CN112185475A - Aiming at SCR denitration system distortion NOxHigh-precision intelligent concentration prediction method - Google Patents

Aiming at SCR denitration system distortion NOxHigh-precision intelligent concentration prediction method Download PDF

Info

Publication number
CN112185475A
CN112185475A CN202011039236.8A CN202011039236A CN112185475A CN 112185475 A CN112185475 A CN 112185475A CN 202011039236 A CN202011039236 A CN 202011039236A CN 112185475 A CN112185475 A CN 112185475A
Authority
CN
China
Prior art keywords
concentration
state
intelligent
inlet
outlet
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011039236.8A
Other languages
Chinese (zh)
Other versions
CN112185475B (en
Inventor
刘国富
李鹤遥
吴俊辉
李佳伟
宋垣波
王庆懿
姜泽旭
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qilu University of Technology
Original Assignee
Qilu University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qilu University of Technology filed Critical Qilu University of Technology
Priority to CN202011039236.8A priority Critical patent/CN112185475B/en
Publication of CN112185475A publication Critical patent/CN112185475A/en
Application granted granted Critical
Publication of CN112185475B publication Critical patent/CN112185475B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics

Landscapes

  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Theoretical Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Analytical Chemistry (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Treating Waste Gases (AREA)

Abstract

The invention relates to the technical field of thermal power engineering, and particularly discloses a method for generating distorted NO of an SCR (selective catalytic reduction) denitration systemxThe high-precision intelligent concentration predicting method comprises the following steps: establishing and SCR system inlet and outlet NOxNO matched with working state of concentration analyzerxA concentration measurement data switching control framework; training to obtain distorted inlet NO under the input of parametersxIntelligent concentration prediction model and distorted state outlet NOxA concentration intelligent prediction model; by individually targeted adjustment of inlet or outlet NOxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are used for realizing NO in a distorted state and a non-distorted statexThe concentration is switched to finally obtain NO with high precision in the whole timexConcentration measurement data. The invention can realize the NO in the distorted state aiming at the SCR denitration systemxThe high-precision intelligent prediction of the concentration,and an accurate data base is provided for the operation optimization adjustment of a subsequent system.

