Search NASA⌕ Search

SEARCH · Search NASA

Results for “emission factor”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6

Soybean fallow and nitrification inhibitors: Strategies to reduce N 2 O emission intensities and N losses in Australian sugarcane cropping systems

The Australian sugar industry is facing mounting pressure to reduce the nitrogen (N) losses and nitrous oxide (N 2 O) emissions associated with N fertiliser use. Research has shown that N 2 O emissions from sugarcane (Saccharum officinarum L.) cropping systems can be reduced with the use of fertilisers coated with nitrification inhibitors, or by sowing legume crops in the fallows between sugarcane crop cycles. However, the efficacy of these two N management strategies across different climatic zones is still unclear, as results from field studies have been contradictory. The objectives of this study were therefore to use the DayCent model to assess the long-term effects of the two strategies (separately or in combination) on N 2 O emissions, N losses and yields. The model was parameterised using data from eight field experiments (39 treatments) conducted across four of the five main districts of the Australian sugarcane industry. A series of long-term scenarios embracing a range of N fertiliser rates (0- 160 kg N ha -1 applied as urea), climatic regions (Tropics and Subtropics), soil textures (fine- and coarse-textured soils) and N-loss risk scenarios (crop fertilised well before or close to the on-set of the wet season) were tested for both N management strategies. Simulations identified that the combined use of soybean (Glycine max L.) fallows and N fertiliser coated with the 3,4-dimethylpyrazole phosphate (DMPP) nitrification inhibitor was the most effective strategy to maintain or even increase current yields while significantly reducing N 2 O and cumulative N (mainly due to nitrate) losses. When used alone, the soybean fallow strategy on average reduced N losses over the entire crop cycle by 29 % compared with current industry best practice. However, this strategy showed a limited capacity to reduce N 2 O emissions over the entire cane crop cycle (median: -3% compared with current industry best practice), or even increased them in scenarios at high-risk of N losses in the Tropics. The use of urea coated with DMPP alone allowed current fertiliser N rates to be reduced by 30 % without any substantial yield penalty. In environments prone to high N losses, DMPP urea abated overall N losses and N 2 O emissions by 41 % and 30 %, respectively. This was the first study to use such an extensive dataset to parameterise a biogeochemical model for yields, N 2 O emissions and N losses in sugarcane cropping systems. Finally, the findings will be instrumental in guiding future research and policies designed to improve the profitability and environmental sustainability of this industry.

54 ENVIRONMENTAL SCIENCES↗

The future of ship engines: Renewable fuels and enabling technologies for decarbonization

Shipping is one of the most efficient transportation modes for moving freight globally. International regulations concerning decarbonization and emission reduction goals drive rapid innovations to meet the 2030 and 2050 greenhouse gas reduction targets. The internal combustion engines used for marine vessels are among the most efficient energy conversion systems. Internal combustion engines dominate the propulsion system architectures for marine shipping, and current marine engines will continue to serve for several decades. However, to meet the aggressive goals of low-carbon-intensity shipping, there is an impetus for further efficiency improvement and achieving net zero greenhouse gas emissions. These factors drive the advancements in engine technologies, low-carbon fuels and fueling infrastructure, and emissions control systems. This editorial presents a perspective on the future of ship engines and the role of low-life cycle-carbon-fuels in decarbonizing the marine shipping sector. A selection of zero-carbon, net-zero carbon, and low-lifecycle-carbon-fuels are reviewed. This work focuses on the opportunities and challenges of displacing distillate fossil fuels for decarbonizing marine shipping. In conclusion, enabling technologies such as next-generation air handling, fuel injection systems, and advanced combustion modes are discussed in the context of their role in the future of low-CO 2 intensity shipping.

33 ADVANCED PROPULSION SYSTEMS↗

Quantum Fluctuations and Lineshape Anomaly in a High‐ β Silver‐Coated InP‐Based Metallic Nanolaser

Abstract Metallic nanocavity lasers provide important technological advancement toward even smaller integrable light sources. They give access to widely unexplored lasing physics in which the distinction between different operational regimes, like those of thermal or coherent light emission, becomes increasingly challenging upon approaching a device with a near‐perfect spontaneous‐emission coupling factor . In fact, quantum‐optical studies have to be employed to reveal a transition to coherent emission in the intensity fluctuation behavior of nanolasers when the input–output characteristic appears thresholdless for nanolasers. Here, a new indicator for lasing operation in high‐ lasers is identified by showing that stimulated emission can give rise to a lineshape anomaly manifested as a transition from a Lorentzian to a Gaussian component in the emission linewidth that dominates the spectrum above the lasing threshold.

