CN104776446B - Combustion optimization control method for boiler - Google Patents
Combustion optimization control method for boiler Download PDFInfo
- Publication number
- CN104776446B CN104776446B CN201510176385.1A CN201510176385A CN104776446B CN 104776446 B CN104776446 B CN 104776446B CN 201510176385 A CN201510176385 A CN 201510176385A CN 104776446 B CN104776446 B CN 104776446B
- Authority
- CN
- China
- Prior art keywords
- boiler combustion
- boiler
- output
- optimization
- input
- 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.)
- Expired - Fee Related
Links
Landscapes
- Feedback Control In General (AREA)
- Control Of Steam Boilers And Waste-Gas Boilers (AREA)
Abstract
The invention discloses a combustion optimization control method for a boiler. The combustion optimization control method is characterized by comprising the following steps: sampling a combustion nonlinear system of the boiler to obtain input/output data at the current moment; training the real-time sampled input/output data by an online incremental learning fuzzy neural network, building an online incremental learning predicting model of the combustion nonlinear system of the boiler; performing a nonlinear prediction control algorithm on the online incremental learning predicting model for realizing the optimization and the control of the combustion process of the boiler. According to the combustion optimization control method for the power station boiler of the online incremental learning fuzzy neural network, the nonlinear optimization problem in the predication control algorithm is solved by utilizing a particle swarm optimization algorithm through the online identification of the boiler combustion optimization model; the real-time optimization and control of the boiler combustion process are realized.
Description
Technical field
The present invention relates to a kind of optimization running technology of power boiler burning system, more particularly to a kind of online incremental learning
The power boiler burning optimization control method of fuzzy neural network, belongs to thermal technics technical field.
Background technology
Burning optimization is to lift Utility Boiler Efficiency, reduce the important means of pollutant emission.Current burning optimization skill
Boiler combustion efficiency and NO are set up offline using learning algorithms such as neutral net or SVMs more than artxDischarge model, to combustion
Burn optimization target values to be optimized using intelligent search algorithms such as genetic algorithms, obtain the manipulation variable of boiler combustion optimization.
Research shows, boiler has very big time-varying characteristics, As time goes on and boiler operatiopn operating mode change,
The learning model of boiler combustion process will occur larger error, and the offline model set up does not adapt to this change, so as to lead
Cause model mismatch.The on-line correction of model needs the longer calculating time so that the process for obtaining manipulation variable is complicated, power station pot
The property regulation real-time of stove is not high, affects the performance of burning optimization.
The features such as the non-linear of boiler combustion process, large time delay, bring to boiler combustion optimization control work certain
It is difficult.Model Predictive Control (MPC) is substantially control method of the class based on optimization, and it is based on forecast model, rolling optimization
With the big feature of feedback compensation three, the control problem of object with big lag is can effectively solve the problem that.The precision and rolling optimization of forecast model
Strategy is the key for affecting MPC performances.The solution of wherein non-linear rolling optimization is difficult to ask for, and typically can only be asked using numerical value optimizing
Solution.
Therefore, it is necessary to find a kind of new method to complete the Nonlinear Model Predictive Control of power boiler burning optimization
Problem, solves two big difficult points mentioned above, i.e., set up accurate Nonlinear Prediction Models in real time and obtain rolling online
Optimum control amount under time domain object function.
The content of the invention
Goal of the invention:For the problem and shortage that above-mentioned prior art is present, it is an object of the invention to provide a kind of online
The power boiler burning optimization control method of incremental learning fuzzy neural network, can with on-line identification boiler combustion process model,
Using the nonlinear optimal problem in PSO Algorithm PREDICTIVE CONTROL, so as to improve the real-time of boiler combustion optimization control
Property.
Technical scheme:For achieving the above object, the technical solution used in the present invention is:
A kind of boiler combustion optimization control method, it is characterised in that comprise the steps:
(1) boiler combustion nonlinear system is sampled, obtains the input/output data at current time;
(2) input/output data that real-time sampling is obtained is trained using online incremental learning fuzzy neural network,
Set up the online incremental learning forecast model of boiler combustion nonlinear system;
(3) nonlinear Model Predictive is used the online incremental learning forecast model, is realized to boiler combustion
The optimal control of journey.
