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As one of the most essential process facilities, the natural gas pipeline may be affected by various hazards and result in frequent accidents. Although Bayesian Network (BN) and other methods have been applied for risk assessment of gas pipelines, most attempts rely on experts instead of data. In this paper, a novel risk assessment model integrating Knowledge Graph (KG), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and BN is proposed to analyze gas pipeline accidents in a data-driven way to minimize the reliance on experts for the current BN-based approach. First, the KG is used to extract and illustrate the causal network from accident reports on the Internet instead of a limited number of experts. Then, DEMATEL is applied to quantify the complex correlations in the causal network to simplify the topology structure and convert it into a BN structure. Moreover, by conducting BN analysis, a probabilistic causation model of gas pipeline accidents is established to identify critical hazards, predict potential consequences and optimize risk reduction strategies. The proposed model can more objectively support the safety management and risk reduction of natural gas pipelines and other process installations in the digital age. • Knowledge graph, DEMATEL, and BN are integrated for gas pipeline risk assessment. • Applying knowledge graph and DEMATEL to elicit BN in a data-driven way. • Critical hazards are identified and potential consequences are quantitatively predicted. • The optimal risk reduction strategy for natural gas pipeline accidents is put forward. [ABSTRACT FROM AUTHOR]

As the scale of the power grid becomes larger, the requirements for transmission reliability are getting higher. Due to the large geographical span and the harsh environment of the power transmission line, it has become the most severely affected equipment of the power grid by natural factors. However, the quantitative assessment of transmission line tripping accidents caused by multiple natural hazards has rarely been investigated. In this study, a risk analysis method to probabilistically analyze the tripping accidents of power transmission lines caused by wildfire, lightning, strong wind and ice storm was proposed. The analysis process consists of comprehensively identifying the risk of tripping accidents based on fault tree analysis and dynamically performing the predictive analysis of tripping accident evolution process in transmission line from causes to consequences based on Bayesian network. Critical risk evolution paths corresponding to four natural hazards are determined through a 72-node BN. The source risks of the four critical risk evolution paths are artificial ignition source from the wildfire path, aging from the lightning path, thoughtless of surrounding environment from the strong wind path and wind effect from the ice storm path. The countermeasures of tripping accidents are derived through the source risks and synergy between risks in three scenario analysis. This study is expected to examine the key challenges of risk management in power grid tripping accidents, which provides technical supports for accident preventing, handling and recovering of tripping accidents of the power transmission line according to "scenario–response"-based hazard response strategy. [ABSTRACT FROM AUTHOR]

• An integrated model based on DHSI, BN modeling and risk analysis is proposed for risk assessment of utility tunnels. • The risk evaluated by economic loss and casualty of UUT accidents can be predicted. • The integrated method is demonstrated to evaluate the risk level of a real-world utility tunnel. With the rapid urbanization, urban underground utility tunnels have seen fast growth in China in the past few years. Urban utility tunnels can house various kinds of city 'lifelines' such as natural gas pipeline, heat pipeline, water supply system, sewer pipeline, electricity and telecommunication cables, which are of great significance to guarantee essential flows of energy, information and logistics for urban life. If a utility tunnel accident occurs, the consequences could be catastrophic. Risk assessment has been an important tool to examine the safety performance of industrial facilities and the effectiveness of safety measures. In this study, an integrated model based on dynamic hazard scenario identification (DHSI), Bayesian network (BN) modeling and risk analysis is proposed for risk assessment of urban utility tunnels. The worst-case scenario of urban utility tunnel accidents is identified by DHSI and modelled by BN. Meanwhile, risk analysis is conducted based on the results of BN considering casualties and economic losses. Finally, the integrated method is applied to evaluate the risk level of a real-world utility tunnel. The results indicate that the integrated quantitative risk assessment framework is an alternative and effective tool for safety assessment and land-use planning of urban utility tunnels. [ABSTRACT FROM AUTHOR]

With the rapid development of the hydrogen industry, an increasing number of hydrogen refueling stations (HRSs) are being built in urban areas with a high density of buildings and population. As a kind of emerging clean fuel, hydrogen is also highly flammable and explosive. So, the risk of multiple accidents in urban HRSs has attracted more and more concern. In this paper, an all-Process Hydrogen Accident Risk Assessment (PHARA) model that combines event tree analysis, fuzzy Bayesian network (FBN), HyRAM, and Probit model is proposed. Based on sensitivity analysis and influence strength analysis of FBN, the critical hazards of hydrogen refueling stations are identified. The risk management priority is determined by comparing the comprehensive risk value of each functional unit of HRSs. Then, the HyRAM-based quantitative consequence analysis is carried out for the high-risk unit, and the flammable zone, fatality zone, and relative safe distances of jet fire and explosion are given in a standardized urban hydrogen refueling station. The proposed PHRAR model can provide technical support for decision-makers to prevent accidents, control risk, and optimize the layout of HRSs. [ABSTRACT FROM AUTHOR]

• A Bayesian-Catastrophe-EPE model for LNG risk assessment is proposed. • The BCEPE model can objectively quantify the causal relationship of LNG risks. • Roll over and misoperation are identified as the critical hazards of LNG leakage. • Risk (hazard) correlations are quantified to provide technical supports for effectively controlling risks. Increasing quantities of natural gas are transported as liquefied natural gas (LNG) worldwide, and LNG is also proposed for use as a clean fuel for ships and trucks. This scenario raises concerns for the safety of LNG bunkering and storage at ports, due to the potentially severe accidents that may arise from LNG leakage. In this paper, an integrated quantitative risk assessment model for LNG bunkering and storage at ports based on Bayesian-Catastrophe-EPE (Energy transfer theory, Preliminary hazard analysis and Evolution tree) method was proposed. The energy-based EPE model was used to derive Bayesian network (BN) causal structure, and the catastrophe theory was employed to deal with experts' judgment to determine the conditional probability tables of BN. The proposed BN-based risk assessment model can provide a novel perspective to identify hazards and risks, and to assess the evolution process of LNG accidents from causes to consequences. The results of scenario analysis of typical LNG accidents demonstrate the soundness and applicability of the proposed model. Moreover, sensitivity analysis was implemented to identify critical hazards and quantify the correlations between each element considered in LNG accidents. The proposed risk assessment framework is of great significance to widen the technical tools available to support safety assessment and loss prevention of LNG bunkering and storage at ports. [ABSTRACT FROM AUTHOR]

• A risk analysis method of terrorist attacks on port's LNG Storage tanks is presented. • This article conducted the risk analysis from multiple dimensions. • Different scenarios are used to analyze potential consequences. In recent years, with the increasing demand for liquefied natural gas (LNG) worldwide, a large amount of LNG is stored, processed, and transported in ports. The port's LNG storage tanks may become a hot target for terrorist attacks because the failure or destruction of these facilities will put markets, cities, and the environment at high risk. This article presents a new risk analysis method of terrorist attacks on the port's LNG Storage tanks. The steps are as follows: (1) Identify risk factors from a multidimensional perspective. (2) Analyze the risk using the Bayesian network and event tree. (3) Evaluate and quantify the accident consequence. (4) Use knowledge graph to store risk knowledge. We verify the effectiveness of the method through case studies. The defence capabilities of the police and different prevention strategies were studied. The results show significant differences in accident consequence between different defensive and emergency response forces. We also conducted nine scenario analysis, which could provide a theoretical basis and method support for public security and urban risk management departments' security prevention decisions. [ABSTRACT FROM AUTHOR]