Safety risk assessment method, system, medium and equipment for long-term service power station boilers

Abstract

The present invention provides a method, system, medium and equipment for safety risk assessment of long-term service power station boilers, which relates to the field of safety risk assessment of long-term service power station boilers. The method includes: analyzing the risk factors affecting the safe operation of the corresponding subsystems according to the equipment conditions, operating conditions and use management conditions of different subsystems of the long-term service power station boilers, and constructing a safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems; based on the safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems, determining the failure probability of different components of different subsystems according to the Bayesian network; and determining the safety risk level of the long-term service power station boiler according to the failure probability. The present invention can reduce the number of non-stop times of the boiler, formulate a scientific and reasonable inspection and maintenance plan, reduce unnecessary losses, and ensure the long-term safe and stable operation of the long-term service power station boiler.

Description

Technical Field

[0001] The present invention relates to the field of safety risk assessment of long-term service power station boilers, and in particular to a safety risk assessment method, system, medium and equipment for long-term service power station boilers. Background Art

[0002] For a long time, long-term service power station boilers have played an important role in promoting economic development and ensuring people's livelihood, but how to ensure their safe operation has become a key issue of common concern to users, supervisory agencies, inspection agencies and even the general public. Due to their complex structure and the involvement of fuel combustion processes, long-term service power station boilers are different from other special equipment. Once an accident occurs, the consequences are often serious and the losses are particularly huge.

[0003] At present, the proportion of coal-fired power units in China that have been in service for more than 10 years has reached 43%, and the 300,000-kilowatt and 600,000-kilowatt coal-fired power units that have been in service for 20 to 30 years have reached or are close to their design life. These units have been transformed many times and are in good condition and have the ability to continue to operate. However, at the same time, as the service time of long-term power station boilers continues to increase, the damage and failure of boiler pressure-bearing components present new characteristics and problems. How to carry out a more scientific and effective comprehensive assessment of the safety risks of long-term power station boilers has become an industry bottleneck technical problem that needs to be solved urgently.

[0004] Long-term service power plant boilers are composed of several subsystems, each of which contains many sub-level devices. As long-term service power plant boilers gradually become larger and more intelligent, their internal structure becomes more and more complex, the system monitoring properties become more diverse, and the data patterns become more difficult to summarize. Therefore, a new comprehensive safety risk assessment method for long-term service power plant boilers is urgently needed to reduce the number of non-stop boilers, help operation and maintenance personnel formulate more scientific and reasonable inspection and maintenance plans, reduce unnecessary losses, and ensure the long-term safe and stable operation of long-term service power plant boilers. Summary of the invention

[0005] The purpose of the present invention is to provide a safety risk assessment method, system, medium and equipment for long-term service power station boilers, which can evaluate the safety risks of long-term service power station boilers, reduce the number of non-stop boilers, formulate scientific and reasonable inspection and maintenance plans, reduce unnecessary losses, and ensure the long-term safe and stable operation of long-term service power station boilers.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A safety risk assessment method for long-term service power station boilers, comprising:

[0008] According to the equipment status, operating conditions and use management of different subsystems of long-term service power station boilers, the risk factors affecting the safe operation of the corresponding subsystems are analyzed, and a safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems is constructed; the different subsystems include header subsystems and heating surface tube subsystems; the header subsystems include boiler drums, startup separators, desuperheaters, water-cooled wall headers, economizer headers, superheater headers and reheater headers; the heating surface tube subsystems include water-cooled wall tubes, economizer tubes, superheater tubes and reheater tubes; the safety risk evaluation index system includes a header subsystem safety risk evaluation index system and a heating surface tube subsystem safety risk evaluation index system;

[0009] Based on the safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems, the failure probability of different components of different subsystems is determined according to the Bayesian network;

[0010] The safety risk level of the long-serving power station boiler is determined according to the failure probability.

[0011] Optionally, the safety risk evaluation index system of the header subsystem includes evaluation items and evaluation indicators corresponding to the evaluation items;

[0012] The evaluation items include cracks, material degradation, quality defects, operating conditions, use management, pipe wall thinning and other problems;

[0013] The evaluation indexes corresponding to the cracks include pipe seat fillet weld cracks, weld surface cracks, weld buried defects and parent material cracks;

[0014] The evaluation index corresponding to the material degradation includes low hardness caused by operation and tissue aging; wherein, low hardness refers to a hardness lower than a set hardness threshold;

