Probabilistic Risk Assessment Model Research Articles

Assessment of potentially toxic and mineral elements in paddy soils and their uptake by rice (Oryza sativa L.) with associated health hazards in district Malakand, Pakistan

Rice, a primary food source in many countries of the world accumulate potentially harmful elements which pose a significant health hazard to consumers. The current study aimed to evaluate potentially toxic and mineral elements in both paddy soils and rice grains associated with allied health risks in Malakand, Pakistan. Rice plants with intact root soil were randomly collected from paddy fields and analyzed for mineral and potentially toxic elements (PTEs) through inductively coupled plasma optical emission spectrometry (ICP‒OES). Through deterministic and probabilistic risk assessment models, the daily intake of PTEs with allied health risks from consumption of rice were estimated for children and adults. The results of soil pH (< 8.5) and electrical conductivity (EC > 400 μs/cm), indicated slightly saline nature. The mean phosphorus concentration of 291.50 (mg/kg) in soil samples exceeded FAO/WHO permissible limits. The normalized variation matrix of soil pH with respect to Ni (0.05), Ca (0.05), EC (0.08), and Mg (0.09), indicated significant influence of pH on PTEs mobility. In rice grains, the mean concentrations (mg/kg) of Mg (463.81), Al (70.40), As (1.23), Cr (12.53), Cu (36.07), Fe (144.32), Mn (13.89), and Ni (1.60) exceeded FAO/WHO safety limits. The transfer factor >1 for K, Cu, P and Zn indicated bioavailability and transfer of these elements from soil to rice grains. Monte Carlo simulations of hazard index >1 for Cr, Zn, As, and Cu with certainties of 89.93% and 90.17%, indicated significant noncarcinogenic risks for children and adults from rice consumption. The total carcinogenic risk (TCR) for adults and children exceeded the USEPA acceptable limits of 1×10−6 to 1×10−4, respectively. The sensitivity analysis showed that the ingestion rate was a key risk factor. Arsenic (As) primarily influenced total cancer risk (TCR) in children, while chromium (Cr) significantly impacted adults. Deterministic cancer risk values slightly exceeded probabilistic values due to inherent uncertainties in deterministic analysis. Rice consumption poses health risks, mainly from exposure to Cr, Ni and As in the investigated area.

A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning (BDL-PRAEW) model considering meteorological conditions is proposed in this paper. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multi-source heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability (HILP) extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

Exhaustive Test Case Generation for Nuclear Safety Software Based on the Software Logic Model

An issue regarding the incorporation of software reliability within the nuclear power plant (NPP) probabilistic risk assessment model has emerged in the licensing processes of digitalized NPPs. Since software failure induces common-cause failure of the processor modules, the reliability of the software used in the NPP safety-critical instrumentation and control systems must be quantified and verified with proper test cases and environments. In this study, a software testing method based on the minimal cut set (MCS)–based exhaustive test case generation scheme is proposed where the software logic model is developed from available information on the software development and the MCSs that represent the necessary and sufficient conditions for the software variables’ states to produce safety software outputs are generated. The MCSs are then converted into the test cases, which can be used as inputs to the test bed to verify that the test cases produce correct outputs after software execution. The effectiveness of the proposed method is demonstrated with the safety-critical trip logic software of the APR-1400 reactor protection system. The method provides a systematic way to conduct exhaustive software testing and prove the functionality of the nuclear safety software based on the test result without uncertainties.

Identifying and quantifying a complete set of full-power initiating events during early design stages of high-temperature gas-cooled reactors

Identifying initiating events is the first step in building a probabilistic risk assessment model. The history of commercial nuclear power plant is primarily light-water reactors, hence, a comprehensive set of possible initiating events that covers most available light-water reactors is identified allowing for a frequentist approach to estimating their associated frequencies. However, other technologies of nuclear power plants do not share the same operating experience, hence, there are no comprehensive lists of initiating events for non-light-water reactors. This paper presents an approach that utilizes top-down deductive methodologies, master logic diagrams, and heat balance fault trees; the bottom-up inductive methodology of failure modes and effects analysis; and legacy and contemporary information to identify a complete list of initiating events for the Xe-100 high-temperature gas-cooled reactor (HTGR). Moreover, the paper presents the approach on how to estimate the frequencies using legacy sources along with deterministic analysis and fault trees. Furthermore, the paper presents an approach to estimate uncertainty parameters associated with each of these initiating events using constrained non-informative distributions to account for lack of operating experience. Finally, the paper presents the list of identified initiating events for the Xe-100 along with their frequencies and uncertainty parameters which serves, along with other contemporary sources, as another building block in having a comprehensive set of initiating events for HTGRs.

