CN112418664B - Particle swarm optimization-based binning combination blending method and system - Google Patents
Particle swarm optimization-based binning combination blending method and system Download PDFInfo
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Abstract
The invention provides a particle swarm algorithm-based sub-bin combination blending combustion method, which comprises the following steps of: step one, setting a boundary constraint condition system; step two, constructing a mathematical expression of a boundary constraint condition system; determining an objective function, and performing global optimization search on the objective function in the constructed mathematical expression by using a particle swarm optimization algorithm to obtain a sub-bin combination blending combustion optimal scheme. The invention also provides a sub-bin combined blending combustion system based on the particle swarm optimization. The method is simple and feasible, has high calculation accuracy, and can realize optimization of most of the existing thermal power generating units in the process of blending the fire coal. The optimized scheme can prepare the coal mixing scheme with optimal conditions on the premise of ensuring the safe and stable operation of the boiler, thereby greatly reducing the blending operation cost and the coal purchasing cost and enabling the coal-fired power plant to obtain long-term benefits.
Description
Technical Field
The invention belongs to the field of blending and burning of boiler coal, and relates to a sub-bin combination blending and burning method and system based on a particle swarm algorithm.
Background
At present, various power generation enterprises commonly have the problems that actual coal types are seriously different from designed or checked coal types, the coal sources are more, the price fluctuation is large, the coal quality difference is large, and a plurality of problems are brought to the safe, environment-friendly and economic operation of the boiler.
Blending coal is the simplest and effective method for solving the problems. The blending coal is blended and burned in various modes, and the combination of the sub-bins is a mode with more complex and optimal effect, namely, different coal in factories are blended in different raw coal bins and then ground, and the corresponding burner burns the blended coal. The separate bin combination mixed combustion has the advantages of flexible control, good load adaptability, high adjustment speed of coal quality entering the furnace, easy grasp of combustion stability and the like. However, the optimal scheme for mixing and burning the coal in different bins is very difficult to realize accurately. For example, 10 single coals are selected, six coal mills, each raw coal bin is not more than two single coals, and the raw coal bin has up to 10% of mixing condition
And (5) a blending result. And as the number of the blendable coal seeds increases, the result increases in power level, and the comparison of the advantages and disadvantages of the blending results one by one is basically impossible. Therefore, if the rapid and accurate separate-bin combined blending combustion can be realized by an advanced method, not only can the problems of boiler combustion be solved, but also considerable economic benefits can be obtained, and a solid foundation is provided for the future intelligent blending combustion technology.
Disclosure of Invention
Aiming at the problem that the separate bin combination blending combustion scheme in the prior art is extremely difficult to accurately realize, the invention provides a separate bin combination blending combustion method and system based on a particle swarm optimization algorithm, which are used for obtaining the coal types and proportions blended in all raw coal bins through calculation of a particle swarm optimization algorithm after meeting the multi-parameter limiting conditions of the combustion performance of all coal mills and boilers.
In order to achieve the above purpose, the present invention has the following technical scheme:
a particle swarm algorithm-based sub-bin combination blending combustion method comprises the following steps:
step one, setting a boundary constraint condition system;
step two, constructing a mathematical expression of a boundary constraint condition system;
determining an objective function, and performing global optimization search on the objective function in the constructed mathematical expression by using a particle swarm optimization algorithm to obtain a sub-bin combination blending combustion optimal scheme.
As a preferable scheme of the sub-bin combination blending combustion method, the boundary constraint conditions comprise coal type constraint conditions, raw coal bin constraint conditions, unit operation constraint conditions and operation grinding constraint conditions.
As a preferable scheme of the sub-bin combination blending combustion method, the method specifically comprises the following steps: setting a coal type constraint condition as selecting all coal types capable of participating in blended combustion, setting the maximum blending proportion and the minimum blending proportion of each coal type, and setting a proportion normalization step length; setting constraint conditions of raw coal bins as the number of coal types which can be blended in the raw coal bins at most, and setting the coal types which cannot be blended or must be blended in each raw coal bin; setting unit operation constraint conditions to set maximum electric load, maximum steam supply, maximum heating amount and maximum sulfur content of the unit during mixed combustion working condition operation; the constraint condition of the operation mill is set as the number of the operation mill, and the mixed coal quality requirement range corresponding to the raw coal bin is configured, wherein the mixed coal quality requirement range comprises heat value, volatile matters, ash, moisture and sulfur.
