Risk-Based Domino Effect Analysis for Fire and Explosion Accidents

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Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities Jie Ji, Qi Tong, Faisal Khan, Mohammed Dadaszadeh, and Rouzbeh Abbassi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00103 • Publication Date (Web): 01 Mar 2018 Downloaded from http://pubs.acs.org on March 8, 2018

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Industrial & Engineering Chemistry Research

Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities Jie Ji*1, Qi Tong1, Faisal Khan2*, Mohammed Dadaszadeh2,3, Rouzbeh Abbassi4 1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China 2. Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada, A1B3X5 3. Hydrogen Safety Engineering and Research Centre (HySAFER), Ulster University, Newtownabbey, BT37 0QB, Northern Ireland, UK 4. National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Launceston 7250, Tasmania, Australia * Correspondence author - Email: (Jie Ji) [email protected]; (Faisal Khan) [email protected] Abstract: Process facilities are vulnerable to catastrophic accidents due to the storage, transportation and processing of large amounts of flammable/explosive materials. Among a variety of accident scenarios, fire and explosion are the most frequent ones. Fire and explosion are interactive events and may cause a ‘chain of accidents’ (also known as the ‘domino effect’). Especially in processing facilities where units are located within a limited distance, fire or explosion occurring in one unit is likely to spread to other units. Currently, there is a lack of proper methodology that considers the effect of fire and explosion interaction. Ignoring this interaction provides uncertainty in the domino effect risk analysis. High complexity and uncertainty, due to the interaction of fire and explosion, thus make it challenging to analyze the domino effect propagation. Fuzzy Inference System (FIS) is known to be an efficient tool for handling uncertainty and imprecision. The current study has developed a new methodology by adopting FIS method to handle the data uncertainties in the dynamic Bayesian network (DBN) to conduct a robust domino effect analysis considering interactions of fire and explosion. Application of the proposed methodology demonstrates that the FIS acts as a quick semi-quantitative method involved in the domino effect analysis. Results obtained from FIS are consistent with those obtained using the DBN. Moreover, it illustrates that DBN is an effective technique to analyze the combination of a fire and explosion accident. Key words: Risk analysis; domino effect; fire and explosion; Fuzzy Inference System; dynamic Bayesian network

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Introduction Process facilities deal with the storage, transportation and processing of chemicals which are potentially flammable, explosive and/or toxic. Their complex geometry and the interaction between various units during an accident are one of the major issues that should be properly considered in risk analysis. Interaction between different events such as fire and explosion is also a concern. Ignoring any of the mentioned issues within the risk analysis of process plants may lead to high uncertainties followed by wrong judgment in providing the appropriate safety measures. This makes the processing plant more vulnerable to catastrophic accidents and more severe consequences involving human and financial loss. An example can be the Piper Alpha accident which occurred in 1988 and which led to the total destruction of the plant and the loss of 165 lives.1 The chain of events started with the accidental release and ignition of hydrocarbons in one module of the plant which led to an explosion. The resulting overpressure opened its way to the adjacent module and caused the rupture of a crude oil transportation line with a consequent fire. The fire spread to the fuel storage units leading to the second explosion. Another accident at the Houston chemical complex of Philips Company, Texas in 1989 in which an initiating explosion wave spread to two neighbor gasoline storage, led to a second explosion. The heat due to the flame then reached a polyethylene reactor and caused the third explosion.2 In 2004, Skikda LNG plant in Algeria experienced a series of explosions, causing 27 deaths and a loss of 900 million dollars.3 Released LNG entered the boiler and caused an explosion. The heat radiation of the explosion reached the vapor cloud, evaporated from the released LNG, and caused a second explosion. Another example was the accident which occurred at BP’s Texas city in 2005 which caused 15 deaths and 180 injuries.4 In this accident, the released flammable liquid became a liquid pool and evaporated, forming a vapor cloud mixing with air under the effect of wind. The vapor cloud was then ignited by a neighboring truck, resulting in a VCE (Vapor Cloud Explosion). The heat radiation of the initiating explosion reached the liquid pool, resulting in a pool fire and several explosions. In 2005, an accident occurred in Buncefield oil tank farm causing 43 injuries and a financial loss of 1.5 billion dollars. The primary accident was a VCE caused by ignition of evaporated gasoline due to tank overfilling. Flame front of the VCE ignited the overfilled gasoline, causing consequent pool fires and damage to storage tanks. In October 2009, multiple explosion and fire accidents occurred at the petroleum terminal of Caribbean Petroleum Corporation in Bayamón, Puerto Rico.5 In this accident, the overfilled gasoline formed a liquid pool followed by the formation of vapor cloud due to evaporation and dispersion. The vapor cloud was ignited by an unknown ignition source and caused a flash fire. Afterwards, the flash fire forced back towards the tank farm and caused a massive explosion. Flame engulfment and heat radiation from the VCE destroyed the nearby tanks and caused a pool fire that continued for 60 hours. The consequence of the accident was destructive damage including ruptures of 17 out of 48 petroleum storage tanks and nearby equipment. In October 2009, an accident occurred at the Indian Oil Corporation refinery, Sitapura.5 The accident chain started with the overflow of fuel from a storage tank forming a 2