Koulas‐Simos, Aris↗

Air Emissions Impacts of Fuel Substitution in the United States Cement Industry

High-heat industrial processes cannot be easily electrified, often rely on comparatively high-emitting solid fuels, and are essential for economic growth. Cement production in particular is critical to supporting construction and the expansion and maintenance of infrastructure systems in countries around the world. Fuels for cement production include petcoke, coal, and waste solvents, and their combustion contributes approximately 40% of the CO 2 emissions from global cement production. Broadening the fuel resources to include agricultural residues and other wastes can reduce emissions and divert waste from landfills. We assess the potential to use 16 different alternative fuels across the 88 integrated cement plants in the United States. The share of alternative fuels in the cement production sector could increase from the current level of 17% to as high as 73%. Fuel substitution can reduce the current fuel-related life-cycle emissions (28.6 ± 5.7 Mton CO 2 /year) by 19–45%. The greatest national-scale human health cobenefits occur when alternative fuels are prioritized at facilities currently using coal and petcoke. NO x emissions outcomes from this strategy are mixed, but SO x and particulate matter emissions reductions drive net human health benefits. These results suggest that targeted fuel substitution in heavy industries can yield outsized emissions benefits.

alternative fuels↗

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗

Biochemical Conversion of Herbaceous Biomass to Renewable Diesel: Biorefinery Marginal Air Quality Impacts and Comparison to Feedstock Production

This study assesses the air quality impacts of an advanced biorefinery that produces renewable diesel blendstock (RDB) from lignocellulosic biomass via aerobic respiration (Davis et al. 2022) by estimating fine particulate matter (PM2.5) impacts from biorefinery emissions. It continues a prior analysis that used a geospatial assessment to identify source regions for biomass feedstocks and studied the impact of feedstock production emissions on air quality (Thind et al. 2022). Thind et al. (2022) identified RDB biorefineries that can use corn stover feedstocks of 2,000; 5,200; and 9,100 dry metric tons per day (DMT/day), based in Iowa, and suggested 7 unique counties can serve as hosts for a biorefinery that draws biomass feedstock from neighboring counties. Given 13 unique county-biorefinery size combinations and two waste lignin end uses at the biorefinery (lignin as a fuel for electricity generation and lignin for pellet production), the air-quality-related sustainability aspects of each of these 26 scenarios are assessed by estimating the annual average impacts of biorefinery emissions on the dispersion and formation of secondary PM2.5 in the atmosphere using a novel reduced-complexity air quality model called the Intervention Model for Air Pollution (InMAP). The 26 biorefinery design combinations help capture how a biorefinery's emissions of air pollutants and their resulting impact on local and regional air quality are influenced by the magnitude of production scale, lignin utilization strategy, and location of a proposed biorefinery. Methods developed in Thind et al. (2022) are applied to estimate the constraints on primary PM2.5 and secondary PM2.5 precursor emissions based on compliance with U.S. Environmental Protection Agency's (EPA's) annual primary National Ambient Air Quality Standard (NAAQS) of PM2.5 (i.e.12.0 micrograms per cubic meter (microgram/m3)) at downwind receptors of a biorefinery. Incremental PM2.5 concentrations caused by the emission of biorefining corn stover into RDB are assessed and compared to those of corn stover production. To illuminate which upstream supply chain stage of renewable diesel production contributes most to air quality impacts, marginal PM2.5 concentrations are compared between both stages at multiple downwind air quality monitor locations. In addition, through a hotspot analysis, we identify the primary contributing factors of emissions within the feedstock production and biorefinery stage operations. In doing so, we provide insights for improving the air pollutant emission-related sustainability of advanced lignocellulosic biofuel production.

