CN110045594B - Intelligent management and control system and method for predicting state risk of four tubes of boiler - Google Patents
Intelligent management and control system and method for predicting state risk of four tubes of boiler Download PDFInfo
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Abstract
The invention relates to an intelligent management and control system and method for predicting state risk of four pipes of a boiler. The existing metal wall temperature monitoring system has fewer measuring points. The invention is characterized in that: the utility boiler is provided with a boiler temperature field furnace top position temperature sensor, a furnace top guided wave sensor, a heating surface pipe metal wall temperature sensor, an oxide skin thickness value manual input device, a boiler temperature field furnace rear temperature sensor, a boiler temperature field furnace rear water wall area temperature sensor, a boiler temperature field furnace front temperature sensor, a furnace front guided wave sensor, a heating surface pipe wall thickness detection data manual input device and a furnace metal wall temperature monitoring device; the furnace top guided wave sensor, the furnace back water-cooled wall area guided wave sensor and the furnace front guided wave sensor are connected to the guided wave acquisition module. The invention solves the problem of less installation of the measuring points of the metal wall temperature monitoring system.
Description
Technical Field
The invention relates to an intelligent management and control system and method for predicting state risk of four pipes of a boiler.
Background
With the development of new industrial revolution represented by the united states "industrial internet" and germany "industry 4.0", and the promulgation of the action program "china manufacture 2025", "internet+" implemented, the digitizing technology is changing the production and operation of industry in an unprecedented way. And the high-capacity and high-parameter units are continuously put into use at present, and particularly, the grade of heat-resistant steel for supercritical and ultra-supercritical is continuously improved, and the importance of leakage prevention and safe operation supervision work of a boiler heating surface pipe is also higher and higher. The intelligent management and control technology of the boiler heating surface pipe is researched, and the significance of boiler state monitoring and risk prediction is great.
The existing metal wall temperature monitoring system has fewer measuring points, no unified theoretical basis exists in data processing, the influence of a temperature field is not considered, metal wall temperature equivalent calculation is not performed according to the thickness of oxide skin of a heated surface tube, and temperature correction is not performed by using a small number of test measuring points in a furnace; no metal overhaul data is input into the system, and trend analysis is carried out on the state of the metal part by utilizing each time of overhaul data; and the judgment leakage of the guided wave sensor is not considered in combination with the metal wall temperature and the hearth temperature field, so that whether the pipe explosion leakage occurs or not is comprehensively analyzed, and the position is accurately judged.
In China patent with publication number CN105760936A, the publication date is 2016, 07 and 13, a boiler four-pipe failure evaluation method based on-site state inspection parameters is disclosed, and risk prediction cannot be performed on the state of the boiler four-pipe.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an intelligent management and control system and method for predicting the state risk of four pipes of a boiler, which can solve the problems of fewer installation of measuring points, unreasonable data processing and inaccurate results of a metal wall temperature monitoring system, input metal overhaul data into the system, perform trend analysis on the state of a metal part by using the overhaul data each time, consider the judging leakage of a guided wave sensor in combination with the metal wall temperature and a hearth temperature field, comprehensively analyze whether the pipe burst leakage occurs and accurately judge the position.
The invention solves the problems by adopting the following technical scheme: this an intelligent management and control system for predicting of boiler four-pipe state risk, its characteristics lie in: the power station boiler is provided with a boiler temperature field furnace top temperature sensor, a furnace top guided wave sensor, a heating surface tube metal wall temperature sensor, an oxide thickness value manual input device, a furnace rear guided wave sensor, a boiler temperature field furnace rear temperature sensor, a boiler temperature field furnace rear water wall area temperature sensor, a furnace rear water wall area guided wave sensor, a boiler temperature field furnace front temperature sensor, a furnace front guided wave sensor, a heating surface tube wall thickness detection data manual input device, a furnace interior metal wall temperature monitoring device, a temperature acquisition module and a guided wave acquisition module; the boiler temperature field furnace top temperature sensor, the heating surface tube metal wall temperature sensor, the boiler temperature field furnace rear water wall area temperature sensor, the boiler temperature field furnace front temperature sensor and the furnace inner metal wall temperature monitoring device are all connected to the temperature acquisition module; the furnace top guided wave sensor, the furnace back water-cooled wall area guided wave sensor and the furnace front guided wave sensor are all connected to the guided wave acquisition module.
The system comprises a temperature processing controller, a guided wave processing redundancy controller, a guided wave processing main controller, a temperature processing redundancy controller, a three-level network main router, a three-level network auxiliary router, an application program main server, a database main server, an application program redundancy server, a database redundancy server, a secondary network router, a primary network system, a secondary network system and a three-level network system, wherein the primary network system is connected with the secondary network system through the secondary network router, and the application program main server, the database main server, the application program redundancy server and the database redundancy server are all connected to the secondary network system; the three-level network system is connected to the application program main server and the database main server through the three-level network auxiliary router, and is connected to the application program redundant server and the database redundant server through the three-level network main router; the temperature acquisition module is connected to the temperature processing controller and the temperature processing redundant controller, and the guided wave acquisition module is connected to the guided wave processing main controller and the guided wave processing redundant controller; the temperature processing controller, the guided wave processing redundant controller, the guided wave processing main controller and the temperature processing redundant controller are all connected with the three-level network system; the manual input device of the oxide skin thickness value and the manual input device of the heating surface pipe wall thickness detection data are connected to a three-level network system.
