Bow-tie Model Research Articles

A risk analysis model for underground gas storage well integrity failure

Loss of underground gas storage (UGS) well integrity can result in serious consequences. Risk analysis is critical to maintaining the integrity of a UGS well and reducing potential accidents. This study proposes a risk analysis model for UGS well integrity failure. First, we employ the bow-tie (BT) approach to establish a systematic well integrity failure model, qualitatively depicting the causal relationship between well integrity failure and potential consequences. Subsequently, hierarchical Bayes analysis (HBA) is used to model source-to-source uncertainty among the aggregated reliability data and provide reasonable estimates for the reliability parameters of components (i.e., well components and safety barriers). This process allows us to consider UGS well attributes using collected data. Furthermore, to address the issues of the BT approach in risk quantification, the developed BT model is mapped to a corresponding Bayesian network (BN). Predictive analysis is performed to estimate the occurrence probabilities of well integrity failure and potential consequences. Probability updating is performed to identify the key contributing factors given the observation of an undesired event. Finally, to obtain a more case-specific result, risk updating is conducted by using probability adapting of the BN and the new information accumulated over time. The results show that the proposed model can effectively handle uncertainty in risk calculation and provide more accurate results. This study has practical value for improving UGS safety.

Energy release and countermeasures for sand casting explosion accidents

Sand casting explosion accidents seriously threaten employees' lives and the harmony and stability of society, such as casualties, equipment trouble and environmental pollution. The main purpose of this study was to determine the water requirement for sand casting explosion accidents, and then corresponding safety measures were proposed to prevent such accidents. First, the relationship between the water condition and temperature for such accidents was achieved based on an energy release equation in compressed gas expansion and the ideal gas state equation. The volume of vapor expanded rapidly as the temperature rises, which was also the immediate cause of sand casting expansion accidents. Also, the water requirement for the sand casting expansion accidents decreased rapidly as the temperature rises. Then, a bow-tie model was adopted for preventing the causes and consequences of sand casting explosion accidents. Three main causes were identified, namely molten metal meeting ponding, failure of cavity exhaust and low strength sand of sand mold, and 11 prevention safety measures were adopted to prevent the causes of such accidents. In addition, three consequences were identified, namely casualties, equipment trouble and environment pollution, and ten mitigation safety measures were adopted to mitigate the consequences of such accidents.

A Bayesian Network model for risk analysis of deepwater drilling riser fracture failure

Drilling risers are crucial connection of subsea wellhead and floating drilling platform. Fracture failure of deepwater drilling risers is the most serious accident in offshore drilling, which would lead to disastrous consequences. This paper presents a Bayesian Network (BN) model to conduct risk analysis for fracture failure of deepwater drilling riser. A Bow-Tie (BT) model is developed to identify the risk factors associated with fracture failure and the potential consequences. Subsequently, evidence theory is used to calculate the prior probability of the input event, and the developed BT model is mapped into BN to carry out risk analysis of fracture failure of drilling riser. Finally, the probability updating is implemented using forward reasoning in BN model when new evidence is obtained, and a dynamic risk profile of fracture failure and consequence status are performed using probability adaption of BN with the occurrence of the new identified critical factors over a period of time. The application of the developed model is demonstrated through a case study, and some suggestions drawn from the investigation are presented to further mitigate the risk and the severity of accident consequences of drilling riser fracture failure during drilling operations.

DETECT - Quantifying the Risk of CO2 Leakage Along Fractures Using an Integrated Experimental, Multiscale Modelling and Monitoring Approach

