CN117991727A - Dynamic risk determination method and system based on equipment reliability and electronic equipment - Google Patents
Dynamic risk determination method and system based on equipment reliability and electronic equipment Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 130
- 238000012423 maintenance Methods 0.000 claims abstract description 36
- 238000004458 analytical method Methods 0.000 claims abstract description 17
- 238000011156 evaluation Methods 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 9
- 238000009472 formulation Methods 0.000 claims description 8
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- 238000003860 storage Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 238000013480 data collection Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000012502 risk assessment Methods 0.000 abstract description 5
- 239000000126 substance Substances 0.000 abstract description 4
- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 description 36
- 125000004805 propylene group Chemical group [H]C([H])([H])C([H])([*:1])C([H])([H])[*:2] 0.000 description 36
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- 235000011470 Adenanthera pavonina Nutrition 0.000 description 2
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Abstract
The embodiment of the invention provides a dynamic risk determination method and system based on equipment reliability and electronic equipment, and belongs to the technical field of petrochemical risk assessment. The method comprises the following steps: determining key equipment capable of generating safety risks for petrochemical processes corresponding to petrochemical devices; constructing a multi-level traceability fault network model according to the petrochemical process; and carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result. According to the petrochemical process of the petrochemical device, the multi-level traceability fault network model is built, the reliability grade of the petrochemical process of the petrochemical device can be determined, and the important safety risk of the petrochemical process of the chemical can be effectively prevented according to the reliability of key equipment. And secondly, the multi-level traceable fault network model can trace the weak root of the fault of the key equipment, customize the maintenance strategy according to the weak root and the reliability thereof, and reduce the risk of petrochemical technology.
Description
Technical Field
The invention relates to the technical field of petrochemical risk protection, in particular to a dynamic risk determination method based on equipment reliability, a dynamic risk determination system based on equipment reliability, electronic equipment and a computer readable storage medium.
Background
The existing petrochemical device risk management method is as follows: according to HAZOP, LOPA, SIL and other general risk assessment technologies, risk event identification analysis is carried out on the device, preventive measures for preventing occurrence of risk events or reducing the consequences of the risk events are determined, the final risk level of the events is assessed, and system units or risk points with higher risk level of the device are determined so as to prevent occurrence of serious risk events of the device.
The traditional device risk assessment management method at present has the following defects:
(1) The analysis of the initial trigger event for the device risk event is deficient;
(2) The reliability level of the preventive measures for preventing occurrence of the risk event or reducing the result of the risk event adopts general data, and the reliability level of the device is not analyzed from the actual application data of the device when the device is required;
(3) The daily maintenance planning of the device equipment facilities is carried out according to a general equipment maintenance scheme, the fault sources of real-time maintenance of key equipment facilities are not traced, and a maintenance strategy is customized;
(4) There is a lack of a dynamic risk level management architecture based on the level of reliability of the equipment critical equipment facilities.
Disclosure of Invention
The embodiment of the invention aims to provide a dynamic risk determination method, a system and electronic equipment based on equipment reliability, so as to at least solve the problem that the reliability level of a device risk assessment management method in the prior art cannot analyze the requirement of the device from actual application data of the device.
To achieve the above object, an embodiment of the present invention provides a dynamic risk determining method based on device reliability, the method including:
Determining key equipment capable of generating safety risks for petrochemical processes corresponding to petrochemical devices;
constructing a multi-level traceability fault network model according to the petrochemical process;
and carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result.
Preferably, the key equipment is from the building of a library of key equipment related to the petrochemical process.
Preferably, the method further comprises: constructing a key equipment library, comprising:
Extracting facility equipment playing a key role on an accident occurrence chain of the petrochemical process based on a key safety implementation evaluation method and/or extracting facility equipment generating a risk event of the petrochemical process based on a risk determination method;
and constructing a key equipment library related to the petrochemical process according to the extracted facility equipment.