In step (1), the input data is boiler operatiopn operating parameter, and the output data is boiler efficiency and cigarette
Gas discharges NOx。
The boiler operatiopn operating parameter stated includes that load, coal-supplying amount, total air, fuel throttle opening, secondary air register are opened
Degree and after-flame throttle opening.
In step (2), the online incremental learning forecast model is:
Wherein, u (k)=(u1(k),u2(k),…,um(k)) represent Boiler Combustion Optimization System controlled quentity controlled variable, y (k)=
(y1(k),y2(k),…,yn(k)) target output of boiler combustion status is represented, d represents that the output of boiler combustion system is prolonged
Late, p and q represent the input/output order of boiler combustion process nonlinear system.
In step (2), totally four layers of the online incremental learning structure of fuzzy neural network:
Input layer, each neuron in this layer represents an input variable of online incremental learning forecast model, wherein
Use X1, X2..., XrRepresent that boiler respectively runs manipulation amount u (k) and associated front p orders output y (k) successively;
Membership function layer, each input variable XiThere is u membership function Aij(j=1,2 ..., u), it is Gauss and is subordinate to letter
Number:
Wherein μijIt is xiJ-th membership function, cijAnd σijRespectively xiJ-th Gaussian function center and width
Degree, u is the quantity of membership function;
Fuzzy rule layer, j-th rule RjOutput be:
Output layer, the output variable of each one input signal weighted sum of node on behalf:
Wherein y be characterize boiler combustion status optimization aim output valve, wjFor result parameter.
In step (3), the method for the optimal control is:
(31) correction of boiler combustion nonlinear system output valve is obtained by the online incremental learning forecast model:
In current sample time k, by the past input/output of boiler combustion nonlinear system and current input u (k) by
Built in line incremental learning forecast model obtains the output estimation value of boiler combustion nonlinear system
By boiler combustion nonlinear system input u (k+1) to be optimized and past input/output, boiler combustion is obtained
Burn the output estimation value of nonlinear system
If the prediction deviation at k moment isUse drift correctionRepaiied
Positive quantity
(32) input to boiler combustion expense linear system is optimized:
Determine that the object function of boiler combustion optimization controlled quentity controlled variable u is according to boiler combustion optimization controlled quentity controlled variable u:
Wherein yirFor the reference locus of i-th boiler combustion significant condition output quantity, by solving boiler combustion optimization
Economic goal function and obtain, yipIt is defeated for prediction of the corresponding i-th boiler combustion significant condition output quantity Jing after feedback compensation
Go out, the dimension that m and n is respectively input into and exports, qiAnd λjFor weight coefficient;
The minimum of a value of above-mentioned object function is obtained by the online rolling optimization in real time of particle cluster algorithm, optimum control amount is obtained
U (k+1), acts on optimum control amount u (k+1) boiler combustion nonlinear system and is optimized control.
Beneficial effect:Compared with prior art, the present invention has advantages below, online incremental learning fuzzy neural network mould
Type can be according to the time-varying characteristics of power boiler burning process nonlinear system, the structure and parameter of on-line tuning model, identification
Process is simple, and adjustable parameter is few, and generalization ability is strong;Nonlinear Model Predictive Control (MPC) is optimized to boiler combustion process
And control, can effectively solve the problem that the large delay characteristic of boiler combustion process, using PSO Algorithm PREDICTIVE CONTROL in it is non-
Linear optimization problem, online rolling optimization in real time determines controlled quentity controlled variable, has preferable control effect to boiler combustion process.
Description of the drawings
Fig. 1 is that the boiler combustion optimization Control system architecture of the online incremental learning fuzzy neural network of the present invention is illustrated
Figure.
Fig. 2 is the online increment fuzzy neural network model schematic diagram of the present invention.
Fig. 3 is the algorithm flow chart of the online increment fuzzy neural network model of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings, further elucidate the present invention, it should be understood that these embodiments be merely to illustrate the present invention and without
In the scope of the present invention is limited, after the present invention has been read, those skilled in the art are to the various equivalent form of values of the invention
Modification falls within the application claims limited range.
