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• Literature on resilience to Natech of the chemical and process industry is reviewed. • Contributions are classified in three areas: Frameworks, quantitative methods, reviews. • Key aspects of the Resilience Engineering for the assessment of Natech scenarios are discussed. • A critical analysis of the existing literature is performed, highlighting gaps and limitations. • Research needs are identified to bridge Resilience Engineering to Natech risk management. The threat of natural hazards and their devastating consequences is challenging the resilience of society. Especially in industrial areas, where relevant quantities of hazardous materials are handled, natural hazards may trigger severe technological accidents (Natech). Resilience Engineering (RE) principles have been recently introduced in the context of process safety and Natech accidents, aiming at the development of systems able to withstand and rapidly recover from unexpected events. A systematic review of the literature addressing the application of resilience drivers to the framework of Natech assessment and management was carried out, providing a critical evaluation of strategies, methods, and tools proposed for resilience to Natech events. The study focused on chemical and process industries and related sectors, as the petrochemical and energy industry, analysing both quantitative and qualitative approaches. The fundamental aspects necessary to develop methods and tools for the quantitative resilience assessment of industrial facilities facing natural hazards have been identified. These mainly address the phases of the resilience evolution process (REP), the features of Natech scenarios, and the resilience metrics. Gaps and limitations in the state of the art, deserving attention in future research, were identified and discussed. These include the development of a detailed framework for the REP, a comprehensive assessment of the post-accident phases, and the integration of specific features of Natech scenarios. Other important aspects identified are the need to bridge the gap between qualitative and quantitative methods and the importance of developing a multidimensional approach to achieve a comprehensive understanding and assessment of the resilience of facilities exposed to natural hazards. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• An integrated approach is proposed for resilience assessment in process systems. • Non-linear interdependencies among human-technical factors are considered. • System dynamic functionality is measured by cascading failure propagation model. • Complex system resilience is quantified based on system functionality. Chemical process systems are becoming more automated and complex, which leads to increased interaction and interdependence between the human and technical elements of process systems. This urges the need for updating the safety assessment method by treating "safety" as an emergent property of a system. Uncertainty comes together with complexity. To enhance system ability of dealing with uncertain disruptions, this paper proposes a quantitative resilience assessment method by modeling the failure propagation (initiated by a disruption) across the functional units of a system. The Functional Resonance Analysis Method (FRAM) is utilized to model the system operation to represent the relationship among its function units and to consider the interactions among human-technical factors. Then, a Cascading Failure Propagation Model (CFPM) is developed to quantify the fault propagation process and reflect the system functionality changes over time for resilience assessment. The proposed method is applied to a propane-feeding control system. The results show that it can help practitioners understand the process of fault propagation and risk increase, identify potential ways to design a more resilient system to respond to uncertain disruptions/attacks, and provide a real-time dynamic resilience profile to support decision-making. [ABSTRACT FROM AUTHOR]

• A dynamic risk analysis model is developed using condition-monitoring data. • The model considers health of critical safety barriers as main contributor to the risk. • Markov Chain Monte Carlo algorithm is used in a Bayesian framework to assess dynamic risk. • The developed model is applied toa oil and gas separator to downstream equipment. • The developed model prompt safety-driven decision-making and enabling timely response to manage risk. Risk assessment models in the oil and gas (O&G) industry has begun to transform from time-static models like fault tree, event tree or bowtie to dynamic risk assessment (DRA) models, as the latter is able to better capture the real-time-dependent risk behavior of safety barriers. Currently, most existing works on DRA in O&G industry mainly rely on event-driven data, such as failures or accident data from similar systems for risk updating. These data can be collected only when accidents or near-misses have occurred, which limits the prediction capability of the DRA model. To address this drawback, a DRA model is developed in this paper that uses condition-monitoring data for risk updating. A significant advantage of using condition-monitoring data is that the risk can be updated before accidents or failures occur, giving the operation team more time to take preventive actions. The developed approach comprises of an offline and an online phase. In the offline phase, a conventional risk assessment is performed based on fault tree models to calculate the risks at the beginning of the operation. Through the offline analysis, we can also identify the most critical safety barriers by examining their contribution to the risk indexes. Critical safety barriers are selected for condition-monitoring. In the online phase, the condition-monitoring data are used to update the reliability of the safety barriers in real-time based on a sequential Markov Chain Monte Carlo (MCMC) algorithm using a Bayesian framework. The updated reliabilities of the safety barriers are, then, used in the offline risk assessment model for a DRA. The developed approach is applied for a DRA of liquid carryover from an oil and gas separator to downstream equipment, using real-time data from the separator's safety barriers. The results show that the developed method provides a more accurate representation of the system's performance, enabling early detection of potential failures and reducing uncertainty in risk estimates. The proposed DRA model demonstrated its effectiveness in predicting failures of safety barriers in real-time, giving operational teams ample opportunity to take corrective action. This leads to improved decision-making in the O&G industry, enabling timely response to changes in risk levels. The use of condition-monitoring data enhances the accuracy of risk estimations, representing a crucial advance in DRA applications in the O&G sector. [ABSTRACT FROM AUTHOR]

