Thermal power boiler combustion optimization method based on multi-parameter feedback control

Abstract

The invention relates to the field of thermal power generation, and discloses a thermal power boiler combustion optimization method based on multi-parameter feedback control, which comprises the following steps: acquiring key parameters such as oxygen concentration, temperature, pulverized coal concentration, air flow, pulverized coal flow and boiler load in boiler operation; establishing a distributed parameter model for describing dynamic changes of oxygen concentration, temperature and pulverized coal concentration in the hearth along with time and space; key characteristic variables representing the combustion state are extracted through multi-scale analysis; dynamically optimizing multiple parameters of different areas in the hearth based on a distributed feedback controller; a global optimization objective function is constructed, and the ratio of the air flow to the pulverized coal flow is dynamically adjusted; and the optimized control instruction is sent to a boiler system, and combustion parameters are adjusted in real time through an execution mechanism. According to the method, through distributed modeling, multi-scale analysis and feedback control, the combustion efficiency is remarkably improved, pollutant emission is effectively reduced, and the method can adapt to coal quality fluctuation and load change.

Description

Technical Field

[0001] The invention relates to the technical field of thermal power generation, and in particular to a thermal power boiler combustion optimization method based on multi-parameter feedback control. Background Art

[0002] As the core equipment of thermal power generation, the combustion efficiency of thermal power boilers is directly related to the economic and environmental performance of power production. Improving combustion efficiency can not only reduce fuel consumption, but also reduce pollutant emissions, while ensuring the long-term stable operation of the equipment. In traditional combustion control methods, feedback regulation technology based on a single parameter is usually adopted, such as regulating the supply of air and fuel by monitoring oxygen concentration or flue gas temperature. However, this single-parameter control method has obvious limitations in complex boiler combustion processes. The combustion process involves a variety of coupled variables (such as oxygen concentration, temperature field, coal powder concentration, etc.), which affect each other and change dynamically. A single parameter cannot fully reflect the actual operating conditions of the boiler combustion state.

[0003] Traditional methods respond slowly when faced with complex working conditions such as coal quality fluctuations and load changes, which can easily lead to unstable combustion states, low combustion efficiency, and excessive pollutant emissions. In addition, it is difficult to achieve global optimization of combustion parameters in the furnace through single parameter adjustment, which may lead to local hot spots or low-oxygen areas inside the boiler, further exacerbating the risks of reduced fuel utilization and pollutant generation. At the same time, the existing technology lacks the ability to monitor and fully regulate dynamic changes in the combustion process in real time, and cannot meet the requirements of efficient energy saving and environmentally friendly emissions for modern thermal power boilers. Therefore, a control method that can comprehensively consider the dynamic characteristics of multiple parameters and optimize the combustion state in real time is needed to overcome the limitations of the existing technology. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a thermal power boiler combustion optimization method based on multi-parameter feedback control, which solves the problems of low combustion efficiency, high pollutant emissions and poor adaptability of the combustion process caused by single parameter feedback in the existing thermal power boiler combustion control.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: A combustion optimization method for a thermal power boiler based on multi-parameter feedback control comprises the following steps: Obtain multiple key parameters during boiler combustion, including oxygen concentration, temperature, pulverized coal concentration, air flow, pulverized coal flow and boiler load; A distributed parameter model is established to describe the combustion state in the boiler furnace, wherein the model represents the dynamic changes of oxygen concentration, temperature and pulverized coal concentration over time and space during the combustion process; Through multi-scale analysis, the time and space scales of the combustion process are decomposed to extract key characteristic variables that characterize the combustion state; According to the distributed parameter model and multi-scale analysis results, the multi-parameter dynamic optimization of different areas in the boiler furnace is realized based on the distributed feedback controller; Based on distributed feedback control, a global optimization objective function is constructed to dynamically adjust the ratio of air flow and coal powder flow. The optimized control instructions are sent to the boiler system, and the combustion parameters are adjusted in real time through the actuator.

