A boiler air-coal ratio control optimization method and system

Abstract

The present invention discloses a method and system for optimizing the control of boiler air-coal ratio, and relates to the technical field of boiler combustion control. The method includes: interacting with target boilers, obtaining boiler operating parameters, calculating the actual air-coal ratio of each combustion zone, and obtaining the target air-coal ratio distribution according to boiler load and coal quality parameters; optimizing with the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization target, generating the optimal primary air parameters and the optimal secondary air parameters, and generating the air-coal ratio control strategy for each combustion zone, and sending the air-coal ratio control strategy to the distributed control unit for coordinated adjustment to achieve balanced combustion of the boiler. The present invention solves the technical problem that the air-coal ratio control effect of the boiler is poor in the prior art, which affects the combustion stability and thermal efficiency, and achieves the technical effect of significantly improving the air-coal ratio control effect, improving the combustion stability, and improving the overall thermal efficiency of the boiler.

Description

Technical Field

[0001] The present invention relates to the technical field of boiler combustion control, and in particular to a method and system for optimizing the control of boiler air-coal ratio. Background Art

[0002] With the continuous growth of energy demand and increasingly stringent environmental protection requirements, higher requirements are placed on boiler combustion efficiency. The traditional air-coal ratio control method is often difficult to adapt to complex and changeable operating conditions, resulting in incomplete combustion, low efficiency and other problems.

[0003] The existing technology has the technical problem that the boiler air-coal ratio control effect is poor, which affects the combustion stability and thermal efficiency. Summary of the invention

[0004] The present application provides a method and system for optimizing the control of boiler air-coal ratio, which is used to solve the technical problem in the prior art that the control effect of boiler air-coal ratio is poor, affecting combustion stability and thermal efficiency.

[0005] In view of the above problems, the present application provides a method and system for optimizing the control of boiler air-coal ratio.

[0006] In a first aspect of the present application, a method for optimizing the control of air-coal ratio of a boiler is provided, the method comprising:

[0007] An interactive target boiler obtains boiler operating parameters, including boiler load, coal quality parameters, primary air parameters and secondary air parameters, wherein the primary air parameter is primary air volume, and the secondary air parameter is secondary air volume; according to the boiler operating parameters, the actual air-coal ratio of each combustion zone is calculated to obtain the actual air-coal ratio distribution; according to the boiler load and coal quality parameters, the target air-coal ratio of each combustion zone is determined to obtain the target air-coal ratio distribution; with the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization goal, the primary air parameters and the secondary air parameters are optimized to generate the optimal primary air parameters and the optimal secondary air parameters; according to the optimal primary air parameters and the optimal secondary air parameters, an air-coal ratio control strategy for each combustion zone is generated, and the air-coal ratio control strategy includes a primary air control curve and a secondary air control curve; the air-coal ratio control strategy is sent to the distributed control unit, and the distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio control strategy to achieve balanced combustion of the boiler.

[0008] The second aspect of the present application provides a remote intelligent control system for a water conservancy gate, the system comprising:

[0009] A boiler operation parameter acquisition module, which is used to interact with the target boiler and acquire boiler operation parameters, including boiler load, coal quality parameters, primary air parameters and secondary air parameters, wherein the primary air parameter is the primary air volume, and the secondary air parameter is the secondary air volume; an actual air-coal ratio distribution acquisition module, which is used to calculate the actual air-coal ratio of each combustion zone according to the boiler operation parameters, and obtain the actual air-coal ratio distribution; a target air-coal ratio distribution acquisition module, which is used to determine the target air-coal ratio of each combustion zone according to the boiler load and coal quality parameters, and obtain the target air-coal ratio distribution; a parameter optimization module, which is used to obtain the target air-coal ratio of each combustion zone according to the actual air-coal ratio distribution The optimization goal is to minimize the deviation from the target air-coal ratio distribution, and to optimize the primary air parameters and the secondary air parameters to generate the optimal primary air parameters and the optimal secondary air parameters; an air-coal ratio control strategy generation module, the air-coal ratio control strategy generation module is used to generate the air-coal ratio control strategy for each combustion zone according to the optimal primary air parameters and the optimal secondary air parameters, and the air-coal ratio control strategy includes a primary air control curve and a secondary air control curve; an air-coal ratio control strategy control module, the air-coal ratio control strategy control module is used to send the air-coal ratio control strategy to the distributed control unit, and the distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio control strategy to achieve balanced combustion of the boiler.

[0010] In a third aspect of the present application, the present application provides an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute a boiler air-coal ratio control optimization method provided in the present application.

[0011] In a fourth aspect of the present application, the present application provides a computer-readable storage medium storing a computer program, and the computer program is used to execute a boiler air-coal ratio control optimization method provided in the present application.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] Interactive target boiler, obtain boiler operating parameters, calculate the actual air-coal ratio of each combustion zone according to the boiler operating parameters, obtain the actual air-coal ratio distribution, obtain the target air-coal ratio distribution according to the boiler load and coal quality parameters, and take the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization goal, optimize the primary air parameters and secondary air parameters, generate the optimal primary air parameters and the optimal secondary air parameters, and generate the air-coal ratio control strategy for each combustion zone, and send the air-coal ratio control strategy to the distributed control unit. The distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio control strategy to achieve balanced combustion of the boiler. The technical effect of significantly improving the air-coal ratio control effect, improving combustion stability, and improving the overall thermal efficiency of the boiler has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A schematic diagram of a flow chart of a method for optimizing the air-coal ratio control of a boiler provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of a flow chart for obtaining actual air-coal ratio distribution in a boiler air-coal ratio control optimization method provided in an embodiment of the present application;

[0017] Figure 3 A schematic diagram of the structure of a boiler air-coal ratio control optimization system provided in an embodiment of the present application;

[0018] Figure 4 A schematic diagram of the structure of an electronic device provided in this application.

[0019] Explanation of the accompanying drawings: boiler operation parameter acquisition module 10, actual air-coal ratio distribution acquisition module 20, target air-coal ratio distribution acquisition module 30, parameter optimization module 40, air-coal ratio regulation strategy generation module 50, air-coal ratio regulation strategy control module 60, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0020] The present application provides a method and system for optimizing the control of the air-coal ratio of a boiler, which is used to solve the technical problem in the prior art that the air-coal ratio control effect of the boiler is poor, affecting the combustion stability and thermal efficiency.

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] Embodiment 1, as Figure 1 As shown, the present application provides a method for optimizing the control of boiler air-coal ratio, the method comprising:

[0023] Step S100: Interact with the target boiler to obtain boiler operating parameters, including boiler load, coal quality parameters, primary air parameters and secondary air parameters, wherein the primary air parameter is the primary air volume, and the secondary air parameter is the secondary air volume.

