CN108764604A - Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm - Google Patents
Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm Download PDFInfo
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Abstract
The Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm that the present invention relates to a kind of, including：According to pulverized coal preparation system thermal balance and operational safety boundary, the mechanism model that the pulverized coal preparation system being coupled with coal characteristic when grinding more coals controls is established in conjunction with expertise；The mechanism model controlled using the pulverized coal preparation system of foundation, the optimal control policy for instructing coal pulverizer to run is searched out based on evolution algorithm.It through the invention can be according to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of pulverized coal preparation system is calculated in advance, under the premise of safe and environment-friendly discharge index can be met the requirements, calculate the economic benefit under different mixed-fuel burning proportions, and find out to obtain the maximum best mixed-fuel burning proportion mixed and burn income, for instructing the optimization of boiler milling system to run.
Description
Technical field
The invention belongs to thermoelectricity technical field more particularly to a kind of Large-sized Coal-fired Power group powder based on Intelligent evolution algorithm
Optimal control method.
Background technology
Since coal resources in China is unevenly distributed, and coal market changes frequently, therefore frequently results in the combustion of Mei electricity enterprises
There is larger difference with coal and design coal.Domestic scholars have carried out a large amount of research to mixed coal combustion, mainly press calorific value
Requirement with volatile matter carries out a certain proportion of blending, and certain effect is achieved on coal adaptability, but is imitated in burning
There are still many problems for rate, slagging dust stratification, pollutant emission etc..Majority coal blending at power plant system is not perfect now, lacks
Sufficient scientific basis, the blindness and randomness of coal blending are all very strong, thus can not ensure coal blending quality.
In fact, mixed coal is mixed with process complexity, it is related to electricity power enterprise and marches into the arena from purchase coal to the whole process for entering stove burning, to whole
The interoperability requirement of a each link of process is higher.In information-based modern society, digitlization that many electricity power enterprises are built
Coal yard is the effective means for substituting old-fashioned artificial coal yard management, preferably solves under labor management mode coal source complexity not
A series of problems, such as manageability, the storage of more coals and resting period control.However the work of this respect is mostly to concentrate on to close at present
The statistics of coal yard coal load quantity is noted, and the influence to power generation downstream links is not given and is considered substantially.To coal process, system in transmission & distribution
Powder system control process even stove combustion process etc. often isolates progress of coming and individually manages and control, and causes more coals
Mix equipment during burning with contribute mismatch, boiler combustion is unstable or the problems such as slagging, seriously affected unit and
The safety of equipment and economy.
At present be directed to more combustion of mixed coal under the conditions of it is milling system optimized, generally use experimental study combination expert manually passes through
The method tested determines that safety margin, equipment is contributed and mixed-fuel burning proportion etc., for example, usually with the variation of coal pulverizer electric current, coal pulverizer into
The vibration of the variation of exit pressure drop, on the spot equipment and sound etc., which are used as, to be judged whether equipment is normal, mixes to match whether reach high specific
Example etc., lacks the judgement of precise quantification, and, outline also more fuzzy to control and the adjustment of Operational Parameters in Powder Making System belongs to one
The management and optimization of kind extensive style.The shortcomings that this optimization, is：There are deviations and uncertainty for the artificial experience of expert, and not
Can be in real time into line trace and guidance, to pulverized coal preparation system adjusting, there is also apparent hysteresis qualitys, can not be with existing coal source, coal rank structure
Present situation accurately matched.
Invention content
The Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm that the object of the present invention is to provide a kind of,
According to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of pulverized coal preparation system is calculated in advance, safe and environment-friendly
Under the premise of discharge index can be met the requirements, the economic benefit under different mixed-fuel burning proportions is calculated, and finds out to obtain maximum and mixes burning
The best mixed-fuel burning proportion of income, for instructing the optimization of boiler milling system to run.
The Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm that the present invention provides a kind of, including：
According to pulverized coal preparation system thermal balance and operational safety boundary, it is coupled with coal characteristic when grinding more coals in conjunction with expertise foundation
Pulverized coal preparation system control mechanism model；
The mechanism model controlled using the pulverized coal preparation system of foundation is searched out based on evolution algorithm and instructs coal pulverizer operation most
Excellent control strategy；
The optimal control policy includes：
It is predicted according to unit load, optimizes the strategy to coal on different run coal bins and coal pulverizer in advance, constantly keep powder processed
System is contributed to be adapted with unit load；
According to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of optimization pulverized coal preparation system, for referring to
Lead the reasonable method of operation of pulverized coal preparation system；Wherein, the safe operation boundary of the pulverized coal preparation system includes that the outlet of separate unit coal pulverizer is mixed
It closes at object temperature bound, separate unit coal pulverizer maximum output, mixture proportion, coal pulverizer combination and the desulphurization system limit
Reason ability；
According to unit operation historical data base and expert knowledge system, optimizing simultaneously formulates the coal pulverizer wind coal under different coals quality
Than curve and control strategy；
Intelligence computation and automatic decision are carried out according to combustion position, unit load and coal quality situation in stove, provides coal pulverizer
The Optimizing Suggestions for the number of units, coal pulverizer combination and each coal pulverizer coal-supplying amount allocation proportion of putting into operation, so that the input of pulverized coal preparation system
Match with stove combustion system.
Further, according to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation side of optimization pulverized coal preparation system
Boundary, for instructing the reasonable method of operation of pulverized coal preparation system to include：
Under the premise of safe and environment-friendly discharge index can be met the requirements, the economic benefit under different mixed-fuel burning proportions is calculated,
And find out to obtain the maximum best mixed-fuel burning proportion mixed and burn income, for instructing the optimization of boiler milling system to run.
Further, which is particle swarm optimization algorithm.
According to the above aspect of the present invention, by the Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm, it can
According to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of pulverized coal preparation system is calculated in advance, safe and environment-friendly
Under the premise of discharge index can be met the requirements, the economic benefit under different mixed-fuel burning proportions is calculated, and finds out to obtain maximum and mixes burning
The best mixed-fuel burning proportion of income, for instructing the optimization of boiler milling system to run.
Above description is only the general introduction of technical solution of the present invention, in order to better understand the technical means of the present invention,
And can be implemented in accordance with the contents of the specification, below with presently preferred embodiments of the present invention and after coordinating attached drawing to be described in detail such as.
Description of the drawings
Fig. 1 is the flow chart of particle swarm optimization algorithm；
Fig. 2 is the multiple-objection optimization flow chart based on particle swarm optimization algorithm.
Specific implementation mode
With reference to the accompanying drawings and examples, the specific implementation mode of the present invention is described in further detail.Implement below
Example is not limited to the scope of the present invention for illustrating the present invention.
Present embodiments provide a kind of Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm, the party
Method according to pulverized coal preparation system thermal balance and operational safety boundary, when grinding more coals in conjunction with expertise foundation with coal characteristic phase coupling
The mechanism model of the pulverized coal preparation system control of conjunction, is then based on evolution algorithm and searches out the optimum control plan for instructing coal pulverizer to run
Slightly.
This optimal control policy meets：
1) it is predicted according to unit load, optimizes the strategy to coal on different run coal bins and coal pulverizer in advance, constantly keep system
Powder system is contributed to be adapted with unit load, solves the problems, such as unit output deficiency common when coal mixing combustion.
2) according to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of optimization pulverized coal preparation system is such as ground
Coal machine exports mixture temperature range, separate unit coal pulverizer maximum output and mixture proportion etc., for instructing the reasonable of pulverized coal preparation system
The method of operation.
3) according to unit operation historical data base and expert knowledge system, optimizing simultaneously formulates the coal pulverizer wind under different coals quality
Coal realizes the optimum state of coal pulverizer operation than curve and control strategy.
