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In order to select the location of fire stations more scientifically and improve the efficiency of emergency management in chemical industry parks (CIPs), an improved risk calculation model for hazardous chemicals has been proposed by taking the domino effect and the identification of major hazardous installations for hazardous chemicals into account. In the analysis of the domino effect, the Monte Carlo simulation was used. Then, a location model of the fire stations was established with the optimization objectives of minimizing total cost and maximizing total risk coverage. The solving procedure of the location model is based on the augmented ε-constraint method combined with the TOPSIS method. Finally, a green chemical industry park was used as a case study for the validation and analysis of the location model. The results showed that the improved model could protect the high-risk areas, which is beneficial for the location decisions of fire stations.

Due to massive flammable chemical materials and dense layout, domino accident chain always occurs in major accidents of atmospheric storage tank farm. In this paper, 136 domino effect accidents of atmospheric tanks are analyzed to achieve relative probability by ETA method. Based on accident probability, an analysis model is proposed to define the most likely accident chain and the key tank in the atmospheric tank farm. To get the real accident scenario, the method adopts the heat radiation model of pool fire under the fire extinguishing conditions. Combined with the dynamic Bayesian network, the escalation model of accident chain is established to figure out the dynamic probability of the tank under the condition of fire extinguishing. Finally, an example is used to verify the feasibility of the method. It is of great theoretical significance and application value for the treatment of domino accident in atmospheric tank farm.

Synergistic effects of pool fires in fire-induced domino accidents can result in greater damage than single pool fire. Existing research mainly adopts a superposition method based on simplified assumptions to analyze the contribution of synergistic effect of pool fires for domino effects. Therefore, the synergistic effect of pool fires has not been well understood. In this study, a new CFD-based method is developed to model the synergistic effect of pool fires. Case studies were carried out taking double pool fires as an example. The influence of key factors, including wind speed, vertical fire location, fuel type, and separation distance has been investigated quantitatively. The results demonstrated that the proposed CFD based approach can model the consequences of pool fires more comprehensively and assess the domino effects under synergistic effect more precisely than the traditional superposition method. Compared with the single pool fire, the synergistic effect of double pool fires can increase the probability of domino accidents for the adjacent tanks at the downwind by six orders of magnitude. In addition, it is observed that the received maximum radiation heat flux and escalation probability of the target tank in the downwind direction rise with increased wind speed. While the maximum radiation heat flux received by target tanks and escalation probability reduced with increased separation distance and vertical fire location. Moreover, according to the minimum safety distance recommended by present design standards, the synergistic effect of double pool fires in diesel storage tank farms will lead to a higher escalation probability of near equipment than gasoline storage tank farms. This study can be used to predict accident consequences and assess the domino effect under the synergistic effect of pool fires, and guide the layout optimization of the chemical storage tank area.

Vapor cloud explosions (VCEs) have been considered as a major hazard in petrochemical industry, accompanying with wide-ranging impact and huge destruction. The existing methods are incapable to make a rapid and accurate estimation when considering multi-factor coupling effects. Therefore, this study proposed a novel methodology of fusing numerical simulation (NS) with neural network (NN) technique for the prediction of explosion consequences. First, 6 parameters of VCEs influencing overpressure are selected as variables of a database. A CFD method is employed for simulating VCEs in a chemical site, by which sufficient blast data are generated. After the architecture of a NN model is determined, data on three generic VCEs are extracted for further model training process. A progressive training method is adopted to develop a general prediction model. Furthermore, data derived from ongoing simulation results are imported into the model for its constant self-improvement. The output of the well-trained model is subsequently transformed into a probabilistic function to assess the domino effect. The integrating NS with NN approach provides an accurate and efficient way to predict the blast effects, which can support more scientific rescue decision-making. Finally, the proposed model is applied to a case study for illustration.

Fire-induced domino accidents are less probable but highly consequent. Although past studies conducted risk assessment of such events, classical models were used for impact estimation. Moreover the influence of varying weather on evolution of fire domino effect was not investigated, where past research necessitates the use of computational fluid dynamics to perform such analysis. This paper adopts a consolidated risk analysis approach to perform consequence modeling using fire dynamics simulator. Various atmospheric conditions were incorporated in the model. The outcomes were used in probabilistic analysis to estimate the escalation probabilities. Risk of domino events was presented as domino levels. It was found that 37 reduction in humidity ratio resulted in 10 decrease in tanks failure time. At 3 and 6 m/s winds, the failure time of tank in flame direction reduced by 56 and 80, whereas the escalation probability increased by 3 and 4 orders respectively. The tank farm failed in 11.3 and 12.85 min at 3 and 6 m/s respectively, which is less than the suggested mitigation time. © 2021 American Institute of Chemical Engineers

Flood events impose great distress on chemical industrial areas, since they may cause Natech accidents involving multiple units. Furthermore, escalation vectors exerted by major accidents can trigger knock-on events, so-called domino effects, causing very severe consequences. In the present study, a methodology is proposed to include domino effects triggered by floods in a quantitative risk assessment, by addressing the frequency assessment of flood-induced domino scenarios. A comprehensive procedure is developed, combining the fragility model for unit damage due to floods, probability estimation for domino escalation, and combinatorial analysis for overall scenarios. Moreover, the flow interference due to the layout of chemical industrial areas is explored to calculate the damage probability more accurately. The methodology has been demonstrated by a case study, the changes in risk indexes and damage zones due to Natech domino effects are discussed. The results show that the overall risk significantly increases with respect to conventional scenarios when considering flood-induced Natech events and domino effects, evidencing the importance of risk analysis of Natech-related domino effects. Finally, some prevention measures have been proposed for chemical industrial areas to make them more resilient and safer when it comes to floods. (C) 2021 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

