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Accepted Manuscript
A safety vulnerability assessment for chemical enterprises: A hybrid of a data
envelopment analysis and fuzzy decision-making
Rui Zhao, Silin Liu, Yiyun Liu, Luziping Zhang, Youping Li
PII:
S0950-4230(18)30439-X
DOI:
10.1016/j.jlp.2018.08.018
Reference:
JLPP 3767
To appear in:
Journal of Loss Prevention in the Process Industries
Received Date: 8 May 2018
Revised Date:
15 August 2018
Accepted Date: 20 August 2018
Please cite this article as: Zhao, R., Liu, S., Liu, Y., Zhang, L., Li, Y., A safety vulnerability assessment
for chemical enterprises: A hybrid of a data envelopment analysis and fuzzy decision-making, Journal of
Loss Prevention in the Process Industries (2018), doi: 10.1016/j.jlp.2018.08.018.
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A Safety Vulnerability Assessment for Chemical
Enterprises: A Hybrid of a Data Envelopment
Analysis and Fuzzy Decision-Making
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Rui Zhaoa∗, Silin Liua, Yiyun Liua, Luziping Zhanga, Youping Lib
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University,
Chengdu 611756, China
School of Environmental Science and Engineering, China West Normal University, Nanchong
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b
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637009, China
Abstract: This study constructs a composite indicator system for vulnerability
assessment based on the disaster-causing factors and hazard-bearing bodies involved
in chemical safety accidents. In such a context, a hybrid model (D-FDM) is built by
combining a data envelopment analysis (DEA) and fuzzy decision-making while
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considering the exposure, sensitivity, and adaptability of chemical enterprises. A case
is employed to verify the proposed hybrid model and demonstrate its practical
application in a safety vulnerability assessment of an ammonia-producing plant in
Sichuan, in southwestern China. The degrees of safety vulnerability related to the
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production and supporting facilities are discerned to provide insights into safety risk
control and management for the case plant. Limitations related to the applicability of
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the methodology are also given to lay the foundation for further improvement.
Keywords: Safety vulnerability; Chemical enterprise; Data envelopment analysis;
Fuzzy decision-making
1. Introduction
With rapid development of China’s national economy, the chemical industry has
become an important pillar industry (Lin and Long, 2014). As a wide variety of
∗
Corresponding author: ruizhaoswjtu@hotmail.com
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chemicals that can be considered harmful or hazardous have been used in product
manufacturing, chemical enterprises are vulnerable to safety accidents, such as fires
and explosions, which pose potential hazards to human health and environment (Lee
et al., 2016; Wang and Dai, 2017). From a disaster formation perspective, safety
accidents are the consequences caused by hazard-causing factors combined with
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hazard-bearing bodies, which closely relate to the vulnerability of receptors (Reniers
et al., 2011; Chakraborty et al., 2018). Thus, assessing chemical enterprises’
vulnerability is critical not only to identifying the risk-related causes and
consequences of safety accidents, but also in providing insights into risk prevention
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and control (Reniers and Amyotte, 2012; Leith and Piper, 2013).
Since proposed by White in 1974, vulnerability has evolved into a conceptual set
consisting of multiple dimensions, such as exposure, sensitivity, adaptability, and
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resilience (White, 1974; Lei et al., 2014). Among these, exposure is categorized as an
external vulnerability, referring to a hazard-bearing body’s extent of exposure as
impacted by a hazard-causing factor (Zhang et al., 2015). In contrast, the latter three
perspectives contribute to internal vulnerability, representing the hazard-bearing
body’s adaptability and resilience (Zhang et al., 2017). The vulnerability of chemical
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enterprises in particular refers to the potential harm they may suffer when exposed to
natural or man-made disturbances, the impact of their inherent sensitivity, and their
adaptability to such a disturbance (Reniers et al., 2014).
Previous vulnerability studies primarily focused on an assessment of enterprises’
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external vulnerability, and specifically to identify hazard sources and predict the
possible consequences of safety accidents (Li et al., 2010; Necci et al., 2015).
