Abstract

Accident modeling is a vital step, which helps in designing preventive measures to avoid future accidents, and thus, to enhance process safety. Bayesian networks (BN) are widely used in accident modeling due to its capability to represent accident scenarios from their causes to likely consequences. However, to assess likelihood of an accident using the BN, it requires exact basic event probabilities, which are often obtained from expert opinions. Such subjective opinions are often inconsistent and sometimes conflicting and/or incomplete. In this work, evidence theory has been coupled with BN to address inconsistency, conflict and incompleteness in the expert opinions. It combines the acquired knowledge from various subjective sources, thereby rendering accuracy in probability estimation. Another source of uncertainty in BN is model uncertainty. To represent multiple interactions of a cause–effect relationship Noisy-OR and leaky Noisy-AND gates are explored in the study. Conventional logic gates, i.e., OR/AND gates can only provide a linear interaction of cause–effect relationship hence introduces uncertainty in the assessment. The proposed methodology provides an impression how dynamic risk assessment could be conducted when the sufficient information about a process system is unavailable. To illustrate the execution of a proposed methodology, a tank equipped with a basic process control system has been used as an example. A real-life case study has also been used to validate the proposed model and compare its results with those using a deterministic approach.

References

References
1.
Kalantarnia
,
M.
,
Khan
,
F.
, and
Hawboldt
,
K.
,
2009
, “
Dynamic Risk Assessment Using Failure Assessment and Bayesian Theory
,”
J. Loss Prev. Process Ind.
,
22
(
5
), pp.
600
606
.10.1016/j.jlp.2009.04.006
2.
Khan
,
F. I.
, and
Abbasi
,
S. A.
,
1998
, “
Models for Domino Effect Analysis in Chemical Process Industries
,”
Process Saf. Prog.
,
17
(
2
), pp.
107
123
.10.1002/prs.680170207
3.
Khan
,
F. I.
,
2001
, “
Use Maximum-Credible Accident Scenarios for Realistic and Reliable Risk Assessment
,”
Chem. Eng. Prog.
,
97
(
11
), pp.
56
64
.https://pdfs.semanticscholar.org/651e/96b8adb7ca210ae08cfd4b8fdfb5e8fea430.pdf
4.
Tan
,
Q.
,
Chen
,
G.
,
Zhang
,
L.
,
Fu
,
J.
, and
Li
,
Z.
,
2014
, “
Dynamic Accident Modeling for High-Sulfur Natural Gas Gathering Station
,”
Process Saf. Environ. Prot.
,
92
(
6
), pp.
565
576
.10.1016/j.psep.2013.03.004
5.
Qureshi
,
Z. H.
,
2008
, “
A Review of Accident Modelling Approaches for Complex Critical Sociotechnical Systems
,”
12th Australian Workshop on Safety Critical Systems and Software and Safety-Related Programmable Systems
, Adelaide, Australia, Aug. 30–31, pp.
47
59
.https://www.researchgate.net/publication/27256803_A_Review_of_Accident_Modelling_Approaches_for_Complex_Critical_Sociotechnical_Systems
6.
Al-shanini
,
A.
,
Ahmad
,
A.
, and
Khan
,
F.
,
2014
, “
Accident Modelling and Analysis in Process Industries
,”
J. Loss Prev. Process Ind.
,
32
, pp.
319
334
.10.1016/j.jlp.2014.09.016
7.
Khakzad
,
N.
,
Khan
,
F.
, and
Amyotte
,
P.
,
2013
, “
Dynamic Safety Analysis of Process Systems by Mapping Bow-Tie Into Bayesian Network
,”
Process Saf. Environ. Prot.
,
91
(
1–2
), pp.
46
53
.10.1016/j.psep.2012.01.005
8.
Badreddine
,
A.
, and
Ben Amor
,
N.
,
2010
, “
A Dynamic Barriers Implementation in Bayesian-Based Bow Tie Diagrams for Risk Analysis
,”
ACS/IEEE International Conference on Computer Systems and Applications
(
AICCSA
), Hammamet, Tunisia, May 16–19, pp. 1–8.10.1109/AICCSA.2010.5587003
9.
Bobbio
,
A.
,
Portinale
,
L.
,
Minichino
,
M.
