CN113887890A - Coal blending and burning optimization method - Google Patents
Coal blending and burning optimization method Download PDFInfo
- Publication number
- CN113887890A CN113887890A CN202111085258.2A CN202111085258A CN113887890A CN 113887890 A CN113887890 A CN 113887890A CN 202111085258 A CN202111085258 A CN 202111085258A CN 113887890 A CN113887890 A CN 113887890A
- Authority
- CN
- China
- Prior art keywords
- coal
- blending
- coal blending
- combustion
- combination
- 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
- 239000003245 coal Substances 0.000 title claims abstract description 238
- 238000002156 mixing Methods 0.000 title claims abstract description 150
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000005457 optimization Methods 0.000 title claims abstract description 15
- 238000002485 combustion reaction Methods 0.000 claims abstract description 47
- 238000012216 screening Methods 0.000 claims abstract description 13
- 238000012937 correction Methods 0.000 claims description 9
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 8
- 238000004519 manufacturing process Methods 0.000 claims description 8
- 239000000843 powder Substances 0.000 claims description 7
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 5
- 229910052717 sulfur Inorganic materials 0.000 claims description 5
- 239000011593 sulfur Substances 0.000 claims description 5
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 238000006477 desulfuration reaction Methods 0.000 claims description 4
- 239000003344 environmental pollutant Substances 0.000 claims description 4
- 229910052757 nitrogen Inorganic materials 0.000 claims description 4
- 231100000719 pollutant Toxicity 0.000 claims description 4
- 239000002893 slag Substances 0.000 claims description 4
- 238000007605 air drying Methods 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 238000002347 injection Methods 0.000 claims description 2
- 239000007924 injection Substances 0.000 claims description 2
- 238000010344 co-firing Methods 0.000 claims 1
- 230000007774 longterm Effects 0.000 abstract 1
- 238000007726 management method Methods 0.000 abstract 1
- 239000000243 solution Substances 0.000 description 4
- 239000000446 fuel Substances 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000023556 desulfurization Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 101150067055 minC gene Proteins 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Regulation And Control Of Combustion (AREA)
Abstract
The invention discloses a blending coal blending combustion optimization method, which relates to the technical field of blending coal blending combustion and comprises the following steps: the method comprises the steps of obtaining all coal blending combinations of at least two coal types in advance, calibrating coal blending proportion constraints, predicting combustion and slagging trends in a furnace based on the obtained coal blending combinations, screening to obtain the coal blending combinations, analyzing a coal blending planning model, and obtaining the coal type combination with the lowest weighted average price and the optimal coal blending proportion as the optimal coal blending combustion. The invention realizes the optimal coal blending scheme obtained based on the coal blending constraint conditions, not only improves the coal blending benefit and can provide guarantee for the economic, safe and stable operation of the blending combustion unit, but also the power plant can flexibly select the number of blending combustion coal types according to the actual situation and can guide the power plant to carry out scientific coal blending combustion management in a long term and real time manner.
Description
Technical Field
The invention relates to the technical field of blending coal and combustion, in particular to a blending coal and combustion optimization method.
Background
The fuel cost is the most significant operating cost in the production operation of a thermal power plant (or unit). The power supply coal consumption reflects the energy efficiency level of a thermal power plant (or a unit), and is generally a relatively stable value under the condition of stable coal quality entering the furnace. Due to the influence of the supply and demand relationship of the coal market, the price difference of fuel purchased from different coal mines is a real-time fluctuation variable, so that the fuel cost under different blending coal combustion proportions is greatly different.
At present, a blending scheme can be given out by a coal blending mode of blending coal through simple calculation, certain guidance effect is provided for blending operation, but considered influence factors are few, the applicability of blended coal cannot be guaranteed fundamentally, potential safety production hazards and environmental emission standard exceeding are easily caused, and the economical efficiency is poor.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a coal blending and burning optimization method to overcome the technical problems in the prior related art.