[0006] Preferably, the distributed parameter model characterizes the dynamic changes of oxygen concentration, temperature and pulverized coal concentration in the boiler furnace over time and space, and specifically includes the following dynamic equations: The oxygen concentration dynamic equation describes the transmission, diffusion and consumption behavior of oxygen and satisfies the following relationship: in, is the oxygen concentration, is the air flow velocity field, is the oxygen diffusion coefficient, is the oxygen consumption rate coefficient, is the coal powder concentration; The temperature dynamic equation describes the behavior of heat transfer, combustion heat release and heat loss, and satisfies the following relationship: in, is the temperature, is the gas density, is the specific heat capacity, is the thermal conductivity, is the enthalpy change of the combustion reaction, is the heat loss coefficient, is the ambient temperature; The dynamic equation of pulverized coal concentration describes the distribution of pulverized coal and the combustion reaction behavior and satisfies the following relationship: Model constraints include initial conditions and boundary conditions; Initial conditions: in, is the oxygen concentration at time The initial distribution value at is the temperature at time The initial distribution value at is the coal powder concentration at time The initial distribution value at time ; Boundary conditions: in, is the boundary normal vector, is the furnace wall temperature.

[0007] Preferably, the step of decomposing the time scale and space scale of the combustion process by multi-scale analysis comprises: On the time scale, oxygen concentration and coal powder concentration are decomposed into fast variables, and temperature is decomposed into a slow variable; On the spatial scale, the average combustion parameters in each area are extracted based on the regional division of the furnace, including the regional average value of oxygen concentration, the regional gradient of temperature and the regional change rate of coal powder concentration.

[0008] Preferably, the step of implementing multi-parameter dynamic optimization of different areas in the boiler furnace based on a distributed feedback controller includes: A local feedback controller is established for each partition separately, and the local controller calculates the control deviation based on the real-time oxygen concentration, temperature and coal powder concentration data in the partition; Using feedback control law, dynamically adjust the distribution of air flow and pulverized coal flow in the partition; Based on the synergistic relationship between regional parameters, the overall combustion performance is optimized through a regional coupling mechanism.

[0009] Preferably, the global optimization objective function is constructed based on the optimization goals of maximizing combustion efficiency, minimizing pollutant emissions, and improving combustion uniformity, and specifically includes: The combustion efficiency objective is to maximize the combustion efficiency by improving the combustion completeness of the fuel and oxygen in the furnace. The combustion efficiency objective function is defined as: in, represents the combustion efficiency function; Pollutant emission target, by reducing the emission of nitrogen oxides and sulfur dioxide generated during the combustion process to minimize pollutant emissions, the pollutant emission objective function is defined as: in, Indicates the amount of NOx generated, Indicates the amount of SOx generated; The combustion uniformity objective is to improve the combustion uniformity by reducing the gradient distribution of the temperature field in the furnace. The combustion uniformity objective function is defined as: in, Represents the square of the gradient of the temperature field; The global optimization objective function is a weighted combination of sub-objective functions to construct a global objective function, which is defined as: in, , , is the weight coefficient.

[0010] Preferably, the weight coefficient of the global optimization objective function is dynamically adjusted according to the boiler operating conditions to adapt to coal quality fluctuations and boiler load changes.

[0011] Preferably, the feedback controller dynamically adjusts the air flow rate and the pulverized coal flow rate by the following control law: Calculate adjustment instructions based on combustion state deviations in each zone; The adjustment results of the air flow and the pulverized coal flow are sent to the actuators, including the air damper valve and the pulverized coal feeder.

[0012] The present invention also provides a thermal power boiler combustion optimization system based on multi-parameter feedback control, comprising: Data acquisition unit, used to collect dynamic data of oxygen concentration, temperature, pulverized coal concentration and external input parameters in the boiler furnace in real time; A distributed parameter modeling unit, used to establish a distributed parameter model of the combustion state in the boiler furnace; Multi-scale analysis unit, used to extract key combustion characteristic variables at both temporal and spatial scales; A distributed feedback control unit, which is used to adjust boiler operating parameters based on real-time data, including air flow and pulverized coal flow; The optimized execution unit is used to execute optimized instructions.

[0013] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the above method is implemented.

[0014] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0015] The present invention provides a combustion optimization method for thermal power boilers based on multi-parameter feedback control. It has the following beneficial effects: 1. The present invention uses multi-parameter feedback control and distributed optimization methods to accurately adjust the ratio of air and coal powder in the boiler furnace, optimize the combustion reaction process of fuel and oxygen, and significantly improve the combustion efficiency. By dynamically adjusting the boiler operating parameters in real time, it can minimize fuel waste and reduce energy consumption costs.

[0016] 2. The present invention constructs a global optimization function with the goal of reducing the generation of pollutants such as NOx and SOx, combines the dynamic regulation of oxygen concentration and temperature to inhibit the generation of NOx in high-temperature areas, and improves the uniformity of coal powder supply to reduce SOx emissions.