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However, hazard-bearing bodies’ inherent receptivity of such accidents is often
neglected. First, consequences might differ even if the same hazard-causing factor
acts on various hazard-bearing bodies. Second, the differences in hazard-bearing
bodies’ sensitivity and adaptability toward a hazard-causing factor may also lead to
substantial differences in their tolerance of safety accidents. For instance, such
adaptability factors as employees’ safety awareness, the frequency of equipment
maintenance, and existing safety measures may mitigate a safety accident’s possible
harm to major hazard-bearing bodies, including the employees, facilities, and
environment (de Koster et al., 2011; John et al., 2016). Consequently, this study
proposes a composite indicator system for safety vulnerability assessment of chemical
enterprises by considering their exposure, sensitivity, and adaptability. A hybrid
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model (D-FDM), combining a data envelopment analysis (DEA) and fuzzy
decision-making, is constructed to aid this assessment, in which the former primarily
measures quantitative indicators, while the latter handles those that are non- or
semi-quantitative. Collectively, these two methods can maximize their individual
advantages by reducing a loss of information and improving the assessment outcomes’
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validity (Han et al., 2015a). A typical ammonia-producing enterprise located in
Southwestern China’s Sichuan province is taken as a case example to verify the
assessment model and suggest effective measures for risk prevention. It is expected
that the study may provide insight into the process safety for chemical industries.
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The remainder of this paper is structured as follows: Section 2 provides a
literature review regarding vulnerability assessments in chemical industries. Section 3
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presents a holistic framework of vulnerability assessment, including construction of
an indicator system, detailed instruction of the hybrid model. Section 4 provides the
case example to demonstrate application of the method, in which the assessment
results and a related discussion are given. Finally, Section 5 concludes and addresses
2. Literature review
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the limitations in the applicability of the methodology in order for further study.
Prior studies on safety vulnerability assessments in chemical enterprises were
based on a risk analysis of hazard-bearing bodies, including workers, equipment, and
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environment, to reveal the consequences of the safety accidents that result from
hazard-causing factors (Aven, 2007; Aven, 2011). Li et al. (2010) focused on
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assessing human vulnerability when exposed to a release of chlorine, and used a
Gaussian spread function to predict health consequences. Topuz et al. (2011) and
Huang et al. (2011) conducted similar studies, in which the former emphasized the
health risks caused by employees’ exposure to the PVC manufacturing process, while
the latter evaluated pesticide production’s health impacts on both employees and
residents near the plant.
Landucci et al. (2015) took industrial facilities as the hazard-bearing body, and
applied the Hopkinson-Cranz method to assess the vulnerability of the facilities in a
chemical enterprise attacked by explosives. Khakzad et al. (2015) applied graph
theory to analyze vulnerability of the equipment in a benzene process plant to identify
the most vulnerable layout that would lead to cascading safety accidents. Jia et al.
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(2017) further proposed a hierarchical framework to assess the equipment
vulnerability within chemical clusters, considering an optimization of the safety
distance and layout. Argenti et al. (2018) adopted the Bayesian network to build a
fragility model to predict the probability of equipment failure in polybutadiene
production facilities. Yang et al. (2018) integrated the Bayesian network with an event
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tree analysis to assess the safety vulnerabilities of chemical storage tanks. Similarly,
Liu et al. (2017) used a set pair analysis to develop a dynamic risk model to assess the
safety vulnerabilities in ammonium nitrate storage sites.
Regarding environmental vulnerability assessment, Si et al. (2012) took an
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anhydrous ammonia distripark as an example to review the possible impacts of
chemical leakage accidents on its surroundings. Peng et al. (2013) considered the
water environment as a hazard-bearing body in analyzing the possible consequences
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of river pollution caused by an aniline leak. Similarly, Zhao et al. (2014) regarded
hazardous materials’ railway transportation as an example to predict the consequences
of sudden environmental pollution accidents and identify impacted areas. Han et al.
(2015b) used fuzzy decision-making in their risk assessment to further discuss the
tolerability of chemical facilities and their associated storage environments. Tian et al.
