Dynamic Risk Assessment and Management of Domino Effects and Cascading Events in the Process Industry

By Valerio Cozzani and Genserik Reniers

Dynamic Risk Assessment and Management of Domino Effects and Cascading Events in the Process Industry provides insights into emerging and state-of-the-art methods for the dynamic assessment of risk and safety barrier performance in the framework of domino effect risk management. The book presents methods and tools to manage the risk of cascading events involving the chemical and process industry. It is an ideal reference for both safety and security managers, industrial risk stakeholders, scientists and practitioners. In addition, laymen may find the state-of-the-art methods concerning domino effects (large-scale accidents) and how to prevent their propagation an interesting topic of study.

• Includes dynamic hazard and risk assessment methods
• Presents methods for safety barrier performance assessment
• Addresses the effect of harsh environment on domino risk assessment
• Relates physical security in relation to domino effects
• Includes innovative methods and tools

• Language: English
• Publisher: Elsevier Science
• Release date: Jun 8, 2021
• ISBN: 9780081028391

Book preview

Dynamic Risk Assessment and Management of Domino Effects and Cascading Events in the Process Industry

Editors

Valerio Cozzani

Genserik Reniers

Table of Contents

Cover image

Title page

Copyright

Contributors

Chapter one. The importance of innovation and new findings in domino effects research

1.1. Introduction

1.2. Domino effect assessment: from regulatory compliance to a research perspective

1.3. Quantitative assessment of domino effect

1.4. Cascading events: the relevance of intentional domino effects

1.5. Prevention and mitigation of domino effect

1.6. Conclusions

Chapter Two. Domino effects in chemical factories and clusters, risk in the eye of the beholder: an historical perspective and discussion

2.1. Introduction

2.2. Materials and methods

2.3. Results

2.4. Discussion and conclusions

Chapter Three. Application of Bayesian network to domino effect assessment

3.1. Introduction

3.2. Bayesian network

3.3. Fire domino modeling in process plants

3.4. Further remarks

3.5. Conclusions

Chapter Four. A Petri net–based methodology for domino effect assessment

4.1. Introduction

4.2. Petri net modeling

4.3. Probability analysis of domino effects

4.4. Conclusions

Chapter Five. Applying agent-based modeling and simulation for domino effect assessment in chemical plants

5.1. Introduction

5.2. Agent-based modeling and simulation

5.3. DAMS—assessing domino effects by using ABMS

5.4. Case studies

5.5. Discussion

5.6. Conclusions

Appendix A

Appendix B

Chapter SIX. Application of graph theory to assessing the vulnerability of tank terminals to domino effects

6.1. Introduction

6.2. Graph theory: metrics and measurements

6.3. Vulnerability assessment of process plants

6.4. Application of graph metrics to safety assessment of process plants

6.5. Conclusions

Chapter SEVEN. Stand-off distances for domino effect caused by intentional acts

7.1. Introduction

7.2. Methodology for the assessment of stand-off distances

7.3. Far-field stand-off distances for explosives and firearms

7.4. Conclusions

Chapter Eight. Vulnerability assessment of chemical plants to intentional acts

8.1. Introduction

8.2. Rings of Protection

8.3. Deter-Detect-Delay

8.4. Security vulnerability assessment

8.5. Comparative analysis of SVA methodologies

8.6. Conclusions

Chapter Nine. Economic model for tackling intentional domino effects in a chemical facility

9.1. Introduction

9.2. Economic foundations

9.3. Threat analysis and vulnerability assessment

9.4. Domino effect analysis

9.5. Cost–benefit analysis

9.6. Cost-effectiveness analysis

9.7. Conclusions

Chapter Ten. Mitigation barriers for domino effect

10.1. Introduction

10.2. The concept of safety barrier and layered protection

10.3. Classification of safety barriers for domino effect

10.4. Description of relevant safety barriers and safety systems

10.5. Managerial aspects related to safety barriers

10.6. Conclusions

Chapter Eleven. Assessment of safety barriers and mitigation of domino scenarios

11.1. Introduction

11.2. Methodology

11.3. Quantitative assessment of safety barrier performance

11.4. Conclusions

Chapter Twelve. Mitigation of fire-induced domino scenarios

12.1. Introduction

12.2. Specific features of domino effect triggered by fire

12.3. Probabilistic assessment of escalation scenarios triggered by fire

12.4. Consequence assessment of mitigated escalation scenarios triggered by fire

12.5. Risk assessment of mitigated domino scenarios triggered by fire

12.6. Tutorial application

12.7. Conclusions

Chapter Thirteen. The influence of harsh environment in the management of safety barriers