The technical scheme of the invention is realized as follows:
a coal blending combustion optimization method comprises the following steps:
step S1, pre-obtaining all coal blending combinations C of at least two coal typesn sAnd calibrating the constraint of the coal blending ratio, and marking the coal blending ratio of each coal as X which is more than or equal to 0i1(i ═ 1,2, 3.., s), wherein n is the total amount of the market coal types, s is the total amount of the selected coal types, and X is equal to or less than 1iIs the proportion of blended coal;
step S2, based on the coal blending combination Cn sPredicting the trend of combustion and slagging in the furnace, and screening and acquiring a coal blending combination C based on combustion slagging characteristic informationn s′；
Step S3, based on coal blending combination Cn s′And analyzing a coal blending planning model, acquiring the coal combination with the lowest weighted average price and the optimal coal blending proportion, and using the coal combination as the optimal coal blending.
Wherein, still include the following step:
step S101, if the current bill of lading mi(i ═ 1,2,3,.. s) < amount of coal supply M, the constraint that calibrates the current proportion of each coal is expressed as:
step S102, calibrating the coal blending ratio of each coal to represent the ratio of each coal, and representing that:
X1+X2+X3+…+Xs＝1。
the prediction of the combustion and slagging tendency in the furnace comprises the following steps:
step S201, combining the obtained coal blending Cn sRespectively acquiring combustion slagging characteristic information including the lowest non-oil-injection stable combustion load rate DminHeat loss q due to incomplete combustion of carbon4And slag formation index S in furnaceuRespectively expressed as:
Dmin＝f(IT,Qnet,ad,qF,M)；
q4＝f(qV,qF,RI,Bp,Qnet,ad,M)；
Su＝f(qF,Sc,Qnet,ad,M)；
wherein IT is the coal powder airflow fire observation temperature, Qnet,adAir drying base lower calorific value, q, for coal blendingF、qVDesigning a hearth section heat load and a volume heat load for a boiler respectively, wherein M is a co-combustion mode, RI is a coal powder thermogravimetric reaction index, Bp is a coal powder one-dimensional furnace burnout rate, and ScThe one-dimensional furnace slagging index of the coal sample is obtained;
step S202, screening and obtaining coal blending combination C based on the obtained combustion slagging characteristic informationn s′。
Wherein, still include the following step:
step S203, combining the obtained coal blending Cn s' performing pollutant emission adaptability prediction, comprising:
calculating NO after blending combustionxWith SO2The amounts of production, expressed as:
NOx＝f(Nar,Vdaf,Qnet.ar,FH,FO2,K)；
SO2＝f(Qnet,ar,ηs,St,ar)；
wherein N isar,St,ar,VdafRespectively the mass fractions of nitrogen element, sulfur element and volatile component of the mixed coal, FHAs a load correction value, FO2For operating oxygen correction, K is boiler correction, ηsThe self-desulfurization efficiency of the fire coal is obtained;
step S204, based on the NO after the mixed burningxWith SO2Production amount, carrying out coal blending combination Cn s′Screening to obtain coal blending combination Cn s″；
Step S205, combining the obtained coal blending Cn s″And analyzing as an input of the coal blending planning model.
The coal blending planning model comprises the following steps:
step S301, recording the coal quality parameter of each coal as A(i,j)(i ═ 1,2, 3.., s), j represents the jth coal quality parameter;
for the coal quality parameter, calibrating the coal blending parameter index after mixing of s kinds of coal, and expressing as follows:
A(1,j)x1+A(2,j)x2+A(3,j)x3+...+A(s,j)xs·bj；
wherein, bjIs the jth coalCoal blending index of quality parameter.
Step S302, a calibration objective function is carried out, and the weighted price expressed as coal blending is the lowest:
C＝C1X1+C2X2+C3X3+...+CsXs
wherein C is the weighted average price.
The invention has the beneficial effects that:
according to the coal blending optimization method, all coal blending combinations of at least two coal types are obtained in advance, coal blending proportion constraint is calibrated, furnace combustion and slagging trend prediction is carried out based on the obtained coal blending combinations, the coal blending combinations are obtained through screening, a coal blending planning model is analyzed, the coal type combination with the lowest weighted average price and the optimal coal blending proportion are obtained and used as the optimal coal blending, and the optimal coal blending scheme obtained based on coal blending constraint conditions is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of a coal blending optimization method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
According to the embodiment of the invention, a coal blending optimization method is provided.
