CN107726358A - Boiler Combustion Optimization System and method based on CFD numerical simulations and intelligent modeling - Google Patents
Boiler Combustion Optimization System and method based on CFD numerical simulations and intelligent modeling Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23N—REGULATING OR CONTROLLING COMBUSTION
- F23N5/00—Systems for controlling combustion
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23N—REGULATING OR CONTROLLING COMBUSTION
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Abstract
The invention discloses a kind of Boiler Combustion Optimization System and method based on CFD numerical simulations and intelligent modeling, system includes DCS control systems interface, CFD computing clusters, sample database cluster, intelligent modeling cluster, central processing cluster and man-machine interface, by the way that CFD analog samples and history run sample are stored, modeled and optimized, realize unit instructed with network load, the optimal air distribution mode real-time matching of the change such as as-fired coal coal characteristic, excess air coefficient.The use of CFD numerical simulation technologies improves the accuracy of modeling in the present invention, and can directly invoke DCS control systems during optimization realizes closed-loop control, makes air distribution mode quick response load variations, realizes unit combustion thermal efficiency and the real-time optimization of NOx emission.
Description
Technical field
The present invention relates to combustion of industrial boiler to optimize system, more particularly to the pot based on CFD numerical simulations and intelligent modeling
Stove combustion optimizing system and method.
Background technology
As country is increasingly strict to fired power generating unit environmental requirement, how to the greatest extent may be used on the premise of disposal of pollutants meets to require
Boiler combustion efficiency can be improved, has become the problem that domestic fired power generating unit generally faces.Air distribution regulation is fired as fired power generating unit
Important regulative mode during burning, it is an important factor for influenceing boiler thermal output and pollutant generation.Therefore, how preferably
Adjusting air distribution mode turns into the most important thing for improving boiler thermal output and reducing pollutant.In the case of existing, most of units are adopted
Air distribution is controlled with tandem oxygen content control air distribution, or by operation instruction principle and artificial experience, which results in boiler combustion
It is difficult to stable in optimum, boiler efficiency is low, NOx discharge is high so as to causing, or even occurs burning partially, the severe feelings such as slagging
Condition.Therefore, need a kind of method that can adjust air distribution mode in real time according to load, ature of coal badly, boiler is being met pollutant row
Highest efficiency of combustion is obtained on the premise of putting requirement.
The intelligent modeling methods such as neutral net are mainly used currently for the prioritization scheme of boiler wind speed adjustment mode, it is few with power plant
It is Sample Establishing boiler characteristics model to measure service data and experimental data, the methods of genetic algorithm is used according to model above to
Wind mode optimizes, such as method disclosed in patent ZL03231306.3 and patent CN106327021A.But above method
Both sides be present in boiler modeling process is carried out, on the one hand because power plant's operating condition has focused largely on high load capacity,
In shortage, the data sample of underload, while in order to ensure the security of unit operation, often keep single air distribution mode,
The data sample coverage that thus be accordingly used in modeling is small and mutual independence is poor；On the other hand, mould in existing modeling process
Type input parameter is more, and over-fitting easily occurs.Two above problem had a strong impact on boiler characteristics model reliability and
Accuracy, further result in optimization failure.How in modeling process abundant data sample reduce parameter input, to improve model
Accuracy and optimization reliability, be boiler wind speed adjustment method optimizing subject matter.Other existing manual adjustment air distribution side
Formula has certain time lag, and network load instruction is continually changing, and this requires one kind to enter to load variations
The air distribution regulating system of row quick response.
The content of the invention
Goal of the invention：The purpose of the present invention is to propose to a kind of boiler combustion based on CFD numerical simulations and intelligent modeling is excellent
Change system and method, the uses of wherein CFD numerical simulation technologies improves the accuracy of modeling, can directly invoke DCS during optimization
Control system realizes closed-loop control, makes air distribution mode quick response load variations, realizes unit combustion thermal efficiency and NOx emission
Real-time optimization.
Technical scheme：Boiler Combustion Optimization System of the invention based on CFD numerical simulations and intelligent modeling controls including DCS
System interface, CFD computing clusters, sample database cluster, intelligent modeling cluster, central processing cluster and man-machine interface, DCS controls
History run sample and CFD analog samples are transferred to by system interface processed respectively with CFD computing clusters by information transmission network
Preserved in sample database cluster；Intelligent modeling cluster calls the data stored in sample database cluster, selects intelligent algorithm
It is modeled, and boiler thermal output forecast model and NOx discharge forecast model is transferred to central processing cluster；Central processing
Cluster calls the parameter of boiler real time execution in DCS control system interfaces to carry out seeking for air distribution mode while model is received
It is excellent, unit combustion thermal efficiency and NOx emission is reached combination optimal value.