[0017] 3. The present invention reduces the non-uniformity of parameters (such as oxygen concentration, temperature and coal powder concentration) in the furnace through regional collaborative optimization of distributed feedback controllers, avoiding the occurrence of local hot spots or insufficient combustion. The optimized combustion uniformity improves the heat transfer efficiency of the boiler, while reducing the operating risks caused by parameter fluctuations and improving the overall stability and reliability of the system.

[0018] 4. The present invention adopts a dynamic weight adjustment mechanism and a closed-loop control method, which can adjust the optimization target and control strategy in real time according to the operating conditions such as boiler load fluctuation, coal quality change or operation mode switching. This method enhances the flexibility and adaptability of boiler operation, effectively ensures efficient combustion and stable operation under complex working conditions, prolongs the service life of the equipment and improves economic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention; Figure 3 It is a schematic diagram of the computer device structure of the present invention.

[0020] Among them, 100, data acquisition unit; 200, distributed parameter modeling unit; 300, multi-scale analysis unit; 400, distributed feedback control unit; 500, optimization execution unit; 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

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

[0022] Please refer to the attached Figure 1 The present invention provides a thermal power boiler combustion optimization method based on multi-parameter feedback control, which realizes dynamic optimization of the boiler combustion process by acquiring multiple key parameters, distributed parameter modeling, multi-scale analysis, distributed feedback control and construction of a global optimization objective function.

[0023] like Figure 1 As shown, the thermal power boiler combustion optimization method based on multi-parameter feedback control may include the following steps: S1. Obtain multiple key parameters during boiler combustion; S2. Establish a distributed parameter model to describe the combustion state in the boiler furnace; S3, extract key characteristic variables of combustion state through multi-scale analysis; S4, multi-parameter dynamic optimization based on distributed feedback controller; S5. Construct a global optimization objective function and dynamically adjust parameter ratios; S6. Optimize the execution of control instructions.

[0024] Each step of the method of the present invention is described in detail below.

[0025] For step S1, in this embodiment, in order to accurately characterize the various operating status parameters during the boiler combustion process and ensure the real-time and comprehensiveness of the combustion optimization process, high-precision sensors and data acquisition systems are used to obtain multiple key parameters in the boiler operation, and real-time transmission and processing are achieved through industrial communication protocols.

[0026] During boiler operation, the combustion state is closely related to multiple core parameters. The present invention collects these key parameters through a variety of sensors and data acquisition devices distributed in the boiler furnace. The key parameters include but are not limited to: Oxygen concentration :Oxygen concentration is one of the most important feedback parameters in the combustion process, reflecting the degree of redox in the combustion process. In this embodiment, oxygen concentration distribution is monitored in real time by oxygen sensors arranged in different areas of the furnace. The oxygen sensor uses a high-precision chemical sensor element and can respond to changes in oxygen content as low as one part per million (ppm).

[0027] The real-time change of oxygen concentration satisfies the following relationship: in: Dynamic relationship between air flow input and combustion area; is the oxygen consumption, which is related to the coal powder concentration and the combustion reaction rate.

[0028] The changing trend of oxygen concentration is directly used for subsequent air-fuel ratio adjustment.

[0029] temperature : Temperature reflects the heat release and transfer during the combustion process and is an important parameter of combustion efficiency. In this embodiment, the temperature distribution at different positions in the furnace is measured in real time by arranging multiple high-temperature sensors on the furnace wall and inside.

[0030] To ensure the accuracy of data collection, the high-temperature sensor covers a temperature range of 2000°C and can effectively characterize the difference between the high-temperature area and the low-temperature area during the combustion process.

[0031] The dynamic change of furnace temperature is expressed by the following equation: in: is the gas density in the furnace; is the specific heat capacity; is the air flow velocity; is thermal conductivity; The heat released by combustion; For heat loss.

[0032] The temperature distribution collected by sensor data is used for subsequent distributed modeling and temperature field optimization.

[0033] Coal powder concentration : Pulverized coal concentration is an important parameter for fuel supply, and its uniformity directly affects combustion efficiency and pollutant generation. In this embodiment, a pulverized coal concentration sensor is used to monitor the spatial distribution of fuel after it is injected into the furnace to ensure uniform fuel supply.

[0034] The dynamic change of coal powder concentration satisfies the following relationship: in: is the conveying speed of pulverized coal; is the reaction rate coefficient of oxygen and coal powder.

