CN116485038B - Dynamic prediction method for secondary domino accident of fire disaster in pressure storage tank area - Google Patents
Dynamic prediction method for secondary domino accident of fire disaster in pressure storage tank area Download PDFInfo
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Abstract
The application discloses a dynamic prediction method of a fire disaster secondary domino accident in a pressure storage tank area, which relates to the field of data processing systems specially used for prediction purposes, considers the influence of the fire accident of the pressure storage tank on the accident spread among adjacent storage tanks and the influence of coupling factors on the accident spread, evaluates the accident chain probability by combining a Bayesian network method, provides a determination method of the most probable accident chain and the most probable accident storage tank of the fire disaster secondary domino accident in the pressure storage tank area under the influence of time and space coupling, carries out effective risk evaluation on the pressure storage tank area, effectively controls and reduces the occurrence of the fire disaster secondary domino accident in the pressure storage tank area, has extremely important theoretical significance and application value for the prevention and control of the fire disaster domino effect in the pressure storage tank area, and provides guidance for the pressure tank area layout and the fire emergency treatment of chemical enterprises.
Description
Technical Field
The application relates to the field of data processing systems specially used for prediction purposes, in particular to a dynamic prediction method of a secondary domino accident of a fire disaster in a pressure storage tank area.
Background
The chemical industry in China is vigorously developed, and the chemical industry is an important support for the rapid development in China. Especially, the income of the chemical industry is continuously increased in 2010, and powerful power is injected into the economic development of China.
There is no mention in the prior art of controlling and reducing the occurrence of secondary domino accidents in pressure tank areas, and there is a need for effective risk assessment of pressure tank areas; the application discloses an LNG tank car accident disaster prediction and emergency rescue system, which comprises a server, wherein the server is respectively connected with M intelligent terminals and a database, the intelligent terminals are provided with rescue APPs, a user collects accident scene information through the intelligent terminals, and the accident rescue request and the accident scene information are sent to the server and the user participating in rescue through the rescue APPs; the server acquires map geological data and weather data of the current accident position from the database according to the accident scene information, establishes a rescue scheme decision model and a disaster prediction model by combining model data and historical accident cases in the database, and sends the rescue scheme decision model and the disaster prediction model to users participating in rescue and users of the accident position. The beneficial effects are that: the response to accidents is quick, the decision scheme is scientific, the rescue is accurate and efficient, and the injury and loss of the accidents are reduced;
the Chinese patent with the patent number of 202011369946.7 discloses an experimental platform and a method for influencing the safety of an adjacent liquid hydrocarbon pipe by gas pipeline injection fire, wherein the experimental platform comprises a liquid hydrocarbon supply loop system, a detection system and a flame system; the liquid hydrocarbon supply loop system comprises a liquid hydrocarbon storage tank, a pipeline pump, a non-test pipe section I, a test pipe section and a non-test pipe section II which are sequentially connected to form a closed loop, wherein the liquid hydrocarbon storage tank is connected with a cooling device, a liquid hydrocarbon outlet pipe is arranged between the liquid hydrocarbon storage tank and the pipeline pump, a stop valve is arranged on the liquid hydrocarbon outlet pipe, and an exhaust valve is arranged on the non-test pipe section II; the detection system comprises a flow detection device, a pressure detection device, a temperature detection device I, a temperature detection device II, a temperature detection device III, a temperature detection device IV and a temperature detection device V; the flame system is located outside the closed loop and in a plane between the ends of the test tube segment. The application can be used for carrying out experimental study on the heat influence of gas combustion on the liquid hydrocarbon pipeline after the natural gas pipeline leaks under the flowing state of the liquid hydrocarbon pipeline;
therefore, in order to control and reduce the occurrence of the fire disaster secondary domino accident in the pressure tank area, an effective risk assessment is necessary to be performed on the pressure tank area, so that a powerful reference basis is provided.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-mentioned and/or existing problems with the design of dynamic prediction methods for secondary domino accidents in pressure tank areas.
Therefore, one of the purposes of the application is to provide a dynamic prediction method of the fire disaster secondary domino accident in the pressure storage tank area, consider the influence of the fire disaster of the pressure storage tank on the accident spread among adjacent storage tanks and the influence of coupling factors on the accident spread, evaluate the accident chain probability by combining with a Bayesian network method, provide a determination method of the most probable accident chain and the most probable accident storage tank of the fire disaster secondary domino accident in the pressure storage tank area under the influence of the time and space coupling, have extremely important theoretical significance and application value for the prevention and control of the fire disaster domino effect in the pressure storage tank area, and provide guidance for the pressure tank area layout and the fire emergency treatment of chemical enterprises.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
a dynamic prediction method for secondary domino accidents in a fire disaster of a pressure storage tank area comprises the following specific steps:
s1: collecting basic data of a pressure tank area to be evaluated;
s2: determining an initial accident storage tank according to the basic data;
s3: determining an accident evolution path according to the initial accident storage tank;
s4: calculating the heat radiation value of the node for each accident evolution path respectively;
s5: calculating the failure time of the storage tank under the coupling of accident chain heat radiation, and judging whether the adjacent storage tank fails;
s6: constructing a static Bayesian network according to the adjacent storage tank accident path;
s7: constructing a dynamic Bayesian network according to the static Bayesian network and the thermal radiation coupling effect;
s8: and calculating dynamic probability according to the dynamic Bayesian network, and determining the most probable expansion accident chain and the key storage tank.
