Dynamic Risk Assessment Framework Research Articles

Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer

Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer

Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.

Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function–based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems.

An Integrated Framework for Dynamic Risk Assessment and Decision Making for Reactor Safety,

An Integrated Framework for Dynamic Risk Assessment and Decision Making for Reactor Safety,

Risk dynamics for marine systems: towards a bio-inspired framework for dynamic risk assessment

AbstractThe development of innovative, complex marine systems, such as autonomous ship concepts, has led to risk-based approaches in design and operation that provide safety level quantification and continuous risk assessment. The existing approaches to dynamic risk assessment mainly aim at updating accident probabilities for specific risk scenarios, based on knowledge of system operation and failure, as well as on past accident and failure information. However, for innovative marine systems that include complex interactions, our ability to identify anything that might go wrong is very limited, which may lead to unidentified risks, and failure data may not be available. This paper presents the foundations of a framework for dynamic risk assessment, which is equally applicable to manned and autonomous ships and mainly relies on information about the safe operational envelope and real-time information regarding deviations from safety. Inspiration is drawn from how the biological immune system identifies the risk of infection in a dynamic environment. The objective is to show the feasibility and benefits of our approach for quantifying the operational risk of marine systems. This paper provides the conceptual basis for developing ship specific applications and describes a process for dynamic risk assessment that is methodologically based on artificial immune systems. To demonstrate the implementation of our framework, we described, an illustrative example that involves a ship in a grounding scenario. The results show that the bio-inspired assessment process and risk description can reflect the changes of the risk level of a marine system.

Dynamic risk assessment framework for industrial systems based on accidents chain theory: The case study of fire and explosion risk of UHV converter transformer

The development of technology leads to the increasing complexity and hugely loss of industrial system, so, it's necessary to assess the risk. This study established a framework for dynamic risk assessment (DRA) of industrial systems based on accidents chain theory, and quantitatively assess risk. In this framework, the theory of accidents chain was proposed, including the structural equation and basic relationships of accidents chain. The risk analysis model, including the risk of initial event, the node risk and the risk of the four interaction relations, was established. The concept and the model of dynamic risk change rate were put forward. In addition, a risk grade classification method was built for dynamic risk evaluation. The DRA framework can be used to construct the risk correlation between the subsystems of the industrial system, and classify the accidents risk involved in the accidents chain. This study took the fire and explosion risk of UHV converter transformer as an example to conduct case analysis. The results showed that the DRA framework proposed in this paper is effective and feasible, which provides basis for risk prevention and control and improves the safety protection level of UHV converter transformer.

Dynamic risk assessment for geologic CO2 sequestration

At a geologic CO2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO2 saturations and pressures in potentially leaky wellbores, CO2/brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the U.S. National Risk Assessment Partnership’s Open Source Integrated Assessment Model (NRAP-Open-IAM) coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.

Can I Sleep Safely in My Smarthome? A Novel Framework on Automating Dynamic Risk Assessment in IoT Environments

Fully automated homes, equipped with the latest Internet of Things (IoT) devices, aiming to drastically improve the quality of lives of those inhabiting such homes, is it not a perfect setting for cyber threats? More than that, this is a fear of many regular citizens and a trending topic for researchers to apply Cyber Threat Intelligence (CTI) for seamless cyber security. This paper focuses on the Risk Assessment (RA) methodology for smarthome environments, targeting to include all types of IoT devices. Unfortunately, existing approaches mostly focus on the manual or periodic formal RA, or individual device-specific cyber security solutions. This paper presents a Dynamic Risk Assessment Framework (DRAF), aiming to automate the identification of ongoing attacks and the evaluation of the likelihood of associated risks. Moreover, DRAF dynamically proposes mitigation strategies when full automation of the decision making is not possible. The theoretical model of DRAF was implemented and tested in smarthome testbeds deployed in several European countries. The resulting data indicate strong promises for the automation of decision making to control the tightly coupled balance between cyber security and privacy compromise in terms of the embedded services’ usability, end-users’ expectations and their level of cyber concerns.

Safety analysis and dynamic risk assessment of community power distribution network using Bayesian network

Safety analysis and risk assessment of Community Power Distribution Network (CPDN) are the key links of safe operation of distribution network system. Reliability indices based risk assessment is the standard method for power distribution network. However, reliability indices are not adequate for the CPDN's intrinsic safety, which is represented by the states of physical components and network's failure events. Meanwhile, the reliability indices is designed for long-term risk assessment and cannot refer to the operation scenarios of CPDN. Therefore, dynamic risk assessment by considering the intrinsic safety of CPDN is a significant and challenge issue. This study proposes a novel safety and dynamic risk assessment framework of CPDN using Bayesian Network (BN). First, based on the standard reliability indices, the failure rate of physical components in CPDN and the failure events are creatively introduced into risk assessment, then a novel risk indices hierarchy is obtained. This indices hierarchy can quantitatively present the dynamic risk of CPDN, with considering the reliability indices and the features of intrinsic safety. Second, a dynamic risk analysis method is proposed, which is based on Fault Tree (FT) and Bayesian Network (BN). A real CPDN is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. The result of the case study suggests that the proposed framework could adequately assess the risk of CPDN and the derived rational safety actions can reduce the risk effectively.

