CN112316703A - Intelligent monitoring method and system for urea solution spray gun for flue gas denitration - Google Patents
Intelligent monitoring method and system for urea solution spray gun for flue gas denitration Download PDFInfo
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- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 title claims abstract description 103
- 239000004202 carbamide Substances 0.000 title claims abstract description 103
- 239000007921 spray Substances 0.000 title claims abstract description 69
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 60
- 239000003546 flue gas Substances 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012544 monitoring process Methods 0.000 title claims abstract description 27
- 238000000889 atomisation Methods 0.000 claims abstract description 24
- 230000000694 effects Effects 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 12
- 238000004519 manufacturing process Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 10
- 238000002485 combustion reaction Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 6
- 238000002955 isolation Methods 0.000 claims description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 4
- 239000001301 oxygen Substances 0.000 claims description 4
- 229910052760 oxygen Inorganic materials 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 abstract description 20
- 229910021529 ammonia Inorganic materials 0.000 abstract description 10
- BIGPRXCJEDHCLP-UHFFFAOYSA-N ammonium bisulfate Chemical compound [NH4+].OS([O-])(=O)=O BIGPRXCJEDHCLP-UHFFFAOYSA-N 0.000 abstract description 6
- 238000005260 corrosion Methods 0.000 abstract description 5
- 230000007797 corrosion Effects 0.000 abstract description 5
- 238000012545 processing Methods 0.000 abstract description 3
- 238000005507 spraying Methods 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 8
- 238000000197 pyrolysis Methods 0.000 description 8
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- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000010531 catalytic reduction reaction Methods 0.000 description 2
- 238000002425 crystallisation Methods 0.000 description 2
- 230000008025 crystallization Effects 0.000 description 2
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- 238000005516 engineering process Methods 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000010924 continuous production Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
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- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention relates to an intelligent monitoring method and system of a urea solution spray gun for flue gas denitration, wherein the method comprises the following steps: detecting various data of a urea spray gun system, comprising: the flow of the main pipe, the flow of each branch pipe, the pressure of each branch pipe, the surface temperature of each branch pipe and the flue gas temperature of a flue; analyzing each item of data by using a machine algorithm; forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm; according to the invention, the atomization effect of the urea spray gun and the fault state of the urea spray gun are monitored in real time by monitoring, processing and early warning on line, so that accurate ammonia spraying is realized, the poor atomization and blockage of the urea solution spray gun can be treated in time, the ammonia escape is reduced, the safety problems of tail flue ammonium bisulfate corrosion and the like are relieved, and the aim of ensuring safe and economic operation of flue gas denitration is achieved.
Description
Technical Field
The invention relates to the technical field of flue gas denitration, in particular to an intelligent monitoring method and system for a urea solution spray gun for flue gas denitration.
Background
The urea solution spray gun is widely used for flue gas denitration technology. The selective catalytic reduction process using urea as a reducing agent is used in the high-temperature pyrolysis process of urea solution electric heating and other forms; in the non-selective catalytic reduction process, the high-temperature flue gas is directly and fully mixed with the direct flue of the spray gun. Chemical properties of urea: 1) is easily soluble in water; 2) the hygroscopicity is strong; 3) easy crystallization. The problems of poor atomization and blockage of a urea solution spray gun caused by easy crystallization of the urea solution or impurities exist, and the safety problems of ammonia escape, ammonia bisulfate corrosion of a flue and the like are easily caused.
Disclosure of Invention
The invention aims to provide an intelligent monitoring method and system for a urea solution spray gun for flue gas denitration, which solve the problems that in the prior art, the urea solution spray gun is poor in atomization and blocked due to the fact that the urea solution is easy to crystallize or impurities exist, ammonia escapes easily, and a flue is corroded by ammonium bisulfate.
The technical purpose of the invention is realized by the following technical scheme:
an intelligent monitoring method of a urea solution spray gun for flue gas denitration comprises the following steps:
detecting various data of a urea spray gun system, comprising: the flow of the main pipe, the flow of each branch pipe, the pressure of each branch pipe, the surface temperature of each branch pipe and the flue gas temperature of a flue;
analyzing each item of data by using a machine algorithm;
and forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm.
