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22-36 

37 

 

 

 

Article 

Research on safety risk assessment of polyimide 

foam production line based on AHP-FCE method 
Ripeng Gao1, Liang Jiang2, Yiting Li2, Xinuo Fang2, Xiaohong Gui2* 

1Aerospace Research Institute of Materials & Processing Technology, Beijing, Beijing 100076, China 
2China University of Mining and Technology, Beijing, Beijing 100083, China 

               A R T I C L E   I N F O 
 

Article history: 
Received 19 September 2025  
Received in revised form 
10 November 2025 
Accepted 01 December 2025 
 
Keywords:  
Polyimide foam, Safety risk assessment, 
Analytic hierarchy process,  
Fuzzy comprehensive evaluation, Index system, 
Risk management 
 
*Corresponding author 
Email address: 
gxhbox@sina.com  
 
DOI: 10.55670/fpll.fusus.4.1.4 
 

A B S T R A C T 
 

Polyimide foam, as a high-performance sandwich composite material, is widely 
used in high-tech manufacturing industries such as aviation and aerospace. 
Nevertheless, its production process involves numerous hazardous chemicals 
and sophisticated machinery, which is extremely hazardous to the system itself. 
Today, the overall assessment tools for multidimensional safety risks on the 
production line are unsatisfactory. To address this, this paper developed a 
safety risk assessment system comprising five dimensions: equipment, 
materials, personnel, environment, and management. The study applied the 
Analytic Hierarchy Process (AHP) to calculate indicator weights and the Fuzzy 
Comprehensive Evaluation Method (FCE) to assess safety risk in a polyimide 
foam line. The results show that the overall risk level of this production line is 
Relatively High, and the main sources of risk are equipment factors and process-
material factors. Simultaneously, personnel and safety management factors 
should also be considered. Based on the evaluation findings, specific risk control 
measures are offered, with both theoretical background and methodological 
underpinning for the safety design and operational management of polyimide 
foam production lines. 

1. Introduction 

Polyimide foam material possesses extensive 

opportunities of application in aviation, aerospace, and other 

high-end equipment manufacturing industries because it has 

excellent high-temperature resistance, light weight, high 

strength, and good chemical stability [1-4]. As high-

performance sandwich composites are in demand for model 

products, the industrialization of polyimide foam is advancing 

rapidly [5,6]. Nevertheless, its manufacturing process 

requires numerous hazardous chemicals and equipment, 

including flammable solvents (tetrahydrofuran and 

methanol) and hazardous processes (high-temperature, high-

pressure foaming, microwave radiation, and dust crushing). 

This puts the production line at several safety risks during 

operation, such as fire, explosions, poisoning, and mechanical 

injuries. Currently, studies on polyimide foam materials are 

conducted at both the local and international levels, with the 

main emphasis on maximizing performance and expanding 

applications [7-10]. On the contrary, studies on assessing 

system safety risks and control measures in the production 

process are lacking. The possible deficiency of systematic 

evaluation techniques and technical assistance in the overall 

assessment of safety risks across the entire process and its 

many dimensions is particularly relevant to realizing large-

scale industrialization at the domestic level. Current safety 

evaluation systems are not without problems, such as the use 

of incomplete indicators, subjective weighting distributions, 

vague evaluation outcomes in the application to production 

systems with complex process characteristics, and numerous 

risk couplings. In a bid to overcome this, this study aims to 

discuss the construction of a polyimide foam production line. 

By incorporating relevant technical documentation, we 

created a safety risk assessment framework comprising five 

dimensions: equipment, materials, personnel, environment, 

and management. The weights of all risk factors were decided 

using the Analytic Hierarchy Process (AHP). Simultaneously, 

the Fuzzy Comprehensive Evaluation Method (FCE) was 

applied to address uncertainties and indeterminate 

information during the evaluation process, yielding a similar 

quantitative analysis of the production line's safety risks. This 

study will provide scientific foundations and risk 

management methods for safety in polyimide foam 

production lines and, thereby, contribute to the safe, 

controlled industrial development of these high-risk process 

lines. 