As shown in fig. 1, the coal blending optimization method according to the embodiment of the invention includes the following steps:
step S1, pre-obtaining all coal blending combinations C of at least two coal typesn sAnd calibrating the constraint of the coal blending ratio, and marking the coal blending ratio of each coal as X which is more than or equal to 0i1(i ═ 1,2, 3.., s), wherein n is the total amount of the market coal types, s is the total amount of the selected coal types, and X is equal to or less than 1iIs the proportion of blended coal;
step S2, based on the coal blending combination Cn sPredicting the trend of combustion and slagging in the furnace, and screening and acquiring a coal blending combination C based on combustion slagging characteristic informationn s′；
Step S3, based on coal blending combination Cn s′And analyzing a coal blending planning model, acquiring the coal combination with the lowest weighted average price and the optimal coal blending proportion, and using the coal combination as the optimal coal blending.
Wherein, still include the following step:
step S101, if the current bill of lading mi(i ═ 1,2,3,.. s) < amount of coal supply M, the constraint that calibrates the current proportion of each coal is expressed as:
step S102, calibrating the coal blending ratio of each coal to represent the ratio of each coal, and representing that:
X1+X2+X3+…+Xs＝1。
the prediction of the combustion and slagging tendency in the furnace comprises the following steps:
step S201, combining the obtained coal blending Cn sRespectively acquiring combustion slagging characteristic information including minimum oil throwingSteady combustion load factor DminHeat loss q due to incomplete combustion of carbon4And slag formation index S in furnaceuRespectively expressed as:
Dmin＝f(IT,Qnet,ad,qF,M)；
q4＝f(qV,qF,RI,Bp,Qnet,ad,M)；
Su＝f(qF,Sc,Qnet,ad,M)；
wherein IT is the coal powder airflow fire observation temperature, Qnet,adAir drying base lower calorific value, q, for coal blendingF、qVDesigning a hearth section heat load and a volume heat load for a boiler respectively, wherein M is a co-combustion mode, RI is a coal powder thermogravimetric reaction index, Bp is a coal powder one-dimensional furnace burnout rate, and Sc is a one-dimensional furnace slagging index of a coal sample;
step S202, screening and obtaining coal blending combination C based on the obtained combustion slagging characteristic informationn s′。
Wherein, still include the following step:
step S203, combining the obtained coal blending Cn s′And performing pollutant emission adaptability prediction, comprising the following steps:
calculating NO after blending combustionxWith SO2The amounts of production, expressed as:
NOx＝f(Nar,Vdaf,Qnet.ar,FH,FO2,K)；
SO2＝f(Qnet,ar,ηs,St,ar)；
wherein N isar,St,ar,VdafRespectively the mass fractions of nitrogen element, sulfur element and volatile component of the mixed coal, FHAs a load correction value, FO2For operating oxygen correction, K is boiler correction, ηsThe self-desulfurization efficiency of the fire coal is obtained;
step S204, based on the NO after the mixed burningxWith SO2Production amount, carrying out coal blending combination Cn s′Screening to obtain coal blending combination Cn s″；
Step S205, combining the obtained coal blending Cn s″And analyzing as an input of the coal blending planning model.
The coal blending planning model comprises the following steps:
step S301, recording the coal quality parameter of each coal as A(i,j)(i ═ 1,2, 3.., s), j represents the jth coal quality parameter;
for the coal quality parameter, calibrating the coal blending parameter index after mixing of s kinds of coal, and expressing as follows:
A(1,j)x1+A(2,j)x2+A(3,j)x3+...+A(s,j)xs·bj；
wherein, bjThe coal blending index of the jth coal quality parameter is obtained.
Step S302, a calibration objective function is carried out, and the weighted price expressed as coal blending is the lowest:
C＝C1X1+C2X2+C3X3+...+CsXs
wherein C is the weighted average price.
By means of the technical scheme, all coal blending combinations of at least two coal types are obtained in advance, coal blending proportion constraint is calibrated, furnace combustion and slagging trend prediction is carried out based on the obtained coal blending combinations, the coal blending combinations are obtained through screening, coal blending planning model analysis is carried out, the coal type combination with the lowest weighted average price and the optimal coal blending proportion are obtained and used as the optimal coal blending, and the optimal coal blending scheme obtained based on coal blending constraint conditions is achieved.
