Chemical clusters can be attractive targets for terrorism, due to the extreme importance of them as well as due to the existence of dangerous materials. Patrolling is scheduled for better securing chemical clusters. However, the current patrolling strategies fail on competing with intelligent attackers and therefore can be non-optimal. The so-called chemical cluster patrolling (CCP) game is proposed in this paper. The CCP game employs game theory as a methodology, aiming at randomly but strategically scheduling security patrols in chemical clusters. The patroller and the attacker are modelled as the two rational players in the CCP game. The patroller's strategy is defined as probabilistically traveling within the cluster or patrolling some plants while the attacker's strategy is formulated as a combination of an attack target, the start time of the attack, and the attack scenario to be used. The Stackelberg equilibrium and a robust solution which takes into consideration of the patroller's distribution-free uncertainties on the attacker's parameters are defined for predicting the outcome of the CCP game. Results of the case study indicate that the patrolling strategy suggested by the CCP game outperforms both the fixed patrolling route strategy and the purely randomized patrolling strategy.

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With rapid urbanization in China, many underground utility tunnels have been established these years. This huge underground construction facilitates city life, but may introduce societal risks due to the installation of high-risk pipelines. Natural gas pipelines have the potential to cause catastrophic accident if a gas leakage and a subsequent explosion occurs. The potential hazards in the gas compartments of a utility tunnel are quite different from those in conventional directly buried gas pipelines. This study developed a dynamic quantitative risk analysis method for natural gas pipelines in a utility tunnel. First, potential accident scenarios of natural gas pipelines situated in a utility tunnel were identified and implemented in a Bow-tie diagram based on case studies of typical gas pipeline accidents and expert experience. Then, a Bayesian network was established from the Bow-tie diagram using a mapping algorithm. Based on a comprehensive analysis of the results of probability updating and sensitivity analysis, critical influencing factors were identified. The proposed framework provides a predictive analysis of the gas pipeline accident evolution process from causes to consequences and examines key challenges in gas pipeline risk management in utility tunnels.

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Game theory has been employed in academia to study the improvement of security in chemical plants. Being able to model intelligent interactions between adaptive adversaries and defenders is the main advantage of game theory, while the main criticisms of the usage of game theory is that it is mathematically complicated and that it over-simplifies reality. The ANSI/API standard 780 on Security Risk Assessment for the petroleum and petrochemical industries (abbreviated as the “API SRA methodology”), conversely, provides a systematic approach for obtaining qualitative or semi-quantitative data, and is criticized on its failure at modelling strategic (and intelligent) adversaries. Integration of game theory and the API SRA methodology for improving chemical plant protection is therefore an interesting domain of study. In this paper, the API SRA methodology bridges the gap between “chemical security reality” and “chemical security theory (that is, game theoretic models)”, by providing quantitative inputs for game theoretic models and by reflecting on game theoretic results with respect to industrial practice.

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Why should game theory be introduced and used in the chemical security practitioners? Security risks are initiated by deliberate behaviours for certain goals. For instance, thieves intentionally intrude a plant for stealing valuable materials, or terrorists maliciously set a fire on a chemical facility to cause societal fear. Initiators of security events (henceforth, attackers) would intelligently observe the defender’s defence plan and then schedule their attack accordingly. Powell (2007) illustrated how resources can be mis-allocated if intelligent interactions between the defender and the attacker are not considered. Game theory was invented in the economic domain for modelling both the cooperative and competitive behaviours in a multiple actors system. In the last 100 years, game theory has been theoretically improved and practically applied to various domains, such as the evolutionary biology, the nuclear balance, computer science etc. These researches have demonstrated the capability of game theory in modelling intelligent interactions. Industrial managers need quantitative recommendations to support their decision making. Conventional security risk assessment methodologies (e.g., the API SRA framework (API, 2013)), being good at studying security systematically, are not able to provide quantitative insights. Moreover, results of these conventional methodologies are not repeatable which means that applying the same methodology to the same plant, different analysts may come to different conclusions. Some quantitative security risk assessment models, for instance, by employing a Bayesian Network framework (e.g., Argenti et al. (2018); Landucci et al. (2017); Fakhravar et al. (2017)), can provide quantitative and repeatable results as well. Nevertheless, these models fail on modelling the intelligent interactions between the defender and the attacker. Game theory, conversely, has a rigorous mathematical foundation and models the intelligent interactions. A game theoretic model explicitly indicates 1) who is involved in the game; 2) what actions can each participant take; 3) what results (numbers) will each participant obtain, for each participants’ strategy combination; 4) how much information that each participant has about the game. Furthermore, outputs of a game theoretic model (i.e., equilibrium) clearly and quantitatively indicates what should the participants do (i.e., the equilibrium strategy) and what will each participant obtain (i.e., the equilibrium payoff). A critical issue is that industrial managers often prefer a qualitative approach and they have difficulties on understanding (the physical meaning of) the quantitative outputs of a game theoretical model. This issue can be addressed by requiring game developers to do a further step work by also translating/mapping their quantitative outputs to qualitative descriptions, and the latter should be expressed in terminologies that industrial practitioners are familiar with. Figure 1 illustrates the idea.@en