To verify and demonstrate successful long-term geological CO2 storage to regulatory bodies and the public, it is critical to improve our understanding of the potential for CO2 migration from storage reservoirs along natural pathways. Currently, there are significant gaps in our understanding of multi-phase fluid migration in faulted and fractured caprocks. Caprocks are typically fine-grained mudstones, carbonates or evaporites, with low matrix permeability and high geochemical reactivity. Potential leakage rates depend on pressure gradients, fluid densities, viscosities and saturations, and the flow properties of the fracture networks. Fracture permeability is highly sensitive to fluid pressure and stress regime, and physical and chemical interactions taking place in the fracture network, including mineral dissolution and precipitation, swelling or shrinkage of clay minerals and hydro-mechanically driven fracture propagation. These combined effects can result in an increase or decrease in fracture permeability and network connectivity over different temporal and spatial scales. The highly coupled nature of these processes makes experimental parameterization and predictive modelling highly challenging, especially at the large temporal and spatial scales relevant to CO2 storage. Although some fundamental laboratory and modelling studies are available in the literature, an integrated study, involving a complete life cycle risk assessment of CO2 leakage through fractured caprocks is lacking. Risk analysis is further complicated by the fact that a leak can only be detected and quantified when geophysical or chemical monitoring tools are able to distinguish relevant changes in gas saturation, pressures or compositions compared to baseline levels. The DETECT research program, cofunded by the European Union and national governments as part the ACT initiative, intends to determine realistic flow rates across fractured and faulted mudstone caprocks, and aims to identify existing monitoring tools capable of detecting such fluid migration. For this purpose, the monitoring performance of state-of-the-art technologies will be compared with flow rate predictions from coupled hydro-mechanical flow and reactive transport simulations at single fracture, fracture network and reservoir-scales, which in turn have incorporated insights from a comprehensive laboratory study of stress and reactivity dependent fracture permeability. This improved understanding of the potential flow rates will feed into an integrated life cycle risk assessment using the established bowtie method to provide an overall picture of the natural paths via which CO2 leaks could occur from subsurface storage reservoirs. The bowtie model will be expanded to include quantitative risk assessment, with the goal of calculating the probability/likelihood of leakage across the caprock and estimating the risk reduction provided by monitoring.

Research of Vulnerability for Fresh Agricultural-Food Supply Chain Based on Bayesian Network

Unbalanced supply and demand, bottleneck of transport capacity, seasonal cycle, and other factors lead to fragile supply chain of fresh agricultural products led by the platform, impeding smooth operation of the supply chain and even causing disruption risk. This paper studies the short-term and long-term vulnerability of the platform leading fresh agricultural product supply chain under the influence of logistics capital flow and information flow, defines its structure and the meaning of its vulnerability, analyzes the vulnerability of each link, and finds out the existing weak links in the supply chain through empirical research. The probability of accident is quantitatively analyzed by using the Bayesian Network. Firstly, the bow-tie model is used to identify the cause and consequence of the accident, and then it is transformed into the Bayesian Network model; then, the “Precursor Incident” information and prior probability are introduced to derive the posterior accident occurrence probability, and the probability of accident occurrence changing with time is quantitatively analyzed; finally, the dynamic risk calculation of fresh agricultural product trading center dominated by a certain platform was carried out. The results show that, with the increase of supply chain operation time and Precursor Incident, the probability of short-term supply chain vulnerability and accident risk present a significant increase trend, while the probability of long-term supply chain vulnerability and accident risk present a significant decrease trend. Therefore, it is suggested that enterprises should establish a dynamic risk evaluation system to monitor and predict the probability of event vulnerability, pay attention to “Precursor Incident,” and take measures to reduce it, such as effective integration of supply chain principal information, timely improvement of information integrated technologies, and comprehensive training on food safety and moral credibility.

Design solutions to improve resilience of metro vehicle to blast events

Considerable effort is being devoted by researchers and stakeholders in order to improve the safety of metro systems with regard to terrorist attacks. The issue has been addressed by many researchers, taking into account the bow-tie model of safety management, in which the node is the terrorist attack, with on one side the causes of the attack (e.g. political issues, technical weaknesses) and on the other side the consequences (e.g. human, organisational, economical). The common goal is to implement a line of defence to isolate these causes and consequences, in order to prevent the attack from occurring and, should it happen, to mitigate its consequences as much as possible. This work takes into account the identified common attack methodologies and the behaviour of the surviving passengers in order to improve the situation management, assist the evacuation of and rescue to survivors. Moreover, technological improvements to the structure and critical systems of the vehicle have been devised and tested in real situation. This paper focuses particularly on the behaviour of people in blast situation, and presents the conclusions of the project on these effects and the related improvements of the vehicles design in order to improve resilience and survivability to blast.