An intelligent control method of the intelligent control system for predicting the state risk of four boiler tubes is characterized by comprising the following steps: the intelligent control method comprises the following steps:
(1) Three-dimensional monitoring: by combining a database technology, a software technology, a network technology and a graphic technology, a three-dimensional digital platform integrating comprehensive service, data information and high visualization is established, the three-dimensional display of the three-dimensional structure, specification model and material of the boiler heating surface tube is realized, and meanwhile, the data and photo information of hidden danger points and leakage points of four tubes in the past year are combined for classification, induction and analysis, and the three-dimensional model is combined for positioning, inquiry and display;
(2) Monitoring leakage of a boiler heating surface pipe: collecting boiler, acoustics, electronics, computer and mechanical multidisciplinary technologies, acquiring noise signals of boiler tube leakage in the boiler through a sensor, performing acoustic spectrum analysis by data processing on the basis of eliminating various complex noise interferences of boiler operation by utilizing the computer technology, realizing early prediction of boiler tube leakage, and judging the position and leakage degree of a leakage area;
(3) Monitoring the wall temperature of a heating surface: establishing a perfect metal wall temperature monitoring system, wherein accident overtemperature data cannot be traced in a plurality of accident cases, suggesting to additionally install wall temperature measuring points one by one, and comprehensively monitoring the combustion working condition of a boiler and the overtemperature condition of a pipe; for the upper vertical pipe, the situation of foreign matter blockage is less, and a small number of wall temperature measuring points are arranged in different areas; the outermost ring of the tubes of the separation screen is provided with at least 1 measuring point per screen along the width direction; the rear screen superheater is provided with measuring points along the width direction at the outermost pipe position of each screen, and full-screen wall temperature measuring points are arranged at 1/4 positions close to the two side walls along the width direction; the high-temperature superheater is provided with 1 measuring point at intervals of a plurality of screens along the width direction, and is arranged on a pipe with the highest calculated value of each screen wall temperature, and meanwhile, 4 to 5 measuring points are arranged on 2 to 3 pipe screens of a high-temperature area along the width direction; the arrangement principle of the high-temperature reheater along the wall temperature measuring points in the width direction is the same as that of the superheater;
(4) Three-dimensional temperature field simulation: the original temperature field model of the boiler is utilized, a large number of temperature measuring points on site are combined, model correction is carried out, and a three-dimensional temperature field which accords with reality is obtained;
(5) Equivalent temperature calculation: estimating the metal temperature of the pipe according to the thickness of an oxide layer on the inner wall of the fire side of the detected pipe sample and the running time of the boiler and the standard DL/T654-2009;
(6) After the equivalent metal wall temperature is estimated, correcting the metal wall temperature data obtained in the step (3) by utilizing the three-dimensional temperature field temperature distribution simulation of the step (4) and the equivalent wall temperature calculation of the step (5), and finally forming a wall temperature final result for overtemperature risk early warning;
(7) And (3) checking detection data storage: inputting the data of the oxide skin thickness of the heating surface subjected to the previous overhaul and the data of the wall thickness of the pipe wall into a system through a manual input device, performing trend analysis on the data by the system, and evaluating the risk state of the heating surface pipe by using a wall temperature and residual life evaluation model;
(8) Historical data trend analysis: self-learning, generating a normal operation data model, and automatically diagnosing the subsequent operation condition according to the data trend;
(9) Status monitoring and risk rating:
Performing risk management on a boiler heating surface pipe, identifying a failure mode existing in the service process, analyzing the possibility of failure and the severity of the result thereof, evaluating the risk level, and performing risk prevention and control through online monitoring, fine depth inspection, fine overhaul modification and health state evaluation measures so as to improve the operation safety of the boiler;
(10) And finally evaluating the risk condition of the state of the heating surface of the boiler according to the analysis and calculation results.
Further, in the step (5), the metal temperature of the 12Cr1MoVG pipe sample is estimated, namely:
lgx = - 6.839869 + 0.003860 T1+ 0.000283 T1lgT
Wherein:
x-thickness of the inner wall oxide layer on the fire side;
t 1 -Rankine temperature;
T-run time of the tube.
In step (8), the power plant DCS system adopts boiler state monitoring single-parameter threshold value alarm, and intelligent control adopts boiler state monitoring multi-parameter self-learning threshold value alarm.