The application is further improved in that the specific content of the S1 is as follows: basic data of a pressure tank field to be evaluated are collected, and the determining process of the tank field to be evaluated is as follows: comparing the types of the stored matters of each tank area with the types of the dangerous goods database, identifying whether the enterprise pressure storage tank areas form important dangerous sources according to comparison data, selecting the pressure storage tank areas which form the important dangerous sources as evaluation objects, and determining the tank areas to be evaluated as risk evaluation objects, wherein the adjacent pressure tank areas forming the important dangerous source pressure storage tank areas;
the collected basic data comprise atmospheric data, pressure storage tank specification parameters, storage tank plane arrangement, storage tank spacing, storage material types and storage tank storage material amounts;
wherein the tank specification parameters include height, diameter, and wall thickness; the atmospheric data comprises temperature, humidity, wind speed and wind direction; the basic data acquisition process comprises a data acquisition strategy, wherein the data acquisition strategy comprises the following specific steps:
s101, extracting sensing data and real data of a sensor in a storage tank environment, wherein the sensing data and the real data are input by taking the storage tank environment and the sensing data as input, and the real data is output by taking the neural network model;
s102, taking a historical temperature curve, a historical sensor sensing data curve, a historical illumination intensity curve and a historical humidity curve in historical environment data as inputs of a neural network model, taking a true value curve as a prediction curve of a multi-feature time sequence prediction neural network model, and training the multi-feature time sequence prediction neural network model;
and S103, inputting the temperature data, the sensing data, the illumination data and the humidity data at the time into a neural network to obtain an output true value.
The application is further improved in that the specific content of the S2 is as follows: after basic data of a tank farm to be evaluated are obtained, taking a storage tank with the largest accident risk and the most serious accident result as an initial storage tank based on the principle of the largest risk and the longest accident chain; the determining process of the initial storage tank comprises the following steps:
s21: checking a storage database to determine a substance coefficient MF of the material;
s22: searching a storage database to inquire a general process risk coefficient F1 and a special process risk coefficient F2 of the stored materials, wherein the general process risk coefficient F1 and the special process risk coefficient F2 are manually set values;
s23: calculating and determining a process unit risk factor f3=f1×f2;
s24: determining a fire and explosion index fei=f3×mf;
s25: calculating a safety measure compensation coefficient C=C1×C2×C3, wherein C1, C2 and C3 are obtained by inquiring in a storage database;
s26: calculating the exposed area s=pi× (0.256×fei) 2 ；
S27: calculating the property value in the exposed area of the exposed area s: m=original cost×0.82×growth coefficient, original cost being cost of devices, materials in the exposed area s; the growth coefficient is a value change coefficient of equipment in the area after being used for a period of time;
s28: determining a basic maximum possible property loss = M x F3;
s29: determining actual maximum probable property loss = base maximum probable property loss x C; the calculated actual maximum possible property loss of the tanks of the tank farm is arranged in descending order, and the tank with the maximum actual maximum possible property loss is considered to have the most serious accident consequence, namely the initial tank.
The application is further improved in that the specific content of the S3 is as follows: when a fire accident occurs in the initial storage tank, the accident evolves on the initial storage tank, the secondary storage tank is influenced by a physical effect generated in the accident evolution process of the initial storage tank and is also influenced by a final accident mode of the initial storage tank, the tertiary storage tank is also influenced by the secondary storage tank and the initial storage tank, the accident evolution process of each storage tank and the physical effect generated by the final evolution accident are sequentially carried out, the next storage tank is caused to generate an accident, an accident evolution path is formed, the fire accident evolution path on the initial storage tank is constructed, namely, the domestic and foreign storage tank accidents are counted to form a database, the database data and the monitoring data are substituted into a similarity calculation formula to obtain database data with the maximum similarity with the monitoring data as a reference object, the propagation path of an accident chain is obtained, and then each accident chain path is counted and analyzed to obtain a result, the accident evolution form and the accident evolution probability of each accident chain form is calculated, so that the accident evolution path is constructed.
