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Licensed Unlicensed Requires Authentication Published by De Gruyter December 18, 2015

Optimization of Facility Layout of Tank farms using Genetic Algorithm and Fireball Scenario

S.R. Nabavi, A.H. Taghipour and A. Mohammadpour Gorji

Abstract

In this study a novel approach is presented to optimize the layout of petrochemical units, considering the most likely scenario. To illustrate the method a real case study, exporting petrochemical tank farm (Esfahan petrochemical, Iran), was selected. Three objective functions like, piping cost, occupied land cost and human losses were combined and the total cost function was minimized. The required data for lethality function were obtained using simulation of different scenario by Phast software. A sigmoid function was fitted to the data which can estimate the lethality based on the distance from incident point. Genetic algorithm (GA) was used to minimize the total cost function. Due to vastness of searching area, more than one optimum layout was obtained. Finally, the optimum layouts were simulated with fire ball scenario and a good agreement was obtained between optimum points and simulated data.

Nomenclature

a

Correlated parameter

b

Correlated parameter

CH

blood money

Closses

human injury cost

CP

piping cost

CLand

land cost

Dh

a constraint between each two risky units

Dr

a constraint between each two residential buildings

Di

distance of ith residential building from incident center

L

length of land

Li

y-coordinate of ith facility

Ll

lower limit for y-coordinate of each facility to prevent locating outside the land region

Lj

y-coordinate of jth facility

LR

y-coordinate of a risky facility

LRB

y-coordinate of a residential building

Lw

lower limit for x-coordinate of each facility to prevent locating outside the land region

Mi,j

probability of death for ith residential building caused by jth tank

Pi,s

piping length between ith tank and L/U shelter

Pi

population of ith housing unit

UL

land cost per square length

Ul

upper limit for y-coordinate of each facility to prevent locating outside the land region

Uw

upper limit for x-coordinate of each facility to prevent locating outside the land region

Up

piping cost per meter

W

width of land

Wi

x-coordinate of ith facility

Wj

x-coordinate of jth facility

WRB

x-coordinate of a residential building

WR

x-coordinate of a risky facility

x0

Correlated parameter

References

1. Park K, Mannan MS, Jo MS, Kim JY, Keren N, Wang Y Incident analysis of Bucheon LPG filling station pool file and BLEVE. J Hazard Mater 2006;137:62–7.10.1016/j.jhazmat.2006.01.070Search in Google Scholar

2. Dole E, Scannell GF. 1990. Phillips 66 company houston chemical complex explosion and fire.Search in Google Scholar

3. Goh YM, Tan S, Lai KC. Learning from the Bhopal disaster to improve process safety management in Singapore. Process Safety Environ Prot 2015;97:102–08.10.1016/j.psep.2015.02.004Search in Google Scholar

4. Khan FI, Amyotte PR. I2SI: a comprehensive quantitative tool for inherent safety and cost evaluation. J Loss Prev Process Ind 2005;18:310–26.10.1016/j.jlp.2005.06.022Search in Google Scholar

5. Cozzani V, Tugnoli A, Salzano E. Prevention of domino effect: from active and passive strategies to inherently safer design. J Hazard Mater 2007;139:209–19.10.1016/j.jhazmat.2006.06.041Search in Google Scholar

6. Tugnoli A, Khan FI, Amyotte PR, Cozzani V. Safety assessment in plant layout design using indexing approach implementing inherent safety perspective Part 2-Domino Hazard Index and case study. J Hazard Mater 2008;160:110–21.10.1016/j.jhazmat.2008.02.091Search in Google Scholar

7. López-Molina A, Vázquez-Román R, Mannan MS, Félix-Flores MG. An approach for domino effect reduction based on optimal layouts. J Loss Prev Process Ind 2013;26:887–94.10.1016/j.jlp.2012.11.001Search in Google Scholar

8. Díaz-Ovalle C, Vázquez-RománR, Sam Mannan M. An approach to solve the facility layout problem based on the worst-case scenario. J Loss Prev Process Ind 2010;23:385–92.10.1016/j.jlp.2010.01.004Search in Google Scholar

9. Díaz-Ovalle C, Vázquez-Román R, Lira-Flores de, J.,Mannan MS. A model to optimize facility layouts with toxic releases and mitigation systems. Expert Syst Appl2013;56:218–27.10.1016/j.compchemeng.2013.05.017Search in Google Scholar