Description

Aiming at SCR denitration system distortion NOxHigh-precision intelligent concentration prediction method
Technical Field
The invention relates to the technical field of thermal energy power engineering, in particular to a method for generating distorted NO of an SCR (selective catalytic reduction) denitration systemxA high-precision intelligent concentration prediction method.
Background
Selective Catalytic Reduction (SCR) denitration system is flue gas Nitric Oxide (NO) mainly applied to current coal-fired equipmentx) The removal system obtains the accurate running state of the unit in real time, and has great significance for guiding the running optimization of the unit. The operating state of the SCR denitration system can be comprehensively described by a plurality of operating parameters, and the inlet NO isxConcentration and outlet NOxThe two concentration measurement parameters are two very key operation parameters of the SCR denitration system; wherein: inlet NOxConcentration characterization of NO entering SCR denitration systemxThe amount of the ammonia can be used for guiding the operator to inject the amount of the ammonia to participate in the denitration oxidation-reduction reaction without causing insufficient ammonia or excessive ammonia; outlet NOxConcentration characterization of NO flowing out of SCR denitration systemxThe amount of the NO can be reflected whether the NO is satisfied by the nationxThe emission control requirement is met, and meanwhile, the method has stronger guiding significance on ammonia injection characteristic evaluation and the like.
However, at present, the inlet or outlet NO of the SCR denitration system of coal burning equipmentxConcentration measurement mode is NO of extraction typexThe concentration measurement method is influenced by the high-dust arrangement mode of the SCR denitration system, the extracted flue gas contains a large amount of fly ash, and continuous long-time online analysis is easy to carry out on NOxThe operation of the concentration analyzer is adversely affected and may even cause clogging of the analyzer, damage to the core analytical unit (optical engine, etc.), and the like. Therefore, in industrial application environment, coal burning equipment SCR denitration system inlet or outlet NOxThe concentration analyzer adopts a working mode of regular maintenance, namely openStopping the sampling of the flue gas at regular intervals, and then using compressed air and other back-blowing sampling pipelines, thereby achieving the purpose of avoiding damaging the core measuring equipment, wherein the NO isxThe duration of single maintenance of the concentration analyzer is as long as about 5-10 minutes. From the above, it is easy to find that NOxThe regular maintenance mode of operation of the concentration analyzer necessarily results in NO during maintenance operationsxInaccuracy, unrepresentative of concentration measurements, and many occurrences of NO after each maintenance runxAbnormal jump in the actual measured value of the concentration, NO in such a distorted statexThe concentration measurement value is very unfavorable for guiding the normal operation of the unit.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides the SCR denitration system distortion state NO with full time, high precision and accurate measurement resultxA high-precision intelligent concentration prediction method.
The invention is realized by the following technical scheme:
aims at SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps of: (1) establishing and SCR system inlet and outlet NOxNO matched with working state of concentration analyzerxA concentration measurement data switching control framework; (2) under the input of the combustion parameters in the furnace, the unit load and the coal mill operation parameters, the distorted state inlet NO is obtained by trainingxA concentration intelligent prediction model; (3) at the inlet of the ammonia injection amount NOxTraining to obtain distorted state outlet NO under the input of concentration and combustion parameters in the furnacexA concentration intelligent prediction model; (4) based on the above 3 steps, the inlet or outlet NO is adjusted individually and specificallyxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are used for realizing NO in a distorted state and a non-distorted statexThe concentration is switched to finally obtain the NO with full time and high precisionxConcentration measurement data.
Firstly, establishing NO at inlet and outlet of SCR systemxFull-time high-precision NO matched with working state of concentration analyzerxConcentration measurement data switching control framework(ii) a Then under the input of the combustion parameters in the furnace, the unit load and the coal mill operation parameters, training is carried out to obtain the NO of the distorted inletxA concentration intelligent prediction model; further, NO is injected into the ammonia injection amount and the inletxTraining to obtain distorted state outlet NO under the input of concentration and combustion parameters in the furnacexA concentration intelligent prediction model; finally by individually targeted adjustment of inlet or outlet NOxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are used for realizing NO in a distorted state and a non-distorted statexThe concentration is switched to finally obtain NO with high precision in the whole timexConcentration measurement data.
The method can realize the NO in the distorted state aiming at the SCR denitration systemxAnd the high-precision intelligent prediction of the concentration provides an accurate data basis for the operation optimization and adjustment of a subsequent system.
The more preferable technical scheme of the invention is as follows:
in step (1), inlet and outlet NOxThe working states of the concentration analyzer are respectively 0 and 1, wherein the state 0 represents NOxNO output from normal operation of concentration analyzerxThe concentration measurement is correct and state 1 indicates NOxThe concentration analyzer is in maintenance operation state, and NO is outputxThe concentration measurement value is in a distorted state, and the actual measurement result is inaccurate.
