CN103576655B - A kind of power boiler burning subspace modeling and Multipurpose Optimal Method and system - Google Patents
A kind of power boiler burning subspace modeling and Multipurpose Optimal Method and system Download PDFInfo
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Abstract
The invention discloses a kind of power boiler burning subspace modeling and Multipurpose Optimal Method and system.It belongs to station boiler optimizing operation field, particularly the method and system that solves of a kind of foundation of high precision combustion model and multiple-objection optimization.Comprise the following steps: the input variable determining burning optimization model; Variable to be optimized is determined from all input variables; Load is divided into several sections of neighborhood overlap, off-line sets up burning subspace ANFIS (adaptive neural network fuzzy system) model of a section; Real-time data acquisition; Carry out Local Subspace Modifying model online; Consider the constraint of unit load, to maximize boiler efficiency and to minimize discharged nitrous oxides for target, adopt ripe global optimization approach, minimize based on integrate-cost and carry out online multiple-objection optimization; Optimum results is separated and implements.The present invention is suitable for generating plant pulverized coal boiler burning optimization and runs, and has the advantages that modeling accuracy is high and optimization speed is fast.
Description
Technical field
The invention belongs to station boiler optimizing operation technical field, particularly a kind of high precision combustion model is set up and the method and system of running optimizatin, specifically a kind of power boiler burning subspace modeling and Multipurpose Optimal Method and system.
Background technology
Very important ingredient in China of coal-fired power plant electrical production, its generated energy, considerably beyond the summation of other various generated energy, does not have large change within the quite a long time.But the whole world is faced with the exhausted crisis of serious primary energy, burned coal price remains high, and problem of environmental pollution is also subject to the extensive concern of countries in the world day by day.Therefore the strategy that national governments take is: greatly develop gigawatt supercritical generating technology on the one hand, being then the optimisation technique research carrying out station boiler coal combustion on the other hand, is namely the research of technique that target carries out improving boiler efficiency and reducing pollutant emission with energy-saving and emission-reduction.
For the burning optimization of high-efficiency low-pollution, the main difficulty faced at present is, the burning of coal in boiler is very complicated gas-particle two-phase flow problem, combustion mechanism aspect is not very ripe, be the mechanism model based on partial differential equation and algebraic equation composition to the study general of its characteristic, and the foundation of these models obtains under the condition of many hypothesis and simplification.For system optimization and control, then mainly based on the method for computational intelligence modeling.
Through finding the open source literature retrieval of prior art, document " QiangXu, JiaYangandYanqiuYang.Identificationandcontrolofboilercom bustionsystembasedonneuralnetworksandantcolonyoptimizati onalgorithm.Proceedingsofthe7thWorldCongressonIntelligen tControlandAutomation, June25-27, 2008, Chongqing, China, pp.765-768 is (based on boiler combustion system identification and the control of neural network and ant colony optimization algorithm, international conference: world's Conference of Intellectual Control and Automation collection of thesis, 2008:765-768) ", have employed method that the neural network in computational intelligence field and ant group optimization combine to carry out burning the design of modeling and controller, better can improve the regulation quality of boiler combustion system, but boiler main can only be solved and want the setting value that controlled parameter tracking is given, and system cannot be made to remain, and Optimum Economic operating mode is run.Document " HaoZhou, KefaCen, JianrenFan.ModelingandoptimizationoftheNOxemissioncharac teristicsofatangentiallyfiredboilerwithartificialneuraln etworks.Energy, 2004, 29:167 – 183 is (based on the tangentially firing boiler discharged nitrous oxides characteristic modeling and control of artificial neural network, International Periodicals: the energy, 2004, 29:167 – 183) ", Multilayer Feedforward Neural Networks is adopted to carry out global modeling and the control of tangentially firing boiler discharged nitrous oxides characteristic, reduction pollutant emission is had great importance, but do not consider boiler efficiency, thus it is incomplete for optimizing.Document (An Enke, Song Yao, Yang Xia. based on the coal-fired power station boiler multiple goal burning optimization of support vector machine and genetic algorithm, energy-conservation, 2008 (10): 22 – 25), support vector machine is adopted to carry out modeling, consider boiler efficiency and pollutant emission problem simultaneously, genetic algorithm is adopted to carry out multiple-objection optimization calculating, but problem is that first to optimize the time long, what next obtained is Pareto optimal solution set, and the solution finally putting on scene only has one group, and how being finally implemented on on-the-spot unique solution is have problem to be solved.And the common issue that above document exists also is: (1) is all carry out overall state modeling by a neural network or support vector machine, for the complicated boiler combustion system with serious nonlinear characteristic, Model approximation performance in global scope is difficult to ensure, the model training time can be very long, especially, when ature of coal coal etc. changes, can there is series of problems in the time length that Modifying model brings and reliability aspect; (2) carry out pollutant emission or/and the optimization of boiler efficiency time do not consider that unit load retrains, the result of such optimization is likely reduction of pollutant emission or/and improve boiler efficiency, but with what to reduce generated energy be cost, therefore consider that the constraint of unit load just becomes important aspect.