Protecting chemical clusters from intentional attacks has been a hot topic during the last decade. Besides intrusion security countermeasures such as cameras, entrances control etc., patrolling also fulfils an important role in the security of chemical facilities and industrial parks. Current patrolling strategies in industry are mainly single-plant driven and purely randomized or based on the patroller’s preference. Such an approach in a chemical industrial park is on the one hand not able to cover the more hazardous facilities more than the less hazardous plants within the park, and on the other hand is not able to deal with strategic (intelligent) human adversaries w.r.t. terrorism. This paper therefore investigates a game theoretic model for optimizing the schedule of patrolling in chemical clusters. The industrial defender and the intelligent/adaptive attackers are modelled as two players in the game. The defender aims at increasing the probability of detecting the attacker, by randomly but strategically scheduling her patrolling route. The attacker aims at causing maximal consequences with highest success probabilities, by choosing a proper attack time and a proper target. The model is further illustrated by a case study.@en

China is a major country producing and using hazardous chemicals. Unfortunately, the hazardous chemical industry is still one of the most high-risk industries in China. In recent years, especially after two devastating hazardous chemical accidents, namely “Qingdao 11.2 Crude Oil Leaking and Explosion Accident” and “Tianjin Port 8.12 Fire and Explosion Accident” which occurred in 2013 and 2015 respectively, China has attached great importance to hazardous chemical safety. The period between 2016 and 2017 is a crucial period for the future direction of hazardous chemical safety in China because China released a series of important government documents (such as ‘Thirteenth Five-Year (2016–2020) Plan for Hazardous Chemical Safety’ and ‘Comprehensive Plan for Hazardous Chemical Safety Management (December 2016–November 2019)’) to promote hazardous chemical safety in the future. What is the future development of China's hazardous chemical safety? To answer this question, this paper attempts to briefly analyze and introduce the opportunities, problems, challenges and tasks of the future of safety with hazardous chemical industrial activities in China, according to the current situation of hazardous chemical safety in China and using the latest government documents and studies. Obviously, this study can provide useful evidence and suggestions for the future of safety management in the hazardous chemical industry both within China and in other countries.

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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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China has been experiencing dynamic industrialization because of rapid economic growth. Even with steady industrial safety improvements in recent years in China, the death rate per accident is increasing, and major accidents (MAs) as well as particularly serious accidents (PSAs) are still occurring every year. Evidently, the risk of industrial accidents, especially of MAs and PSAs is still high. Moreover, China has entered a bottleneck period for the prevention and control of MAs and PSAs. In a word, MAs and PSAs have become a significant challenge for China's industrial, social, and economic development. In recent years, especially since 2016, great attention of the Chinese government has been given to the prevention and control of MAs and PSAs. China launched its nationwide safety campaigns for firmly curbing MAs and PSAs. Some potentially effective measures and strategies in a series of safety policy documents (e.g., the ‘Guidelines for Comprehensively and Resolutely Curbing MAs and PSAs’ and the ‘Thirteenth-Five-Year Plan for Work Safety’) were also proposed, to reduce MAs and PSAs. Firstly, this paper makes a statistical analysis of China's MAs and PSAs between the year of 2002 and 2016 to figure out the current status of MAs and PSAs in China. Then this article reviews some latest major events of the prevention and control of MAs and PSAs in China to introduce the recent efforts in the prevention and control of MAs and PSAs in China. Finally, according to a series of safety policy documents in China, and the scientific research literature from other countries, this study gives a brief introduction to the future prospects of the prevention and control of MAs and PSAs in China. Obviously, this study can provide useful evidence and suggestions for the future prevention and control of MAs and PSAs both within China and in other countries.

