simple way to pattern solid metals in an additive fashion (syringe-based extrusion) and with high resolution (molding) at or near room temperature. Here, the use of a paste that solidifies provides a novel route for 3D printing of solid metals at ambient temperatures as well as the creation of micro- and nanostructured metallic surfaces that may be useful for optics, non-wetting surfaces, or electronic microcomponents.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Unraveling Local Structure of Molten Salts via X-ray Scattering, Raman Spectroscopy, and Ab Initio Molecular Dynamics

In this work, we resolve a long-standing issue concerning the local structure of molten MgCl 2 by employing a multimodal approach, including X-ray scattering and Raman spectroscopy, along with the theoretical modeling of the experimental spectra based on ab initio molecular dynamics (AIMD) simulations utilizing several density functional theory (DFT) methods. We demonstrate the reliability of AIMD simulations in achieving excellent agreement between the experimental and simulated spectra for MgCl 2 and 50 mol % MgCl 2 + 50 mol % KCl, and ZnCl 2 , thus allowing structural insights not directly available from experiment alone. A thorough computational analysis using five DFT methods provides a convergent view that octahedrally coordinated magnesium in pure MgCl 2 upon melting preferentially coordinates with five chloride anions to form distorted square pyramidal polyhedra that are connected via corners and to a lesser degree via edges. This is contrasted with the results for ZnCl 2 , which does not change its tetrahedral coordination on melting. Although the five-coordinate MgCl 5 3– complex was not considered in the early literature, together with an increasing tendency to form a tetrahedrally coordinated complex with decreasing the MgCl 2 content in the mixture with alkali metal chloride systems, current work reconciles the results of most previous seemingly contradictory experimental studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Cryo-EM structures of DNA-free and DNA-bound BsaXI: architecture of a Type IIB restriction–modification enzyme

Abstract We have determined multiple cryogenic electron microscopy (cryo-EM) structures of the Type IIB restriction–modification enzyme BsaXI. Such enzymes cleave DNA on both sides of their recognition sequence and share features of Types I, II, and III restriction systems. BsaXI forms a heterotrimeric (RM)2S assemblage in the presence and absence of bound DNA. Two unique structural motifs—a multi-helical “knob” and a long antiparallel double-helical “paddle”—are involved in DNA binding and cleavage. Binding of the DNA target triggers a large conformational change from an ‘open’ to ‘closed’ configuration, resulting in a mixture of two different conformations with respect to the positioning of the S subunit and its target recognition domains on the enzyme’s bipartite DNA target site. Structure-guided mutagenesis studies implicated two clusters of residues in the RM subunit as being critical for DNA cleavage, both are located proximal to a DNA cleavage site. One corresponds to a canonical PD-(D/E)xK endonuclease site in the N-terminal endonuclease domain, while the other corresponds to residues clustered within the paddle motif (near to the C-terminal end of the RM subunit). This analysis facilitates a comparison of three potential mechanisms by which such enzymes cleave DNA on each side of the bound target.

Shen, Betty W.↗

DOE OSTI2022

Electrically controlling and optically observing the membrane potential of supported lipid bilayers

Supported lipid bilayers are a well-developed model system for the study of membranes and their associated proteins, such as membrane channels, enzymes, and receptors. These versatile model membranes can be made from various components, ranging from simple synthetic phospholipids to complex mixtures of constituents, mimicking the cell membrane with its relevant physiochemical and molecular phenomena. In addition, the high stability of supported lipid bilayers allows for their study via a wide array of experimental probes. In this work, we describe a platform for supported lipid bilayers that is accessible both electrically and optically, and demonstrate direct optical observation of the transmembrane potential of supported lipid bilayers. We show that the polarization of the supported membrane can be electrically controlled and optically probed using voltage-sensitive dyes. Membrane polarization dynamics is understood through electrochemical impedance spectroscopy and the analysis of an equivalent electrical circuit model. In addition, we describe the effect of the conducting electrode layer on the fluorescence of the optical probe through metal-induced energy transfer, and show that while this energy transfer has an adverse effect on the voltage sensitivity of the fluorescent probe, its strong distance dependency allows for axial localization of fluorescent emitters with ultrahigh accuracy. We conclude with a discussion on possible applications of this platform for the study of voltage-dependent membrane proteins and other processes in membrane biology and surface science.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

The Modeling of the Synfuel Production Process: Process models of Fischer-Tropsch production with electricity and hydrogen provided by various scales of nuclear plants

