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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Waste management strategies for military-generated waste in the United States

Sustainable and energy efficient municipal solid waste (MSW) management is vital for the health and performance of deployed soldiers, as they play an important role in the safety and dignity of a country. As the United States targets net-zero carbon-free emission goals, research and development are essential to surpassing current practices for management of MSW at military installations, both at home and abroad. To achieve this, it is important to act upon and implement the policies and instructions set by government agencies such as the Environmental Protection Agency (EPA), the Department of Energy (DOE), and the Department of Defense (DoD). Understanding the composition of waste and disposal methods that are in use at military installations is a crucial step to success. Here, the authors show this through analyzed data from trusted sources, such as government agencies, reports, magazines, journals, and other publications on MSW management techniques. By defining these challenges, possible solutions can be better understood for improved management of MSW at military installations in the United States. Finally, an integrated approach with deployable units capable of waste-to-energy technology using waste reduction, diversion, and recycling strategies shows to be an effective pathway for MSW conversion into energy and other useful products.

09 BIOMASS FUELS↗
DOE OSTI2025

Land change, fire, and climate weaken carbon sink in the conterminous United States

The land carbon sink of the conterminous United States was evaluated using a bottom-up modeling framework and 30-meter land change data from 1985 to 2020. This cross-scale, cross-landscape, and cross-system approach tracked fractional land cover changes and applied regional model calibration. Results show average terrestrial and aquatic carbon sinks of +110 ± 37 and +19 ± 0.5 teragrams of carbon per year, respectively. The terrestrial carbon sink, showing no clear trend, peaked in the 1990s, with more years as a carbon source since 2000, contradicting recent national and global studies. Land change had the largest impact (−70 ± 5.5 teragrams of carbon per year), exceeding impacts of climate (−33 ± 48 teragrams of carbon per year), wildfire (−7.7 ± 2.4 teragrams of carbon per year), and erosion transport (−1.9 ± 0.13 teragrams of carbon per year). The positive CO 2 fertilization effect (+69 ± 12 teragrams of carbon per year) was insufficient to maintain the carbon sink strength. Our framework reveals key paths of carbon loss, with implications for carbon budget and energy policies in the United States and beyond.

Liu, Jinxun [US Geological Survey, Reston, VA (Uni↗
DOE OSTI2013

Improving Simulations of Fine Dust Surface Concentrations over the Western United States by Optimizing the Particle Size Distribution

To improve estimates of remote contributions of dust to fine particulate matter (PM2.5) in the western United States, new dust particle size distributions (PSDs) based upon scale-invariant fragmentation theory (Kok_PSD) with constraints from in situ measurements (IMP_PSD) are implemented in a chemical transport model (GEOS-Chem). Compared to initial simulations, this leads to reductions in the mass of emitted dust particles with radii <1.8 mm by 40%-60%. Consequently, the root-mean-square error in simulated fine dust concentrations compared to springtime surface observations in the western United States is reduced by 67%-81%. The ratio of simulated fine to coarse PM mass is also improved, which is not achievable by reductions in total dust emissions. The IMP_PSD best represents the PSD of dust transported from remote sources and reduces modeled PM2.5 concentrations up to 5 mg/m3 over the western United States, which is important when considering sources contributing to nonattainment of air quality standards. Citation: Zhang, L., J. F. Kok, D. K. Henze, Q. Li, and C. Zhao (2013), Improving simulations of fine dust surface concentrations over the western United States by optimizing the particle size distribution, Geophys. Res. Lett., 40, 3270-3275, doi:10.1002/grl.50591.

distribution↗
DOE OSTI2022

Stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach

We propose a solution method for the large-scale stochastic unit commitment (SUC) problem with weekly-dispatched energy storage and significant weather-dependent stochastic generating capacity. Weekly storage facilities that mostly charge during weekends and discharge during weekdays require a weekly scheduling of generating units, which result in a large-scale optimization problem. This SUC problem is formulated as a two-stage stochastic model and we use the conditional value-at-risk as a risk measure. Using a Benders framework, the proposed solution method decomposes the problem into a mixed-integer linear master problem and linear and continuous subproblems. The master problem corresponds to the first-stage decisions throughout the week and includes all the commitment (binary) variables and their corresponding constraints. The subproblems correspond to the actual dispatch of the generating units on a weekly basis. Based on the success of column-and-constraint generation algorithms to solve robust optimization problems, we improve the low communication between the master problem and the subproblems in the standard Benders decomposition by adding primal variables and constraints from the subproblems to the master problem, which provides a better approximation of the recourse function. Furthermore, our computational experiments demonstrate the effectiveness of the proposed decomposition method using an instance of the South Carolina synthetic system with 90 generating units under 40 scenarios.

25 ENERGY STORAGE↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. Here, we also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.

