On January 8, 2020, a pandemic pathogen was confirmed as a new coronavirus; WHO named this new coronavirus "COVID-19" (Tu et al., 2020) . On January 30, the WHO announced that the COVID-19 was listed as a "Public Health Emergency of International Concern" (PHEIC). As of May 3, 2020, the number of confirmed cases surpassed 3,349,000, continuing to rise (WHO, 2020) . The outbreak has had a severe impact on a global scale, and many industries face enormous challenges due to the strict quarantine policies adopted by many countries. As one of the pandemic safety measures, many chemical plants have limited personnel at workplaces and shift significantly to remote working. Only employees in important production and management positions are required to reduce the number of people returning to work. Due to the shortage of human resources, the workers' workload and pressure will increase substantially, resulting in plants facing higher risks than usual. These measures have caused severe disruptions to normal operations of chemical plants. This has created a more challenging environment for the process industry to manage the risks of major process accidents. For example, Tertiary Butyl Catechol (TBC), a chemical inhibitor, was found unavailable on the site and was not added to the tank for one and half months (Mathur, 2020) .
The shortage of human resources caused by the pandemic will increase the staff workload. The on-site workers speed up operations to complete a large amount of work, thereby increasing the probability of operational errors; besides, inadequate or no supervision due to human resources shortage will increase the probability of operational errors turning into accidents. The impact of the pandemic on the accident stage is mainly reflected in the daily inspection and maintenance, as well as the emergency response time and efficiency. For example, the shortage of human resources will reduce the number of on-site inspections and routine maintenance, significantly increasing the potential risks of the plant. Besides, when an accident occurs, operators' response speed and efficiency will also be affected to different degrees. The specific impact of the pandemic on the process industries is discussed in Section 2.
In order to analyze the risks in the system, many accident models have been proposed. Reason's (1990) Swiss cheese model significantly affects the understanding of an accident. He believes that the occurrence of an accident is the result of a combination of factors, including potential and direct factors. Leveson (2004) proposed a new accident model based on system theory concepts, called the system-theoretic accident model and process (STAMP). In her opinion, accidents in complex systems occur because the control system fails to deal with external disturbances or non-functional interactions between system components promptly, rather than merely due to the failure of independent components. These models cannot represent the overall view of system safety, nor can they be adapted to the modeling of multiple causal factors. They are mainly descriptive models, not predictive models (Rathnayaka et al., 2011) . Rathnayaka et al. (2011) developed a process accident model, named SHIPP (System hazard identification, prediction and prevention). It combines the concepts of Bow-Tie to model causality. The probability of accidents can be updated using the Bayesian update mechanism. Bayesian network is a popular method for conducting quantitative risk analysis for process systems. Abimbola and Khan (2014) used the Bayesian network to evaluate the dynamic risk of the drilling system, and he used the value of the safety barrier failure probability changing with time to obtain the relationship between the accident occurrence probability and time. Zarei et al. (2019) used the fuzzy Bayesian network (FBN) to perform a quantitative risk assessment on a natural gas station. They compared the Bayesian network with FBN, and show the advantages of FBN. Yang et al. (2013) proposed a framework that used a precursor-based hierarchical Bayesian approach (HBA) for rare event frequency estimation and demonstrated it with the BP Deepwater Horizon accident in the Gulf of Mexico. Vianello et al. (2019) used an API risk-based inspection assessment approach to reduce maintenance costs and, increase the plant's reliability and availability. Li et al. (2019) proposed a risk-based accident model to analyze the problem of subsea pipeline leakage quantitatively and effectively predicted the probability of subsea pipeline leakage accidents. Milazzo et al. (2015) proposed a quantitative risk assessment approach to analyze the uncertainties related to the results of the analysis, which derive from assumption in the application of the standard models. Vianello et al. (2016) used the Inspection Manager software to overcome the complexity and time-consuming data collection in RBI.
