CN109225640A - A kind of wisdom electric precipitation power-economizing method - Google Patents
A kind of wisdom electric precipitation power-economizing method Download PDFInfo
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- CN109225640A CN109225640A CN201811199073.2A CN201811199073A CN109225640A CN 109225640 A CN109225640 A CN 109225640A CN 201811199073 A CN201811199073 A CN 201811199073A CN 109225640 A CN109225640 A CN 109225640A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C3/00—Separating dispersed particles from gases or vapour, e.g. air, by electrostatic effect
- B03C3/34—Constructional details or accessories or operation thereof
- B03C3/66—Applications of electricity supply techniques
- B03C3/68—Control systems therefor
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Abstract
The present invention provides a kind of wisdom electric precipitation power-economizing method, the step of the method includes: to acquire basic work information；Create model instance；Acquire the real-time discharge amount of dust；Acquire the influence factor variation of the real-time discharge amount of dust；Machine learning obtains objective result, and objective result is transmitted in online knowledge knowledge network, is ranked up to objective result, obtains optimum operation scheme；Online knowledge knowledge network is inquired, field worker reasonable operation deduster is instructed.Power-economizing method proposed by the present invention can recommend optimum operation scheme, have directive function to field worker operation deduster, operated by guidance, overcome in dust removal process, the case where electric field, electric current and voltage unreasonable allocation, has the advantages that energy-saving and emission-reduction, and under systematic direction, field worker being capable of small step iteration tests, preferred plan is micro-adjusted, advanced optimizes operation scheme, simultaneously, can also log history operation scheme, convenient for checking and contrast operation's scheme.
Description
Technical field
The present invention relates to energy-saving field more particularly to a kind of wisdom electric precipitation power-economizing methods.
Background technique
In recent years, the discharge for the pollutant that strict control fire coal generates becomes electricity with the rapid development of electric utility
The important component of power career development.SO2 emissions and dust discharge amount in heat power plant boiler tail flue gas
Control is an important ring for control atmosphere pollution object.
Electric precipitator is the corollary equipment of thermal power plant indispensability, its function is by coal-fired or oil burning boiler discharge flue gas
In particle dust removed, so that the fume amount being discharged into atmosphere be greatly lowered, this is to improve environmental pollution, is improved
The important environmental protection equipment of air quality.
Thermal power plant is usually mounted with dry electric precipitator and wet electric precipitator simultaneously, due to the perspective of environmental protection equipment performance and
Requirement of the country to coal quality is promoted, and the remote super reality of the general power of deduster needs at present, but field worker lacks effectively guidance, easily
Waste of energy is caused, operation cost is improved.
Therefore, it is necessary to intelligent guidance field worker, electric current, the voltage of each electric field of reasonable disposition electric precipitation are guaranteed with reaching
Reduction power dissipation obj ectives under the conditions of dust emission is up to standard.
Summary of the invention
The present invention provides a kind of wisdom electric precipitation power-economizing method, the step of the method, includes:
S10: basic work information is acquired by the data flow track following module in system；
S20: by the basic work information divided rank, each basic operating condition corresponds to a kind of model instance；
S30: the acquisition real-time discharge amount of dust, and compare discharge standard；
S40: changed by the influence factor that the data flow track following module in system acquires the real-time discharge amount of dust, institute
State power supply mode, current limitation and the voltage limit that influence factor includes each electric field of deduster；
S50: the machine learning in same model instance, when the influence factor changes, in triggering system
Module carries out machine learning, obtains objective result, and the objective result includes current deduster operation scheme, corresponding current total
Energy consumption and corresponding current dust emission concentration；And objective result is transmitted in online knowledge knowledge network, in model instance of the same race, no
Same objective result is ranked up according to the height of current total energy consumption, can be retained by default if being discharged into first n,
And optimum operation scheme is obtained, otherwise skip over.It, can be using existing algorithm such as quick sorting algorithm and random in sequencer procedure
Deep woods algorithm.
S60: inquiring online knowledge knowledge network, obtains model instance of the same race and reaches the current total energy consumption under the conditions of discharge standard most
Low historical operation scheme recommends prioritization scheme to field personnel, instructs field worker reasonable operation deduster.Carry out
When the recommendation of prioritization scheme, such as random deep woods algorithm of the algorithm that can be used.
The step of the method, sequence was not completely fixed, and such as S30 step, monitoring to the real-time discharge amount of dust can
To use full-time monitoring, can also arbitrarily can be adjusted using interval time section monitoring, the sequence of S30 step.
Optionally, the basic work information includes boiler load information and chemistry point information, and by boiler load information
Be divided into several grades with chemistry point information, for example, by boiler load be divided into 40% or less, 40%-60%, 60%-80%,
80% or more four grade.
Optionally, the chemistry point information includes As-received ash, As-received carbon, As-received hydrogen, moisture as received coal, receives
Various As-received are done 16 by one of base nitrogen, As-received oxygen, net calorific value as received basis, As-received sulphur or a variety of combinations
Equal part, each equal part correspond to a kind of model instance.
Optionally, the deduster includes dry electric precipitator and wet electric precipitator.
