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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Effects of Carbonate Minerals on Shale-Hydraulic Fracturing Fluid Interactions in the Marcellus Shale

Natural gas extracted from tight shale formations, such as the Marcellus Shale, represents a significant and developing front in energy exploration. By fracturing these formations using pressurized fracturing fluid, previously unobtainable hydrocarbon reserves may be tapped. While pursuing this resource, hydraulic fracturing operations leave chemically complex fluids in the shale formation for at least two weeks. This provides a substantial opportunity for the hydraulic fracturing fluid (HFF) to react with the shale formation at reservoir temperature and pressure. In this study, we investigated the effects of the carbonates on shale-HFF reactions with a focus on the Marcellus Shale. We performed autoclave experiments at high temperature and pressure reservoir conditions using a carbonate-rich and a decarbonated or carbonate-free version of the same shale sample. We observed that carbonate minerals buffer the pH of the solution, which in turn prevents clay dissolution. Carbonate and bicarbonate ions also scavenge reactive oxidizing species (ROS), which prevents oxidation of shale organic matter and volatile organic compounds (VOCs). Carbonate-free samples also show higher pyrite dissolution compared to the carbonate-rich sample due to chelation reactions. This study demonstrates how carbonate minerals (keeping all other variables constant) affect shale-HFF reactions that can potentially impact porosity, microfracture integrity, and the release of heavy metals and volatile organic contaminants in the produced water.

58 GEOSCIENCES↗

Deep Realistic Extragalactic Model (DREaM) Galaxy Catalogs: Predictions for a Roman Ultra-deep Field

In the next decade, deep galaxy surveys from telescopes such as the James Webb Space Telescope and Roman Space Telescope will provide transformational data sets that will greatly enhance the understanding of galaxy formation during the epoch of reionization (EoR). In this work, we present the Deep Realistic Extragalactic Model (DREaM) for creating synthetic galaxy catalogs. Our model combines dark matter simulations, subhalo abundance matching and empirical models, and includes galaxy positions, morphologies, and spectral energy distributions. The resulting synthetic catalog extends to redshifts z ~ 12, and galaxy masses log 10 (M/M ⊙ ) = 5 covering an area of 1 deg 2 on the sky. We use DREaM to explore the science returns of a 1 deg 2 Roman ultra-deep field (UDF), and to provide a resource for optimizing ultra-deep survey designs. We find that a Roman UDF to ~30 m AB will potentially detect more than 10 6 M UV < -17 galaxies, with more than 10 4 at redshifts z > 7, offering an unparalleled data set for constraining galaxy properties during the EoR. Our synthetic catalogs and simulated images are made publicly available to provide the community with a tool to prepare for upcoming data.

79 ASTRONOMY AND ASTROPHYSICS↗

Baseload Hydrogen Production Using Nuclear and Renewable Energy: A Comparative Analysis

As the global push towards net zero carbon gains momentum, the demand for clean hydrogen is expected to grow rapidly across various sectors, including transportation, industries and electrical grids. To meet this growing hydrogen demand, baseload hydrogen production facilities capable of providing a continuous and reliable supply of hydrogen will be necessary throughout the world. This paper explores the technoeconomic feasibility of establishing baseload electrolytic hydrogen production facilities in the United States, utilizing different clean generation resources. The key criteria include maintaining a consistent supply of clean hydrogen without putting baseload demand stress to already vulnerable power grid. In order to do that, the proposed facilities will host onsite clean power generation and energy storage technologies. The proposed facilities can capitalize on available investment and production incentives and have ability to export excess electricity to the utility at a bulk price. Several scenarios are considered based on the clean energy resources to support the electrolysis process including light water reactors (LWRs) currently evaluating retirement options, wind, solar PVs, and advanced small modular reactors (SMRs). For each scenario, a hypothetical hydrogen production facility is considered in a location in the US where the primary generation resource is at its peak strength. Comparative analysis in this paper reveal that the nuclear power plants are most economically viable for baseload hydrogen production facilities, outperforming renewable-based facilities with significantly lower levelized cost of hydrogen (LCOH). Even under best-case scenarios for resource availability, incentives and export prices, renewable-based facilities face challenges due to daily and seasonal generation variability, resulting in large installation sizes and lower capacity factors. Among renewable-based facilities, complementarity hybrids, providing more stable power supply, demonstrate superior economics compared to facilities based on a single renewable technology. While LWR-powered facility can achieve a negative LCOH with incentives, SMR-powered facilities can provide economic hydrogen supply with LCOH below $1/kg with high temperature electrolysis option. The analysis in this paper underscores the pivotal role of nuclear energy in the future hydrogen economy.

