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Journal of Loss Prevention in the Process Industries 26 (2013) 895e903

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Journal of Loss Prevention in the Process Industries

journal homepage: www.elsevier .com/locate/ j lp

Application of a multi-plant QRA: A case study investigating the riskimpact of the construction of a new plant on an existing chemicalplant’s risk levels

Shahabaldin Baesi a, Bahman Abdolhamidzadeh a,*, Che Rosmani Che Hassan a,Mahar Diana Hamid a, Genserik Reniers b,c

aDepartment of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiabAntwerp Research Group on Safety and Security (ARGoSS), University of Antwerp, Prinsstraat 13, 2000 Antwerpen, BelgiumcHogeschool-UniversiteitBrussel/KULeuven, Belgium

a r t i c l e i n f o

Article history:Received 10 April 2012Received in revised form25 October 2012Accepted 6 November 2012

Keywords:Quantitative Risk assessmentIndividual riskSocietal riskDomino accidentsChemical clusterProcess industries

* Corresponding author.E-mail address: abdolhamidzadeh@um.edu.my (B.

0950-4230/$ e see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.jlp.2012.11.005

a b s t r a c t

The construction of chemical clusters whereby a variety of chemical plants are located next to each otherprovides great economic benefits. However, in such clusters, due to the mere scale on which hazardousmaterials are processed, stored and handled, the potential of various accidents is much higher than insingle companies. Furthermore, the close proximity of process installations and storage tanks in suchareas gives rise to the risk of domino effects. Therefore, land use planning and layout design has alwaysbeen a challenge within such clusters.

In this paper, a Quantitative Risk Assessment (QRA) is carried out and used as a decision making tool toevaluate the acceptability of constructing a new chemical plant adjacent to an existing one. For thispurpose, standard parameters such as individual risk and societal risk were quantified, before and afterthe new plant would come into operation. Given the experience of past accidents in the processindustries, the likelihood of domino accidents in the two neighboring plants has also been analyzed.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Due to the flammable and toxic nature of substances which arebeing handled in the oil-, gas- and petrochemical industries,installations within chemical plants have a high potential to causesubstantial damages in terms of fatalities, serious injuries, propertydamages and environmental degradation. In addition, largeinventories of hydrocarbons, intense temperature and pressureconditions and inherent congestion in process installationswhereby process equipment is often situated in close proximity toone another, increases the probability of catastrophic accidents andamplifies their potential consequences (Abdolhamidzadeh, Abbasi,Rashtchian, & Abbasi, 2010; Khan & Abbasi, 1999a, 1999b; Reniers,2010). For example, in Toulouse, France, 30 people were killed and2242 injured in 2001 due to an ammonium nitrate explosion, and inTexas city, USA, 15 persons lost their lives and 170 others wereinjured in 2005 due to a refinery disaster (Dechy, Bourdeaux,Ayrault, Kordek, & Le Coze, 2004; Kalantarnia, Khan, & Hawboldt,2010). We refer e.g. to Lees (1996), Wells (1997), Kletz (1999a,

Abdolhamidzadeh).

All rights reserved.

2003), and Atherton and Gil (2008) for many other examples ofmajor accidents in the chemical industries. Such catastrophicevents also happen in developing countries and especially in theMiddle East (Khan & Abbasi, 1999a). This may be related to the evergrowing number of chemical clusters in this particular region dueto the existence of massive sources of energy in the Persian Gulf. Inchemical clusters, upstream activities such as production andseparation are increasingly carried out in chemical plants beingphysically located nearby chemical companies with downstreamactivities, such as refineries.

One of the important issues in the construction of chemicalclusters is so-called land use planning. Land use planning is influ-enced by economic, operational and safety aspects. The highnumber of hazardous activities and substances in chemical clusters,in combinationwith high levels of congestion, demand an adequateand solid decision making process for situating new plants in thearea, whereby individual and group risks are taken into account.