10. Rahman SMT, Salim MT, Syeda SR. Facility layout optimization of an ammonia plant based on risk and economic analysis. Procedia Eng 2014;90:760–5.10.1016/j.proeng.2014.11.810Search in Google Scholar

11. Meng YF, Zhao DF, Zhao. Preliminary study on safety performance evaluation of petrochemical plant layout. Procedia Eng 2013;52:277–83 & Z.Q.10.1016/j.proeng.2013.02.140Search in Google Scholar

12. Jung S, Ng D, Lee J, Vázquez-Román R, Mannan MS. An approach for risk reduction (methodology) based on optimizing the facility layout and siting in toxic gas release scenarios. J Loss Prev Process Ind 2010;23:139–48.10.1016/j.jlp.2009.06.012Search in Google Scholar

13. Galeev AD, Starovoytova EV, Ponikarov SI. Numerical simulation of the consequences of liquefied ammonia instantaneous release using FLUENT software. Process Safety Environ Prot 2013;91:191–201.10.1016/j.psep.2012.05.002Search in Google Scholar

14. Cheng YH, Shih C, Jiang SC, Weng TL. Development of accident dose consequences simulation software for nuclear emergency response applications. Ann Nucl Energy 2008;35:917–26.10.1016/j.anucene.2007.09.001Search in Google Scholar

15. Han K, Cho S, Yoon ES. Optimal layout of a chemical process plant to minimize the risk to humans. Procedia Comp Sci 2013;22:1146–55.10.1016/j.procs.2013.09.201Search in Google Scholar

16. Aiello G, Scalia GL, Enea M. A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding. Expert Syst Appl 2012;39:10352–8.10.1016/j.eswa.2012.01.125Search in Google Scholar

17. Roberts T, Gosse A, Hawksworth S. Thermal radiation from fireballs on failure of liquefied petroleum gas storage vessels. Process Safety Environ Prot 2000;78:184–92.10.1205/095758200530628Search in Google Scholar

18. Satyanarayana K, Borah M, Rao PG. Prediction of thermal hazards from fireballs. Journal Prev Process Ind 1991;4:344–7.10.1016/0950-4230(91)80048-YSearch in Google Scholar

19. Crowl DA, Louvar JF. Chemical process safety fundamentals with applications, 2nd ed. New Jersey: Prentice-Hall PTR, 2002.Search in Google Scholar

20. Castell CML, Lakshmanan R, Skilling JM, Bañares-Alcántara R. Optimization of process plant layout using genetic algorithms. Comp Chem Eng 1998;22:993–6.10.1016/S0098-1354(98)00198-7Search in Google Scholar

21. Ayyub BM. Risk analysis in engineering and economics. New York: CHAPMAN & HALL/CRC, 2003.10.1201/9780203497692Search in Google Scholar

22. Hupt RL, Hupt SE. Practical genetic algorithms. New York: John Wily & Sons, 2004.Search in Google Scholar

23. Emami A, Noghreh P. New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renewable Energy 2010;35:1559–64.10.1016/j.renene.2009.11.026Search in Google Scholar

24. Azadeh A, Motevali Haghighi S, Asadzadeh SM, Saedi H. A new approach for layout optimization in maintenance workshops with safety factors: The case of a gas transmission unit. J Loss Prev Process Ind 2010;26:1457–65.10.1016/j.jlp.2013.09.014Search in Google Scholar

25. Cox RA. Improving risk assessment for process plant. J Hazard Mater 1982;6:249–60.10.1016/0304-3894(82)80013-7Search in Google Scholar

26. Shao H, Duan G. Risk Quantitative Calculation and ALOHA Simulation on the Leakage Accident of Natural Gas Power Plant. Procedia Eng 2012;45:352–9.10.1016/j.proeng.2012.08.170Search in Google Scholar

27. Jung S, Ng D, Diaz-Ovalle C, Vazquez-Roman R, Mannan MS. New approach to optimizing the facility siting and layout for fire and explosion scenarios. Ind Eng Chem Res 2011;50:3928–37.10.1021/ie101367gSearch in Google Scholar

Received: 2015-8-8
Revised: 2015-11-28
Accepted: 2015-11-28
Published Online: 2015-12-18
Published in Print: 2016-6-1

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