The application is further improved in that the specific content of the S4 is as follows: calculating a heat radiation value I and a flame height value H of a fire tank area adjacent to a target storage tank, wherein the calculation formula of the flame height value H is as follows:
windless condition:
the wind conditions are that:
wherein H is flame height, m; ρ o For air density, 1.2kg/m was taken 3 The method comprises the steps of carrying out a first treatment on the surface of the D is the diameter of the pool fire, m; g gravity acceleration of 9.8m/s 2 The method comprises the steps of carrying out a first treatment on the surface of the mass burn rate, kg/s/m, with m' being the unit liquid surface area 2 ；u * Is the fire point position wind speed, m/s, whereinu is the wind speed at a distance of 10 meters from the fire point, m/s; ρ v To the density of the combustible liquid vapor, kg/m 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the mass combustion rate m' is calculated as: m "=m * (1-e -kβD ) In this formula, m' is the mass combustion rate, kg/(m) 2 ·s)；m * Is the maximum mass burn rate; k is flame attenuation coefficient, m -1 The method comprises the steps of carrying out a first treatment on the surface of the Beta is an average ray length correction coefficient; d is the diameter of the pool fire, m;
the calculation mode of the heat radiation value of the fire disaster tank area adjacent to the target storage tank is as follows:
i=e' F tau; wherein the method comprises the steps of
Wherein E' is the average radiation intensity of the flame surface, kW/m 2 The method comprises the steps of carrying out a first treatment on the surface of the F is a geometric view factor; τ is the atmospheric transmittance; η is an efficiency factor and is 0.13-0.35; ΔH c kJ/kg for liquid heat of combustion; x is the horizontal distance from the center of the liquid pool of the target storage tank, and h is the ratio of flame height to flame radius; s is the ratio of the distance of the observer from the center of the liquid pool to the flame radius; f (F) H And F V Representing the horizontal and vertical view factors of the vertical cylinder, respectively, A, B representing the intermediate coefficients; d is the diameter of the pool fire, I is the heat radiation amount received by the adjacent storage tank; m' is the mass combustion rate per unit cell area;
when an initial accident occurs, the generated fire heat radiation acts on adjacent target storage tanks at the same time, the target storage tanks are sequentially invalid due to different storage tank distances, the heat radiation intensity of the adjacent storage tanks under the coupling action of the heat radiation is calculated for the storage tanks possibly receiving the coupling action of the heat radiation in the evolution path of the accident chain, the coupling heat radiation intensity value is obtained by adding the heat radiation of the received surrounding storage tanks, and the calculation formula is as follows:
I＝I 1 +I 2 +...+I n
wherein I is the heat radiation value of the adjacent target storage tank; i n Is the heat radiation of the accident storage tank.
The application is further improved in that the specific content of the S5 is as follows: substituting a single heat radiation value or a coupling heat radiation value received by an adjacent target storage tank into a failure time formula to calculate the corresponding failure time, wherein the failure time calculation formula is as follows:
in ttf 1 For the failure time of the target tank when exposed to a single thermal radiation,is subject to T 1 Heat radiation amount of the tank->Ttf is the volume of the target tank 2 For the time of failure of the target tank when it is coupled by the heated radiation,/->For the amount of heat radiation from the surrounding tank +.>Is the volume of the target storage tank;
the obtained failure time is brought into a pressure storage tank failure probability calculation model, and the model formula is as follows:
Y＝11.27-1.641ln(ttf)
wherein Y represents a probability unit value of the equipment failure probability; ttf represents the time in s for the adjacent tank to fail under the action of heat radiation;
probability of failure P of adjacent target tank d The expression is as follows:
wherein P is d Representing a failure probability of the target device; y represents a unit of the device destruction probability,
and taking the storage tank after the primary accident expansion as an initial accident storage tank, determining a secondary expansion storage tank in which the accident is likely to happen, obtaining expansion accident chains, calculating the expansion probability of each expansion accident chain, and constructing a fire accident chain of the adjacent storage tank according to the expansion accident chains and the corresponding expansion probabilities.
The application is further improved in that the specific content of the S6 is as follows: according to the constructed fire accident path of the adjacent storage tank, the calculated expansion probability of the expansion accident chain is input into a conditional probability table, a static Bayesian network is drawn, and the expansion accident chain and the key accident storage tank with the maximum damage probability under the static condition are determined through prior probability and posterior probability analysis and are used for the subsequent verification of the expansion accident chain probability calculated by the dynamic Bayesian network.
The application is further improved in that the specific content of the S7 is as follows: aiming at the selected pressure tank area, analyzing the pressure tank area, calculating time parameters related to the tank, wherein the calculated parameters are burnout time ttb and failure time ttf; burnout time ttb: under the condition that the material in the storage tank is not interfered by external factors, the time from the beginning of combustion to the burnout of the material is obtained through the relation between the material storage quantity in the tank and the mass combustion rate of the material, and the failure time ttf is: the time of the adjacent storage tank to be destroyed under the physical effect of the accident storage tank is calculated by means of a probit model; comparing the sizes of the two: if ttb is smaller than ttf, the fire domino accident of the storage tank can not happen; ttb is larger than ttf, fire domino accidents occur in the storage tank, storage tank failure nodes are set through calculation results, the dynamic Bayesian network is divided into different time segments for calculation, and an accident chain transmission scene with time dimension is constructed.
The application further improves that the specific content of the S8 is as follows: a dynamic Bayesian network is established by using GeNIe software, auxiliary nodes L1, L2 and L3 are set to represent first-level, second-level and third-level domino accidents, conditional probability of the accidents is input in the software, the Bayesian network is calculated to obtain prior probability of each point, then the auxiliary nodes are respectively set, posterior probability of the accidents of each storage tank when different domino accidents occur is calculated, storage tank probability change conditions of each accident chain are determined by comparing the prior probability and the posterior probability of the accidents, one accident chain with the highest probability is analyzed, and the storage tank with the largest influence on the probability of adjacent storage tanks is determined.
