Pool fire is a typical example of fire accidents in chemical process industries. Since fire researchers have implemented a variety of measurements to gain insights into pool fire and to prevent fire disasters, there is a need to illustrate how pool fire models influence the risk assessment results. This review intends to consecutively discuss the effect of different physical factors on the burning behavior of pool fire and fire risk assessment. For the most part, this review extracts representative works from abundant pool fire articles in the last years and is subdivided into mass burning rates, entrainment, flame height, pulsation, radiation transfer sections, and risk assessment. On the basis of the latest research, it is indicated that new fire models can provide more accurate and reliable assessment results than previous models. They are not only to reduce the cumbersome work and resources but also to validate computational fluid dynamics (CFD) models that are essential components of performance-based design in fire prevention. Consequently, providing the latest information about how pool fire evolves and how risk assessment is affected, this review paper would be advantageous to fire experts in the future.

Vapor cloud explosion (VCE) accidents such as the Jaipur explosion in 2005 manifest that VCEs may lead to unpredicted overpressures, resulting in catastrophic domino effects. Many attempts have been made to assess VCEs and the subsequent domino effects in the process and chemical industry, whereas little attention has been paid to the spatial–temporal evolution of VCEs. Thus, this chapter provides a dynamic methodology based on the discrete dynamic event tree to assess the likelihood of VCEs and possible subsequent domino effects. The developed methodology includes six steps: identification and characterization of loss of containment scenarios, analysis of vapor cloud dispersion, identification and characterization of ignition sources, explosion frequency assessment, overpressure calculation, and escalation assessment. Given a release scenario, by applying the developed methodology, we can obtain the probability of VCEs, the likelihood of domino effects, and the damage probabilities of installations exposed to overpressure.

Domino effects may be induced by both intentional and unintentional threats. To deal with possible intentional and unintentional domino effects, this chapter develops an integrated safety and security management framework. First, safety and security management principles are introduced to show the similarities and differences between safety management and security management. Then an integrated safety and security management framework is proposed based on risk assessment and management principles. In this framework, safety measures and security measures are integrated and divided into three categories: detection measures, delay measures, and emergency response actions. This framework mainly consists of six parts: chemical plant description, threat and hazard identification, the vulnerability of installations subject to hazards and threats, the vulnerability of installations exposed to domino effects, consequence analysis, risk treatment, and risk reduction. According to the framework, protection strategies encompassing both safety and security can be formulated to obtain an acceptable domino effect risk.

Barriers are used in various forms to assure the safety of chemical plants. A deep understanding of the literature related to safety barriers is essential to tackle the challenges in improving their design and management. This paper first provides an overview of the history of the development of the safety barrier concept. Subsequently, this paper elaborates a systematic review of the definition, classification, evaluation, performance assessment, and management of safety barriers in the chemical process industries. Based on the literature review, this study proposes a practical classification of safety barriers benefiting the identification of performance indicators and the collection of indicator-related data for safety barriers. The safety barrier functions are extended and illustrated by involving the resilience concept. Performance assessment criteria are proposed corresponding to the adaptability and recoverability of the safety barriers. Finally, the management of safety barriers is discussed. The roadmap for future studies to develop integrated management of safety and security barriers to ensure the resilience of chemical plants is suggested.

To objectively grasp the current situation and development trend of resilient cities or communities (RC) research. The articles in Web of Science (WOS) Core Collection databases from 1995 to 2022 were used as a sample, and bibliometrics was used to statistically analyze the year of publication and number of articles, highly cited documents and keyword hotness in this field. VOSviewer was used to explore the knowledge graph of RC research documents. The results show that: the development process is roughly divided into 3 periods: no attention (1995–2004), starting (2005–2014), and rapid growth (2015–2021). The journal “Sustainability” and “International journal of disaster risk reduction” are the key journals publishing RC research. Serre and Shaw are the most productive authors. The USA is still the leading country in this field of RC. Colorado State Univ, Texas a&m Univ, and Delft Univ Technol are the main research institutions. The keyword analysis indicates the hot topics in different periods. Moreover, several limitations and some recommendations for future research on RC are also given based on this.

This book introduces three domino effect models that can be used for risk assessment of both intentional domino effects and unintentional domino effects. Based on these risk assessment models, three chapters on domino effect management are provided to prevent and mitigate domino effects triggered by unintentional events or intentional attacks.

Data uncertainties caused by the lack of knowledge and nature variation or randomness present vital challenges to domino effects modelling. To improve the assessment of the propagation probabilities and consequences of the domino-effect accidents, the influence of various types of uncertainties on risk assessment results needs to be investigated. However, a systematic identification of data uncertainties in domino effects has not been studied yet. In the current study, the data uncertainties in different categories (accidental, Natech, and intentional) of domino events are identified thoroughly based on historical data and literature. Meanwhile, the possible sources of the identified uncertainties are analysed by considering the environment, safety management, and operation factors. Finally, we discuss possible solutions to model uncertainties in risk assessments of domino effects. This study is a pilot study for uncertainty analysis and helps to identify the critical uncertainties that are of necessity to be considered in the domino effect risk assessments.