As a preferable scheme of the binning combination blending method, the mathematical expression of the boundary constraint condition system constructed in the second step is as follows:
C max ≤nc
r i,min ≤R i ≤r i,max
R step ＝r step
wherein nc is the number of the most blended coal seeds in the raw coal bin, r i,min And r i,max I is the minimum and maximum blending proportion of i coal typesIs a positive integer; r is (r) step Step length is regulated for proportion;
S j,max ≤ns j
coal j，i ∈S j，coal or (b)
Ns in j The j raw coal bins are mixed with the coal types at most, and coal j,i Coal types which are j raw coal bins and are necessary to be mixed or can not be mixed;
η rh ＝B BMCR Q SJ η B,SJ /L SJ
L real ＝load max +(stream max ·H Stream +heat max )/(η p η rh )
load in max 、stream max 、heat max 、star max The maximum electric load, the maximum steam supply and the maximum heating amount of the unit during the mixed combustion working condition operation and the maximum sulfur content in the furnace are respectively obtained; l (L) real 、B BMCR 、Q SJ 、η B,SJ 、L SJ 、η p 、H stream 、n mill 、n run 、Q all 、S all The method comprises the steps of blending combustion working condition heat load, BMCR working condition coal quantity, design coal heat value, design boiler efficiency, unit design capacity, pipeline efficiency, steam supply enthalpy value, coal mill quantity, operation grinding quantity, furnace coal heat value and furnace coal sulfur content;
Mill j ∈Mill run
q j,min ≤Q j ≤q j,max
v j,min ≤V j ≤v j,max
a j,min ≤A j ≤a j,max
m j,min ≤M j ≤m j,max
s j,min ≤S j ≤s j,max
q in j 、V j 、A j 、M j 、S j The heat value, the volatile component, the ash content, the moisture and the sulfur content of the mixed coal in the j coal mill are respectively, and the left and right parameters of the inequality are respectively the lower limit value and the upper limit value; parameter Q j 、V j 、A j 、M j 、S j The calculation formula of (2) is as follows:
q in j,i 、v j,i 、a j,i 、m j,i 、s j,i The parameters are the heat value, volatile matter, ash content, moisture and sulfur content of the coal type i of the j coal mill respectively, and the corresponding r is the proportion of the coal type i in the j coal mill.
In the particle swarm optimization algorithm, the lowest comprehensive power generation cost is used as an objective function, and the objective function is subjected to global optimization search in all constructed mathematical expressions by using the particle swarm optimization algorithm to obtain the optimal mixed combustion scheme of the sub-bin combination.
As a preferable scheme of the binning and combining co-firing method, the particle swarm optimization algorithm is set to be 1000 in iteration number, 500 in population number and 0.6 in acceleration factor.
As a preferred scheme of the sub-bin combination blending combustion method, the proportion of each raw coal bin blending combustion coal type in the optimal scheme results is vertically rearranged according to a set step length, all raw coal bin rearranging results are arranged and combined, then global constraint condition judgment is carried out on each result, the scheme meeting all constraint conditions is recorded, and the scheme output result with the lowest comprehensive power generation cost is selected.
As a preferred scheme of the warehouse-division combined blending combustion method, the step three sequentially replaces various coal types in the optimized scheme after the normalization with other coal types, one scheme is formed by replacement once, all schemes are subjected to global constraint condition judgment, and the result is output according to the lowest comprehensive power generation cost by using the other coal types which can be replaced in the schemes meeting all constraint conditions.
The invention also provides a sub-bin combined blending combustion system based on the particle swarm optimization, which comprises the following steps:
the constraint condition setting module is used for setting a boundary constraint condition system;
the mathematical expression construction module is used for constructing a mathematical expression of the boundary constraint condition system;
the optimal scheme calculating module is used for determining an objective function, and carrying out global optimizing search on the objective function in the constructed mathematical expression by utilizing a particle swarm optimization algorithm to obtain the sub-bin combination blending combustion optimal scheme.
