CN109325313A - Based on improvement quantum telepotation boiler of power plant NOXPrediction model device - Google Patents
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- 239000002245 particle Substances 0.000 claims abstract description 55
- 238000005457 optimization Methods 0.000 claims abstract description 40
- 239000003245 coal Substances 0.000 claims abstract description 30
- 238000004891 communication Methods 0.000 claims abstract description 14
- 238000009434 installation Methods 0.000 claims abstract description 10
- 230000007423 decrease Effects 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 9
- 230000003247 decreasing effect Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 239000003546 flue gas Substances 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 2
- 239000007789 gas Substances 0.000 claims 1
- 230000008901 benefit Effects 0.000 abstract description 3
- 239000003344 environmental pollutant Substances 0.000 abstract description 2
- 231100000719 pollutant Toxicity 0.000 abstract description 2
- 239000000779 smoke Substances 0.000 description 7
- 238000000034 method Methods 0.000 description 5
- 238000010248 power generation Methods 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 3
- 238000013178 mathematical model Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
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- 230000006399 behavior Effects 0.000 description 1
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- 230000001276 controlling effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000005610 quantum mechanics Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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Abstract
The invention discloses one kind based on improvement quantum telepotation boiler of power plant NOxPrediction model device, including on-site data gathering device, computer installation and communication device.On-site data gathering device is to establish boiler NO in next stepxConcentration of emission prediction model is prepared；Computer installation implementation model algorithm operation using minimizing the error as target for training data predicted value and actual value, and then obtains accurate NO using the initial parameter of improved quantum particle swarm optimization optimization extreme learning machinexDischarge model.Advantages of the present invention: extreme learning machine optimal initial parameter efficiently can quickly be calculated by improved quanta particle swarm optimization, and then obtain accurate power plant boiler NOxModel is discharged, pollutant emission is reduced for coal unit and is of great significance.
Description
Technical field
The present invention relates to coal-fired plant boiler NOxEmitted smoke technical field, more particularly to one kind based on improvement quanta particle
Group's optimization boiler of power plant NOxPrediction model device.
Background technique
Coal is one of the main energy sources in China, accounts for 70% or so of non-renewable energy production and consumption, this based on coal
Energy resource structure determines that coal-fired thermal power generation is occupied an leading position in the power generation in China.According to China Electricity Council
The statistical data of announcement, annual thermal power output accounts for the 78% of total power generation within 2012.As can be seen that thermal power generation is still
The major way of China's power generation.The NO that coal-fired power plant's fuel combustion generatesxBe atmosphere pollution main harmful substance it
One.Construct accurate NOxThe necessary condition that emitted smoke model controls it.Therefore, effective NO is establishedxEmission performance prediction
It is particularly important that model reduces pollutant emission to power station.However, generating NOxReaction it is extremely complex, be difficult to establish accurate NOx
First mathematical model of discharge, "black box" data-driven modeling method neural network based can ignore model reaction principle,
It has been widely used in each engineering field.But there are the training time is long and be easy to appear " over-fitting " etc. and ask for traditional neural network
Topic.Extreme learning machine (ELM) is a kind of novel feedforward neural network, has pace of learning fast, and adjustment parameter is few, and estimated performance is high
The advantages of.But input layer initial weight and hidden layer biasing are determining at random in ELM training process, this will affect limit study
The stability of machine.In this regard, proposing the new optimization algorithm of one kind combines modeling with ELM.Quanta particle swarm optimization (QPSO) exists
In basic principle equally with particle swarm algorithm (PSO), the iterative process of each particle is all based on individual optimal and group most
Excellent information is updated.Unlike PSO algorithm, QPSO algorithm carries out each particle with the behavior of quantum
Movement, each particle can appear in solution space probabilityly and any one of work as position, largely enhance grain
The randomness of son movement and validity quantum particle swarm (QPSO) algorithm of algorithm global optimizing are on the basis of particle swarm algorithm
A kind of intelligent optimization algorithm for combining quantum mechanics correlation theory and being formed.But QPSO algorithm search late convergence
Slow and low search precision phenomenon, for this solution it is required that proposing new improved quantum particle swarm optimization optimization
Extreme learning machine model inner parameter, to establish accurate coal fired boiler of power plant NOxDischarge model.
Summary of the invention
In view of the above problems, it is an object of the invention to be related to a kind of accurate boiler NOxConcentration of emission prediction model device, with
Solve current boiler NOxThe situation of concentration prediction model inaccuracy, and then convenient for controlling and reducing boiler NOxEmission effect.