Usually, so-called Quantitative Risk Assessment, or abbreviatedQRA, is used for this purpose. Previous studies indicate that QRA isa useful tool for land use planning, layout design andmodification inthe process industries. Khan and Abbasi (1999b) have estimated theoverall individual risk posed by a chemical cluster to adjacent

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903896

residential areas in order to compare the result with safetycriteria and eventually recommendations were given to makemodifications in the plants since the criteria were not met. Jo andCrowl (2008) specified the minimum safety distances betweena high pressure gas pipeline and residential areas. Risk and conse-quence analyses of toxic chemicals in certain warehouses to thenearby villages were analyzed by Rigas and Sklavounos (2002) toinvestigate the extent of compliance to exposure threshold limits.The study by Papazoglou, Nivolianitou, Aneziris, Christou, andBonanos (1999) demonstrated that a high level of risk is exposedto passengers crossing the highway adjacent to a specific refineryand therefore a new trajectorywith the safety distance calculated byQRA should be constructed. These are a limited number of examplesshowing that land use planning is vital for locating process facilitiesnear to public areas and a QRA could be an effective technique forthis purpose. In this method, in addition to identifying the hazards,also their associated consequences and the frequencies of occur-rences are quantitatively estimated (Kletz, 1999b). Furthermore,parameters such as ‘individual risk’ and ‘group risk’ (the latter alsobeing called ‘societal risk’) are parameters which support the deci-sion making process for land use planning. Several other relevantstudies can be found elsewhere (Bubbico, Maschio, Mazzarotta,Milazzo, & Parisi, 2006; Gharabagh et al., 2009; Milazzo et al.,2002; Yet-Pole, Chi-Min, & Ching-Hong, 2009).

The proliferation of chain accidents suggests that the probabilityof domino effects should also be considered as an importantparameter in land use planning for chemical clusters. A recent studyon past domino events indicated that the number of cascadingaccidents in the process industries has increased globally in recentdecades (Abdolhamidzadeh, Abbasi, Rashtchian, & Abbasi, 2011). Inthe same study there are some accidents mentioned in which aninitiating event in a single plant has led to a catastrophe withina cluster of chemical plants. A recent example could be Shazand, Iraninwhich 30 peoplewere killed and 38 others were seriously injuredin 2008 due to an initial explosion that led to othermajor explosionsand fires in the neighboring plants. The dominant accidentthat caused the majority of fatalities was the major explosions thatwere triggered by the initial blast (Abdolhamidzadeh et al., 2011).

As already mentioned, in land use planning, possible scenariosare reviewed from different points of view, one being the safetyperspective. In many cases, different chemical plants in a chemicalindustrial area do not come into operation at the same point intime. For example, it is common practice to build plants to consumethe product(s) of an existing plant. It is obvious that the construc-tion of new plant(s) adjacent to existing one(s) will affect theoverall risk of the industrial area, regardless of the fact whether riskassessments have been performed for individual plants.

In the remainder of this article, QRA has been applied to eval-uate the risk-based effects of constructing a new chemical plant(which is called BSPC in this article) adjacent to an existing one(called AKPC in this paper). These two plants forming our case-study, are located in one of the largest energy zones in the worldin southern Iran (called ‘PETZONE’). Parameters such as the indi-vidual risk and the societal risk have been assessed, before and afterthe new plant came into operation. Furthermore, the possibilityand the likelihood of domino events in the two neighboring plantswere investigated. We mainly focus on safety aspects in our article,as the feasibility of the planning from both an economic and anoperational perspective has been carried out in an earlier stage ofthe land-use planning decision process.

2. Quantitative Risk Assessment (QRA)

QRA along with other techniques have been used in one way oranother in the process industries for loss prevention from 1970

onwards (Abbasi, Krishnakumari, & Khan, 1998; Khan & Abbasi,1998). QRA is a method that provides quantified estimation forthe risk posed by a group of hazards (Kletz, 1999). Hence, thistechnique enables risk mitigation methods to be evaluated in orderto bring the risk to tolerable levels without resorting to too costlyprotective systems (Pula, Khan, Veitch, & Amyotte, 2006). AlthoughQRAhas itsweaknesses and drawbacks, it is often the only approachto get grip on possible risks and to tame complexity, as indicated byPasman, Jung, Prem, Rogers, and Yang (2009). QRA involves thefollowing main steps: scenario selection, frequency estimation,consequence assessment, and risk quantification (CCPS, 2000;Khan, Sadiq, & Husain, 2002). We will discuss these steps hereafter.