The power boiler burning optimization control method of a kind of online incremental learning fuzzy neural network that the present invention is provided, leads to
Cross carries out real-time sampling to boiler combustion nonlinear system, using online incremental learning fuzzy neural network to real-time sampling data
It is trained, sets up boiler combustion optimization data-driven model, it is pre- using nonlinear model to the boiler combustion optimization model
Observing and controlling system (MPC) algorithm is optimized and controls.The power boiler burning optimization control method is comprised the following steps that:
(1) boiler combustion process nonlinear system is sampled, obtains the input/output data at current time;
(2) real-time sampling is trained using online incremental learning fuzzy neural network, sets up boiler combustion optimization pre-
Survey model;
(3) to the model, boiler combustion process is optimized using Nonlinear Model Predictive Control (MPC) algorithm and
Control.
For using boiler structure, burner arrangement form the features such as choose suitable boiler operatiopn operating parameter and make
For the input quantity of combustion model, boiler efficiency and environment protection emission NOxFor optimization aim output quantity, so as to obtain boiler combustion system
The real-time input/output data of system model;
Described real-time boiler operatiopn operating parameter includes load, coal-supplying amount, total air, fuel throttle opening, secondary
Throttle opening, after-flame throttle opening;
Boiler efficiency and flue gas NO are set up using a kind of online incremental learning fuzzy neural networkxModel.For building
The |input paramete of mould and the output parameter of sign boiler combustion status are expressed asWherein xkRepresent kth group as defeated
Enter the boiler operating parameter vector of data, yk+dRepresent that kth group characterizes the vector of boiler combustion status as output parameter, d is represented
The output of boiler combustion system postpones.Such as the online incremental learning fuzzy neural network model schematic diagram that Fig. 2 is the present invention, model
Structure has four layers:
1. input layer, each neuron in this layer represents input variable X of boiler combustion system modeli(i=1,
2 ..., r), r is input variable number;
2. membership function layer, each input variable XiThere is u membership function Aij(j=1,2 ..., u), it is Gauss and is subordinate to
Function:
Wherein μijIt is xiJ-th membership function, cijAnd σijThe center of respectively j-th Gaussian function and width, u
It is the quantity of membership function;
3. fuzzy rule layer, the node layer number has reacted number of fuzzy rules, for calculating T- models of each rule triggering power
Son is figured for multiplication, j-th rule RjOutput be:
4. output layer, the output variable of each one input signal weighted sum of node on behalf:
Wherein y be characterize boiler combustion status optimization aim output valve, wjFor result parameter.
As shown in figure 3, online incremental learning fuzzy neural network algorithm is broadly divided into 4 parts:The generation of neuron, premise
Parameter Estimation, weight adjustment and tailoring technique.Model during iteration, according to the system of setting miss by computation model error
Difference parameter keProduce neuron;In addition, model can also calculate the distance for being input to the neuronal center for having existed, according to setting
Boundary sizes kdProduce neuron.The k of settingeAnd kdAlso dynamic can adjust during iterative calculation.Neuron is produced
Afterwards, the center c of its member functionijAnd width csjSize give an initial value, in iterative calculation below, can according to kd
And keSize is being accordingly adjusted.Weight adjustment refers to weight w of neuron output layerkSize it is true by linear least square
It is fixed.Tailoring technique is mainly model can calculate importance index of the neuron to system, according to importance come dynamic cutting footpath
Can be adjusted according to input data dynamic to base unit, therefore model structure, will not be excessively complicated.