13.1. Introduction

13.2. Methodological approach

13.3. Tutorial application to demonstrate case studies

13.4. Discussion

13.5. Conclusions

Chapter Fourteen. Optimal firefighting to prevent domino effects

14.1. Introduction

14.2. Firefighting in chemical and process plants

14.3. Influence diagrams

14.4. Application influence diagram to optimal firefighting

14.5. Discussion

14.6. Conclusions

Chapter Fifteen. Multiplant emergency planning in the event of domino effects

15.1. Introduction

15.2. Methodology

15.3. Case study

15.4. Conclusions

Appendix (Fig. A1; Table A1)

Chapter Sixteen. Conclusions

16.1. Quantitative assessment of domino effect and escalation scenarios

16.2. Dynamic risk assessment of domino effect and safety barriers

16.3. Safety and security synergies

16.4. Concluding remarks

List of acronyms

Glossary

Index

Copyright

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Contributors

Chao Chen,     Safety and Security Science Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands

Valerio Cozzani,     Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Università di Bologna, Bologna, Italy

Behnaz Hosseinnia,     Norwegian University of Science and Technology, Trondheim, Norway

Nima Khakzad,     School of Occupational and Public Health, Ryerson University, Toronto, ON, Canada

Faisal Khan,     Memorial University of Newfoundland, St. John’s, NL, Canada

Gabriele Landucci,     Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy

Alessio Misuri,     Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Università di Bologna, Bologna, Italy

Federica Ovidi,     Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy

Nicola Paltrinieri

Department of Mechanical and Industrial Engineering, NTNU - Norwegian University of Science and Technology, Trondheim, Norway

Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Universita di Bologna, Bologna, Italy

Genserik Reniers

Safety and Security Science Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands

Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), University Antwerp, Antwerp, Belgium

CEDON, KULeuven, Campus Brussels, Brussels, Belgium

Ernesto Salzano,     Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Università di Bologna, Bologna, Italy

Paul Swuste,     Safety and Security Science Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands

Karolien van Nunen

Research Chair Vandeputte, Universiteit van Antwerpen, Antwerpen, Belgium

Law Enforcement, Rechtenfaculteit, Universiteit van Antwerpen, Antwerpen, Belgium

Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), University Antwerp, Antwerp, Belgium

Laobing Zhang,     Safety and Security Science Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands

Jianfeng Zhou,     School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China

Chapter one: The importance of innovation and new findings in domino effects research

Valerio Cozzani ¹ , and Genserik Reniers ² , ³ , ⁴       ¹ Laboratory of Industrial Safety and Environmental Sustainability - DICAM, Alma Mater Studiorum - Università di Bologna, Bologna, Italy      ² Safety and Security Science Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands      ³ Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), University Antwerp, Antwerp, Belgium      ⁴ CEDON, KULeuven, Campus Brussels, Brussels, Belgium

Abstract

Cascading events in which an escalation triggered by a domino effect takes place are high-impact low-probability accidents that require specific prevention and mitigation in design and operation of industrial facilities and industrial parks. A domino effect phenomenon was responsible of several catastrophic accidents that affected the chemical and process industry, as well as critical infrastructures for energy and other industrial sectors where relevant quantities of hazardous substances are stored or processed. Although to date most of these events were caused by unintentional domino effects, related to process safety accidents, intentional, security-related, domino effects may cause similar scenarios. Significant regulatory requirements and technical standards address the prevention and mitigation of domino effects in the design and operation of industrial sites. However, the complexity of the topic and the limited data available result in a poor agreement on assessment procedures to address escalation hazards possibly resulting in domino scenarios. Even if methods, models, and tools developed now facilitate the quantitative assessment of domino scenarios in risk analysis and in the safety management of industrial sites, a number of open points still remain. In recent years, a constant progress was made in improving the quantitative methods for understanding and assessing domino effects, and in addressing and reducing the uncertainty. Recent research efforts dedicated to the exploration of domino effects allowed the development of methods for dynamic risk assessment. These may now be complemented by specific methods addressing the quantitative performance of safety barriers to prevent the escalation. In parallel, a relevant effort was dedicate to methods addressing the control and prevention of intentional domino effects. In the present chapter, an overview of the more active research areas in domino effects is provided, and the importance of innovation and new findings for the assessment and the management of the risk caused by cascading events triggered by domino effects is provided.

