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Dynamic risk assessment of corrosion in CO2 transport pipelines
under multi-factor coupling and failure probability modeling
Xinxin Fan , Guangyu Liu , Xin Ouyang , Feng Yan , Qihui Hu ,
Cailin Wang , Yuxing Li
PII:
DOI:
Reference:
S2667-1433(25)00089-7
https://doi.org/10.1016/j.jpse.2025.100342
JPSE 100342
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Journal of Pipeline Science and Engineering
Received date:
Revised date:
Accepted date:
13 July 2025
1 August 2025
19 August 2025
Please cite this article as: Xinxin Fan , Guangyu Liu , Xin Ouyang , Feng Yan , Qihui Hu ,
Cailin Wang , Yuxing Li , Dynamic risk assessment of corrosion in CO2 transport pipelines under
multi-factor coupling and failure probability modeling, Journal of Pipeline Science and Engineering
(2025), doi: https://doi.org/10.1016/j.jpse.2025.100342
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Dynamic risk assessment of corrosion in CO₂ transport
pipelines under multi-factor coupling and failure probability
modeling
Xinxin Fana,b#, Guangyu Liua,b#, Xin Ouyangc, Feng Yanc, Qihui Hua,b, Cailin Wanga,b*, Yuxing Lia,b*
a
: College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao,
266580, China
b
: Shandong Provincial Key Laboratory of Oil, Gas and New Energy Storage and Transportation
Safety, China University of Petroleum (East China), Qingdao, 266580, China
c: Pipe China Institute of Science and Technology, Tianjin, 300450, China
*Corresponding authors:
Cailin Wang, E-mail address: clwang0330@163.com
Yuxing Li, E-mail address: liyx@upc.edu.cn
#
These authors contributed equally to this work and should be considered co-first author
Abstract：
Internal corrosion poses a major threat to the integrity of CO₂ transport pipelines, which are
essential to CCUS technology. However, accurate risk assessment faces significant challenges. This
study employs gray correlation analysis and triangular fuzzy hierarchical analysis methods to calculate
the contribution weights of factors such as gas impurities and operating parameters to internal
corrosion, coupled with the influence of service life on pipeline internal corrosion, and establishes
evaluation criteria for the classification of various factors. Based on the results of weight contribution
calculations and factor evaluation classification criteria, combined with fuzzy mathematics methods, a
corrosion risk rating model for CO₂ transport pipelines was established. Comprehensively considering
the factors influencing corrosion inside CO₂ pipelines, a T-S fuzzy failure tree for corrosion inside CO₂
pipelines was established. Based on this, a Bayesian network was constructed, and the probabilities of
root nodes and conditional probabilities of intermediate nodes were determined using factor evaluation
criteria. The corrosion failure probability inside CO₂ transmission pipelines was quantitatively
calculated, and a failure probability calculation model was established. This study proposes, for the first
time, an integrated framework for CO₂ pipeline corrosion risk assessment that combines multi-factor
coupling analysis, dynamic POF modeling considering service life, fuzzy risk grading, and
probabilistic fault tree analysis. The developed model significantly enhances the ability to dynamically
assess corrosion risk levels and quantitatively predict failure probabilities, providing a scientific and
reliable tool for optimizing pipeline integrity management and safety in CCUS operations.
Keywords: CO2 pipeline; Dynamic failure assessment; risk management; T-S fuzzy
failure fault tree.
1. Introduction
Carbon capture, utilization, and storage (CCUS) is a key technology for mitigating anthropogenic
CO2 emissions and achieving climate goals(Liu et al., 2025a, Sun et al., 2023). The core of CCUS
infrastructure is long-distance pipelines for transporting dense or supercritical CO 2 (Simonsen et al.,
2024). However, internal corrosion in long-distance pipelines is not negligible(Simonsen et al., 2025).
This corrosion is mainly caused by trace impurities (such as H2O, O2, SO2, NO2, and H2S)(Ding et al.,
2025, Sun et al., 2022, Liu et al., 2025b) in the CO2 stream interacting with the pipeline steel under
high pressure and flow coupling conditions. Therefore, effective corrosion risk management urgently
requires timely prediction of failure, identification of high-risk pipeline sections, and the ability to
comprehensively consider the complex characteristics of pipeline operating conditions, environmental
conditions, and material degradation over time. However, most existing models are static or quasi-static,
often treating corrosion rates or failure probabilities as constant or slowly changing values, and
ignoring the synergistic effects of operating variables (such as pressure, temperature, and impurity
concentration), resulting in significant shortcomings in the assessment of corrosion risks under
transient conditions.
To overcome these shortcomings, Dynamic Risk Assessment (DRA) was developed as a core
theoretical framework for handling risks in time-varying systems(Yang et al., 2024). DRA refers to
modeling and reasoning methods that update the risk level of a system in real time as the system's
operating conditions, structural state, and environmental factors change. Unlike traditional static
methods, DRA integrates sensor data, degradation mechanisms, and probabilistic inference algorithms
to capture the dynamic evolution path of system failure probability over time. In this field, Professor
Faisal Khan (Han et al., 2024, Yazdi et al., 2023, Dawuda et al., 2021)and his C-RISE team have
consistently been at the forefront of international research. They have systematically developed a range
of dynamic risk modeling tools, including Dynamic Bayesian Networks (DBN), Object-Oriented
Bayesian Networks (DOOBN), and Fuzzy Logic Inference Systems, and successfully applied these
tools to the modeling of MIC corrosion in subsea pipelines Dao (Dao et al., 2023a)and the risk
assessment of sedimentation corrosion under varying flow conditions (UDC) Yazdi (Yazdi et al., 2022).
The main contributions of these models include: capturing the nonlinear time-coupling relationships
between corrosion factors; achieving multi-source sensor fusion and state monitoring; and providing an
interpretable probabilistic inference structure that facilitates practical operations and decision support
for engineers. Among them, the MIC dynamic model developed by Dao et al. is (Dao et al.,
2023b)based on a time-driven DOOBN framework, which simulates bacterial growth, membrane layer
formation, and corrosion rate evolution. Its predictive accuracy has been verified under multiple sets of
experimental data, significantly outperforming static Bayesian and purely physical models. Guo et al.
(Guo et al., 2021)extended DRA to urban gas pipeline networks and proposed a dynamic failure
probability assessment model that considers the uncertainty of user load fluctuations, pipeline aging,
