Applications of 3D QRA Technique to the Fire-explosion Simulation and Hazard Mitigation Within a Naphtha-cracking Plant

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Applications of 3D QRA technique to the fire/explosion simulationand hazard mitigation within a naphtha-cracking plant

Yet-Pole I*, Chi-Min Shu, Ching-Hong Chong

Department of Safety, Health and Environmental Engineering, National Yunlin University of Science and Technology, Douliou, Taiwan

a r t i c l e i n f o

Article history:

Received 30 November 2008

Received in revised form

3 April 2009

Accepted 3 April 2009

Keywords:

CFD

Fire and explosion

Maximum physical effect

Naphtha-cracking plant

QRA

a b s t r a c t

This research employed computational fluid dynamics (CFD) fire and explosion simulation software,

FLACS, to evaluate the possible hazards of different worst-case scenarios within a petrochemical plant.

After the effect factors (overpressure, pressure impulse, and thermal radiation temperature) were

calculated, the results of interest were, in turn, adopted to a self-developed risk analysis module to

estimate the corresponding 3D individual risk value. The latter value can further be utilized as an index

for assessing suitable preventive action against specific hazard before any accident happens. The whole

procedure was realized by applying a preliminary consequence analysis on a naphtha-cracking plant site.

The simulation results can assist the relevant personnel in understanding all the varied conditions during

an accident and can also be used as a reference for emergency response planning. According to the

results, the size of the vapor cloud has great influence on the hazard consequence. Furthermore, a water

spray system alone has limited potential on a disastrous accident and its mitigation effect can be

observed via the corresponding 3D risk values. With the aid of the 3D dynamic consequence simulation

and QRA (quantitative risk assessment) technique, the plant site safety can be effectively improved by

means of implementing the preliminary hazard assessment and suitable preventive control strategies.

Crown Copyright 2009 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Owing to the continuous growth of global population, the

requirement of raw materials and energy has also increased during

the past decades. The petrochemical industry, which plays such

a majorrole inthe dailylives ofhumanbeings,has also becomemore

prosperous and even larger at the same time. Common petro-

chemical plants usually have complex production processes and

store a large amount of hazardous substances. A very serious

consequence may happen once theseflammable or toxic substances

are ignited or released accidentally; therefore, people have to pay

close attention to the environment and public security problems for

the industry. Among all the available safety analysis techniques,

computational fluid dynamics (CFD) is a very useful tool that canestimate possible hazardous consequences and their corresponding

mitigation measures beforehand (Dharmavaram, Hanna, & Hansen,

2005; Coirier, Kim, & Marella, 2005; Hanna et al., 2006; Hanna,

Hansen, Ichard, & Strimaitis, 2009; Kisa & Jelemensky, 2009).

With the rapid developmentof computer hardware as well as the

abundant explosion data of field experiments during recent years,

the high-speed computing capability has substantially improved the

maturity of CFD technology as applied to fire and explosion simu-

lation. Hansen, Talberg, and Bakke (1999) utilize CFD software to

implement a gas explosion simulation, and the result is highly

consistent with the experimental data. Berg, Bakke, Fearnley, and

Brewerton (2000) employ CFD to simulate the explosion conse-

quence of a floating production, storage and offloading vessel (FPSO)

and have proposed many explosion mitigation measures at the same

time.Ryder, Sutula, Schemel, Hamer, and Van Brunt (2004) use CFD

software to simulate a fire that takes place within a tank farm. The

simulation results show an ignited chemical, which released from

a partially opened tank roof, in combination with changing wind

conditions can severely influence the thermal plume and the radiant

flux incident on the adjoining tanks. One can see that while one tank

is severally exposed and at risk, tanks equally as far away are not indanger from radiant flux at the time. I, Chiu, and Wu (2009)utilize

CFD software to reconstruct the air recirculating flow pattern within

a semiconductor fab. Different released scenarios were applied in

their study to investigate the corresponding hazard range and

severity. Mitigation measures, such as a water spray system and

a pressure relief panel, were also provided to study their effective-

ness to relieve thermal radiation and overpressure hazards within

a confined working place.