Compared with the prior art, the application has the following beneficial effects: the method for determining the most probable accident chain and the most probable accident storage tank of the fire disaster secondary domino accident in the pressure storage tank area under the influence of the coupling action of time and space is provided by considering the influence of the fire accident of the pressure storage tank on the accident expansion among adjacent storage tanks and the influence of the coupling factors on the accident propagation and combining a Bayesian network method to evaluate the accident chain probability, has extremely important theoretical significance and application value for the prevention and control of the domino effect of the fire disaster in the pressure storage tank area and provides guidance for the pressure tank area layout and the fire emergency treatment of chemical enterprises.
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FIG. 1 is a schematic flow diagram of a dynamic prediction method for a fire disaster secondary domino accident in a pressure storage tank area.
Detailed Description
In order that the technical means, the creation characteristics, the achievement of the objects and the effects of the present application may be easily understood, it should be noted that in the description of the present application, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "a", "an", "the" and "the" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The application is further described below in conjunction with the detailed description.
Examples
According to the embodiment, the influence of fire accidents of the pressure storage tank on accident spread among adjacent storage tanks and the influence of coupling factors on accident propagation are considered, the probability of an accident chain is evaluated by combining a Bayesian network method, a method for determining the most probable accident chain and the most probable accident storage tank of the fire disaster secondary domino accident of the pressure storage tank area under the influence of time and space coupling is provided, the method has extremely important theoretical significance and application value for preventing and controlling the fire disaster domino effect of the pressure storage tank area, and guidance is provided for the pressure tank area layout and fire emergency treatment of chemical enterprises.
S1: basic data of a pressure tank area to be evaluated are collected, and the specific contents are as follows: basic data of a pressure tank field to be evaluated are collected, and the determining process of the tank field to be evaluated is as follows: comparing the types of the stored matters of each tank area with the types of the dangerous goods database, identifying whether the enterprise pressure storage tank areas form important dangerous sources according to comparison data, selecting the pressure storage tank areas which form the important dangerous sources as evaluation objects, and determining the tank areas to be evaluated as risk evaluation objects, wherein the adjacent pressure tank areas forming the important dangerous source pressure storage tank areas;
the collected basic data comprise atmospheric data, pressure storage tank specification parameters, storage tank plane arrangement, storage tank spacing, storage material types and storage tank storage material amounts;
wherein, the specification parameters of the storage tank comprise height, diameter and wall thickness; the atmospheric data includes temperature, humidity, wind speed, wind direction; the basic data acquisition process comprises a data acquisition strategy, wherein the data acquisition strategy comprises the following specific steps:
s101, extracting sensing data and real data of a sensor in a storage tank environment, wherein the sensing data and the real data are input by taking the storage tank environment and the sensing data as input, and the real data is output by taking the neural network model;
s102, taking a historical temperature curve, a historical sensor sensing data curve, a historical illumination intensity curve and a historical humidity curve in historical environment data as inputs of a neural network model, taking a true value curve as a prediction curve of a multi-feature time sequence prediction neural network model, and training the multi-feature time sequence prediction neural network model;
and S103, inputting the temperature data, the sensing data, the illumination data and the humidity data at the time into a neural network to obtain an output true value.
S2: the initial accident storage tank is determined according to the basic data, and the concrete contents are as follows: after basic data of a tank farm to be evaluated are obtained, taking a storage tank with the largest accident risk and the most serious accident result as an initial storage tank based on the principle of the largest risk and the longest accident chain; the determination process of the initial storage tank is as follows:
s21: checking a storage database to determine a substance coefficient MF of the material;
s22: searching a storage database to inquire a general process risk coefficient F1 and a special process risk coefficient F2 of the stored materials, wherein the general process risk coefficient F1 and the special process risk coefficient F2 are manually set values;
s23: calculating and determining a process unit risk factor f3=f1×f2;
s24: determining a fire and explosion index fei=f3×mf;
s25: calculating a safety measure compensation coefficient C=C1×C2×C3, wherein C1, C2 and C3 are obtained by inquiring in a storage database;
s26: calculating the exposed area s=pi× (0.256×fei) 2 ；
S27: calculating the property value in the exposed area of the exposed area s: m=original cost×0.82×growth coefficient, original cost being cost of devices, materials in the exposed area s; the growth coefficient is a value change coefficient of equipment in the area after being used for a period of time;
s28: determining a basic maximum possible property loss = M x F3;
s29: determining actual maximum probable property loss = base maximum probable property loss x C; the calculated actual maximum possible property loss of the storage tanks of the tank farm is arranged in a descending order, and the storage tank with the maximum actual maximum possible property loss is regarded as the most serious accident result, namely the storage tank is used as the initial storage tank;