NOxConcentration measurement data switching control framework pair NOxOperating period T with concentration analysis state of 01Taking NOxNO normally output from concentration analyzerxA concentration measurement; and for NOxOperating time period T with concentration analyzer state of 12Taking in NO through inlet or outletxNO output by intelligent concentration prediction modelxAnd (5) predicting the concentration.
In step (2), the distorted state is entered as NOxIn the concentration intelligent prediction model, the combustion parameters in the furnace comprise total components, total coal quantity, upper layer over-fire air door opening and upper layer secondary small air door opening, the unit load is the real power of the unit, and the coal mill operation parameters comprise the start-stop working state and the instantaneous coal feeding quantity of each coal mill.
Step (ii) of(3) Medium, distorted state outlet NOxIn the intelligent concentration prediction model, the ammonia injection amount is the instantaneous ammonia injection flow of a reactor on the corresponding side of the SCR denitration system, and NO is introduced into an inletxConcentration of corresponding side inlet NOxNO output from concentration analyzerxThe actual measurement value of the concentration, and the combustion parameters in the furnace comprise total component, total amount, opening degree of an upper layer burn-out air door, opening degree of an upper layer secondary small air door and flue gas flow.
The distorted state inlet NOxThe intelligent concentration prediction model is characterized in that the model inputs are furnace combustion parameters, unit load and coal mill operation parameters; model output as Inlet NOxPredicting the concentration value; NO is carried out by jointly applying fuzzy tree model and support vector machine modelxAnd (4) predicting the concentration.
The distorted state outlet NOxThe intelligent concentration prediction model is characterized in that the model inputs comprise ammonia injection amount and inlet NOxConcentration, furnace combustion parameters; model output as Outlet NOxPredicting the concentration value; NO is carried out by jointly applying fuzzy tree model and support vector machine modelxAnd (4) predicting the concentration.
Preferably, NOxThe concentration prediction is mainly based on the prediction output of a fuzzy tree model, and the prediction output of a support vector machine model is paid, and the specific combined application rule is as follows:
Figure DEST_PATH_IMAGE002
，
Figure DEST_PATH_IMAGE004
；
calculating the variation trend of the prediction result of the fuzzy tree model at the adjacent time
Figure DEST_PATH_IMAGE006
And the variation trend of the prediction result of the fuzzy tree model at the adjacent time
Figure DEST_PATH_IMAGE008
；
Figure DEST_PATH_IMAGE010
，
Wherein:
Figure 300778DEST_PATH_IMAGE002
is composed oftInlet or outlet NO at moment +1xThe output value of the intelligent concentration prediction model is predicted;
Figure 380729DEST_PATH_IMAGE004
is composed oftInlet or outlet NO based on fuzzy tree model at +1 momentxPredicting the concentration value;
Figure 672033DEST_PATH_IMAGE006
is composed oftInlet or outlet NO based on support vector machine model at time +1xAnd (5) predicting the concentration.
In the step (4), the NOxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are embodied as preset delay time tau, and the setting rule is as follows:
Figure DEST_PATH_IMAGE018
；
wherein tau is the delay time of the delay module to be set,
Figure DEST_PATH_IMAGE020
is corresponding to inlet or outlet NOxThe total duration of the single maintenance operation state of the concentration analyzer, wherein kappa is an empirically set coefficient and takes any value between 1/8 and 1/4 according to the operation condition.
Said full time, high precision NOxIn the concentration measurement data, the total time is determined by NOxNormal running time period of concentration analyzer, NOxThe maintenance operation time of the concentration analyzer is short; wherein NO is due to the setting of the delay time τxIn a short time after the maintenance of the concentration analyzer is finished, NO is still takenxNO output by intelligent concentration prediction modelxAnd (5) predicting the concentration.
Compared with the prior art, the invention has the advantages that: provides a method for SCR denitration system distortion NOxThe high-precision intelligent concentration predicting method establishes NO at the inlet and outlet of SCR systemxFull-time high-precision NO matched with working state of concentration analyzerxThe concentration measurement data switching control frame establishes a distorted inlet or outlet NO by combining the fuzzy tree and the support vector machine modelxThe concentration intelligent prediction model realizes the NO in a distorted state and a non-distorted state by utilizing a delay modulexThe concentration is switched to obtain NO with high precision in the whole timexConcentration measurement data. The application of the invention can compensate NOxThe concentration analyzer has the defect of distorted concentration measurement values during maintenance and operation, realizes accurate description of the key operation state of SCR denitration in the whole period, and can provide an accurate data base for operation optimization and adjustment of a subsequent system.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic process flow diagram of the present invention;
FIG. 2 shows inlet NO in an embodiment of the present inventionxIntelligent concentration prediction characteristics;
FIG. 3 shows the outlet NO in the example of the present inventionxThe concentration intelligent prediction characteristic.
Detailed Description
The present invention is described in detail below with reference to the accompanying drawings, which are used for implementing the present invention on the premise of the technical solution, and it should be understood that the implementation case is for illustrating the present invention, but the protection scope of the present invention is not limited to the implementation case.
