Probabilistic Risk Assessment Techniques Research Articles

Applicability of 'Toolkit for Safety Assessment' tool to interventional radiology using probabilistic risk assessment techniques.

Interventional radiology brings extensive benefits to patients. Nevertheless, certain procedures may result in high doses of radiation, leading to health risks to occupationally exposed individuals (OEIs). Therefore, a more comprehensive risk analysis is essential to ensuring safety and minimising radiation exposures for all OEIs. The Toolkit for Safety Assessment (TOKSA) tool performs risk assessments based on the concepts described in 'General Safety Requirements' Part 3 (Radiation Protection and Safety of Radiation Sources: International Basic Safety Standards) and Part 4 (Safety Assessment for Facilities and Activities). This tool was developed based on the 'Ibero-American Forum of Radiological and Nuclear Regulatory Agencies' risk models and can promote the use of the risk assessment processes by OEIs. The aim of this study was to experimentally analyse the applicability of the TOKSA tool in interventional radiology with the use/support of probabilistic risk assessment techniques. The results were used to reduce the risks associated with a hemodynamics room in a hospital in Belo Horizonte, Brazil.

Modelling potential human exposure to the urease inhibitor NBPT through the environment-food pathway

The urease inhibitor N-(n-butyl) thiophosphoric triamide (NBPT) has recently attracted a lot of attention attributing to its efficiency in reducing ammonia loss from urea fertiliser applied to temperate grassland soils. Ammonia gas lost to the environment causes soil acidification, eutrophication and contributes to global warming through increased greenhouse gas emissions and ozone layer depletion. The active chemical NBPT blocks the soil microbial enzyme (urease) and reduces ammonia emission. Furthermore, NBPT’s use in agriculture might benefit farmers by reducing reliance on expensive nitrate fertiliser and aiding in a shift to more urea-based fertiliser (using NBPT co-applied with urea is more cost-effective). The present study was carried out to characterise the potential transfer of NBPT from grass to liquid milk and compute the associated human health risks. Using probabilistic risk assessment techniques, an exposure assessment model was developed to calculate the Estimated Daily Intake (EDI) of NBPT from milk, following co-application of NBPT with a urea N-fertiliser. Results show that the predicted mean concentration of NBPT in milk is 2.5 × 10−8 mg NBPT/kg milk, while the mean daily intake (EDI) of NBPT is 5 × 10−11 mg NBPT /kg BW/day). Back-calculations revealed that, under the studied conditions, for the EDI to exceed ADI of 3 × 10−2 mg NBPT/kg BW/ day, the NBPT application rate would need to exceed the NBPT fertiliser limit (0.09–0.2% by mass of urea nitrogen) set in the Commission Regulation (EC) No 1107/2008, and the bio-transfer factor would need to be over 100% (implausible). Sensitivity analysis revealed soil pH (SPH), phytoaccumulation factor (PF), NBPT permissible levels in fertiliser (NBPT%), pasture cover (P), and grazing rotation length (t) as critical factors influencing the EDI of NBPT. The present study concludes that NBPT presents negligible risk to human health under the conditions and assumptions studied.

Reducing the risk of intentional domino effects in process plants: A risk‐based minimax strategy

AbstractCompared with safety assessment, security risk assessment in chemical and process plants is more challenging. On top of uncertain environmental and operational parameters and interdependent failures, which are common in the safety risk assessment of complex systems and infrastructures, there are other uncertain parameters such as the likelihood of attack scenarios and attackers' expected outcomes. As such, the application of probabilistic risk assessment (PRA) techniques, which have long been applied to safety risk assessment and management, to security risk management may result in nonoptimal or suboptimal decisions. In the present study, we will demonstrate how a combination of PRA and game theory may outperform PRA and lead to a more cost‐effective allocation of security measures. For this purpose, the outcome of a dynamic Bayesian network—as a PRA technique—is used as input to the minimax strategy—as a game theoretic strategy—for security risk management of a tank terminal under attacks with a homemade bomb. The proposed risk‐based minimax strategy alleviates the need for estimation of attack likelihoods or attacker payoffs, which would have otherwise been too challenging to estimate if the analyst solely depended on a PRA technique.