Technological accidents triggered by natural disasters (Natech) have become a significant threat to the safety of coastal energy infrastructure. The domino accidents involved could propagate rapidly in a short duration and amplify an accident exponentially. In previous studies of Natech-related domino accidents, the scope of natural disasters was mainly focused on the phase of primary events, and the subsequent accident cascades were entirely driven by the domino effect. This simplification essentially weakens the real-time intervention of ongoing disasters in the evolution of accidents, making it difficult to accurately model the evolutionary patterns of domino accidents. Accordingly, a Natech-related domino evolution graph (NT-DEG) is proposed in this paper for dynamic modeling of accident evolution under the real-time disturbance of natural disasters. In addition, a stochastic process and a deterministic process are innovatively used to control the generation of primary events and the update of the evolution network at each timestamp. Moreover, using a dynamic community detection algorithm and graph metrics, an identification method for critical units in dynamic domino accidents is proposed. The application of NT-DEG to typhoon-related Natech indicates that continuous-onset natural disasters may cause new failure units that could participate in the original domino accident at any moment. This real-time intervention noticeably changes the propagation path and scale of the original accident and accelerates accident evolution. In addition, the validation based on the integrated simulation reveals that the proposed identification method can accurately identify the critical units, and the safety measures against them can noticeably reduce the scale of accident cascades. [ABSTRACT FROM AUTHOR]

• A Multi-Objective Optimization of safety barriers performance parameters is developed. • The solution suggests the design improvements to guarantee the best mitigation at the minimum cost. • A chemical facility exposed to NaTech is considered as case study. • The facility is equipped with active, passive, and procedural safety barriers. • Meaningful insights on the budget to be allocated and on the actions to be performed are collected. Safety barriers are to be designed to bring the largest benefit in terms of accidental scenarios consequences mitigation at the most reasonable cost. In this paper, we formulate the problem of the identification of the optimal performance parameters of the barriers that can at the same time allow for the consequences mitigation of Natural Technological (NaTech) accidental scenarios at reasonable cost as a Multi-Objective Optimization (MOO) problem. The MOO is solved for a case study of literature, consisting in a chemical facility composed by three tanks filled with flammable substances and equipped with six safety barriers (active, passive and procedural), exposed to NaTech scenarios triggered by either severe floods or earthquakes. The performance of the barriers is evaluated by a phenomenological dynamic model that mimics the realistic response of the system. The uncertainty of the relevant parameters of the model (i.e., the response time of active and procedural barriers and the effectiveness of the barriers) is accounted for in the optimization, to provide robust solutions. Results for this case study suggest that the NaTech risk is optimally managed by improving the performances of four-out-of-six barriers (three active and one passive). Practical guidelines are provided to retrofit the safety barriers design. [ABSTRACT FROM AUTHOR]

