Process safety and process security share the common goal of protecting people, assets, and the environment, yet they remain largely fragmented in regulation and practice. This separation obscures the coupled nature of accidental and intentional risks in the chemical process industry and creates blind spots in how safety–security interactions are managed. To address this challenge, this study develops the Resilience-oriented Process Safety and Process Security (RoPSS) framework, which integrates both domains through four resilience capabilities (Anticipation, Absorption, Adaptation, and Ascension) embedded within a six-step management cycle. The framework introduces Ascension as an evolutionary capability that consolidates restoration, learning, continuous improvement, and prevention, extending resilience beyond recovery toward longer-term system strengthening. A structured catalogue of 50 performance indicators, organized by disruption type, resilience capability, and indicator category, provides measurable means to assess both operational and governance resilience. These indicators were defined and refined through focused expert elicitation, including an importance-availability assessment used as a formative step for indicator prioritization. An illustrative example in a chlor-alkali plant shows how RoPSS supports integrated disruption mapping, shared objective setting, and resilience-enhancing strategies. Overall, the framework offers an expert-informed conceptual basis for managing coupled safety–security risks and provides a foundation for future empirical evaluation, industrial application, and resilience benchmarking.

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.

Chemical industrial park cyber physical systems combine process equipment, utility systems, control systems, and information infrastructure. A local disturbance can therefore spread across domains through state dependent relations, propagation delays, and changes in operating load. Existing topology-based models often do not represent these mechanisms or the timing of recovery actions. This study develops an event-driven heterogeneous multilayer network model and an Adaptive Recovery Priority Scheduling strategy for a plant representative system with 251 nodes and 369 directed relations. The model separates engineering load from structural centrality, represents multiple operating states and relation specific consequences, and resolves all events at the same timestamp before the simulation clock advances. Cascade influence is estimated as a source to target conditional probability matrix from stable event-driven realizations. ARPS ranks eligible repairs using projected mission weighted functionality change, cascade exposure, and service-ready recovery time. The framework is evaluated under 12 stress conditions, three disruption scenarios, six recovery strategies, and 30 matched physical worlds for each scenario and stress condition. All 24 checkpoint comparisons satisfied the normalized Frobenius and NCI Spearman convergence criteria. Thirteen nodes remained in the highest 10 % of the NCI ranking under all stress conditions, although membership near the cutoff varied. ARPS achieved the lowest mean recovery loss in all 36 base groups. Among 180 predefined paired comparisons, 179 had 95 % confidence intervals with upper bounds below zero. These results suggest that event-driven modeling combined with safety-oriented recovery criteria can contribute to improved resilience in industrial cyber–physical systems.

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.

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 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.

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 conflicts 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 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.

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.

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.

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.