[0024] Specifically, information interaction with the target boiler refers to establishing contact or exchanging information with a specific boiler. By interacting with the target boiler, a series of data related to the boiler's operating status can be collected. Boiler load refers to the amount of work tasks or output power currently undertaken by the boiler, reflecting the boiler's working intensity. Coal quality parameters involve various characteristic indicators of the coal used, such as calorific value, ash content, volatile matter, etc. These parameters have an important impact on the combustion process and boiler performance. The primary air parameter is the primary air volume. The primary air mainly undertakes the important task of blowing the coal powder into the furnace smoothly, and also provides some oxygen required in the initial stage of combustion. The primary air volume is like the force that pushes the coal powder. If the air volume is too large, the coal powder may be blown too dispersed, which is not conducive to combustion; if the air volume is too small, the coal powder may not be able to reach the furnace smoothly or burn incompletely. The secondary air parameter is the secondary air volume. The secondary air further provides oxygen during the combustion process to help the fuel burn more completely. Reasonable control of the secondary air volume is very critical for adjusting the intensity of combustion, flame shape, combustion efficiency, etc. By accurately mastering and regulating the primary and secondary air parameters, the boiler's combustion process can be optimized, ensuring safe and stable operation of the boiler, improving combustion stability, and increasing the overall thermal efficiency of the boiler.

[0025] Step S200: Calculate the actual air-coal ratio of each combustion zone according to the boiler operating parameters to obtain the actual air-coal ratio distribution.

[0026] Specifically, the actual air-coal ratio of each combustion zone is calculated according to the boiler operating parameters and the actual air-coal ratio distribution is obtained. First, the various combustion zones of the boiler are identified, and special measuring instruments are used to accurately obtain the primary air volume, secondary air volume and corresponding coal volume data of each combustion zone. For each combustion zone, the measured primary air volume and secondary air volume are added together to obtain the total air volume. The total air volume is then divided by the coal volume in the area to calculate the actual air-coal ratio of each combustion zone. The actual air-coal ratios of all combustion zones are comprehensively analyzed and compared, so as to clearly grasp the specific distribution of the air-coal ratio in the entire boiler furnace. Based on this information, the combustion efficiency and operating status of the boiler are deeply evaluated, and targeted adjustments and optimizations are made according to actual needs, ultimately achieving improved combustion stability and thermal efficiency.

[0027] Step S300: Determine the target air-coal ratio of each combustion zone according to the boiler load and coal quality parameters, and obtain the target air-coal ratio distribution.

[0028] Specifically, based on the boiler load and coal quality parameters, the combustion state parameters of each combustion zone are determined, such as burnout rate, ignition temperature and combustion temperature, etc. These parameters can be obtained through experimental measurement. According to the combustion state parameters of each combustion zone, the corresponding air-coal ratio model is selected from the preset air-coal ratio model library. The air-coal ratio model is obtained by analyzing and fitting a large amount of experimental data or historical operation data, and describes the relationship between the combustion state parameters and the optimal air-coal ratio. The combustion state parameters of each combustion zone are input into the corresponding air-coal ratio model to obtain the target air-coal ratio of each combustion zone, thereby obtaining the target air-coal ratio distribution, and realizing balanced combustion and efficient operation of the boiler.

[0029] Step S400: Taking the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization goal, the primary air parameters and the secondary air parameters are optimized to generate the optimal primary air parameters and the optimal secondary air parameters.

[0030] Specifically, the actual air-coal ratio distribution is calculated by the current air-coal measurement data of each combustion zone of the boiler, reflecting the air-coal ratio in the actual operation of the boiler; the target air-coal ratio distribution is the ideal air-coal ratio distribution determined according to the boiler load and coal quality parameters. The optimization goal is to minimize the deviation and try to make the actual air-coal ratio distribution close to the target air-coal ratio distribution. It is necessary to adjust and optimize the primary air parameters (such as primary air volume, wind speed, etc.) and secondary air parameters (such as secondary air volume, wind speed, etc.). In the optimization process, the optimization algorithm can be used to continuously try different combinations of primary air parameters and secondary air parameters within a certain range, calculate the deviation value between the actual air-coal ratio distribution and the target air-coal ratio distribution under each combination, and finally find the set of primary air parameters and secondary air parameters that minimize the deviation after a lot of calculations and comparisons. This is the optimal primary air parameter and the optimal secondary air parameter. Through the optimization process, the air-coal ratio in the boiler combustion process can be accurately controlled to improve the combustion efficiency and stability.

[0031] Step S500: Generate an air-coal ratio control strategy for each combustion zone according to the optimal primary air parameters and the optimal secondary air parameters, wherein the air-coal ratio control strategy includes a primary air control curve and a secondary air control curve.

[0032] Specifically, based on the optimal primary air parameters and combined with the characteristics of the specific combustion zone, the supply volume and supply rate of the primary air in different operating stages of the combustion zone are accurately calculated. Through data analysis and model simulation, the specific values ​​of the primary air under various working conditions are determined, so as to draw a detailed and accurate primary air control curve, which clearly indicates how the primary air should be adjusted in different combustion scenarios to achieve the best effect. For the optimal secondary air parameters, a similar method is also adopted, fully considering the special requirements of the combustion zone and the synergistic relationship with the primary air, determining the reasonable supply volume and distribution method of the secondary air under different conditions, and constructing a secondary air control curve through in-depth research and repeated verification, so that it can effectively cooperate with the primary air to achieve the optimization of the entire combustion process. In the process of generating the control strategy, the following factors must also be considered comprehensively: temperature distribution in the combustion zone, combustion speed of the fuel, airflow distribution in the furnace, etc. At the same time, experiments and tests should be carried out continuously, and the control curve should be continuously corrected and improved according to the data feedback of actual operation to ensure its accuracy and adaptability.

[0033] Step S600: Send the air-coal ratio control strategy to the distributed control unit, and the distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio control strategy to achieve balanced combustion of the boiler.

