Hybrid layer of protection analysis and bow tie analysis with fuzzy approach for quantitative risk assessment

1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 01-11 © IAEME
1
HYBRID LAYER OF PROTECTION ANALYSIS AND BOW-TIE ANALYSIS
WITH FUZZY APPROACH FOR QUANTITATIVE RISK ASSESSMENT
Rachid Ouache1
, Ali A.J Adham2
, Noor AzlinnaBinti Azizan3
1, 2, 3
Faculty of Technology, University Malaysia Pahang, 26300, Malaysia
ABSTRACT
Quantitative risk assessment and reliability are essential issues in modern safety to make
reliable decision. Risk assessment approaches are designed primarily to reduce the existing risk
inherent in engineering system to a tolerable level and maintain it over time. This reduction is often
achieved by successive interposition of several protective barriers between the source of danger,
which can be an industrial process, and potential targets as people, property and environment. Layer
of protection analysis is an approach to estimate the risk by quantifying risk results. A fuzzy set is a
new mathematical tool to model inaccuracy and uncertainty of data based on the surgeon method. In
this study, new model proposed to deal with quantitative risk assessment and precise the severity of
the scenario and determine the safety integrity level SIL based on LOPA and Bow-tie analysis using
fuzzy set, and the results illustrates that this models is more powerful than logical and arithmetic
computation.
Keywords: Quantitative Risk Assessment, Reliability, Layer of Protection Analysis, Bow-Tie
Analysis and Fuzzy Sets.
1. INTRODUCTION
The increasing complexity of engineering system has imposed substantial uncertainties and
imprecise associated with data in risk assessment problems. Reliability of system is the ability to
operate under designated operating conditions for a designated period of time or number of cycles
through a probability. The improve of reliability for prolonging the life of the item based on two
steps essential, on the one hand, study reliability issues and on the other hand, estimate and reduce
the failure rate (Mohammad, 1999; Dasgupta, 1991). Quantitative Risk Assessment (QRA) for
objective to estimates the outcome event probability of event tree and uses crisp probabilities of
events to estimate the outcome event probability or frequency (Kenarangui, 1991; Lees, 2005;
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING
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ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 01-11 © IAEME
2
Ferdous, 2006; A.Nieto-Morote, 2011). The classifications of uncertainty, aleatory and epistemic
uncertainties are the major classes (Thacker and Huyse, 2003; Adam, 2010; ThallesVitelli, 2014;
Mohammad, 1999; H.J. Pasman, 2009; N. Ouazraouia, 2013).Expert systems can be built based on
fuzzy logic and they provide reasonably accurate outcomes useful in systems analysis (LÁSZLÓ,
2002). Layer of protection analysis LOPA can be used a screening tool for QRA(CCPS, 2001).
In this study, LOPA and Bow-tie analysis using fuzzy logic are proposed to solve problem of
quantitative risk assessment. Fuzzy inference (Sugeno model) is the approach used in this paper; the
frequency of the mitigated scenario is calculated using fault tree analysis and event tree analysis by
generic data for the initiating event frequency and PFD of the independent protection layers
(KambizMokhtari, 2011; Anjuman, 2012; Adam S,2011).
2. FUZZY LAYER OF PROTECTION ANALYSIS
2.1 LAYER OF PROTECTION ANALYSIS LOPA
2.1.1 Definition: Layer of protection analysis is semi-quantitative approach, It can be viewed as a
simplification of the quantitative risk analysis methods using event tree analysis based on selection
and estimation of magnitude the scenarios for enhance the system by the protection needs
(ChunyangWei, 2008; SohrabKhaleghi, 2013).
Figure.1: Integration Layer of protection analysis in the Event tree analysis
2.1.2 LOPA and Risk Decisions Making
The flow chart shown in Figure.2 illustrates one organization for three approaches qualitative,
semi-quantitative and quantitative risk assessment (CCPS, 2001).
I
P
L
I
P
L
I
P
L ConsequenceOccurs
SafeOutcome
Undesired but
tolerableoutcome
Initiating Event
Undesired but
tolerableoutcome
Consequenceexceeding
criteria
success
Failure
success
success
Failure
Failure