The fuzzy rule of network model is gradually changed from scratch, is decided whether to increase according to the sample of training
Or reduce by a rule, and set response parameter.The following detailed description of algorithmic procedure, detailed algorithm flow chart such as Fig. 3:
1) initialize and predefine initial parameter εmin, εmax, emin, emax, kmf, ks, keer, wherein εminAnd εmaxIt is fuzzy
The setting value of the minimum and maximum of regular perfect set, eminIt is preferable Accuracy Error, emaxIt is maximum error, keAnd kdFor pre-
The threshold value being associated with e and ε respectively is first set, it can be with dynamic adjustment, k in learning processmfIt is the adjacent membership function of control
The constant of similarity, keerIt is default constant for regular importance;
2) first group of sample data (x1, y1) obtain after, produce first fuzzy rule, parameter is as follows:
c1=x1,σ1=σ0,ω1=ω0, wherein ω0,σ0For predefined parameter;
3) from the beginning of second group of sample, to each group of new samples (xk, yk) calculate mdkJ (), it is observation data and j-th strip
The mahalanobis distance of the central point of fuzzy rule, finds out mdk,min=mdk(J),And computing system is missed
Difference
4) md is worked ask,min>kd, ek>keMono- fuzzy rule of Shi Zengjia, it is assumed that u fuzzy rule has been produced is new to produce rule
Initial parameter then is allocated according to the following rules:Multidimensional input variable xkProject to corresponding one-dimensional membership function empty
Between, calculate dataWith boundary set ΦiEuclidean distance between (j)Wherein Φi∈{ximin,ci1,
ci2,...,ciu,ximax, while findingIf edi(jn)≤kmf, thenIt is not used in the dimension to produce
The new membership function of life.Otherwise distribute a new Gaussian function, the width of Gaussian function and center are respectively:
5) after producing new rule, importance η of calculation error reduction rate and j-th strip rulej(j=1,2 ..., u), error
Reduce rate matrix Δ=(ρ1,ρ2,...,ρu), wherein jth arranges (r+1) individual error slip of j-th rule of correspondence.If ηj<kerr, then j-th rule is deleted;
6) if new rule need not be increased, condition md is metk,min<kd, ek>ke, then input variable xiMembership function
Width csijIt is modified to σnew,ij=ζ * σij, wherein ζ is decay factor;
7) result parameter is adjusted, and n observation data sample of r input variable produces u fuzzy rule, its matrix form
For W φ=Y.Optimized parameter W*Determination be formulated as minimize | | W φ-T | |2Linear problem, T be desired output (i.e.
Sample is exported).W is determined using Generalized Inverse Method*For:W*=T (φTφ)-1φT。
8) see whether to complete learning process, if the return to step 3 without if), otherwise terminate whole learning process.
It is to the model that the nonlinear system of boiler combustion process is set up using online incremental learning fuzzy neural network:
Wherein, u (k)=(u1(k),u2(k),…,um(k)) represent Boiler Combustion Optimization System controlled quentity controlled variable, y (k)=
(y1(k),y2(k),…,yn(k)) output quantity of boiler combustion status is represented, p and q represents boiler combustion process nonlinear system
Input/output order.
Determine that the output of boiler combustion nonlinear system postpones d by the fitting precision of off-line model, in present sample
K is carved, the output of system is obtained by built forecast model by the past input/output of boiler combustion system and current input u (k)
EstimateBy system input u (k+1) to be optimized and past input/output, the output estimation of system is obtained
ValueDue to the reasons such as noise jamming or model mismatch, forecast model outputWith reality output y (k+d)
Deviation is commonly present, if the prediction deviation at k moment isUse drift correctionObtain
Obtain correction
Boiler combustion process model is nonlinearity, then for solving the optimization problem of PREDICTIVE CONTROL signal sequence
It is Solution of Nonlinear Optimal Problem.Shorter control signal sequence can reduce the complexity of optimization problem, increase control
Robustness.Using one-step prediction control signal, the object function for determining boiler combustion optimization controlled quentity controlled variable u is:
Wherein yirFor the reference locus of i-th boiler combustion significant condition output quantity, boiler combustion is solved by off-line model
Burn the economic goal function of optimization and obtain;yipIt is corresponding i-th boiler combustion significant condition output quantity Jing after feedback compensation
Prediction output;The dimension that m and n is respectively input into and exports;qiAnd λjFor weight coefficient.
For above-mentioned Predictive Control of Nonlinear Systems algorithm, the fitness function for determining boiler combustion optimization controlled quentity controlled variable is formula
(5), the minimum of a value of formula (5) object function is obtained as Rolling optimal strategy using particle swarm optimization algorithm, obtains boiler combustion
Optimum control amount u (k+1) of optimization, acts on optimum control amount u (k+1) boiler combustion process nonlinear system and is controlled
System.
