Deviations of process parameters from their normal ranges are the primary causes of accidents in chemical process systems. Traditional risk assessment methods largely rely on static probability analysis based on historical data, which struggles to capture the dynamic influence of real-time parameter variations on risk and lacks the capability to predict risk evolution trends. This paper proposes a dynamic risk prediction method for chemical process systems based on enhanced feature engineering and XGBoost. First, key process parameters (KPPs), including temperature, liquid level and flow rate are identified through process analysis. A comprehensive risk indicator is then constructed using Dempster-Shafer (D-S) evidence theory to achieve dynamic quantification of system risk. Second, a dynamic simulation model is established using Aspen Plus, simulating operations under normal, disturbed, and extreme conditions to generate time-series data of KPPs. On this basis, multi-scale sliding window techniques are employed to extract enhanced features, including temporal, statistical, trend, and disturbance features. Finally, an XGBoost-based risk prediction model is developed. A continuous stirred-tank reactor (CSTR) is employed to demonstrate the proposed methodology. The results indicate that the proposed methodology achieves an RMSE of 0.159, MAE of 0.122 and an R² of 0.7237, outperforming traditional methods by significant margins. The results validate the effectiveness of combining enhanced feature engineering with XGBoost for risk prediction in chemical processes.

Rising concerns over carbon emissions from fossil fuels have fueled interest in renewable energies. Hydrogen, as a clean energy source, stands out for its free of pollution and high calorific value. However, challenges in safely storing and transporting hydrogen, such as embrittlement, fire and explosion risks, are critical. This study reviews hydrogen storage and transportation safety research through a bibliometric approach, analyzing 948 relevant publications obtained from the Web of Science Core Collection, SCOPUS, and Science Direct literature databases since 2007. Then, a bibliometric analysis is conducted to obtain the publication's distribution, organization, source, and cooperation networks. Besides, the research hotspots in different periods are identified, and the evolution trend of hot topics is analyzed. Moreover, this paper proposes the possible future research needs in this field. The main hot topics in the field of hydrogen storage and transportation safety research include microstructure, crack, susceptibility, and hydrogen embrittlement and they change over time. In the future, research topics such as hydrogen damage in materials, compatibility of hydrogen in natural gas pipelines, and risk assessment should obtain more attention.

In the post-disruption phase, the resilience of LNG terminal system largely depends on maintenance resources—the more maintenance resources there are, the stronger the system's restoration capability and resilience. However, as maintenance resources increase, so do the associated maintenance costs. To enhance system resilience while controlling costs, a well-formulated optimization methodology is crucial. A process parameter-driven resilience optimization method for LNG terminal system considering the resilience enhancement rate (RER), cost and the maximum acceptable restoration time (MART) is proposed. The system resilience and RER are assessed by system performance curve, which is determined by time-dependent process parameters obtained from process simulations. The maintenance resources are represented by the number of maintenance team, including human resources, necessary equipment and materials, etc. A cost function model considering inherent cost, the cost of maintenance resources secondment and operating costs is established to represent the cost factors involved in the entire maintenance activity. According to derived results of the resilience assessment and cost analysis, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to solve the multi-objective optimization model developed in this study. The resilience enhancement optimization for the LNG terminal system is utilized to demonstrate the proposed methodology.

Chemical Process Systems (CPSs) exhibit complex characteristics and inherent dangers that can lead to serious accidents when disrupted. Accurate quantification and assessment of system resilience are crucial for effectively responding to potential undesired events. To address this, we propose a multiparametric resilience assessment methodology for CPSs that considers system dynamics and Independent Protection Layers (IPLs). This method integrates multiple CPS parameters using the Best Worst Method (BWM) to establish a comprehensive performance indicator. A dynamic simulation model incorporating IPLs is developed to monitor real-time changes in system parameters under disruptive influences. Additionally, a resilience metric is introduced, utilizing time-varying parameters to quantify system resilience under various disruptions. A case study involving a two-column pressure-swing distillation process with top recycling, designed to separate a minimum-boiling azeotrope of tetrahydrofuran and water, demonstrates the applicability of this method to complex CPSs. The results indicate that, compared to traditional resilience assessment methods based on reliability, the proposed approach provides time-dependent process parameters, reducing the uncertainty of reliability data. Furthermore, by considering IPLs, this method offers valuable decision support for the design and optimization of these protective layers.