Quantitative microbial risk assessment of haemolytic uremic syndrome associated with Argentinean kosher beef consumption in Israel.

The aim of this study was to perform a quantitative microbial risk assessment (QMRA) of Shiga toxin-producing Escherichia coli hemolytic uremic syndrome (STEC-HUS) linked to the consumption of Kosher beef produced in Argentina and consumed in Israel in children under 14 years. A probabilistic risk assessment model was developed to characterize STEC prevalence and contamination levels in the beef supply chain (cattle primary production, cattle transport, processing and storage in the abattoir, for export and at retail, and home preparation and consumption). The model was implemented in Microsoft Excel 2016 with the @Risk add-on package. Results of 302 surveys with data collected in Israel were as follows: 92.3% of people consumed beef, mostly at home, and 98.2% preferred levels of cooking that ensured STEC removal from the surface of beef cuts. The preferred degree of ground beef doneness was "well-done" (48.2%). Cooking preference ranged from red to "medium-well done" (51.8%). Median HUS probability from Argentinean beef cut and ground beef consumption in children under 14 years old was <10-15 and 8.57x10-10, respectively. The expected average annual number of HUS cases and deaths due to beef cut and ground beef consumption was zero. Risk of infection and HUS probability correlated with salting effect on E. coli count, processing raw beef before vegetables, ways of storage and refrigeration temperature at home, joint consumption of salad and beef cuts, degree of beef doneness and cutting board washing with detergent after each use with beef and vegetables. The STEC-HUS risk in Israel from consumption of bovine beef produced in Argentina was negligible. The current QMRA results were similar to those of previous beef cut consumption QMRA in Argentina and lower than any of the QMRA performed worldwide in other STEC-HUS linked to ground beef consumption. This study confirms the importance of QMRA to estimate and manage the risk of STEC-HUS from beef consumption. The impact variables identified in the sensitivity analysis allowed us to optimize resources and time management, to focus on accurate actions and to avoid taking measures that would not have an impact on the risk of STEC-HUS.

Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment

The failure probability of the target event might not be linear to that of the component in the probabilistic risk assessment model if the component is correlated with another one or it has redundancies. If the failure probability of the component is uncertain in the non-linear model, the failure probability of the target event cannot be accurately obtained only from the expectation value of the component failure probability. Therefore, generally, a lot of Monte Carlo simulations are required for obtaining the importance measure such as FV, RRW, RAW, and BI. Since the calculation cost of the simulation is large in the analysis of complicated systems, the present paper proposes the estimation technique based on the sensitivity coefficient for importance measures. In the proposed method, the sensitivity coefficient is estimated from one Monte Carlo simulation result, thus the calculation cost is much less than the direct Monte Carlo simulations. As the non-linear model, the fault tree model with correlated components and that with the β factor method were analyzed. The results show that the proposed method based on the sensitivity coefficient can accurately estimate the importance measure for these models.

Chronicles of security risk assessment in process industries: Past, present and future perspectives

Security incidents around the world causes enormous loss in terms of life, economy, and environment. The 9/11 ′triggered the serious onset of research initiatives in the field of security with a more specific focus on the protection of high sensitive and hazardous facilities. It is well known that technology is advancing in a very rapid phase, proportionally increasing the security risk of the facilities. Conventional deterministic risk techniques widely found their applications in the earlier stage but owing to the increasing dynamic nature of the security risk, probabilistic dynamic risk assessment models were developed over time. In this article, the authors attempted to present the evolution of security risk concepts, their present status, and their future scope in a precise and consolidated form. The last 20 years of development in the security risk assessment concept involving deterministic and dynamic risk assessment models and applications in security risk assessment relevant to physical security are discussed in this article. The objective of the article is to outline the past, current and future directions of security risk highlighting their strength, weakness and limitations.