Synthetic fuels (synfuels), also known as electro-fuels (E-fuels), are hydrocarbon fuels produced from waste CO2 streams and water electrolysis, with electricity as the primary source of energy. To achieve substantial reductions in greenhouse gas (GHG) emissions, electricity sources must release zero carbon or near-zero carbon, as is the case with solar, wind, hydro, and nuclear power. Nuclear power is one of the largest and steadiest domestic sources of clean energy in the United States. Moreover, nuclear power has the potential to produce hydrogen economically for less than $2/kg, reaching the DOE near-term target price. Thus, using nuclear power to produce synfuels has the unique potential to significantly reduce the GHG emissions of hydrocarbon fuels production and end-use applications. Fisher-Tropsch or FT fuel (a mixture of naphtha, jet fuel, and diesel) is of great interest because it is a drop-in fuel that can be blended with conventional petroleum counterparts and is compatible with existing infrastructure. By using the ASPEN Plus model, this report develops FT fuel production models on three scales, corresponding to nuclear plants with capacities of 1000 MWe, 437 Mwe, and 100 MWe, respectively. The FT model case with energy from a 437-MWe nuclear plant is used as a baseline case. This report summarizes the baseline ASPEN Plus model results with a detailed mass and energy analysis. Our modeled facility produces 507 MT/day (185,000 gal/day) of FT fuel by converting 255 MT/day of hydrogen and 1,580 MT/day of CO2. The FT fuel production energy efficiency from hydrogen and electricity energy inputs is 70% (lowerheating-value or LHV-based). Including the high-temperature electrolyzer in the system boundary, the FT fuel production LHV efficiency from electricity and thermal energy inputs is 51%, considering 39.8 kWh/kg of electricity and 6.86 kWh/kg of thermal energy use from a nuclear plant for hydrogen production. The FT production efficiency can potentially be increased by further integrating the heat exchange between nuclear plant and FT process, and this study is underway. The carbon conversion ratio in the baseline case is 99%, with process CO2 capture and recirculation and oxy-combustion using the oxygen by-product from water electrolysis. The hydrogen consumption is 1.38 kg/gal-FT fuel and the CO2 consumption is 8.56 kg/gal-FT fuel in the baseline case. With different FT production scales determined by the nuclear plant capacity, the FT model was scaled using the same operating parameters, which led to the same conversion efficiency regardless of scale. However, the different FT plant scales will impact the economics of FT fuel production; this impact will be examined in the next phase of this study.

Zang, Guiyan↗

DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗

DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2026

AGR-5/6/7 Thermal Model with Non-uniform Gas Gaps

The daily as-run thermal analysis for the Advanced Gas Reactor (AGR)-5/6/7 experiment was documented in ECAR-5633 (Hawkes 2021). A finite element model was created for the entire test train, which consisted of five capsules. The fuel compacts, graphite holders, stainless-steel capsule walls, and all other major components were individually modeled, including all thermocouples (TCs). Daily heat rates for each compact and component in the model were input from daily as-run physics analyses. Daily gas compositions for each capsule were also input. The thermal conductivity of the compacts and graphite holders varied with fast neutron fluence. Gas mixture thermal conductivity was implemented using experimentally attained values from the literature. Fluence and temperature-dependent thermal conductivity was used for the graphite components and the fuel compacts. Radiation heat transfer was implemented. The model was tuned in an attempt to match the TC readings during the first cycle by adjusting the Neolube (graphite lubricant) thickness.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Computational analysis of flame initiation, quenching, and re-ignition in a prechamber natural gas engine under varying EGR-dilution levels

The on-road natural-gas (NG) fueled transportation relies on stoichiometric spark-ignition engines for the advantages of simple after-treatment system despite the efficiency penalty relative to lean-burn combustion strategies. Exhaust gas recirculation (EGR) has the potential to reduce this efficiency gap at low to moderate loads without the need for complex lean-exhaust aftertreatment systems. However, EGR dilution leads to reduced combustion stability and increased cycle-to-cycle variability. A promising technology that has the potential to achieve reliable operation under diluted conditions is the prechamber ignition (or turbulent jet ignition) which uses chemically active turbulent jets generated from combustion inside a prechamber to initiate, stabilize and accelerate combustion of the mixture inside the main chamber. The present work focusses on developing a RANS-based CFD approach to accurately reproduce in-cylinder phenomena in a stoichiometric NG prechamber-assisted heavy-duty engine without relying on complex combustion models that account for turbulence-chemistry interactions. This is necessary because reactive prechamber jets at high EGR dilution tend to extinguish while emerging into the main chamber, which is followed by a phase of re-ignition — a phenomenon that conventional G-equation or well-stirred reactor combustion models cannot reproduce. With addition of a damping multiplier to the well-stirred reactor model, the predictions are seen to show good agreement with experimental pressure evolution and combustion images acquired from a single cylinder Cummins N-14 optical diesel engine retrofitted with a prechamber ignition system. Model predictions of local heat release in the flame and temperature evolution inside the flame are used to investigate combustion dynamics in the prechamber and the main chamber. It is seen that the well-stirred reactor model with the inclusion of damping is able to reproduce the temporary reduction in heat release within the flame, which can be considered equivalent to quenching of jets, and the subsequent re-ignition of the flame inside the main chamber. The delay between quenching and re-ignition depends on the amount of dilution, as explained by an illustration of flame evolution in a Borghi diagram.

Prechamber ignition↗