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At least 379 records · Page 21

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DOE OSTI2020

Sources of Conservatism in Making Corrective Action Decisions at the NNSS - 20477

During the course of closing nuclear testing release sites at the Nevada National Security Site (NNSS) under the Federal Facility Agreement and Consent Order (FFACO) using the Risk-Based Corrective Action (RBCA) evaluation process, conservative assumptions and estimates were used in many phases of the work. These assumptions and estimates were agreed to during Data Quality Objective (DQO) meetings and published in the FFACO documents. However, the potential cumulative effect of these multiple sources of conservatism was not evaluated. The RBCA process ensures that corrective action decisions minimize the risk of making Type I errors (not applying corrective action to areas that exceed action levels) at the expense of making Type II errors (applying corrective action to areas that do not exceed action levels). Rather than fully characterizing the spatial distribution of contaminants throughout each site, the RBCA process uses a bounding methodology that conservatively estimates spatial boundaries beyond which contamination is not present that exceed corrective action levels. This process accepts that these boundaries may encompass areas that do not exceed corrective action levels and, therefore, may be somewhat larger than for fully characterized sites. This is justified at sites where the reduction in investigation costs are more than any potential increase in corrective action costs. To minimize the risk of making Type I errors, conservatism was used throughout the process. This study documents the many sources of conservatism used in the RBCA process. Conservatism was used in the RBCA process in determining potential receptors, determining action levels, collecting samples, converting sample results into receptor dose, and accounting for uncertainty. This provides decision-makers with a high level of confidence that unacceptable risk to receptors will not result from exposure to residual contamination outside the corrective action boundaries. However, it does not provide an estimate of the actual extent of areas exceeding action levels. This study provides an evaluation of the effects of compounding conservatism on RBCA process decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Hyperplane decision trees as piecewise linear surrogate models for chemical process design

Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.

Decision trees↗

DOE OSTI2023

A decision support tool for e-waste recycling operations using the hen-and-chicks bio-inspired optimization metaheuristic

E-waste from end-of-life electrical and electronic devices is one of the fastest growing waste streams from households and businesses. E-waste recycling yields environmental sustainability and economic benefits. Due to continuous changes in e-waste types and compositions, recycling businesses face challenges to optimize their operational configuration to achieve better economic and environmental performance. To help e-waste recyclers mitigate this problem, we have developed a modular decision support tool called the Comprehensive Manufacturing Assessment Tool (CMAT) that can simulate both e-waste recycling operations and economics. This tool can give valuable insights regarding the profitability of the entire operation and different e-waste types. In addition, a new bio-inspired metaheuristic optimization algorithm, hen-and-chicks optimization (HACO), was developed to assign manpower to different workstations to maximize operational efficiencies. According to the results of our case study, laptops, desktops, and computer peripherals are the three electronic waste products that produce the most profit. Our examination of the sensitivity of material prices shows that the price of steel has the most significant influence on total profit, because it is the most widely used material in the majority of electronic devices. We have released the decision support tool as open-source software under a general public license. It could be customized for other recycling industries beyond e-waste to achieve business sustainability by making their operations more efficient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Ecological connectivity and in-kind mitigation in a regulatory decision framework: A case study with an amphibian habitat specialist

Ecological connectivity is critical to the survival and long-term viability of populations but is often overlooked in regulatory frameworks. We integrated landscape-level processes into a mitigation strategy for impacts to aquatic resources on the U.S. Department of Energy (DOE) Oak Ridge Reservation (ORR) in eastern Tennessee. Wetlands on the ORR, which contain significant breeding populations of the imperiled four-toed salamander (Hemidactylium scutatum) and tubercled rein orchid (Platanthera flava var. herbiola), will be impacted by construction of an environmental waste disposal facility under the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA). Here, we used a modified Kepner-Tregoe decision analysis to select general mitigation options that balanced regulatory requirements and interest group perspectives. We emphasized habitat connectivity through models that prioritized an area's importance to natural area connectivity (centrality) and maintenance of population structure for an affected habitat specialist (four-toed salamanders). We also emphasized in-kind mitigation through the preservation and enhancement of ecologically similar resources and the translocation and establishment of a new subpopulation of four-toed salamanders elsewhere on the ORR. We ultimately released over 500 juvenile salamanders that originated from the impacted site into the chosen mitigation wetlands. By doing so under the constraints of a time-sensitive CERCLA remediation effort and exceeding its substantive requirements, this work underscores feasibility. Ecological connectivity and the conservation of species that are not afforded explicit regulatory processes can be effectively and efficiently integrated into environmental decision-making and land use planning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems

Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Risk

The interaction between power systems and wildfires can be dangerous and costly. Distribution grids can be liable for the outbreak of wildfires during extreme weather. In wildfire-prone areas, investment planning should consider the impact of operational actions on wildfire-related uncertainties affecting line failure likelihood. Here, in this case, endogenous-based uncertainty modeling should comprise the backbone of the investment planning model viz-a-viz the inability of standard exogenous-based uncertainty modeling. Therefore, we propose a decision-dependent uncertainty (DDU) aware methodology to optimize investment portfolios for distribution systems, considering that high power-flow levels in high-threat areas can ignite wildfires and increase line failure probability. The methodology identifies the best combination of upgrades (new lines, hardening existing lines, and placing switching devices). Methodologically, we propose a two-stage distributionally robust planning optimization problem with DDU that considers the distribution system's multiperiod operation. The first stage determines optimal switching actions and line investments, and the second stage evaluates the worst-case expected operational cost under a DDU framework designed to account for the endogenous impact of power-flow levels and hardening investment decisions in the line failure probabilities. An iterative method is tailored to handle the problem and numerical experiments demonstrate a more prepared grid to deal with wildfire risk.

Power systems investment planning↗

DOE OSTI2021

A Rapid Decision Support Tool for Estimating Impacts of a Vadose Zone Volatile Organic Compound Source on Groundwater and Soil Gas

Diminishing rates of subsurface volatile contaminant removal by soil vapor extraction (SVE) oftentimes warrants an in‐depth performance assessment to guide remedy decision‐making processes. Such a performance assessment must include quantitative approaches to better understand the impact of remaining vadose zone contamination on soil gas and groundwater concentrations. The spreadsheet‐based Soil Vapor Extraction Endstate Tool (SVEET) software functionality has recently been expanded to facilitate quantitative performance assessments. The updated version, referred to as SVEET2, includes expansion of the input parameter ranges for describing a site (site geometry, source characteristics, etc.), an expanded list of contaminants, and incorporation of elements of the Vapor Intrusion Estimation Tool for Unsaturated‐zone Sources software to provide soil gas concentration estimates for use in vapor intrusion evaluation. As part of the update, SVEET2 was used to estimate the impact of a tetrachloroethene (PCE) vadose zone source on groundwater concentrations, comparing SVEET2 results to field‐observed values at an undisclosed site where SVE was recently terminated. PCE concentrations from three separate monitoring wells were estimated by SVEET2 to be within the range of 6.0–6.7 μg/L, as compared to actual field concentrations that ranged from 3 to 11 μg/L PCE. These data demonstrate that SVEET2 can rapidly provide representative quantitative estimates of impacts from a vadose zone contaminant source at field sites. Finally, in the context of the SVE performance assessment, such quantitative estimates provide a basis to support remedial and/or regulatory decisions regarding the continued need for vadose zone volatile organic compound remediation or technical justification for SVE termination, which can significantly reduce the cost to complete for a site.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A behavior and physiology-based decision support tool to predict thermal comfort and stress in non-pregnant, mid-gestation, and late-gestation sows

Background: Although thermal indices have been proposed for swine, none to our knowledge differentiate by reproductive stage or predict thermal comfort using behavioral and physiological data. The study objective was to develop a behavior and physiology-based decision support tool to predict thermal comfort and stress in multiparous (3.28 ± 0.81) non-pregnant (n = 11), mid-gestation (n = 13), and late-gestation (n = 12) sows. Results: Regression analyses were performed using PROC MIXED in SAS 9.4 to determine the optimal environmental indicator [dry bulb temperature (T DB ) and dew point] of heat stress (HS) in non-pregnant, mid-gestation, and late-gestation sows with respiration rate (RR) and body temperature (T B ) successively used as the dependent variable in a cubic function. A linear relationship was observed for skin temperature (T S ) indicating that TDB rather than the sow HS response impacted TS and so TS was excluded from further analyses. Reproductive stage was significant for all analyses (P < 0.05). Heat stress thresholds for each reproductive stage were calculated using the inflections points of RR for mild HS and T B for moderate and severe HS. Mild HS inflection points differed for non-pregnant, mid-gestation, and late gestation sows and occurred at 25.5, 25.1, and 24.0 °C, respectively. Moderate HS inflection points differed for non-pregnant, mid-gestation, and late gestation sows and occurred at 28.1, 27.8, and 25.5 °C, respectively. Severe HS inflection points were similar for non-pregnant and mid-gestation sows (32.9 °C) but differed for late-gestation sows (30.8 °C). These data were integrated with previously collected behavioral thermal preference data to estimate the T DB that non-pregnant, mid-gestation, and late-gestation sows found to be cool (T DB < T DB preference range), comfortable (T DB = T DB preference range), and warm (T DB preference range < T DB < mild HS). Conclusions: The results of this study provide valuable information about thermal comfort and thermal stress thresholds in sows at three reproductive stages. The development of a behavior and physiology-based decision support tool to predict thermal comfort and stress in non-pregnant, mid-gestation, and late-gestation sows is expected to provide swine producers with a more accurate means of managing sow environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2024 Annual Workshop Presentation

