CN112529412A - Dynamic risk analysis method for hazardous chemical substance fire domino accident under multi-disaster coupling - Google Patents
Dynamic risk analysis method for hazardous chemical substance fire domino accident under multi-disaster coupling Download PDFInfo
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Abstract
The invention discloses a dynamic risk analysis method for a hazardous chemical substance fire domino accident under multi-disaster coupling, which comprises the steps of collecting basic information; analyzing the vulnerability of the equipment under the action of natural disasters to determine a possible initial accident scene; analyzing the equipment states at different time steps by using a distribution model of accident parameters; counting the states of the devices appearing at different time steps through Monte Carlo simulation; after all Monte Carlo simulations are completed, the dynamic domino accident probability is calculated. Under the coupling of multiple disasters, the evolution process of the fire domino accident has close time correlation, and the accident risk is larger. By adopting the method, the dynamic risk assessment of the fire domino accident of the large-area chemical equipment can be realized, and important decision support is provided for domino accident prevention and control.
Description
Technical Field
The invention belongs to the technical field of chemical process safety, and particularly relates to a dynamic risk analysis method for a hazardous chemical substance fire domino accident under multi-disaster coupling.
Background
China is vast in breadth, natural disasters occur frequently, and chemical industry gathering areas are mostly concentrated in coastal areas along rivers which are easily affected by natural disasters. According to the principle of 'industrial aggregation, centralized layout and intensive land utilization', the distribution mode of the chemical device is further enlarged and intensified, so that the amount of dangerous chemicals is highly concentrated, and initial accidents are easy to generate chain reaction to cause domino effect accidents. Compared with other industrial accidents, the consequences of a multi-disaster coupled accident caused by a natural disaster are more serious, and the main reasons include: the accident influence range is large, and the initial accident may involve a plurality of units; the fire-fighting equipment may be damaged and cannot be started; insufficient emergency personnel and equipment; the lifeline project is damaged, and the emergency rescue is prevented from being carried out. With the change of climate and environment and the convergence development normality of chemical industry, the frequency of occurrence of multi-disaster coupled accidents is higher and higher, so that natural disaster factors need to be comprehensively considered to carry out risk analysis of the multi-disaster coupled domino accidents.
In recent years, scholars at home and abroad adopt a method based on risk index (for example, Huanghai Swallow et al, "Bayesian Network-based accident multi-level domino effect calculation method research [ J ]. Chinese safety production science technology, 2019,015(006): 157-161", Wang et al, "a chemical park real-time quantitative risk assessment method based on multi-disaster real-time coupling [ P ], CN 106651153A", Luying et al, a PTVA model-based multi-disaster coupling physical vulnerability assessment method [ P ], CN109784602A ", Verseveivel H et al," Modeling multi-hazard humouring data on lower basis of a Bayesian approach with a Bayesian approach [ J ]. dynamic Engineering,2015,103(9): 1-14), and a complex Network-based method (for example, Lubobing B et al, simulation of devices [ J ]. 363, dynamic Engineering, 132, simulation, Research on a complex network-based disaster chain risk assessment method [ J ]. systematic engineering theory and practice, 2015,35(2): 466-. However, the related work only involves the analysis of subsequent domino effect accidents which may be caused, and most of the related work is static research on a spatial scale and lacks dynamic research on a time scale. In an actual scene (particularly fire), the evolution of the accident is a dynamic process and is closely related to time factors, and the static analysis method is difficult to evaluate risks at different time steps. In addition, high-order domino accidents are more likely to occur under the condition of multi-disaster coupling, and due to the fact that the number of possible accident combinations is large, accurate analysis is difficult to achieve through an existing dynamic risk analysis method, and the risk of the multi-disaster coupling accidents is underestimated.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a dynamic risk analysis method for a domino accident of a hazardous chemical substance fire under multi-disaster coupling. The method considers the spatial synergistic effect and the parallel effect of disaster factors in the actual domino accident evolution process and the temporal superposition effect, analyzes the dynamic risk of the fire domino accident under the multi-disaster coupling from two dimensions of time and space, and can effectively provide decision support for accident prevention and control and emergency rescue. The specific implementation steps are as follows:
s1: collecting basic information for dynamic domino effect risk analysis;
s2: calculating the vulnerability of each device under the action of a disaster according to a device vulnerability analysis model under the disaster, analyzing a possible initial accident scene, and calculating scene probability;
s3: respectively analyzing the state of each device in each time step for each of the possible initial accident scenes by adopting a Monte Carlo simulation analysis method, and respectively obtaining the dynamic domino effect probability in each initial accident scene;
s4: and combining the dynamic domino effect probabilities of all possible initial accident scenes to obtain the overall dynamic domino probability.