To solve the above problems, the present invention provides one kind based on improvement quantum telepotation boiler of power plant NOxPrediction model
Device, including on-site data gathering device, computer installation and communication device.On-site data gathering device include measuring cell,
DCS data collection station and communication interface.Computer installation include host (built-in model algorithm and storage hard disk), communication interface,
Display and keyboard.Communication device includes communication module and signal wire, and wherein signal wire is connected with field control station.
Further, described based on the boiler of power plant NO for improving quantum telepotationxPrediction model device, described in scene
Data acquisition device is to establish NO in next stepxConcentration of emission prediction model is prepared.When due to the variation of unit service condition, such as coal
Kind replacement, the variation of unit AGC load instruction frequent fluctuation, burning condition (such as air distribution mode, excess air coefficient), all can
Make flue gas NOxOccur compared with large disturbances.By to coal unit boiler combustion and NOxFormation mechanism analysis, determines NOxConcentration of emission
The input variable of prediction model.
The boiler of power plant NO based on improvement quantum telepotationxPrediction model device, described in computer installation
Implementation model algorithm operation.In order to improve extreme learning machine (ELM) modeling accuracy, propose more than one kind in conjunction with cosine decreasing function
String successively decreases quantum particle swarm optimization (COSQPSO), and using using cosine successively decrease quantum particle swarm optimization optimize
ELM mode input layer weight and hidden layer bias, establish effective NOxEmitted smoke model.Wherein, cosine successively decreases quantum
Particle swarm optimization algorithm is described in detail below:
(a) initialization algorithm parameter determines search space Search Range, determines the dimension of objective optimisation problems, sets population number
Mesh, maximum number of iterations and primary position x；
(b) particle initial position in population is updated in the function of objective optimisation problems, it is optimal calculates primary individual
Value pbestWith population global optimum gbest；
Wherein, l is the sequence being randomly generated, and the size of l is population quantity, and d is to obey equally distributed random number, ave_
Best is the average of single search particle optimal value, and b is the scaling coefficient of from 1 to 0.5 linear decrease.
(c) identical as quanta particle swarm optimization, kind of group mean desired positions ave_best and each is calculated according to the following formula
Particle is between pbestAnd gbestBetween random site P；
Wherein,For random number equally distributed on [0,1]；Pid(t) be i-th particle iteration t times when individual optimal value；
pgd(t) be the t times iteration when global optimum；P (t) is between individual optimal value Pid(t) with global optimum pgd(t) it
Between a random value；β is converging diverging coefficient.
(d) different from quanta particle swarm optimization, the converging diverging factor beta of improved quanta particle swarm optimization is by improved plan
It calculates, i.e.,
β=1-cos ((1-t/T) pi/2)
(e) new β calculation method is substituted into following formula, all particles in population is carried out more according to particle more new formula
Newly
Wherein, u is the equally distributed random number on [0,1]；
(f) the new fitness of each particle is calculated, and according to principle of optimality to original pbestWith gbestIt is replaced or retains；
(g) judge whether the target value after whether iteration reaches maximum times or optimization reaches aimed at precision, if then algorithm
Iteration is terminated, otherwise return step (c) continues iteration.
It being randomly generated due to ELM model parameter, this affects the precision of prediction of ELM model to a certain extent, therefore, benefit
With the weight and threshold value of the DEQPSO algorithm optimization model proposed, it is optimal network structure.
Boiler load (WM), total blast volume (th-1), coal pulverizer A coal-supplying amount have been selected respectively by Analysis on Mechanism and actual conditions
(th-1), coal pulverizer B coal-supplying amount (th-1), coal pulverizer C coal-supplying amount (th-1), coal pulverizer D coal-supplying amount (th-1), mill
Coal machine E coal-supplying amount (th-1), coal pulverizer F coal-supplying amount (th-1), two sides secondary air flow (th-1), two after-flame windshield plates
Aperture (%), six coal pulverizer First air air quantity (th-1), six secondary baffle openings (%) become as the input of model
Amount.
The present invention optimizes extreme learning machine model built using improved quanta particle swarm optimization, to solve current boiler smoke
NOxThe insufficient situation of precision of prediction.For power plant boiler NOxDischarge model foundation difficulty and quanta particle swarm optimization are being searched for
Later period has that search precision is insufficient, and present invention combination cosine decreasing function proposes that a kind of cosine quantum particle swarm that successively decreases is excellent
Change algorithm (COQPSO).Successively decreased quantum particle swarm optimization optimization ELM Parameters in Mathematical Model using cosine, is established accurate
NOxEmitted smoke model.
