Chemical process industries are threatened by accidental and intentional major events that may lead to catastrophic consequences due to hazardous materials' production, operation, and storage. Remarkably, the digitalization of industrial facilities brings emerging cyber-physical attack risks, which calls for a holistic and integrated safety and security risk assessment and management. Considering the dynamic aspects of risks, the continuous monitoring and assessment of risk-related variations plays a vital role in making timely adaptions to risk treatment strategies and, therefore, accommodating increasing risks. To this end, this study proposes a comprehensive framework for risk-based safety and security barrier management, handling challenges in assessing integrated safety and security risks and deriving timely and cost-efficient barrier improvement strategies in case undesired risks are increasing to unacceptable levels. The fundamental ideas and applicable procedures are elaborated before a case study is demonstrated to offer insights into its feasibility. The case study shows that implementing this framework holds advantages in managing safety and security risks in a unified way, considering the interplays between safety and security and making continuous risk-treatment adaptions to sustain the safety and security of digitalized chemical process systems. Furthermore, the principles and precautionary considerations pertinent to this new framework are discussed to foster its application in real-world settings.

Explosion-induced domino accidents in the chemical industry, such as the 2005 Buncefield and 2019 Xiangshui accidents, can lead to catastrophic losses. Recent studies commonly use probit models (simplified linear regression models) to predict the probability of accident escalation caused by equipment failure due to overpressure conditions but necessitate distinct equations for different equipment types. In order to simplify the number of models and improve their accuracy, this study introduced three machine learning models (random forest model, convolutional neural network model, and deep neural network model), addressing complex nonlinear relationships that conventional regression models may not fully capture. By model training, the DNN model has the highest accuracy (99 %), followed by CNN (94 %) and random RF (95 %). The DNN model was selected as the optimal data-driven model for equipment vulnerability assessment due to their feedforward mechanism's capability to dynamically align parameters with evolving data distributions. The approach developed can not only predict the probability of equipment damage by integrating values related to peak overpressure and equipment type but also effectively address the accuracy validation issues associated with traditional regression models. Besides, this approach can be considered open source model and more explosion data may be used in the future to further improve the model.

Introduction: Recent developments in a wide variety of cyber-physical system (CPS) designs have resulted in many safety and security concerns. These are caused by increasing complexity, connectivity to the outside world, and a variety of component types, all contributing to increased system vulnerability. Simultaneously, the use of digital models, big data analytics, remote computing, online services, and machine learning extends manufacturing systems far beyond their physical and visible boundaries. Focusing on industry and healthcare settings, this study explores the challenges faced by safety and security management originating from these developments.

Method: The scoping review method was used to gather literature on safety and security concerns. These are grouped and linked to the CPS components. Subsequently, their impact is reviewed from the perspective of safety and security management systems.

Results: The findings indicate that safety and security management become entangled, many new risks need to be considered, and the availability of online digital real-time system components and employee skills are critical factors. Some of the sensors and safety metrics software must now be classified as safety-critical because robots and people have started to share the same workspace. The rapid developments and many unresolved issues on design, ethics, data and software quality, external supervision, standards and guidance, legal frameworks, and online system components abroad urgently require attention. In the discussion, several recommendations are made regarding how safety and security management can address these challenges.

Conclusions: While robots and people increasingly mingle on shop floors and in care situations, safety management is lagging behind current CPS developments. New critical safety aspects require the attention of safety and security management. Practical applications: These findings in safety and security management systems will contribute to avoiding harm to people and the environment in companies operating CPS. One sentence summary: This study presents a structured way to deal with safety management of Cyber-Physical-Systems.

The rapid industrialization and urbanization in China require a high level of safety management and thus urge the development of safety risk assessment in China. In the past two decades, many safety risk assessment research findings have been published in international journals by Chinese scholars, while it is not clear the development progress and China's contributions to the world in this research field. Therefore, a systematic and thorough literature review is conducted to investigate risk assessment research in China. Firstly, the research publications authored by Chinese scholars are searched from the well-known literature database Web of Science to support the analysis of risk assessment research in China. Secondly, a bibliometric analysis is conducted for the obtained literature related to risk assessment research in China to find out publication trends, research organizations, research authors, research topics, and research methods. Then, a thorough analysis of research topics and research methods is carried out to present the research progress. Finally, possible future research issues in the risk assessment research domain are discussed based on this literature review. According to the discussion, more attention in China should be paid to the risk of digital or autonomous systems, the risk related to extreme events, and the risk in large cities.