Hydrogen–gasoline hybrid refueling stations can minimize construction and management costs and save land resources and are gradually becoming one of the primary modes for hydrogen refueling stations. However, catastrophic consequences may be caused as both hydrogen and gasoline are flammable and explosive. It is crucial to perform an effective risk assessment to prevent fire and explosion accidents at hybrid refueling stations. This study conducted a risk assessment of the refueling area of a hydrogen–gasoline hybrid refueling station based on the improved Accident Risk Assessment Method for Industrial Systems (ARAMIS). An improved probabilistic failure model was used to make ARAMIS more applicable to hydrogen infrastructure. Additionally, the accident consequences, i.e., jet fires and explosions, were simulated using Computational Fluid Dynamics (CFD) methods replacing the traditional empirical model. The results showed that the risk levels at the station house and the road near the refueling area were 5.80 × 10−5 and 3.37 × 10−4, respectively, and both were within the acceptable range. Furthermore, the hydrogen dispenser leaked and caused a jet fire, and the flame ignited the exposed gasoline causing a secondary accident, considered the most hazardous accident scenario. A case study was conducted to demonstrate the practicability of the methodology. This method is believed to provide trustworthy decisions for establishing safe distances from dispensers and optimizing the arrangement of the refueling area.

Due to complexities and uncertainties, risk management of domino effects in chemical plants in operation is challenging. A global optimal risk management scheme can hardly be obtained, therefore, finding a targeted local optimal risk management scheme can be prudent. In the present study, a novel risk management approach is proposed to reduce fire-induced domino effect risk by leveraging loading/unloading demands based on risk aggregation and inventory management. The proposed approach uses the aggregation of three risk indicators as part of the risk management strategy. The first risk indicator is the loss of containment (LOC) risk of chemical installations, which indicates how likely a storage tank may cause a primary fire accident. The second risk indicator is the secondary fire accident inducing ability of chemical installations, which indicates how likely a primary tank may cause secondary fire accidents only. The third risk indicator is the inter-unit closeness degree of chemical installations, which indicates how severe the overall consequence a primary tank may cause. Combining the aggregate risk with the inventory availability of storage tanks, candidate risk management schemes are proposed based on inventory management on chemical loading/unloading demands. The optimal risk management scheme is determined based on the potential losses of fire-induced domino effects. A case study demonstrates the effectiveness of the proposed methodology and risk management strategy for fire-induced domino effects.

Emergency response plays an important role in preventing or delaying the domino effect of chemical accidents. Requirements for emergency response to a major accident may exceed the capacity of the emergency department within the region of responsibility for the accident, in which case multi-department emergency response is necessary. In this work, modeling problems related to the arrival of emergency teams at different times in multi-department emergency response are discussed. These problems will dynamically influence the time to failure of adjacent facilities in the event of a fire. A timed colored hybrid Petri-net (TCHPN) approach is proposed to solve these problems and analyze multi-department emergency response processes. An example of responding to a tank fire illustrates the proposed approach. An emergency response process is simulated, and probabilities of fire escalation prevention under different fire levels are analyzed. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Major industrial accidents occurring at so-called major hazard installations may cause domino accidents which are among the most destructive industrial accidents existing at present. As there may be many hazard installations in an area, a primary accident scenario may potentially propagate from one installation to another, and correlations exist in probability calculations of domino effects. In addition, during the propagation of a domino effect, accidents of diverse types may occur, some of them having a synergistic effect, while others do not. These characteristics make the analytical formulation of domino accidents very complex. In this work, a simple matrix-based modeling approach for domino effect analysis is proposed. Matrices can be used to represent the mutual influences of different escalation vectors between installations. On this basis, an analysis approach for accident propagation as well as a simulation-based algorithm for probability calculation of accidents and accident levels is provided. The applicability and flexibility of this approach is discussed while applying it to estimate domino probabilities in a case study.

Atmospheric storage tank fires are relatively rare, but because of storing large amount of flammable liquid in them, they have catastrophic consequences. This study presents a review of atmospheric storage tank fire scenarios causes and costs. The fire accidents on the atmospheric storage tanks were collected from accident databases, papers and other sources. The causes and costs of the 104 accidents were classified. Initiating event of 38% of the accidents was environmental factors, 20% was equipment factors, 15% was operation factors (human error), 13% was domino effect, 10% was maintenance factor and 4% was terrorist attack. Lightning strikes were the most frequent cause of the accidents triggered by the environmental factors (21.73%). The cost of accidents that have produced domino effects was eight times more than cost of accidents without domino effects. The average cost for accidents that have produced domino effects was approximately 24,181,673$, and for accidents without domino effects, it was 205,191,298$. Identification of storage tank accident causes and cost can provide insights into how managers make cost–benefit analysis and implement effective corrective actions and safety barriers.

