The Arctic is known for its abundant reserve of natural resources. Last decade has seen some exploration and production activities in this region. The assurance of safe operations in this region is a critical and challenging task because of the harsh environment, the remoteness of operation sites, the limited infrastructure, and resources available in response to emergent situations, the application of costly equipment and facilities, and the sensitive marine environment. For complex process systems operating in a harsh environment, the scope of conventional risk assessment is not enough because of the highly uncertain environment, and its impacts on equipment performance. Risk assessment needs to be extended to include both the pre-failure and the post-failure phases. Additionally, risk assessment approaches under normal operating, and environmental conditions may not be applicable in the Arctic regions with unique and uncertain characteristics of the harsh environment. Therefore, this study aims to develop a quantitative resilience assessment method for process units operating under Arctic extreme conditions. Dynamic Bayesian network (DBN) is applied to model the probabilistic relationships between causes and effects in a dynamic manner. The proposed method is applied to the resilience assessment of a separator (as part of an oil production system). The proposed approach will help reveal the critical operating parameters under extreme conditions for process units. It also helps identify potential design improvement to enhance process safety.

The authors regret that an imprecise statement was made in this article, and wish to offer this Corrigendum as clarification. The authors would like to apologise for any inconvenience caused. Imprecise statement: Misuri et al. (2018, pp. 70) state in their work: “Differently from EN [evidential network], CN [credal network] can be used both to conduct forward analysis and to update probabilities”. This statement implies that EN cannot be used for belief updating and should be mapped into a corresponding CN for that purpose. Correction: The foregoing statement may be correct for conventional ENs that are based on Dempster's combination rule, but does not hold true for the EN developed by Simon and co-workers (2008, 2009) based on Bayesian network (BN) inference algorithms (herein, BN-based EN). Simon and Weber (2009) explicitly mention in their work that the developed EN can be used for belief updating: “The computation mechanism is based on the Bayes theorem, which is extended to the representation of uncertain information according to the framework of Dempster-Shafer theory. Specific evidence (Hard evidence) is modeled by a mass of 1 on one of the focal elements of the frame of discernment. Non-specific evidence (Soft evidence) corresponds to a mass distribution on the focal elements of the frame of discernment.” Proof: Fig. 1 displays the BN-based EN developed in Misuri et al. (2018) for security vulnerability assessment of a process plant where the belief masses have been updated given the evidence “Attack = Success”. Misuri et al. (2018) used the BN-based EN for predicting the probability of a successful attack (forward analysis), but to update the probabilities (backward analysis) they mapped the BN-based EN into an equivalent CN and calculated the updated probabilities given “Attack = Success”. They used two packages, JavaBayes and GL2U, to implement the CN, concluding that JavaBayes results in more consisting updated probabilities with regard to the evidence (the 2nd column in Table 1). In the present corrigendum, we used the BN-based EN (Fig. 1) for belief updating given “Attack = Success”. The updated beliefs were subsequently used to calculate updated probability intervals (the 3rd column in Table 1), showing a good agreement between the results of CN and BN-based EN. Conclusion: The EN developed by Simon and co-workers (2008, 2009) based on BN can be used for belief mass updating the same way BN can be used for probability updating, with no need for using CN.

Domino effects are high-impact phenomena that have caused catastrophic damage to several chemical and process plants around the world through secondary incidents caused by primary ones. With the increasing trend of cyberattacks targeting critical infrastructures, there is a concern that such cyberattacks may trigger domino effects, by manipulating industrial control systems in such a way that the physical consequences are likely to escalate. In this study, we have demonstrated that via network segmentation of industrial control systems, the plant robustness against cyberattack-related domino effects can be improved. To this end, a risk-based decision-making methodology is developed based on Bayesian network and graph theory to investigate and evaluate the robustness of segmentation alternatives. The application of the methodology to an illustrative case study shows the efficacy of the approach as a viable cyber risk mitigation measure in chemical and process plants.

Global warming and the subsequent increase in the frequency and severity of wildfires demand for specialized risk assessment and management methodologies to cope with the ever-increasing risk of wildfires in wildland-industrial interfaces (WIIs). Wildfires can jeopardize the safety and integrity of industrial plants, and trigger secondary fires and explosions especially in the case of process plants where large inventory of combustible and flammable substances is present. In the present study, by modeling the WII as a two dimensional lattice, we have developed an innovative methodology for modeling and assessing the risk of wildfire spread in WIIs by combining dynamic Bayesian network and wildfire behavior prediction models. The developed methodology models the spatial and temporal spread of fire, based on the most probable path of fire, both in the wildland and in the industrial area.

