The chemical industry has an important role in our modern society. Due to the existence of hazardous materials and possible extreme producing conditions, chemical facilities are also considered dangerous. Research has pointed out that a successful attack on chemical plants may cause mass casualties, in the United States. Game theory has been employed to improve the protection of chemical plants, and current literature on chemical plant protection games assume a ‘rational’ attacker. The present paper studies a game-theoretic model, which is played by a rational defender and a ‘bounded rational’ attacker, for improving chemical plant protection. The attacker modeled in this paper is assumed to play higher payoff strategies with higher probabilities, which is innovative from the current chemical security literature. Attackers in the current chemical plant protection games would always play the strategy with the highest payoff (probability of 100%). Distribution-free uncertainties on attacker's parameters are also integrated into the model. An algorithm for solving the game presented in this paper is also proposed. A case study reveals that although a bounded rational attacker would reduce the defender's expected payoff, the defender's equilibrium strategy from the present model is robust to different attacker behaviors.@en

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

Terrorists often take the chemical clusters as the attacking target because of the adverse impacts of a chemical accident on society and the environment. In addition to some fixed countermeasures, previous studies have verified the feasibility of a patrol in addressing adversarial attacks. However, the previous patrolling practices fail to tackle the terrorist attacking problems in a large-scale area cost-effectively. To further tackle the protection issue with cost-beneficial solutions in a large-scale scenario, i.e., in a chemical cluster, we propose an area-partition-involved collaborative patrolling (APCP) game. We first leverage the proposed greedy deployment algorithm to determine the initial deployment of defenders (patrollers), including the quantity and position of patrol vehicles. Then, the large-scale area is partitioned into multiple smaller areas by using the collaborative idea of static partitioning. In the meantime, corresponding patrolling graphs are constructed based on graphic modeling methods. Finally, the APCP game is built between patrol vehicles (namely defender) and potential terrorists (namely attacker), in which patrol vehicles aim at detecting attack behaviors of terrorists by intelligently scheduling the patrolling routes. After formalizing the problem into a sequential game, we compute the Stackelberg equilibrium through the MultiLPs algorithm. Through case studies of three practical chemical cluster scenarios, the results explicitly show the superiority of our proposed APCP game by saving up to 25.48 % patrolling costs in a one-shot game compared to the results before partition. As for the collaborative patrolling problem in a large-scale area, the methods and models proposed in this paper can facilitate the management department of chemical clusters with intelligently scheduled patrolling routes, which can effectively reduce the cost of patrollers, and better protect the chemical cluster.

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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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Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency.

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Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applications is still quite limited. Many current mobile AR applications of point clouds lack fluent interactions with users. In our paper, a cLoD (continuous level-of-detail) method is introduced to filter the number of points to be rendered considerably, together with an adaptive point size rendering strategy, thus improve the rendering performance and remove visual artifacts of mobile AR point cloud applications. Our method uses a cLoD model that has an ideal distribution over LoDs, with which can remove unnecessary points without sudden changes in density as present in the commonly used discrete level-of-detail approaches. Besides, camera position, orientation and distance from the camera to point cloud model is taken into consideration as well. With our method, good interactive visualization of point clouds can be realized in the mobile AR environment, with both nice visual quality and proper resource consumption.

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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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Chemical production- or storage facilities may have a strong appeal to terrorists due to their potential for causing great losses and possible huge societal impact. In view of the deficiency of attack trees, especially the impact of attacker numbers on the attack time, a timed colored Petri-net based attack process modeling approach, as well as a simulation based security failure probability analysis approach for security management, is proposed in this paper. The number of attackers has a key influence on the logic relationship of attack events. For performing the events represented by the logical gates (mainly AND gate and OR gate) of an attack tree, the influence of a different number of attackers on the attack time is discussed, and corresponding timed colored Petri-net based modeling approaches are provided. Comparing the duration of an attack process with an assumed interval of security inspection, a security failure probability can be obtained.

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Developing a group of good safety professionals is a key foundation for improving a country's work safety and health. In China, the widespread concern and discussion on the development of safety professionals, or more specifically Certified Safety Engineers (CSEs) started in the early 1990s. In recent years, especially after 2002, China's CSEs entered into a holistic, systematic and rapid development period because of the formal establishment and implementation of the professional qualification system for CSEs in 2002. Moreover, CSE is now becoming the most important profession in the field of work health and safety in China, and it has been officially approved by the central government. China has rich experiences on the development and cultivation of CSEs. However, these experiences were little known to the outside world due to the lack of efficient communication. Firstly, this paper briefly reviews the history and development of CSEs in China. Then, the administrative regulation and important requirements (such as capacity requirements and the knowledge framework) regarding CSEs in China are briefly introduced. This research aims at promoting the cooperation and exchange of information on the development and cultivation of safety professionals between China and other countries, to offer useful evidence and suggestions for the development of safety professionals.

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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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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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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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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

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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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

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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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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