Therefore, the present invention is based on some Subspace partition of load data, set up the adaptive neural network fuzzy system model of each subspace of combustion system respectively, comprehensively obtain overall condition model on this basis, optimizing process not only considers the constraint of unit load, introduce the integrate-cost function of boiler efficiency and oxides of nitrogen simultaneously, multi-objective optimization question is converted into single-object problem process.
Summary of the invention
The object of the present invention is to provide that a kind of step is clear and definite, clear concept, computing be simple, the modeling and optimization method and system of the boiler combustion of reliable results, the modeling method invented all is significantly improved in model training speed and Generalization accuracy, the optimization method invented also has good improvement in optimization precision and optimal speed, the modeling and optimization that can be applicable to various Combustion System of Boiler Burning Fine controls, and has very strong practicality.
For achieving the above object, one aspect of the present invention provides a kind of power boiler burning subspace modeling and Multi objective optimization system, specifically by the following technical solutions:
It comprises boiler (1), the distributed monitoring control system (2) be connected with described boiler (1) and coal property test instrument (3), and the burning optimization workstation (4) to be connected with coal property test instrument (3) with DCS (2), described burning optimization workstation (4) comprises interconnective communication module (5), interface module (6), optimize module (7) and model module (8), described communication module (5) obtains data from DCS (2) and coal property test instrument (3), optimum results is sent to DCS (2) by communication module (5) and is sent to boiler (1) further.
The present invention on the other hand, provides a kind of power boiler burning subspace modeling and Multipurpose Optimal Method, adopts
Technical scheme realizes according to following steps:
Step (1). according to concrete boiler type and the interact relation to burning, determine the input variable z of burning optimization model;
Step (2). from all input variable z, determine variable to be optimized
x;
Step (3). determine optimization method;
Step (4). off-line model is set up;
Step (5). real-time data acquisition;
Step (6). on-time model correction;
Step (7). on-line optimization;
Step (8). optimum results is separated and implements;
Step (9). repeat step (5) ~ (8).
In described step (1) the input variable z of burning optimization model include stove ature of coal, enter stove total coal amount, enter stove total air, each layer secondary air register aperture, each grate firing throttle opening, bellows burner hearth differential pressure and oxygen content of smoke gas to the greatest extent, optionally also comprise according to boiler concrete condition, enter stove each feeder aperture, each coal pulverizer ventilation, burner pivot angle, each main steam temperature, each reheat steam temperature, main steam flow, total Feedwater Flow, each reheating attemperation water flow, described as-fired coal matter comprises coal net calorific value, volatile matter, ash content and total moisture.
Variable to be optimized in described step (2)
xcomprise each layer secondary air register aperture, each grate firing throttle opening and oxygen content of smoke gas to the greatest extent, optionally also comprise each feeder aperture, each coal pulverizer ventilation, burner pivot angle according to boiler concrete condition.
In described step (3), optimization method is ripe colony intelligence optimized algorithm, and wherein global search adopts differential evolution algorithm, and Local Search adopts simulated annealing, gives full play to the advantage of two kinds of optimized algorithms.
In described step (4), off-line model is set up and is comprised the steps:
Step (a1). obtain unit operation historical data from DCS or the SIS database of process industry widespread use, comprise the input variable z of the burning optimization model described in step (2), and boiler efficiency E, oxides of nitrogen N and unit load L;
Step (a2). according to data mining Selection Model training sample from unit operation historical data;
Step (a3). according to load, model training sample is divided into
iindividual section, i.e. L
1: [0,
a 2], L
2: [
a 1,
a 3] ..., L
i-1
: [
a i-2
,
a i ], L
i : [
a i-1
,
a i+ 1
],
Wherein,
a i be the number between 0 and unit rated load MCR, and meet relation: 0≤
a i <
a i+ 1
≤ 1.1MCR(
q=1,2 ...,
i);
Step (a4). according to above-mentioned
iindividual section sample data sets up boiler efficiency Local Subspace model F respectively
1,
i (
x,
y) (
i=1,2 ...,
i), set up discharged nitrous oxides Local Subspace model F
2,
i (
x,
y) (
i=1,2 ...,
i), set up unit load Local Subspace model F
3,
i (
x,
y) (
i=1,2 ...,
i).Modeling method is adaptive neural network fuzzy system (ANFIS).