Although more and more scholars have used CFD to simulate fires

and explosions (Di Benedetto, 2009), littleof themhave explored risk

analysis issues. In this research, a flame acceleration simulator

* Corresponding author. Tel.: 886 5 5342601x4421, 4493 or 4496; fax: 886 5

5312069.

E-mail address: [email protected](Y.-P. I).

Contents lists available atScienceDirect

Journal of Loss Prevention in the Process Industries

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j l p

0950-4230/$ see front matter Crown Copyright 2009 Published by Elsevier Ltd. All rights reserved.

doi:10.1016/j.jlp.2009.04.002

Journal of Loss Prevention in the Process Industries 22 (2009) 506515
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(FLACS), which is CFD software, was employed on an intricate

naphtha-cracking process area and its fire and explosion simulation

results were used to calculate the individual risk by a self-developed

3D risk analysis technique.

The traditional risk analysis technique can only predict risk

value in a 2D static format; the result is quite different from a real

situation since it neglects the cavity effect caused by terrain and

buildings. The 3D risk analysis technique employed in this research

not only can take the risk values of different height levels and

obstacles into consideration, but also can estimate the effects of

different fire and explosion measures.

2. Simulation background

A petrochemical process area, mainly designed for propylene

separation and purification within a naphtha-cracking plant, was

selected to conduct the fire and explosion risk analysis. The target

process contains four reactors, five distillation towers, four storage

tanks, 31 heat exchangers, and 18 pumps, and is divided into four

regions (Structures P, R, R1 and M; see Fig.1). Usually the distribution

of population can directly affect the results of risk analysis, and

workers will not alwaysstay at the same place allday long. Therefore,

a survey for population distribution, appearance probability and

density shouldbe done before risk calculationin order to reflecta real

situation. During a normal operating period, it is supposed that only

one person appears hourly around the process area to perform

a 10 min job of equipment inspection and meter recording, which

results in an appearanceprobabilityof 10/60 0.167. Two persons areassigned to execute a two-week equipment-repair job during the

annual repair period, which results in an appearanceprobability of 2/

52 0.038. The average appearance probability at the process area

thus becomes (1/3) 0.167 (2/3) 0.038 0.081 (see Table 1).On

the other hand, theappearance probability of vacant lots is set as 0.5/

60 0.008 since these places do not have any equipmentand people

usually spendhalf a minute to pass through them in order to get into

the process area. Fig. 2 diagrams the related information about

population distribution within the simulation site.

3. Research methods

Risk analysis, which originated in the aviation industry and the

nuclear power industry, aims at finding and preventing the

potential hazards within a system. The related risk analysis tech-

niques include hazard identification, consequence analysis,

frequency analysis, and risk quantification (Lees, 1986).

Three worst-case scenarios (WCSs) were studied in this research

(seeTable 2for details). Case 1 was a large scale vapor cloud igni-

tion case that originated from a distillation tower collapse. The

released chemical contained 95% propylene (with 5% propane) and

formed a 38 m diameter, 100% concentrated spherical gas cloud.

After 11 s dispersion, the flammable gas cloud was ignited by an

ignition point.Fig. 3shows the location of the gas cloud and the

ignition point. Cases 2 and 3 discussed the deflagration accidents

(arising from a reflux tank rupture followed by the ignition of static

electricity) without and with a water spray mitigation system. The

released chemical contained 100% propylene and formed a 15 mdiameter, 100% concentrated spherical gas cloud.Fig. 4depicts the

position of the gas cloud and its ignition point. Furthermore, Fig. 5

shows the layout of 20 water spray zones, where the blue and the

black frames represent double and single layer, respectively.

3.1. Consequence analysis

Consequence analysis is mainly used to investigate the hazard

severities and their possible ranges when incidents occur within

a simulation site. Usually, consequence analysis includes two parts.

One is the physical model, used for evaluating the characteristics of

all hazard factors and their corresponding impact zones caused by

different incidents. The other one is the effect model, mainly used

forestimating the damagelevels to employees or equipment withinthe impact zones by different hazard factors.

