CN109766596A - A kind of expert system construction method of denitration economical operation - Google Patents
A kind of expert system construction method of denitration economical operation Download PDFInfo
- Publication number
- CN109766596A CN109766596A CN201811595158.2A CN201811595158A CN109766596A CN 109766596 A CN109766596 A CN 109766596A CN 201811595158 A CN201811595158 A CN 201811595158A CN 109766596 A CN109766596 A CN 109766596A
- Authority
- CN
- China
- Prior art keywords
- denitration
- expert system
- adjustable parameter
- parameter
- economical operation
- 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.)
- Pending
Links
- 238000010276 construction Methods 0.000 title claims abstract description 16
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims abstract description 83
- 229910021529 ammonia Inorganic materials 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 40
- 239000003245 coal Substances 0.000 claims abstract description 29
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 28
- 239000003546 flue gas Substances 0.000 claims abstract description 28
- 238000005507 spraying Methods 0.000 claims abstract description 22
- 238000002485 combustion reaction Methods 0.000 claims abstract description 13
- 239000007789 gas Substances 0.000 claims abstract description 10
- 239000000779 smoke Substances 0.000 claims abstract description 5
- 229910000069 nitrogen hydride Inorganic materials 0.000 claims description 15
- 230000001537 neural effect Effects 0.000 claims description 9
- 239000007921 spray Substances 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 8
- 239000002245 particle Substances 0.000 claims description 5
- 238000001727 in vivo Methods 0.000 claims description 4
- 240000002853 Nelumbo nucifera Species 0.000 claims description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims description 2
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims description 2
- 244000131316 Panax pseudoginseng Species 0.000 claims description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 claims description 2
- 235000008434 ginseng Nutrition 0.000 claims description 2
- FGIUAXJPYTZDNR-UHFFFAOYSA-N potassium nitrate Chemical compound [K+].[O-][N+]([O-])=O FGIUAXJPYTZDNR-UHFFFAOYSA-N 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 3
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 43
- 230000008569 process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 241001269238 Data Species 0.000 description 2
- 238000010531 catalytic reduction reaction Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000010977 unit operation Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000003054 catalyst Substances 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Abstract
The present invention provides a kind of expert system construction methods of denitration economical operation, are related to power station denitration technology field, can reduce the operating cost of power station SCR denitration system, realize the Optimum Economic of SCR denitration system operation；This method step includes: S1, selection parameter；S2, using adjustable parameter and non-adjustable parameter as input variable, establish boiler combustion model, carry out the prediction of the net coal consumption rate of flue gas NOx concentration, flue gas flow, exhaust gas temperature and unit；S3, using the boiler smoke NOx concentration, the flue gas flow, the exhaust gas temperature and ammonia spraying amount as input variable, establish SCR denitration system model, carry out the prediction of exit NOx concentration and the escaping of ammonia；S4, denitration operating cost model is established；S5, adjustable parameter is optimized；S6, building expert system.During technical solution provided by the invention is suitable for power station denitration operation.
Description
[technical field]
The present invention relates to power station denitration technology field more particularly to a kind of expert system building sides of denitration economical operation
Method.
[background technique]
One of the main reason for discharge of nitrogen oxides (NOx) is haze formation in coal fired generation process, it is national in recent years
The NOx emission of coal fired power plant is required increasingly strict.In order to reduce NOx emission, current domestic in-service and newly-built Large-sized Coal-fired Power
Group is most of to have installed selective catalytic reduction flue gas denitration (Selective Catalytic Reduction, SCR) system.
Liquefied ammonia (NH is used when SCR denitration system is run3) reducing agent is made, it reacts under the action of catalyst with the NOx in flue gas, it is raw
At harmless nitrogen and water, to achieve the purpose that denitrating flue gas.The main target of SCR denitration system is by exit NOx concentration
And the escaping of ammonia control is in claimed range.The cost that can be run to power station that introduces of SCR system brings certain increase, mainly
Increase and SCR spray ammonia cost including unit net coal consumption rate etc..