42 ENGINEERING↗
DOE OSTI2014

Modeling transmission line constraints in two-stage robust unit commitment problem

Integration of renewable energy sources and demand response poses new challenges to system operators as they increase the uncertainty of the power supply and demand. Recently, robust optimization techniques are applied to the unit commitment problem with uncertainty as an alternative to the stochastic programming approaches. However, it remains challenging to solve the robust unit commitment model with full transmission line constraints. In this paper, we propose novel acceleration techniques for solving two-stage robust unit commitment problem with consideration of full transmission line constraints. We use 1) the cutting-plane algorithm for the master problem, which dynamically includes critical transmission line constraints, and 2) column-generation methods, including the branch-and-price-and-cut algorithm and heuristic approaches, for the subproblems, which add only necessary transmission line dual variables on the fly. Furthermore, computational results for the modified IEEE 118-bus system show that the combination of the cutting-plane algorithm and the heuristic column-generation approach greatly reduces the total solution time of the two-stage robust unit commitment problem.

Heuristic algorithms↗
DOE OSTI2015

Biogeochemical consequences of regional land use change to a biofuel crop in the southeastern United States

Here, the United States has mandated the production of 80 billion liters of second–generation biofuel by 2022, and several approaches to meet this target focus on using ligno–cellulosic ethanol from perennial grasses and non–food crops. The large–scale deployment of biofuel agronomy should consider high–yielding crops that meet ethanol production goals, choose appropriate landscapes for biofuel crops from a climate and food production standpoint, and a full consideration of the environmental impact of large–scale land use change. The southeastern United States has a long growing season conducive for producing high–yielding crops, and is relatively less important to US food production than the rain–fed Midwestern states that have been extensively studied for biofuel crops. We use the DayCent biogeochemical model to run simulation experiments to test the hypotheses that converting a large swath of traditional agriculture in the southeastern United States that is already utilized for bioenergy production (assuming 35% of current corn–soy, and 10% of grazed pasture hectares; ~950,000 ha) to energy cane will result in greater biomass production, increased soil C storage, decreased soil N losses and lower greenhouse gas emissions than a landscape of corn–soy rotations and interspersed grazed pasture. Our simulations suggest that energy cane above–ground productivity on former pasture and corn–soy fields would be between 52–59 million Mg dry mass per year, resulting in 21.1–23.7 billion liters of ligno–cellulosic ethanol, or ~28% of the 2022 US government mandate. DayCent did not predict significant changes in soil C flux from land conversion to energy cane, but simulations predicted lower rates of N loss compared to current agriculture. GHG emissions from energy cane landscapes were substantially higher on former pasture, but an order of magnitude lower when compared to corn–soy hectares. While further study is needed to ascertain the full economic and industrial feasibility of converting nearly 1,000,000 ha of land to energy cane production, our results suggest that such an undertaking could meet a sizeable fraction of the US ethanol mandate, reduce N pollution and GHG emissions, and avoid compromising land devoted to food production in the southeastern United States.

southeastern United States↗
DOE OSTI2014

Qualification of Daiichi Units 1, 2, and 3 Data for Severe Accident Evaluations - Process and Illustrative Examples from Prior TMI-2 Evaluations

The accidents at the Three Mile Island Unit 2 (TMI-2) Pressurized Water Reactor (PWR) and the Daiichi Units 1, 2, and 3 Boiling Water Reactors (BWRs) provide unique opportunities to evaluate instrumentation exposed to severe accident conditions. Conditions associated with the release of coolant and the hydrogen burn that occurred during the TMI-2 accident exposed instrumentation to harsh conditions, including direct radiation, radioactive contamination, and high humidity with elevated temperatures and pressures. As part of a program initiated in 2012 by the Department of Energy Office of Nuclear Energy (DOE-NE), a review was completed to gain insights from prior TMI-2 sensor survivability and data qualification efforts. This initial review focused on the set of sensors deemed most important by post-TMI-2 instrumentation evaluation programs. Instrumentation evaluation programs focused on data required by TMI-2 operators to assess the condition of the reactor and containment and the effect of mitigating actions taken by these operators. In addition, prior efforts focused on sensors providing data required for subsequent forensic evaluations and accident simulations. To encourage the potential for similar activities to be completed for qualifying data from Daiichi Units 1, 2, and 3, this report provides additional details related to the formal process used to develop a qualified TMI-2 data base and presents data qualification details for three parameters: primary system pressure; containment building temperature; and containment pressure. As described within this report, sensor evaluations and data qualification required implementation of various processes, including comparisons with data from other sensors, analytical calculations, laboratory testing, and comparisons with sensors subjected to similar conditions in large-scale integral tests and with sensors that were similar in design to instruments easily removed from the TMI-2 plant for evaluations. As documented in this report, results from qualifying data for these parameters led to key insights related to TMI-2 accident progression. Hence, these selected examples illustrate the types of activities completed in the TMI-2 data qualification process and the importance of such a qualification effort. These details are documented in this report to facilitate implementation of similar process using data and examinations at the Daiichi Units 1, 2, and 3 reactors so that BWR-specific benefits can be obtained.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States