Modernizing risk assessment: A systematic integration of PRA and PHM techniques

Recent advances in sensing and computing technologies have resulted in an abundance of data in various formats and more processing power for using this data. Consequently, there has been an interest in using these advances to enhance modeling and assessment techniques for safety and reliability of a variety of systems. To date, this has occurred under two distinct aspects of reliability engineering. Prognostics and Health Management (PHM) has developed powerful new algorithms for understanding and predicting the mechanical and electrical devices’ health. For complex systems, the techniques of Probabilistic Risk Assessment (PRA), which provide a system-level perspective, have become increasingly dynamic. Both PHM and PRA bring unique advantages and limitations. PHM excels at data handling and supports prediction, but the methods applicable at the component level are not suitable for modeling complex engineering systems (CES). PRA provides a comprehensive approach suitable for drawing together many types of data and assessing complex systems, but is limited in the ability to exploit advanced ML methods or enable prediction. In this paper, we explore how to systematically draw together the advances in PHM and PRA to provide a more forward-looking, model- and data-driven approach for assessing and predicting the risk and health of CES.

Dynamic risk assessment with bayesian network and clustering analysis

While traditional probabilistic risk assessment (PRA) using event tree and fault tree is mainly concerned with static uncertainties, dynamic PRA techniques address the timeline response of plants in a systematic manner. Major technical challenges of dynamic PRA are related to a very large number of possible scenarios and many iterative simulations to accommodate the possible changes in the probability distribution of input variables. In this study, we propose a novel probabilistic mapping method using a dynamic Bayesian network with clustering analysis for discretized system space in time, which enables one to physically and logically reduce the number of required simulations as well as to quantify system evolution in a probabilistic manner. The mean shift clustering algorithm is used to cluster datasets from similar scenarios so that the proposed approach can be applied in practice at a manageable computational cost without the burden of running too many additional iterative simulations for a large variety of operational conditions of a target system. The risk effect quantification of variations in control units’ configuration would lead to the verification and improvement of dynamic system safety.

Finite-horizon semi-Markov game for time-sensitive attack response and probabilistic risk assessment in nuclear power plants

Cybersecurity has drawn increasing attention in the nuclear industry. To improve the cyber-security posture, it is important to develop effective methods for cyber-attack response and cyber-security risk assessment. In this research, we develop a finite-horizon semi-Markov general-sum game between the defender (i.e., plant operator) and the attacker to obtain the time-sensitive attack response strategy and the real-time risk assessment in nuclear power plants. We propose methods for identifying system states of concern to reduce the state space and for determining state transition probabilities by integrating probabilistic risk assessment techniques. After a proper discretization of the developed continuous-time model, we use dynamic programming to derive the time-varying and state-dependent strategy of the defender based on the solution concept of the mixed-strategy Nash equilibrium. For risk assessment, three risk metrics are considered, and an exact analytical algorithm and a Monte Carlo simulation-based algorithm for obtaining the metrics are developed. Both players’ strategies and the risk metrics are illustrated using a digital feedwater control system used in pressurized water reactors. The results show that the proposed method can support plant operators in timely cyber-attack response and effective risk assessment, reduce the risk, and improve the resilience of nuclear power plants to malicious cyber-attacks.