[0035] The coal powder concentration data is used as the input of the subsequent distributed feedback controller.

[0036] Air flow : The air flow rate determines the oxygen supply and is a key control parameter for achieving sufficient combustion. In this embodiment, a high-precision flow meter in the air duct measures the air flow rate entering the furnace in real time.

[0037] The input relationship between air flow and oxygen concentration meets the following conditions: in, The opening of the control valve for air flow, It is the controller's instruction to adjust the air flow.

[0038] The measurement of air flow provides a direct basis for subsequent oxygen concentration adjustment.

[0039] Pulverized coal flow : The pulverized coal flow rate is the total amount control parameter of the fuel supply, and its real-time measurement determines the heat output of the combustion. In this embodiment, the dynamic flow rate of the fuel at the outlet of the pulverized coal feeder is measured by an online pulverized coal flow meter.

[0040] The relationship between pulverized coal flow and load demand meets the following conditions: in: is the current heat load demand of the boiler; is the combustion efficiency correction factor.

[0041] The monitoring results of pulverized coal flow are directly used for fuel supply adjustment under load changes.

[0042] Boiler load Boiler load is an important input condition of the combustion process, reflecting the external heat output demand. In this embodiment, the boiler control system collects load signals in real time and dynamically adjusts the combustion parameters in combination with the current air flow and coal powder flow.

[0043] The boiler load is used to guide the global optimization of subsequent combustion parameters.

[0044] In order to ensure the accuracy and real-time performance of the above parameter collection, this embodiment uses industrial communication protocols (such as Modbus and Profibus) to transmit the parameter data to the central data processing unit, and processes and stores the data in real time.

[0045] The data provides basic input conditions for subsequent distributed modeling, multi-scale analysis and feedback controllers, enabling comprehensive monitoring and optimization of the combustion process.

[0046] For step S2, in this embodiment, in order to accurately describe the dynamic change law of each parameter in the combustion process in the boiler furnace, a distributed parameter model is established for the distribution characteristics of key parameters such as oxygen concentration, temperature and coal powder concentration over time and space. This model fully considers the transportation, diffusion and reaction behavior of oxygen, heat and fuel in the combustion process, and mathematically describes the combustion state in the furnace through a group of partial differential equations (PDEs).

[0047] The distributed parameter model specifically includes: Oxygen concentration dynamic model: The oxygen concentration is affected by air flow, molecular diffusion and chemical reaction consumption during the combustion process. Its dynamic change is described by the following equation: in, Indicates oxygen concentration; Indicates time; It represents the air velocity field, and its direction and magnitude are related to the flow state in the furnace; is the diffusion coefficient of oxygen molecules; is the rate coefficient of the reaction between oxygen and coal powder; Indicates the concentration of coal powder.

[0048] The above oxygen concentration dynamic equation reflects the relationship between oxygen supply and consumption, and its distribution state has a decisive influence on combustion efficiency and pollutant generation. In the model, Indicates the rate at which oxygen is consumed in a combustion reaction.

[0049] Temperature dynamic model: The temperature distribution in the furnace is the core parameter of the combustion process, which is affected by factors such as the heat released by the combustion reaction, heat conduction and heat loss. The dynamic change of temperature is described by the following equation: in, is temperature; is the gas density in the furnace; is the specific heat capacity of the gas; is the thermal conductivity; represents the enthalpy change of the combustion reaction (i.e. the heat released by combustion); is the heat transfer coefficient; Indicates the ambient temperature outside the furnace.

[0050] In this model, The heat released by the combustion reaction, Represents heat loss. This model can accurately describe the temperature field changes at different locations in the furnace, providing support for subsequent optimization of combustion uniformity.

[0051] Dynamic model of coal powder concentration: The distribution of pulverized coal concentration is a direct reflection of the fuel supply and combustion reaction, and its dynamic changes are described by the following equation: in, Indicates the concentration of pulverized coal; Indicates the consumption rate of coal powder combustion reaction.

[0052] The model accurately reflects the supply and consumption status of fuel by considering the transportation and combustion reaction of pulverized coal, providing a basis for the subsequent optimization of fuel distribution.

[0053] Boundary conditions and initial conditions: In order to make the model practically applicable, this embodiment further introduces boundary conditions and initial conditions to constrain the initial values ​​and distribution ranges of oxygen concentration, temperature and coal powder concentration.