2.1. Scenario selection

Possible scenarios in the process industries may be differentmodes of fire, explosion and toxic dispersion (Arunraj & Maiti,2009; Markowski, 2007). Neglecting any possible scenario canlead to a risk underestimation and eventually affect the overall riskvalues (Van Sciver, 1990). Scenario selection is usually based onexpert’s opinion, history of past accidents and safety reviews. Forour study, after selecting the credible scenarios based on a combi-nation of mentioned methods, Event Tree Analysis (ETA) wasapplied for scenario development. The quantitative feature of ETAwas then used for incident frequency calculation.

2.2. Frequency estimation

Once the final outcomes and their sequences are predicted foreach scenario, the scenario frequency should be determined. Forthis purpose, the failure frequency of each initiating event and theprobability of every intermediate event such as immediate ignition,delayed ignition, vapor cloud fire, etc., are used. The frequencyvalues which can be found in literature, are mostly based onhistorical data of previous incidents (Beerens, Post, & Uijt de Haag,2006). The frequency of a scenario specifies the number of occur-rences of that scenario in a specified timeframe (which is usuallyone year). The probability of an intermediate event is a dimension-less value between0 and 1 which indicates the possibility ofoccurrence of that event in a specific period of time. For this paper,the failure frequencies for the initiating events and the intermediatevalues were obtained from the Purple Book (CPR,1999). Applicationof failure frequencies in the Purple Book and theBEVIManual duringthe past years was industry standard. Also software such as ARIPAR-GIS (Spadoni, Egidi, & Contini, 2000) or DomPrevPlanning (Reniers& Dullaert, 2007) use these values. The following equation showshow the initiating event frequency, intermediate probabilities andfinal outcome frequency are related to each other (CCPS, 2000):

fi ¼ FIPo;iPoc;i (1)

where, fi is the frequency of the final outcome of scenario i arisingfrom incident I (1/yr),FI is the frequency of incident I which causesdifferent outcomes (1/yr),Po;i is probability of occurrence for anintermediate outcome of incident Iwhich causes a number of i finaloutcomes, and Poc;i is the probability of occurrence for a finaloutcome which is a subset of outcomes arising from incident I.

2.3. Consequences assessment

During the consequence assessment, the effects and magnitudeof the final outcome of each scenario should be estimated. Theconsequences of potential scenarios could be classified as follows(Lees, 1996): fire: thermal radiation; explosion: overpressure andfragment projection; and toxic dispersion and exposure.

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903 897

Besides serious physical human harm and eventually casualties,property damage and environmental effects are other conse-quences among the overall potential loss (Pintaric, 2007).The measure of damage in QRA is fatality, since other types of lossare more complex to assess in comparison to human casualties(Pula et al., 2006).

A chain of rigorous calculations is needed to estimate theintensity of the physical harm posed by the mentioned scenariooutcomes in a chemical cluster. Software packages for the proce-dure of consequence assessment are used to estimate the numer-ical value of thermal heat load, explosion overpressure and toxicconcentration at various spots around the release point. For thisstudy, the well-known PHAST (Process Hazard Analysis Safety Tool)software was used for consequence assessment. Once the appro-priate failure frequencies are given, this particular software con-taining fine discharge, dispersion, evaporation, and rainout models,is a powerful tool for the prediction of effect zones (Pintaric, 2007).In addition to the results of consequence modeling, fatality probitequations were used to quantify the expected percentage oflethality for the exposed population.

2.4. Risk quantification

The frequency and severity (rate of fatality) of every specificscenario are combined, to obtain a measure of the correspondingrisk. Risk quantification results are presented in two conventionalcategories which are known as ‘individual risk’ and ‘group risk’(also called ‘societal risk’).