s3: determining an accident evolution path according to the initial accident storage tank, wherein the accident evolution path comprises the following specific contents: when a fire accident occurs in the initial storage tank, the accident evolves on the initial storage tank, the secondary storage tank is influenced by a physical effect generated in the accident evolution process of the initial storage tank and is also influenced by a final accident mode of the initial storage tank, the tertiary storage tank is also influenced by the secondary storage tank and the initial storage tank, the accident evolution process of each stage of storage tank and the physical effect generated by the final evolution accident all lead to the accident of the next stage of storage tank, so that an accident evolution path is formed, the fire accident evolution path on the initial storage tank is constructed, namely, the domestic and foreign storage tank accidents are counted to form a database, the database data and the monitoring data are substituted into a similarity calculation formula to obtain database data with the maximum similarity with the monitoring data as a reference object, the propagation path of an accident chain is obtained, and then each accident chain path is counted and analyzed to obtain a result, the accident evolution form is obtained, and the accident evolution probability of each accident chain form is calculated, so that the accident evolution path is constructed;
s4: the heat radiation value of the node is calculated for each accident evolution path respectively, and the specific contents are as follows: calculating a heat radiation value I and a flame height value H of a fire tank area adjacent to a target storage tank, wherein the calculation formula of the flame height value H is as follows:
windless condition:
the wind conditions are that:
wherein H is flame height, m; ρ o For air density, 1.2kg/m was taken 3 The method comprises the steps of carrying out a first treatment on the surface of the D is the diameter of the pool fire, m; g gravity acceleration of 9.8m/s 2 The method comprises the steps of carrying out a first treatment on the surface of the mass burn rate, kg/s/m, with m' being the unit liquid surface area 2 ；u * Is the fire point position wind speed, m/s, whereinu is the wind speed at a distance of 10 meters from the fire point, m/s; ρ v To the density of the combustible liquid vapor, kg/m 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the mass combustion rate m' is calculated as: m "=m * (1-e -kβD ) In this formula, m' is the mass combustion rate, kg/(m) 2 ·s)；m * Is the maximum mass burn rate; k is flame attenuation coefficient, m -1 The method comprises the steps of carrying out a first treatment on the surface of the Beta is an average ray length correction coefficient; d is the diameter of the pool fire, m;
the calculation mode of the heat radiation value of the adjacent target storage tank of the fire disaster tank area is as follows: i=e' F tau;
wherein the method comprises the steps of
Wherein E' is the average radiation intensity of the flame surface, kW/m 2 The method comprises the steps of carrying out a first treatment on the surface of the F is a geometric view factor; τ is the atmospheric transmittance; η is an efficiency factor and is 0.13-0.35; ΔH c kJ/kg for liquid heat of combustion; x is the horizontal distance from the center of the liquid pool of the target storage tank, and h is the ratio of flame height to flame radius; s is the ratio of the distance of the observer from the center of the liquid pool to the flame radius; f (F) H And F V Representing the horizontal and vertical view factors of the vertical cylinder, respectively, A, B representing the intermediate coefficients; d is the diameter of the pool fire, I is the heat radiation amount received by the adjacent storage tank; m' is the mass combustion rate per unit cell area;
when an initial accident occurs, the generated fire heat radiation acts on adjacent target storage tanks at the same time, the target storage tanks are sequentially invalid due to different storage tank distances, the heat radiation intensity of the adjacent storage tanks under the coupling action of the heat radiation is calculated for the storage tanks possibly receiving the coupling action of the heat radiation in the evolution path of the accident chain, the coupling heat radiation intensity value is obtained by adding the heat radiation of the received surrounding storage tanks, and the calculation formula is as follows:
I＝I 1 +I 2 +...+I n
wherein I is the heat radiation value of the adjacent target storage tank; i n Heat radiation for the accident tank;
s5: calculating the failure time of the storage tank under the accident chain heat radiation coupling, and judging whether the adjacent storage tank fails or not, wherein the specific contents are as follows: substituting a single heat radiation value or a coupling heat radiation value received by an adjacent target storage tank into a failure time formula to calculate the corresponding failure time, wherein the failure time calculation formula is as follows:
in ttf 1 Is the object ofFailure time of the tank when subjected to a single heat radiation,is subject to T 1 Heat radiation amount of the tank->Ttf is the volume of the target tank 2 For the time of failure of the target tank when it is coupled by the heated radiation,/->For the amount of heat radiation from the surrounding tank +.>Is the volume of the target storage tank;
the obtained failure time is brought into a pressure storage tank failure probability calculation model, and the model formula is as follows:
Y＝11.27-1.641ln(ttf)
wherein Y represents a probability unit value of the equipment failure probability; ttf represents the time in s for the adjacent tank to fail under the action of heat radiation;
probability of failure P of adjacent target tank d The expression is as follows:
wherein P is d Representing a failure probability of the target device; y represents a unit of the device destruction probability,
taking a storage tank with a primary accident expansion as an initial accident storage tank, determining a secondary expansion storage tank with a possible accident, obtaining expansion accident chains, calculating the expansion probability of each expansion accident chain, and constructing fire accident chains of adjacent storage tanks according to the expansion accident chains and the corresponding expansion probabilities;