Compared with the prior art, the invention has the following beneficial effects:
the method is simple and feasible, has high calculation accuracy, and can realize optimization of most of the existing thermal power generating units in the process of blending the fire coal. The optimized scheme can prepare the coal mixing scheme with optimal conditions on the premise of ensuring the safe and stable operation of the boiler, thereby greatly reducing the blending operation cost and the coal purchasing cost and enabling the coal-fired power plant to obtain long-term benefits.
Furthermore, the invention carries out up-down normalization on the proportion of the raw coal bunker blended coal in the optimal scheme result according to the set step length, and the normalization function is to consider that the operation is easy to be executed in the actual coal process.
Further, according to the method, each coal type in the optimized scheme after the normalization is replaced by other coal types in sequence, one scheme is formed by replacing the coal types once, global constraint condition judgment is carried out on all schemes, and in the scheme meeting all constraint conditions, the results are output according to the lowest comprehensive power generation cost in sequence by using the other coal types which can be replaced by each coal type. The scheme is applied to the condition that the mixed coal is exhausted, and can meet production requirements by replacing other coal types.
Drawings
FIG. 1 is a flow chart of a sub-bin combination blending combustion method based on a particle swarm algorithm;
FIG. 2 is a diagram showing the results of the binning combination blending optimization scheme of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, the method for mixing and burning by the sub-bin combination based on the particle swarm algorithm of the invention specifically comprises the following steps:
s1, constructing a boundary constraint condition system of a coal, a raw coal bin, a unit running condition and a coal mill, wherein the boundary constraint condition system comprises the following steps of:
constructing constraint conditions of coal types: selecting all coal types which can participate in the blended combustion, setting the maximum blending proportion and the minimum blending proportion of each coal type, and setting the proportion normalization step length;
constructing raw coal bin constraint conditions: setting the quantity of coal types which are mixed in the raw coal bins to the maximum, and setting the coal types which cannot be mixed in each raw coal bin or are required to be mixed in each raw coal bin;
constructing unit operation constraint conditions: setting the maximum electric load, the maximum steam supply and the maximum heating amount of the unit under the mixed combustion working condition, and the maximum sulfur content in the furnace;
constructing a commissioning mill constraint condition: selecting the number of the coal mill to be put into operation, and configuring the coal quality requirement range of the mixed coal corresponding to the raw coal bin, wherein the coal quality requirement range comprises heat value, volatile matters, ash, moisture and sulfur;
s2, constructing mathematical expression formulas in a boundary constraint condition system, wherein the mathematical expression formulas are respectively as follows:
C max ≤nc
r i,min ≤R i ≤r i,max i＝1,2,3... (1)
R step ＝r step
nc in the formula (1) is the number of the coal seeds which are mixed in the raw coal bin to the maximum extent, r i,min And r i,max For the minimum and maximum blending proportion of i coal types, r step Step sizes are normalized for the ratio.
Ns in (2) j The j raw coal bins are mixed with the coal types at most, and coal j,i The coal types which are the j raw coal bins and are necessary to be blended or can not be blended.
η rh ＝B BMCR Q SJ η B,SJ /L SJ
L real ＝load max +(stream max ·H Stream +heat max )/(η p η rh )
Load in (3) max 、stream max 、heat max 、star max Respectively the maximum electric load, the maximum steam supply and the maximum heating quantity of the unit during the mixed combustion working condition operation and the maximum sulfur content in the furnace；L real 、B BMCR 、Q SJ 、η B,SJ 、L SJ 、η p 、H stream 、n mill 、n run 、Q all 、S all The method is characterized by respectively mixing the heat load of the combustion working condition, the coal quantity of the BMCR working condition, the heat value of the design coal, the efficiency of the design boiler, the design capacity of a unit, the efficiency of a pipeline, the enthalpy value of steam supply steam, the quantity of coal mills, the quantity of the operation grinding, the heat value of the coal in the furnace and the sulfur content of the coal in the furnace.
Mill j ∈Mill run
q j,min ≤Q j ≤q j,max
v j,min ≤V j ≤v j,max
a j,min ≤A j ≤a j,max j＝A,B,C... (4)
m j,min ≤M j ≤m j,max
s j,min ≤S j ≤s j,max
Q in (4) j 、V j 、A j 、M j 、S j The heat value, the volatile component, the ash content, the moisture and the sulfur content of the mixed coal in the j coal mill are respectively the lower limit and the upper limit of the left and the right parameters of the inequality.