Keywords

Chemical industrial parks; Domino effect; Escalation; Major accident hazard; Quantitative risk assessment; Research

1.1 Introduction

1.2 Domino effect assessment: from regulatory compliance to a research perspective

1.3 Quantitative assessment of domino effect

1.4 Cascading events: the relevance of intentional domino effects

1.5 Prevention and mitigation of domino effect

1.6 Conclusions

References

1.1. Introduction

The features of domino accidents, and their potentially catastrophic impact, attracted a lot of attention in the international scientific and technical literature on these high-impact low-probability (HILP) cascading accident scenarios since the early times of research in process safety and risk assessment (Reniers and Cozzani, 2013). It may be inferred that the accident scenarios where a domino effect took place are those that caused the highest impact in terms of asset damage and economic loss, if not of life loss and damage to environment (Khan and Abbasi, 1999; CCPS, 2001; Mannan, 2005; Abdolhamidzadeh et al., 2011). Thus, it is not surprising that regulatory authorities and technical standards issued significant requirements addressing the prevention and mitigation of domino effects, aimed at the control and reduction of domino hazards. Such requirements demand for relevant investments during design and operation of industrial sites, required by the implementation of specific inherently safer design, active and passive technical safety barriers, and operational procedural and emergency measures for domino effect prevention and mitigation (Reniers and Cozzani, 2013). Additional measures are required in the case of industrial clusters and parks, where the limited safety distances and the interdependency of utilities may increase the likelihood of escalation (Reniers et al., 2005b). More recently, the possibility of intentional domino effects became of concern (Reniers and Audenaert, 2014).

Thus, both regulators and company managers are looking at research to provide innovative methods allowing, on the one hand, a more effective protection from cascading events triggered by domino effects, and, on the other hand, a more effective use of the resources devoted to the control and reduction of risk due to a domino effect.

Although in recent years an increasing number of publications address domino effects, still the subject has been afforded by a relatively limited number of researchers, as shown by a recent analysis of the international scientific literature (Li et al., 2017). As a result, the specific features of domino scenarios are still poorly known, and there is not even an agreement on the definition of domino effect (Necci et al., 2015). Table 1.1 reports a summary of the definitions of domino effect: as evident, most are complementary, but each stresses different specific features of domino scenarios. The more comprehensive definition, encompassing most of the features of domino accidents present in previous references, is the one provided by Reniers and Cozzani (2013) that will be applied in this and in the following chapters of the present book.

Since neither is there a widely accepted definition of domino effect, nor an agreement of the specific features of domino effect that deserve more attention, most of the studies on domino effects have been carried out independently and focus either on particular aspects of accident escalation processes, as equipment vulnerability models, or on the definition of methodologies for hazard and/or risk assessment of domino scenarios. Comprehensive reviews of the state of the art of research addressing domino effect are available in the literature (Reniers and Cozzani, 2013; Necci et al., 2015; Chen et al., 2020). Fig. 1.1 shows some of the topics addressed in domino effect research in the last two decades.

As shown in the figure, research on the domino effect phenomenon first addressed methods for the quantitative assessment of risk. In the late 1990s, also due to the limitations in computational resources still present at the time, simplified methodologies for quantitative assessment were addressed. Since then, a relevant progress was done in developing quantitative methods, and active research is facing now the development of dynamic risk assessment tools for studying domino effects. The effort done to develop thresholds for equipment damage and for escalation, and models for equipment damage in domino scenarios, influenced and supported methodology development, reducing the uncertainty in quantitative risk assessment. More recently, the growing attention devoted to security issues in industrial sites influenced the research addressing quantitative assessment methodologies, orienting several researchers toward the exploration of intentional domino effects.

