CN114510885B - A method for finding the best operating conditions of an oxygen-enriched combustion boiler - Google Patents
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Abstract
The invention relates to an optimizing method of an optimal working condition of an oxyfuel combustion boiler, which comprises the following steps of selecting a fluid dynamics model in Ansys Fluent based on the structure and the size of the oxyfuel combustion boiler, determining working condition parameters which need to be input for numerical simulation calculation, determining a result output through the numerical simulation calculation, determining the number of variables and a limiting range according to the determined working condition parameters which need to be input by utilizing a genetic algorithm in MATLAB, generating an initial population, setting the working condition parameters in the numerical simulation calculation of the Ansys Fluent, constructing a numerical simulation calculation model of boiler combustion, constructing a Pareto front multi-target optimizing model which takes four parameters of heat quantity, flame center position, flame maximum temperature and boiler thermal efficiency of the boiler as optimizing target parameters, setting termination conditions, and performing multi-target optimizing calculation to obtain the optimal working condition parameters.
Description
Technical Field
The invention belongs to the field of regulation of an oxygen-enriched combustion boiler, and particularly relates to a optimizing method for the optimal working condition of the oxygen-enriched combustion boiler.
Background
Along with the improvement of environmental protection standards, power generation enterprises or heating enterprises are expected to reduce the emission of pollutants (NO x) in coal-fired boilers, and on the basis, the combustion efficiency of coal dust is improved as much as possible, the running stability and safety of the boilers are ensured, and the effective utilization of heat of the boilers is ensured.
The oxygen-enriched combustion technology is an efficient energy-saving combustion technology and an emission reduction technology which is easy to realize. The technology is that pure oxygen and recirculated flue gas with the main component of CO 2 are mixed in a certain proportion (the mixed gas is called as fuel gas or combustion gas) and then sent into a hearth to be mixed and burned with fuel. As nitrogen in the traditional combustion mode is almost completely eradicated, the concentration of CO 2 in the flue gas generated by oxygen-enriched combustion is higher. Part of flue gas containing high-concentration CO 2 generated by combustion is mixed with pure oxygen in a recycling mode and then enters a hearth for drying, delivering fuel and controlling combustion temperature of the hearth, and the other part of flue gas can directly enter compression equipment for CO 2 storage after relatively simple impurity removal treatment such as condensation, drying and the like. The technology is suitable for the transformation of the old boiler of the power plant, can be used for a newly built boiler, has the advantages of low cost, high reliability and good inheritance with the existing boiler combustion technology, and is also a CO 2 emission reduction technical route which is most easily accepted by the traditional power generation enterprises.
When the oxyfuel combustion technology is adopted, N 2 is replaced by CO 2. Because of specific volume of CO 2 and gas radiation characteristics, local highest temperature can be reduced, flame center is moved downwards, combustion characteristics are worse than that of adopting air combustion, mixing proportion of O 2 and CO 2 is changed, proportion of primary air quantity, secondary air quantity and tertiary air quantity is adjusted, comprehensive performance of an oxygen-enriched combustion boiler is improved, and therefore finding out optimal mixing proportion of O 2 and CO 2 and adjusting proportion of primary air quantity, secondary air quantity and tertiary air quantity are of great significance for application of the oxygen-enriched combustion boiler.
Disclosure of Invention
The invention provides an optimizing method for the optimal working condition of an oxygen-enriched combustion boiler. The method is realized based on the collaborative simulation of Ansys Fluent and MATLAB, and the working condition parameters (optimal ratio of O 2 to CO 2: k, 1-k, optimal ratio of primary air quantity, secondary air quantity and tertiary air quantity: a, b, c, a+b+c=1) of which the comprehensive performance is closer to that of the air combustion are screened out. In order to achieve the above purpose, the technical scheme of the invention is as follows:
an optimizing method of the optimal working condition of an oxygen-enriched combustion boiler is realized based on Ansys Fluent and MATLAB collaborative simulation, and comprises the following steps:
Step 1, based on the structure and the size of an oxygen-enriched combustion boiler in Ansys Fluent, a fluid dynamic model is selected, working condition parameters which need to be input for numerical simulation calculation are determined, a result output through the numerical simulation calculation is determined, and the determined result output through the numerical simulation calculation comprises four parameters of effective utilization heat of the boiler, flame center position, flame maximum temperature and thermal efficiency of the boiler;
And 2, determining the number of variables and the limiting range by utilizing the genetic algorithm in MATLAB, determining the working condition parameters to be input determined in the step1, generating an initial population, and setting the working condition parameters in Ansys Fluent numerical simulation calculation.
And 3, constructing a numerical simulation calculation model of boiler combustion in Ansys Fluent, starting numerical simulation calculation by using the constructed numerical simulation calculation model, and outputting a calculation result.