The frequency at which a particular individual being fatallyharmed when standing at a certain distance from a potentialhazard, is known as “individual risk” (Gooijer, Cornil, & Lenoble,2012). The overall individual risk at any location (x,y) inside oroutside the industrial plant is the summation of all individual risksat that specific point. The individual risk at any location (x,y) iscalculated by the following equation (CCPS, 2000):

IRx;y ¼Xn

i¼1

IRx;y;i (2)

In other words, the risk of any identified scenario will be indi-vidually calculated at a specific location (x,y), and subsequently all

Fig. 1. Layout of the study area (case-study) showin

risks are summed to estimate the overall risk at that specific point.To calculate the individual risk at location (x,y) arising fromscenario i the following equation is applied (CCPS, 2000):

IRx;y;i ¼ fiPf ;i (3)

where fi is the frequency of the final outcome of scenario i arisingfrom an incident (1/yr), andPf,iis the fatality probability of the finaloutcome of scenario i at the geographical location (x,y). Individualrisk contours represent the final results of this step. The individualrisk value in every position, when compared to universally orregionally accepted values, is one of the criteria in risk-baseddecision making for land use planning.

Societal risk provides a risk evaluation for a group of peoplelocated in the vicinity of the accident location. In other words, thenumber of people affected by all final outcomes is estimated(Renjith & Madhu, 2010). Similar to the individual risk, the societalrisk is a function of frequency of occurrence and rate of fatality.Another important and determinant factor for calculating thesocietal risk is the population density around the incident location.

The societal risk is presented in formof FeN(FrequencyeNumberof fatalities) curves where the cumulative frequency of finaloutcomes is plotted against the number of fatalities arising from anoutcome in a logarithmic scale. To calculate the number of fatalitiesof each final outcome the following equation is used (CCPS, 2000):

Ni ¼X

x;yPx;ypf ;i (4)

whereNirepresents the number of fatalities of the final outcome ofscenario i, Px,yis the number of individuals at the geographicallocation (x,y), and pf,i is the probability that the final outcome ofscenario i causes death at the geographical location (x,y).

The results obtained above, are used in Equation (5) to calculatethe ultimate data required for plotting the FeN curve. This equationwhich is known as the cumulative frequency equation is expressedas below (CCPS, 2000):

FN ¼X

i

Fi for all final outcome case I which Ni > N (5)

where FN is the cumulative frequency for all final outcomes whichresult in fatalities of more than N persons,Fi is the frequency of the

g the two neighboring plants; AKPC and BSPC.

Table 1List of AKPC storages in this study.

TK-item Compound Type Temperature(�C)

Pressure(barg)

Volume(m3)

Inventory(tonne)

TK-101 Propylene Spherical 40 25 2200 1044TK-102 Propylene Spherical 40 25 2200 1044TK-103 Propylene Spherical 40 25 2200 1044TK-104 Propylene Spherical 40 25 2200 1044TK-105 Ethylene Cylindrical �104 Atm. 14,000 7968

Table 2List of BSPC storages in this study.

TK-item Compound Type Temperature(�C)

Pressure(barg)

Volume(m3)

Inventory(tonne)

TK-201 Naphtha Cylindrical 30 0.01 33,118.8 24,839TK-202 Naphtha Cylindrical 30 0.01 33,118.8 24,839TK-203 Naphtha Cylindrical 30 0.01 33,118.8 24,839TK-204 Naphtha Cylindrical 30 0.01 16,272 12,204TK-205 Naphtha Cylindrical 30 Atm. 16,272 12,204TK-206 P-Xylene Cylindrical 30 Atm. 17,085.6 14,693TK-207 P-Xylene Cylindrical 30 Atm. 17,085.6 14,693TK-208 P-Xylene Cylindrical 40 0.003 1500 1267TK-209 Acetic acid Cylindrical 45 0.014 1500 1554TK-210 Acetic acid Cylindrical 45 0.014 1500 1554

Table 4Accident scenarios and the relevant failure frequencies.

Scenariono.

TK-item no. Containment Scenario type Frequency(1/yr)

1e4 TK-101,102,103,104 Propylene Leakage 1 � 10�5

5e8 TK-101,102,103,104 Propylene Rupture 5 � 10�7

9 TK-105 Ethylene Leakage 1 � 10�5

10e14 TK-201,202,203,204,205

Naphtha Leakage 1 � 10�5

15e17 TK-206,207,208 P-Xylene Leakage 1 � 10�5

18e19 TK-209, 210 Acetic acid Leakage 1 � 10�5

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903898

final outcome of scenario i, and Ni is the number of fatalities for thefinal outcome of scenario i. Finally, having the quantities of FN andN, the cumulative frequency of final outcomes is plotted against thenumber of fatalities. The obtained FeN curved will be comparedagainst the intended criteria to evaluate the acceptability of thepotential societal risk.