Determine boiler combustion future controlled quentity controlled variable u (k+1)=(u (k using the online rolling optimizations in real time of PSO1+1),u(k2+
1),…,u(km+1)).Particle population size is L, and particle i is expressed as popi=(ui,vi,li,Fibest,Fi), wherein:ui=
(ui1,ui2,…,uim) represent particle i position vector, vi=(vi1,vi2,…,vim) represent that the history that particle i is passed through is best
Position, FibestRepresent the adaptive optimal control value of particle i, FiRepresent the current adaptive value of particle i.In the t time iteration, the speed of particle i
Degree, displacement more new formula, the adjustment formula of inertia weight is as follows:
Wherein:c1,c2For accelerated factor, r1,r2For the random number between [0,1].All particles find most in whole colony
Good position is g=(g1,g2,…,gm), it is that PSO optimizes the optimum control amount for obtaining, the i.e. optimum control of boiler combustion system
Amount.Whole boiler combustion optimization control algolithm step is as follows:
1) initialization system state, forecast model and PSO parameters, and in order to improve the arithmetic speed of model, first to pot
Stove burning historical data carries out off-line training, obtains Nonlinear Prediction Models under boiler original state;
2) sampling instant k, on the basis of Nonlinear Prediction Models under the offline boiler original state set up, using online
Incremental learning fuzzy neural network is trained to present sample data, online amendment boiler combustion optimization forecast model in real time;
3) to fixed boiler combustion optimization controlled quentity controlled variable u (k), the output y (k+d) of system, by online incremental learning
Fuzzy Neural Network Prediction Model is obtainedIf boiler combustion controlled quentity controlled variable u (k+1) of optimization undetermined is particle in PSO
Position vector, brings forecast model into, the estimation output of etching system when obtaining k+1By drift correction, the estimation is defeated
Go out and obtain the adaptation value function F of particle;
4) current adaptive value F of each particle is comparediWith itself adaptive optimal control value FibestIf, Fi<Fibest, then update
FibestAnd li.Compare the adaptive optimal control value and global optimum's adaptive value of particle, if Fibest<Fglobal, then F is updatedglobalAnd g;
5) weight of each particle, speed and displacement are updated by formula (6).Check whether and reach maximum iteration time, be then
Exit, g is controlled quentity controlled variable u (k+1) for optimizing, otherwise continue PSO iteration;
6) optimum control amount u (k+1) is acted on into boiler combustion optimization nonlinear system, reality is carried out to boiler combustion process
When control;
7) k, i.e. k+1 → k, return to step 2 are increased), the whole calculating process of repetition.
Claims (4)
1. a kind of boiler combustion optimization control method, it is characterised in that comprise the steps:
(1) boiler combustion nonlinear system is sampled, obtains the input/output data at current time;
(2) input/output data that real-time sampling is obtained is trained using online incremental learning fuzzy neural network, is set up
The online incremental learning forecast model of boiler combustion nonlinear system;
(3) nonlinear Model Predictive is used the online incremental learning forecast model, is realized to boiler combustion process
Optimal control;
In step (2), the online incremental learning forecast model is:
Wherein, u (k)=(u1(k),u2(k),…,um(k)) represent Boiler Combustion Optimization System controlled quentity controlled variable, y (k)=(y1(k),
y2(k),…,yn(k)) represent boiler combustion status target output, d represent boiler combustion system output postpone, p and q tables
Show the input/output order of boiler combustion process nonlinear system;
In step (3), the method for the optimal control is:
(31) correction of boiler combustion nonlinear system output valve is obtained by the online incremental learning forecast model:
In current sample time k, by the past input/output of boiler combustion nonlinear system and current input u (k) by being built
Online incremental learning forecast model obtains the output estimation value of boiler combustion nonlinear system
By boiler combustion nonlinear system input u (k+1) to be optimized and past input/output, boiler combustion is obtained non-
The output estimation value of linear system
If the prediction deviation at k moment isUse drift correctionObtain correction
(32) input to boiler combustion expense linear system is optimized:
Determine that the object function of boiler combustion optimization controlled quentity controlled variable u is according to boiler combustion optimization controlled quentity controlled variable u:
Wherein yirFor the reference locus of i-th boiler combustion significant condition output quantity, by the economy for solving boiler combustion optimization
Object function and obtain, yipThe prediction for being corresponding i-th boiler combustion significant condition output quantity Jing after feedback compensation output, m
The dimension for being respectively input into n and exporting, qiAnd λjFor weight coefficient;
The minimum of a value of above-mentioned object function is obtained by the online rolling optimization in real time of particle cluster algorithm, optimum control amount u (k+ is obtained
1), optimum control amount u (k+1) is acted on into boiler combustion nonlinear system and is optimized control.
