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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition

Neodymium-iron-boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.

42 ENGINEERING↗
DOE OSTI2020

RELOG

RELOG is a supply chain optimization package focusing on reverse logistics and reverse manufacturing. For example,the package can be used to determine where to build recycling plants, what sizes should they have and which customers should be served by which plants. The package supports customized reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods.

ILOEJE, CHUKWUNWIKE↗
DOE OSTI2022

Self Configuring Digital Twin for Optimizing E-Waste Recycling

Electronic waste recycling industry needs a decision support tool for optimizing their processes to become cost competitive. We developed a software called CMAT, Comprehensive Manufacturing Assessment Tool. The aim of CMAT is to provide the e-waste recycling companies with a fully customizable decision support framework that analyzes the optimal supply chain configurations. The software optimizes the logistics operations, helps to identify the best recycling process configuration, and generates valuable insights regarding the economic performance of different categories of e-waste. The ultimate purpose of the tool is to assist the users developing a digital twin of their processes and to provide insights on questions pertinent to the e-waste recycling industry including how to increase efficiency and reduce costs, energy consumption, and greenhouse gas emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Improved Advanced Biomass Logistics Utilizing Woody and other Feedstocks in the Northeast and Pacific Northwest (Final Report)

Willow and poplar short rotation woody crops (SRWC) have shown promise with regards to environmental benefits and rural development but wide adoption lags due to underdeveloped markets and supply systems. High costs associated with harvesting, handling and transportation (40-60% of delivered cost) have impeded expansion. A better understanding of these systems will create opportunities to improve efficiency, reduce costs, and realize environmental benefits and impacts. The project’s goal was to lower the delivered cost of hybrid poplar in the Northwest and willow in the Northeast by optimizing harvesting and logistics supply systems while maintaining or improving biomass quality along the supply chain. Over 3,400 Mg of biomass and 300 ha of willow and poplar were monitored over a range of crop and field conditions. Feedstock quality as affected by storage and preprocessing were shown to improve or maintain feedstock quality. Modeled harvesting costs ranged from $38–61 Mg -1 dry; when including delivery and preprocessing feedstock costs ranged between $79-83 Mg -1 dry for willow and $106-116 Mg -1 dry for poplar. Costs for willow minimized when hot water extraction and high-moisture densification preprocessing were used. Models also suggest that social and regional factors could further reduce costs. Results will give guidance to feedstock growers, harvesting and logistic operations, biorefinery project developers, and policy makers developing SRWC to support a growing bioeconomy.

, biomass↗
DOE OSTI2024

Next-Generation Reverse Logistics Networks of Photovoltaic Recycling: Perspectives and Challenges

With the growing adoption of solar energy as a key component of the global energy transition and its new industrial policy (the Inflation Reduction Act and others), the United States is witnessing a significant increase in solar investments. This surge in installations and domestic and reshored manufacturing of solar photovoltaic (PV) panels brings with it a pressing issue: the proper management of end-of-life (EoL) PV panels. As these panels are decommissioned, either due to reaching the end of their lifespans or due to breakage across the various stages of the forward supply chain, it becomes crucial to establish efficient reverse supply chain logistics systems to address the challenges associated with their disposal, while also unlocking the value of the inherent materials that are of critical value for other forward supply chains. This perspective article examines the challenges regarding EoL PV panels and relevant reverse supply chain and logistics networks, and proposes future research directions based on the gaps observed among academic research, industry, and policy-making challenges. We identify the main bottlenecks and hurdles including, among others, the lack of supportive regulations and absence of structured, optimized recycling infrastructure. To this end, it is proposed that the key to achieving a sustainable reverse supply chain network for solar PV panels lies in relentless end-to-end supply chain cost optimization efforts supported by enabling policies. Moreover, it is proposed that designing systematic decision-making modeling frameworks is vital for examining different possibilities and scenarios for state, region or nation-wide optimization of solar PV reverse supply chain networks. Indicative to this effect, we discuss the development of a Resource-Task-Network (RTN)-based model and demonstrate its application and benefits through a case study. We wrap up with conclusions and future research directions.

circular economy↗
DOE OSTI2020

Optimization of harvest and logistics for multiple lignocellulosic biomass feedstocks in the northeastern United States

A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $\$67.90$ to $\$86.97$ per dry Mg with an average of $\$79.58$ /dry Mg. The biomass delivered costs by county were from $\$67.90$ to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $\$85.30$ /dry Mg for forest residues, $\$84.47$ /dry Mg for hybrid willow, $\$99.68$ for switchgrass and $\$97.87$ per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Lastly, our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

09 BIOMASS FUELS↗
DOE OSTI2026

Biofuel Infrastructure, Logistics, and Transportation (BILT) Model for Supply Chain Analysis: Documentation for BILT

The Biofuel Infrastructure, Logistics, and Transportation (BILT) model for supply chain analysis developed by Oak Ridge National Laboratory for the US Department of Energy is an accounting and optimization model that tracks the entire biomass-to-end-use supply chain (e.g., biomass production, transportation, and processing) for potential feedstocks, pathways, and end uses. This report summarizes the development, function, and operation of the BILT model.