In addition, in the technical scheme, the furnace combustion and slagging trend prediction comprises the following steps: respectively using the lowest fuel-injection-free stable combustion load rate DminHeat loss q due to incomplete combustion of carbon4And in-furnace slagging indexSuTo predict the ignition stability, burnout and slag formation in the furnace.
And for the pollutant emission adaptability prediction, NO after the mixed combustion is calculatedxWith SO2And (4) generating amount, predicting whether the processing capacity of the denitration and desulfurization system reaches the standard or not.
In addition, in the technical scheme, the coal blending ratio is calibrated and restrained: each sub model has s kinds of coal, and the coal blending ratio of each kind of coal is recorded as X being more than or equal to 0i≤1(i＝1,2,3,...,s)；
If the bill of lading m is consideredi(i 1,2, 3.., s) may be less than the coal load M, then the proportion of each coal must satisfy the constraint of
Meanwhile, the coal loading of all coal types is added together to be equal to the total coal loading, and the coal blending ratio of each coal type can be used to represent the proportion of each coal type after normalization, and is represented as:
X1+X2+X3+…+Xs＝1
in addition, the combustion stability, environmental protection index and safe operation index of the boiler of the coal blending are considered, and the specific coal quality parameters are moisture, volatile matters, calorific value, sulfur content, nitrogen content and the like. The coal quality parameter of each coal is recorded as A(i,j)(i ═ 1,2, 3.., s), j represents the jth coal quality parameter. For the coal quality parameter, the coal blending parameter after the mixing of s kinds of coal cannot exceed a given index, which is expressed as:
A(1,j)x1+A(2,j)x2+A(3,j)x3+...+A(s,j)xs·bj
wherein, bjThe coal blending indexes of the jth coal quality parameter are critical values of moisture, ash content, volatile components, total sulfur and the like. For some special coal quality parameters, such as calorific value, it is necessary to meet a given criterion or more.
In addition, specifically, the objective function is calibrated. Namely, on the premise of meeting the constraint conditions, the cost is saved as much as possible. Thus, it is possible to provideThe model is targeted to the lowest weighted price for coal blending, i.e., the lowest cost. If the price of each coal is CiThen, the objective function of the model is expressed as:
C＝C1X1+C2X2+C3X3+...+CsXs
wherein, C is the final weighted average price, and the unit is: element/t;
it should be particularly noted that, in the present technical solution, obtaining the minimum coal blending price value under the condition that the constraint condition is satisfied includes the following steps:
the target to be optimized, i.e. the minimum weighted price, is calibrated as:
minC＝C1X1+C2X2+C3X3+...+CsXs；
and calibrating the normalized coal blending ratio, and expressing as:
X1+X2+X3+…+Xs＝1；
the critical values to be met by various coal quality parameters are calibrated and are expressed as:
A(1,j)x1+A(2,j)x2+A(3,j)x3+...+A(s,j)xs·bj；
the coal blending ratio of each coal is calibrated to be more than or equal to 0 and less than or equal to 1 and is expressed as follows:
obtaining the optimal coal blending ratio X under the condition of meeting the constraint conditioniAnd the lowest weighted price for coal blending.
In summary, according to the technical scheme of the invention, all coal blending combinations of at least two coal types are obtained in advance, coal blending proportion constraint is calibrated, furnace combustion and slagging trend prediction is carried out based on the obtained coal blending combinations, the coal blending combinations are obtained by screening, and coal blending planning model analysis is carried out to obtain the coal type combination with the lowest weighted average price and the optimal coal blending proportion as optimal coal blending, so that the optimal coal blending scheme obtained based on coal blending constraint conditions is realized.
While the foregoing is directed to the preferred embodiment of the present invention, other and further embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (5)
1. A coal blending and burning optimization method is characterized by comprising the following steps:
all coal blending combinations C for pre-obtaining at least two coal typesn sAnd calibrating the constraint of the coal blending ratio, and marking the coal blending ratio of each coal as X which is more than or equal to 0i1(i ═ 1,2, 3.., s), wherein n is the total amount of the market coal types, s is the total amount of the selected coal types, and X is equal to or less than 1iIs the proportion of blended coal;
coal blending combination C based on acquisitionn sPredicting the trend of combustion and slagging in the furnace, and screening and acquiring a coal blending combination C based on combustion slagging characteristic informationn s′；
Based on coal blending combination Cn s′And analyzing a coal blending planning model, acquiring the coal combination with the lowest weighted average price and the optimal coal blending proportion, and using the coal combination as the optimal coal blending.