3.1.1. Physical model

This study employed FLACS as a tool of the physical model to

estimate the results of 3D physical parameters. The governing

equations in FLACS included mass, momentum, and energy

conservation equations and turbulent energy equations. By using

FLACS software, one can simulate all the necessary physical

parameters within a certain period of time and space, and the

results can be expressed as Fi(x,y,z, t). To predict the hazard level at

the simulation site, the maximum physical effects in each specific

coordinate (x, y, z) within the hazard elapsed period were calcu-

lated during this research. Different physical parameters (Fi(x,y,z,

t)) were accessed from the FLACS output files and processed as

Nomenclature

i index of the hazardous physical effect (dimensionless)

n total number of the hazardous physical effect

(dimensionless)t time (s)

te radiation elapse time interval (s)

u dummy variable (dimensionless)

(x,y,z) Cartesian coordinates (m)ER equivalence ratio is a measure for concentration of fuel

(flammable gas cloud) compared to its stoichiometric

concentration;ER is defined asF=O=F=Ostoichiometric,

where (F/O) is the ratio of fuel to oxygen.ER equals 1 at

stoichiometric concentration; from no fuel to pure fuel

(no oxygen and other inert gases) ER varies from zero

to infinity (dimensionless)

Fi different physical parameters calculated via FLACS

software (F1,F2, andF3equalP,J, andT

correspondingly)

Fi,max maximum physical effects (maximum value ofFiwithin a certain period of time)

FI incident frequency (1/yr)

IR individual risk at specific location (death/yr)

I thermal radiation (kW/m2)J pressure impulse (Pa s)

Ki,1

,Ki,2

conversion factors of the corresponding physical

effects (seeTable 3)

NW northwest directionP overpressure (barg)

PDi death percentage (%)

PI ignition probability (dimensionless)

PWIND probability of wind direction (dimensionless)PZi appearance probability of employees (dimensionless)

T radiation temperature (K)

Ta initial ambient temperature (K)

V directional wind velocity projected on y-axis (m/s)

VVEC wind velocity vector (m/s)

Yi probit value of different physical effects

(dimensionless)

Y.-P. I et al. / Journal of Loss Prevention in the Process Industries 22 (2009) 506515 507


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time-independent maximum physical effects (Fi,max(x, y, z))

according to Eq. (1) (Cheng, 2005). Where Fi(x, y, z, t) can be

substituted as P(x,y,z,t),J(x,y, z, t), orT(x, y, z, t) while the over-

pressure, pressure impulse, or thermal radiation is calculated. It is

necessary to convert the maximum temperature value into radia-

tion dose with Eq.(2)before calculating the thermal radiation term

(CPR-14E, 1997).

Fi;maxx;y;z Maxt 1;2;.n

Fix;y;z; t (1)

Ix;y;z 5:67 108

Tx;y;z4T4a

(2)

3.1.2. Effect model

An effect model can adopt the simulation results from a physical

model. It can evaluate the degrees of personnel casualty or facility

damage affected by overpressure, pressure impulse, or thermal

radiation. The theory of the effect model is proposed byEisenberg,

Lynch, and Breeding (1975). In this study, the effect model was

constructed with Fortran 95 language and the METFOR graphical

library. Necessary data were accessed from the FLACS output files in

order to calculate the fire and explosion effects, and the final results

were displayed by a series of 3D death percentage diagrams. When

brought into practice, first the maximum physical effects are

converted into personnel casualty probit values (Yi(x, y, z))

according to Eq. (3), where Ki,1and Ki,2 represent the conversion

factors of the physical effects, and their corresponding values are

listed inTable 3(Lees, 1986). Later the probit values are converted

into personnel death percentages (PDi(x, y, z)) that are used for

calculating the death toll of certain hazard incidents (see Eq. (4)

proposed byFinney, 1971).

Fig. 1. Equipment layout of the simulation site.

Table 1

Population distribution and its appearance probability at the simulation site.