Preferably, constructing a multi-level traceable fault network model according to the petrochemical process includes:
Dividing the petrochemical process into a plurality of levels, each level having a plurality of facility units, one facility unit of each level being associated with a plurality of facility units in a next level;
Acquiring a fault logic relationship between a facility unit of each level and a plurality of facility units corresponding to the next level;
And constructing a multi-level traceable fault network model according to the fault logic relationship.
Preferably, the fault logical relationship includes an and logical relationship and an or logical relationship.
Preferably, the reliability analysis is performed on the key device based on a multi-level traceability fault network model, including:
Constructing a field database of the petrochemical device, wherein the field database comprises basic information and fault information of the petrochemical device;
extracting multi-dimensional evaluation input parameters from a site database;
calculating the failure rate of the key equipment according to the extracted multidimensional evaluation input parameters;
and sorting and classifying failure rate grades according to the magnitude of the failure rates, wherein the failure rate grades are used for representing the reliability of key equipment.
Preferably, the multi-dimensional evaluation input parameters are the equipment quantity, equipment use time, data collection time, equipment effective time and equipment failure times of petrochemical equipment in the on-site database under different suppliers and different models.
Preferably, calculating the failure rate of the key device according to the extracted multi-dimensional evaluation input parameters includes:
calculating to obtain a multi-dimensional device factor according to the device effective time and the number of devices with different dimensions;
calculating to obtain equipment failure factors according to the effective time of the equipment, the number of the equipment with different dimensions and the equipment failure times;
and calculating the failure rate of the key equipment according to the multi-dimensional equipment factors and the equipment failure factors.
Preferably, the method further comprises: calculating failure rates for each level of facility units, comprising:
Calculating the failure rate of the facility units of the next level of each level, wherein the failure rate of the facility units of the lowest level is the failure rate of key equipment;
and calculating the failure rate of the facility units of each level according to the failure rate of the facility units of the next level of each level and the fault logic relationship between the facility units of each level and the plurality of facility units corresponding to the next level.
Preferably, the method further comprises: and carrying out dynamic risk management on the petrochemical process according to the reliability result.
Preferably, the dynamic risk management includes one or more of risk level assessment of risk event, fault tracing of key equipment, maintenance policy formulation of key equipment and maintenance feedback formulation.
The embodiment of the invention provides a dynamic risk determining system based on equipment reliability, which is used for realizing the dynamic risk determining method based on equipment reliability, and comprises the following steps:
the acquisition module is used for determining key equipment capable of generating safety risks for petrochemical processes corresponding to the petrochemical devices;
The model construction module is used for constructing a multi-level traceability fault network model according to the petrochemical process;
And the analysis module is used for carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result.
The embodiment of the invention provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the dynamic risk determination method based on the device reliability when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by the processor, implements the above-mentioned dynamic risk determination method based on device reliability.
According to the technical scheme, the multi-level traceability fault network model is built according to the petrochemical process of the petrochemical device, the reliability grade of the petrochemical process of the petrochemical device can be determined, and the important safety risk of the petrochemical process of the chemical resolution can be effectively prevented according to the reliability of key equipment.
And secondly, the multi-level traceable fault network model can trace the weak root of the fault of the key equipment, customize the maintenance strategy according to the weak root and the reliability thereof, and reduce the risk of petrochemical technology.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for dynamic risk determination based on device reliability provided by one embodiment of the present invention;
FIG. 2 is a flow chart of a key device database construction method provided by an alternative embodiment of the present invention;
FIG. 3 is a flow chart of a facility for extracting risk events from the petrochemical process based on a risk determination method provided by an alternative embodiment of the present invention;
FIG. 4 is a flowchart of a method for constructing a multi-level traceable fault network model according to an alternative embodiment of the present invention;
FIG. 5 is a partial architectural diagram of a multi-level traceable fault network model provided by an alternative embodiment of the present invention;
FIG. 6 is a flow chart of reliability analysis of the critical device based on a multi-level traceable fault network model provided by an alternative embodiment of the present invention;
FIG. 7 is a flow chart of a method for calculating failure rate provided by an alternative embodiment of the present invention;
Fig. 8 is a block diagram of a device reliability based dynamic risk determination system provided by an alternative embodiment of the present invention.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Example 1
Fig. 1 is a flowchart of a method for determining a dynamic risk based on device reliability according to a facility mode of the present invention, and as shown in fig. 1, a method for determining a dynamic risk based on device reliability includes:
Step S101: and determining key equipment capable of generating safety risks for petrochemical processes corresponding to the petrochemical devices.