[0054] The initial conditions are: in, , , are the initial distribution values ​​of oxygen concentration, temperature and coal powder concentration respectively.

[0055] The boundary conditions are: in, represents the boundary no-flux condition for oxygen, is the boundary normal vector; represents the temperature boundary heat transfer condition, is the furnace wall temperature; Indicates the boundary no-flux condition of coal powder.

[0056] In this embodiment, based on the above-mentioned distributed parameter model, the model is discretized and solved by numerical calculation methods (such as finite difference method or finite element method) to achieve real-time simulation and dynamic tracking of oxygen concentration, temperature and coal powder concentration in the furnace.

[0057] During the specific implementation process, the distribution data of oxygen concentration, temperature and coal powder concentration are obtained in real time through the industrial data acquisition system as the input conditions of the model; by solving the above equations, the dynamic distribution of various parameters in the furnace is obtained, providing basic support for subsequent control and optimization.

[0058] The model is run in sync with the data collected by the sensors to ensure that the distributed parameter model can be updated in real time and reflect the boiler operating status.

[0059] The distributed parameter model established in this embodiment can accurately describe the dynamic changes of oxygen concentration, temperature and coal powder concentration during boiler combustion, and provide a scientific basis for subsequent optimization control and the construction of the global objective function.

[0060] For step S3, in this embodiment, in order to accurately extract the key characteristic variables in the boiler combustion process and better characterize the variation patterns of core parameters such as oxygen concentration, temperature, and coal powder concentration at different time and space scales, a multi-scale analysis method is used to decompose and quantify the combustion process.

[0061] The core idea of ​​multi-scale analysis is to divide the complex combustion process into two dimensions: time and space, extract key features from the two levels of fast and slow variables and regional distribution, and establish a simplified and efficient expression of characteristic variables to provide support for subsequent controller design and optimization.

[0062] Time scale analysis: Different physical parameters in the combustion process have significant time scale differences, for example, the combustion reaction rate changes quickly, while the temperature transfer changes relatively slowly. Therefore, this embodiment uses a time scale decomposition method to separate fast variables from slow variables to improve the description accuracy of the model.

[0063] Oxygen concentration and coal powder concentration It is regarded as a fast variable, and its rate of change is significantly affected by the combustion reaction rate. The change of fast variables can be formally described as follows: in: represents the reaction consumption rate of oxygen; Indicates the reaction consumption rate of coal powder; is the combustion reaction rate constant.

[0064] temperature It is regarded as a slow variable, and its change is mainly controlled by heat transfer and loss, showing a slow time evolution. The change relationship of the slow variable can be expressed as: in: It represents the heat released by the combustion reaction; Represents heat loss.

[0065] By decomposing the time scale, this example extracts the rate of change of oxygen concentration , the change rate of coal powder concentration And the time trend of temperature , these characteristic quantities are directly used as input parameters for controller design.

[0066] Spatial scale analysis: The combustion process inside the boiler furnace has significant spatial distribution characteristics. The oxygen concentration, temperature and coal powder concentration in different areas may vary significantly. This spatial non-uniformity will directly affect the combustion efficiency and pollutant generation. Therefore, this embodiment quantifies the regional distribution characteristics of combustion parameters through spatial scale decomposition.

[0067] For the convenience of analysis, the furnace area Divided into multiple sub-areas , the combustion parameters inside each sub-region are considered to be relatively uniformly distributed. The following feature quantities are extracted for each sub-region: in: Indicates sub-area Volume; are the average values ​​of oxygen concentration, temperature and pulverized coal concentration in the sub-area, respectively.

[0068] In addition, in order to further characterize the inhomogeneity of parameters between regions, the gradient characteristics between adjacent regions are calculated: in: For adjacent areas and The geometric center distance; , , are the spatial gradients of oxygen concentration, temperature and coal powder concentration, respectively.

[0069] Through the above-mentioned spatial scale analysis, this embodiment extracts the average parameter value of each sub-region and the gradient characteristics of the adjacent regions for evaluating the combustion uniformity and parameter distribution state.

[0070] In actual operation, this embodiment uses an industrial data acquisition system to obtain real-time distribution data of oxygen concentration, temperature and coal powder concentration, and extracts key feature quantities based on the above multi-scale analysis method. The extraction results are input into the controller in real time to guide regional parameter optimization and combustion uniformity improvement.