The beneficial effects of the present invention are: by based on improved cosine successively decrease quantum particle swarm optimization optimization ELM
Model can be effectively to NOxDischarge is predicted, and precision of prediction is higher, provides a kind of feasible coal-fired pot for thermal power plant
Furnace NOxEmitted smoke method.
Detailed description of the invention
Fig. 1 is in the present embodiment based on the boiler of power plant NO for improving quantum telepotationxThe structure of prediction model device is shown
It is intended to, in which: 1, measuring cell, 2, DCS data collection station, 3, communication interface, 4, (built-in model algorithm and storage are hard for host
Disk), 5, communication interface, 6, display, 7, keyboard, 8, communication module, 9, signal wire, 10, field control station.
Fig. 2 is in the present embodiment based on the boiler of power plant NO for improving quantum telepotationxComputer in prediction model device
The algorithm steps flow chart of device.
Fig. 3 is to improve quanta particle swarm optimization in the present embodiment to optimize extreme learning machine model flow figure；
Specific embodiment
Embodiments of the present invention are described in detail with reference to the accompanying drawing.It is of the invention based on improving quantum telepotation
Boiler of power plant NOxPrediction model device, specifically includes:
(1) on-site data gathering device, by related data in historical data base in acquisition DCS system, to establish pot in next step
Furnace NOxConcentration of emission prediction model is prepared.When due to the variation of unit service condition, such as coal replacement, unit AGC load instruction
The variation of frequent fluctuation, burning condition (such as air distribution mode, excess air coefficient), can all make flue gas NOxOccur compared with large disturbances.
In addition, boiler NOxThe factors such as concentration of emission and coal-supplying amount, primary air flow, secondary air flow, secondary air register baffle opening are related.Most
NO is determined afterwardsxThe input variable of concentration of emission prediction model is respectively as follows: boiler load (WM), total blast volume (th-1), coal pulverizer A
Coal-supplying amount (th-1), coal pulverizer B coal-supplying amount (th-1), coal pulverizer C coal-supplying amount (th-1), coal pulverizer D coal-supplying amount (t
H-1), coal pulverizer E coal-supplying amount (th-1), coal pulverizer F coal-supplying amount (th-1), two sides secondary air flow (th-1), two combustions
Windshield plate aperture (%), six coal pulverizer First air air quantity (th-1), six secondary baffle openings (%) are as model to the greatest extent
Input variable, but not limited to this, in practical applications, those skilled in the art can adjust variable according to the actual situation.
(2) computer installation, implementation model algorithm operation.In order to improve the modeling accuracy of ELM, in order to improve extreme learning machine
(ELM) modeling accuracy, present invention combination cosine decreasing function propose that a kind of cosine successively decreases quantum particle swarm optimization
(COQPSO).Using cosine successively decrease quantum particle swarm optimization optimization ELM Parameters in Mathematical Model, establish accurate NOxRow
Put prediction model.
The sampling period is set as 30s, acquires the operation data of correlated variables, and is pre-processed, comprising: coarse value is rejected and filter
Wave.Wherein, it is based on improved difference quanta particle swarm optimization
(a) initialization algorithm parameter determines search space Search Range, determines the dimension of objective optimisation problems, sets population number
Mesh, maximum number of iterations and primary position x；
(b) particle initial position in population is updated in the function of objective optimisation problems, it is optimal calculates primary individual
Value pbestWith population global optimum gbest；
Wherein, l is the sequence being randomly generated, and the size of l is population quantity, and d is to obey equally distributed random number, ave_
Best is the average of single search particle optimal value, and b is the scaling coefficient of from 1 to 0.5 linear decrease.
(c) identical as quanta particle swarm optimization, kind of group mean desired positions ave_best and each is calculated according to the following formula
Particle is between pbestAnd gbestBetween random site P；
Wherein,For random number equally distributed on [0,1]；Pid(t) be i-th particle iteration t times when individual optimal value；
pgd(t) be the t times iteration when global optimum；P (t) is between individual optimal value Pid(t) with global optimum pgd(t) it
Between a random value；β is converging diverging coefficient.
(d) different from quanta particle swarm optimization, the converging diverging factor beta of improved quanta particle swarm optimization is by improved plan
It calculates, i.e.,
β=1-cos ((1-t/T) pi/2)
(e) new β calculation method is substituted into following formula, all particles in population is carried out more according to particle more new formula
Newly
Wherein, u is the equally distributed random number on [0,1]；
(f) the new fitness of each particle is calculated, and according to principle of optimality to original pbestWith gbestIt is replaced or retains；
(g) judge whether the target value after whether iteration reaches maximum times or optimization reaches aimed at precision, if then algorithm
Iteration is terminated, otherwise return step (c) continues iteration.