As a further optimization of this embodiment, the key device is derived from constructing a key device library related to the petrochemical process, and the data information of the key device is obtained from the key device library.
As shown in fig. 2, the method further includes: the key equipment library is constructed, which comprises the following steps:
Step a1: extracting facility equipment playing a key role on an accident occurrence chain of the petrochemical process based on a key safety implementation evaluation method and/or extracting facility equipment generating a risk event of the petrochemical process based on a risk determination method;
Step a2: and constructing a key equipment library related to the petrochemical process according to the extracted facility equipment.
In this embodiment, the key safety implementation evaluation method preferably adopts the method, the memory and the system for evaluating the key safety facilities of the chemical industry disclosed in patent number CN202010465926.3, and the key equipment of the petrochemical device is evaluated by the method, and the specific evaluation steps are not excessively detailed in this embodiment.
In this embodiment, the petrochemical process takes a polypropylene production process (propylene process for short) as an example, and the facility equipment such as a propylene outlet pipeline check valve, a prepolymerization reactor safety valve, a reactor pressure control loop and the like in the propylene process is identified as key equipment by a key safety implementation evaluation method, and meanwhile, identification ID information of the facility equipment is respectively extracted according to the basic data recording condition of the facility equipment, for example: 200219840, 200219827, 200109156, etc., and importing data of the type, function, location in petrochemical process, etc. of facility equipment associated with the ID information into a key equipment library.
As a further optimization of the present embodiment, as shown in fig. 3, the risk determination method preferably employs a HAZOP/LOPA risk assessment tool, and in step a1, extracting facility equipment for generating a risk event for the petrochemical process based on the risk determination method includes:
step a101: compiling risk units of petrochemical processes, analyzing nodes and compiling codes of risk events.
In this embodiment, taking propylene process as an example, the process is divided into a catalytic unit 1, a polymerization unit 2, a high-low pressure flash evaporation unit 3, a steaming drying unit 4, a raw material refining unit 5 and a public engineering unit 6, wherein the public engineering unit 6 comprises a discharge system node 6.1 and a public engineering system node 6.2, 21 risk events are analyzed in the public engineering system node 6.2, and compiling codes of the 21 risk events are 6.2.1,6.2.2, … and 6.2.21 respectively.
Step a102: judging whether an initial triggering event of the risk event is related to the fault of the facility equipment, and if so, extracting identification ID information of the facility equipment to a key equipment facility library; if not, the facility equipment information is not extracted.
Step a103: judging whether the risk event is the last unit and the last node terminates the risk event, if not, analyzing the next node risk event; if yes, the extraction and identification process is finished.
For example: the initial trigger event of the risk event with the compiling code of 6.2.4 is that the condensate pump fails and stops, and the equipment fails and is related, so that the ID information of the condensate pump, the related equipment model, the equipment function position and other data are extracted and imported into a key equipment library; meanwhile, judging that the risk event is not a termination risk event, and returning to the next risk event 6.2.5 of the node; when analyzing the risk event 6.2.21, the initial trigger event is that the upstream temperature is too high, the risk event is irrelevant to the equipment fault, the equipment information is not extracted, and the risk event 6.2.21 is the final unit of the propylene process and the final node is the final risk event, so that the extraction and identification process is ended.
Step S102: and constructing a multi-level traceable fault network model according to the petrochemical process.
As a further optimization of this embodiment, as shown in fig. 4, constructing a multi-level traceable fault network model according to the petrochemical process includes:
Step b1: dividing the petrochemical process into a plurality of levels, each level having a plurality of facility units, one facility unit in each level being associated with a plurality of facility units in a next level;
As a first level, catalytic systems for propylene processes, polymerization systems, high-low pressure flash systems, vapor drying systems, feedstock refining systems, utility systems, and the like.