[0034] Specifically, the generated detailed air-coal ratio control strategy is accurately transmitted to each distributed control unit through a specific communication channel. These distributed control units are distributed in different key positions of the boiler system and bear the important responsibility of accurately controlling local equipment. When the distributed control unit receives the air-coal ratio control strategy, it accurately adjusts the opening, flow and other parameters of the primary air valve of each combustion zone according to the primary air control curve specified in the control strategy. In this way, it is ensured that the supply volume and supply speed of the primary air are highly matched with the combustion demand, providing suitable conditions for the combustion of the fuel; for the secondary air valve, according to the secondary air control curve, coordinate its opening degree, wind direction, etc., so that the secondary air can effectively cooperate with the primary air according to the requirements of the strategy, promote the combustion, enhance the combustion effect, and improve the combustion efficiency. During the entire adjustment process, the distributed control unit continuously monitors and feedbacks the actual operation data, and compares and analyzes it in real time with the air-coal ratio control strategy. If any deviation or abnormality occurs, it will be fine-tuned in time to ensure that the boiler always maintains a state of balanced combustion. Through this coordinated adjustment method, the primary air valve and the secondary air valve of each combustion zone can cooperate closely to achieve balanced combustion of the boiler according to the specific air-coal ratio control strategy.

[0035] In one possible implementation, Figure 2 , step S200 further includes:

[0036] Step S210: Divide the boiler combustion zone into multiple combustion zones, each combustion zone is provided with an air-coal ratio detection unit, the air-coal ratio detection unit includes a primary air volume sensor, a secondary air volume sensor and a coal volume sensor, and the multiple combustion zones have a regional topological relationship.

[0037] Step S220: In each combustion zone, the primary air volume, secondary air volume and coal volume of each combustion zone are respectively obtained through the primary air volume sensor, the secondary air volume sensor and the coal volume sensor.

[0038] Step S230: Calculate the actual air-coal ratio of each combustion zone based on the primary air volume, secondary air volume and coal volume of each combustion zone.

[0039] Step S240: Based on the actual air-coal ratio of each combustion zone and the regional topological relationship, obtain the actual air-coal ratio distribution.

[0040] Specifically, the boiler combustion zone is carefully divided into multiple combustion zones in order to more accurately monitor and control the combustion conditions in different zones. Each combustion zone is equipped with a special air-coal ratio detection unit, which includes a primary air volume sensor, a secondary air volume sensor and a coal volume sensor. This configuration ensures that the key parameters of the combustion zone can be fully acquired. The regional topological relationship refers to the spatial relationship between multiple combustion zones, which describes the relative position and connection method of the combustion zones. The regional topological relationship will affect the processes such as airflow, heat transfer and chemical reaction in the combustion zone. By understanding and analyzing the regional topological relationship of the combustion zone, the combustion system can be better designed and optimized to improve combustion efficiency.

[0041] The primary air volume sensors, secondary air volume sensors and coal volume sensors in each combustion zone are used to accurately collect the primary air volume, secondary air volume and coal volume data of each combustion zone. These sensors monitor and feedback data in real time, providing timely and accurate original information for subsequent calculations and analysis.

[0042] For each combustion zone, add the primary air volume and the secondary air volume to get the total air volume, and divide the total air volume by the coal volume to get the actual air-coal ratio of the combustion zone. The specific formula can be expressed as: actual air-coal ratio = (primary air volume + secondary air volume) ÷ coal volume.

[0043] The actual air-coal ratio calculated for each combustion zone is combined with the regional topological relationship between them. For example, each combustion zone is regarded as a node, the actual air-coal ratio is an attribute value of the node, and the regional topological relationship is like the lines connecting these nodes, building a network-like structure. By integrating this information, the distribution of the actual air-coal ratio in different areas of the entire boiler combustion zone can be clearly presented. Such actual air-coal ratio distribution is crucial for a comprehensive understanding of the overall status of boiler combustion, and can help operators or control systems quickly discover possible imbalances or abnormal areas, so as to make targeted adjustments and optimizations.

[0044] In a possible implementation, step S300 further includes:

[0045] Step S310: Based on the boiler load and coal quality parameters, the combustion state parameters of each combustion zone are determined, wherein the combustion state parameters include burnout rate, ignition temperature and combustion temperature.

[0046] Step S320: According to the combustion state parameters of each combustion zone, a corresponding air-coal ratio model is selected from a preset air-coal ratio model library, the input parameters of the air-coal ratio model include boiler load, coal quality parameters and combustion state parameters, and the output parameter is the optimal air-coal ratio.

[0047] Step S330: input the combustion state parameters of each combustion zone into the corresponding air-coal ratio model to obtain the target air-coal ratio of each combustion zone.

[0048] Step S340: generating a target air-coal ratio distribution according to the target air-coal ratio of each combustion zone and the regional topological relationship.

[0049] Specifically, the combustion state parameters of each combustion zone are determined by comprehensively considering the boiler load and coal quality parameters. The boiler load reflects the working intensity of the boiler and is determined by monitoring parameters such as steam flow and pressure, while the coal quality parameters are related to the characteristics of coal, including volatile matter, calorific value, ash content, moisture and other parameters, which affect the combustion characteristics of coal. Use coal quality parameters and boiler load data to establish a combustion model. These models can be based on empirical formulas, experimental data or numerical simulations, input coal quality parameters and boiler load into the combustion model, and calculate the combustion state parameters such as burnout rate, ignition temperature and combustion temperature of each combustion zone. The boiler has multiple combustion zones, and the differences between the zones need to be considered, such as the layout of the burner, air volume distribution and other factors, and the combustion state parameters need to be appropriately corrected. The calculated combustion state parameters need to be verified and adjusted. The accuracy of the calculation results is verified through actual combustion tests, monitoring data or comparison with empirical values, and necessary adjustments are made.

[0050] The combustion state parameters of each combustion zone are compared one by one with the typical combustion state parameters corresponding to each model in the air-coal ratio model library. When selecting the air-coal ratio model from the air-coal ratio model library, the Euclidean distance between the combustion state parameters of each combustion zone and the typical combustion state parameters corresponding to each model in the air-coal ratio model library is calculated, and the air-coal ratio model with the smallest distance is selected as the target air-coal ratio calculation model for the combustion zone. Exemplarily, assuming that the combustion state parameters of combustion zone A are a set of specific values, then calculate its Euclidean distance with the typical combustion state parameters of model 1 in the model library, the Euclidean distance with model 2, and so on. If the Euclidean distance with model 3 is the smallest, it means that the typical combustion state parameters of model 3 are closest to the state of combustion zone A, so model 3 is selected as the target air-coal ratio calculation model for combustion zone A.

[0051] After the air-coal ratio model corresponding to each combustion zone is determined, the specific combustion state parameters are inputted, and the actual combustion state information is passed to the model, so that the model can accurately calculate the most suitable air-coal ratio for the combustion zone, i.e. the target air-coal ratio, according to its internally set algorithm and logic. This process reflects the precise control and optimization of the combustion process, which helps to achieve efficient, stable and clean combustion in each combustion zone, thereby improving the overall operating performance and efficiency of the boiler.