The blast load distribution is an important factor affecting the accuracy of structural damage and domino effect analysis caused by explosions in petroleum and chemical industries. The distribution is usually treated as the uniform or non-uniform and the latter is more suitable for the real scenarios. However, the applicability of the uniform distribution has not been studied in details. In the present study, both the blast load intensity model (BLIM) and blast damage intensity model (BDIM) are developed to represent uniform or non-uniform blast loads quantitatively. Three explosion types (free air burst, air burst and surface burst) and three target structural surfaces (rectangular plates, cylindrical shells and spherical shells) are considered in BLIM and BDIM. The element superposition method based on finite element model (FEM) is proposed, which can solve BLIM and BDIM accurately. Furthermore, the relative difference between the uniform and non-uniform distribution can be obtained on basis of BLIM and BDIM. Finally, the application condition for the rectangular plates - critical stand-off distances with the relative difference of 5%, is defined and verified to distinguish the uniform and non-uniform distribution. The study can provide an insight into the proper application of blast load distribution.

A fire accident is one the most dangerous accidents that may lead to knock-on effects, especially in the plants dealing with large quantities of hazardous substances. Different from other accidents, e.g., explosions, fires can last certain period of time and the thermal radiations emitted by them have a synergistic effect. At the same time, the heat-up of target installations under the effect of thermal radiation is also a process that takes a certain amount of time. These characteristics make fire induced domino effects a dynamic process. During an emergency response due to a fire accident, emergency personnel may arrive at the fire scene at different times, so they may face different accident situations. In this work, an adaptive timed Petri-net (ATPN) based approach is proposed to model the propagation of fires and perform a dynamic analysis of potential domino effects. The definition of ATPN as well as the enabling and execution rules is provided. Through simulations, not only the probabilities of fires in different installations, but also the probabilities of the fire extension propagated over time can be obtained. An example of a tank farm fire illustrates the approach. Our developed approach for carrying out a dynamic analysis of domino effects is helpful for emergency preparation.

Although the effects of jet fires are often limited to rather short distances, if their flames impinge on a pipe or a vessel collapse can occur in very short times. In such cases, the heat flux on the affected equipment is very high and wall temperature can increase very rapidly. This can happen in parallel pipelines, if a release occurs and impinges on another one. Nevertheless, jet fire impingement has been scarcely studied. In this communication the results obtained from an experimental set-up are presented. Sonic jet fires impinged on a pipe containing stagnant air or water. The temperatures of the flames impinging on it were measured for the worst case (flame front-bright zone), as well as the evolution with time of the pipe wall temperature at different locations. Initial temperature increases up to around twenty degrees C/s were registered for the air inside, with maximum values of up to 600 degrees C reached in 2.5 min, and 800 degrees C in approximately 9 min. In the case of pipe containing water, in the zone of the wall in contact with the liquid the heating rates were much lower, the maximum temperature reached being up to approximately 150 degrees C. From the temperatures of the jet flames and of the pipe, the heat fluxes reaching the pipe and the corresponding heat transfer coefficients were obtained. The results obtained emphasized that safe distances are essential in pipelines, together with fire proofing and other safety measures.

Fires are major primary events in domino effects in chemical and process industries, and released thermal radiation is a main cause of accident propagation. In fire-induced domino effects, synergistic effect of multiple burning units will increase risk of domino effects, and the synergistic effect is time-dependent. In this study, a superimposition based new approach is proposed to model the contribution of synergistic effect of fires for domino effects, and a numerical solution of the approach is developed. In this approach, the synergistic effect of fires is modeled dynamically through time-variant target unit wall temperature and received thermal flux, and the receivable thermal dose is proposed as failure criterion and is modeled. The contributions of synergistic effect on the risk of domino effects are assessed by time to failure and escalation probability of target unit. The proposed approach is able to not only model the synergistic effect of fires, but also to understand the temporal evolution of synergistic effect when higher-level accidents occur. A case study demonstrates the effectiveness, advantages, extension of the proposed approach to model the contribution of synergistic effect for domino effects risk.

Global warming and the subsequent increase in the frequency and severity of wildfires demand for specialized risk assessment and management methodologies to cope with the ever-increasing risk of wildfires in wildland-industrial interfaces (WIIs). Wildfires can jeopardize the safety and integrity of industrial plants, and trigger secondary fires and explosions especially in the case of process plants where large inventory of combustible and flammable substances is present. In the present study, by modeling the WII as a two dimensional lattice, we have developed an innovative methodology for modeling and assessing the risk of wildfire spread in WIIs by combining dynamic Bayesian network and wildfire behavior prediction models. The developed methodology models the spatial and temporal spread of fire, based on the most probable path of fire, both in the wildland and in the industrial area.

Wind loads are a major threat for old corroded steel structures constructed near the sea where wind speeds can be very high. This paper presents a case study of a wind-induced failure analysis of an existing steel structure and the proposed retrofitting methods. The examined steel structure was constructed in the 1970s in Syros, Greece and is currently operating as an athletic centre. The first part of this study presents the wind-induced failure analysis, from which a domino effect is documented. A corroded bracing that was buckled due to wind load governs the reduction of vertical load carrying of the steel structure and creates an asymmetry under horizontal loading before a number of other steel members failed due to buckling. To understand the structure's performance, failure analysis, as well as time history and incremental dynamic analysis, were performed. The second part of this paper presents the proposed retrofitting methods for improving the vertical load carrying capacity under wind loads. The goal was to improve the load-carrying capacity of the structure so as to comply with current design European codes. In addition, enhancement of the dynamic properties of the strengthened structure was demonstrated using modal analyses. The structural behaviour was determined in a more precise manner via non-linear wind time-history and incremental static analyses. The analytical results explain the development of failures in the existing structure.