Q in (4) j 、V j 、A j 、M j 、S j The calculation formula of the parameter is shown as formula (5), wherein q j,i 、v j,i 、a j,i 、m j,i 、s j,i The parameters are the heat value, volatile matter, ash content, moisture and sulfur content of the coal type i of the j coal mill respectively, and the corresponding r is the proportion of the coal type i in the j coal mill.
S3, in the particle swarm optimization algorithm, the sub-bin combination blending combustion optimization scheme uses the lowest comprehensive power generation cost as an objective function, the iteration number is set to be 1000, the population number is set to be 500, the acceleration factor is set to be 0.6, and the particle swarm optimization algorithm is utilized to conduct global optimization search on the objective function under all set constraint conditions, so that the sub-bin combination blending combustion optimization scheme is obtained.
Optionally, the proportion of the blended coal types of each raw coal bin in the optimal scheme result is vertically regulated according to a set step length, and the regulating function is to consider that the operation is easy to execute in the actual coal process. For example, the coal types mixed and burned in the bin A are a:b=23.8%: 76.2%, and the results after the coal types are respectively regulated up and down according to the step length of 10% are a:b=3:7 and a:b=2:8. The method comprises the steps of arranging and combining all raw coal bin sorting results, judging global constraint conditions of all the results, recording schemes meeting all the constraint conditions, and selecting the scheme with the lowest comprehensive power generation cost to output the results.
Optionally, replacing other coal types of the optimized scheme after the normalization in sequence, replacing the coal types to form one scheme at a time, judging global constraint conditions of all schemes, and sequencing and outputting the other coal types which can be replaced by each coal type in the scheme meeting all constraint conditions according to the lowest comprehensive power generation cost. For example, the replacement result of the two kinds of coal a and b is a (b, c, d …), b (a, c, d …), namely, the mixed combustion of a and b can be replaced by the combustion of b, c, d … coal types in sequence if the coal a is insufficient, and the like. The scheme is applied to the condition that the mixed coal is exhausted, and other coal can be adopted for replacement to meet the production requirement.
The embodiment is carried out on a 300MW bituminous coal boiler unit, and the specific implementation steps of the invention are as follows:
(1) Selecting a, b, c, d, e, f and g coal as coal types capable of participating in blended combustion, wherein the minimum blending proportion and the maximum blending proportion of each coal type are respectively defaulted to 0% and 100%, and the proportion normalization step length is 10%;
(2) The number of the coal types blended in the raw coal bins is defaulted to be 2 at most, and the coal types which cannot be blended or must be blended in each raw coal bin are defaulted to be none;
(3) Setting the maximum electric load of the unit during the mixed combustion working condition operation as 200MW, the maximum steam supply amount of 25t/h, the maximum heating amount of 20GJ/h and the maximum sulfur content of 1.8 percent;
(4) The number of the selected coal mill is A, B, C, D, and the coal quality requirement range of the mixed coal of each raw coal bin is as follows:
(5) After the parameter setting is finished, a particle swarm optimization algorithm is utilized, the lowest comprehensive power generation cost is used as an objective function, global optimization search is carried out under all set constraint conditions, and a sub-bin combined mixed combustion optimal scheme is obtained;
a particle swarm algorithm-based binning combined blending combustion system, comprising:
the constraint condition setting module is used for setting a boundary constraint condition system;
the mathematical expression construction module is used for constructing a mathematical expression of the boundary constraint condition system;
the optimal scheme calculating module is used for determining an objective function, and carrying out global optimizing search on the objective function in the constructed mathematical expression by utilizing a particle swarm optimization algorithm to obtain the sub-bin combination blending combustion optimal scheme.
The result of the scheme shown in fig. 2 includes the precisely calculated coal types and proportions, and further includes the proportions and the supplemental coal types that can be performed after the normalization. In the actual production process of the power plant, only a few parameters with frequent fluctuation are required to be set each time when the blended combustion scheme is formulated, and other parameters remain default in advance, so that the scheme optimization result can be obtained within a few seconds.