The above studies focus on the probability of accidents and the risks of comprehensive accident causes. Those methods cannot represent the risk of a single path. However, due to the pandemic's unique and complex impact on the chemical industry, it is essential to identify and eliminate the most vulnerable risk factors in a limited time. In light of the above, it is necessary to design a method that can quickly find the most likely accident path and corresponding risk factors.
This paper aims to establish a risk-based model for hazardous material leakage accidents in the case of human resources shortage during a pandemic. The model is divided into two parts. The first part uses qualitative risk analysis to identify the potential risk factors and the possible accident consequences during a pandemic. In the second part, quantitative risk analysis is applied to determine the accident's evolution from causes to consequences, quickly identifying the most likely path, compute its corresponding probability, and finally discover the critical risk factors in the path. The research provides strong support for decision-making during the outbreak.
The remaining parts of this paper are organized as follows. The influence of pandemic in the industry is presented in Section 2. A brief description of the proposed method, including the window of opportunity, complex network, and risk entropy, is shown in Section 3. The qualitative risk analysis based on the window of opportunity and the quantitative risk analysis based on the complex network are presented in Section 4. Section 5 compares the proposed method with Bow-Tie and Bayesian networks for accident modeling. Finally, conclusions are drawn in Section 6.
In the face of any pandemic's rapid spread, and especially in case of the COVID-19 pandemic some governments decided to suspend public transport and impose a temporarily lockdown to reduce the population flow. Even so, it could be observed in many countries that the pandemic still spread dramatically and adversely affected people's lives and economy. The delay of the loch-to-work situation caused by this outbreak has had a significant impact on the chemical industry. The impact of the outbreak on the chemical industry is divided into two parts: i) the impact on human errors, and ii) the impact on accident stages.
The outbreak in China began in early 2020. After the Spring Festival, employees were about to return to work. Due to the pandemic's impact, some non-local employees were not allowed to return to work, or 14 days of quarantine observation before resuming work (Hao et al., 2020; Kumar et al., 2020) . This led to a shortage of workers in the plant. Besides, in order to implement the pandemic prevention and control requirements, some employees were seconded to other departments, which made the shortage of some positions more serious. During the pandemic, some international routes and ports were shut down (Fu et al., 2020) , which caused a large amount of hazardous materials to be stored in the plant. As the storage tanks are full for a long time and lack staff, this has caused delays in daily inspections, supervision, and other work.
The Human Factors Analysis and Classification System (HFACS) classifies the causes of accidents into four categories: unsafe acts, the preconditions for unsafe acts, unsafe supervision, and organization influence (Shappell and Wiegmann, 2000) . With its systematic methodology and taxonomic nature, the HFACS reduces the incompleteness caused by experts' limited knowledge and missing information during the identification and classification of human and organizational factors (Fu et al., 2020) . According to the unique impact of the pandemic, the causes of human error are classified into four categories in this present work, namely, operational error (M1), the preconditions for unsafe acts (M2), unsafe supervision (M3), and organizational influence (M4). Fig. 1 demonstrates the logical relationship between the factors caused by the pandemic.
For operational errors, due to the impact of staff shortage and secondment, the tasks assigned to the staff during the pandemic may not be familiar to them. Insufficient or no training for the staff may reduce the technical ability, safety awareness, and effectiveness of the staff's supervision. For the preconditions for unsafe acts, in response to the call for pandemic prevention and control, the staff must wear masks and keep a proper distance, which increases the difficulty of communication and dramatically reduces the effectiveness of information transmission. Besides, due to the long-term wearing of masks and high work pressure, employees are likely to make a mistake. This also increases the probability of human errors. For organizational influence, due to the shortage of personnel and the increase in the absenteeism rate of the personnel, the daily production, and management of the plant can only rely on a few employees. In this case, there will be unclear task assignments, unreasonable scheduling, night work, and over time, which increases the staff's burden and increases the probability of human error. The impact of the pandemic on workers will be throughout the entire production process. Table 1 demonstrates the risk factors of human errors. The specific analysis of the human errors is shown in Section 4.2.
