CN109977559A - A kind of Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving based on improved adaptive GA-IAGA - Google Patents
A kind of Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving based on improved adaptive GA-IAGA Download PDFInfo
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Abstract
The present invention relates to a kind of Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving based on improved adaptive GA-IAGA, comprising the following steps: establish the model of the water screen tube diabatic process convection current Radiant exothermicity in Pulverized Fuel Boiler Furnace, and relevant parameter is set；According to the model and relevant parameter of the water screen tube diabatic process convection current Radiant exothermicity in the Pulverized Fuel Boiler Furnace built, heat exchange amount and estimation heat exchange amount setting objective function are solved with forward direction, and determine constraint condition；Real coding is carried out to objective function, constraint condition, determines correlated variables；Inverting is carried out to heat exchange amount during the water screen tube convection current radiant heat transfer in Pulverized Fuel Boiler Furnace using Revised genetic algorithum；The optimal value that Revised genetic algorithum is sought is the solution of inverse radiation analysis.Present invention improves more excellent solution is lost in original algorithm, ability of searching optimum and convergence rate are improved, has very extensive Practical significance in the reverse temperature intensity for solving the water screen tube convection current radiant heat transfer process in Pulverized Fuel Boiler Furnace.
Description
Technical field
The present invention relates to a kind of inverse radiation analysis method for solving based on improved adaptive GA-IAGA.
Background technique
The basic thought of genetic algorithm is to safeguard the group for representing the potential solution of problem, which can be selected by operation
It selects, intersect, mutation operator is evolved.In genetic algorithm, the effective solution of each of institute's Solve problems is referred to as " a dyeing
Body ", concrete form are the coded strings generated using specific coding mode.Each of coded strings coding unit is known as
" gene ".Modern genetics based on mendelism propose the reform patterns of hereditary information.In the heredity of organism
In the process, chromosome is hereditary information-gene carrier, and gene is combined according to certain order on chromosome.From life
The two most important theories of object science provide preciousness for the Holland method for seeking effectively to study artificial Adaptable System
Idea inspirations.On the basis of thousand people carry out biosimulation with computer, Holland has found the biological heredity of nature
Evolutionary system has been successfully set up the model of genetic algorithm with the similitude of artificial Adaptable System, and to Genetic algorithm searching
Validity carried out theoretical proof.Genetic algorithm using N genome at group searched in D dimension problem space it is optimal
Solution, in searching process, algorithm will do it iterative cycles, and the initial value of each iteration all is influenced greatly, to lose more excellent solution, can sought
It easily falls into that local optimum, search capability are poor, convergence rate is slow during excellent, affects low optimization accuracy and speed.
For inverse radiation analysis, in early stage research, mainly pass through the tradition optimization such as Gauss-Newton method, conjugate gradient method
Method is solved, but such algorithm acquired results are larger by the interference of initial value, easily falls into local optimum, and calculating
Information largely based on gradient is needed in journey.In recent years, more and more scholars begin trying intelligent algorithm being applied to radiation
In the solution research of indirect problem.Intelligent optimization algorithm is to designing various optimizing by the swarm intelligence principle of natural imitation circle
A kind of general designation of algorithm mainly includes genetic algorithm, ant group algorithm, simulated annealing, particle swarm algorithm etc..They have
Concept is concise, it is easy to accomplish, it is not necessarily to gradient information, calculates the advantages that relatively easy.
Coal-powder boiler is the boiler plant using coal dust as fuel.It is rapid with burning, complete, capacity is big, high-efficient, adaptation
Coal is wide, is convenient for the advantages that controlling to adjust.The burning feature of coal-powder boiler is that fuel with air enters combustion chamber, and is suspending
It burns under state.
It is close according to the moulded dimension parameter of the water screen tube convection current radiant heat transfer in known Pulverized Fuel Boiler Furnace, system hot-fluid
The operating conditions such as degree, the physical parameter of model each section of the water screen tube convection current radiant heat transfer in inverting Pulverized Fuel Boiler Furnace, temperature point
The unknown parameters such as cloth, boundary condition.To be applied to engineering construction problem in have very strong Practical significance.And it is used at present with mesh
Classic algorithm based on the gradient information of scalar functions carries out inverting, can encounter dyscalculia, problem is misread or acquisition can not
The problems such as row solution；Although being not necessarily to gradient information using some intelligent algorithms, calculating is relatively easy, can also encounter and fall into part most
Excellent, the problems such as convergence rate is slow, precision is low, it is unable to reach accurate inversion solution.