Using CFD numerical simulations and the method for the Boiler Combustion Optimization System of intelligent modeling, comprise the following steps：
(1) CFD analog samples are calculated by CFD computing clusters；
(2) analyze and improve history run sample；
(3) CFD analog samples and power plant's actual history operation sample after improving are stored in sample database cluster,
So that intelligent modeling cluster calls；
(4) intelligent modeling cluster receives the sample in sample database cluster, is trained using intelligent algorithm；
(5) optimization of air distribution mode is carried out in central processing cluster；
(6) man-machine interface is established to ensure monitoring and control of the operations staff to whole burning optimization blowing system.
In step (1), feelings are arranged according to the practical structures of optimization aim boiler, each heating surface deployment scenarios, air channel flue
The parameter such as condition and burner nozzle type and position, boiler physical model is built, using Computational Fluid Dynamics couplingization
The method for learning reaction is simulated to stove combustion process；During simulation, for different load, as-fired coal coal characteristic, excessive sky
The factors such as gas coefficient, coal pulverizer combination and air distribution mode are simulated, and obtain corresponding unburned carbon in flue dust, NOx discharge
Boiler thermal output is obtained etc. parameter, and by thermodynamic computing.
In step (2), by boiler combustion optimization Adjustment Tests, the unburned carbon in flue dust of unit under different operating conditions is obtained
And boiler slag carbon content；With load, as-fired coal coal characteristic, oxygen amount, coal pulverizer combination, air distribution mode etc. to input, flying dust
Phosphorus content and boiler slag carbon content are output, establish nonlinear model；Using power plant's actual operating data as input, unburned carbon in flue dust and
Boiler slag carbon content obtains prediction unburned carbon in flue dust and boiler slag carbon content under practical operation situation for output, anti-by boiler heat
Balance method calculates, and obtains the boiler thermal output of history run sample.
In step (6), unit operation personnel read sample by human-computer interface control sample database cluster, manually or certainly
Dynamic renewal boiler thermal output forecast model and NOx discharge forecast model；By selection, manually or automatically Optimizing Mode, control are matched somebody with somebody
The optimal way of wind.
In step (3), the data in sample database include load, as-fired coal coal characteristic, excess air coefficient, air distribution
The information such as mode, boiler thermal output, NOx discharge.
In step (4), using including parameters such as load, as-fired coal coal characteristic, excess air coefficient, air distribution modes to be defeated
Enter, using boiler thermal output, NOx discharge as output, establish boiler thermal output forecast model and NOx discharge forecast model, lead to
Crossing addition regularization term prevents the situation of over-fitting from occurring.
In step (5), central processing cluster reads Real-time Load, as-fired coal coal characteristic, mistake from DCS control systems interface
The parameters such as air coefficient are measured, are inputted as fixed；The boiler thermal output forecast model trained using intelligent modeling cluster
With NOx discharge forecast model, optimizing is carried out to air distribution mode using optimized algorithm, obtains corresponding under operating condition this moment most
Excellent air distribution mode.
Brief description of the drawings
Fig. 1 is the Boiler Combustion Optimization System figure based on CFD numerical simulations and intelligent modeling；
Fig. 2 is the Boiler combustion optimization general flow chart based on CFD numerical simulations and intelligent modeling；
Fig. 3 is the intelligent modeling algorithm flow chart by taking the BP neural network of regularization as an example；
Fig. 4 is the central processing cluster burning optimization algorithm flow chart by taking genetic algorithm as an example.
Embodiment
Embodiment is described further below in conjunction with the accompanying drawings.
Built as shown in figure 1, the system includes DCS control systems interface, CFD computing clusters, sample database cluster, intelligence
Mould cluster, central processing cluster and man-machine interface.Wherein, DCS control systems interface and CFD computing clusters pass through information transfer net
History run sample and CFD analog samples are transferred in sample database cluster and preserved respectively by network；Intelligent modeling cluster is adjusted
With the data stored in sample database cluster, intelligent algorithm is selected to be modeled, and by boiler thermal output forecast model and NOx
Forecasting of discharged quantity model is transferred to central processing cluster；Central processing cluster calls DCS control systems while model is received
The parameter of boiler real time execution carries out the optimizing of air distribution mode in interface, and optimizing result can provide open loop to unit operation personnel and refer to
Lead or directly invoke DCS control systems and realize closed-loop control, unit combustion thermal efficiency and NOx emission is reached combination optimal value.