The concept of window of opportunity (WO) is mostly used in the medical field to indicate the best time to treat a disease (Ismail et al., 2017; Langer et al., 2011; Sweeney, 1997; Andersen, 2003) . WO represents the best time to invest in a business and the best time to catch up with competitors (Kwak and Yoon, 2020; Yap and Truffer, 2019) . In this research, the time from the accident precursor stage to the time before the accident termination is defined as the WO representing the best time for the system to prevent and control accidents. The life cycle of an accident can be divided into two stages, each stage corresponding to a WO. The first stage is the accident precursor stage, corresponding to the first type of WO (FWO). The second stage is the accident evolution stage, corresponding to the second type of WO (SWO). The pandemic's impact is different at each stage, but one thing in common is that it shortens the window of opportunity. During the accident precursor period before the accident initiation stage (i.e., FWO), the operators' main task is daily inspections to identify and eliminate risk factors in time. The FWO can be divided into three segments, as shown in Fig. 2 (a) . Under normal circumstances, routine inspection and maintenance will promptly and effectively discover and eliminate risk factors, thus reducing the probability of accidents and extending the time of FWO. However, due to the shortage of human resources during the pandemic, the number and frequency of daily inspections are relatively low. Besides, with the increased workload and pressure of workers, the effectiveness of inspection and maintenance will be reduced, resulting in a reduced probability of finding and eliminating risk factors. This means that the number of risk factors present in the plant during the pandemic is higher than usual significantly shortening the FWO. For the accident evolution stage (SWO), the operators' main task is to take emergency measures to prevent the escalation of the accident. The SWO can be divided into three segments, as shown in Fig. 2 (b) . Whether and what measures were taken in each time period would lead to the accident develop in different directions. Due to the shortage of human resources and emergency supplies, the response speed of workers, the speed of taking measures and the effectiveness will be affected during the pandemic. Therefore, the development path of the accident cannot be effectively cut off in time, which will shorten the SWO and lead to the rapid escalation of the accident into a catastrophe.
In the last section, we analyzed the special impact of the pandemic on chemical process safety. The methodology is developed in this section to deal with those special risk factors, which include identifying the potential risk factors, accident scenarios, and assessing the accident shortest path and its probability under the influence of a pandemic. The proposed methodology is comprised of several steps, as demonstrated in Fig. 3 . Each step of the methodology is discussed in detail in the following section.
In recent decades, a large number of catastrophic accidents have occurred in the process industry. The most common example of these accidents is personnel poisoning, fire, and vapor cloud explosion (VCE). Leakages are often the causes of these accidents. Therefore, this paper focuses on investigating leakage-induced accident risks in the process industry under the pandemic situation. Dangerous gas leakage accidents can be divided into several stages on the time axis, and each stage will exhibit different dynamic characteristics. The time from the accident precursor stage to the time before the accident termination is called the window of opportunity (WO)，as shown in Fig. 4 . Whether and what measures are taken in each period would lead to the accident develop in different directions.
There are two types of WO: the first type of WO (FWO) refers to the stage of the accident precursor stage. Potential risk factors exist in this stage, but they are not eliminated in a timely and effective manner. As time goes on, they eventually lead to accidents. Notably, during the pandemic situation, the impact of human resources shortage shortened the FWO. It may increase the probability of human errors. Therefore, studying the FWO is beneficial to deal with the special effects of the pandemic. The purpose of studying the FWO is to discover and eliminate the risk factors before the accident occurs and extend the FWO, to prevent the occurrence of accident fundamentally. The second type of WO (SWO) refers to the time from the accident occurrence to the time before the accident terminates. During this period, the initial accident may lead to a disastrous accident. Due to the pandemic's impact, the speed of emergency response will be reduced when an accident occurs. This increases the probability that the initial accident evolves into a catastrophe. SWO aims to analyze the cause of barrier failure and to take adequate measures to control the development path of accidents and reduce their consequences. The relationship between the WO and the accident life cycle is shown in Fig. 4 .