Further, the basic information in step S1 includes geographical location information, weather information, equipment spatial distribution information, and hazardous chemical information stored in the equipment.
Further, step S2 specifically includes:
calculating damage probability of each device according to a device vulnerability analysis model under a certain disaster;
and calculating scene probability of the corresponding possible initial accident scene according to the damage probability of each device.
All initial accident scenes are obtained in a permutation and combination mode, and the scene probability of the corresponding initial accident scene is calculated according to the damage probability of each device;
and determining all possible initial accident scenes through an accident probability threshold.
Further, when the disaster is an earthquake, the damage probability
Wherein Y is a Probit model variable, and Y is k1+k2 ln(102PGA)，k1And k2PGA is the peak ground acceleration, and u represents the integral variable, as parameters related to the tank type, hazardous chemical fill rate.
Further, in step S3, each initial accident scenario adopts a monte carlo simulation analysis method to analyze the state of each device in each time step, and obtains a dynamic domino effect probability, which specifically includes the following steps:
taking a certain initial accident scene and the scene probability thereof as the input of Monte Carlo simulation;
carrying out Monte Carlo simulation initialization processing, and setting basic parameters of each simulation;
analyzing the states of each device in different time steps in a random sampling mode according to an accident parameter distribution model of the initial accident scene;
continuously carrying out Monte Carlo simulation until the simulation times reach a set value, and counting the accident state frequency of each time step in each simulation;
and calculating the dynamic domino effect probability of the initial accident scene according to the accident state frequency.
Further, the dynamic domino effect probability of the initial accident scene can be obtained by dividing the accident state frequency counted at each time step in the initial accident scene by the total simulation frequency.
Further, when the initial accident scene is a fire disaster, the accident parameters in the accident parameter distribution model comprise effective emergency rescue time, complete combustion time of dangerous chemicals in the equipment and failure time of the equipment under the action of heat radiation of the fire disaster.
Further, when the initial accident scenario is a fire, the states of the respective devices include a safety state, a fire accident state, and a fire complete state.
Compared with the prior art, the invention can realize the following beneficial effects:
(1) on the basis of analyzing the vulnerability of natural disasters to equipment, the method considers the scene of potential fire domino accidents, analyzes the serious consequences possibly caused by the multi-disaster coupling accidents from the two aspects of the immediate accidents and the potential secondary accidents, and better accords with the actual situation.
(2) The method fully considers all possible high-order fire domino accident scenes, and can solve the complex combination problem related to the propagation of high-order domino links in a plurality of equipment scenes by adopting a Monte Carlo simulation method, thereby greatly improving the calculation efficiency.
(3) On the basis of the existing domino accident analysis method, the method considers the propagation process of the fire domino link in space and also considers the evolution process of the fire domino link in time, and can realize the update of real-time domino effect probability according to the latest accident scene information.
(4) The method can evaluate equipment with high risk of domino accidents under the condition of multi-disaster coupling in a risk analysis stage, and provides a basis for reasonable arrangement of safety protection facilities; in the emergency rescue stage after the accident occurs, the most possible propagation path and evolution time of the domino accident under the multi-disaster coupling are analyzed, and decision support is provided for the development of the emergency rescue action.
Drawings
FIG. 1 is a plan view of a typical chemical accumulation area for the case where the present invention is applied.
Fig. 2 is a schematic view of a dynamic risk analysis process of a hazardous chemical substance fire domino accident under the condition of multi-disaster coupling according to an embodiment of the present invention.
Fig. 3 is an algorithm flow diagram of a monte carlo simulation.
FIGS. 4 (a) - (p) are graphs of the results of the present invention in case illustration.
Detailed Description
The following further describes the practice of the present invention with reference to the drawings, but the scope of the present invention is not limited thereto.