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(2017) also performed a quantitative risk assessment to show how an industrial park’s
work environment is vulnerable to explosion accidents.
Most of these studies mainly focused on vulnerability assessment of specific
hazard-bearing bodies, such as employees, equipment, or the environment, but seldom
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considered the enterprise itself as an entity exposed to hazards. Additionally, these
prior studies paid little attention to chemical enterprises’ inherent adaptability to
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hazards while assessing their vulnerabilities. Therefore, this study integrates chemical
enterprises’ external exposure and internal adaptability to construct an indicator
system to assess their safety vulnerability. A hybrid model combining a DEA and
fuzzy decision making (D-FDM) is introduced to evaluate the vulnerability of various
production and ancillary facilities in a chemical enterprise. The assessment enables a
more efficient identification of key hazards during the enterprise’s operation,
facilitating risk prevention and control.
3. Method
3.1 Construction of an indicator system for vulnerability assessment
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This study constructs an indicator system for vulnerability assessment of
chemical enterprise based on three dimensions: exposure, sensitivity, and adaptability,
as Table 1 illustrates. On the one hand, exposure and sensitivity are classified as
physical vulnerability indicators, which include the vulnerability of humans, the
environment, and equipment (Birkmann et al., 2013). On the other hand, adaptability
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concerns an enterprise’s adaptive capacity regarding safety accidents, or more
specifically, its inherent resilience during safety accidents’ occurrence (Huang et al.,
2011; Shirali et al., 2012).
Table 1. Indicator system of the vulnerability assessment for chemical enterprise
First-Level Index
Second-Level Index
Concentration of hazardous
Employees’ exposure
materials
Work hours
Number of employees per
facility
Employees’ density
Production facility’s size
Residential density
Externally
Number of sensitive areas
Number of enterprises
environmental hazard
nearby
Amount of hazardous
materials
Internally
Variety of hazardous
materials
environmental hazard
Latent chemical energy in
the hazardous materials
Equipment
Duration of service
identification
Operating pressure
Number of injuries
Number of fatalities
Possible consequence
Economic loss
Environmental loss
Frequency of safety training for employees
Frequency of emergency training
Close-circuit television monitoring system
Enterprise’s surveillance
Maintenance cycles of equipment
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Physical
Vulnerability
Indicators
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Category
Adaptability
Indicators
Employees’ vulnerability is represented by the employees’ exposure and density
(Bell et al., 2013). The former relates to the toxic or harmful chemical substances with
which the employees come into contact, and the employees’ work hours (Shi et al.,
2013). The employees’ exposure is measured based on an exposure-dose assessment,
representing the chronic harmfulness of employees due to exposure to toxic
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substances (van Leeuwen and Vermeire, 2007). Glendon et al. (2016) highlighted
such assessment by quantifying the possible health terminal effect of employees when
posing to toxic substances, based upon the intensity, frequency and duration time of
⁄
where
.
=
×
×
×
×
×
(1)
/
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exposure, given as follows:
denotes the concentration of air pollutants, or
;
is the
⁄!; "# refers to the exposure time, !⁄$; "% is the exposure
inhalation rate,
frequency, $⁄&; '( signifies the exposure duration, &; )* is the body weight in
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, , and -. is the average time of contact, $.
The employees’ density correlates with the number of employees in production
/=
01
2
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or supporting facilities, which are measured as follows:
where / stands for the density, 3 45 ⁄
3 45 ; and 2 is the size of the facility,
6
6
.
(2)
; 01 is the number of employees,
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An enterprise’s environmental vulnerability is divided into internally and
externally environmental hazard. The former (internally environmental hazard)
manifests as the storage of hazardous materials, given as follows (AIChE, 1994):
7=
∑:;<= 9 × 7
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where 7 is the hazard index;
(3)
is the variety of hazardous materials; 9 denotes
is
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the ratio of the actual storage of hazardous materials to the critical mass; and 7
the hazard coefficient.