Complex networks are between regular networks and random networks connected by logical operators. Initial events may evolve into result events through different network paths (Meng et al., 2019) . The subtle relationship between the nodes is a guarantee that the entire system will normally complete the assigned tasks. The complex network abstracts the basic events and intermediate events as discrete points and abstracts the relationship between events as a directed edge with weights. The directed edge represents the relationship between events, and the weight represents the degree of connection. It is described by a directed acyclic sparse matrix connection graph G= (N, E, W) , where N = (1, 2, … n) is the set of nodes; E = (e 1 , e 2 , … e n ) is the set of edges; W = (w 1 , w 2 , … w n ) is the set of weights of the edges. This paper introduce the Dijkstra algorithm to calculate the shortest path of an accident under the impact of pandemic. The implementation of the algorithm is based on greedy thinking. The basic idea is to traverse all nodes from one vertex until finding the shortest path to the endpoint. The algorithm adds the edge weights contained in each path, and the path with the smallest total edge weight is the shortest path from the start point to the end. In a complex network, the nodes' edge weights are represented by probabilities, the shortest path is:
In the formula, i and j are any two nodes in a complex network； when x ij = 1, it means that there is risk transmission path between nodes i and j; when x ij = 0 means that there is no direct connection between i and j; P ij represents the probability of risk transfer from node i to node j. The maximum value is the optimal solution, that is, the path with the highest probability of accident.
Since the algorithm adds all the edge weights to find the shortest path, and the probability cannot be added, so the risk entropy with additivity is introduced to express the edge weights between nodes. Entropy is a state function introduced by Clausius in 1867 to complete the quantification of the second law of thermodynamics, which has evolved into a measure of system disorder or uncertainty (Clausius, 1867). Shannon (1948) used information entropy to describe the uncertainty of an information source. Drawing on the definition of self-information in information theory, this paper uses self-information to represent the edge weights between nodes, called risk entropy. For the event x i with probability P(x i ), its self-information is I(x i ) (Shannon, 1948) :
The calculation of an accident shortest path can be converted into the optimal solution problem. Since the higher the probability of an event, the smaller the self-information; therefore, the shortest path of an accident is the path with the lowest risk entropy. The objective function is transformed into a risk entropy function, as shown in the following equation:
The flue gas generated by coal combustion in thermal power plants contains enormous nitrogen oxides. To prevent environmental pollution, the flue gas should be denitrified. SCR technology refers to the process of reducing agent under the action of catalyst to convert nitrogen oxides in flue gas into nitrogen and water. Ammonia gas is usually selected as a reducing agent, and the reaction temperature is 280-420 • C, the specific reaction process is shown in Fig. 5 .
Ammonia as a reducing agent is used in the denitration process. Fig. 6 represents the process flow diagram for the ammonia storage and transportation system in the SCR denitration process. The liquid ammonia from the transport vehicle is discharged into the liquid ammonia storage tank, and the evaporated gas is discharged into the evaporator and buffer tank. The liquid ammonia is pumped to the evaporator for evaporation. Then the evaporated ammonia gas is discharged into the buffer tank, which stabilizes the supply of ammonia gas. Finally, the ammonia gas enters the SCR denitrification system. The residual NH 3 in the storage tank, evaporator, and buffer tank is absorbed by the industrial water in the ammonia dilution tank. Then it is discharged into the wastewater tank. Finally, it is pumped to the industrial wastewater station.
(1) Hazard identification
The research of FWO aims to identify potential risk factors to prevent accidents. In this period, the Fault tree model is used to find potential risk factors according to the process flow in Fig. 7 .
