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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2021

Low soil phosphorus availability triggers maize growth stage specific rhizosphere processes leading to mineralization of organic P

Phosphorus (P) is one of the essential nutrients for all living organisms. High-quality mineral P fertilizer is a finite resource with only an estimated 80-100 years of reserves remaining globally. Both plants and microbes have developed several mechanisms such as secretion of organic acids, acidification of the rhizosphere, and production of extracellular enzymes to enhance the acquisition of P from soils. Increased activity of phosphatase occurs in response to P deficiency as part of P starvation responses which catalyze the hydrolysis of P o from soil organic matter (SOM) and release inorganic P (P i ). However, the specific interactions between plants and microbial communities in the rhizosphere soil triggered by P deficiency are largely unknown. In a greenhouse study we grew maize plants in low and high P containing soils and examined the role of rhizosphere processes on mineralization of organic P (P o ) in soils with low and high P availability and different P speciation 23 (Po dominated versus P i dominated) and determine its effect on plant growth. Plant biomass was determined and rhizosphere soils, and soil samples from pots with and without plants were collected at key vegetative growth stages (VGS). A variety of biogeochemical parameters (e.g. microbial biomass C and P, potential phosphatase activities, plant biomass P, soil P species etc.) were determined using both conventional (sequential chemical extraction) and advanced (e.g. high resolution mass spectrometry (FTICR-MS) and 3129 P solution NMR) techniques. Here, we used phospholipid fatty acid (PLFA) and DNA based microbial community analysis to track changes in microbial community structure and diversity. Low P availability induced changes in biogeochemical processes and microbial community composition in the rhizosphere soils of plants grown in low P containing soil. At early VGS, the plants in low P soil were visually struggling which correlated with greater rhizosphere potential acid phosphatase activity and a reduction in P o fractions. At late VGS, the plants appeared to recover which correlated with a decrease in Meh (III) extractable P, an increase in microbial biomass C and P and greater total P in the plant biomass (roots, shoots and leaves). In high P containing soil, on the other hand, greater P availability masked these rhizosphere processes. Our results confirmed the degradation of SOM and Po mineralization in rhizosphere soils driven by microbe and plant need for P. P deficiency may have favored the abundance of fungi which utilized easily degradable root exudates for the production of extracellular enzymes responsible for the mineralize of SOM P o . This study highlights the intertwined mechanisms involved, and emphasizes the importance of, P o sources for plant nutrition and survival in marginal soils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information

Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This work investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.

42 ENGINEERING↗
DOE OSTI2022

The “Fresnel Equations” for Diffuse radiation on Inclined photovoltaic Surfaces (FEDIS)

Here the well-known Fresnel equations solve for the reflection and transmission of light for precise incident angles. The transmission of diffuse radiation incident on a planar or domed surface is often needed for real-world applications. Due to the complexity of the Fresnel equations, the analytical solution of the integration has hitherto been unobtainable over the last centuries. Therefore, this problem was numerically solved by integrating the angular transmittances in space often leading to substantial computing burden and bias in the results. To efficiently estimate the solar energy resource for a glass-covered photovoltaic (PV) module, we derive an analytical solution of diffuse transmission based on the rigorous integration of an alternate form of the Fresnel equations. The approach leads to a simple yet accurate relative transmittance model that reconciles the solar energy sensed by pyranometers and PV panels. With limited and clearly stated approximations, the complex mathematical derivation resulted in an elegant solution. An experiment using 1-year of data at the National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) shows that the new model dramatically decreases the disparity between the solar radiation measurements by a Kipp and Zonen CM Pyranometer 22 (CMP22) and an IMT reference cell on a 1-axis tracking system. The solution in this paper can be widely used in scientific and engineering research, development, and applications wherever the Fresnel equations are used.

14 SOLAR ENERGY↗
DOE OSTI2024

Exponential concentration in quantum kernel methods

Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guaranteed to find the optimal model’s parameters due to the convexity of the training landscape. However, this is based on the assumption that the quantum kernel can be efficiently obtained from quantum hardware. In this work we study the performance of quantum kernel models from the perspective of the resources needed to accurately estimate kernel values. We show that, under certain conditions, values of quantum kernels over different input data can be exponentially concentrated (in the number of qubits) towards some fixed value. Thus on training with a polynomial number of measurements, one ends up with a trivial model where the predictions on unseen inputs are independent of the input data. We identify four sources that can lead to concentration including expressivity of data embedding, global measurements, entanglement and noise. For each source, an associated concentration bound of quantum kernels is analytically derived. Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration. Our results are verified through numerical simulations for several QML tasks. Altogether, we provide guidelines indicating that certain features should be avoided to ensure the efficient evaluation of quantum kernels and so the performance of quantum kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spatially structured bacterial interactions alter algal carbon flow to bacteria