 

 

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February 2026| Volume 04 | Issue 01 | Pages 37-44 

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DS. Balami et al. /Future Sustainability                                                                                February 2026| Volume 04 | Issue 01 | Pages 22-36 

38 

 

2. Research methods 

To systematically and scientifically evaluate safety risks 

in polyimide manufacturing lines, this study uses a complex 

assessment tool combining qualitative and quantitative 

evaluation. Specifically, the Analytic Hierarchy Process (AHP) 

is initially used to create an evaluation index for safety risks 

and to compute the weights of each indicator, reflecting the 

relative significance of various factors in the comprehensive 

risk. Based on this, the Fuzzy Comprehensive Evaluation 

(FCE) technique is presented to address the inherent two-

sidedness and uncertainty in safety risk assessment and to 

convert the expert's experience and judgment into 

quantitative assessment outcomes. The combined use of AHP 

and FCE can not only ensure the scientific validity and 

consistency of indicator weights but also provide an 

opportunity to conduct a thorough quantitative assessment of 

complex risk factors and to support the methodological 

support for further risk analysis and decision-making [11]. 

2.1 Hierarchy analysis method 

The Analytic Hierarchy Process (AHP) is a multi-criteria 

decision-making technique that uses both quantitative and 

qualitative methods to compare the relative significance of 

elements, assign weights at various levels, and rank and select 

the best alternative [12,13]. This method was proposed by the 

American operations researcher, T. L. Saaty. It has found 

extensive application across most fields and has been 

demonstrated to be effective and universal in making 

complex decisions [14]. The steps for implementing the AHP 

analysis method are shown in Figure 1. 

 
Figure 1. Steps in the analytic hierarchy process (AHP) analysis 

It is possible to divide the AHP weighting process into four 
steps: 
(1) Build a hierarchical organization: Compare the 

relationships between the elements of evaluation to create a 

hierarchical system. Based on a precise grasp of the essence 

of the problem, clarify the decision-making objectives, 

decompose the problem into different levels, and determine 

the target level, criterion level, and solution level. 

(2) Constructing the judgment matrix: Using the 1-9 
proportional scale [15] as shown in Table 1, pairwise 
comparisons are conducted between elements within the 
same level, with appropriate scale values selected based on 
their relative importance. Judgment matrices are then 
constructed for each criterion level according to the pairwise 
comparison results. 
(3) Determine the maximum eigenvalue λmax and its 

corresponding eigenvector ω of the judgment matrix. 

(4) Consistency check. Calculate the consistency index CI as 

shown in Equation (1). 

𝐶𝐼 =
𝜆𝑚𝑎𝑥−𝑛

𝑛−1
                              (1) 

Here, λmax denotes the maximum eigenvalue of the judgment 

matrix, while n represents the matrix's order. The 

Consistency Index (CI) measures the internal consistency of 

the matrix. 

Then calculate the random consistency ratio (CR) as shown in 

Equation (2). 

𝐶𝑅 =
𝐶𝐼

𝑅𝐼
             (2) 

Relative Importance (RI) represents the average random 

consistency index of the judgment matrix at the 

corresponding order. The Consistency Ratio (CR), typically 

set at 0.1, is used to assess whether the matrix demonstrates 

acceptable consistency. The CR < 0.1 implies that the matrix 

satisfies the consistency requirements. When this happens, 

the eigenvector ω, which is the eigenvalue of the largest 

eigenvalue λmax, is scaled to get the evaluation index weights. 

On the other hand, when CR is greater than 0.1, it indicates 

high levels of inconsistency in the judgment matrix, requiring 

that the pairwise comparisons be re-examined and tweaked 

iteratively until a tolerable level of consistency is attained. 