The polymerization system in the first hierarchy includes a reaction feed system, a reactor heat removal system, and a reactor agitation system, and then the reaction feed system, the reactor heat removal system, and the reactor agitation system are taken as the second hierarchy.
The reaction feed system in the second stage comprises a catalyst feed system, a raw propylene feed system, then the catalyst feed system, the raw propylene feed system are the third stage.
The catalyst feeding system in the third level comprises a catalyst metering pump and a catalyst metering instrument, and the raw material propylene feeding system comprises a propylene flow regulating loop and an instrument wind system, wherein the catalyst metering pump, the catalyst metering instrument, the propylene flow regulating loop and the instrument wind system serve as a fourth level.
The catalyst metering pump and the catalyst metering instrument are key facility equipment which generates risks for petrochemical technology, and the catalyst metering pump and the catalyst metering instrument are used as key equipment.
The propylene flow regulating loop in the fourth level includes a propylene feed flow meter, a BPCS (process control) system and a propylene flow regulating valve, and then the propylene feed flow meter, the BPCS system and the propylene flow regulating valve are taken as the fifth level.
The propylene feeding flow meter, the BPCS system and the propylene flow regulating valve are key facility equipment which generates risks for petrochemical process, and the propylene feeding flow meter, the BPCS system and the propylene flow regulating valve are used as key equipment.
Step b2: a fault logical relationship between a facility unit of each level and a plurality of facility units corresponding to a next level is obtained.
In this embodiment, the fault logic relationship includes an and logic relationship and an or logic relationship, where the fault logic relationship is determined by the property of the propylene process itself, and in this embodiment, for example, two implementation units or more than two facility units of the next level corresponding to one facility unit of a certain level.
Taking two implementation units of the next level as an example, as shown in fig. 5, in the fourth level, the catalyst metering pump and the catalyst meter are failed, so that the catalyst feeding system in the third level fails, and the failure logic relationship between the facility unit of the level and the plurality of facility units of the next level is an and logic relationship.
In the fourth level, when any one of the propylene flow regulating circuit and the meter wind system fails, the raw material propylene feeding system in the third level fails to be abnormal, and the failure logic relationship between the facility unit of the level and the facility units corresponding to the next level is an OR logic relationship.
The fault logic relationship determining method of one facility unit of a certain level corresponding to more than two facility units of a next level is the same as the determining method of the fault logic relationship.
Step b3: and constructing a multi-level traceable fault network model according to the obtained fault logic relationship structure between petrochemical process layers, wherein the local structure of the multi-level traceable fault network model is shown in fig. 5, the "·" in fig. 5 represents an "AND" logic relationship, and the "+" in fig. 5 represents an "OR" logic relationship.
Step S103: and carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result.
Specifically, as shown in fig. 6, the reliability analysis of the key device based on the multi-level traceability fault network model includes:
step c1: and constructing an on-site database of the petrochemical device, wherein the on-site database comprises basic information and fault information of the petrochemical device, namely counting the petrochemical device which is put into use in the propylene process.
As a further optimization of this embodiment, the construction of the field database comprises the steps of:
Step c101: constructing a type framework according to the petrochemical device put into use;
Specifically, all types of equipment of the petrochemical device are statistically arranged, wherein the equipment comprises key equipment, and a first-stage equipment framework is formed according to classification forms of dynamic equipment, static equipment, electric equipment, instrument equipment and other equipment; and then building a secondary equipment framework on the primary equipment framework, for example: the secondary equipment framework of the movable equipment is provided with a pump, a compressor, a fan and the like; and building a three-stage equipment framework for the two-stage equipment framework respectively, wherein the three-stage equipment framework of the pump comprises a centrifugal pump, a reciprocating pump and the like.
Step c102: and constructing an on-site database according to the collected basic data and fault data of the petrochemical device.