The evolving energy landscape in Europe is showing concrete signals that hydrogen will play a central role in the energy transition scenario. In this light, a report of the European Hydrogen Backbone pinpoints no less than forty existing projects focused on the commissioning of several kilometers of hydrogen pipelines in the following years. Hence, ensuring a safe operability of these systems represents a topic worthy of investigation and marked by significant challenges, especially given the unique properties that make hydrogen a potentially hazardous substance. Established techniques may prove helpful in supporting the development of dedicated prevention and mitigation strategies for hydrogen systems. Among these, Risk-Based Inspection (RBI) could represent an effective tool to design inspection programs aimed at the detection of hydrogen-induced damages, especially for components working in pressurized environments, including pipeline materials. However, the lack of operational experience associated with emerging technologies may lead to the adoption of over-conservative safety measures, which could impact the economic attractiveness of these systems. Therefore, this study proposes an evolution of conventional RBI planning by implementing concepts of safety economics and optimization modelling, thus building a novel approach named “Cost-Informed Risk-Based Inspection” (CIRBI). The proposed methodology is therefore applied to a case study of inspection techniques potentially suitable for pipeline materials (i.e., API X-series pipeline steels), showcasing its potential as a self-standing approach for inspection planning while also demonstrating the insight that it may provide to ensure a safe operability of hydrogen pipelines.

Integrating process safety and process security risk management is increasingly essential for enhancing resilience in the chemical process industry. This study addresses how practitioners perceive the integration of these two domains, identifying key benefits, barriers, and strategies for effective implementation. A mixed-methods approach was applied, combining quantitative survey data from 47 industry professionals with qualitative insights from open-ended responses. The findings highlight significant advantages of integration, such as optimized resource use, reduced operational redundancies, and improved risk management. However, barriers such as knowledge gaps, resource constraints, and communication silos were identified. Respondents emphasized the importance of adopting a resilience-oriented approach involving proactive risk management, continuous improvement, and adaptability in both safety and security practices. Critical enablers for integration include strong leadership, alignment of societal values, cross-disciplinary training, and integrated risk assessment methodologies. Emerging technologies and regulatory alignment were also identified as critical factors in facilitating integration. The study contributes to the theoretical understanding of integrated risk management by supporting resilience engineering and systems theory. It offers actionable strategies for overcoming barriers and leveraging enablers, laying the groundwork for developing a resilience-oriented framework for process safety and process security risk management.

In recent years, the relationship between academia and the fossil fuel industry has become a focal point of intense debate. This concern arises from the fear that corporate funding might skew research activities. A significant development in this area is the adoption of policies by a Dutch university, and discussions in several others, prohibiting research funded by the fossil fuel industry. These policies aim to safeguard academic freedom and integrity. Despite this, there has been little discussion on the myriad challenges, implications, and possible unintended consequences, particularly in the realm of safety-and-security research. As such, this manuscript delves into the complex transition towards a fossil-fuel-free society, examining it through the lenses of safety science and sociotechnical systems. It emphasizes the vital importance of collective responsibility in ensuring systemic safety and security as we navigate towards achieving the sustainable development goals. This journey requires a delicate balance between the objectives of safety and sustainability, along with a deep understanding of the security implications of decreasing our dependence on the fossil fuel industry. The strategy of distancing academic research from fossil fuel industries, commonly seen as a positive step, also demands a nuanced consideration of its broader impacts, including the setting of precedents for addressing other existential and systemic risks. Instead, we argue for the establishment of robust governance structures rooted in restorative justice principles. Such frameworks can facilitate productive dialogue with underrepresented groups, motivate the fossil fuel industry towards sustainable practices, and safeguard the integrity of scholarly research. This approach not only addresses immediate concerns related to fossil fuels but also lays the groundwork for a more inclusive and equitable model of climate risk research, essential for tackling the multifaceted challenges of our era.

Domino effects are a complex phenomenon of accident escalation with high uncertainty, which could lead to catastrophic consequences. Predicting the probability of domino effects presents a great challenge in the field of process safety. In multi-level domino chains, synergistic effects of accidents would further raise the complexity of probability prediction since escalation vectors emitted from those accidents may be coupled. In this paper, four categories of synergistic effects are classified according to the type of accidents and their place on the domino accident sequence. Subsequently, specific models for estimating escalation probability under different synergistic accident scenarios are proposed based on the widely-used probit models, allowing the analysis of the coupling effects of escalation vectors. A probability prediction method for domino chains is further developed using Bayesian Network. The application of the developed method is demonstrated by a case study, and the domino probability is estimated accounting for the synergistic effects and all possible accident scenarios. The key units for promoting accident propagation are further identified through posterior probability analysis. The method would be helpful for domino risk assessment and management of any chemical industrial area.