Wherein,
yremove variable to be optimized in whole input variable z of Optimized model
xthe rear remaining part input variable without the need to optimizing.
Step (a5). integrate boiler efficiency world model F respectively according to above-mentioned Local Subspace model
1(
x,
y), discharged nitrous oxides world model F
2(
x,
y), unit load world model F
3(
x,
y), computing formula is as follows:
In formula,
In described step (5), real-time data acquisition comprises the real time data of the signal such as input variable z, boiler efficiency E, oxides of nitrogen N, unit load L gathering burning optimization model.
In described step (6), on-time model correction comprises the steps:
Step (b1). calculate boiler efficiency model respectively according to real time data and export E
m(k)=F
1(
x,
y), nitrogen oxide emission models export N
m(k)=F
2(
x,
y), unit load model export L
m(k)=F
3(
x,
y);
Step (b2). calculate the nearest model calculated of sampling for Q time based on rolling window and export E
m(k – Q+1:k), N
m(k – Q+1:k), L
m(k – Q+1:k) and unit actual operating data E, composition error J between N, L, if composition error J is little, skip this step without the need to on-time model correction, if composition error J is large, revises model according to up-to-date unit data; As preferably, the computing method of described composition error J are
In formula,
represent the mean value of E (k-Q+1:k), N (k-Q+1:k), L (k-Q+1:k) respectively.
The criterion that composition error J is large or little is: if J>
then think that composition error J is large; If J≤
then think that composition error J is little.
generally get 0.01 ~ 10, concrete numeral is determined according to actual boiler parameter debugging.
On-time model correction in described step (6), only needs to revise described in step (a4) at every turn
i1 in individual boiler efficiency Local Subspace model or 2,
i1 in individual discharged nitrous oxides Local Subspace model or 2 and
i1 or 2 in individual unit load Local Subspace model, the pattern number defining method that revise is: if unit load L meets L<
a 1, then correction model 1; If unit load L meets
a i ≤ L<
a i+ 1
(
i=1,2 ...,
i-1), then correction model
iwith
i+ 1; If unit load L meet L>=
a i , then correction model
i.
In described step (7), on-line optimization adopts constraint Multipurpose Optimal Method to obtain best optimized variable
x=
x opt, target maximizes boiler efficiency E, minimizes discharged nitrous oxides N, and maintain unit load L almost constant, objective function is simultaneously
Min[E(k)–E
M(k)]E
﹩+[N
M(k)–N(k)]N
﹩
s.t.E
M(k)=F
1(
x,
y),
N
M(k)=F
2(
x,
y),
L
M(k)=F
3(
x,
y),
L
M(k)≥L(k)–△L,
x iL≤
x i≤
x iH.
In formula, E
﹩it is the cost that boiler efficiency reduces a unit; N
﹩it is the cost that oxides of nitrogen raises a unit; △ L is the unit load constant interval of allowing before and after optimizing, and namely burning optimization can not be reduced to cost with generated energy, generally gets △ L=L
0× 5 ‰ (L
0represent unit rated load);
x iHwith
x iLthe bound constraint of variable to be optimized respectively.
In described step (8), optimum results is separated and implements to be the optimum optimization variable by obtaining
x=
x optin each layer secondary air register aperture, each grate firing to the greatest extent throttle opening, each feeder aperture, each coal pulverizer ventilation, burner pivot angle, send to on-the-spot topworks as new regulated quantity; By the optimum optimization variable obtained
x=
x optin oxygen content of smoke gas send to oxygen content of smoke gas automatic control unit, as new oxygen content of smoke gas setting value.
The invention has the beneficial effects as follows: training time and the on-line amending time of model shorten all greatly, generalization ability also obviously strengthens, reliability increases, optimizing process considers the constraint of unit load, introduces the integrate-cost function of boiler efficiency and oxides of nitrogen simultaneously, multi-objective optimization question is converted into single-object problem process, not only clear concept, optimum results is clear and definite, and optimal speed is fast, often can obtain the optimum solution of problem in the short period of time.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of system.