and leak propagation, providing an operational logical framework for corrosion modeling in real urban
pipeline networks. In addition to traditional Bayesian networks, the Copula-Bayesian framework
proposed by Li et al.(Li et al., 2022) provides a new method for handling multivariate complex
dependencies in DRA, particularly suitable for scenarios with asymmetry and tail coupling behavior
between corrosion paths. In retirement risk assessment, this method effectively identifies critical failure
paths that cannot be captured by traditional risk matrices. Li et al.(Li et al., 2024) developed a dynamic
risk-based life assessment method for subsea pipelines. The identification of risk factors for pipeline
failure was achieved through fault tree analysis, with these risk factors then being converted into
network nodes and a Bayesian network model capable of probability updates constructed.
In recent years, artificial intelligence (AI) has also received widespread attention in the field of
corrosion prediction, especially in pitting depth prediction and remaining life assessment. For example,
Akhlaghi et al. (Akhlaghi et al., 2023)developed a point corrosion prediction model based on deep
neural networks, achieving higher accuracy than traditional models in multiple datasets. Mesghali and
Abbassi
(Mesghali et al., 2024)used integrated tree models to identify key variables affecting the
corrosion evolution of buried pipelines. However, although such AI models perform well in pattern
recognition, they generally suffer from problems such as missing causal structures, poor interpretability,
and strong data dependency. In scenarios with complex corrosion mechanisms, sparse data, or
significant fluctuations, the results are less reliable and traceable. The DRA framework, with its clear
structure, traceable logic, and ability to integrate expert knowledge, is particularly suitable for
operational decision-making scenarios.
Although DRA has made significant progress in corrosion prediction and system safety
assessment, its application in CO2 pipeline internal corrosion risk assessment still faces the following
issues: difficulty in comprehensively coupling time-varying driving factors (such as impurity
concentration, corrosion depth, etc.); insufficient modeling of corrosion degradation path uncertainty;
and lack of an integrated model framework for inferring failure paths from multi-factor interactions.
Therefore, this study proposes a hybrid method combining gray correlation analysis (GCA) with
the triangular fuzzy analytic hierarchy process (TFAHP). Through this method, the correlation between
various factors (temperature, pressure, flow velocity, H2O, O2, NO2, SO2, H2S) and corrosion rate in the
intrinsic system of complex pipeline corrosion is quantified. Based on the theory of residual strength
and failure probability of pipelines, a new dynamic relationship between corrosion rate, corrosion depth,
nominal wall thickness, and service life has been proposed. A pitting factor has been introduced to
determine the dynamic failure probability criterion based on pitting and uniform corrosion. Through
comprehensive analysis, the factors influencing internal corrosion in CO2 pipelines have been
identified, and a T-S fuzzy failure tree for internal corrosion in CO2 pipelines has been established. A
Bayesian network was constructed using T-S fuzzy failure trees, and the probability of each root node
and the conditional probability of intermediate nodes were determined using factor evaluation criteria.
The probability of corrosion failure in carbon dioxide pipelines was quantitatively calculated, and a
failure probability calculation model was established. The CO2 pipeline segment risk rating assessment
model and failure probability calculation model established in this study provide effective technical and
data support for carbon dioxide transport pipeline risk control, integrity management, and on-site
operation. The proposed risk ranking and failure probability assessment model for CO 2 pipeline
segments is designed for seamless integration into SCADA systems, enabling real-time corrosion risk
visualization and dynamic maintenance strategy adjustment. The model is particularly well-suited for
the following engineering scenarios: Subsea or buried pipelines operating under challenging conditions
such as MIC, UDC, or supercritical CO2 environments; Aging pipeline segments characterized by
structural degradation and high uncertainty in remaining useful life; Complex operational settings
where multi-parameter diagnosis and predictive failure assessment are required under incomplete or
uncertain data conditions. By combining the interpretability of dynamic risk assessment (DRA) with
the uncertainty tolerance of fuzzy logic inference, this study offers a robust methodological pathway
toward the development of next-generation corrosion risk modeling tools. The proposed framework
provides technical and analytical support for risk control, integrity management, and field-level
operational decision-making in CO2 transport pipelines.
2. Model Establishment
2.1 Weighting Model for Factors Affecting Internal Corrosion in CO2 Pipelines
In light of the findings from prior research in this paper and experimental data derived from a
comprehensive literature review, the corrosion rate is identified as the primary target layer. The factor
layer encompasses a range of pivotal factors that exert an influence on the corrosion rate, chief among
them being temperature, pressure, flow rate, moisture content, nitrogen oxides, sulfur oxides, oxygen,
and other variables. The numerical layer signifies the specific numerical values of each factor layer. The
numerical layer signifies the specific numerical values of each factor layer. The corrosion environment of
CO2 transport pipelines is regarded as a gray system, exhibiting clear external results but ambiguous
internal characteristics. That is to say, the influence of each individual factor variable on the target layer
is evident; however, the coupling relationships among the factors and their influence on the target layer
remain opaque. Consequently, gray correlation analysis is employed to quantify the coupling correlation
between the corrosion rate and various influencing factors.
The ensuing investigation employed a multifaceted approach, integrating the outputs of a gray
correlation analysis with the triangular fuzzy hierarchical analysis method. The root mean square method
was then utilized to calculate the contribution weights of each influencing factor to the corrosion rate.
Concurrently, to mitigate the impact of subjectivity on the calculation outcomes, triangular fuzzy
numbers were employed to minimize the subjectivity of the judgment matrix.
The specific model establishment process is as follows:
(1)
Let M be a fuzzy number on the real number set R = (–∞, +∞). When its membership function
μM: R → [0, 1] satisfies the following equation, it is called a triangular fuzzy number.
l
 1
 m  l x  m  l , x  [l , m]

