Risk-based techniques such as risk graph and Layer of Protection Analysis (LOPA) are used to determine the Safety Integrity Level (SIL) of safety instrumented functions to ensure that risk is reduced to a tolerable level. However, these techniques have some drawbacks. For instance, they need absolute and precise numbers to evaluate SIL parameters, which are rarely available or are highly uncertain. In addition, they are incapable of considering individual and societal risks simultaneously. Moreover, risk tolerance criteria are likely to be used incorrectly in the LOPA technique, and risk graph is difficult to calibrate. In the current paper, a novel comprehensive fuzzy arithmetic model has been developed to determine the required SILs in process industries. The fuzzy required Risk Reduction Factor (RRF) is calculated for both individual and societal risks. Fuzzy numbers are developed from crisp intervals, based on the expected interval of the fuzzy numbers. Expert fuzzy-scaled elicitation has been applied to obtain the SIL parameters. In the proposed model, the overall risk tolerance criterion and apportionment factor are defined as SIL parameters for both individual and societal risks to ensure that the applied risk criteria are compliant with the requirements of the system. In addition, an approach is introduced for determining the required SIL based on the fuzzy required RRF. The proposed methodology was demonstrated to alleviate the limitations, and thus, can be considered as a more precise alternative to the conventional methods.

Frequent unpredictable earthquake disasters such as the Turkey Earthquake in 2023 pose an increasing threat to oil tank farms since they may trigger major accidents and domino effects, resulting in casualties, economic losses, and environmental pollution. Unpredictable earthquakes are definitely difficult to prevent and thus resilience strategies such as emergency response should be applied to reduce losses. However, little attention has been paid to the quantitative resilience modeling of oil tank farms, resulting in difficulties in decision-making on resilience assessment and management. Therefore, this study proposes a quantitative seismic resilience model of oil storage tanks by using a dynamic agent-based modeling approach. This approach models the storage tank, active fault, and the environment as three independent agents with their attributes and behaviors. The interaction between agents can also be modeled through disaster evolution rules, and the consequences of interactions can be adjusted through adaptation and recovery strategies. The dynamic propagation of earthquake accidents and the evolution of potential domino effects can be quantified from a bottom-up perspective, thereby quantifying the seismic resilience of oil tank farms. A case study is carried out to illustrate the application of the developed model in oil tank farms and to analyze the sensitivity of different model parameters. The results show that the developed model can dynamically characterize the evolution of earthquake-induced domino effects as well as the emergency and restoration processes, supporting the decision-making on the allocation of resilience measures.

Frequent unpredictable earthquake disasters such as the Turkey Earthquake in 2023 pose an increasing threat to oil tank farms since they may trigger major accidents and domino effects, resulting in casualties, economic losses, and environmental pollution. Unpredictable earthquakes are definitely difficult to prevent and thus resilience strategies such as emergency response should be applied to reduce losses. However, little attention has been paid to the quantitative resilience modeling of oil tank farms, resulting in difficulties in decision-making on resilience assessment and management. Therefore, this study proposes a quantitative seismic resilience model of oil storage tanks by using a dynamic agent-based modeling approach. This approach models the storage tank, active fault, and the environment as three independent agents with their attributes and behaviors. The interaction between agents can also be modeled through disaster evolution rules, and the consequences of interactions can be adjusted through adaptation and recovery strategies. The dynamic propagation of earthquake accidents and the evolution of potential domino effects can be quantified from a bottom-up perspective, thereby quantifying the seismic resilience of oil tank farms. A case study is carried out to illustrate the application of the developed model in oil tank farms and to analyze the sensitivity of different model parameters. The results show that the developed model can dynamically characterize the evolution of earthquake-induced domino effects as well as the emergency and restoration processes, supporting the decision-making on the allocation of resilience measures.