Embodiment
Following examples are used for Implement methodology of the present invention is described, but are not used for limiting the scope of the invention.
Below for the coal-fired monoblock of certain 600MW, in conjunction with the specific embodiment of the present invention, a kind of power boiler burning subspace modeling and Multipurpose Optimal Method are described.Concrete steps are as follows:
1. determine the input variable z of burning optimization model.Specifically comprise as-fired coal net calorific value, as-fired coal volatile matter, as-fired coal ash content, as-fired coal total moisture, enter stove total coal amount, enter stove total air, each layer secondary air register aperture, each grate firing to the greatest extent throttle opening, bellows burner hearth differential pressure, oxygen content of smoke gas, each feeder aperture, each coal pulverizer ventilation, burner pivot angle.
2., according to boiler situation, from all input variable z, determine variable to be optimized
x.Specifically comprise each layer secondary air register aperture, each grate firing throttle opening, oxygen content of smoke gas to the greatest extent.Optionally burner pivot angle is also comprised according to boiler concrete condition.
3. determine optimization method.Optimization method is ripe colony intelligence optimized algorithm, and wherein global search adopts differential evolution algorithm, and Local Search adopts simulated annealing.
4. off-line model is set up.Comprise the steps:
4.1. read unit operation history data from historical data base, comprise the input variable z of all burning optimization models of definition in step 1, and boiler efficiency E, oxides of nitrogen N and unit load L;
4.2. data digging method Selection Model training sample 600 groups from unit operation historical data is adopted;
4.3. according to load, model training sample is divided into 7 sections, i.e. L
1: [0,
a 2], L
2: [
a 1,
a 3], L
3: [
a 2,
a 4], L
4: [
a 3,
a 5], L
5: [
a 4,
a 6], L
6: [
a 5,
a 7], L
7: [
a 6,
a 8],
Wherein,
a 1=0.4,
a 2=0.5,
a 3=0.6,
a 4=0.7,
a 5=0.8,
a 6=0.9,
a 7=1.0,
a 8=1.1;
4.4. according to the section sample data of 7 in step 4.3, adaptive neural network fuzzy system (ANFIS) is adopted to set up boiler efficiency Local Subspace model F respectively
1,1(
x,
y), F
1,2(
x,
y), F
1,3(
x,
y), F
isosorbide-5-Nitrae(
x,
y), F
1,5(
x,
y), F
1,6(
x,
y), F
1,7(
x,
y), set up discharged nitrous oxides Local Subspace model F
2,1(
x,
y), F
2,2(
x,
y), F
2,3(
x,
y), F
2,4(
x,
y), F
2,5(
x,
y), F
2,6(
x,
y), F
2,7(
x,
y), set up unit load Local Subspace model F
3,1(
x,
y), F
3,2(
x,
y), F
3,3(
x,
y), F
3,4(
x,
y), F
3,5(
x,
y), F
3,6(
x,
y), F
3,7(
x,
y);
Wherein,
yremove variable to be optimized in whole input variable z of Optimized model
xthe rear remaining part input variable without the need to optimizing.
4.5. boiler efficiency world model F is integrated respectively according to the Local Subspace model in step 4.4
1(
x,
y), discharged nitrous oxides world model F
2(
x,
y), unit load world model F
3(
x,
y), computing formula is as follows:
In formula,
5. real-time data acquisition.Obtaining current data unit operation is as-fired coal net calorific value 25462kj/kg, as-fired coal volatile matter 29.22%, as-fired coal ash content 15.75%, as-fired coal total moisture 11.31%, enter the total coal amount 241.56t/h of stove, enter stove total air 2572t/h, each layer secondary air register aperture (54.3%, 56.2%, 57.7%, 54.1%, 50.7%, 44.6%), each grate firing is throttle opening (62.1% to the greatest extent, 52.2%), bellows burner hearth differential pressure 0.82kPa, oxygen content of smoke gas (3.32%), each feeder aperture (86.4%, 87.3%, 83.1%, 83.6%, 80.4%, 0.4%), each coal pulverizer ventilation (102.4t/h, 107.6t/h, 101.7t/h, 93.3t/h, 97.5t/h, 0.1t/h), burner pivot angle 60.5%, and boiler efficiency 92.5%, oxides of nitrogen 687.8mg/Nm
3, unit load 581.3MW.