Locationa Employee

number

Appearance

probability

Employee number

appearance probability

Ground areas 3 0.081 0.243

P-2 area 3 0.081 0.243

P-3 area 3 0.081 0.243

R-2 area 3 0.081 0.243

R-3 area 3 0.081 0.243

R1-2 area 3 0.081 0.243

R1-3 area 3 0.081 0.243

R1-4 area 3 0.081 0.243

M-2 area 2 0.038 0.076

M-3 area 2 0.038 0.076

M-4 area 2 0.038 0.076

Vacant lots 1 0.008 0.008

a The numbers after - represents floor number.

Fig. 2. Population distribution used in this study, different colors represent different

locations where their corresponding numerical values were shown in Table 1 (red:

Structure P-2/P-3 areas, green: Structure R-2/R-3 areas, orange: Structure R1-2/R1-3/

R1-4 areas, blue: Structure M, pink: ground area, light-blue: vacant lots area). (For

interpretation of the references to color in this figure legend, the reader is referred to

the web version of this article.)

Y.-P. I et al. / Journal of Loss Prevention in the Process Industries 22 (2009) 506515508


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Yix;y;z Ki;1Ki;2ln

Fi;maxx;y;z

(3)

PDix;y;z 1

2p1=2

ZYix;y;z5

N

exp

u2

2

du (4)

3.2. Frequency analysis

The containment failure frequencies used in this study were

based on the bibliography and experience from previous

researches. According toI, Tsai, and Her (1999), the general rupture

frequency for a petrochemical vessel is around 2 1061105/yr.

Due to the lack of relevant information as well as to facilitate theassessment process, the incident frequencies used in this study

were roughly divided into three categories: which are possible

(frequency 1 105/yr), less likely (frequency1 106/yr) and

very unlikely (frequency 1 107/yr). Since all the incidents

discussed may cause disastrous accidents, their frequencies were

categorized as very unlikely to occur, which is 1 107/yr.

3.3. Risk quantification

This paper focused on the damage caused by fires and explo-

sions. Therefore, the risk value was assumed to be a function of

containment failure frequency, gas cloud ignition probability, wind

direction probability, personnel appearance probability, and

personnel death probability. The calculation algorithm for indi-vidual risk was a revised form from Considine (1984) as was

expressed in Eq.(5). HereIR(x,y,z) stands for the individual risk at

a specific location; i and n stand for the index and the number of the

hazardous physical effects (for overpressure, pressure impulse, and

thermal radiation; n equals 3); FIstands for incident frequency

(1 107/yr was chosen in this study); PI denotes for ignition

probability of the released cloud (1 was chosen to represent 100%

ignition); PWIND expresses probability of wind directions, which can

be found from the local meteorological data (set at 0.0015 for

southeast wind in this study); PZi(x,y,z) andPDi(x,y,z) represent

employee appearance probability and death percentage, respec-

tively, both of them belong to the function of the coordinate. The

total individual risk was the cumulative summation of risk values

under different hazardous physical effects from certain enumerated

incidents. The final result was displayed in a 3D iso-surface form

and superimposed with the 3D plant facilities layout to enable

better understanding by the relevant personnel.

IRx;y;z Xni 1

FIPIPWINDPZix;y;zPDix;y;z (5)

4. Results and discussion

4.1. FLACS simulation results

Wind can directly affect the gas cloud dispersion and lead to

a different hazard scope. After the wind direction was deliberately

chosen, a prior 120 s wind field simulation was performed to

stabilize the wind before the explosion simulation (see Fig. 6).

Figures 79show the simulation results of Case 1. InFig. 7we see

that most of the gas cloud was moving to the right of the graph and

the affected sites were far to the right of the target process (the redsquare) since the southeast wind was blowing from the left of the

graph. The hazard impact zone may become very large if the gas

cloud were ignited after 60 s of dispersion (the figure shown here

was a pure dispersion simulation and the gas cloud was not

Table 2

Simulation cases and their related parameters.