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 domino effect in chemical industrial parks represents a complex phenomenon where accidents such as leaks, fires, and explosions can occur either simultaneously or in sequence. The progression of domino accidents is highly uncertain, making it difficult to anticipate the spatial-temporal development of such accidents. This paper presents a model that aims to forecast the evolution of domino effects by considering the critical thermal dose and utilizing the Probit model to assess the escalation of incidents caused by thermal radiation and overpressure. To tackle the complexities associated with multiple installations, high order, and various accident types in modeling domino effect accidents, the model incorporates Monte Carlo simulation methods. The model validation and case studies have demonstrated the effectiveness of this approach in simulating the progression of domino accidents initiated by a range of primary accidents. This approach enables the prediction of potential accident chains and the dynamic failure probability of hazardous installations, including the identification of the initial installation likely to fail. The insights gained from this research offer guidance for the prevention and mitigation of the domino effect in chemical accidents.

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.

Understanding and enhancing the resilience of transport networks against climate-induced extreme events, such as wildfires, is critical to minimizing disruptions and their societal impacts. In this context, resilience is essential for effectively coping with these hazards, as road disruptions can hinder evacuation efforts, reduce accessibility, and lead to significant economic losses. Despite scientific progress, existing resilience assessment frameworks have limitations, including scenario-specific results and limited consideration of the underlying resilience concepts. To address these limitations, this paper introduces a resilience framework based on dynamic thresholds and characteristic curves to evaluate system recovery capacity. The framework incorporates a temporal dimension, allowing for the analysis of recovery time and recovery rate, which depend on the resources available for recovery activities. The characteristic curves illustrate system resilience by capturing key information on the preparedness, response, and recovery capacities inherent in each network. Consequently, the framework offers a more comprehensive view of system behavior during the recovery stage, as demonstrated through its application to a Portuguese case study. The insights gained can assist stakeholders in determining the feasibility of strengthening system resilience through enhanced response and recovery efforts, as well as in identifying when it is critical to reinforce resilience at earlier stages through adaptation measures.

Corrosion is a deterioration phenomenon of buried long-distance pipelines involving complex dynamic processes. The complexity poses challenges to addressing the safety concerns caused by corrosion. In recent years, the concept of resilience has been introduced into the assessment of engineering systems. However, there is a limited effort in quantitatively assessing the resilience of a pipeline's response to corrosion. This work aims to develop a novel framework to quantify the resilience of pipelines against corrosion while considering the resilience evolution induced by future corrosion growth, dynamic in-line inspection (ILI) plans, and distinct repair strategies (re-coating, composite material reinforcements, and pipe replacement). Pipeline Service Resilience (PSR) is modeled as a function of absorption, adaptability, and restoration capabilities based on the time-dependent burst pressure metric. Dynamic Monte Carlo Simulation technique is employed to model the potential resilience evolution scenarios to predict the PSR. The proposed framework is demonstrated on an in-service pipeline. The case results show that the PSR value ranges from 0.8943 to 1 due to the uncertainty of the resilience evolution process. Noteworthy impacts on PSR include repair time, ILI intervals, anti-corrosion ability, decision-making time, corrosion depth growth rate, and corrosion length growth rate (in decreasing order of sensitivity). The proposed methodology can potentially emerge as a significant tool for evaluating pipeline resilience under corrosion.

Pipelines are the most widely used system for transporting liquid and gaseous energy materials, but throughout their lifespan, they are exposed to various detrimental factors, such as corrosion and deviations in process variables. In recent years, the concept of resilience has gained significant attention as a means to analyze infrastructure behavior during failure states. This study introduces a novel metric for assessing pipeline resilience based on reliability. The proposed method involves an aging study of pipelines, considering the interaction of potential failures—such as corrosion, pressure variations, temperature fluctuations, and changes in fluid velocity—and subsequently analyzes ways to restore the system to its original conditions. The method offers an assessment approach for the three phases that constitute a resilience curve: absorption, adaptation, and restoration. This approach not only identifies the system's time to failure, but also through analysis of the resilience curve, facilitates the comparison of the effects of potential preventive, mitigative, and repair actions. A case study is presented to validate the method's efficacy. The results suggest that the proposed approach could be a valuable tool in the decision-making process within the asset integrity management (AIM) framework, aiming to optimize pipeline resilience by implementing the most effective safety solutions.