Phytoplankton account for nearly half of global photosynthetic carbon fixation, and the fate of that carbon is regulated in large part by microbial food web processing. We currently lack a mechanistic understanding of how interactions among heterotrophic bacteria impact the fate of photosynthetically fixed carbon. Here, we used a set of bacterial isolates capable of growing on exudates from the diatom Phaeodactylum tricornutum to investigate how bacteria-bacteria interactions affect the balance between exudate remineralization and incorporation into biomass. With exometabolomics and genome-scale metabolic modeling, we estimated the degree of resource competition between bacterial pairs. In a sequential spent media experiment, we found that pairwise interactions were more beneficial than predicted based on resource competition alone, and 30% exhibited facilitative interactions. To link this to carbon fate, we used single-cell isotope tracing in a custom cultivation system to compare the impact of different "primary" bacterial strains in close proximity to live P. tricornutum on a distal "secondary" strain. We found that a primary strain with a high degree of competition decreased secondary strain carbon drawdown by 51% at the single-cell level, providing a quantitative metric for the "cost" of competition on algal carbon fate. Additionally, a primary strain classified as facilitative based on sequential interactions increased total algal-derived carbon assimilation by 7.6 times, integrated over all members, compared to the competitive primary strain. Our findings suggest that the degree of interaction between bacteria along a spectrum from competitive to facilitative is directly linked to algal carbon drawdown.

genome-scale metabolic model↗
DOE OSTI2025

A Digital Twin for an Inverter-Based Resource Power Plant: Real-time data streaming unlocks situation awareness

Here, this study presents the development and successful implementation of a digital twin specifically designed for a grid-connected IBR power plant. By integrating a reduced-order model of the IBR system and dynamically updating the grid impedance with real-time data, the digital twin effectively captures and replicates the behavior of the physical system. Its accuracy and reliability are validated through critical test scenarios, including a three-phase fault and a line-tripping event. The results confirm that the digital twin closely emulates its physical counterpart, demonstrating its strong potential for real-time analysis, system monitoring, and predictive decision making in modern power systems.

Digital twins↗
DOE OSTI2022

FEDIS (The “Fresnel Equations” for Diffuse radiation on Inclined photovoltaic Surfaces) [SWR-22-64]

The well-known Fresnel equations solve for the reflection and transmission of light for precise incident angles. The transmission of diffuse radiation incident on a planar or domed surface is often needed for real-world applications. Due to the complexity of the Fresnel equations, the analytical solution of the integration has hitherto been unobtainable over the last centuries. Therefore, this problem was numerically solved by integrating the angular transmittances in space often leading to substantial computing burden and bias in the results. To efficiently estimate the solar energy resource for a glass-covered photovoltaic (PV) module, we derive an analytical solution of diffuse transmission based on the rigorous integration of an alternate form of the Fresnel equations. The approach leads to a simple yet accurate relative transmittance model that reconciles the solar energy sensed by pyranometers and PV panels. With limited and clearly stated approximations, the complex mathematical derivation resulted in an elegant solution.

Xie, Yu↗
DOE OSTI2021

Newly acquired core enhances geologic understanding of the northern Paradox Basin Cane Creek play, southeastern Utah