09 BIOMASS FUELS↗

Effect of soot concentration on radiative transport in a 2 m JP-8 pool fire

Hubbard et al. (2024) investigated conjugate heat transfer in a 7.9 m JP-8 pool fire, utilizing both heavily sooting and non-sooting fuel models. Their findings indicated that all models produced pool radiative heat fluxes within the measurement uncertainty for large-scale outdoor experiments. This study aims to further investigate the influence of soot concentration on radiative transport. To this end, Large Eddy Simulations (LES) of a 2 m diameter JP-8 pool fire were conducted, employing four turbulent combustion chemistry models: the Eddy Dissipation Concept (EDC) and three variants of flamelet model. Additionally, three variations of the one-equation subgrid scale turbulence kinetic energy model, and multiple mesh resolutions were utilized. The primary validation metrics were pool radiative heat flux and plume radiative fraction, with simulation data compared to experimental results published by Blanchat et al. (2010). The EDC model produced high soot concentrations near the pool surface, resulting in a significant shielding effect. In contrast, the flamelet combustion model has different kinetic limiters for soot production, leading to soot formation higher in the flame, where it can be more readily oxidized. Flamelet models incorporating both acetylene and benzene contributions to soot growth demonstrated better agreement with experimental data. To assess the sensitivity of pool radiative heat flux to soot mass concentration, we used a one-dimensional approximation over a wide range of soot concentrations and compared it to three-dimensional solutions obtained using Discrete Ordinates. Furthermore, this analysis illustrates the impacts of soot shielding and Turbulence Radiation Interactions (TRI), with TRI emission enhancement factors predicted to be in the range of 3–5 near the edges of the plume.

Computational fluid dynamics↗

Effect of alloying on intrinsic ductility in WTaCrV high entropy alloys

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit excellent irradiation resistance. The ductility evaluations are carried out using a criterion based on the competition between the critical stress intensity factors for emission (K Ie ) and cleavage (K Ic ) in the {110} slip planes and {110} crack planes, which are evaluated within the linear elastic fracture mechanics framework and computed using density functional theory. The results suggest that increasing the alloying concentrations of V and reducing the concentrations of W can significantly improve the ductility in these HEAs. The elastic anisotropy for these HEAs is analyzed using the Zener anisotropy ratio and its correlation with the concentration of W in the alloys is studied. Results indicate that these alloys tend to be fairly isotropic independently from the concentration of W in them. The computed data for the elastic constants of these HEAs is also compared against available experimental data. The results are in good agreement, validating the robustness and accuracy of the computational methods. Multiple phenomenological ductility metrics were also computed and analyzed against the analytical model. Some metrics, mainly the surrogate D parameter, show a good correlation with the Rice model. The potential of these empirical metrics to serve as surrogate screening models for optimizing the compositional space is also discussed.

Anisotropy↗

Techno-Economic Analysis of Repurposing Natural Gas Transmission Pipeline Networks to Accommodate Hydrogen Blends

Blending hydrogen into natural gas infrastructure could supplement natural gas supply and increase resilience for applications such as ammonia production, peaking and load-following power plants, and heating. The United States has an extensive network of natural gas pipelines, but the feasibility of employing this infrastructure to transport hydrogen is unclear. We analyze the costs associated with repurposing three distinct natural gas transmission pipelines in different locations within the United States to carry blends of hydrogen up to 100% via three different pipeline network modification methods and compare against the cost of building a new dedicated hydrogen pipeline. We conduct a sensitivity analysis on the hoop stress limit of the existing pipe, techno-economic parameters, emissions, and relative capacity. The results show that the capital costs required to upgrade a pipeline can vary from tens of millions to billions of dollars depending on the length and capacity of the existing pipeline section, whether the existing pipeline already operates at or below its maximum allowable operating pressure, whether future demand is expected to increase or decrease, the network modification method selected, and pipe material costs. The delivered cost of energy to end users is impacted less by the levelized cost of transporting hydrogen blends than by the cost of the natural gas and hydrogen fuels being transported. The emissions impact of blending hydrogen into natural gas transmission networks scales proportionally with the amount of energy displaced with low-emission hydrogen (such as from natural gas with carbon capture and sequestration or electrolysis powered by nuclear, renewable, or geothermal electricity), therefore low blend ratios (e.g., < 20% vol. hydrogen) will result in low emissions impacts. Factors such as permitting and right-of-way costs, the proximity of the pipeline to hydrogen demand and production, and the compatibility of and/or retrofitting costs of end-use gas-fueled technologies will likely be greater drivers in determining whether converting a particular natural gas pipeline to carry hydrogen makes economic sense.