[0052] Based on the known topological relationships between the combustion zones, such as adjacent, relative, and interval, combined with the target air-coal ratio value of each combustion zone, for those combustion zones that have a large impact on each other and are closely related during the combustion process, their air-coal ratios should be adapted to each other to achieve the optimization of the overall combustion effect. According to factors such as the location importance of the combustion zone and the distribution of combustion load, the target air-coal ratio is reasonably adjusted and redistributed, so that each combustion zone can obtain an air-coal ratio that suits its own situation and meets the requirements of the overall combustion layout, thereby ensuring efficient, stable and safe boiler combustion.

[0053] In a possible implementation, step S320 further includes:

[0054] Step S321: Collect historical operation data of the target boiler, wherein the historical operation data includes multiple groups of historical data samples, each group of historical data samples includes boiler load, coal quality parameters, combustion state parameters, and corresponding optimal air-coal ratio.

[0055] Step S322: performing cluster analysis on the multiple groups of historical data samples according to the combustion state parameters to obtain multiple historical sample sets.

[0056] Step S323: Based on multiple historical sample sets, with boiler load, coal quality parameters and combustion state parameters as input features and the optimal air-coal ratio as output target, multiple air-coal ratio models are trained and obtained.

[0057] Step S324: adding a plurality of air-coal ratio models to the air-coal ratio model library.

[0058] Specifically, the past operating data of the target boiler is comprehensively obtained. Multiple groups of samples of historical operating data can provide rich information. The boiler load reflects the load degree of the boiler when it is working, which directly affects the energy demand and combustion state. Coal quality parameters such as coal composition and calorific value have an important influence on the combustion effect and characteristics. The combustion state parameters cover key indicators such as flame conditions and temperature distribution, reflecting the actual combustion conditions. The corresponding optimal air-coal ratio is the ratio that achieves the best combustion effect under specific conditions. It is a key data verified by practice and is crucial for subsequent model establishment and optimization.

[0059] Cluster analysis is a method of grouping data objects so that objects in the same group are similar to each other, while objects in different groups are different from each other. Through cluster analysis, historical data samples can be divided into different categories, each category represents a specific combustion state mode. In this step, the combustion state parameters are used to perform cluster analysis on multiple groups of historical data samples, and similar samples are classified into the same category, thereby obtaining multiple historical sample sets.

[0060] For each determined historical sample set, the boiler load, coal quality parameters and combustion state parameters are sorted and preprocessed so that they can be effectively used by the model. These input features can be converted into a suitable data format, such as a numerical vector, etc., and a suitable machine learning or deep learning algorithm, such as a neural network, a decision tree, etc., is selected to establish a mapping relationship between the input features and the optimal air-coal ratio. During the training process, the algorithm will continuously adjust the parameters of the model to minimize the error between the predicted air-coal ratio and the actual optimal air-coal ratio. Through a large amount of training data and iterations, the model gradually learns the inherent rules between the input features and the optimal air-coal ratio. For example, when using a neural network, a network structure containing multiple neurons can be constructed, with the boiler load, coal quality parameters and combustion state parameters as nodes of the input layer, and information processing and transmission are performed through the middle hidden layer, and finally the predicted optimal air-coal ratio is obtained in the output layer. By training for multiple historical sample sets separately, multiple air-coal ratio models with specific targets can be obtained to meet the needs under different boiler operating conditions.

[0061] Adding multiple trained air-coal ratio models to the air-coal ratio model library allows the appropriate model to be called according to the specific situation in actual applications, providing a variety of choices that can better adapt to different operating scenarios and changing conditions, and improving the flexibility and accuracy of air-coal ratio control during boiler combustion. By continuously enriching and improving this model library, the optimization level of boiler operation can be continuously improved.

[0062] In a possible implementation, step S340 further includes:

[0063] Step S341: setting an air-coal ratio difference constraint matrix according to the regional topological relationship of multiple combustion zones, wherein the air-coal ratio difference constraint matrix is ​​an upper and lower limit constraint matrix of the target air-coal ratio difference between adjacent combustion zones.

[0064] Step S342: Substitute the target air-coal ratio of each combustion zone in the target air-coal ratio distribution into the air-coal ratio difference constraint matrix, and perform constraint verification on the target air-coal ratio of each combustion zone.

[0065] Step S343: When the constraint verification passes, the target air-coal ratio distribution is output; otherwise, the target air-coal ratio distribution is regenerated.

[0066] Specifically, there are certain physical position relationships and thermal connections between multiple combustion zones in the boiler. These relationships are crucial to the stability and efficiency of combustion. By analyzing and understanding these relationships, it can be determined within what range the target air-coal ratio difference between adjacent combustion zones should be limited. For example, due to the mutual influence of heat transfer or combustion characteristics between certain combustion zones, their air-coal ratio difference cannot be too large or too small, otherwise it may cause problems such as unstable combustion, local overheating or incomplete combustion. According to this physical position relationship and thermal connection, the upper and lower limits of the target air-coal ratio difference between adjacent combustion zones are clarified, and these upper and lower limits are presented in the form of a matrix to form an air-coal ratio difference constraint matrix. Each element in this matrix represents the allowable range of the air-coal ratio difference between specific adjacent combustion zones, which provides an important basis and restriction for the subsequent adjustment and optimization of the air-coal ratio, and helps to achieve more accurate and effective combustion control.

[0067] The target air-coal ratio distribution is a vector or matrix containing the target air-coal ratios of each combustion zone. The air-coal ratio difference constraint matrix is ​​a matrix constructed based on the topological relationship between the combustion zones, which is used to limit the range of the target air-coal ratio difference between adjacent combustion zones. Substitute the target air-coal ratios of each combustion zone in the target air-coal ratio distribution into the air-coal ratio difference constraint matrix to check whether the target air-coal ratios of each combustion zone meet the constraint conditions. By performing constraint verification on the target air-coal ratios of each combustion zone, it can be ensured that the adjustment of the air-coal ratio will not cause combustion instability or other problems, thereby achieving more accurate and effective combustion control.

[0068] When the target air-coal ratio of each combustion zone is substituted into the air-coal ratio difference constraint matrix, if all the target air-coal ratios meet the constraints set by the matrix, that is, they do not exceed the specified difference range, then it can be determined that the target air-coal ratio distribution is feasible and reasonable, and it is output for subsequent use or further processing. However, if it is found during the substitution verification that some or all of the target air-coal ratios do not meet the conditions of the constraint matrix, it means that the current target air-coal ratio distribution may lead to some unexpected situations, such as unstable combustion and other problems. At this time, it is necessary to regenerate the target air-coal ratio distribution, adjust the air-coal ratio settings of certain combustion zones, and then substitute the verification again until the target air-coal ratio distribution that passes the constraint verification is obtained. The purpose of this is to ensure that the target air-coal ratio distribution finally adopted is within a safe, reasonable range that meets the operating requirements of the combustion system.