The Cane Creek interval is an emerging unconventional heterolithic play within the Pennsylvanian Paradox Formation of the northern Paradox Basin, southeastern Utah. Structural controls (basement uplifts, salt movement, faults, fractures) likely control production from the Cane Creek, where total oil production of ~10 MMBO is just a fraction of current estimates of the undiscovered resource (up to 1.2 BBOE). Oil production has been most successful from unstimulated horizontal wells in the Cane Creek Unit in the central play area, whereas areas to the north (Greentown and Gunnison Valley) have to date proven to be largely unproductive from vertical and horizontal wells drilled without the benefit of 3D seismic or the deployment of modern hydraulic fracturing techniques. However, the limited historical drilling has shown significant promise (e.g., significant shows). The lack of core data in the northern part of the play has impeded thorough assessment of reservoir quality, character, and lithological facies heterogeneity. The Cane Creek is informally divided into three distinct zones. The upper A and basal C zones are composed of fabric destructive and wavy bedded anhydrite, dolomitic mudstone, and organic-rich algal laminated mudstones (source rock). The middle B zone is generally wave rippled and burrowed, low-permeability sandstone-siltstone (reservoir) with subordinate displacive anhydrite. Recent potash cores collected from the Salt Valley anticline near Crescent Junction, Utah (but east of the main play area), show thicker reservoir sandstone/siltstone packages (~40 ft) in the B zone, and parts of the C zone, in the northern part of the Paradox Basin in comparison to the central (~35 ft) and southern (~30 ft) areas. Source rock analyses from northern core/cuttings also indicate that it is well positioned within the oil window (VRo ~0.8), with thin organic-rich mudstone beds containing up to 20 wt% TOC. Because the siliciclastic reservoir rocks have low permeabilities (0.009–0.202 mD) and variable porosities (6–17%) due to dolomite-anhydrite cement, naturally occurring and possibly stimulated fractures may be essential for hydrocarbon recovery. To better understand the northern part of the Cane Creek play, a new core was obtained from the research stratigraphic well State 16-2 (January 2021). Details from this new core will help characterize reservoir quality via core analyses, petrography, fracture orientations, geomechanics, and correlation of facies and depositional trends. Additionally, an evaluation of matrix porosity/permeability will be conducted to determine its contribution to productivity.

02 PETROLEUM↗
DOE OSTI2023

Estimating National-Scale Wind Potential Using Spatially Explicit Turbine Layout Optimization

National renewable energy potential assessments play a broad and critical role in analysis of the clean energy transition by providing foundational estimates of developable clean resources. Common to all past wind potential assessments is an assumption that wraps the complexity of wind plant layout (arrangement of turbines) into a single metric known as capacity density, or rated power capacity per unit of land area. Quite often, a singular capacity density or rotor-diameter driven capacity density is used in wind potential assessments across broad geographies despite the complexities of local drivers. Here, we present a new wind technical potential assessment for the United States, leveraging a spatial optimization approach in lieu of the traditional uniform capacity density. The optimization approach is a spatially explicit method for determining the potential locations of individual wind turbines-taking into account the turbine configuration, plant economics and losses, wind resource, and siting considerations. Our approach accounts for the interactions between wind technology design, wind plant layout, and the vast array of regulatory, land use and infrastructure conflicts with wind development. Our results highlight the potential ability of larger turbines to enable increased wind capacity, up to a point, and increased generation when siting turbines in and around spatial constraints; moreover, they demonstrate and capture the LCOE benefit of relatively lower capacity densities and reduced wake losses when land is relatively abundant. These insights provide foundational knowledge for the wind sector as it develops and pursues future turbine models and as wind energy markets expand in zero-carbon futures. Further, when applied in capacity expansion models, supply curves developed by these methods can provide detailed local insights about where wind turbines might be deployed in and among known siting constraints for those regions where wind energy is determined to be economic, providing critical nuance to local decision-makers and stakeholders.

17 WIND ENERGY↗
DOE OSTI2024

On the connection between least squares, regularization, and classical shadows

Classical shadows (CS) offer a resource-efficient means to estimate quantum observables, circumventing the need for exhaustive state tomography. Here, we clarify and explore the connection between CS techniques and least squares (LS) and regularized least squares (RLS) methods commonly used in machine learning and data analysis. By formal identification of LS and RLS ``shadows'' completely analogous to those in CS---namely, point estimators calculated from the empirical frequencies of single measurements---we show that both RLS and CS can be viewed as regularizers for the underdetermined regime, replacing the pseudoinverse with invertible alternatives. Through numerical simulations, we evaluate RLS and CS from three distinct angles: the tradeoff in bias and variance, mismatch between the expected and actual measurement distributions, and the interplay between the number of measurements and number of shots per measurement. Compared to CS, RLS attains lower variance at the expense of bias, is robust to distribution mismatch, and is more sensitive to the number of shots for a fixed number of state copies---differences that can be understood from the distinct approaches taken to regularization. Conceptually, our integration of LS, RLS, and CS under a unifying ``shadow'' umbrella aids in advancing the overall picture of CS techniques, while practically our results highlight the tradeoffs intrinsic to these measurement approaches, illuminating the circumstances under which either RLS or CS would be preferred, such as unverified randomness for the former or unbiased estimation for the latter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Measuring Success: A Refined Methodology for Estimating Long-term Continuous Improvement