3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 01-11 © IAEME
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Figure.2: Flowchart shows the relationship between Qualitative approach, semi-quantitative and
Quantitative risk assessment
2.1.3 Determining the Frequency of Scenarios
The following is the general procedure for calculating the frequency for are lease scenario
with a specific consequence endpoint.
݂௜
஼
ൌ ݂௜
ூ
ෑ ܲ‫ܦܨ‬௜௝
௃
௝ୀଵ
ൌ ݂௜
ூ
ൈ ܲ‫ܦܨ‬௜ଵ ൈ ܲ‫ܦܨ‬௜ଶ ൈ ‫ڮ‬ ൈ ܲ‫ܦܨ‬௜௝ ሺ1ሻ
ܴ௞
஼
ൌ ݂௞
஼
ൈ ‫ܥ‬௞ሺ2ሻ
݂௜
஼
ൈ ܲ‫ܦܨ‬௉௅ ൑ ܴܶ ሺ3ሻ
ܴ‫ܨܨ‬௉௅ ൒
݂௜
஼
ܴܶ
ሺ4ሻ
2.1.4 Classification of consequence: The outcome’s prediction of damage can be by experimental
values or simulated values available for the chemicals.
Table.1: Definition of categories of consequence
Consequence
class
Plant
personnel
Community Environment
1 and 2 No lost time No hazard No notification
3 Single injury Odour /noise Permit violation
4 >1 injury One or more injuries Serious offsite impact
5 Fatality One or more severe injuries Serious offsite impact
NO
NO
YESYES
YES
YES
Qualitative study
Complete study based
on qualitative judgment
Is consequence
II ou III ?
Is frequency
of consequence > 1/10
years?
Is a SIL required
for one or more SIF?
Complete study Based on
QRA results
Management
review
Full QRA
study on all or
part of the
Management
review
Is frequency of initiating
event understood?
Are IPLs and PFDs
understood?
Complete study based
on LOPA judgment
Is risk
still in immediate action
or action atnext
opportunity box?
Ensure initiating events
and IPLs are truly
independent
LOPA
Cost/
benefitanalysis
of alternatives
Managemen
t review
Is risk
in immediate action or
action at next
opportunity box?
Is risk
in optional action
box?
NO
NO
NO
YES
YES
YES
YES
NO
NO

4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
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Table.2: Risk tolerance criteria
Frequency of
consequence (/yr)
Consequence category
Category
1
Category
2
Category
3
Category
4
Category
5
100
– 10-1
Not acceptable
10-1
– 10-2
10-2
– 10-3
Intermediate range
10-3
– 10-4
10-4
– 10-5
Acceptable
10-5
– 10-6
10-6
– 10-7
2.2 FUZZY LOGIC FL AND FUZZY SET THEORY FS: The fuzzy logic provides an inference
structure that enables appropriate human reasoning capabilities.
2.2.1 Fuzzy Sets FS: The utility of fuzzy sets lies in their ability to model uncertain or ambiguous
data, FS is important to observe that there is an intimate connection between Fuzziness and
Complexity. Fuzzy sets provide means to model the uncertainty associated with vagueness,
imprecision, and lack of information regarding a problem or a plant, etc (Dubois, 1980, Zadeh,
1978).The uncertainty is found to arise from ignorance, from chance and randomness, due to lack of
knowledge, from vagueness. (Canos, 2008; R. Nait-Said, 2008, 2009; Bouchon et al, 1995;
RadimBris, 2013).
2.2.2 FUZZY NUMBERS: The membership functionµ஺෨(x) has the following characteristics
(Dubois & Prade 1978).The membership function of the number ‫ܣ‬ሚ can be expressed as follows.
µ஺෨(x) =
µ஺෩
௅ ሺ‫ݔ‬ሻ, ܽ ൑ ‫ݔ‬ ൑ ܾ
1, ܾ ൑ ‫ݔ‬ ൑ ܿ
µ஺෩
ோ ሺ‫ݔ‬ሻ, ܿ ൑ ‫ݔ‬ ൑ ݀
(5)
ߤ஺෩
௅
ሺ‫ݔ‬ሻ= ೣషೌ
್షೌ
(6)
ߤ஺෩
ோ
ሺ‫ݔ‬ሻ ൌ ೏షೣ
೏ష೎
(7)
Figure.3: Trapezoidal and triangular fuzzy numbers
0,otherwise
µ஺෨(x)
db
0
ߤ஺෩
௅
ሺ‫ݔ‬ሻ ߤ஺෩
ோ
ሺ‫ݔ‬ሻ
1
x
ca 1
µ஺෨(x)
db =c
0
ߤ஺෩
௅
ሺ‫ݔ‬ሻ ߤ஺෩
ோ
ሺ‫ݔ‬ሻ
1
x
a 1