Step 4, constructing a Pareto front multi-objective optimization model which takes four parameters of the effective utilization heat of a boiler, the flame center position, the flame maximum temperature and the boiler thermal efficiency as optimization target parameters in a genetic algorithm in MATLAB according to the calculation result output in the step 3, setting termination conditions, and carrying out multi-objective optimization calculation;
And 5, if the termination condition set in the step 4 is not met, the population is mutated, cross selection is carried out, the adverse mutation is screened out according to the existing result, then the calculation is carried out in the step 3, the mutation is ended until the termination condition is met, and the optimal result is output, so that the optimal working condition parameters are obtained.
Preferably, in the step 1, the determined working condition parameters required to be input for numerical simulation calculation comprise the ratio of O 2 to CO 2, namely k (1-k), and the ratio of primary air quantity, secondary air quantity and tertiary air quantity, namely a: b (c=1-a-b).
In step 4, the specific steps of constructing the Pareto front multi-objective optimization model are as follows:
(1) The four parameters of the effective utilization heat of the boiler, the flame center position, the highest flame temperature and the thermal efficiency of the boiler are expressed as follows as optimization targets:
fj,j=1,2,3,4 (1)
Wherein j represents an optimization target parameter sequence number, and f 1,f2,f3,f4 represents the effective utilization heat of the boiler, the flame center position, the flame maximum temperature and the boiler heat efficiency respectively;
The result set obtained by the numerical simulation calculation is expressed as follows:
fij,i=1,2,3,...,n;j=1,2,3,4 (2)
Wherein i represents individuals in different groups, n in total, and f i1,fi2,fi3,fi4 respectively represents the effective utilization heat of boilers, flame center position, flame maximum temperature and boiler thermal efficiency values of different individuals in the groups;
(2) The effective heat quantity Q 0, flame center position h 0, flame maximum temperature T 0, and boiler heat efficiency η 0 of the boiler obtained by air combustion are set as target values, and are respectively expressed as follows:
(3) The target value is subjected to dimensionless treatment and expressed as Performing dimensionless treatment on the result data set, wherein the dimensionless treatment is denoted as F ij;
(4) The approach degree between the calculated result and the target value under the working condition parameter is calculated, and the closer to the target value, the closer to the optimal result is represented, and the approach degree calculating method is as follows:
Setting a termination calculation value d *, and setting a termination condition such that the degree of approach d ij between the calculation result and the target value is equal to or less than the termination calculation value d *;
When the termination condition is met, obtaining the minimum distance between the calculation result and the target value;
And selecting the population individuals corresponding to the minimum distance S * and the k, a, b and c values corresponding to the population individuals to obtain the optimal working condition parameters.
The formula for performing the target value dimensionless treatment in the step (3) is as follows:
The formula for carrying out dimensionless treatment on the result data set in the step (3) is as follows:
The termination calculation is:
the invention has the beneficial effects that:
1. The method is realized based on Ansys Fluent and MATLAB collaborative simulation, and can quickly find out the working condition that the combustion characteristic of the boiler is close to that of the boiler when air combustion is adopted under the condition of oxygen-enriched combustion, namely the proportion k of O 2, the proportion of CO 2 is 1-k, the primary air quantity proportion a, the secondary air quantity proportion b and the tertiary air quantity proportion c.
2. In the method, a Pareto front LINMAP multi-target optimization model is constructed in the optimizing process, various important parameters in the boiler combustion are comprehensively considered, and the found optimal working condition is relatively close to the boiler characteristic when air combustion is adopted in various aspects.
Drawings
FIG. 1 is a flow chart of a optimizing method for the optimal working condition of an oxyfuel combustion boiler
FIG. 2 is a schematic diagram of an oxy-fuel combustion boiler system
In the attached figure 2, 1, an ash cooling hopper, 2, a primary air inlet, 3, a secondary air inlet, 4, a tertiary air inlet, 5, a cold water wall, 6, a partition screen superheater, 7, a rear screen superheater, 8, a final superheater, 9, a final reheater, 10, a vertical low-temperature superheater, 11, a vertical low-temperature superheater, 12, a horizontal low-temperature superheater, 13 and an economizer.
Detailed Description
The invention provides an improvement strategy aiming at the problems in the prior art. In order to better illustrate the present invention, the present invention will be further described with reference to fig. 1 and 2.
The invention is realized based on Ansys Fluent and MATLAB, so that the realization of collaborative simulation of Ansys Fluent and MATLAB is important. According to the invention, MATLAB and Ansys Fluent are required to be started simultaneously, and indirect parameter transfer is realized by accessing the shared data folder through I/O operation of the file.
