Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Critical”

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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Critical Asset Management at the Start of the Pandemic

COVID-19 has changed the daily lives of so many people and forced many companies to examine their way of doing business, including their Asset Management programs. This virus has given way to an unprecedented set of challenges to continue conducting business as usual when everything is unusual. On June 16, 2020, members of the Northern California Chapter of the Association of Asset Management Professionals came together for a panel discussion on Critical Asset Management during the COVID-19 pandemic and beyond. Participants were representatives from health care, water, and national security, which are all considered essential industries.

Administration and management↗
DOE OSTI2021

Critical Literature Review of Quantitative Sustainability Assessment Methods for the Circular Economy

The circular economy (CE) has been proposed to be an operational framework for sustainable development that decouples economic growth from resource consumption. The CE ambition is to maximize the retention of value in products, materials, and resources in the economy over time with the help of CE strategies such as selling a service rather than a product, reusing and repairing products or their components, and recycling. The social, economic, and environmental performances of CE strategies need to be measured against their linear counterparts to avoid strategies that increase circularity but have other unintended externalities. However, there is currently no tool specifically designed to compare circular to linear systems and, thus, various methods from different fields have been applied. This session aims at reviewing, contrasting, and critiquing different methods that have been applied to assess the CE until now, along with an up to date state of the science in this field. Methods from the industrial ecology field have most often been applied to study the CE. However, the transition to CE involves both technological improvements as well as social changes. New business models such as collaborative consumption models (e.g., Uber or Airbnb) are examples of the new patterns of production and consumption of the CE, which may be difficult to analyze from a purely industrial ecology perspective. Methods from complexity science and humanities could therefore complement the industrial ecology perspective to extend the scope of the analysis. Such an approach could also answer a longstanding criticism of the CE which, in contrast with sustainability, solely focuses on the environment and the economy. Moreover, the hybridization of two or more existing methods can yield additional capabilities, which may enable the exploration of additional CE-related research questions. The 90 minutes session will have several presentations and conclude with a moderated, interactive panel discussion on how methods from different fields could be combined to harness their relative strengths.

circular economy↗
DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration: Preprint

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions, i.e., the reserve requirement, renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained off-line using historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine and battery. Case studies demonstrated that the proposed method outperforms other operating reserve determination methods.

distribution system↗
DOE OSTI2022

Y-12 Nuclear Criticality Safety Program Health Model: Development, Use and Benefits

The Y-12 National Security Complex (Y-12) operates seven diverse enriched uranium processing facilities and maintains a comprehensive nuclear criticality safety (NCS) program. Given the magnitude of the hazard, the NCS program receives significant oversight. Describing the scope and health of the Y-12 NCS program to sometimes non-expert stakeholders with limited time had historically focused on recent events and “hot topics” which did not always convey sufficient context (i.e., how to differentiate between an individual performance issue and a systemic concern). Y-12 created the NCS Program Health (NCSPH) Model to provide a complete and holistic framework to quantify and communicate NCS program health. The model is constructed in a tiered fashion with the top tier broken into three (3) Tier 2 elements, fourteen (14) Tier 3 elements, and 104 Tier 4 and 5 elements. This granularity ensures every feature of the NCS Program is accounted for and stakeholders can see how these features support the collective whole. The model is used as the framework for NCS communications including program plans, reports, health surveys, and meeting agendas. The model has substantially improved stakeholder appreciation for the entirety of the NCS program and how events and assessment results factor into an overarching conclusion on NCS program health. While some details are Y-12 specific, the NCSPH model can easily be tailored for any other site with an NCS program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗
DOE OSTI2022

Identification of Critical Material Attributes in Lignin Streams Based on Pseudomonas putida Performance

Variability in chemical composition of corn stover feedstocks has been suggested to play a role in biocatalyst performance during the conversion of lignocellulosic biomass to value-added compounds. In previous studies, we investigated the performance of Pseudomonas putida CJ781 - an engineered bacterium that converts aromatic compounds to muconate - on various lignin streams produced from the deacetylation of corn stover, and we found significant differences on muconate production. To better understand the origin of these differences, we down selected twenty-two compounds from literature - that had previously been detected as extractives in lignin streams - and we analyzed their concentrations in 12 lignin streams generated from different feedstocks. P. putida CJ781 was then cultivated in mock lignin streams supplemented with various concentrations of these compounds. Bacterial growth was tracked in microtiter plates and the performance was measured by comparing maximum growth rates among conditions. At maximum concentrations that may be found in lignin streams, sodium was the material attribute that limited growth rate in a greater extent, with a reduction in growth rate by 8 to 37%, depending on the counter ion, with sodium nitrate being the most inhibitory, followed by sodium acetate, sodium chloride, and sodium sulfate. Ammonium acetate was also found to be inhibitory at average concentrations of ammonium in lignin streams, reducing maximum growth rate by 18%. Overall, the data from this study has allowed to down select critical material attributes in lignin streams and process parameters that significantly affect the performance of P. putida and will be used as input to predictive models that aim to reduce risks attributed to feedstock variability in lignocellulosic biorefineries.