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liquid pool followed by the evaporation of fuel. The liquid pool was then ignited by a generator station, causing a pool fire. Flame front of the pool fire ignited the evaporated vapor cloud and caused a subsequent explosion and multiple pool fires. Multiple fires could influence each other and make the fire more destructive and uncontrollable6,7. Finally, the pool fires spread over the entire tank farm and continued for a week. An overview of the past accidents may lead to two main conclusions. First, the interaction between the fire and explosion is more likely to occur in processing plant accidents rather than each event individually evolving. Second, the complex geometry and huge amount of flammable material makes such plants more vulnerable to the spread of events from one unit to another. Previous published studies of fire and explosion analysis in process industries include Dow fire and explosion index, Mond index, IFAL( Instantaneous Fractional Annual Loss) index, MACC( Maximum Credible Accident Analysis), HIRA (Hazard Identification and Ranking Analysis), MOSEC (Modeling and Simulation of Fire and Explosion in Chemical process industries), computer automated DOMIFFECT analysis and ORA(Optimal Risk Analysis). Most of the risk analysis methodologies overlooked the interactions of fire and explosion and effects of events propagation.8,9 Terminology for a ‘chain of accidents’ is the ‘domino effect’ in which a primary accident, initiated in one unit, spreads to the adjacent unit through different effects, i.e. heat radiation, overpressure or blast fragments. This leads to the secondary or even higher order of accidents that increase the failure probability of the units and causes the severity of the consequences. Domino effects have low probability but have more severe consequences due to complex industrial settings. The risk analysis associated with domino effect considering fire and explosion individually has made great progress in recent years. However, there has been less attention devoted to the quantitative risk analysis (QRA) studies of domino events considering the integration of the fire and explosion accidents. Early research assessing domino effect are limited to qualitative analysis and description of chain of events in historical accidents.10,11 A comprehensive framework of domino effect analysis was developed by Khan and Abbasi for chemical processing industries, in which models in cases where different primary accidents for analyzing domino effect and corresponding escalation probabilities, are presented.12 More recently, a series of works conducted by Cozzani and coworkers demonstrated a QRA of domino effect with discussion on appropriate safety measures.13-16 There were also studies on the escalation threshold values conducted by Cozzani et al. to analyze domino effects within various installations, aiming at correcting the general applied threshold values in a probit model, which is widely applied to the calculation of spreading probabilities, probabilities for heat and overpressure effects and units vulnerabilities.17 A series of studies conducted by Landucci and coworkers present various preventive safety measures whenever facing domino effects and the interaction between safety measures.18-20 Analyses of domino effects’ probabilities and evaluating the performance of safety measures in industrial plants were also conducted through the application of the Bayesian Network 3