s6: the static Bayesian network is constructed according to the accident path of the adjacent storage tank, and the specific contents are as follows: according to the constructed fire accident path of the adjacent storage tank, the calculated expansion probability of the expansion accident chain is input into a conditional probability table, a static Bayesian network is drawn, and the expansion accident chain and the key accident storage tank with the maximum damage probability under the static condition are determined through prior probability and posterior probability analysis and are used for the subsequent verification of the expansion accident chain probability calculated by the dynamic Bayesian network;
s7: the dynamic Bayesian network is constructed according to the static Bayesian network and the thermal radiation coupling effect, and the specific contents are as follows: aiming at the selected pressure tank area, analyzing the pressure tank area, calculating time parameters related to the tank, wherein the calculated parameters are burnout time ttb and failure time ttf; burnout time ttb: under the condition that the material in the storage tank is not interfered by external factors, the time from the beginning of combustion to the burnout of the material is obtained through the relation between the material storage quantity in the tank and the mass combustion rate of the material, and the failure time ttf is: the time of the adjacent storage tank to be destroyed under the physical effect of the accident storage tank is calculated by means of a probit model; comparing the sizes of the two: if ttb is smaller than ttf, the fire domino accident of the storage tank can not happen; ttb > ttf, the fire domino accident will occur in the storage tank, the storage tank failure node is set through the calculation result, the dynamic Bayesian network is divided into different time segments for calculation, and the accident chain propagation scene of the time dimension is constructed;
s8: according to the dynamic Bayesian network, calculating the dynamic probability, determining the most probable expansion accident chain and the key storage tank, wherein the specific contents are as follows: a dynamic Bayesian network is established by using GeNIe software, auxiliary nodes L1, L2 and L3 are set to represent first-level, second-level and third-level domino accidents, conditional probability of the accidents is input in the software, the Bayesian network is calculated to obtain prior probability of each point, then the auxiliary nodes are respectively set, posterior probability of the accidents of each storage tank when different domino accidents occur is calculated, storage tank probability change conditions of each accident chain are determined by comparing the prior probability and the posterior probability of the accidents, one accident chain with the highest probability is analyzed, and the storage tank with the largest influence on the probability of adjacent storage tanks is determined.
By this embodiment: considering the influence of fire accidents of the pressure storage tank on the accident expansion among adjacent storage tanks and the influence of coupling factors on the accident propagation, evaluating the accident chain probability by combining a Bayesian network method, providing a method for determining the most probable accident chain and the most probable accident storage tank of the fire disaster secondary domino accident of the pressure storage tank area under the influence of time and space coupling, having extremely important theoretical significance and application value for preventing and controlling the fire disaster domino effect of the pressure storage tank area, and providing guidance for the pressure tank area layout and the fire emergency treatment of chemical enterprises.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A dynamic prediction method for a secondary domino accident in a fire disaster in a pressure storage tank area is characterized by comprising the following steps of: the method comprises the following specific steps:
s1: collecting basic data of a pressure tank area to be evaluated;
s2: determining an initial accident storage tank according to the basic data;
s3: determining an accident evolution path according to the initial accident storage tank;
s4: calculating the heat radiation value of the node for each accident evolution path respectively;
s5: calculating the failure time of the storage tank under the coupling of accident chain heat radiation, and judging whether the adjacent storage tank fails;
s6: constructing a static Bayesian network according to the adjacent storage tank accident path;
s7: constructing a dynamic Bayesian network according to the static Bayesian network and the thermal radiation coupling effect;
s8: calculating dynamic probability according to the dynamic Bayesian network, and determining the most probable expansion accident chain and the key storage tank; the specific content of the S1 is as follows: basic data of a pressure tank field to be evaluated are collected, and the determining process of the tank field to be evaluated is as follows: comparing the types of the stored matters of each tank area with the types of the dangerous goods database, identifying whether the enterprise pressure storage tank areas form important dangerous sources according to comparison data, selecting the pressure storage tank areas which form the important dangerous sources as evaluation objects, and determining the tank areas to be evaluated as risk evaluation objects, wherein the adjacent pressure tank areas forming the important dangerous source pressure storage tank areas;
the collected basic data comprise atmospheric data, pressure storage tank specification parameters, storage tank plane arrangement, storage tank spacing, storage material types and storage tank storage material amounts;
wherein the tank specification parameters include height, diameter, and wall thickness; the atmospheric data comprises temperature, humidity, wind speed and wind direction;
the basic data acquisition process comprises a data acquisition strategy, wherein the data acquisition strategy comprises the following specific steps:
s101, extracting sensing data and real data of a sensor in a storage tank environment, wherein the sensing data and the real data are input by taking the storage tank environment and the sensing data as input, and the real data is output by taking the neural network model;
s102, taking a historical temperature curve, a historical sensor sensing data curve, a historical illumination intensity curve and a historical humidity curve in historical environment data as inputs of a neural network model, taking a true value curve as a prediction curve of a multi-feature time sequence prediction neural network model, and training the multi-feature time sequence prediction neural network model;