Therefore, it is necessary to research and develop a kind of economical and practical denitration Economic Operation Expert System, with reduce power station denitration operation at
This.The present invention is based on power station denitration operation datas, optimize to the adjustable parameter of boiler and SCR denitration system and construct expert
System provides guidance for power station denitration economical operation.
[summary of the invention]
In view of this, the present invention provides a kind of expert system construction method of denitration economical operation, this method can drop
The operating cost of low power station SCR denitration system realizes the Optimum Economic of SCR denitration system operation.
On the one hand, the present invention provides a kind of expert system construction method of denitration economical operation, which is characterized in that step packet
It includes:
S1, selection parameter；The parameter includes adjustable parameter and non-adjustable parameter；
S2, using the adjustable parameter and the non-adjustable parameter as input variable, establish boiler combustion model, carry out pot
Kiln gas NOx concentration, flue gas flow, exhaust gas temperature and unit net coal consumption rate prediction；
S3, become using the boiler smoke NOx concentration, the flue gas flow, the exhaust gas temperature and ammonia spraying amount as input
Amount, establishes SCR denitration system model, and carry out the prediction of exit NOx concentration and the escaping of ammonia；
S4, the prediction result for S2 and the ammonia spraying amount, establish denitration operating cost model；
S5, according to S4's as a result, being optimized to adjustable parameter；
S6, the optimum results according to S5 construct expert system.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the adjustable ginseng
Number includes total blast volume, primary wind pressure, secondary air register aperture, after-flame throttle opening and ammonia spraying amount；The non-adjustable parameter includes negative
Lotus and coal-supplying amount；The economy examination parameter includes net coal consumption rate and SCR spray ammonia cost.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation is built in the S4
The method of vertical denitration operating cost model is to carry out money demand elasticity to the ammonia spraying amount under net coal consumption rate and unit generated energy, obtains list
Denitration operating cost model under the kilowatt hour generated energy of position.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the tool of the S5
Hold in vivo are as follows: be based on optimization method, adjustable parameter is optimized, denitration operating cost when making per kilowatt under generated energy is most
It is low, and obtain the optimal value of adjustable parameter under least cost.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the tool of the S6
Hold in vivo are as follows: expert system is constructed using load and coal-supplying amount as mode input using the optimal value of adjustable parameter as output,
Obtain the adjustable parameter combination under different load and coal-supplying amount.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the Secondary Air
Door aperture includes 6 layers of aperture parameter；The after-flame throttle opening includes 2 layers of aperture parameter.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation establishes the pot
RBF neural method is utilized when furnace combustion model；RBF neural method is utilized when establishing the SCR denitration system model；
Particle group optimizing method is used when optimizing to the adjustable parameter.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation is established described de-
Money demand elasticity formula when nitre operating cost model are as follows: C=Ccc+CNH3in；ccc=ycc·pcc, cNH3in=xNH3in/xload·
pNH3；Wherein, cccIt is coal consumption cost model, cNH3inIt is spray ammonia cost model, yccIt is net coal consumption rate, xNH3inIt is under unit generated energy
Ammonia spraying amount, pcc、pNH3It is coal unit price and spray ammonia unit price respectively.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the population
The formula of optimization method are as follows: minc=ycc·pcc+xNH3in/xload·pNH3, and meet constraint condition yNOxout≤δNOx, yNH3out
≤δNH3；Wherein, δNOxIt is the limit value of NOx emission, δNH3It is the limit value of the escaping of ammonia.
Compared with prior art, the present invention can be obtained including following technical effect: the present invention is based on power station denitration operations
Data select boiler combustion parameter relevant to SCR denitration system operation to be analyzed, and optimize to operating parameter, energy
Enough realize the Optimum Economic of power station denitration operation.
Certainly, it implements any of the products of the present invention it is not absolutely required to while reaching all the above technical effect.
[Detailed description of the invention]
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this field
For those of ordinary skill, without creative efforts, it can also be obtained according to these attached drawings other attached drawings.
Fig. 1 is the expert system building flow diagram of denitration economical operation in power station provided by one embodiment of the present invention；
Fig. 2 is the model structure of denitration economical operation in power station provided by one embodiment of the present invention.