Table 1.1

From Necci, A., Cozzani, V., Spadoni, G., Khan, F., 2015. Assessment of domino effect: state of the art and research Needs. Reliability Engineering & System Safety 143, 3–18.

Figure 1.1 Significant topics addressed by domino effect research in the last two decades.

The methodologies developed for quantitative assessment supported the development of innovative approaches and tools dedicated to domino effect management. Although the number of influencing factors and side topics developed to support the research in this area is far more than those evidenced in Fig. 1.1, in the four key areas pertaining to risk management of domino scenarios some of the more important innovations were obtained. Key performance indicators and metrics allowing the management of domino risk in design and operation are now available. The complexity of industrial clusters and of industrial parks is now better understood, and specific management tools are available to manage external domino effects in industrial parks.

A further area of active research is that concerning the management of safety barriers. In recent years, this area attracted several relevant contributions (Landucci et al., 2015), addressing the probability of failure on demand and the effectiveness of technical safety barriers, as well as the assessment and improvement of procedural and emergency operational barriers. The results obtained allow a more comprehensive management of domino risk, also supporting decision-making concerning the optimal allocation of resources dedicated to the implementation and maintenance of safety barriers.

The available methods for the quantitative assessment of intentional domino effects are also allowing a closer interaction of safety and security research, with growing synergies among the two research areas, which lead to the integration of methods and procedures for the safety and security risk management of cascading events triggered by domino effects.

The present book addresses most of these topics, aiming at presenting recent results and innovative methodologies available for the quantitative assessment and the management of risk due to domino effect. In the following, the main areas addressed by contributions collected in the book are introduced.

1.2. Domino effect assessment: from regulatory compliance to a research perspective

About one century ago, chemical industries began to cluster in extended industrial areas, integrating production and sharing utilities and workforce, encouraged by the reduction of fixed and operating costs. The so-called chemical clusters or industrial parks, even nowadays, are a recognized model of efficiency and sustainability, implementing the concept of industrial symbiosis. However, the relevant complexity and the size of such industrial clusters also caused a growth in the inventory of hazardous substances present, thus increasing the potential for severe accidents involving domino effects.

Since the early times of process safety, the potential hazard coming from accidents involving a domino effect was recognized. Thus, when the European Union (actually the European Economic Community, at the time) decided the adoption of an advanced legislation for the control of major accident hazards (the so-called Seveso Directive—Directive 82/501/EEC), a specific requirement addressing domino effect was present. Actually, the possibility of domino effects should have been assessed in all the sites falling under the obligations of the Directive. However, at the time no method or specific tool existed to allow the identification and assessment of domino effect and escalation scenarios.

Thus, since the 1980s, at first in Europe, than worldwide, an increasing interest of regulators and of company managers oriented research efforts toward the definition of escalation threshold criteria and safety distances for domino accidents. However, complexity is the main conceptual issue posed by domino effect to process safety science. In the framework of domino effect assessment, the performance of an entire complex system needs to be considered in order to prevent its failure, being not sufficient to assess the correct performance of each of its parts (Leveson, 2004). Thus, it took about a decade before the first systematic study on domino effect in the chemical and process industry was published (Bagster and Pitblado, 1991). Several other pioneering studies followed (Contini et al., 1996; Delvosalle, 1998; Lines et al., 1998), but were limited by the insufficient development of integrated software tools, and by the lack of knowledge on structural damage leading to equipment failure. Only in the last 10 years several research breakthroughs paved the way toward the quantitative assessment of domino scenarios aimed at the prevention of escalation and at the management of risk associated to domino accidents. A further decade was required before comprehensive methodologies were issued, overcoming such limitations (Khan and Abbasi, 1998b, 2001; Cozzani et al., 2005, 2006; Reniers et al., 2005a; Reniers and Dullaert, 2007). The progress achieved allowed the consolidation of methodologies for quantitative risk assessment of domino accidents as well as for the identification and the management of risks related to domino scenarios. The relevant work carried out in the field allowed the achievement of ready-to-use and up-to-date tools for domino risk assessment, able to provide the support for a further step toward the design and operation of safer and more sustainable chemical industrial facilities, infrastructures, and industrial parks. Such historical perspective in the evolution of understanding the domino effect phenomenon is detailed in Chapter 2.

