Feasibility study of CSNS as an ATLAS ITk sensor QA irradiation site
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Harmonized geospatial data to support infrastructure siting feasibility planning for energy system transitions
Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 264 suitability layers for use with 56 power plant technologies in a harmonized format that can be easily ingested by geospatially-enabled modeling software allowing for customization to robustly address science objectives when evaluating varying future conditions.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Opportunities for Pumped Storage Hydropower under the Inflation Reduction Act [Slides]
The Inflation Reduction Act (IRA) creates significant incentives for clean energy technologies including pumped storage hydropower (PSH). The investment tax credit (ITC) is expected to sunset in 2033 (or later). This decade-long window of opportunity can accommodate the lead times typically necessary for developing PSH. The ITC for PSH likely ranges from 6%-50%. Portions of the ITC are spatially dependent. 22 states have the potential for deployment of PSH at a feasible site with the maximum ITC of 50%, based on currently defined areas under the energy community tax credit bonus. Regions including the central Rockies, Appalachia and the California-Nevada border have especially high combined potential for site feasibility and ITC.
13 HYDRO ENERGY↗
IM3 Projected US Data Center Locations
IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Mongird, Kendall (ORCID:0000000328077088)↗
IM3 Projected US Data Center Locations
IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Mongird, Kendall (ORCID:0000000328077088)↗
Investigating Benefits and Challenges of Converting Retiring Coal Plants into Nuclear Plants
A coal-to-nuclear (C2N) transition means siting a nuclear reactor at the site of a recently retired coal power plant. Three overarching questions from the C2N transition guide this research: where in the United States are retired coal facilities located and what factors make a site feasible for transition; what factors of technology, cost, and project timeline drive investor economics over such a decision; and how will C2N impact local communities? The study team evaluated the siting characteristics of recently retired plants and those operating coal-fired power plant sites run by a utility or an independent power producer utilizing publicly available data to screen U.S. coal power plant sites to nuclear-feasible locations. After screening all retired coal sites to a set of 157 potential candidates and screening operating sites to a set of 237 candidates, the study team estimates that 80% of retired and operating coal power plant sites that were evaluated have the basic characteristics needed to be considered amenable to host an advanced nuclear reactor. For the recently retired plant sites evaluated, this represents a capacity potential of 64.8 GWe to be backfit at 125 sites. For the operating plant sites evaluated, this represents a capacity potential of 198.5 GWe to be backfit at 190 sites. This report evaluates a case study for the detailed impacts and potential outcomes from a C2N transition. Based on the nuclear technology choices and sizes evaluated to replace a large coal plant of 1,200 MWe generation capacity at the case study site, nuclear overnight costs of capital could decrease by 15% to 35% when compared to a greenfield construction project, through the reuse of infrastructure from the coal facility. Nuclear replacement designs can have a lower capacity size because nuclear power plants run at higher capacity factors than coal power plants. In the case study replacing coal capacity with 924 GWe of nuclear capacity, the study team found regional economic activity could increase by as much as $\$275 million$ and add 650 new, permanent jobs to the region of analysis. The evaluated site choice in the report is hypothetical for analysis purposes only and based on available data and documented assumptions. Consequently, the findings only inform at a general level. A community, investor, or other interested stakeholder can use these results to set up a detailed, in-depth analysis for a specific application of interest, such as evaluating a C2N transition of a specific coal power plant and a specific nuclear technology design. The report was subjected to independent peer reviews by experts in systems engineering and regional economic modeling to evaluate analysis and assumptions.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Assessing the suitability of sites near Pine Island Glacier for subglacial bedrock drilling aimed at detecting Holocene retreat–readvance
Abstract. Unambiguous identification of past episodes of ice sheet thinning below the modern surface and grounding line retreat inboard of present requires recovery and exposure dating of subglacial bedrock. Such efforts are needed to understand the significance and potential future reversibility of ongoing and projected change in Antarctica. Here we evaluate the suitability for subglacial bedrock drilling of sites in the Hudson Mountains, which are located in the Amundsen Sea sector of West Antarctica. We use an ice sheet model and field data – geological observations, glaciological observations and bedrock samples from nunataks, and ground-penetrating radar from subglacial ridges – to rate each site against four key criteria: (i) presence of ridges extending below the ice sheet, (ii) likelihood of increased exposure of those ridges if the grounding line was inboard of present, (iii) suitability of bedrock for drilling and geochemical analysis, and (iv) accessibility for aircraft and drilling operations. Our results demonstrate that although no site in the Hudson Mountains is perfect for this study when assessed against all criteria, the accessibility, N–S orientation and basaltic bedrock lithology of Winkie Nunatak's southernmost ridge (74.86° S, 99.77° W) make it a feasible site both for drilling and subsequent cosmogenic nuclide analysis. Furthermore, the ridge is strewn with glacial erratics at all elevations, providing valuable constraints on its early Holocene deglacial history. Based on our experiences during this study, we conclude with a series of recommendations for assessing site suitability for future bedrock drilling campaigns. We emphasise the importance of consulting a range of expertise prior to drilling and ensuring that sufficient field reconnaissance is undertaken (including obtaining detailed grids of radar survey data and bedrock samples).