[0015] The evaluation indicators corresponding to the quality defects include support and hanger defects, welding defects, low hardness, high hardness, design defects, improper processing technology and unqualified original material quality; among which, high hardness refers to hardness higher than the set hardness threshold;

[0016] The evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, safety valve failure, overtemperature and overpressure; wherein, overtemperature refers to a temperature exceeding a set temperature threshold, and overpressure refers to a pressure exceeding a set pressure threshold;

[0017] The evaluation indicators corresponding to the said use management include statutory inspections not being carried out in accordance with regulations, fault records, personnel certification issues, and non-standard emergency rescue and inspections;

[0018] The evaluation indicators corresponding to the pipe wall thinning include erosion thinning, steam oxidation thinning, water side corrosion thinning and outer wall corrosion thinning;

[0019] Evaluation indicators corresponding to the other problems include unqualified water quality or improper cleaning.

[0020] Optionally, the safety risk evaluation index system of the heating surface pipe subsystem includes: evaluation items and evaluation indicators corresponding to the evaluation items;

[0021] The evaluation items include pipe wall thinning, cracks, material degradation, quality defects, operating conditions, use management and other issues;

[0022] The evaluation indicators corresponding to the tube wall thinning include flue gas dew point corrosion thinning, pitting corrosion thinning, coal ash corrosion thinning, high temperature sulfur and chlorine corrosion thinning, high temperature steam oxidation thinning, fly ash wear thinning, coal particle wear thinning, soot blowing wear thinning, slag wear thinning and mechanical wear thinning; wherein, high temperature is a temperature higher than a set temperature threshold;

[0023] The evaluation indicators corresponding to the cracks include creep cracks, mechanical fatigue cracks, stress corrosion cracks, hydrogen damage cracks, corrosion fatigue cracks and welding cracks;

[0024] The evaluation indicators corresponding to the material degradation include low hardness caused by operation, tissue aging and decreased mechanical properties;

[0025] The evaluation indicators corresponding to the quality defects include welding defects, low hardness, high hardness, design defects, improper processing technology and unqualified original material quality; among which, low hardness refers to hardness below the set hardness threshold; high hardness refers to hardness above the set hardness threshold;

[0026] The evaluation indicators corresponding to the operating conditions include deep peak load regulation and rapid load change, over-temperature and over-pressure; wherein, over-temperature is a temperature exceeding a set temperature threshold, and over-pressure is a pressure exceeding a set pressure threshold;

[0027] The evaluation indicators corresponding to the said use management include statutory inspections not being carried out in accordance with regulations, fault records, personnel certification issues, and non-standard emergency rescue and inspections;

[0028] The evaluation indicators corresponding to the other problems include unqualified water quality, improper cleaning, coking, slagging, deformation, displacement, out of the queue, bulging, swelling and burning.

[0029] Optionally, based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems, the failure probability of different components of different subsystems is determined according to the Bayesian network, specifically including:

[0030] Based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems, the evaluation levels of different indicators of different components of different subsystems are determined;

[0031] Based on the evaluation levels of different indicators of different components of different subsystems, combined with the probability of event occurrence and running time, the prior probabilities of different indicators of different components of different subsystems are determined;

[0032] According to the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems and the prior probability of different indicators of different components of different subsystems, Bayesian networks corresponding to different subsystems and different components are constructed;

[0033] The failure probabilities of the different components of the different subsystems are determined according to the Bayesian networks corresponding to the different components of the different subsystems.

[0034] Optionally, the failure probability is:

[0035]

[0036] Where P(A|B) is the conditional probability of event A given event B; P(A) is the probability of event A; P(B|A) is the conditional probability of event B given event A; P(B|E i ) In event E i The conditional probability of event B if it occurs; P(E i ) is event E i The probability of occurrence; E i is the i-th event; where an event refers to whether different components of different subsystems fail.

[0037] Optionally, the safety risk level of the long-serving power plant boiler is determined according to the failure probability, and then the following step is further included:

[0038] Develop an inspection and maintenance plan based on the safety risk level.

[0039] A safety risk assessment system for long-term service power station boilers, comprising:

[0040] The safety risk assessment index system construction module is used to analyze the risk factors that affect the safe operation of the corresponding subsystems according to the equipment conditions, operating conditions and use management of different subsystems of long-term service power station boilers, and to construct a safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems; the different subsystems include header subsystems and heating surface tube subsystems; the header subsystems include boiler drums, start-up separators, desuperheaters, water-cooled wall headers, economizer headers, superheater headers and reheater headers; the heating surface tube subsystems include water-cooled wall tubes, economizer tubes, superheater tubes and reheater tubes; the safety risk assessment index system includes a header subsystem safety risk assessment index system and a heating surface tube subsystem safety risk assessment index system;

[0041] The failure probability determination module is used to determine the failure probability of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems according to the Bayesian network;

[0042] The safety risk level determination module is used to determine the safety risk level of the long-term service power station boiler according to the failure probability.