Externally environmental hazard is measured by the residential density outside
the chemical enterprise, including the number of sensitive areas and similar
enterprises nearby, which is given as follows (Jia et al., 2010):
2> =
2? + 2 + 24
3
(4)
where 2> is the enterprise’s externally environmental vulnerability. Further, 2?
denotes the residential density, categorized into 4 levels: “high density” (≥
1,500 3 45 ⁄
600 3 45 ⁄
6
6
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), “medium density” (600-1,500 3 45 ⁄
6
), “low density” (≤
), and “uninhabited.” Each level is assigned a value of 1, 0.75, 0.5,
or 0.25, respectively (Li et al., 2010). Moreover, 2 is the sensitive area index,
divided into 4 levels: “high sensitivity” (the number of environmentally sensitive
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areas is ≥ 5), “medium sensitivity” (≥ 3), “low sensitivity” (≥ 1), and “zero sensitivity”
(0), each of which is assigned a value of 1, 0.75, 0.5, or 0.25, respectively (Jia et al.,
2010). The number of similar chemical enterprises nearby is represented by 24.
When this is ≥ 7, the value is 1; when the number is ∈[5,7], ∈[3,5], or ∈[1,3], the
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values are assigned as 0.75, 0.5, or 0.25, respectively (Jia et al., 2010).
Equipment vulnerability is represented by the equipment identification index,
∑F;<= E5
B = C1 +
G×
3
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measured as follows (Huang et al., 2011):
where B is the equipment identification index;
(5)
is the base equipment identification
number; E5 is factor influencing equipment identification; and 5 denotes the
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sequence number of the influencing factors, with 5 = 1, 2, 3 corresponding to the
three influencing factors: the service duration, operating temperature and pressure,
and maintenance funds.
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Generally, the possible consequence is determined by the frequency of accidents
and the severity of consequences (Casal, 2017). However, different influencing
factors may have the same risk rating in actual operations, which makes it impossible
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to discriminate between their consequences. Hence, this study introduces the risk
level index, represented by the Borda count method, as follows (Tsai et al., 2014):
= J0 − L M + 0 − 1N
;
where
(6)
stands for the Borda ranking of the 5th facility; 0 denotes the number of
;
facilities with a sequence order, and 1N is the sequence order of the consequence
probability. Further,
as follows:
L
=
L
L
is the sequence order of the consequence intensity, measured
+ 1 + 7L ⁄2
(7)
where
L
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= ∑O<= 7O P > 1 ,
LR=
=
= 0; 7L represents the number of facilities with a
severity of 9L P ≤ 4 , with 9=, 96 , 9F , and 9V denoting severities of I, II, III, and
IV, respectively. Moreover,
where
N
1N =
N
+ 1 + 0N ⁄2
= ∑NR=
O<= 0O W > 1 ,
=
(8)
= 0; 0N denotes the number of facilities with a
probabilities of grades A, B, C, D, and E, respectively.
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probability ranking of BN W ≤ 5 , with B= , B6 , BF , BV , and BY representing
All the indicators in this study that describe the four levels of adaptability are
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qualitative, as Table 2 indicates.
Table 2. Classification of adaptability assessment indicators
Quarterly
Within all units
Automated
online
surveillance
Compulsory
periodic
maintenance
IV
Semiannually
Annually
None
Semiannually
Annually
None
Within
production units
Within some
production units
None
Regular
surveillance
Irregular
surveillance
None
Regular
maintenance
Irregular
maintenance
None
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Equipment
maintenance
Quarterly
Classification of Assessment
II
III
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Enterprise’s
surveillance
I
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Indicator
Frequency of safety
training
Frequency of
emergency training
Closed-circuit
television
monitoring system
3.2 Construction of the hybrid model
The indicator system for enterprise’s vulnerability is composed of the physical
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vulnerability indicators and the adaptability indicators, in which the former are mainly
quantitative, whilst the latter are qualitative. Thus, a hybrid D-FDM model is
proposed to assess the safety vulnerability of chemical enterprise in the study. The
assessment is divided into two processes, i.e. primary and secondary, as Figure 1
illustrates. DEA is employed to handle with the quantitative indicators, which
produces assessment results of physical vulnerability. Such results and the data related
to adaptability indicators are then fuzzified simultaneously via membership functions
(MFs). The outcomes of fuzzification are taken as the parameters for fuzzy
assessment to obtain the final results, i.e. the comprehensive vulnerability assessment
results. The DEA overcomes the subjectivity in fuzzy decision-making, and fuzzy
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processing of the DEA results ensures interrelationships of assessment results without
information loss (Liu et al., 2013). Integration of these two methods may give rise to a
maximization of their advantages, and facilitate the multi-attributive decision making
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on vulnerability assessment.