In one embodiment, the analyzing each item of data by using a machine algorithm specifically includes:
judging the flow speed and the circulation condition of the urea solution in the main pipeline according to the flow of the main pipeline;
judging the flow speed and the flow condition of the urea solution in each branch pipeline according to the flow of each branch pipe and the pressure of each branch pipe;
recording the absolute value of the deviation between the surface temperature of each branch pipe of the urea spray gun system and the ambient temperature, and comparing the real-time deviation between the surface temperature of each branch pipe and the absolute value with the variation trend of the deviation from the absolute value to judge the solution circulation condition in the spray gun;
and judging the temperature condition in the flue according to the flue gas temperature.
In one embodiment, the determining the flow speed and the flow condition of the urea solution in the main pipe according to the flow of the main pipe specifically includes:
if the flow of the main pipe is small, the flow speed in the main pipe is slow, the circulation condition is not good, and the main pipe is blocked;
if the flow of the main pipe is large, the flow velocity block in the main pipe is good in circulation condition, and the main pipe is not blocked.
In one embodiment, the determining the flow rate and the flow condition of the urea solution in each branch pipe according to the flow rate of each branch pipe and the pressure of each branch pipe specifically includes:
if the flow of each branch pipe is small and the pressure rises, the flow speed in each branch pipe is slow, the circulation condition is not good, and the main pipe is blocked;
if the flow of each branch pipe is large and the pressure is reduced, the flow velocity blocks in each branch pipe have good circulation condition, and the main pipe is not blocked.
In one embodiment, the determining the temperature in the flue according to the flue gas temperature specifically includes:
judging the local heat exchange condition and the medium disturbance condition of a measuring point in the flue according to the flue gas temperature; and meanwhile, judging the load condition, the combustion efficiency condition and the combustion working condition of the flue boiler.
In one embodiment, before analyzing the items of data using the machine algorithm, the method further includes:
sending each item of detection data to a field bus acquisition front end; the field acquisition front end is connected to the distributed control system through a DCS special communication module; the distributed control system is accessed to a factory-level production management information system or a special data server through a communication interface machine, an isolation gateway and a mirror image server, and various data are analyzed by the factory-level production management information system or the special data server through a machine algorithm.
In one embodiment, the analyzing each item of data by using a machine algorithm specifically includes:
by importing various detection data of the operation of the denitration system, the optimizing system carries out abnormity marking when discovering abnormal change of the denitration efficiency and relevant signal values, incidence relations and influence factors deviate from the distribution rule; classifying and identifying the characteristic data of the abnormal marks in the data of all the time periods of the data set;
identifying and judging whether the influence of the atomization effect on the denitration efficiency is accurate or not, and completing the operation by establishing a test set; the data of the test set is from production field operation logs or overhaul ledgers; the accuracy of the mark with poor atomization effect represents the accuracy of a learning algorithm and a training model of the machine;
fitting an atomization effect function of the urea spray gun: e ═ NOX，FE,TT,T1...TnIn which NO isXFor denitration efficiency, TT is flue gas temperature FE is main pipe flow, T1...TnIs the flow rate of each branch pipe.
In one embodiment, the forming a limited data set according to each item of data specifically includes:
the data set is the key for the learning algorithm to correctly fit and converge and successfully establish the model; the associated data items in the data set comprise various items of detection data; introducing boiler load, NOx concentration before and after an SCR device, flue gas oxygen content and hearth outlet flue gas temperature as measuring point signal data; by deriving these data from the historical database, a matrix of relational data is constructed to form a finite data set.
In one embodiment, the performing of the early warning according to the early warning algorithm specifically includes:
the early warning algorithm for the blockage of the urea spray gun comprises the following steps: eb＝{FE,T1...Tn}; FE is the flow of the main pipe, T1...TnFor flow of each branchAn amount;
constructing an expert knowledge base system, wherein the expert knowledge base system comprises the relation between the data such as denitration efficiency, urea atomization effect, urea flow, flue gas temperature, urea spray gun branch pipe surface temperature, environment temperature and the like and the blockage of a urea solution spray gun;
and the DCS configuration self-built self-defined function module is used for realizing the early warning function of the expert knowledge base.
The utility model provides a urea solution spray gun intelligent monitoring system for flue gas denitration, includes:
a detection module: detecting various data of a urea spray gun system;
an analysis module: analyzing each item of data by using a machine algorithm;
the early warning module: and forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm.