08 HYDROGEN↗

Robust highly durable solid oxide fuel cell cathodes – Improved materials compatibility & self-regulating surface chemistry

Solid oxide fuel cells (SOFCs) are electrochemical conversion devices that directly transform hydrogen or hydrocarbon fuels to electricity, with energy efficiencies as high as 90%, coupled with reduced emissions. Several factors, however, remain to be addressed when considering scale-up of SOFC technology, including the need to overcome decreased performance due to sluggish rates of the oxygen reduction reaction (ORR) at the cathode under reduced temperatures and susceptibility to degradation in performance from surface poisoning e.g. from chromia, while limiting the use of critical raw materials (lanthanides and transition metals) present in high performing mixed ionic electronic conducting electrodes like (La,Sr)CoO 3 (LSC). In this project we explored the key descriptors for determining ORR activity in SOFC electrodes and tried to recover performance degradation by applying them to SOFC electrodes. In order to do this, we first selected a model mixed ionic electronic conducting (MIEC) oxide, Pr-doped CeO 2 (Pr 0.1 Ce 0.9 O 2-δ , PCO), which is a chemically stable fluorite and free of inherent poison sources (e.g. Sr segregation in LSC) that potentially react with external impurities such as Cr-species vaporized from the interconnect. The three approaches originally planned in this project are as follows: 1) evaluation of scavenger exsolution characteristics, 2) study of scavengers gettering efficacy following Cr and Si poisoning and 3) integration of new compositions into porous electrodes. Among them, exceptional progress has been made in 2) and 3), especially understanding the role of surface infiltrants in impacting electrode performance and degradation of PCO materials. We found that the Smith acidity scale for binary oxides serves as a powerful descriptor for tuning and predicting the oxygen exchange kinetics on MIEC PCO surfaces. As a result, with infiltration with binary oxides, ranging from strongly basic (Li 2 O) to strongly acidic (SiO 2 ) onto the surface of porous PCO, it was possible to systematically vary the chemical surface exchange coefficient (k chem ) by 6 orders of magnitude! L i2 O increased k chem by nearly 1,000 times over that of pristine PCO, while SiO 2 decreased k chem by nearly the same factor. Strikingly, although the pre-exponential of k chem scales linearly with the acidity of the infiltrated binary oxide, there is nearly no change in the activation energy. With this insight, we attributed the origin of these dramatic changes in k chem values to the systematic increase and decrease in the surface electron density induced by infiltrated binary oxides. More interestingly, although both Cr 2 O 3 and SiO 2 were determined to be acidic by Smith, suggesting that this feature could likely be the primary reason that these compounds serve to poison the ORR on SOFC cathodes, the effect of poisoning could be subsequently tuned by adding multiple infiltrants and controlling their relative surface acidities. We also systematically examined the effect of serial infiltration of basic and acidic oxides. It turned out that serial infiltration of Li not only recovers approximately 20-fold degraded k chem of PCO by acidic Cr 2 O 3 but its k chem is enhanced even beyond that of the non-infiltrated PCO by more than three orders of magnitude. This was further verified with a screen-printing PCO symmetric cell in terms of the electrode performance (area-specific resistance, ASR) related to approach 3). These observations point to acidity as a key descriptor not only in tuning and predicting the ORR activity of SOFC cathodes that largely determines the overall performance of SOFC, but in mitigating and reactivating poisoned electrode performance. This work provides novel guidelines for making the electrode performance much more active and robust in SOFCs, which can further be applied to all applications requiring oxygen exchange reaction, such as electrolyzers, permeation membranes and gas sensors.

30 DIRECT ENERGY CONVERSION↗

Impact of a dynamic grid mix and climate on operational carbon emissions modeling for different building typologies and climate zones

Calculating operational carbon emissions through a building’s lifecycle is complex due to the dynamic nature of influencing factors such as climate and energy grid mix. This paper introduces a novel methodology for modeling 30-year operational carbon impacts of buildings and applies this method to mid-rise office and residential typologies across various US climate zones. The method accounts for these temporal variabilities using new and scarcely cited data sources. Key findings indicate that future changes in the climate, while impactful, play a relatively modest role in operational carbon emissions compared to significant reductions with modeling scenarios using the projected decarbonization of the electricity grid. Here, the study also finds that using annual, month-hourly, or hourly grid emission factors have a minimal impact on carbon accounting, except in certain climates and program types where emission patterns do not align with a building’s energy consumption. Warmer climates like Miami, Florida and Tucson, Arizona, which rely heavily on cooling, demonstrate larger variations in carbon emissions when using higher temporal resolution emission factors. Ultimately, this study underscores the critical role of grid decarbonization in reducing long-term emissions and the importance of incorporating this variable in life cycle assessment (LCA) modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Coupling of a single tin-vacancy center to a photonic crystal cavity in diamond