4) intelligence computation and automatic decision are carried out according to the conditions such as combustion position, unit load, coal quality situation in stove, provided
Coal pulverizer puts into operation the Optimizing Suggestions such as number of units, coal pulverizer combination, each coal pulverizer coal-supplying amount allocation proportion, makes the throwing of pulverized coal preparation system
Enter and match with stove combustion system, promotes pulverized coal preparation system and mix the ability with low price colm.
Optimization fitness function (Fitness Function) used in the present embodiment is a kind of based on more combustion of mixed coal
Boiler economics evaluation model, under the premise of safe and environment-friendly discharge index can be met the requirements, in real time calculate difference mix burning
Burning economic benefit is mixed under ratio, and the coal blending in advance of boiler, upper coal, powder processed can be instructed according to final calculated maximum return
Equal work.
Evolution algorithm used in the present embodiment is particle swarm optimization algorithm (Particle Swarm
Optimization), the Optimal Control Problem that pulverized coal preparation system how is successfully solved using the algorithm is introduced below.
Invention is further described in detail below.
1, the principle analysis of particle swarm optimization algorithm.
In the practical application of particle optimization algorithm, the potential solution of each optimization problem can be imagined as D dimension search skies
Between in a particle, all particles all there are one by object function (Fitness Function) determine fitness value
(Fitness Value), these particles are flown in search space with certain speed, and the size and Orientation of speed is according to grain
The flying experience of son itself and the flying experience of entire population are adjusted into Mobile state.With that is, all particles can follow it is current most
Excellent particle is searched in solution space.
Assuming that found in minimum problems at one, need to find under optimal solution x makes multidimensional objective function f (x) meet
Formula,
X=argminf (x)；
In the target search space of D dimensions, a group is formed by N number of particle, wherein i-th of particle is expressed as one
The vector of a D dimensionsThat is position of i-th of the particle in D dimension morals search space is
In other words, the position of each particle is exactly a potential solution of optimization problem.It willSubstitute into object function (Fitness
Function its adaptive value) can be calculated, size is obtained according to adaptive value to weighQuality.If the adaptation of each particle
Value is Fitnessi(i∈[1,N]).The flying speed of i-th of particle is also the vector of D dimensions, is denoted asRemember that the optimal location that i-th of particle searches so far isIt is whole
The optimal location that a population searches so far is gbest=(g1,g2,...,gD).The operational mode of each particle is not only
Depending on flying experience (the i.e. p of itselfbest), also suffer from influence (the i.e. g of the flying experience of entire populationbest).Therefore, grain
Subgroup optimization algorithm can ensure that final result is global optimum, rather than be trapped in local optimum.Particle Swarm Optimization
The process of method is as described below：
1) initial value of N number of particle, each particle x are setiOne potential solution of representing optimized problem, i ∈ [1, N].It is right
In minimum value optimization problem, the adaptive value Fitness of each particlei, the optimal location of each particleMost with entire population
Excellent position gbestAll it is set as infinitely great.
2) reach the greatest iteration number t of setting in iterations tmaxBefore, alternatively, not meeting the feelings of certain end condition
Under condition, repeat the steps of in each iteration：
A, the adaptive value of each particle, Fitness are calculatedi=f (xi)；
B, the optimal location that each particle searches so far is updated
C, the optimal location that entire population searches so far is updated
It is d, improved according to the following formula,
xi,t+1=xi,t+ui,t+1,
Wherein, u in formulai,t+1It is defined as
Wherein, ui,tI-th of particle is represented in the flying speed of time period t, ui,t+1I-th of particle is represented in the next time
The flying speed of section t+1, ω is less than 1 constant, for feed back particle time period t flying speed to next time period t+
The influence of 1 flying speed.xi,tRepresent the current position of i-th of particle.Studying factors c1And c2It is that these variables fly to determining
The weighted value that scanning frequency degree influences.r1And r2It is the arbitrary constant between [0,1], enchancement factor is added to algorithm.
T=t+1.
3) iteration terminates, it can be deduced that meets the optimal solution x of multidimensional objective function f (x).
The flow chart of particle swarm optimization algorithm is as shown in Figure 1.