Optionally, when the machine learning module carries out machine learning, learning time is equal to current learning time and delay
The sum of learning time, i.e., in learning process, systematic learning current operation can also learn field worker to the further of current operation
Adjustment.
Optionally, when the machine learning module carries out machine learning, detect that electric spark occurs in electric field, voltage is at this time
Zero, dust collection capacity zero needs to reduce voltage, and electric spark is prevented to generate, and terminates study at this time.
Optionally, when the machine learning module carries out machine learning, objective result is handled using weighting averaging method,
Merge the historical knowledge point of identical operating condition, time gap current time is closer, and empirical weight is higher.
Optionally, the S50 includes S51: operating deduster for a period of time by skilled engineer, system carries out machine
After device study, system can instruct field worker to operate.
Optionally, the S60 includes S61: small step iteration tests, i.e. field worker are sentenced by operating by a small margin by system
Whether disconnected operation direction is reasonable.
By the above-mentioned description of this invention it is found that compared to the prior art, a kind of wisdom electric precipitation proposed by the present invention
Power-economizing method has the advantages that
1, recommend optimum operation scheme, there is directive function to field worker operation deduster；
2, by guidance operation, overcome in dust removal process, the operation of deduster is single, and electric field, electric current and voltage configuration are not
Reasonable situation, has the advantages that energy-saving and emission-reduction；
3, log history operation scheme, convenient for checking and contrast operation's scheme；
4, under systematic direction, field worker can small step iteration, preferred plan is micro-adjusted, behaviour is advanced optimized
Make scheme.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes a part of the invention, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.
Wherein:
Fig. 1 is a kind of flow chart of wisdom electric precipitation power-economizing method of the present invention.
Specific embodiment
In order to be clearer and more clear technical problems, technical solutions and advantages to be solved, tie below
Drawings and examples are closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
Embodiment one: a kind of wisdom electric precipitation power-economizing method, the step of the method include:
S10: basic work information is acquired by the data flow track following module in system；
S20: by the basic work information divided rank, each basic operating condition corresponds to a kind of model instance, this implementation
In example, the basis work information includes boiler load information and chemistry point information, and divides boiler load information and chemistry to letter
Breath is divided into several grades.Boiler load is such as divided into 40% or less, 40%-60%, 60%-80%, 80% or more four
Grade, each grade correspond to a kind of model instance.The chemistry point information includes As-received ash, As-received carbon, As-received
One of hydrogen, moisture as received coal, As-received nitrogen, As-received oxygen, net calorific value as received basis, As-received sulphur or a variety of groups
It closes, various As-received is done into 16 equal parts, each equal part corresponds to a kind of model instance.
S30: the acquisition real-time discharge amount of dust, and compare discharge standard；
S40: find that the influence factor of the real-time discharge amount of dust changes by the data flow track following module in system, institute
State power supply mode, current limitation and the voltage limit that influence factor includes each electric field of dry electric precipitator and wet electric precipitator；
S50: the machine learning module in same model instance, when finding influence factor variation, in triggering system
Machine learning is carried out, study plan is generated, execute study plan and obtains objective result, the objective result includes current dedusting
Device operation scheme, corresponding current total energy consumption and corresponding current dust emission concentration；And objective result is uploaded to and is known online
Know in net, in model instance of the same race, different objective results is ranked up according to the height of current total energy consumption, can pass through system
Setting, if n reservations before being discharged into, and optimum operation scheme is obtained, otherwise skip over.In order to guarantee that system can be to field worker
Carry out effective job guide, system use initial stage, deduster is operated for a period of time by skilled engineer, system into
After row machine learning, system can instruct field worker to operate.
When the machine learning module carries out machine learning, learning time is equal to current learning time and delay learning time
The sum of, i.e., in learning process, systematic learning current operation can also learn further adjustment of the field worker to current operation, together
When, in learning process, system carries out the rule such as zero spark and checks, detects that electric spark occurs in electric field, voltage is zero at this time, is removed
Dirt ability is zero, needs to reduce voltage, and electric spark is prevented to generate, and terminates study at this time.
S60: inquiring online knowledge knowledge network, obtains model instance of the same race and reaches the current total energy consumption under the conditions of discharge standard most
Low historical operation scheme recommends prioritization scheme to field personnel, instructs field worker reasonable operation deduster.Live work
People in operation, can carry out small step iteration tests, i.e., field worker by grasping by a small margin on the basis of optimum operation scheme
Make, judges whether operation direction is reasonable by system.
Embodiment two: in view of electric field device aging, device update can all cause to produce under identical operating condition and influence factor
Raw different power consumption and dust emission uses weighting to objective result as a result, when the machine learning module carries out machine learning
The processing of averaging method, merges the historical knowledge point of identical operating condition, and time gap current time is closer, and empirical weight is higher, this
The knowledge that kind method obtains online knowledge knowledge network has timing property.