Investigating the applicability of human reliability analysis methods during early design stages of non-light-water nuclear power plants

Human interventions are an integral part of any system, whether through design, operation, maintenance, or upgrading. Moreover, although the reliance on human intervention in safety-related actions in advanced reactors (e.g., Generation IV) is expected to be reduced or completely replaced by automated actions, nuclear power plants (NPP) require human actions throughout their lifecycle from design, construction, operation, and decommissioning. Hence, the impacts of all operator actions are required to be captured and incorporated in the probabilistic risk assessment (PRA) of the modeled plant. Furthermore, a risk-informed and performance-based design and licensing approach expects that a PRA model, including human reliability analysis (HRA), is developed starting from the early design stages and used to inform all design iterations. However, due to the lack of details during the early design stages, HRA is often postponed until the design is mature enough. Conducting HRA in the final design stages, though adequate in capturing pre-, at-, and post-initiators, comes short of informing the design itself in the iterative design lifecycle. Moreover, due to most HRA methodologies being developed utilizing mostly the operating experience of existing light water reactors, limited guidance is available to the applicability of different HRA methodologies during the early stages of the design. Limited guidance is available, as well, on how to utilize the results of HRA in informing the design of later iterations. Hence, this study presents an investigation of the applicability of different HRA methodologies during the early stages of the design. The structure of a representative set of nine HRA methodologies is assessed against the available operating and emergency procedures within different design stages. Furthermore, these different HRA methodologies are assessed based on the availability of guidance on how to use their results to risk-inform the design in an iterative design process. Finally, the applicability of Open Preliminary Human Importance (OpenPHI) methodology, introduced specifically to implement HRA during early design stages, is assessed during the early stages of the design and compared against other methodologies.

A combined strategy for dynamic probabilistic risk assessment of fission battery designs using EMRALD and DEPM

The notion of nuclear reactors with battery-like capabilities, called fission batteries, puts forth system requirements and design constraints that have so far been unseen in the nuclear power production industry. Such restrictions require fission batteries to be modular, integrated, autonomous, tamper-proof (i.e., resilient, fault-tolerant, all-weather, and safe), and affordable. With design requirements specifying no human intervention for operation, and minimal connectivity to remote monitoring networks, fission batteries are unique among existing nuclear power plants and emerging advanced reactor designs. Given these attributes, traditional probabilistic risk assessment (PRA) of fission batteries is expected to require dynamic methods to model advanced aspects, such as self-diagnosis, self-adjustment, and duration-prediction capabilities, as they are key ingredients for unattended operations. In addition, availability models need to integrate autonomous control, associated error-detection algorithms, and adversarial human actions. Currently, no existing framework demonstrably assesses these advanced attributes. This paper introduces and demonstrates an integrated framework for the dynamic modeling of fission battery designs. The proposed framework comprises a combined modeling strategy that uses the dual-graph error propagation methodology (DEPM) based on the continuous-time Markov chain (CTMC) models implemented in OpenPRA Error Propagation (OpenErrorPro) and the dynamic PRA tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), based on discrete dynamic event trees (DDET). This combination overcomes some of the limitations of the tools when used independently. It enables detailed dynamic analysis to produce time explicit results to support the development of fission battery traditional PRA models. To evaluate the utility of this novel approach, a demonstration case is shown that models the hypothesized response of a fission battery design to an external fire event. DEPM CTMCs and alternative failure approaches are coupled with EMRALD to characterize and quantify the likelihood of the event sequences. The results show that the combined framework effectively captures the dynamic aspects of fission battery design in terms of the timing and realism of modeled events. Given the complexity of the failure scenarios, we believe that EMRALD and DEPM are necessary and complementary when the need for high-resolution analysis offsets the challenges of detailed modeling.

Non-negligible health risks caused by inhalation exposure to aldehydes and ketones during food waste treatments in megacity Shanghai

Aldehydes and ketones in urban air continue to receive regulatory and scientific attention for their environmental prevalence and potential health hazard. However, current knowledge of the health risks and losses caused by these pollutants in food waste (FW) treatment processes is still limited, especially under long-term exposure. Here, we presented the first comprehensive assessment of chronic exposure to 21 aldehydes and ketones in urban FW-air environments (e.g., storage site, mechanical dewatering, and composting) by coupling substantial measured data (383 samples) with Monte Carlo-based probabilistic health risk and impact assessment models. The results showed that acetaldehyde, acetone, 2-butanone and cyclohexanone were consistently the predominant pollutants, although the significant differences in pollution profiles across treatment sites and seasons (Adonis test, P < 0.001). According to the risk assessment results, the estimated cancer risk (CR; mean range: 1.6 × 10−5-1.12 × 10−4) and non-cancer risk (NCR; mean range: 2.98–22.7) triggered by aldehydes and ketones were both unacceptable in most cases (CR: 37.8%–99.3%; NCR: 54.2%–99.8%), and even reached the limit of concern to CR (1 × 10−4) in some exposure scenarios (6.18%–16.9%). Application of DALYs (disability adjusted life years) as a metric for predicting the damage suggested that exposure of workers to aldehydes and ketones over 20 years of working in FW-air environments could result in 0.02–0.14 DALYs per person. Acetaldehyde was the most harmful constituent of all targeted pollutants, which contributed to the vast majority of health risks (>88%) and losses (>90%). This study highlights aldehydes and ketones in FW treatments may be the critical pollutants to pose inhalation risks.