Quantitative risk assessment (QRA) has played an effective role in improving safety of process systems during the last decades. However, QRA conventional techniques such as fault tree and bow-tie diagram suffer from drawbacks as being static and ineffective in handling uncertainty, which hamper their application to risk analysis of process systems. Bayesian network (BN) has well proven as a flexible and robust technique in accident modeling and risk assessment of engineering systems. Despite its merits, conventional applications of BN have been criticized for the utilization of crisp probabilities in assessing uncertainty. The present study is aimed at alleviating this drawback by developing a Fuzzy Bayesian Network (FBN) methodology to deal more effectively with uncertainty. Using expert elicitation and fuzzy theory to determine probabilities, FBN employs the same reasoning and inference algorithms of conventional BN for predictive analysis and probability updating. A comparison between the results of FBN and BN, especially in critically analysis of root events, shows the outperformance of FBN in providing more detailed, transparent and realistic results.

Complex networks play a vital role in reliability analysis of real-world applications, demanding for precise and accurate analysis methods for optimal allocations of cost and reliability. Since the configuration of a system may change with every feasible solution of cost allocation optimization equation, finding the best arrangement of the system can become very challenging. This paper presents a novel methodology by combining Genetic Algorithm (GA) and Monte Carlo (MC) simulation approaches to simultaneously optimize cost allocation and system configuration in complex network. GA is used to generate configuration-cost pairs while MC is used to evaluate the reliability of the system for each pair. The application of the developed methodology is demonstrated for power grids as an example of critical complex networks. The results show that the proposed methodology can be readily used in practice.

The industrial clustering process in the chemical industry is becoming progressively more important due to economic, social and political issues. Industrial clustering means agglomeration of companies in the same geographical area in order to increase productivity and reduce costs. Nonetheless, clustering also has some important safety and security implications. The aim of this study is twofold: firstly, the development of an algorithm for the classification of chemical industrial clusters with regards to safety and security risks. Secondly, considering the importance of a multi-plant safety and security management system, highlighting the greater efficiency in the reduction of risk where adequate cooperation exists. The methodology is divided in three main steps, namely, “selection” (of the chemical parks to be processed), “assessment” of average hazard and vulnerability of installations within the cluster area, followed by an analysis of the relationships within companies in terms of strategic and operational cooperation, and “ranking”. The last step evaluates the strong influences of the above-mentioned parameters through the analytic network process (ANP) and leads to a final classification.

Past accident surveys reveal that loading and unloading operations (LUOs) are responsible for 11% of fire-related domino accidents. This study investigates the domino accidents during LUOs in the last two decades and identifies the main causes and features of these domino effects. An index-based approach is proposed to assess these domino effects, measuring the periodic escalation capability of installations. The proposed escalation capability index takes into account the special features being present in these accidents, including the spread of vapor cloud due to delayed ignition, multiple fires caused by vapor cloud explosion (VCE), the quantity variation of hazardous substances, and the change of primary event risk due to operations. From a risk management view, an emergency strategy is proposed to tackle the risk caused by LUOs. Therefore, this methodology can identify the most critical areas with regard to the starting or escalating of domino events during LUOs and support the decision-making of alert levels.

Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.

Chemical industrial areas comprising various hazardous installations may be attacked by adversaries, triggering possible intentional domino effects. Compared with accidental domino effects, intentional domino effects may be more difficult to prevent since intelligent and strategic adversaries can adapt their tactics according to protection measures. However, how and to what extent domino effects affect security management is ignored in previous studies. This study proposes a methodology to prevent and mitigate intentional domino effects taking into consideration economic issues in the decision-making process on safety and security resources. The methodology is divided into five parts: threat analysis, vulnerability analysis of installations with respect to intentional attacks, vulnerability analysis of installations subject to possible domino effects caused by the attacks, cost-benefit analysis, and optimization. Net present value of benefits (NPVB) is employed and quantified in the cost-benefit analysis to determine whether a protection strategy (a combination of safety and security measures) is profitable, or not. Besides, an optimization algorithm called “PROTOPT” based on “maximin” strategy is developed to achieve the most profitable protection strategy. An illustrated case study shows that domino effects can not be ignored in security management since they may have a profound impact on adversaries’ strategies.

Risk analysis in process systems is very important to design effective strategies for preventing and mitigating potential major accidents. Although conventional techniques as Bow-tie (BT) have widely been used in risk assessment of process systems, they fall short in effectively modelling epistemic uncertainty which is prevailing in risk assessment of process systems. The present study is aimed at alleviating this shortcoming by incorporating fuzzy set theory into BT, developing a so-called fuzzy extended Bow tie (FEBT) model. FEBT, compared with previous fuzzy BT methods, uses the intuitionistic fuzzy numbers and thus provides a more accurate cause – consequence model of accident scenarios. A natural gas transmission network is used to demonstrate the application of FEBT.