[0043] Optionally, the failure probability determination module specifically includes:

[0044] An evaluation level determination unit is used to determine the evaluation levels of different indicators of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems;

[0045] A priori probability determination unit is used to determine the priori probabilities of different indicators of different components of different subsystems based on the evaluation levels of different indicators of different components of different subsystems, combined with the probability of event occurrence and the running time;

[0046] A Bayesian network construction unit is used to construct Bayesian networks corresponding to different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems and the prior probabilities of different indicators of different components of different subsystems;

[0047] The failure probability determination unit is used to determine the failure probabilities of different components of different subsystems according to the Bayesian networks corresponding to the different components of the different subsystems.

[0048] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program so that the electronic device executes the above-mentioned safety risk assessment method for a long-term service power station boiler.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned safety risk assessment method for a long-term service power station boiler.

[0050] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the embodiments of the present invention analyze the risk factors affecting the safe operation of the corresponding subsystems according to the equipment conditions, operating conditions and usage management of different subsystems of the long-serving power station boiler, and construct a safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems, so as to determine the failure probability of different components of the same subsystem to obtain the safety risk level of the long-serving power station boiler, thereby reducing the number of non-stop times of the boiler, being able to formulate a scientific and reasonable inspection and maintenance plan, reducing unnecessary losses, and ensuring the long-term safe and stable operation of the long-serving power station boiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 Develop a technical roadmap for comprehensive safety risk assessment of long-term service power plant boilers;

[0053] Figure 2 A flow chart of the safety risk assessment method for long-term service power station boilers;

[0054] Figure 3 The schematic diagram of Bayesian network for different components of the header subsystem;

[0055] Figure 4 This is a sample diagram of a Bayesian network. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The purpose of the present invention is to provide a safety risk assessment method, system, medium and equipment for a long-term service power station boiler, which reduces the number of non-stop boiler shutdowns, can formulate a scientific and reasonable inspection and maintenance plan, reduce unnecessary losses, and ensure the long-term safe and stable operation of the long-term service power station boiler.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Embodiment 1

[0060] The dangerous factors affecting the safe operation of boilers in long-term service are analyzed from three aspects: equipment condition, operating conditions and use management. A safety risk evaluation index system under multi-factor and multi-scale coupling of header and heating surface pipe subsystems is established. The prior probability of different components and different indicators of the subsystem is determined in combination with the on-site inspection. At the same time, based on the Bayesian network risk evaluation technology, the quantitative evaluation of the safety risk of long-term service power plant boilers is realized according to the basic framework of "component-subsystem-boiler as a whole". The technical route is as follows: Figure 1 shown.

[0061] like Figure 2As shown, the present invention provides a safety risk assessment method for a long-term service power station boiler, comprising:

[0062] Step 201: Analyze the risk factors that affect the safe operation of the corresponding subsystems according to the equipment conditions, operating conditions and use management of different subsystems of long-term service power station boilers, and construct a safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems; the different subsystems include header subsystems and heating surface tube subsystems; the header subsystem includes a boiler drum, a start-up separator, a desuperheater, a water-cooled wall header, an economizer header, a superheater header and a reheater header; the heating surface tube subsystem includes water-cooled wall tubes, economizer tubes, superheater tubes and reheater tubes; the safety risk assessment index system includes a header subsystem safety risk assessment index system and a heating surface tube subsystem safety risk assessment index system.

[0063] Step 202: Based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems, the failure probability of different components of different subsystems is determined according to the Bayesian network.

[0064] In practical applications, step 202 specifically includes: determining evaluation levels of different indicators of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems; determining the prior probabilities of different indicators of different components of different subsystems based on the evaluation levels of different indicators of different components of different subsystems, combined with the probability of event occurrence and the running time; constructing Bayesian networks corresponding to different components of different subsystems according to the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems and the prior probabilities of different indicators of different components of different subsystems; and determining the failure probabilities of different components of different subsystems according to the Bayesian networks corresponding to different components of different subsystems.

[0065] Step 203: Determine the safety risk level of the long-term service power station boiler according to the failure probability.