u
 1
 M ( x)  
x
, x  [m,u]
mu
m  u
0, other



(1)
In the formula, l  m  u , l and u represent the lower and upper bounds of M, respectively, and m
is the median of M. In general, the triangular fuzzy number M can be denoted as (l, m, u).
(2)
If μM and μN represent the membership functions of two triangular fuzzy numbers M, N, then
the membership function of the triangular fuzzy number T=f (M, N) is given by the following equation 2:
T ( z ) 
sup
(x,y)R2 ,z f (x,y)
 min   ( x),  ( y) 
M
N
(2)
Based on Equation 2, let M1 = (l1, m1, u1) and M2 = (l2, m2, u2) be two triangular fuzzy numbers.
Combining the triangular fuzzy number algorithm, we have:
M1  M 2  l1 , m1 , u1   l2 , m2 , u2   l1  l2 , m1  m2 , u1  u2 
(3)
M1
M 2   l1 , m1 , u1 
l2 , m2 , u2   l1l2 , m1m2 , u1u2 
 1 1 1
M 1  (l , m, u )1   , , 
u m l 
(3)
(4)
(5)
The importance of indicator j relative to indicator i is represented by the triangular fuzzy
number aij-1, utilizing triangular fuzzy numbers (l, m, u). Following the provision of n(n-1)/2 fuzzy
judgments, a fuzzy judgment matrix A = (aij)n×n composed of triangular fuzzy numbers can be obtained.
The determination of the median m of the triangular fuzzy numbers in the fuzzy judgment matrix is
based on the 1–9 scale method of the hierarchical analysis method.
In the Analytic Hierarchy Process (AHP), the negative average of the remaining eigenroots of the
judgment matrix, excluding the maximum eigenroot, is introduced as an indicator of the deviation of
the judgment matrix from consistency. This phenomenon is expressed as follows: The calculation of CI
is derived from the following equation: CI = (λmax - n) / (n - 1). The consistency of the judgment matrix
is optimized when the CI value is minimal, and it is compromised when the CI value is maximal. In the
event that the judgment matrix is found to be consistent, CI is equivalent to 0.
When making judgments, it is sufficient for the judgment matrix to exhibit satisfactory
consistency. Therefore, it is necessary to introduce the average random consistency index of the
judgment matrix to assess whether judgment matrices of different orders exhibit satisfactory
consistency. In practical applications, the ratio of the consistency index CI of a judgment matrix to the
average random consistency index RI of the same order is called the random consistency ratio, denoted
as C, where C = CI/RI. When CR is less than 0.10, the judgment matrix is considered to have
satisfactory consistency, and hierarchical analysis can be conducted.
After passing the consistency test, the fuzzy evaluation factor matrix E is constructed as shown in
Equations 6:
u l

1
1  12 12

2m12

 u21  I 21
1
1  2m
E   eij   
21
nn


1  un1  ln1 1  un 2  ln 2

2mn1
2mn 2
u l 
1  1n 1n 
2m1n

u2 n  l2 n 
1
2m2 n 



1

(6)
Among them, the elements of matrix E other than 1 are defined as standard deviation rates eij. The
larger eij is the greater the ambiguity and the lower the credibility; the smaller eij is the smaller the
ambiguity and the higher the credibility.
Based on this, the adjusted judgment matrix Q is calculated as shown in Equations 7 and 8:
Q  M E
(7)
 m11 m12
m
m22
Q   21


 mn1 mn 2
u l

1
1  12 12

2m12
m1n  
 u l
1
m2 n  1  21 21
2m21


 
mnm  
1  un1  ln1 1  un 2  ln 2

2mn1
2mn 2
u l 
1  1n 1n 
2m1n

u2 n  l2 n 
1
2m2 n 



1

(8)
Convert the judgment matrix Q by columns into a judgment matrix Q' with a diagonal of 1, then
use the square root method to calculate the weights of each indicator. Calculate the nth root of all
elements in each row, then normalize them to obtain the weight calculation results, as shown in
Equations 9:
w
i  n i , i  1, 2, , n
 i
(9)
i 1
2.2 CO2 Pipeline Internal Corrosion Risk Assessment Model
Based on the weight calculation model for corrosion influencing factors in CO 2 pipelines,
combined with the POF failure probability classification criteria(Leira et al., 2016, Mahmoodian and Li,
2017), risk assessment criteria for each influencing factor were established. A fuzzy comprehensive
evaluation method was used to construct a graded evaluation fuzzy subset, and the fuzzy evaluation
indicators were quantified to determine the maximum membership degree. Using fuzzy transformation
principles, a comprehensive analysis of each indicator was conducted to obtain the overall evaluation
grade. The specific model establishment process is as follows:
(1)
First, establish a set of evaluation factors for the fuzzy evaluation content, referred to as
factor set X, as shown in Equations 10:
X  {x1 , x2 ,..., xn }
(10)
Among them, xi is the ith factor affecting the system, which divides the whole into relatively
independent parts. A corresponding set of comments Y is established for each part, as shown in
Equations 11:
Y  { y1 , y2 ,..., yn }
(11)
yj is the jth grade of the evaluation result.
Based on the evaluation results, a fuzzy set matrix R for the comprehensive evaluation can be
obtained, which is the Cartesian product of the single-factor evaluation matrices X and Y, as shown in
Equation 12:
 r11 , r12 ,..., r1k 
 r , r ,..., r 
2k 
R   21 22
....



 rn1 , rn 2 ,..., rnk 
(2)
(12)
For factors with concentrated evaluation factors, their impact on the overall system is not the
same, meaning that their contribution weights in the comprehensive evaluation are not the same. In
order to determine their importance in the evaluation, it is necessary to determine the “weight” that
represents their importance, i.e., the weight coefficient set, as shown in Equation 13:
A  (a1 , a2 ,....an )
(13)
Among them, a1, a2, …, an are the weight coefficients corresponding to factors x1, x2,…xn in the
factor set.
(3)
Perform fuzzy mathematical comprehensive evaluation calculations to obtain the results. Use
the algebraic product operation of fuzzy sets to obtain the fuzzy comprehensive evaluation matrix, as
shown in Equation 14:
 r11 , r12 ,..., r1k 
 r , r ,..., r 
2k 
B  A * R   a1 , a2 ,..., an   21 22
....



