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DOE OSTI2020

Optimal Efficiency and Operational Cost Savings: A Framework for Automated Rooftop PV Placement

Residential energy consumers are charged based on a utility rate structure, such as net metering or feed-in tariff. To lower consumers' electricity bills, expensive batteries are deployed to reduce the electricity fed from the grid during peak hours. However, strategic photovoltaic (PV) panel placement enables the reduction of operational energy cost while considering the spatial feasibility and efficiency for hosting rooftop PV. In this paper, we present a framework to automatically identify the optimal location of rooftop PV panels on residential buildings. Our framework integrates multiple workflows, including energy and environmental simulation, parametric modeling, and optimization to identify the ideal location of PV panels to balance the demand and supply at a building and community scale. These workflows are linked using the Grasshopper plug-in for Rhinoceros CAD software. The framework includes two different workflows, each satisfying a target for optimal PV placement: (a) maximizing PV panel efficiency, where users aim to maximize energy generation, and (b) minimizing operational energy cost, where "best'' panels are selected considering utility rates for operational energy cost. Our framework is demonstrated in a residential community in Fort Collins, Colorado, to generate the optimal PV placement for each of the two aforementioned targets. Results from the two workflows are compared to illustrate the effect of PV location and orientation on solar energy production efficiency and operational energy cost. The developed workflows are introduced as tools within the Grasshopper plug-in to investigate the solar potential of rooftop PV panels while taking into account factors such as contextual shading, utility rate structures, and buildings' energy demand profiles.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

National Emission Standards for Hazardous Air Pollutants – Radionuclide Emissions Calendar Year 2020

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) operates the Nevada National Security Site (NNSS) and the North Las Vegas Facility (NLVF). From 1951 through 1992, the NNSS was the continental testing location for U.S. nuclear weapons. Radionuclides in air from NNSS activities have been monitored since the initiation of atmospheric testing. After 1962, testing was limited to underground detonations, which greatly reduced radiation exposure to the public. Since the end of nuclear testing in 1992, radiation monitoring has focused on detecting airborne radionuclides from historically contaminated soils because this sources dominates the potential offsite dose. These radionuclides are derived from re-suspension of soil (primarily by wind) and emission of tritium-contaminated soil moisture through evapotranspiration. Low amounts of legacy-related tritium are also emitted to air at the NLVF, an NNSS support complex in North Las Vegas. To protect the public from harmful levels of manmade radiation, the Clean Air Act, National Emission Standards for Hazardous Air Pollutants (NESHAP), specifically the National Emission Standards for Emissions of Radionuclides Other Than Radon From Department of Energy Facilities (40 CFR 61, Subpart H, 2020) limits the release of radioactivity from a DOE facility to that which would cause 10 millirem per year (mrem/y) effective dose equivalent (EDE) to any member of the public. This limit does not include radiation unrelated to NNSS activities. Unrelated doses could come from naturally occurring radioactive elements, from sources such as medically or commercially used radionuclides, or from sources outside of the United States, such as Japan’s Fukushima nuclear power plant, which was damaged in 2011. NNSA/NFO demonstrates compliance with the NESHAP limit by reporting environmental measurements of radionuclide air concentrations at critical receptor locations on the NNSS. This alternative was proposed and formerly submitted to the U.S. Environmental Protection Agency (EPA) in 2001 (EPA 2001a) and has been the method used to demonstrate compliance with the 40 CFR 61.92 dose standard since 2005. Six locations on the NNSS have been established to act as critical receptor locations to demonstrate compliance with the NESHAP limit. These locations are closer to radionuclide releases than where the public resides so they act as protective substitutes for public receptor locations. Compliance is demonstrated if the measured annual average concentration is less than the NESHAP Concentration Level (CL) for Environmental Compliance listed in Table 2 of 40 CFR 61, Appendix E. For multiple radionuclides, compliance is demonstrated when the sum of the fractions (determined by dividing each radionuclide’s concentration by its CL and then adding the fractions together) is less than 1.0. The EPAapproved air transport model, called the Clean Air Package 1988 (CAP88-PC) is also used to calculate the effective dose equivalent to the maximally exposed individual from NNSS air emissions. CAP88-PC was also used to calculate the population dose, or the collective EDE (expressed as person-rem [roentgen equivalent man] per year [person-rem/y]) for all individuals combined who reside within 80 kilometers (km) of NNSS emission sources. In 2020, the potential dose from radiological emissions to air from both current and past NNSS activities was well below the 10 mrem/y dose limit. This is demonstrated by both the air sampling data collected at critical receptor air monitoring stations and CAP88-PC modeling. The average concentrations of radioactivity at air critical receptor stations ranged from 0.2% to a maximum of 4.2% of the allowed NESHAP limit. CAP88-PC modeling of all 2020 NNSS radionuclide emissions showed the maximally exposed individual to be in Amargosa Valley and this individual received a potential dose of 0.063 mrem/y. The collective dose was calculated to be 0.29 person-rem/year for the 521,300 people who lived within 80 km of NNSS emission sources.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