Due to the special impact of the pandemic on the WO and human errors, the probability of equipment failure being detected and eliminated is reduced, while the probability of human errors has increased. This makes accidents' probability higher than normal. Equipment and human factors are the main causes of leakage accident. The equipment factors of liquid ammonia storage and transportation system can be divided into two parts, namely storage equipment and pipeline equipment. In the pandemic situation, human factors are mainly composed of four parts, which are operation errors, poor information transmission, unreasonable work design and poor working environment. There are 28 basic events and 19 intermediate events in the fault tree, which means that there are 28 risk factors that may cause an accident in the FWO under the impact of a pandemic. If these risk factors can be eliminated in a timely and effective manner during FWO, the leakage accident can be avoided. The identified risk factors are shown in Fig. 7 and Table 2. (2) Accident sequence When an accident occurs, the safety barrier's effectiveness determines the accident propagation scenarios, and each scenario can be represented by an accident evolution path. Due to the impact of the epidemic, the frequency and efficiency of safety barrier inspection and maintenance are reduced, leading to an increased possibility of safety barrier failure. Besides, the shortage of human resources reduces the speed of emergency response. The research of SWO aims to identify the potential accident evolution path. In this period, the Event Tree model can be used to find out the possible failure reasons of the safety barriers and the potential development path of the accident. Ammonia is not article are mainly divided into three categories, namely the dispersion prevention barrier, the ignition prevention barrier, and the emergency response barrier. The effectiveness of the safety barrier is affected by multiple risk factors. In Fig. 8A and B, C, D, E means safe, near miss, poisoning accident, explosion accident, and catastrophe, respectively. The details are shown in Fig. 8 and Table 2 .
The study of WO can effectively identify the cause, evolution path, and consequences of the accident under the impact of a pandemic. In order to quantitatively analyze the accident risk, the complex network is introduced to integrate the two types of WO (FWO and SWO) to calculate the shortest path and the probability of the accident. The fuzzy set theory can be used to present subjective, vague, linguistic and imprecise data and information effectively, in which fuzzy numbers scored by invited experts will characterize the probability values of primary events, and subsequently fuzzy number in linguistic term can be transformed into fuzzy failure (prior) probability of factors Zarei et al., 2019) . In the present work, expert elicitation is introduced to calculate the failure probabilities of the events. The specific calculation procedure can be seen in Zarei et al. (2019) . Table Appendix A demonstrates expert judgments, aggregation of fuzzy numbers, fuzzy possibility, and the probability of root events in Figs. 7 and 8.
The complex network model ( Fig. 9 ) was developed based on the identified risk factors (Fig. 7) and their relationships (Fig. 8) . In order to simplify the modeling process of a complex network, node types are divided into two categories: equipment factors and human factors. There are 85 nodes and 131 edges in the complex network model, and the risk factors associated with each node are shown in Table 3 .
The complex network's edge weights are expressed by probabilities. In this paper, when a node i has only one parent node j, the edge weight W ji between the two is the failure probability of the node i. The Fuzzy set theory is used to calculate the specific failure probability of factors (Lavasani et al., 2011) . When a node has more than one parent node, the node represents an intermediate event. At this point, the edge weights between the node and its parent nodes are obtained according to the relationship between the AND gate and the OR gate in Figs. 7 and 8. In the complex network, the edge direction is used to indicate the risk transitivity, and the edge weight is used to indicate the risk value. According to Eq. (2), all edge weight values of risk factors are converted into risk entropy, as shown in Table 4 .
The purpose of accident scenario calculation is to search for the shortest path from the initial event to the resulting event. The shortest path of a dangerous gas leakage accident is equivalent to the path with the lowest risk entropy. Based on the Dijkstra algorithm and Equation (3), MATLAB software is used to calculate the shortest path of the accident caused by various risk factors. The results are shown in Table 5 .
The shortest paths of leakage and escalation accidents caused by different risk factors are listed in Table 5 . The nodes in each path are the principal risk factors in the accident evolution. According to Table 5 , an ammonia leakage accident caused by human errors is the shortest, followed by equipment factors. It can be seen that the probability of human factors increase due to the impact of the pandemic has increased, thereby increasing the probability of leakage accidents. The shortest leakage accident path is 2 → 26→40 → 46→48, and its probability is 1.49E-02, indicating that after a few steps, the initial event can cause a leakage accident. When ammonia leakage occurs, the dispersion prevention barrier will eventually fail due to the automatic detector failure, long delayed inspection and manual detection failure, resulting in a large amount of ammonia leakage and dispersion. As ammonia gas is highly corrosive and toxic, workers will have obvious uncomfortable reactions when inhaled, such as cough, dizziness and dyspnea. This has seriously affected the speed and efficiency of personnel emergency response. Ammonia is not easily ignited. However, due to the failure to take effective measures to control the leakage of ammonia gas, a large amount of ammonia gas accumulates, and explosion accidents will occur under the conditions of the existence of ignition sources, which further caused damage to personnel and the plant. The shortest escalation accident path is 2 → 26→40 → 46→48 → 52→74 → 81→83 → 64→84 → 68→79 → 85, and its probability is 1.06E-08.
