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.

In the process industry, hazardous chemical accidents can easily cause heavy loss, and emergency response is an important approach to reduce it. In the process of emergency response, many emergency tasks have different characteristics in their execution. This study analyzes the impact of the dynamic changes of emergency response personnel on the emergency tasks, and deduces the relationship between the execution time of an emergency task and the number of people with the time they join the task. Aiming to model the emergency response process under the possible dynamic change of the emergency task execution time, the dynamic timed Petri-net (DTPN) is proposed by improving the timed Petri-net (TPN) on the basis of the execution mechanism of transition, and the corresponding models are established according to several basic execution characteristics of the emergency tasks. The proposed approach is used to model the emergency response of on-site responders to a fire in a storage tank, and the time analysis is carried out. The results show that the approach can well solve the problem of modeling and analysis of emergency tasks with dynamic execution characteristics.

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.

In the fire related domino effect analysis of storage tanks, time to failure is a very important issue, which helps to analyze the time characteristics of accident propagation in a certain area, and is also an important parameter for some methods to determine the failure probability of target tanks. This study carries out a numerical simulation to analyze the time to failure of tanks under fire with respect to domino effects, taking the time at which the tank wall stress exceeds the yield strength as the failure time of the corresponding tank. Two scenarios of fire escalation in domino effects are studied, and the time to failure under the synergistic effect of thermal radiations is analyzed. More importantly, this study performs the analysis of time to failure of the target tank considering heat radiation accumulation, that is, under the circumstance of successive multiple tank fires, the fire thermal radiations from multiple fires do not simultaneously influence the target storage tank, but they have an accumulation effect in the target tank, and this way influence the time to failure of the tank.

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.

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.

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.

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.

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.

Domino effects triggered by fire are responsible for most of the cascading accidents which occurred in the chemical and process industry. Their occurrence is highly dependent on the performance of fire protection barriers, as well as on the primary fire scenario. Active and passive safety barriers are largely used to prevent or mitigate domino effects. However, a blinkered allocation approach of safety measures based on a subjective strategy, which does not consider cascading effects possibly triggered by an accident, can lead to a deployment of a safety plan that is not effective in effectively mitigating the consequences of a domino accident. In the present study, an innovative methodology supporting and optimizing the decision-making process for fire prevention in a chemical site has been developed. The methodology provides an optimal allocation of safety measures, aimed at preventing and mitigating fire escalation under budget constraints. A Cost-Effectiveness Analysis with an optimization algorithm is used to allocate a limited budget in terms of fire safety barriers. A novel tool, named Cost Variable Decision Tree, has been developed to support fire prevention investment decisions. The methodology is able to assess the economics of safety plan implementation, considering both the costs of safety measures and the hypothetical benefits deriving from avoided accidents. An illustrative case-study was carried out, confirming that the proposed methodology can effectively support the decision-making process addressing safety barriers allocation.

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.

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.

In the built environment, often too much focus is put on compliance instead of seeking the optimal fire-safety solution for the building. Because of a lack of tangible incentives for building owners, the benefits of implementing fire safety measures and their societal contribution are often not recognized and therefore, not considered. By means of a literature review and a survey, we have indicated necessary fire-safety related attributes and tool features and analyzed the currently available fire risk assessment tools. This study shows that a limited number of these tools can provide a partial fire risk analysis of a building. A total of 26 tools were found. No tools were found that included all the identified fire consequence-related attributes to ensure fire-safe buildings. However, we did identify 11 tools that have the potential to assess between 32 % and 52 % of the found attributes of building fire safety. To stimulate the development of such tools, this paper provides 12 factors by which to assess fire risk assessment tools – quantifying the overall ‘quality’ of the assessment tool – which can incentivize industry to refine the existing ones with enhanced predictability of the potential consequences of fire incidents.

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.

Fire accidents in oil tank farms can trigger domino effects, leading to multiple tank fires with catastrophic consequences. Preventing losses in large-scale tank farms requires a dynamic assessment of fire-induced domino accidents. Existing research often focuses on calculating the time to failure (TTF) of storage tanks but overlooks the influence of failure modes. This study develops numerical models to explore failure modes of oil storage tanks with uniform and stepwise walls exposed to thermal radiation. Factors such as the flame heights of combustion tank, adjacent spacings, wall thickness, and tank volumes are considered. The numerical model employs a solid double-layer flame model to determine thermal radiation intensity and temperature, followed by a dynamic stress–strain and buckling analysis to obtain time to buckling (TTB) and time to yielding (TTY). If TTB < TTY, the failure model is buckling; otherwise, the failure model is yielding. Results indicate that failure modes in nonuniform thermal fields include buckling and yielding, with stepwise walls favoring buckling and uniform walls favoring yielding. When the wall thickness is below the critical value, failure is yielding; otherwise, it is buckling. These findings support risk management and emergency response for fire-induced domino effects in oil tank farms.

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.

In recent years, nature-induced urban disasters in high-density modern cities in China have raised great concerns. The delayed and imprecise understanding of the real-time post-disaster situation made it difficult for the decision-makers to find a suitable emergency rescue plan. To this end, this study aims to facilitate the real-time performance and accuracy of on-site victim risk identification. In this article, we propose a victim identification model based on the You Only Look Once v7-W6 (YOLOv7-W6) algorithm. This model defines the “fall-down” pose as a key feature in identifying urgent victims from the perspective of disaster medicine rescue. The results demonstrate that this model performs superior accuracy (mAP@0.5, 0.960) and inference speed (5.1 ms) on the established disaster victim database compared to other state-of-the-art object detection algorithms. Finally, a case study is illustrated to show the practical utilization of this model in a real disaster rescue scenario. This study proposes an intelligent on-site victim risk identification approach, contributing significantly to government emergency decision-making and response.

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.

When a major chemical accident occurs, emergency response is an important measure to reduce accident losses. Some actions in an emergency response may be more important than others in reducing accident losses, so they need to be carried out as a matter of priority. The impact of the number of emergency personnel on the relationship between emergency response actions and time of emergency response process is analyzed in this paper in the circumstance that the number of responders is not enough to carry out emergency actions with priority at the same time, and a Petri-net based approach is proposed to model and analyze the actions. An emergency response action that needs to consider its priority is divided into two stages, one is to determine the personnel for executing the action, the other is the execution of the action. This division facilitates the handling of dynamic changes in the relationship between actions and differences in start time of the actions. The approach is illustrated by taking the emergency response of on-site emergency personnel of a chemical plant to a fire as an example. The efficiency of the emergency process is analyzed, and for the action of rescuing the wounded, which has the highest priority in the example, the time and the probability when considering the priority of emergency response actions are compared with those when not considering the priority.

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.