In this work, we demonstrate optical coupling between a single tin-vacancy (SnV) center in diamond and a free-standing photonic crystal nanobeam cavity. The cavities are fabricated using quasi-isotropic etching and feature experimentally measured quality factors as high as ~11000. We investigate the dependence of a single SnV center's emission by controlling the cavity wavelength using a laser-induced gas desorption technique. Under resonance conditions, we observe an intensity enhancement of the SnV emission by a factor of 12 and a 16-fold reduction of the SnV lifetime. Based on the large enhancement of the SnV emission rate inside the cavity, we estimate the Purcell factor for the SnV zero-phonon line to be 37 and the coupling efficiency of the SnV center to the cavity, the β factor, to be 95%. Our work paves the way for the realization of quantum photonic devices and systems based on efficient photonic interfaces using the SnV color center in diamond.

42 ENGINEERING↗

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r >0.95 for ML and r =0.14~0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r =0.74 for ML and r <0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM 2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

54 ENVIRONMENTAL SCIENCES↗

Calculating Potential Radiological Emissions for Waste Management Activities at INL - 20068

At Idaho National Laboratory (INL), work involving radionuclides is evaluated for potential emissions from a project in order to comply with the National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations, 40 CFR 61 Subpart H. Emission calculations are documented in an Air Permitting Applicability Determination (APAD) to analyze unmitigated and mitigated emissions and determine if an Application to Construct (ATC) or continuous monitoring is required. To calculate the unmitigated and mitigated emissions, a spreadsheet was developed to provide ease in determining potential emissions by providing the maximum operating temperature and the material being used. The spreadsheet aids in determining the potential emissions for research projects and waste management activities at Materials and Fuels Complex (MFC) and other locations across the INL site. Furthermore, it can also be used for periodic confirmatory measurements (PCM) to justify low emissions. Elements that factor into the unmitigated and mitigated calculations include the amount of each radionuclide used (in curies or grams), specific activity (if amount is given in grams), the temperature the material is heated to in Celsius, the dose conversion factor which is derived from Clean Air Act Assessment Package - 1988 (CAP-88) modeling, and the number of HEPA filters used for mitigated measures. The main drivers for calculating the unmitigated emissions for a project are the amount used per radionuclide, the maximum operating temperature, and the location of the work. The maximum operating temperature determines the airborne release factor which is dependent on the physical state of the radionuclide. Prior to October 2017, if the radionuclide was heated to greater than 100 deg. C, the radionuclide was assumed to be a gas, which has the highest airborne release factor. This assumption would be overly conservative for radionuclides with high melting and boiling points, which provided a challenge to demonstrate low emissions. In October 2017, the Environmental Protection Agency (EPA) approved an alternative method for INL. This method allows the airborne release factor to be determined by using the melting point and 90% of the boiling point of the radionuclide. This methodology was included in the spreadsheet to allow unmitigated emission calculations for APADs to be completed more efficiently and effectively. Results show a reduction in time completing air emission calculations as well as lower total emissions across all facilities at INL. MFC annual emissions were reduced by 62% from the previous year and Research and Education Campus (REC) facilities were reduced by 38% due to implementation of the approved alternative method. Time spent on APADs, PCMs, and documentation for the annual NESHAP report was also reduced significantly. The spreadsheet provided in Table I provides the potential emission calculations for the 'Advanced Retrieval and Disposition Techniques for Remote Handled Mixed Low Level Waste (RH MLLW) at the Radioactive Scrap and Waste Facility (RSWF)' project. Calculations show the Potential Effective Dose Equivalent (PEDE) at RSWF to be 7.27 E-04 mrem/yr (7.27 E-09 Sv/yr) which is well below the 0.1 mrem/yr threshold. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment

Methane (CH 4 ) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH 4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH 4 emissions at the facility-level. Near-field quantification of methane (CH 4 ) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH 4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH 4 h −1 . Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.

gaussian plume↗

Decarbonizing or illusion? How carbon emissions of commercial building operations change worldwide