A Robust Approach to Sequential Information Theoretic Planning

In many sequential planning applications a natural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Through the use of robust M-estimators we obtain bounds on absolute deviation of estimated MI. Moreover, we propose a sequential algorithm which integrates inference and planning by maximally reusing particles in each stage. We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the benefits of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Here, our method shows improvements over a recent method which selects intervention experiments based on the same MI objective.

Zheng, Sue↗

DOE OSTI2021

Coupled Microbial-Conversion and Computational-Fluid-Dynamics (CFD) Models for Butanediol Production in Micro-Aerated Reactors

Microbial conversion of substrates to macromolecules has been widely used in the synthesis of value-added products in pharmaceutical and biotechnology industries. These bioreactions are also being investigated in the production of low-value commodities such as biofuels [1]. Gas-liquid mass-transfer and transport-reaction coupling are important challenges when designing and scaling up these reactor systems. Experiments in wellmixed small-scale reactors have enabled characterization of microbial reactivity, while their coupling with macroscale transport remains relatively unexplored. In this work, we use a coupled metabolic-CFD model to study the action of a genetically engineered microbe Zymomonas mobilis [2] on sugars to produce 2,3-Butanediol (BDO). BDO is an important hydrocarbon intermediate that can be catalytically upgraded to several fuels and chemicals [3]. An important aspect to this particular microbial conversion is the need for micro-aerated environments as opposed to traditional aerobic fermentation. Slight variations in oxygen concentration can result in competing reaction pathways that disable BDO production. Hence, gas-liquid mass transfer and transport need to be optimized in large-scale reactors to maximize BDO production, for which CFD is a valuable tool. The aerobic-fermentation CFD model previously developed by the authors [5] for simulating bubble-column and airlift reactors at scale was used in this study. The Reynolds-averaged mass, momentum and energy transport equations for interpenetrating gas and liquid phase are solved in this model along with the transport and interphase mass transfer of oxygen. Our previous work used a phenomenological model for microbial oxygen uptake that neglected microbial growth and other reaction pathways. In this work, a detailed metabolic model enabled prediction of product formation and inhibition pathways. In order to manage computational cost, we used a subcycling technique [6] that takes advantage of the clear separation in transport (~ 200 sec) and reaction (~ 2-3 hours) timescales. The CFD model is first solved to steady state, after which the metabolic model is advanced at every cell in the computational domain using the local oxygen concentration. The CFD model is then run to achieve a new steady state that provides a new oxygen distribution for the metabolic model. This process, where reaction and fluid updates are interleaved together, is iterated until reactants are completely exhausted. This work will examine the performance of different reactor designs such as bubble column and airlift reactors at scale (250-500 m3). Oxygen mass-transfer coefficient and distribution are critically analyzed among reactors, and optimization studies pertaining to aeration is presented. Furthermore, it has been observed in experiments that high BDO production may be achieved by manipulating the aerobic environment over the course of reaction, such that oxygen concentration is high during the growth phase, and very low as sugar is depleted. This characteristic will be addressed by our simulations for which a timedependent scheduling strategy for aeration is presented that maximizes BDO production. [1] Humbird, D., Davis, R., and McMillan, J., Aeration costs in stirred-tank and bubble column bioreactors, Biochemical Engineering Journal, 127, 161—166, 2017 [2] Yang, S., Mohagheghi, A., Franden, M. A., Chou, Y.-C., Chen, X., Dowe, N., Himmel, M. E., and Zhang, M., Metabolic engineering of zymomonas mobilis for 2, 3-butanediol production from lignocellulosic biomass sugars. Biotechnology for biofuels, 9(1):189, 2016 [3] Kim, S. J., Sim, H. J., Kim, J. W., Lee, Y. G., Park, Y. C., and Seo, J. H., Enhanced production of 2,3-butanediol from xylose by combinatorial engineering of xylose metabolic pathway and cofactor regeneration in pyruvate decarboxylase-deficient Saccharomyces cerevisiae. Bioresource Technology, 245:1551–1557, 2017 [4] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Opportunities and Challenges for Nuclear-Renewable Hybrid Energy Systems

Nuclear-renewable hybrid energy systems (also known as integrated nuclear-renewable systems) are conceptual systems that have two or more energy inputs and produce two or more products. They maximize profitability, and usually equipment utilization, by adjusting between the products depending upon the availability of resources and market value. For example a nuclear-renewable hybrid energy system that produces electricity and hydrogen would maximize sales of electricity when its price is high and shift to hydrogen production when the electricity price is low. This presentation summarizes the results of several economic analyses of nuclear-renewable hybrid energy systems and they key overall conclusions from those analyses.