 rn1 , rn 2 ,..., rnk 
(14)
The result of fuzzy comprehensive evaluation is the membership degree of the evaluated object to
each fuzzy subset, i.e., a fuzzy vector. Let the fuzzy comprehensive evaluation result vector be b as
shown in Equation 15:
b  (b1 , b2 ,...bn )
(15)
br  max[b j ]
(16)
If:
i  j m
The evaluation system as a whole is classified under the r level.
In this paper, specific comments are discussed in terms of high risk, medium risk, and low risk. If
the evaluation results are:
B  ( Z1 , Z 2 , Z 3 )
(17)
According to the principle of maximum membership degree, if Z 1 > Z2, Z3, the evaluation is low
risk. If Z2 > Z1, Z3, the evaluation is medium risk. If Z3 > Z2, Z1, the evaluation is high risk.
Calculation Model for the Probability of Corrosion Failure in CO 2 Pipelines
2.3 Calculation Model for the Probability of Corrosion Failure in CO2 Pipelines
2.3.1 Corrosion-induced T-S fuzzy failure in CO₂ pipelines
In light of the extant research findings on the subject of corrosion, an accident tree for internal
corrosion failure in CO₂ pipelines was established. The accident tree was pruned and optimized to
establish a T-S fuzzy failure fault tree for internal corrosion in CO₂ pipelines. The fault tree
methodology employs internal corrosion failure in CO₂ pipelines as the top event, while
comprehensively considering all factors affecting pipeline internal corrosion. These include eight
factors such as operating temperature, operating pressure, flow velocity, water content, and impurity
components, which are designated as bottom events. Based on extant research results, four factors—pH,
droplet spreading radius, wall shear stress, and CO₂ density—were selected as intermediate events. The
fundamental event failure states of the accident tree were transformed into multi-fault states, and the
operational relay gates were converted into T-S gates to establish a T-S fuzzy fault tree for CO₂ pipeline
internal corrosion.
2.3.2 Bayesian network establishment for internal corrosion of CO₂ pipelines
The objective is to map the structure of the T-S fault tree for corrosion in CO₂ pipelines to a
Bayesian network. The basic events of the T-S fault tree should be mapped to the root nodes of the
Bayesian network, intermediate events to intermediate nodes, and top events to leaf nodes. The input
events of the T-S gates should be mapped to parent nodes, and the output events should be mapped to
child nodes. The corresponding nodes in the Bayesian network model should be connected with
directed edges. A Bayesian network diagram for corrosion failure in CO₂ transport pipelines should be
constructed based on the rules. The T-S gate rules should be utilized to assign values to the conditional
probability tables of the corresponding nodes in the Bayesian network. Furthermore, all T-S gate rules
should be converted into conditional probabilities of the nodes in the Bayesian network.
2.3.3 Determination of the failure probability of root nodes in Bayesian networks for internal
corrosion
This study is founded on a comprehensive review of pertinent current standards, including the
Pipeline Risk Management Manual, the Pipeline Integrity Management Specification SY/T
1180-2014(Binglin et al., 2015), and the Pipeline Internal Corrosion Control Specification GB/T
23258-2020. In addition, the study draws upon the characteristics of CO₂ transmission pipelines and the
impact of pipeline service life on internal corrosion. By taking a multifaceted approach, this study aims
to establish the coupled weights of various influencing factors on internal corrosion. In light of extant
experimental findings for single-factor variables and the extant experimental data from the existing
literature, fuzzy subsets are employed to describe the failure probability of root nodes. The fuzzy
subsets of failure probabilities for each root node of the Bayesian network for internal corrosion failure
of CO₂ transmission pipelines are determined based on the failure probability classification criteria
using service life and POF. The specific process is outlined as follows:
(1)
Determine the service life of the pipeline based on pipeline operating data, and then
determine the corrosion rate range corresponding to the different probabilities of internal corrosion
failure occurring under the current pipeline condition.
(2)
The determination of the failure probability pm for each root node under different service
times is to be made using single-factor variable experiments or existing experimental data from the
literature, in accordance with relevant standards.
(3)
Fuzzy processing is performed on pm. To reflect generality, a trapezoidal membership
function is employed to determine pl and pr, where pl = pr = 0.15pm. Finally, the fuzzy subset of the
fault probability of the root node is determined, where p m, pl, and pr represent the center, upper bound,
and lower bound of the fuzzy subset, respectively.
(4) The weighting calculation model for internal corrosion influencing factors yielded
results that informed the assignment of distinct weight values to each root node. The relevant T-S
and Bayesian network calculation formulas are employed to calculate the fuzzy subsets of
corrosion failure probabilities for each intermediate and leaf node, thereby determining the safety
status of internal corrosion in CO₂ transport pipelines.
3. Results and Discussion
3.1 Key factors contributing to corrosion inside CO2 pipelines
As illustrated in Table 1, recent studies have yielded findings pertinent to the issue of corrosion in
CO₂ transmission pipelines. These findings are supported by publicly accessible datasets(Sun et al.,
2025). These data were then integrated with the experimental data from the preceding sections of this
paper to calculate the corrosion key factor identification method. For the eight studied factors, since the
interrelationships among these influencing factors are not yet clearly defined, but they have a clear
correspondence with corrosion rates, they are treated as fuzzy systems with ambiguous attributes. The
gray correlation method is employed to perform dimensionless processing on these factors, thereby
obtaining the correlation coefficients between each factor and corrosion rates. A gray association
analysis calculation program was developed using MATLAB, and the results of the association degrees
for each factor are shown in Table 1 and Fig.1.
Table 1
Results of grey correlation analysis method
research
moisture
temperature
pressure
factors
relevance
flow
O2
SO2
NO2
H2S
content
0.3771
0.3762
0.3775
velocity
0.3742
0.3741
0.3729
0.3740
0.3775
Fig.1.
Comparison matrix of grey correlation coefficients among various factors
In order to achieve quantitative selection of the median of triangular fuzzy numbers in the
judgment matrix, the correlation values calculated for different research factors are compared in pairs.
This process is repeated until the ratio range and the corresponding median m of triangular fuzzy
numbers in the ratio range are determined. The specific selection principles are delineated in Table 2.
Table 2
Refer to the value of triangular fuzzy number center value m
factor correlation
(1,1.1)
[1.1,1.2)
[1.2,1.3)
[1.3,1.5)
[1.5,2)
[2,3)
[3,4)
[4,+∞)
[1,2)
[2,3)
[3,4)
[4,5)
[5,6)
[6,7)
[7,8)
[8,9]
coefficient ratio
m value
According to the above rules, construct the mean matrix of the factor layer triangular fuzzy judgment using
the 1-9 scale method as the principle M:
1.00
1.20

0.91

1.50
M 
1.70

1.90
1.80

0.91
0.83
1.00
0.74
1.43
1.45
1.85
1.47
0.74
1.10
1.35
1.00
1.81
1.87
1.95
1.89
1.00
0.67
0.70
0.55
1.00
1.02
1.30
1.03
0.55
0.59
0.69
0.53
0.98
1.00
1.28
1.02
0.53
0.53
0.54
0.51
0.77
0.78
1.00
0.80
0.51
0.56
0.68
0.53
0.97
0.98
1.25
1.00
0.53
1.10 
1.35 
1.00 

1.81 
1.87 

1.95 
1.89 

1.00 
(18)
It is imperative to refrain from deriving the judgment median matrix based on the triangular fuzzy
judgment matrix M. The maximum Eigenvalue, designated as λ max, of the median matrix M must be
calculated. Conducting consistency testing on λ max is imperative to ensure the accuracy of the results.
Following a thorough calculation, it was determined that this median matrix successfully passed the
one-time test.
According to the calculation, adjust the decision matrix Q and normalize the diagonal of matrix Q
to obtain Q':