The invention has the beneficial effects that: according to the invention, the atomization effect of the urea spray gun and the fault state of the urea spray gun are monitored in real time by monitoring, processing and early warning on line, so that accurate ammonia spraying is realized, the poor atomization and blockage of the urea solution spray gun can be treated in time, the ammonia escape is reduced, the safety problems of tail flue ammonium bisulfate corrosion and the like are relieved, and the aim of ensuring safe and economic operation of flue gas denitration is achieved.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic diagram of the steps of a urea solution spray gun intelligent monitoring method and system for flue gas denitration;
FIG. 2 is a schematic diagram of a system structure of an intelligent monitoring method and system for a urea solution spray gun for flue gas denitration.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
Referring to fig. 1, the method for intelligently monitoring a urea solution spray gun for flue gas denitration according to the present invention is shown, and the method includes the following steps:
100. detecting various data of a urea spray gun system, comprising: the flow of the main pipe, the flow of each branch pipe, the pressure of each branch pipe, the surface temperature of each branch pipe and the flue gas temperature of a flue;
in the embodiment of the invention, all detection data are sent to a field bus acquisition front end; the field acquisition front end is connected to the distributed control system through a DCS special communication module; the distributed control system is accessed to a factory-level production management information system or a special data server through a communication interface machine, an isolation gateway and a mirror image server, and various data are analyzed by the factory-level production management information system or the special data server through a machine algorithm.
200. Analyzing each item of data by using a machine algorithm;
specifically, the flow speed and the circulation condition of the urea solution in the main pipeline are judged according to the flow of the main pipeline;
judging the flow speed and the flow condition of the urea solution in each branch pipeline according to the flow of each branch pipe and the pressure of each branch pipe;
recording the absolute value of the deviation between the surface temperature of each branch pipe of the urea spray gun system and the ambient temperature, and comparing the real-time deviation between the surface temperature of each branch pipe and the absolute value with the variation trend of the deviation from the absolute value to judge the solution circulation condition in the spray gun;
and judging the temperature condition in the flue according to the flue gas temperature.
In the embodiment of the invention, the flow speed and the circulation condition of the urea solution in the main pipeline are judged according to the flow of the main pipeline: if the flow of the main pipe is small, the flow speed in the main pipe is slow, the circulation condition is not good, and the main pipe is blocked; if the flow of the main pipe is large, the flow velocity block in the main pipe is good in circulation condition, and the main pipe is not blocked.
In the embodiment of the invention, the flow speed and the flow condition of the urea solution in each branch pipeline are judged according to the flow of each branch pipe and the pressure of each branch pipe: if the flow of each branch pipe is small and the pressure rises, the flow speed in each branch pipe is slow, the circulation condition is not good, and the main pipe is blocked; if the flow of each branch pipe is large and the pressure is reduced, the flow velocity blocks in each branch pipe have good circulation condition, and the main pipe is not blocked.
In the embodiment of the invention, the temperature condition in the flue is judged according to the flue gas temperature: judging the local heat exchange condition and the medium disturbance condition of a measuring point in the flue according to the flue gas temperature; and meanwhile, judging the load condition, the combustion efficiency condition and the combustion working condition of the flue boiler.
In the embodiment of the invention, by importing various detection data of the operation of the denitration system, the optimizing system detects abnormal change of the denitration efficiency, and carries out abnormal marking when relevant signal values, incidence relations and influence factors deviate from the distribution rule; classifying and identifying the characteristic data of the abnormal marks in the data of all the time periods of the data set;
identifying and judging whether the influence of the atomization effect on the denitration efficiency is accurate or not, and completing the operation by establishing a test set; the data of the test set is from production field operation logs or overhaul ledgers; the accuracy of the mark with poor atomization effect represents the accuracy of a learning algorithm and a training model of the machine;
fitting an atomization effect function of the urea spray gun: e ═ NOX，FE,TT,T1...TnIn which NO isXFor denitration efficiency, TT is flue gas temperature FE is main pipe flow, T1...TnIs the flow rate of each branch pipe.
300. And forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm.
In the embodiment of the invention, the data set is the key for judging whether the learning algorithm can be correctly fitted and converged and successfully establishing the model; the associated data items in the data set comprise various items of detection data; introducing boiler load, NOx concentration before and after an SCR device, flue gas oxygen content and hearth outlet flue gas temperature as measuring point signal data; by deriving these data from the historical database, a matrix of relational data is constructed to form a finite data set.