s103, inputting the temperature data, the sensing data, the illumination data and the humidity data at the time into a neural network to obtain an output true value;
the specific content of the S4 is as follows: calculating heat radiation value of adjacent target storage tank in fire tank areaAnd flame height value->Wherein the flame height value +.>The calculation formula of (2) is as follows:
windless condition:；
the wind conditions are that:；
wherein,flame height, m; />For air density, 1.2kg/m was taken 3 ；/>The diameter of the pool fire is m; />Acceleration of gravity of 9.8m/s 2 ；/>Mass burn rate per liquid surface area, kg/s/m 2 ；/>For fire point position wind speed, m/s, wherein +.>，/>The wind speed is m/s at a position 10 meters away from the fire point; />To the density of the combustible liquid vapor, kg/m 3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the mass burn rate->The calculation formula of (2) is as follows: />In this formula, < >>For mass burn rate, kg/(m) 2 ·s)；/>Is the maximum mass burn rate; />For flame attenuation coefficient, m -1 ；/>Correcting the coefficient for the average ray length; />The diameter of the pool fire is m;
the calculation mode of the heat radiation value of the fire disaster tank area adjacent to the target storage tank is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of；
；
；
；
；
；
In the method, in the process of the application,for average radiation intensity of flame surface, kW/m 2 The method comprises the steps of carrying out a first treatment on the surface of the F is a geometric view factor; τ is the atmospheric transmittance; η is an efficiency factor and is 0.13-0.35; />kJ/kg for liquid heat of combustion; x is the horizontal distance from the center of the liquid pool of the target storage tank, and h is the ratio of flame height to flame radius; s is the ratio of the distance of the observer from the center of the liquid pool to the flame radius; f (F) H And F V Representing the horizontal and vertical view factors of the vertical cylinder, respectively, A, B representing the intermediate coefficients; d is the diameter of the pool fire, I is the heat radiation amount received by the adjacent storage tank; m' is the mass combustion rate per unit cell area;
when an initial accident occurs, the generated fire heat radiation acts on adjacent target storage tanks at the same time, the target storage tanks are sequentially invalid due to different storage tank distances, the heat radiation intensity of the adjacent storage tanks under the coupling action of the heat radiation is calculated for the storage tanks possibly receiving the coupling action of the heat radiation in the evolution path of the accident chain, the coupling heat radiation intensity value is obtained by adding the heat radiation of the received surrounding storage tanks, and the calculation formula is as follows:
；
wherein the method comprises the steps ofI is the heat radiation value of the adjacent target storage tank;heat radiation for the accident tank;
the specific content of the S5 is as follows: substituting a single heat radiation value or a coupling heat radiation value received by an adjacent target storage tank into a failure time formula to calculate the corresponding failure time, wherein the failure time calculation formula is as follows:
；
；
in the method, in the process of the application,for the failure time of the target tank under the action of a single heat radiation,/->Is subject to T 1 The amount of heat radiation from the storage tank,for the volume of the target tank>For the time of failure of the target tank when it is coupled by the heated radiation,/->For the amount of heat radiation from the surrounding tank +.>Is the volume of the target storage tank;
the obtained failure time is brought into a pressure storage tank failure probability calculation model, and the model formula is as follows:
；
wherein Y represents a probability unit value of the equipment failure probability;the time of the adjacent storage tank to fail under the action of heat radiation is expressed as s;
probability of failure P of adjacent target tank d The expression is as follows:；
in the method, in the process of the application,representing a failure probability of the target device; y represents a unit of the device destruction probability,
and taking the storage tank after the primary accident expansion as an initial accident storage tank, determining a secondary expansion storage tank in which the accident is likely to happen, obtaining expansion accident chains, calculating the expansion probability of each expansion accident chain, and constructing a fire accident chain of the adjacent storage tank according to the expansion accident chains and the corresponding expansion probabilities.
2. The method for dynamically predicting a fire secondary domino accident in a pressurized storage tank farm according to claim 1, wherein: the specific content of the S2 is as follows: after basic data of a tank farm to be evaluated are obtained, taking a storage tank with the largest accident risk and the most serious accident result as an initial storage tank based on the principle of the largest risk and the longest accident chain; the determining process of the initial storage tank comprises the following steps:
s21: checking a storage database to determine a substance coefficient MF of the material;
s22: searching a storage database to inquire a general process risk coefficient F1 and a special process risk coefficient F2 of the stored materials, wherein the general process risk coefficient F1 and the special process risk coefficient F2 are manually set values;
s23: calculate and determine workRisk coefficient of art unit；
S24: determining fire and explosion index；
S25: calculating a safety measure compensation coefficient C=C1×C2×C3, wherein C1, C2 and C3 are obtained by inquiring in a storage database;
s26: calculating the exposed area s=pi× (0.256×fei) 2 ；
S27: calculating the property value in the exposed area of the exposed area s: m=original cost×0.82×growth coefficient, original cost being cost of devices, materials in the exposed area s; the growth coefficient is a value change coefficient of equipment in the area after being used for a period of time;
s28: determining a basic maximum possible property loss = M x F3;
s29: determining actual maximum probable property loss = base maximum probable property loss x C; the calculated actual maximum possible property loss of the tanks of the tank farm is arranged in descending order, and the tank with the maximum actual maximum possible property loss is considered to have the most serious accident consequence, namely the initial tank.