ENERGY PLANNING, POLICY, AND ECONOMY,ENVIRONMENTAL↗

DOE OSTI2021

Opportunities and Challenges for Nuclear-Renewable Hybrid Energy Systems

economic analysis↗

DOE OSTI2021

Regional Hybrid Energy Systems Technoeconomic Analysis

The National Renewable Energy Laboratory (NREL) has been working with the Idaho National Laboratory (INL) and Argonne National Laboratory (ANL) to analyze the potential that hybridizing Xcel Energy's two nuclear power plants in Minnesota might have. The hybrid nuclear power plants would maximize sales of electricity when its price is high and reduce electricity sales to maximize hydrogen production when the price of electricity is low. This presentation summarizes the project's approach and status and communicates future direction.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDROGEN↗

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 OSTI2020

Real-time Ridesharing for Transportation Hubs with Demand and Supply Uncertainty

Transportation hubs in major cities generate a significant amount of trips by taxis and for-hail vehicles (FHV), with many of the trips sharing similar destinations. This suggests promising opportunities to leverage the collective travel needs with dedicated ridesharing solutions to reduce the externalities of excessive traffic at transportation hubs. In this study, we develop a novel dynamic ridesharing approach to serve trips from the transportation hub by considering (1) demand (new passengers) and supply (newly available vehicles) in the near future and (2) the uncertainty of future predictions. Our approach consists of two stages. In the first stage, we develop a data structure called hub mobility tree to generate potential combinations of shareable trips as candidate schedules efficiently. Then the generated schedules are used in the second stage to formulate the stochastic hub-based ridesharing problem (SHRP), which is a stochastic integer programming problem with the objective to maximize the total expected ridesharing profit over time. Due to the prohibitive number of shareable trips, we then approximately solve SHRP by the sample average approximate method (SAA), and a dual Lagrangian technique is implemented to further improve the scalability of the solution approach. We demonstrate the performances of the proposed method by simulating the ridesharing service at JFK airport using NYC taxi and FHV data. The results indicate that the proposed method outperforms the myopic ridesharing (maximize profit for a single time step) and the rolling horizon method with point estimation of future demand and supply.

dynamic ridesharing↗

DOE OSTI2022

Single-Axis Tracker Control Optimization Potential for the Contiguous United States: Preprint

Conventional tracker control algorithms maximize collection of direct irradiance with no regard for collection of diffuse irradiance. Therefore, a tracker control algorithm that optimizes for maximal total irradiance, not just direct, might realize improved insolation collection. Using weather data gridded at 0.25° by 0.25° latitude/longitude spacing covering the contiguous United States, we evaluate the insolation gain of two alternative control algorithms optimized for improved total irradiance collection in monofacial arrays and present annual and monthly geographic heatmaps showing the gains across the contiguous United States. Certain locations show potential annual insolation gains approaching 1.0%, but most locations with recently-built tracker systems show annual gains between 0.1% and 0.4%. We also demonstrate a relationship between a climate's annualized diffuse insolation fraction and its potential tracker optimization insolation gain.

diffuse↗

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

Verification of Upcoming MCNP Features For Estimating Nuclear Data Sensitivities in Fixed Source Simulations [Abstract]

Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Self-tuning wave energy converter (WEC) controller for changing sea states

Systems and methods for a WEC controller that uses a self-tuning proportional-integral control law prescribing motor torques to maximize electrical power generation and automatically tune the controller to maximize power absorption. In an embodiment, the controller may be part of any resonant WEC system. The control law relies upon an identified model of device intrinsic impedance to generate a frequency-domain estimate of the wave-induced excitation force and measurements of device velocities. The control law was tested in irregular sea-states that evolved over hours (a rapid, but realistic time-scale) and that changed instantly (an unrealistic scenario to evaluate controller response).