For example: the basic data of pumps put into use in the propylene process are 74 in total, wherein the basic data of the reciprocating pumps are 10, and the 10 basic data are classified into the reciprocating pumps of the three-stage equipment framework of the field database. And similarly, if the total number of fault data in the operation period of the reciprocating pump is 9, the fault data are classified into the reciprocating pump of the three-level equipment framework of the field database, and the field database contains the basic data and the corresponding fault data of each petrochemical device.
Wherein, the basic data of the reciprocating pump includes: ID information: "202019736", device type: "reciprocating pump", apparatus said functional position: "propylene process catalytic unit", equipment manufacturer: "XX Pump Co., ltd.", device model: "EKM210S GR.10", device on time: "201407", etc.
Some fault information of the reciprocating pump includes: fault identification ID information: "202019736", device type: "reciprocating pump", faulty component: "diaphragm", failure mode: "flow limited", failure cause: "mechanical wear", failure impact: "Single plant off-line", plant failure start time: "20191030", equipment failure end time: "20191031", etc.
Step c2: the method comprises the steps of extracting multi-dimensional evaluation input parameters from a site database, wherein the multi-dimensional evaluation input parameters are the equipment number N i (i=1, 2, k), the equipment application time T i1, the data collection time T i2, the equipment effective time T i and the equipment failure frequency F i of petrochemical equipment in the site database under different suppliers and different models.
Step c3: and calculating the failure rate of the key equipment according to the extracted multidimensional evaluation input parameters.
As a further optimization of the present embodiment, as shown in fig. 7, the calculation of failure rate of the key device from the extracted multidimensional evaluation input parameters includes:
Step c301: and calculating a multi-dimensional device factor e i according to the device effective time T i and the device number N i of different dimensions.
Specifically, the calculation formula of the multidimensional device factor e i is:
wherein the device valid time T i＝min{Ti1,Ti2, i.e., the smaller of the device on time and the data collection time.
Step c302: and calculating the equipment failure factor F i according to the equipment effective time T i, the equipment number N i of different dimensions and the equipment failure times F i.
Specifically, the calculation formula of the equipment failure factor f i is:
Step c303: calculating the failure rate lambda of the key equipment according to the multi-dimensional equipment factor e i and the equipment fault factor f i;
where k is the number of multi-dimensional samples of the critical device.
X W pump industry Co., ltd. According to the field database record: the number of devices N 1 =3, the device commissioning time T 11 = 74309, the device data collection time T 12 = 53904, the device effective time T 1 = 53904, and the device failure number F 1 =5 of the EK M210S gr.5 type reciprocating pump.
X W pump Co., ltd.): the number of equipment N 2 =2, the equipment effective time T 2 = 53904, and the equipment failure number F 2 =4 of the EKM210S gr.10 type reciprocating pump.
W pump engineering company: the number of reciprocating pump devices of P1/SSPPP/TNU/TF/STF/model N 3 =2, the device effective time T 3 = 53904, the device failure number F 3 =0.
O company: the number of reciprocating pump devices N 4 =2, the device effective time T 4 = 53904, the device failure times F 4 =0, of the doxa.l-a10×25 model.
The multi-dimensional equipment factor and the fault factor of the reciprocating pump are e 1、e2、f1、f1 respectively; since the other number of faults is zero, its effect is not considered.
In this embodiment, the reciprocating pump belongs to a key device, and the reciprocating pump is a facility unit of a lowest level, which is determined according to an actual petrochemical process structure, for example, the lowest level may be a facility unit of a fifth level or a fourth level. Further optimizing the present embodiment, the method further includes: calculating failure rates for each level of facility units, comprising:
Step c311: calculating the failure rate of the facility units of the next level of each level, and adopting the steps c301 to c303 to calculate; wherein the failure rate of the facility units at the lowest level is the failure rate of the key equipment;
Step c312: and calculating the failure rate of the facility units of each level according to the failure rate of the facility units of the next level of each level and the fault logic relationship between the facility units of each level and the plurality of facility units corresponding to the next level.
Specifically, in the multi-level traceable fault network model, when a fault logic relationship between a facility unit of one level and a plurality of facility units corresponding to the next level is an and logic relationship, the failure rate of the facility unit of the level is a positive factor product of failure rates of a plurality of facility units of the next level associated with the level.