[0069] In a possible implementation, step S400 further includes:

[0070] Step S410: Establish a hierarchical optimization model for the air-coal ratio deviation, and divide the optimization process into a primary optimization stage and an advanced optimization stage according to the size of the air-coal ratio deviation.

[0071] Step S420: In the primary optimization stage, the optimization goal is to minimize the sum of the absolute values ​​of the air-coal ratio deviations, and a global optimization is performed with the value ranges of the primary air parameters and the secondary air parameters as constraints to obtain a suboptimal solution for the air-coal ratio deviation.

[0072] Step S430: In the advanced optimization stage, the optimization goal is to minimize the mean square error of the air-coal ratio deviation, and the value range of the primary air parameter and the secondary air parameter in the neighborhood of the suboptimal solution is constrained to perform local optimization to obtain the local optimal solution of the air-coal ratio deviation.

[0073] Step S440: setting an optimization stage switching condition according to the convergence speed of the air-to-fuel ratio deviation and the optimization effect, and performing stage switching between the primary optimization stage and the advanced optimization stage based on the optimization stage switching condition.

[0074] Step S450: performing hierarchical optimization, iteratively optimizing the suboptimal solution and the local optimal solution until the convergence condition is met, and outputting the optimal primary air parameters and the optimal secondary air parameters.

[0075] Specifically, to construct a hierarchical optimization model for the deviation of the air-coal ratio, it is necessary to clarify the goal to be achieved through optimization, such as minimizing the sum of the absolute values ​​of the deviation of the air-coal ratio or the mean square error. According to the size of the air-coal ratio deviation, the optimization process is divided into the primary optimization stage and the advanced optimization stage. A deviation threshold is set. When the deviation is greater than the threshold, the primary optimization stage is entered, and when the deviation is less than or equal to the threshold, the advanced optimization stage is entered. Select the variables that need to be adjusted, such as the primary air parameter and the secondary air parameter, consider the actual situation, determine the value range of the optimization variable and other constraints to ensure the feasibility and safety of the optimization results, select a suitable optimization algorithm, such as genetic algorithm, particle swarm optimization algorithm, etc., combine the optimization objectives, optimization variables, constraints and optimization algorithms, and construct a hierarchical optimization model for the deviation of the air-coal ratio. According to the convergence speed and optimization effect of the air-coal ratio deviation, set reasonable stage switching conditions to determine when to switch from the primary optimization stage to the advanced optimization stage, or when to end the optimization process. Use the constructed optimization model for optimization calculation, iteratively optimize the suboptimal solution and the local optimal solution until the convergence conditions are met, and output the optimal primary air parameter and secondary air parameter, as well as the corresponding air-coal ratio deviation. When establishing a hierarchical optimization model, it is necessary to comprehensively consider factors such as the size of the wind-coal ratio deviation, optimization objectives, constraints and optimization algorithms to achieve effective wind-coal ratio optimization. The reason for grading is that the wind-coal ratio deviation may exist to different degrees. According to the specific wind-coal ratio deviation value, the entire optimization process is divided into two different stages, namely the primary optimization stage and the advanced optimization stage. In the primary optimization stage, the relatively large wind-coal ratio deviation is mainly dealt with, and a relatively simple and direct method that can quickly achieve certain results is used for optimization. The purpose is to roughly improve the deviation first. The advanced optimization stage is a more detailed and in-depth optimization of smaller wind-coal ratio deviations. In this stage, more complex and accurate algorithms will be used to further reduce the deviation to achieve better optimization results.

[0076] In the primary optimization stage, minimizing the sum of the absolute values ​​of the air-coal ratio deviations as the optimization goal means making the sum of the absolute values ​​of the air-coal ratio deviations as small as possible. The sum of the absolute values ​​of the air-coal ratio deviations is an indicator to measure the overall deviation degree. By pursuing its minimization, the system can be driven to develop in the direction of reducing the air-coal ratio deviation. The value range of the primary air parameter and the secondary air parameter is used as a constraint to ensure that during the optimization process, the primary air parameter and the secondary air parameter will not exceed the prescribed reasonable value range. Performing global optimization means conducting extensive searches and attempts in the entire possible parameter space to find a relatively good solution, that is, a suboptimal solution to the air-coal ratio deviation. Although this suboptimal solution is not the final optimal solution, it is already a relatively good result in the primary stage, laying the foundation for the subsequent advanced optimization stage.

[0077] The mean square error is an indicator to measure the degree of data discreteness. By minimizing it, the air-to-coal ratio deviation can be adjusted more accurately to make the deviation more uniform and stable. The suboptimal solution neighborhood means a small range near the suboptimal solution obtained in the previous primary stage. The local optimization is restricted to this neighborhood because there is already a relatively good starting point (suboptimal solution). More detailed exploration around it can not only improve efficiency, but also avoid blindly searching in the entire parameter space. For example, for the specific size setting of this local range, consider the following situations. If the value of the primary wind parameter is 50 and the value of the secondary wind parameter is 80 in the suboptimal solution obtained in the primary optimization stage, the fluctuation of the air-to-coal ratio deviation is usually around 10 units. Then the local range can be set to fluctuate 5 units above and below the suboptimal solution. For example, the primary wind parameter can be [45, 55] and the secondary wind parameter can be [75, 85]. Alternatively, if we know from past experience that it is easier to find a better solution within 3 units of the suboptimal solution, then we can narrow the local range accordingly, and set the primary wind parameter to [47, 53] and the secondary wind parameter to [77, 83]. Alternatively, considering the computing resources and time constraints, if the resources are sufficient, we can set the local range to be relatively large, such as the primary wind parameter [40, 60] and the secondary wind parameter [70, 90]. If the resources are limited, we can narrow the range appropriately, such as the primary wind parameter [48, 52] and the secondary wind parameter [78, 82]. By fine-tuning the primary wind parameter and the secondary wind parameter within this limited range, we can find the specific parameter setting that can minimize the mean square error of the air-to-coal ratio deviation, and this parameter setting corresponds to the local optimal solution of the air-to-coal ratio deviation.