2. boiler combustion optimization control method according to claim 1, it is characterised in that in step (1), the input number
According to for boiler operatiopn operating parameter, the output data is boiler efficiency and fume emission NOx。
3. boiler combustion optimization control method according to claim 2, it is characterised in that described boiler operatiopn manipulation ginseng
Number includes load, coal-supplying amount, total air, fuel throttle opening, secondary air register aperture and after-flame throttle opening.
4. boiler combustion optimization control method according to claim 1, it is characterised in that in step (2), the online increasing
Amount totally four layers of structure of fuzzy neural network of study:
Input layer, each neuron in this layer represents an input variable of online incremental learning forecast model, wherein using X1,
X2..., XrRepresent that boiler respectively runs manipulation amount u (k) and associated front p orders output y (k) successively;
Membership function layer, each input variable XiThere is u membership function Aij, it is Gauss member function:
Wherein μijIt is xiJ-th membership function, j=1,2 ..., u, cijAnd σijRespectively xiJ-th Gaussian function in
The heart and width, u is the quantity of membership function;
Fuzzy rule layer, j-th rule RjOutput be:
Output layer, the output variable of each one input signal weighted sum of node on behalf:
Wherein y be characterize boiler combustion status optimization aim output valve, wjFor result parameter.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510176385.1A CN104776446B (en) | 2015-04-14 | 2015-04-14 | Combustion optimization control method for boiler |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510176385.1A CN104776446B (en) | 2015-04-14 | 2015-04-14 | Combustion optimization control method for boiler |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104776446A CN104776446A (en) | 2015-07-15 |
CN104776446B true CN104776446B (en) | 2017-05-10 |
Family
ID=53618077
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510176385.1A Expired - Fee Related CN104776446B (en) | 2015-04-14 | 2015-04-14 | Combustion optimization control method for boiler |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104776446B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110017470A (en) * | 2019-02-27 | 2019-07-16 | 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 combustion multiobjective optimization control method based on differential evolution algorithm |
Families Citing this family (30)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107615186A (en) * | 2015-08-13 | 2018-01-19 | 华为技术有限公司 | The method and apparatus of Model Predictive Control |
CN105605610A (en) * | 2016-01-13 | 2016-05-25 | 上海交通大学 | Boiler combustion optimization method based on particle swarm algorithm |
CN106125555A (en) * | 2016-08-29 | 2016-11-16 | 西安西热控制技术有限公司 | A kind of online dynamic prediction control method based on main vapour pressure of boiler historical data |
CN106354011A (en) * | 2016-10-11 | 2017-01-25 | 沈阳化工大学 | Improved fuzzy PID (Proportion Integration Differentiation) control method for combustion of gas-fired boiler |
CN106842955B (en) * | 2017-03-15 | 2019-08-20 | 东南大学 | CO after burning with exhaust gas volumn Disturbance Rejection2Trapping system forecast Control Algorithm |
CN106991507A (en) * | 2017-05-19 | 2017-07-28 | 杭州意能电力技术有限公司 | A kind of SCR inlet NOx concentration on-line prediction method and device |
CN107180279B (en) * | 2017-06-14 | 2020-08-25 | 重庆科技学院 | QPSO-DMPC-based reaction regeneration system optimization control method |
CN107045290A (en) * | 2017-06-14 | 2017-08-15 | 重庆科技学院 | Reaction-regeneration system optimal control method based on MQPSO DMPC |