Summary of the invention
When solving for Pulverized Fuel Boiler Furnace inverse radiation analysis in the prior art, genetic algorithm initial value influences greatly, easily to fall into
The deficiencies of local optimum, the technical problem to be solved in the present invention is to provide one kind, and ability of searching optimum and convergence speed can be improved
Degree improves the Pulverized Fuel Boiler Furnace inverse radiation analysis solution based on improved adaptive GA-IAGA that more excellent solution defect is lost in original algorithm
Method.
In order to solve the above technical problems, The technical solution adopted by the invention is as follows:
The present invention plants the Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving based on improved adaptive GA-IAGA, comprising the following steps:
1) model of the water screen tube diabatic process convection current Radiant exothermicity in Pulverized Fuel Boiler Furnace is established, and related ginseng is set
Number；
2) according to the model and correlation of the water screen tube diabatic process convection current Radiant exothermicity in the Pulverized Fuel Boiler Furnace built
Parameter solves heat exchange amount and estimation heat exchange amount setting objective function with forward direction, and determines constraint condition；
3) real coding is carried out to objective function, constraint condition, determines correlated variables；
4) using Revised genetic algorithum to heat exchange amount during the water screen tube convection current radiant heat transfer in Pulverized Fuel Boiler Furnace
Carry out inverting；The optimal value that Revised genetic algorithum is sought is the solution of inverse radiation analysis.
In step 1), model is outer without having two kinds of situations of slagging outside slagging and pipe in the presence of pipe, carries out respectively to two kinds of situations anti-
Drill solution.
In step 2), the convection current Radiant exothermicity on unit water-cooling wall obtained when according to positive Solve problems, by anti-
Every relevant parameter that model is calculated, including convective heat-transfer coefficient h are drilled, the emissivity ε of water screen tube manages interior water temperature T1With
And flue-gas temperature T2, heat exchange amount and estimation heat exchange amount setting objective function are solved with forward directionΦ* i,measuredFor positive solution value, Φ* i,eatimatedFor algorithm estimated value.
In step 4), Revised genetic algorithum is improved on the basis of the genetic algorithm of standard, and specific steps are such as
Under:
401) group is initialized；
402) fitness value of every chromosome is calculated separately according to valuation functions；
403) according to the fitness value of chromosome, selection behaviour is carried out using chromosome of the roulette selection algorithm to group
Make；
404) according to crossover probability PcTwo parent chromosomes are selected from population, are handed over using improved crossover operator
The child chromosome of fork operation, generation replaces parent chromosome to enter new population；According to mutation probability PmSelected chromosome, and it is right
Group carries out mutation operation；
405) after mutation operation, the fitness value of each chromosome in group is recalculated；
406) it evaluates whether to meet termination condition, such as meets termination condition, then algorithm terminates；Otherwise return step 402)
In step 404), improved crossover operator particular content is as follows:
According to crossover probability PcTwo chromosomes are selected from t generation Crossover operation is carried out, wherein (1≤c1≤c2≤N), d is chromosome length, and two are handed over
The chromosomal gene of fork is(1≤i≤d), the correspondence gene generated after intersection are(1≤i≤
d)；
New chromosome xc1'Middle geneFrom following three chromosome, (1≤i≤d):
That is, child chromosomeWherein, rand equally distributed random number between [0,1]；For chromosome；In i-th of gene,For chromosome
In i-th of gene；
Newly generated child chromosomeGene is chosen in chromosome among random slave above three, similarly, son
For chromosome xc2'GeneAlso it is obtained in the intermediate chromosome of three generated from the above.
The invention has the following beneficial effects and advantage:
1. the inverse radiation analysis method for solving the present invention is based on improved adaptive GA-IAGA overcomes the initial of existing classic algorithm
Value influences big, the disadvantages of easily falling into local optimum, by being compared with other algorithms, improve lost in original algorithm compared with
The shortcomings that excellent solution, improves ability of searching optimum and convergence rate, is solving the water screen tube convection current radiation in Pulverized Fuel Boiler Furnace
The reverse temperature intensity of diabatic process has very extensive Practical significance.
2. the Revised genetic algorithum of the method for the present invention application, the crossover operator with memory function are contaminated with two parents
Based on colour solid, global optimum's individual and individual optimum individual are taken full advantage of during crossover operation, so that in group
Chromosome is directionally close to the optimal solution of objective function.