58 GEOSCIENCES↗
Evolution of carbide precipitates in Haynes® 282 superalloy processed by wire arc additive manufacturing
Precipitation hardening regulated with heat treatments is a critical technique for tuning the mechanical properties of Ni-based superalloys fabricated with additive manufacturing, where mechanical deformation is no longer viable for microstructural refinement. As one of the key precipitates, carbides prevail in the additively manufactured alloys, whose precipitation kinetics remain largely vague. This work studies the influence of heat treatments on the evolution of MC- and M 23 C 6 -type carbides within a Haynes® 282 superalloy prepared by wire arc additive manufacturing (WAAM). SEM-EDS analysis was performed to identify the evolution pathways of the carbides, and TEM and XRD characterizations were carried out to track the crystallographic features. The MC-type carbides are originally present in the as-printed alloy, which distribute uniformly across the whole material and remain largely unchanged in size during the heat treatments. By contrast, the incipiently non-existent M 23 C 6 -type carbides precipitate during an aging treatment at 1010 °C, which majorly extend along the grain boundaries as kinetically feasible sites. Further experimental and computational analysis confirms the enhancing effect of the M 23 C 6 -type carbides on the Vickers hardness. For the first time, this work revealed the evolution pathways of carbides within a Haynes® 282 superalloy prepared by WAAM, providing critical information for tunning the microstructure and mechanical properties. Finally, the carbide evolution kinetics established in this work can be potentially extended to other alloys prepared with WAAM, providing critical information to prevent mechanical failures associated with carbide coarsening.
36 MATERIALS SCIENCE↗
Techno-Economic Analysis Using REopt for Community Solar on Multifamily Affordable Housing Properties [Slides]
Multifamily affordable housing (MFAH) providers can identify and prioritize properties in their portfolios for which community solar development is feasible by following portfolio screening steps in a process outlined by NREL. Once MFAH providers have identified the most feasible sites, they can conduct more detailed analyses for a select number of sites to assess how distributed energy resources such as solar plus storage can help them meet their goals. The step is completed using NREL's REopt (https://reopt.nrel.gov/tool), a free techno-economic optimization model that determines DER sizes and dispatch strategies that minimize the life cycle cost of energy at a site. This presentation describes how to perform this step using REopt to help MFAH providers answer questions such as: What size solar PV system will result in the most energy bill savings at this site? What size solar-plus-storage system would be needed to power critical loads through a utility grid outage? What is the financial impact of rate switching, net metering, and/or meter aggregation? What percentage of the site's load can be offset with renewable energy? What are the emissions benefits of this renewable generation?
14 SOLAR ENERGY↗
Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study
Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.
Sun, Alexander↗
Marine Fuel Innovation in the Great Lakes and St Lawrence Seaway
NLR collaborated with five Great Lakes ports to conduct analysis on potential risks and opportunities for making additional fuels available at their ports. The project included: Identifying existing and anticipated fuel production within the region; examining current port infrastructure and activities; exploring potential siting feasibility of additional energy infrastructure based on available area; introducing the Alternative Energy for Ports Explorer (APEX) tool to help ports understand existing energy resources and infrastructure. This presentation summarizes the work we did and our key findings.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
STAND Quickstart Guide
STAND is a flexible web application designed to identify and examine potentially feasible sites where advanced nuclear facilities might be welcomed by host communities. STAND allows the user to explore and provide insight on socioeconomic, proximity, and safety data, generate county reports, review regulatory data, and complete a comparative analysis across multiple sites by following several paths, either individually or sequentially based on the user’s needs and where they are in the deployment process. STAND is best used for the contiguous United States. STAND is not a substitute for the in-depth studies required to qualify a site for hosting a nuclear facility, nor is it a substitute for the necessary community engagement to build trust and seek consent. It can, however, be a valuable resource to aid in the siting process.
99 GENERAL AND MISCELLANEOUS↗
STAND Quickstart Guide
STAND is a flexible web application designed to identify and examine potentially feasible sites where advanced nuclear facilities might be welcomed by host communities. STAND allows the user to explore and provide insight on socioeconomic, proximity, and safety data, generate county reports, review regulatory data, and complete a comparative analysis across multiple sites by following several paths, either individually or sequentially based on the user’s needs and where they are in the deployment process.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study
This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.