It can be seen from Table 5 that different initial events cause leakage accidents, and to avoid the occurrence of leakage accidents, corresponding measures can be taken in the FWO, such as strengthening routine inspection, reduce work stress and have a reasonable work schedule. When the leakage accident occurs, the shortest development path of the accident is almost the same. A long delay in inspection, static electricity, and inadequate human resources are the leading causes of the failure of dispersion prevention barriers, ignition prevention barriers, and emergency response barriers, respectively. When the leakage accident occurs, to avoid the escalation of the accident, corresponding measures can be taken in SWO to cut off the expansion path of the accident, to avoid the occurrence of the escalation of the accident. The shortest path of an explosion accident caused by human factors and equipment factors are shown in Fig. 10 . The blue line in the figure represents the shortest accident path caused by equipment factors; the green line represents the shortest accident path caused by human factors, and the red line represents the common path of the accident caused by both. There are some identical risk factors in the shortest path of different accidents.
The risk factors of each path are interlinked. Targeted measures can be taken to cut off the development path of the accident and make the accident develop in a relatively favorable direction. For equipment factors, targeted inspection should be carried out as much as possible in the case of human resources shortage. Especially for components with high failure frequency (such as valve failure in this case), the effectiveness of inspection and maintenance should be increased. This can not only effectively reduce the number of risk factors and the failure probability, but also extend the time of FWO to carry out more inspections and repairs. Besides, it can compensate for some of the impact of the human resources shortage. For human errors, reasonable work arrangement and communication can not only reduce the workers' pressure but also reduce the impact caused by the poor working environment. Targeted development of emergency plans during the pandemic can reduce the decision time and the probability of decision failure. It can also improve the effectiveness of emergency response, and control the direction of accidents in a limited time to reduce accident consequences.
Bow-Tie (BT) and Bayesian network (BN) model are widely used as risk analysis techniques in the field of chemical process safety (Ferdous et al., 2012; Khakzad et al., 2013; Abimbola and Khan, 2014; Zarei et al., 
18.364 1.06 × 10 -8 Fig. 10 . The shortest paths of different initial events during the pandemic.
The difference between the proposed method and BT and BN. . BT is used to represent the relationship between the cause of an accident, the path of the accident, the consequences of the accident and the measures to prevent the accident. It is a risk analysis method that is easy to use and operate, and it is highly visual. BT can graphically represent the entire life cycle of an accident, and help workers establish effective measures to prevent accidents. However, most of the modern complex industrial systems suffer from multiple failure modes and exhibit dynamic failure behaviour. Therefore, conventional tool is unable to deal with dynamic failure behaviour of complex systems (Kabir, 2017) . BT, as a conventional risk analysis method, uses generic failure data. This makes it to be non-case-specific and introduce uncertainty into the results (Li et al., 2016) . Besides, it cannot represent the risk of a single path. BN is a probabilistic inference technique for reasoning in uncertain situations, which can relax the limitation of conventional methods and consider conditional dependence and common failures in the process of accident modeling (Yuan et al., 2015) . However, BN focus on solving the overall risk of accidents, and cannot represent the risk of a single path. During the pandemic, due to the shortage of human resources, it is essential to identify and eliminate the most vulnerable risk factors in limited time. This can increase WO's time so as to make more rounds of inspection and maintenance. Compared with the BN method, this method fully displays the risk factors on the network graph systematically and intuitively on the basis of considering the uncertainty. The detailed difference between the proposed method and BT and BN is shown in Table 6 .