According to the method for analyzing the dynamic risk of the hazardous chemical substance fire domino accident under the condition of the multi-disaster coupling, provided by the embodiment of the invention, a device vulnerability analysis model under the action of natural disasters is combined with the calculation of the dynamic probability of the domino accident, so that on one hand, a high-order domino accident scene is considered, on the other hand, the spatial synergistic effect and the parallel effect of disaster-causing factors in the actual domino effect process are considered, and the dynamic risk of the hazardous chemical substance fire domino accident under the condition of the multi-disaster coupling in an area range is analyzed through the temporal superposition effect. In this embodiment, a high-order domino accident scene often occurs in a domino accident caused by a natural disaster, because an initial scene of such an accident may involve a plurality of devices, and a lifeline project is easily damaged, and emergency rescue is blocked, so that the domino accident is rapidly propagated, and the consequences are further amplified. The synergistic effect of the disaster causing factors on the space refers to the process that the disaster causing factors generated by a plurality of accident equipment act on the same disaster-affected equipment, the parallel effect on the space refers to the process that the disaster causing factors generated by the same accident equipment act on the plurality of disaster-affected equipment, and the superposition effect on the time refers to the process that the equipment is ineffective due to the gradual reduction of the strength under the action of the disaster causing factors.
In this embodiment, a chemical tank farm including 16 atmospheric storage tanks is selected as a research object, and the plan layout is shown in fig. 1. Wherein the No. 1-10 are non-anchored storage tanks with specification of phi 22.0m × 7.5m and volume of 2500m31300t of kerosene are stored respectively; 11# -16# are all anchoring storage tanks with the specification of phi 17.0m multiplied by 6.0m and the volume of 1300m3Respectively storing 700t of kerosene. The area is set to have an intensity of 0.4 g. The wind speed of the area is 2m/s, the atmospheric stability is B, the ambient temperature is 21 ℃, and the relative humidity is 0.67. Under the condition of not being influenced by the earthquake, the emergency rescue time follows normal distribution with the mean value of 12.5min and the variance of 5.875 min. Under the condition of being influenced by earthquake, emergency rescue can be hindered, and normal distribution with the mean value of 30min and the variance of 10min is obeyed.
The method for analyzing dynamic risk of a domino accident caused by a hazardous chemical substance under multi-disaster coupling provided by the embodiment specifically comprises the following steps:
step 1: collect the basic information that carries out dynamic domino effect risk analysis, including geographical position information, meteorological information, equipment spatial distribution information and the danger article information that equipment stored, as above-mentioned show, compile the relevant information of danger article that equipment stored, include:
n: the number of devices in the area; v: 1 x n dimensional matrix, element viCharacterizing the volume of a device i (i ∈ {1,2, L, n }); c: 1 Xn dimensional matrix, element ciCharacterizing the storage capacity of dangerous chemicals in equipment i (i belongs to {1,2, L, n }); RT: 1 × n dimensional matrix, element rtiCharacterizing a thermal radiation threshold of a device i (i ∈ {1,2, L, n }); r0: n x n dimensional matrix, element rijThe method comprises the steps of characterizing the heat radiation intensity of a device i to a device j (i belongs to {1,2, L, n }, j belongs to {1,2, L, n }); μ _ tte: the mean value of the effective emergency rescue time; sigma _ tte is the standard deviation of effective emergency rescue time; μ _ TTB: 1 Xn-dimensional matrix, element μ _ ttbiRepresenting the average value of the sustained combustion time of the equipment i after the fire disaster occurs, and obtaining the following result through a model: m ═ m "∞×(1-e-kβD) Calculation, where m "represents the combustion rate of the material in still air (kg/m)2S), D denotes the pool fire diameter (m), m "∞And k β is an empirical factor; σ _ TTB: 1 Xn-dimensional matrix, element σ _ ttbiAnd (3) representing the standard deviation of the sustained combustion time of the equipment i after the fire disaster occurs.