Deterministic and probabilistic health risk assessment techniques to evaluate non-carcinogenic human health risk (NHHR) due to fluoride and nitrate in groundwater of Panipat, Haryana, India

Human interferences have caused groundwater contamination in alluvial aquifers which subsequently affects the health of exposed population. In the present study, 74 groundwater samples from the semi-arid region of Panipat district, falling under Yamuna sub-basin, India was evaluated to know the potential non-carcinogenic human health risk in local adult and child population. The major objective of the present study was to know the non-carcinogenic human health risk due to intake of fluoride and nitrate contaminated water, using two different approaches: deterministic and probabilistic (Monte Carlo simulation). The values of hazard quotient (HQ) determined by deterministic as well as probabilistic approach were nearly identical. The hazard index (HI) value of 40.8% samples was above the unity in case of adults while 69.7% samples indicated HI value greater than unity for children thus indicating children are more prone to non-carcinogenic health risk than the adult population. Sensitivity analysis was performed to identify the influence of the non-carcinogenic human health risk predictor variables for the prediction of risk and concentration factor (CF) was the most influential variable. Multivariate statistical techniques were employed to know the positive and negative relationship of fluoride and nitrate with other parameters. Results of principal component analysis/factor analysis (PCA/FA) indicated that the concentration of fluoride is controlled by the presence of calcium due to their negative correlation in groundwater samples. The hierarchical agglomerative cluster analysis (HCA) also supported the outcome of PCA/FA and both indicated anthropogenic sources of fluoride and nitrate in groundwater.

A perceptual computing–based method to prioritize intervention actions in the probabilistic risk assessment techniques

AbstractProbabilistic risk assessment techniques as the systematic tools have been widely used on the different type of industrial sectors to reduce the estimated risk to an acceptable level. The fact is to design inherently safety; hazards have to be eliminated and reduced in risk as much as possible with the consideration of several interventions. In this regard, multicriteria decision making (MCDM) science is commonly integrated with the probabilistic risk assessment techniques to improve the safety performance of a system. Thus, it has been widely used to assist decision makers in controlling the identified process hazards in a different type of engineering applications. However, by increasing the complexity of industrial sectors as well as human being judgments, typical MCDM methods cannot highly guarantee their output results. According to this point, proposing MCDM methods based on mathematical programming have been interested in scholars due to high reliability and feasibility of the results. In this paper, we extended integration of MULTIMOORA approach with the Choquet integral under subjectivity circumstances to prioritize corrective actions in a typical probabilistic risk assessment technique. To illustrate the efficiency and feasibility of the proposed method, it has been applied in a real case study.

Spatiotemporal variations and characterization of the chronic cancer risk associated with benzene exposure

A spatiotemporal analysis of benzene was performed in east of the USA and in a representative station in Baltimore County, in order to assess its trend over a 25-year time span between 1993 and 2018. A novel time series analysis technique known as TBATS (an ensemble of Trigonometric seasonal models, Box-Cox transformation, ARMA error plus Trend and Seasonal components) was applied for the first time on an air contaminant. The results demonstrated an annual seasonality and a continuously declining trend in this respect. The success of Reformulated Gasoline Program (RFG), initiated in 1995, was obviously detected in time series data since the daily benzene concentrations reduced to one-sixth of its original level in 1995. In this regard, the respective values of mean absolute scaled error (MASE) were 0.35 and 0.45 for training and test series. Given the observed concentrations of benzene, the hot spot areas in east of the US were identified by spatial analysis, as well. A chronic cancer risk was followed along the study area, by both a deterministic and probabilistic risk assessment (PRA) techniques. It was indicated that children are at higher risk than that of adults. The range of estimated risk values for PRA was higher and varied between 6.45 × 10−6 and 1.68 × 10−4 for adults and between 8.13 × 10−6 and 8.29 × 10−4 for children. According to the findings of PRA, and referring to the threshold level of 1 × 10−4, only 1.2% of the adults and 28.77% of the children were categorized in an immediate risk group.