1.3. Quantitative assessment of domino effect

The analysis of the literature (Necci et al., 2015; Chen et al., 2020) indicates the quantitative assessment of domino effect as a central point in research on domino effects in the last two decades, as shown in Fig. 1.1. As shown in Fig. 1.2, the quantitative assessment of domino effect requires four main tasks: the identification of primary scenarios and the assessment of escalation vectors (red [dark gray in printed version] steps in Fig. 1.2); the assessment of equipment vulnerability to primary accident scenarios and the assessment of escalation frequencies (yellow [light gray in printed version] step in Fig. 1.2); the identification and frequency assessment of domino scenarios (blue [gray in printed version] steps in Fig. 1.2); and the calculation of final consequences, including loss of life, in complex domino scenarios (green [mild gray in printed version] step in Fig. 1.2).

The very first studies on the quantitative assessment of domino scenarios (Bagster and Pitblado, 1991; Delvosalle, 1998; Khan and Abbasi, 1998a) considered the secondary scenarios as independent accidents, therefore only their frequency and their consequences were considered for the assessment of risk. These early methodologies only allowed considering simple domino chains (a primary event causing a single secondary event, that may further escalate causing a tertiary event, and so on). The calculation of the frequencies of combined simultaneous secondary accidents is considered in the methods proposed by Cozzani and coworkers (Cozzani et al., 2005, 2006) and allows the identification and assessment of all the possible first-level domino scenarios involving the simultaneous failure of secondary units, allowing model application to industrial clusters and industrial parks (Antonioni et al., 2009). An extension of the combinatorial methodology proposed by Cozzani et al. (2005) to consider higher level escalation was demonstrated (Cozzani et al., 2014), but may benefit in perspective of dedicated mathematic formulations for domino frequency estimation.

Figure 1.2 Main steps needed for the quantitative assessment of domino accident scenarios.

Since 2013, Khakzad and coworkers are aiming at the application of Bayesian networks to the calculation of accident escalation probabilities and to the analysis of domino scenarios (e.g. see (Khakzad et al., 2013)). Bayesian networks are nowadays a mature tool for quantitative risk assessment, and their application to domino effect assessment provides new insights in the spatial and temporal robust modeling of domino effects, and in the vulnerability assessment of chemical and process plants. More recently, agent-based methods (Zhang et al., 2018) were introduced to study the propagation of domino effects, from a bottom-up perspective able to model higher-level domino effects and synergistic effects, also accounting for temporal dependencies. Petri nets (Zhou and Reniers, 2017; Kamil et al., 2019) were also proposed to allow the analysis of the probabilities of higher level domino effects. The applicability of graph theory and graph metrics was also proposed for the modeling and analysis of the vulnerability of process units and process plants with respect to domino effects (Khakzad et al., 2017). Considering domino effect as a directed graph, graph metrics elements such as closeness and betweenness can be effectively used for identifying critical units within a process plant. Chapters 3 to 6 deal with these innovative methodologies for domino effect quantitative assessment, aiming at presenting them in detail and in highlighting the potential advantages of such innovative approaches in the quantitative assessment of cascading events triggered by domino effect.

1.4. Cascading events: the relevance of intentional domino effects

Industrial sites 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. When intentional direct attacks are carried out, 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. Escalation may result in cascading events that may heavily involve and damage the industrial facility of concern. In this framework, the assessment of the vulnerability of industrial facilities is a key element for the prediction, the prevention, and the mitigation of domino events triggered by malevolent actions, including the design of appropriate security systems and barriers. Chapters 7–9 address, respectively, the safety distances for direct attacks, the procedures and the methodologies currently adopted worldwide to assess the vulnerability of industrial facilities to malicious acts of interference, and the economic framework for the allocation of protection barriers to reduce the vulnerability of chemical facilities to intentional domino effects.