Step 2: according to an equipment vulnerability analysis model under a certain disaster (the equipment vulnerability analysis model of the corresponding disaster is the prior art), calculating the vulnerability of each equipment under the action of the disaster and analyzing possible initial accidentsAnd (4) calculating scene probability. The selected disaster is an earthquake, and according to an equipment vulnerability analysis model under an earthquake scene: k is1+k2 ln(102PGA), where Y is the Probit model variable, k1And k2The PGA is the peak ground acceleration, and the damage probability of each device can be calculated for the parameters related to the storage tank type and the hazardous chemical filling rate
u represents an integral variable. The damage probability of the 1# -10# storage tank under the earthquake intensity is 0.11, and the damage probability of the 11# -16# storage tank is 0.04. All initial accident scenes can be obtained through a permutation and combination mode, and the scene probability of the initial scene can be obtained by multiplying the damage probability of the equipment in the damage state and the non-damage probability of the equipment in the non-damage state in the corresponding scene:
wherein I represents the number of devices in a damaged state, Pe,iRepresents the impairment probability of the ith (I ∈ {1, 2.., I }), J represents the number of devices in the non-impaired state, Pe,jRepresenting the impairment probability of the jth (J e {1, 2.., J }) non-impaired device. By taking 1X 10-5Screening out the initial accident scene which is larger than the accident probability threshold value, and determining the possible initial accident scene. In this process, the worst possible accident consequence is taken into account, i.e. after a failure of the device, it is considered to be ignited immediately due to the presence of numerous ignition sources. For the present case, the failure case was considered to be a tank wall bottom leak with a hole diameter of 200 mm.
And step 3: and respectively analyzing the state of each device in each time step for each of the possible initial accident scenes by adopting a Monte Carlo simulation analysis method, and respectively obtaining the dynamic domino effect probability in each initial accident scene, wherein a flow chart of the algorithm is shown in FIG. 3. The method comprises the following specific steps:
step 3.1: inputting basic information for carrying out dynamic domino effect risk analysis, and selecting an initial accident scene and scene probability thereof from all possible initial accident scenes as the input of Monte Carlo simulation, wherein the scene after the equipment in the selected initial accident scene has an accident is a fire;
step 3.2: carrying out Monte Carlo simulation initialization processing, and setting basic parameters of each simulation, wherein the setting comprises the following steps:
SP: a 1 × n dimensional matrix representing an initial state of each device, determined by an initial accident scenario; TPF: a 1 × n dimensional matrix (initial value is 0 matrix) representing the duration of the device in the pool fire state in each simulation; r: a T x n dimensional matrix (T is the total time step, the initial value is 0 matrix) representing the intensity of the thermal radiation to which each device is subjected at each time step; TD: a 1 × n dimensional matrix (initial value is 0 matrix) representing the time duration after which the device is exposed to thermal radiation exceeding a threshold; TTE: a 1 x n dimensional matrix, each element representing a sampling value of the corresponding device for taking effective emergency rescue time; tem _ SA, Tem _ PF, Tem _ B: a T × n dimensional matrix (initial value is 0 matrix) for calculating temporary variables of the dynamic probability.
Step 3.3: due to uncontrollable environmental conditions, related parameters of fire accident evolution can generate a symmetrical distribution around a mean value in a time dimension, the symmetrical distribution can be regarded as a normal distribution, and the state of each device in each time step is analyzed in a random sampling mode. The accident related parameters comprise effective emergency rescue time, complete combustion time of dangerous chemicals in the equipment, failure time of the equipment under the action of fire heat radiation, and the state of each equipment can be any one of a safety state, a fire accident state and a complete combustion state.
At time t-1, the device is in an initial accident state: ST (1:) ═ SP, where ST is a T × n dimensional matrix, representing the state of the device at all time steps.
At t>At the moment 1, firstly determining the equipment in the fire state at the last moment (t-1), and using a 1 x n dimensional matrix PF to represent the equipment, if the corresponding element is in the fire state, setting the value to be 1, otherwise, setting the value to be 0; the thermal radiation value to which each device is subjected is then calculated: r (t:): PF × R0-i,yi,ziThe elements of the ith (i ∈ {1,2, L, n }) position of the matrices X, Y and Z, respectively,
and then analyzing the state of the equipment at the time t, and dividing into three cases:
i) if device j was in the safe State (SA) at the last time (t-1), i.e. S (t-1, j) ═ SA:
judging whether the intensity of the heat radiation received by the device j at the time t exceeds a threshold value RT (j) of the device j, and if the intensity of the heat radiation received by the device j at the time t does not exceed the threshold value, namely R (t, j) < RT (j), the state of the device j at the time t is S (t, j) < SA; if the threshold value is exceeded, i.e. R (t, j) ≧ RT (j), the time to failure TTF (j) of the device j at time t is first calculated for the intensity of the thermal radiation R (t, j):
ln(TTF(j))＝-1.13×logR(t,j)-2.67×10-5×V(j)+9.9
TTF(j)＝eln(TTF(j))/60
if the device j is just exposed to the heat radiation exceeding the threshold, i.e. the time td (j) that lasts after the device is exposed to the heat radiation exceeding the threshold is 1, the time to failure S _ ttf (j) of the device j can be obtained by sampling: s _ ttf (j) norm (ttf (j), σ _ ttf), where norm (ttf (j), σ _ ttf) is a random sample of a normal distribution with the mean ttf (j) and the standard deviation σ _ ttf.