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.

Seaport infrastructure requires considerable resources and time for a full recovery from accidents caused by hazardous cargo. Despite their severity, the risk to seaport infrastructure from hazardous cargo operations has been insufficiently explored. This study aims to fill that gap by examining the risks to seaport infrastructure from the complex effects of hazardous cargo operations. It draws on literature, incident reports, and expert consultations to identify comprehensive risk factors and their interconnections. The study employs expert judgments alongside logistic regression to develop Conditional Probability Tables (CPTs) and conducts a risk analysis using Bayesian networks (BN). Our findings indicate that, under typical operating conditions, fire and explosion, corrosion, and improper handling are the most significant contributors to seaport infrastructure risk with probabilities of 8.73 %, 5.88 %, and 5.61 % respectively. Inverse propagation indicates that the contribution of improper handling and corrosion is enhanced by 153 % and 96 % respectively towards the increased risk. A sensitivity analysis was carried out to pinpoint critical risk factors. Based on these insights, the study suggests practical measures like the use of tracking and monitoring systems along with third-party audits for effective handling, augmented and virtual reality for advanced training, and automation technology for reduced human roles to subside risks to seaport infrastructure and promote uninterrupted operations.

As supply chains in today's world become more complex and fragile, enhancing the resilience of maritime transport is increasingly imperative. The COVID-19 epidemic in 2020 exposed the vulnerability of existing supply chains, causing substantial impacts such as supply shortages, procurement constraints, logistics delays and port congestion, highlighting the need to build resilient maritime transportation networks (MTNs) and reigniting research on the resilience of maritime transport. Based on science mapping, we quantitatively analysed the domain of resilience of MTNs. We mainly study the resilience of MTNs from the following aspects: the construction of MTNs and their topological characterization, vulnerability-orientated resilience analysis of MTNs, recovery-orientated resilience analysis of MTNs, investment decision-orientated resilience analysis of MTNs, climate change-orientated resilience analysis of MTNs and pandemic-orientated resilience analysis of MTNs. This study reviews recent advances in MTN resilience research, highlighting research topics, shortcomings and future research agenda.

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.

The estimation of failure probability is challenging in hydrogen embrittlement in steel pipelines due to the complexity of the synergistic effect of multiple factors. The present study proposed a hybrid methodology to estimate the failure probability of steel pipelines due to hydrogen embrittlement. The methodology integrates the fault tree analysis with a fuzzy comprehensive evaluation. Fault tree analysis captures the logical relationships between influencing indicators to develop a new assessment model of hydrogen embrittlement in steel pipelines. An improved fuzzy fault tree analysis method was proposed to process aleatoric and epistemic uncertainties to estimate the probability of each basic event due to the difficulty in obtaining the actual probabilities. The failure probability of blended hydrogen natural gas pipelines was estimated by considering the correlation of events. A case study demonstrated the applicability of the proposed method. Maintenance measures can be implemented according to the evaluation results to ensure pipeline safety.

System resilience denotes the capacity to uphold desired system performance in the face of disruptions. Evaluating the resilience of a process system necessitates a thorough consideration of the intricate interplay between its components and the pivotal role of process parameters in reflecting the repercussions of disruptions on the system. This paper introduces an integrated methodology that takes into account component interactions and leverages process data for the resilience assessment of a process system. The proposed methodology comprises four key components: system structure analysis, disruption impacts analysis, process simulation, and resilience assessment. Firstly, the system structure is meticulously scrutinized using a P-graph model. This analysis encompasses the assessment of the significance and interplay of components, as well as the evaluation of how component failures affect the system's overall processes. Secondly, a Markov model is devised to examine the state transition process of components and quantifies the maintenance time needed for failed components. Subsequently, a simulation model is formulated to acquire real-time process parameters in the presence of disruptive events. Finally, the system's performance response function (PRF) is derived from the normalization of these process parameters. Building upon this foundation, a resilience assessment is conducted with a focus on the PRF. To illustrate the effectiveness of this methodology, an LNG terminal system is employed as an exemplar.