6. on-time model correction.Step is as follows:
6.1. calculate boiler efficiency model respectively according to real time data and export E
m(k)=F
1(
x,
y), nitrogen oxide emission models export N
m(k)=F
2(
x,
y), unit load model export L
m(k)=F
3(
x,
y);
6.2. calculate the nearest model calculated of sampling for 30 times based on rolling window and export E
m(k – 29:k), N
m(k – 29:k), L
m(k – 29:k) and unit actual operating data E, composition error J between N, L, as shown in the formula
In formula,
represent the mean value of E (k-29:k), N (k-29:k), L (k-29:k) respectively.
Result of calculation obtains J=0.7.Getting the large or little criterion of composition error J is
=1, result J<
then without the need to carrying out model on-line amending, the adaptive neural network fuzzy system subspace model being indicated as combustion system foundation has enough precision.
7. on-line optimization.According to actual conditions, get the cost E that boiler efficiency reduces a unit
﹩=1000$, oxides of nitrogen raises the cost N of a unit
﹩=0.003$, unit load constant interval △ L=600 × 5 ‰ of allowing before and after optimizing, the bound constraint of variable to be optimized is respectively within the scope of topworks's physical restriction ± and 30%.Optimize following objective function
Min[E(k)–E
M(k)]E
﹩+[N
M(k)–N(k)]N
﹩
s.t.E
M(k)=F
1(
x,
y),
N
M(k)=F
2(
x,
y),
L
M(k)=F
3(
x,
y),
L
M(k)≥L(k)–△L,
x iL≤
x i≤
x iH.
The result that is finally optimized is each layer secondary air register aperture (56.8%, 55.1%, 47.3%, 48.5%, 55.3%, 57.2%), each grate firing throttle opening (56.9%, 50.5%), oxygen content of smoke gas 2.98%, burner pivot angle 56.2% to the greatest extent.Organizing boiler efficiency corresponding to optimum results with this is 93.3%, and discharged nitrous oxides is 498.5mg/Nm
3, unit load is 585.4MW.Visible, boiler efficiency improves 0.8%, and discharged nitrous oxides reduces 189.3mg/Nm
3, unit load adds 4.1MW.This shows, by Modeling and optimization method provided by the invention, when Current fuel consumes, boiler efficiency can be improved, and discharged nitrous oxides can be reduced, and unit load slightly increases.
8. optimum results is separated and implements.By the best each layer secondary air register aperture obtained, each grate firing to the greatest extent throttle opening, burner pivot angle, send to DCS as new regulated quantity and and then send to on-the-spot topworks; The best oxygen content of smoke gas obtained is sent to DCS thermal control process loop, is regulated by DCS as new oxygen content of smoke gas setting value, thus make oxygen content of smoke gas actual value reach optimal value.
9. repeat the new operating mode of step 5 ~ 8 pair next round to continue to optimize, thus remain that unit burns in optimum state.
Claims (8)
1. power boiler burning subspace modeling and a Multipurpose Optimal Method, is characterized in that: specifically comprise the steps:
Step (1). according to concrete boiler type and the interact relation to burning, determine the input variable z of burning optimization model;
Step (2). from all input variable z, determine variable x to be optimized;
Step (3). determine optimization method;
Step (4). off-line model is set up;
Step (5). real-time data acquisition;
Step (6). on-time model correction;
Step (7). on-line optimization;
Step (8). optimum results is separated and implements;
Step (9). repeat step (5) ~ (8);
In described step (1) the input variable z of burning optimization model include stove ature of coal, enter stove total coal amount, enter stove total air, each layer secondary air register aperture, each grate firing to the greatest extent throttle opening, bellows burner hearth differential pressure, oxygen content of smoke gas, enter stove each feeder aperture, each coal pulverizer ventilation, burner pivot angle, each main steam temperature, each reheat steam temperature, main steam flow, total Feedwater Flow, each reheating attemperation water flow, described as-fired coal matter comprises coal net calorific value, volatile matter, ash content and total moisture.
2. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, is characterized in that: variable to be optimized in described step (2)
xcomprise each layer secondary air register aperture, each grate firing throttle opening and oxygen content of smoke gas to the greatest extent.
3. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, it is characterized in that: in described step (3), optimization method is ripe colony intelligence optimized algorithm, wherein global search adopts differential evolution algorithm, Local Search adopts simulated annealing, gives full play to the advantage of two kinds of optimized algorithms.
4. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, is characterized in that: in described step (4), off-line model is set up and comprised the steps:
Step (a1). obtain unit operation historical data from DCS or the SIS database of process industry widespread use, comprise the input variable z of the burning optimization model described in step (2), and boiler efficiency E, oxides of nitrogen N and unit load L;
Step (a2). according to data mining Selection Model training sample from unit operation historical data;
Step (a3). according to load, model training sample is divided into
iindividual section, i.e. L
1: [0,
a 2], L
2: [
a 1,
a 3] ..., L
i-1
: [
a i-2
,
a i ], L
i : [
a i-1
,
a i+ 1
],
Wherein,
a i be the number between 0 and unit rated load MCR, and meet relation: 0≤
a i <
a i+ 1
≤ 1.1MCR(
q=1,2 ...,
i);
Step (a4). according to above-mentioned
iindividual section sample data sets up boiler efficiency Local Subspace model F respectively
1,
i (
x,
y) (
i=1,2 ...,
i), set up discharged nitrous oxides Local Subspace model F
2,
i (
x,
y) (
i=1,2 ...,
i), set up unit load Local Subspace model F
3,
i (
x,
y) (
i=1,2 ...,
i);
Modeling method is adaptive neural network fuzzy system (ANFIS);
Wherein,
yremove variable to be optimized in whole input variable z of Optimized model
xthe rear remaining part input variable without the need to optimizing;
Step (a5). integrate boiler efficiency world model F respectively according to above-mentioned Local Subspace model
1(
x,
y), discharged nitrous oxides world model F
2(
x,
y), unit load world model F
3(
x,
y), computing formula is as follows:
In formula,
。
5. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, is characterized in that: in described step (5) real-time data acquisition comprise gather burning optimization model input variable z, boiler efficiency E, oxides of nitrogen N, unit load L signal real time data.
6. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, is characterized in that: in described step (6), on-time model correction comprises the steps:
Step (b1). calculate boiler efficiency model respectively according to real time data and export E
m(k)=F
1(
x,
y), nitrogen oxide emission models export N
m(k)=F
2(
x,
y), unit load model export L
m(k)=F
3(
x,
y);
Step (b2). calculate the nearest model calculated of sampling for Q time based on rolling window and export E
m(k – Q+1:k), N
m(k – Q+1:k), L
m(k – Q+1:k) and unit actual operating data E, composition error J between N, L, if composition error J is little, skip this step without the need to on-time model correction, if composition error J is large, revises model according to up-to-date unit data; The computing method of described composition error J are
In formula,
represent the mean value of E (k-Q+1:k), N (k-Q+1:k), L (k-Q+1:k) respectively;
The criterion that described composition error J is large or little is: if J>
then think that composition error J is large; If J≤
then think that composition error J is little;
generally get 0.01 ~ 10, k and represent sampling number corresponding to current time.
7. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, is characterized in that: in described step (7), on-line optimization adopts constraint Multipurpose Optimal Method to obtain best optimized variable
x=
x opt, target maximizes boiler efficiency E, minimizes discharged nitrous oxides N, and maintain unit load L almost constant, objective function is simultaneously
Min[E(k)–E
M(k)]E
﹩+[N
M(k)–N(k)]N
﹩
s.t.E
M(k)=F
1(
x,
y),
N
M(k)=F
2(
x,
y),
L
M(k)=F
3(
x,
y),
L
M(k)≥L(k)–△L,
x iL≤
x i≤
x iH
In formula, E
﹩it is the cost that boiler efficiency reduces a unit; N
﹩it is the cost that oxides of nitrogen raises a unit; △ L is the unit load constant interval of allowing before and after optimizing, and namely burning optimization can not be reduced to cost with generated energy, generally gets △ L=L
0× 5 ‰, L
0represent unit rated load;
x iHwith
x iLbe the bound constraint of variable to be optimized respectively, k represents the sampling number corresponding to current time.
8. a kind of power boiler burning subspace modeling according to claim 1 and Multipurpose Optimal Method, is characterized in that: in described step (8), optimum results is separated and implements to be the optimum optimization variable by obtaining
x=
x optin each layer secondary air register aperture, each grate firing to the greatest extent throttle opening, each feeder aperture, each coal pulverizer ventilation, burner pivot angle, send to on-the-spot topworks as new regulated quantity; By the optimum optimization variable obtained
x=
x optin oxygen content of smoke gas send to oxygen content of smoke gas automatic control unit, as new oxygen content of smoke gas setting value.
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