If the device j is not just subjected to a heat radiation effect exceeding the threshold, i.e. td (j) ≠ 1, then it is necessary to calculate the remaining time to failure rttf (j) of the device j at time t, taking into account the superposition of the damage of the device under the previous heat radiation effect: RTTF (t, j) ═ R (t, j)/R (t-1, j)c(RTTF (t-1, j) -1), wherein the parameter c is related to emergency rescue time. The random sample values of the Monte Carlo simulation are as follows: s _ rttf (j) ═ norm (rttf (j)), σ _ rttf, represents a random sampling of a normal distribution with a mean value rttf (j) and a standard deviation σ _ rttf.
Next it is determined whether the device j in the safe State (SA) will evolve into a pool fire state (PF): if S _ RTTF (j) <1 and TTE (j) > t, namely at the current time t, the equipment j fails, the effective emergency rescue time is longer than the total accident time, the equipment j in the safe state evolves to a pool fire state, and otherwise, the safe state is still maintained.
ii) if device j was in the pool fire state (PF) at the last time (t-1), i.e. S (t-1, j) ═ PF:
the time required for the hazardous material in device j to burn completely (as a randomly sampled mean μ _ ttb (j)) can be calculated from the burn rate: m ═ m "∞×(1-e-kβD) Where m "represents the combustion rate of the material in still air, D represents the diameter of the pool fire, m"∞And k β is an empirical factor. The random sample values of the Monte Carlo simulation are as follows: ttb (j) norm d (μ _ ttb (j)), σ _ ttb (j)), and ttb (j) represents the time that the device i can continuously burn after fire occurs, if ttb (j) is greater than the time tpf (j) when the device j is in the pool fire state, it indicates that the material is not completely burned, the device j is still in the pool fire state, and if ttb (j) is less than the time tpf (j) when the device j is in the pool fire state, the state of the device j evolves from the pool fire to complete combustion.
iii) if the plant j was in the combustion complete state (B) at the last moment (t-1), i.e. S (t-1, j) ═ B:
at the present time tset j is still in the combustion complete state, i.e., S (t, j) ═ B.
This step requires analysis of each time step, and step 3.3 is cycled until all time steps have not been analyzed;
step 3.4: and after the Monte Carlo simulation is finished each time, counting the accident state frequency occurring on different time steps of each device in the finished simulation. This step needs to judge whether the number of simulations completed reaches the set value, and the steps 3.1-3.4 are circulated before all simulations are not analyzed.
Step 3.5: after all the monte carlo simulations in the initial accident scene are completed, the accident state frequency occurring at each time step in the step 3.4 is divided by the total simulation times, and the dynamic domino effect probability of the initial accident scene can be obtained. And circulating step 3.1-step 3.5 before all possible initial accident scenes are analyzed. And 3.1-3.5, calculating the dynamic probability of the fire domino effect under the multi-disaster coupling of a single scene.
And 4, step 4: after the dynamic domino effect probability caused by all possible initial accident scenes is completed, the overall dynamic domino probability can be calculated by combining the scene probability of each initial accident scene:
in the formula, PDM _ SA, PDM _ PF and PDM _ B are T × n dimensional matrixes which respectively represent the overall probability of the dynamic domino effect of each device in a safe state, a pool fire state and a combustion complete state, and P _ SAk、P_PFkAnd P _ BkDynamic domino effect probability of fire, PS, under disaster coupling representing the kth initial accident scenariokRepresenting the scene probability of the kth initial accident scene.