3.2.1 Data envelopment analysis
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Fig.1. The DEA coupled with fuzzy assessment
The DEA uses a relative efficiency measurement as a basis to determine each
decision-making unit’s input-output ratio (Cook et al., 2014). From the input-output
analysis perspective, chemical enterprises’ vulnerability is indicated by their “disaster
efficiency”: a higher efficiency means that accidents may be likely to occur, and the
there are
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enterprise has greater vulnerability (Huang et al., 2013; Alper et al., 2015). Assume
facilities in a chemical enterprise as decision-making units ( 7ZL );
inputs and W outputs are then randomly assigned to the
vectors are [L = J[=L , [6L … []L M
^
7ZL . The input and output
the corresponding weight vectors are > = >= , >6… >]
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^
and _L = J_=L , _6L … _NL M , respectively, while
^
and ` = `= , `6… `N
^
,
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respectively; a denotes the efficiency index. Thus, the constructed efficiency ratio
Charnes, Cooper, and Rhodes (CCR) model is given as follows:
a=
`^ ∙ _L
> ^ ∙ [L
(9)
When a tends toward 1, this indicates that the enterprise has a greater safety
vulnerability. The following optimization is generated by taking a ≤ 1 as the
constraint condition for all decision-making units
7ZL P = 1,2, …
:
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`^ _L
g ^
> [L
f
d
d
`^ _L
W. . ^ ≤ 1, P = 1,2, …
e
> [L
d
d `h ≥ 0, = 1,2, … W
c >L ≥ 0, P = 1,2, …
According to the Charnes-Cooper transformation,
1
> ^ [L
ej= >
c k= `
(11)
and Equation (10) transforms into Equation (12):
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g`^ _L = >l m n
f
dW. . j^ [ − k ^ _ ≥ 0, P = 1,2, …
L
L
^
j [L = 1
e
d
j ≥ 0, k ≥ 0
c
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f =
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(10)
(12)
.
Eventually, substituting the input and output data from n decision-making units
3.2.2 Fuzzification
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into Equation (9) yields the efficiency index a for each assessment unit.
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For the quantitative indicators, fuzzification occurs by using the triangular
membership functions to transform the DEA assessment outcomes into corresponding
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membership degrees, and the procedures are given as follows (Zadeh, 1992; Song et
al., 2014):
1. Determine the index set Z of the assessment object:
Z = o`= , `6 … `: p
(13)
q = o>= , >6 … >] p
(14)
2. Determine the assessment set q:
3. Solve the membership matrix
, as follows:
r==
=r ⋮
?:=
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⋯ ?=]
⋱
⋮ w,0 ≪ rij ≪ 1
⋯ ?:]
(15)
For the results [; obtained by DEA, the matrix is developed as follows:
[; to different levels are as follows:
?;= = 1, ?;6 = ?;F = ?;V = 0.
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If [; exceeds the level I standard, or [; > q=, then the membership degrees of
(16)
If [; falls between the level j and j + 1 standards, or qL > [; > qL{= , then the
L
= 1 − ?;
L{=
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?;
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membership degrees of [; to different levels are as follows:
>L − g;
?; L{= =
>L − >L{=
(17)
(18)
From Eq.(17) to (18), the typical triangular membership functions are shown in
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Fig.2.
Fig.2 Triangular membership functions used in the study
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If [; is below the level IV standard, or [; | qV, then the membership degrees of
[; to different levels are as follows:
?;V = 1, ?;= = ?;6 = ?;F = 0
(19)
Similarly, the membership functions corresponding to the adaptability
assessment indicators (shown in Table 2) can be also determined by using Eq. (16) to
Eq. (19).
