CN114417689A - Ultra-clean emission energy-saving optimization control method for flue gas of main-pipe boiler - Google Patents

Ultra-clean emission energy-saving optimization control method for flue gas of main-pipe boiler Download PDF

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CN114417689A
CN114417689A CN202110815901.6A CN202110815901A CN114417689A CN 114417689 A CN114417689 A CN 114417689A CN 202110815901 A CN202110815901 A CN 202110815901A CN 114417689 A CN114417689 A CN 114417689A
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boiler
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邢莉华
顾蓉
艾军
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Shanghai Meishan Iron and Steel Co Ltd
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Abstract

The invention relates to an energy-saving optimization control method for ultra-clean emission of flue gas of a main-pipe boiler, which comprises the following steps: step 1, carrying out coordinated distribution of boiler combustion through data mining analysis, and step 2, carrying out iterative optimization by using a Particle Swarm Optimization (PSO) to solve a coefficient solution under the boiler combustion operation on the premise of the step 1: the method comprises the steps of gas weight coefficient, load sharing coefficient and gas variety proportioning coefficient, step 3, realizing automatic generation of the set value of SO2, calculating the set value of SO2 in real time by adopting a dynamic programming algorithm, and step 4, adopting a fuzzy control strategy, and adjusting P and I parameters of PID according to the actual working condition, SO that the PID has stronger regulation capability and self-adaption capability.

Description

Ultra-clean emission energy-saving optimization control method for flue gas of main-pipe boiler
Technical Field
The invention relates to a control method, in particular to an energy-saving optimization control method for ultra-clean emission of flue gas of a main-pipe boiler, belonging to the technical field of combustion power generation of gas furnaces.
Background
For most gas furnace units, an SDS dry desulfurization and cloth bag dust removal process is mainly adopted, and Na2CO3 with strong activity is in full contact with SO2 in flue gas and other acidic media under the action of high-temperature flue gas to react, SO that absorption and purification are carried out. The desulfurized product enters a bag-type dust collector along with the air flow for further dust removal. The content of SO2 in the boiler flue gas is related to the sulfur content of blast furnace gas and coke oven gas, when the coke amount of the blast furnace gas is large, the concentration of the SO2 in the boiler flue gas is higher and often breaks through 100mg/m3, even if a large amount of converter gas is mixed and burned, the converter gas cannot be reduced to be within 100mg/m3, the load of the boiler can be reduced, even the boiler is shut down, and a plurality of gas furnace units are desulfurized and modified according to the situation. However, when desulfurization is performed by using conventional PID control, the control effect is often poor and some negative effects are brought due to the problem of large delay and large inertia of the system, mainly the consumption of NaHCO3, the desulfurization power consumption and the nitrogen consumption of each boiler are too high. The improvement of the operation cost of the desulfurization system brings not little pressure to the operation cost accounting of the boiler, and the intelligent control is adopted: algorithms including neural networks, MPCs, simulated annealing and the like are used as control modes, parameter setting and structure are often complex, and large-scale popularization and application are often difficult due to the restriction of actual field software and hardware conditions. Therefore, a new solution to solve the above technical problems is urgently needed.
Disclosure of Invention
The invention provides an energy-saving optimization control method for ultra-clean emission of flue gas of a main-pipe boiler aiming at the problems in the prior art, and the method is suitable for ultra-low emission of flue gas of a main-pipe gas furnace and improves the economy and stability of a unit in an operation period.
In order to achieve the purpose, the technical scheme of the invention is as follows, and the method for the ultra-clean emission energy-saving optimization control of the flue gas of the main-pipe boiler is characterized by comprising the following steps of:
step 1, carrying out coordinated distribution of boiler combustion through data mining analysis,
step 2, under the premise of the step 1, iterative optimization is carried out by utilizing a Particle Swarm Optimization (PSO) to solve a coefficient solution under the boiler combustion operation: comprises a coal gas weight coefficient, a load sharing coefficient and a coal gas variety proportioning coefficient,
step 3, realizing the automatic generation of the set value of SO2, adopting a dynamic programming algorithm to calculate the set value of SO2 in real time,
and 4, adopting a fuzzy control strategy to adjust the P and I parameters of the PID according to the actual working condition, so that the PID has stronger regulation capability and self-adaptive capability.
As an improvement of the present invention, step 1, coordinated distribution of boiler combustion is performed through data mining analysis, specifically as follows: selecting historical data of different periods under stable working conditions, wherein the historical data comprises total gas consumption C; the coal gas consumption data of each boiler is C1, C2, … … and Cn; load data of each boiler N1, N2, … …, Nn; actual production amount data f (1), f (2), … …, f (n) of each gas furnace SO 2; the load sharing coefficient of each gas furnace is etanThe weight coefficients of the gas quantity of each gas furnace are K11, K21, … … and Kn 1; the weight of the load sharing coefficient K12, K22, … …, Kn 2; the gas variety proportion coefficient of each gas furnace is lambdanThe gas variety proportioning coefficient weights K13, K23, … … and Kn3, parameters need to be adjusted and set according to actual operation conditions, and the minimum value of the gas consumption of each furnace is Cmin (1), Cmin (2), … … and Cmin (n); the maximum value of the gas consumption of each furnace is Cmax (1), Cmax (2), … …, Cmax (n), wherein n represents the number of gas furnaces, and n is {1, 2, 3,, k }; the gas consumption Max and Min needs to be modified by combining actual operation data on the basis of boiler design parameters,
Figure RE-GDA0003581227110000021
η1+η2+…+ηn＝1 (2)
C1+C2+…+Cn＝C (3)
λ1+λ2+…+λn＝1 (4)
Cmin(n)≤Cn≤Cmax(n) (5)
0＜ηn＜1 (6)
0＜λn＜1 (7)。
as an improvement of the invention, step 2, iterative optimization is carried out by utilizing a particle swarm optimization PSO to solve a coefficient solution under the combustion operation of the boiler,
under the limitation of the above formulas (1) to (7) and on the premise of historical data, the particle swarm optimization PSO is used for iterative optimization solution of the coefficient solution under the boiler combustion operation, including the coal gas quantity weight coefficient, the load sharing coefficient and the coal gas variety proportioning coefficient, SO that the generation amount of SO2 is as small as possible, namely
Figure RE-GDA0003581227110000022
Specifically, all parameters of the PSO algorithm are initialized, the maximum iteration number is set to 20000, the acceleration factors c1 are set to 1.4 and c2 are set to 1.4, the inertia weight w is 0.8, the population size sizepop is set to 200, the speed is limited, and the value range of the variable is [ -1,1]The function dimension dim is 6, the number of independent variables of a target function is 3, the position information is the whole variable search space, the initial position and the initialization speed are 0 and 0.1, each particle in a population independently searches an optimal solution in the search space and records the optimal solution as an individual extreme value of the current particle, the individual extreme value is shared with other particles, the optimal individual extreme value is searched as the current global optimal solution of the whole particle swarm, all the particles in the particle swarm adjust the speed and the position of the particle according to the current individual extreme value found by the particle and the current global optimal solution shared by the whole particle swarm until the optimal solution of the function is found: kn1, ηnAnd λnAnd the determined set of solutions is used as the distribution basis of the operation, under which the distribution adjustment of the actual combustion process is performed, and the actual value of SO2 generated after the combustion distribution adjustment operation is used as the input PV in step four.
As an improvement of the invention, step 3, the automatic generation of the set value of SO2 is realized, and a dynamic programming algorithm is adopted to calculate the set value of SO2 in real time, specifically as follows:
the set value of SO2 is automatically generated, specifically: in order to prevent the measured value of SO2 from fluctuating beyond the environmental index limit in the dynamic operation process, the initial concentration of SO2 at the time 0 is set to 35mg/Nm3, the current hour is divided into 60 segments according to 1-minute time intervals, and the concentration of SO2 at 0-1min of the current hour is recorded as PV 2so2(k) Setting the forgetting factor sigma at the current momentkDefinition of 1Concentration of SO2 at PV during the-2 min periodso2(k +1) setting the forgetting factor at the corresponding time to sigmak+1And the concentration of SO2 in the 59-60min period is defined as PV by analogy of successive time periodsso2(k +59) setting the forgetting factor at the corresponding time to sigmak+59The actual SO2 concentration values in the 0-n period are weighted and averaged to obtain a whole, the unknown SO2 concentration value at the next time is taken as another part, the concentration value after weighted and averaged is 35, and therefore the set value of the SO2 concentration in the n- (n +1) th min period
Figure RE-GDA0003581227110000031
Where n denotes the time period and n ∈ (1, 60)]。
Step 4, adopting a fuzzy control strategy to adjust P and I parameters of PID according to actual working conditions, wherein the fuzzy control strategy is implemented by the following steps: recording the measured value PV of the concentration of SO2 in the step 2 and the set value SP of the concentration of SO2 in the step 3, setting the error e to be SP-PV, and performing differential processing on the error e to obtain delta e as the input of a fuzzy control parameter model, setting the boundary value of the concentration of SO2 to be 35mg/Nm3 based on the actual operation of a site, defining the variation range of the error e to be-5-0, -10-5, -15-10, -20-15, -25-20, and expressing-5, -10, -15, -20 with fuzzy sets of normal (ZO), small Negative (NS), medium Negative (NM) and large Negative (NB); defining the variation range of the deviation variation delta e as 0.2-1, 0.05-0.2, 0-0.05, -0.05-0, -0.2-0.05, -1-0.2, and using the fuzzy set of 0.2, 0.05, 0, -0.05, -0.2: positive large (PB), Positive Small (PS), normal (ZO), Negative Small (NS), negative large (NB) representations; the membership degree function part selects a Gaussian type, so that the membership degrees of the error e at the left side and the right side of the corresponding interval are respectively a and 1-a, and the membership degrees of the error change delta e at the left side and the right side of the corresponding interval are respectively b and 1-b, so that the membership degrees of the output values under the fuzzy rule are respectively a multiplied by b, a multiplied by (1-b), (1-a) multiplied by b, (1-a) multiplied by (1-b); for the control output part, the better effect can be achieved by considering PI control, therefore, the change condition of PID parameters only takes delta Kp and delta Ki, and the change range delta Kp of P is defined as: -1 to-0.5, -0.5 to-0.15, -0.15 to 0, 0 to 0.15, 0.15 to 0.5, 0.5 to 1, wherein-0.5, -0.15, 0, 0.15, 0.5 is represented by fuzzy sets minus size (NB), minus size (NS), normal (ZO), Plus Size (PS), plus size (PB) of Δ Kp, and the variation range Δ Ki of I is defined as: -8 to-3, -3 to-1, -1 to 0, 0 to 1,1 to 3, 3 to 8, and-3, -1, 0, 1, 3 is represented by a fuzzy set of Δ Ki: negative large (NB), Negative Small (NS), normal (ZO), Positive Small (PS), and positive large (PB), and the fuzzy rule is determined according to the actual situation, which is specifically shown in table 1 and table 2; table 1: fuzzy control Δ Kp rule, table 2: fuzzy control delta Ki rule;
table 1: fuzzy control deltaKpRules
Figure RE-GDA0003581227110000041
Table 2: fuzzy control deltaKiRules
Figure RE-GDA0003581227110000042
In operation, controller parameters are automatically calculated to obtain the output Δ Kp, Δ Ki of the fuzzy controller, wherein the calculation of Δ Kp is shown as the following formula:
ΔKp＝a×b×Ra,b+a×(1-b)×Ra,1-b+(1-a)×b×R1-a,b+(1-a)×(1-b)×R1-a,1-b；
in the formula, Ra,bRepresenting the output fuzzy set of input a, b corresponding to the fuzzy rule of delta Kp, Ra,1-bRepresenting the input a, 1-b corresponding to the output fuzzy set under the fuzzy rule of delta Kp, R1-a,bRepresenting that input 1-a, b corresponds to the output fuzzy set under the fuzzy rule of delta Kp, R1-a,1-bIndicating that inputs 1-a, 1-b correspond to output fuzzy sets under the Δ Kp fuzzy rule. Similarly, Δ Ki is calculated in a similar manner.
Dynamically updating Kp to be delta Kp + Kp and Ki to be delta Ki + Ki on the basis of the parameters of the original PID controller Kp and Ki, thereby realizing the self-adaptive adjustment of the PID controller of the desulfurization system.
Compared with the prior art, the invention has the advantages that 1) the technical scheme calculates the weight coefficient of the gas quantity, the load sharing coefficient and the gas variety proportioning coefficient of each boiler through data mining analysis to guide the combustion of the boiler, SO that the generation amount of SO2 is as small as possible; 2) the scheme realizes the automatic generation technology of the set value of SO2, and a dynamic programming algorithm is adopted to calculate the set value of SO2 in real time; 3) the scheme adopts a fuzzy control strategy to change PID parameters in real time, so that the desulfurization has self-adaptive capacity. The desulfurization cost is reduced.
Table: real-time data comparison of SO2 at outlet of 1 boiler before and after improvement
0：15 1：15 2：15 3：15 4：15 5：15 6：15 7：15 8：15 9：15 10：15 11：15
Before improvement 27.93 27.43 25.82 25.91 26.21 28.45 27.93 27.97 23.12 22.82 24.69 28.4
After improvement 31.29 32.05 31.04 31.99 34.08 32.2 32.27 31.3 33.64 33.85 33.36 32.89
12：15 13：15 14：15 15：15 16：15 17：15 18：15 19：15 20：15 21：15 22：15 23：15
Before improvement 22.54 21.82 21.69 22.95 22.71 20.77 22.39 25.74 25.49 22.9 22.33 24.18
After improvement 31.64 32.93 31.2 32.82 31.35 32.65 32.01 32.73 32.4 30.82 30.55 31.72
Drawings
FIG. 1: a combustion coordination control map;
FIG. 2: a PSO algorithm flow chart;
FIG. 3: and the desulfurization control overall flow chart.
The specific implementation mode is as follows:
for the purpose of enhancing an understanding of the present invention, the present embodiment will be described in detail below with reference to the accompanying drawings.
Example 1: referring to fig. 1-3, a method for controlling super-clean emission energy-saving optimization of flue gas of a main-pipe boiler comprises the following steps:
step 1, carrying out coordinated distribution of boiler combustion through data mining analysis,
step 2, under the premise of the step 1, iterative optimization is carried out by utilizing a Particle Swarm Optimization (PSO) to solve a coefficient solution under the boiler combustion operation: comprises a coal gas weight coefficient, a load sharing coefficient and a coal gas variety proportioning coefficient,
step 3, realizing the automatic generation of the set value of SO2, adopting a dynamic programming algorithm to calculate the set value of SO2 in real time,
and 4, adopting a fuzzy control strategy to adjust the P and I parameters of the PID according to the actual working condition, so that the PID has stronger regulation capability and self-adaptive capability.
Referring to fig. 1, step 1, coordinated distribution of boiler combustion is performed through data mining analysis, specifically as follows: selecting historical data of different periods under stable working conditions, wherein the historical data comprises total gas consumption C; the coal gas consumption data of each boiler is C1, C2, … … and Cn; load data of each boiler N1, N2, … …, Nn; actual production amount data f (1), f (2), … …, f (n) of each gas furnace SO 2; the load sharing coefficient of each gas furnace is etanThe weight coefficients of the gas quantity of each gas furnace are K11, K21, … … and Kn 1; the weight of the load sharing coefficient K12, K22, … …, Kn 2; the gas variety proportion coefficient of each gas furnace is lambdanThe gas variety proportioning coefficient weights K13, K23, … … and Kn3, parameters need to be adjusted and set according to actual operation conditions, and the minimum value of the gas consumption of each furnace is Cmin (1), Cmin (2), … … and Cmin (n); the maximum value of the gas consumption of each furnace is Cmax (1), Cmax (2), … …, Cmax (n), wherein n represents the number of gas furnaces, and n is {1, 2, 3,, k }; the gas consumption Max and Min needs to be modified by combining actual operation data on the basis of boiler design parameters,
Figure RE-GDA0003581227110000061
η1+η2+…+ηn＝1 (2)
C1+C2+…+Cn＝C (3)
λ1+λ2+…+λn＝1 (4)
Cmin(n)≤Cn≤Cmax(n) (5)
0＜ηn＜1 (6)
0＜λn＜1 (7)。
referring to FIG. 2, step 2, iterative optimization is carried out by utilizing particle swarm optimization PSO to solve a coefficient solution under the boiler combustion operation,
under the limitation of the above formulas (1) to (7) and on the premise of historical data, the particle swarm optimization PSO is used for iterative optimization solution of the coefficient solution under the boiler combustion operation, including the coal gas quantity weight coefficient, the load sharing coefficient and the coal gas variety proportioning coefficient, SO that the generation amount of SO2 is as small as possible, namely
Figure RE-GDA0003581227110000062
Specifically, all parameters of the PSO algorithm are initialized, the maximum iteration number is set to 20000, the acceleration factors c1 are set to 1.4 and c2 are set to 1.4, the inertia weight w is 0.8, the population size sizepop is set to 200, the speed is limited, and the value range of the variable is [ -1,1]With a function dimension dim of 6The number of independent variables of an objective function is 3, position information is a whole variable search space, the initial position and the initialization speed are 0 and 0.1, each particle in a population independently searches an optimal solution in the search space, the optimal solution is recorded as an individual extreme value of a current particle, the individual extreme value is shared with other particles, the optimal individual extreme value is searched as a current global optimal solution of the whole particle swarm, all the particles in the particle swarm adjust the speed and the position of the particles according to the current individual extreme value found by the particles and the current global optimal solution shared by the whole particle swarm until the optimal solution of the function is found: kn1, ηnAnd λnAnd the determined set of solutions is used as the distribution basis of the operation, under which the distribution adjustment of the actual combustion process is performed, and the actual value of SO2 generated after the combustion distribution adjustment operation is used as the input PV in step four.
Referring to fig. 3, step 3, implementing automatic generation of the set value of SO2, and calculating the set value of SO2 in real time by using a dynamic programming algorithm, specifically as follows:
the set value of SO2 is automatically generated, specifically: in order to prevent the measured value of SO2 from fluctuating beyond the environmental index limit in the dynamic operation process, the initial concentration of SO2 at the time 0 is set to 35mg/Nm3, the current hour is divided into 60 segments according to 1-minute time intervals, and the concentration of SO2 at 0-1min of the current hour is recorded as PV 2so2(k) Setting the forgetting factor sigma at the current momentkDefining the concentration of SO2 as PV during a 1-2min periodso2(k +1) setting the forgetting factor at the corresponding time to sigmak+1And the concentration of SO2 in the 59-60min period is defined as PV by analogy of successive time periodsso2(k +59) setting the forgetting factor at the corresponding time to sigmak+59The actual SO2 concentration values in the 0-n period are weighted and averaged to obtain a whole, the unknown SO2 concentration value at the next time is taken as another part, the concentration value after weighted and averaged is 35, and therefore the set value of the SO2 concentration in the n- (n +1) th min period
Figure RE-GDA0003581227110000071
Where n denotes the time period and n ∈ (1, 60)]。
Step 4, adopting a fuzzy control strategy to adjust P and I parameters of the PID according to the actual working condition, wherein the fuzzy control strategy is specifically implemented as follows: recording the measured value PV of the concentration of SO2 in the step 2 and the set value SP of the concentration of SO2 in the step 3, setting the error e to be SP-PV, and performing differential processing on the error e to obtain delta e as the input of a fuzzy control parameter model, setting the boundary value of the concentration of SO2 to be 35mg/Nm3 based on the actual operation of a site, defining the variation range of the error e to be-5-0, -10-5, -15-10, -20-15, -25-20, and expressing-5, -10, -15, -20 with fuzzy sets of normal (ZO), small Negative (NS), medium Negative (NM) and large Negative (NB); defining the variation range of the deviation variation delta e as 0.2-1, 0.05-0.2, 0-0.05, -0.05-0, -0.2-0.05, -1-0.2, and using the fuzzy set of 0.2, 0.05, 0, -0.05, -0.2: positive large (PB), Positive Small (PS), normal (ZO), Negative Small (NS), negative large (NB) representations; the membership degree function part selects a Gaussian type, so that the membership degrees of the error e at the left side and the right side of the corresponding interval are respectively a and 1-a, and the membership degrees of the error change delta e at the left side and the right side of the corresponding interval are respectively b and 1-b, so that the membership degrees of the output values under the fuzzy rule are respectively a multiplied by b, a multiplied by (1-b), (1-a) multiplied by b, (1-a) multiplied by (1-b); for the control output part, the better effect can be achieved by considering PI control, therefore, the change condition of PID parameters only takes delta Kp and delta Ki, and the change range delta Kp of P is defined as: -1 to-0.5, -0.5 to-0.15, -0.15 to 0, 0 to 0.15, 0.15 to 0.5, 0.5 to 1, wherein-0.5, -0.15, 0, 0.15, 0.5 is represented by fuzzy sets minus size (NB), minus size (NS), normal (ZO), Plus Size (PS), plus size (PB) of Δ Kp, and the variation range Δ Ki of I is defined as: -8 to-3, -3 to-1, -1 to 0, 0 to 1,1 to 3, 3 to 8, and-3, -1, 0, 1, 3 is represented by a fuzzy set of Δ Ki: negative large (NB), Negative Small (NS), normal (ZO), Positive Small (PS), and positive large (PB), and the fuzzy rule is determined according to the actual situation, which is specifically shown in table 1 and table 2; table 1: fuzzy control Δ Kp rule, table 2: fuzzy control delta Ki rule;
TABLE 1 fuzzy control ΔKpRules
Figure RE-GDA0003581227110000072
Table 2: fuzzy control deltaKiRules
Figure RE-GDA0003581227110000081
In operation, controller parameters are automatically calculated to obtain the output Δ Kp, Δ Ki of the fuzzy controller, wherein the calculation of Δ Kp is shown as the following formula:
ΔKp＝a×b×Ra,b+a×(1-b)×Ra,1-b+(1-a)×b×R1-a,b+(1-a)×(1-b)×R1-a,1-b；
in the formula, Ra,bRepresenting the output fuzzy set of input a, b corresponding to the fuzzy rule of delta Kp, Ra,1-bRepresenting the input a, 1-b corresponding to the output fuzzy set under the fuzzy rule of delta Kp, R1-a,bRepresenting that input 1-a, b corresponds to the output fuzzy set under the fuzzy rule of delta Kp, R1-a,1-bIndicating that inputs 1-a, 1-b correspond to output fuzzy sets under the Δ Kp fuzzy rule. Similarly, Δ Ki is calculated in a similar manner.
Dynamically updating Kp to be delta Kp + Kp and Ki to be delta Ki + Ki on the basis of the parameters of the original PID controller Kp and Ki, thereby realizing the self-adaptive adjustment of the PID controller of the desulfurization system.
It should be noted that the above-mentioned embodiments are not intended to limit the scope of the present invention, and all equivalent modifications and substitutions based on the above-mentioned technical solutions are within the scope of the present invention as defined in the claims.

Claims (5)

1. A method for controlling the ultra-clean emission energy-saving optimization of the flue gas of a header boiler is characterized by comprising the following steps:
step 1, carrying out coordinated distribution of boiler combustion through data mining analysis,
step 2, under the premise of the step 1, iterative optimization is carried out by utilizing a Particle Swarm Optimization (PSO) to solve a coefficient solution under the boiler combustion operation: comprises a coal gas weight coefficient, a load sharing coefficient and a coal gas variety proportioning coefficient,
step 3, realizing the automatic generation of the set value of SO2, adopting a dynamic programming algorithm to calculate the set value of SO2 in real time,
and 4, adopting a fuzzy control strategy to adjust the P and I parameters of the PID according to the actual working condition, so that the PID has stronger regulation capability and self-adaptive capability.
2. The ultra-clean emission energy-saving optimization control method for the flue gas of the main-pipe boiler according to claim 1, wherein in the step 1, the coordinated distribution of the boiler combustion is performed through data mining analysis, and the method specifically comprises the following steps:
selecting historical data of different periods under stable working conditions, wherein the historical data comprises total gas consumption C; the coal gas consumption data of each boiler is C1, C2, … … and Cn; load data of each boiler N1, N2, … …, Nn; actual production amount data f (1), f (2), … …, f (n) of each gas furnace SO 2; the load sharing coefficient of each gas furnace is etanThe weight coefficients of the gas quantity of each gas furnace are K11, K21, … … and Kn 1; the weight of the load sharing coefficient K12, K22, … …, Kn 2; the gas variety proportion coefficient of each gas furnace is lambdanThe gas variety proportioning coefficient weights K13, K23, … … and Kn3, parameters need to be adjusted and set according to actual operation conditions, and the minimum value of the gas consumption of each furnace is Cmin (1), Cmin (2), … … and Cmin (n); the maximum value of the gas consumption of each furnace is Cmax (1), Cmax (2), … …, Cmax (n), wherein n represents the number of gas furnaces, and n is {1, 2, 3,, k }; the gas consumption Max and Min needs to be modified by combining actual operation data on the basis of boiler design parameters,
Figure RE-FDA0003581227100000011
η1+η2+…+ηn＝1 (2)
C1+C2+…+Cn＝C (3)
λ1+λ2+…+λn＝1 (4)
Cmin(n)≤Cn≤Cmax(n) (5)
0＜ηn＜1 (6)
0＜λn＜1 (7)。
3. the super-clean emission energy-saving optimization control method for the flue gas of the main-pipe boiler according to claim 2, characterized in that in step 2, iterative optimization is performed by using particle swarm optimization PSO to solve a coefficient solution under the combustion operation of the boiler,
under the limitation of the above formulas (1) to (7) and on the premise of historical data, the particle swarm optimization PSO is used for iterative optimization solution of the coefficient solution under the boiler combustion operation, including the coal gas quantity weight coefficient, the load sharing coefficient and the coal gas variety proportioning coefficient, SO that the generation amount of SO2 is as small as possible, namely
Figure RE-FDA0003581227100000021
Specifically, all parameters of the PSO algorithm are initialized, the maximum iteration number is set to 20000, the acceleration factors c1 are set to 1.4 and c2 are set to 1.4, the inertia weight w is 0.8, the population size sizepop is set to 200, the speed is limited, and the value range of the variable is [ -1,1]The function dimension dim is 6, the number of independent variables of a target function is 3, the position information is the whole variable search space, the initial position and the initialization speed are 0 and 0.1, each particle in a population independently searches an optimal solution in the search space and records the optimal solution as an individual extreme value of the current particle, the individual extreme value is shared with other particles, the optimal individual extreme value is searched as the current global optimal solution of the whole particle swarm, all the particles in the particle swarm adjust the speed and the position of the particle according to the current individual extreme value found by the particle and the current global optimal solution shared by the whole particle swarm until the optimal solution of the function is found: kn1, ηnAnd λnAnd the determined set of solutions is used as the distribution basis of the operation, under which the distribution adjustment of the actual combustion process is performed, and the actual value of SO2 generated after the combustion distribution adjustment operation is used as the input PV in step four.
4. The super-clean emission energy-saving optimization control method for the flue gas of the main-pipe boiler according to claim 3, wherein in the step 3, the set value of SO2 is automatically generated, and a dynamic programming algorithm is adopted to calculate the set value of SO2 in real time, and the method specifically comprises the following steps: the set value of SO2 is automatically generated, specifically: in order to prevent the measured value of SO2 from fluctuating beyond the environmental index limit in the dynamic operation process, the initial concentration of SO2 at the time 0 is set to 35mg/Nm3, the current hour is divided into 60 segments according to 1-minute time intervals, and the concentration of SO2 at 0-1min of the current hour is recorded as PV 2so2(k) Setting the forgetting factor sigma at the current momentkDefining the concentration of SO2 as PV during a 1-2min periodso2(k +1) setting the forgetting factor at the corresponding time to sigmak+1And the concentration of SO2 in the 59-60min period is defined as PV by analogy of successive time periodsso2(k +59) setting the forgetting factor at the corresponding time to sigmak+59The actual SO2 concentration values in the 0-n period are weighted and averaged to obtain a whole, the unknown SO2 concentration value at the next time is taken as another part, the concentration value after weighted and averaged is 35, and therefore the set value of the SO2 concentration in the n- (n +1) th min period
Figure RE-FDA0003581227100000022
Where n denotes the time period and n ∈ (1, 60)]。
5. The super-clean emission energy-saving optimization control method for the flue gas of the main-pipe boiler according to claim 3 or 4, characterized in that step 4, a fuzzy control strategy is adopted, and the P and I parameters of PID are adjusted according to the actual working condition, wherein the fuzzy control strategy is implemented specifically as follows: recording the measured value PV of the concentration of SO2 in the step 2 and the set value SP of the concentration of SO2 in the step 3, setting the error e to be SP-PV, and performing differential processing on the error e to obtain delta e as the input of a fuzzy control parameter model, setting the boundary value of the concentration of SO2 to be 35mg/Nm3 based on the actual operation of a site, defining the variation range of the error e to be-5-0, -10-5, -15-10, -20-15, -25-20, and expressing-5, -10, -15, -20 with fuzzy sets of normal (ZO), small Negative (NS), medium Negative (NM) and large Negative (NB); defining the variation range of the deviation variation delta e as 0.2-1, 0.05-0.2, 0-0.05, -0.05-0, -0.2-0.05, -1-0.2, and using the fuzzy set of 0.2, 0.05, 0, -0.05, -0.2: positive large (PB), Positive Small (PS), normal (ZO), Negative Small (NS), negative large (NB) representations; the membership degree function part selects a Gaussian type, so that the membership degrees of the error e at the left side and the right side of the corresponding interval are respectively a and 1-a, and the membership degrees of the error change delta e at the left side and the right side of the corresponding interval are respectively b and 1-b, so that the membership degrees of the output values under the fuzzy rule are respectively a multiplied by b, a multiplied by (1-b), (1-a) multiplied by b, (1-a) multiplied by (1-b); for the control output part, the better effect can be achieved by considering PI control, therefore, the change condition of PID parameters only takes delta Kp and delta Ki, and the change range delta Kp of P is defined as: -1 to-0.5, -0.5 to-0.15, -0.15 to 0, 0 to 0.15, 0.15 to 0.5, 0.5 to 1, wherein-0.5, -0.15, 0, 0.15, 0.5 is represented by fuzzy sets minus size (NB), minus size (NS), normal (ZO), Plus Size (PS), plus size (PB) of Δ Kp, and the variation range Δ Ki of I is defined as: -8 to-3, -3 to-1, -1 to 0, 0 to 1,1 to 3, 3 to 8, and-3, -1, 0, 1, 3 is represented by a fuzzy set of Δ Ki: negative large (NB), Negative Small (NS), normal (ZO), Positive Small (PS), and positive large (PB), and the fuzzy rule is determined according to the actual situation, which is specifically shown in table 1 and table 2;
table 1: fuzzy control delta pKRules
Figure RE-FDA0003581227100000031
Table 2: fuzzy control deltaKiRules
Figure RE-FDA0003581227100000032
In operation, controller parameters are automatically calculated to obtain the output Δ Kp, Δ Ki of the fuzzy controller, wherein the calculation of Δ Kp is shown as the following formula:
ΔKp＝a×b×Ra,b+a×(1-b)×Ra,1-b+(1-a)×b×R1-a,b+(1-a)×(1-b)×R1-a,1-b；
in the formula, Ra,bRepresenting the output fuzzy set of input a, b corresponding to the fuzzy rule of delta Kp, Ra,1-bRepresenting the input a, 1-b corresponding to the output fuzzy set under the fuzzy rule of delta Kp, R1-a,bRepresenting that input 1-a, b corresponds to the output fuzzy set under the fuzzy rule of delta Kp, R1-a,1-bIndicating that inputs 1-a, 1-b correspond to output fuzzy sets under the Δ Kp fuzzy rule.
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