The implementation case is based on the specific implementation of a SCR system of a front-wall and rear-wall opposed firing boiler of a certain 600MW coal-fired thermal power generating unit, and the provided SCR denitration system distortion state NO is developedxAnd (3) carrying out engineering application research on a high-precision intelligent concentration prediction method. The specific implementation process is as follows:
step 1: establishing and SCR system inlet and outlet NOxFull time interval matched with working state of concentration analyzerHigh precision NOxA concentration measurement data switching control framework;
inlet and outlet NO of the unitxThe concentration analyzer adopts the working modes of extraction type measurement and regular maintenance back flushing; inlet and outlet NOxThe concentration analyzer does not perform 1-time maintenance back flushing every 2 hours, and the total time of single maintenance is 255 seconds; inlet NOxMaintenance time and outlet NO of concentration analyzerxThe maintenance time of the concentration analyzer is not overlapped and is carried out independently. Inlet and outlet NO of the unitxThe working state of the concentration analyzer is 0 and 1, and the state 0 represents NOxThe concentration analyzer is working normally, and state 1 shows NOxThe concentration analyzer is in a maintenance operation state.
For this setting, for NOxOperating time period T with state of concentration analyzer being 01Taking NOxNO normally output from concentration analyzerxA concentration measurement; and for NOxOperating time period T with state of concentration analyzer being 02Taking in NO through inlet or outletxNO output by intelligent concentration prediction modelxAnd (5) predicting the concentration.
Step 2: under the input of the combustion parameters in the furnace, the unit load and the coal mill operation parameters, the distorted state inlet NO is obtained by trainingxA concentration intelligent prediction model;
the used furnace combustion parameters, unit load and coal mill operation parameters are shown in (part of) tables 1 and 2, and continuous operation data are used to respectively train and obtain inlet NO based on fuzzy treexConcentration prediction model, support vector machine-based inlet NOxA concentration prediction model.
Figure DEST_PATH_IMAGE022
Figure DEST_PATH_IMAGE024
Based on the prediction output of fuzzy tree model, support vector machineEstablishing inlet NO by jointly applying fuzzy tree model and support vector machine model based on principle that model prediction output is auxiliaryxAnd (3) a concentration intelligent prediction model.
FIG. 2 shows the inlet NO of the present method in an embodiment of the present inventionxConcentration intelligent prediction characteristic, and NO at inlet can be seenxEnabling inlet NO in distorted state during maintenance of concentration analyzerxMore accurate prediction of concentration, and in NOxNO does not appear after the maintenance of the concentration analyzer is finishedxA significant jump in concentration.
And step 3: at the inlet of the ammonia injection amount NOxTraining to obtain distorted state outlet NO under the input of concentration and combustion parameters in the furnacexA concentration intelligent prediction model;
amount of injected ammonia used, inlet NOxThe concentrations and the combustion parameters in the furnace are shown in tables 1, 2 and 3 (part), and outlet NO based on the fuzzy tree is obtained by using continuous operation data through training respectivelyxConcentration prediction model and support vector machine-based outlet NOxA concentration prediction model.
Figure DEST_PATH_IMAGE026
Establishing the export NO by jointly applying the fuzzy tree model and the support vector machine model according to the principle that the prediction output of the fuzzy tree model is taken as the main part and the prediction output of the support vector machine model is taken as the auxiliary partxAnd (3) a concentration intelligent prediction model.
FIG. 3 shows the export NO of the method according to an embodiment of the present inventionxThe intelligent concentration prediction characteristic can show that NO is at the outletxEnabling distorted state NO to be exported during maintenance of the concentration analyzerxMore accurate prediction of concentration, and in NOxNO does not appear after the maintenance of the concentration analyzer is finishedxA significant jump in concentration.
And 4, step 4: based on the aforementioned 3 steps, by individually and specifically adjusting the inlet or outlet NOxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are used for realizing NO in a distorted state and a non-distorted statexThe switching of the concentration is carried out,finally obtain NO with high precision in the whole timexConcentration measurement data.
In this embodiment, the inlet and outlet NO's are empirically setxThe characteristic parameter of the delay module downstream of the concentration intelligent prediction model is tau =50 seconds, and the empirical setting coefficient is about 0.196 at this time.
The embodiment can show that the invention discloses a method for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method can realize the purpose of predicting the NO in the SCR denitration system in a distorted statexThe high-precision intelligent prediction of the concentration can provide an accurate data base for the operation optimization and adjustment of a subsequent system.
As described above, although the embodiments of the present invention have been described in connection with the embodiments and the accompanying drawings, they should not be construed as limiting the present invention itself. On the basis of the technical scheme of the invention, various modifications or changes which can be made by any unit or person without creative labor are still within the protection scope of the invention.