Tracing and prediction analysis of an urban pipeline leakage accident based on the catastrophe DBN model

With trends in urbanization, many underground pipelines have been constructed, forming a huge spider web-like network, and accidents in such systems can lead to disastrous consequences, as observed in the Qingdao and Kaohsiung gas explosion accidents. For such a complex system, questions related to identifying the accident causes and predicting the evolving trends represent research challenges. Such accidents are often catastrophic and dynamic, and traditional methods are only appropriate for static accident analyses. Based on the above problems, an accident tracing and prediction method based on the bow-tie model and the catastrophe theory dynamic Bayesian network (DBN) model (C-DBN) is proposed in this paper. First, a fishbone diagram (FD) is used to analyze all possible causes of the accident, and the bow-tie and Bayesian network (BN) models are established according to the logical relationship. The conditional probability tables (CPTs) of the BN are obtained using the transformation rule and catastrophe theory. Dynamic analyses of the accident causes and development trends can be conducted using the powerful reasoning ability of the DBN, which can supply guidance for emergency response and rescue. Finally, the Kaohsiung gas explosion accident is used as an example to verify the feasibility of the method. The results show that the analysis results are highly consistent with the real scenario.

Safety assessment of petrochemical enterprise using the cloud model, PHA-LOPA and the bow-tiemodel.

Safe production is the foundation of the normal operations of petrochemical enterprises, and it helps maintain social stability. The main purpose of this study is to prevent petrochemical enterprise accidents by proposing a composite safety assessment approach based on the cloud model, preliminary hazard analysis–layer of protection analysis (PHA–LOPA) and the bow-tie model. First, the petrochemical enterprise and its relevant indicators were evaluated based on the cloud model. Second, the quantitative effect of the uncertainty transformation on the evaluation result of the cloud model was further analysed. This mainly includes the error analysis of the numerical characteristics under the conditions of few samples and small values. Third, the critical indicators such as shock and noise can be weakened and prevented by corresponding safety measures based on PHA–LOPA and the bow-tie model. After adopting two independent protection layers, the risk levels of shock and noise decrease from 3 to 2. Then, shock and noise were analysed in depth with the bow-tie model, and the causes and consequences were identified. Moreover, corresponding safety measures were taken to prevent accidents. The case study validated the validity and feasibility of the composite safety assessment approach proposed here.

Risk Assessment of Rail Haulage Accidents in Inclined Tunnels with Bayesian Network and Bow-Tie Model

Risk Assessment of Rail Haulage Accidents in Inclined Tunnels with Bayesian Network and Bow-Tie Model

Risk assessment of process system considering dependencies

Risk assessment is conducted in process systems to identify potential accident scenarios and estimate their likelihood and associated consequences. The bow-tie (BT) technique is most frequently used to conduct the risk assessment. It is a simple, comprehensive and straightforward technique; however, it considers independence among the causation factors (initiating events) of an accident scenario and the safety barriers in place to minimize the impact of the accident scenario. This is a serious limitation and can lead to erroneous results. This paper presents a simple yet robust approach to revise the Bow-tie technique considering interdependence. It employs copula functions to model the joint probability distributions of causations in the BT model of the accident scenario. This paper also analyzes the impact of dependence on two common logic gates used to represent the potential accident scenario. The probability of a potential accident scenario in a hexane distillation unit using both the traditional BT technique and the revised approach is compared. Results confirm that the revised approach is reliable and robust.

Research on a new aviation safety index and its solution under uncertainty conditions

Aviation safety has been an eternal theme since aircraft is invented, and safety risk assessment is taken as the important method to evaluate the aircraft safety, but it is usually difficult to compute and compare because the aviation risk is often composite, discontinuous and difficult to quantitative. To overcome these drawbacks, a new aircraft safety index under uncertainty conditions is proposed after the definition of aircraft safety was suggested based on the safety concept. After that, the safety performance function under given accident severity was provided through analyzing and describing uncertain factors of the aviation action. Combing with the application of Bow-tie model in the aviation accident fields, the flowchart and solution are suggested to compute the proposed aircraft safety index. Finally, taking the tire burst accident as an example, the feasibility of the proposed index and property of the solution are testified.