Based on the implementation of the Ansys Fluent and MATLAB co-simulation, the method specifically comprises the following steps:
And 1, constructing an oxycombustion boiler model in Ansys Fluent based on the actual structure and size of the boiler, wherein the specific model is shown in figure 2. The device comprises an ash cooling hopper, a primary air inlet 2, a secondary air inlet 3, a tertiary air inlet 4, a cold water wall 5, a separation screen superheater 6, a rear screen superheater 7, a final-stage superheater 8, a final-stage reheater 9, a vertical low-temperature superheater 10, a vertical low-temperature superheater 11, a horizontal low-temperature superheater 12 and an economizer 13. The following models are adopted in the numerical simulation calculation process, namely an N-S equation in fluid dynamics, a turbulence Realizable k-epsilon model, an Euler-Lagrange method random orbit model, a two-step competition reaction rate model, a P1 radiation heat transfer model and a turbulent chemical reaction as an Eddy Dissipation (EDM) model. The numerical simulation calculation needs to input working condition parameters including the proportion of O 2 and CO 2, namely k and 1-k, and the proportion of primary air quantity, secondary air quantity and tertiary air quantity, namely a, b and c, wherein c=1-a-b. The numerical simulation calculation result comprises four parameters of effective heat utilization of the boiler, flame center position, flame maximum temperature and thermal efficiency of the boiler.
And 2, generating an initial population by a genetic algorithm in the MATLAB program according to the number of variables and the limiting range, wherein N initial data are randomly generated, each data is an individual, and the N individuals form the population. The chromosome of each individual is composed of three independent characteristic values, namely (1) the proportion k of O 2, the proportion of CO 2 is 1-k, (2) the primary air volume proportion a, and (3) the secondary air volume proportion b, and the tertiary air volume proportion c=1-a-b. And the input Ansys Fluent is set as a working condition parameter required by numerical simulation calculation.
And 3, inputting and setting working condition parameters required by the Ansys Fluent numerical simulation calculation in the step 1 and the setting in the step 2 after encoding by MATLAB, and starting the numerical simulation calculation by the model established in the Ansys Fluent in the step 1 and outputting a calculation result.
And 4, inputting the calculation result in the step 3 into a Pareto front multi-target optimization model which is constructed in MATLAB based on a genetic algorithm and takes four parameters of the effective utilization heat of a boiler, the flame center position, the flame maximum temperature and the boiler thermal efficiency as optimization target parameters, namely a LINMAP multi-target optimization model, and performing multi-target optimization calculation. The Pareto front LINMAP multi-objective optimization model is specifically as follows:
The four parameters of the effective utilization heat of the boiler, the flame center position, the highest flame temperature and the thermal efficiency of the boiler are expressed as follows as optimization targets:
fj,j=1,2,3,4 (1)
j represents the optimization target parameters, and the total number is 4. f 1,f2,f3,f4 represents the effective heat utilization of the boiler, the flame center position, the maximum flame temperature and the thermal efficiency of the boiler, respectively.
The result set obtained by the numerical simulation calculation is represented as follows:
fij,i=1,2,3,...,n;j=1,2,3,4 (2)
i represents individuals in different populations, and n are total. And f i1,fi2,fi3,fi4 respectively represents the effective utilization heat of the boiler, the flame center position, the highest flame temperature and the thermal efficiency of the boiler of different individuals in the population.
The effective heat utilization amount (Q 0), flame center position (h 0), flame maximum temperature (T 0), and boiler heat efficiency (η 0) of the boiler obtained by air combustion are set as target values, and are expressed as follows:
If the dimensions of the four parameters are different, the boiler effectively utilizes the heat, the flame center position, the flame maximum temperature and the boiler thermal efficiency, and in order to neglect the influence of the different dimensions, the target value and the result data set are required to be subjected to dimensionless treatment respectively when multi-target optimization calculation is carried out:
And calculating the degree of approach between the calculated result and the target value under the working condition parameters, wherein the closer to the target value, the closer to the optimal result is represented. The approach degree calculation method is as follows:
The termination condition was set such that the degree of proximity between the calculated result and the target value was 95%. The concrete representation is as follows:
When the value of (6) is equal to or less than the formula (7), the termination condition is satisfied, and the minimum distance between the result and the target value is obtained as follows:
S*＝Min(dij),i=1,2,3,...,n;j=1,2,3,4 (8)
And selecting the population individuals corresponding to the minimum distance S * and the k, a, b and c values corresponding to the population individuals to obtain the optimal working condition parameters.
And 5, if the termination condition of the formula (7) in the step 4 is not met, the initial population generates variation, cross selection is performed, the calculation is performed in the step 3 after the unfavorable variation is screened out according to the existing result, until the termination condition of the formula (7) is met, the variation is ended, and an optimal result (the values of k, a, b and c when the closest target values are comprehensively compared) is obtained according to the formula (8).
The foregoing is merely illustrative of specific embodiments of the present invention and is not intended to limit the scope of the claims. All equivalent changes and modifications made according to the claims and the specification of the present invention are within the scope of the present invention.