08 - HYDROGEN↗

Assessment of Commercial Building Lighting as a Frequency Regulation Resource

This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Insights into Methodologies and Operational Details of Resource Adequacy Assessment: A Case Study with Application to a Broader Flexibility Framework

Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1-2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1-2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

Experimentation in Exploring Photovoltaic Inverter Dynamics Under Different Irradiance Levels Through a Data-Driven Approach

As conventional direct connections of synchronous generators are being phased out, inverter-based resources (IBRs) with grid support functions are increasingly being integrated into power systems. This transition requires the development of accurate dynamic models for IBRs to predict how power systems will adapt to varying levels of IBRs penetration, establish grid code requirements, and ensure compliance. Here, this study introduces an active probing signal-based data-driven modeling technique to accurately derive the dynamics model of a smart photovoltaic inverter operating in Volt-Watt and Freq-Watt modes, in compliance with the IEEE 1547–2018 standard. The paper focuses on investigating how the dynamics of the PV inverter model respond to fluctuations in solar irradiance, utilizing real-time digital simulator experimentation. The experimental analysis demonstrates that the amplitude of dynamics fluctuates with changes in irradiance across both operational modes and confirms the active power’s dependence on irradiance levels. Furthermore, the nature of inverter dynamics varies distinctly between the different modes of activation. Critically, our findings indicate that dynamic models require DC-gain adjustments to accommodate contrasting irradiance levels, highlighting a negative gradient linear relationship between the DC-gain of each model and the irradiance.

14 SOLAR ENERGY↗

Remining and Restoring Abandoned US Mining Sites: The Case for Materials Needed for Zero-Carbon Transition

The electricity generation sector is responsible for 25% of the world’s greenhouse gas (GHG) emissions and thus has been the focus of efforts to transition to clean energy and sustainable development in many nations. In the past 20 years, renewable energy sources have been the fastest-growing energy source in the world, comprising almost 29% of the world’s electricity generation in 2020. Renewable energy sources are expected to comprise nearly 95% of the world’s power capacity growth through 2026, with a share of planned capacity expansion of up to 46% in 2026. The rapid development of renewable energy sources and technologies will require an enormous amount of raw materials to replace coal and gas plants and increase in the capacity to handle growing electricity demand because renewable energy sources have a low-power density and intermittent behavior. Accounting for the expected scale of rapidly deploying renewable energy sources that require rare earth elements (REEs), cement, and steel, the mining industry may face a supply problem for the materials critical for clean energy. And, as the exploration and development of new mining sites can be expensive and risky, the mining industry may require economic stimuli to grow supply. The increasing demand for renewable energy resources makes mining a threat to the environment unless proper regulations are established and calls for remining, cleanup, and circular economic development are made. Mining also contributes to environmental injustices related to the exploitation and pollution of lands near communities that are dependent on biodiversity in the area, while not always benefiting from technological advancements provided by the use of renewable energy and technologies. This study explored the opportunity of remining abandoned mining waste to extract metals and minerals essential for the production of renewable energy sources. The authors analyzed materials used in the production of these technologies, materials readily available in the United States, and which materials can be extracted locally in the United States from abandoned mine waste. The authors also studied environmental injustices that populations near mining sites experience and ways to mitigate these injustices, such as providing more control over extraction and cleanup activities, providing more job opportunities in those areas, and offsetting costs associated with cleanup and land restoration projects.

42 ENGINEERING↗

An overview of switchgrass phenotypes variability across diverse populations and their implications for conversion to fuels