This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

REopt: Energy Decision Analysis

REopt transforms complex energy project decisions into actionable results for building owners, utilities, and industry. Based on decades of NREL decision-support expertise, REopt guides investment in economic, resilient energy technologies. This presentation provides an overview of how REopt can help building owners, utilities, developers, and industry optimize the economic and resilience benefits of distributed energy resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

USAID Energy Storage Decision Guide for Policymakers

The purpose of this report is to arm relevant decision makers with the initial layer of information they need to understand energy storage and to make informed policy, regulatory, and investment decisions around grid-connected energy storage. While many of the case studies presented in this report are based on experiences from the U.S. and Europe, the lessons learned can be applied to power sectors in emerging economies. Importantly, this report covers topics related grid-connected energy storage for power sector applications. The term “grid-connected” implies that the storage system is interconnected to a centralized power system. Topics related to off-grid, micro-grid and mini-grid energy storage applications are not covered in this report, nor are procurement practices for energy storage.

25 ENERGY STORAGE↗

DOE OSTI2022

REopt: Energy Decision Analysis [Slides]

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

SPECs Early-Stage Decision Model: User Manual

The SPECs Early-Stage Decision (ESD) model is central to the SPECs procurement solutions toolkit. The ESD model is an Excel-based spreadsheet model, which provides information about the economic and strategic value of a proposed battery-storage project or solar-plus-storage (solar-plus) project. The model can be used to explore combinations of storage-related project value streams in order to define a potential project, while educating co-op decision-makers about project benefits and costs. A sensitivity analysis function speeds the development of "what-if" scenarios. A gap analysis function solves for top-priority metrics and supports the inclusion of hard-to-monetize strategic values, such as the value of storage to defer costly system upgrades in light of increasing distributed solar and other distributed energy resources (DERs). Model outputs include the utility data, assumptions, and use-case scenarios that are recommended content for the requests for proposals (RFPs). The model may also provide an initial "sanity check" for RFP responses, supporting further discussions among utility staff, vendors and stakeholders. The ESD is not a "finance-grade" modeling tool, and users are cautioned to be mindful of its limitations, but the model has been reviewed by users, who recommend it as a way to drive faster and better project design and planning, as well as to facilitate better communications with vendors, grid partners, and stakeholders.

14 SOLAR ENERGY↗

DOE OSTI2022

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models

We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $\$$653 and $\$$685, respectively, at a willingness-to-pay (WTP) threshold of $\$$66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $\$$809 at the same WTP threshold. Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Visualization and Decision Making Design Under Uncertainty

This report documents the program and the outcomes of Dagstuhl Seminar 22331 "Visualization and Decision Making Design Under Uncertainty". The seminar brought together 33 researchers and practitioners from different domains concerned with visualization and decision making under uncertainty including visualization, visual analytics, human-computer interaction, artificial intelligence, climate research, geography and geology. The programme was organized in two parts: In the first part which lasted two days, participants gave short talks where they discussed current practices and the uncertainty visualization challenges they encountered in their own research. At the end of day two, participants brainstormed collectively around the main uncertainty visualization research challenges across domains and applications. In the second part, participants voted for the following three main challenges they wished to discuss for the remainder of the seminar (one and a half days): applications, human-centered uncertainty visualization, a design process for uncertainty visualization. Thus three break-out groups were formed to discuss these challenges. Abstracts for the individual talks and the break-out group activities are included in this report.

decision making↗