The proposed methodology leverages the strength of BT and BN, which mapped from Fault Tree and Event Tree, and considers the data uncertainty. It can solve the probability of the path between any nodes, and quickly identify the shortest path of accidents, and most the vulnerable risk factors, to provide more targeted decision support for accident prevention and control during a pandemic.
The pandemic's impact on the process industry is mainly reflected in two aspects: human errors and accident stages. Human errors should be analyzed from four aspects: organization, unsafe supervision, preconditions for unsafe acts, and unsafe acts. Due to the shortage of human resources, there will be secondments during the epidemic, workers' ambiguity about new tasks, and insufficient training will increase the probability of human errors. Besides, organizations will take various measures to deal with the impact of the pandemic. The manager may make wrong decisions or policies because of lack of experience in the pandemic. For the influence of unsafe supervision, the lack of human resources will reduce or even eliminate normal supervision efficiency, thereby increasing the risk of accidents. For the preconditions of unsafe acts, the shortage of human resources caused by the pandemic increases the workers' stress and workload. The on-site workers speed up operations to complete many tasks, thereby increasing the probability of operational errors.
The impact of the pandemic on the accident stage is mainly reflected in the daily inspection and maintenance as well as the emergency response time and efficiency. In the FWO, the shortage of human resources will reduce the number of on-site inspections and routine maintenance. Due to the lack of normal inspection and maintenance, the possibility of accidents in the process industry is greatly increased. Due to the impact of the pandemic, personnel's emergency efficiency and response time will be greatly affected in the event of an accident, thus shortening SWO. This means that when the initial accident occurs, the probability of accident escalation increases greatly, increasing the risk of the process industry.
According to the shortest path calculation results, the main reason for the leakage accident in the process industry is the valve failure caused by insufficient maintenance, and the delayed inspection finally leads to the leakage accident. When the automatic gas detector fails, the shortage of human resources leads to manual detection failure, which leads to the failure of the dispersion prevention barrier. Due to the pandemic's impact, no fire prevention measures were taken after the leakage accident, which eventually led to the explosion accident. Emergency rescue is not carried out in a timely and effective manner because of the pandemic's impact and personnel poisoning, which causes severe casualties, property losses, and environmental damage.
The special influence of a pandemic on chemical process safety is analyzed, and is divided into two parts: the impact on human errors and the accident stages. For human errors, operational errors, information transmission failure, unreasonable working hours, and poor working environment are the main causes of human errors. For accident stages, the number of workers is reduced during the pandemic, thereby increasing the number of risk factors and the probability of failures, shortening the window of opportunity and increasing the possibility of accidents.
The WO concept is proposed to analyze the special risk factors during a pandemic, and to divide the accident life cycle into two parts. It reveals the possible risk factors, accident scenarios and accident consequences of chemical process safety under the impact of a pandemic.
Based on a qualitative risk analysis, the complex network is introduced to integrate the WO. The complexity of accident process is displayed intuitively on the network model. The Dijkstra algorithm is used to find the shortest path of an accident and to identify the shortest path caused by different risk factors. Since the probabilities cannot be added, the use of the Dijkstra algorithm is limited. In order to overcome this shortcoming, the concept of risk entropy is proposed to convert probability into risk entropy to represent edge weights. Human error caused by high working pressure as the initial event leads to the shortest path of escalation accident.
The advantages and disadvantages of the BT and BN models are analyzed. The results show that BT and BN have their own strengths, but they cannot calculate the risk of a single path. The proposed method combines the advantages of BT and BN to make the modeling process and results more scientific. It can more effectively reduce the probability of accidents during the pandemic within a limited time. Taking targeted measures can cut off the development of the accident and make the accident develop in a relatively favorable direction. It has certain engineering significance for reducing the probability of accidents and controlling the consequences of accidents during any pandemic.
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.
Expert judgment, fuzzy possibilities (FPs) and fuzzy probabilities (FPr) of root events in fault tree and event tree. 