[0066] In practical applications, the safety risk evaluation index system of the header subsystem includes evaluation items and evaluation indicators corresponding to the evaluation items; the evaluation items include cracks, material degradation, quality defects, operating conditions, use management, pipe wall thinning and other problems; the evaluation indicators corresponding to the cracks include pipe seat corner weld cracks, weld surface cracks, weld buried defects and parent material cracks; the evaluation indicators corresponding to the material degradation include low hardness caused by operation and tissue aging; wherein, low hardness is hardness below the set hardness threshold; the evaluation indicators corresponding to the quality defects include support and hanger defects, welding defects, low hardness, high hardness, design defects, improper processing technology, etc. When the quality of the original material is unqualified; wherein, the high hardness refers to the hardness that is higher than the set hardness threshold; the evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, safety valve failure, overtemperature and overpressure; wherein, the overtemperature is the temperature exceeding the set temperature threshold, and the overpressure is the pressure exceeding the set pressure threshold; the evaluation indicators corresponding to the use management include statutory inspections not performed in accordance with regulations, fault records, personnel certification issues, and emergency rescue and inspections that are not standardized; the evaluation indicators corresponding to the pipe wall thinning include erosion thinning, steam oxidation thinning, water side corrosion thinning and outer wall corrosion thinning; the evaluation indicators corresponding to other problems include unqualified water quality or improper cleaning.

[0067] The safety risk assessment method for the long-term service power station boiler header subsystem is as follows:

[0068] According to the structural characteristics of the components and the operating environment conditions, the boiler drum, start-up separator, desuperheater, water-cooled wall header, economizer header, superheater header, reheater header, etc. are classified into the header subsystem. First, a safety risk assessment index system under multi-factor and multi-scale coupling of the header subsystem is established; secondly, the prior probabilities of different components and different indicators of the subsystem are determined in combination with the on-site inspection situation; finally, the failure probability of different components of the header subsystem is obtained based on the Bayesian network. The specific steps are as follows.

[0069] Step 1-1: Establish a safety risk assessment indicator system for container subsystems.

[0070] The risk factors affecting the safe operation of boiler header subsystems in long-term service power stations are analyzed from three aspects: equipment condition, operating conditions and use management. A safety risk assessment index system for header subsystems under multi-factor and multi-scale coupling is established, including seven major evaluation items and 26 corresponding small evaluation indicators, including cracks (cracking), material degradation, quality defects, operating conditions, use management, pipe wall thinning and other problems, as shown in Table 1.

[0071] Table 1 Safety evaluation index system and on-site inspection form for container subsystem

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] Step 1-2: Determine the prior probabilities of different components and different indicators of the header subsystem.

[0078] The evaluation levels of different components and indicators of the subsystem are determined based on the on-site inspection situation. Then, based on the possibility of event occurrence and the operating time, typical prior probability values ​​are selected in Table 2.

[0079] Table 2 Typical prior probability values ​​of different components and indicators of header subsystem

[0080]

[0081] Step 1-3: Construct the Bayesian network of different components of the container subsystem.

[0082] According to the safety risk assessment index system of the container subsystem established in step 1-1 and the prior probabilities of different components and different indicators of the container subsystem determined in step 1-2, the Bayesian network of different components of the container subsystem is constructed, such as Figure 3 shown.

[0083] Step 1-4: Calculate the failure probability of different components of the header subsystem.

[0084] The failure probability of different components of the header subsystem is calculated based on the Bayesian theory. The calculation formula is shown in formula (1):

[0085]

[0086] Where P(A|B) is the conditional probability of event A given event B; P(A) is the probability of event A; P(B|A) is the conditional probability of event B given event A; P(B|E i ) In event E i The conditional probability of event B if it occurs; P(E i ) is event E i The probability of occurrence; E i is the i-th event; where an event refers to whether different components of different subsystems fail.

[0087] like Figure 4As shown, the Bayesian network is analyzed, and the conditional probabilities of nodes C and D are calculated through the prior probabilities of nodes A and B, as shown in Tables 3 to 5. Among them, Y indicates that failure occurs, and N indicates that failure does not occur.