[specific embodiment]
For a better understanding of the technical solution of the present invention, being retouched in detail to the embodiment of the present invention with reference to the accompanying drawing
It states.
It will be appreciated that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Base
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
Its embodiment, shall fall within the protection scope of the present invention.
The term used in embodiments of the present invention is only to be not intended to be limiting merely for for the purpose of describing particular embodiments
The present invention.In the embodiment of the present invention and the "an" of singular used in the attached claims, " described " and "the"
It is also intended to including most forms, unless the context clearly indicates other meaning.
The present invention is based on power station denitration operation datas, and boiler and SCR denitration system operating parameter are optimized and constructed
Expert system provides guidance for power station denitration economical operation.
The present invention is based on power station denitration operation data, select boiler combustion parameter relevant to SCR denitration system operation into
Row analysis, and operating parameter is optimized, to realize the Optimum Economic of power station denitration operation, on this basis, building is special
Family's system.In order to achieve the goal above, the process for the scheme that the present invention uses is as shown in Figure 1, comprising the following steps:
S1, the influence factor for qualitatively analyzing station boiler and SCR denitration system economical operation, mainly consider net coal consumption rate
Examination parameter with SCR spray ammonia cost as economical operation；Select total blast volume, primary wind pressure, secondary air register aperture, after-flame air door
Aperture, ammonia spraying amount are as adjustable parameter, load and coal-supplying amount as non-adjustable parameter；
It can choose operating parameter related with station boiler and SCR denitration system economical operation in the selection of parameter,
It is not limited to listed parameter.
S2, with total blast volume, primary wind pressure, secondary air register aperture, after-flame throttle opening, load and coal-supplying amount history run
Data establish boiler combustion model as input variable, using RBF neural method, realize boiler smoke NOx concentration, flue gas
The predicted value of the net coal consumption rate of flow, flue-gas temperature and unit.
The boiler combustion model of the application and hereinafter SCR system model are not only individually to export, but enter more more
Model out, this boiler combustion model have 4 outputs, are flue gas NOx concentration, flue gas flow, flue-gas temperature and net coal consumption rate respectively,
SCR system model hereinafter has 2 outputs.The modeling method of the application is simultaneously not specific to a certain kind, any building based on data
Mould method is ok.
S3, using flue gas NOx concentration, flue gas flow, exhaust gas temperature, ammonia spraying amount history data as input variable,
SCR system model is established using RBF neural method, realizes the prediction of SCR exit NOx concentration and the escaping of ammonia.
In the present embodiment, the modeling in second step and third step is realized using the method for neural network.But this
The modeling method of application is not limited to RBF neural method, further includes support vector machines, statistical regression and relevant number
According to modeling method, these methods are all based on the description that power station operation data carrys out implementation model input and output parameter relationship.
S4, according to S2 and S3's as a result, under net coal consumption rate and unit generated energy ammonia spraying amount carry out money demand elasticity, obtain
Denitration operating cost model when per kilowatt under generated energy.
S5, according to S4's as a result, using particle group optimizing method, to adjustable parameter, i.e. total blast volume, primary wind pressure, secondary
Throttle opening, after-flame throttle opening, ammonia spraying amount optimize, and denitration operating cost when making per kilowatt under generated energy is minimum,
And obtain the optimal value of adjustable parameter under least cost.
The optimization method of the application is not limited to particle group optimizing method, further includes genetic algorithm and other are relevant
Optimization method.
S6, according to S5's as a result, using load and coal-supplying amount as mode input, the optimal value of adjustable parameter as output,
Expert system is constructed using fuzzy Decision Making Method, adjustable parameter combination optimal under different load and coal-supplying amount is obtained, thus real
Existing least cost operation.
Wherein, construction method used in expert system is constructed in S6 and be not limited to fuzzy Decision Making Method, further include it
His relevant expert decision-making method.