In the step (9), after all data of the heated surface subjected to the previous large and small repair are input, short-time overheat time, short-time overheat times, corrosion conditions, soot blowing scouring, wall thickness, aging grades, hardness, running time, start-stop times and temperature rising and falling rates are statistically analyzed, and an empirical model is built; calculating a corresponding normal working condition judgment constant space according to the correlation factor fuzzy control model; after self-learning correction, automatic real-time calculation is carried out subsequently, and potential risks are judged; and under the fault working condition, calculating a fault constant space, and accurately judging the fault risk.
Compared with the prior art, the invention has the following advantages and effects: based on a sensing detection device and an information network, filtering and processing information by utilizing a data mining technology, a data identification technology, an artificial intelligence technology and the like, and acquiring safety risks in advance through intelligent decision support to provide auxiliary decisions for operation management staff.
The method can predict the state risk of the four pipes of the boiler on line according to the needs, enriches the means of metal technical supervision, and is also beneficial to improving the safety of unit operation; meanwhile, fault analysis can be carried out on the basis of furnace shutdown and shutdown, and leakage points can be accurately judged; meanwhile, a large amount of metal wall temperature and hearth temperature data can be recorded and analyzed, the wall thickness and the oxide skin thickness of the four tubes of the boiler are corrected by combining the size, the real wall temperature of the four tubes of the boiler is calculated, and abnormal working conditions such as over-temperature and the like are accurately judged; the state of the four pipes of the boiler can be judged according to the trend of the overhaul data. Therefore, the intelligent control of the state risk of the boiler four pipes is realized, the problem that the boiler four pipes are controlled to burst by manual adjustment and optimized operation at present is solved, the economic benefit and the social benefit are better, and the technical problem that the boiler four pipes are difficult to control at present is solved.
Drawings
In order to more clearly illustrate the embodiments of the invention and/or the technical solutions of the prior art, the drawings that are required in the description of the embodiments and/or the prior art will be briefly described below, it being obvious that the drawings in the description below are only some embodiments of the invention and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an intelligent management and control system for predicting the state risk of four tubes of a boiler according to an embodiment of the present invention.
In the figure: the boiler comprises a power station boiler 1, a boiler temperature field furnace top temperature sensor 2, a furnace top guided wave sensor 3, a heating surface pipe metal wall temperature sensor 4, an oxidation skin thickness value manual input device 5, a furnace back guided wave sensor 6, a boiler temperature field furnace back temperature sensor 7, a boiler temperature field furnace back water wall area temperature sensor 8, a furnace back water wall area guided wave sensor 9, a boiler temperature field furnace front temperature sensor 10, a furnace front guided wave sensor 11, a heating surface pipe wall thickness detection data manual input device 12, a furnace metal wall temperature monitoring device 13, a temperature acquisition module 14, a guided wave acquisition module 15, a temperature processing controller 16, a guided wave processing redundancy controller 17, a guided wave processing main controller 18, a temperature processing redundancy controller 19, a three-stage network main router 20, a three-stage network auxiliary router 21, an application main server 22, a database main server 23, an application redundancy server 24, a database redundancy server 25, a two-stage network router 26, a one-stage network system 27, a two-stage network system 28 and a three-stage network system 29.
Detailed Description
The present invention will be described in further detail by way of examples with reference to the accompanying drawings, which are illustrative of the present invention and not limited to the following examples.
Examples
Referring to fig. 1, the intelligent management and control system for predicting the risk of the four-tube state of the boiler in the present embodiment includes a utility boiler 1, a boiler temperature field top temperature sensor 2, a top guided wave sensor 3, a heated surface tube metal wall temperature sensor 4, an oxide skin thickness value manual input device 5, a post-furnace guided wave sensor 6, a boiler temperature field post-furnace temperature sensor 7, a boiler temperature field post-furnace water wall area temperature sensor 8, a post-furnace water wall area guided wave sensor 9, a boiler temperature field pre-furnace temperature sensor 10, a pre-furnace guided wave sensor 11, a heated surface tube wall thickness detection data manual input device 12, a furnace metal wall temperature monitoring device 13, a guided wave temperature acquisition module 14, a guided wave acquisition module 15, a temperature processing controller 16, a processing redundancy controller 17, a processing main controller 18, a temperature processing redundancy controller 19, a three-level network main router 20, a three-level network sub router 21, an application main server 22, a database main server 23, an application server 24, a database server 25, a two-level network router 26, a two-level network system 29, and a three-level network system 29.