Fires are the most common scenarios in domino effect accidents, responsible for most of the domino effects that occurred in the process and chemical industry. The escalation induced by fire is delayed since the build-up of heat radiation needs time. As a result, a fire-induced domino effect is a spatial–temporal evolution process of fires. To address the dynamic characteristics, a Domino Evolution Graph (DEG) model based on dynamic graphs is developed in this chapter. The DEG model considers synergistic effects, parallel effects, and superimposed effects and overcomes the limitations of “probit models” in the second and higher-level propagations. Compared with past risk assessment methods for domino effects, the DEG model can rapidly deliver the evolution graphs (paths), the evolution time, the likelihood of domino effects, and the damage probability of installations. Therefore, the DEG model can be applied to domino risk assessment at the chemical cluster level and support the allocation of safety and security resources for preventing and mitigating fire-induced domino effects.

We witness many severe accidents in different sectors worldwide every year, resulting in fatalities, injuries, environmental pollution, property loss, etc. Safety management aims to use interventions to prevent these undesired events and thus avoid different kinds of loss. Various interventions that have different safety performances and costs are available for managers; one safety intervention may have multiple functions, such as avoiding fatalities and protecting the environment. As a result, we need to know the value of safety when deciding on investment in interventions. To support decision-making on safety management, the Safety & Security Science Group in Delft University of Technology (TUD) conducted a project on the value of safety to get insight into the values considered in the context of safety. Four research questions have been answered, as follows: what are the values of safety? what methods are used to measure the value of safety? what are the limitations of past research? what gaps have been identified? what is the roadmap for future safety management?

In light of possible severe consequences of unintentional and intentional domino effects, an integrated domino effect management framework was introduced in Chap. 5 to prevent and mitigate domino effect risk. In this chapter, an economic approach based on safety economics is developed to obtain the optimal protection strategy. First, we introduce the concepts and approaches used in safety economics (Chen et al. in Saf Sci 14, 2021). Then, a domino effect management approach is developed based on cost–benefit analysis and game theory (Chen et al. in Process Saf Environ Prot 134:392–405, 2020). In this approach, the disproportion factor (DF) is employed in the cost–benefit analysis to determine whether a protection strategy is recommended. Besides, an optimization algorithm called “PROTOPT” is developed to achieve the optimal protection strategy.

Since the 1990s, domino effects have obtained increasing attention in the process and chemical industry. This chapter reviews the research on the assessment and management of domino effects. This chapter first summarizes the definitions, characteristics, and classifications of domino effects. The vulnerability models related to domino effects are reviewed, including deterministic methods, probabilistic methods, and CFD/FEM methods. Next, modeling methods are divided into three categories, and management strategies are divided into five categories. Then the representative assessment methods and management strategies in chemical plants and clusters are analyzed in detail. Besides, these assessment and management approaches are compared and analyzed to propose their applications. Moreover, this chapter identifies research gaps in this field, which provides the motivations of the chapters of this book.

Chemical process industries are threatened with accidental and intentional adverse events because of the storage and operation of large quantities of hazardous substances. Safety and security barriers play important roles in protecting the chemical plants from safety and security-related undesired events and mitigating the potentially catastrophic consequences. Aiming to identify major accident scenarios in terms of both safety and security and determine the corresponding safety and security barriers, a novel approach based on MIMAH (methodology for identifying major accident hazards) and historical data analysis is proposed. In this approach, the MIMAH is extended to identify accident scenarios related to safety, physical security, and cyber security by using a combination of bow-tie analysis and attack tree analysis. Then, data analysis is conducted to supplement the identified major accident scenarios before the critical safety and security barriers can be identified and illustrated based on an integrated bow-tie and attack tree model. This study helps to identify major hazards considering both safety and security perspectives and supports the integrated assessment and management of safety and security barriers in the chemical process industries.

In a coupled domino effect, hazardous scenarios such as toxic release, VCE, and fire can simultaneously or sequentially occur. Chapters 2 and 3 only consider domino effects that are caused by fire or VCE. Therefore, this study develops a dynamic method called “Dynamic Graph Monte Carlo” (DGMC) to model the evolution of coupled domino effects and assess the vulnerability of humans and installations exposed to such scenarios. In the DGMC model, a chemical plant is represented by a system with multiple agents. The system consists of three types of agents: hazardous installations, ignition sources, and humans. Monte Carlo simulation is used to address the uncertainties in the evolution and interdependencies among the agents. By applying the developed algorithm, the death probability of humans and the failure probability of installations exposed to multiple possible hazardous scenarios can be obtained. Moreover, we can also obtain the possible evolution paths, evolution time nodes, and ignition times by using the developed model and algorithm.