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(BN),21-23 Dynamic Bayesian Network (DBN) 24,25 and graph theory 26,27. These later studies proved BN to be an effective and precise tool for domino effect analysis. The current work is aimed at developing a comprehensive methodology to analyze both the fire and explosion propagation in a domino effect accident. Applying the Bayesian network to model the domino effect has been extensively published in the previous studies.21-26 However, those works only presented a methodology of how to use Bayesian network to analyze the domino effect propagation. Additionally, those works only analyze fire and explosion separately, which is not the truth in most cases. From the summary of typical domino effect accidents in the past, we know that fire and explosion always interact with each other and aggravate the accident. Considering the aforementioned research, there are limited studies on interaction of fire and explosion and their consequences on human lives and financial risk. Therefore, this work tries to develop a combination of two sources of accidents, i.e. fire and explosion. Another problem of the domino effect analysis in the previous work is the data uncertainty. Domino effects in processing facilities are considered to be very complicated scenarios with high uncertainties. There are various sources of uncertainties, i.e. lack of information about potential targets to determine the propagation of domino effect; atmospheric conditions to determine the heat radiation and overpressure distribution; and personnel and property distributions which affect the consequence in domino effect. This information is hard to confirm or is uncertain. This unknown and uncertain information is a huge issue to be solved in a domino effect analysis. Previous studies analyze the fire or explosion based on several assumptions. Obviously, those assumptions bring the uncertainties to the work and thus make the results imprecise. Under this circumstance, we have adopted the fuzzy logic, which is proven to be an effective tool for handling uncertainty and imprecision.28 It handles the uncertainties and considers the interactions of fire and explosion accidents in the complex domino effect chains. This work goes beyond the use of fuzzy theory to BN. It is a development of methodology to handle data uncertainty in the dynamic Bayesian network to conduct robust domino effect analysis. A fuzzy risk analysis is proposed to evaluate uncertainties and help to develop a robust domino effect analysis. The basic procedure proceeds as follows. The fuzzy inference system is not only used to provide the probability but also to help identify the most critical unit with highest risk. The most critical unit is defined as highest risk consisting of high probability of occurrence and severe consequence with respect to human injuries, deaths, and financial loss. DBN is employed to analyze the time-dependent probability of fire and explosion in each unit and to confirm the validity of fuzzy risk analysis. Moreover, probit models for assessing the heat radiation and overpressure effect on humans and properties are devoted to assess the human and financial loss. Integrating the human and financial loss into the probability provided by DBN, identifies the most critical units with highest risk. Furthermore, DBN is applied to determine the most vulnerable unit with the highest probability of accident. Units with the highest increase ratio of accident probability are considered as contributing most to the propagation of domino

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effect. Finally, the appropriate safety measure allocation to reduce the risk associated with domino effect is presented. This paper is organized as follows. The fundamentals and terminologies of domino effect are discussed in Section 2. Background information about FIS and DBN are introduced in Section 3. Section 4 proposes the methodology for quantitative risk analysis of domino effect. Section 5 presents a case study to validate the proposed methodology. The allocations of safety measures based on the modeling results are given in Section 6. Conclusions are summarized in Section 7. 2. Domino effect propagation 2.1. Domino effect In quantitative analysis of processing facilities, a domino effect, which is also known as a ‘chain of accidents’, is described as a phenomenon where a primary accident such as fire and explosion in a unit triggers a secondary or higher order accidents in other units. This results in more severe overall consequences than the primary accident itself. In fact, domino effect is often identified as of low frequency and with severe consequence scenarios. There are several key elements during the domino effect of an accident as follows: • A primary accident which is initiated in the first unit and is able to spread to other units through physical effects with sufficient intensity; • Escalation vectors of a physical effect such as heat radiation and fire engulfment in fire accident, overpressure and blast projectiles in case of explosion. In fact, escalation vectors are essentially released energy from the primary accident; • A target unit, which receives the escalation vector, originating from the lower order accident and which may generate a higher order accident. Primary accident, particularly in processing facilities, mainly includes fire and explosion. Escalation vector is determined by the type of accident occurring in the primary unit. Furthermore, whether the accident can spread to a higher order of accidents depends on a variety of factors, including the type of primary accident and associated escalation vector; overall energy released from the primary accident which is mainly determined by the inventory, the intensity of escalation vector which can be calculated through empirical models12,16,17,29,30 or elaborate CFD models, distance between primary and secondary units; escalation thresholds for different installations, and vulnerability of the target unit which may be quite different due to different operating conditions and materials of the equipment. Various escalation thresholds with regards to different equipment have been studied by Cozzani et al.15-17 and Landucci et al..31 2.2 Domino-induced failure rate The domino-induced failure rate, which is the escalation probability caused by domino effects, is determined by several factors. A widely-used model for calculation of the escalation probability is a probit model introduced in the work conducted by Eisenberg et al.32 and used by Khan et al.12,30 as follows: = + ln , 1 5