[0078] The optimization stage switching conditions should be determined based on the convergence speed of the air-coal ratio deviation and the final optimization effect, and the optimization accuracy and efficiency should be improved by switching from the primary optimization stage to the advanced optimization stage. If the convergence speed of the air-coal ratio deviation is fast, it means that the current optimization strategy is more effective and can quickly make the deviation develop in the desired direction; if the convergence speed is slow, it is necessary to consider switching to different methods. The optimization effect directly reflects the actual results achieved by the current optimization strategy. If after a period of primary optimization, it is found that the optimization effect is not ideal and has not achieved the expected improvement, then consider switching to the advanced optimization stage to seek a better solution. Setting the optimization stage switching conditions is to clarify specific indicators or standards. When these conditions are met, the stage switching is performed. When the convergence speed of the deviation is lower than a certain threshold, or when the optimization effect is not significantly improved within a certain period of time, the stage switching is performed. Exemplarily, the convergence speed threshold of the deviation is set to a deviation reduction of less than 0.5% after each round of iteration. During the optimization process, it is initially in the primary optimization stage. As the iteration proceeds, it is found that the reduction in deviation after three consecutive iterations is less than 0.5%. At this time, the stage switching condition is met, and the stage is switched from the primary optimization stage to the advanced optimization stage. Alternatively, if the reduction in the mean square error of the wind-coal ratio deviation is less than 10% within 10 iterations, the stage switching is performed. If after a period of optimization, it is found that this improvement effect has not been achieved within 10 iterations, then the stage switching is performed to try a more refined advanced optimization strategy to further improve the optimization effect.

[0079] According to the pre-determined switching conditions, the system automatically switches between the primary optimization stage and the advanced optimization stage at the appropriate time. This allows the optimization strategy to be dynamically adjusted according to actual conditions to achieve better optimization effects on the air-coal ratio deviation.

[0080] In a possible implementation, step S500 further includes:

[0081] Step S510: setting a combustion monitoring cycle, extracting multiple groups of air-coal ratio control strategies within the combustion monitoring cycle, and chronologically arranging them according to strategy generation time.

[0082] Step S520: respectively obtain multiple groups of combustion results corresponding to multiple groups of air-coal ratio control strategies, analyze the multiple groups of combustion results according to the time series arrangement, and obtain the change trend of the combustion results.

[0083] Step S530: performing a strategy analysis on multiple groups of air-coal ratio control strategies according to the variation trend of the combustion results, and optimizing the primary air parameters and the secondary air parameters according to the strategy analysis results.

[0084] Specifically, the combustion monitoring cycle is set to regularly monitor and analyze the combustion process. By extracting multiple groups of air-coal ratio control strategies within the combustion monitoring cycle and arranging them in chronological order according to the strategy generation time, we can understand the control measures taken at different time points and their impact on the combustion process.

[0085] The combustion results generated after the implementation of each air-coal ratio control strategy are collected separately. These combustion results include various indicators that can reflect the combustion status, such as combustion efficiency, temperature distribution, and pollutant emission levels. By analyzing these combustion results in the same order according to the time sequence of the previous air-coal ratio control strategy, we can clearly understand the specific impact of different control strategies on the combustion results, not just looking at the effect of a single strategy in isolation, but considering it comprehensively from the perspective of time series.

[0086] After obtaining the trend of the combustion results, the primary and secondary air parameters are optimized according to the results of the strategy analysis. If it is found that some strategies lead to poor combustion effects, it may be necessary to adjust the primary air volume, wind speed and other parameters, or change the timing of the secondary air intervention, air volume distribution, etc. In this way, the air-coal ratio control strategy can be continuously adapted to the actual combustion situation, so as to achieve better combustion effects. For example, if the analysis finds that in the trend of the combustion results, the combustion efficiency decreases in a certain period of time, and the primary air volume in the corresponding air-coal ratio control strategy is too large, then the primary air volume can be appropriately reduced for optimization; or if it is found that the timing of the secondary air intervention is late, resulting in incomplete combustion, the secondary air intervention time can be advanced to improve the combustion condition.

[0087] In summary, the embodiments of the present application have at least the following technical effects:

[0088] The present application provides a method for optimizing the control of the air-coal ratio of a boiler, which relates to the technical field of boiler combustion control, and achieves the technical effects of significantly improving the control effect of the air-coal ratio, improving combustion stability, and improving the overall thermal efficiency of the boiler.

[0089] Embodiment 2, based on the same inventive concept as the method for optimizing the air-coal ratio of a boiler in the above embodiment, Figure 3 As shown, the present application provides a boiler air-coal ratio control optimization system, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0090] The boiler operation parameter acquisition module 10 is used to interact with the target boiler and acquire boiler operation parameters, including boiler load, coal quality parameters, primary air parameters and secondary air parameters, wherein the primary air parameter is the primary air volume, and the secondary air parameter is the secondary air volume.

[0091] The actual air-coal ratio distribution acquisition module 20 is used to calculate the actual air-coal ratio of each combustion zone according to the boiler operating parameters to obtain the actual air-coal ratio distribution.

[0092] The target air-coal ratio distribution acquisition module 30 is used to determine the target air-coal ratio of each combustion zone according to the boiler load and coal quality parameters, and obtain the target air-coal ratio distribution.

[0093] The parameter optimization module 40 is used to optimize the primary air parameters and the secondary air parameters with the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization target, so as to generate the optimal primary air parameters and the optimal secondary air parameters.

[0094] The air-coal ratio control strategy generation module 50 is used to generate the air-coal ratio control strategy of each combustion zone according to the optimal primary air parameters and the optimal secondary air parameters. The air-coal ratio control strategy includes a primary air control curve and a secondary air control curve.

[0095] The air-coal ratio regulation strategy control module 60 is used to send the air-coal ratio regulation strategy to the distributed control unit, and the distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio regulation strategy to achieve balanced combustion of the boiler.

[0096] Furthermore, the actual air-coal ratio distribution acquisition module 20 further includes:

[0097] An air-coal ratio detection setting unit is used to divide the boiler combustion zone into multiple combustion zones, each combustion zone is provided with an air-coal ratio detection unit, the air-coal ratio detection unit includes a primary air volume sensor, a secondary air volume sensor and a coal volume sensor, and the multiple combustion zones have a regional topological relationship.

[0098] The primary air volume, secondary air volume and coal volume acquisition unit is used to obtain the primary air volume, secondary air volume and coal volume of each combustion zone through the primary air volume sensor, secondary air volume sensor and coal volume sensor in each combustion zone.

[0099] An actual air-coal ratio calculation unit is used to calculate the actual air-coal ratio of each combustion zone based on the primary air volume, secondary air volume and coal volume of each combustion zone.