2, mixture proportion is calculated using PSO Multipurpose Optimal Methods.
(1) each particle represents a kind of potential feasible mixture proportion.
(2) it is directed to each particle, i.e. each mixture proportion, pulverizer outlet temperature t can be calculated2Upper and lower limit.
I. upper limit calculation formula is as follows：
A) (after separator) direct-firing to medium-speed pulverizer：
Work as VdafWhen < 40%,
Work as VdafWhen >=40%, t2=60~70 DEG C.
B) to low speed coal mill bunker formula (after coal pulverizer)：
Meager coal, 100 DEG C~130 DEG C；
Bituminous coal, 70 DEG C~90 DEG C；
Lignite, 60 DEG C~70 DEG C.
C) double-in and double-out ball mill is direct-firing (after coal pulverizer)：
Meager coal, 100 DEG C~130 DEG C；
Bituminous coal, 70 DEG C~90 DEG C；
Lignite, 60 DEG C~70 DEG C.
Ii. lower limit calculation formula is as follows：
A) pulverizer outlet temperature t2It should be higher than that dew-point temperature tdp, and cannot be below 60 DEG C, i.e., the two takes high level.
For the ball type pulverizer system：t2min=tdp+5℃；
For unit pulverized-coal system：t2min=tdp+2℃。
In formula：tdp--- dew-point temperature, DEG C.
B) dew-point temperature calculates
The dew-point temperature of coal pulverizer outlet is calculated, because the free surface moisture in raw coal has been introduced into wind powder mixture
(can simplify and think that free surface moisture is all introduced into wind powder mixture), so the water capacity in air should include in raw coal at this time
Free surface moisture.
Work as d2When=3.8g/kg~60g/kg,
Work as d2When=61g/kg~825g/kg,
In formula：Pa --- local BAP Barometric Absolute Pressure, kPa；
D2 --- every kilogram of drier (air) water capacity is (i.e. in wind powder mixture：Included former moisture in coal containing wet
Amount), g/kg.
It is calculated as follows when only making drier with air：
In formula：g1--- enter the drying dosage of coal pulverizer, can be incited somebody to action when calculating dew-point temperatureAs dry
Drying prescription amount；
Kle--- pulverized coal preparation system air leak rate of air curtain, value are as follows：Low speed coal mill, warehouse formula is 0.2~0.4, direct-firing to be
0.25；Medium-speed pulverizer, direct-firing negative pressure is 0.2；
D --- air humidity content usually takes d=10g/kg；
Δ M --- the water that every kilogram of raw coal is evaporated by drying.
In formula：Mar--- it is raw coal moisture as received coal, %；
Mpc--- the moisture of coal dust, % are exported for coal pulverizer.
(3) mathematical model of milling system optimized control problem is：
Max, which mixes, burns income (each mixed-fuel burning proportion particle can calculate corresponding mix and burn income)
min|qin-qout| (each mixed-fuel burning proportion particle, can calculate corresponding qinAnd qout)
S.t. each mixture proportion corresponds to the upper limit≤pulverizer outlet temperature t2≤each mixture proportion and corresponds to lower limit.
(4) the multiple-objection optimization flow based on particle swarm optimization algorithm is as shown in Figure 2.
Correlation computations formula is as follows：
1, the calculating that mill drying is contributed
1) total heat q is inputtedin(kJ/kg)
qin=qag1+qmac；
In formula：qag1--- the physical thermal of drier, kJ/kg；
qmac--- the machinery heat that coal pulverizer work generates, kJ/kg.
qag1=cag1t1g1；
In formula：t1--- the mixed just temperature (being regarded as coal pulverizer inlet wind-warm syndrome) of each ingredient drier, DEG C；
cag1--- in t1At a temperature of specific heat capacity of mass after each ingredient drier weighted average, kJ/ (kg DEG C)；
qmac=Kmace；
In formula：Kmac--- mechanical thermal transition coefficient；To low speed coal mill, it is taken as 0.7；To medium-speed pulverizer, it is taken as 0.6；
E --- unit coal-grinding power consumption；Low speed coal mill burn anthracite when e=90~110, burn bituminous coal when e=55~90, burn
E=35~65 when lignite, e=30~58 when burning slum, E types middling speed and HP (RP) type medium-speed pulverizer e=22~36, MPS
(ZGM) type coal pulverizer e=20~30, kJ/kg.