1.5. Prevention and mitigation of domino effect

Safety barriers are a key element in the control and the mitigation of risk due to domino effects. Beside design-based strategies aimed at the introduction of inherently safer measures (Tugnoli et al., 2012a, 2012b), technical and operational barriers are frequently applied to prevent domino effects and escalation of primary scenarios. Specific engineered technical barriers as active (emergency shut-down system, water deluges, water curtains, etc.) and passive safety systems (catch basins, passive fire protection and fireproofing, blast walls, etc.) are usually implemented to prevent and mitigate domino effects. The potential severity of escalation scenarios triggered by domino effect justifies such measures in the case of critical domino targets, even if the cost of barrier implementation and maintenance may be relevant.

The displacement of procedural barriers, as an appropriate planning and implementation of operational and first response strategies, is also crucial to mitigate domino effect: e.g., a relevant reduction of the potential for fire escalation is obtained by a rapid displacement of emergency teams and the activation of local fire monitors.

An appropriate barrier management thus emerges as a key point to control the risk due to domino effect and to support the implementation of quantitative safety barrier performance assessment aimed at including the effect of safety barrier performance in the quantitative assessment of risk due to domino scenarios (Landucci et al., 2015). A specific quantitative approach, based on a modified event tree analysis (ETA), was developed to calculate the frequency of escalation accounting for the presence of safety barriers (Landucci et al., 2015, 2016). The decrease in the performance of safety barriers in harsh environment should also be considered when carrying out a quantitative assessment of domino risk (Landucci et al., 2017; Bucelli et al., 2018). Besides technical barriers, also emergency barriers are crucial to prevent domino effect. Specific multiplant emergency response planning is required for an effective response to domino effect in chemical clusters (Hosseinnia et al., 2018; Khakzad et al., 2018).

Chapters 10–15 are dedicated to discuss in detail the above issues. In particular, Chapters 10–12 address the management and quantitative performance assessment of technical and operational barriers to prevent and mitigate domino effects, Chapter 13 addressed the specific issue of harsh environment and its effect on safety barriers, while Chapters 14 and 15 are concerned with emergency planning and emergency strategies in the specific framework of escalation prevention.

1.6. Conclusions

Domino effect is a well-known hazard in several industrial sectors, ranging from the chemical industry to oil and gas and other industrial activities where relevant quantities of hazardous substances are stored and/or processed. Regulatory requirements aimed at the prevention and mitigation of domino effects called for a relevant research in the area, and several methodologies and approaches are now available for the quantitative assessment and management of risk due to possible cascading events triggered by domino effects. In the following chapters, innovative methodologies addressing three main areas are provided: (i) the quantitative assessment of unintentional domino effect scenarios; (ii) the assessment of intentional, security-related domino effects; and (iii) the management of technical and operational safety barriers aiming at the prevention and mitigation of domino effects.

References

1. Abdolhamidzadeh B, Abbasi T, Rashtchian D, Abbasi S.A. Domino effect in process-industry accidents – an inventory of past events and identification of some patterns.  Journal of Loss Prevention in the Process Industries . 2011;24:575–593.

2. Antonioni G, Bonvicini S, Spadoni G, Cozzani V. Development of a framework for the risk assessment of Na-Tech accidental events.  Reliability Engineering & System Safety . 2009;94:1442–1450.

3. Bagster D.F, Pitblado R.M. The estimation of domino incident frequencies—an approach.  Process Safety and Environmental Protection . 1991;69:195–199.

4. Bucelli M, Landucci G, Haugen S, Paltrinieri N, Cozzani V. Assessment of safety barriers for the prevention of cascading events in oil and gas offshore installations operating in harsh environment.  Ocean Engineering . 2018;158:171–185.

43. CCPS.  Guidelines for Consequence Analysis of Chemical Releases . New York (US): Center for Chemical Process Safety American Institute of Chemical Engineers; 1999.

5. CCPS.  Layer of Protection Analysis: Simplified Process Risk Assessment . New York: AIChE–CCPS; 2001.

6. Chen C, Reniers G, Khakzad N. A thorough classification and discussion of approaches for modeling and managing domino effects in the process industries.  Safety Science . 2020;125. .

7. Contini S, Boy S, Atkinson M, Labath N, Banca M, Nordvik J. Domino effect evaluation of major industrial installations: a computer aided methodological approach.  Proceedings of the European Seminar on Domino Effects . 1996:1.