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will result in varying degrees and various types of risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative risk level and distribution in a HHMS to control major risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative risk analysis process more difficult. Additionally, a variety of uncertain risk data hinder the realization of quantitative risk analysis. A few quantitative risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative risk analysis method for HHMS was completed to obtain the risk level and distribution of high-risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain risk data for the occurrence probability (OP) and consequence severity (CS) in the risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative risk assessment result according to the analysis results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative risk analysis of one reciprocating pump system (RPS). The quantitative risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.

Risk analysis of urban gas pipeline network based on improved bow-tie model

Gas pipeline network is a major hazard source in urban areas. In the event of an accident, there could be grave consequences. In order to understand more clearly the causes and consequences of gas pipeline network accidents, and to develop prevention and mitigation measures, the author puts forward the application of improved bow-tie model to analyze risks of urban gas pipeline network. The improved bow-tie model analyzes accident causes from four aspects: human, materials, environment and management; it also analyzes the consequences from four aspects: casualty, property loss, environment and society. Then it quantifies the causes and consequences. Risk identification, risk analysis, risk assessment, risk control, and risk management will be clearly shown in the model figures. Then it can suggest prevention and mitigation measures accordingly to help reduce accident rate of gas pipeline network. The results show that the whole process of an accident can be visually investigated using the bow-tie model. It can also provide reasons for and predict consequences of an unfortunate event. It is of great significance in order to analyze leakage failure of gas pipeline network.

Hazard-Based Design of the Bow-Tie Method to Prevent and Mitigate Mine Accidents

The mining trade involves many complicated and interrelated variables—its complex environment, abundant machinery and a plethora of other contributors to accidents. In both developed and developing countries, mining accidents have caused many casualties. However, a universal risk assessment method for mining accidents is has not yet been implemented. Among risk assessment methods, the bow-tie has been used in different industry processes and systems and has proven effective. In this paper, the bow-tie model is utilized to investigate the relationship among mining accident risks, safety measures and possible consequences. The paper illustrates the hazards of mining accidents using US mine accident data. It also shows how the consequences of mine accidents are summarized by laws and regulations of different countries. This paper also introduces a series safety measures from Chinese safety standards and how the safety measures prevent and mitigate risks. At the end of the paper, a case of mine water inrush is applied using the bow-tie approach. The results show that the method is effective for analyzing mine safety.

Modelling the risks remotely piloted aircraft pose to people on the ground

Regulations for remotely piloted aircraft systems (RPAS) should have a strong foundation in, and traceability to, the management of the safety risks. This paper describes a new approach for modelling the risks associated with RPAS operations near populous areas to support the development of regulations and safety cases. A review found that existing models do not provide a simple means for incorporating the wide range of technical and operational controls into the risk analysis process. A Barrier Bow Tie Model (BBTM) is proposed as it focuses risk analysis, evaluation, and decision-making activities on the practical devices, people, and processes that can be used to reduce risk. Existing literature and practical controls were reviewed and used to define the components of the model and a case study is used to exemplify its application. More than 50 practical controls were incorporated into the model. The template barriers, controls, and graphical nature of the BBTM facilitated a simple comparison of the two case study RPAS operations and a more structured approach to the setting of airworthiness requirements taking into consideration the wide range of technical and operational factors that can be used to manage risk. The model provides the linkage between a regulation, associated controls, and how the controls contribute to a reduction in risk, which is necessary for the adoption of a risk-based approach to the regulation of RPAS. The BBTM provides a generic framework that can be used to structure the development of safety cases for any RPA operation.

Research on Controlled Flight Into Terrain Risk Analysis Based on Bow-tie Model and WQAR Data

Controlled flight into the terrain (CFIT) is the main types of civil aircraft accidents. In this paper, we bulid the risk model for CFIT event, which is based on Bow-tie analysis model. At the same time, we calculated and verified this model by using China civil aviation flight quality monitoring station’s A320 fleets of flight data, and analyzed the results based on the time dimension and China's domestic airport dimension, finally achieved the warning function of CFIT event risk.