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where Y represents the probit function for the target equipment, and are probit coefficients for different types of equipment. D represents the dose of escalation vector, i.e. peak static overpressure in case of explosion or time to failure (ttf) in case of fire. Literatures extensively demonstrated the probit coefficient, i.e. and , for various equipment and verified the model with the previous accidents and experiments.13,15 In current work, we only consider the atmospheric and pressurized tank as targets to be studied. Table 1 lists the values of , and D, specified for atmospheric and pressurized equipment. Table 1. Parameter details for probit model as showed in Equation 1 Escalation Equipment D vector condition ln(ttf ) = − 1.128 ln(Q ) − 2.667 *10 −5 * V + 9.877 2

Atmospheric

12.54

-1.847

Pressurized

12.54

-1.847

Heat radiation13

Q(kW/m ) and stands for the heat radiation received by the target equipment, V(m3)is the volume for target equipment. ln( ttf ) = − 0.947 ln( Q ) + 8.835 * σ 0.032

(Same

definition as above.) 2.44 Peak static overpressure(kPa) Overpressure Atmospheric -18.96 13,15 Pressurized -42.44 4.33 Peak static overpressure(kPa) The value of Y, obtained from Equation 1, is used to calculate the escalation probability as Equation 2.33 P=

1 2π

∫

Y −5

−∞

e

−u 2 2

du ,

(2)

In this study, instead of integration of Equation 2, error function () presented in Equation 3 is used.34 = 0.5 × 1 + erf

√

,

(3)

Probability of the secondary accident (!"#$%&'() ), given the probability of the primary accident (*(+,'() ), is then calculated by using Equation 4. Ps e c o n d a r y = P p r im a r y * Pe s c a la ti o n

where

Pes c ala tio n

(4)

is the escalation probability as illustrated above. Probability of

occurrence of tertiary accident or even higher order of accidents can also be calculated following the same procedures discussed above. The critical issue relating to domino effect propagation is synergistic effect, generally known as the joint effect (joint escalation vector intensity), of units in the same or different order. Synergistic effect may increase the failure probability of other units and thus trigger further accidents. Methods for calculating the synergistic effect are extensively studied in the work conducted by Khakzad and coworkers.21,22,25,26 In

6


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order to provide a more accurate analysis of failure rate caused by the joint effects of escalation vector, synergistic effect is considered in this work. 3. Background 3.1. Fuzzy risk analysis In domino effect risk analysis, the uncertainties arise due to the lack of two sources of information. First, the uncertainties presented by the complexity and randomness of domino effect and accuracy of analysis procedures, and secondly the uncertainties due to the experts’ subjective perspectives about the risk analysis. These perspectives are subject to the experts’ standpoint and professional knowledge and might differ from one individual to another which may initiate another source of uncertainty. These sources of uncertainties are the main reasons that information about different variables in an accident scenario is not generally crisp and precise. To avoid such uncertainties, the fuzzy logic and fuzzy sets were introduced to deal with the situations in which the boundaries and values of a problem are not specified precisely.35 The framework of fuzzy set theory was developed later in the work by Bellman and Zadeh.36 The current study considers a methodology for quick ranking risk associated with domino effect propagation and the precise value of the risk is not the main concern. To this end, employing fuzzy logic as a semi-quantitative assessment method identifies the most critical units. The fuzzy logic simplicity and uncertainty-handling ability makes it easy to analyze the risk associated with each unit.28,37,38 The FIS, also known as fuzzy expert system, is a mathematical system, which transforms human perspectives to fuzzy sets based on fuzzy logic. It analyses analog inputs according to the knowledge-based fuzzy if-then rules which are generated from engineering knowledge by the collection of if-then statements.37 The basic structure of FIS is presented in Figure 1.39

Figure 1. Basic structure and elements of FIS. As demonstrated in Figure 1, the main elements of FIS are as follows: • Fuzzification which means transforming the crisp numbers into fuzzy sets based on the membership functions which are knowledge based. • Fuzzy if-then rules are then applied to map fuzzy input numbers to the output numbers based on the collected database for fuzzy rules. • Defuzzification that processes all if-then rules in each fuzzy set and transforms the fuzzy numbers into single crisp numbers. In this study, defuzzification type is centroid. 7