EP3474090A1 (en) * | 2017-10-20 | 2019-04-24 | aixprocess GmbH | Method for regulating a process within a system, in particular the combustion process of a boiler or furnace |
CN107977539A (en) * | 2017-12-29 | 2018-05-01 | 华能国际电力股份有限公司玉环电厂 | Improvement neutral net boiler combustion system modeling method based on object combustion mechanism |
CN108644805A (en) * | 2018-05-08 | 2018-10-12 | 南京归图科技发展有限公司 | Boiler intelligent combustion optimal control method based on big data |
CN108954375B (en) * | 2018-07-18 | 2020-06-19 | 厦门邑通软件科技有限公司 | Coal-saving control method for boiler |
CN109063359A (en) * | 2018-08-16 | 2018-12-21 | 燕山大学 | A kind of dynamic modelling method of Process of Circulating Fluidized Bed Boiler |
CN110888401B (en) * | 2018-09-11 | 2022-09-06 | 京东科技控股股份有限公司 | Combustion control optimization method and device for thermal generator set and readable storage medium |
CN109709907A (en) * | 2018-11-16 | 2019-05-03 | 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 | The boiler combustion process model integrated learning method and system of genetic programming algorithm based on tree |
KR102106827B1 (en) * | 2018-11-30 | 2020-05-06 | 두산중공업 주식회사 | System and method for optimizing boiler combustion |
US12050441B2 (en) | 2019-04-10 | 2024-07-30 | Aixprocess Gmbh | Method for controlling a process within a system, particularly a combustion process in a boiler or furnace |
CN110274258A (en) * | 2019-05-09 | 2019-09-24 | 国网河北能源技术服务有限公司 | A kind of feedforward control firing optimization method based on combustion zone temperature field prediction |
CN111061149B (en) * | 2019-07-01 | 2022-08-02 | 浙江恒逸石化有限公司 | Circulating fluidized bed coal saving and consumption reduction method based on deep learning prediction control optimization |
CN110673485B (en) * | 2019-10-21 | 2020-11-24 | 京东城市(南京)科技有限公司 | Model training method, device, electronic apparatus, and medium for combustion control |
CN110647042B (en) * | 2019-11-11 | 2022-04-26 | 中国人民解放军国防科技大学 | Robot robust learning prediction control method based on data driving |
CN111539615B (en) * | 2020-04-20 | 2023-04-07 | 上海发电设备成套设计研究院有限责任公司 | Boiler combustion process state monitoring method and system based on deep learning |
CN111829003B (en) * | 2020-06-22 | 2023-04-07 | 嘉兴汇智诚电子科技有限公司 | Power plant combustion control system and control method |
CN112947546B (en) * | 2021-01-27 | 2022-02-25 | 涵涡智航科技(玉溪)有限公司 | Ground-imitating flying method of unmanned aerial vehicle |
CN113091088B (en) * | 2021-04-14 | 2022-10-25 | 南京邮电大学 | Boiler combustion generalized predictive control method based on two-stage neural network |
CN113418188B (en) * | 2021-06-21 | 2022-06-14 | 中国人民解放军国防科技大学 | Double-swirl combustion instability control method and system |
CN113589693B (en) * | 2021-07-22 | 2023-05-09 | 燕山大学 | Cement industrial decomposing furnace temperature model predictive control method based on neighborhood optimization |
CN113654078A (en) * | 2021-08-20 | 2021-11-16 | 常州工学院 | Optimization method and system for boiler combustion air distribution structure |
CN113962435A (en) * | 2021-09-14 | 2022-01-21 | 国能神福(石狮)发电有限公司 | Boiler combustion parameter judgment method and device |
CN116974206B (en) * | 2023-09-06 | 2024-02-02 | 武昌理工学院 | Mill control method based on predictive fuzzy control algorithm |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101498457A (en) * | 2009-03-02 | 2009-08-05 | 杭州电子科技大学 | Boiler combustion optimizing method |
CN102679391A (en) * | 2012-05-21 | 2012-09-19 | 常州市新港热电有限公司 | Combustion online optimizing method of boiler |
CN103759290A (en) * | 2014-01-16 | 2014-04-30 | 广东电网公司电力科学研究院 | Large coal-fired unit online monitoring and optimal control system and implementation method thereof |
-
2015
- 2015-04-14 CN CN201510176385.1A patent/CN104776446B/en not_active Expired - Fee Related
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101498457A (en) * | 2009-03-02 | 2009-08-05 | 杭州电子科技大学 | Boiler combustion optimizing method |