biocatalyst↗
DOE OSTI2021

Dynamic Simulation of a Sub-Critical Coal Fired Power Plant

In order to address the demanding operating conditions for remaining coal-fired power plants, a dynamic model and a suite of tools have been developed for studying load cycling and to find optimization opportunities. A sub-critical steam cycle power plant was modeled in a flow-sheet modeling tool, APROS™. The model represented the firing system, economizer, evaporator, superheat, and reheat systems. Four loads from 100% TMCR to 25% TMCR were calibrated and tested such that low-to-high cycling could be studied. The model was run through various load cycles; one of which is presented here. This modeling is a prototype for general use in developing cutting edge controls products and for maximizing economic, low-emissions, and efficient operation of the existing coal power fleet.

Braun, Timothy↗
DOE OSTI2023

Primal-Dual Differentiable Programming for Distribution System Critical Load Restoration: Preprint

Swift and reliable critical load restoration (CLR) can help make a distribution system resilient towards extreme events. To optimally achieve that, alongside practical concerns such as limiting online computational burden, some studies leverage model-free reinforcement learning (RL) to train control policies. Despite the advantages provided by RL algorithms, these approaches suffer from two issues: 1) the lack of a proper mechanism for constraint enforcement, and 2) poor sample efficiency. Therefore, in this paper, a primal-dual differentiable programming (PDDP) method is developed for guiding the training leading to a constraint-satisfying policy. Additionally, the model-based nature of the proposed method aims at improving sample efficiency. The experiment on a CLR problem demonstrates that PDDP can effectively train a control policy that both achieves desirable performance and satisfies required constraints.

differentiable programming↗
DOE OSTI2020

A Tool for the Risk-Informed Management of Critical Mission Resilience

We describe a methodology and tool for the risk-informed management and planning of mission resilience. By mapping concepts of resilience onto the elements of a streamlined risk model we are able to tap the substantial portfolio of established risk concepts to provide rapid insights in the evaluation and high-level screening of prospective resilience enhancement measures. This provides a risk-informed, levelized basis for the comparison of disparate resilience solutions and the means of establishing preferences. The methodology begins with identification of critical missions met by a site, and establishment of the supporting physical assets. Scenarios that would result in failure of these assets are systematically identified. Each scenario comprises three elements: realization of a hazard or threat resulting in loss of resources (the current focus being on power, natural gas, and water) to the asset, failure of measures in place to protect the asset against those losses, and realization of the consequent impacts. These scenarios are quantified in terms of their probabilities of occurrence and the magnitude of the resultant consequences (mission outage time), allowing risk-prioritization to focus resilience enhancement considerations. What-If? analyses are conducted through adjusting elements of the risk calculation to reflect the deployment of prospective resilience measures, by which means the efficacies of each of those measures can be compared using common risk metrics across diverse resilience strategies. This paper will also describe the insights from example applications.

resilience, risk, risk assesment, energy, water↗
DOE OSTI2023

Critical Minerals from Waste Streams in the Powder River Basin of Wyoming and Montana, USA

Background/Objectives. Critical minerals (CM) are essential for numerous industrial and defense applications, including green technologies that will help to meet carbon emission reduction goals. Many CM are currently mined and processed in countries that lack stringent environmental and labor regulations. Unconventional sources such as existing industrial waste streams could play a part in building an ethical CM supply chain. Additionally, waste streams created in the extraction and processing of CM could be used for other industrial or commercial purposes, thereby reducing waste and advancing a circular economy. Approach/Activities. The Department of Energy funded Powder River Basin (PRB) CORE-CM project is exploring all aspects of the carbon ore, rare earth element (REE), and CM value chain, including the potential for extraction of CM from industrial waste streams in the basin. Waste streams that have potential as CM feedstocks are being inventoried to assess the concentration of CM in each waste stream and to estimate volume and accessibility. Although basin-specific technologies for extraction of CM are still in development, waste streams that may be produced during these processes are being cataloged and mapped to potential secondary uses. Results/Lessons Learned. Initial studies of the CM potential of PRB coal ash show that this waste stream contains concentrations of greater than 300 ppm total REE (Bagdonas et al., 2022, Renewable and Sustainable Energy Reviews). Moreover, calcium-rich PRB coal ash is amenable to REE extraction (Taggart et al., 2016, Environmental Science and Technology). Wyoming coal is shipped to 28 states, meaning that coal ash produced at power stations across the US represents a potential widespread resource for the extraction of REE. In addition to coal mining and coal fired electricity generation, the PRB is home to other energy industries including oil and gas production, in-situ uranium mining, and bentonite mining. Historically, precious and base metal mining has taken place on the perimeter of the PRB. Waste streams from these industries are currently being evaluated for their CM potential. Assessing the CM resource potential of waste streams could contribute to the development of an ethical CM supply chain. Results from these studies can be replicated for other waste streams, increasing the likelihood of successful creation of circular economies.