Table 1.  Meaning of the 1-9 scale 

 

2.2 Fuzzy comprehensive evaluation method 

The Fuzzy Comprehensive Evaluation Method (FCE) is a 

quantitative measurement based on fuzzy mathematics. It is 

practical in nature, as it applies the principle of fuzzy relation 

synthesis to measure factors whose boundaries in the system 

are unclear, thereby providing a complete evaluation [16,17]. 

The basic steps of the fuzzy comprehensive evaluation 

method are as follows: 

Scale Definition 
1 The i factor is as important as the j factor. 

3 The i factor is slightly more important 
than the j factor. 

5 The i factor is more important than the j 
factor 

7 The i factor is significantly more 
important than the j factor. 

9 The i factor is absolutely more important 
than the j factor. 

2，4，6，8 The comparison result between the i and j 
factors falls within the median of the 
adjacent judgments listed above. 

Count backwards Compare factor j with factor i to obtain 
the judgment value 𝑎𝑗𝑖 = 1/𝑎𝑖𝑗 . 



DS. Balami et al. /Future Sustainability                                                                                February 2026| Volume 04 | Issue 01 | Pages 22-36 

39 

 

(1) Determine the set of evaluation factors: Identify all 

evaluation factors for the subject. Assuming n factors 

influencing safety assessment, the critical factors set is 

obtained: 

𝑈 =（𝑢1, 𝑢2, . . . 𝑢𝑛）            (3) 

(2) Determine the evaluation level set: Set evaluation levels, 

typically divided into multiple categories such as severe, 

relatively severe, moderate, relatively weak, and no impact. 

These evaluation levels form a set: 

𝑉 =（𝑣1, 𝑣2, . . . 𝑣𝑛）            (4) 

(3) Determine the weight set: In fuzzy evaluation of various 

factors, the determination of weight is the key to evaluation. 

According to the importance of evaluation factors, the weight 

of evaluation indicators is determined to form a weight set: 

𝐴 =（𝑎1, 𝑎2, . . . 𝑎𝑛）            (5) 

The value ∑ 𝑎𝑖
𝑛
𝑖=1 𝑎𝑖𝑢𝑖 of 1 represents the weight of the 

evaluation factor, which can be determined through the 

Analytic Hierarchy Process (AHP). 

(4) Establish the fuzzy relation matrix R: The membership 

degree reflects the extent to which an evaluated object 

belongs 𝑟𝑖𝑗𝑢𝑖𝑣𝑖to a specific evaluation level under a given 

factor, denoted as, where represents the membership degree 

of the factor in the level. All factor membership degrees 

collectively form a fuzzy evaluation matrix: 

𝑅 = [

𝑟11 𝑟12

𝑟21 𝑟22

⋯
⋯

𝑟1𝑛

𝑟2𝑛

⋮        ⋮ ⋱ ⋮
𝑟𝑚1 𝑟𝑚2 ⋯ 𝑟𝑚𝑛

]           (6) 

(5) Fuzzy comprehensive evaluation: The fuzzy 

comprehensive evaluation result B is: 

𝐵 = 𝐴𝑅 = (𝑎1, 𝑎2, . . . 𝑎𝑚) [

𝑟11 𝑟12

𝑟21 𝑟22

⋯
⋯

𝑟1𝑛

𝑟2𝑛

⋮        ⋮ ⋱ ⋮
𝑟𝑚1 𝑟𝑚2 ⋯ 𝑟𝑚𝑛

] =

(𝑏1, 𝑏2, . . . 𝑏𝑚）             (7) 

Here, B denotes the membership vector, with bi representing 

the membership degree of the comprehensive evaluation 

result in the i-th evaluation level. 