As shown in Fig. 2 the method includes the steps of：
(1) CFD analog samples are calculated by CFD computing clusters, according to the practical structures of optimization aim boiler, each heating surface
The parameter such as deployment scenarios, air channel flue deployment scenarios and burner nozzle type and position, build the boiler thing of complete and accurate
Model is managed, wherein secondary air duct controls each layer secondary air flow since air preheater entrance, using baffle opening.
The structure of mathematical modeling is as follows：The discrete of the differential equation has used Finite Volume Method for Air, and pressure x velocity coupling uses three
Stability maintenance state SIMPLE algorithms calculate, and Equations of Turbulence is using the double equation simulation turbulent air flow flowings of standard k- ε, using random orbit mould
Type is tracked to pulverized coal particle movement locus.The chemistry occurred in combustion using non-premixed combustion modeling coal dust
Combustion reaction and each component transport, and gas phase turbulance burning is using Hybrid analysis-probability density function simulation (PDF) model；
Radiation heat transfer uses P1 radiation patterns.
After Mathematical Models, respectively for different load, as-fired coal coal characteristic, excess air coefficient, coal-grinding
Machine combination, air distribution mode are simulated, and obtain the parameters such as corresponding unburned carbon in flue dust, NOx discharge, and pass through heating power meter
Calculation obtains the boiler combustion thermal efficiency.
(2) analyze and improve history run sample.By boiler combustion optimization Adjustment Tests, obtain under different operating conditions
The unburned carbon in flue dust and boiler slag carbon content of unit.As shown in figure 3, using BP neural network algorithm, it is special with load, as-fired coal ature of coal
Property, oxygen amount, coal pulverizer combination, air distribution mode be input, unburned carbon in flue dust and boiler slag carbon content are output, establish 3 layers of BP
Neural network model.Using power plant's actual operating data as input, unburned carbon in flue dust and boiler slag carbon content actual fortune is obtained for output
Prediction unburned carbon in flue dust and boiler slag carbon content in the case of row.Calculated by boiler heat back balance method, obtain history run
The boiler thermal output of data.
(3) CFD analog samples and power plant's actual history operation sample after improving are stored in sample database cluster,
So that intelligent modeling cluster calls.Data in sample database include load, as-fired coal coal characteristic, excess air coefficient,
The information such as air distribution mode, boiler thermal output, NOx discharge.The increase of random groups operating condition, it can be controlled by Power Plant DCS
The mode that system interface calls in history run sample updates sample database cluster.In addition, in the case of some abnormal runnings,
When running on the lower load or lower heat of combustion coal, corresponding combustion conditions are simulated by CFD and provided for sample database
Sample, the real-time update of sample database is realized, ensure all standing of the sample to running situation.
(4) intelligent modeling cluster receives the sample in sample database cluster, is trained using intelligent algorithm.Together
Sample uses BP neural network, by the sample including CFD analog samples and history run sample in sample database cluster
Import in intelligent modeling cluster, using load, as-fired coal coal characteristic, excess air coefficient, air distribution mode as input, with boiler hot
Efficiency, NOx discharge are output, establish the BP neural network model of 3 layers of regularization.Hidden layer and output layer use sigmoid
Function is as activation primitive, after initializing each layer weight, the output net of i-th of node of hidden layer of networkiFor：
The output o of i-th of node of hidden layeriFor：
Wherein, φ is the excitation function of hidden layer, and φ is logsig functions in this formula；M is input layer number, i.e.,
Load, excess air coefficient, as-fired coal coal characteristic, air distribution mode；wijSaved for i-th to hidden layer for j-th of node of input layer
Weights between point；θiRepresent the threshold value of hidden layer.
The output net of k-th of node of output layerkFor：
The output o of k-th of node of output layerkFor：
Wherein, ψ is the excitation function of output layer, and ψ be logsig functions in this formula, and q is output layer neuron number,
It is q=2, i.e. the boiler combustion thermal efficiency, NOx discharge in formula；wkiFor implicit i-th of node layer by layer to k-th of node of output layer
Between weights；akRepresent the threshold value of output layer.