3. Case study

“AKPC” and “BSPC” are two neighboring complexes located inthe so-called PETZONE. In this industrial region, 15 petrochemicalplants are situated in 5 distinct sites within an area of approxi-mately 20 km2 and therefore, it is known to be one of the biggestenergy zones in the world. The PETZONE lies in the northern coastof the Persian Gulf and expands to the southwestern city of Mah-shahr, in southern Iran.

Before and during the construction of many plants in the PET-ZONE, until present only economic and operational factors havebeen taken into account, leaving safety aspects to be largelyneglected in the layout design of those plants. This was also thecase for our case study of the two plants AKPC and BSPC. The olefinunit of AKPC produces ethylene and propylene and thesesubstances are later used in the polymerization unit to producehigh density polyethylene, low density polyethylene and poly-propylene. Regarding BSPC, its main products are naphtha, p-xylene and acetic acid.

These two plants present an interesting case study due to theirhigh number of storage tanks and huge inventory of chemicals. Asone of these plants provides a portion of the other one’s feed,constructing them close together seemed a wise choice from aneconomic and operational point of view. However, the complianceof risk criteria had not been an item of consideration. After occur-rence of some minor process accidents with limited consequencesin these plants, the risk of accident escalation from one plant toanother was highlighted more than ever. Therefore, the idea ofconstructing AKPC and BSPC in proximity of each other, whichseemed a defendable choice once, has been challenged. In thepresent study, the two adjacent chemical complexes AKPC andBSPC are both subjected to one QRA (treating both plants as oneplant), allowing us to analyze the effects of the construction of apetrochemical plant, adjacent to an existing one, on the overall risk.The purpose was to verify whether it was indeed a wise choice toconstruct these two plants this close to each other (for productionpurposes), or not (for safety reasons). Fig. 1 represents the layout ofthe two petrochemical plants.

Table 3Prevailing weather conditions.

Category Atmospheric stability Avespe

Hot season (daytime) Neutral (D class) 5Hot season (nighttime) Stable (F class) 1.5Cold season (daytime) Neutral (D class) 5Cold season (nighttime) Stable (F class) 1.5

Based on a safety review and on a safety screening carried outas a preliminary step, the major hazard sources of the study area(as displayed in Fig. 1), appear to be the storage tanks of these twoplants. Although there are obviously other sources of hazardswithin this area, their contribution in the overall risk could beregarded negligible (compared with the present storage tanks).AKPC houses 5 major operational storage tanks containingethylene and propylene, while BSPC contains 10 major atmo-spheric storage tanks containing naphtha, p-xylene and aceticacid. Tables 1 and 2 provide a list of the chemical substanceinventories in AKPC and BSPC. Although there were some otherstorage tanks or process equipment, no one passed the screeningstep for scenario selection due to their low inventory of hazardousmaterials and due to the lesser inherent hazardousness of thematerials.

The atmospheric data used in this study, were established by theIranian Meteorological Organization (IMO) for the Mahshahr portfrom 1988 to 2006 (IRIMO, 2006). The result of the meteorologicaldata analysis indicates that two prevailing weather conditions (hotseason and cold season) can be intended for the QRA study duringdaytime and nighttime to cover almost all of the probable condi-tions. Table 3 summarizes the average meteorological data whichare used for the consequence analysis.

3.1. Accident scenarios

As stated previously, the storage tanks have been identified tolead to the most hazardous scenarios in the case under study.

rage winded (m/s)

Average ambienttemp. (�C)

Average relativehumidity (%)

40 5025 6020 8010 90

Fig. 2. Bowtie diagram for propylene tank leakage.

Fig. 3. Bowtie diagram for propylene tank rupture.

Table 5Consequence modeling results for the selected scenarios.