The positive factor product of the failure rate is expressed as follows:
where j represents the number of lower-level facility units, i represents the lower-level ith facility unit, and λ i represents the failure rate of the lower-level ith facility unit.
When the failure logic relationship between the facility units of one level and the facility units corresponding to the next level is an OR logic relationship, the failure rate of the facility units of the level is the inverse factor product of the failure rates of the facility units of the next level associated with the level, and the inverse factor of the inverse factor product is taken.
The failure rate inverse factor product formula is as follows:
where j represents the number of lower-level facility units, i represents the lower-level ith facility unit, and λ i represents the failure rate of the lower-level ith facility unit.
In FIG. 4, the failure rates of the catalyst meter, the reaction pressure meter, the BPCS system, the pressure regulating valve, and the catalyst meter calculated in steps c301 to c303 were 4.3X10 -6/h、2.01×10-6/h、1.41×10-5/h、7.24×10-6/h、4.66×10-5/h, respectively.
Therefore, after the catalyst metering pump and the catalyst metering instrument are failed in the multi-level traceable fault network model, the catalyst feeding system is failed, and the failure rate of the catalyst feeding system is 2.0 multiplied by 10 -10/h.
For the failure of the reaction pressure control loop, whether the failure of the pressure instrument, the failure of the BPCS or the failure of the control valve can lead to the failure, the failure rate of the reaction pressure control loop is 2.33 multiplied by 10 -5/h, and the annual failure probability of the reaction pressure control loop is as follows:
2.33×10-5/h*365*24＝0.2/y。
Step c4: and sorting and classifying failure rate grades according to the magnitude of the failure rates, wherein the failure rate grades are used for representing the reliability of key equipment.
Specifically, the failure rates of all key devices are ordered from small to large, the failure rate sequences are divided into four sequences, and the four sequences of the failure rates are the four levels of reliability; for example: the number of key devices is 80, the first sequence is that the failure rate is from small to large and is 20, the second sequence is that the failure rate is from small to large and is 21-40, the third sequence is that the failure rate is from small to large and is 41-60, and the fourth sequence is that the failure rate is from small to large and is 61-80; the higher failure rate in this embodiment represents lower reliability.
When the number of the four sequences is not an integer, for example, the number of the key devices is 79, the first sequence is the failure rate from small to large and is 19, the second sequence is the failure rate from small to large and is 20-39, the third sequence is the failure rate from small to large and is 40-59, and the fourth sequence is the failure rate from small to large and is 60-79.
Example two
The difference between this embodiment and the first embodiment is that the method further includes: and carrying out dynamic risk management on the petrochemical process according to the reliability result.
Specifically, the dynamic risk management includes one or more of risk level assessment of risk events, fault tracing of key equipment, maintenance policy formulation of key equipment and maintenance feedback formulation.
For risk level assessment of risk events, such as high pressure risk events in the reactor of propylene processes, the reactor may be explosive and casualtic. In the risk event, the reactor pressure control loop can be controlled when the reactor is over-pressurized, but the probability that the reactor pressure control loop fails to be used when needed is the failure probability when needed, and the failure probability of the international general standard reactor pressure control loop is 0.1/y. When the high risk event of the propylene process reactor pressure is evaluated, the international universal standard failure probability of the control loop is generally used for carrying out the degradation of the risk event, wherein the degradation in the high risk event of the propylene process reactor pressure is 0.1, the degradation result is the failure probability of the reactor, the failure probability of the pressure control loop is calculated by the probability of the reactor failure, and the degradation result is 0.1/y, the probability of the reactor failure is calculated by the probability of 0.1=0.01/y. However, the international general standard is only a recommended value, and cannot represent the actual situation of the field application of each petrochemical enterprise. Depending on the analysis flow of the embodiment, the on-site database of the pressure control loop is utilized to determine that the failure probability of the reactor pressure control loop is 0.2/y when the actual demand of the reactor pressure control loop is in actual demand, the degradation in the high risk event of the propylene process reactor pressure is 0.2, the degradation result is 0.1/y 0.2=0.02/y, the failure probability of the reactor pressure control loop when the actual demand of the reactor pressure control loop is related to the high risk event of the propylene process reactor pressure, and the risk event grade degradation evaluation is carried out again.