This example is to realize the building process of power station denitration Economic Operation Expert System, electricity using certain power station operation data
The process of denitration Economic Operation Expert System of standing building is as follows:
Step 1: the influence factor of station boiler and SCR denitration system economical operation is qualitatively analyzed, it is main to consider power supply
Coal consumption cccAmmonia cost c is sprayed with SCRNH3inExamination parameter as economical operation；Select total blast volume xta, primary wind pressure xfa, Secondary Air
Door aperture xsa, after-flame throttle opening xofa, ammonia spraying amount xNH3inAs adjustable parameter, load xloadWith coal-supplying amount xcoalAs can not
Parameter is adjusted, secondary air register aperture includes 6 layers of aperture parameter, x heresa=[xsa1, xsa2, xsa3, xsa4, xsa5, xsa6], after-flame air door
Aperture xofaIncluding 2 layers of aperture parameter xofa=[xofa1, xofa2]；
Step 2: to select total blast volume xta, primary wind pressure xfa, secondary air register aperture xsa, after-flame throttle opening xofa, load
xloadWith coal-supplying amount xcoalAs input variable, it is based on data unit operation, establishes boiler combustion using RBF neural method
Model obtains flue gas NOx concentration yNOxgas, flue gas flow yfg, flue-gas temperature yftWith the net coal consumption rate y of unitccPredicted value；
Step 3: with flue gas NOx concentration yNOxgas, flue gas flow yfg, exhaust gas temperature yft, ammonia spraying amount xNH3inBecome as input
Amount is based on data unit operation, establishes SCR system model using RBF neural method, obtain exit NOx concentration yNOxout、
The escaping of ammonia yNH3outPredicted value；
Step 4: according to step 2 and step 3 as a result, to net coal consumption rate ycc, ammonia spraying amount x under unit generated energyNH3inInto
Row money demand elasticity establishes denitration operating cost model；
C=ccc+cNH3in
Wherein ccc=ycc·pcc, cNH3in=xNH3in/xload·pNH3, p herecc、pNH3It is standard coal and spray ammonia respectively
Unit price；
Step 5: the model obtained according to step 4 is based on particle group optimizing method, to adjustable parameter, i.e. total blast volume xta, one
Secondary wind pressure xfa, secondary air register aperture xsa, after-flame throttle opening xofa, ammonia spraying amount xNH3inIt optimizes, is meeting pollutant emission
Denitration operating cost c when making per kilowatt under it is required that under generated energy is minimum, namely solves following optimization problem:
Minc=ycc·pcc+xNH3in/xload·pNH3
st.yNOxout≤δNOx
yNH3out≤δNH3
Wherein, δNOxIt is the limit value of NOx emission, δNH3It is the limit value of the escaping of ammonia；This optimization problem is solved, least cost is obtained
The optimal value of lower adjustable parameter；
Step 6: according to step 5 as a result, with load xloadWith coal-supplying amount xcoalAs mode input, the total wind of adjustable parameter
Measure xta, primary wind pressure xfa, secondary air register aperture xsa, after-flame throttle opening xofa, ammonia spraying amount xNH3inOptimal value as output, structure
Expert system is built, adjustable parameter value optimal under different load and coal-supplying amount is obtained, to realize that least cost is run.
Based on above-mentioned selected route, the model for establishing power station denitration economical operation using the present invention is as shown in Figure 2.Building
Boiler combustion model, SCR system model and operating cost model；According to load, coal-supplying amount, total blast volume, primary wind pressure, secondary
Throttle opening and after-flame throttle opening utilize boiler combustion model prediction flue gas NOx concentration, flue gas flow, flue-gas temperature and confession
The value of electric coal consumption；According to flue gas NOx concentration, flue gas flow, flue-gas temperature and ammonia spraying amount, escaped using SCR system model prediction ammonia
The value of ease and exit NOx concentration；According to load, ammonia spraying amount and net coal consumption rate, when obtaining per kilowatt using operating cost model
Optimized operation cost under generated energy.