An accident within a chemical plant may trigger escalation effects, leading to a catastrophic degradation of operating performance. Due to possible severe consequences of domino effects, safety and security measures are needed to prevent and mitigate domino effects in chemical industrial areas. However, safety and security measures may be insufficient for tackling unpredictable and unpreventable domino effects induced by multi-target attacks or natural disasters. Therefore, This chapter develops a resilient-based approach for domino effect management in the process and chemical industry (Chen et al. in Reliab Eng Syst Saf, 2021; Chen in A dynamic and integrated approach for modeling and managing domino-effects. Delft University of Technology, 2021). A dynamic stochastic methodology is developed to quantify the resilience of chemical plants. In this methodology, a “resilience evolution scenario” consists of four stages: disruption, escalation, adaptation, and restoration. A resilient chemical plant depends on resistant capability, mitigation capability, adaptation capability, and restoration capability. The uncertainties in the disruption and escalation stages are modeled by the dynamic Monte Carlo method. Possible resilience scenarios are obtained by sampling random data. Then, the resilience of a chemical plant can be determined based on the resilience scenarios.

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.

Process and chemical industrial areas consist of hundreds and even thousands of installations situated next to each other, where quantities of hazardous (e.g., flammable, explosive, toxic) substances are stored, transported, or processed. These installations are mutually linked in terms of the hazard level they pose to each other in the system. As a result, a primary undesired disruption (e.g., an accidental event, intentional attack, or natural disaster) may escalate to nearby installations, triggering a chain of accidents. This phenomenon is well known as the potential for “knock-on effects” or so-called “domino effects”. This dissertation is devoted to modeling the spatial-temporal evolution of domino effects, preventing the escalation, mitigating the consequences, thereby developing a safer, securer, and more resilient chemical industrial area.

Many construction accidents occur in China each year, leading to a large number of deaths, injures, and property losses. Due to the outbreak of COVID-19, little attention is paid to construction safety, resulting in severe accidents. To prevent construction accidents and learn to how address safety issues in future pandemics, this study proposed an improved STAMP (Systems Theoretic Accident Modeling and Processes) model to analyze the collapse accident of the Xinjia Express Hotel used for COVID-19 quarantine in China. Through the application of the STAMP approach, the causes of the construction accident and the relationship between various causal factors are analyzed from a systematic perspective. The identified causes are divided into five categories: contractors, management of organizations, technical methods, participants, and interactive feed-back. Finally, safety recommendations are drawn from this study to improve construction safety and safety management in pandemics.

Subsea gas release is an industrial hazard that can impose fire hazards on offshore facilities near the gas surfacing area. However, risk assessment of the fire caused by subsea gas release is challenged due to inadequate recognition of the knowledge of subsea gas release mechanism and resulting hazards. At present, minimal researches involving risk assessment of offshore fire resulting from a subsea gas release were reported, and this paper is an extension of the previous works on subsea gas behavior. This paper focuses on modeling fire risk on offshore facilities due to subsea gas release. A numerical simulation is carried out using the Computational Fluid Dynamic technique of Fire Dynamics Simulator (FDS) to analyze fire propagation characteristics and assess the impact of fire on personnel and assets. A probit model is adopted to calculate the probabilities of injury or death caused by fire hazards. This study also investigates the effect of wind speed, gas release rate and the distance between gas pool and platform on fire impacts and casualty probabilities. The present study can support safety measure design to mitigate or avoid the impacts of offshore fire events from subsea gas release.

A disruption to hazardous (flammable, explosive, and toxic) material (HAZMAT) storage plants may trigger escalation effects, resulting in more severe storage performance losses and making the performance restoration more difficult. The disruption, such as an intentional attack, may be difficult to predict and prevent, thus developing a resilient HAZMAT storage plant may be a practical and effective way to deal with these disruptions. This study develops a dynamic stochastic methodology to quantify the resilience of HAZMAT storage plants. In this methodology, resilience evolution scenarios are modeled as a dynamic process that consists of four stages: disruption, escalation, adaption, and restoration stages. The resistant capability in the disruption stage, mitigation capability in the escalation stage, adaption capability in the adaption stage, and restoration capability in the restoration stage are quantified to obtain the HAZMAT storage resilience. The uncertainties in the disruption stage and the mitigation stage are considered, and the dynamic Monte Carlo method is used to simulate possible resilience scenarios and thus quantify the storage resilience. A case study is used to illustrate the developed methodology, and a discussion based on the case study is provided to find out the critical parameters and resilience measures.