Ecological risk assessment of tire and road wear particles: A preliminary screening for freshwater sources in Canada

Abrasion of tires on road surfaces leads to the formation of tire and road wear particles (TRWPs). Approximately 5.9 million tonnes/year of TRWPs are emitted globally, and 12–20% of emissions generated on roads are transmitted into surface waters, where they can release (i.e., leach) chemical compounds that adversely affect aquatic species. To better understand the ecological risk of TRWPs, an acute, probabilistic ecological risk assessment model was developed and applied. This was a screening-level, conceptual ecological risk assessment (ERA) based on secondary data from published scientific studies. The model was demonstrated using British Columbia (BC) Highway 97 (TRWP source) and Kalamalka Lake (receiving water) in Canada, considering two spatial scenarios with varied highway (HWY) lengths and lake volumes. TRWP-derived chemical leachates considered for ERA were aniline, anthracene (ANT), benzo(a)pyrene (B(a)P), fluoranthene (Fl), mercaptobenzothiazole (MBT), and zinc (Zn). An assumed ‘total TRWP-derived leachate set’ was also assessed, representing all compounds present in tire-derived leachate test solutions. The results indicated the risk to aquatic species in two spatial scenarios. In scenario 1, ecotoxicity risk was high from exposure to TRWP-derived zinc and the total TRWP-derived leachate set. Scenario 2 results indicated acute risk was high from all TRWP-derived chemicals examined, except MBT. This preliminary ecological risk screening provides an early signal that freshwater lakes adjacent to busy highways may be at risk from TRWP contamination, indicating a need for further research. This research is the first ERA of TRWPs in Canada, and the results and methodology provide a foundation for future research and solutions development.

Probabilistic risk assessment of dietary exposure to pentachlorophenol in Guangzhou, China

Pentachlorophenol (PCP) is a ubiquitous environmental contaminant commonly existing as its sodium salt (NaPCP), which enters the human body primarily through long term but low-level dietary exposure. PCP contributes to chemical carcinogenesis and teratogenesis. In this study, the probabilistic risk of dietary exposure to PCP in Guangzhou citizens was investigated. In total, 923 food samples in the categories of pork, livestock (beef and lamb), poultry, offal, eggs, and freshwater fish (considered to be relatively susceptible to PCP contamination) were collected from various markets in Guangzhou and tested for PCP. Probabilistic risk assessment model calculations for PCP dietary exposure and margin of exposure (MOE) values were performed using @RISK software, based on a Monte Carlo simulation with 10,000 iterations. The overall detection rate of PCP (above 1 μg kg−1, the detection limit) was 19.9% (184/923), with an average of 7.9 μg kg−1. The highest rate of PCP detection, 28.2%, was in livestock (beef and lamb). The MOE value for dietary PCP exposure in general Guangzhou residents averaged 400, which was far below 5,000 (the borderline for judging a health risk). The lowest MOE value, 190, was observed in the 3- to-6-year old population and indicates a significant risk. In conclusion, this study suggests that PCP exposure in Guangzhou residents is of considerable health risk, especially for the pre-school young children.

Probabilistic risk assessment model for released tritium into the environment for CFETR

The China Fusion Engineering Test Reactor (CFETR) is the next facility in the Chinese roadmap to realize sustainable-clean fusion energy. Because of the large tritium inventory of the CFETR, safety analyses are necessary. During the current design phase of the CFETR, performing the risk analysis of tritium released into the environment could reveal the design weaknesses and indicate improvements aimed at trtium risk (TR) reduction but also form a part of the licensing process for future CFETR construction and operation. Several CFETR safety analyses using deterministic approaches have been conducted, but relatively few studies based on probabilistic approaches are currently available. In this paper, a probabilistic risk assessment (PRA) model for tritium release into the environment for CFETR was proposed, in which the frequency and the radioactivity of the released tritium in accident sequences are considered. The event tree/fault tree (ET/FT) method was applied to investigate primary factors contributing to the tritium release into the environment and to estimate the frequency of accident sequences. We presented a model for preliminary and rapid assessment of the radioactivity of released tritium in accident sequences and developed a calculation code. The application process and validity of the model were demonstrated through the assessment of TR of the CFETR for selected postulated initiating events.