42. Council Directive 2003/105/EC, . Seveso II Directive on the control of major-accident hazards involving dangerous substances with amendments..  Off. J. Europ. Union . 2003;L345:97–105.

8. Cozzani V, Antonioni G, Landucci G, Tugnoli A, Bonvicini S, Spadoni G. Quantitative assessment of domino and NaTech scenarios in complex industrial areas.  Journal of Loss Prevention in the Process Industries . 2014;28:10–22.

9. Cozzani V, Antonioni G, Spadoni G. Quantitative assessment of domino scenarios by a GIS-based software tool.  Journal of Loss Prevention in the Process Industries . 2006;19:463–477.

10. Cozzani V, Gubinelli G, Antonioni G, Spadoni G, Zanelli S. The assessment of risk caused by domino effect in quantitative area risk analysis.  Journal of Hazardous Materials . 2005;127:14–30.

11. Delvosalle C. A methodology for the identification and evaluation of domino effects. In:  Rep. CRC/MT/003, Belgian Ministry of Employment and Labour, Bruxelles (B) . 1998.

46. Gorrens B, De Clerck W, De Jongh K, Aerts M.  Domino effecten van en naar Seveso-inrichtingen, Rep. 07.0007 . Brussels (B): Flemish Ministry of Environment, Nature and Energy; 2009.

38. Health and Safety Executive.  Health and Safety Executive. The Control of Major Hazards, Third Report of the HSC Advisory Committee . London: HMSO; 1984.

12. Hosseinnia B, Khakzad N, Reniers G. Multi-plant emergency response for tackling major accidents in chemical industrial areas.  Safety Science . 2018;102:275–289.

13. Kamil M.Z, Taleb-Berrouane M, Khan F, Ahmed S. Dynamic domino effect risk assessment using petri-nets.  Process Safety and Environmental Protection . 2019;124:308–316.

14. Khakzad N, Khan F, Amyotte P, Cozzani V. Domino effect analysis using Bayesian networks.  Risk Analysis . 2013;33:292–306.

15. Khakzad N, Landucci G, Cozzani V, Reniers G, Pasman H. Cost-effective fire protection of chemical plants against domino effects.  Reliability Engineering & System Safety . 2018;169:412–421.

16. Khakzad N, Landucci G, Reniers G. Application of graph theory to cost-effective fire protection of chemical plants during domino effects.  Risk Analysis . 2017;37:1652–1667.

17. Khan F.I, Abbasi S. Models for domino effect analysis in chemical process industries.  Process Safety Progress . 1998;17:107–123.

18. Khan F.I, Abbasi S. Major accidents in process industries and an analysis of causes and consequences.  Journal of Loss Prevention in the Process Industries . 1999;12:361–378.

19. Khan F.I, Abbasi S. An assessment of the likelihood of occurrence, and the damage potential of domino effect (chain of accidents) in a typical cluster of industries.  Journal of Loss Prevention in the Process Industries . 2001;14:283–306.

20. Khan F.I, Abbasi S.A. DOMIFFECT (DOMIno eFFECT): user-friendly software for domino effect analysis.  Environmental Modelling & Software . 1998;13:163–177.

21. Landucci G, Argenti F, Spadoni G, Cozzani V. Domino effect frequency assessment: the role of safety barriers.  Journal of Loss Prevention in the Process Industries . 2016;44:706–717.

22. Landucci G, Argenti F, Tugnoli A, Cozzani V. Quantitative assessment of safety barrier performance in the prevention of domino scenarios triggered by fire.  Reliability Engineering & System Safety . 2015;143:30–43.

23. Landucci G, Bonvicini S, Cozzani V. A methodology for the analysis of domino and cascading events in Oil & Gas facilities operating in harsh environments.  Safety Science . 2017;95:182–197. .

39. Lees F.P.  Loss Prevention in the Process Industries-Hazard Identification, Assessment, and Control . Oxford: Butterworth-Heinemann; 1996.

24. Leveson N. A new accident model for engineering safer systems.  Safety Science . 2004;42:237–270.

25. Li J, Reniers G, Cozzani V, Khan F. A bibliometric analysis of peer-reviewed publications on domino effects in the process industry.  Journal of Loss Prevention in the Process Industries . 2017;49:103–110.