09 BIOMASS FUELS↗
DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2024

Large-scale spatially explicit analysis of carbon capture at cellulosic biorefineries

The large-scale production of cellulosic biofuels would involve spatially distributed systems including biomass fields, logistics networks and biorefineries. Better understanding of the interactions between landscape-related decisions and the design of biorefineries with carbon capture and storage (CCS) in a supply chain context is needed to enable efficient systems. Here we analyse the cost and greenhouse gas mitigation potential for cellulosic biofuel supply chains in the US Midwest using realistic spatially explicit land availability and crop productivity data and consider fuel conversion technologies with detailed CCS design for their associated CO 2 streams. Optimization methods identify trade-offs and design strategies leading to systems with attractive environmental and economic performance. Strategic and operational decisions depend on underlying spatial features and are sensitive to biofuel demand and CCS incentives. US CCS incentives neglect to motivate greenhouse gas mitigation from all supply chain emission sources, which leverage spatial interactions between CCS, electricity prices and the biomass landscape.

09 BIOMASS FUELS↗
DOE OSTI2026

Biorefinery siting and sizing to achieve the US Billion‐Ton Bioeconomy vision: A case study using a gasification–Fischer–Tropsch process

Achieving a secure, abundant, and affordable energy future requires a robust and adaptable energy strategy, with bioenergy playing a pivotal role. Biomass-based energy presents a promising pathway to use domestic resources while fostering economic opportunities in rural areas. Despite the potential to source more than 1 billion dry short tons of biomass annually in the US, significant infrastructure and economic barriers hinder full utilization for energy production. This study used the Biofuel Infrastructure, Logistics, and Transportation (BILT) model to assess biorefinery siting and scale and determine the number and size of facilities required to maximize use of the US biomass potential. A spatially agnostic approach first assessed the effects of facility capacity and transportation constraints on biomass use. Then, a spatially explicit analysis integrated county-level biomass availability from the US Department of Energy's 2023 Billion-Ton Report and technoeconomic assessments to evaluate different biorefinery deployment scenarios. The results indicate that an optimized mix of facility sizes is essential to leverage biomass resources fully across varying regional production densities to maximize use of the US biomass potential. Larger biorefineries or co-located smaller facilities significantly enhance biomass use while reducing costs through economies of scale. These findings underscore the importance of strategically balancing facility capacity and spatial distribution to optimize the bioenergy supply chain. In conclusion, this study provides critical insights for advancing the US bioenergy economy by aligning biorefinery deployment with biomass resource availability and economic viability.

BILT Model↗
DOE OSTI2021

Next Generation Logistics Systems for Delivering Optimal Biomass Feedstocks to Biorefining Industries in the Southeastern U.S.

The diverse portfolio of biomass sources that is available in the Southeastern U.S., including a significant supply of pine “residue”, represents a valuable strategic position for the region. Through blends formulated based on critical properties, this project will take full advantage of the range in biomass properties afforded by the portfolio to produce a consistent, high-performance feedstock for the industry, while lowering cost. Key developments being targeted to enable this potential include whole-tree transport to a state-of-the-art merchandising depot that will further access biomass from ongoing, forest industry operations. The approach will more effectively utilize the tree and distribute cost, while minimizing in-woods contamination of the woody biomass component. To implement this vision, information on the chemical composition and changes that are induced during multiple preprocessing steps (size reduction, moisture removal, densification, etc.) is needed. New NIR sensor technology will be developed for online monitoring of important biomass properties. The data will be incorporated into a statistical process control platform to improve process efficiency and meet required specifications. Advanced process models are being developed to inform the techno-economic and life-cycle assessment of the program’s impact. The new system will ultimately reduce operational risks from supply chain disruptions, and allow operation of larger-scale biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2026

Spatial Optimization of Multiscale Biorefinery Deployment for a Diversified Bioeconomy in the United States