The calculation result of the case is shown in fig. 4, and the probability that each device is in different states at different time steps is intuitively shown. The probability of domino accidents in fires of T1-T10 equipment is higher than that of accidents of T11-T16, not only because the equipment has higher damage probability under the action of natural disasters, but also because the equipment has high heat radiation intensity when the equipment is used as an accident unit to cause pool fires, peripheral equipment is easy to cause domino accidents. The probability of domino occurring for equipment located in the central region of the tank farm is high relative to the edge locations, with the highest probability of accidents at T5 and T6, since these equipment are highly central and most susceptible to high intensity upgrade vectors.
By replacing equipment vulnerability models of different types of natural disasters such as flood, thunder, hurricane and the like, the dynamic risk of the fire domino accident under the condition of multi-disaster coupling caused by the disasters can be analyzed.
In summary, according to the present embodiment, the dynamic risk assessment is performed on the fire domino accident under the multi-disaster coupling through the monte carlo simulation according to the existing equipment vulnerability assessment model, so that the dynamic analysis of the multi-initial accident unit and the high-order domino accident scene can be realized, and a reference is provided for the dynamic risk prevention and control and the real-time emergency rescue decision of the accidents.
The foregoing description of the preferred embodiment of the present invention is not intended to limit the scope of the present invention, which is defined by the claims and their equivalents, but rather by the claims and their equivalents.
Claims (8)
1. A dynamic risk analysis method for a domino accident of a hazardous chemical substance fire under multi-disaster coupling is characterized by comprising the following steps:
s1: collecting basic information for dynamic domino effect risk analysis;
s2: calculating the vulnerability of each device under the action of a disaster according to a device vulnerability analysis model under the disaster, analyzing a possible initial accident scene, and calculating scene probability;
s3: respectively analyzing the state of each device in each time step for each of the possible initial accident scenes by adopting a Monte Carlo simulation analysis method, and respectively obtaining the dynamic domino effect probability in each initial accident scene;
s4: and combining the dynamic domino effect probabilities of all possible initial accident scenes to obtain the overall dynamic domino probability.
2. The method for analyzing the dynamic risk of the domino-coupled hazardous chemical substance fire domino accident according to claim 1, wherein the basic information in the step S1 includes geographical location information, meteorological information, equipment spatial distribution information, and equipment-stored hazardous chemical substance information.
3. The method for analyzing the dynamic risk of the domino accident of hazardous chemical substances in the multi-disaster coupling environment according to claim 1, wherein the step S2 specifically comprises:
calculating damage probability of each device according to a device vulnerability analysis model under a certain disaster;
all initial accident scenes are obtained in a permutation and combination mode, and the scene probability of the corresponding initial accident scene is calculated according to the damage probability of each device;
and determining all possible initial accident scenes through an accident probability threshold.
4. The method according to claim 3, wherein the damage probability is determined when the disaster is an earthquake
Wherein Y is a Probit model variable, and Y is k1+k2ln(102PGA)，k1And k2PGA is the peak ground acceleration, and u represents the integral variable, as parameters related to the tank type, hazardous chemical fill rate.
5. The method for analyzing the dynamic risk of the domino-coupled hazardous chemical substance fire domino accident according to any one of claims 1 to 4, wherein each initial accident scenario in the step S3 adopts a Monte Carlo simulation analysis method to analyze the state of each device in each time step and obtain the dynamic domino effect probability, and the method specifically comprises the following steps:
taking a certain initial accident scene and the scene probability thereof as the input of Monte Carlo simulation;
carrying out Monte Carlo simulation initialization processing, and setting basic parameters of each simulation;
analyzing the states of each device in different time steps in a random sampling mode according to an accident parameter distribution model of the initial accident scene;
continuously carrying out Monte Carlo simulation until the simulation times reach a set value, and counting the accident state frequency of each time step in each simulation;
and calculating the dynamic domino effect probability of the initial accident scene according to the accident state frequency.
6. The method according to claim 5, wherein the dynamic domino effect probability of the initial accident scenario is obtained by dividing the statistical accident state frequency at each time step in the initial accident scenario by the total number of simulations.
7. The method for analyzing the dynamic risk of the domino-coupled fire domino accident with hazardous chemicals as claimed in claim 5, wherein when the initial accident scenario is a fire, the accident parameters in the accident parameter distribution model include effective emergency rescue time, complete combustion time of hazardous chemicals in equipment, and failure time of equipment under the action of thermal radiation of the fire.
8. The method according to claim 5, wherein when the initial accident scenario is a fire, the states of the devices include a safety state, a fire accident state, and a complete combustion state.
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