[0088] Table 3 Prior probability table of node A and node B

[0089] P(A=Y) P(A=N) P(B=Y) P(B=N) 0.9 0.1 0.6 0.4

[0090] Table 4 Conditional probability table of node C when nodes A and B are clear

[0091] A B P(C=Y) P(C=N) Y Y 0.5 0.5 Y N 0.9 0.1 N Y 0.2 0.8 N N 0.7 0.3

[0092] Table 5 Conditional probability table of node D when nodes A and B are clear

[0093] A C P(D=Y) P(D=N) Y Y 0.6 0.4 Y N 1.0 0.0 N Y 0.2 0.8 N N 0.6 0.4

[0094] In practical applications, the safety risk evaluation index system of the heating surface pipe subsystem includes: evaluation items and evaluation indicators corresponding to the evaluation items; the evaluation items include pipe wall thinning, cracks, material degradation, quality defects, operating conditions, use management and other issues; the evaluation indicators corresponding to the pipe wall thinning include flue gas dew point corrosion thinning, pitting thinning, coal ash corrosion thinning, high temperature sulfur chlorine corrosion thinning, high temperature steam oxidation thinning, fly ash wear thinning, coal particle wear thinning, soot blowing wear thinning, slag wear thinning and mechanical wear thinning; wherein, high temperature is a temperature higher than a set temperature threshold; the evaluation indicators corresponding to the cracks include creep cracks, mechanical fatigue cracks, stress corrosion cracks, hydrogen damage cracks, corrosion fatigue cracks and welding cracks; the evaluation indicators corresponding to the material degradation include low hardness caused by operation , tissue aging and degradation of mechanical properties; the evaluation indicators corresponding to the quality defects include welding defects, low hardness, high hardness, design defects, improper processing technology and unqualified original material quality; among which, low hardness refers to the hardness below the set hardness threshold; high hardness refers to the hardness above the set hardness threshold; the evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, overtemperature and overpressure; among which, overtemperature refers to the temperature exceeding the set temperature threshold, and overpressure refers to the pressure exceeding the set pressure threshold; the evaluation indicators corresponding to the use management include statutory inspections not performed in accordance with regulations, fault records, personnel certification issues, and emergency rescue and inspections that are not standardized; the evaluation indicators corresponding to other problems include unqualified water quality, improper cleaning, coking, slagging, deformation, displacement, decoupling, bulging, swelling and burning.

[0095] The safety risk assessment method for heating surface tubes of long-term service power station boilers is as follows:

[0096] According to the structural characteristics of the components and the operating environment conditions, water-cooled wall tubes, economizer tubes, superheater tubes, reheater tubes, etc. are classified into the heating surface tube subsystem. First, a safety risk assessment index system under multi-factor and multi-scale coupling of the heating surface tube subsystem is established; secondly, the prior probabilities of different components and different indicators of the subsystem are determined in combination with the on-site inspection situation; finally, the failure probability of different components of the heating surface tube subsystem is obtained based on the Bayesian network. The specific steps are as follows.

[0097] Step 2-1: Construct a safety risk assessment index system for the heating surface pipe subsystem.

[0098] The risk factors affecting the safe operation of header subsystems of boilers in long-term service power stations are analyzed from three aspects: equipment condition, operating conditions and use management. A safety risk assessment index system for header subsystems under multi-factor and multi-scale coupling is established, including 7 major evaluation items and 39 corresponding minor evaluation indicators, such as tube wall thinning, cracks, material degradation, quality defects, operating conditions, use management and other issues, as shown in Table 6.

[0099] Table 6 Safety evaluation index system and on-site inspection form for heating surface pipe subsystem

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] Step 2-2: Determine the prior probabilities of different components and different indicators of the heating surface pipe subsystem.

[0108] The evaluation levels of different components and indicators of the subsystem are determined based on the on-site inspection situation. Then, based on the possibility of event occurrence and the operating time, typical prior probability values ​​are selected in Table 7.

[0109] Table 7 Typical prior probability values ​​of different components and indicators of the heating surface pipe subsystem

[0110]

[0111]

[0112] Step 2-3: Construct the Bayesian network of different components of the heating surface pipe subsystem.

[0113] According to the safety risk assessment index system of the heating surface pipe subsystem established in step 2-1 and the prior probabilities of different components and different indicators of the heating surface pipe subsystem determined in step 2-2, the Bayesian network of different components of the heating surface pipe subsystem is constructed. Figure 3 .

[0114] Step 2-4: Calculate the failure probability of different components of the header subsystem.

[0115] The failure probability of different components of the heating surface pipe subsystem is calculated based on the Bayesian theory. The process refers to steps 1-4.

[0116] In practical applications, the safety risk level of the long-serving power station boiler is determined according to the failure probability, and then the following step further includes: formulating an inspection and maintenance plan according to the safety risk level.

[0117] Step 3: Comprehensive evaluation of safety risks of long-term service power station boilers.

[0118] According to the above-calculated failure probabilities of different components of the header and heating surface pipe subsystems, combined with Table 8, a Bayesian network is established, and finally the overall failure probability of the long-term service power plant boiler can be calculated.