The Search Range of third step, setting extreme learning machine weight determines extreme learning machine model hidden layer node number, optimizes
The objective function of process is
In formula, yiActual value is expressed as,Represent predicted value.
By the Modeling Calculation of computer installation, the higher NO of precision is obtainedxEmitted smoke model, can be in this model
Correlated variables, control and reduction NO are adjusted on basisxDischarge.
Fig. 1 is in the present embodiment based on the boiler of power plant NO for improving quantum telepotationxThe structure of prediction model device is shown
It is intended to；Fig. 2 is in the present embodiment based on the boiler of power plant NO for improving quantum telepotationxComputer in prediction model device
The algorithm steps flow chart of device；Fig. 3 is to improve quanta particle swarm optimization in the present embodiment to optimize extreme learning machine model flow
Figure；
The foregoing is merely a specific embodiment of the invention, but scope of protection of the present invention is not limited thereto, any to be familiar with
Those skilled in the art are all in range disclosed by the invention, and made similar variation or equivalent replacement should all cover
Within the scope of the present invention.
Claims (4)
1. one kind is based on improvement quantum telepotation boiler of power plant NOxPrediction model device, which is characterized in that described device packet
Include: on-site data gathering device, computer installation and communication device, the on-site data gathering device include measuring cell, DCS
Data collection station and communication interface, the computer installation include host (built-in model algorithm and storage hard disk), communication interface,
Display and keyboard, the communication device include communication module and signal wire, and wherein signal wire is connected with field control station.
2. according to claim 1 a kind of based on improvement quantum telepotation boiler of power plant NOxPrediction model device,
It is characterized in that, the on-site data gathering device is to establish boiler NO in next stepxConcentration of emission prediction model is prepared, due to machine
When group service condition variation, such as coal-supplying amount, unit AGC load instruction frequent fluctuation, burning condition (such as air distribution mode, excessive sky
Gas coefficient etc.) variation, can all make flue gas NOxOccur compared with large disturbances, by coal unit boiler and NOxMechanism of production analysis,
Determine NOxThe input variable of concentration of emission prediction model is boiler load, total blast volume, coal pulverizer A coal-supplying amount, coal pulverizer B to coal
Amount, coal pulverizer C coal-supplying amount, coal pulverizer D coal-supplying amount, coal pulverizer E coal-supplying amount, coal pulverizer F coal-supplying amount, two sides secondary air flow, two
Burnout degree baffle opening, six coal pulverizer First air air quantity, six secondary baffle openings.
3. according to claim 1 a kind of based on improvement quantum telepotation boiler of power plant NOxPrediction model device,
The computer installation implementation model algorithm operation proposes to combine cosine to improve extreme learning machine (ELM) modeling accuracy
Decreasing function proposes that a kind of cosine successively decreases quantum particle swarm optimization (COSQPSO), and utilizes cosine decrement seed
Subgroup optimization algorithm optimizes the weight and hidden layer bias of ELM mode input layer, establishes effective NOx emission characteristic mould
Type, wherein be described in detail below based on quanta particle swarm optimization is improved:
(a) initialization algorithm parameter determines search space Search Range, determines the dimension of objective optimisation problems, sets population number
Mesh, maximum number of iterations and primary position x；
(b) particle initial position in population is updated in the function of objective optimisation problems, it is optimal calculates primary individual
Value pbestWith population global optimum gbest；
(c) identical as quanta particle swarm optimization, kind of group mean desired positions ave_best and each is calculated according to the following formula
Particle is between pbestAnd gbestBetween random site P；
Wherein,For random number equally distributed on [0,1]；Pid(t) be i-th particle iteration t times when individual optimal value；
pgd(t) be the t times iteration when global optimum；P (t) is between individual optimal value Pid(t) with global optimum pgd(t) it
Between a random value；β is converging diverging coefficient；
(d) different from quanta particle swarm optimization, the converging diverging factor beta of improved quanta particle swarm optimization is by improved plan
It calculates, i.e.,
β=1-cos ((1-t/T) pi/2)
(e) new β calculation method is substituted into following formula, all particles in population is carried out more according to particle more new formula
Newly
Wherein, u is the equally distributed random number on [0,1]；
(f) the new fitness of each particle is calculated, and according to principle of optimality to original pbestWith gbestIt is replaced or retains；
(g) judge whether the target value after whether iteration reaches maximum times or optimization reaches aimed at precision, if then algorithm
Iteration is terminated, otherwise return step (c) continues iteration.