CN102679391A (en) * | 2012-05-21 | 2012-09-19 | 常州市新港热电有限公司 | Combustion online optimizing method of boiler |
CN103759290A (en) * | 2014-01-16 | 2014-04-30 | 广东电网公司电力科学研究院 | Large coal-fired unit online monitoring and optimal control system and implementation method thereof |
Non-Patent Citations (4)
Title |
---|
基于广义动态模糊神经网络的电厂锅炉燃烧优化建模;赵敏;《热力发电》;20101231;第39卷;第19-21页 * |
基于神经网络模型的锅炉燃烧优化控制研究;于旭;《中国学位论文全文数据库》;20091226;第29-36页 * |
基于神经网络的电厂锅炉燃烧系统建模及优化研究;赵敏;《中国学位论文全文数据库》;20100125;第30-47页 * |
火电机组锅炉燃烧系统建模与优化研究;潘锐;《中国学位论文全文数据库》;20140630;全文 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110017470A (en) * | 2019-02-27 | 2019-07-16 | 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 combustion multiobjective optimization control method based on differential evolution algorithm |
Also Published As
Publication number | Publication date |
---|---|
CN104776446A (en) | 2015-07-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104776446B (en) | Combustion optimization control method for boiler | |
Le et al. | Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO | |
CN111413872B (en) | Air cavity pressure rapid active disturbance rejection method based on extended state observer | |
Pal et al. | Self-tuning fuzzy PI controller and its application to HVAC systems | |
CN113552797A (en) | Heating furnace temperature control method and system based on improved particle swarm optimization | |
CN111260117B (en) | CA-NARX water quality prediction method based on meteorological factors | |
CN111738477B (en) | Power grid new energy consumption capability prediction method based on deep feature combination | |
CN113253779A (en) | Heat pump temperature control system based on particle swarm fuzzy PID algorithm | |
CN108167802B (en) | Multi-model intelligent optimizing and predicting control method for boiler load under low load | |
Rojas et al. | Adaptive fuzzy controller: Application to the control of the temperature of a dynamic room in real time | |
CN106991507A (en) | A kind of SCR inlet NOx concentration on-line prediction method and device | |
Gouadria et al. | Comparison between self-tuning fuzzy PID and classic PID controllers for greenhouse system | |
Lei et al. | Online optimization of fuzzy controller for coke-oven combustion process based on dynamic just-in-time learning | |
CN111077771A (en) | Self-tuning fuzzy PID control method | |
CN112180733B (en) | Fuzzy logic-based building energy consumption system prediction control parameter setting method | |
CN118011805A (en) | Ultra-supercritical unit model predictive control method based on data driving and Tube optimization | |
CN117215190A (en) | Prediction control method for furnace temperature model in urban solid waste incineration process | |
CN115586801B (en) | Gas blending concentration control method based on improved fuzzy neural network PID | |
CN105334730A (en) | Heating furnace oxygen content IGA optimization T-S fuzzy ARX modeling method | |
CN114740713B (en) | Multi-objective optimization control method for wet flue gas desulfurization process | |
CN115289450A (en) | Full-load dynamically-adjusted boiler staged combustion real-time control method and system | |
Fabro et al. | Fuzzy-neuro predictive control, tuned by genetic algorithms, applied to a fermentation process | |
CN113870549B (en) | Method for optimizing iterative learning gain of traffic subarea by self-adaptive fine-tuning algorithm | |
Wang et al. | Method on PID controller optimization based on the data-driven technique | |
CN118192702A (en) | Hearth temperature control method based on I2T2FB-MPC |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
EXSB | Decision made by sipo to initiate substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170510 Termination date: 20210414 |