Dischargerate (kg/s)

Dischargevelocity (m/s)

Liquidfraction

Max. poolradius (m)

Time toreach LEL (s)

Propylene tankleakage

247.6 282 0.6 15.23 18.7

Propylene tankrupture

e 262 0.59 e 18.7

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903 899

Table 4 shows the possible scenarios and their frequencies for theselected storage tanks for this study. The failure frequencies weremainly obtained from the Purple Book (CPR, 1999).

The data provided in Table 4 indicates that 19 specific scenarioshave been identified for the consequence analysis. Furthermore,only the storage tanks containing propylene have the potential ofinstantaneous release (tank rupture) as well as continuous release.This is due to the high pressure under which the propylene isstored. Other storage vessels are operating under atmosphericpressure and therefore leakage is the only accidental release visu-alized for those tanks.

3.2. Release categories and the final outcomes

As already mentioned, Event Tree Analysis was applied forpredicting all possible scenarios arising from an initial incident. Forinstance, bowtie diagrams are constructed in Figs. 2 and 3 to showthe scenario development for a propylene tank leakage and rupturescenario based on the recommended structure available in theliterature (CPR, 1999; Vílchez, Espejo, & Casal, 2011). Although thestructure remained almost constant, some minor modifications

both in the structure and probability values have been done inorder to customize the case for the existing conditions of the caseunder study. The possible events leading to the initiating events areshown in the fault tree side conceptually.

Since safety principles e both in design and in operation- arehighly adhered in the tank yard, the probability of immediateignition for a liquid leakage scenario is very low. Contrary, tankrupture usually occurs when a storage tank is engulfed in flamesand hence, the probability of immediate ignition is considerablyhigher for the rupture case. The latter explains the differencebetween the values used for the immediate ignition probability in

Fig. 4. A, B individual risk contours before (for AKPC) and after (for cluster composed of AKPC and BSPC) constructing the new plant.

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903 901

the leakage case and in the rupture case. Moreover, based on theplant layout review and due to the low level of equipmentcongestion in this particular area under study, the probability ofa flash fire is higher than that of a Vapor Cloud Explosion (VCE),since higher congestion is known to increase the probability ofa VCE rather than that of a flash fire.

Fig. 5. A, B societal risk (FeN curve) before (A)

4. Result and discussion

In order to evaluate the risk-based effects of constructing BSPCadjacent to AKPC, calculations were made for both individual riskand societal risk before and after the new plant came under oper-ation adjacent to the existing one. The quantified risk can then be

and after (B) constructing the new plant.

Table 6Target equipment affected by domino accident (due to overpressure).

scenario

Magnitude of overpressure caused by different AKPC scenarios at target equipment (bar)

TK

-201

TK

-202

TK

-203

TK

-204

TK

-205

TK

-206

TK

-207

TK

-208

TK

-209

TK

-210

1-4 0.2 0.1 0.1 0.08 0.08 0.17 0.15 0.15

5-8 1 0.4 0.2 0.2 0.14 0.14 0.25 0.23 0.22

9 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Affected by Dominoaccident

Not Affected by Dominoaccident

1 1

1

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903902

compared against the criteria to assess the acceptability of the risklevels. The likelihood of domino effects needs to be assessed aswell.

4.1. Calculation of individual risk

Using the results of frequency estimation and a detailedconsequence assessment for all the possible scenarios, the indi-vidual risk contours were obtained and are shown on a satelliteview of the study area. Key outputs of the consequence modelingare presented in Table 5 to give more insight in the two selectedscenarios. Fig. 4A and B demonstrate the individual risk levelsbefore (A) and after (B) the second plant came operational. Thenumerical value of each risk contour represents the frequency atwhich a particular individual is fatally harmed when standingwithin the contour boundary.

The results of individual risk calculations reveal that inside thechemical cluster composed of both plants (AKPC and BSPC), as wellas within the single plant AKPC, the individual risk values do notexceed the tolerable magnitude of 10�5/yr before and after the newplant came under operation. Fig. 3A and B also demonstrate that forpublic areas (such as the main roads around AKPC and BSPC) therisk level does not exceed the magnitude of 10�6/yr, except fora short interval of 100 m at the southern main road of AKPC.Nevertheless, this issue was already present even before theconstruction of BSPC. In general, we can state that in terms ofindividual risk, constructing the new plant adjacent to the existingone, did not affect the acceptability of risk levels.