Building and using dynamic risk-informed diagnosis procedures for complex system accidents

Accidents pose unique challenges for operating crews in complex systems such as nuclear power plants, presenting limitations in plant status information and lack of detailed monitoring, diagnosis, and response planning support. Advances in severe accident simulation and dynamic probabilistic risk assessment provide an opportunity to garner detailed insight into accident scenarios. In this article, we demonstrate how to build and use a framework which leverages dynamic probabilistic risk assessment, simulation, and dynamic Bayesian networks to provide real-time monitoring and diagnostic support for severe accidents in a nuclear power plant. We use general purpose modeling technology, the dynamic Bayesian network, and adapt it for risk management of complex engineering systems. This article presents a prototype model for monitoring and diagnosing system states associated with loss of flow and transient overpower accidents in a generic sodium fast reactor. We discuss using this framework to create a risk-informed accident management framework called Safely Managing Accidental Reactor Transients procedures. This represents a new application of risk assessment, expanding probabilistic risk assessment techniques beyond static decision support into dynamic, real-time models which support accident diagnosis and management.

Benthic invertebrate exposure and chronic toxicity risk analysis for cyclic volatile methylsiloxanes: Comparison of hazard quotient and probabilistic risk assessment approaches

This study utilized probabilistic risk assessment techniques to compare field sediment concentrations of the cyclic volatile methylsiloxane (cVMS) materials octamethylcyclotetrasiloxane (D4, CAS # 556-67-2), decamethylcyclopentasiloxane (D5, CAS # 541-02-6), and dodecamethylcyclohexasiloxane (D6, CAS # 540-97-6) to effect levels for these compounds determined in laboratory chronic toxicity tests with benthic organisms. The concentration data for D4/D5/D6 in sediment were individually sorted and the 95th centile concentrations determined in sediment on an organic carbon (OC) fugacity basis. These concentrations were then compared to interpolated 5th centile benthic sediment no-observed effect concentration (NOEC) fugacity levels, calculated from a distribution of chronic D4/D5/D6 toxicologic assays per OECD guidelines using a variety of standard benthic species. The benthic invertebrate fugacity biota NOEC values were then compared to field-measured invertebrate biota fugacity levels to see if risk assessment evaluations were similar on a field sediment and field biota basis. No overlap was noted for D4 and D5 95th centile sediment and biota fugacity levels and their respective 5th centile benthic organism NOEC values. For D6, there was a small level of overlap at the exposure 95th centile sediment fugacity and the 5th centile benthic organism NOEC fugacity value; the sediment fugacities indicate that a negligible risk (1%) exists for benthic species exposed to D6. In contrast, there was no indication of risk when the field invertebrate exposure 95th centile biota fugacity and the 5th centile benthic organism NOEC fugacity values were compared.

A new approach to quantify safety benefits of disaster robots

Abstract Remote response technology has advanced to the extent that a robot system, if properly designed and deployed, may greatly help respond to beyond-design-basis accidents at nuclear power plants. Particularly in the aftermath of the Fukushima accident, there is increasing interest in developing disaster robots that can be deployed in lieu of a human operator to the field to perform mitigating actions in the harsh environment caused by extreme natural hazards. The nuclear robotics team of the Korea Atomic Energy Research Institute (KAERI) is also endeavoring to construct disaster robots and, first of all, is interested in finding out to what extent safety benefits can be achieved by such a disaster robotic system. This paper discusses a new approach based on the probabilistic risk assessment (PRA) technique, which can be used to quantify safety benefits associated with disaster robots, along with a case study for seismic-induced station blackout condition. The results indicate that to avoid core damage in this special case a robot system with reliability > 0.65 is needed because otherwise core damage is inevitable. Therefore, considerable efforts are needed to improve the reliability of disaster robots, because without assurance of high reliability, remote response techniques will not be practically used.

