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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.

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.

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.

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.

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 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.

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.

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.

In most dust explosion accidents, a series of explosions consisting of a primary (dust) explosion and one or more subsequent secondary dust explosion(s) has been reported. Such chain of dust explosions can be referred to as a dust explosion domino effect (DEDE). DEDEs are capable of causing severe onsite and offsite damages to human, assets, and the environment, thus requiring a detailed understanding of the causes, consequences, probabilities, and escalation mechanisms thereof to prevent and mitigate the potential damages. In this research, we have developed a methodology for the probability estimation of DEDEs based on Bayesian network. The application and efficacy of the methodology have been demonstrated via a real-world case study. The results illustrate that the developed methodology can effectively portend the propagation of DEDEs while calculating the respective probabilities.

© 2018 Institution of Chemical Engineers On February 2007, a massive fire in a propane de-asphalting unit in an oil refinery in Texas, USA happened due to liquid propane release from a cracked pipe in a control station injuring four people, damaging extensive equipment, causing significant business interruption, and resulting in more than $50 million losses. The accident was triggered by a natural hazard: freezing of piping at a control station caused an inlet pipe elbow to crack, which in turn, led to the release of high-pressure liquid propane which was rapidly ignited. In addition, there were two near-miss events due to potential domino effects. In fact, the accident could reasonably have resulted in much more severe consequences due to the exposure of large butane storage spheres and chlorine containers, increasing the possibility of a catastrophic domino effect. This paper develops a Natech (natural hazard triggering technological disasters) risk assessment methodology that relies upon Bayesian network capabilities and takes into account the potential Natech domino effects. The methodology is implemented in the intended refinery and mathematically graphically represents the dynamic cause–effect relations between units involved in the scenario, and handles uncertainties among the interactions. In addition, the methodology can provide a risk value for the entire scenario that can be used further for risk-based decision making.

The uncertainty and the complexity associated with the domino effect is a barrier to assessing the frequency of such accidents analytically. The use of simulation techniques, such as Monte Carlo, to examine the domino effect instead of analytical techniques has shown great promise. In this paper, a new method to assess the frequency of domino accidents is proposed—FREEDOM II—which is an improvement on the recent algorithm proposed by the authors (FREEDOM). The modifications on FREEDOM were carried out to overcome a limitation of the method and to extend its capabilities. A key shortcoming of the earlier method was its inability to handle multiple failure scenarios. This shortcoming has been overcome in FREEDOM II. A new and improved algorithm has been developed that carries out the simulation in a significantly shorter run time. The applicability of the new model is shown by performing a multi-scenario case study.