3. the present invention is the algorithm realized based on real coding, the variation of chromosome is acted on gene, is calculated improving
In method, according to mutation probability PmJudge whether each gene of chromosome makes a variation in new population after intersecting, then with testing
Function carries out test verifying, it can be deduced that the innovatory algorithm has certain advantage than other algorithms.
4. improved adaptive GA-IAGA proposed by the present invention proposes a kind of new crossover operator, improves in original algorithm
The shortcomings that losing more excellent solution improves ability of searching optimum and convergence rate, according to the water-cooling wall in known Pulverized Fuel Boiler Furnace
The operating conditions such as the moulded dimension parameter of pipe convection current radiant heat transfer, system heat flow density, the water screen tube pair in inverting Pulverized Fuel Boiler Furnace
The unknown parameters such as the physical parameter, Temperature Distribution, the boundary condition of model each section of radiant heat transfer are flowed, Pulverized Fuel Boiler Furnace is being solved
In water screen tube convection current radiant heat transfer process inverse problem and engineering construction problem solving have very extensive Practical significance.
Detailed description of the invention
Figure 1A is a kind of inverse radiation analysis method for solving general flow chart based on improved adaptive GA-IAGA of the present invention；
Figure 1B is the method for the present invention detail flowchart；
Fig. 2 is completely without the water screen tube of lime-ash；
Fig. 3 has the water screen tube of uniform lime-ash；
Fig. 4 is fitness value change curve of the Sphere function in algorithms of different；
Fig. 5 is fitness value change curve of the Schwefel Problem function in algorithms of different.
Specific embodiment
The present invention is further elaborated with reference to the accompanying drawings of the specification.
As shown in Figure 1A, a kind of inverse radiation analysis method for solving based on improved adaptive GA-IAGA of the present invention,
The following steps are included:
1) model of the water screen tube diabatic process convection current Radiant exothermicity in Pulverized Fuel Boiler Furnace is established, and related ginseng is set
Number；
2) according to the model and correlation of the water screen tube diabatic process convection current Radiant exothermicity in the Pulverized Fuel Boiler Furnace built
Parameter solves heat exchange amount and estimation heat exchange amount setting objective function with forward direction, and determines constraint condition；
3) real coding is carried out to objective function, constraint condition, determines correlated variables；
4) using Revised genetic algorithum to heat exchange amount during the water screen tube convection current radiant heat transfer in Pulverized Fuel Boiler Furnace
Carry out inverting；The optimal value that Revised genetic algorithum is sought is the solution of inverse radiation analysis.
In step 1), there are two kinds of situations for model: pipe is outer to have slagging without slagging and outside managing.Two kinds of situations are built respectively
Mould, specifically:
Pipe it is outer completely without lime-ash when, as shown in Fig. 2, it is as follows to establish model:
Heat exchange amount Φ on unit length pipelAre as follows:
Φl=Φlcon+Φlrad
The quantity of heat convection Φlcon:
Φlcon=Al×h×(T2-T1)
Radiant exothermicity Φlrad:
Φlrad=Al×σ0×ε×(T2 4-T1 4)
When pipe is outer one layer of uniform lime-ash, as shown in figure 3, it is as follows to establish model:
When the slagging of water screen tube surface, grey heat resistance of the scale RAAre as follows:
The entire thermal resistance ∑ R of heat convection and radiation heat transfer are as follows:
Wherein hrad=ε σ0(T2 3+T2 2Td+T2Td 2+Td 3)
After water screen tube slagging, black dirt surface temperature TdMeet following formula
Radiant exothermicity Φlcon,rad:
Φlcon,rad=Al(h+hrad)(T2-Td)
Wherein, Φ1For the heat exchange amount on unit length pipe, Φ1conFor the quantity of heat convection, Φ1radFor Radiant exothermicity,
A1For heat exchange area, h is convective heat-transfer coefficient, hconFor convection transfer rate, hradFor radiation heat transfer coefficient, T1For water temperature in pipe,
T2For flue-gas temperature, TdFor black dirt surface temperature, ε is emissivity, and λ is thermal coefficient, σ0For constant, ddAnd d0Respectively water cooling tube
Inside and outside diameter, RAFor grey heat resistance of the scale.
In step 2), the convection current Radiant exothermicity on unit water-cooling wall obtained when according to positive Solve problems can be anti-
The parameters for drilling the system of being calculated, such as convective heat-transfer coefficient h, the emissivity ε of water screen tube manages interior water temperature T1And flue gas
Temperature T2, heat exchange amount and estimation heat exchange amount setting objective function are solved with forward direction
Φ* i,measuredFor positive solution value, Φ* i,eatimatedFor algorithm estimated value.