In the embodiment of the present invention, the performing of the early warning according to the early warning algorithm specifically includes:
urea sprayGun blockage early warning algorithm: eb＝{FE,T1...Tn}; FE is the flow of the main pipe, T1...TnIs the flow rate of each branch pipe.
Constructing an expert knowledge base system, wherein the expert knowledge base system comprises the relation between the data such as denitration efficiency, urea atomization effect, urea flow, flue gas temperature, urea spray gun branch pipe surface temperature, environment temperature and the like and the blockage of a urea solution spray gun;
and the DCS configuration self-built self-defined function module is used for realizing the early warning function of the expert knowledge base.
In order to achieve the above object, the present invention further provides an intelligent monitoring system for a urea solution spray gun for flue gas denitration, comprising:
a detection module: detecting various data of a urea spray gun system; the method comprises the following steps: the flow of the main pipe, the flow of each branch pipe, the pressure of each branch pipe, the surface temperature of each branch pipe and the flue gas temperature of a flue;
in the embodiment of the invention, all detection data are sent to a field bus acquisition front end; the field acquisition front end is connected to the distributed control system through a DCS special communication module; the distributed control system is accessed to a factory-level production management information system or a special data server through a communication interface machine, an isolation gateway and a mirror image server, and various data are analyzed by the factory-level production management information system or the special data server through a machine algorithm.
An analysis module: analyzing each item of data by using a machine algorithm;
specifically, the flow speed and the circulation condition of the urea solution in the main pipeline are judged according to the flow of the main pipeline;
judging the flow speed and the flow condition of the urea solution in each branch pipeline according to the flow of each branch pipe and the pressure of each branch pipe;
recording the absolute value of the deviation between the surface temperature of each branch pipe of the urea spray gun system and the ambient temperature, and comparing the real-time deviation between the surface temperature of each branch pipe and the absolute value with the variation trend of the deviation from the absolute value to judge the solution circulation condition in the spray gun;
and judging the temperature condition in the flue according to the flue gas temperature.
In the embodiment of the invention, the flow speed and the circulation condition of the urea solution in the main pipeline are judged according to the flow of the main pipeline: if the flow of the main pipe is small, the flow speed in the main pipe is slow, the circulation condition is not good, and the main pipe is blocked; if the flow of the main pipe is large, the flow velocity block in the main pipe is good in circulation condition, and the main pipe is not blocked.
In the embodiment of the invention, the flow speed and the flow condition of the urea solution in each branch pipeline are judged according to the flow of each branch pipe and the pressure of each branch pipe: if the flow of each branch pipe is small and the pressure rises, the flow speed in each branch pipe is slow, the circulation condition is not good, and the main pipe is blocked; if the flow of each branch pipe is large and the pressure is reduced, the flow velocity blocks in each branch pipe have good circulation condition, and the main pipe is not blocked.
In the embodiment of the invention, the temperature condition in the flue is judged according to the flue gas temperature: judging the local heat exchange condition and the medium disturbance condition of a measuring point in the flue according to the flue gas temperature; and meanwhile, judging the load condition, the combustion efficiency condition and the combustion working condition of the flue boiler.
The early warning module: and forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm.
In the embodiment of the invention, the data set is the key for judging whether the learning algorithm can be correctly fitted and converged and successfully establishing the model; the associated data items in the data set comprise various items of detection data; introducing boiler load, NOx concentration before and after an SCR device, flue gas oxygen content and hearth outlet flue gas temperature as measuring point signal data; by deriving these data from the historical database, a matrix of relational data is constructed to form a finite data set.
In the embodiment of the present invention, the performing of the early warning according to the early warning algorithm specifically includes:
constructing an expert knowledge base system, wherein the expert knowledge base system comprises the relation between the data such as denitration efficiency, urea atomization effect, urea flow, flue gas temperature, urea spray gun branch pipe surface temperature, environment temperature and the like and the blockage of a urea solution spray gun;
and the DCS configuration self-built self-defined function module is used for realizing the early warning function of the expert knowledge base.