Frequent unpredictable earthquake disasters such as the Turkey Earthquake in 2023 pose an increasing threat to oil tank farms since they may trigger major accidents and domino effects, resulting in casualties, economic losses, and environmental pollution. Unpredictable earthquakes are definitely difficult to prevent and thus resilience strategies such as emergency response should be applied to reduce losses. However, little attention has been paid to the quantitative resilience modeling of oil tank farms, resulting in difficulties in decision-making on resilience assessment and management. Therefore, this study proposes a quantitative seismic resilience model of oil storage tanks by using a dynamic agent-based modeling approach. This approach models the storage tank, active fault, and the environment as three independent agents with their attributes and behaviors. The interaction between agents can also be modeled through disaster evolution rules, and the consequences of interactions can be adjusted through adaptation and recovery strategies. The dynamic propagation of earthquake accidents and the evolution of potential domino effects can be quantified from a bottom-up perspective, thereby quantifying the seismic resilience of oil tank farms. A case study is carried out to illustrate the application of the developed model in oil tank farms and to analyze the sensitivity of different model parameters. The results show that the developed model can dynamically characterize the evolution of earthquake-induced domino effects as well as the emergency and restoration processes, supporting the decision-making on the allocation of resilience measures.

Due to the COVID-19 pandemic in 2020, the trade-off between economics and epidemic prevention (safety) has become painfully clear worldwide. This situation thus highlights the significance of balancing the economy with safety and health. Safety economics, considering the interdependencies between safety and micro-economics, is ideal for supporting this kind of decision-making. Although economic approaches such as cost-benefit analysis and cost-effectiveness analysis have been used in safety management, little attention has been paid to the fundamental issues and the primary methodologies in safety economics. Therefore, this paper presents a systematic study on safety economics to analyze the foundational issues and explore the possible approaches. Firstly, safety economics is defined as a transdisciplinary and interdisciplinary field of academic research focusing on the interdependencies and coevolution of micro-economies and safety. Then we explore the role of safety economics in safety management and production investment. Furthermore, to make decisions more profitable, economic approaches are summarized and analyzed for decision-making about prevention investments and/or safety strategies. Finally, we discuss some open issues in safety economics and possible pathways to improve this research field, such as security economics, risk perception, and multi-criteria analysis.

The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under uncertain and unpredictable circumstances, resilience becomes an essential property of a chemical process system that helps it better adapt to disruptions and restore from surprising damages. The resilience assessment needs to be enhanced to identify the accident's root causes on the level of technical-human-organizational interactions, and development of the specific resilience attributes to withstand or recover from the disruptions. The outcomes of resilience assessment are valuable to identify potential design or operational improvements to ensure complex process system functionality and safety. The current study integrates the Functional Resonance Analysis Method and dynamic Bayesian Network for quantitative resilience assessment. The method is demonstrated through a two-phase separator of an acid gas sweetening unit. Aspen Hysys simulator is applied to estimate the failure probabilities needed in the resilience assessment model. The study provides a useful tool for rigorous quantitative resilience analysis of complex process systems on the level of technical-human-organizational interactions.

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.

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.

Bayesian Network (BN) has been increasingly exploited to improve different aspects of Human Reliability Analysis (HRA), resulting in a new generation of HRA techniques, known as BN-HRA models. However, validating and evaluating the accuracy of BN-HRA models is still a challenging task. In this study, we have assessed and compared the performance of some of well-known BN-HRA techniques using human performance data obtained from an offshore evacuation simulation. Based on the role of data in quantifying the BN-HRA models, three categories of BN-HRA models have been considered: (i) BN-CREAM and BN-SPARH, which are based on predefined rules (rule-based methods), (ii) Bayesian Parameter Learning (BPL), which is entirely based on the available data (data-based method), and (iii) BN-SLIM model which is based on both the available data and the predefined rules (hybrid method). The results of the present study show that the data-based methods, i.e., BN-SLIM and BPL, in general outperform the rule-based methods. Cross-validation analysis further demonstrates the superiority of BN-SLIM over BPL, particularly in case of data scarcity.