Phillips, Erin↗
DOE OSTI2023

Building Environmental Justice and Equity into the Development of Critical Mineral Industries

The global transition toward low-carbon energy not only means an increase in demand for clean electricity and renewable resources, but also an increase in demand for the critical minerals (CMs) and rare earth elements (REEs) that these technologies rely upon. Indeed, low-carbon energy technologies, such as those used for wind turbines and electric vehicle motors, require significantly more lithium, nickel, cobalt, manganese, graphite, and other CMs than fossil-based energy technologies. The growing demand for new forms of low- carbon energy will necessitate a proportional scale-up of CM and REE extraction and processing. Presently, the majority of extraction and processing activities for CMs and REEs are concentrated in very few countries, primarily in China and the Global South. CM and REE supply chain activities conducted in or controlled by these countries are commonly associated with widespread and well- documented human rights abuses and environmental degradation. As a result of concerns related to supply chain security, worker welfare, and the overall need for additional sources of CMs and REEs, governments worldwide have begun to explore policy pathways for the development of new supply chains. The development of new supply chains for CMs and REEs re s a multifold opportunity to accelerate the global deployment of low-carbon energy fleets, reduce global carbon emissions, build political resilience—and also to enhance policy objectives of environmental justice and equity in the clean energy transition. Fulfilling these policy objectives requires energy producers to navigate a maze of global supply chain policies designed to shape and accelerate the growth of new markets. In the United States, for instance, President Biden recently implemented fiscal and trade policies that incentivize both domestic and global reliance on U.S.-produced CMs and REEs, leveraging key relationships in Asia and Europe to ensure the accelerated buildout of the U.S. supply chain. The Biden Administration’s framework also emphasizes that new supply chains for CMs, REEs, and other low-carbon energy pathways must generate benefits for marginalized and disadvantaged communities, build energy equity, and contribute to the Biden Administration’s vision of environmental justice. present CM and REE supply chains include several stages, including mining, processing, transport, utilization, and disposal, each of which involves different environmental justice considerations and potential injustices. This study explores how technical innovation, paired with responsible community engagement and empowerment, can help inform the development of energy equity and environmental justice at all stages of new supply chains. The study highlights these opportunities on a broad scale. We also lend a specific focus to research at the University of Wyoming School of Energy Resources that aims to guide the development of CM and REE industries in communities with high economic dependence on coal and other fossil fuel industries, identifying pathways to grow a U.S. domestic supply chain by producing CMs from coal, coal by-products, and coal waste streams. These production pathways represent a potential new supply for CMs across the U.S. and elsewhere, thereby enhancing national security and accelerating the widespread deployment of low-carbon energy technologies, while also generating alternative applications for remaining coal reserves and aiding in a just transition for rural energy-producing communities.

Gerace, Selena↗
DOE OSTI2023

CRITICAL MINERAL RESOURCE ASSESSMENT OF COALS FROM THE POWDER RIVER BASIN, WYOMING, USA

Coal and associated sediments are potential sources of Rare Earth Elements (REE) and other critical minerals necessary for many consumer and defense-related products and development of energy technologies. For this study, 15 samples were sourced from Dry Fork Mine within the Powder River Basin near Gillette, Wyoming, to determine REE content along with select transition metals (t-metals) of interest in non-combusted coals and associated sediments. An excavator collected samples along a mine highwall, within benched intervals representing a foot to a few feet of coal strata. Homogenized sample subsets were crushed in an aluminum-ceramic vial, then passed through a 106-micron sieve. Sieved samples were pyrolyzed by a LECO TGA-108, which concurrently measured sample moisture, volatile, and ash content. Residual ash was then fused into a glass bead with lithium metaborate at a 6:1 Li-metaborate to sample ratio and then dissolved in 20% nitric acid. The nitric acid solutions containing digested beads were then diluted and passed through a 0.45 micron syringe filter. Filtered samples were analyzed by ICP-MS for REE and select t-metals. Reagent blanks were analyzed to assess for any potential contamination. Total REE plus Y (REE+Y) concentrations across the 15 samples range from 52 ppm to 1015 ppm with an average of 477 ppm on a dry ash basis. When normalized to upper continental crust (UCC; Taylor and McLennan, 1985) values, 13 of the 15 samples show average enrichment factors (EF) for REE+Y greater than 1. EF values indicate M-type enrichment (Sm through Er), as opposed to distinct preferential enrichment of light or heavy REE. T-metals with the highest average concentrations are V, Cu, Ni, and Zr at 215, 243, 123, and 118 ppm, respectively. Multiple t-metals had EF>1; however, Cu, Ge, and Sb have the highest average EF relative to UCC for t-metals, at 9.7, 8.0, and 17.0, respectively.

Copeland, Grant↗