Advanced Reactor Passive System Reliability Demonstration Analysis for an External Event

Abstract Many advanced reactor designs rely on passive systems to fulfill safety functions during accident sequences. These systems depend heavily on boundary conditions to induce a motive force, meaning the system can fail to operate as intended because of deviations in boundary conditions, rather than as the result of physical failures. Furthermore, passive systems may operate in intermediate or degraded modes. These factors make passive system operation difficult to characterize within a traditional probabilistic framework that only recognizes discrete operating modes and does not allow for the explicit consideration of time-dependent boundary conditions. Argonne National Laboratory has been examining various methodologies for assessing passive system reliability within a probabilistic risk assessment for a station blackout event at an advanced small modular reactor. This paper provides an overview of a passive system reliability demonstration analysis for an external event. Considering an earthquake with the possibility of site flooding, the analysis focuses on the behavior of the passive Reactor Cavity Cooling System following potential physical damage and system flooding. The assessment approach seeks to combine mechanistic and simulation-based methods to leverage the benefits of the simulation-based approach without the need to substantially deviate from conventional probabilistic risk assessment techniques. Although this study is presented as only an example analysis, the results appear to demonstrate a high level of reliability of the Reactor Cavity Cooling System (and the reactor system in general) for the postulated transient event.

Soil Cleanup Goal for Dioxin Using Probabilistic Risk Assessment Techniques

ABSTRACTState regulators in Florida recently approved a first-of-its-kind probabilistic risk assessment (PRA) for determining an alternative residential Soil Cleanup Target Level (SCTL) for dioxin (32 ng/kg TEQ). The default residential SCTL (7 ng/kg TEQ) is based on a single, deterministic calculation with numerous conservative assumptions, resulting in an overly conservative value far beyond the regulatory mandate (i.e., 10−6 increase in cancer risk). Conversely, this PRA used a Monte Carlo simulation to estimate risk for all members of a large population using a combination of scientific data and professional judgment, with final details developed during negotiations with regulators. The simulation parameters were defined probabilistically and reflect the ranges of values for the following exposure variables: body weight, exposure duration, exposure frequency, fraction from contaminated source, soil ingestion rate, and relative bioavailability. Other variable and uncertain parameters were treated deterministically per direction from the regulators. The state also required that a pre-supposed high-risk subpopulation be analyzed separate from the full receptor population. Despite the conservativeness of the alternative SCTL, this PRA represents a significant step toward more realistic estimates of human health risks caused by environmental contaminant exposure.

A Game-Theoretical Model to Improve Process Plant Protection from Terrorist Attacks.

The New York City 9/11 terrorist attacks urged people from academia as well as from industry to pay more attention to operational security research. The required focus in this type of research is human intention. Unlike safety-related accidents, security-related accidents have a deliberate nature, and one has to face intelligent adversaries with characteristics that traditional probabilistic risk assessment techniques are not capable of dealing with. In recent years, the mathematical tool of game theory, being capable to handle intelligent players, has been used in a variety of ways in terrorism risk assessment. In this article, we analyze the general intrusion detection system in process plants, and propose a game-theoretical model for security management in such plants. Players in our model are assumed to be rational and they play the game with complete information. Both the pure strategy and the mixed strategy solutions are explored and explained. We illustrate our model by an illustrative case, and find that in our case, no pure strategy but, instead, a mixed strategy Nash equilibrium exists.

Branch-and-Bound algorithm applied to uncertainty quantification of a Boiling Water Reactor Station Blackout

Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peak clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical state. Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. Unfortunately DPRA methods introduce issues associated with combinatorial explosion of states. This paper presents a methodology to address combinatorial explosion using a Branch-and-Bound algorithm applied to Dynamic Event Trees (DET), which utilize LENDIT (L – Length, E – Energy, N – Number, D – Distribution, I – Information, and T – Time) as well as a set theory to describe system, state, resource, and response (S2R2) sets to create bounding functions for the DET. The optimization of the DET in identifying high probability failure branches is extended to create a Phenomenological Identification and Ranking Table (PIRT) methodology to evaluate modeling parameters important to safety of those failure branches that have a high probability of failure. The PIRT can then be used as a tool to identify and evaluate the need for experimental validation of models that have the potential to reduce risk. In order to demonstrate this methodology, a Boiling Water Reactor (BWR) Station Blackout (SBO) case study is presented.