5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 01-11 © IAEME
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2.2.3 Fuzzy inference system FIS
Sugeno method is most commonly used fuzzy inference method (sugeno, 1985). A typical
rule in sugeno fuzzy model has the form, if input 1=x and input 2 =y, then output
z= ax+by+c(8)
The final output of the system is weighted average of all the rule output which is given as:
Final output =
∑ ௪೔ ௭೔
ಿ
೔సభ
∑ ௪೔
ಿ
೔సభ
(9)
Figure.4: Sugeno rule operates diagram
A FIS with five functional block described in Figure.5.
Figure.5: Fuzzy inference system
3. CASE STUDY
The storage tank is designed to hold a flammable liquid under slight nitrogen positive
pressure under controls pressure (PICA-I). (CCPS, 2000). To demonstrate the proposed approaches,
this was earlier reported by CCPS (2000).
(
Database Rule base
Knowledge base
Fuzzification interface
Defuzzification interface
Decision-making unit FuzzyFuzzy
CrispCrisp
OUTPUTINPUT
Output
level
RuleWeight
(firingstrength)
Z=ax+by+c
Input 1
x
y
AND
Input MF
Input MF
Input 2
F2(x)
F1(x)
W
Z
Output MF

6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print),
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Figure.6: Flammables liquid storage tank
Table.3: LPG tank release using fuzzy inference methods (Gate.1)
Equipment and valves Instruments
FV = Flow control Valve
T = Tank, P = pump
PV =Pressure control Valve
RV = Relief Valve,
1 = 1 inch size
P = Pressure, T = Temperature
L = Level, F = Flow,
I = Indicator, C = Controller
A = Alarm; H = High, L = Low
Calculate probability using Boolean algebras method
I
f
Tank rupture due
to reaction
A
N
D
Tank overfill and
release
via RV-1
A
N
D
Failure BPCS
and
human action
T
H
E
N
LPG tank
release
M1=1.10-8
M2=1.10-5
PFD=1.10-1
T=1.10-6
Calculate probability using fuzzy inference method
1
I
F
M1=1.10-8
A
N
D
M2=1.10-5
A
N
D
PFD=1.10-1
T
H
E
N
T=1.6*10-6
2 M1=5.03*10-9
M2=5.09*10-6
PFD=0.0509 T=1.55*10-6
3 M1=1.5*10-8
M2=1.5*10-5
PFD=0.149 T=1.65*10-6
1῎
PV-2 1῎
PICA-1
H
PV-1
V-7
To atmosphere
Nitrogene
To flare
RV-1
V-8
1
῎
From tank
trucks
V-1
FlammableLiquid
Storage Tank
T=1
4
῎
LIA-1
TIA-1
V-3
1῎
P=1
PI-1
V-4
FICA
-1
H
H
L
FV-1
To process
L

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ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 01-11 © IAEME
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Figure.7: Simulation of inputs and outputs of fuzzy inference using sugeno
Table.4: Frequency of consequences for large LPG leakage using fuzzy inference methods
Figure.8: Simulation of inputs and outputs of fuzzy inference using sugeno approach
a. Rules inferences process of LPG tank
release, three inputs with one output
b. Three dimensional diagram, LPG tank
release, Tank rupture due to reaction and
Tank overfill and release via RV-1
c. Two dimensional diagram, LPG tank
release with Tank rupture due to reaction
d. Two dimensional diagram, LPG tank
release with failure BPCS and human
action
Calculate frequency of outcome using classical method
IF AND AND THEN
large LPG leakage BPCS Safety Instrumented
System SIS
BLEVE
P=1.6*10-6
PFD=1.10-1
PFD=1.10-1
P=1.6*10-8
Calculate frequency of outcome using fuzzy inference method
0.00016 0.1 0.1 2.42*10-8
a. Rules inferencess process
for BLEVE, three inputs with
one output
b.Three dimensional diagram of
BLEVE by large LPG leakage
and BPCS
c. Two dimensional diagram
shows relationship between
BLEVE and SIS