The utility boiler 1 in the embodiment is provided with a boiler temperature field furnace top position temperature sensor 2, a furnace top guided wave sensor 3, a heating surface pipe metal wall temperature sensor 4, an oxide skin thickness value manual input device 5, a furnace back guided wave sensor 6, a boiler temperature field furnace back temperature sensor 7, a boiler temperature field furnace back water wall area temperature sensor 8, a furnace back water wall area guided wave sensor 9, a boiler temperature field furnace front temperature sensor 10, a furnace front guided wave sensor 11, a heating surface pipe wall thickness detection data manual input device 12 and a furnace inner metal wall temperature monitoring device 13; the boiler temperature field furnace top temperature sensor 2, the heating surface tube metal wall temperature sensor 4, the boiler temperature field furnace rear temperature sensor 7, the boiler temperature field furnace rear water-cooled wall area temperature sensor 8, the boiler temperature field furnace front temperature sensor 10 and the furnace metal wall temperature monitoring device 13 are all connected to the temperature acquisition module 14; the furnace top guided wave sensor 3, the furnace back guided wave sensor 6, the furnace back water wall area guided wave sensor 9 and the furnace front guided wave sensor 11 are all connected to the guided wave acquisition module 15.
The primary network system 27 in the present embodiment is connected to the secondary network system 28 through the secondary network router 26, and the application main server 22, the database main server 23, the application redundant server 24, and the database redundant server 25 are all connected to the secondary network system 28; the tertiary network system 29 is connected to the application primary server 22 and the database primary server 23 through the tertiary network secondary router 21, and the tertiary network system 29 is connected to the application redundant server 24 and the database redundant server 25 through the tertiary network primary router 20; the temperature acquisition module 14 is connected to the temperature processing controller 16 and the temperature processing redundant controller 19, and the guided wave acquisition module 15 is connected to the guided wave processing main controller 18 and the guided wave processing redundant controller 17; the temperature processing controller 16, the guided wave processing redundant controller 17, the guided wave processing main controller 18 and the temperature processing redundant controller 19 are all connected with the three-stage network system 29; the manual input device 5 of the oxide scale thickness value and the manual input device 12 of the wall thickness detection data of the heated surface pipe are connected to a three-level network system 29.
The working steps of the intelligent management and control system for predicting the state risk of the four pipes of the boiler in the embodiment are as follows:
(1) And (5) three-dimensional monitoring. By combining database technology, software technology, network technology and graphic technology, a three-dimensional digital platform integrating comprehensive business, data information and high visualization is established, the intuitive three-dimensional display of all parameters such as a three-dimensional structure, a specification model, materials and the like of a boiler heating surface pipe is realized, and meanwhile, the information such as data, pictures and the like of hidden danger points and leakage points of four pipes in the past is classified, generalized and analyzed, and the positioning, inquiring and displaying are performed by combining a three-dimensional model.
(2) And monitoring leakage of a heating surface pipe of the boiler. The method is characterized by integrating multidisciplinary technologies such as a boiler, acoustics, electronics, a computer, machinery and the like, acquiring noise signals of boiler tube leakage in the boiler through a sensor, performing acoustic spectrum analysis by data processing on the basis of eliminating various complex noise interferences of boiler operation by utilizing the computer technology, realizing early prediction of boiler tube leakage, and judging the position and the leakage degree of a leaked region.
(3) And monitoring the wall temperature of the heating surface. Establishing a perfect metal wall temperature monitoring system, wherein accident overtemperature data cannot be traced in a plurality of accident cases, suggesting to additionally install wall temperature measuring points one by one, and comprehensively monitoring the combustion working condition of a boiler and the overtemperature condition of a pipe; and for the upper vertical pipe, the situation of foreign matter blockage and the like is less, and a small number of wall temperature measuring points can be arranged in different areas. The outermost tube of the dividing screen should have at least 1 station per screen arranged in the width direction. The rear screen superheater is provided with measuring points along the width direction at the outermost pipe of each screen, and full-screen wall temperature measuring points are arranged at 1/4 positions close to the two side walls along the width direction. The high-temperature superheater is provided with 1 measuring point at intervals of several screens along the width direction, and is arranged on a pipe with the highest calculated value of each screen wall temperature, and meanwhile, 4 to 5 measuring points are arranged on 2 to 3 pipe screens of a high-temperature area along the width direction. The arrangement principle of the wall temperature measuring points of the high-temperature reheater along the width direction is the same as that of the superheater, and the low-temperature superheater and the low-temperature reheater generally do not require more wall temperature measuring points.
(4) Three-dimensional temperature field simulation
With the development of large-scale power station boilers, the capacity of the large-scale power station boilers is larger and the size of the hearth is larger, so that all required temperature values are difficult to actually measure on site, and three-dimensional temperature field simulation analysis is needed. And (3) carrying out model correction by utilizing an original temperature field model of the boiler and combining a large number of temperature measuring points on site to obtain a three-dimensional temperature field which accords with reality.
(5) Equivalent temperature calculation
The tube metal temperature was estimated according to the standard DL/T654-2009 based on the detected oxide layer thickness of the tube sample to the fire side inner wall and the boiler run time. For example, the metal temperature of a 12Cr1MoVG pipe sample is estimated, namely:
lgx = - 6.839869 + 0.003860 T1+ 0.000283 T1lgT
Wherein:
x-thickness of the inner wall oxide layer on the fire side;
t 1 -Rankine temperature;
T-run time of the tube.