Strategic biorefinery siting is critical for a diversified bioeconomy, yet industry, policy, and research often focus on either large-scale biofuel plants or smaller-scale specialty bioproduct facilities, with limited coordination across scales. We address this gap by modeling biorefinery deployment spanning a 28-fold difference in capacity. We developed an open-source, spatially explicit framework integrating techno-economic analysis with logistics and refinery cost surrogate models to evaluate multiscale miscanthus-derived biorefineries across the rainfed U.S. for the production of ethanol, succinic acid, lactic acid, potassium sorbate, and acrylic acid. Overall costs change little as feedstock density increases, while transport distances decrease by ∼30 to 67% (∼100 km) and siting flexibility improves. Specifically, a 5-fold feedstock density increase (2% to 10% of suitable land) reduces minimum selling prices by <10% (e.g., 0.27 USD·gal –1 for ethanol). This limited economic sensitivity suggests dense planting is not required for competitive deployment, particularly for smaller-scale facilities. Representing collection areas as irregular rather than circular expands the feasible space under low-density scenarios. While large-scale refineries anchor regional supply chains, smaller facilities retain spatial flexibility even when large refineries are established. These findings highlight the importance of spatial representation and multiscale coordination for robust, regionally tailored biomanufacturing networks to advance renewable carbon integration without extensive land conversion.

biorefinery siting↗
DOE OSTI2022

Coffee supply chain planning under climate change

The growing demand but uncertain supply makes the sustainability of the coffee industry a shared concern for all participants along the coffee supply chain. This study proposed a decision-making model that comprises the cultivation management, including shade management and annual agriculture management, and the supply chain logistics. A two-stage stochastic program is presented and used within a rolling horizon scheme that periodically updates input data information to deal with uncertainty associated with future climate scenarios. The program minimizes the total expected cost of the entire supply chain of arabica coffee. The study applied the model to the real case study of arabica coffee bean supply to the U.S. market, trying to answer whether arabica coffee supply can meet the U.S. demand from 2022 to 2050 and how to best mitigate any shortage through corporate-farmer partnerships. The results show that the coffee supply will have a 3% shortage in the future; medium-level shade management and more irrigation and fertilization are possible mitigation strategies. These results demonstrate the need for all participants to adopt suitable technologies for the sustainability of global coffee supply chains together.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nth-plant scenario for blended pellets of Miscanthus, Switchgrass, and Corn Stover using multi-modal transportation: Biorefineries and depots in the contiguous U.S.

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Here, this study addresses the logistic challenge of mobilizing national cellulosic feedstock supplies for a sustainable bioenergy industry. A Mixed Integer Linear Programming (MILP) model was developed and updated to deliver on-spec biomass that considers both a desired quantity and quality at the biorefinery. Our supply chain analysis includes multi-modal transport (truck and rail), varying depot and biorefinery sizes, and feedstock blends of corn stover (harvested by either a two- or three-pass method), switchgrass, and miscanthus. The following US states: Illinois, Kansas, Missouri, North Carolina, Oklahoma, Georgia, and Texas were identified as key locations for producing accessible miscanthus. Based on our most optimistic scenario, using trucks as the only transportation mode in 2040 with a cost target of $\$$79/dt, corn stover, switchgrass, and miscanthus could help meet 48% of the EPA target, 173 million dry tons that translate into 7.8 billion GGE. The addition of rail transportation for biomass delivery to biorefineries could help meet 79% of the EPA target, 283 million dry tons that translate into 12.7 billion GGE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Available land for cellulosic biofuel production: a supply chain centered comparison

The land that is potentially available to produce dedicated cellulosic bioenergy crops, often referred to as 'marginal' land, depends heavily on the underlying assumptions used to classify and identify it. In this study we compare three definitions and types of marginal land to identify the interactions between the bioenergy landscape and the logistics networks needed for the biofuel supply chain. Typical studies of the scale, cost, and greenhouse gas (GHG) mitigation potential of cellulosic biofuel take a land-centered approach which may neglect to account for the trade-offs between establishing bioenergy crops and the supply chain design decisions needed to allow those crops to be converted to liquid fuel. A mathematical programming approach is used to minimize the total annualized cost of a large-scale field-to-product system producing bioethanol in the USA midwest. Results show that a high concentration of marginal land leads to efficient systems and that the bioenergy landscape design becomes increasingly important with a higher emphasis on GHG mitigation. Additionally, targeted landscape design (including fertilization) with a focus on fields with high soil carbon sequestration potential can greatly reduce the system-wide GHG emissions for only a small increase in the unit cost of biofuel.

09 BIOMASS FUELS↗