CN113591389A - Coal proportioning determination method for power generation process of thermal power plant - Google Patents
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Abstract
The invention discloses a coal proportioning determination method in the power generation process of a thermal power plant, which comprises the following steps: s1: acquiring basic parameter data of each batch of coal; s2: establishing a coal batch combination model; s3: setting coal batch combination model parameters according to the quality index and the power generation requirement and solving a combination result by using the model; s4: constructing value intervals of each parameter and constructing a target preference function; s5: establishing an optimal coal blending ratio searching model by using a Genetic Algorithm (GA); s6: and setting a model parameter for searching the optimal coal blending ratio, inputting an expected parameter value, combining with S4 to obtain an objective function, and solving the addition ratio of each coal by using the model. The invention solves the problems of unreasonable coal addition types and proportions and no consideration of economy and environmental protection in the power generation process.
Description
Technical Field
The invention relates to the field of power generation control of thermal power plants, in particular to a method for determining coal proportioning.
Background
Because the energy structure of China is mainly thermal power generation, for coal-fired power plants, in order to guarantee the power production, a certain stock coal amount generally needs to be kept, and is influenced by the coal market, electricity) coal has to be purchased at multiple points, so that the actual coal types for combustion are more and more diversified, the calorific value Q, the volatile component V, the ash content A, the moisture content M and the sulfur content S of different types and batches of coal are generated, and in the actual power generation and coal combustion process, the coals of different batches are often combined for use due to the existence of the coal stock and the requirement of the feeding components each time. However, the following problems need to be considered in combination: the rationality of the current combination cannot be considered only when combining, but also when combining subsequently. In the actual production process, the total combination number is too large due to more stock batches and coal types, and the calculation complexity of the total combination number is far beyond the calculation capability of the human brain.
The national requirements for gas emission after the coal burning process of power generation are strict. Therefore, the adding quality of each coal pollution needs to be accurately calculated before the coal is fed into the furnace, so that each finally discharged index meets the national environmental protection requirement. In practice, due to the fact that factors influencing the generation amount of nitrogen oxides and sulfur dioxide are more, the emission amount of the pollution gas is difficult to obtain through mechanisms or expert experience, and the coal blending is challenging.
The corresponding mathematical model can be established to quickly and accurately calculate the required batch combination of the coal and the reasonable adding quality of each raw material according to the content of each component of the current coal, so that the coal is as economical as possible on the premise that the coal proportion meets the production requirement and the emission requirement. The coal proportioning model provided by the invention overcomes the defects of high manual calculation difficulty and low efficiency, and can ensure that thermal power generation has more economical efficiency and environmental protection.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for determining the coal proportion in the power generation process of a thermal power plant, and solves the problems that the manual calculation difficulty is high, the efficiency is low, and the environmental protection performance and the economic performance cannot be considered at the same time.
The invention adopts the technical scheme that the method for determining the coal proportion in the power generation process of the thermal power plant comprises the following steps:
s1: acquiring basic parameter data of each batch of coal;
s2: establishing a coal batch combination model;
s3: setting coal batch combination model parameters according to the quality index and the power generation requirement and solving a combination result by using the model;
s4: constructing value intervals of each parameter and constructing a target preference function;
s5: establishing an optimal coal blending ratio searching model by using a Genetic Algorithm (GA);
s6: and setting a model parameter for searching the optimal coal blending ratio, inputting an expected parameter value, combining with S4 to obtain an objective function, and solving the addition ratio of each coal by using the model.