This research explores how process safety and process security risk management methods may be integrated into a unified framework, thereby considering the resilience engineering paradigm. Employing a systematic literature review and expert surveys with industrial practitioners alongside representatives from key regulatory bodies to understand the complexities and opportunities of such integration. The interactions that result from a mixed-method study ensure the gaining of insights into the practical challenges and opportunities of integration in diverse operational and regulatory environments. Utilizing Microsoft Forms analytics tools and ATLAS.ti for qualitative analysis, the research identifies patterns and conditions, as well as the context, which is pivotal for constructing a comprehensive, adaptable risk management framework. This paper introduces a novel, holistic risk management perspective, advocating for the amalgamation of process safety and process security within the chemical industry, adopting the System-Theoretic Accident Model and Processes (STAMP) model underscored by resilience principles.

Frequent unpredictable earthquake disasters such as the Turkey Earthquake in 2023 pose an increasing threat to oil tank farms since they may trigger major accidents and domino effects, resulting in casualties, economic losses, and environmental pollution. Unpredictable earthquakes are definitely difficult to prevent and thus resilience strategies such as emergency response should be applied to reduce losses. However, little attention has been paid to the quantitative resilience modeling of oil tank farms, resulting in difficulties in decision-making on resilience assessment and management. Therefore, this study proposes a quantitative seismic resilience model of oil storage tanks by using a dynamic agent-based modeling approach. This approach models the storage tank, active fault, and the environment as three independent agents with their attributes and behaviors. The interaction between agents can also be modeled through disaster evolution rules, and the consequences of interactions can be adjusted through adaptation and recovery strategies. The dynamic propagation of earthquake accidents and the evolution of potential domino effects can be quantified from a bottom-up perspective, thereby quantifying the seismic resilience of oil tank farms. A case study is carried out to illustrate the application of the developed model in oil tank farms and to analyze the sensitivity of different model parameters. The results show that the developed model can dynamically characterize the evolution of earthquake-induced domino effects as well as the emergency and restoration processes, supporting the decision-making on the allocation of resilience measures.

Emergency response is a procedural safety barrier of paramount importance for the mitigation of fire scenarios and the prevention of escalation. However, in Natech scenarios, emergency response may be affected by the natural event impacting the site. Indeed, when contrasting Natech accidents, emergency responders have to face both the natural event and the cascading technological scenario. Despite the criticality of the issue, limited attention was devoted to date to the analysis of emergency response in cascading sequences triggered by natural events. The present study provides a novel and technically sound methodology to assess the performance of emergency response and the required intervention time in Natech scenarios. An expert survey combined with a Bayesian Network model was used to assess the performance of the emergency response. The routing and setup phases were identified as those mostly affected by natural events. Monte Carlo simulations were used to obtain baseline data and specific probability distributions for the time required to carry out the emergency response considering the factors that may hinder the response during natural events. In Natech accidents, the time for effective mitigation resulted higher of at least a factor 2 with respect to that expected in the case of conventional accidents. The methodology developed may be used to support the improvement of the emergency management of Natech scenarios, allowing for a detailed definition of site-specific emergency response plans. Moreover, the results may be used to provide a more accurate assessment of the fire-driven escalation probability in Natech events.

An earthquake-triggered fire domino scenario (E-FDS) is an example of a typical multi-hazard coupling event. The seismic damage can affect the fire resistance of engineering structures, leading to significant mutually amplified phenomena. In this work, a two-stage experimental program is designed to expound the earthquake-fire coupling failure mechanism of steel cylindrical tanks (SCTs). Quasi-static tests are adopted to simulate the damage characteristics of SCTs under seismic excitation (Stage I). Fire tests are adopted to investigate the fire-resistance performance of pre-damaged SCTs (Stage II). The influences of seismic damage on the fire resistance of SCTs are particularly of interest. Three potential seismic damage degrees are considered. The experimental results show that tank specimens exhibit typical diamond-shaped buckling after Stage I. The coupling failure analysis of SCTs is conducted through sequential thermodynamic coupling simulations. Due to factors such as geometric deformation, residual stress, and thermal radiation absorption capacity, the fire resistance of SCTs is significantly attenuated by seismic damage. For the three damage states, fire resistance time attenuation coefficients (0.868, 0.716, 0.511) and critical temperature attenuation coefficients (0.910, 0.779, 0.672) were obtained. This work provides pivotal insights into the mutually amplified phenomena in E-FDSs.