(6) Processing of evaluation results: Based on the fuzzy 
comprehensive operation result B, the final rating of the 
evaluated object can be determined using either the 
maximum membership principle or the weighted average 
method. The maximum membership principle selects the 
highest corresponding rating in bi as the evaluation result, 
while the weighted average method calculates the weighted 
average of all evaluation ratings to obtain the final result. In 
practice, the AHP and FCE methods can be implemented 
effectively using specialized software tools for efficient, 
accurate computation and analysis. The implementation 
platform of this study was Yaahp software. First, we prepared 
a survey questionnaire based on the hierarchical structure of 
the AHP, in which we invited experts to make pairwise 
comparisons and score indicators at each level. After 
collecting the data from the questionnaires, the data were 
processed in Yaahp to obtain judgment matrices, calculate 
indicator weights, and test consistency. Based on this, we 
have integrated the software's fuzzy overall analysis 
capability. To determine the final assessment results, we 

multiplied the experts' scores for subordinate factors by the 
weights derived using AHP. All calculations of weight and the 
total FCE evaluation process went along smoothly with the 
assistance of Yaahp. Such an approach was not only the most 
effective in enhancing research efficiency and standardization 
but also in rendering risk assessment results scientifically 
valid and reliable. 

3. Construction of the safety evaluation index system 

Science and a rational risk assessment system are the 

basis of accurate safety risk analysis. To make the assessment 

results effectively and impartially illustrate the safety 

situation in the production lines of polyimide foams, this 

study systematically analyzed the production process, the 

properties of hazardous substances, and the types of 

accidents that may occur in the manufacturing lines. Our 

analysis of project documentation and related literature 

enabled us to develop a multi-level, multi-dimensional system 

of safety risk evaluation indices. 

3.1 Basis and principles of indicator system construction 

To develop a comprehensive and precise evaluation 

index system for safety risks in polyimide foam production 

line, it must be grounded in solid theoretical foundations and 

clear design principles [18]. This study, after 

comprehensively examining domestic and international 

chemical process safety standards (e.g., GB 45673-2025) and 

the characteristics of polymer material synthesis processes, 

established the following four core principles to guide the 

scientific construction of the index system: 

(1) Systematic principle: The index system should be able to 
comprehensively cover the key safety dimensions, such as 
"people-machine-material-method-environment", ensure 
that the factors are relatively independent and have internal 
logical connections, and constitute a hierarchical and 
complete organic whole, to avoid the omission or repeated 
evaluation of important risk sources. 
(2) Scientific principle: The selection, definition, and 
hierarchical attribution of each indicator must have a solid 
theoretical basis (such as accident causation theory, system 
engineering principles) or come from the clear support of 
industry standards and technical specifications, to ensure 
that it can accurately and objectively reflect the essence and 
impact path of a specific risk. 
(3) Operability Principle: Indicators must be observable, 
measurable, and comparable. The required data for 
evaluation should be obtained through feasible methods, such 
as on-site inspections, reviewing equipment operation 
records, querying safety management archives, and expert 
on-site assessments, to ensure the evaluation work can be 
effectively implemented. 
(4) The principle of dynamism and orientation: The index 

system is not only used for static risk status assessment, but 

also should pay attention to the dynamic process of risk 

management, guide enterprises to pay attention to the 

continuous improvement of safety management, and provide 

directional guidance for future risk early warning and 

prevention and control priorities. 

3.2 Construction of safety evaluation index system 

First, by systematically reviewing domestic and 

international literature on chemical process safety and 

polymer production process safety, we identified common 

risk factors, including equipment reliability, hazardous 



DS. Balami et al. /Future Sustainability                                                                                February 2026| Volume 04 | Issue 01 | Pages 22-36 

40 

 

chemical management, and personnel safety behavior [19-

22]. Secondly, we conducted an in-depth analysis of safety 

design documents, operating procedures, and safety 

management regulations related to polyimide foam 

production lines. This enabled us to identify specific risk 

points unique to polyimide foam manufacturing, including 

radiation control in microwave foaming processes, 

fire/explosion risks associated with solvents like 

tetrahydrofuran, and interlock controls during high-pressure 

vulcanization. Building on this foundation, we applied the 

Analytic Hierarchy Process (AHP) methodology to 

decompose complex safety risk issues into three hierarchical 

levels: objectives, criteria, and indicators. This resulted in a 

clear hierarchical structure model. The specific risk 

evaluation index system for polyimide foam production lines 

is detailed in Table 2.  