The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention in any way, and it will be understood by those skilled in the art that the present invention may be embodied with many simple modifications and substitutions without departing from the spirit and principles of the invention, which are also within the scope of the appended claims.
Claims (7)
1. The method for mixing and burning the sub-bin combination based on the particle swarm optimization is characterized by comprising the following steps:
step one, setting a boundary constraint condition system;
step two, constructing a mathematical expression of a boundary constraint condition system;
determining an objective function, and performing global optimization search on the objective function in the constructed mathematical expression by using a particle swarm optimization algorithm to obtain a sub-bin combination blending combustion optimal scheme;
the boundary constraint conditions comprise coal constraint conditions, raw coal bin constraint conditions, unit operation constraint conditions and operation mill constraint conditions;
the mathematical expression of the boundary constraint condition system constructed in the second step is as follows:
C max ≤nc
r i,min ≤R i ≤r i,max
R step ＝r step
wherein nc is the number of the most blended coal seeds in the raw coal bin, r i,min And r i,max The mixing proportion of the coal types is i, namely the minimum and maximum mixing proportion of the coal types, wherein i is a positive integer; r is (r) step Step length is regulated for proportion;
S j,max ≤ns j
coal j,i ∈S j,coal or (b)
Ns in j The j raw coal bins are mixed with the coal types at most, and coal j,i Coal types which are j raw coal bins and are necessary to be mixed or can not be mixed;
η rh ＝B BMCR Q SJ η B,SJ /L SJ
L real ＝load max +(stream max ·H Stream +heat max )/(η p η rh )
load in max 、stream max 、heat max 、star max The maximum electric load, the maximum steam supply and the maximum heating amount of the unit during the mixed combustion working condition operation and the maximum sulfur content in the furnace are respectively obtained; l (L) real 、B BMCR 、Q SJ 、η B,SJ 、L SJ 、η p 、H stream 、n mill 、n run 、Q all 、S all The method comprises the steps of blending combustion working condition heat load, BMCR working condition coal quantity, design coal heat value, design boiler efficiency, unit design capacity, pipeline efficiency, steam supply enthalpy value, coal mill quantity, operation grinding quantity, furnace coal heat value and furnace coal sulfur content;
Mill j ∈Mill run
q j,min ≤Q j ≤q j,max
v j,min ≤V j ≤v j,max
a j,min ≤A j ≤a j,max
m j,min ≤M j ≤m j,max
s j,min ≤S j ≤s j,max
q in j 、V j 、A j 、M j 、S j The heat value, the volatile component, the ash content, the moisture and the sulfur content of the mixed coal in the j coal mill are respectively, and the left and right parameters of the inequality are respectively the lower limit value and the upper limit value; parameter Q j 、V j 、A j 、M j 、S j The calculation formula of (2) is as follows:
q in j,i 、v j,i 、a j,i 、m j,i 、s j,i The parameters are the heat value, volatile matter, ash content, moisture and sulfur content of the coal type i of the j coal mill respectively, and the corresponding r is the proportion of the coal type i in the j coal mill.
2. The method for binning and blending combustion based on particle swarm optimization according to claim 1, wherein the method specifically comprises the following steps: setting a coal type constraint condition as selecting all coal types capable of participating in blended combustion, setting the maximum blending proportion and the minimum blending proportion of each coal type, and setting a proportion normalization step length; setting constraint conditions of raw coal bins as the number of coal types which can be blended in the raw coal bins at most, and setting the coal types which cannot be blended or must be blended in each raw coal bin; setting unit operation constraint conditions to set maximum electric load, maximum steam supply, maximum heating amount and maximum sulfur content of the unit during mixed combustion working condition operation; the constraint condition of the operation mill is set as the number of the operation mill, and the mixed coal quality requirement range corresponding to the raw coal bin is configured, wherein the mixed coal quality requirement range comprises heat value, volatile matters, ash, moisture and sulfur.
3. The method for binning and combining and blending combustion based on a particle swarm optimization according to claim 1, wherein the method comprises the following steps: and thirdly, in the particle swarm optimization algorithm, taking the lowest comprehensive power generation cost as an objective function, and carrying out global optimization search on the objective function in all constructed mathematical expressions by utilizing the particle swarm optimization algorithm to obtain a sub-bin combined blending combustion optimal scheme.