4. according to claim 1 a kind of based on improvement quantum telepotation boiler of power plant NOxPrediction model device,
It is characterized in that, the Search Range of setting extreme learning machine weight determines extreme learning machine model hidden layer node number, optimizes
The objective function of journey is
In formula, yiActual value is expressed as,Represent predicted value.
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| CN109492807A (en) * | 2018-11-01 | 2019-03-19 | 大唐环境产业集团股份有限公司 | Based on the boiler NO for improving quanta particle swarm optimizationXPrediction model optimization method |
| CN110262415A (en) * | 2019-06-03 | 2019-09-20 | 山东和信智能科技有限公司 | A kind of power generation station-service integrated information platform |
| CN111177864A (en) * | 2019-12-20 | 2020-05-19 | 苏州国方汽车电子有限公司 | Particle swarm algorithm-based internal combustion engine combustion model parameter optimization method and device |
| CN111765445A (en) * | 2020-07-01 | 2020-10-13 | 河北工业大学 | Boiler on-line combustion optimization control method and system and computer equipment |
| CN113217922A (en) * | 2021-02-25 | 2021-08-06 | 华南理工大学 | Method and system for predicting source output of NOx generated in waste incineration |
| CN113705890A (en) * | 2021-08-27 | 2021-11-26 | 太原理工大学 | Diesel engine pollutant emission control method based on approximate model |
| CN113864814A (en) * | 2021-09-15 | 2021-12-31 | 华能国际电力股份有限公司上海石洞口第一电厂 | Boiler combustion optimization method, device and medium based on variable screening |
| CN114459052A (en) * | 2022-01-27 | 2022-05-10 | 东北电力大学 | Coal-fired boiler NOx emission optimization method and device based on improved SSA |
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| CN113864814B (en) * | 2021-09-15 | 2024-04-26 | 华能国际电力股份有限公司上海石洞口第一电厂 | Variable screening-based boiler combustion optimization method, device and medium |
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Cited By (15)
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|---|---|---|---|---|
| CN109492807A (en) * | 2018-11-01 | 2019-03-19 | 大唐环境产业集团股份有限公司 | Based on the boiler NO for improving quanta particle swarm optimizationXPrediction model optimization method |
| CN110262415A (en) * | 2019-06-03 | 2019-09-20 | 山东和信智能科技有限公司 | A kind of power generation station-service integrated information platform |
| CN111177864B (en) * | 2019-12-20 | 2023-09-08 | 苏州国方汽车电子有限公司 | Particle swarm optimization-based internal combustion engine combustion model parameter optimization method and device |
| CN111177864A (en) * | 2019-12-20 | 2020-05-19 | 苏州国方汽车电子有限公司 | Particle swarm algorithm-based internal combustion engine combustion model parameter optimization method and device |
| CN111765445A (en) * | 2020-07-01 | 2020-10-13 | 河北工业大学 | Boiler on-line combustion optimization control method and system and computer equipment |
| CN111765445B (en) * | 2020-07-01 | 2021-10-15 | 河北工业大学 | Boiler on-line combustion optimization control method and system and computer equipment |
| CN113217922A (en) * | 2021-02-25 | 2021-08-06 | 华南理工大学 | Method and system for predicting source output of NOx generated in waste incineration |
| CN113705890A (en) * | 2021-08-27 | 2021-11-26 | 太原理工大学 | Diesel engine pollutant emission control method based on approximate model |
| CN113705890B (en) * | 2021-08-27 | 2023-06-20 | 太原理工大学 | Diesel engine emission pollutant control method based on approximate model |
| CN113864814A (en) * | 2021-09-15 | 2021-12-31 | 华能国际电力股份有限公司上海石洞口第一电厂 | Boiler combustion optimization method, device and medium based on variable screening |
| CN113864814B (en) * | 2021-09-15 | 2024-04-26 | 华能国际电力股份有限公司上海石洞口第一电厂 | Variable screening-based boiler combustion optimization method, device and medium |
| CN114459052A (en) * | 2022-01-27 | 2022-05-10 | 东北电力大学 | Coal-fired boiler NOx emission optimization method and device based on improved SSA |
| CN114459052B (en) * | 2022-01-27 | 2022-09-09 | 东北电力大学 | Coal-fired boiler NOx emission optimization method and device based on improved SSA |
| CN116954058A (en) * | 2023-07-13 | 2023-10-27 | 淮阴工学院 | Boiler NOx concentration prediction and intelligent control method and system |
| CN116954058B (en) * | 2023-07-13 | 2024-02-23 | 淮阴工学院 | Boiler NOx concentration prediction and intelligent control method and system |
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