Claims (9)

1. Aims at SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps of: (1) establishing and SCR system inlet and outlet NOxNO matched with working state of concentration analyzerxA concentration measurement data switching control framework; (2) under the input of the combustion parameters in the furnace, the unit load and the coal mill operation parameters, the distorted state inlet NO is obtained by trainingxA concentration intelligent prediction model; (3) at the inlet of the ammonia injection amount NOxTraining to obtain distorted state outlet NO under the input of concentration and combustion parameters in the furnacexA concentration intelligent prediction model; (4) based on the above 3 steps, the inlet or outlet NO is adjusted individually and specificallyxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are used for realizing NO in a distorted state and a non-distorted statexThe concentration is switched to finally obtain the NO with full time and high precisionxConcentration measurement data.
2. The method as claimed in claim 1 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: in step (1), inlet and outlet NOxThe working states of the concentration analyzer are respectively 0 and 1, wherein the state 0 represents NOxNO output from normal operation of concentration analyzerxThe concentration measurement is correct and state 1 indicates NOxThe concentration analyzer is in maintenance operation state, and NO is outputxThe concentration measurement value is in a distorted state, and the actual measurement result is inaccurate.
3. The method as claimed in claim 2 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: in step (1), NOxConcentration measurement data switching control framework pair NOxOperating period T with concentration analysis state of 01Taking NOxNO normally output from concentration analyzerxA concentration measurement; and for NOxOperating time period T with concentration analyzer state of 12Taking in NO through inlet or outletxNO output by intelligent concentration prediction modelxAnd (5) predicting the concentration.
4. The method as claimed in claim 1 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: in step (2), the distorted state is entered as NOxIn the concentration intelligent prediction model, the combustion parameters in the furnace comprise total components, total coal quantity, upper layer over-fire air door opening and upper layer secondary small air door opening, the unit load is the real power of the unit, and the coal mill operation parameters comprise the start-stop working state and the instantaneous coal feeding quantity of each coal mill.
5. The method as claimed in claim 1 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: in step (3), the outlet NO is distortedxIn the intelligent concentration prediction model, the ammonia injection amount is the instantaneous ammonia injection flow of a reactor on the corresponding side of the SCR denitration system, and NO is introduced into an inletxConcentration ofIs a corresponding side inlet NOxNO output from concentration analyzerxThe actual measurement value of the concentration, and the combustion parameters in the furnace comprise total component, total amount, opening degree of an upper layer burn-out air door, opening degree of an upper layer secondary small air door and flue gas flow.
6. The method as claimed in claim 1 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: in the steps (2) and (3), the inlet NO of the distorted statexIntelligent concentration prediction model and distorted state outlet NOxThe intelligent concentration prediction model jointly applies a fuzzy tree model and a support vector machine model to carry out NOxAnd (4) predicting the concentration.
7. The method as claimed in claim 6 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: NOxThe concentration prediction is mainly based on the prediction output of a fuzzy tree model, and the prediction output of a support vector machine model is paid, and the specific combined application rule is as follows:
Figure DEST_PATH_IMAGE001
，
Figure 687771DEST_PATH_IMAGE002
(ii) a Calculating the variation trend of the prediction result of the fuzzy tree model at the adjacent time
Figure DEST_PATH_IMAGE003
And the variation trend of the prediction result of the fuzzy tree model at the adjacent time
Figure 159904DEST_PATH_IMAGE004
；
Figure DEST_PATH_IMAGE005
Wherein:
Figure 1958DEST_PATH_IMAGE006
is composed oftEntry at time +1Or outlet NOxThe output value of the intelligent concentration prediction model is predicted;
Figure DEST_PATH_IMAGE007
is composed oftInlet or outlet NO based on fuzzy tree model at +1 momentxPredicting the concentration value;
Figure 397167DEST_PATH_IMAGE008
is composed oftInlet or outlet NO based on support vector machine model at time +1xAnd (5) predicting the concentration.
8. The method as claimed in claim 1 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: in the step (4), the NOxThe characteristic parameters of a delay module system at the downstream of the concentration intelligent prediction model are embodied as preset delay time tau, and the setting rule is as follows:
Figure DEST_PATH_IMAGE009
(ii) a Wherein tau is the delay time of the delay module to be set,
Figure 273857DEST_PATH_IMAGE010
is corresponding to inlet or outlet NOxThe total duration of the single maintenance operation state of the concentration analyzer, wherein kappa is an empirically set coefficient and takes any value between 1/8 and 1/4 according to the operation condition.
9. The method as claimed in claim 8 for SCR denitration system distortion state NOxThe high-precision intelligent concentration prediction method is characterized by comprising the following steps: said full time, high precision NOxIn the concentration measurement data, the total time is determined by NOxNormal running time period of concentration analyzer, NOxThe maintenance operation time of the concentration analyzer is short; wherein NO is due to the setting of the delay time τxIn a short time after the maintenance of the concentration analyzer is finished, NO is still takenxNO output by intelligent concentration prediction modelxAnd (5) predicting the concentration.
CN202011039236.8A 2020-09-28 2020-09-28 Distortion state NO for SCR denitration system x High-precision intelligent prediction method for concentration Active CN112185475B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011039236.8A CN112185475B (en) 2020-09-28 2020-09-28 Distortion state NO for SCR denitration system x High-precision intelligent prediction method for concentration

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011039236.8A CN112185475B (en) 2020-09-28 2020-09-28 Distortion state NO for SCR denitration system x High-precision intelligent prediction method for concentration