Engineering risk management is comprised of managing operational safety risks on the one hand and managing physical security risks on the other. Although some basic management principles are obviously the same for both safety and security, some important conceptual and calculation differences exist, as is explained in this paper. For instance, safety risk is usually calculated based on the scenarios’ consequences and likelihoods, while security needs to be determined by the assessment of vulnerability, the likelihood of attack and potential consequences. Nonetheless, there are also many similarities. Conceptual models, metaphors and principles that have been elaborated in the safety domain during the past century, many of them based on major accidents and their investigation, can easily be translated to the security domain. In the present study, we will explain how physical security should be seen in relation to safety, and what models and principles, derived from safety science, can be employed to manage the security aspects associated with physical threats.

In the chemical industry, multi-hazard (toxic, flammable, and explosive) materials such as acrylonitrile are stored, transported, and processed in large quantities. A release of multi-hazard materials can simultaneously or sequentially lead to acute toxicity, fire and explosion. The spatial-temporal evolution of hazards may also result in cascading effects. In this study, a dynamic methodology called “Dynamic Graph Monte Carlo” (DGMC) is developed to model the evolution of multi-hazard accident scenarios and assess the vulnerability of humans and installations exposed to such hazards. In the DGMC model, chemical plants are modeled as a multi-agent system with three kinds of agents: hazardous installations, ignition sources, and humans while considering the uncertainties and interdependencies among the agents and their impacts on the evolution of hazards and possible escalation effects. A case study is analyzed using the DGMC methodology, demonstrating that the risk can be underestimated if the spatial-temporal evolution of multi-hazard scenarios is neglected. Vapor cloud explosion (VCEs) may lead to more severe damage than fire, and the safety distances which are implemented only based on fire hazards are not sufficient to prevent from the damage of VCEs.

Vapor cloud explosion (VCE) accidents in recent years such as the Buncefield accident in 2005 indicate that VCEs in process plants may lead to unpredicted overpressures, resulting in catastrophic disasters. Although a lot of attempts have been done to assess VCEs in process plants, little attention has been paid to the spatial-temporal evolution of VCEs. This study, therefore, aims to develop a dynamic methodology based on discrete dynamic event tree to assess the likelihood of VCEs and the vulnerability of installations. The developed methodology consists of six steps: (i) identification of hazardous installations and potential loss of containment (LOC), (ii) analysis of vapor cloud dispersion, (iii) identification and characterization of ignition sources, (iv) explosion frequency and delayed time assessment using the dynamic event tree, (v) overpressure calculation by the Multi-Energy method and (vi) damage assessment based on probit models. This methodology considers the time dependencies in vapor cloud dispersion and in the uncertainty of delayed ignitions. Application of the methodology to a case study shows that the methodology can reflect the characteristics of large VCEs and avoid underestimating the consequences. Besides, this study indicates that ignition control may be regarded as a delay measure, effective emergency actions are needed for preventing VCEs.

Corrosion is a significant concern causing tremendous losses to all pipeline operators. To combat this operational issue, new methods and tools are needed to analyze and model degradation, to predict failure, and finally to develop strategies for prevention, control, and mitigation of corrosion in pipelines. A practical inspection and maintenance program is crucial to prevent pipeline failures due to corrosion. Risk-based inspection (RBI) is an increasingly popular and trusted method to assess and develop inspection plans. However, the determination of optimal inspection intervals is still challenging in RBI. The present study aims to develop a dynamic Bayesian network (DBN)-based approach for optimization of inspection intervals. Based on inline inspection data and analytical corrosion propagation models, DBN is applied for the estimation of both the internal and external corrosion damage as well as the probability of failure (PoF). The cost of failure (CoF) is estimated based on typical cost categories relevant to pipeline accidents. Risk is calculated as the product of PoF and CoF. A utility function to combine both the risk and the annual cost of the inspection program is also developed. The optimal interval can be found based on the curve of the utility function. The proposed approach is demonstrated through a real-world case study on an operating pipeline.