The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.

Accidents resulting in industrial fires in chemical and process installations and in industrial parks where relevant quantities of hazardous substances are stored or processed may cause domino effects. Probit models developed and used in a multitude of studies can provide the probability of equipment failure, but they do not consider the effect of multiple radiation sources, and thus fail to capture the effects of severe scenarios as those where multiple fires start at different times in different units. In the present study, a critical thermal dose for equipment failure is defined. A direct procedure for the calculation of ttf based on the critical thermal dose is then introduced, which is able to account for the time at which the different secondary fires start or are extinguished. This allows considering the effects of the primary and of several secondary fire scenarios in causing a domino effect, updating the time to failure on the basis of the dynamic evolution of multiple fire scenarios. The proposed approach is demonstrated through case-studies addressing fire-induced domino effects in an oil storage tank farm. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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.

Technological accidents triggered by natural hazards (Natech accidents) are likely to escalate in cascading scenarios with severe consequences. Indeed, safety barriers implemented in process plants to prevent and mitigate accidents may be affected by natural hazards as well. The present study proposes a novel comprehensive method to assess safety barriers and protection systems performance modification during natural hazards, as well as the resulting modification in the expected frequency of secondary technological scenarios that may arise. In particular, the probability and frequency of domino scenarios initiated by Natech events are assessed considering the possible concurrent degradation of safety barrier performance in case of floods and earthquakes. An approach based on layer of protection analysis is adopted to quantify safety barrier performance degradation, accounting for the modification of barrier availability and effectiveness. A dedicated event tree analysis is applied to domino effect assessment and quantification of overall escalation scenarios. The results obtained allowed a detailed assessment of the expected frequency of secondary mitigated escalation scenarios, considering the possible effect of barriers degradation within Natech events.

Natural hazards may cause severe technological accidents involving hazardous substances (Natech accidents). Along with process equipment also safety critical elements as safety barriers might be impacted by such events, thus reducing the protection provided and the possibility to prevent escalation and cascading effects. In the present study a comprehensive methodology is developed to address the quantitative assessment of the risk caused by the escalation of Natech accidents, specifically addressing the effect of the performance modification of safety barriers caused by the impact of the natural hazard. Barrier performance depletion is modelled through an innovative multi-level approach, and it is then introduced in the quantitative risk assessment procedure by a modified event tree analysis. A demonstrative application of the proposed methodology to a case study is provided, showing a relevant increase in risk figures deriving from the degradation of safety barrier performance caused by natural events. The proposed framework extends the systemic assessment of Natech scenarios to encompass the specific criticalities introduced by safety barrier performance modification induced by natural events, providing a more effective support to decision-making in the management and control of risk deriving from the interaction of natural hazards with technological installations.

Managing Wildland-Urban-Interface (WUI) fires is a challenging task due to the inherent complexity of the WUI environment. To ensure the success of strategies for the protection of population and structures, safety measures have to be implemented at different scales (landscape, community and homeowner). The present study is focused on the homeowner scale and deals with the threat related to the presence of LPG domestic tanks in a WUI fire scenario. Recent accidents have demonstrated that the risk associated with this type of installation is real, but often disregarded by residents. A methodology was developed, providing a set of indicators that may easily be compared with risk acceptance criteria, assessing whether the integrity of an LPG tank exposed to WUI fire scenarios is compromised or not. The methodology is applicable to a vast range of situations and at a different level of detail according to available data. A number of case studies were carried out, showing that WUI fire scenarios impacting on domestic LPG tanks complying with regulations currently adopted in several Mediterranean countries cannot be deemed safe. The methodology proposed represents an advanced tool to assist on safety distances sizing to be prescribed by standards, driving regulators towards better-informed decision-making.

Target equipment damage is a key factor in the quantitative risk assessment of domino effects caused by blast fragments. However, the damage process of target equipment caused by blast fragments in domino effect accidents has not been studied clearly. In this paper, a finite element (FE) model of large vertical storage tanks impacted by blast fragments is built to study the damage process. A small-scaled fragment impact experiment is carried out to validate the FE modeling approach. Based on accident statistical data and empirical formulas, the reasonable ranges of blast fragment parameters are obtained. The comparison between the rigid and the elastic-plastic fragment impact is conducted. The effects of filling degree, tank volume, impact height, roof-impact angle, fragment type, impact velocity and impact angle on the damage process are discussed. It can be concluded that: (1) The "added mass" effect of the inner liquid on the impact process increases with the filling degree. The fragment deformation has important effects on the impact process. (2) The impact process includes multiple collisions rather than only one collision. (3) The reduction of tank volume and the increase of impact height on tank wall can increase the damage degree. The impact on roof edge can make the fragment penetration easier. (4) End-caps for identical kinetic energy are more harmful to tanks than plates. The end-cap impact with horizontal impact angle of 35 degrees causes the most serious deformation. This study provides insights into the damage mechanism and prevention of domino effects caused by blast fragments.