2) pulverized coal preparation system drying grinds the heat q that the ribbings 1kg go out and consumeout(kJ/kg)
qout=qev+qag2+qf+q5；
In formula：qev--- the heat of water consumption, kJ/kg in evaporation raw coal；
qag2--- the heat that steam exhaust drier is taken out of, kJ/kg；
qf--- the heat of heating fuel consumption, kJ/kg；
q5--- equipment cooling loses, kJ/kg.
qev=Δ M (2500+c "H2Ot2-4.187trc)；
In formula：c″H2O--- vapor is in t2At a temperature of average specific heat at constant pressure hold, kJ/ (kg DEG C)；
T --- steam temperature exports medium temperature t for coal pulverizer herein2, DEG C；
trc--- raw coal temperature is right(QNet, arUnit is MJ/kg, MtFor raw coal total moisture) height
Moisture fuel trc=20 DEG C, to other fuel trc=0 DEG C, DEG C.
In formula：ca2--- in temperature t2When humid air specific heat capacity, kJ/ (kg DEG C)；
cda--- the specific heat capacity of dry air, kJ/ (kg DEG C)；
The temperature of t --- dry air exports medium temperature t for coal pulverizer herein2, DEG C；
c″H2O--- the vapor specific heat capacity at temperature identical as dry air, kJ/ (kg DEG C)；
The water content of d --- air, g/kg.
When using bituminous coal：cdc=0.0034t+0.8796
When using meager coal：cdc=0.0032t+0.8136
When using lignite：cdc=0.0031t+0.9332
When using anthracite：cdc=0.0011t+0.7684
When using slum：cdc=0.0014t+0.8562
In formula：cdc--- the specific heat capacity of moisture-free coal, with the sum of the out temperature of coal, kJ/ (kg DEG C).
Equipment cooling loses q5It can be calculated as follows：
When using warehouse formula system, q5=0.05qin；
When using direct-firing system, q5=0.02qin。
2, burning income calculation is mixed
A) coal price calculates：
Coal i marks are single：Wherein, Qnet ' ar are the low heat valve of raw coal；Marking coal calorific value is
7000kcal/kg or 29308kJ/kg, P are to be manually entered value.
B) net coal consumption rate calculates：
Coal consumption for power generation：Wherein, turbine heat rate is calculated according to 8000kg/kwh；Pipe
Road efficiency=0.99 calculates.
Net coal consumption rate：Wherein, Lfcy is station service power consumption rate.
c)
D) desulphurization system supplies consumption calculates：
Increase cost：
Unit increases cost：
The present invention calculates the safe operation side of pulverized coal preparation system in advance according to the characteristic of different types of coal and different pulverized coal preparation systems
Boundary, such as separate unit coal pulverizer exports mixture temperature bound, separate unit coal pulverizer maximum output, coal pulverizer combination and takes off
Sulphur system limits processing capacity etc..Under the premise of safe and environment-friendly discharge index can be met the requirements, different mixed-fuel burning proportions are calculated
Under economic benefit, and find out to obtain the maximum best mixed-fuel burning proportion mixed and burn income, the optimization for instructing boiler milling system
Operation.Therefore, it although burned coal price and coal property are all changing at any time, still can finally be determined according to burning benefit is mixed
Optimal dynamic mixed-fuel burning proportion.
The above is only a preferred embodiment of the present invention, it is not intended to restrict the invention, it is noted that for this skill
For the those of ordinary skill in art field, without departing from the technical principles of the invention, can also make it is several improvement and
Modification, these improvements and modifications also should be regarded as protection scope of the present invention.