Bacelli, Giorgio↗

DOE OSTI2017

Exposure Assumptions Underlying Cleanups to Industrial Versus Unrestricted Uses: Comparison of State and Federal Approaches - 17228

The Environmental Protection Agency (EPA) and various states have designated a variety of cleanup levels ranging from restricted (too much residual contamination to allow any use) to unrestricted or residential. Although there are many potential exposure scenarios, the States of Washington and New Jersey identify only two levels for cleanup of chemical contaminants in soil: 'unrestricted' and 'industrial' (Washington) and 'residential' vs 'non-residential' (New Jersey). Other agencies such as EPA and DOE recognize several levels of recreational use, between unrestricted and industrial, and likewise there are several levels of residential use as well. Cleanup criteria may allow or preclude the use of ground water for drinking, swimming pools, gardens, or lawns. Tribal unrestricted scenarios include the use of water (probably surface water in most scenarios) for sweat lodges, and have calculated high level exposure pathways for both dermal and inhalation pathways as well as ingestion. For residential scenarios, inadvertent ingestion of soil, drinking of ground water, and consumption of local foods represent the highest exposure pathways. Intermediate, recreational pathways may be non-consumptive (hiking, bird watching, photography) or consumptive (hunting, fishing, harvesting plants and medicines), the latter entailing much higher potential exposures. For each land-use there is a corresponding exposure scenario, with different assumptions about the maximally exposed individual, their age, and the amounts of air, water, soil, and foods they consume. For each exposure pathway one can compute the ratio between the exposure assumptions for any two scenarios. The product of the ratios comprises a Vulnerability Index. The index is higher for children than adults. For Washington this Index is reflected in the 'industrial' vs 'unrestricted' soil cleanup levels, which are 43.7 times lower for 'unrestricted'. This paper explores the similarities and differences in the exposure scenarios and vulnerability of the maximally exposed individuals and the consequences of a mismatch between current cleanup levels and possible future land uses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Application of Structured Decision Making to Hanford Site Radiological Air Monitoring - 17924

Radiological air monitoring is a component of an Environmental Surveillance (ES) program that has been in operation at the Department of Energy Hanford Site for several decades. The primary objectives of the ES program are protection of human health and the environment (on and off-site) as well as regulatory compliance. The ES program was designed decades ago when the plutonium-production reactors and related facilities at the Hanford Site were operational and air emissions were more widespread and significant than today. We employed a structured decision making (SDM) approach that integrates science with values and preferences to a Hanford Site air monitoring program decision. Specifically, a decision was necessary pertaining to the possible replacement of three air monitoring stations located in the 100 Area of the Hanford Site because the power line that supports these stations was near the end of its lifecycle. Although focused on this specific decision, the SDM model is more broadly applicable to decisions pertaining to adapting the air monitoring network to current Hanford operations. The value focused thinking approach that underlies SDM facilitates transparent and defensible decision making, and aids in communicating monitoring rationale. Some of the fundamental objectives elicited from the decision makers regarding management options for the three air monitoring stations included maximizing the ability to measure releases, minimizing environmental impacts, and maximizing social and economic sustainability. The fundamental objectives were linked to six objectives that could be measured (quantified) to allow a decision-analytic solution to the decision problem. Value functions and preference weights were developed for each of the objectives measures based on information obtained during a project workshop as well as by review and analysis of environmental data. Lastly, a set of five management options was identified that might achieve at least one of the objectives. A Bayes Network was developed as a solution to the decision problem pertaining to the three air stations (the 3 air stations decision). The Bayes Network describes the decision model graphically, and also serves as the computational engine for the decision analysis. The Bayes Network defines the relationship among the five management options and six objectives measures. In addition to the weights and value functions noted above, key inputs to the Bayes Network include a set of probabilities related to the different model states defined by the network. These include the probability of a significant release occurring, the probability that a release would be detected under each of the five management options, and the probability of achieving the other objective measures under each management option. In this manner the Bayesian Network captures the uncertainty in the components of the decision model. Replacing the three air monitoring stations with thermoluminescent dosimeters was identified as the preferred management option. A brief report was developed documenting the SDM model, including the workshop notes and analyses that were used to develop the objectives and model input variable values. This application demonstrates SDM's utility for supporting monitoring program decisions and, critically, providing a transparent and traceable basis for the decision. This relatively simple SDM model also provides a basis for an SDM model for monitoring decisions related to the more complex and holistic goal of achieving ES program objectives. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

Final Closure of the Maxey Flats Low-Level Radioactive Waste Disposal Site - 16088