3. The method for dynamically predicting a fire secondary domino accident in a pressurized storage tank farm according to claim 2, wherein: the specific content of the S3 is as follows: when a fire accident occurs in the initial storage tank, the accident evolves on the initial storage tank, the secondary storage tank is influenced by a physical effect generated in the accident evolution process of the initial storage tank and is also influenced by a final accident mode of the initial storage tank, the tertiary storage tank is also influenced by the secondary storage tank and the initial storage tank, the accident evolution process of each storage tank and the physical effect generated by the final evolution accident are sequentially carried out, the next storage tank is caused to generate an accident, an accident evolution path is formed, the fire accident evolution path on the initial storage tank is constructed, namely, the domestic and foreign storage tank accidents are counted to form a database, the database data and the monitoring data are substituted into a similarity calculation formula to obtain database data with the maximum similarity with the monitoring data as a reference object, the propagation path of an accident chain is obtained, and then each accident chain path is counted and analyzed to obtain a result, the accident evolution form and the accident evolution probability of each accident chain form is calculated, so that the accident evolution path is constructed.
4. A method for dynamic prediction of a fire secondary domino event in a pressurized storage tank compartment according to claim 3, wherein: the specific content of the S6 is as follows: according to the constructed fire accident path of the adjacent storage tank, the calculated expansion probability of the expansion accident chain is input into a conditional probability table, a static Bayesian network is drawn, and the expansion accident chain and the key accident storage tank with the maximum damage probability under the static condition are determined through prior probability and posterior probability analysis and are used for the subsequent verification of the expansion accident chain probability calculated by the dynamic Bayesian network.
5. The method for dynamically predicting a fire secondary domino accident in a pressurized storage tank farm according to claim 4, wherein: the specific content of the S7 is as follows: for the selected pressure tank area, analyzing the pressure tank area, calculating a time parameter related to the storage tank, wherein the calculated parameter is burnout timeTime to failure->The method comprises the steps of carrying out a first treatment on the surface of the Burn-out time->: under the condition of no intervention of external factors, the time from the start of combustion to the burnout of the materials in the storage tank is obtained through the relation between the material storage quantity in the storage tank and the mass combustion rate of the materials, and the failure time is +.>: the time of the adjacent storage tank to be destroyed under the physical effect of the accident storage tank is calculated by means of a probit model; comparing the sizes of the two: if->＜/>The fire domino accident of the storage tank can not happen;＞/>and setting failure nodes of the storage tank according to a calculation result, dividing the dynamic Bayesian network into different time slices for calculation, and constructing an accident chain propagation scene of a time dimension.
6. The method for dynamically predicting a fire secondary domino accident in a pressurized storage tank farm according to claim 5, wherein: the specific content of the S8 is as follows: a dynamic Bayesian network is established by using GeNIe software, auxiliary nodes L1, L2 and L3 are set to represent first-level, second-level and third-level domino accidents, conditional probability of the accidents is input in the software, the Bayesian network is calculated to obtain prior probability of each point, then the auxiliary nodes are respectively set, posterior probability of the accidents of each storage tank when different domino accidents occur is calculated, storage tank probability change conditions of each accident chain are determined by comparing the prior probability and the posterior probability of the accidents, one accident chain with the highest probability is analyzed, and the storage tank with the largest influence on the probability of adjacent storage tanks is determined.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787677A (en) * | 2016-03-31 | 2016-07-20 | 交通运输部水运科学研究所 | Petrochemical port area accident risk area quantitative assessment method |
| CN111242504A (en) * | 2020-01-23 | 2020-06-05 | 南京工业大学 | Coal gasification device risk probability calculation method based on domino effect |
| CN112529412A (en) * | 2020-12-11 | 2021-03-19 | 华南理工大学 | Dynamic risk analysis method for hazardous chemical substance fire domino accident under multi-disaster coupling |
| CN114861988A (en) * | 2022-04-15 | 2022-08-05 | 沈阳化工大学 | Method for predicting normal-pressure storage tank group accidents through dynamic Bayesian network and fault tree model |
| WO2022227129A1 (en) * | 2021-04-27 | 2022-11-03 | 山东交通学院 | Road transportation risk assessment method and system for hazardous chemical |
| CN115358514A (en) * | 2022-07-08 | 2022-11-18 | 广东石油化工学院 | Risk superposition compensation calculation method for domino accident probability of multiple major hazard sources |
-
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- 2023-06-08 CN CN202310672231.6A patent/CN116485038B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787677A (en) * | 2016-03-31 | 2016-07-20 | 交通运输部水运科学研究所 | Petrochemical port area accident risk area quantitative assessment method |
| CN111242504A (en) * | 2020-01-23 | 2020-06-05 | 南京工业大学 | Coal gasification device risk probability calculation method based on domino effect |
| CN112529412A (en) * | 2020-12-11 | 2021-03-19 | 华南理工大学 | Dynamic risk analysis method for hazardous chemical substance fire domino accident under multi-disaster coupling |
| WO2022227129A1 (en) * | 2021-04-27 | 2022-11-03 | 山东交通学院 | Road transportation risk assessment method and system for hazardous chemical |
| CN114861988A (en) * | 2022-04-15 | 2022-08-05 | 沈阳化工大学 | Method for predicting normal-pressure storage tank group accidents through dynamic Bayesian network and fault tree model |
| CN115358514A (en) * | 2022-07-08 | 2022-11-18 | 广东石油化工学院 | Risk superposition compensation calculation method for domino accident probability of multiple major hazard sources |
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