2. The coal blending optimization method according to claim 1, wherein the coal blending proportion constraint further comprises the following steps:
if the current bill of lading mi(i ═ 1,2,3,.. s) < amount of coal supply M, the constraint that calibrates the current proportion of each coal is expressed as:
and calibrating the coal blending ratio of each coal to represent the proportion of each coal type, and representing that:
X1+X2+X3+…+Xs＝1。
3. the coal blending combustion optimization method according to claim 2, wherein the prediction of the combustion and slagging tendency in the furnace comprises the following steps:
combining the obtained coal blending Cn sRespectively acquiring combustion slagging characteristic information including the lowest non-oil-injection stable combustion load rate DminHeat loss q due to incomplete combustion of carbon4And slag formation index S in furnaceuRespectively expressed as:
Dmin＝f(IT,Qnet,ad,qF,M)；
q4＝f(qV,qF,RI,Bp,Qnet,ad,M)；
Su＝f(qF,Sc,Qnet,ad,M)；
wherein IT is the coal powder airflow fire observation temperature, Qnet,adAir drying base lower calorific value, q, for coal blendingF、qVDesigning the heat load of the cross section of a hearth and the volume heat load of a boiler respectively, wherein M is a co-combustion mode, RI is a thermogravimetric reaction index of the pulverized coal, Bp is a one-dimensional furnace burnout rate of the pulverized coal, and Sc is the one-dimensional furnace slagging index of the coal sample;
screening and obtaining coal blending combination C based on obtained combustion slagging characteristic informationn s′。
4. The coal blending and burning optimization method according to claim 3, further comprising the following steps:
combining the obtained coal blending Cn s′And performing pollutant emission adaptability prediction, comprising the following steps:
calculating NO after blending combustionxWith SO2The amounts of production, expressed as:
NOx＝f(Nar,Vdaf,Qnet.ar,FH,FO2,K)；
SO2＝f(Qnet,ar,ηs,St,ar)；
wherein N isar,St,ar,VdafRespectively the mass fractions of nitrogen element, sulfur element and volatile component of the mixed coal, FHAs a load correction value, FO2For operating oxygen correction, K is boiler correction, ηsThe self-desulfurization efficiency of the fire coal is obtained;
based on NO after co-firingxWith SO2Production amount, carrying out coal blending combination Cn s′Screening to obtain coal blending combination Cn s″；
Combining the obtained coal blending Cn s″And analyzing as an input of the coal blending planning model.
5. The coal blending optimization method according to claim 4, wherein the coal blending planning model comprises the following steps:
the coal quality parameter of each coal is recorded as A(i,j)(i ═ 1,2, 3.., s), j represents the jth coal quality parameter;
for the coal quality parameter, calibrating the coal blending parameter index after mixing of s kinds of coal, and expressing as follows:
A(1,j)x1+A(2,j)x2+A(3,j)x3+...+A(s,j)xs·bj；
wherein, bjThe coal blending index of the jth coal quality parameter is obtained.