In step 3), Revised genetic algorithum is improved on the basis of the genetic algorithm of standard, and specific steps are such as
Shown in Figure 1B, specifically:
401) group is initialized；
402) fitness value of every chromosome is calculated separately according to valuation functions；
403) according to the fitness value of chromosome, selection behaviour is carried out using chromosome of the roulette selection algorithm to group
Make；
404) according to crossover probability PcTwo parent chromosomes are selected from population, are intersected using mproved crossover operators
The child chromosome of operation, generation replaces parent chromosome to enter new population.According to mutation probability PmSelected chromosome, and to group
Body carries out mutation operation；
405) after mutation operation, the fitness value of each chromosome in group is recalculated；
406) it evaluates whether to meet termination condition, such as meets termination condition, then algorithm terminates；Otherwise return step 402).
404) in, in Revised genetic algorithum, crossover operator particular content is as follows:
Crossover probability selects two chromosomes from t generation Crossover operation is carried out, wherein (1≤c1≤c2≤N), d is chromosome length, and two are handed over
The chromosomal gene of fork is,(1≤i≤d), the correspondence gene generated after intersection are(1≤i≤
d)。
New chromosome xc1'Middle geneFrom following three chromosome, (1≤i≤d):
That is, child chromosomeWherein, rand equally distributed random number between [0,1]；For chromosomeIn i-th of gene,For chromosome
In i-th of gene.
Newly generated child chromosomeChromosome (1) among random slave above three, chooses base in (2) (3)
Cause, the child chromosome generated by such crossover operator maintain the portion gene of parent chromosome, and can be purposeful
Ground is close to the present age optimal individual consciously, so as to avoid local optimum is fallen into too early.Similarly, child chromosome Xc2'
GeneAnd obtained in the three intermediate chromosomes generated from the above.
In step 4), the water screen tube convection current radiant heat transfer in Pulverized Fuel Boiler Furnace is changed in the process with Revised genetic algorithum
Heat carries out inverting, it is assumed that model saturated water temperature T1=600K.The outer smoke convection by T2=1500K of pipe conducts heat, convective heat transfer
Coefficient h=100W/ (m2·K).Pipe it is outer completely without slagging when, water screen tube emissivity e=0.8, when managing outer slagging, lime-ash surface
Emissivity e=0.9.The optimal value that Revised genetic algorithum is sought is the solution of inverse radiation analysis.
By a kind of inverse radiation analysis method for solving based on improved adaptive GA-IAGA of the present invention, Revised genetic algorithum and other
When eight kinds of algorithms are tested, Fig. 4 is fitness value change curve of the Sphere function in algorithms of different, and Fig. 5 is
Fitness value change curve of the Schwefel Problem function in algorithms of different, speed of searching optimization and low optimization accuracy, Dou Yaogao
In other several algorithms, and it is increasingly closer to optimal value in an iterative process, precision is also higher and higher, achieves and preferably seeks
Excellent effect.Inverse radiation analysis method for solving based on improved adaptive GA-IAGA is compared with other two kinds of algorithm inversion results,
As table 1 be water screen tube without slagging when Inversion Calculation obtain each parameter estimated value, as can be seen from the data in the table, contrast table
In the actual value that provides, h=100W/ (m2K), e=0.8, T1 *=0.4, T2=1500K quotes method (GA of the invention
Memory) seek result very close to actual value, the water screen tube emissivity e=0.8001 that inverting obtains, with actual value
Relative error is 0.01%, and the mean error of four parametric results is only 0.4%, and accuracy is far superior to other two kinds calculations
Method.When having slagging such as 2 water screen tube of table Inversion Calculation obtain each parameter estimated value, although accuracy and other two kinds of algorithms
It is not much different, but solution accuracy of the invention is higher, the relative error of convection transfer rate is 0.7%, other three ginsengs
The relative error of several estimated value and actual value is all 0.1% hereinafter, the average relative error of four parameters is only 0.22%.
Table 1
Table 2
In conclusion the present invention has preferable directive significance to the solution of other inverse radiation analysis and practical application.
By Revised genetic algorithum, according to the model scale of the water screen tube convection current radiant heat transfer in known Pulverized Fuel Boiler Furnace
The operating conditions such as very little parameter, system heat flow density, model each section of the water screen tube convection current radiant heat transfer in inverting Pulverized Fuel Boiler Furnace
The unknown parameters such as physical parameter, Temperature Distribution, boundary condition, calculate fast, precision is high, solution to other inverse radiation analysis and
Practical application has very strong directive significance.