In the embodiment of the invention, referring to fig. 2, FE is a urea solution mother pipe, I-f1.. Fn is a urea solution branch pipe, and I-p1.. n are thermometers of 1 st, 2 nd, 3.. n urea solution branch pipes, corresponding to n urea solution spray guns; e-1.. n is a urea solution spray gun, and YD is a flue or a pyrolysis tank. The urea solution is conveyed to a metering and distributing platform through a main pipe, distributed to each branch, enters a urea solution spray gun, is atomized by the spray gun and then is sprayed into a flue or a pyrolysis tank. FE. I-F, X-F are respectively: a urea solution main pipe flowmeter, a urea solution main pipe flowmeter and a urea solution main pipe flow remote transmission signal; i-t1.. n, X-t1.. n are: 1, 2, 3.. n surface temperature meter and temperature remote signal of urea solution branch pipe spray gun; I-T, X-T are respectively: and (4) a thermometer and a temperature remote signal of a flue (pyrolysis tank).
In the embodiment of the invention, all field detection signals (X-F, X-T1.. n, X-T) are firstly accessed to a field bus (Modbus protocol) acquisition front end; the field acquisition front end is connected to a DCS (distributed control system) through a DCS special communication module; the DCS is accessed to an SIS (plant-level production management information system) or a special data server through a communication interface machine, an isolation network gate, a mirror image server and the like; the data source used for big data optimization and machine learning is the SIS or a non-real-time database of a special data server.
The urea solution atomization pyrolysis process in the denitration process is a real-time continuous process, and during the process, a measuring point arranged on a monitoring system generates a large amount of real-time data and stores the real-time data in a historical database. In the continuous operation process of a denitration system (including urea solution atomization and pyrolysis), fault conditions such as spray gun blockage or poor atomization are inevitable, monitoring data of the fault conditions and fault time periods are recorded and stored, and the rule of finding related data and finding data change from the massive data is the process of data optimization and machine learning.
The purpose of machine learning is to mine the historical data rule, perfect the artificial intelligence model and deploy real-time application. At the present stage, an AI model or an AI algorithm Python library can be used for realizing the training of the model and the inspection of the model effect until the deployment of the application model, and finally the real-time database is accessed for realizing the real-time diagnosis on-line operation.
According to the invention, the urea solution is atomized at high strength and then sprayed into a reaction flue or a pyrolysis tank, so that the pyrolysis efficiency and effect of the urea solution are improved; through on-line monitoring, processing data and carrying out the early warning, real time monitoring urea spray gun atomization effect and urea spray gun fault state realize accurate ammonia that spouts, can in time handle urea solution spray gun atomization badness and jam, reduce ammonia escape and alleviate safety problems such as afterbody flue ammonium bisulfate corrosion, reach the purpose of ensureing the safe economic operation of flue gas denitration.
The invention can comprehensively, dynamically and accurately reflect the working state of the urea solution spray gun, and the E and Eb functions can accurately reflect state information and fault information through multi-parameter detection and data analysis. The application of advanced sensor and industrial bus technology realizes the field acquisition of a large amount of data and effectively reduces the system cost. Accurate ammonia spraying, real-time state and fault early warning really realize the environmental protection and economic operation of the flue gas denitration system. Accurate control and active prevention effectively reduce ammonia escape, and relieve the safety negative effects of the ABS phenomenon (adhesion, blockage, corrosion and the like caused by ammonium bisulfate) of the tail flue. The industrial big data, the machine learning and other artificial intelligence are applied to obviously improve the modernization level of denitration control.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. The intelligent monitoring method of the urea solution spray gun for flue gas denitration is characterized by comprising the following steps of: the method comprises the following steps:
detecting various data of a urea spray gun system, comprising: the flow of the main pipe, the flow of each branch pipe, the pressure of each branch pipe, the surface temperature of each branch pipe and the flue gas temperature of a flue;
analyzing each item of data by using a machine algorithm;
and forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm.
2. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 1, characterized in that: the analyzing of each item of data by using a machine algorithm specifically comprises:
judging the flow speed and the circulation condition of the urea solution in the main pipeline according to the flow of the main pipeline;
judging the flow speed and the flow condition of the urea solution in each branch pipeline according to the flow of each branch pipe and the pressure of each branch pipe;
recording the absolute value of the deviation between the surface temperature of each branch pipe of the urea spray gun system and the ambient temperature, and comparing the real-time deviation between the surface temperature of each branch pipe and the absolute value with the variation trend of the deviation from the absolute value to judge the solution circulation condition in the spray gun;
and judging the temperature condition in the flue according to the flue gas temperature.
3. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 2, characterized in that: the specific flow speed and circulation condition of the urea solution in the main pipeline according to the flow of the main pipeline are as follows:
if the flow of the main pipe is small, the flow speed in the main pipe is slow, the circulation condition is not good, and the main pipe is blocked;
if the flow of the main pipe is large, the flow velocity block in the main pipe is good in circulation condition, and the main pipe is not blocked.
4. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 2, characterized in that: the specific flow speed and flow condition of the urea solution in each branch pipeline is judged according to the flow of each branch pipeline and the pressure of each branch pipeline as follows:
if the flow of each branch pipe is small and the pressure rises, the flow speed in each branch pipe is slow, the circulation condition is not good, and the main pipe is blocked;
if the flow of each branch pipe is large and the pressure is reduced, the flow velocity blocks in each branch pipe have good circulation condition, and the main pipe is not blocked.
5. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 2, characterized in that: the judgment of the temperature condition in the flue according to the flue gas temperature specifically comprises the following steps:
judging the local heat exchange condition and the medium disturbance condition of a measuring point in the flue according to the flue gas temperature; and meanwhile, judging the load condition, the combustion efficiency condition and the combustion working condition of the flue boiler.
6. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 1, characterized in that: before the analyzing each item of data by using the machine algorithm, the method further comprises the following steps:
sending each item of detection data to a field bus acquisition front end; the field acquisition front end is connected to the distributed control system through a DCS special communication module; the distributed control system is accessed to a factory-level production management information system or a special data server through a communication interface machine, an isolation gateway and a mirror image server, and various data are analyzed by the factory-level production management information system or the special data server through a machine algorithm.
7. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 1, characterized in that: the analyzing of each item of data by using a machine algorithm specifically comprises:
by importing various detection data of the operation of the denitration system, the optimizing system carries out abnormity marking when discovering abnormal change of the denitration efficiency and relevant signal values, incidence relations and influence factors deviate from the distribution rule; classifying and identifying the characteristic data of the abnormal marks in the data of all the time periods of the data set;
identifying and judging whether the influence of the atomization effect on the denitration efficiency is accurate or not, and completing the operation by establishing a test set; the data of the test set is from production field operation logs or overhaul ledgers; the accuracy of the mark with poor atomization effect represents the accuracy of a learning algorithm and a training model of the machine;
fitting an atomization effect function of the urea spray gun: e＝{NOX，FE,TT,T1...TnIn which NO isXFor denitration efficiency, TT is flue gas temperature FE is main pipe flow, T1...TnIs the flow rate of each branch pipe.
8. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 1, characterized in that: the forming of the limited data set according to each item of data specifically includes:
the data set is the key for the learning algorithm to correctly fit and converge and successfully establish the model; the associated data items in the data set comprise various items of detection data; introducing boiler load, NOx concentration before and after an SCR device, flue gas oxygen content and hearth outlet flue gas temperature as measuring point signal data; by deriving these data from the historical database, a matrix of relational data is constructed to form a finite data set.
9. The intelligent monitoring method for the urea solution spray gun for flue gas denitration according to claim 1, characterized in that: the early warning according to the early warning algorithm specifically comprises the following steps:
the early warning algorithm for the blockage of the urea spray gun comprises the following steps: eb＝{FE,T1...Tn}; wherein FE is the flow of the main pipe, T1...TnIs the flow rate of each branch pipe.
Constructing an expert knowledge base system, wherein the expert knowledge base system comprises the relation between the data such as denitration efficiency, urea atomization effect, urea flow, flue gas temperature, urea spray gun branch pipe surface temperature, environment temperature and the like and the blockage of a urea solution spray gun;
and the DCS configuration self-built self-defined function module is used for realizing the early warning function of the expert knowledge base.
10. The utility model provides a urea solution spray gun intelligent monitoring system for flue gas denitration which characterized in that: the method comprises the following steps:
a detection module: detecting various data of a urea spray gun system;
an analysis module: analyzing each item of data by using a machine algorithm;
the early warning module: and forming a limited data set according to each item of data, and carrying out early warning according to an early warning algorithm.
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