Development of a framework for dynamic risk assessment of environmental impacts in chemicals warehouse using CFD-BN

Chemical warehouses are one of the high-risk areas in the process industries due to the high diversity and quantity of stored chemicals. Risk assessment is a useful tool for developing appropriate strategies to prevent and control the risks. In this study, computational fluid dynamics (CFD) and Bayesian network (BN) approaches were proposed for dynamic risk assessment. Initially, bow tie (BT) method was used for identifying basic events and modeling the consequences. In order to determine the consequences intensity (heat flux and CO and CO2 concentration), fire dynamics simulator (FDS) and solid flame model were used. A total of 21 causes or failures were identified in the chemical spills, 13 cases of which were related to basic events. Out of the identified causes, the forklift and drum strike basic event had the most contribution of the chemical spills in the warehouse, and the probability of the major spill event scenario 1.25495E−11 was estimated by Bayesian networks. The estimated risk by CFD in combination with BN, after updating, is unacceptable compared to the UK risk criterion. Bayesian networks and CFD approach for dynamic assessment of environmental impact risk in chemical warehouses provide the capability to quantitatively and dynamically assess the consequences of chemical spills by modeling its cause and effect in the warehouse. Based on the results of this method, effective preventive measures can be taken to control the consequences of chemical spills in the warehouse.

A framework for dynamic risk assessment with condition monitoring data and inspection data

In this paper, a framework is proposed for integrating condition monitoring and inspection data in Dynamic risk assessment (DRA). Condition monitoring data are online-collected by sensors and indirectly relate to component degradation; inspection data are recorded in physical inspections that directly measure the component degradation. A Hidden Markov Gaussian Mixture Model (HM-GMM) is developed for modeling the condition monitoring data and a Bayesian network (BN) is developed to integrate the two data sources for DRA. Risk updating and prediction are exemplified on an Event Tree (ET) risk assessment model. A numerical case study and a real-world application on a Nuclear Power Plant (NPP) are performed to demonstrate the application of the proposed framework.

Impact of wildfires on Canada's oil sands facilities

Abstract. Exponential growth of oil and gas facilities in wildlands from one side and an anticipated increase of global warming from the other have exposed such facilities to an ever-increasing risk of wildfires. Extensive oil sands operations in Canadian wildlands, especially in the province of Alberta, along with the recent massive wildfires in the province, require the development of quantitative risk assessment (QRA) methodologies which are presently lacking in the context of wildfire-related technological accidents. The present study is an attempt to integrate Canadian online wildfire information systems with current QRA techniques in a dynamic risk assessment framework for wildfire-prone process plants. The developed framework can easily be customized to other process plants potentially exposed to wildfires worldwide, provided that the required wildfire information is available.

Quantitative assessment of wildfire risk in oil facilities

Despite frequent occurrence of wildfires around the world, the role of wildfires has rarely been taken into account in risk assessment of process plants in wildlands, especially that large inventory of flammable petroleum products in contact with the heat of wildfire can lead to severe domino effects. We have developed a dynamic risk assessment framework by integrating available models of fire spread and domino effect analysis with online maps of wildfire characteristics such as ignition probability and heat intensity to investigate the impact of wildfires on oil facilities. The framework is modular, so one can readily enhance its accuracy by replacing the current techniques with more sophisticated ones. The application of the methodology is demonstrated on an oil terminal.