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3.2.3 D-FDM assessment
The final results of comprehensive vulnerability are derived from the fuzzy
assessment. It further ranks the objects through a fuzzy linear transformation based
upon principle of maximum membership degree (Zadeh, 1965; Han et al., 2015b).
First, the indicators’ weighted coefficient vector
= o =,
6…
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The procedures are given as follows (Zadeh, 1965; Guo et al., 2009):
should be given as:
:p
(20)
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An analytic hierarchy process (AHP) is adopted to assign the weighted values for
the indicators, expressed as follows (Saaty, 1980; Calabrese et al., 2016):
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1. Construct a judgment matrix and perform paired comparisons of indicators to
obtain the relative importance, measured on a scale of one to five;
2. Solve the judgment matrix and use each column’s geometric mean to obtain
the row vector }; = }= , }6 … }: ^ , 5 = 1,2 … , ; subsequently, normalize }; to
obtain the weighted vector }; ’ ;
and consistency ratio
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3. Compute the consistency index
from the
judgement matrix to test its consistency:
=
≤ 0.1, the matrix is considered consistent.
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When
•]€• −
−1
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=
Finally, the final assessment vector will be composed:
(21)
(22)
= ° , where R is the
membership matrix from Eq.(15). The fuzzy operator in this study, “ ° ,” uses ordinary
additive operations within bounded constraints:
h
=
]
5 ƒ1, „
L<=
5 J}L , ?Lh M… , P = 1,2, …
(23)
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4. An illustrative case example
This study focuses on the case of a chemical plant that synthesizes ammonia
from natural gas in Sichuan province, to assess its safety vulnerability. Seventeen
facilities are in the industrial site, including such main production facilities as the gas
distribution station and the desulfuration, decarburization and methanation,
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compression and synthesis, and urea production departments. These facilities also
include supporting departments, such as those related to the desalinized water supply,
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maintenance, warehousing, and laboratory. Figure 3 displays the plant’s layout.
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1. Wastewater treatment department 2. Water supply 3. Power supply unit 4. Liquid ammonia
storage 5. Urea production 6. Laboratory 7. Compression and synthesis department 8.
Decarburization and methanation department 9. Desulfuration department 10. Waste gas treatment
11. Nitrogen generation department 12. Gas distribution station 13. Warehouse 14. Office building
15. Desalted water supply 16. Heat supply 17. Maintenance department
Fig.3. The layout of the investigated chemical plant
The input data for the physical vulnerability assessment are primarily obtained
through a field survey and review of the case plant’s related statistics. The primary
data are handled using Equations (1) to (8), and normalized for input into MaxDea
software for the DEA analysis, as Table 3 indicates. Regarding its adaptability, five
senior management executives specializing in safe production within the case plant
are invited to rank the importance of each adaptability indicator. The mean scores are
substituted into the AHP model to obtain the weightings, as Figure 4 illustrates.
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0.0025
2.085
0
0
0
0
0
0
0
0
1.877
0
0
0
0.0027
0.0021
0.0056
0.002
0.0018
0.0036
0.0017
0.0021
0.0041
0.0026
0.004
0.0046
0.211
Possible
consequence
0
15.57
0.0189
34.5
47.81
48.21
6
8
7
0.417
6.2
49.58
8
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.417
0.056
0
0
0
0
0
0
0
0
40
0
0
0
47.085
37.5
30
20
25
20
35
30
30
70
30
20
20
8
7
6
6
6
6
8
7
7
9
6
6
6
0.417
0.417
0.417
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1.97
Equipment
identification
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0.0022
0.0036
0.0058
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0.189
0.73
0.19
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Gas distribution station
Desulfuration department
Decarburization and methanation
department
Compression and synthesis
department
Urea production
Desalted water supply
Nitrogen generation department
Maintenance
Warehouse
Laboratory
Heat supply
Wastewater treatment department
Waste gas treatment
Liquid ammonia storage
Water supply
Power supply unit
Office building
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Unit
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Table 3. Input data of physical vulnerability indicators
Externally
Internally
Employee’s Employee’s
environmental environmental
exposure
density
hazard
hazard
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Score
0.45
0.30
0.24
0.17
0.15
0.11
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0.07
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0.42
0.00
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Frequency of Frequency of Close-circuit
Enterprise's
Safety Training Emergency
Television
Surveillance
Training
Monitor system
Equipment
Maintenance
Adaptability
Fig.4. Weight of the indicators for adaptive capacity
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4.1 Results of the physical vulnerability
Figure 5 illustrates the results of the case enterprise’s physical vulnerability
assessment. Clearly, the production facilities are more physically vulnerable than the
supporting facilities. For instance, the desulfuration and decarburization and
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methanation departments are more vulnerable. This finding parallels Darbra et al.’s
(2010) conclusion, in that chemical accidents are more likely to occur during the
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production process.