ESASeedPARAM: A seed treatment model for threatened and endangered bird species in the United States.

The USEPA, National Marine Fisheries Service, and Fish and Wildlife Service are required to assess the risks of pesticides undergoing registration or reregistration to threatened and endangered (i.e., listed) species. Currently, the USEPA lacks a refined model to assess the risks of seed treatments to listed bird species. We developed the Endangered Species Assessment Seed Treatment Probabilistic Avian Risk Assessment Model (ESASeedPARAM) to incorporate species-specific diets, body weights, and food ingestion rates for potentially exposed listed bird species. The model also incorporates information on dissipation of seed residues after planting, and metabolism and elimination by birds during exposure. The ESASeedPARAM estimates hourly intake from ingestion of treated seeds for up to 50 days after planting. For each simulated bird, maximum retained dose (= body burden) and maximum rolling average total daily intake are estimated for acute and chronic exposure, respectively. The model is probabilistic and estimates exposure and risk for 20 birds on each of 1000 fields. The model accounts for interfield variation in the amount of waste grain on the soil surface in tilled, reduced till, and untilled fields. To estimate the fate of each bird from acute exposure, a random value is selected from the appropriate dose-response relationship and compared with the maximum retained dose. If acute exposure exceeds the randomly chosen effects value, mortality is assumed. For chronic risk, the most sensitive No Observed Adverse Effects Level (NOAEL) and Lowest Observed Adverse Effects Level (LOAEL) for an apical endpoint (survival, growth, reproduction) are compared with maximum rolling average total daily intake. In this article, we describe a case study conducted with the ESASeedPARAM for imidacloprid used as a seed treatment on wheat and soybean. Integr Environ Assess Manag 2023;19:527-546. © 2022 SETAC.

Automatic Generation of Event Trees and Fault Trees: A Model-Based Approach

In the past few decades, the increasing complexity of modern engineering systems has been driven by the integration of a large number of components whose operations may involve many disciplines (e.g., thermal hydraulics, plant operations, cybersecurity). Most computational tools used by industry and regulators for system safety and reliability assessments are still based on the traditional fault tree (FT) and event tree (ET) approach, which may not be able to capture complex interactions among system constituents. The use of simulation tools has widely increased in the past few decades to improve the fidelity of the reliability and safety analyses. However, the direct use of simulation tools as part of dynamic probabilistic risk assessment (DPRA) methods is not getting traction since (1) modeling the whole system under consideration with DPRA methods may be computationally expensive and unnecessary, and (2) the manual integration of DPRA models into existing state-of-practice probabilistic risk assessment models (i.e., based on FTs and ETs) can be time consuming and prone to errors. In this paper we propose a procedure to overcome this limitation by presenting several algorithms designed to automatically construct subsystem ETs and FTs from DPRA methods for integration into an existing ET/FT system model.

Editorial: Natural-hazard risk assessment in developing countries

Developing countries are disproportionately affected by natural hazards and lack of coping capacities. 15 This combination sets back progress on poverty alleviation and slows long-term development, 16 contributing the aggravation of the many obstacles already faced by these countries and their 17 communities. Probabilistic risk assessment models are increasingly popular tools for estimating 18 potential human and economic loss due to natural hazards. Within such a context, risk modeling for 19 developing countries presents specific challenges in terms of quantity and quality of the available input 20 data at all components of risk: hazard, exposure, and vulnerability. As such, widening the types of 21 hazards and ensuring that models are contextualized to the local needs of these countries is also crucial 22 and may require innovative or, some times, simplified, yet sound, approaches that tackle the well-23 known deficiencies in this context. Some of the above-mentioned challenges include: (i)

Risk assessment in railway traffic planning - assumptions for the method

Every activity, including within the railway system, is subject to risk, i.e., the possibility of uncertain phenomena that may affect it (both positively and negatively). One of the operational subsystems of the Union Railway System is railway traffic. It consists of four processes - the second is traffic planning. This process is also subject to risk. As it directly relates to traffic safety, conducting research and analyzing the risks involved makes sense. This article aims to develop the assumptions for a risk assessment method in railway traffic planning. This method, once created, will be used to carry out a risk assessment of the indicated process from two points of view: the railway undertaking and the infrastructure manager. As a result of the considerations, it was assumed that risk identification would be performed using the risk description principle from the M_o_R methodology. The risk estimation will be adapted to use a probabilistic risk assessment model. This assessment will be performed using the Monte Carlo simulation method, which is strongly recommended for use by the Office of Railway Transport (the national railway safety body).