(6) After the equivalent metal wall temperature is estimated, the metal wall temperature data obtained in the step (3) is corrected by utilizing the three-dimensional temperature field temperature distribution simulation of the step (4) and the equivalent wall temperature calculation of the step (5), and a final wall temperature result is finally formed and is used for overtemperature risk early warning.
(7) Verification test data storage
The data of the oxide skin thickness of the heating surface and the wall thickness of the pipe wall which are subjected to the previous overhaul are input into a system through a manual input device, the system performs trend analysis on the data, and the risk state of the heating surface pipe can be estimated by using a wall temperature and residual life estimation model.
(8) Historical data trend analysis
And (3) self-learning, generating a normal operation data model, and automatically diagnosing the subsequent operation condition according to the data trend. At present, the DCS system of the power plant adopts boiler state monitoring single-parameter threshold value alarm, and intelligent control adopts boiler state monitoring multi-parameter self-learning threshold value alarm.
(9) State monitoring and risk rating
The risk management is carried out on the boiler heating surface pipe, the failure mode existing in the service process is identified, the possibility of failure and the severity of the result are analyzed, the risk level is evaluated, and the risk prevention and control are carried out through measures such as online monitoring, refined depth inspection, refined overhaul transformation, health state evaluation and the like, so that the operation safety of the boiler is improved.
After all data of the heated surface subjected to the previous large and small repair are input in a gear building manner, parameters such as short-time overheat time, short-time overheat times, corrosion conditions, soot blowing scouring, wall thickness, ageing grade, hardness, running time, start-stop times, temperature rising and falling speed are statistically analyzed, and an empirical model is built. Calculating a corresponding normal working condition judgment constant space according to the correlation factor fuzzy control model; and after self-learning correction, automatic real-time calculation is performed subsequently to judge the potential risk. And under the fault working condition, calculating a fault constant space, and accurately judging the fault risk.
The method comprises the following steps: the normal condition ：k1X1+k2X2+……+knXn={y1,y2,y3,……,yn}, results are within a fuzzy control range; the fault condition ：k1X1+k2X2+……+knXn={C1,C2,C3,……,Cn}, results in a range of fuzzy control.
(10) And finally evaluating the risk condition of the state of the heating surface of the boiler according to the analysis and calculation results.
In addition, it should be noted that the specific embodiments described in the present specification may vary from part to part, from name to name, etc., and the above description in the present specification is merely illustrative of the structure of the present invention. All equivalent or simple changes of the structure, characteristics and principle according to the inventive concept are included in the protection scope of the present patent. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions in a similar manner without departing from the scope of the invention as defined in the accompanying claims.
Claims (5)
1. An intelligent management and control method for an intelligent management and control system for predicting risk of four-tube states of a boiler is provided, wherein the intelligent management and control system for predicting risk of four-tube states of the boiler comprises a power station boiler (1), a boiler temperature field furnace top position temperature sensor (2), a furnace top guided wave sensor (3), a heating surface tube metal wall temperature sensor (4), an oxide skin thickness value manual input device (5), a furnace back guided wave sensor (6), a boiler temperature field furnace back temperature sensor (7), a boiler temperature field furnace back water-cooling wall area temperature sensor (8), a furnace back water-cooling wall area guided wave sensor (9), a boiler temperature field furnace front temperature sensor (10), a furnace front guided wave sensor (11), a heating surface tube wall thickness detection data manual input device (12), a furnace inner metal wall temperature monitoring device (13), a temperature acquisition module (14) and a guided wave acquisition module (15), wherein the power station boiler (1) is provided with the boiler temperature field furnace top position temperature sensor (2), the furnace top guided wave sensor (3), the heating surface tube metal wall temperature sensor (4), the oxide skin thickness manual input device (5), the furnace back water-cooling wall area temperature sensor (8), the furnace back water-cooling wall area temperature sensor (10) and the furnace back water-cooling wall area temperature sensor (8) A furnace front guided wave sensor (11), a heating surface pipe wall thickness detection data manual input device (12) and a furnace inner metal wall temperature monitoring device (13); the boiler temperature field furnace top temperature sensor (2), the heating surface tube metal wall temperature sensor (4), the boiler temperature field furnace rear temperature sensor (7), the boiler temperature field furnace rear water wall area temperature sensor (8), the boiler temperature field furnace front temperature sensor (10) and the furnace inner metal wall temperature monitoring device (13) are all connected to the temperature acquisition module (14); the furnace top guided wave sensor (3), the furnace back guided wave sensor (6), the furnace back water-cooled wall area guided wave sensor (9) and the furnace front guided wave sensor (11) are connected to the guided wave acquisition module (15); the method is characterized in that: the intelligent control method comprises the following steps:
(1) Three-dimensional monitoring: by combining a database technology, a software technology, a network technology and a graphic technology, a three-dimensional digital platform integrating comprehensive service, data information and high visualization is established, the three-dimensional display of the three-dimensional structure, specification model and material of the boiler heating surface tube is realized, and meanwhile, the data and photo information of hidden danger points and leakage points of four tubes in the past year are combined for classification, induction and analysis, and the three-dimensional model is combined for positioning, inquiry and display;
(2) Monitoring leakage of a boiler heating surface pipe: collecting boiler, acoustics, electronics, computer and mechanical multidisciplinary technologies, acquiring noise signals of boiler tube leakage in the boiler through a sensor, performing acoustic spectrum analysis by data processing on the basis of eliminating various complex noise interferences of boiler operation by utilizing the computer technology, realizing early prediction of boiler tube leakage, and judging the position and leakage degree of a leakage area;
(3) Monitoring the wall temperature of a heating surface: establishing a perfect metal wall temperature monitoring system, wherein accident overtemperature data cannot be traced in a plurality of accident cases, suggesting to additionally install wall temperature measuring points one by one, and comprehensively monitoring the combustion working condition of a boiler and the overtemperature condition of a pipe; for the upper vertical pipe, the situation of foreign matter blockage is less, and a small number of wall temperature measuring points are arranged in different areas; the outermost ring of the tubes of the separation screen is provided with at least 1 measuring point per screen along the width direction; the rear screen superheater is provided with measuring points along the width direction at the outermost pipe position of each screen, and full-screen wall temperature measuring points are arranged at 1/4 positions close to the two side walls along the width direction; the high-temperature superheater is provided with 1 measuring point at intervals of a plurality of screens along the width direction, and is arranged on a pipe with the highest calculated value of each screen wall temperature, and meanwhile, 4 to 5 measuring points are arranged on 2 to 3 pipe screens of a high-temperature area along the width direction; the arrangement principle of the high-temperature reheater along the wall temperature measuring points in the width direction is the same as that of the superheater;
(4) Three-dimensional temperature field simulation: the original temperature field model of the boiler is utilized, a large number of temperature measuring points on site are combined, model correction is carried out, and a three-dimensional temperature field which accords with reality is obtained;
(5) Equivalent temperature calculation: estimating the metal temperature of the pipe according to the thickness of an oxide layer on the inner wall of the fire side of the detected pipe sample and the running time of the boiler and the standard DL/T654-2009;
(6) After the equivalent metal wall temperature is estimated, correcting the metal wall temperature data obtained in the step (3) by utilizing the three-dimensional temperature field temperature distribution simulation of the step (4) and the equivalent wall temperature calculation of the step (5), and finally forming a wall temperature final result for overtemperature risk early warning;
(7) And (3) checking detection data storage: inputting the data of the oxide skin thickness of the heating surface subjected to the previous overhaul and the data of the wall thickness of the pipe wall into a system through a manual input device, performing trend analysis on the data by the system, and evaluating the risk state of the heating surface pipe by using a wall temperature and residual life evaluation model;
(8) Historical data trend analysis: self-learning, generating a normal operation data model, and automatically diagnosing the subsequent operation condition according to the data trend;
(9) Status monitoring and risk rating:
Performing risk management on a boiler heating surface pipe, identifying a failure mode existing in the service process, analyzing the possibility of failure and the severity of the result thereof, evaluating the risk level, and performing risk prevention and control through online monitoring, fine depth inspection, fine overhaul modification and health state evaluation measures so as to improve the operation safety of the boiler;
(10) And finally evaluating the risk condition of the state of the heating surface of the boiler according to the analysis and calculation results.
2. The intelligent management and control method for an intelligent management and control system for boiler four-tube state risk prediction according to claim 1, wherein: in the step (5), the metal temperature of the 12Cr1MoVG pipe sample is estimated, namely:
lgx = - 6.839869 + 0.003860 T1 + 0.000283 T1 lgT
Wherein:
x-thickness of the inner wall oxide layer on the fire side;
t 1 -Rankine temperature;
T-run time of the tube.
3. The intelligent management and control method for an intelligent management and control system for boiler four-tube state risk prediction according to claim 1, wherein: in the step (8), the power plant DCS system adopts boiler state monitoring single-parameter threshold value alarm, and intelligent control adopts boiler state monitoring multi-parameter self-learning threshold value alarm.
4. The intelligent management and control method for an intelligent management and control system for boiler four-tube state risk prediction according to claim 1, wherein: in the step (9), after all data of the past large and small repair heating surfaces are input in a gear building manner, short-time overheat time, short-time overheat times, corrosion conditions, soot blowing flushing, wall thickness, ageing grades, hardness, running time, start and stop times and temperature rising and falling rates are statistically analyzed, and an empirical model is built; calculating a corresponding normal working condition judgment constant space according to the correlation factor fuzzy control model; after self-learning correction, automatic real-time calculation is carried out subsequently, and potential risks are judged; and under the fault working condition, calculating a fault constant space, and accurately judging the fault risk.