Successful resource management systems require current and detailed feedback on operational and corporate-level performance. As corporate accountability concerns intensify, the precision and reliability of these performance metrics have become crucial. Traditional savings estimation methods can be difficult to understand, particularly linear regression, and can provide varying results. This paper reviews common efficiency metrics and highlights underlying mathematical inconsistencies when estimating total and percent savings with current methods. A refined approach to calculating long-term utility savings is proposed that simplifies current methodologies utilizing ratios to define an adjusted baseline, allowing for consistent and fair aggregation of results across multiple scales from resources to corporate performance. A simplified example demonstrates how the proposed methodology improves upon existing methods, especially in intermediate years. This paper’s major contributions are the simplified approach for converting modeled utility usage into estimated savings and the consistent roll-up methodology enabling more comparable, aggregable, and actionable results across scales.

Price, Chris [ORNL] (ORCID:0000000202007906)↗
DOE OSTI2022

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Furthermore, our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep Direct-Use Geothermal Feasibility Study for Hawthorne NV

The Hawthorne Nevada, deep direct-use geothermal study is a two-year effort funded by the U.S. Department of Energy to determine the techno-economic feasibility of implementing a large-scale, direct-use facility for the Hawthorne Army Depot (HAD) and the public facilities of the city of Hawthorne and Mineral County. The approach links a production side analysis (PSA) and a demand side analysis (DSA) into a whole-system analysis (WSA) to provide an integrated assessment of the resource and the probability of delivering economically viable direct-use energy to Hawthorne. Hawthorne, Nevada is in the western part of the Basin and Range province and has been the focus of geothermal investigations for over 40 years. Over the last 15 years, several studies completed by the U.S. Navy Geothermal Program Office (GPO) in conjunction with industry professionals quantified the existence of several low temperature geothermal prospects, the most promising of which is called Prospect A. The promise of Prospect A is based on drilling and flow testing that produced ~100 °C water at flow rates of up to 31 l/s (500 gallons per minute). Measured productivity indexes range from 40-85 l/s/MPa, suggesting a warm and productive heat source. Despite the promise of the resource, uncertainties in its spatial extent and long-term sustainability mean that techno-economic analyses must include probabilities of the sustainability of the resource under different operating scenarios. Here, the PSA is conducted by integrating a wide range of disparate data to estimate lognormal P90, P50, and P10 resource capacities. These capacities are used as input to a thermal-hydrologic (T-H) model to estimate thermal drawdown for each capacity estimate for several different DSA scenarios. Using a systems-based approach, the WSA links the dynamic T-H simulations of the PSA/DSA combinations with the techno-economic model GEOPHIRES to account for both the temporal dynamics and uncertainties in the system to produce probabilistic distributions of several performance metrics including the levelized cost of heat (LCOH) and the return on investment (ROI). This report is the final delivery for the project and documents the study's activities and results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Critical Minerals and Rare Earth Elements in Powder River Basin Coal and Associated Sediments, Wyoming, USA