[0100] An actual air-coal ratio distribution obtaining unit is used to obtain the actual air-coal ratio distribution based on the actual air-coal ratio of each combustion zone and the regional topological relationship.

[0101] Furthermore, the target air-coal ratio distribution acquisition module 30 further includes:

[0102] A combustion state parameter determination unit is used to determine the combustion state parameters of each combustion zone based on boiler load and coal quality parameters, wherein the combustion state parameters include burnout rate, ignition temperature and combustion temperature.

[0103] The corresponding air-coal ratio model selection unit is used to select a corresponding air-coal ratio model from a preset air-coal ratio model library according to the combustion state parameters of each combustion zone. The input parameters of the air-coal ratio model include boiler load, coal quality parameters and combustion state parameters, and the output parameter is the optimal air-coal ratio.

[0104] A target air-coal ratio acquisition unit is used to input the combustion state parameters of each combustion zone into the corresponding air-coal ratio model to obtain the target air-coal ratio of each combustion zone.

[0105] A target air-coal ratio distribution generation unit is used to generate a target air-coal ratio distribution according to the target air-coal ratio of each combustion zone and the regional topological relationship.

[0106] Furthermore, the corresponding air-coal ratio model selection unit further includes:

[0107] A historical operation data collection unit, wherein the historical operation data collection unit is used to collect historical operation data of a target boiler, wherein the historical operation data includes multiple groups of historical data samples, each group of historical data samples includes boiler load, coal quality parameters, combustion state parameters, and a corresponding optimal air-coal ratio.

[0108] A plurality of historical sample set acquisition units are used to cluster and analyze the plurality of groups of historical data samples according to the combustion state parameters to acquire a plurality of historical sample sets.

[0109] Multiple air-coal ratio model acquisition units are used to train and acquire multiple air-coal ratio models based on multiple historical sample sets, with boiler load, coal quality parameters and combustion state parameters as input features and the optimal air-coal ratio as output target.

[0110] The air-coal ratio model adding unit is used to add multiple air-coal ratio models to the air-coal ratio model library.

[0111] Furthermore, the target air-coal ratio distribution generating unit further includes:

[0112] An air-coal ratio difference constraint matrix setting unit, wherein the air-coal ratio difference constraint matrix setting unit is used to set an air-coal ratio difference constraint matrix according to the regional topological relationship of multiple combustion zones, and the air-coal ratio difference constraint matrix is ​​an upper and lower limit constraint matrix of the target air-coal ratio difference between adjacent combustion zones.

[0113] A target air-coal ratio constraint verification unit is used to substitute the target air-coal ratio of each combustion zone in the target air-coal ratio distribution into the air-coal ratio difference constraint matrix to perform constraint verification on the target air-coal ratio of each combustion zone.

[0114] When the constraint verification passes, the target air-coal ratio distribution is output, otherwise, the target air-coal ratio distribution is regenerated.

[0115] Furthermore, the parameter optimization module 40 further includes:

[0116] The hierarchical optimization model building unit is used to build a hierarchical optimization model of the air-coal ratio deviation, and divide the optimization process into a primary optimization stage and an advanced optimization stage according to the size of the air-coal ratio deviation.

[0117] The unit for obtaining a suboptimal solution of the air-coal ratio deviation is used to perform global optimization in the primary optimization stage, with minimizing the sum of the absolute values ​​of the air-coal ratio deviation as the optimization goal, and with the value ranges of the primary air parameters and the secondary air parameters as constraints, to obtain a suboptimal solution of the air-coal ratio deviation.

[0118] The unit for obtaining the local optimal solution of the air-coal ratio deviation is used to perform local optimization in the advanced optimization stage, with minimizing the mean square error of the air-coal ratio deviation as the optimization goal, and with the value range of the primary air parameter and the secondary air parameter in the neighborhood of the suboptimal solution as the constraint, to obtain the local optimal solution of the air-coal ratio deviation.

[0119] An optimization stage switching condition setting unit, wherein the optimization stage switching condition setting unit is used to set the optimization stage switching condition according to the convergence speed and optimization effect of the air-coal ratio deviation, and perform stage switching between the primary optimization stage and the advanced optimization stage based on the optimization stage switching condition.

[0120] The optimal wind parameter output unit is used to perform hierarchical optimization, iteratively optimize the suboptimal solution and the local optimal solution until the convergence condition is met, and output the optimal primary wind parameter and the optimal secondary wind parameter.

[0121] Furthermore, the air-coal ratio control strategy generation module 50 further includes:

[0122] A combustion monitoring cycle setting unit, which is used to set the combustion monitoring cycle, extract multiple groups of air-coal ratio control strategies within the combustion monitoring cycle, and arrange them in chronological order according to the strategy generation time.

[0123] A combustion result change trend acquisition unit is used to respectively acquire multiple groups of combustion results corresponding to multiple groups of air-coal ratio control strategies, analyze the multiple groups of combustion results according to time series arrangement, and acquire the combustion result change trend.

[0124] The air-coal ratio control strategy analysis unit is used to perform strategy analysis on multiple groups of air-coal ratio control strategies according to the change trend of the combustion results, and optimize the primary air parameters and the secondary air parameters according to the strategy analysis results.

[0125] Embodiment 3, Figure 4 The structural schematic diagram of the electronic device provided in the third embodiment of the present application shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode of the present invention. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 4 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 4 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0126] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to a method for optimizing the air-coal ratio of a boiler in an embodiment of the present application. The processor 21 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 22, that is, realizing the above-mentioned method for optimizing the air-coal ratio of a boiler.