Preferably, the specific steps of step S1 are as follows:
firstly, obtaining the total Batch number sumbch and Batch set Batch in the current inventory1The basic parameter data of each batch comprise calorific value Q, volatile component V, ash A, moisture M and sulfur S; batch1Is defined as follows:
Batch1＝{1，2，3，…，sumbch}。
optimally, the step of S2 includes the following substeps:
s21, calculating the average number of each parameter after different batches of combination, wherein the calculation method comprises the following steps:
wherein the content of the first and second substances,
represents the mean of the parameters J after combination, JiThe basic parameter represents the batch i, J belongs to { Q, V, A, M, S }, and n represents the number of batches contained in a combination;
s22. in Batch1In solving the first combination combi1After combination, J needs to satisfy the following constraints:
wherein, JstadRepresents
Standard value of (3), errorJRepresents
Maximum fluctuation range of,%;
the solving method adopts a random generation method, namely, in Batch1Randomly selecting n elements, and calculating
And judging whether the constraint is satisfied, if so, combi1Solving successfully; if not, continuing random generation until finding the combi meeting the constraint1；
After the first combination is successfully solved, the solution is continued to solve a second combination combi in the same way2，Batch2The calculation method is as follows:
Batch2＝Batch1-combi1
when combi1When it is not reasonable, Batch2There will be no solution; define the maximum random number ranmaxWhen the number of executed solution times is greater than ranmaxAnd combi2When the solution is not successful, the combi is solved again1(ii) a In Batch3Solution to combi3When the number of the executed solution times is more than ranmaxAnd combi3When the solution is not successful, the combi is solved again1；Batch3The calculation method of (a) is shown as follows:
Batch3＝Batch2-combi2
the rest can be done in the same way until the combi is solvedpmaxAnd pmax is the number of combinations that need to be solved.
Optimally, the step of S3 includes the following substeps:
s31, setting coal batch combination model parameters according to quality indexes and power generation requirements, wherein the parameters comprise Jstad、errorJN and pmax;
s32, after the model parameters are set, inputting the coal batch information in the step S1, and solving pmax combinations by using a coal batch combination model.
Optimally, the step of S4 includes the following substeps:
s41, constructing value intervals of each parameter and constructing a target preference function; determining a heat value preference function, setting an expected heat value Q as Q actually, and dividing a value interval of the heat value as follows:
the interval-1: the domain: q is more than Q-100 and less than or equal to Q;
interval-2, 3, desired domain: q is more than Q-200 and less than or equal to Q-100 or Q is more than Q and less than or equal to Q + 100;
interval-4.5, tolerable domain: q is more than Q-400 and less than or equal to Q-200 or Q +100 and less than or equal to Q + 300;
interval-6, 7, undesired domain: q is more than Q-450 and less than or equal to Q-400 or Q +300 and less than or equal to Q + 450;
interval-8, 9, highly undesirable domain: q is more than Q-600 and less than or equal to Q-400 or Q +300 and less than or equal to Q + 500;
interval-10, 11, no accept domain: q is not less than Q-600 or Q +500 is less than Q;
constructing a second derivative of the heat value preference function over the preference interval k:
wherein:
λk＝gq(k)-gq(k-1)and a and b are expressions of a preference function obtained by integrating a strictly positive real number:
δqk＝(λk)4[a/12(ξk)2+b(ξk-1)4]+cλkξk+d
preference function value delta from interval end pointq(k-1)，δq(k)And its slope h(k-1)，h(k)The following can be obtained:
wherein the content of the first and second substances,
is the average slope of the preference function over the interval-1; determining the endpoint information of the preference function in the interval:
taking deltaq＝0.1；
②Δδq(k)＝βncΔδq(k-1)，k2，3，4，5，β＞1，ncThe number of design targets;
③δq(1)＝Δδq1，δq(k)＝δq(k-1)+Δδq(k)；
④
h(k)＝(h(k))min+αΔh (k)0 < alpha < 1, from a, b strictly positive, we can obtain:
for the interval-0, set: deltaq＝δq1·exp[(h1/δq1)(gq-gq1)]
Through optimization and comparison, the values of a, b, c and d of each section can be obtained by taking alpha as 0.07 and beta as 1.2, and the preference functions of other parameters can be obtained in the same way; wherein the expected value of the volatile component is set as v, and the setting interval is as follows:
(v-18，v-15]、(v-13，v-10]、(v-10，v-8]、(v-8，v-4]、(v，v+6]、(v+6，v+10]、(v+10，v+16]、(v+12，v+14]
(v-4，v]is a very desirable domain; SO (SO)2The expected value is s;
the setting interval is:
(s-1200，s-900]、(s-900，s-500]、(s-500，s-300]、(s-300，s-100]、(s，s+100]、(s+100，s+300]、(s+300，s+500]、(s+500，s+900]
(s-100，s]is a desired interval; NOxThe expected value is no;
the setting interval is:
(no，no+5]、(no+5，no+10]、(no+10，no+20]、(no+20，n+40)
slag formation index RzThe expected value is rz;
the setting interval is:
(rz，rz+0.25]、(rz+0.25，rz+0.5]、(rz+0.5，rz+0.75]、(rz+0.75，rz+1]
natural index RspThe expected value is rs;
the setting interval is:
(rs，rs+0.4]、(rs+0.4，rs+0.8]、(rs+0.8，rs+1.2]、(rs+1.2，rs+1.6]
s42, obtaining R of mixed coalsp，RzQ three preference functions, deltafA preference function representing the F-th parameter, F (x) is an interval endpoint value, and then a safety objective function F is constructed after three target indexes of the safety objective function are constructeds：
Obtaining the price, P, of each coal typeiRepresents the price of the i-th coal, XiThe proportion of the i-th coal is expressed; the price is directly calculated by adopting a weighted value, is a balance magnitude, and is multiplied by a coefficient of 0.01 to construct an economic objective function Fe：
Obtaining NO of the coal blendx，SO2V three preference functions, δfA preference function representing the F parameter, F (x) is an interval endpoint value, and then an environmental protection objective function F is constructedp
Synthesizing the three objective functions according to the weight to obtain a total objective function F0：
F0＝β1Fs+β2Fe+β3Fp
Wherein beta is1，β2，β3Respectively taking 0.3, 0.5 and 0.2.
Optimally, the step of S5 includes the following substeps:
s51, searching for an optimal coal blending ratio by using a Genetic Algorithm (GA), determining combination of each batch, and setting constraint conditions as follows:
the single coal proportion: xi≥0
Heat generation amount: qa≤Q＝fQ(Xi，Qi)≤Qb
Volatile components: a. thea≤A＝fv(Xi，Ai)≤Ab
Moisture content: m ═ fM(Xi，Mi)≤Mb
S52, constructing a fitness function fit (x) of the genetic algorithm by the selected total objective function:
fit(x)＝β1Fs+β2Fe+β3Fp
s53, generating an initial population Chrom with the number of individuals being M by using a random algorithm, wherein M is not more than 200, and the initial population cannot cover the whole solving space. The coal blending coding mode adopts real number coding, the sequence is according to the natural sequence of coal combination, and the real number represents the proportion of corresponding coal types in the batch of combustion;
s54, calculating the fitness value of each individual in the population by using a loop statement;
s55, directly copying the D individuals with the front adaptive value ranking to the next generation by adopting an elite strategy;
s56, selecting, crossing and mutating to generate the rest M-D individuals to form a new population and cover Chrom;
and S57, repeating S54-S56 for iteration until the fitness of each individual of the population tends to be stable or the iteration times reaches the maximum set iteration times K times, and finishing.
Optimally, the step of S6 includes the following substeps:
s61, parameters in the model are set according to power generation requirements, and the set parameters comprise:
Jstad、errorj、n、pmax、q、v、s、no、rz、rs；
and S62, inputting basic information of each batch and type of coal, and solving the optimal coal addition ratio by using the optimization model of S5.
The method for determining the coal proportion in the power generation process of the thermal power plant has the following beneficial effects:
1. the method for determining the coal proportioning in the power generation process of the thermal power plant can quickly and accurately calculate the required batch combination according to the basic parameters of the current batches of coal for subsequent proportioning optimization.
2. According to the method for determining the coal proportion in the power generation process of the thermal power plant, the adding proportion of each coal in each current batch combination can be accurately calculated according to the batch combination basic parameters of the coal and the objective function and the optimization algorithm, so that each index of combustion meets the production requirement.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a graph comparing the results of manual combination with those of the coal batch combination model of the example.
FIG. 3 is a graph comparing the results of artificial and example coal blending.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a method for determining a coal blending ratio in a power generation process of a thermal power plant includes the following steps:
s1: obtaining basic information data of calorific value Q, volatile component V, ash content A, moisture M, sulfur content S and the like of each batch of coal;
s2: selecting n batches in a batch set by adopting a random selection method, calculating the average value of each parameter after different batch combinations, then judging whether the batch group meets constraint conditions, if not, reselecting, removing the combination which meets the constraints from the batch set, then solving the other combinations in the same way, and so on to solve all coal batch combinations, thereby establishing a coal batch combination model;
s3: setting parameters of a coal combination model according to environmental limiting conditions such as nitrogen oxide emission, sulfur dioxide emission and fly ash emission, load requirements controlled and determined by a power grid and heat requirements required by a user, and solving a coal batch combination result by using the model;
s4: constructing value intervals of heat value, volatile components, sulfur dioxide, nitrogen oxides, slagging index and natural index, constructing respective target preference functions, and obtaining R of the mixed coalsp，RzAnd after Q three objective functions, constructing a safety objective function, obtaining the prices of various coals, constructing an economic objective function, and obtaining NO of mixed coalx，SO2And v, constructing an environment-friendly objective function after the three objective functions are completed, and finally synthesizing the safety objective function, the economic objective function and the environment-friendly objective function according to the weight to obtain a total objective functionCounting;
s5: establishing the coal information of each batch as an initial population through a genetic algorithm, calculating the fitness value of each individual in the population according to a total objective function, generating new individuals through selection, intersection and variation to form a new population, and repeating iteration until the fitness of each individual in the population tends to be stable, thereby establishing an optimal coal blending ratio searching model;
s6: setting parameters in the model according to the power generation demand, wherein the set parameters comprise:
Jstad、errorj、n、pmax、q、v、s、no、rz、rs
and then inputting basic information of each batch and type of coal, and solving the optimal coal addition ratio by using an optimization model.
Coal is the power foundation of a thermal power plant, but basic parameters of various kinds of coal and batches are different, and the total Batch number sumbch and Batch set Batch in the current inventory need to be obtained firstly1The basic parameter data of each batch comprise calorific value Q, volatile component V, ash A, moisture M and sulfur S; batch1Is defined as follows:
Batch1＝{1，2，3，…，sumbch}
then, calculating the average of each parameter after different batches are combined, wherein the calculation method comprises the following steps:
wherein the content of the first and second substances,
represents the mean of the parameters J after combination, JiThe basic parameter represents the batch i, J belongs to { Q, V, A, M, S }, and n represents the number of batches contained in a combination;
in Batch1In solving the first combination combi1After combination, J needs to satisfy the following constraints:
wherein, JstadRepresents
Standard value of (3), errorJRepresents
Maximum fluctuation range of,%;
the solving method adopts a random generation method, namely, in Batch1Randomly selecting n elements, and calculating
And judging whether the constraint is satisfied, if so, combi1Solving successfully; if not, continuing random generation until finding the combi meeting the constraint1；
After the first combination is successfully solved, the solution is continued to solve a second combination combi in the same way2，Batch2The calculation method is as follows:
Batch2＝Batch1-combi1
when combi1When it is not reasonable, Batch2There will be no solution; define the maximum random number ranmaxWhen the number of executed solution times is greater than ranmaxAnd combi2When the solution is not successful, the combi is solved again1(ii) a In Batch3Solution to combi3When the number of the executed solution times is more than ranmaxAnd combi3When the solution is not successful, the combi is solved again1；Batch3The calculation method of (a) is shown as follows:
Batch3＝Batch2-combi2
then, the parameters of the coal batch combination model are set according to the current power generation condition and the power generation requirement, including Jstad、error JN, and pmax. After the model parameters are set, the coal batch information described in S1 is input, and pmax combinations can be solved by using the coal batch combination model.
Then, we need to construct an objective function to obtain R of the mixed coalsp，RzQ three preference functions, deltafA preference function representing the F-th parameter, F (x) is an interval endpoint value, and then a safety objective function F is constructed after three target indexes of the safety objective function are constructeds：
Obtaining the price, P, of each coal typeiRepresents the price of the i-th coal, XiThe proportion of the i-th coal is expressed; the price is directly calculated by adopting a weighted value, is a balance magnitude, and is multiplied by a coefficient of 0.01 to construct an economic objective function Fe：
Obtaining NO of the coal blendx，SO2V three preference functions, δfA preference function representing the F parameter, F (x) is an interval endpoint value, and then an environmental protection objective function F is constructedp
Synthesizing the three objective functions according to the weight to obtain a total objective function F0：
F0＝β1Fs+β2Fe+β3Fp
Wherein beta is1，β2，β3Respectively taking 0.3, 0.5 and 0.2.
After the total objective function is obtained, an optimization model needs to be constructed to find the optimal coal blending ratio. The process is as follows:
using GA genetic algorithm to search the optimal coal blending ratio, and after determining the combination of each batch, firstly setting the constraint conditions as follows:
the single coal proportion: xi≥0
Heat generation amount: qa≤Q＝fQ(Xi，Qi)≤Qb
Volatile components: a. thea≤A＝fv(Xi，Ai)≤Ab
Moisture content: m ═ fM(Xi，Mi)≤Mb
S52, constructing a fitness function fit (x) of the genetic algorithm by the selected total objective function:
fit(x)＝β1Fs+β2Fe+β3Fp
then, a random algorithm is used to generate an initial population Chrom with an individual number of M, wherein M is not more than 200, and the initial population cannot cover the whole solving space. The coal blending coding mode adopts real number coding, the sequence is according to the natural sequence of coal combination, and the real number represents the proportion of corresponding coal to the batch of combustion. Calculating the fitness value of each individual in the population by using a loop statement, directly copying D individuals with the fitness value ranked at the front to the next generation by adopting an elite strategy, and generating the rest M-D individuals by selection, intersection and variation to form a new population and cover Chrom. And repeating the iteration until the fitness of each individual of the population tends to be stable.
And finally, setting parameters in the model according to the power generation demand, wherein the set parameters comprise:
Jstad、errorj、n、pmax、q、v、s、no、rz、rs。
and solving the optimal coal addition proportion by using the basic information of each batch and each type of coal through an optimization model of S5.
FIG. 2 is a graph comparing the combination result of the manual combination with the combination result of the coal batch combination model of the present invention.
FIG. 3 is a comparison of the results of the artificial coal blending and the model optimization calculation in the coal blending determination method in the power generation process of the thermal power plant. And selecting the comparison condition of coal price, sulfur dioxide discharge and nitrogen oxide discharge under the condition of manual proportioning and model optimization under the condition of the same heat value.
Claims (7)
1. A coal proportioning determination method in the power generation process of a thermal power plant is characterized by comprising the following steps:
s1: acquiring basic parameter data of each batch of coal;
s2: establishing a coal batch combination model;
s3: setting coal batch combination model parameters according to the quality index and the power generation requirement and solving a combination result by using the model;
s4: constructing value intervals of each parameter and constructing a target preference function;
s5: establishing an optimal coal blending ratio searching model by using a Genetic Algorithm (GA);
s6: setting a model parameter for finding the optimal coal blending ratio, inputting an expected parameter value and combining S4 to obtain a target function; and solving the adding proportion of each coal by using the model.
2. The method for determining the coal blending ratio in the power generation process of the thermal power plant as claimed in claim 1, wherein the step S1 is as follows:
firstly, obtaining the total Batch number sumbch and Batch set Batch in the current inventory1The basic parameter data of each batch comprise calorific value Q, volatile component V, ash A, moisture M and sulfur S; batch1Is defined as follows:
Batch1＝{1，2，3，…，sumbch}。
3. the method for determining the coal blending ratio in the power generation process of the thermal power plant as claimed in claim 2, wherein the step S2 comprises the following sub-steps:
s21, calculating the average number of each parameter after different batches of combination, wherein the calculation method comprises the following steps:
wherein the content of the first and second substances,
represents the mean of the parameters J after combination, JiThe basic parameter represents the batch i, J belongs to { Q, V, A, M, S }, and n represents the number of batches contained in a combination;
s22. in Batch1In solving the first combination combi1After combination, J needs to satisfy the following constraints:
wherein, JstadRepresents
Standard value of (3), errorJRepresents
Maximum fluctuation range of,%;
the solving method adopts a random generation method, namely, in Batch1Randomly selecting n elements, and calculating
And judging whether the constraint is satisfied, if so, combi1Solving successfully; if not, continuing random generation until finding the combi meeting the constraint1；
After the first combination is successfully solved, the solution is continued to solve the second combination Combi in the same way2，Batch2The calculation method is as follows:
Batch2＝Batch1-Combi1
when combi1When it is not reasonable, Batch2There will be no solution; define the maximum random number ranmaxWhen the number of executed solution times is greater than ranmaxAnd combi2When the solution is not successful, the combi is solved again1(ii) a In Batch3In solving for combi3When the number of the executed solution times is more than ranmaxAnd combi3When the solution is not successful, the combi is solved again1；Batch3The calculation method of (a) is shown as follows:
Batch3＝Batch2-combi2
the rest can be done in the same way until the combi is solvedpmaxAnd pmax is the number of combinations that need to be solved.
4. The method for determining the coal blending ratio in the power generation process of the thermal power plant as claimed in claim 3, wherein the step S3 comprises the following substeps:
s31, setting coal batch combination model parameters according to quality indexes and power generation requirements, wherein the parameters comprise Jstad、errorJN and pmax;
s32, after the model parameters are set, inputting the coal batch information in the step S1, and solving pmax combinations by using a coal batch combination model.
5. The method for determining the coal blending ratio in the power generation process of the thermal power plant according to claim 4, wherein the step S4 is as follows:
s41, constructing value intervals of each parameter and constructing a target preference function; determining a heat value preference function, setting an expected heat value Q as Q actually, and dividing a value interval of the heat value as follows:
the interval-1: the domain: q is more than Q-100 and less than or equal to Q;
interval-2, 3, desired domain: q is more than Q-200 and less than or equal to Q-100 or Q is more than Q and less than or equal to Q + 100;
interval-4.5, tolerable domain: q is more than Q-400 and less than or equal to Q-200 or Q +100 and less than or equal to Q + 300;
interval-6, 7, undesired domain: q is more than Q-450 and less than or equal to Q-400 or Q +300 and less than or equal to Q + 450;
interval-8, 9, highly undesirable domain: q is more than Q-600 and less than or equal to Q-400 or Q +300 and less than or equal to Q + 500;
interval-10, 11, no accept domain: q is not less than Q-600 or Q +500 is less than Q;
constructing a second derivative of the heat value preference function over the preference interval k:
wherein:
a, b are expressions of a preference function by integration of a strictly positive real number:
δqk＝(λk)4[a/12(ξk)2+b(ξk-1)4]+cλkξk+d
preference function value delta from interval end pointq(k-1)，δq(k)And its slope h(k-1)，h(k)The following can be obtained:
wherein the content of the first and second substances,
is the average slope of the preference function over the interval-1; determining the endpoint information of the preference function in the interval:
taking deltaq＝0.1；
②Δδq(k)＝βncΔδq(k-1)，k2，3，4，5，β＞1，ncThe number of design targets;
③δq(1)＝Δδq1，δq(k)＝δq(k-1)+Δδq(k)；
④
h(k)＝(h(k))min+αΔh(k)0 < alpha < 1, from a, b strictly positive, we can obtain:
for the interval-0, set: deltaq＝δq1·exp[(h1/δq1)(gq-gq1)]
Through optimization and comparison, the values of a, b, c and d of each section can be obtained by taking alpha as 0.07 and beta as 1.2, and the preference functions of other parameters can be obtained in the same way; wherein the expected value of the volatile component is set as v, and the setting interval is as follows:
(v-18，v-15]、(v-13，v-10]、(v-10，v-8]、(v-8，v-4]、(v，v+6]、(v+6，v+10]、(v+10，v+16]、(v+12，v+14]
(v-4，v]is a very desirable domain; SO (SO)2The expected value is s;
the setting interval is:
(s-1200，s-900]、(s-900，s-500]、(s-500，s-300]、(s-300，s-100]、(s，s+100]、(s+100，s+300]、(s+300，s+500]、(s+500，s+900]
(s-100，s]is a desired interval; NOxThe expected value is no;
the setting interval is:
(no，no+5]、(no+5，no+10]、(no+10，no+20]、(no+20，n+40)
slag formation index RzThe expected value is rz;
the setting interval is:
(rz，rz+0.25]、(rz+0.25，rz+0.5]、(rz+0.5，rZ+0.75]、(rz+0.75，rz+1]
natural index RspThe expected value is rs;
the setting interval is:
(rs，rs+0.4]、(rs+0.4，rs+0.8]、(rs+0.8，rs+1.2]、(rs+1.2，rs+1.6]
s42, obtaining R of mixed coalsp，RzQ three preference functions, deltafA preference function representing the F-th parameter, F (x) is an interval endpoint value, and then a safety objective function F is constructed after three target indexes of the safety objective function are constructeds：
Obtaining the price, P, of each coal typeiRepresents the price of the i-th coal, XiThe proportion of the i-th coal is expressed; the price is directly calculated by adopting a weighted value, is a balance magnitude, and is multiplied by a coefficient of 0.01 to construct an economic objective function Fe：
Obtaining NO of the coal blendx，SO2V three preference functions, δfA preference function representing the F parameter, F (x) is an interval endpoint value, and then an environmental protection objective function F is constructedp
Synthesizing the three objective functions according to the weight to obtain a total objective function F0：
F0＝β1Fs+β2Fe+β3Fp
Wherein beta is1，β2，β3Respectively take0.3，0.5，0.2。
6. The method for determining the coal blending ratio in the power generation process of the thermal power plant as claimed in claim 5, wherein the step S5 comprises the following sub-steps:
s51, searching for an optimal coal blending ratio by using a Genetic Algorithm (GA), determining combination of each batch, and setting constraint conditions as follows:
the single coal proportion: xi≥0
Heat generation amount: qa≤Q＝fQ(Xi，Qi)≤Qb
Volatile components: a. thea≤A＝fv(Xi，Ai)≤Ab
Moisture content: m ═ fM(Xi，Mi)≤Mb
S52, constructing a fitness function fit (x) of the genetic algorithm by the selected total objective function:
fit(x)＝β1Fs+β2Fe+β3Fp
s53, generating an initial population Chrom with the number of individuals of M by using a random algorithm, wherein M is not more than 200, and the initial population is required to be incapable of covering the whole solving space; the coal blending coding mode adopts real number coding, the sequence is according to the natural sequence of coal combination, and the real number represents the proportion of corresponding coal types in the batch of combustion;
s54, calculating the fitness value of each individual in the population by using a loop statement;
s55, directly copying the D individuals with the liveness values ranked at the top to the next generation by adopting an elite strategy;
s56, selecting, crossing and mutating to generate the rest M-D individuals to form a new population and cover Chrom;
and S57, repeating S54-S56 for iteration until the fitness of each individual of the population tends to be stable or the iteration times reaches the maximum set iteration times K times, and finishing.
7. The method for determining the coal blending ratio in the power generation process of the thermal power plant as claimed in claim 6, wherein the step S6 comprises the following substeps:
s61, parameters in the model are set according to power generation requirements, and the set parameters comprise:
Jstad、errorj、n、pmax、q、v、s、no、rz、rs；
and S62, inputting basic information of each batch and type of coal, and solving the optimal coal addition ratio by using the optimization model of S5.
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