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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

Since the 9/11 attack in New York in 2001, a lot of attention has been paid to the protection of critical infrastructures. Chemical industries are without doubt critical infrastructures due to their extreme importance for society in combination with their vulnerability. They play important roles in modern-life society, from producing and providing daily necessities such as food and energy, to making modern medicine. They are thus truly essential to our modern way of living. Process plants usually store dangerous goods in large quantities, which may pose an important threat to themselves as well as to their surroundings. Moreover, due to a variety of benefits of scale, process plants tend to build their factories geographically together, potentially aggravating the danger. Therefore, the importance of protecting industrial process plants (including those in the chemical industry, the food industry, the energy industry, and others) cannot be overestimated. Risks caused by human behaviours with the intention to cause losses are defined as security risks. For instance, thieves intentionally intruding a plant for stealing valuable materials, or terrorists maliciously setting a fire on a chemical facility to cause societal fear. Initiators of security events (henceforth, attackers) would intelligently observe the defender’s defence plan and then schedule their attack accordingly. Literature has actually shown how resources can be misallocated if intelligent interactions between the defender and the attacker are not considered. Game theory was developed in the economic domain for modelling both cooperative and competitive behaviours in a multiple actors system. In the last 100 years, game theory has been theoretically improved and practically applied to various domains, such as evolutionary biology, computer science etc. These researches have demonstrated the capability of game theory in modelling intelligent interactions. Several security management systems based on game theory have been developed and deployed in reality, such as the ARMOR system for the Los Angeles airport, the PROTECT system for the US coast guard, etc. In this research, game theory is employed to study the protection of chemical industrial areas. Four models are proposed: i) DAMS – an agent-based modelling and simulation approach for assessing domino effects in chemical plants; ii) CPP game – a game theoretic model for single plant protection; iii) CCP game – a game theoretic model for multiple plants protection, by optimizing patrolling; and iv) PPG – a game theoretic model aiming at optimizing pipeline patrolling within or between chemical plants. These models are briefly explained hereafter.@en

Past accident analyses indicate that fire escalation is responsible for most of the domino effects that happened in the process industries. The evolution of domino accidents triggered by fire is different from domino accidents triggered by other primary scenarios, since the escalation caused by heat radiation is delayed with respect to the start of the fire. In this study, a methodology involving a Domino Evolution Graph (DEG) model and a Minimum Evolution Time (MET) algorithm is proposed to model the spatial-temporal evolution of domino accidents. Synergistic effects and parallel effects of the spatial evolution, as well as superimposed effects of the temporal evolution possibly occurring in complex domino evolution processes, are considered in this study. A case study demonstrates that the methodology is able to not only capture the spatial-temporal dimension but also to overcome the limitation of the “probit model” w.r.t only able to estimate the damage probability of the first level propagation. Besides, different from simulation or Bayesian approaches, our methodology can quickly provide evolution graphs (paths), the evolution time and the corresponding probability given a primary scenario. Therefore our approach can also be applied to domino risk assessment within an industrial park level and provide support for the allocation decision of safety and security resources.

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Security-related risks of oil and gas pipelines are assessed in this paper using the technique of game theory in combination with a security risk assessment approach. A Socio-political index is defined and embedded in an innovative and comprehensive assessment method, considering the effects of social, economic and political elements on pipeline attractiveness and vulnerability. After having analysed the security threats, security measures, aimed at increasing the security level of a pipeline system, are assessed by using a game-theory model. The pipeline segments which are the most probable to be attacked are determined. In addition, having assessed the possible outcomes of attacks to each segment, the security of different segments of specific pipeline routes can be further improved. Our approach can efficiently allocate limited security resources to decrease the security risk along a pipeline route. It should be noted that although this study focuses on oil and gas pipelines, the proposed methodology could be easily adapted to other pipeline systems.