[0127] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0128] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0130] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the control of boiler air-coal ratio, characterized in that: The method comprises: Interact with the target boiler to obtain boiler operation parameters, including boiler load, coal quality parameters, primary air parameters and secondary air parameters, wherein the primary air parameter is the primary air volume, and the secondary air parameter is the secondary air volume; According to the boiler operating parameters, the actual air-coal ratio of each combustion zone is calculated to obtain the actual air-coal ratio distribution; According to boiler load and coal quality parameters, the target air-coal ratio of each combustion zone is determined, and the target air-coal ratio distribution is obtained; Taking the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization goal, optimizing the primary air parameters and the secondary air parameters to generate the optimal primary air parameters and the optimal secondary air parameters; Generate an air-coal ratio control strategy for each combustion zone according to the optimal primary air parameters and the optimal secondary air parameters, wherein the air-coal ratio control strategy includes a primary air control curve and a secondary air control curve; The air-coal ratio control strategy is sent to the distributed control unit, and the distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio control strategy to achieve balanced combustion of the boiler; Among them, taking the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization goal, optimizing the primary air parameters and the secondary air parameters to generate the optimal primary air parameters and the optimal secondary air parameters include: A hierarchical optimization model of air-coal ratio deviation is established, and the optimization process is divided into primary optimization stage and advanced optimization stage according to the size of air-coal ratio deviation. In the primary optimization stage, the optimization objective is to minimize the sum of the absolute values ​​of the air-coal ratio deviations, and the value ranges of the primary and secondary air parameters are used as constraints to perform global optimization, thus obtaining a suboptimal solution for the air-coal ratio deviation. In the advanced optimization stage, the optimization goal is to minimize the mean square error of the air-coal ratio deviation, and the local optimal solution of the air-coal ratio deviation is obtained by taking the value range of the primary air parameter and the secondary air parameter in the neighborhood of the suboptimal solution as the constraint. Setting an optimization stage switching condition according to the convergence speed of the air-to-coal ratio deviation and the optimization effect, and performing stage switching between the primary optimization stage and the advanced optimization stage based on the optimization stage switching condition; Perform hierarchical optimization, iteratively optimize the suboptimal solution and the local optimal solution until the convergence condition is met, and output the optimal primary air parameters and the optimal secondary air parameters.

2. The method according to claim 1, characterized in that According to the boiler operating parameters, the actual air-coal ratio of each combustion zone is calculated to obtain the actual air-coal ratio distribution, including: The boiler combustion zone is divided into a plurality of combustion zones, each combustion zone is provided with an air-coal ratio detection unit, the air-coal ratio detection unit comprises a primary air volume sensor, a secondary air volume sensor and a coal volume sensor, and the plurality of combustion zones have a regional topological relationship; In each combustion zone, the primary air volume, secondary air volume and coal volume of each combustion zone are obtained respectively through the primary air volume sensor, the secondary air volume sensor and the coal volume sensor; Calculate the actual air-coal ratio of each combustion zone based on the primary air volume, secondary air volume and coal volume of each combustion zone; Based on the actual air-coal ratio of each combustion zone and the regional topological relationship, the actual air-coal ratio distribution is obtained.

3. The method according to claim 2, characterized in that According to the boiler load and coal quality parameters, the target air-coal ratio of each combustion zone is determined, and the target air-coal ratio distribution is obtained, including: Based on the boiler load and coal quality parameters, determining the combustion state parameters of each combustion zone, wherein the combustion state parameters include burnout rate, ignition temperature and combustion temperature; According to the combustion state parameters of each combustion zone, a corresponding air-coal ratio model is selected from a preset air-coal ratio model library, wherein the input parameters of the air-coal ratio model include boiler load, coal quality parameters and combustion state parameters, and the output parameter is the optimal air-coal ratio; The combustion state parameters of each combustion zone are input into the corresponding air-coal ratio model to obtain the target air-coal ratio of each combustion zone; According to the target air-coal ratio of each combustion zone and the regional topological relationship, a target air-coal ratio distribution is generated.

4. The method according to claim 3, characterized in that The method further comprises: Collecting historical operation data of the target boiler, wherein the historical operation data includes multiple groups of historical data samples, each group of historical data samples includes boiler load, coal quality parameters, combustion state parameters, and corresponding optimal air-coal ratio; Performing cluster analysis on the multiple groups of historical data samples according to the combustion state parameters to obtain multiple historical sample sets; Based on multiple historical sample sets, boiler load, coal quality parameters and combustion state parameters are used as input features, and the optimal air-coal ratio is used as the output target to train and obtain multiple air-coal ratio models; A plurality of air-coal ratio models are added to the air-coal ratio model library.

5. The method according to claim 3, characterized in that: The method further comprises: An air-coal ratio difference constraint matrix is ​​set according to the regional topological relationship of multiple combustion zones, wherein the air-coal ratio difference constraint matrix is ​​an upper and lower limit constraint matrix of the target air-coal ratio difference between adjacent combustion zones; Substituting the target air-coal ratio of each combustion zone in the target air-coal ratio distribution into the air-coal ratio difference constraint matrix, and performing constraint verification on the target air-coal ratio of each combustion zone; When the constraint verification passes, the target air-coal ratio distribution is output, otherwise, the target air-coal ratio distribution is regenerated.

6. The method according to claim 1, characterized in that The method further comprises: Setting a combustion monitoring cycle, extracting multiple groups of air-coal ratio control strategies within the combustion monitoring cycle, and arranging them in chronological order according to the strategy generation time; Obtain multiple groups of combustion results corresponding to multiple groups of air-coal ratio control strategies respectively, analyze the multiple groups of combustion results according to the time sequence arrangement, and obtain the change trend of the combustion results; According to the changing trend of the combustion results, a strategy analysis is performed on multiple groups of air-coal ratio control strategies, and the primary air parameters and secondary air parameters are optimized according to the strategy analysis results.

7. A boiler air-coal ratio control optimization system, characterized in that: The system is used to implement a boiler air-coal ratio control optimization method according to any one of claims 1 to 6, and the system comprises: A boiler operation parameter acquisition module, which is used to interact with the target boiler and acquire boiler operation parameters, including boiler load, coal quality parameters, primary air parameters and secondary air parameters, wherein the primary air parameter is the primary air volume, and the secondary air parameter is the secondary air volume; An actual air-coal ratio distribution acquisition module, which is used to calculate the actual air-coal ratio of each combustion zone according to the boiler operating parameters to obtain the actual air-coal ratio distribution; A target air-coal ratio distribution acquisition module, which is used to determine the target air-coal ratio of each combustion zone according to boiler load and coal quality parameters, and obtain the target air-coal ratio distribution; A parameter optimization module, wherein the parameter optimization module is used to optimize the primary air parameters and the secondary air parameters with the minimum deviation between the actual air-coal ratio distribution and the target air-coal ratio distribution as the optimization target, and generate the optimal primary air parameters and the optimal secondary air parameters; An air-coal ratio control strategy generation module, the air-coal ratio control strategy generation module is used to generate an air-coal ratio control strategy for each combustion zone according to an optimal primary air parameter and an optimal secondary air parameter, the air-coal ratio control strategy including a primary air control curve and a secondary air control curve; The air-coal ratio regulation strategy control module is used to send the air-coal ratio regulation strategy to the distributed control unit, and the distributed control unit coordinates and adjusts the primary air valve and the secondary air valve of each combustion zone according to the air-coal ratio regulation strategy to achieve balanced combustion of the boiler.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; Wherein, the processor is used to execute a boiler air-coal ratio control optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, so the computer program is used to execute the boiler air-coal ratio control optimization method described in any one of claims 1 to 6.

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