Above to a kind of expert system construction method of denitration economical operation provided by the embodiment of the present application, carry out in detail
It is thin to introduce.The description of the example is only used to help understand the method for the present application and its core ideas；Meanwhile for ability
The those skilled in the art in domain, according to the thought of the application, there will be changes in the specific implementation manner and application range, comprehensive
Upper described, the contents of this specification should not be construed as limiting the present application.
Some vocabulary has such as been used to censure specific components in specification and claims.Those skilled in the art
, it is to be appreciated that hardware manufacturer may call the same component with different nouns.Present specification and claims are not
In such a way that the difference of title is as component is distinguished, but with the difference of component functionally as the criterion of differentiation.Such as
It is an open language in "comprising", " comprising " of the specification and claims in the whole text mentioned in, therefore " packet should be construed to
Containing/including but not limited to "." substantially " refer within the acceptable error range, those skilled in the art can centainly miss
The technical problem is solved in poor range, basically reaches the technical effect.Specification subsequent descriptions be implement the application compared with
Good embodiment, so the description is being not intended to limit the scope of the present application for the purpose of the rule for illustrating the application.
The protection scope of the application is subject to view the appended claims institute defender.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Include, so that commodity or system including a series of elements not only include those elements, but also including not clear
The other element listed, or further include for this commodity or the intrinsic element of system.In the feelings not limited more
Under condition, the element that is limited by sentence "including a ...", it is not excluded that in the commodity or system for including the element also
There are other identical elements.
It should be appreciated that term "and/or" used herein is only a kind of incidence relation for describing affiliated partner, indicate
There may be three kinds of relationships, for example, A and/or B, can indicate: individualism A, exist simultaneously A and B, individualism B these three
Situation.In addition, character "/" herein, typicallys represent the relationship that forward-backward correlation object is a kind of "or".
Above description shows and describes several preferred embodiments of the present application, but as previously described, it should be understood that the application
Be not limited to forms disclosed herein, should not be regarded as an exclusion of other examples, and can be used for various other combinations,
Modification and environment, and the above teachings or related fields of technology or knowledge can be passed through in application contemplated scope described herein
It is modified.And changes and modifications made by those skilled in the art do not depart from spirit and scope, then it all should be in this Shen
It please be in the protection scope of the appended claims.
Claims (9)
1. a kind of expert system construction method of denitration economical operation, which is characterized in that step includes:
S1, selection parameter；The parameter includes adjustable parameter and non-adjustable parameter；
S2, using the adjustable parameter and the non-adjustable parameter as input variable, establish boiler combustion model, carry out boiler smoke
Gas NOx concentration, flue gas flow, exhaust gas temperature and unit net coal consumption rate prediction；
S3, using the boiler smoke NOx concentration, the flue gas flow, the exhaust gas temperature and ammonia spraying amount as input variable, build
Vertical SCR denitration system model, and carry out the prediction of exit NOx concentration and the escaping of ammonia；
S4, the prediction result for S2 and the ammonia spraying amount, establish denitration operating cost model；
S5, according to S4's as a result, being optimized to adjustable parameter；
S6, the optimum results according to S5 construct expert system.
2. the expert system construction method of denitration economical operation according to claim 1, which is characterized in that the adjustable ginseng
Number includes total blast volume, primary wind pressure, secondary air register aperture, after-flame throttle opening and ammonia spraying amount；The non-adjustable parameter includes negative
Lotus and coal-supplying amount；The economy examination parameter includes net coal consumption rate and SCR spray ammonia cost.
3. the expert system construction method of denitration economical operation according to claim 2, which is characterized in that built in the S4
The method of vertical denitration operating cost model is to carry out money demand elasticity to the ammonia spraying amount under net coal consumption rate and unit generated energy, obtains list
Denitration operating cost model under the kilowatt hour generated energy of position.
4. the expert system construction method of denitration economical operation according to claim 2, which is characterized in that the tool of the S5
Hold in vivo are as follows: be based on optimization method, adjustable parameter is optimized, denitration operating cost when making per kilowatt under generated energy is most
It is low, and obtain the optimal value of adjustable parameter under least cost.