And (3) calibrating an objective function, wherein the weighted price expressed as coal blending is the lowest:
C＝C1X1+C2X2+C3X3+...+CsXs
wherein C is the weighted average price.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111085258.2A CN113887890A (en) | 2021-09-16 | 2021-09-16 | Coal blending and burning optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111085258.2A CN113887890A (en) | 2021-09-16 | 2021-09-16 | Coal blending and burning optimization method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113887890A true CN113887890A (en) | 2022-01-04 |
Family
ID=79009561
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111085258.2A Pending CN113887890A (en) | 2021-09-16 | 2021-09-16 | Coal blending and burning optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113887890A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104992028A (en) * | 2015-07-17 | 2015-10-21 | 华北电力大学（保定） | Fossil power generation unit coal blending scheme acquisition method |
| CN107316104A (en) * | 2017-06-07 | 2017-11-03 | 西安西热锅炉环保工程有限公司 | The coal mixing combustion forecast system of assessment system after a kind of band |
| CN109376945A (en) * | 2018-11-13 | 2019-02-22 | 华能国际电力股份有限公司上海石洞口第电厂 | A kind of coal mixing combustion optimization system based on more coals |
| KR102271070B1 (en) * | 2020-03-31 | 2021-06-29 | 조선대학교산학협력단 | Method and apparatus for determining mixed coal combination |
-
2021
- 2021-09-16 CN CN202111085258.2A patent/CN113887890A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104992028A (en) * | 2015-07-17 | 2015-10-21 | 华北电力大学（保定） | Fossil power generation unit coal blending scheme acquisition method |
| CN107316104A (en) * | 2017-06-07 | 2017-11-03 | 西安西热锅炉环保工程有限公司 | The coal mixing combustion forecast system of assessment system after a kind of band |
| CN109376945A (en) * | 2018-11-13 | 2019-02-22 | 华能国际电力股份有限公司上海石洞口第电厂 | A kind of coal mixing combustion optimization system based on more coals |
| KR102271070B1 (en) * | 2020-03-31 | 2021-06-29 | 조선대학교산학협력단 | Method and apparatus for determining mixed coal combination |
Non-Patent Citations (1)
| Title |
|---|
| 何荣强等: "一种基于智能算法的燃煤配煤掺烧技术", 《能源与节能》, no. 10, 31 December 2019 (2019-12-31), pages 1 - 4 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110319455B (en) | Boiler coal blending method | |
| Harmsen et al. | Co-benefits of black carbon mitigation for climate and air quality | |
| Xu et al. | Carbon emissions reductions oriented dynamic equilibrium strategy using biomass-coal co-firing | |
| Truong et al. | Reducing emissions of the fast growing Vietnamese coal sector: The chances offered by biomass co-firing | |
| CN111091230A (en) | Scientific coal blending co-combustion system based on coal full-value chain management of coal-fired power plant | |
| Ubando et al. | Optimal integration of a biomass‐based polygeneration system in an iron production plant for negative carbon emissions | |
| CN108074020A (en) | A kind of intelligence coal mixing combustion optimization method and its system | |
| CN110135699A (en) | A kind of coal-burning power plant's inventory management system and method based on relation between supply and demand | |
| Sommersacher et al. | Combustion related characterisation of Miscanthus peat blends applying novel fuel characterisation tools | |
| Li et al. | An inexact fuzzy programming approach for power coal blending | |
| CN114925328A (en) | Coal blending and burning calculation method and system for coal-fired unit | |
| Masum et al. | Replacing coal in Georgia's power plants with woody biomass to increase carbon benefit: A mixed integer linear programming model | |
| Collado et al. | Almond shells and exhausted olive cake as fuels for biomass domestic boilers: optimization, performance and pollutant emissions | |
| Wils et al. | Reduction of fuel side costs due to biomass co-combustion | |
| Roslyakov et al. | Optimal choice of the best available technologies for Russian thermal power plants | |
| Bhatt et al. | Emission factors of industrial boilers burning biomass-derived fuels | |
| CN113887890A (en) | Coal blending and burning optimization method | |
| Jasinskas et al. | Investigation of boiler efficiency and harmful emissions while burning of plant biomass briquettes | |
| Adams | Sustainability of biomass for cofiring | |
| CN115345512A (en) | Coal-fired boiler mixed-combustion sludge coupling power generation regulation and control method, system, equipment and medium | |
| Dayton | Summary of NOx Emissions Reduction from Biomass Cofiring | |
| Hakala et al. | Biocarbon from bark or black pellets as an alternative for coal in steelmaking–techno-economic evaluation | |
| CN115221772B (en) | Dangerous waste incineration compatibility optimization method and device, terminal equipment and storage medium | |
| Zapata et al. | Combustion Performance of Agropellets in an Experimental Fixed Bed Reactor versus a Commercial Grate Boiler. Validation of Ash Behavior | |
| Lahai et al. | Technical considerations for the design and selection of improved cookstoves: a review |
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 |