To lead the low-carbon transition in global buildings, this study is the first to use the generalized Divisia index method (GDIM) to identify the factors driving carbon emissions and assess the decarbonization performance in commercial building operations (CBOs) of sixteen countries during 2000–2019. Results show that (1) while the global carbon emissions from CBOs have increased at a modest rate of 0.9%/yr, this trend runs counter to the declining emissions in the U.S. (-1.1%/yr) and the significant growth in China (14.4%/yr), which can be attributed to the impact of economy-related factors. (2) The U.S. and China, as the largest emitters, contributed 66.8% of the samples’ decarbonization of CBOs (919.1 million tons of carbon dioxide). (3) Most countries’ CBOs had a decarbonization efficiency level of less than 10%, except for Spain (27.8%). Spain excelled in both areas of per capita and per floor area with an average efficiency of nearly 30%. Moreover, ridge regression successfully confirms the stability of GDIM results and it should be noted GDIM has limitations in characterizing the end-use activity. Overall, this study tracks the historical decarbonization of global CBOs and offers benchmarks for different emitters to forecast the dynamic of building emissions along with economic booms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Preparation and Qualification of Preproduction SSR2 Jacketed Cavities for PIP-II

The qualification of 325 MHz Single Spoke Resonators type 2 (SSR2) jacketed cavities to meet technical requirements represents a significant milestone in the development of the SSR2 cryomodules for the PIP-II Project at Fermilab. This poster reports the procedures and lessons learned in processing and preparing these cavities for horizontal cold testing prior to integration into a cavity string assembly, with a focus on addressing the field emission issues observed during the cold testing. A comprehensive root cause analysis identified critical fabrication, processing, and handling factors impacting field emission onset. New techniques were successfully developed and implemented to achieve field emission-free SSR2 cavities, and efforts were made to correlate radiation levels measured at the test stand with expected levels in the LINAC tunnel. Additionally, the evolution of field emission through assembly steps was thoroughly investigated, leading to a reassessment of design choices and enhancing our understanding of their effects on cavity performance.

Grassellino, L. [Fermilab]↗

Flexible FlueCO2

Carbon dioxide (CO2) emission reductions remain a significant challenge on the path to clean energy. There are increasing legislative, social, and environmental factors motivating CO2 emissions reduction from power plants with carbon capture and storage (CCS). CCS in natural gas combined cycle (NGCC) power plants is critical to achieve a net-zero carbon electricity grid. Enhanced 45Q tax credits provide new incentives, but currently available technologies are unable to profitably operate in grids with deep variable renewable penetration which require flexible NGCC operation. Luna Labs has developed the FlueCO2 membrane to enable a profitable NGCC-CCS process. The FlueCO2 membrane couples steam transport across the membrane to CO2 transport in the opposite direction, enabling high capture efficiencies and low energy costs even at low CO2 concentrations. The dual-phase membrane can operate in the range of typical flue gas temperatures and pressures and does not require temperature or pressure cycling. Luna Labs’ FlueCO2 technology enables flexible and profitable operation of NGCC plants with lower capital investment and impact on electricity prices. In this Phase 1 project, Luna Labs utilized experimental testing, modeling, process simulation, and standardized costing methodologies to evaluate the techno-economic value of a 650 MW greenfield NGCC plant with FlueCO2 (NGCC-FlueCO2). Key design requirements for operation were established and plant performance under load-leveling conditions was validated through computational fluid dynamics and process modeling. Luna Labs developed a dynamic modeling tool which modeled plant operational modes across a variety of tax structures and electricity pricing scenarios to project the overall Net Present Value (NPV) of the NGCC-FlueCO2. FlueCO2 minimizes the impact of CCS integration on plant operation by integrating directly into the NGCC heat recovery steam generator (HRSG). By tapping into the plant’s low-pressure (LP) steam, operators can divert LP steam to the greenfield NGCC and/or CCS process in response to dynamic markets. Since FlueCO2 will not significantly affect HRSG (or NGCC) operation, CCS only turns off during peak power demand (>$250/MWh). Under baseload conditions, FlueCO2 lowers the capital (37%), energy (36%) and carbon capture (<$40/tonne) costs and can increase the overall plant lifetime NPV by approximately ~$1B in comparison with NGCC solvent-based capture reference cases (NETL Case 31B). Luna Labs has shared its costing tools with several interested partners and customers, which follows a generalizable approach to costing analysis.

Kelly, Jesse↗