High-pressure gaseous methane release is a relevant safety-related problem mainly in the Oil and Gas industry. As well documented, the reason for these safety concerns is connected with the severe consequences of the domino effect subsequent to the possible ignition. In risk assessment activities, estimation of the damage area is of primary importance in order to draw up proper safety guidelines. To do this, loss prevention specialists use quick and well-established numerical tools (i.e., integral models) in their daily activities. However, the presence of an obstacle in the flow field of the jet (e.g., the ground) is a more probable situation to deal with. It is known that integral models fail in this kind of scenario, leading to unreliable predictions. Hence, the present work investigates how an industrial ground surface influences the LFL cloud size of a horizontal high-pressure methane jet. An innovative quick procedure is proposed allowing to determine the height below which the ground begins to influence the LFL cloud size and the extent of such influence. Therefore, this procedure allows practitioners to establish when integral models can be used and when not to use them, and also provides a simple and reliable alternative to their use. These analytical instruments are derived from an extensive computational fluid dynamics analysis performed with Ansys Fluent 19.0.

Fragment projection from equipment failure has been extensively recognized as a cause of cascading events and of severe domino scenarios. In recent years several mathematical models suitable for the quantitative assessment of risk due to domino effects and cascading events were developed and validated, but a systematic methodology for quantitative risk assessment caused by fragment projection and impact is still missing. In the present study, a step-by-step approach is proposed for the assessment of domino risk indices due to fragment projection. The approach builds on available sub-models for the quantitative assessment of fragment generation, impact and damage probabilities. Altogether, the proposed model supports a quantification of the risk due to escalation triggered by fragment impact that can be easily automated and integrated in risk assessment studies.

Rail transport of hazardous material (RTHM) plays a vital role in the supply chain of raw materials and products. However, RTHM can pose severe risks due to the large quantities of flammable and explosive chemicals transported over rail tracks crossing residential and industrial areas and possible human and technical failures. Among the potential safety issues, the domino effect is one of the most feared events, which can have devastating consequences despite its relatively low probability. As the first study, the present investigation develops a dynamic risk analysis model for analyzing domino effects in RTHM based on Dynamic Bayesian Network. Accident scenarios such as pool fire, flash fire, fire ball, vapor cloud explosion, and BLEVE are considered to analyze domino effects. The model performance is tested on a real RTHM (i.e., gasoline transportation), demonstrating the effectiveness of the proposed model in simulating the domino-driven effects in terms of both consequences and probability escalation and in dealing with the parameter and model uncertainties.

Processing facilities are faced with spatial-temporal dependent multiple-hazards. These hazards could cause catastrophic consequences. A novel vulnerability model called the “fire and explosion synergistic effect model” (FESEM) is proposed in the present study to model equipment vulnerability under the spatial-temporal synergistic of heat radiation and overpressure. On the basis of the fire synergistic effect model, the FESEM firstly model the temperature elevation and the yield strength reduction of the storage tank wall under fire heat radiation. Then, based on the von Mises yield criterion, the FESEM models the equivalent stress under overpressure. Combining the lowered yield strength induced by fire heat radiation and equivalent stress induced by overpressure under the synergistic effect, the logistic function is used to estimate time to failure and escalation probability. The application of the model is demonstrated in the case study, which confirms that the synergistic effect is significant and closer to reality. The parameters that have considerable effects on time to failure and escalation probability are discussed. The proposed method will serve as a rapid quantitative risk assessment tool of multi-hazard coupling, including the domino effect. The model will serve as an essential guide for preventing and controlling domino effects.

The layout design for storage vessels is an important issue to keep safe process plants, since the accidents originated in storage areas are almost one-third of all reported accidents. In order to reduce fire and explosion damages different mathematical approaches have been proposed; however, most of them are not suitable for real storage facilities design. In this work, a MILP approach has been developed to find the optimal layout of storage vessels, seeking to minimize the cost associated with potential damage of fire and explosion. This approach allows allocation of suitable sites for storage vessels, reducing accidents propagation, and domino effect while keeping safety as much as possible the plant assets. In this way, the optimization approach provides substantial support for decision-makers during the design stage. The study case used to test the mathematical model considers 19 vessels, six buildings, five process facilities and 25 possible scenarios, which includes 16 fires and 9 explosions.

If flammable gas is mixed with air, and the mixture is ignited, it is possible to form a vapor cloud explosion (VCE) which may be very destructive, and easy to trigger a domino effect of accidents because of its large extent of impact. A VCE accident may induce secondary VCE accidents, then tertiary VCE accidents, and so on. This is called the cascading effect of VCE accidents, which requires an understanding of probabilities and propagation patterns to prevent and mitigate the potential damages. In this work, a methodology based on Petri-net is proposed to model the cascading effect of VCE accidents and perform probability analysis, taking the mutual influence between the accidents into account. The deficiency in probability analysis of VCE accidents is discussed. According to the limits of states and their changes which reflect characteristics of VCE propagation, a Petri-net approach is provided for modeling and analysis of VCE cascading effect, and the modeling approach and analysis process of VCE cascading effect are presented. The application and efficacy of the methodology are demonstrated via an example of VCE accidents occurring in a gasoline tank storage area. The results show that the developed methodology can effectively reveal the propagation patterns of VCEs cascading and calculate the respective probabilities of VCE accidents.