Claims (5)
1. a kind of Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving based on improved adaptive GA-IAGA, it is characterised in that including following step
It is rapid:
1) model of the water screen tube diabatic process convection current Radiant exothermicity in Pulverized Fuel Boiler Furnace is established, and relevant parameter is set；
2) model and relevant parameter of the water screen tube diabatic process convection current Radiant exothermicity in the Pulverized Fuel Boiler Furnace that basis is built,
Heat exchange amount and estimation heat exchange amount setting objective function are solved with forward direction, and determines constraint condition；
3) real coding is carried out to objective function, constraint condition, determines correlated variables；
4) heat exchange amount during the water screen tube convection current radiant heat transfer in Pulverized Fuel Boiler Furnace is carried out using Revised genetic algorithum
Inverting；The optimal value that Revised genetic algorithum is sought is the solution of inverse radiation analysis.
2. the Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving according to claim 1 based on improved adaptive GA-IAGA, special
Sign is: in step 1), model is outer without having two kinds of situations of slagging outside slagging and pipe in the presence of pipe, carries out invertings to two kinds of situations respectively
It solves.
3. the Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving according to claim 1 based on improved adaptive GA-IAGA, special
Sign is: in step 2), the convection current Radiant exothermicity on unit water-cooling wall obtained when according to positive Solve problems passes through inverting
Every relevant parameter of model, including convective heat-transfer coefficient h is calculated, the emissivity ε of water screen tube manages interior water temperature T1And
Flue-gas temperature T2, heat exchange amount and estimation heat exchange amount setting objective function are solved with forward directionΦ* i,measuredFor positive solution value, Φ* i,eatimatedFor algorithm estimated value.
4. the Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving according to claim 1 based on improved adaptive GA-IAGA, special
Sign is: in step 4), Revised genetic algorithum is improved on the basis of the genetic algorithm of standard, and specific steps are such as
Under:
401) group is initialized；
402) fitness value of every chromosome is calculated separately according to valuation functions；
403) according to the fitness value of chromosome, selection operation is carried out using chromosome of the roulette selection algorithm to group；
404) according to crossover probability PcTwo parent chromosomes are selected from population, and intersection behaviour is carried out using improved crossover operator
Make, the child chromosome of generation replaces parent chromosome to enter new population；According to mutation probability PmSelected chromosome, and to group
Carry out mutation operation；
405) after mutation operation, the fitness value of each chromosome in group is recalculated；
406) it evaluates whether to meet termination condition, such as meets termination condition, then algorithm terminates；Otherwise return step 402).
5. the Pulverized Fuel Boiler Furnace inverse radiation analysis method for solving according to claim 4 based on improved adaptive GA-IAGA, special
Sign is: in step 404), improved crossover operator particular content is as follows:
According to crossover probability PcTwo chromosomes are selected from t generation Crossover operation is carried out, wherein (1≤c1≤c2≤N), d is chromosome length, and two are handed over
The chromosomal gene of fork is(1≤i≤d), the correspondence gene generated after intersection are(1≤i≤
d)；
New chromosome xc1'Middle geneFrom following three chromosome, (1≤i≤d):
That is, child chromosomeWherein, rand equally distributed random number between [0,1]；For
Chromosome；In i-th of gene,For chromosomeIn i-th
A gene；
Newly generated child chromosomeGene is chosen in chromosome among random slave above three, similarly, filial generation dye
Colour solid Xc2'GeneAlso it is obtained in the intermediate chromosome of three generated from the above.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113237447A (en) * | 2021-04-21 | 2021-08-10 | 武汉钢铁有限公司 | Method for estimating thickness of carbon brick on side wall of blast furnace hearth |
| CN114818505A (en) * | 2022-05-10 | 2022-07-29 | 南京净环热冶金工程有限公司 | Method for predicting temperature distribution of steel billet in heating furnace based on particle swarm optimization algorithm |
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Application publication date: 20190705 Assignee: SHENYANG DONGBO THERMAL TECHNOLOGY Co.,Ltd. Assignor: Northeastern University Contract record no.: X2023210000302 Denomination of invention: A Solution Method for the Inverse Problem of Coal Powder Furnace Radiation Based on Improved Genetic Algorithm Granted publication date: 20230113 License type: Common License Record date: 20231213 |