[0119] Table 8 Comprehensive safety risk evaluation table for long-term service power station boilers

[0120]

[0121] The present invention can not only quantitatively evaluate risks, but also scientifically rank the dangerous factors that affect the safe operation of boilers. Compared with the existing technology, it has great advantages in solving failures caused by uncertainty and correlation in complex systems, and the calculation accuracy is higher through repeated iterations. At the same time, the graphical results provide an easy-to-understand model, which can easily correct data to analyze the correlation and sensitivity of parameters. This method can effectively reduce the number of non-stop boilers, reduce unnecessary losses, guide users to further optimize inspection and maintenance plans, and provide technical support for the long-term safe operation and intelligent supervision of China's long-serving power plant boilers.

[0122] Embodiment 2

[0123] In order to execute the method corresponding to the above-mentioned embodiment 1 and realize the corresponding functions and technical effects, a safety risk assessment system for boilers in a long-term service power station is provided below.

[0124] A safety risk assessment system for long-term service power station boilers, comprising:

[0125] The safety risk assessment index system construction module is used to analyze the risk factors that affect the safe operation of the corresponding subsystems according to the equipment status, operating conditions and use management of different subsystems of long-term service power station boilers, and to construct a safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems; the different subsystems include header subsystems and heating surface tube subsystems; the header subsystems include boiler drums, start-up separators, desuperheaters, water-cooled wall headers, economizer headers, superheater headers and reheater headers; the heating surface tube subsystems include water-cooled wall tubes, economizer tubes, superheater tubes and reheater tubes; the safety risk assessment index system includes a header subsystem safety risk assessment index system and a heating surface tube subsystem safety risk assessment index system.

[0126] The failure probability determination module is used to determine the failure probability of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems according to the Bayesian network.

[0127] The safety risk level determination module is used to determine the safety risk level of the long-term service power station boiler according to the failure probability.

[0128] In practical applications, the failure probability determination module specifically includes:

[0129] The evaluation level determination unit is used to determine the evaluation levels of different indicators of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems.

[0130] The prior probability determination unit is used to determine the prior probabilities of different indicators of different components of different subsystems based on the evaluation levels of different indicators of different components of different subsystems, combined with the probability of event occurrence and running time.

[0131] The Bayesian network construction unit is used to construct the Bayesian networks corresponding to different components of different subsystems according to the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems and the prior probabilities of different indicators of different components of different subsystems.

[0132] The failure probability determination unit is used to determine the failure probabilities of different components of different subsystems according to the Bayesian networks corresponding to the different components of the different subsystems.

[0133] Embodiment 3

[0134] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program so that the electronic device executes the above-mentioned method for assessing the safety risk of a long-term service power plant boiler.

[0135] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned safety risk assessment method for a long-term service power station boiler.

