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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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Dynamic modeling of a direct reduced iron shaft furnace to enable pathways towards decarbonized steel production
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Decarbonizing nitrogen fertilizer production via the electrochemical nitrogen oxidation reaction
Nitric acid is an important commodity chemical with extensive applications in both agricultural and industrial sectors. However, current production methods involve a combination of the Haber–Bosch and Ostwald processes, which are both energy and carbon emission intensive. The electrochemical nitrogen oxidation reaction (NOR) to produce nitric acid or nitrates shows great potential as an environmentally friendly method for producing fertilizers under mild conditions. The key to progress in this field lies in understanding the fundamental mechanistic insights and establishing robust experimental methods, which is essential for the efficient design and synthesis of electrocatalysts for NOR. Additionally, poor gas mass transport in conventional electrochemical reactors at present lead to lower NOR activity, thereby limiting the progress in this field. In this work, we present a synergistic computational and experimental approach to map out the potential chemical and electrochemical steps and determine the energetics on PtO 2 catalyst to gain mechanistic insights into NOR. Here, this study marks the first attempt to perform NOR in a vapor-fed reactor designed using advanced (additive) manufacturing. The vapor-fed reactor significantly improved the N 2 mass transport to the catalyst, allowing us to report the highest rate for nitrate production to date at 3.3 μmol cm -2 h -1 at 2.01 V vs RHE.
Process intensification and comparison of electrolytic hydrogen green steel plants for industrial decarbonization
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The role of hydrogen in decarbonizing U.S. industry: A review
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Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems
Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.
Energy sufficiency for deep decarbonization: A modeling framework
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Diverse decarbonization pathways under near cost-optimal futures
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Cost-competitive offshore wind-powered green methanol production for maritime transport decarbonization
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An integrated transportation-power system model for a decarbonizing world
Rising demand for electricity from electric vehicles (EVs) will require new paradigms to guarantee reliable and low-cost electricity. This study couples an agent-based travel demand simulator and an electricity grid model to assess the economic costs of supplying power to meet EVs' added demand across the Chicago region. Results suggest that shifting from personal EVs to a fleet of shared, fully-automated all-electric vehicles (SAEVs) could lower per-mile emissions, congestion, and embodied vehicle and charging infrastructure emissions. Further, the results should compel policymakers to shift the cost of providing power onto commercial customers, like electric ride-hail fleets, through price-indexed electricity prices, which can shift charging to off-peak periods or away from resource-scarce hours.
LLUDA (Low-cost Logging for Ubiquitous Decarbonization and Analysis) [SWR-24-131]
This repository features an Arduino-based GPS logger, called LLUDA, which uses the Adafruit Ultimate GPS FeatherWing with the Adafruit Feather M0 Adalogger, designed to capture and log GPS data in CSV format onto an SD card (embedded inside the Feather M0 Adalogger). The FeatherWing is powered through the vehicle's auxiliary power (from cigarette lighter) socket via a micro USB cable. The data it collects can be used to analyze vehicle operations and support vehicle modeling applications, helping to assess the potential for electrification.
Application of the Sourdough Fuel Cycle for Rapid Decarbonization of Electric Grids
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Carbon Capture through Membranes - Leveraging Multiphysics Modeling, Dimensional Analysis and Machine Learning to Scale up and Optimize Devices and Processes for Decarbonization
We study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow (DFfeed). Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.
Influence of Hybrid Financial Instruments in Decarbonizing the Industrial Sector
This presentation details the results of an analysis performed to examine the influence of a hybrid financial instrument consisting of the Section 45Q tax credit and a tax on unabated emissions of CO2, on the decision of industrial facilities to retrofit with carbon capture technology.
EverGREEN 2045: An Energy Mix to Decarbonize Washington State
Washington State’s future resource mix is likely to be comprised of intermittent renewables and carbon-free generation including hydropower by 2045. Generation will likely be comprised of wind, solar photovoltaic (PV), batteries, pumped storage hydropower (PSH), existing nuclear power plants, and potentially enhanced geothermal systems and advanced nuclear reactor technologies. To assess the cost and stability of the future resource mix, we partner with X-energy (advanced reactor design) and AltaRock Energy (enhanced geothermal system) for proprietary cost data. After estimating the costs of these two new technologies, we design plausible future resource mix scenarios to meet Washington State’s Clean Energy Transformation Act which transitions the state to carbon-free generation by 2045. We assess the cost and stability of the future resource mix with power system analysis tools, finding revenues are sufficient to cover variable operating and maintenance costs for most technologies providing power in 2030 and 2045, but capacity payments or power purchase agreements will likely be necessary for flexible resources, including enhanced geothermal systems and advanced nuclear reactors, to participate in the future resource mix.
From Waste to Clean Fuel: Using Microwave Chemistry to Achieve Process Decarbonization
Microwave co-gasification of plastics and biomass has several advantages over conventional gasification, including enhanced hydrogen production and greater solid-to-gas conversion, as opposed to undesirable char and tar -- via process electrification route. A previous study at NETL has revealed the efficient role of microwave heating, which can liberate the oxygenated free radicals and encourage bond cleavage in non-microwave active plastics, if combined with microwave-active corn stover. However, further reduction of the unwanted tar must be achieved for future process scale-up and higher energy efficiency, which can be realized via a microwave-assisted catalytic approach. This continuous study investigates a series of microwave-active Fe catalysts that can efficiently enhance H2 production and reduce tar for plastic-corn stover co-gasification. The results suggest that in presence of magnetite, hydrogen yield increase 100% as compared to non-catalytic system at similar reaction conditions.