4.2. Calculation of societal risk

Although the previous section indicates that individual risklevels do not exceed the tolerable levels, the impact of any potentialaccident on operators and public should also be investigated.Fig. 4A and B provide the societal risk calculation results before andafter the new plant came into operation respectively. As it can beseen in Fig. 5A, the societal risk posed by AKPC individually, falls inthe ALARP (As Low As Reasonably Practicable) region, as defined byHSE (UK).Fig. 5Bshows that after constructing the new plant, thesocietal risk curve gets closer to the maximum risk criteria, but stillremains in the ALARP region. Therefore, constructing the new plantBSPC adjacent to the existing one AKPC, seems to have only limitedimpact upon the change of tolerability of the societal risk in ourcase study.

4.3. Domino effects assessment

The distance from the storage tanks located in AKPC to theclosest operational storage tank in BSPC is about 800 m. Based onthe values reported by Cozzani, Gubinelli, and Salzano (2006) thisdistance suggests that potential cascading accidents between theneighboring plants caused by thermal radiation are highlyimprobable. Therefore, the only factor that can trigger a secondaryaccident in the other neighboring plant, is the overpressure causedby an unconfined Vapor Cloud Explosion. Considering the volatilityof the materials involved, the operational conditions of storage, theaverage ambient temperature and the prevailing wind direction(west), we may conclude that only materials stored in AKPC’sstorages are capable of forming an effective vapor cloud.

There are different values reported for damage threshold due tooverpressure in literature, but the overpressure of 1bar wouldsurely destroy any target equipment (Salzano & Cozzani, 2005).This threshold has been chosen to evaluate the possibility ofdomino accidents on the storage tanks in BSPC. Table 6 providesthe magnitude of overpressure, caused by different AKPC scenarios

(1e9), at the location of BSPC’s storage tanks. These values arebased on performing a detailed consequence modeling.

As it can be seen in Table 6, the closest storage tanks to AKPC(TK-201 and TK-202) are possibly damaged due to overpressurecaused by a propylene explosion after a leakage or tank rupture,eventually triggering secondary accidents. In other words, theaccidental release of propylene in AKPC, either continuous(scenarios 1e4) or instantaneous (scenarios 5e8), has the capa-bility of causing cascading accidents in BSPC. This suggests that ifdecision-makers insist on not changing the new plant’s layout (e.g.due to large economic and operational benefits), countermeasuresshould be taken into consideration in order to minimize theheightened risk of domino accidents.

Both choices for the Purple Book and the UK risk tolerancecriteria are individual choices for this study, and they should beseen and respected as such. We are aware that failure frequenciesare subject to constant optimization, and that the Purple Book hasits limitations (Pasman, 2011). Nonetheless, for this study of twoplants situated in Iran, using the frequencies reported in this well-known and much-used work, was a justifiable choice.

5. Conclusions

The risk-based effects of constructing a new chemical plantadjacent to an existing one, forming a chemical cluster, is analyzedand discussed in this paper. For this purpose, a QRAwas carried outto assess different parameters of risk. Individual and societal riskswere evaluated before and after the new plant came under oper-ation. The results indicate that the new plant did not have a majorimpact on the risk levels, since the risk levels stay within thetolerability limits. Although no major impact was observed, therewas a significant increase in the societal risk. Moreover, a dominoeffect analysis indicates that some storage tanks in the new plantentail a high potential of being affected by events originating in theexisting plant.

This research and this case study illustrate that in order to makean objective plant lay out decision for a chemical cluster withregard to operational risks, not only conventional risk assessmentsshould be carried out, but a domino effects analysis should beperformed as well, since otherwise some risks may be under-estimated and overlooked.

It is undoubtedly true that themore accurate information is used(e.g. concerning probabilities), the more accurate risk calculationscan bemade. However, one should always remember that, basically,calculated risks are relative risks, and that absolute values are proneto variability and uncertainty. As referenced by Pasman et al. (2009),

S. Baesi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 895e903 903

EU-studies have shown that a factor of 10e100 both ways in riskresults is not uncommon. Hence, future research might include riskdistributions and confidence limits, allowing risk analysts to evenbetter understand the uncertainties accompanying the results.

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