The disaster efficiency of the heat supply, compression and synthesis, and liquid
ammonia storage departments equals one, which indicates that these three facilities
have the greatest safety vulnerabilities. This finding relates to the production nature of
chemical industry. As the compression and synthesis as well as liquid ammonia
departments are both key facilities along the production line, they involve much
equipment related to chemical reactions, and a complex reaction environment (Kidam
et al., 2013). Thus, they may be more vulnerable, and more likely to experience safety
accidents. A field survey of the case plant revealed that the heat supply department
installed a gas boiler operating at 22 ton/h that has had relatively frequent
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malfunctions over the last decade due to various failures, including an improper
ignition and unstable flame. This demonstrates that the department is physically
vulnerable.
Score
1.0
0.8
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0.6
0.4
0.2
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0.0
Facility
Fig.5. Assessment results of the chemical enterprise’s physical vulnerability
4.2 Results of the adaptability
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Figure 6 displays each facility’s adaptability assessment results. The heat supply
department exhibits extremely high adaptability, which encompasses 50% of the
overall ratio. The compression and synthesis, decarburization and methanation, liquid
ammonia storage, and desulfuration departments indicate high adaptability. As these
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departments are key facilities in the case plant, more attention is paid to risk
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prevention, thus decreasing the probability of safety accidents.
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Score
1.0
low
0.8
medium
0.4
high
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0.6
0.2
extremely-high
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0.0
Facility
Fig.6. Assessment results of the chemical enterprise’s adaptive capacity
4.3 Results of the comprehensive vulnerability
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Figure 7 illustrates the results of the case plant’s comprehensive vulnerability.
Among all the facilities, the liquid ammonia storage, compression and synthesis,
decarburization and methanation, heat supply, and desulfuration departments all have
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a disaster efficiency of greater than 0.7. This also indicates that the production
facilities are more vulnerable than the supporting facilities. The field survey indicates
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that two liquid ammonia tanks of 980 m3 are located in the liquid ammonia storage
department, which may emit unorganized NH3 emissions thus result in potential
health impacts to workers. This indicates why the liquid ammonia department
presents a high vulnerability.
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Score
0.8
0.6
0.4
0.0
Facility
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0.2
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Fig.7. The chemical enterprise’s comprehensive vulnerability
Figure 8 indicates that the enterprise’s comprehensive vulnerability, which
includes adaptability assessment results, is lower than physical vulnerability. Despite
that, the production facilities’ comprehensive vulnerability remains greater than that
of the supporting facilities. Nevertheless, after incorporating the adaptability
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assessment results, scores from the liquid ammonia storage, compression and
synthesis, and heat supply departments decease from 1 to 0.7653, 0.7524, and 0.7484,
respectively. This may closely relate to the abundant chemicals used, as well as the
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complex equipment and environment involved in the production facilities.
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Score
1.0
Physical vulnerability
0.8
Comprehensive vulnerability
0.6
0.4
0.0
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Facility
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0.2
Fig.8. Comparison of the comprehensive and physical vulnerabilities
4.4 Discussion
This study identified that production facilities are more vulnerable than
supporting facilities. Dahl and Kongsvik (2018) pointed out that chemical accidents
were more likely to occur during chemical production, which indirectly validated our
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results. This study further indicated that different quantities of hazardous chemicals
were stored in the enterprise’s liquid ammonia storage, desulfuration, and urea
production departments. Thus, a certain risk of leakage exists, which leads to a
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comparatively high vulnerability. Zhang and Zheng (2012) verified our results,
revealing that 41.5% of the accidents were caused by these chemicals leaking in fixed
facilities. Although implementing compensating safety measures in the production
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facilities may decrease safety risks to a certain extent, these facilities’ inherent
vulnerability still remains high. Thus, it is necessary to reinforce prevention in
improving process safety (Khan et al., 2016).
Some uncertainties exist in the assessment: 1. The case enterprise’s data has yet
to be validated due to the complexity in the data accessibility and availability. For
example, most of the production and supporting facilities have yet to install an
automated surveillance system. 2. As the weighing of adaptability indicators was
determined by the scores given by five in-site department executives, this process is
subject to their preferences, which may impact the assessment outcomes.
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5. Conclusion and further study
This study constructs an indicator system for a safety vulnerability assessment
from the perspectives of enterprises’ exposure, sensitivity, and adaptability to hazards.
The DEA is a hybrid with fuzzy decision-making to develop an assessment model
(D-FDM). A chemical plant that synthesizes ammonia from natural gas in Sichuan
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province, China, is selected as an example to demonstrate the application. Through
the identification of facilities that are most likely to fail, this assessment provides
insight into the improvement of process safety.
The results indicate that production facilities, including the liquid ammonia
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storage, compression and synthesis, decarburization and methanation, heat supply,
and desulfuration departments, are more vulnerable than the supporting facilities.
Although the introduction of adaptive and compensating measures may decrease
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safety risks in all involved facilities, the need remains to reinforce regulation for the
promotion of safe production and operations.
This study contributes to the method improvement of risk assessment. By taking
a typical method of risk assessment “Hazard and Operability Analysis” (HAZOP) as
an example, it is effective to identify possible causes and consequences resulted from
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deviation of process parameters, ultimately to lead actions taken on improvement of
industrial safety (Kościelny et al., 2017). However, the main disadvantage emerges in
its inherent structure of qualitative analysis that highly depends upon experienced
professionals resulting in uneven quality of analysis reports (Dunjó et al., 2010). The
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hybrid method employed in this study fills such gap by ranking the vulnerability of
various production and auxiliary facilities in a chemical enterprise through a
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quantitative measurement, which may overcome subjective randomness in risk
assessment.
However, this study has certain limitations: first, all the adaptability indicators
are qualitative and susceptible to subjectivity, which may result in uncertainties in the
assessment outcomes. Second, all the indicator parameters are certain and static, in
that these parameters’ dynamic temporal changes have been omitted. Especially, some
of the parameters are simply defined in order for the quantitative measurement, e.g.
only toxic substances are considered for the measurement of employees’
exposure-response. Further studies can improve the assessment by introducing more
case scenarios that reflect the safety characteristics of chemical enterprises to validate
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the hybrid model. Uncertainty analysis will be applied in examining dynamic
parameter changes’ impacts on the assessment outcomes.
Acknowledgement
This study is sponsored by National Natural Science Foundation of China (No.
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41571520), Sichuan Province Circular Economy Research Center Fund (No.
XHJJ-1802), The Fundamental Research Funds for the Central Universities (No.
A0920502051408), The State-province Joint Engineering Laboratory of Spatial
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Information Technology for High-Speed Railway Safety (No. IRT13092).
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Research Highlights
Construct a composite indicator system for safety vulnerability assessment on
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chemical enterprise.
Provide a hybrid model (D-FDM) by combining a data envelopment analysis (DEA)
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and fuzzy decision-making for vulnerability assessment.
Chemical enterprises’ inherent adaptability to hazards has been considered while
assessing their vulnerabilities.
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A case example is provided to demonstrate the model application.
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