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There are two approaches for FIS calculation: Mamdani method in which output member functions are fuzzy sets and Sugeno method using the linear member functions of inputs to generate outputs.40 In the current study, Mamdani method is employed as there is no linear relation between the inputs and outputs. FIS has been applied in various research areas such as prioritization of environmental issues,38 piping risk assessment,41 process safety analysis,28 risk assessment of occupational accidents,42 process hazard uncertainties analysis,43 fuzzy risk matrix,39 fuzzy risk analysis for explosion risk assessment,37 and risk assessment of liquefied natural gas terminals.44 In this work, variables of domino effects are studied in FIS in order to analyze the risk associated with domino effect. The fuzzy risk analysis for domino effect is illustrated in the following section. 3.2. The fuzzy risk matrix Combination of fire and explosion risk matrix consists of several independent variables, namely, probability of accident, severity of the consequence for personnel loss and financial loss, environmental pollution and company reputation considering an overall risk assessment perspective. However, for illustration purposes, only probability of fire or explosion and consequences with respect to human and financial loss are included in this work. The data flow of risk associated with human and financial loss is shown in Figure 2 presenting the main three steps employed. The proposed FIS is aimed at the risk assessment of domino effect instead of risk analysis of general accidents. It is based on the basic idea of the fuzzy logic, however, in order to apply the fuzzy logic to specifically study the risk of domino effect, modifications based on the traditional FIS are made due to different characteristics between a domino effect accident and a general accident. For instance, the input parameter “closeness” is added because it is a critical parameter determining which initiating unit would affect more units in the domino effect propagation.26,27 Additionally, the “inventory” is another parameter which determines the “ttb”, i.e. time to burn out.22,25 In other words, the “inventory” determines whether there is a failure in the target unit and whether there is an overlapping of heat radiation emitting from units in different orders of the accident.22,25 Therefore, the “inventory” can influence the escalation probability and synergetic effect of domino effect propagation and consequently determines how many units would get involved in the domino effect accident.

8


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Figure 2. The data flow for risk analysis of processing tanks. The fuzzy logic is applied to the calculation of probability of fire and explosion occurrence depending on three factors. The first factor is the frequency of leakage (LE) due to corrosion or overfilling. Another influential factor is the probability of presence of ignition (IG) source with enough energy to ignite the flammable materials. The last factor is the flammable property of the fuels which determines the ease of ignition, i.e. flash point (FP) in case of fire and explosion range(ER) in case of explosion. For instance, highly reactive materials such as acetylene and ethylene are more likely to be ignited than materials with medium reactivity such as methane.29 The severity of consequence for each accident scenario also consists of three elements: Inventory (IN) of each unit which is a fundamental data for measuring the released total energy; Closeness (CL) of each unit which is a typical variable for determining how many units are likely to get involved in the domino effect. Physical interpretation of closeness is that units with higher closeness score are able to affect more nearby units. The definition of closeness can be found in the studies by Khakzad et al..26,27 Exposure duration (ED) reflects the presence of personnel in the hazard area with respect to the human loss. In this study, exposure duration to personnel is replaced by the value of present properties (PV) with respect to property loss. Finally, after obtaining the probability of occurrence (PO) and severity of consequence (SC) for each unit, the risk assessment of each unit is carried out through FIS following the rules shown in Table 2. The probability of accident occurrence and associated consequences are identified as input variables of this stage. Table 2. Rules used in fuzzy logic application of risk analysis R SC PO Insignificant Minor Moderate Major Catastrophic Almost Medium High Critical Critical Critical certain Likely Medium High High Critical Critical Possible Low Medium High Critical Critical 9



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Unlikely Low Low Medium High Critical Rare Low Low Medium High High 3.3 Fundamentals of the dynamic Bayesian network analysis 3.3.1 Bayesian network BN is a probabilistic directed acyclic graphical model which represents the random variables and their conditional dependency by nodes and arcs respectively.45 The type and strength of dependency among nodes are represented by the conditional probability tables (CPT). Nodes are connected by the directed arcs which direct from the causes (i.e. parent nodes) to its consequences (i.e. child nodes). As an extension of joint probability distribution, BN is superior to traditional risk analysis methodology such as fault tree and event tree for the advantage of posterior analysis based on the available observations. Furthermore, critical factors contributing most to an accident can be obtained by the comparison of prior and posterior probabilities when BN takes in new observations as evidence. In this study particularly, comparison of prior and posterior probability of accident occurring at each unit can be implemented to identify the units which contribute most to the domino effect. Another advantage of BN is the incorporation of multiple states of each node, common cause failure and conditional probability. These qualities make BN more flexible and applicable over traditional risk assessment methods. BN has been widely applied in various fields such as risk analysis, reliability analysis and safety analysis.46-49 3.3.2 Dynamic Bayesian network Dynamic Bayesian network is an extension of Bayesian network with an additional feature of time-dependent probabilities. The DBN relates nodes over a discretized time line. In order to model the temporal evolution of nodes, the continuous time line is divided into a series of discretized time slices, which makes the node at time step t dependent not only on its parental nodes pa ( X ) , which at contemporary time step t, but t i