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Process Safety in the oil and gas industry is managed through a robust Process Safety Management (PSM) system that involves the assessment of the risks associated with a facility in all steps of its life cycle. Risk levels tend to fluctuate throughout the life cycle of many processes due to several time varying risk factors (performances of the safety barriers, equipment conditions, staff competence, incidents history, etc.). While current practices for quantitative risk assessments (e.g. Bow-tie analysis (BT), Layer of protection analysis (LOPA) etc.) have brought significant improvements in the management of major hazards, they are static in nature and do not fully take into account the dynamic nature of risk and how it improves risk-based decision making.In an attempt to continually enhance the risk management in process facilities, the oil and gas industry has put in very significant efforts over the last decade toward the development of process safety key performance indicators (KPI or parameters to be observed) to continuously measure or gauge the efficiency of safety management systems and reduce the risks of major incidents. This has increased the sources of information that are used to assess risks in real-time. The use of such KPIs has proved to be a major step forward in the improvement of process safety in major hazards facilities. Looking toward the future, there appears to be an opportunity to use the multiple KPIs measured at a process plant to assess the quantitative measure of risk levels at the facility on a time-variant basis.ExxonMobil Research Qatar (EMRQ) has partnered with the Mary Kay O'Connor Process Safety Center – Qatar (MKOPSC-Q) to develop a tool that monitors, in real time, the potential increases in risk levels as a result of pre-identified risk factors and process safety related data, using Bayesian Belief Networks (BN). The development of the tool involved two phases: 1) Development of a methodology that establishes the framework for the tool and 2) Development of the tool itself with the use of JAVA programming language. The overall tool is to be called PULSE, which stands for Process Unit Life Safety Evaluation.In this context, the paper presents a case study of the quantitative risk assessment of a process unit using BN. The different steps of the development of the BN are detailed, including: translation of a Bowtie into a skeletal BN, modification of the skeletal BN to incorporate reliability data, and insertion of equipment failure evidence into the BN for dynamic modeling. In addition, an overview of PULSE is presented. The outcomes of the dynamic modeling of the BN with real time insertion of evidence are discussed and recommendation for the framework for a dynamic risk assessment tool are made.

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study.

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety.

The Dynamic Risk Assessment and Management System: An Assessment of Immediate Risk of Violence for Individuals with Offending and Challenging Behaviour

Purpose Research on dynamic risk assessment has developed over the last 10 years and a number of variables have emerged as being possible predictors of future sexual and violent offences. These variables include hostile attitude/anger and compliance with routine. In 2002, Thornton (Sexual Abuse: A Journal of Research & Treatment14, 139) developed a framework for dynamic risk assessment which also includes distorted and dysfunctional attitudes, socio‐affective functioning, self‐management and, for sex offenders, sexual interests. In 2000, Hanson & Harris (Criminal Justice & Behaviour27, 6) provided empirical support for the predictive power of a number of dynamic factors which they split into stable and acute variables. In their research they found poor social supports, antisocial lifestyle, and poor self‐management strategies to be important in addition to co‐operation with supervision and hostile attitude. The present report describes the development of the Dynamic Risk Assessment and Management System (DRAMS) – an assessment for dynamic/proximal risk factors in people with intellectual disabilities.Method In a field trial, the DRAMS was administered by staff unfamiliar with the formal requirements of the assessment. They were familiar with other ward‐based assessments. Ten subjects were assessed for reliability purposes (total of 45 assessments) and five subjects were assessed consistently over a 3‐month period. Eighteen aggressive incidents were recorded in these five subjects and the relationship between DRAMS assessments and the incidents was calculated. Assessments were recorded on the day of the incident, the day before the incident and on a control day, which was at least 7 days before or after an incident.Results Four items achieved high reliability – mood, psychotic symptoms, self‐regulation, compliance with routine – in addition to total score. Two items achieved intermediate reliability – antisocial behaviour and problems with thinking/attitudes, and three items were not applicable to this client group in that none of the participants was ever rated on them – substance abuse, renewal of emotional relationships and victim access. One item had poor reliability – therapeutic alliance. There were significant differences between the DRAMS assessment on the control day and assessments both on the day prior to the incident and the day of the incident for three of the six subscales and total score.Conclusion Initial results would suggest that the DRAMS is a reliable instrument apart from the therapeutic alliance category. Initial signs suggest that it may be predictive of aggressive incidents in residential settings. Several developments to other settings and the client groups are discussed.

Constructing and testing a framework for dynamic risk assessment.

This paper describes the construction and testing of a framework for dynamic risk assessment. A review of previous studies identified 4 domains into which dynamic risk factors for sexual offending seem to fall. These were sexual interests, distorted attitudes, socioaffective functioning, and self-management. Psychometric indicators for 3 of the domains were identified, and 2 studies are reported using these indicators to test the framework. Study 1 divided men serving a prison sentence for a sexual offense against a child into 2 groups--those with a previous conviction of this kind (Repeaters) and those for whom this was the only time they had been sentenced for such an offense (Current Only). The Repeaters were found to show more distorted attitudes, worse socioaffective functioning, and poorer self-management than did the Current Only group. Study 2 used a simple algorithm to combine these psychometric indicators into an overall "Deviance" classification. Reconviction data was obtained for offenders classified as high, moderate, or low on Deviance. Sexual reconviction was found to be monotonically associated with the Deviance classification. Logistic regression analysis showed that both static variables (Static-99) and the Deviance classification made independent contributions to prediction. It is suggested that risk assessment procedures should combine these 2 approaches.