In conclusion compared to the prior art the present invention, has the advantages that
1, recommend optimum operation scheme, there is directive function to field worker operation deduster；
2, by guidance operation, overcome in dust removal process, the operation of deduster is single, and electric field, electric current and voltage configuration are not
Reasonable situation, has the advantages that energy-saving and emission-reduction；
3, log history operation scheme, convenient for checking and contrast operation's scheme；
4, under systematic direction, field worker can small step iteration, preferred plan is micro-adjusted, behaviour is advanced optimized
Make scheme.
The present invention is exemplarily described above in conjunction with attached drawing, it is clear that the present invention implements not by aforesaid way
Limitation, as long as the improvement for the various unsubstantialities that the inventive concept and technical scheme of the present invention carry out is used, or without changing
It is within the scope of the present invention into the conception and technical scheme of the invention are directly applied to other occasions.
Claims (9)
1. a kind of wisdom electric precipitation power-economizing method, which is characterized in that the step of the method includes:
S10: basic work information is acquired by the data flow track following module in system；
S20: by the basic work information divided rank, each basic operating condition corresponds to a kind of model instance；
S30: the acquisition real-time discharge amount of dust, and compare discharge standard；
S40: changed by the influence factor that the data flow track following module in system acquires the real-time discharge amount of dust, the shadow
The factor of sound includes power supply mode, current limitation and the voltage limit of each electric field of deduster；
S50: the machine learning module in same model instance, when the influence factor changes, in triggering system
Machine learning is carried out, obtains objective result, the objective result includes current deduster operation scheme, corresponding current total energy consumption
With corresponding current dust emission concentration；And objective result is transmitted in online knowledge knowledge network, it is different in model instance of the same race
Objective result is ranked up according to the height of current total energy consumption, and obtains optimum operation scheme；
S60: inquiring online knowledge knowledge network, obtains model instance of the same race and to reach the current total energy consumption under the conditions of discharge standard minimum
Historical operation scheme instructs field worker reasonable operation deduster.
2. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the basis work information
Divide information including boiler load information and chemistry, and boiler load information and chemistry point information are divided into several grades.
3. a kind of wisdom electric precipitation power-economizing method according to claim 2, which is characterized in that the chemistry divides packet
Include As-received ash, As-received carbon, As-received hydrogen, moisture as received coal, As-received nitrogen, As-received oxygen, net calorific value as received basis,
Various As-received are done 16 equal parts by one of As-received sulphur or a variety of combinations, each equal part corresponds to a kind of model instance.
4. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the deduster includes dry
Electric precipitator and wet electric precipitator.
5. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the machine learning module
When carrying out machine learning, learning time is equal to the sum of current learning time and delay learning time.
6. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the machine learning module
When carrying out machine learning, detects that electric spark occurs in electric field, then terminate study.
7. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the machine learning module
When carrying out machine learning, objective result is handled using weighting averaging method, merges the historical knowledge point of identical operating condition, time interval
Closer from current time, empirical weight is higher.
8. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the S50 includes S51:
Deduster is operated by skilled engineer, system carries out machine learning.
9. a kind of wisdom electric precipitation power-economizing method according to claim 1, which is characterized in that the S60 includes S61:
Small step iteration tests, i.e. field worker judge whether operation direction is reasonable by operating by a small margin, by system.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811199073.2A CN109225640A (en) | 2018-10-15 | 2018-10-15 | A kind of wisdom electric precipitation power-economizing method |
| PCT/CN2019/089475 WO2020078008A1 (en) | 2018-10-15 | 2019-05-31 | Energy saving method employing smart electrostatic precipitation |
| CN201910762320.3A CN110624696B (en) | 2018-10-15 | 2019-08-19 | Intelligent electric dust removal energy-saving method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811199073.2A CN109225640A (en) | 2018-10-15 | 2018-10-15 | A kind of wisdom electric precipitation power-economizing method |
Publications (1)
| Publication Number | Publication Date |
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| CN109225640A true CN109225640A (en) | 2019-01-18 |
Family
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| Application Number | Title | Priority Date | Filing Date |
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| CN201811199073.2A Withdrawn CN109225640A (en) | 2018-10-15 | 2018-10-15 | A kind of wisdom electric precipitation power-economizing method |
| CN201910762320.3A Active CN110624696B (en) | 2018-10-15 | 2019-08-19 | Intelligent electric dust removal energy-saving method |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201910762320.3A Active CN110624696B (en) | 2018-10-15 | 2019-08-19 | Intelligent electric dust removal energy-saving method |
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| WO (1) | WO2020078008A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020078008A1 (en) * | 2018-10-15 | 2020-04-23 | 厦门邑通软件科技有限公司 | Energy saving method employing smart electrostatic precipitation |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113426264A (en) * | 2021-07-15 | 2021-09-24 | 国电环境保护研究院有限公司 | Intelligent operation control method and control platform for flue gas purification island |
| CN114114921A (en) * | 2021-11-26 | 2022-03-01 | 华能平凉发电有限责任公司 | Control method and device of dust removal power supply |
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Also Published As
| Publication number | Publication date |
|---|---|
| CN110624696A (en) | 2019-12-31 |
| WO2020078008A1 (en) | 2020-04-23 |
| CN110624696B (en) | 2021-05-28 |
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