5. The intelligent management and control method for an intelligent management and control system for boiler four-tube state risk prediction according to claim 1, wherein: the intelligent management and control system for predicting the state risk of the boiler four pipes further comprises a temperature processing controller (16), a guided wave processing redundancy controller (17), a guided wave processing main controller (18), a temperature processing redundancy controller (19), a three-level network main router (20), a three-level network auxiliary router (21), an application program main server (22), a database main server (23), an application program redundancy server (24), a database redundancy server (25), a second-level network router (26), a first-level network system (27), a second-level network system (28) and a three-level network system (29), wherein the first-level network system (27) is connected with the second-level network system (28) through the second-level network router (26), and the application program main server (22), the database main server (23), the application program redundancy server (24) and the database redundancy server (25) are all connected to the second-level network system (28); the three-level network system (29) is connected to the application program main server (22) and the database main server (23) through the three-level network auxiliary router (21), and the three-level network system (29) is connected to the application program redundant server (24) and the database redundant server (25) through the three-level network main router (20); the temperature acquisition module (14) is connected to the temperature processing controller (16) and the temperature processing redundant controller (19), and the guided wave acquisition module (15) is connected to the guided wave processing main controller (18) and the guided wave processing redundant controller (17); the temperature processing controller (16), the guided wave processing redundant controller (17), the guided wave processing main controller (18) and the temperature processing redundant controller (19) are all connected with the three-level network system (29); the manual input device (5) for the oxide skin thickness value and the manual input device (12) for the wall thickness detection data of the heating surface pipe are connected to a three-level network system (29).
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Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4939754A (en) * | 1987-10-06 | 1990-07-03 | Abb Atom Ab | Device for limitation of a flow through a density lock for a nuclear reactor |
| JPH09119603A (en) * | 1995-10-27 | 1997-05-06 | Babcock Hitachi Kk | Diagonosing and estimating method of corrosion, fatigue and damage in boiler water wall tube |
| KR100639785B1 (en) * | 2005-09-15 | 2006-10-31 | 주식회사 아이스기술 | Method for measuring temperature on the boiler |
| CN101013067A (en) * | 2007-02-06 | 2007-08-08 | 济南丰采电子科技有限公司 | High temperature furnace pipe residue lifetime estimation method and device |
| CN101839795A (en) * | 2010-05-10 | 2010-09-22 | 任振伟 | The leak diagnostic systems of pressure-bearing pipe of boiler and method |
| CN103216812A (en) * | 2013-04-15 | 2013-07-24 | 王衡 | Method for on-line monitoring and alarming of convection heating face flue gas temperature field of power station boiler |
| KR20130088557A (en) * | 2012-01-31 | 2013-08-08 | 하나에버텍 주식회사 | Boiler tube monitoring system |
| WO2013136472A1 (en) * | 2012-03-14 | 2013-09-19 | 中国電力株式会社 | Tube leak detection device and tube leak detection method |
| KR20140017237A (en) * | 2012-07-31 | 2014-02-11 | 주식회사 지오네트 | A analytical method for industrial boiler condition using pattern recognition |
| CN104142272A (en) * | 2014-07-22 | 2014-11-12 | 广东电网公司电力科学研究院 | Ultra supercritical boiler super-heat and re-heater heating surface life evaluation method |
| CN204404153U (en) * | 2014-12-31 | 2015-06-17 | 大唐鸡西第二热电有限公司 | CFBB four-tube leakage checkout gear |
| CN104881535A (en) * | 2015-05-21 | 2015-09-02 | 东南大学 | Improved thermal power plant boiler temperature field reconstruction temperature measuring algorithm |
| CN105113370A (en) * | 2015-07-28 | 2015-12-02 | 安徽省新路建设工程集团有限责任公司 | Asphalt-pavement compacting construction method based on kneading compaction |
| CN105258100A (en) * | 2015-11-23 | 2016-01-20 | 重庆安迈科技有限公司 | Boiler monitoring system based on Internet of Things |
| CN105698158A (en) * | 2015-11-23 | 2016-06-22 | 大唐鸡西第二热电有限公司 | Method for detecting four-tube leak of circulating fluidized bed boiler |
