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This paper intends to present the design and the production of a software for the Occupational Health and Safety Integrated Management System with the view to a rapid drawing up of the system documents in the field of occupational health and safety.
This report summarizes the R&D activities of the Plant Health Management (PHM) project during fiscal year 2020 (FY20). This project focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate system sources of risk. This project development lives in cooperation with the Risk Informed Asset Management (RIAM) project. The RIAM project, in fact, uses the reliability models and data generated by the PHM project to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture). This year’s activities for the PHM project focused mainly on the development of two classes of models. The first one includes a series of component reliability models which include aging, testing and maintenance. The second class includes a series of system reliability models which focus on the secondary side of exiting U.S. reactors. These models are based on fault tree logic structures such that they can be used by existing plant PRA software. We also started to focus on the management of health data and we tackled this issue in two directions. The first one focuses on the integration of monitor data with simulation models to assess component health. This approach is moving from a classical data based to a model+data based approach with the goal of improving higher component health information. The second direction focuses on linking equipment reliability data (e.g., maintenance/failure reports, component monitoring data) directly to system reliability models using a safety margin based language rather than a probability based language. The main advantage of a safety margin based language is that it can provide to system engineers more tangible information on system/component health and how it propagates to the system level.
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. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein 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. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.
Idaho National Laboratory (INL) performed an Annual Effectiveness Review of the Integrated Safety Management System (ISMS), per 48 Code of Federal Regulations (CFR) 970.5223 1, “Integration of Environment, Safety and Health into Work Planning and Execution.” The annual review assessed Integrated Safety Management (ISM) effectiveness, provided feedback to maintain system integrity, and identified target areas for focused improvements and assessments for Fiscal Year (FY) 2014. Results of the FY 2013 annual effectiveness review demonstrate that the INL’s ISMS program is “Effective” and continually improving and shows signs of being significantly strengthened. Although there have been unacceptable serious events in the past, there has also been significant attention, dedication, and resources focused on improvement, lessons learned and future prevention. BEA’s strategy of focusing on these improvements includes extensive action and improvement plans that include PLN 4030, “INL Sustained Operational Improvement Plan, PLN 4058, “MFC Strategic Excellence Plan,” PLN 4141, “ATR Sustained Excellence Plan,” and PLN 4145, “Radiological Control Road to Excellence,” and the development of LWP 20000, “Conduct of Research.” As a result of these action plans, coupled with other assurance activities and metrics, significant improvement in operational performance, organizational competence, management oversight and a reduction in the number of operational events is being realized. In short, the realization of the fifth core function of ISMS (feedback and continuous improvement) and the associated benefits are apparent.
The primary purpose of this report is to summarize the performance of Ames National Laboratory’s environmental programs, present highlights of significant environmental activities, and confirm compliance with environmental regulations and requirements for calendar year 2021. This report is a working requirement of Department of Energy Order 231.1B, Environment, Safety and Health Reporting. It includes descriptions of the Laboratory’s site, mission, the status of its compliance with applicable environmental regulations, its planning and activities to maintain compliance, and a comprehensive review of its environmental protection, surveillance and monitoring activities. Ames National Laboratory is located on the campus of Iowa State University (ISU) and occupies 13 buildings owned by the Department of Energy (DOE). See the Laboratory’s Web page for location and Laboratory overview. The Laboratory also leases space in ISU owned buildings. In 2021, the Laboratory accumulated and disposed of hazardous waste under a U.S. Environmental Protection Agency (EPA) issued generator number. All waste was handled according to applicable EPA, State, and local regulations and DOE Orders. The Laboratory operates as a Small Quantity Generator (SQG) of hazardous waste. There were no radiological air emissions or exposures to the general public due to Laboratory activities in 2021 (See U.S. Department of Energy Air Emissions Annual Report in Appendix A.) The Laboratory has an established Environmental Radiological Protection Program (Plan 10200.041) per DOE Order 458.1 requirements. Plans, policies, and procedures are in place to protect the public and the environment against undue risk from radiation associated with DOE radiological activities. As indicated in prior Site Environmental Reports, formal pollution prevention awareness, waste minimization and recycling programs have been in practice since 1990, with improvements implemented most recently in 2017 with Iowa State University’s shift toward single-stream recycling. Included in recycling efforts are items such as batteries, monitors, corrugated cardboard, lamps, miscellaneous electronic office equipment, mixed paper, newsprint, food/beverage containers, and laboratory glassware. Ames National Laboratory also recycles/reuses salvageable metal, used oil, and foamed polystyrene peanuts, and encourages chemical redistribution and sharing among research groups. Ames National Laboratory reported its contractual performance to DOE-Ames Site Office (AMSO) through the Laboratory’s Performance Evaluation Measurement Plan (PEMP), and a performance level of “B+” was achieved in 2021 for Sustain Excellence and Enhance Effectiveness of Integrated Safety, Health, and Environmental Protection As reported in Site Environmental Reports for prior years, the Laboratory’s Environmental Management System (EMS) has been integrated into the Laboratory’s Integrated Safety Management System (ISMS) since 2005. The integration of EMS into Laboratory business practices allows the Laboratory to systematically review, address and respond to environmental impacts. In addition to DOE-identified objectives and targets, the EMS Steering Committee recommends annual environmental goals for the Laboratory. Due to the COVID-19 pandemic and limited onsite work staff, goals of reducing water usage and travel/commuting to promote the reduction of scope 3 greenhouse gases were achieved. All contract deliverables and environmental compliance activities were still met during this time.