4. Risk assessment 

4.1 Indicator weighting 

This study developed an expert survey questionnaire on 

risk indicators for polyimide foam production lines, based on 

the established risk assessment framework. Industry experts 

were invited to complete the questionnaire. Using the 

collected data, judgment matrices were constructed in Yaahp 

software to aggregate expert opinions through group 

decision-making, ultimately determining the final weights of 

each indicator. First, based on the risk index evaluation 

system for polyimide foam production lines, a hierarchical 

model was constructed in the Yaahp software, as shown in 

Figure 2. 

The expert data was then imported into the Yaahp 

software to construct judgment matrices for each expert. All 

matrices demonstrated a consistency ratio (CR) below 0.1, 

passing the consistency test to ensure the scientific validity 

and rationality of the weight distribution. The final weight 

allocation results are presented in Table 3. As shown in Table 

3, the key risk points with higher weights constitute the 

primary risk profile of this production line. The top three 

factors are all equipment-related: B11 Core Equipment 

Stability and Reliability Design (0.1068), B12 Safety Interlock 

and Protective Device Effectiveness (0.0994), and B14 

Electrostatic Protection and Grounding System Reliability 

(0.0780). Following closely are B42 Safety Layout of 

Hazardous Materials Production Facilities (0.0557), B13 

Compliance of Special Equipment Safety Accessories 

(0.0547), B21 Management of Hazardous Chemical Storage 

and Usage (0.0450), B51 Coverage of Safety Education and 

Specialized Training (0.0405), and B31 Management of 

Workers' Physical and Mental Health Status (0.0407). This 

clearly identifies the priority areas for risk control resource 

allocation. 

4.2 Fuzzy comprehensive evaluation 

After determining the weights of each indicator, experts 
were invited to rate the relevance  
V= {Low Risk 1, Relatively Low Risk 2, General Risk 3, 
Relatively High Risk 4, High Risk 5}  

 of all secondary indicators in the evaluation set. The results 
were imported into the Yaahp software, which performed 
fuzzy calculations to generate a comprehensive safety risk 
assessment score for the polyimide foam production line. The 
final evaluation report is shown in Figure 3. 

The overall safety risk assessment score for the 

polyimide foam production line is 3.7246, as shown in Figure 

3. This score is considered a Relatively High Risk according to 

the set-out evaluation scale. This means the entire safety 

scenario in the production line is critical, and the 

management should take urgent action and implement 

effective measures. The most significant factors were 

equipment-related (B1) and process material (B2), with 

scores of 4.05 and 3.98, respectively, and were all considered 

high risk. This not only confirms the findings of the AHP 

weight analysis but also establishes these two dimensions as 

the main factors contributing to high overall risk levels. The 

scores for personnel factors (B3), safety management factors 

(B5), and environmental risk factors (B4) were 3.46, 3.30, and 

3.2,5, respectively, which are within the upper band of 

General Risk and on the border of Relatively High Risk. These 

results reveal a significant lack in these fields that cannot be 

ignored. 

4.3 Recommendations for countermeasures 

This section will present a detailed safety risk analysis of 

the polyimide foam production lines and an indicator-

weighted fuzzy overall assessment. It will offer specific risk-

prevention and improvement suggestions in five main areas: 

equipment and facilities, process materials, personnel 

management, safety management, and environmental 

control. These are measures to improve overall safety 

management capabilities and mitigate systemic risks. 

(1) Strengthen the inherent safety of equipment and facilities, 

focusing on high-weight risk points 

1. Enhancement of the reliability of core equipment: To 

develop and actively introduce a preventative maintenance 

and life cycle maintenance system for major equipment (such 

as high-temperature pressure tanks and microwave foaming 

furnaces), as well as the implementation of equipment-based 

monitoring technology, to detect the trend of equipment 

deterioration in advance. 