Sun, Alexander↗
TCCSP Site Characterization and Feasibility Report
The Tulare County Carbon Storage Project (TCCSP) focused on establishing the geological and commercial viability of a 50 million metric ton (MMT) carbon storage complex located in Tulare County, California. The geologic scope of work included collecting new and existing subsurface data, such as two-dimensional (2D) seismic data, advanced geophysical logs, core and fluid samples, and specialized core-analysis datasets, to support the integration and interpretation of findings from earlier tasks. These efforts contributed to the comprehensive feasibility study for commercial carbon capture and storage (CCS) development at the TCCSP site and in the surrounding region. The TCCSP project team evaluated the feasibility of the Lower Monterey Group (Santa Margarita Sand Member or its equivalent), Temblor Formation sand members (Olcese, Jewett, Vedder), and Lodo-Martinez Zone (Domengine Sandstone, Walker Formation, Lodo Formation, and the Martinez Sand) to serve as commercial CCS reservoirs along with potential for the Upper Monterey Group (Reef Ridge, Antelope Members) and the Kreyenhagen Formation to vertically seal underlying reservoirs from shallow above-zones.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Building Capacity: Solar Photovoltaics Site Assessment and Feasibility Study
While Bhutan is abundant with clean energy hydropower, there are dry months in winter when solar energy could reduce expensive power inputs and bolster Bhutan's energy resilience and independence. As the country explores solar photovoltaic (PV) development as an option to achieve that goal, grid planners and renewable energy experts are partnering with the South Asia Group for Energy (SAGE) to determine the benefits and challenges of building solar energy systems in Bhutan.
14 SOLAR ENERGY↗
Coal-dependent Communities in Transition: Identifying Best Practices to Ensure Equitable Outcomes
The U.S. coal industry is experiencing a sharp increase in the numbers of retired and/or decommissioned coal-fired power plants across the U.S. In the years between 2010-2019, around 102 gigawatts (GW) of coal-fired generating capacity has been announced to be decommissioned, representing more than 546 coal-fired power plant units, and an additional 17 GW is planned to be decommissioned by 2025. This change in the energy production landscape presents an impact on the social, environmental, and economic prospects of coal-dependent communities. This report examined the role of communities in the coal power plant decommissioning process and provided community-identified best practices to ensure an equitable process. The experiences of four coal-dependent communities—Wise County, VA, Muskegon, MI, Anderson County, TN, and Becker, MN—are presented as case studies to understand the impacts of the decommissioning process, and associated best practices, from the communities’ perspective. The report results highlight the need to recognize that the decommissioning decision-making process must be community-based to be equitable. Each community’s input is key to the transition away from coal power because there is no one-size-fits-all development plan. In other words, each community’s trajectory through the decommissioning process—from the retirement decision-making stage to the final site redevelopment phase—is unique because each community has distinct needs and wants from the energy transition. What is best for one community may not be suited for another. Ultimately, the framework for site development and community revitalization post-decommissioning cannot be universal because each community’s profile—from a social, cultural, and economic perspective—is different. Community impacts of power plant decommissioning are not limited to job and revenue losses. Communities are likely to be impacted culturally, socially, environmentally, and have long-term health-based impacts that should be acknowledged and addressed in post-retirement plans. Commonly identified decommissioning best practices include: Early and continued engagement throughout, with a number of mediums for communication and feedback (e.g., in-person sessions, virtual meetings, written comment opportunities); Early planning of post-decommissioning projects to replace lost jobs, revenue, and economic activity; Recognition (and mitigation, if possible) of social impacts on the community due to plant closure; Transparency throughout the process, with trusted information being provided about the decommissioning process and timeline; potential impacts on the workforce, economy, and environment; and the feasibility of alternative site uses; Identification of funding sources, technical experts, and/or strategic partnerships to support decommissioning and the affected communities upfront; and, Acknowledgment of communities as stakeholders who have a role in the conversation and right to determine their futures. Three key areas for assisting coal-dependent communities affected by the energy transition: Technical assistance: assessment of site feasibility for alternative uses or to repower with new technologies; Cross-partnership engagement and collaboration: facilitate knowledge-sharing of “lessons learned” about the decommissioning process between communities and provide guidance for decision-making processes; and, Financial assistance: access to grant and/or loan programs to assist with redevelopment survey, bolster community economic security through job creation, and cover environmental clean-up costs. Technical assistance, cross-partnership engagement, and financial aid can be mobilized to help communities throughout various stages of the decommissioning process, including the retirement decision, the site reclamation phase, and eventual revitalization of the site and surrounding community.
01 COAL, LIGNITE, AND PEAT↗
Assessing the stability of Pd-exchanged sites in zeolites with the aid of a high throughput quantum chemistry workflow
Abstract Cation exchanged-zeolites are functional materials with a wide range of applications from catalysis to sorbents. They present a challenge for computational studies using density functional theory due to the numerous possible active sites. From Al configuration, to placement of extra framework cation(s), to potentially different oxidation states of the cation, accounting for all these possibilities is not trivial. To make the number of calculations more tractable, most studies focus on a few active sites. We attempt to go beyond these limitations by implementing a workflow for a high throughput screening, designed to systematize the problem and exhaustively search for feasible active sites. We use Pd-exchanged CHA and BEA to illustrate the approach. After conducting thousands of explicit DFT calculations, we identify the sites most favorable for the Pd cation and discuss the results in detail. The high throughput screening identifies many energetically favorable sites that are non-trivial. Lastly, we employ these results to examine NO adsorption in Pd-exchanged CHA, which is a promising passive NO x adsorbent (PNA) during the cold start of automobiles. The results shed light on critical active sites for NO x capture that were not previously studied.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