[0136] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0137] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A safety risk assessment method for long-term service power station boilers, characterized in that: include: According to the equipment conditions, operating conditions and use management of different subsystems of long-term service power station boilers, the risk factors affecting the safe operation of the corresponding subsystems are analyzed, and a safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems is constructed; the different subsystems include header subsystems and heating surface tube subsystems; the header subsystems include boiler drums, startup separators, desuperheaters, water-cooled wall headers, economizer headers, superheater headers and reheater headers; the heating surface tube subsystems include water-cooled wall tubes, economizer tubes, superheater tubes and reheater tubes; the safety risk evaluation index system includes a header subsystem safety risk evaluation index system and a heating surface tube subsystem safety risk evaluation index system; Based on the safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems, the failure probability of different components of different subsystems is determined according to the Bayesian network; The failure probability is: Where P(A|B) is the conditional probability of event A given event B; P(A) is the probability of event A; P(B|A) is the conditional probability of event B given event A; P(B|E i ) In event E i The conditional probability of event B if it occurs; P(E i ) is event E i The probability of occurrence; E i is the i-th event; where the event refers to whether different components of different subsystems fail; Determine the safety risk level of the long-term service power plant boiler according to the failure probability, quantitatively evaluate the risk, and scientifically rank the risk factors that affect the safe operation of the boiler; The safety risk evaluation index system of the header subsystem includes evaluation items and evaluation indicators corresponding to the evaluation items; the evaluation items include cracks, material degradation, quality defects, operating conditions, use management, pipe wall thinning and other problems; the evaluation indicators corresponding to the cracks include pipe seat corner weld cracks, weld surface cracks, weld buried defects and parent material cracks; the evaluation indicators corresponding to the material degradation include low hardness caused by operation and tissue aging; wherein, low hardness is hardness below the set hardness threshold; the evaluation indicators corresponding to the quality defects include support and hanger defects, welding defects, low hardness, high hardness, design defects, improper processing technology and original The quality of the initial material is unqualified; wherein, the hardness is too high and is higher than the set hardness threshold; the evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, safety valve failure, overtemperature and overpressure; wherein, the overtemperature is the temperature exceeding the set temperature threshold, and the overpressure is the pressure exceeding the set pressure threshold; the evaluation indicators corresponding to the use management include statutory inspections not being carried out in accordance with regulations, fault records, personnel certification issues, and emergency rescue and inspections that are not standardized; the evaluation indicators corresponding to the pipe wall thinning include erosion thinning, steam oxidation thinning, water side corrosion thinning, and outer wall corrosion thinning; the evaluation indicators corresponding to other problems include unqualified water quality or improper cleaning; The safety risk evaluation index system of the heating surface pipe subsystem includes: evaluation items and evaluation indicators corresponding to the evaluation items; the evaluation items include pipe wall thinning, cracks, material degradation, quality defects, operating conditions, use management and other issues; the evaluation indicators corresponding to the pipe wall thinning include flue gas dew point corrosion thinning, pitting thinning, coal ash corrosion thinning, high temperature sulfur chlorine corrosion thinning, high temperature steam oxidation thinning, fly ash wear thinning, coal particle wear thinning, soot blowing wear thinning, slag wear thinning and mechanical wear thinning; wherein, high temperature is a temperature higher than a set temperature threshold; the evaluation indicators corresponding to the cracks include creep cracks, mechanical fatigue cracks, stress corrosion cracks, hydrogen damage cracks, corrosion fatigue cracks and welding cracks; the evaluation indicators corresponding to the material degradation include low hardness caused by operation, aging of the structure, etc. and degradation of mechanical properties; the evaluation indicators corresponding to the quality defects include welding defects, low hardness, high hardness, design defects, improper processing technology and unqualified original material quality; among which, low hardness refers to the hardness below the set hardness threshold; high hardness refers to the hardness above the set hardness threshold; the evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, overtemperature and overpressure; among which, overtemperature refers to the temperature exceeding the set temperature threshold, and overpressure refers to the pressure exceeding the set pressure threshold; the evaluation indicators corresponding to the use management include statutory inspections not performed in accordance with regulations, fault records, personnel certification issues, and emergency rescue and inspections that are not standardized; the evaluation indicators corresponding to other problems include unqualified water quality, improper cleaning, coking, slagging, deformation, displacement, out of line, bulging, swelling and burning.

2. The safety risk assessment method for long-term service power station boilers according to claim 1 is characterized in that: Based on the safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems, the failure probability of different components of different subsystems is determined according to the Bayesian network, including: Based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems, the evaluation levels of different indicators of different components of different subsystems are determined; Based on the evaluation levels of different indicators of different components of different subsystems, combined with the probability of event occurrence and running time, the prior probabilities of different indicators of different components of different subsystems are determined; According to the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems and the prior probability of different indicators of different components of different subsystems, Bayesian networks corresponding to different subsystems and different components are constructed; The failure probabilities of the different components of the different subsystems are determined according to the Bayesian networks corresponding to the different components of the different subsystems.

3. The safety risk assessment method for long-term service power station boilers according to claim 1 is characterized in that: Determining the safety risk level of the long-term service power plant boiler according to the failure probability, and then further comprising: Develop an inspection and maintenance plan based on the safety risk level.