A method of ecological and economic risk assessment during the development of the shelf based on mathematical modelling

This paper proposes a method of mathematical modelling of ecological risk based on a synthesis of dynamic and probabilistic risk assessment techniques. The probability of assessment of an acceptable probability of an anthropogenic impact to minimize economic costs is proposed. The dependence of an acceptable probability of an anthropogenic impact on the ecological risk is demonstrated with an example calculation. The results of the modelling of the state of a shelf ecosystem based on the dynamic model are used for the calculation as source information. Based on this synthesis, the calculation results bring about the opportunity to balance ecological–economic goals of achieving safe development of the shelf and to satisfy the involuntary necessity to reduce the costs on environmental protection measures, while maintaining the priority of environmental requirements.

Integrated risk assessment for multi-unit NPP sites—A comparison

Most of the nuclear power producing sites in the world houses multiple units. Such sites are faced with hazards generated from external events: earthquake, tsunami, flood, etc. and can threaten the safety of nuclear power plants. Further, risk from a multiple unit site and its impact on the public and environment was evident during the Fukushima nuclear disaster in March 2011. It is therefore important to evolve a methodology to systematically assess the risk from multi-unit site. For a single unit site, probabilistic risk assessment technique identifies the potential accident scenarios, their consequences, and estimates the core damage frequency that arise due to internal and external hazards. This challenging task becomes even more complex for a multiple unit site, especially when the external hazards that has the potential to generate one or more correlated hazards or a combination of non-correlated hazards are to be modelled. This paper presents an approach to evaluate risk for multiple NPP sites and also compare the risk for sites housing single, double and multiple nuclear plants.

PO-0996: A novel probabilistic risk assessment technique for radiotherapy

PO-0996: A novel probabilistic risk assessment technique for radiotherapy

Occurrence and ecological risks from fipronil in aquatic environments located within residential landscapes

This study investigated the occurrence of fipronil and its metabolites in aquatic environments in residentially-developed landscapes, including five canals and three retention ponds. Fipronil was detected at four of the sites, with concentrations of 0.5–207.3ngL−1. Fipronil sulfone and fipronil sulfide were detected at three sampling sites, with concentrations ranging from 0.46 to 57.75 and 0.40–26.92ngL−1, respectively. Multiple risk assessment methods were performed to characterize potential ecological risks, including deterministic screening and probabilistic risk assessment techniques. The deterministic method indicated no risk to certain biotic groups (i.e. aquatic plants, fish, molluscs, and algae–moss–fungi), but did indicate risks to larval insects and crustaceans. Results from the probabilistic risk assessment indicated significant ecological risks (acute and chronic) ranging from 0.75 to 58.9% and 3.9–35.0% when organisms were exposed to the maximum and median concentrations detected, respectively. The potentially affected fraction of species (PAF) likely to be acutely impacted ranged from 4.6 to 8.1% (fipronil), 0.2–1.6% (fipronil sulfone), and 1.9–3.1% (fipronil sulfide) in the ponds with frequent detectable concentrations. The PAF likely to be impacted at chronic toxicity levels ranged from 16.5 to 23.8% for fipronil. Joint probability curve analysis indicated that concentrations exceeded the LC50 of the most sensitive 5% of species 8.5–18.8% of the time at two of the sites with the most frequent detections. Using the more conservative NOEC/LOEC values, there was a 75–78% probability that concentrations were high enough to negatively affect the most sensitive 5% of species at the same two sites, indicating significant risks for chronic toxicity. JPCs indicated a ≤2.6% probability of fipronil sulfone exceeding the LC50 concentrations for the most sensitive 5% of species at the same two sites; and a 4.3–6.8% probability of fipronil sulfide exceeding the LC50 concentrations at the same sites. Results indicate that fipronil and its sulfone and sulfide degradation products may present significant risks to aquatic organisms in some residentially-developed areas.