It is well known that lithium-ion batteries(LIBs) plays an important role in the energy storage space. This bibliometric study presents a visualized analysis of research trends related to lithium-ion batteries thermal runaway(LIBsTR) from 1996 to 2024. The annual publication trend shows exponential growth, transitioning from a niche concern to a mainstream priority in battery science. China leads in research output, followed by the United States, while European nations contribute high-quality but fragmented work. Chinese academic institutions are highly productive in this field. Co-authorship analysis reveals a centralized research ecosystem dominated by Chinese teams, with emerging open-collaboration models. The Journal of Energy Storage and the Journal of Power Sources are among the most productive journals, and the latter has the highest citation influence. Citation analysis indicates that top-cited papers have significantly impacted the field, with Chinese-affiliated authors becoming prominent post-2015. Keyword analysis shows that “thermal runaway,” “lithium-ion battery,” and “safety” are core topics. Four keyword clusters identify different research focuses, including battery components, thermal management, battery state diagnosis, and thermal runaway mechanisms. The research evolution has shifted from basic understanding to practical applications. Future research should focus on data-driven methods, early-warning systems, battery thermal management, and the development of high-power and high-energy-density battery safety technologies. However, this study has limitations, such as relying solely on the WoS database and potential tool-related restrictions.

With the advent of the artificial intelligence (AI) era, the chemical industrial park cyber-physical system (CIPCPS) has been widely regarded as an effective strategy for promoting the intelligent upgrading of traditional chemical industrial parks. However, it is accompanied by both opportunities and challenges. Traditional chemical industry risk assessment methods mainly focus on physical space, thereby overlooking the risks arising from the cyber space. To ensure the safety and security of CIPCPS, this study explores the following questions: (i) What emerging risks is CIPCPS currently facing? (ii) Regarding these risks, what are the current research advancements in assessment methods, and what limitations do they have? (iii) How to conduct an integrated risk assessment for CIPCPS? The results show that current research on CIPCPSs has not yet provided a systematic classification of various risk types and specific risk factors. Gaps are identified in the traditional risk assessment methods, including insufficient multi-source data fusion capabilities, limited algorithmic interpretability, and the failure to consider the unique consequence amplification effect of domino accidents in CIPCPSs. To address these issues, this study classifies the specific risk types and factors through historical accident case analysis. On this basis, an integrated risk evolution framework for CIPCPSs is developed, dividing the process into three phases: Disturbance-System induction, System-Accident evolution, and Accident-System feedback. Finally, four recommendations are proposed to guide the conduct of integrated risk assessment, providing a theoretical basis for future risk management of CIPCPSs.

This study proposes a dynamic decision-making framework for fire emergency management in hazardous chemical storage tank areas, based on Bayesian networks and influence diagrams. The framework comprises two core components: the generation of initial response strategies and the evaluation of them. By analyzing the thermal radiation coupling effect between tanks, this study introduces the closeness centrality metric to identify key tank nodes, for which multiple emergency response strategies are further formulated. To evaluate these emergency response strategies, this study employs influence diagrams to evaluate the utility of each strategy and proposes a simplified utility function. The study further evaluates the impact of domino effects by dynamically updating the influence diagram, recalculating the closeness centrality, and adjusting the key nodes based on the assumption that a fire spreads to adjacent tanks. A case study illustrates that, across a range of firefighting and cooling efficiency assumptions, the framework successfully adapts to changing scenarios, optimizing strategies to improve the timeliness and accuracy of emergency responses. This provides a viable optimization method for complex tank fire emergency decision-making.

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.

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.