This paper describes the final closure remedy for the former Maxey Flats Low-Level Radioactive Waste Disposal Site (MFDS), the challenges presented at the site due to the condition of the disposal facility and waste, and their resolution through comprehensive geo-synthetic design. The MFDS is an inactive landfill located in Kentucky on a ridge surrounded by a buffer zone. Historically, waste disposal occurred without reducing voids or without compacting the placed waste. Settlement and water management issues led to releases of radioactive contamination into surrounding waters, resulting in the closure of the facility in 1977. The final closure remedy consists of 1) development and implementation oversight of a sump abandonment design to ensure no direct pathways for infiltrating water to contact the waste and 2) development and implementation oversight of the Final Closure Period (FCP) cap design. Challenges successfully addressed by the FCP scope included: Abandon sumps to minimize storm water migrating into the waste; Address potential existing groundwater waste interaction; Maximum use of on-site cap construction materials; Design cap to fit very restricted site footprint; Assess and address potential isolated differential settlement; Maximize passive storm water management; Provide alternative site access during construction. The FCP remedy and resultant design satisfies these challenges through use of layers of geo-grid-reinforced soil and geo-synthetic materials, contours and slopes that minimize the quantities of construction materials while maximizing the use of available material, probabilistic subsidence modeling, an integrated storm water management system ensuring the slow release of storm water into receiving streams, and a dedicated construction haul road system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Strategic Planning and Decisions Tools to Enhance EM Mission Execution - 16552

Maximizing the success of an organization depends on many things with a key one being the focus and quality of the management decisions made by the organisation's senior leadership team. Successfully advancing an organisation's agenda requires operational, tactical, and strategic decisions. These decisions involve both decisions on specific actions, as well as decisions about planning. In addition to the day-to-day decisions of running the business, the leadership team should be allocating appropriate time toward developing organizational strategic objectives. Strategic planning is an organisation's process of defining its direction and strategies, and plotting how to accomplish those strategies. Strategic planning is essential to the long term viability of the organization. The pathway to success with strategic planning is not nearly so obvious and straight forward as with operational decisions. Organizational leaders must resist the temptation to minimize the time allocated to strategic thinking and strategic planning. Strategic thinking and strategic planning are centered around making sure that your organization is doing the right things, instead of just doing things right. Management guru Peter Drucker noted that 'There is nothing more wasteful than becoming highly efficient at doing the wrong things'. Strategic thinking is used in deciding what to do. Tactical thinking is used in deciding how to do it. Decision tools can be utilized to improve the results of the strategic planning process. In certain situations, properly selected decision tools will provide a mechanism or a process for making better decisions, or for channeling strategic thought and strategic planning toward enlightened alternative approaches. Decision tools can also provide for assigning a quantitative measure of merit or goodness, or at least a semi-quantitative measure of merit to multiple decision options. Properly selected and utilized, decision tools provide the leadership team with additional insight into the consequences of their decisions. Appropriately selected decision tools can be utilized to maximize the effectiveness of your strategic planning efforts. Decision tools can be used to quantify strategic decision options within a particular framework, providing insights into alternatives to improve an organisation's most effective execution of the DOE EM clean up mission. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Evaluation of Potential Dose to Off-site Receptors from On-site Removable Contamination - 18521

Calculations used in making corrective action decisions estimate the maximum potential dose to on-site receptors exposed to contamination from nuclear testing activities at U.S. Department of Energy, Environmental Management Nevada Program Soils Activity release sites. At many of these release sites, removable contamination is present at levels that require controls to be in place under Title 10 Code of Federal Regulations Part 835, 'Occupational Radiation Protection.' When significant levels of removable contamination are present at a release site, it may be necessary to evaluate the potential dose to off-site receptors from removable contamination that has the potential to be transported off the contaminated site. The protocols described herein could be used to evaluate the potential dose to off-site receptors to ensure that corrective action decisions consider both on-site and off-site potential receptors. The calculation of estimated dose to a receptor can be accomplished by establishing a scenario that describes the exposure (e.g., time and pathway) of the individual to the radiological contamination and by using the properties of the contaminants (e.g., dose conversion factors). Radiological contamination transported from the site has the potential to expose off-site receptors under a potentially large number of exposure scenarios that cannot be comprehensively defined. However, a scenario was developed to represent a maximally exposed off-site receptor. Because the estimated dose to the maximally exposed off-site receptor based on maximum post-closure release site activity levels is less than the on-site dose decision level, corrective action decisions at the Nevada National Security Site Soils Activity release sites are also protective of potential off-site receptors when significant levels of removable contamination are present. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