There have been substantial changes to the human lifestyle over the past two centuries, which are reflected in the amount of fuel we consume to power our day-to-day needs. The way we use these resources has indeed manifested in an overdependence on non-renewable energy sources, such as coal and petroleum, for generating electricity and powering our transportation needs. There is a pressing need to explore alternative ways of fueling our current lifestyle without impacting the environment. Biofuels have long been touted as a sustainable solution for use as drop-in fuels in aviation and maritime applications. Still, they have yet to establish themselves as a competitive commercial alternative, necessitating further research and development. Lignocellulosic biomass is an underutilized resource that is widely accessible for the commercial processing of renewable biofuels. Bioenergy crops, such as switchgrass (Panicum virgatum L.), which can be cultivated on marginal lands with minimal competition for agricultural land, are an ideal and promising candidate for bulk-scale biofuel synthesis. Over the past 30 years, significant progress has been made in breeding and genetically modifying these grasses to enhance their drought resilience and subsequent yields. However, discrepancies in biomass composition can lead to irregular feedstocks for downstream operations, which in turn affect overall production targets for biofuels. Here, this review examines the variability in switchgrass (P. virgatum L.) biomass phenotypes across diverse populations and plant components, and their implications for biofuel conversion. The study highlights significant variations in biomass yield, composition, and cell wall chemistry both between switchgrass genotypes and within individual cultivars. Key findings include differences in cellulose, hemicellulose, and lignin content between leaves and stems, which affect biomass digestibility and ethanol yield. The review also discusses the impact of lignin chemistry, particularly the syringyl/guaicyl (S/G) ratio, on the efficiency of biomass saccharification. Furthermore, it explores how these variations respond differently to various pretreatment techniques, affecting overall biofuel production. We conclude that understanding and quantifying this variability is crucial for optimizing switchgrass as a feedstock for commercial biofuel production, thereby potentially addressing the pressing need for sustainable energy sources in sectors such as aviation.

Kousika, Rohit [Univ. of Tennessee, Knoxville, TN ↗

Exploratory Investigation of Coal in Nonequilibrium Plasma

Coal is an abundant natural resource and there is motivation to find new uses for it that do not intrinsically involve combustion. One approach is to explore new ways of processing coal, and in this work, we focus on the transformation of coal in a nonequilibrium plasma generated from an equimolar mixture of nitrogen and hydrogen. The outcome of the nonequilibrium plasma reaction is fundamentally different than a thermal control reaction carried out using the same gas composition, pressure, and temperature range. The nonequilibrium plasma produces a gas mixture that is enriched in acetylene and its derivatives. Furthermore, when compared to the thermal control experiment, the solid char byproduct of the nonequilibrium plasma has a very reactive surface and is spontaneously combustible at ambient temperature. Experiments performed to characterize the reaction kinetics of coal in the plasma suggest that the mechanism proceeds through a sequential process by which the coal particle temperature rises to a point where devolatilization can occur, the devolatilization reaction happens, followed by parallel reactions of released organic vapors in the plasma phase and surface activation. In conclusion, the reaction rate appears to be limited by the time it takes for the coal particle temperature to rise, consistent with previous results reported for reactions of coal in thermal plasma.

Acetylene↗

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗

Assessing pore network heterogeneity across multiple scales to inform CO2 injection models

Geologic heterogeneity is a key feature that must be considered when translations of scaled data are performed. This paper presents the assessment of geologic heterogeneity using a multiscale workflow that includes image analysis-based methods coupled with well log analysis to provide data in which fractals and machine learning methods estimate the carbon dioxide (CO 2 ) storage resource potential of a reservoir. The heterogeneity of rock properties of the complex Bell Creek reservoir in Montana, USA, was explored at the pore scale (~nm to mm), core scale (~mm to m), and well scale (~cm to m). The data used in this study included advanced image analysis of micro-CT (computed tomography) images (pore scale), thin sections (pore scale), plugs and core images (core scale) and well logs (well scale). The micro-CT images were segmented using a U-net segmentation approach into objects of pores and grains. Further, the segmented images were reconstructed into subvolumes of different sizes. Physical properties (porosity and permeability) and fractal dimensions were calculated for the various subvolumes, and Lorenz coefficient (Lc) values, a single parameter to describe the degree of heterogeneity within a pay zone section, were calculated from thin-section images and well logs. Porosity and fractal dimension values were used to estimate the 188-µm threshold of representative elementary volume (REV) in this study. Both the Lc and fractal dimension values were found to be negatively correlated. When these two parameters are combined, it is possible to discern differences in the complex porous networks of the samples analyzed in this study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Simulation of adiabatic quantum computing for molecular ground states

Quantum computation promises to provide substantial speedups in many practical applications with a particularly exciting one being the simulation of quantum many-body systems. Adiabatic state preparation (ASP) is one way that quantum computers could recreate and simulate the ground state of a physical system. In this paper, we explore a novel approach for classically simulating the time dynamics of ASP with high accuracy and with only modest computational resources via an adaptive sampling configuration interaction scheme for truncating the Hilbert space to only the most important determinants. We verify that this truncation introduces negligible error and use this new approach to simulate ASP for sets of small molecular systems and Hubbard models. Furthermore, we examine two approaches to speeding up ASP when performed on quantum hardware: (i) using the complete active space configuration interaction (CASCI) wave function instead of the Hartree–Fock initial state and (ii) a nonlinear interpolation between the initial and target Hamiltonians. We find that starting with a CASCI wave function with a limited active space yields substantial speedups for many of the systems examined, while nonlinear interpolation does not. In additional, we observe interesting trends in the minimum gap location (based on the initial state) as well as how state preparation time can depend on certain molecular properties, such as the number of valence electrons. Importantly, we find that the required state preparation times do not show an immediate exponential wall that would preclude an efficient run of ASP on actual hardware.