Claims (3)
1. a kind of Large-sized Coal-fired Power group pulverizing optimization control method based on Intelligent evolution algorithm, which is characterized in that including：According to
The system being coupled with coal characteristic when grinding more coals is established on pulverized coal preparation system thermal balance and operational safety boundary in conjunction with expertise
The mechanism model of powder system control；
The mechanism model controlled using the pulverized coal preparation system of foundation, the optimal control for instructing coal pulverizer to run is searched out based on evolution algorithm
System strategy；
The optimal control policy includes：
It is predicted according to unit load, optimizes the strategy to coal on different run coal bins and coal pulverizer in advance, constantly keep pulverized coal preparation system
It contributes and is adapted with unit load；
According to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of optimization pulverized coal preparation system is used for guidance system
The reasonable method of operation of powder system；Wherein, the safe operation boundary of the pulverized coal preparation system includes separate unit coal pulverizer outlet mixture
Temperature bound, separate unit coal pulverizer maximum output, mixture proportion, coal pulverizer combination and desulphurization system limit process energy
Power；
According to unit operation historical data base and expert knowledge system, optimizing simultaneously formulates the coal pulverizer coal-air ratio song under different coals quality
Line and control strategy；
Intelligence computation and automatic decision are carried out according to combustion position, unit load and coal quality situation in stove, coal pulverizer is provided and puts into operation
The Optimizing Suggestions of number of units, coal pulverizer combination and each coal pulverizer coal-supplying amount allocation proportion, so that the input and stove of pulverized coal preparation system
Combustion system matches.
2. the Large-sized Coal-fired Power group pulverizing optimization control method according to claim 1 based on Intelligent evolution algorithm, special
Sign is, according to the characteristic of different types of coal and different pulverized coal preparation systems, the safe operation boundary of optimization pulverized coal preparation system, for instructing
The reasonable method of operation of pulverized coal preparation system includes：
Under the premise of safe and environment-friendly discharge index can be met the requirements, the economic benefit under different mixed-fuel burning proportions is calculated, and look for
Go out to obtain the maximum best mixed-fuel burning proportion mixed and burn income, for instructing the optimization of boiler milling system to run.
3. the Large-sized Coal-fired Power group pulverizing optimization control method according to claim 1 or 2 based on Intelligent evolution algorithm,
It is characterized in that, the evolution algorithm is particle swarm optimization algorithm.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112183924A (en) * | 2020-08-25 | 2021-01-05 | 华能国际电力股份有限公司上安电厂 | Coal blending and blending combustion method for thermal power generating unit |
| CN112418527A (en) * | 2020-11-24 | 2021-02-26 | 西安热工研究院有限公司 | Optimal coal blending ratio calculation and judgment method based on boiler side index and fuel price |
| CN113654075A (en) * | 2021-07-06 | 2021-11-16 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Method and device for predicting lignite blending combustion ratio of coal-fired boiler |
| CN114415601A (en) * | 2021-11-25 | 2022-04-29 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Boiler overall coordination real-time intelligent optimization system and method for thermal power generating unit |
| CN114647191A (en) * | 2022-03-28 | 2022-06-21 | 华北电力大学 | Optimized scheduling method of pulverizing system based on boiler heat load balanced distribution |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645523A (en) * | 2012-05-10 | 2012-08-22 | 北京华电天仁电力控制技术有限公司 | Moisture as received coal on-line identification method based on heat balance of powder process system |