Knock-on effects or so-called domino effects in the process industries may cause much greater losses than merely a primary event. Probability analysis of accidents resulting from domino effects is important for risk assessment. However, for the accident occurrence of a unit there may be mutual influences between the units in the area influenced by the accidents due to a domino effect, and this makes the calculation of probabilities of the accidents rather difficult. A matrix-based approach is proposed to model the influences between units influenced by a fire-induced domino effects, and the analysis approach for accident propagation as well as a simulation-based algorithm for probability calculation of accidents is provided. The synergistic effect of thermal radiation is taken into account during the accident propagation. The proposed approach is flexible to model and analyze domino effects in various conditions of primary fires by only changing the value of the initial matrix indicating the fire states. Two examples illustrate analyzing the fire propagation among tanks storing flammable liquids. The results show that this approach is simple but effective for offering an insight in the accident propagation process and for knowing the probabilities of equipment getting on fire.

A lightning strike is the main cause of fire accidents in chemical storage tanks, and the thermal radiation produced by a large storage tank fire may lead to a domino effect. A prediction method aiming to evaluate the probability of a domino effect at different levels triggered by lightning in the chemical tank farm is proposed. The developed method takes into account both the probability calculation model of fire triggered by lightning and the assessment method of the subsequent domino effect probability. Firstly, the accident scenarios and causes of fire triggered by lightning are analyzed by the event tree method, and a probability calculation model of fire accident triggered by lightning is developed. Secondly, the graph of chains of accidents is built considering synergistic effects and multi-level domino effects, and the Bayesian network is applied to calculate the probability of each accident chain. The most dangerous primary equipment is identified by comparison with the probabilities of the domino effect at different levels. By setting up the failure states of different tanks, the probabilities of events are updated under a given situation, and the most susceptible target equipment with respect to the domino effect are identified. Finally, the method is illustrated with two case studies in a chemical industry park. The results will be helpful for the prevention of domino effects based on the theory of chain-cutting disaster mitigation.

In industrial chemistry, many flammable materials are handled and/or stored in various facilities. It is possible that major fires occur at these facilities possibly leading to domino effects due to the failure of neighboring facilities caused by thermal radiation. It will take a certain time that thermal radiation causes nearby facilities to fail and that a domino effect occurs. The time needed for escalation to take place allows emergency actions as a response to the primary fire to prevent the propagation of the fire. In the present study, a Timed Colored Hybrid Petri-net (TCHPN) based methodology is introduced to evaluate different emergency response actions based on their efficiency in preventing or delaying the propagation of domino effects. A TCHPN model of emergency response to flammable liquid tank fire is established, and a time based analysis of emergency response actions for preventing domino effects is performed. Based on the simulation analysis, the probability of domino effects is calculated and response actions are compared.

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases.

Industrialization exposes more petrochemical facilities in the slope-industrial interfaces (SIIs) to the impact range of landslides, demanding practical assessment approaches for addressing associated damage and its potential consequences. However, accurately predicting the expansion and upgrade of landslide hazards in industrial plants is challenging, involving a series of cascading-event trigger-response analyses. Accordingly, this paper develops a hybrid Bayesian network-based landslide risk assessment (HBN-LRA) model to evaluate the landslide risk on storage tanks in SIIs. This model decomposes the landslide risk into three submodules: slope stability, failure of targets, and proactive remedial measures and updates. It transmits the landslide risk through conditional dependence between subsystems. The results of applying the HBN-LRA model toward quantifying the landslide risk in a typical SII area indicate that the distance from the slope in the system risk factors directly determines storage tank damage. Landslide risk is sensitive to geological and geomorphic conditions, such as soil depth, cohesion, and unit weight; their relative importance all exceed 0.15. Tests on the case slope demonstrate that reducing the drainage paving distance from 10 to 8 m can improve slope stability by 50%. This result highlights the potential of the proactive remedial module in evaluating and designing slope drainage systems.

The production and storage of liquefied petroleum gas (LPG) is gradually becoming larger and more intensive, which greatly increases the risk of the domino effect of an explosion accident in a storage tank area while improving production and management efficiency. This paper describes the construction of the domino effect scene of an explosion accident in an LPG storage tank area, the analysis of the characteristics of the LPG tank explosion shock wave and the target storage tank failure, and the creation of an ANSYS numerical model to derive the development trend and expansion law of the domino accident in the LPG storage tank area. The research showed that: 400 m3 tank T1 explosion shock waves spread to T2, T4, T5, T3, and T6, and the tank overpressures of 303 kPa, 303 kPa, 172 kPa, 81 kPa, and 61 kPa respectively. The critical values of the target storage tank failure overpressure-range threshold were 70 kPa and 60 m. After the explosion of the initial unit T1 tank, at 38 ms, the T2 and T4 storage tanks failed and exploded; at 56 ms, the T5 storage tank exploded for the third time; at 82 ms, the T3 storage tank exploded for the fourth time; and at 102 ms, the T6 storage tank exploded for the fifth time. With the increase of explosion sources, the failure overpressure of the target storage tank increased, and the interval between explosions continuously shortened, which reflected the expansion effect of the domino accident. The domino accident situation deduction in the LPG storage tank area provided a scientific basis for the safety layout, accident prevention and control, emergency rescue, and management of a chemical industry park.

Fire is one of the most costly accidents in process plants due to the inflicted damage and the required firefighting resources. If the firefighting resources are sufficient, firefighting will include the suppression and cooling of all the burning units and exposed units, respectively. However, when the resources are inadequate, optimal firefighting strategies to answer “which burning units to suppress first and which exposed units to cool first?” would be essential to delay the fire spread until more resources become available. The present study demonstrates the application of two decision support techniques to optimal firefighting under uncertainty and limited resources: (i) Dynamic influence diagram, as an extension of dynamic Bayesian network, and (ii) mathematical programming. Both techniques are illustrated to be effective in identifying optimal firefighting strategies. However, unlike the dynamic influence diagram, the mathematical programming is demonstrated not to suffer from an exponential growth of decision alternatives, making it a more efficient technique in the case of large process plants and complicated fire spread scenarios.