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The chemical industry is very important for the world economy and this industrial sector represents a substantial income source for developing countries. However, existing regulations on controlling atmospheric pollutants, and the enforcement of these regulations, often are insufficient in such countries. As a result, the deterioration of surrounding ecosystems and a quality decrease of the atmospheric environment can be observed. Previous works in this domain fail to generate executable and pragmatic solutions for inspection agencies due to practical challenges. In addressing these challenges, we introduce a so-called Chemical Plant Environment Protection Game (CPEP) to generate reasonable schedules of high-accuracy air quality monitoring stations (i.e., daily management plans) for inspection agencies. First, so-called Stackelberg Security Games (SSGs) in conjunction with source estimation methods are applied into this research. Second, high-accuracy air quality monitoring stations as well as gas sensor modules are modeled in the CPEP game. Third, simplified data analysis on the regularly discharging of chemical plants is utilized to construct the CPEP game. Finally, an illustrative case study is used to investigate the effectiveness of the CPEP game, and a realistic case study is conducted to illustrate how the models and algorithms being proposed in this paper, work in daily practice. Results show that playing a CPEP game can reduce operational costs of high-accuracy air quality monitoring stations. Moreover, evidence suggests that playing the game leads to more compliance from the chemical plants towards the inspection agencies. Therefore, the CPEP game is able to assist the environmental protection authorities in daily management work and reduce the potential risks of gaseous pollutants dispersion incidents.

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A common criticism on game theoretic risk analysis of security threats is that it requires quantitative parameters of both the defender and the attacker, whereby the parameters of the attackers especially are difficult to estimate. In the present paper, a game theoretic model for chemical plant protection, able to deal with the defender's distribution-free uncertainties on the attacker's parameters (Interval CPP Game), is proposed. The Interval CPP Game only requires the interval(s) in which the attacker's parameter(s) is (are) located, instead of the exact number of the parameter(s). Two algorithms are developed, namely the Interval Bi-Matrix Game Solver (IBGS) and the Interval CPP Game Solver (ICGS), for solving general bi-matrix games with interval payoff uncertainties and especially for solving interval CPP games, respectively. Both algorithms are based on mixed integer linear programming (MILP). Theoretic analysis as well as a case study shows that including the defender's uncertainties on the attacker's parameters would reduce her equilibrium payoff.

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As large amounts of hazardous chemicals are handled in the petrochemical industries, the plants in these industries are attractive for terrorists because they can cause great losses and have important social impact. Security risk assessment is important to determine the risk level of a plant in order to take targeted measures to reduce the security risk. Based on Security Risk Factor Table (SRFT) which covers the essential elements for the security risk assessment, a weighted fuzzy Petri-net (WFPN) based security risk assessment approach is proposed in this paper. This approach can easily model different relationships between the risk factors as well as their importance, and use the analysis method of Petri-nets to perform the risk assessment. Two WFPN models of security risk assessment are established according to different relationships between the factors, and a matrix operation based security risk inference method is developed. An illustrative example is used to demonstrate the approach. The results show that correctly determining and modeling the relationships among the risk factors is important to assess the security risk.

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An approach based on game theory is proposed to schedule security patrolling for a pipeline system. The method proposes numbers of patrolling paths according to the risk of security incidents on the pipeline system, in order to allow the patrolling team to covering high-risk segments more than low-risk segments. Patrolling of the pipeline system was modeled mathematically, based on time and distance discretization. The overall approach also examines the presence of security countermeasures on a pipeline system, and their effects on the patrolling schedule. The application of the method is explained by an illustrative case study.

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The New York City 9/11 terrorist attacks urged people from academia as well as from industry to pay more attention to operational security research. The required focus in this type of research is human intention. Unlike safety-related accidents, security-related accidents have a deliberate nature, and one has to face intelligent adversaries with characteristics that traditional probabilistic risk assessment techniques are not capable of dealing with. In recent years, the mathematical tool of game theory, being capable to handle intelligent players, has been used in a variety of ways in terrorism risk assessment. In this article, we analyze the general intrusion detection system in process plants, and propose a game-theoretical model for security management in such plants. Players in our model are assumed to be rational and they play the game with complete information. Both the pure strategy and the mixed strategy solutions are explored and explained. We illustrate our model by an illustrative case, and find that in our case, no pure strategy but, instead, a mixed strategy Nash equilibrium exists.

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