| CN105760936A (en) * | 2016-02-24 | 2016-07-13 | 西安西热电站信息技术有限公司 | Boiler 'four tubes' failure evaluation method based on field state inspection parameters |
| CN108254080A (en) * | 2018-02-01 | 2018-07-06 | 天津德尔泰热控系统有限公司 | The system and method in station boiler temperature field is established using gas infra-red radiation superimposed characteristics |
| CN108266715A (en) * | 2018-02-13 | 2018-07-10 | 华电电力科学研究院有限公司 | A kind of discharge smoke temperature of power station boiler intelligent test system |
| CN109118110A (en) * | 2018-08-30 | 2019-01-01 | 太仓港协鑫发电有限公司 | A kind of explosion-proof expert system of boiler wear resistant |
| CN109253870A (en) * | 2018-08-21 | 2019-01-22 | 嘉兴新嘉爱斯热电有限公司 | The assessment device and method in biomass fuel boiler heat-exchange tube service life |
| CN209624993U (en) * | 2019-03-25 | 2019-11-12 | 华电电力科学研究院有限公司 | A kind of managing and control system for four main tubes of boiler state risk profile |
-
2019
- 2019-03-25 CN CN201910228050.8A patent/CN110045594B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4939754A (en) * | 1987-10-06 | 1990-07-03 | Abb Atom Ab | Device for limitation of a flow through a density lock for a nuclear reactor |
| JPH09119603A (en) * | 1995-10-27 | 1997-05-06 | Babcock Hitachi Kk | Diagonosing and estimating method of corrosion, fatigue and damage in boiler water wall tube |
| KR100639785B1 (en) * | 2005-09-15 | 2006-10-31 | 주식회사 아이스기술 | Method for measuring temperature on the boiler |
| CN101013067A (en) * | 2007-02-06 | 2007-08-08 | 济南丰采电子科技有限公司 | High temperature furnace pipe residue lifetime estimation method and device |
| CN101839795A (en) * | 2010-05-10 | 2010-09-22 | 任振伟 | The leak diagnostic systems of pressure-bearing pipe of boiler and method |
| KR20130088557A (en) * | 2012-01-31 | 2013-08-08 | 하나에버텍 주식회사 | Boiler tube monitoring system |
| WO2013136472A1 (en) * | 2012-03-14 | 2013-09-19 | 中国電力株式会社 | Tube leak detection device and tube leak detection method |
| KR20140017237A (en) * | 2012-07-31 | 2014-02-11 | 주식회사 지오네트 | A analytical method for industrial boiler condition using pattern recognition |
| CN103216812A (en) * | 2013-04-15 | 2013-07-24 | 王衡 | Method for on-line monitoring and alarming of convection heating face flue gas temperature field of power station boiler |
| CN104142272A (en) * | 2014-07-22 | 2014-11-12 | 广东电网公司电力科学研究院 | Ultra supercritical boiler super-heat and re-heater heating surface life evaluation method |
| CN204404153U (en) * | 2014-12-31 | 2015-06-17 | 大唐鸡西第二热电有限公司 | CFBB four-tube leakage checkout gear |
| CN104881535A (en) * | 2015-05-21 | 2015-09-02 | 东南大学 | Improved thermal power plant boiler temperature field reconstruction temperature measuring algorithm |
| CN105113370A (en) * | 2015-07-28 | 2015-12-02 | 安徽省新路建设工程集团有限责任公司 | Asphalt-pavement compacting construction method based on kneading compaction |
| CN105258100A (en) * | 2015-11-23 | 2016-01-20 | 重庆安迈科技有限公司 | Boiler monitoring system based on Internet of Things |
| CN105698158A (en) * | 2015-11-23 | 2016-06-22 | 大唐鸡西第二热电有限公司 | Method for detecting four-tube leak of circulating fluidized bed boiler |
| CN105760936A (en) * | 2016-02-24 | 2016-07-13 | 西安西热电站信息技术有限公司 | Boiler 'four tubes' failure evaluation method based on field state inspection parameters |
| CN108254080A (en) * | 2018-02-01 | 2018-07-06 | 天津德尔泰热控系统有限公司 | The system and method in station boiler temperature field is established using gas infra-red radiation superimposed characteristics |
| CN108266715A (en) * | 2018-02-13 | 2018-07-10 | 华电电力科学研究院有限公司 | A kind of discharge smoke temperature of power station boiler intelligent test system |
| CN109253870A (en) * | 2018-08-21 | 2019-01-22 | 嘉兴新嘉爱斯热电有限公司 | The assessment device and method in biomass fuel boiler heat-exchange tube service life |
| CN109118110A (en) * | 2018-08-30 | 2019-01-01 | 太仓港协鑫发电有限公司 | A kind of explosion-proof expert system of boiler wear resistant |
| CN209624993U (en) * | 2019-03-25 | 2019-11-12 | 华电电力科学研究院有限公司 | A kind of managing and control system for four main tubes of boiler state risk profile |
Non-Patent Citations (6)
| Title |
|---|
| The study of the control of the bed temperature in the Circulating fluidized bed boiler based on the fuzzy control system;Chao-Ying Liu等;《2010 International Conference on Computer and Communication Technologies in Agriculture Engineering》;全文 * |
| 加热管中的壁温和流动湿蒸汽温度场分析;李炎锋等;动力工程(第05期);全文 * |
| 四角切圆炉膛三维温度分布优化控制建模研究;王淅芬, 等;工程热物理学报(第04期);全文 * |
| 基于彩色CCD炉膛火焰温度场测量方法研究;张玉杰等;量子电子学报(第05期);全文 * |
| 超临界机组锅炉过热器和再热器炉内壁温在线监测;顾山;;热力发电(第04期);全文 * |
| 锅炉低温过热器爆管分析及预防措施;陈贤贤等;华电技术(第11期);全文 * |
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