Subjective machines: Probabilistic risk assessment based on deep learning of soft information.

For several years machine learning methods have been proposed for risk classification. While machine learning methods have also been used for failure diagnosis and condition monitoring, to the best of our knowledge, these methods have not been used for probabilistic risk assessment. Probabilistic risk assessment is a subjective process. The problem of how well machine learning methods can emulate expert judgments is challenging. Expert judgments are based on mental shortcuts, heuristics, which are susceptible to biases. This paper presents a process for developing natural language-based probabilistic risk assessment models, applying deep learning algorithms to emulate experts' quantified risk estimates. This allows the risk analyst to obtain an a priori risk assessment when there is limited information in the form of text and numeric data. Universal sentence embedding (USE) with gradient boosting regression (GBR) trees trained over limited structured data presented the most promising results. When we apply these models' outputs to generate survival distributions for autonomous systems' likelihood of loss with distance, we observe that for open water and ice shelf operating environments, the differences between the survival distributions generated by the machine learning algorithm and those generated by the experts are not statistically significant.

Review of Probabilistic Risk Assessment Models for Ship Collisions with Structures

Researchers have always been concerned about collision risks between ships and structures on busy waterways, as the consequences can be catastrophic. The models for determining the probabilities of these accidents, however, vary widely, with discrepancies between different model results in the same assessment. The models sometimes lack critical elements or are inherently flawed, and therefore do not represent reality. This paper aims to review the existing probabilistic risk models for ship and structure collisions. The advantages and disadvantages of each model are discussed, which leads to a better method for future model development. This paper reviews the existing literature for the probabilistic risk assessment (PRA) between nautical traffic and offshore infrastructures. This paper differentiates the existing models into three categories: statistics of collision rates, statistical models, and simulation models, as the models are evolving from statistical models to simulation models to derive more accurate results. The advantages and disadvantages of the statistical models were evaluated by comparing the details of the elements contributing to risk. Simulation models with virtual autonomous ships can better reflect the reality and include more risk elements than those described in the existing models. The cores of simulation models and the advantages of different models are elaborated and compared, thus supporting future work in this area.

A framework to implement human reliability analysis during early design stages of advanced reactors

Nuclear power plants require human actions throughout their lifecycle from design, construction, operation, and decommissioning. However, for advanced reactors (e.g., Generation IV), the reliance on human intervention in safety-related actions is expected to be reduced or completely replaced by automated actions. The Probabilistic Risk Assessment (PRA) Standard for Advanced Non-LWR Nuclear Power Plants requires that the impacts of all operator actions are captured and incorporated in the risk of the modeled plant. Moreover, the Modernization of Technical Requirements for Licensing Advanced Reactors requires human reliability analysis (HRA) to be included throughout all design and PRA development stages. However, due to the lack of details during the early design stages, HRA is often postponed until the design is mature enough. Conducting HRA in later design stages, though it may be adequate in capturing pre-, at-, and post-initiators comes short of informing the design itself in the iterative design lifecycle. Hence, this paper presents a framework to include HRA during the design's early stages, pre-conceptual or conceptual. The proposed framework provides a process for the removal of operator actions that do not contribute to the risk and the identification of all key operator actions that are critical to the safety of the design. The results of this framework are then used to inform the design of those safety-related operator actions to update the design further. Then, using information from the updated design, this framework can be reapplied to investigate the impact of the design update on human reliability. The PRA model of the X-energy's pre-conceptual Xe-100 high-temperature gas-cooled pebble-bed reactor (HTGR-PB) design is used to demonstrate the approach. In the pre-conceptual Xe-100 PRA model, also called Phase 0 PRA model, human actions were considered an integral part of analyzing the plant response to different initiating events. Hence, in this paper, all possible human actions in the Xe-100 PRA model are identified, analyzed, and removed to emulate a design relying only on the available automated control systems. The preliminary results of this assessment show how safe the Xe-100 design is even without crediting any human actions. The results also list necessary sequences in which operator actions are critical to the risk profile of the design.