Case no. Scenario Gas volume (m3) Gas concentration (%) Ignition time (s) Ignition coordinates (m) Mitigation measure

1 Distillation column collapse 28,731 100 11 (59, 25, 1) No

2 Reflux tank rupture 1767 100 0.5 (16, 12, 3) No

3 Reflux tank rupture 1767 100 0.5 (16, 12, 3) Water spray

Fig. 3. Position and ignition point of a flammable gas cloud released from a distillation column.



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ignited). The 3D temperature diagram of Fig. 8 indicates the

deflagration flame in the air (22 s after ignition) is slightly shifted to

the northwest due to the wind effect. The high temperature at the

bottomis distributed widely beyondthe target area and it mayhave

a very serious effect on the nearby process sites.Fig. 9shows that

the overpressure is also distributed widely; however, its value is

rather small since the maximum value at the explosion center is

only 0.0227 barg (seeTable 4).

Cases 2 and 3 represent the same scenario (seeTable 2) without

and with water spray. The gas cloud was ignited immediately after

the reflux tank was ruptured. The macroscopic views of the simu-

lation results for the two cases do not show a significant difference

(the related figures are not shown here due to space limitations).

One can only find the appearing time of the highest overpressure in

Case 3 (2.5 s after ignition) was about 0.5 s earlier than that of

Case 2, which might be the result of turbulent effects caused by the

water spray activation. A turbulent flow can accelerate the mixing

effect of the released gas and the surrounding air and push the gas

cloud to reach its flammable limit earlier. A similar phenomenon

can also be observed inTable 4, where the maximum temperature

value of Case 3 does not decrease as expected. In addition, the

maximum overpressure and pressure impulses are ever higher

than those of Case 2. It was postulated that the turbulent flow can

accelerate the flame speed, and thus simultaneously increase the

maximum value of overpressure and pressure impulse.

To further investigate whether the water spray has a positive

mitigation effect, two monitoring points (P9 and P18, see Fig. 4)

were observed and compared at the same time (where P9 was

Fig. 4. Position and ignition point of a flammable gas cloud released from a reflux tank.

Fig. 5. Layout of 20 water spray zones (blue: double layer, black: single layer). (For interpretation of the references to color in this figure legend, the reader is referred to the web

version of this article.)



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located inside of both the gas cloud and the water spray zones,

while P18 was located outside of the gas cloud but within the

water spray zones). Table 5shows that after water spray is acti-

vated, the overpressure values of these two monitoring points are

still larger than those without mitigation. However, it was

observed that both temperature values of these two points with

water spray were actually lower than those without mitigation

(P9 decreased 300 K, while P18 decreased 650 K). These results

indicated that although water spray cannot reduce the maximum

temperature near the explosion center, nevertheless, it can still

alleviate the thermal radiation hazard of the surrounding area. In

this study, the hazard impact zones of Cases 2 and 3 were obvi-

ously much smaller than that of Case 1 and mainly concentrated

at the surrounding of the target area. However, the impact can

become more severe should the ignition time be delayed until the

concentration of most gas cloud is within the flammable limit. An

additional simulation case, Case 4, was implemented to prove

such speculation. Case 4 is similar to Case 2 except the gas cloud

was located at the center of the target area and its concentration

equaled 1.5 times of stoichiometric concentration of the complete

combustion reaction. It was observed that the overpressure of

Case 4 (0.0144 barg, not shown here) was about 3 times as large

as that of Case 2. Case 2 behaved much more like a fireball

phenomenon instead of a deflagration since its gas cloud

concentration was 100%; therefore, the flammable cloud center

Table 3

Effect models of different hazard categories.

Hazard category Effect model

Thermal radiation Y1x;y;z 14:9 2:56 ln

teIx;y;z

4=3

104

Pressure impulse Y2x;y;z 46:1 4:82 lnJx;y;z

Overpressure Y3x;y;z 77:1 6:91 lnPx;y;z

Fig. 6. Side view of wind field simulation result, where VVEC stands for velocity vector and Vdenotes directional velocity projected ony-axis.

Fig. 7. Gas cloud dispersion result of Case 1 (60 s after released, without ignition).