This report is the second in a series of Electrification Futures Study (EFS) publications, a multi-year research project to explore widespread electrification in the future energy system of the United States. The report presents scenarios of electric end-use technology adoption and resulting electricity consumption in the United States. The scenarios are used to characterize changes to end-use sectors under futures with increased electrification and to quantify how those changes might impact the amount and shape of electricity consumption. These scenarios were developed primarily using expert judgment and an energy system accounting framework. Of course, technology adoption will ultimately depend on a set of complex considerations that are not fully assessed using our methodology, but which are discussed extensively throughout the report. These interacting factors include technology and fuel cost trade-offs, infrastructure needs, environmental policies, and consumer preference. Within each of these factors are barriers that might challenge increased electrification - such as higher upfront costs or unfamiliarity with new electric technologies - but also opportunities that could yield greater adoption - such as increased productivity or expanded value streams enabled by electric and/or grid-connected technologies. Understanding and quantifying these factors are needed to both evaluate the implications of a potential increased electrification future and to influence the degree of future electrification. Overall, the report represents an initial step to inform researchers and decision-makers with data and context to plan for a potential future in which electricity powers an expanded share of the U.S. energy economy.This report is the second in a series of Electrification Futures Study (EFS) publications, a multi-year research project to explore widespread electrification in the future energy system of the United States. The report presents scenarios of electric end-use technology adoption in all major sectors - transportation, commercial and residential buildings, and industry - and resulting electricity consumption in the United States. The scenarios are used to characterize changes to end-use sectors under futures with increased electrification and to quantify how those changes might impact the amount and shape of electricity consumption. These scenarios were developed primarily using expert judgment and an energy system accounting framework. Of course, technology adoption will ultimately depend on a set of complex considerations that are not fully assessed using our methodology, but which are discussed extensively throughout the report. These interacting factors include technology and fuel cost trade-offs, infrastructure needs, environmental policies, and consumer preference. Within each of these factors are barriers that might challenge increased electrification - such as higher upfront costs or unfamiliarity with new electric technologies - but also opportunities that could yield greater adoption, such as increased productivity or expanded value streams enabled by electric and grid-connected technologies. Understanding and quantifying these factors are needed to both evaluate the implications of a potential increased electrification future and to influence the degree of future electrification. Overall, the report represents an initial step to inform researchers and decision-makers with data and context to plan for a potential future in which electricity powers an expanded share of the U.S. energy economy.

2050↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Heat Pump Rooftop Units With Exhaust Air Energy Recovery

This technical report documents the Advanced Rooftop Unit Control End Use Savings Shapes (EUSS) measure, including providing a description of the technology, modeling methods, and the energy savings calculated from applying the measure to applicable across the US commercial building stock using ComStock to quantify its potential. This measure replaces gas furnace and electric resistance rooftop units with high-efficiency variable speed heat pump rooftop units (HP-RTU) that include exhaust air heat or energy recovery. The measure uses the same assumptions and technology as the variable speed HP-RTU measure from EUSS 2023 release 1 but adds energy recovery to precondition outdoor ventilation air to reduce HVAC loads. The HP-RTU compressor lockout temperature is modeled as 0 degrees Fahrenheit; below this temperature, the heat pump is set to shut off. The unit is sized based on the design cooling loads, with backup electric resistance heating addressing any remaining loads including heating hours below the compressor lockout temperature when there is no heat pump heating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2018

Unit Testing for Command and Control Systems

Unit tests were created to evaluate the functionality of a Data Generation and Publication tool for a command and control system. These unit tests are developed to constantly evaluate the tool and ensure it functions properly as the command and control system grows in size and scope. Unit tests are a crucial part of testing any software project and are especially instrumental in the development of a command and control system. They save resources, time and costs associated with testing, and catch issues before they become increasingly difficult and costly. The unit tests produced for the Data Generation and Publication tool to be used in a command and control system assure the users and stakeholders of its functionality and offer assurances which are vital in the launching of spacecraft safely.

testing↗
DOE OSTI2011

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

Bio-gasification↗
DOE OSTI2017

Recent Developments in the United Kingdom Geological Disposal Facility Programme - 17558