The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares, (e.g., Seafood Spoilage Predictor) have evolved the use of information systems in the food safety management. Such tools are updateable with new food-pathogen specific models containing cardinal parameters and multiple dependent variables, including plate counts, concentration of metabolic products, or even expression levels of certain genes. Then, these tools may further serve as decision-support tools which may assist in product logistics, based on their scientifically-based and “momentary” expressed spoilage and safety level.
This report describes research results to date in support of the integration and demonstration of diagnostics technologies for prototypical AdvSMR passive components (to establish condition indices for monitoring) with model-based prognostics methods. The focus of the PHM methodology and algorithm development in this study is at the localized scale. Multiple localized measurements of material condition (using advanced nondestructive measurement methods), along with available measurements of the stressor environment, enhance the performance of localized diagnostics and prognostics of passive AdvSMR components and systems.
As Hanford celebrates its 75. anniversary, the challenge to complete the cleanup mission and simultaneously prepare for new nuclear operations puts increasing pressure on an infrastructure system that in some places is nearly the same age. While the cleanup mission began in earnest in 1989 and continues today, completing construction of the country's largest Waste Treatment and Immobilization Plant will put nuclear operations back into the Hanford footprint. Ensuring that the infrastructure originally built to support plutonium production as part of the Manhattan Project is the right size and reliable for the remaining missions is a delicate balance of funding priorities, strategy and a predictable project delivery. The DOE Richland Operations Office (DOE-RL) and other sites across the DOE complex have recognized the need for increased investment in site infrastructure. Combined with this increasing need is the funding reality of variable fiscal budgets that creates challenges for even the best strategists. The current Hanford infrastructure priority list has approximately 100 reliability projects with a combined value over $380 million dollars. At current funding levels, there is a 10 to 15-year backlog. To meet this challenge head on, DOE-RL and Mission Support Alliance (MSA) regularly evaluate project priorities and ongoing maintenance actions to ensure the highest risk systems are addressed. This is done by integrating historical and operational knowledge, system health reports, and maintenance records then coordinating with the Facility Information Management System (FIMS). Sometimes with decades old infrastructure, it can seem as if everything is a number one priority. Recently, in a random testing of approximately 20% of the wooden power poles on site, approximately 92% of the poles were found to be in satisfactory condition with 8% percent failing. MSA Electrical Utilities group immediately replaced one pole, found to have only 8% strength remaining. This type of severely degraded infrastructure is not conducive to reliable mission execution. Given the historical prioritization of funding for cleanup, then operations, followed by infrastructure, it is not surprising that the commitment of funds on infrastructure projects such as sewer collection systems or replacing old electrical transformers has been hard fought. The acknowledgement and re-prioritization of limited funds will work towards creating a more reliable and sustainable infrastructure portfolio that will support and even enhance the cleanup and operations missions. This paper/presentation will outline Hanford's approach to executing reliability projects that will serve the remaining missions on site for years to come. (authors)
We report soil serves many important ecological functions and is an integral part of our existence as a society. However, concerns for soil health are growing globally, in part due to the negative impacts of agricultural management on soil resources. The production of perennial bioenergy crops on marginal land in row-crop production systems is one solution that could improve land-use efficiency and address the sustainability of cropland management. Because the relationship between crop management and the environment is complex, more research is needed to evaluate the potential benefits perennial bioenergy crop production has on soil health, as well as other ecosystem services. In this study, shrub willow buffers were strategically integrated into a corn-soybean cropping system with the main objective of reducing nitrate-N leaching from grain crop production while producing biomass for bioenergy. Two buffer systems (defined by landscape positions) were included for comparison, one on marginal land with exposure to nitrate-N leaching from upslope grain (southern plots) and one on fertile soils with less nitrate-N leaching potential (northern plots). Evaluation of soil (chemistry, bulk density, microbial community) and shrub willow vegetation properties ( fine roots, leaf litter decomposition, and nutrient uptake dynamics), showed that landscape position plays an important role in (1) the dynamics of soil chemical properties, (2) shrub willow's influence and productivity, and (3) the provision of additional ecosystem services such as reductions in nitrous oxide emissions and nitrate-N leaching. In addition, the combination of crop type and landscape position (N-grain, N-willow, S-grain, and S-willow) influenced the species composition of the soil microbial community, resulting in unique and identifiable communities. These results highlight the potential application of shrub willow buffers for ecosystem service provision and support of ecosystem processes; however, understanding the relationship between the microbial community, crop type, and landscape is important for understanding the sustainability of the design.
Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. Here, we describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.
Enhanced risk monitors (ERMs) for active components in advanced reactor concepts use predictive estimates of component failure to update, in real time, predictive safety and economic risk metrics. These metrics have been shown to be capable of use in optimizing maintenance scheduling and managing plant maintenance costs. Integrating this information with plant supervisory control systems increases the potential for making control decisions that utilize real-time information on component conditions. Such decision making would limit the possibility of plant operations that increase the likelihood of degrading the functionality of one or more components while maintaining the overall functionality of the plant. ERM uses sensor data for providing real-time information about equipment condition for deriving risk monitors. This information is used to estimate the remaining useful life and probability of failure of these components. By combining this information with plant probabilistic risk assessment models, predictive estimates of risk posed by continued plant operation in the presence of detected degradation may be estimated. In this paper, we describe this methodology in greater detail, and discuss its integration with a prototypic software-based plant supervisory control platform. In order to integrate these two technologies and evaluate the integrated system, software to simulate the sensor data was developed, prognostic models for feedwater valves were developed, and several use cases defined. The full paper will describe these use cases, and the results of the initial evaluation.
We report a national need is to prepare for and respond to accidental or intentional disasters categorized as chemical, biological, radiological, nuclear, or explosive (CBRNE). These incidents require specific subject-matter expertise, yet have commonalities. We identify 7 core elements comprising CBRNE science that require integration for effective preparedness planning and public health and medical response and recovery. These core elements are (1) basic and clinical sciences, (2) modeling and systems management, (3) planning, (4) response and incident management, (5) recovery and resilience, (6) lessons learned, and (7) continuous improvement. A key feature is the ability of relevant subject matter experts to integrate information into response operations. We propose the CBRNE medical operations science support expert as a professional who (1) understands that CBRNE incidents require an integrated systems approach, (2) understands the key functions and contributions of CBRNE science practitioners, (3) helps direct strategic and tactical CBRNE planning and responses through first-hand experience, and (4) provides advice to senior decision-makers managing response activities. Recognition of both CBRNE science as a distinct competency and the establishment of the CBRNE medical operations science support expert informs the public of the enormous progress made, broadcasts opportunities for new talent, and enhances the sophistication and analytic expertise of senior managers planning for and responding to CBRNE incidents.
The reliability consideration of LED products includes both luminous flux drop and color shift. Previous research either talks about luminous maintenance or color shift, because luminous flux degradation usually takes very long time to observe. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (20%) after a short training of the estimator.
Enhanced risk monitors (ERMs) for active components in advanced reactor concepts use predictive estimates of component failure to update, in real time, predictive safety and economic risk metrics. These metrics have been shown to be capable of use in optimizing maintenance scheduling and managing plant maintenance costs. Integrating this information with plant supervisory control systems increases the potential for making control decisions that utilize realtime information on component conditions. Such decision making would limit the possibility of plant operations that increase the likelihood of degrading the functionality of one or more components while maintaining the overall functionality of the plant. ERM uses sensor data for providing real-time information about equipment condition for deriving risk monitors. This information is used to estimate the remaining useful life and probability of failure of these components. By combining this information with plant probabilistic risk assessment models, predictive estimates of risk posed by continued plant operation in the presence of detected degradation may be estimated. In this paper, we describe this methodology in greater detail, and discuss its integration with a prototypic software-based plant supervisory control platform. In order to integrate these two technologies and evaluate the integrated system, software to simulate the sensor data was developed, prognostic models for feedwater valves were developed, and several use cases defined. The full paper will describe these use cases, and the results of the initial evaluation. (authors)
Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.