2. Assurance of the functionality of combustible gas alarm 

interlocks and mechanical protective devices: The 

functionality of combustible gas alarm interlocks and 

mechanical protective devices should be regularly checked 

and inspected to be responsive and effective. Create an 

interlock system management ledger that has well-

established maintenance responsibilities and cycles. 

3. Comprehensive electrostatic protection system: Have 

periodic tests and maintenance of the grounding resistance of 

all equipment and pipelines that carry any of the flammable 

and explosive media to offer continuous and dependable 

grounding. The use of anti-static materials and humidification 

in processes is likely to generate static, such as during 

crushing and conveying. 

(2) Optimize the risk control of process materials and strictly 

prevent the loss of control of hazard sources 

1) Accurate control over hazardous chemicals: Introduce the 

one book one label system (safety data sheets and safety 

labels) of all substances, standardize storage and usage 

conditions of solvents, including tetrahydrofuran and 

methanol, and enhance integrity checks in leak prevention 

facilities. 

 

 



DS. Balami et al. /Future Sustainability                                                                                February 2026| Volume 04 | Issue 01 | Pages 22-36 

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Table 2. Risk assessment index system for polyimide foam production line 

Evaluation 
goal 

Primary 
indicator 

Secondary indicator Subdivide secondary indicators 

Polyimide Foam 
Wire 
Production Line 
Safety Risk 
Assessment A 

Device Factor 
B1 

Core Equipment Stability and Reliability 
Design B11 

Design life, failure rate, and maintenance cycle of high-
temperature pressure tanks, microwave foaming furnaces, 
vulcanizing machines, etc., as well as material selection and 
system design for pressure-bearing pipelines 

Safety interlock and protective device 
effectiveness B12 

The combustible gas detector is interlocked with the 
emergency ventilation system, while the fire damper is 
interlocked with the corresponding air conditioner. 
Protective measures are implemented for mechanical 
processing equipment such as vulcanizing machines and 
cutting machines. 

Special Equipment Safety Accessories 
Compliance B13 

Pass rate of pressure gauge, safety valve, and quick-opening 
interlock device inspection 

Electrostatic Protection and Grounding 
System Reliability B14 

Electrostatic grounding of crushing equipment, pipelines, 
fans, etc. 

Explosion-proof Electrical Equipment 
Selection and Installation Compliance B15 

Explosion-proof marking, explosion-proof clearance, and 
cable sealing devices meet the standards 

Completeness of the equipment online 
monitoring and early warning system B16 

Coverage of real-time electrical fire monitoring, vibration 
monitoring, and temperature/pressure collection 

Process 
Material 
Factor B2 

Hazardous Chemicals Storage and Use 
Management B21 

Storage conditions and anti-leakage measures for polyimide 
resin, tetrahydrofuran, methanol, alcohol, isocyanate, etc. 

Asphyxiation gas risk prevention and control 
B22 

Nitrogen leak detection, accident ventilation and alarm 
interlock 

Control of combustible gas and dust 
concentration B23 

Dust and steam concentration monitoring and control in 
crushing, foaming and oven processes 

High-temperature and high-pressure 
interlock control B24 

Temperature and pressure dual-limit interlock and over-
limit protection for pressure vessels and other equipment 

Fire Prevention in Cleaning Operations B25 Fire and explosion prevention facilities, anti-static flooring, 
and ventilation in alcohol consumption areas 

Waste gas and harmful substance control B26 Hydrogen fluoride, methanol, and cyclopentadiene 
emissions purification and compliance monitoring 

Personnel 
Factor B3 

Workforce Health Management B31 Health examination and fatigue monitoring before high 
temperature, high altitude and confined space operations 

Compliance with operating procedures and 
violation control B32 

Violation of regulations and operation 

Job Qualifications and Skill Set B33 Certification rates for special operations, explosion-proof 
electrical maintenance, and pressure vessel operation 

Job standard compliance rate B34 The wearing rate of work protective equipment, the 
implementation rate of the "three certificates" in confined 
spaces, and the orderly arrangement of items and tools in 
the workplace 

Security awareness and risk identification 
ability B35 

Can you identify the risks of chemicals, poisoning, explosion 
and other risks specific to this process 

Emergency response capability B36 Emergency response proficiency for on-site fires, leaks, and 
explosions 

Environmental 
risk factor B4 

Noise hazard control B41 Noise levels of vacuum pumps and cooling systems ≤85 dB, 
along with the proportion of ear protection worn. 