| US20130030573A1 (en) * | 2010-03-24 | 2013-01-31 | Abb Research Ltd | Computer-based method and device for automatically providing control parameters for a plurality of coal mills supplying coal powder to a plant |
| CN103345213A (en) * | 2013-06-09 | 2013-10-09 | 华电电力科学研究院 | Device and method for fire coal management and combustion strategy optimization in variable coal quality conditions of coal-fired thermal power generating unit |
| CN105844369A (en) * | 2016-04-18 | 2016-08-10 | 东南大学 | Pulverizing system optimal distribution method based on self-adaptive chaos particle swarm |
| CN107016176A (en) * | 2017-03-24 | 2017-08-04 | 杭州电子科技大学 | A kind of hybrid intelligent overall boiler burning optimization method |
| CN107274027A (en) * | 2017-06-22 | 2017-10-20 | 湖南华润电力鲤鱼江有限公司 | A kind of many coal coal mixing combustion optimization methods of coal unit |
| CN107301477A (en) * | 2017-06-22 | 2017-10-27 | 湖南华润电力鲤鱼江有限公司 | A kind of coal-fired procurement decisions method based on many coal coal mixing combustion optimizing models |
| 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 |
-
2018
- 2018-04-04 CN CN201810300690.0A patent/CN108764604B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130030573A1 (en) * | 2010-03-24 | 2013-01-31 | Abb Research Ltd | Computer-based method and device for automatically providing control parameters for a plurality of coal mills supplying coal powder to a plant |
| CN102645523A (en) * | 2012-05-10 | 2012-08-22 | 北京华电天仁电力控制技术有限公司 | Moisture as received coal on-line identification method based on heat balance of powder process system |
| CN103345213A (en) * | 2013-06-09 | 2013-10-09 | 华电电力科学研究院 | Device and method for fire coal management and combustion strategy optimization in variable coal quality conditions of coal-fired thermal power generating unit |
| CN105844369A (en) * | 2016-04-18 | 2016-08-10 | 东南大学 | Pulverizing system optimal distribution method based on self-adaptive chaos particle swarm |
| CN107016176A (en) * | 2017-03-24 | 2017-08-04 | 杭州电子科技大学 | A kind of hybrid intelligent overall boiler burning optimization 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 |
| CN107274027A (en) * | 2017-06-22 | 2017-10-20 | 湖南华润电力鲤鱼江有限公司 | A kind of many coal coal mixing combustion optimization methods of coal unit |
| CN107301477A (en) * | 2017-06-22 | 2017-10-27 | 湖南华润电力鲤鱼江有限公司 | A kind of coal-fired procurement decisions method based on many coal coal mixing combustion optimizing models |
Non-Patent Citations (2)
| Title |
|---|
| 梁学东 等: "1000MW机组锅炉低热值煤种掺烧的经济性分析", 《热力发电》 * |
| 陈刚 等: "火电机组混煤掺烧全程动态优化***开发与应用", 《中国电力》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112183924A (en) * | 2020-08-25 | 2021-01-05 | 华能国际电力股份有限公司上安电厂 | Coal blending and blending combustion method for thermal power generating unit |
| CN112183924B (en) * | 2020-08-25 | 2022-05-24 | 华能国际电力股份有限公司上安电厂 | Coal blending and blending combustion method for thermal power generating unit |
| CN112418527A (en) * | 2020-11-24 | 2021-02-26 | 西安热工研究院有限公司 | Optimal coal blending ratio calculation and judgment method based on boiler side index and fuel price |
| CN112418527B (en) * | 2020-11-24 | 2023-04-07 | 西安热工研究院有限公司 | Optimal coal blending ratio calculation and judgment method based on boiler side index and fuel price |
| CN113654075A (en) * | 2021-07-06 | 2021-11-16 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Method and device for predicting lignite blending combustion ratio of coal-fired boiler |
| CN113654075B (en) * | 2021-07-06 | 2024-03-26 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Method and device for predicting lignite blending combustion proportion of coal-fired boiler |
| CN114415601A (en) * | 2021-11-25 | 2022-04-29 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Boiler overall coordination real-time intelligent optimization system and method for thermal power generating unit |
| CN114647191A (en) * | 2022-03-28 | 2022-06-21 | 华北电力大学 | Optimized scheduling method of pulverizing system based on boiler heat load balanced distribution |
| CN114997529A (en) * | 2022-07-18 | 2022-09-02 | 西安热工研究院有限公司 | Full life cycle management method, system, equipment and storage medium for powder process system |
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