Claims (8)
1. An optimizing method of the optimal working condition of an oxygen-enriched combustion boiler is realized based on Ansys Fluent and MATLAB collaborative simulation, and comprises the following steps:
Step 1, based on the structure and the size of an oxygen-enriched combustion boiler in Ansys Fluent, a fluid dynamic model is selected, working condition parameters which need to be input for numerical simulation calculation are determined, a result output through the numerical simulation calculation is determined, and the determined result output through the numerical simulation calculation comprises four parameters of effective utilization heat of the boiler, flame center position, flame maximum temperature and thermal efficiency of the boiler;
Step2, utilizing a genetic algorithm in MATLAB, determining the working condition parameters to be input determined in the step1, determining the number of variables and a limiting range, generating an initial population, and setting the working condition parameters in Ansys Fluent numerical simulation calculation;
step 3, constructing a numerical simulation calculation model of boiler combustion in Ansys Fluent, starting numerical simulation calculation by using the constructed numerical simulation calculation model, and outputting a calculation result;
Step 4, constructing a Pareto front multi-objective optimization model which takes four parameters of the effective utilization heat of a boiler, the flame center position, the flame maximum temperature and the boiler thermal efficiency as optimization target parameters in a genetic algorithm in MATLAB according to the calculation result output in the step 3, setting termination conditions, and carrying out multi-objective optimization calculation;
And 5, if the termination condition set in the step 4 is not met, the population is mutated, cross selection is carried out, the adverse mutation is screened out according to the existing result, then the calculation is carried out in the step 3, the mutation is ended until the termination condition is met, and the optimal result is output, so that the optimal working condition parameters are obtained.
2. The optimizing method for the optimal operation condition of the oxy-fuel combustion boiler according to claim 1, wherein in the step 1, the determined operation condition parameters required to be input for numerical simulation calculation comprise the ratio of O 2 to CO 2, namely k (1-k), and the ratio of primary air quantity, secondary air quantity and tertiary air quantity, namely a: b (c=1-a-b).
3. The optimizing method for the optimal working condition of the oxy-fuel combustion boiler according to claim 2, wherein in the step 4, the specific steps of constructing the Pareto front multi-objective optimizing model are as follows:
(1) The four parameters of the effective utilization heat of the boiler, the flame center position, the highest flame temperature and the thermal efficiency of the boiler are expressed as follows as optimization targets:
fj,j=1,2,3,4 (1)
Wherein j represents an optimization target parameter sequence number, and f 1,f2,f3,f4 represents the effective utilization heat of the boiler, the flame center position, the flame maximum temperature and the boiler heat efficiency respectively;
The result set obtained by the numerical simulation calculation is expressed as follows:
fij,i=1,2,3,L,n;j=1,2,3,4 (2)
Wherein i represents individuals in different groups, n in total, and f i1,fi2,fi3,fi4 respectively represents the effective utilization heat of boilers, flame center position, flame maximum temperature and boiler thermal efficiency values of different individuals in the groups;
(2) The effective heat quantity Q 0, flame center position h 0, flame maximum temperature T 0, and boiler heat efficiency η 0 of the boiler obtained by air combustion are set as target values, and are respectively expressed as follows:
(3) The target value is subjected to dimensionless treatment and expressed as Performing dimensionless treatment on the result data set, wherein the dimensionless treatment is denoted as F ij;
(4) The approach degree between the calculated result and the target value under the working condition parameter is calculated, and the closer to the target value, the closer to the optimal result is represented, and the approach degree is calculated as follows:
Setting a termination calculated value d *, setting a termination condition that the approaching degree d ij between the calculated result and the target value is smaller than or equal to the termination calculated value d *, obtaining the minimum distance between the calculated result and the target value when the termination condition is met, selecting a population individual corresponding to the minimum distance S * and k, a, b and c corresponding to the minimum distance, and obtaining the optimal working condition parameters.
4. The optimizing method for the optimal operation condition of an oxycombustion boiler according to claim 3, wherein the formula of the target value non-dimensionality processing performed in the step (3) is as follows:
5. the optimizing method for optimal operation of an oxycombustion boiler of claim 4, wherein the formula of the dimensionless processing of the result data set in step (3) is as follows:
6. the optimizing method for optimum operation of oxy-combustion boiler according to claim 5, wherein the calculated value is terminated
7. The method of optimizing optimal operation of an oxycombustion boiler of claim 1, wherein the selected fluid dynamics model comprises an N-S equation, a turbulent Realizable k-epsilon model, a euler-lagrangian random orbit model, a two-step competitive reaction rate model, a P1 radiant heat transfer model, and a turbulent chemical reaction model.
8. The method for optimizing optimal conditions of an oxycombustion boiler of claim 7, wherein the turbulent chemical reaction model is a vortex dissipation EDM model.
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