The performance of fire protection measures plays a key role in the prevention and mitigation of fire escalation (fire domino effect) in process plants. In addition to passive and active safety measures, the intervention of firefighting teams can have a great impact on fire propagation. In the present study, we have demonstrated an application of dynamic Bayesian network to modeling and safety assessment of fire domino effect in oil terminals while considering the effect of safety measures in place. The results of the developed dynamic Bayesian network-prior and posterior probabilities-have been combined with information theory, in the form of mutual information, to identify optimal firefighting strategies, especially when the number of fire trucks is not sufficient to handle all the vessels in danger.

The evaluation of vulnerability of process equipment to natural events is a central issue in the analysis of NaTech risks (Natural events triggering Technological accidents). Among others, the recent event in Japan has alerted the public opinion regarding the occurrence of tsunamis, which may result in dramatic consequences, either related to economic losses due to service interruption and repair costs, or for escalation of the tsunami towards severe catastrophic scenarios related to the loss of containment for the damaged equipment. Hence, environmental pollution, toxic dispersions, fires or explosions, depending on the stored, processed or transported fluid, on the structural design, and on the type of process operations. In this work, the hazard related to tsunami impact on industrial equipment has been analyzed, either for the natural event characterization (intensity measure, hazard), or for the particular critical infrastructure. Vulnerability (or Fragility) functions have been specifically defined with respect to tsunami wave and debris.

This article proposes a risk-based method for domino effect characterization and potential escalation for process equipment affected by thermal radiation (i.e., fires). This methodology intends to answer two key questions: (1) which process equipment is impacted by a heat flux capable of resulting in escalation due to equipment failure; and (2) what is the associated time to the process equipment failure; that is, Time to Failure (TTF). The first phase consists of developing dedicated heat flux exceedance curves for a given location of interest. The second phase involves a dynamic simulation for the prediction of the TTF due to fires impacting the equipment identified in phase one. A two-step approach is proposed for ensuring accurate results: (1) vessel wall segmentation to determine how the Ultimate Tensile Strength (UTS) of the material decreases as a function of temperature, and (2) the UTS is then compared with the Hoop stress by considering the equipment internal pressure combined with the installed overpressure protection performance. This article defines step-by-step how to conduct a risk-based assessment and determine the TTF using a case study. It demonstrates the applicability and accuracy of this approach, which helps the decision-making process on how potential mitigation measures can be implemented. © 2017 American Institute of Chemical Engineers Process Saf Prog, 2017

The barrier net was put between accident source and objective vessel to reduce the probability of target damage due to fragments generated by vessel explosion, and to offset the requirement of safety distance. The fragment trajectory, impact probability of fragment, ruptured probability of the impacted target, and domino effect risk were respectively calculated using Monte-Carlo simulations under the barrier net. The effects of the height of barrier net on the probability of fragments impact were mainly investigated to obtain the reasonable height of barrier net. The results showed that the domino effect risk was decreased by nearly an order of magnitude, due to the significant reduction of impact probability of fragments. The relative differences demonstrated that 70%–90% reduction in the domino effect risk was performed by the barrier net. The impact probability of fragments decreased, but the intercepting probability of the barrier net increased linearly with the height of barrier net. To save cost and leave space for emergency escape and rescue, the barrier net should be installed on an initial height level. Under the same height of barrier net, the intercepting probability will be increased much more than that under the barrier net directly installed on the ground.

The recent interest in optimizing land use through integrated risk assessment, which intensified after Buncefield oil depot incident in 2005, calls for, among other things, determination of domino effects, which can be severe. The recommended tanks spacing and water application rates by design codes vary widely and are sometimes contradicting, if not subjective. This paper introduces a novel framework to determine the water application rate for protection of storage tanks against thermal radiation from an external non-contacting fire through first principles modeling. This new approach has been applied to assess the appropriate cooling water rate needed for the protection of an existing crude oil tank farm which includes three one-million barrel and two 500,000-barrel floating roof tanks. The tanks are to be protected from the thermal radiation of an adjacent tank with a full surface fire by application of cooling water and in the present arrangement they are so widely spaced that this is only attributable to a generous ‘overdesign’. It has been shown that applying the new approach could have resulted in at least 25% saving in tank farm area.

The present study is aimed at defining a structured approach to the quantitative assessment of cascading events triggered by fire, accounting for the influence of harsh environmental conditions on the emergency response and on the performance of hardware safety barriers. A specific metric was defined in order to consider the external factors related to harsh environments on the determination of hardware and emergency safety barriers availability, with a specific focus on operations in extremely cold climates. The metric allows evaluating the time scale of emergency and the related efficiency of barriers in contrasting accident escalation. The values obtained for availability and effectiveness of barriers were input to a specific event tree analysis in order to support the quantitative assessment of accident frequency associated to cascading events. The approach is tested by its application to a case study, aimed at the assessment of the influence of extremely cold environmental conditions on the risk due to cascading events in an industrial site.