The error cost function of neutral net is：
Wherein,For j-th of sample, the actual value of k-th of neuron of output layer；λ is regularization parameter, and effect is anti-
Only over-fitting；L is the neutral net number of plies, and sl is certain layer of neuron number, and from i=1, i.e. input layer starts to calculate.
The target of neural metwork training is exactly to make the difference of network output and actual y values minimum, and J (w) reaches minimum, uses
Gradient descent method solves, and output layer error is：
δ(3)=o(3)-y
Wherein 3 represent the outermost layer of neutral net, i.e. output layer.
Hidden layer error is：
δ(2)=(w(2))Tδ(3).*g'(o(2))
The variable gradient of each layer of weights is：
Δ(l)=Δ(l)+δ(l+1)(net(l))T
I.e.
Input layer is constantly updated to hidden layer, the connection weight of hidden layer to output layer by above formula, receives Algorithm Error
Hold back designated value ε.Boiler thermal output forecast model and NOx discharge forecast model can now be obtained.
Two above model can manually or automatically update：Manual mode is by operations staff in man-machine interface
Manually select renewal；Automated manner is settable to be automatically updated after new samples quantity reaches arranges value.
(5) optimization of air distribution mode is carried out in central processing cluster.Central processing cluster is read from DCS control systems interface
The parameters such as Real-time Load, as-fired coal coal characteristic, excess air coefficient are taken, are inputted as fixed；Utilize intelligent modeling collection
The boiler thermal output forecast model and NOx discharge forecast model that group trains, optimizing is carried out to air distribution mode, corresponded to
Air distribution mode optimal under operating condition this moment.As shown in figure 4, using genetic algorithm, specific implementation process is：
1. given constrained parameters, i.e., the span of each input parameter, including load, excess air coefficient, as-fired coal
Coal characteristic, air distribution mode, the boiler combustion thermal efficiency, NOx discharge.Initialized, initial evolutionary generation t=0 is set, most
Macroevolution algebraically t=T, random n individual of generation are used as parent and filial generation.
2. evaluating the fitness of individual, the foundation of fitness function is the boiler thermal effect that BP neural network is calculated
Rate and NOx discharge, if NOx discharge meets group setup standard, the individual adaptation degree is：
Eva=exp (- 0.005*Eff)
Wherein Eff is the boiler thermal output of BP neural network output.
If NOx discharge exceeds group setup standard, the ideal adaptation angle value is arranged to 0.01, during evolution by
Eliminate.
3. n individual composition parent of selection and female generation from all individuals, to parent and mother for each variable in gene
The random coefficient between one [- 0.25,1.25] is taken, arithmetic friendship is then carried out between male parent and female parent according to the random coefficient
Fork, obtain 2n new individuals.Random variation operation is carried out to individual.
4. carrying out Fitness analysis to 2n newly-generated individual, 2 individuals of fitness highest in 2n individual are selected
New population is added, others are eliminated, and so far complete a wheel and evolve.The condition terminated of evolving is the continuous n of evolutionary process for most
Excellent individual object function is basically unchanged, or when maximum algebraically exceedes certain value T, loop termination.
5. after loop termination, in last obtained generation, is individual, as optimal adaptation degree individual.The individual is represented in given pot
On the premise of stove load, excess air coefficient, as-fired coal coal characteristic, how air distribution mode is adjusted, boiler thermal effect can be reached
The combination optimal value of rate and NOx discharge.
Wherein, optimal air distribution mode can select optimal multiple-objection optimization value, can also select to control NOx discharge not
The optimal value of optimal boiler thermal output is obtained in the case of exceeded.
(6) man-machine interface is established to ensure monitoring and control of the operations staff to whole burning optimization blowing system.Pass through
Man-machine interface, unit operation personnel can control sample database cluster to read sample, update manually or automatically update boiler hot
EFFICIENCY PREDICTION model and NOx discharge forecast model.In addition, operations staff can also be by selecting optimization or Automatic Optimal manually
Pattern, control the optimal way of air distribution, the form that manual mode can be instructed by open loop, by the recommendation air distribution mode after optimization
It is shown in man-machine interface, is adjusted manually by operations staff；Automated manner can directly adjust air distribution side by DCS control systems
Formula, realize the closed loop regulation of optimization system.