5. the expert system construction method of denitration economical operation according to claim 2, which is characterized in that the tool of the S6
Hold in vivo are as follows: expert system is constructed using load and coal-supplying amount as mode input using the optimal value of adjustable parameter as output,
Obtain the adjustable parameter combination under different load and coal-supplying amount.
6. the expert system construction method of denitration economical operation according to claim 2, which is characterized in that the Secondary Air
Door aperture includes 6 layers of aperture parameter；The after-flame throttle opening includes 2 layers of aperture parameter.
7. the expert system construction method of denitration economical operation according to claim 1, which is characterized in that establish the pot
RBF neural method is utilized when furnace combustion model；RBF neural method is utilized when establishing the SCR denitration system model；
Particle group optimizing method is used when optimizing to the adjustable parameter.
8. the expert system construction method of denitration economical operation according to claim 3, which is characterized in that establish described de-
Money demand elasticity formula when nitre operating cost model are as follows: c=ccc+cNH3in；ccc=ycc·pcc, cNH3in=xNH3in/xload·
pNH3；Wherein, cccIt is coal consumption cost model, cNH3inIt is spray ammonia cost model, yccIt is net coal consumption rate, xNH3inIt is under unit generated energy
Ammonia spraying amount, pcc、pNH3It is coal unit price and spray ammonia unit price respectively.
9. the expert system construction method of denitration economical operation according to claim 4, which is characterized in that the population
The formula of optimization method are as follows: minc=ycc·pcc+xNH3in/xload·pNH3, and meet constraint condition yNOxout≤δNOx, yNH3out
≤δNH3；Wherein, δNOxIt is the limit value of NOx emission, δNH3It is the limit value of the escaping of ammonia.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811595158.2A CN109766596A (en) | 2018-12-25 | 2018-12-25 | A kind of expert system construction method of denitration economical operation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811595158.2A CN109766596A (en) | 2018-12-25 | 2018-12-25 | A kind of expert system construction method of denitration economical operation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109766596A true CN109766596A (en) | 2019-05-17 |
Family
ID=66451584
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811595158.2A Pending CN109766596A (en) | 2018-12-25 | 2018-12-25 | A kind of expert system construction method of denitration economical operation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109766596A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110263452A (en) * | 2019-06-25 | 2019-09-20 | 华电国际电力股份有限公司技术服务分公司 | Flue gas Annual distribution characteristic analysis method, system and denitrating system in a kind of flue |
| CN111006240A (en) * | 2019-11-22 | 2020-04-14 | 华北电力大学 | Biomass boiler furnace temperature and load prediction method |
| CN111460726A (en) * | 2020-01-22 | 2020-07-28 | 杭州电子科技大学 | Optimization method for ammonia escape of coal slime fluidized bed boiler denitration system |
| CN111589301A (en) * | 2020-05-29 | 2020-08-28 | 广东电科院能源技术有限责任公司 | Method, device, equipment and storage medium for predicting SCR denitration performance of coal-fired power plant |
| CN111589302A (en) * | 2020-05-29 | 2020-08-28 | 广东电科院能源技术有限责任公司 | Method, device, equipment and storage medium for predicting SCR denitration performance of coal-fired power plant |
| CN111750341A (en) * | 2020-07-08 | 2020-10-09 | 湖南大学 | Oxygen-enriched combustion system and control method thereof |
| CN112461995A (en) * | 2020-11-03 | 2021-03-09 | 西安热工研究院有限公司 | Method for predicting ammonia escape of thermal power plant |