In the reactor pressure control loop of the propylene process, when the reactor pressure control loop fails, the weak root of the failure is traced according to a multi-level tracing failure network model and is a reaction pressure instrument, a BPCS system, a pressure regulating valve and the like.
For the maintenance policy formulation of the key equipment, determining the maintenance frequency of the maintenance policy according to the reliability level, wherein the maintenance activity maintenance frequency of the key equipment in the first sequence becomes 1.25 times, the original maintenance frequency of the key equipment in the second sequence becomes 1.5 times, the maintenance frequency of the key equipment in the third sequence becomes 1.75 times, and the maintenance frequency of the key equipment in the fourth sequence becomes 2 times; the maintenance activity is changed from general maintenance to the establishment of maintenance activity aiming at weak link reaction pressure instruments, BPCS systems, pressure regulating valves and the like.
For example: the reliability level of the reactor pressure control loop of the propylene process is in the fourth sequence of the reliability level of the key equipment, the maintenance activity frequency of the control loop is doubled, namely, the initial 'two years one check' is changed into 'one year one check', the maintenance node is one year, the maintenance initial time 2021 is set for 11 months, and the maintenance post personnel Liu Mou and the enterprise management post personnel Li Mou are determined.
For maintenance feedback formulation of key equipment, when the maintenance node of the reactor pressure control loop of the propylene process is one year and the time reaches 11 months of 2022, maintenance activities are required to be carried out aiming at weak links, and the maintenance strategy information is transmitted to enterprise management post personnel Li Mou and maintenance post personnel Liu Mou. Meanwhile, the failure probability is changed to 1 when the loop before maintenance activities is required, and the risk level of the risk event of the propylene process related to the failure probability is correspondingly improved by one level. When Li Mou supervises the maintenance post personnel Liu Mou to complete maintenance activities and feed back information, the failure probability is changed to 0.2 when the pressure loop of the reactor is required, the risk level of the associated event is stable, and the dynamic evaluation management of the risk event level of the propylene process is completed.
Example III
Fig. 8 is a block diagram of a device reliability-based dynamic risk determination system according to an alternative embodiment of the present invention, and as shown in fig. 8, the system is configured to implement the above-described device reliability-based dynamic risk determination method, and the system includes:
the acquisition module is used for determining key equipment capable of generating safety risks for petrochemical processes corresponding to the petrochemical devices;
The model construction module is used for constructing a multi-level traceability fault network model according to the petrochemical process;
And the analysis module is used for carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result.
According to the petrochemical process of the petrochemical device, the multi-level traceability fault network model is built, the reliability grade of the petrochemical process of the petrochemical device can be determined, and the important safety risk of the petrochemical process of the chemical can be effectively prevented according to the reliability of key equipment.
And secondly, the multi-level traceable fault network model can trace the weak root of the fault of the key equipment, customize the maintenance strategy according to the weak root and the reliability thereof, and reduce the risk of petrochemical technology.
The embodiment of the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the dynamic risk determination method based on equipment reliability when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by the processor, implements the above-mentioned dynamic risk determination method based on device reliability.
The foregoing details of the optional implementation of the embodiment of the present invention have been described in conjunction with the accompanying drawings, but the embodiment of the present invention is not limited to the specific details of the foregoing implementation, and various simple modifications may be made to the technical solution of the embodiment of the present invention within the scope of the technical concept of the embodiment of the present invention, where all the simple modifications belong to the protection scope of the embodiment of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, various possible combinations of embodiments of the present invention are not described in detail.
Those skilled in the art will appreciate that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, including instructions for causing a single-chip microcomputer, chip or processor (processor) to perform all or part of the steps of the methods of the embodiments described herein. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In addition, any combination of various embodiments of the present invention may be performed, so long as the concept of the embodiments of the present invention is not violated, and the disclosure of the embodiments of the present invention should also be considered.
Claims (14)
1. A method for dynamic risk determination based on device reliability, the method comprising:
Determining key equipment capable of generating safety risks for petrochemical processes corresponding to petrochemical devices;
constructing a multi-level traceability fault network model according to the petrochemical process;
and carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result.
2. The method of claim 1, wherein the key equipment is from a key equipment library associated with the petrochemical process.
3. The method according to claim 2, wherein the method further comprises: constructing a key equipment library, comprising:
Extracting facility equipment playing a key role on an accident occurrence chain of the petrochemical process based on a key safety implementation evaluation method and/or extracting facility equipment generating a risk event of the petrochemical process based on a risk determination method;
and constructing a key equipment library related to the petrochemical process according to the extracted facility equipment.
4. The method of claim 2, wherein constructing a multi-level traceable fault network model from the petrochemical process comprises:
dividing the petrochemical process into a plurality of levels, each level having a plurality of facility units, one facility unit in each level being associated with a plurality of facility units in a next level;
Acquiring a fault logic relationship between a facility unit of each level and a plurality of facility units corresponding to the next level;
and constructing a multi-level traceable fault network model according to the fault logic relationship.
5. The method of claim 4, wherein the failed logical relationship comprises an and logical relationship and an or logical relationship.
6. The method of claim 4 or 5, wherein performing reliability analysis on the critical device based on a multi-level traceable fault network model comprises:
Constructing a field database of the petrochemical device, wherein the field database comprises basic information and fault information of the petrochemical device;
extracting multi-dimensional evaluation input parameters from a site database;
calculating the failure rate of the key equipment according to the extracted multidimensional evaluation input parameters;
and sorting and classifying failure rate grades according to the magnitude of the failure rates, wherein the failure rate grades are used for representing the reliability of key equipment.
7. The method of claim 6, wherein the multi-dimensional evaluation input parameters are the number of equipment, equipment time, data collection time, equipment availability time, and equipment failure times for petrochemical plants in the field database for different suppliers and different models.
8. The method of claim 7, wherein calculating failure rates of key devices based on the extracted multi-dimensional evaluation input parameters comprises:
calculating to obtain a multi-dimensional device factor according to the device effective time and the number of devices with different dimensions;
calculating to obtain equipment failure factors according to the effective time of the equipment, the number of the equipment with different dimensions and the equipment failure times;
and calculating the failure rate of the key equipment according to the multi-dimensional equipment factors and the equipment failure factors.
9. The method of claim 6, wherein the method further comprises: calculating failure rates for each level of facility units, comprising:
Calculating the failure rate of the facility units of the next level of each level, wherein the failure rate of the facility units of the lowest level is the failure rate of key equipment;
and calculating the failure rate of the facility units of each level according to the failure rate of the facility units of the next level of each level and the fault logic relationship between the facility units of each level and the plurality of facility units corresponding to the next level.
10. The method of claim 6, wherein the method further comprises: and carrying out dynamic risk management on the petrochemical process according to the reliability result.
11. The method of claim 10, wherein the dynamic risk management comprises one or more of risk level assessment of risk events, fault tracing of critical devices, maintenance policy formulation of critical devices, and maintenance feedback formulation.
12. A device reliability based dynamic risk determination system for implementing the device reliability based dynamic risk determination method of any one of claims 1-11, the system comprising:
the acquisition module is used for determining key equipment capable of generating safety risks for petrochemical processes corresponding to the petrochemical devices;
The model construction module is used for constructing a multi-level traceability fault network model according to the petrochemical process;
And the analysis module is used for carrying out reliability analysis on the key equipment based on the multi-level traceability fault network model to obtain a reliability result.
13. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the device reliability based dynamic risk determination method of any of claims 1-11 when executing the computer program.
14. A computer readable storage medium having stored thereon a computer program, which when executed by the processor implements the device reliability based dynamic risk determination method of any of claims 1-11.
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