also on its states

X

t −1 i

and parental nodes pa ( X

t −1 i

) pa ( X it − 2 )... pa ( X i0 )

at previous time steps.

The joint probability distribution P(X) of a series of variables X=(X1, X2, X3,…,Xn) is expanded as illustrated in Equation 5. In this study, only two time steps are considered and Equation 5 is modified as Equation 6. More details about application of DBN to risk analysis can be found in studies.25,50-52 n

P( X t ) = P( X1t , X2t , X3t ,...X nt ) = ∏P(Xit Xit −1 , pa( Xit ), pa( Xit −1) pa( Xit −2 )... pa( Xi0 )) ,

(5)

i =1

n

P( X t ) = P( X1t , X 2t , X3t ,...X nt ) = ∏P(Xit Xit −1 , pa( Xit ), pa( Xit −1))

(6)

i =1

4. The proposed Methodology 4.1. Risk analysis framework of domino effect In the risk analysis of domino effect in a processing facility, three tasks are of great importance. This includes: 10


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•

Which unit in the tank farm must be assigned as having the highest risk in terms of human loss and financial loss? • Which unit in the tank farm is the most vulnerable when considering its failure probability and domino-induced failure rate? In other words, which units are more likely to catch fire or explode? • After assigning the most vulnerable unit in a tank farm, which unit would contribute the most to the domino propagation effect given to be the most vulnerable at an accident? The mentioned issues are considered in the risk analysis methodology developed in the current study and illustrated in Figure 3.

Figure 3. Proposed methodology to assess the risk of a domino effect. 4.2.Analysis process of the methodology 4.2.1. Risk analysis through FIS In the first step, FIS is implemented to semi-quantitatively analyze the risk index of a unit given the unit involved in an accident (fire or explosion). For each unit, average of input variables in case of fire and explosion is used as the inputs of the FIS. In other words, fire and explosion are employed with equal weight of 50%. Risk ranking is employed to identify the most critical units with highest risk with respect to different consequence categories. In order to develop a fuzzy risk assessment, input variables and if-then rules are provided. Details of fuzzy sets applied in the fuzzification step are summarized in Tables 3, 5 and 7. For instance, Tables 3, 5, 7 provide the input variables which are divided into different linguist descriptions with relevant range. We adopted a “Triangular” membership function for its effectiveness and simplicity with relevant parameters which are employed in the Matlab.38 Further, total number of 27 (3*3*3) if-then rules applied in each FIS is presented in Tables 4 and 6. If-then rules are established based on the published data with the modification required to fulfill the requirement of the proposed methodology.37,39 For instance, as shown in Table 4, if the probability of leakage (LE) is low, probability of ignition (LG) is low, and flash point/explosion range (FP/ER) is combustible/narrow, then the corresponding probability of occurrence (PO) is rare. The Matlab software was employed to realize FIS type of Mamdani. For illustration purpose, a layout of the storage tank farm is demonstrated in Figure 4. Four tanks (units) are considered. For 11



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instance, given tank 1 in Figure 4 to be in a fire or an explosion accident, variables associated with this tank are imported in FIS to calculate the corresponding risk index illustrated in section 3.1. The similar assessment procedure is applied to all other tanks and risk index associated with each tank in the tank farm is then obtained.

Figure 4. The layout of storage tank farm. Table 3. Details of FIS input variables and membership functions for estimating the likelihood probabilities Input Linguistic Membership Description range Parameters variables Description function Log(LE) Low 10(-6)LE

Risk-Based Domino Effect Analysis for Fire and Explosion Accidents

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