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Fig. 8. 3D temperature diagram of Case 1 (22 s after ignition).

Fig. 9. 3D cross-sectional overpressure diagram of Case 1 (3.5 s after ignition).



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could not have been burnt unless its concentration was diluted

below the upper explosion limit (UEL).

4.2. 3D risk analysis results

The risk analysis module adopted the FLACS output value for

risk calculation and presented the calculation results in a 3D

format. From the 3D risk diagram (figure not shown here), Struc-ture R1 had the highest risk value once the Case 1 accident

occurred. The mortality rate of the target area could be as high as

98.6%, which means almost no one could have survived under such

high temperature circumstance. In addition, a domino effect could

also have been triggered since there were many different processes

surrounding the target area. Figs. 10 and 11 show the results of

thermal radiation effect and its 3D risk value for Cases 2 and 3,

where different graphs represent (a) temperature iso-surfaces, (b)

mortality rate iso-surfaces, and (c) 3D risk iso-surfaces (the

temperature and the mortality rate iso-surfaces of Case 3 are not

Table 4

Maximum values of different simulation hazards.

Case Temperature (K) Overpressure (barg) Pressure impulse (Pa s)

1 2351 0.0227 3233

2 2277 0.0046 2055

3 2277 0.0051 2061

Table 5

Comparison of water spray effect at different monitoring points.

Ca se Monitori ng p oi nt Temp er ature (K) Overp ressure (ba rg)

2 P9 1700 0.0025

2 P18 1900 0.0025

3 P9 1400 0.0034

3 P18 1250 0.0031

Fig. 10. (a) Maximum temperature iso-surfaces of Case 2. (b) Mortality rate iso-surfaces of Case 2. (c) 3D risk iso-surfaces of Case 2.



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shown here due to space limitations). They showed that the relatedhazardous impact contours ofFigs. 10 and 11are similar, no matter

whether the water spray activated or not; nevertheless, the risk

values of Structure R (indicated by red squares) for the two cases

were obviously different as can be seen by their colors. The risk

value of Structure R for Case 3 was 1.0 1012 person/yr, which is

somewhat smaller (has lighter color) than that of Case 2 (1.4

1012 person/yr). Such phenomena can also be seen by conse-

quence analysis from the mortality rate of employees at the target

area, where Case 2 was 53.7% but Case 3 decreased to 51.2%.

Therefore, the water spray still had a positive effect on relief of

thermal radiation hazard even during a disastrous accident.

The individual risk values of three simulation cases at different

floors and regions of the target area are shown inTable 6. Because

of the largest gas cloud, Case 1 had the highest risk value (from8.0 1020 to 3.4 1012 person/yr) all over the regions except for

Structures P and R since some of the flammable gas had been

dispersed to the other processes via wind effect (seeFigs. 7 and 8).

For Cases 2 and 3, Structures P and R had the largest risk values

among all the regions. The risk values of Case 3 at these two

structures (6.0 1013 and 1.0 1012 person/yr) were smaller

than those of Case 2 (7.0 1013 and 1.4 1012 person/yr), the

reason for which had already been analyzed in the previous para-

graph. One can notice that Structure R1 has zero risk values for

Cases 2 and 3 because their gas clouds were relatively smaller and

were ignited immediately. Therefore, the corresponding thermal

hazards were relatively concentrated around the leak point and did

not spread so widely as that of Case 1. One can also observe that the

risk values on the high floors were all larger than those on the

ground areas, since thermal radiation played the major role in

the mortality rate in all these cases and the fireball became larger

and larger once it rose up to the sky (seeFig. 8).