In the chemical process industry, large quantities of different flammable substances are stored in tank farms. Due to potential mutual impacts among the tanks, severe domino accidents may occur after a tank catches fire. The emergency response to a tank fire may influence the development of the accident and impact the occurring of escalating events or so-called domino effects. In this paper, a fuzzy Petri-net (FPN) based reversed reasoning approach is proposed to analyze emergency response actions impacting domino effects. FPN is utilized to deduce the consequence-antecedent relationship between an accident and the emergency response actions. To analyze and compare the impacts of the actions on a domino effect, the backward reasoning is of special interest and often preferable when the occurrence probability of domino effects is known. As a tank fire accident in an oil depot usually lasts for a certain period of time, and as it may be greatly influenced by emergency response actions, it is taken as an example to illustrate the proposed approach. Abstract: In the chemical process industry, large quantities of different flammable substances are stored in tank farms. Due to potential mutual impacts among the tanks, severe domino accidents may occur after a tank catches fire. The emergency response to a tank fire may influence the development of the accident and impact the occurring of escalating events or so-called domino effects. In this paper, a fuzzy Petri-net (FPN) based reversed reasoning approach is proposed to analyze emergency response actions impacting domino effects. FPN is utilized to deduce the consequence-antecedent relationship between an accident and the emergency response actions. To analyze and compare the impacts of the actions on a domino effect, the backward reasoning is of special interest and often preferable when the occurrence probability of domino effects is known. As a tank fire accident in an oil depot usually lasts for a certain period of time, and as it may be greatly influenced by emergency response actions, it is taken as an example to illustrate the proposed approach. (C) 2017 Published by Elsevier Ltd.

In the literature, many studies have outlined the main existing methods and software tools used for the study and analysis of domino effects. One of these is the MADS–MOSAR model, which provides a schematic representation of the process of domino effects in the form of black boxes. The exploitation of these boxes for the deduction of short and long scenarios is based on the experience of the users of this model. Hence, the difficulty encountered by some practitioners of the model MADS–MOSAR not experienced for the modeling of domino effects. To overcome this difficulty, this paper presents a modeling of black boxes of the MADS–MOSAR model in the form of networks which allow a better exploration of the “Source-Flow-Target” triptych that intervene in the process of domino effects.

The use of probits is essential for the study of domino effect in chemical process industries, to enable the assessment of the probability of escalation of accidents. This paper reports a more comprehensive and reliable method for formulating the probits than has been accomplished by the earlier reported methods. It is based on a larger data set, a finer level of classification of the equipment and a different method of linking the qualitative description of the damage to the quantitative probability. The probits thus obtained have significantly higher coefficients of determination (R2) than those obtained by the previous authors, indicating a much better interpretation of the past data for calculating the probits. In most cases, the R2 was greater than 0.9.

s afety and security in chemical industrial plants require special attention due to the related concentrated hazards. Actually, primary accidents such as fire and explosions, can escalate into domino effects in the storage tanks depending on the relative distance between tanks, the flammability and explosiveness of the stored material, the fragility to structural defects of the surrounding tank etc. Therefore, reducing the probability of domino effect within an industrial area has become a crucial concern in industrial risk analysis and assessment. The present paper redefines the vulnerability of tanks in the case of domino effect and creates the formation mechanism and evaluation model of multiple accident coupling scenarios. Based on the occurrence, propagation and influence of the primary accident (single accident or multi-accident scenario) and the fusion of matter element extension theory, entropy weight method, complex network model, TOPSIS (technique for order preference by similarity to ideal solution) model and risk matrix, the weight I, weight II of evaluation index and the evaluation system are established which can determine easily the vulnerability level of tanks. For illustrative purposes, the application of Bayesian method indicated that the global site probability of failure (i.e. the failure of the surrounding tanks, after a first sequence accident) can be reduced by removing the most vulnerable tanks, which confirms the effectiveness of the assessment method.

Domino effects triggered by fire can cause extremely severe damages to the chemical and process plants. In the need of a more effective prevention of fire domino effects, the present study focuses on firefighting which has received less attention compared to passive and active fire protection systems. Considering both the vulnerability and recoverability phases during fire domino effects, we have introduced a methodology for optimal identification of firefighting strategies so as to increase the resiliency of process plants in dealing with fire escalation scenarios. The area above the resilience curve (AARC), which is equal to the accumulation of loss of resilience over time, was considered as the metric to identify the optimal firefighting strategies. In other words, the strategy leading to the lowest AARC can be selected as the optimal strategy from a resiliency perspective.

Process facilities handling hazardous chemicals in large quantities and elevated operating conditions of temperature/pressure are attractive targets to external attacks. The possibility of an external attack on a critical installation, performed with an intention of triggering escalation of primary incidents into secondary and tertiary incidents, thereby increasing the severity of consequences needs to be effectively analysed. A prominent Petrochemical Industry located in Kerala, India was identified for studying the possibility of a deliberately induced domino effect. In this study, a dedicated Bayesian network is developed to model the domino propagation sequence in the chemical storage area of the industry, and to estimate the domino probabilities at different levels. This method has the advantage of accurately quantifying domino occurrence probabilities and identifying possible higher levels of escalations. Moreover, the combined effect from multiple units can be modelled easily and new information can be added into the model as evidences to update the probabilities. Phast (Process hazard analysis) software is used for consequence modelling to determine the impact zones of the identified primary and secondary incidents. The results of the case study show that such analyses can greatly benefit green field and brown field projects in determining the appropriate safety and security measures to be implemented or strengthened so as to reduce its attractiveness to external threat agents.