'
Q 






1.0000
1.1771
0.9044
1.6097
1.6724
1.9561
1.7085
0.9044
0.8495
1.0000
0.7683
1.3675
1.4208
1.6618
1.4514
0.7683
1.1057
1.3016
1.0000
1.7799
1.8492
2.1629
1.8891
1.0000
0.6212
0.7313
0.5618
1.0000
1.0389
1.2152
1.0613
0.5618
0.5979
0.7038
0.5408
0.9625
1.0000
1.1696
1.0216
0.5408
0.5112
0.6018
0.4623
0.8229
0.8550
1.0000
0.8734
0.4623
0.5853
0.6890
0.5294
0.9422
0.9789
1.1450
1.0000
0.5294
1.1057 
1.3016 
1.0000 

1.7799 
(19)
1.8492 

2.1629 
1.8891 

1.0000 
The weights of each factor layer for the target layer were calculated using the square root method,
as shown in Table 3.
Table 3
The weight ranking of the factor layer to the target layer
research
moisture
temperature
pressure
factors
weight
flow
O2
SO2
NO2
H2S
content
0.1063
0.1153
0.1013
velocity
0.1375
0.1407
0.1551
0.1425
0.1013
As demonstrated by the data, nitrogen oxides emerge as the predominant factor influencing the
severity of corrosion within pipelines. Furthermore, O 2 and sulfides have been shown to have a
relatively high relative weight as influencing factors on the severity of pipeline corrosion. In the
context of long-distance 2 pipelines, field monitoring should prioritize the composition and
concentration of impurities within the pipeline. The contribution weights of various influencing factors
to corrosion rates are ranked as follows: The sequence of gases considered is NO 2 > SO2 > O2 > H2S,
with considerations of pressure, temperature, and flow rate/water content.
3.2 Standard for Corrosion Failure and Risk Assessment of CO2 Pipelines
Pipeline corrosion analysis is conducted to estimate the remaining strength and failure probability
of pipelines. This analysis facilitates the development of relevant risk assessment and risk control plans.
According to the research by Larin (Larin et al., 2016) et al., the corrosion rate/corrosion severity can
be correlated with the pipeline failure probability (POF) through the corrosion depth (d) and the
nominal wall thickness (h) of the pipeline. Related studies have shown(Li et al., 2009) that under
normal operating conditions, when the corrosion depth reaches half of the nominal wall thickness, the
pipeline is highly susceptible to plastic deformation, leading to leakage, perforation, or fracture failure.
In the context of pipeline operations, manual manipulation of valves (e.g., closing or opening) can pose
significant risks to pipelines with localized defects. When the corrosion depth reaches approximately
30% of the pipeline's nominal wall thickness, the probability of pipeline failure due to corrosion can
reach as high as 70%. A more conservative value is selected to establish a pipeline corrosion failure
model, with the objective of preventing pipeline failure caused by corrosion. When the corrosion depth
is less than 10% of the pipe's nominal wall thickness, the probability of corrosion failure is considered
to be virtually impossible. When the corrosion depth is between 10% and 20% of the pipe's nominal
wall thickness, the probability of corrosion failure is considered to be extremely low. In the range of 20%
to 30% of the pipe's nominal wall thickness, the probability of corrosion failure is designated as
occasional. However, when the corrosion depth surpasses 30% of the pipe's nominal wall thickness, the
probability of corrosion failure is categorized as extremely likely, as illustrated in Equations 20 to 23.
d
 0.1
h
d
0.1   0.2
h
d
0.2   0.3
h
d
 0.3
h
(20)
(21)
(22)
(23)
In this equation, d is the corrosion depth and h is the nominal wall thickness of the pipe.
In actual pipeline operation, the actual corrosion depth of the pipeline can only be measured using
a pipeline cleaning device; however, this is not economically feasible. Therefore, the corrosion depth of
the pipeline can be expressed using the pitting corrosion rate CRr and time, as shown in Equation 24:
d  CRr  t
(24)
The value of α ranges from 0.3 to 1.0. Based on the relevant research results on CO2
corrosion(Peng and Zeng, 2015), α is set to 0.6 in this study.
Therefore, for actual CO2 long-distance transmission pipelines, the pipeline corrosion failure
model can be expressed as a relationship between the corrosion rate and the pipeline service life. The
pipeline corrosion failure model corrected for service life is shown in Equations 5-25 to 5-28:
CRr 
0.1h
t
(25)
0.1h
0.2h
 CRr  

t
t
(26)
0.2h
0.3h
 CRr  

t
t
(27)
CRr 
0.3h
t
(28)
Obviously, as the service life increases, the corrosion rate that the pipeline can withstand
decreases. Based on the influence of service life on corrosion rate and pipeline strength, a standard for
the probability of internal corrosion failure of CO2 pipelines has been established, as shown in Table 4.
Table 4
Standards for the possibility of internal corrosion failure in CO 2 pipelines and risk
evaluation standards (pitting corrosion rate standards)
rank
CRr 
0.3h
t
0.2h
0.3h
 CRr  

t
t
CRr 
0.2h
t
possibility
probability
risk possibility
Possible
(0.01，0.1)
Severe
occasionally
(0.001，0.01)
Moderate
rare
(0.0001，0.001)
Minor
In order to determine the numerical ranges corresponding to different failure probabilities for each
factor, the range of pitting corrosion rates corresponding to the standards for the likelihood of corrosion
failure in CO2 pipelines must first be determined. This can be achieved by combining the
aforementioned standards with the experimental results of single-factor variables for each factor.
Accordingly, the failure probabilities for each root node and the corresponding risk assessment
comments for each root node must be determined, taking into account the actual site conditions.
Uniform corrosion rates are more easily tested than pitting corrosion rates in laboratory and field
engineering environments. Corrosion rates are also commonly used in risk assessment processes for
other pipelines. Related studies typically use the pitting corrosion coefficient to compare uniform and
pitting corrosion. Zhao and Hua (Wu et al., 2004) (Sun et al., 2016) investigated the range of pitting
corrosion coefficients for X65 steel in CO₂ transport pipelines under different experimental conditions.
Their results showed that the average pitting corrosion coefficient in CO₂ transport pipeline corrosion
environments was about 20. Thus, a pitting corrosion coefficient of 20 was adopted and Table 5 was
converted into an assessment standard for uniform corrosion rates based on the evaluation criteria for
pitting corrosion rates (see Table 5).
Table 5
Standards for the possibility of internal corrosion failure in CO2 pipelines and risk
evaluation standards (average corrosion rate standards)
rank
CR 
0.3h
20t 
0.2h
0.3h
 CR 