4. A safety risk assessment system for long-term service power station boilers, characterized in that: include: The safety risk assessment index system construction module is used to analyze the risk factors that affect the safe operation of the corresponding subsystems according to the equipment conditions, operating conditions and use management of different subsystems of long-term service power station boilers, and to construct a safety risk assessment index system under multi-factor and multi-scale coupling of different subsystems; the different subsystems include header subsystems and heating surface tube subsystems; the header subsystems include boiler drums, start-up separators, desuperheaters, water-cooled wall headers, economizer headers, superheater headers and reheater headers; the heating surface tube subsystems include water-cooled wall tubes, economizer tubes, superheater tubes and reheater tubes; the safety risk assessment index system includes a header subsystem safety risk assessment index system and a heating surface tube subsystem safety risk assessment index system; The failure probability determination module is used to determine the failure probability of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems according to the Bayesian network; the failure probability is: Where P(A|B) is the conditional probability of event A given event B; P(A) is the probability of event A; P(B|A) is the conditional probability of event B given event A; P(B|E i ) In event E i The conditional probability of event B if it occurs; P(E i ) is event E i The probability of occurrence; E i is the i-th event; where the event refers to whether different components of different subsystems fail; A safety risk level determination module is used to determine the safety risk level of the long-term service power plant boiler according to the failure probability, quantitatively evaluate the risk, and scientifically rank the risk factors that affect the safe operation of the boiler; The safety risk evaluation index system of the header subsystem includes evaluation items and evaluation indicators corresponding to the evaluation items; the evaluation items include cracks, material degradation, quality defects, operating conditions, use management, pipe wall thinning and other problems; the evaluation indicators corresponding to the cracks include pipe seat corner weld cracks, weld surface cracks, weld buried defects and parent material cracks; the evaluation indicators corresponding to the material degradation include low hardness caused by operation and tissue aging; wherein, low hardness is hardness below the set hardness threshold; the evaluation indicators corresponding to the quality defects include support and hanger defects, welding defects, low hardness, high hardness, design defects, improper processing technology and original The quality of the initial material is unqualified; wherein, the hardness is too high and is higher than the set hardness threshold; the evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, safety valve failure, overtemperature and overpressure; wherein, the overtemperature is the temperature exceeding the set temperature threshold, and the overpressure is the pressure exceeding the set pressure threshold; the evaluation indicators corresponding to the use management include statutory inspections not being carried out in accordance with regulations, fault records, personnel certification issues, and emergency rescue and inspections that are not standardized; the evaluation indicators corresponding to the pipe wall thinning include erosion thinning, steam oxidation thinning, water side corrosion thinning, and outer wall corrosion thinning; the evaluation indicators corresponding to other problems include unqualified water quality or improper cleaning; The safety risk evaluation index system of the heating surface pipe subsystem includes: evaluation items and evaluation indicators corresponding to the evaluation items; the evaluation items include pipe wall thinning, cracks, material degradation, quality defects, operating conditions, use management and other issues; the evaluation indicators corresponding to the pipe wall thinning include flue gas dew point corrosion thinning, pitting thinning, coal ash corrosion thinning, high temperature sulfur chlorine corrosion thinning, high temperature steam oxidation thinning, fly ash wear thinning, coal particle wear thinning, soot blowing wear thinning, slag wear thinning and mechanical wear thinning; wherein, high temperature is a temperature higher than a set temperature threshold; the evaluation indicators corresponding to the cracks include creep cracks, mechanical fatigue cracks, stress corrosion cracks, hydrogen damage cracks, corrosion fatigue cracks and welding cracks; the evaluation indicators corresponding to the material degradation include low hardness caused by operation, aging of the structure, etc. and degradation of mechanical properties; the evaluation indicators corresponding to the quality defects include welding defects, low hardness, high hardness, design defects, improper processing technology and unqualified original material quality; among which, low hardness refers to the hardness below the set hardness threshold; high hardness refers to the hardness above the set hardness threshold; the evaluation indicators corresponding to the operating conditions include deep peak regulation and rapid load change, overtemperature and overpressure; among which, overtemperature refers to the temperature exceeding the set temperature threshold, and overpressure refers to the pressure exceeding the set pressure threshold; the evaluation indicators corresponding to the use management include statutory inspections not performed in accordance with regulations, fault records, personnel certification issues, and emergency rescue and inspections that are not standardized; the evaluation indicators corresponding to other problems include unqualified water quality, improper cleaning, coking, slagging, deformation, displacement, out of line, bulging, swelling and burning.

5. The long-term service power plant boiler safety risk assessment system according to claim 4 is characterized in that: The failure probability determination module specifically includes: An evaluation level determination unit is used to determine the evaluation levels of different indicators of different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems; A priori probability determination unit is used to determine the priori probabilities of different indicators of different components of different subsystems based on the evaluation levels of different indicators of different components of different subsystems, combined with the probability of event occurrence and the running time; A Bayesian network construction unit is used to construct Bayesian networks corresponding to different components of different subsystems based on the safety risk evaluation index system under multi-factor and multi-scale coupling of different subsystems and the prior probabilities of different indicators of different components of different subsystems; The failure probability determination unit is used to determine the failure probabilities of different components of different subsystems according to the Bayesian networks corresponding to the different components of the different subsystems.

6. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the safety risk assessment method for a long-term service power plant boiler as described in any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the safety risk assessment method for a long-term service power station boiler as described in any one of claims 1 to 3.

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