A lack of safety awareness in industrial companies can cause substantial harm to people and the environment. This study explores how fading of safety awareness influences safety-related decisions in (petro)chemical companies. Drawing on ethical fading theory, the research aims to better understand the mechanism that causes safety to decline and to identify ways to prevent this process and reduce safety incidents. Semi-structured interviews were conducted within the (petro)chemical industry to explore this phenomenon. The findings suggest that self-interest plays a more significant role in safety incidents than previously assumed and manifests in multiple forms that contribute to the fading of safety awareness. Moreover, self-interest was seldom identified as a formal root cause of incidents, likely because the fading process occurs largely at a subconscious level, as described in ethical fading theory. Finally, the study found that neutralization techniques were frequently used to justify unsafe behavior, both ex ante and ex post. These insights extend existing theory by linking ethical fading to safety management and highlight the need for interventions that address subconscious drivers of unsafe decision-making.

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.

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.

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.

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.

Battery-powered applications are rapidly spreading in handheld, domestic, business and power storage appliances and in propelling a range of electric vehicles. Fast developments of new battery technology sparked an equally fast development of a new and wide range of applications, showing new safety problems at the same time. The acceptability of these new safety risks across the range has so far not been thoroughly assessed due to lack of statistical incident data. This study explores the wide range of new technology-based battery applications where people are exposed to these hazards, gathers credible incident scenarios and assesses currently available means for incident prevention and mitigation. Battery fire, explosion and toxic fume incidents are emerging as key safety issues in aerospace, shipping, transport and storage, waste handling, the high-risk chemical industry, domestic appliances, industrial power storage, road traffic and carparks. Incidents are causing severe injuries, death and considerable environmental impacts and financial losses. Implementation of both preventive and repressive safety measures is ongoing, yet complicated due to re-ignition and chemicals involved in battery fires. New firefighting strategies and techniques are needed. The authors present an indicative risk assessment based on the presence of risk factors, as derived from a triangulation of experiences reported from practice, scientific literature findings and expert interviews, thereby initiating a risk-based perspective. Several ways to move forward are recommended.

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.

Decisions in complex systems need support from formal risk management. Traditional risk management, based on a "rational" idea of risk (the actual damage linked with probabilities), often diverges from public perceptions, leading to conflict between expert-led evaluations and societal acceptance. Two research directions have emerged to bridge this gap: enhancing stakeholder participation in risk management and incorporating emotional factors into risk assessment. Building on these efforts, we propose a systematic methodology integrating stakeholders' emotional considerations into formal risk management. Our approach combines a refined risk conceptualization with a structured stakeholder engagement process. An illustrative example involving an ammonia plant site-selection risk problem is presented to demonstrate the applicability of the proposed approach. The proposed approach offers a potential way to resolve conflicts and enhance public trust in risk management.

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.

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.

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.

Focusing on the effective configuration of emergency response systems in utility tunnels, this study proposes an innovative approach to optimize existing emergency response systems based on a consequence rapid prediction model and genetic algorithm. In the proposed approach, the interactions between different emergency response components are considered to perform a rapid gas dispersion prediction. Furthermore, the predicted gas concentration distribution is employed to estimate the quantitative explosion risks by combining the equivalent cloud method and the Baker-Strehlow model. Finally, the cumulative and cascading risk index are proposed and combined for systematic optimization by using a genetic algorithm. A case study is performed to demonstrate the feasibility of the proposed approach. The results indicate that the optimized emergency response systems effectively reduce both the cumulative and cascading risk level. This study provides technical support for emergency response system design and helps to improve the safety-risk-control capabilities of utility tunnels.

Major accidents in the chemical process industry occur with low frequency but may lead to severe damages affecting a myriad of stakeholders. Managing major accident risks of chemical industrial systems is regulated in the Seveso Directive of the European Union. However, the conventional risk assessment mainly focuses on the objective aspects of risks and lacks in incorporating public concerns and context-related issues. Aiming to overcome this limitation and enhance the public’s engagement and trust in risk assessment and management, the present study built an integrated risk index for ranking risks considering both technical aspects and societal concerns. A hypothetical case-study is used to demonstrate the application of the proposed risk index. At last, the outcomes of using the integrated risk index and the conventional risk assessment approach are compared and discussed.

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.