4. The method for binning and combining and blending combustion based on particle swarm optimization according to claim 3, wherein the method comprises the following steps: the particle swarm optimization algorithm is set to be 1000 in iteration times, 500 in population quantity and 0.6 in acceleration factor.
5. The method for binning and combining and blending combustion based on a particle swarm optimization according to claim 1, wherein the method comprises the following steps:
and thirdly, vertically rectifying the proportion of the blended coal types in each raw coal bin in the optimal scheme result according to a set step length, arranging and combining all raw coal bin rectifying results, judging global constraint conditions of each result, recording schemes meeting all constraint conditions, and selecting a scheme with the lowest comprehensive power generation cost to output the result.
6. The particle swarm optimization-based binning combination blending method according to claim 5, wherein the method is characterized by comprising the following steps:
and step three, replacing each coal type in the optimized scheme after the normalization with other coal types in sequence, replacing the coal types once to form one scheme, judging global constraint conditions of all schemes, and sequencing and outputting a result according to the lowest comprehensive power generation cost by using the other coal types which can be replaced by each coal type in the scheme meeting all constraint conditions.
7. The particle swarm optimization-based binning combined blending combustion system is characterized by comprising the following components:
the constraint condition setting module is used for setting a boundary constraint condition system;
the mathematical expression construction module is used for constructing a mathematical expression of the boundary constraint condition system;
the optimal scheme calculating module is used for determining an objective function, and carrying out global optimizing search on the objective function in the constructed mathematical expression by utilizing a particle swarm optimization algorithm to obtain a sub-bin combination blending combustion optimal scheme;
the boundary constraint conditions comprise coal constraint conditions, raw coal bin constraint conditions, unit operation constraint conditions and operation mill constraint conditions;
the mathematical expression of the boundary constraint condition system constructed in the second step is as follows:
C max ≤nc
r i,min ≤R i ≤r i,max
R step ＝r step
wherein nc is the number of the most blended coal seeds in the raw coal bin, r i,min And r i,max The mixing proportion of the coal types is i, namely the minimum and maximum mixing proportion of the coal types, wherein i is a positive integer; r is (r) step Step length is regulated for proportion;
S j,max ≤ns j
coal j,i ∈S j,coal or (b)
Ns in j The j raw coal bins are mixed with the coal types at most, and coal j,i Coal types which are j raw coal bins and are necessary to be mixed or can not be mixed;
η rh ＝B BMCR Q SJ η B,SJ /L SJ
L real ＝load max +(stream max ·H Stream +heat max )/(η p η rh )
load in max 、stream max 、heat max 、star max The maximum electric load, the maximum steam supply and the maximum heating amount of the unit during the mixed combustion working condition operation and the maximum sulfur content in the furnace are respectively obtained; l (L) real 、B BMCR 、Q SJ 、η B,SJ 、L SJ 、η p 、H stream 、n mill 、n run 、Q all 、S all The method comprises the steps of blending combustion working condition heat load, BMCR working condition coal quantity, design coal heat value, design boiler efficiency, unit design capacity, pipeline efficiency, steam supply enthalpy value, coal mill quantity, operation grinding quantity, furnace coal heat value and furnace coal sulfur content;
Mill j ∈Mill run
q j,min ≤Q j vq j,max
v j,min ≤V j ≤v j,max
a j,min ≤A j ≤a j,max
m j,min ≤M j ≤m j,max
s j,min ≤S j ≤s j,max
q in j 、V j 、A j 、M j 、S j The heat value, the volatile component, the ash content, the moisture and the sulfur content of the mixed coal in the j coal mill are respectively, and the left and right parameters of the inequality are respectively the lower limit value and the upper limit value; parameter Q j 、V j 、A j 、M j 、S j The calculation formula of (2) is as follows:
q in j,i 、v j,i 、a j,i 、m j,i 、s j,i The parameters are the heat value, volatile matter, ash content, moisture and sulfur content of the coal type i of the j coal mill respectively, and the corresponding r is the proportion of the coal type i in the j coal mill.
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