5. Conclusions

A CFD model, FLACS, was employed in this study to simulate thefireand explosionconsequences of three WCS cases withina complex

naphtha-cracking process area.With a self-developed riskcalculation

module, the FLACS simulation results can be used to analyze the 3D

risk value inside the target area. The 3D risk analysis technique can

overcome the limitations of the traditional methods that can only

predict the risk value on the ground. Unlike the traditional methods

that usually neglect the influence of terrain and buildings, the 3D

method can differentiate the risk difference at different heights with

the help of the CFD algorithm. The WCS simulation results showed

thatin most places employeesat high levelswouldexperience higher

risks (1.0 10125.7 1013 person/yr), while the risk values on the

ground areas and vacant lots would be the lowest (6.0 1020

8.0 1020 person/yr). Therefore, an apparent risk difference existed

between different heights at the same location.In addition, this study also investigated whether a fire and

explosion hazard can be mitigated by installing a water spray

system. According to the simulation results, water spray activation

alone has very limited potential to directly reduce the hazardous

consequences of a disastrous incident. Besides, the turbulent effect

caused by water droplets can also shorten the initiation time of an

explosion and slightly increase the overpressure. All these subtle

differences can be observed viathe 3D risk values. One believes that

the 3D risk analysistechniquenot merelycan be usedin the fire and

explosion simulation in this study, but can also be employed as

a test platform in the future for validating the effectiveness of any

novel mitigating measure for chemical release (Gupta, 2005) before

it can be formally implemented in a process plant. Such application

is being studied by the authors currently. It is foreseeable there are

more and more engineering projects that related to the process

safety design can be evaluated via using this technique.

Acknowledgments

The authors are grateful to GexCon AS, Norway for providing the

FLACS software and partial financial aid (NSC 96-2628-E-224-001-

MY3) from the National Science Council of Taiwan to support this

study.

References

Berg, J.T., Bakke, J.R., Fearnley, P., Brewerton, R.B. (2000). A CFD layout sensitivitystudy to identify optimum safe design of a FPSO. OTC paper number 12159.

Cheng, T.L. (2005).The development of 3D risk analysis technology and its applicationon hazardous substances release simulation within a petrochemical storage area .Master Thesis, National Yunlin University of Science and Technology, Yunlin,Taiwan.

Coirier, W. J., Kim, S., & Marella, S. (2005). Progress towards a coupled mesoscaleand microscale modeling capability. In American meteorological society seventhsymposium on the urban environment. Paper 4.1. http://www.ametsoc.org.

Considine, M. (1984). The assessment of individual and societal risks. SRD Report R-310, the Safety and Reliability Directorate. Warrington: UK Atomic EnergyAuthority.

CPR-14E. (1997). Methods for the calculation of the physical effects due to releases ofhazardous materials (TNO Yellow Book). Hague, Netherlands: Committee for thePrevention of Disasters.

Dharmavaram, S., Hanna, S. R., & Hansen, O. R. (2005). Consequence analysis usinga CFD model for industrial sites.Process Safety Progress, 24, 316327.

Di Benedetto, A. (2009). CFD simulations for explosion phenomena. Journal of LossPrevention in the Process Industries, 22, 257.

Eisenberg, N. A., Lynch, C. J., & Breeding, R. J. (1975). Vulnerability model: A simu-

lation system for assessing damage resulting from marine spills . Springfield, VA,

Fig. 11. 3D risk iso-surfaces of Case 3.

Table 6

Comparison of individual risk value of three cases at different floors and regions.

Location Case 1 Case 2 Case 3

Ground areas 8.0 1020 6.0 1020 6.0 1020

P-2 area 5.7 1013 7.0 1013 6.0 1013

P-3 area 5.7 1013 7.0 1013 6.0 1013

R-2 area 5.7 1013 1.4 1012 1.0 1012

R-3 area 5.7 1013 1.4 1012 1.0 1012

R1-2 area 3.4 1012 0 0

R1-3 area 3.4 1012 0 0

R1-4 area 3.4 1012 0 0

M-2 area 8.0 1020 6.0 1020 6.0 1020

M-3 area 8.0 1020 6.0 1020 6.0 1020

M-4 area 8.0 1020 6.0 1020 6.0 1020

Vacant lots 8.0 1020 6.0 1020 6.0 1020

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Documents

Applications of 3D QRA Technique to the Fire-explosion Simulation and Hazard Mitigation Within a Naphtha-cracking Plant