Kremenetski, Vladimir↗

Transmission and Distribution Coordination

This webinar explores planning the future grid and interconnected energy system while examining balancing generation (G), transmission (T), distribution (D), & customer (C) resources all while also increasingly capturing engineering details in planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Existing Methods for Grid Strength Assessment and Role of Hydropower in Future Grids

The power system is undergoing rapid evolution with increasing penetration of inverter-based resources (IBRs), large loads, microgrids, and power electronic devices. Ensuring reliable and stable operation of the modern power grid is a multi-faceted challenge that requires detailed understanding of this complex system. Dynamic stability is a major concern in maintaining the security of power grids as the generation mixes and large loads transitions to include high shares of power electronic devices. The operation of IBRs in regions with low system strength and higher grid impedances has been found to be the main reason behind many of the power system instabilities that manifest themselves in various types of oscillations and interactions that, if not properly addressed and damped, can jeopardize the reliable operation of the power system. Weak grid conditions compound such stability problems, particularly when many IBRs operate in proximity to and connect to weak power grids. Therefore, it is important to assess grid strength for planning, integration and operation of IBRs for a given power system. In this report, we aim to understand and classify existing methodologies for grid strength assessment, along with their limitations and future needs. Further, we aim to utilize the huge untapped potential in utilizing hydro energy resources in addressing some of the pertinent challenges of grid strength and reliable operation of modern power systems. Hydropower, historically valued for its flexibility and dispatchability, now faces new constraints due to reduced share of synchronous machines in the generation mix and seasonal variability of available water resources. Yet, these same plants present untapped potential beyond energy generation - notably, as providers of critical grid services. This report explores how hydropower plants, particularly through operation as synchronous condensers, can play a pivotal role in strengthening the grid amid evolving system dynamics.

13 HYDRO ENERGY↗

Technical Potential for Hydropower Capacity at Non-powered Dams

In the last decade, retrofits of existing nonpowered dams (NPDs) have made up the largest share of capacity increases for US hydropower. Accurate estimates of potential capacity and generation at NPDs help identify sites that may be worth investing in detailed feasibility analyses and design exploration. This dataset consists of NPDs in the conterminous US with at least 100kW of theoretical potential based on earlier resource assessments. Historical daily streamflow (modeled or from USGS gauge records), hydraulic head (based on historical observations or primary purpose and dam height) are the main inputs for HydroGenerate which determines design flow and turbine efficiencies and then calculates nominal capacity, daily generation, and capacity factor. These outputs are summarized on a monthly basis (i.e., generation (MWh) and capacity factor averaged for each month from January to December) and overall (i.e., nominal capacity, average annual generation (MWh), and average annual capacity factor). A total of 4.1 GW capacity is estimated across all 2,564 NPDs included in the dataset.

13 HYDRO ENERGY↗

Sustainable recovery of Rare Earth Elements (REEs) from coal and coal ash through urban mining: A Nature Based Solution (NBS) for circular economy

The demand for rare earth elements (REEs) has surged in recent years, driven by their crucial role in various industrial applications and their uneven geological distribution. As a result, urban mining from secondary resources, particularly coal and coal ash, has gained traction as a sustainable solution within a circular economy framework. This study highlights the significant presence of REEs in coal and coal ash, revealing that certain samples contain REE concentrations that rival traditional ores. Notably, coal ash has the potential to yield approximately 312,000 tons of REEs annually, far exceeding global demand. The research delves into advanced techniques for analyzing REEs, including elemental, isotopic, and mineralogical studies. Additionally, it explores innovative extraction methods such as the use of green solvents, nature-based solutions, and bioleaching and biosorption. By leveraging coal and its byproducts as secondary resources, this study underscores the opportunity to reduce dependence on conventional mining, enhancing the sustainability of REE recovery. Here, a comprehensive literature review was conducted to highlight technological advancements and emerging opportunities that can address current challenges in this field.

Circular economy↗

Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14,341 locations including 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering is applied to the geochemical data matrix that automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivity as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin.

15 GEOTHERMAL ENERGY↗