If a fire occurs in an area with multiple installations storing or handling flammable materials, it may escalate from one installation to another to form so-called domino effects. The propagation of fire-induced domino effects has temporal properties due to the heating effect of the thermal radiation of fires. Under the action of thermal radiation, the surrounding installations may be damaged, and the probability of damage can be estimated by Probit models. As a result, the propagation path exhibits characteristics of randomness. As new installations catch fire due to domino effects, the thermal radiation received by a target installation changes, so that the damage probability changes dynamically throughout the accident process. This study aims at further improve the previous study of the matrix modeling approach for fire-induced domino effects by considering the heating process of facilities under fire accidents. The analysis process incorporating the damage probability calculation algorithm is presented. The improved approach is illustrated by the study of two cases.

Risk assessment can reveal the level of a safety situation which can be very important info for company safety management. Considering the multiple factors in risk assessment of a system and the vagueness nature of many factors, and using the benefits of the Petri-net in modeling and reasoning, a weighted fuzzy Petri-net (WFPN) based risk assessment approach which combines WFPN with the SPA-fuzzy method is proposed in this paper. The SPA-fuzzy method is utilized to establish the membership functions for fuzzy assessment, taking its advantage in comparing identity, contrary and discrepancy features of two different sets. In this study, the WFPN is redefined to model relationships between assessment factors, and the matrix-based fuzzy reasoning algorithm is provided to assess the factors in parallel. The application of the proposed risk assessment approach is illustrated by case studies: the risk assessment of a chemical storage tank area and the ignition accident assessment of hydrogen refueling stations.

Fire-induced domino effect is one of the main threats to hazardous material storage tanks, and many attempts have been conducted to assess the vulnerability of storage tanks exposed to fire to evaluate domino effect risk. However, past research ignored the influence of wind load on the thermal buckling behavior of storage tanks exposed to fire, which may underestimate the risk of exposed tanks. This paper thus conducts a numerical simulation of the thermal buckling behavior of steel vertical dome storage tanks under the synergistic effect of static wind loads and thermal effects. The effects of wind parameters and heat radiation parameters on the thermal post-buckling behavior and the time to failure (ttf) of storage tanks are investigated to analyze the synergistic effects of fire and wind loads. By comparing the circumferential and meridional stresses before and after the thermal post-buckling stage, it is found that under the disturbing effect of the positive wind pressure load, the thermal post-buckling of the tanks on downwind occurs earlier and more severe. Besides, the effects of wind angle, fire location height, and diameter on buckling damage were investigated. The comparative analysis of different scenarios shows that the tanks in the windy scenario are more prone to thermal post-buckling, and the deformation is intensified, with an increased likelihood of failure.

Chemical facilities face threats from accidental and intentional events, including the rising concern of cyber-physical (C2P) attacks in the digitized industrial control system era. Addressing major accident risks from safety hazards and C2P attacks requires an immediate unified framework for safety and security barrier management. This study presents a systematic risk-based approach to integrate conventional safety risks with emerging C2P attack risks. Adverse scenarios are identified, integrated into an attack-tree-bow-tie diagram, and modelled using a Bayesian network (BN). A vulnerability assessment model is developed to quantify industrial control system vulnerability to C2P attacks, considering uncertainties in attackers' knowledge levels. Monte Carlo simulations are used to handle uncertainty propagation in risk assessment, allowing the use of probability distributions for BN root nodes. Sensitivity analysis identifies critical factors/events, guiding the proposal of candidate strategies for barrier improvements. Combining cost-effectiveness analysis with a risk matrix yields the optimal strategy for safety and security barrier enhancements based on risk estimations. A hypothetical case study demonstrates the proposed approach's effectiveness in integrated safety and security barrier management, considering security vulnerability patching and safety barrier maintenance scheduling from a cost-effective perspective.

