Historical data analysis shows that escalation accidents, a so-called domino effect, may have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different to the analytical or Monte Carlo simulation approaches, which normally study the domino effect phenomenon at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents while the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can easily be applied to large-scale complicated cases.@en

The growing trend of interconnecting two or more chemical process or storage facilities represents a critical safety issue, since an accident can easily escalate from an industrial establishment to the nearby plants resulting in a domino effect. However, common safety analyses often ignore cascading events in chemical tank farms, their complex and transient evolution, and mitigation effects of add-on safety measures. The aim of the present work is to develop a structured approach for the assessment of complex domino events accounting for the influence of safety barriers. The approach is based on the adoption of Agent-based Model and Simulation for the assessment of Domino effect in presence of add-on Protections (DAMS-P). For the first time, the assessment of mitigated cascading events in chemical tank farms is carried out accounting for the transient evolution of multiple scenarios and related synergistic effects, and the effect of safety barriers and their possible time-dependent degradation. A verification of DAMS-P is firstly performed through the comparison against analytic probability evaluation based on event tree analysis and tested through the application of industrial cases. The results obtained constitute a useful support for decision-making and for the identification of critical barriers and their performance evaluation.

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Industrial facilities where relevant quantities of hazardous chemicals are stored or processed may be direct or indirect targets of malicious acts of interference, including cyber or physical attacks. In the case of direct attacks, one or more process units may be damaged within the installation, often with the clear willingness of triggering domino effects escalating the primary event and possibly involving the entire installation. Also indirect attacks may be relevant, where the damage or destruction of neighboring sensible civilian targets is the primary aim of the action, but domino effects may be generated by the effects of the interaction with primary, large-scale accidents occurring outside the physical border of the facility. Escalation may result in cascading events that may heavily involve and damage the industrial facility of concern.@en

Physical Security in the Process Industry: Theory with Applications deals with physical security in the field of critical infrastructures where hazardous materials are a factor, along with the state-of-the-art thinking and modeling methods for enhancing physical security. The book offers approaches based on scientific insights, mainly addressing terrorist attacks. Moreover, the use of innovative techniques is explained, including Bayesian networks, game-theory and petri-networks. Dealing with economic parameters and constraints and calculating the costs and benefits of security measures are also included. The book will be of interest to security (and safety) scientists, security managers and the public at large.

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The propagation of accidents among process units may cause amplification of accident magnitude, resulting in a domino effect chain. Several catastrophic accidents occurred in the process and chemical industry presented these features. Hence, research efforts have been given to the analysis of the domino effects in order to enhance prevention and mitigation strategies. In this work, challenges of analysing domino effects in the chemical industries are discussed, highlighting that quantitative analytic approaches suffer from the complexity on assessing domino effects, especially when dealing with simultaneous accidents propagating among multiple units. Therefore, a bottom-up modelling approach, namely, the agent based modelling and simulation (ABM&S) approach, is introduced for analysing domino effects. Moreover, a prototype model for assessing domino effects in the chemical industries by using agent based modelling and simulation (DAMS) is given and further extensions of the prototype model is also discussed, highlighting the potential benefits.@en

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases.

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The propagation of fire-induced domino effects in chemical plants largely depends on the primary fire scenario, on separation distances between the units, and on the presence of fire protection barriers. Passive and active safety barriers are widely employed to prevent or delay the initiation or propagation of domino effects. In the present study, a methodology has been developed based on Bayesian network to account for the impact of such safety barriers on the propagation of fire domino scenarios. The Bayesian network has been extended to a limited memory influence diagram in order to identify a cost-effective allocation of additional safety barriers to further mitigate the fire propagation. The application of the methodology has been demonstrated using a chemical tank farm. The results are in good agreement with the results of a graph theoretic approach developed in a previous study, proving the reliability of the developed methodology in cost-effective protection of process plants.@en

The threat originating from deliberate attacks against process and chemical sites where relevant quantities of hazardous substances are stored or processed is a credible one, as confirmed by the attacks carried out in France in the summer of 2015. Although such chemical sites implement multiple layers of protection to address safety-related accidents, they often get behind with providing adequate attention and resources to the protection of assets against external adversaries or, in other words, terrorist threats. The lack of a consolidated practice in the risk-informed implementation of security countermeasures goes hand in hand with the existence of very few rigorous approaches to the effectiveness assessment of security systems, and in particular of physical protection systems (PPSs). The present study addresses the issue of quantitatively assessing the effectiveness of physical security systems adopted to protect process and storage facilities, recognized as high-consequence loss physical assets. To this purpose, the security functions of PPSs were analyzed to guide the selection of performance parameters. Experts from the industry were involved to identify variables and influencing factors relevant to the successful accomplishment of security functions. Experience-based judgments on performance, to be expressed in terms of probabilistic estimates, were also obtained. An insight into the belief that experts hold on the probability of PPSs effectively preventing the success of an external attack and in the identification of critical elements was obtained. The application of the results to the quantitative assessment of the contribution of security controls in reducing vulnerability was also addressed.

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Chemical and process facilities may be the target of external acts of interference, aimed at causing cascading events, which may escalate into severe fires, explosions or toxic dispersions. Recent accidents that occurred in European chemical facilities presented these features, showing that industry must address with the greatest urgency the need of increasing the attention to security issues. Objective, performance-based methods to verify the adequateness of the resources dedicated to the protection of assets against external attacks are needed. In the present study, a probabilistic risk analysis approach supported by a model based on Bayesian Networks is adopted to address the quantitative assessment of the attack likelihood and to incorporate the functional analysis of physical protection systems (PPS) applied the security of process and storage installations. A case study of industrial interest is analysed to exemplify the methodology, which may be adopted to evaluate the PPS in place in a given facility. The methodology also allows for a quantitative evaluation of attack success credibility and for the identification of the more critical escalation scenarios, thus supporting safety and security reviews of chemical and process facilities.

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