Claims (9)
- A kind of 1. Boiler Combustion Optimization System based on CFD numerical simulations and intelligent modeling, it is characterised in that：Controlled including DCS System interface, CFD computing clusters, sample database cluster, intelligent modeling cluster, central processing cluster and man-machine interface, it is described DCS control systems interface is respectively passed history run sample and CFD analog samples by information transmission network with CFD computing clusters In the defeated cluster to sample database and preserve；The intelligent modeling cluster calls the data stored in sample database cluster, choosing Select intelligent algorithm to be modeled, and boiler thermal output forecast model and NOx discharge forecast model are transferred to central processing collection Group；The central processing cluster calls the parameter of boiler real time execution in DCS control system interfaces to enter while model is received The optimizing of row air distribution mode, unit combustion thermal efficiency and NOx emission is set to reach combination optimal value.
- A kind of 2. Boiler combustion optimization based on CFD numerical simulations and intelligent modeling, it is characterised in that：Including following step Suddenly：(1) CFD analog samples are calculated by CFD computing clusters；(2) analyze and improve history run sample；(3) CFD analog samples and power plant's actual history operation sample after improving are stored in sample database cluster, for Intelligent modeling cluster calls；(4) intelligent modeling cluster receives the sample in sample database cluster, is trained using intelligent algorithm；(5) optimization of air distribution mode is carried out in central processing cluster；(6) man-machine interface is established to ensure monitoring and control of the operations staff to whole burning optimization blowing system.
- 3. the Boiler combustion optimization according to claim 2 based on CFD numerical simulations and intelligent modeling, its feature exist In：In the step (1), according to the practical structures of optimization aim boiler, each heating surface deployment scenarios, air channel flue deployment scenarios And the parameter such as burner nozzle type and position, boiler physical model is built, using Computational Fluid Dynamics coupling chemistry Reaction method is simulated to stove combustion process.
- 4. the Boiler combustion optimization according to claim 3 based on CFD numerical simulations and intelligent modeling, its feature exist In：When being simulated using CFD to combustion process, for different load, as-fired coal coal characteristic, excess air coefficient, coal-grinding The factor such as machine combination and air distribution mode is simulated, and obtains the parameters such as corresponding unburned carbon in flue dust, NOx discharge, and lead to Cross heating power and boiler thermal output is calculated.
- 5. the Boiler combustion optimization according to claim 2 based on CFD numerical simulations and intelligent modeling, its feature exist In：In the step (2), by boiler combustion optimization Adjustment Tests, the unburned carbon in flue dust of unit under different operating conditions is obtained And boiler slag carbon content；With load, as-fired coal coal characteristic, oxygen amount, coal pulverizer combination, air distribution mode etc. to input, flying dust Phosphorus content and boiler slag carbon content are output, establish nonlinear model；Using power plant's actual operating data as input, unburned carbon in flue dust and Boiler slag carbon content obtains prediction unburned carbon in flue dust and boiler slag carbon content under practical operation situation for output, anti-by boiler heat Balance method calculates, and obtains the boiler thermal output of history run sample.
- 6. the Boiler combustion optimization according to claim 2 based on CFD numerical simulations and intelligent modeling, its feature exist In：In the step (6), unit operation personnel read sample by human-computer interface control sample database cluster, manually or certainly Dynamic renewal boiler thermal output forecast model and NOx discharge forecast model；By selection, manually or automatically Optimizing Mode, control are matched somebody with somebody The optimal way of wind.
- 7. the Boiler combustion optimization according to claim 2 based on CFD numerical simulations and intelligent modeling, its feature exist In：In the step (3), the data in sample database include load, as-fired coal coal characteristic, excess air coefficient, air distribution The information such as mode, boiler thermal output, NOx discharge.
- 8. the Boiler combustion optimization according to claim 2 based on CFD numerical simulations and intelligent modeling, its feature exist In：In the step (4), using including the parameters such as load, as-fired coal coal characteristic, excess air coefficient, air distribution mode as input, Using boiler thermal output, NOx discharge as output, boiler thermal output forecast model and NOx discharge forecast model are established, by adding Regularization term is added to prevent the situation of over-fitting from occurring.
- 9. the Boiler combustion optimization according to claim 2 based on CFD numerical simulations and intelligent modeling, its feature exist In：In the step (5), central processing cluster reads Real-time Load, as-fired coal coal characteristic, mistake from DCS control systems interface The parameters such as air coefficient are measured, are inputted as fixed；The boiler thermal output forecast model trained using intelligent modeling cluster With NOx discharge forecast model, optimizing is carried out to air distribution mode using optimized algorithm, obtains corresponding under operating condition this moment most Excellent air distribution mode.
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