26. Lines I, Gledhill J, Health, Safety Executive, L..  Development of Methods to Access the Significance of Domino Effects from Major Hazard Sites . Sudbury: HSE Books; 1998.

27. Mannan S.  Lees’ Loss Prevention in the Process Industries . Butterworth-Heinemann; 2005.

28. Necci A, Cozzani V, Spadoni G, Khan F. Assessment of domino effect: state of the art and research Needs.  Reliability Engineering & System Safety . 2015;143:3–18.

29. Reniers G, Cozzani V.  Domino Effects in the Process Industries, Modeling, Prevention and Managing . Amsterdam, The Netherlands: Elsevier; 2013.

44. Post J.G, Bottelberghs P.H, Vijgen L.J, Matthijsen A.J.C.M.  Instrument Domino Effecten . Bilthoven (NL): RIVM; 2003.

30. Reniers G.L.L, Audenaert A. Preparing for major terrorist attacks against chemical clusters: intelligently planning protection measures w.r.t. domino effects.  Process Safety and Environmental Protection . 2014;92:583–589.

31. Reniers G.L.L, Dullaert W. DomPrevPlanning©: user-friendly software for planning domino effects prevention.  Safety Science . 2007;45:1060–1081.

32. Reniers G.L.L, Dullaert W, Ale B.J.M, Soudan K. Developing an external domino accident prevention framework: Hazwim.  Journal of Loss Prevention in the Process Industries . 2005;18:127–138.

33. Reniers G.L.L, Dullaert W, Ale B.J.M, Soudan K. The use of current risk analysis tools evaluated towards preventing external domino accidents.  Journal of Loss Prevention in the Process Industries . 2005;18:119–126.

34. Tugnoli A, Cozzani V, Padova A, Barbaresi T, Tallone F. Mitigation of fire damage and escalation by fireproofing: A risk-based strategy.  Reliability Engineering and System Safety . 2012;105:25–35. doi: 10.1016/j.ress.2011.11.002.

37. Tugnoli A, Landucci G, Salzano E, Cozzani V. Supporting the selection of process and plant design options by Inherent Safety KPIs.  J Loss Prev. Process Ind.  2012;25:830–842. doi: 10.1016/j.jlp.2012.03.008.

40. Uijt de Haag P.A.M, Ale B.J.M.  Guidelines for quantitative risk assessment (Purple book) . The Hague (NL): Committee for the Prevention of Disasters; 1999.

41. Vallee A, Bernuchon E, Hourtolou D.  MICADO: Méthode pour l’identification et la caractérisation des effets dominos, Rep. INERIS-DRA-2002-25472 . Paris (F): Direction des Risques Accidentels; 2002.

35. Zhang L, Landucci G, Reniers G, Khakzad N, Zhou J. DAMS: a model to assess domino effects by using agent-based modeling and simulation.  Risk Analysis . 2018;38:1585–1600.

36. Zhou J, Reniers G. Petri-net based cascading effect analysis of vapor cloud explosions.  Journal of Loss Prevention in the Process Industries . 2017;48:118–125.

Chapter Two: Domino effects in chemical factories and clusters, risk in the eye of the beholder: an historical perspective and discussion

Paul Swuste ¹ , Karolien van Nunen ² , ³ , ⁴ , Genserik Reniers ¹ , ⁴ , ⁵ , and Nima Khakzad ⁶       ¹ Safety and Security Science Group, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands      ² Research Chair Vandeputte, Universiteit van Antwerpen, Antwerpen, Belgium      ³ Law Enforcement, Rechtenfaculteit, Universiteit van Antwerpen, Antwerpen, Belgium      ⁴ Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), University Antwerp, Antwerp, Belgium      ⁵ CEDON, KULeuven, Campus Brussels, Brussels, Belgium      ⁶School of Occupational and Public Health, Ryerson University, Toronto, ON, Canada

Abstract

Major accidents in Western countries, receiving a lot of media attention in the 1970s, are starting point for research into internal and external domino effects in the chemical and petrochemical sectors and clusters. Initially, these reports are published by government institutions and government-related