20t
20t 
CR 
0.2h
20t 
possibility
probability
risk possibility
Possible
(0.01，0.1)
Severe
occasionally
(0.001，0.01)
Moderate
rare
(0.0001，0.001)
Minor
According to the research results in Section 3.1, temperature, pressure, and impurity content
directly influence the internal corrosion of CO₂ pipelines. Therefore, establishing a quantitative
correlation model between corrosion severity and influencing factors can effectively assess corrosion
risk levels within pipelines. The corrosion failure and risk assessment criteria for CO₂ pipelines based
on service time are dynamic standards. Since CO₂ applications in China are still in their infancy and
there is limited operational data, this study uses research experimental data that was summarized and
published by Liu Jing et al. for illustrative examples(Sun et al., 2025). Assuming a CO₂ transport
pipeline with a wall thickness of 25 mm and an operating time of five years, the risk probability
evaluation criteria for each influencing factor are categorized based on the classification criteria in
Table 4 and 5, combined with publicly available datasets (see Table 6).
Table 6
Evaluation limits of various indicators for CO2 transmission pipelines that have been in
operation for five years
influencing factors
O2/ ppm
NO2/ ppm
SO2/ ppm
H2S/ ppm
H2O/ ppm
T/ ℃
V/ (m/s)
P/ MPa
evaluation limit
risk possibility
O2>1000
Severe
475<O2≤1000
Moderate
O2≤475
Minor
NO2>244
Severe
154<NO2≤244
Moderate
NO2≤154
Minor
SO2>295
Severe
162<SO2≤295
Moderate
SO2≤162
Minor
H2S>90
Severe
50<H2S≤90
Moderate
H2S≤50
Minor
H2O>475
Severe
284<H2O≤475
Moderate
H2O≤284
Minor
30~32 ℃
Severe
25~30 ℃
Moderate
>32 ℃
Minor
0<V<0.5
Severe
0.5≤V<1
Moderate
V≥1
Minor
12≤P<15
Severe
10≤P<12
Moderate
8≤P<10
Minor
The experimental results of Dugstad and Liu(Sun et al., 2025) are used for illustrative purposes.
Example 1: At a temperature of 25 °C, a pressure of 10 MPa, a water content of 490 ppm, an SO₂
content of 138 ppm, an NO₂ content of 191 ppm, and a flow rate of 0.5 m/s, the uniformly measured
corrosion rate was 0.017 mm/a. According to GB/T 23258-2020: Specification for Control of Internal
Corrosion in Steel Pipelines, this corrosion rate is considered low risk.
According to the method described in Section 2.3 and the evaluation criteria in Table 6, at a
temperature of 25 °C, the temperature factor is in the medium-risk range with an evaluation vector of
(0, 1, 0). The moisture content is 490 ppm, and the moisture content factor is in the high-risk range
with an evaluation vector of (1, 0, 0). The sulfur dioxide (SO₂) content is 138 ppm, and the SO₂ content
factor is considered to be in the low-risk range, with an evaluation vector of (0, 0, 1). Evaluation
vectors for other influencing factors are determined using the same rules described above.
Therefore, the overall evaluation vector matrix R for the experimental conditions in Example 1
can be obtained, as shown in Equation 29:
 0,1, 0 
 0,1, 0 


 0, 0,1


0, 0,1

R
 0, 0,1


 0, 0,1
1, 0, 0 


 0,1, 0 
(29)
The evaluation vectors in the matrix R correspond to temperature, pressure, O2 content, NO2
content, SO2 content, H2S content, water content, and flow velocity from top to bottom.
After obtaining the evaluation vector matrices for various factors, combining the weight
calculation results of key factors for corrosion in CO2 pipelines, and based on the principle of
maximum membership degree, a comprehensive evaluation of the corrosion risk level under the
experimental conditions can be performed to obtain the comprehensive evaluation matrix B. The
calculation process is as follows:
B  A R
(30)
A   0.1063,0.1153,0.1375,0.1551,0.1407,0.1425,0.1013,0.1013
(31)
B  A  R   0.1013,0.3229,0.5758
(32)
According to the principle of maximum membership degree, the corrosion risk assessment level
under the experimental conditions is low risk. Based on GB/T 23258-2020: Specification for Control of
Internal Corrosion of Steel Pipelines, the corrosion rate measured in the experiment is considered to be
within the low-risk range. The risk level evaluation method established in this study is consistent with
the evaluation results based on corrosion rate assessment in the standard.
Example 2: Temperature: 50 °C; Pressure: 10 MPa; Moisture content: 500 ppm; SO₂ content: 200
ppm; NO₂ content: 200 ppm; O₂ content: 200 ppm; H₂S content: 1000 ppm. The uniformly measured
corrosion rate was 0.095 mm/year. The rules for determining the values of the evaluation vectors for
each factor are the same as in Example 1. Therefore, the overall evaluation vector matrix R under the
experimental conditions of Example 2 can be obtained as follows:
 0, 0,1
 0,1, 0 