• A virtual-experimental approach to quantification of complex system resilience. • Bridging system resilience to material resilience. • Measuring system resilience based on "Stress-Strain curve". Resilience is an emergent property of a system, which changes with various internal and external factors. Resilience is also a hidden property of a system that cannot be observed. Thus, experiments should be performed for a given system to measure its resilience. However, physical experiments are practically impossible. Inspired by the tensile test for the stress-strain curve in Material Science, this paper proposes a virtual experiment for measuring system resilience and applies it to a chemical process system. The physical parameters of system resilience of a process system are mapped to those of material resilience. A process system is viewed as a 'specimen' in this experiment. The system performance variation caused by disruptions is seen as the displacement of the specimen caused by the applied load. In absorption phase, the decrease speed of system performance is determined by the failure rate of components under disruptive condition. Response time, including fault diagnosis time and resource allocation time, is used to represent adaptation ability. Restoration ability depends on repair rate of components. For simplicity purpose, the proposed method is applied to resilience assessment of a release prevention barrier system used in the Chevron Richmond refinery crude unit and its associated upstream process. [ABSTRACT FROM AUTHOR]

The goal of leading indicators for safety is to identify the potential for an accident before it occurs. Past efforts have focused on identifying general leading indicators, such as maintenance backlog, that apply widely in an industry or even across industries. Other recommendations produce more system-specific leading indicators, but start from system hazard analysis and thus are limited by the causes considered by the traditional hazard analysis techniques. Most rely on quantitative metrics, often based on probabilistic risk assessments. This paper describes a new and different approach to identifying system-specific leading indicators and provides guidance in designing a risk management structure to generate, monitor and use the results. The approach is based on the STAMP (System-Theoretic Accident Model and Processes) model of accident causation and tools that have been designed to build on that model. STAMP extends current accident causality to include more complex causes than simply component failures and chains of failure events or deviations from operational expectations. It incorporates basic principles of systems thinking and is based on systems theory rather than traditional reliability theory. [ABSTRACT FROM AUTHOR]

• A network-based approach is developed to model the NaTech-related domino effect in process clusters. • Expected escalation vectors are used to characterize the pairwise connection strength. • Two constraints, escalation and probability thresholds, are imposed to reduce the computational complexity of traversing potential propagation pathways. • Improved community detection algorithm can discover process clusters in which dominoes effects occur intensively. • NaTech-related domino effect network structure is sensitive to the natural hazard intensity. Extreme disasters and their resulting cascading accidents are always being a considerable threat to process cluster safety. However, many current assessments of the domino effects within process clusters do not consider the uncertainty of the primary accidents triggered by natural hazards. Additionally, high-efficiency methods to model the complex dependencies of large-scale process clusters are still lacking. Hence, this paper expands a hazard scenario module at the front end of the assessment framework of a domino effect to consider the response of installations to hazard loads. Simultaneously, a network-based approach is developed to model the NaTech-related domino effect. The constructed network imposes two constraints, escalation and probability thresholds, to reduce the computational complexity of traversing potential propagation pathways. As a result, the proposed method can be applied to large-scale process clusters. The safety analysis of an oil storage base subject to hurricanes and concurrent flooding shows that the proposed method can clarify the role of each unit in a local and cross-community domino effect at the node and community levels, respectively. The results inform the implementation of emergency responses to accidents. Furthermore, sources of uncertainty indicate that the network structure of the NaTech-related domino effect is sensitive to hazard intensity. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Pursuing process safety in a world of continuously increasing requirements is not a simple matter. Keeping balance between producing quality and volume under budget constraints while maintaining an adequate safety level proves time and time again a difficult task given that evidently major accidents cannot be avoided. Lack of resilience from an organizational point of view to absorb unwanted and unforeseen disturbances has in recent years been put forward as a major cause, while organizational erosive drift is shown to be responsible for complacency and degradation of safety attitude. A systems approach to safety provides a new paradigm with the promise of new comprehensive tools. At the same time, one realizes that risk assessment will fall short of identifying and quantifying all possible scenarios. First, human error is in most assessments not included. It is even argued that determining human failure probability by decomposing it to basic elements of error is not possible. Second, the crux of the systemic approach is that safety is an emergent property, which means the same holds for the technological aspect: risk is not fully predictable from failure of components. By surveying and applying recent literature, besides analysing, this paper proposes a way forward by considering resilience of a socio-technical system both from an organizational and a technical side. The latter will for a large part be determined by the plant design. Sufficient redundancy and reserve shall be kept to preserve sufficient resilience, but the question that rises is how. Available methods are risk assessment and process simulation. It is helpful that the relation between risk and resilience analysis has been recently defined. Also, in a preliminary study the elements of resilience of a process have become listed. In the latter, receiving and interpreting weak signals to boost situational awareness plays an important role. To maintain alertness on the functioning of a safety management system, the process industry is monitoring safety performance indicators. The critical intensity level upon which management must be alarmed is less simple. Risk assessment may be improved, made dynamic, and be a tool of process control by taking account of short-term risk fluctuations based on sensor signals and the influence of human factors with its long-term changes via indicators. Bayesian network can provide the infrastructure. The paper will describe various complexities when applying a holistic control of safety to a process plant in general, and it will more specifically focus on safeguarding measures such as barriers and other controls with some examples. [Copyright &y& Elsevier]