Safety Layout of Hazardous Materials 
Production Facilities B42 

The location, evacuation distance, and fire prevention 
distance of Class A operation area comply with the 
specifications 

Fire Protection Facilities: Configuration and 
Effectiveness B43 

The completeness rate of fire hydrants, fire extinguishers, 
fireproof roller shutters, and fire water sources 

Microwave radiation control B44 Microwave oven cavity shielding effect pass rate 
Evacuation corridor accessibility B45 No items in the channel. The indicator light is working 

properly. 
Ventilation and Dust Removal System 
Performance B46 

Local exhaust and accident ventilation air volume 
compliance rate 

Environmental temperature and humidity 
and chemical stability B47 

The temperature and humidity control records in the 
storage and production areas meet the process 
requirements 

Safety 
management 
factor B5 

Safety education and specialized training 
coverage rate B51 

Training ratio for high-temperature, high-pressure, 
confined space, explosion-proof areas, and chemical 
operations 

Safety responsibility system implementation 
B52 

Workplace safety responsibility signing rate and assessment 
results 

Emergency Plan Development and Drill B53 Fire, explosion, leakage, electric shock and other special 
plans and exercise frequency 

Operational Procedures and System 
Completeness B54 

Update and operability of core equipment and hazardous 
work procedures 

Closed-loop management of safety 
inspections and hazard rectification B55 

Monthly inspection frequency and closed-loop rectification 
rate 

 

 



DS. Balami et al. /Future Sustainability                                                                                February 2026| Volume 04 | Issue 01 | Pages 22-36 

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Figure 2. Hierarchical structure model 

 

 

 

Figure 3. Comprehensive evaluation report 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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2) Enhanced process safety parameter interlock: Re-examine 

and optimize alarm and interlock settings for critical process 

parameters (temperature, pressure, concentration) to ensure 

automatic downgrading or shutdown procedures when limits 

are exceeded, preventing accident escalation. 

(3) Improve personnel safety literacy and behavioral norms 

1) Special training and capacity building: Develop special 

training modules for the risks of the polyimide foam 

production process and adopt the combination of case 

teaching and practical exercise to improve the risk 

identification and emergency response ability of employees. 

2) Operation process supervision and behavior correction: 

Promote the "behavior safety observation" activity, and use 

video surveillance and other technical means to strengthen 

remote supervision of high-risk operations (such as limited 

space, high temperature cleaning), timely intervention in 

violation of rules, and analysis of root causes. 

(4) Improve the safety management system and promote 

closed-loop management 

1) Link the implementation of the responsibility system with 

performance assessment: include safety performance 

indicators (such as the rate of hidden danger rectification and 

the number of violations) into the annual assessment of 

departments and individuals, clarify the rules of rewards and 

punishments, and enhance the safety responsibility 

awareness of all staff. 

2) Closed-loop optimization of hazard identification and 

management: Through digital systems (e.g., mobile 

inspection apps), the entire process of hazard reporting, 

rectification, and verification is tracked, ensuring timely 

resolution and feedback for each identified issue, thereby 

forming a closed-loop management system. 

(5) Improve working environment and emergency support 

conditions 

1) Layout optimization and emergency facility maintenance: 

Regularly review fire separation distances between Class A 

zones and adjacent facilities to ensure unobstructed 

evacuation routes. Establish a monthly inspection system for 

fire protection facilities to maintain their readiness at all 

times. 

2) Improvement of local environmental control ability: 

optimize the airflow organization of local exhaust hood for 

dust (gas) production processes, such as foaming and 

crushing, and regularly measure the air volume and air speed 

to ensure that the capture efficiency meets the occupational 

exposure limit requirements. 