| CN113110046A (en) * | 2021-04-02 | 2021-07-13 | 玖禾智控(北京)科技有限公司 | Desulfurization system control method based on big data self-learning prediction control |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103752170A (en) * | 2014-01-16 | 2014-04-30 | 华中科技大学 | Denitration operation optimization method for SCR (Selective Catalytic Reduction) system of tangential firing pulverized coal boiler |
| CN104190254A (en) * | 2014-09-09 | 2014-12-10 | 国家电网公司 | Method for optimizing SCR (Selective Catalytic Reduction) ammonia spraying |
| CN107504473A (en) * | 2017-07-13 | 2017-12-22 | 上海电力学院 | A kind of boiler combustion and denitration linkage operation method based on multiple-objection optimization |
| CN107694337A (en) * | 2017-11-03 | 2018-02-16 | 吉林省电力科学研究院有限公司 | Coal unit SCR denitrating flue gas control methods based on network response surface |
-
2018
- 2018-12-25 CN CN201811595158.2A patent/CN109766596A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103752170A (en) * | 2014-01-16 | 2014-04-30 | 华中科技大学 | Denitration operation optimization method for SCR (Selective Catalytic Reduction) system of tangential firing pulverized coal boiler |
| CN104190254A (en) * | 2014-09-09 | 2014-12-10 | 国家电网公司 | Method for optimizing SCR (Selective Catalytic Reduction) ammonia spraying |
| CN107504473A (en) * | 2017-07-13 | 2017-12-22 | 上海电力学院 | A kind of boiler combustion and denitration linkage operation method based on multiple-objection optimization |
| CN107694337A (en) * | 2017-11-03 | 2018-02-16 | 吉林省电力科学研究院有限公司 | Coal unit SCR denitrating flue gas control methods based on network response surface |
Non-Patent Citations (1)
| Title |
|---|
| 郭凯旋: "考虑脱硝运行成本的燃煤锅炉在线燃烧优化控制系统的研究与应用", 《中国优秀博硕士学位论文全文数据库（硕士） 工程科技II辑》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110263452A (en) * | 2019-06-25 | 2019-09-20 | 华电国际电力股份有限公司技术服务分公司 | Flue gas Annual distribution characteristic analysis method, system and denitrating system in a kind of flue |
| CN111006240B (en) * | 2019-11-22 | 2020-11-13 | 华北电力大学 | Biomass boiler furnace temperature and load prediction method |
| CN111006240A (en) * | 2019-11-22 | 2020-04-14 | 华北电力大学 | Biomass boiler furnace temperature and load prediction method |
| CN111460726A (en) * | 2020-01-22 | 2020-07-28 | 杭州电子科技大学 | Optimization method for ammonia escape of coal slime fluidized bed boiler denitration system |
| CN111460726B (en) * | 2020-01-22 | 2023-11-14 | 杭州电子科技大学 | Optimization method for ammonia escape of coal slime fluidized bed boiler denitration system |
| CN111589301A (en) * | 2020-05-29 | 2020-08-28 | 广东电科院能源技术有限责任公司 | Method, device, equipment and storage medium for predicting SCR denitration performance of coal-fired power plant |
| CN111589302A (en) * | 2020-05-29 | 2020-08-28 | 广东电科院能源技术有限责任公司 | Method, device, equipment and storage medium for predicting SCR denitration performance of coal-fired power plant |
| CN111750341A (en) * | 2020-07-08 | 2020-10-09 | 湖南大学 | Oxygen-enriched combustion system and control method thereof |
| CN111750341B (en) * | 2020-07-08 | 2023-03-14 | 湖南大学 | Oxygen-enriched combustion system and control method thereof |
| CN112461995A (en) * | 2020-11-03 | 2021-03-09 | 西安热工研究院有限公司 | Method for predicting ammonia escape of thermal power plant |
| WO2022095534A1 (en) * | 2020-11-03 | 2022-05-12 | 西安西热锅炉环保工程有限公司 | Method for predicting ammonia escaping from thermal power plant |
| CN113110046A (en) * | 2021-04-02 | 2021-07-13 | 玖禾智控(北京)科技有限公司 | Desulfurization system control method based on big data self-learning prediction control |
| CN113110046B (en) * | 2021-04-02 | 2022-06-07 | 玖禾智控(北京)科技有限公司 | Desulfurization system control method based on big data self-learning prediction control |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109766596A (en) | A kind of expert system construction method of denitration economical operation | |
| Tan et al. | Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory | |
| CN105629738B (en) | SCR flue gas denitrification systems control method and equipment | |
| CN104826493B (en) | A kind of control method of selective catalytic reduction flue gas denitrification system | |
| CN107526292B (en) | A method of the regulation ammonia spraying amount based on inlet NOx concentration prediction | |