Ammonia is a promising fuel for marine propulsion and generation, yet its acute toxicity and associated safety challenges necessitate careful consideration. Current regulations recognize the hazards of ammonia, introducing numerous technical safety measures in response. However, the effectiveness of these measures in ensuring acceptable risk levels for future vessels remains to be fully assessed. This study estimates the risk level onboard ammonia-powered ships, identifying the aspects with the largest and controllable influence on it. Three hypothetical concepts of ammonia fuel supply were developed in this study on an example tanker vessel and analyzed using the quantitative risk assessment (QRA) methodology. The obtained risk profiles were evaluated against the risks by an equivalent liquefied natural gas-fueled system, serving as a benchmark. The results demonstrate that ammonia-fueled ships exhibit individual risk levels for engineering crew with periodic duties in fuel preparation rooms (FPR), or similar compartments, which are 1–1.5 orders of magnitude higher than those observed for a conventional gas-fueled alternative. An analogous increase has been noted for the public potentially present onboard or in proximity. The study underscores the importance of managing human-machine interactions, enhancing the reliability of supply systems, and managing the systems’ complexity to mitigate risks in FPRs. Regarding public safety, the analysis highlights new risk mechanisms introduced by ammonia and examines how storage conditions affect exposure levels. By offering a detailed QRA framework, this research contributes to the development of effective risk management strategies for ammonia-powered ships.

Earthquake-triggered fire domino scenarios (E-FDSs) arise frequently from the interaction between earthquakes and chemical installations, resulting in catastrophic multi-hazard coupling events. The complicated mutually amplified phenomena between natural disasters and chemical accidents significantly aggravates the escalation of domino accidents, which has posed great challenges for modeling and preventing E-FDSs. Under this impetus, this work proposes an advanced type-2 fuzzy probabilistic methodology to obtain the time-dependent failure probability of steel cylindrical tanks (SCTs) subjected to the earthquake-fire sequence. To cope with the limited prior knowledge on E-FDSs, a basic universal is established to describe the fire resistance attenuation caused by the seismic damage. The coupling failure criterion of SCTs is formulated by a type-2 fuzzy time-dependent limit state equation. A credibility-based stochastic simulation algorithm is developed for the hybrid uncertainty analysis (combining ambiguity and stochasticity). The proposed methodology is validated by case studies of a 5000 m³ fixed roof tank. Compared to the existing accident probability model, the proposed methodology can not only capture the fire resistance attenuation caused by the seismic damage but also provide a dynamic estimation of tank failure probability with respect to the fire exposure time. The proposed methodology can effectively and dynamically capture the accident evolution process, which in turn helps mitigate and prevent the spatiotemporal propagation of domino effects.

Risk-based techniques such as risk graph and Layer of Protection Analysis (LOPA) are used to determine the Safety Integrity Level (SIL) of safety instrumented functions to ensure that risk is reduced to a tolerable level. However, these techniques have some drawbacks. For instance, they need absolute and precise numbers to evaluate SIL parameters, which are rarely available or are highly uncertain. In addition, they are incapable of considering individual and societal risks simultaneously. Moreover, risk tolerance criteria are likely to be used incorrectly in the LOPA technique, and risk graph is difficult to calibrate. In the current paper, a novel comprehensive fuzzy arithmetic model has been developed to determine the required SILs in process industries. The fuzzy required Risk Reduction Factor (RRF) is calculated for both individual and societal risks. Fuzzy numbers are developed from crisp intervals, based on the expected interval of the fuzzy numbers. Expert fuzzy-scaled elicitation has been applied to obtain the SIL parameters. In the proposed model, the overall risk tolerance criterion and apportionment factor are defined as SIL parameters for both individual and societal risks to ensure that the applied risk criteria are compliant with the requirements of the system. In addition, an approach is introduced for determining the required SIL based on the fuzzy required RRF. The proposed methodology was demonstrated to alleviate the limitations, and thus, can be considered as a more precise alternative to the conventional methods.

Industrial production intensification greatly enhances resource utilization efficiency and production efficiency within the modern petrochemical industry (MPI). However, densely located hazardous installations pose significant threats to workers, society and environment. Under this impetus, an advanced pareto-based optimization methodology is proposed for chemical tank farm (CTF) design. The objectives of domino risk minimization and land resource utilization efficiency maximization can be achieved through optimizing the locations and dimensions of storage tanks. A simplified quantitative domino risk assessment procedure is developed within a grid-based Cartesian coordinate system, which links the design parameters and risk values. A bi-objective optimization model is developed for problem formulation and a well-designed simulated annealing-based multi-objective particle swarm optimization is proposed for model solving. A CTF with six floating roof diesel tanks is adopted for case study. The simulated annealing-based jumping mechanism can effectively avoid the local optimum, which makes the algorithm easier to obtain the trade-off with great convergence and diversity. The proposed methodology can provide safer and more cost-effective design solutions. Results indicate that the design parameters can significantly affect the regional domino risk distribution. The conflicting nature between safety and economy is discussed. This work is of great significance for the safety and reliability of MPI.