5. Conclusion 

This paper mathematically constructs a multidimensional 
assessment model that includes five aspects, such as 
equipment, materials, personnel, environment, and 
management, to evaluate the degree of safety risks in 
polyimide foam manufacturing lines. The paper uses the 
Analytic Hierarchy Process (AHP) and the Fuzzy Credibility 
Evaluation (FCE) method to provide quantitative risk 
assessment and classification. The key findings are as follows: 
(1) Through AHP weight analysis, the identification of key risk 
factors such as the stability and reliability design of core 
equipment, the effectiveness of safety interlock and 
protective devices, and the reliability of the electrostatic 
protection and grounding system are the priority areas of risk 
control. 
(2) The comprehensive evaluation based on FCE indicates 
that the production line's overall risk is classified as 
"Relatively High Risk". The highest scores were attributed to 
equipment and process material factors, which are the 
primary contributors to the elevated risk level. 
(3) Although the personnel, environment, and safety 
management factors are at the level of "General Risk", they 
are still close to the threshold of "Relatively High Risk", 
indicating that there are obvious shortcomings in the risk 
prevention and control system, which need to be 
strengthened systematically. 

Table 3. Weight ranking of risk indicators 

Risk indicator  Weight  

B11 Core Equipment Stability and 
Reliability Design 

0.1068 

B12 safety interlock and protection device 
effectiveness 

0.0994 

B14 Static protection and grounding system 
reliability 

0.0780 

B42 Safety layout of hazardous materials 
production facilities 

0.0557 

B13 Compliance of Safety Accessories for 
Special Equipment 

0.0547 

B21 Management of storage and use of 
hazardous chemicals 

0.0450 

B31 Personnel physical and mental state 
management 

0.0407 

B51 safety education and special training 
coverage 

0.0405 

B41 Noise hazard control 0.0346 

B15 Explosion-proof Electrical Equipment 
Selection and Installation Compliance 

0.0345 

B16 equipment online monitoring and early 
warning system completeness 

0.0295 

B22 Asphyxiating gas risk prevention and 
control 

0.0295 

B52 Safety responsibility system 
implementation 

0.0279 

B26 Waste gas and harmful substance 
control 

0.0269 

B44 Microwave Radiation Control 0.0254 

B32 Compliance with Operating Procedures 
and Violation Control 

0.0252 

B53 Emergency Plan preparation and 
exercise 

0.0245 

Fire prevention in B25 cleaning operations 0.0238 

B54 Operational Procedures and System 
Completeness 

0.0225 

B23 combustible gas and dust 
concentration control 

0.0213 

B43 Fire protection facilities and 
effectiveness 

0.0213 

B46 ventilation and dust removal system 
performance 

0.0212 

B24 High-temperature and high-pressure 
interlock control 

0.0188 

B34 Work Specification Compliance Rate 0.0182 

B33 Job Qualifications and Skill Set 0.0164 

B45 evacuation channel accessibility 0.0162 

B47 Environmental temperature and 
humidity and chemical stability 

0.0127 

B55 closed-loop management for safety 
inspection and hazard rectification 

0.0106 

B35 Safety Awareness and Risk 
Identification 

0.0103 

B36 Emergency response capability 0.0080 

 

 



DS. Balami et al. /Future Sustainability                                                                                February 2026| Volume 04 | Issue 01 | Pages 22-36 

44 

 

(4) Based on the evaluation results, specific risk prevention 
and control measures are put forward from the aspects of 
equipment inherent safety, process parameter control, 
personnel behavior management, closed-loop system 
operation, and environmental emergency support, which 
provide an operational practice path for the safe operation 
and continuous improvement of the polyimide foam 
production line. 
This study provides methodological support for risk 
identification, classification, and the formulation of control 
strategies in high-risk production systems. Future research 
may further incorporate dynamic risk-monitoring data and 
intelligent early-warning technologies to improve the real-
time and prospective character of risk assessment. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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