The demand for Rare Earth Elements (REE) and other critical minerals (CM) required for consumer products, defense-related applications, and low-carbon energy technology is increasing rapidly. Unconventional sources of REE/CM, such as coal and associated sediments, could prove very important in building resilient and ethical energy supply chains. Utilization of existing infrastructure and the highly trained energy workforce in traditionally coal-producing regions in emerging REE/CM industries could provide an economic boost to coal communities. The Powder River Basin (PRB) of Wyoming and Montana, USA, is a prime candidate to investigate the feasibility of extracting REE/CM from coal and associated sediments. The PRB hosts thick (>50 ft) coal seams that are mined at the surface and more than 40% of the coal produced in the US comes from the PRB. Geochemical data from multiple locations in PRB coal systems show REE enrichments at the top and bottom margins and at internal partings in coal seams. This trend is exhibited in two cores drilled at Peabody’s North Antelope Rochelle mine in the east central PRB, the largest coal mine in the world. The two cores include the Wyodak Anderson coal zone as well as the over- and underlying shale units. The Wyodak Anderson coal zone is part of the Paleocene Tongue River Member of the Fort Union Formation in which most of the coal resources in the PRB reside. To understand REE/CM enrichment, a total of 188 samples were analyzed for their major and trace element chemistry from both cores, sampled at one foot or smaller intervals. The two cores show distinct REE enrichments in the uppermost and lowermost 3 to 12 feet of the Wyodak Anderson coal zone, as well as enrichments in bounding carbonaceous shale units. Total REE+Y (REY) concentrations as high as 2510 ppm (all concentrations reported on an ash basis), or 15 times average upper continental crust values (Taylor and McLennan, 1995), were identified in the coal. Partings within the coal zone also show relative REE enrichment, with concentrations up to 485 ppm REY. The remaining interior portions of the coal zone contain lower concentrations of REE, resulting in an average REY for all samples from both cores of 288 ppm. The proportion of high-value critical REE (Nd, Eu, Tb, Dy, Er, Y) compared to REY averages 36%, which is higher than the critical REE proportion in average upper continental crust of 33%. This finding highlights the relative enrichment of middle REE compared to light REE, in contrast to many conventional REE deposits. Other critical trace elements that show enrichments above average upper continental crust include Ga, Nb, and V. Within this sample suite, Ga concentrations range from 2.8 to 213 ppm, with an average of 29 ppm; Nb from 2.9 to 166 ppm with an average of 26 ppm; and V from 27 to 1695 ppm with an average of 188 ppm. None of these trace elements exhibit the same distinct pattern of enrichment as REE in the upper and lower bounding layers and internal partings of the coal zone. Major element chemistry indicates CaO concentrations averaging ~25% in the coal samples from both cores, which has important implications for the extractability of REE from coal. Calcium-rich PRB coal has been shown to be more amenable to REE extraction than lower calcium coals by some methods (Stuckman et al., 2019; Taggart et al., 2016), highlighting the importance of REE extractability in addition to REE concentration in assessing the value of potential feedstocks. The original coal resource in the PRB is estimated at 1.16 trillion short tons (Luppens et al., 2015). Thus, the PRB represents an important potential unconventional source for REE and other CM. Ongoing research to fully characterize the REE/CM resource, identify enrichment mechanisms, and further develop and scale extraction technologies is necessary to understand the full potential of this resource.

Phillips, Erin↗
DOE OSTI2023

Multi-Sourced Collaboration for the Production and Refining of Rare Elements and Critical Metals (Final Technical Report)