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ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 01-11 © IAEME
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Figure.9: Bow-tie analysis with results of fuzzy sets (sugeno approach) to calculate probability
of the top event and consequences LPG tank release
Table.5: Alpha-cut at left for scenario (*10-8
/yr)
α-
cut
Seq1 Seq2 Seq3 Seq4 Seq5 Seq6 Seq7 Seq8 Seq9 Seq10
0 2.228 27.57 1.531 0.3662 4.203 33.14 3.61 4.226 46.21 410.6
0.1 2.2472 27.743 1.5659 0.37008 4.2377 33.356 3.641 4.2604 46.529 412.74
0.2 2.2664 27.916 1.6008 0.37396 4.2724 33.572 3.672 4.2948 46.848 414.88
0.3 2.2856 28.089 1.6357 0.37784 4.3071 33.788 3.703 4.3292 47.167 417.02
0.4 2.3048 28.262 1.6706 0.38172 4.3418 34.004 3.734 4.3636 47.486 419.16
0.5 2.324 28.435 1.7055 0.3856 4.3765 34.22 3.765 4.398 47.805 421.3
0.6 2.3432 28.608 1.7404 0.38948 4.4112 34.436 3.796 4.4324 48.124 423.44
0.7 2.3624 28.781 1.7753 0.39336 4.4459 34.652 3.827 4.4668 48.443 425.58
0.8 2.3816 28.954 1.8102 0.39724 4.4806 34.868 3.858 4.5012 48.762 427.72
0.9 2.4008 29.127 1.8451 0.40112 4.5153 35.084 3.889 4.5356 49.081 429.86
1 2.42 29.3 1.88 0.405 4.55 35.3 3.92 4.57 49.4 432
An
d
Insuffici
ent
volume
in tank
to
unload
truck,
B5=1.10
-2
Failure
of or
ignoring
LIA-I,
B6=1.10
-2
LPG
tank
releas
e
T=1.6
*10-6
Tank
rupture
due to
reactio
n
M1=1.
81*10-8
Tank
overfil
ls and
release
via
RV-1,
M2=1.
62*10-
5
T=2.1
Pressure
rise
exceeds
capacity
of PV-I,
B4=1.10-1
Reagent reacts
with unloaded
material,
B3=1.10-1
Tank truck
not sampled
before
unloading,
B2=1.10-
2
Wrong
material
in tank
truck,
B1=1.10
-3
Or
An
d
3
35.3x10-8
0.405x10-8
4.55x10-8
3.92x10-8
4.57x10-8
49.4x108
432x10-8
1.88x10-8
BLEVE
Flash fire
and
Local
Thermal
hazard
VCE
Flash fire
Safe
dispersal
VCE
Flash fire
and
Flash fire
Safe
dispersal
2.42x10-8
29.3x10-8
PFD
1- PFD

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Figure.10: Fuzzy and classical frequency sequence 1
4. DISCUSSION
The model proposed in this study is to calculate the frequencies and consequences of
initiating event. LOPA and Bow-tie analysis using fuzzy sets is a model which allows us to precisely
the values of IE, consequences and risk. To get best results Bow-tie analysis devised to two method
Fault tree analysis and Event tree analysis, where each method used alone with fuzzy, this for
facilitating the task of calculating the value of risk.
The results have shown on form of figures and tables, the figures also divided to four kinds;
rules inferences process where we can demonstrate it by the equation (9) and the figure.2, three
dimensional diagram, two dimensional diagram where the equation.10 can demonstrate the results
for both two and three dimensional, and comparison between two methods fuzzy and classical
(logical and arithmetic computation) using the equations (6), (7) and (8).The tables illustrate the
advantages of fuzzy where can give different results for IE and consequences, and fuzzy inference
best tool to help for understand the variation of outputs by inputs.
According to value of tolerable risk in our system which must be 10-7
, the values got by fuzzy
approach are more precise than logical regarding that pessimist values are prefers by the analysts.
Thus fuzzy sets is more appropriate and more powerful to assess the risk in engineering system.
Combination between five methods HAZOP, FTA, ETA, Bow-tie and LOPA using fuzzy are the
greatest model to precise the value of risk and the best model for reliability quantitative risk
assessment, which they allow to understand the system completely, minimize the risk from side and
maximize the value of safety from the other side.
Optimization of IPL in systems based on LOPA after Bow-tie analysis allow to maximize the
value of safety in engineering system and minimize the value of risk
5. CONCLUSION
In this study, we proposed a new modelto deal with problem of uncertainty and imprecise of
risk assessment. Qualitative, semi quantitative, and quantitative are three approaches based on five
methods HAZOP, Bow-tie analysis based on FTA ETA, and LOPA using Fuzzy sets as a new model
for risk assessment. The results which have gotten in this study are more powerful while authorizing
us to say the proposed model is the best solution for reliability quantitative risk assessment and
improve safety integrity level SIL. The frequency of consequences calculated by the frequency of IE
and PFD of IPL using fuzzy-sugeno is more powerful than logical method, and consider as
complementary. The assessment of IPL for engineering system is the best continuity for the proposed
model.
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5 2 2.5 3
Membershipdegree
Frequency (*10-8/yr)
Sequence 1
classical
fuzzy

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