| US8185216B2 (en) | Plant controlling device and method, thermal power plant, and its control method | |
| CN107243257B (en) | It is suitble to the intelligence spray ammonia control system of full load | |
| CN112580250A (en) | Thermal power generating unit denitration system based on deep learning and optimization control method | |
| CN105629736B (en) | The fired power generating unit SCR denitration Disturbance Rejection forecast Control Algorithm of data-driven | |
| CN105597537B (en) | Denitration control method based on Prediction and Control Technology | |
| CN104715142B (en) | A kind of station boiler NOxDischarge dynamic soft-measuring method | |
| CN106599586B (en) | SCR neural network based intelligently sprays ammonia optimization method and device | |
| CN104061588A (en) | Low-nitrogen combustion control method and system based on secondary air door air regulation control | |
| CN104102138B (en) | Soft measurement based ammonia injection grid partition control method | |
| CN108905554A (en) | A kind of minimum continuous spray ammonia temperature online real-time predicting method of SCR flue gas denitrification equipment | |
| CN112418284A (en) | Control method and system for SCR denitration system of full-working-condition power station | |
| CN111522290A (en) | Denitration control method and system based on deep learning method | |
| CN113175678B (en) | Garbage incineration monitoring method and device | |
| CN109670625A (en) | NOx emission concentration prediction method based on Unscented kalman filtering least square method supporting vector machine | |
| CN106054608A (en) | Fuzzy control method and system for waste incineration flue gas denitration SNCR (Selective Non Catalytic Reduction) | |
| Matino et al. | Application of echo state neural networks to forecast blast furnace gas production: pave the way to off-gas optimized management | |
| CN115245735A (en) | Cement kiln flue gas iSNCR control method based on predictive control model | |
| CN112613237B (en) | CFB unit NOx emission concentration prediction method based on LSTM | |
| CN109766666A (en) | Boiler smoke based on low nitrogen burning and SNCR-SCR Collaborative Control discharges NOxConcentration prediction method | |
| CN112365065A (en) | WFGD self-adaptive online optimization scheduling method |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20190814 Address after: 830011 No. 200 Hengda Street, Urumqi High-tech Industrial Development Zone, Xinjiang Uygur Autonomous Region Applicant after: STATE GRID XINJIANG ELECTRIC POWER CO., LTD., ELECTRIC POWER Research Institute Applicant after: Urumqi Electric Power Construction and Debugging Institute, Xinjiang Xinneng Group Co.,Ltd. Applicant after: NORTH CHINA ELECTRIC POWER University Address before: 830011 No. 200 Hengda Street, Urumqi High-tech Industrial Development Zone, Xinjiang Uygur Autonomous Region Applicant before: STATE GRID XINJIANG ELECTRIC POWER CO., LTD., ELECTRIC POWER Research Institute Applicant before: XINJIANG ELECTRIC POWER CONSTRUCTION AND COMMISSIONING CO.,LTD. Applicant before: NORTH CHINA ELECTRIC POWER University |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20190906 Address after: Room 801, Block B, Shanghai Building, 430 East Hebei Road, Urumqi City, Xinjiang Uygur Autonomous Region Applicant after: Urumqi Electric Power Construction and Debugging Institute, Xinjiang Xinneng Group Co.,Ltd. Applicant after: STATE GRID XINJIANG ELECTRIC POWER CO., LTD., ELECTRIC POWER Research Institute Applicant after: NORTH CHINA ELECTRIC POWER University Address before: 830011 No. 200 Hengda Street, Urumqi High-tech Industrial Development Zone, Xinjiang Uygur Autonomous Region Applicant before: STATE GRID XINJIANG ELECTRIC POWER CO., LTD., ELECTRIC POWER Research Institute Applicant before: Urumqi Electric Power Construction and Debugging Institute, Xinjiang Xinneng Group Co.,Ltd. Applicant before: NORTH CHINA ELECTRIC POWER University |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190517 |
|
| RJ01 | Rejection of invention patent application after publication |