 0, 0,1


0,1, 0 

R
 0,1, 0 


1, 0, 0 
 0,1, 0 


 0, 0,1
(33)
The results of the comprehensive evaluation of matrix B are shown in equation 34:
B  A  R   0.1425,0.5124,0.3451
(34)
According to the principle of maximum membership degree, the corrosion risk assessment level
under this experimental condition is classified as medium risk. Based on GB/T 23258-2020:
Specification for Control of Internal Corrosion in Steel Pipelines, the corrosion rate measured in this
experiment is considered to fall within the medium risk range. The risk grading evaluation method
established in this study is consistent with the evaluation results based on corrosion rate assessment in
the relevant standard.
Based on the two examples above, the established method for evaluating internal corrosion risk in
CO₂ pipelines has been validated as applicable. Ten sets of data were selected from the literature and
publicly available datasets for assessment, as shown in Table 7, to verify the accuracy of the internal
corrosion risk evaluation method. As Table 7 show, the established risk assessment method is accurate.
Table 7
Temperature/
Pressure/MP
℃
a
O2/ppm
Verification of the risk assessment method for internal corrosion in CO 2 pipelines
SO2
H2S
moisture
flow
corrosion
Model evaluation
GB/T23258 Evaluation
/ppm
/ppm
content/ppm
velocity/m/s
ratemm/a
rating
Grade
NACE SP 0106-2018
NO2/ppm
50
10
500
150
0
0
500
0.5
0.116
Moderate
Moderate
Moderate
50
9.5
0
0
0
0
3570
0.5
0.016
Minor
Minor
Minor
35
8
0
0
0
0
3000
1
0.003
Minor
Minor
Minor
35
8
20
0
100
0
1200
0.5
0.01
Minor
Minor
Minor
50
8
1000
0
1000
200
3600
1
3.2
Severe
Severe
Severe
45
10
30000
0
0
50
0
0.5
0.031
Moderate
Moderate
Moderate
50
10
500
0
200
200
1000
0.5
0.178
Moderate
Moderate
Moderate
25
10
0
0
200
0
450
0.5
0.017
Moderate
Moderate
Moderate
25
10
1000
0
2000
0
2000
1
1.1
Severe
Severe
Severe
40
10
1000
0
500
0
2000
1
0.6
Severe
Severe
Severe
3.3 Corrosion-induced T-S Fuzzy Fault Tree and Bayesian Network for CO2
Pipelines
A comprehensive analysis of the failure factors for internal corrosion in CO 2 pipelines is
conducted, drawing upon extant research findings on internal corrosion in CO2 pipelines. A fault tree
for internal corrosion failures in CO2 pipelines is established, followed by pruning and optimization of
the fault tree. The AND gates are converted into T-S gates, resulting in a T-S fuzzy fault tree for
internal corrosion failures in CO2 pipelines, as illustrated in Fig.2. In Fig.2, CO2 internal corrosion
failure is designated as the top event, comprising 4 intermediate events, 8 basic events, and 5
T-S
gates. The corresponding event types are enumerated in Table 8.
Fig.2.
T-S fuzzy fault tree for corrosion in CO2 conveying pipelines
Table 8
serial number
T-S fuzzy fault tree event type
event
serial number
event
T
Corrosion failure in CO2 pipes
x3
Fluid velocity inside the pipe
y1
pH
x4
moisture content
y2
droplet radius
x5
Nitrogen oxide impurity content
y3
shear stress
x6
Sulfur oxide impurity content
y4
CO2 density
x7
Hydrogen sulfide impurity content
x1
temperature
x8
Oxygen impurity content
x2
pressure
As illustrated in Fig.3, the Bayesian network diagram of CO2 pipeline corrosion failure is derived
from the T-S fuzzy fault tree of CO2 pipeline corrosion.
Fig.3.
Bayesian network diagram of corrosion failure in the CO 2 conveying pipeline
The establishment of the rules for each T-S gate is informed by extant research findings and
experience. This study stipulates that the fuzzy subsets of fault probabilities for fault states 0.5 and 1
are congruent. When the root node manifests with a low probability, the fault state is designated as 0,
and the fuzzy subsets of fault probabilities for states 0.5 and 1 are [0.9985, 0.999, 0.9995] and [0.00045,
0.0005, 0.00055], respectively. When the root node occurs with an occasional probability, the fault
probability subsets for fault states 0 and 0. The values 5 and 1 are found to be [0.985, 0.99, 0.995] and
[0.0045, 0.005, 0.0055], respectively. The probability of root node occurrence is a possibility, and the
fault probability subsets for fault states 0, 0.5, and 1 are [0.85, 0.9, 0.95], [0.045, 0.05, 0.055], and
[0.045, 0.05, 0.055], respectively.
The following rules govern the process of assigning conditional probabilities to intermediate
nodes: In the event that an intermediate node, designated as y, contains m root nodes, among which n
nodes are in a fault state of 0.5, and q nodes are in a fault state of 1, the conditional probability
assignments for the intermediate node's fault states 0, 0.5, and 1 are 1-(n+q/m), n/2m, and n+2q/2m,
respectively, where m and n are both greater than 0. If all root nodes are in fault state 0, then the
intermediate node's fault state must be 0.
Take T-S-4 doors as an example, which is the conditional probability table for the intermediate
event y3, as shown in Table 9:
Table 9
T-S-4 gate Conditional probability assignment table
x1
x2
x3
0
0
1
0
y3
0
0.5
1
0
1
0
0
0
0.7
0
0.3
0
1
0
0.7
0
0.3
0
0
1
0.7
0
0.3
1
1
0
0.3
0
0.7
1
0
1
0.3
0
0.7
0
1
1
0.3
0
0.7
1
1
1
0
0
1
0.5
0
0
0.85
0.15
0
0
0.5
0
0.85
0.15
0
0
0
0.5
0.85
0.15
0
0.5
0.5
0
0.7
0.3
0
0.5
0
0.5
0.7
0.3
0
0
0.5
0.5
0.7
0.3
0
0.5
1
0
0.4
0.15
0.45
0.5
1
1
0.1
0.15
0.75
0.5
0
1
0.4
0.15
0.45
1
0.5
1
0.1
0.15
0.75
1
0.5
0
0.4
0.15
0.45
0
0.5
1
0.4
0.15
0.45
1
1
0.5
0.1
0.15
0.75
1
0
0.5
0.4
0.15
0.45
0
1
0.5
0.4
0.15
0.45
0.5
0.5
1
0.1
0.3
0.6
1
0.5
0.5
0.1
0.3
0.6
0.5
1
0.5
0.1
0.3
0.6
0.5
0.5
0.5
0.55
0.45
0
3.4 Calculation of the probability of corrosion failure in CO2 transport
pipelines
In accordance with the corrosion failure and risk grading criteria for CO₂ pipelines as outlined in
Section 3.2, in conjunction with the Bayesian network diagram for CO₂ pipeline internal corrosion
failure and the conditional probability tables for each T-S gate as established in Section 3.3, the
probability of CO₂ internal corrosion failure is determined. In order to facilitate comprehension of the
subsequent discussion, an example calculation is first performed. The experimental conditions and
parameters from Example 1 in Section 3.2 are utilized, as are the assessment limit values for various
indicators of the pipeline after five years of operation, as presented in Table 6. The specific calculation
process is as follows:
The experimental conditions for Example 1 are as follows: temperature 25 °C, pressure 10 MPa,
moisture content 490 ppm, SO2 content 138 ppm, NO2 content 191 ppm, flow rate 0.5 m/s. As
indicated by Tables 5 and 6, the probability of fuzzy subsets of each root node is as follows:
[0.985,0.99,0.995],[0.0045,0.005,0.0055],[0.0045,0.005,0.0055];[0.985,0.99,0.995],[0.0045,0.005,
0.0055],[0.0045,0.005,0.0055];[0.9985,0.999,0.9995],[0.00045,0.0005,0.00055],[0.00045,0.0005,0.000
55];[0.9985,0.999,0.9995],[0.00045,0.0005,0.00055],[0.00045,0.0005,0.00055];[0.9985,0.999,0.9995],
[0.00045,0.0005,0.00055],[0.00045,0.0005,0.00055];[0.9985,0.999,0.9995],[0.00045,0.0005,0.00055],[
0.00045,0.0005,0.00055];[0.85,0.9,0.95],[0.045,0.05,0.055],[0.045,0.05,0.055];[0.985,0.99,0.995],[0.0
045,0.005,0.0055],[0.0045,0.005,0.0055].
Take node y3 as an example to calculate the failure probability of node y3. The same applies to
other nodes. The specific calculation for node y3 is shown in Equations 5-35 to 5-43:
p( y3  0) 
 p( x1, x2, x3; y3  0)
(35)
x1, x 2, x 3
 P( y3  0 x1, x2, x3)  p( x1)  p( x2)  p( x3)

(36)
x1, x 2, x 3
p( y3  0.5) 
 p( x1, x2, x3; y3  0.5)
(37)
x1, x 2, x 3

 P( y3  0.5 x1, x2, x3)  p( x1)  p( x2)  p( x3)
(38)
x1, x 2, x 3
p( y3  1) 
 p( x1, x2, x3; y3  1)
(39)
x1, x 2, x 3
 P( y3  1 x1, x2, x3)  p( x1)  p( x2)  p( x3)
(40)
p( y3  0)  0.9995,0.9998,0.9999
(41)
p( y3  0.5)  5.2 104 ,5.8 104 , 6.4 10 4 
(42)
p( y3  1)  8.4 104 ,9 104 ,9.6 104 
(43)

x1, x 2, x 3
Similarly, after calculating the failure probabilities of nodes y1 to y5, the failure probability of the
top event CO2 internal corrosion is calculated as shown in Equations 5-44 to 5-51:
p(T  0) 

p( y1, y 2, y3, y 4; T  0)
(44)
y1, y 2, y 3, y 4


P(T  0 y1, y 2, y3, y 4)  p( y1)  p( y 2)  p( y3)  p( y 4)
(45)
y1, y 2, y 3, y 4
p(T  0.5) 

p( y1, y 2, y3, y 4; T  0.5)
(46)
y1, y 2, y 3, y 4


P(T  0.5 y1, y 2, y3, y 4)  p( y1)  p( y 2)  p( y3)  p( y 4)
(47)
y1, y 2, y 3, y 4
p(T  1) 

p( y1, y 2, y3, y 4; T  1)
y1, y 2, y 3, y 4


P(T  1 y1, y 2, y3, y 4)  p( y1)  p( y 2)  p( y3)  p( y 4)
(48)
y1, y 2, y 3, y 4
p(T  0)  0.920,0.916,0.910
(49)
p(T  0.5)  0.055,0.056,0.57
(50)
p(T  0)  0.0275,0.028,0.0285
(51)
In summary, assuming that a pipeline operates for five years under the experimental conditions of
Example 1, the probability of pipeline failure due to internal corrosion is 8.4%. This indicates that
although the pipeline is currently operating normally, if the degree of internal corrosion further
intensifies, the probability of pipeline failure due to internal corrosion will increase. Internal corrosion
of pipelines can lead to serious safety hazards.
For factors influencing CO2 pipeline internal corrosion, operational parameters such as
temperature, pressure, and flow rate can be monitored along the pipeline and calculated using
theoretical models to obtain actual values for critical sections. For gaseous impurities such as nitrogen
oxides and sulfur oxides, it can be assumed that they are uniformly distributed within the pipeline
during operation. However, the distribution of water within the pipeline is uneven due to external
environmental factors and changes in pipeline elevation. Therefore, the in-situ detection of water
content in different sections of long-distance pipelines is an urgent issue to be addressed. Compared to
quantitative CO2 pipeline corrosion risk grading evaluation systems, CO2 internal corrosion failure
probability calculations can further precisely calculate the specific failure probabilities of different
sections of long-distance CO2 pipelines, providing a more accurate description of the current
operational safety status of the pipeline and technical support for actual pipeline operations.
4. Conclusions
This study addresses the unique environmental conditions of CO₂ transport pipelines and
calculates the coupled contribution weights of various influencing factors on corrosion rates. It
establishes dynamic assessment criteria for each influencing factor. A quantitative assessment model
for corrosion risk levels in CO₂ pipelines and a quantitative calculation model for internal corrosion
failure probability are developed, leading to the following conclusions:
The coupled contribution weights of various factors to the corrosion rate were calculated using
gray correlation analysis and triangular fuzzy hierarchical analysis. The coupled contribution weights
of various influencing factors to the corrosion rate were ranked as follows: NO₂ > SO₂ > O₂ > H₂S >
pressure > temperature > flow rate > water content.
Grounded in the theories of residual strength and failure probability for internal pipeline corrosion,
a rigorous criterion has been formulated to define the relationship between corrosion depth and wall
thickness. By further integrating the influence of service time on corrosion behavior, a dynamic
framework for assessing the probability of corrosion-induced failure has been established.
The research results of a dynamic corrosion failure probability evaluation system and corrosion
weight calculation, combined with fuzzy mathematics theory and maximum membership evaluation
rules, were used to establish a corrosion risk grading assessment model for dynamic CO2 transport
pipelines. The model was compared with actual measurement data and demonstrated high accuracy.
A CO2 pipeline internal corrosion failure probability calculation model was developed based on a
T-S fuzzy fault tree and Bayesian network. The classification criteria for fuzzy subsets of failure
probabilities at the root nodes of the Bayesian network were established. By incorporating the coupling
contribution weights of various factors to the internal corrosion rate, rules for assigning conditional
probabilities to intermediate nodes and the fuzzy algorithm for failure probability were determined.
This framework constitutes a comprehensive model for evaluating the probability of internal corrosion
failure in CO₂ transport pipelines.
CRediT authorship contribution statement
Fan Xinxin: Writing-original draft, Methodology, Investigation, Formal analysis, Conceptualization.
Liu Guangyu: Writing-original draft, Methodology, Investigation, Formal analysis, Conceptualization.
Wang Cailin: Visualization, Investigation. Xin Ouyang: Writing – review & editing. Feng Yan:
Investigation. Hu Qihui: Writing – review & editing. Li Yuxing: Writing – review & editing, Resources,
Funding acquisition, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
Acknowledgements
This research is supported by National Natural Science Foundation of China (52302445), Shandong
Provincial Natural Science Foundation (ZR2023QE029) and Fundamental Research Funds for the
Central Universities (25CX06004A).
Data availability
The data available on request from the authors.
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Declaration of Interest Statement
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.