Idaho National Laboratory’s (INL’s) Integrated Safety Management System (ISMS) effectiveness review of fiscal year (FY) 2016 shows that INL has integrated management programs and safety elements throughout the oversight and operational activities performed at INL. The significant maturity of Contractor Assurance System (CAS) processes, as demonstrated across INL’s management systems and periodic reporting through the Management Review Meeting process, over the past two years has provided INL with current real-time understanding and knowledge pertaining to the health of the institution. INL’s sustained excellence of the Integrated Safety and effective implementation of the Worker Safety and Health Program is also evidenced by other external validations and key indicators. In particular, external validations include VPP, ISO 14001, DOELAP accreditation, and key Laboratory level indicators such as ORPS (number, event frequency and severity); injury/illness indicators such as Days Away, Restricted and Transfer (DART) case rate, back & shoulder metric and open reporting indicators, demonstrate a continuous positive trend and therefore improved operational performance over the last few years. These indicators are also reflective of the Laboratory’s overall organizational and safety culture improvement. Notably, there has also been a step change in ESH&Q Leadership actions that have been recognized both locally and complex-wide. Notwithstanding, Laboratory management continues to monitor and take action on lower level negative trends in numerous areas including: Conduct of Operations, Work Control, Work Site Analysis, Risk Assessment, LO/TO, Fire Protection, and Life Safety Systems, to mention a few. While the number of severe injury cases has decreased, as evidenced by the reduction in the DART case rate, the two hand injuries and the fire truck/ambulance accident were of particular concern. Aggressive actions continue in order to understand the causes and define actions needed to prevent similar injuries and accidents in the future. While our injury rate is not where we want it to be, it is not the only indicator that defines our ISMS program, safety culture, and efforts to be a continuous learning organization. When reviewing the entire year’s performance, and all areas that integrate ISMS principles and core functions, INL has an “effective” ISMS program that is continually improving.
Personnel at Sandia National Laboratories (hereinafter referred to as Sandia) comply with United States Department of Energy (DOE) Policy 450.4A, Chg 1, Integrated Safety Management Policy, and implement an Integrated Safety Management System (ISMS) to ensure safe operations. Sandia personnel integrate safety into management and work practices at all levels so missions are accomplished while protecting Members of the Workforce, the public, and the environment. As a result, safety is effectively integrated into all facets of work planning and execution. Thus the management of safety functions becomes an integral part of mission accomplishment and meets the requirements outlined in the DOE Acquisition Regulation (DEAR) 970.5223-1, Integration of Environment, Safety, and Health into Work Planning and Execution, clause incorporated by reference into the Prime Contract. The DEAR 970.5223-1, Integration of Environment, Safety, and Health into Work Planning and Execution, clause requires DOE contractors to manage and perform work in accordance with a documented Safety Management System that fulfills conditions of the DEAR clause at a minimum. For purposes of this clause, safety encompasses environment, safety, and health (ES&H), including pollution prevention and waste minimization.
Personnel at Sandia National Laboratories (hereinafter referred to as Sandia) comply with U.S. Department of Energy (DOE) P 450.4A, Chg 1, Integrated Safety Management Policy, and implement an Integrated Safety Management System (ISMS) to ensure safe operations. Sandia personnel integrate safety into management and work practices at all levels so that missions are accomplished while protecting Members of the Workforce, the public, and the environment. As a result, safety is effectively integrated into all facets of work planning and execution. Thus, the management of safety functions becomes an integral part of mission accomplishment and meets the requirements outlined in the DOE Acquisition Regulation (DEAR) 970.5223-1, Integration of Environment, Safety, and Health into Work Planning and Execution clause incorporated into the Prime Contract. The DEAR 970.5223-1, Integration of Environment, Safety, and Health into Work Planning and Execution clause requires DOE contractors to manage and perform work in accordance with a documented Safety Management System that fulfills conditions of the DEAR clause, at a minimum. For purposes of this clause, safety encompasses environment, safety, and health, including pollution prevention and waste minimization.