, and
Ciancamerla
,
E.
,
2001
, “
Improving the Analysis of Dependable Systems by Mapping Fault Trees Into Bayesian Networks
,”
Reliab. Eng. Syst. Saf.
,
71
(
3
), pp.
249
260
.10.1016/S0951-8320(00)00077-6
10.
Marsh
,
D. W. R.
,
Bearfield
,
G.
, and
Marsh
,
W.
,
2008
, “
Generalizing Event Trees Using Bayesian Networks
,”
Proc. Inst. Mech. Eng., Part O
,
222
(
2
), pp.
105
114
.
11.
Yuan
,
Z.
,
Khakzad
,
N.
,
Khan
,
F.
, and
Amyotte
,
P.
,
2015
, “
Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks
,”
Risk Anal.
,
35
(
2
), pp.
278
291
.10.1111/risa.12283
12.
Abimbola
,
M.
,
Khan
,
F.
,
Khakzad
,
N.
, and
Butt
,
S.
,
2015
, “
Safety and Risk Analysis of Managed Pressure Drilling Operation Using Bayesian Network
,”
Saf. Sci.
,
76
, pp.
133
144
.10.1016/j.ssci.2015.01.010
13.
Adedigba
,
S. A.
,
Khan
,
F.
, and
Yang
,
M.
,
2016
, “
Dynamic Safety Analysis of Process Systems Using Nonlinear and Non-Sequential Accident Model
,”
Chem. Eng. Res. Des.
,
111
, pp.
169
183
.10.1016/j.cherd.2016.04.013
14.
Ferdous
,
R.
,
Khan
,
F.
,
Sadiq
,
R.
,
Amyotte
,
P.
, and
Veitch
,
B.
,
2013
, “
Analyzing System Safety and Risks Under Uncertainty Using a Bow-Tie Diagram: An Innovative Approach
,”
Process Saf. Environ. Prot.
,
91
(
1–2
), pp.
1
18
.10.1016/j.psep.2011.08.010
15.
Markowski
,
A. S.
,
Mannan
,
M. S.
, and
Bigoszewska
,
A.
,
2009
, “
Fuzzy Logic for Process Safety Analysis
,”
J. Loss Prev. Process Ind.
,
22
(
6
), pp.
695
702
.10.1016/j.jlp.2008.11.011
16.
Abrahamsson
,
M.
,
2002
, “
Uncertainty in Quantitative Risk Analysis-Characterisation and Methods of Treatment
,” Fire Safety Engineering and Systems Safety, Department of Fire Safety Engineering Lund University, Sweden, p.
88
.
17.
Ferdous
,
R.
,
Khan
,
F.
,
Sadiq
,
R.
,
Amyotte
,
P.
, and
Veitch
,
B.
,
2009
, “
Handling Data Uncertainties in Event Tree Analysis
,”
Process Saf. Environ. Prot.
,
87
(
5
), pp.
283
292
.10.1016/j.psep.2009.07.003
18.
Thacker
,
B. H.
, and
Huyse
,
L. J.
,
2007
, “
Probabilistic Assessment on the Basis of Interval Data
,”
Struct. Eng. Mech.
,
25
(
3
), pp.
331
345
.10.12989/sem.2007.25.3.331
19.
Wilcox
,
R. C.
, and
Ayyub
,
B. M.
,
2003
, “
Uncertainty Modeling of Data and Uncertainty Propagation for Risk Studies
,”
Fourth International Symposium on Uncertainty Modeling and Analysis
(
ISUMA
), College Park, MD, Sept. 21–24, p.
184
.10.1109/ISUMA.2003.1236160
20.
Stewart
,
R. T.
, and
Quintana
,
I. O.
,
2018
, “
Probabilistic Opinion Pooling With Imprecise Probabilities
,”
J. Philos. Logic
,
47
(
1
), pp.
17
45
.10.1007/s10992-016-9415-9
21.
Sentz
,
K.
, and
Ferson
,
S.
,
2002
, “
Combination of Evidence in Dempster-Shafer Theory
,” Sandia National Laboratories, Albuquerque, NM, Report No.
SAND2002-0835
.https://pdfs.semanticscholar.org/a800/a3890c75038703f5d7848fc0586c944c38e2.pdf
22.