The United Kingdom Government remains committed to the policy of geological disposal. All major nuclear nations are actively pursuing geological disposal. It is internationally recognised that geological disposal represents the safest and most sustainable option as the end point of the management of high level waste and spent fuel considered as waste. Other countries that have also decided on a policy of geological disposal include Canada, Finland, France, Switzerland, Sweden and the United States of America. The United Kingdom Government continues to favour an approach to siting a Geological Disposal Facility that is based on the willingness of local communities to participate in the siting process. Construction and operation of a Geological Disposal Facility will be a multi-billion pound project that will provide skilled employment for hundreds of people over many decades. It will contribute greatly to the local economy and wider socio-economic framework. There are also likely to be spin-off industry benefits, infrastructure investments, benefits to local education or academic resources, and positive impacts on local service industries that support the facility and its workforce. It is also likely to involve major investments in local transport facilities and other infrastructure, which would remain after the facility had been closed. The United Kingdom Government published a renewed process for siting a Geological Disposal Facility in 2014 - the Implementing Geological Disposal White Paper. This paper discusses the status of activities in the 2014 White Paper that have been accomplished or are underway through to 2016. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Scaling High-Resolution Soil Organic Matter Composition to Improve Predictions of Potential Soil Respiration Across the Continental United States

Despite the importance of microbial soil organic matter (SOM) respiration in regulating the flux of carbon between soils and the atmosphere, soil carbon cycling models remain primarily based on climate and soil properties, leading to large uncertainty in predictions. To address this knowledge gap, we analyzed high-resolution water-extractable SOM profiles from soil cores collected across the United States by the 1,000 Soils Pilot of the Molecular Observation Network. Our innovation lies in using machine learning to distill thousands of SOM formula into tractable units; and it enables integrating data from molecular measurements into soil respiration models. In surface soils, SOM chemistry provided better estimates of potential soil respiration than soil physicochemistry, and using them combined yielded the best prediction. Overall, we identify specific subsets of organic molecules that may improve predictions of global soil respiration and create a strong basis for developing new representations in process-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structural and Electronic Complexities of a Sulfur‐Bridged Di‐Iron Complex Composed of Mono‐ and Di‐Nitrosyl Units

The delocalized, thermodynamically stable cation, [(N 2 S 2 )Fe(NO)•Fe(NO) 2 ] + , an adduct of mono-nitrosyl and dinitrosyl iron units, is analyzed to address the unusual stability of the sulfur-bridged diiron complex in its three overall redox levels, +, 0, and −. X-ray diffraction and myriad spectroscopic techniques probe products of sequential electron uptake in the corresponding neutral and anionic species. Conundrums include unified blueshifts of the overall 3-band, ν(NO), pattern with added electrons. One-electron reduction changes the anti-ferromagnetically coupled, S = 0, cationic diiron species to the neutral analog, S = ½, with unpaired spin mainly localized on the MNIU, which decreases its ∠Fe–N–O angle by 10 degrees in response to the extra electron density. Subsequent reduction to the anionic species, S = 1, involves a major geometric change at the MNIU, which moves the Fe in {Fe(NO)} 8 out of the N 2 S 2 plane. Site-specific 15 N labeling of nitrosyl in the MNIU confirms the IR analysis and shows rapid NO exchange between the MNIU/DNIU (mono-nitrosyl iron unit/dinitrosyl iron unit) pairs during its synthesis at RT. Mössbauer spectroscopy, S K-edge XAS, and molecular orbital calculations confirm the ability of NO and the versatility of sulfur bridges to buffer and distribute electrons, a key to their major importance in metalloenzymes.

DNIC↗
DOE OSTI2025

Systems Analysis and Optimization of Circular PET Packaging Supply Chains in the United States: Environmental and Socioeconomic Impacts

Many actions are underway at global, national, and local levels to address the plastic waste problem and transition toward a circular economy of plastics. Studies evaluating environmental and socioeconomic impacts of such a transition are lacking. Here, the purpose of this study is to conduct a national systems analysis of polyethylene terephthalate (PET) packaging supply chains in the United States. Material flow data was combined with environmental and socioeconomic indicators to evaluate and compare the sustainability of the linear PET packaging supply chain, current (2019) supply chain, and possible future circular supply chain options in the United States. Environmentally optimal circular US PET packaging material flows showed 31% and 38% savings of GHG emissions and energy demand, respectively, with a circularity of 77% when compared with a linear supply chain. Additionally, the environmentally optimal system showed higher employment (29%) and wages (31%) than a linear system, but with a 5% decrease in revenue generation. A socioeconomically optimal circular PET supply chain showed increased employment (by 52%), wages (by 67%), and revenues (by 1%), with a circularity of 59% when compared with the linear system. However, it showed 14% higher GHG emissions than a linear system, indicating a trade-off between environmentally and socioeconomically optimal circular PET packaging systems. Overall, linear-to-circular material flow transition may not necessarily lead to increased revenues and decreased environmental impacts of the entire system, but it does benefit society due to increased employment and wages. Future systems analysis work should focus on improving data quality for environmental and socioeconomic dimensions.

PET↗