The project objective was to develop a feasible and cost-effective method for recovering rare earth elements (REEs) and critical materials (CMs) from coal and coal byproducts, resulting in high-purity individually separated REEs and CMs. The targeted REEs included Y, Pr, Nd, Gd, Dy, and Sm, with a purity of over 99.5%, while the CMs included Co, Mn, Ga, Sr, Li, Ni, Zn, and Ge, with a purity of over 90%. The project aimed to design a prototype facility capable of producing 1-3 tonnes/day of high-purity REO mixes. The work was divided into four designated circuits: 1) REE extraction and concentration, 2) REE separation and purification, 3) RE metal production, and 4) CM production. To achieve these goals, the project involved 11 tasks, including technology reviews, research, process flow diagram development, mass balance estimation, and preliminary technical-economic analysis. The project team included researchers from the University of Kentucky, University of Alabama and Virginia Tech as well as process specialists from Argonne National Laboratory. MP Materials provided technical support regarding rare earth markets and processing while Alliance Coal performed resource assessment. The project included a market analysis for Nd/Pr, Tb, Dy, Gd, Y, Co, Mn, Li, Sr, Ga, Ni, Zn, and Ge. These analyses provided insights into the supply and demand trends as well as historic and future projections of market price relative to purity requirements for these elements. Two coal resources were selected for the project: the West Kentucky No. 13 (Baker) Seam and an undisclosed lignite resource in the Illinois coal basin. The estimated quantities of REEs in these resources were calculated based on production samples and drilling data. It was estimated that there is adequate supply for an operation producing one metric ton daily of higher purity mixed rare earth oxides (MREO) for approximately 20 years at a site located in western Kentucky. In Circuit 1, project data was obtained from a pilot heap leach and REE concentration facility. It was concluded that the existing circuit, which generated a MREO concentrate, two types of CM mixed products, and Li- and Sr-containing waters, would be suitable feed for circuits 2-4. Data from the first-of-its-kind coal coarse refuse heap leach pilot pad played a crucial role in estimating reliable elemental concentrations of the pregnant leaching solution (PLS). The average total REE concentration in the PLS was found to be 28.6 ppm. In Circuit 2, several concepts were explored including a novel process referred to as solvent-assisted chromatography (SAC). This concept involved a novel columnar reactor that incorporated multiple mixer/settlers, thereby enabling the operation of counter-flowing aqueous and organic phases. Unfortunately, due to project time constraints, a complete fundamental modeling analysis could not be completed to fully evaluate the technology. Molten salt electrowinning was considered as an alternative for circuit 3 following circuit 2 purification circuit utilizing the novel SAC process. A mass and energy balance of Nd reduction to metal in a fluoride containing molten salt electrolyte was conducted. Comparisons were made with the current state of Asian molten salt electrorefining, and potential improvements in siphoning rare earth metals (REM) from the reactor were presented. A cost estimate was performed for the production of 1 tonne per day, which yielded a total of $2.29 million for the nine electrowinning (EW) cells required. The selected option for circuits 2 and 3 was a plasma distillation process, which initially separates rare earth elements (REEs) from other elements. This is followed by selective electrowinning in various ionic liquids. The selection was made on the basis of thermodynamic modeling and experimental data previously published by a project partner. The combination offers an innovative approach to integrated refining and RE metal production. For Circuit 4, an extensive literature review was conducted for the processing of the CMs. The ultimate decision was to utilize a combined plasma and ionic liquid process as well to produce individual high-purity concentrates of Zn, Ni, Co, Mn, and Mg. A separate flowsheet for Li and Sr was recommended, which would yield carbonates of these elements. Due to the lack of suitable experimental data at this time, a process recommendation could not be provided but several methods have been proposed for consideration. Lastly, a techno-economic analysis (TEA) was conducted to assess the effectiveness of the proposed process for further investigation. The TEA results revealed a capital expense (CapEx) of $737 million and an annual operational expense (OpEx) of $220 million. Due to the selected elements, the hypothetical heap leach pad can produce 1 metric tonne per day of REO equivalent, but a conscious decision was made to only treat targeted REEs, resulting in the production of 0.4 metric tonne of REM. An estimated annual revenue of $90.87 million was projected based on standard market pricing information provided by the funding agency. During the TEA, ten different modules were evaluated for costing purposes. The precipitation circuit was identified as the largest single operational expense, followed by the Mg/Mn process due to the amount of treated metal. In terms of capital expenditures, the heap leach process incurred the highest cost, followed by the Mg/Mn process. The scalability of the plasma process is a crucial consideration since the reactors cannot be scaled beyond the largest demonstrated size due to their reliance on surface area of the slag and vapor phase. The purity estimate for the REEs are generally 98%±2% to produce a metal. The purity level being lower than the project objective was due to the lack of specific experimental data needed to tighten the tolerance of the estimates. Based on literature and previous experience, the CMs are estimated as follows; Ga (95%+, metal), Sr (95%+, carbonate), Li (95%+, carbonate), Ni (98%±2%, metal), Zn (95%+, metal sponge), Ge (95%+, metal), Co (98%±2%, metal), and Mn (98%±2%, metal).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

A Systematic Review and Integrated Approach to Modeling of Aging Utility Scale PV Systems

The growing deployment of utility-scale photovoltaic (PV) systems has increased the importance of techno-economic modeling operational photovoltaic (PV) systems for predicting energy yield, optimizing asset management, and informing financial decisions. Through a systematic review of literature and current industry practices, we review the different common modeling practices of a system's configuration and age, performance and degradation, operation and maintenance (O&M), while also focusing on specific considerations for repowering, revamping, and decommissioning. Building on the synthesis, we develop a structured framework for techno-economic modeling of operating PV systems that integrate performance and degradation analysis, a decommissioning and repowering cost model that estimates the system's end-of-life costs to reduce uncertainty quantifications and improve consistency across the sector. This research contributes to improved modeling methodologies and potentially to reduced financial performance requirements by providing practitioners with input resources and practical approaches to estimate performance, degradation, and costs associated with continued operation, revamping, repowering, or decommissioning decisions.

14 SOLAR ENERGY↗
DOE OSTI2020

A convective wind resource model for Solar Vortex power generation

Climate change has increased the need for clean, nonpolluting energy sources to decrease dependence on fossil fuels. Alternative energy sources, mainly solar and horizontal wind, have been the primary focus for producing clean energy. New technologies are being developed, such as the Solar Vortex (SoV), which was developed at the Georgia Institute of Technology, and relies on a vertical wind resource to generate power. The National Renewable Energy Lab (NREL) has resource models representing solar and horizontal wind resources across the 48 United States. This research developed a vertical wind resource model that is comparable in resolution to NREL’s solar and horizontal wind resource models and uses the model for estimating power output for the SoV. This model complements NREL’s existing resource models and supports the deployment of an additional clean energy generation technology. The model was applied to Mesa, Arizona to find feasible sites for a small-scale vertical wind farm.

14 SOLAR ENERGY↗