Abstract: Dynamic safety analysis methodologies are an attractive approach to tackle systems with complex dynamics (i.e. with behavior highly dependent on the values of the process parameters): this is often the case in various areas of the chemical industry. The present paper compares analyses with Probabilistic Safety Assessment (PSA)/Quantitative Risk Assessment (QRA) methods with those from a dynamic methodology (Monte Carlo simulation). The results of a case study for a chemical batch reactor from the literature, overall risk figure and main contributors, are examined. The comparison has shown that, provided that the event success criteria are appropriately defined, consistent results can be obtained; otherwise important accident scenarios, identifiable by the dynamic Monte Carlo simulation, are possibly missed in the application of conventional methods. Defining such criteria was quite resource-intensive: for the analysis of this small system, the success criteria definitions required many system simulation runs (about 1000). Such large numbers of runs may not be practical in industrial-scale applications. It is shown that success criteria obtained with fewer simulation runs could have led to different quantitative PSA results and to the omission of important accident scenario variants. [Copyright &y& Elsevier]

• Probabilistic analysis based on agent modelling for domino effect triggered by fire. • Agent-based modelling is able to analyse cascading events in complex layouts. • Implementation of passive, active and procedural measures with dynamic evaluation. • Identification of critical equipment and safety barriers supporting decision-making. The growing trend of interconnecting two or more chemical process or storage facilities represents a critical safety issue, since an accident can easily escalate from an industrial establishment to the nearby plants resulting in a domino effect. However, common safety analyses often ignore cascading events in chemical tank farms, their complex and transient evolution, and mitigation effects of add-on safety measures. The aim of the present work is to develop a structured approach for the assessment of complex domino events accounting for the influence of safety barriers. The approach is based on the adoption of Agent-based Model and Simulation for the assessment of Domino effect in presence of add-on Protections (DAMS-P). For the first time, the assessment of mitigated cascading events in chemical tank farms is carried out accounting for the transient evolution of multiple scenarios and related synergistic effects, and the effect of safety barriers and their possible time-dependent degradation. A verification of DAMS-P is firstly performed through the comparison against analytic probability evaluation based on event tree analysis and tested through the application of industrial cases. The results obtained constitute a useful support for decision-making and for the identification of critical barriers and their performance evaluation. [ABSTRACT FROM AUTHOR]

• The present study analyzed whether the pool fire alone can cause a domino event in processing and storage facilities. • Two models are used to simulate the escalation vector caused by a pool fire. • The proposed concept of escalation vector and numerical models are tested using two past accidents. • The results confirm that the pool fire alone has the ability to cause a domino event. The chain of accidents, also known as the domino effect, is responsible for several severe accidents in the chemical and process industries. The pool fire is often blamed as one of the primary accidents triggering a domino event. The present study is devoted to analyzing whether the pool fire alone can cause a domino event in processing and storage facilities. Two models, including a solid flame model and computational fluid dynamic model, are applied to simulate the escalation vector caused by a pool fire. The escalation vector probability is calculated using a probit model for a potential domino effect. This study also investigates the possible factors that can cause a domino effect and determine credible accident scenarios. The proposed concept of escalation vector and numerical models are tested using two past accidents. The study estimates the possibility of pool fires alone causing a domino effect. The results of this study show that although the pool fire alone has the ability to cause a domino event, it is unlikely to occur if a safe distance separates the equipment and proper mitigation measures are employed. [ABSTRACT FROM AUTHOR]