Publications (2)

Publication Number Publication Date
CN112185475A true CN112185475A (en) 2021-01-05
CN112185475B CN112185475B (en) 2023-07-14

Family

ID=73944473

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011039236.8A Active CN112185475B (en) 2020-09-28 2020-09-28 Distortion state NO for SCR denitration system x High-precision intelligent prediction method for concentration

Country Status (1)

Country Link
CN (1) CN112185475B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113217922A (en) * 2021-02-25 2021-08-06 华南理工大学 Method and system for predicting source output of NOx generated in waste incineration

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108803309A (en) * 2018-07-02 2018-11-13 大唐环境产业集团股份有限公司 It is a kind of that ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and model adaptation
CN108837699A (en) * 2018-07-02 2018-11-20 大唐环境产业集团股份有限公司 It is a kind of that ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and PREDICTIVE CONTROL
CN110865623A (en) * 2019-12-13 2020-03-06 西安西热控制技术有限公司 NO in SCR denitration controlxMeasurement signal substitution system and control method thereof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108803309A (en) * 2018-07-02 2018-11-13 大唐环境产业集团股份有限公司 It is a kind of that ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and model adaptation
CN108837699A (en) * 2018-07-02 2018-11-20 大唐环境产业集团股份有限公司 It is a kind of that ammonia optimization method and system are intelligently sprayed based on the SCR denitration of hard measurement and PREDICTIVE CONTROL
CN110865623A (en) * 2019-12-13 2020-03-06 西安西热控制技术有限公司 NO in SCR denitration controlxMeasurement signal substitution system and control method thereof

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
包文运等: "SCR系统喷氨自动控制逻辑应用诊断与优化研讨", 《化学工程与装备》, pages 270 - 271 *
高文松等: "600MW机组SCR脱硝系统运行特性", 《广东电力》, pages 28 - 32 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113217922A (en) * 2021-02-25 2021-08-06 华南理工大学 Method and system for predicting source output of NOx generated in waste incineration

Also Published As

Publication number Publication date
CN112185475B (en) 2023-07-14

Similar Documents

Publication Publication Date Title
CN101617112B (en) Internal combustion engine exhaust gas system and control method of the same
CN103629018B (en) A kind of reclaimer of cooler for recycled exhaust gas and regeneration method
CN103148473B (en) Optimal operation method and system for utility boiler based on CO
CN106681381A (en) SCR denitration system ammonia spraying quantity optimal control system and method based on intelligent feedforward signals
CN104715142B (en) A kind of station boiler NOxDischarge dynamic soft-measuring method
CN104226110A (en) Coal-fired boiler SCR (Selective Catalytic Reduction) denitration control method and system
CN108837698A (en) Based on advanced measuring instrumentss and the SCR denitration of advanced control algorithm spray ammonia optimization method and system
CN106247396B (en) A kind of control system of burner optimization burning
CN109059570A (en) For using the energy-saving control system and method for the heating furnace of mixed gas
CN112185475A (en) Aiming at SCR denitration system distortion NOxHigh-precision intelligent concentration prediction method
CN113856464A (en) High-followability SCR denitration control system and method
CN115738622B (en) Tail gas emission detection system of desulfurization equipment
JP2009198136A (en) Gas concentration estimation device and gas concentration estimation method for coal-fired boiler
CN107670474B (en) SNCR (selective non-catalytic reduction) denitration system control device and denitration control method
CN112717690B (en) SCR denitration commissioning method under deep peak regulation working condition of coal-fired unit
CN110737198A (en) Large-scale coal-fired power plant CO based on BP neural network2Capture system prediction control method
CN108021027B (en) Output power prediction system and method for supercritical circulating fluidized bed unit
KR102262883B1 (en) Apparatus for filter testing capable of constant space velocity control
CN112058067A (en) Accurate ammonia spraying control method and system for circulating fluidized bed boiler and sampling device
CN207478283U (en) A kind of fired power generating unit denitration real-time control apparatus
CN210269174U (en) Combustor test bench
CN1322955A (en) In-line boiler efficiency monitoring method based on fume component analysis
CN113418207A (en) Power station hearth combustion monitoring and diagnosing device and method
CN214151507U (en) Structure for rapidly measuring and feedback-controlling SCR ammonia injection amount based on concentration of NOx at outlet of induced draft fan
CN102207289B (en) Device and method for regulating flue gas ingredients of side wall water cooling wall of front-rear wall hedging combustion boiler automatically

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant