This application claims priority to Chinese Patent Application No. 202311101016.7 with a filing date of Aug. 29, 2023. The content of the aforementioned application, including any intervening amendments thereto, is incorporated herein by reference.
The present invention relates to the technical field of thermal power unit operation optimization and, in particular, to an intelligent pollution and carbon reduction method based on combustion control and load distribution for a multi-source fuel blending combustion unit.
The blending combustion of low-carbon/zero-carbon fuels such as sludge and biomass can reduce fuel costs for power plants and also reduce carbon emissions from power plants. It has become a key way to deeply reduce carbon emissions in the power/cogeneration industry and has been applied in many power plants at home and abroad. Compared with a traditional coal-fired header-system cogeneration unit, the header-system cogeneration unit with coal-sludge multi-source fuel blending combustion can reduce fuel costs for power plants and maximize the optimal allocation of resources, thereby becoming an effective way to break the siege of sludge and garbage. However, due to the unstable calorific value and different physical and chemical properties of multi-source fuels, it is difficult to establish a stable fuel-load mapping relationship, which limits the economic efficiency of the unit.
In the process of operation and control of thermal power plants, due to the different equipment and operation levels of different power plants and different performance of units, it is necessary to adjust the effective configuration between energy resources and the operating status of the units to comprehensively analyze the optimal operation mode and optimal load distribution plan of units in the plants, and fully tap the economic potential of units in the plants. In addition, taking a cogeneration unit as an example, the traditional operation mode of determining electricity by heat is subject to the constraints of the unit's operating status. In the process of frequent load changes, it may lead to energy waste under low-load conditions and insufficient energy supply under high-load conditions. The flexibility of the unit operation has a large room for improvement, and it is necessary to establish a more flexible unit operation strategy through a more reasonable energy planning method.
The flexible operation of thermal power units puts forward higher requirements on the unit's deep peak regulation, load ramp and rapid start-stop capabilities. How to establish a parameter optimization strategy for a single unit in the face of rapid and large-scale variable load conditions, obtain the optimization instructions of key combustion operation parameters in real time, and make timely and accurate control adjustments to the optimization instructions has become a difficult problem that needs to be solved in a boiler combustion system under the background of flexible operation control. The existing multi-objective combustion optimization based on data mining and intelligent optimization algorithms is still an open-loop optimization method, and it is difficult to achieve the multi-objective optimization of the system under the variable load conditions of the boiler.
In order to overcome the shortcomings of the prior art, the present invention provides a concept of “multi-unit load distribution optimization-single unit combustion control” for flexible control of multi-level and multi-energy flows, establishes an intelligent pollution and carbon reduction method based on combustion control and load distribution, and solves the problems of pollution and carbon reduction at the source of boilers and flexible control of multi-unit operation under complex working conditions such as variable load/variable fuel. Specifically, the intelligent pollution and carbon reduction method based on combustion control and load distribution is manifested as follows: starting from the overall situation of a coal-fired power plant, in view of the load distribution and flexible operation difficulties caused by the variable coal quality and complex fuel characteristics of a multi-source fuel unit, an optimal load distribution and flexible operation method for a multi-source fuel blending combustion unit with economy as an objective is provided to adapt to complex fuels, solve the operation optimization difficulty of the key production process of a multi-source fuel blending combustion cogeneration unit, such as sludge drying-steam distribution, and realize the optimal header-system multi-unit load distribution and flexible operation of a multi-source fuel unit. For single units with variable loads and fuels, existing combustion control methods have the following disadvantages: difficulty in modeling, complex optimization calculations, and difficulty in implementing them under closed-loop conditions. Based on an idea of mechanism analysis, optimization model construction, closed-loop simulation verification and parameter adjustment, that is, by firstly analyzing the mechanism of the key operation parameters of boiler combustion, and in combined with a furnace combustion mechanism, determining the air-coal ratios of auxiliary air, close-coupled over fire air and separated over fire air as decision parameters, and further constructing an optimization model of total air volume optimization for the boiler and stratified air distribution optimization for the boiler to optimize the total air volume and stratified air distribution of the boiler, carrying out parameter setting and closed-loop simulation verification under stable load and variable load conditions, and also adding mechanism correction parameters to an algorithm loop to improve model generalization ability, the present invention achieves the improvement of unit energy efficiency and reduction of pollutant emissions.
The intelligent pollution and carbon reduction method based on combustion control and load distribution uses a data processing layer, a multi-unit load distribution and operation optimization layer and a single-unit boiler multi-objective combustion optimization layer; the data processing layer, the multi-unit load distribution and operation optimization layer and the single-unit boiler multi-objective combustion optimization layer are embedded in a power plant information system in the form of modules, and communicate with the power plant information system in real time;
Preferably, the data clustering submodule is configured to acquire the daily multi-source fuel blending ratio, the calorific value of coal entering the boiler, and the fuel calorific value of sludge and garbage from historical operation data, and cluster the data with the blending ratio and the fuel calorific value as variables, and segment the data into different fuel working conditions in consideration of the characteristics of the clustered data, wherein the calorific values and physical and chemical properties of fuels under the working conditions are similar, and the clustering effect is expressed as follows:
where SC(i) is a clustering effect coefficient; a(i) is an average distance between each data point i and all other data points in the same cluster; b(i) is an average distance between the data point i and all data points in another cluster closest to the data point i.
Preferably, the data delay processing submodule is configured to select, from the historical operation data, a variable load condition segment where the boiler inlet fuel amount changes and the main air flow parameters of primary air and secondary air remain unchanged, analyze the delay of the change of the boiler outlet steam flow and the boiler inlet fuel amount, and eliminate the time difference of response to the data about boiler outlet steam flow and boiler inlet fuel amount in a subsequent process.
Preferably, the data filtering submodule further performs data filtering on the data about the boiler outlet steam flow and the boiler inlet fuel amount after data delay processing, and uses a Savitzky-Golay filter to filter the data in consideration of the data characteristics of the boiler inlet fuel amount to eliminate the high-frequency noise and jitter of a boiler inlet fuel signal.
The multi-unit load distribution optimization model sets start-stop constraints, capacity constraints and load ramp constraints for units with coal consumption economy as an objective, realizes the load distribution optimization of multiple units in an upper structure, and obtains the load distribution optimization plan of each unit with economy as an objective when the output of multiple units is determined; in the multi-unit load distribution optimization with fuel consumption economy as an objective, in order to balance the best economic benefits obtained by power plant operation, steam consumption rate, heat consumption rate, power supply cost, fuel consumption and other indicators are usually used to measure the economy of multi-unit operation. However, since the unit energy output forms include heat, electricity, gas, cold and other energy sources, the operation economy of multiple units cannot be evaluated just by steam consumption rate and heat consumption rate. Similarly, the power supply cost cannot be directly linked to the economy of the cogeneration unit. The fuel cost plays a decisive role in the change of the economic benefits of multiple units. The fuel consumption can directly reflect the economy of units when a power plant operates stably. Therefore, fuel consumption is considered herein as an objective function.
Preferably, the fuel consumption-steam flow model is expressed as:
where, Bi is the fuel consumption of an i-th unit, Fi (χi) is a steam flow function sign, xi is the boiler outlet steam flow of the i-th unit, and ai, bi and ci are correlation coefficients of fuel consumption and boiler outlet steam flow.
Preferably, the objective function of fuel consumption is expressed as:
where, Jeco represents the objective function of fuel consumption, Bi represents the fuel consumption of the i-th unit; xi is the load of the i-th unit, and the load of a circulating fluidized bed unit is characterized by main steam flow rate; n represents the number of units operating in the power plant; Fi(χi) represents the fuel consumption characteristic model of the i-th unit.
Preferably, the setting start-stop constraints, capacity constraints and load ramp constraints for units is to optimize the load distribution of multiple units when the start-stop status of each unit is determined, and also set a unit steam balance constraint, a unit load constraint and a unit load ramp rate constraint; Further preferably, the unit steam balance constraint is expressed as:
where, X is the steam flow of multiple units, and χi is the steam flow of the i-th operating unit.
Further preferably, in order to maintain safe and stable operation of the units, the load of each unit needs to be controlled within a certain range, and the unit load constraint is expressed as:
x
imin
<x
i
<x
imax
where χimin is the minimum load of the i-th operating unit, and χimax is the maximum load of the i-th operating unit.
Further preferably, the unit load ramp rate constraint is expressed as:
where, xit and xi,t−1 represent the main steam flow rates of the i-th unit at time t and the time before time t respectively; Δt represents the time of load change; vi+ and vi− represent the load increase velocity and load decrease velocity of the i-th unit respectively.
Preferably, in the multi-unit flexible operation optimization model, the fuel consumption of multiple units is used as an objective function, but unlike the load distribution optimization model, the total fuel consumption within a period of time is used to measure the objective function of the flexible operation optimization model.
Further preferably, the total fuel consumption of the multiple units within the period of time is expressed as:
J′
eco=min(Σt=1t′BtΔT)
where, Jeco′ represents the total fuel consumption of multiple units in a period of time, t′ represents flexible operation time, Bt represents the total fuel consumption of multiple units at time t, and ΔT represents a unit interval time.
Further preferably, the constraints for optimizing the steam flow of multiple units include a constraint on steam consumption for sludge drying and a constraint on total steam capacity.
Further preferably, the constraint on steam consumption for sludge drying is expressed as:
0<x′t<x′max
where x′t represents the steam consumption for sludge drying at time t, and x′max represents the maximum steam consumption when a sludge dryer is running at full load.
Further preferably, the fuel consumption is reduced by redistributing the steam flow at each time, so that in the same time period, the total steam consumption for drying all the sludge is unchanged before and after optimization, and the constraint on total steam capacity is expressed as:
Q=Q′
where Q represents steam consumption for drying all sludge before optimization, and Q′ represents steam consumption for drying all sludge after optimization.
Preferably, the structure of the adaptive pollution and consumption reduction optimization algorithm is as follows: the first step is to initialize the total steam flow X of multiple units and the start-stop status of multiple units, the second step is to initialize algorithm parameters and particle properties, randomly generate a certain number of particles, and determine the position and velocity of each particle, and calculate particle fitness, the third step is to determine whether the particle fitness meets mutation conditions, and determine the position and velocity of each particle when the particle fitness meets the mutation conditions; the fourth step is to determine the steam flow xi of each unit through the particle fitness calculation, and the fifth step is to output the steam flow xi of each unit when the constraint on steam consumption for sludge drying and the constraint on total steam capacity are met; the sixth step is to return to update the particle velocity and particle position and re-calculate the particle fitness when the constraints are not met.
Further preferably, during an algorithm iteration process, the mutation probability of the algorithm is dynamically adjusted according to the rate of change of an objective function value; when the objective function value changes slowly, the mutation probability is appropriately increased to promote the global search of the algorithm; when the objective function value changes rapidly, the mutation probability is appropriately reduced to prevent the search from falling into the local optimal solution too early; when the search state distribution is even and few local optimal solutions appear, a mutation range is appropriately expanded to promote the global search; when the search state distribution is uneven and multiple local optimal solutions appear, the mutation range is appropriately narrowed to prevent the search from falling into the local optimal solution too early.
Preferably, in the optimization model of total air volume optimization for the boiler and stratified air distribution optimization for the boiler, after the air-coal ratio setting value combined with the AGC (automatic generation control) load instruction is issued, a DCS directly calculates an amount of coal fed, a current boiler inlet air volume is further calculated according to the amount of coal fed, and the boiler inlet volume control of auxiliary air, close-coupled over fire air and separated over fire air is realized by valve control; further calculations are performed to obtain the comprehensive objective function value of current kilowatt-hour coal consumption and NOx emission concentration, and data is transmitted to an ACRAA optimization controller, an ACRCCOFA optimization controller and an ACRSOFA optimization controller to optimize three air-coal ratios online, thus completing a round of online optimization control cycle. In the online optimization loop of the three air-coal ratios, the mechanism correction parameters of the auxiliary air, close-coupled over fire air and separated over fire air are added to achieve parameter fine-tuning of control models under different initial conditions, so as to improve the response speed of the air-coal ratios to the objective functions and make the algorithm converge better. Under multiple combustion conditions, the values of the mechanism correction parameters of auxiliary air, close-coupled over fire air and separated over fire air are between 1 and 1.1, which depends on the NOx emission concentration and coal consumption per kilowatt-hour under the initial conditions. By adding this module, part of the prior mechanism knowledge is integrated into the optimization control method to improve the generalization of the model.
Preferably, in the optimization model of total air volume optimization for the boiler and stratified air distribution optimization for the boiler, fuel price, boiler operation data and simulation data are substituted into a fuel cost model and a denitrification cost model to calculate fuel cost and denitrification cost, and the weight in the comprehensive objective function is further defined from the perspective of cost; the comprehensive objective function value of the coal consumption per kilowatt-hour and the NOx concentration is expressed as:
where, JMESC represents the comprehensive objective function value of coal consumption per kilowatt-hour and NOx concentration, p and q are weight factors of fuel cost and denitrification cost in the comprehensive objective function and satisfy p+q=1; MCCR represents the fuel cost; MNOx represents the denitrification cost.
Further preferably, the coal consumption per kilowatt-hour is expressed as:
wherein, b is the coal consumption per kilowatt-hour of a unit; Bc is the amount of coal fed to the boiler; Qnet,ar is the low calorific value of the coal fed into the boiler; and P is the active power of a unit.
Further preferably, the fuel cost is expressed as:
where, b is the coal consumption per kilowatt-hour of a unit; mc is the fuel price; and MCCR is the fuel cost.
Further preferably, the denitrification cost is expressed as:
where, MNOx is the denitrification cost; CNOχ is the NOχ concentration at the boiler outlet; B is the amount of coal fed; Vgv is the dry flue gas volume; QNH3 is the theoretical amount of ammonia required for NOχ removal; λ is ammonia nitrogen ratio; MNH
Preferably, in the optimization model of stratified air distribution optimization for the boil, the constraints on the air-coal ratio of auxiliary air, the air-coal ratio of close-coupled over fire air, and the air-coal ratio of separated over fire air are:
ψACR
ψACR
ψACR
wherein, ψACR
Preferably, in the optimization model of total air volume optimization for the boiler and stratified air distribution optimization for the boiler, a multi-input dynamic extremum seeking control algorithm is introduced for the problem of stratified air distribution optimization for the boiler, and parameter setting and closed-loop simulation verification are carried out under stable load and variable load conditions. The main parameters of the multi-input dynamic extremum seeking control algorithm include high-pass filter parameter ωh, low-pass filter parameter ωl, adaptive gain parameter k, disturbance amplitudes α, β and disturbance frequency ω, which have a certain influence on the convergence speed, stability and extremum seeking accuracy of the control algorithm. Therefore, on the basis of considering improving the extremum seeking accuracy of the system, the convergence speed and stability of the algorithm are improved by setting the parameters such as high-pass filter parameter ωh, low-pass filter parameter ωl, adaptive gain parameter k, disturbance amplitude α, β and disturbance frequency ω.
Further preferably, during the parameter setting of the multi-input dynamic extremum seeking control algorithm, in the structural design of each input parameter loop, two control structures that adapt to a variation range of the comprehensive objective function are designed. In actual control, the specific control structure can also be selected on the basis of prior mechanism knowledge and initial combustion state parameters.
The present invention has the following beneficial effects:
In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings required for use in the embodiments are briefly introduced below.
In order to more clearly illustrate the technical solution of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments, but the scope of the present invention is not limited thereto. In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. It should be understood that the implementation of the present invention is not limited to the following embodiments, and any formal modifications and/or changes made to the present invention will fall within the scope of the present invention.
For sludge-combustion circulating fluidized bed cogeneration units 0 #-5 #, an intelligent pollution and carbon reduction system and method based on combustion control and load distribution are provided.
Referring to
The data clustering submodule is configured to acquire the daily multi-source fuel blending ratio, the calorific value of coal entering the boiler, and the fuel calorific value of sludge and garbage from historical operation data, and cluster the data with the blending ratio and the fuel calorific value as variables, and segment the data into different fuel working conditions in consideration of the characteristics of the clustered data, wherein the calorific values and physical and chemical properties of fuels under the working conditions are similar, and the clustering effect is expressed as follows:
where SC(i) is a clustering effect coefficient; a(i) is an average distance between each data point i and all other data points in the same cluster; b(i) is an average distance between the data point i and all data points in another cluster closest to the data point i.
K-means clustering is performed on the selected historical data of blending and coal calorific value based on different cluster numbers. When the number of clusters is 5, the silhouette coefficient is the highest, reaching 0.77707. In this case, the fuel characteristics distribution is divided into 5 clusters. The fuel characteristics of the data points within each cluster are similar. Relatively speaking, clustering can be used to obtain a more stable calorific value of fuel fed in the boiler. In view of this, follow-up research is carried out based on the historical operation data of the center data of the 5th cluster, where the blending ratio is about 1.15 and the coal calorific value is 24688 KJ/kg.
The data delay processing submodule is configured to select, from the historical operation data, a variable load condition segment where the boiler inlet fuel amount changes and the main air volume parameters of primary air and secondary air remain unchanged, analyze the delay of the change of the boiler outlet steam flow and the boiler inlet fuel amount, and eliminate the time difference of response to the data about boiler outlet steam flow and boiler inlet fuel amount in a subsequent process.
As shown in
The delay analysis of other units is shown in Table 1. Similarly, in the subsequent construction process of the fuel consumption-steam flow model, the historical data of each unit needs to be delayed.
The data filtering submodule further performs data filtering on the data about the boiler outlet steam flow and the boiler inlet fuel amount after data delay processing, and uses a Savitzky-Golay filter to filter the data in consideration of the data characteristics of the boiler inlet fuel amount to eliminate the high-frequency noise and jitter of a boiler inlet fuel signal.
The multi-unit load distribution optimization model sets start-stop constraints, capacity constraints and load ramp constraints for units with coal consumption economy as an objective, realizes the load distribution optimization of multiple units in an upper structure, and obtains the load distribution optimization plan of each unit with economy as an objective when the output of multiple units is determined; in the multi-unit load distribution optimization with fuel consumption economy as an objective, in order to balance the best economic benefits obtained by power plant operation, steam consumption rate, heat consumption rate, power supply cost, fuel consumption and other indicators are usually used to measure the economy of multi-unit operation. However, since the unit energy output forms include heat, electricity, gas, cold and other energy sources, the operation economy of multiple units cannot be evaluated just by steam consumption rate and heat consumption rate. Similarly, the power supply cost cannot be directly linked to the economy of the cogeneration unit. The fuel cost plays a decisive role in the change of the economic benefits of multiple units. The fuel consumption can directly reflect the economy of units when a power plant operates stably. Therefore, fuel consumption is considered herein as an objective function.
The fuel consumption-steam flow model is expressed as:
where, Bi is the fuel consumption of an i-th unit, Fi (χi) is a steam flow function sign, χi is the boiler outlet steam flow of the i-th unit, and ai, bi and ci are correlation coefficients of fuel consumption and boiler outlet steam flow.
The correlation coefficients in the fuel consumption-steam flow model of different units are shown in Table 2.
The structure of the adaptive pollution and consumption reduction optimization algorithm is as follows: the first step is to initialize the total steam flow X of multiple units and the start-stop status of multiple units, the second step is to initialize algorithm parameters and particle properties, randomly generate a certain number of particles, and determine the position and velocity of each particle, and calculate particle fitness, the third step is to determine whether the particle fitness meets mutation conditions, and determine the position and velocity of each particle when the particle fitness meets the mutation conditions; the fourth step is to determine the steam flow xi of each unit through the particle fitness calculation, and the fifth step is to output the steam flow xi of each unit when the constraint on steam flow consumed for sludge drying and the constraint on total steam flow are met; the sixth step is to return to update the particle velocity and particle position and re-calculate the particle fitness when the constraints are not met.
The algorithm solution stability of the adaptive pollution and consumption reduction optimization algorithm under different load conditions is shown. Through 10 repeated experiments, the optimization stability of the algorithm under various load conditions such as 700 t/h, 800 t/h, 900 t/h and 1000 t/h is explored. The results are shown in Table 3. The results show that a small standard deviation of fuel consumption can be figured out under different load conditions, further demonstrating the stability of the constructed optimization model in the load distribution optimization problem.
As shown in
The multi-unit flexible operation optimization model redistributes the steam flow involved in sludge drying, thereby obtaining the minimum total fuel consumption of multiple units within the time period. Therefore, unlike the load distribution optimization model, the multi-unit flexible operation optimization model prioritizes the optimization of the total steam flow of multiple units. The optimization process is as follows:
As shown in
As shown in
In the optimization model of total air volume optimization for the boiler and stratified air distribution optimization for the boiler, fuel price, boiler operation data and simulation data are substituted into a fuel cost model and a denitrification cost model to calculate fuel cost and denitrification cost, and the weight in the comprehensive objective function is further defined from the perspective of cost; the comprehensive objective function value of the coal consumption per kilowatt-hour and the NOx concentration is expressed as:
where, JMESC represents the comprehensive objective function value of coal consumption per kilowatt-hour and NOx concentration, p and q are weight factors of fuel cost and denitrification cost in the comprehensive objective function and satisfy p+q=1; MCCR represents the fuel cost; MNOx represents the denitrification cost.
The coal consumption per kilowatt-hour is expressed as:
wherein, b is the coal consumption per kilowatt-hour of a unit; Bc is the amount of coal fed to the boiler; Qnet,ar is the low calorific value of the coal fed into the boiler;
and P is the active power of a unit.
The fuel cost is expressed as:
where, b is the standard coal consumption per kilowatt-hour of a unit; mc is the fuel price; and MCCR is the fuel cost.
The denitrification cost is expressed as:
where, MNOx is the denitrification cost; CNOx is the NOχ concentration at the boiler outlet; B is the amount of coal fed; Vgv is the dry flue gas volume; QNH3 is the theoretical amount of ammonia required for NOχ removal; λ is ammonia nitrogen ratio; MNH
The constraints on the air-coal ratio of auxiliary air, the air-coal ratio of close-coupled over fire air and the air-coal ratio of separated over fire air are:
ψACR
ψACR
ψACR
wherein, ψACR
In the optimization model of total air volume optimization for the boiler and stratified air distribution optimization for the boiler, a multi-input dynamic extremum seeking control algorithm is introduced for the problem of stratified air distribution optimization for the boiler, and parameter setting and closed-loop simulation verification are carried out under stable load and variable load conditions. The main parameters of the multi-input dynamic extremum seeking control algorithm include high-pass filter parameter ωh, low-pass filter parameter ωl, adaptive gain parameter k, disturbance amplitudes α, β and disturbance frequency ω, which have a certain influence on the convergence speed, stability and extremum seeking accuracy of the control algorithm. Therefore, on the basis of considering improving the extremum seeking accuracy of the system, the convergence speed and stability of the algorithm are improved by setting the parameters such as high-pass filter parameter ωh, low-pass filter parameter ωl, adaptive gain parameter k, disturbance amplitude α, β and disturbance frequency ω.
Table 4 shows the main parameters of the multi-input extremum seeking control algorithm. During the parameter setting of the multi-input dynamic extremum seeking control algorithm, in the structural design of each input parameter loop, two control structures that adapt to a variation range of the comprehensive objective function are designed. In actual control, the specific control structure can also be selected on the basis of prior mechanism knowledge and initial combustion state parameters. It should be noted that the selection and combination of specific control structures will not cause adverse guidance on the convergence of the algorithm. This is because the three air-coal ratio parameters are highly coupled to each other and have different effects on the comprehensive objective function under different load conditions. In the solving process of extremum seeking, different air-coal ratio parameter solution sets can be searched in the solution space to meet the requirements of extremum optimization of the comprehensive objective function.
As shown in
At t3, a load reduction instruction is given, and the system slowly reduces the load from 1000 MW to 950 MW and converges for ACRAA, ACRCCOFA, and ACRSOFA at 2.40, 1.38, and 2.31, respectively. The CCR decreased accordingly with the decrease of boiler load in a high load range, and is affected by the change of air volume, and stabilized to 277.81 g/kWh at t4.
Similarly, after the system quickly reduces the load from 950 MW to 900 MW during a period of t5 to t6, the comprehensive objective function value JMESC decreases to 270.91, but unlike what happened at t3, the load change rate at t5 is twice the original by setting parameters for the model.
At t7, a load increase instruction is given, and the rate is twice that at t5. The system quickly increases the load from 900 MW to 1000 MW, and converges for ACRAA, ACRCCOFA, and ACRSOFA at 1.83, 1.37, and 2.60, respectively. At this time, the CCR reaches the optimal value of 280.80 g/kWh under the current load condition, and the NOx concentration drops to 246.32 mg/m3. After multiple rounds of online optimization, the comprehensive objective function value JMESC at t8 is lower than that under the initial condition and under the optimized condition, reaching 279.04 g/kWh, and a good multi-objective optimization effect is obtained.
The optimization results are shown in Table 5. The total air volume does not change greatly before and after the four rounds of optimization. The reduction of the comprehensive objective function value is achieved by optimizing the stratified air distribution for the boiler. In the three rounds of optimization under variable load conditions with different rates, load increase and decrease at different rates have little effect on an ACR solution set, and only the ACRAA is adjusted in a small range, showing the high stability of the model. In addition, in this embodiment, in order to improve the convergence ability of the model under variable load conditions, the mechanism correction parameter of loop 1, i.e., an ACRAA loop, is adjusted from 1 to 1.05. The results shows that after multiple rounds of online optimization, the comprehensive objective function value JMESC at t8 is lower than that under the initial condition and under the optimized condition, reaching 279.04, and a good multi-objective optimization effect is obtained, and the positive role of the mechanism correction parameter in improving the convergence ability of the model is further verified.
The actual application results show that under complex combustion conditions such as high coal consumption per kilowatt-hour and high NOx, low coal consumption kilowatt-hour and high NOx, and high coal consumption kilowatt-hour and low NOx, the unit's coal consumption per kilowatt-hour and NOx emission concentration are both reduced, with the maximum reduction reaching 9 g/kWh and 120 mg/m3 respectively. The model has good stability and high adaptive optimization capability under variable load conditions and coal quality disturbance conditions.
The invention is described in detail above in combination with the embodiments, but the contents described are only specific embodiments of the invention, and cannot be understood as limiting the scope of the invention. It should be pointed out that for those of ordinary skills in the art, without departing from the concept of the invention, all modifications and improvements made according to the application scope of the invention should still fall within the scope of the invention.
Number | Date | Country | Kind |
---|---|---|---|
202311101016.7 | Aug 2023 | CN | national |