Heckerman
,
D.
,
Mamdani
,
A.
, and
Wellman
,
M. P.
,
1995
, “
Real-World Applications of Bayesian Networks
,”
Commun. ACM
,
38
(
3
), pp.
24
26
.10.1145/203330.203334
23.
Neapolitan
,
R.
,
1990
,
Probabilistic Reasoning in Expert Systems
,
Wiley
,
New York
.
24.
Pearl
,
J.
,
1988
,
Probabilistic Reasoning in Intelligent Systems
, Vol.
88
,
Morgan Kauffmann
,
San Mateo, CA
, p.
552
.
25.
Nielsen
,
T. D.
, and
Jensen
,
F. V.
,
2009
,
Bayesian Network and Decision Graph
, 2nd ed., Springer, New York.
26.
Diez
,
F. J.
, and
Druzdzel
,
M. J.
,
2007
, “
Canonical Probabilistic Models for Knowledge Engineering
,”
Inf. Sci.
,
9
, pp.
1
59
.https://pdfs.semanticscholar.org/2ed3/d3e4bad4f9480338e50934d36120894b119e.pdf
27.
Heckerman
,
D.
, and
Breese
,
J. S.
,
1996
, “
Causal Independence for Probability Assessment and Inference Using Bayesian Networks
,”
IEEE Trans. Syst. Man, Cybern. Part A: Syst. Hum.
,
26
(
6
), pp.
826
831
.10.1109/3468.541341
28.
Ayyub
,
B. M.
, and
Klir
,
G. J.
,
2006
,
Uncertainty Modeling and Analysis in Engineering and the Sciences
, Chapman and Hall/CRC, Boca Raton, FL.
29.
Ferdous
,
R.
,
Khan
,
F.
,
Sadiq
,
R.
,
Amyotte
,
P.
, and
Veitch
,
B.
,
2011
, “
Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations
,”
Risk Anal.
,
31
(
1
), pp.
86
107
.10.1111/j.1539-6924.2010.01475.x
30.
Druschel
,
B. R.
,
Ozbek
,
M.
, and
Pinder
,
G. F.
,
2006
, “
Application of Dempster-Shafer Theory to Hydraulic Conductivity
,” CMWR–XVI, Conference Program, Copenhagen, Denmark.
31.
Bae
,
H.-R.
,
Grandhi
,
R. V.
, and
Canfield
,
R. A.
,
2004
, “
An Approximation Approach for Uncertainty Quantification Using Evidence Theory
,”
Reliab. Eng. Syst. Saf.
,
86
(
3
), pp.
215
225
.10.1016/j.ress.2004.01.011
32.
Cheng
,
Y.
,
2000
, “
Uncertainties in Fault Tree Analysis
,”
J. Appl. Sci. Eng.
,
3
(
1
), pp.
23
29
.
33.
Lefevre
,
E.
,
Colot
,
O.
, and
Vannoorenberghe
,
P.
,
2002
, “
Belief Function Combination and Conflict Management
,”
Inf. Fusion
,
3
(
2
), pp.
149
162
.10.1016/S1566-2535(02)00053-2
34.
Zadeh
,
L. A.
,
1984
, “
Review of a Mathematical Theory of Evidence
,”
AI Mag.
,
5
(
3
), p.
81
.10.1609/aimag.v5i3.452
35.
Vesely
,
W. E.
,
Goldberg
,
F. F.
,
Roberts
,
N. H.
, and
Haasl
,
D. F.
,
1981
,
Fault Tree Handbook
,
U.S. Nuclear Regulatory Commission
,
Washington, DC
.
36.
Smets
,
P.
,
Hsia
,
Y. T.
,
Saffiotti
,
A.
,
Kennes
,
R.
,
Xu
,
H.
, and
Umkehrer
,
E.
,
1991
, “
The Transferable Belief Model
,”
Symbolic and Quantitative Approaches to Uncertainty
(Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 548), Springer, Berlin.
37.
CSB
,
2009
, “
Caribbean Petroleum Tank Terminal Explosion and Multiple Tank
,” U.S. Chemical Safety Board, Bayamon, PR.
38.
Hugin Expert
,
2008
, “
Hugin Researcher API
,” Hugin Expert A/S, Aalborg, Denmark.
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