Quantitative risk prognostics framework based on Petri Net and Bow-Tie models

A simulation framework based on the Petri Net model is proposed in this paper used for performing quantitative risk prognosis through extending the Bow-Tie model. A Petri Net model is built to include features, specific to assets, such as the condition of the asset, the projected operational usage, inspection and maintenance policies and degradation process, so that the future condition of the asset over time can be estimated. Several new Petri Net modelling features which advance the traditional Bow-Tie approach are proposed, such as asset usage generating and usage dependent transitions, and the possibility of entering evidence about the actual condition of the asset through the use of truncated distributions. Monte Carlo simulation method is used to simulate the developed Petri Net model over a selected time frame, in order to obtain statistics necessary to perform risk assessment using the Bow-Tie model. The paper reports on the overall proposed methodology and then focusses on the development of the Petri Net model. The methodology is applied in risk prognostics of operating an underground passenger lift. In particular, the combination of the Petri Net and the Bow-Tie models is illustrated to predict the likelihood and the consequences of an event when a lift gets stuck in a shaft between landings.

The Application of Bow-Tie Method in Hydrogen Sulfide Risk Management Using Layer of Protection Analysis (LOPA)

Safety systems need to be used in strong and stable ways to achieve the objectives and goals of organizations. The main role of safety systems is highlighted ever than before in maintaining personnel health, environmental protection and improves the reputation of the organizations. Proper functioning of safety system depends on the reliability and the failure probability of the system, which determines the integrated system safety. In this regard, this study aimed to H2S risk management using bow-tie model with an emphasis on Layer of Protection Analysis (LOPA). An oil processing and gas injection plant is selected as a case of study with considering the high concentration of H2S (130,000 ppm) as well as very high pressure of gas injection (410 bars). This work commences when hazardous regions is categorized according to H2S gas leakage resources which followed by H2S risk assessment (bow-tie model). In the following stage, intelligent safety systems were investigated as one of the LOPAs. Thus, the elements of intelligent safety systems are specified. Based upon the software-defined logic, block diagrams were determined. Then, Probability of Failure on Demand (PFD) and Safety Integrity Level (SIL) were attained. PFD of block diagrams was calculated, and corresponding SIL was obtained using Reliability Block Diagram and the relationships between PFD and reliability. As a result, each of elements or block diagrams was considered the weak points. Accordingly, solutions were proposed to reduce the adverse effects and promote SIL to improve safety performance of plant.

Study of dynamic risk management system for flammable and explosive dangerous chemicals storage area

In order to improve the ability of emergency management for storage area of dangerous chemicals, a framework of risk management technical system about the flammable and explosive dangerous chemicals was proposed. Combined with the dynamic changes of dangerous sources, risk analysis for storage area of dangerous chemicals was conducted based on the Bow-Tie model. Dynamic hazards analysis method and classification system were established based on Set Pair Analysis, updating results in real time with the storage capacity and accident characteristics. Integrating with the GIS, Internet of Things, detection and sensing information technology, a dynamic security monitoring system was proposed. Based on the database technology, the emergency decision support system of dangerous chemicals was made, including storage date, emergency resource, emergency plans, information of risk, etc. Using the dynamic risk management system, it can effectively achieve the goal of dynamic supervision, risk identification, real-time monitoring as well as assisting the emergency decision making of dangerous chemicals in the whole life cycle. All models are comprehensively composed of the framework of dynamic risk management system, including identification, classification, assessment, supervision and emergency management.

Risk Analysis on Leakage Failure of Natural Gas Pipelines by Fuzzy Bayesian Network with a Bow-Tie Model

Pipeline is the major mode of natural gas transportation. Leakage of natural gas pipelines may cause explosions and fires, resulting in casualties, environmental damage, and material loss. Efficient risk analysis is of great significance for preventing and mitigating such potential accidents. The objective of this study is to present a practical risk assessment method based on Bow-tie model and Bayesian network for risk analysis of natural gas pipeline leakage. Firstly, identify the potential risk factors and consequences of the failure. Then construct the Bow-tie model, use the quantitative analysis of Bayesian network to find the weak links in the system, and make a prediction of the control measures to reduce the rate of the accident. In order to deal with the uncertainty existing in the determination of the probability of basic events, fuzzy logic method is used. Results of a case study show that the most likely causes of natural gas pipeline leakage occurrence are parties ignore signage, implicit signage, overload, and design defect of auxiliaries. Once the leakage occurs, it is most likely to result in fire and explosion. Corresponding measures taken on time will reduce the disaster degree of accidents to the least extent.