[0071] In this embodiment, the characteristic variables extracted by multi-scale analysis include the oxygen concentration change rate, the coal powder concentration change rate, the temperature change trend, the regional average value and the inter-regional gradient. These characteristic variables accurately characterize the dynamic characteristics and spatial distribution characteristics of the combustion state, and provide support for the subsequent distributed feedback control and the construction of the global optimization objective function.

[0072] For step S4, in this embodiment, in order to accurately and dynamically optimize the combustion process in the boiler furnace, a distributed feedback controller is used to independently adjust key parameters such as oxygen concentration, temperature and coal powder concentration in different areas of the furnace, and the global combustion performance is improved through collaborative optimization between regions.

[0073] The design of the distributed feedback controller is based on the distributed parameter model and multi-scale analysis results established above. Its goal is to achieve an ideal combustion state in each area of ​​the boiler through real-time monitoring and dynamic adjustment of multiple parameters, thereby improving combustion efficiency, reducing pollutant emissions and optimizing combustion uniformity.

[0074] The structure of the local feedback controller: In this embodiment, the furnace is divided into multiple areas, and each area is independently equipped with a local feedback controller for real-time adjustment of the combustion parameters in the area.

[0075] The control law of the local feedback controller is as follows: in: For Region Control inputs, including air flow adjustment And coal powder flow adjustment ; For Region The state vector of includes oxygen concentration, temperature and coal powder concentration; For Region The target state vector is determined according to the optimization objective; is the regional feedback gain matrix, which determines the response strength of the controller to the deviation; For Region With adjacent areas The coupling weight is used to coordinate the parameter distribution between adjacent regions; For the region A collection of adjacent regions.

[0076] The control law consists of two parts: the first is the local feedback adjustment item within the area, which directly calculates the adjustment command according to the combustion parameter deviation in this area; the second is the coupling adjustment item between regions, which optimizes the synergistic relationship between regions through the gradient information of the parameters.

[0077] Dynamic calculation of control inputs: According to the control law, the controller calculates the air flow in real time and coal powder flow Adjustment amount: in, and It is the air and coal flow adjustment function calculated based on the control law, and the specific deviation And the difference between adjacent areas Related.

[0078] The dynamic adjustment of air flow directly affects the supply of oxygen concentration, while the dynamic adjustment of pulverized coal flow affects the heat released by combustion. The coordinated effect of the two ensures that the combustion process reaches an ideal state.

[0079] Setting of regional target status: Target state The setting is based on the global optimization objective function to ensure that the combustion parameters of each area meet the following conditions: Oxygen concentration : It is in the range corresponding to the ideal air-fuel ratio to avoid over-oxygen or hypoxia; temperature : It is in the optimal range of combustion thermal efficiency, while reducing the high temperature area as much as possible to inhibit the generation of NOx; Coal powder concentration : Ensure uniform feeding to avoid local excess or deficiency.

[0080] The dynamic adjustment of the target state is based on changes in boiler operating load and coal quality conditions, and is updated in real time through the online optimization module.

[0081] Collaborative optimization between regions: The distributed feedback controller in this embodiment realizes the coordinated optimization among multiple regions through the regional coupling term. Specifically, it is as follows: This item adjusts the control input according to the parameter differences between adjacent areas, so that the oxygen concentration, temperature and coal powder concentration gradually tend to balance in the entire furnace.

[0082] For example, when there is a significant temperature gradient between adjacent zones, the controller will increase the fuel supply to the low-temperature zone while reducing the fuel supply to the high-temperature zone to reduce the temperature difference and improve combustion uniformity.

[0083] In actual operation, the distributed feedback controller achieves dynamic optimization through the following steps: Receive real-time data on oxygen concentration, temperature and coal powder concentration in the area; Calculate the deviation between the current state and the target state, and calculate the adjustment amount based on the parameter differences of adjacent areas; Generate adjustment instructions for air flow and pulverized coal flow and send them to actuators (such as air damper valves and pulverized coal feeders); Continuously update control inputs and adjust control strategies based on real-time data to ensure continuous optimization of the combustion process.

[0084] This embodiment achieves multi-parameter optimization of different areas in the boiler furnace through dynamic adjustment of the distributed feedback controller. The controller can quickly respond to changes in oxygen concentration, temperature and coal powder concentration, so that the combustion state of each area tends to be ideal. Further, through the coordinated optimization between regions, the parameter gradient in the furnace is reduced, and the combustion uniformity is significantly improved.

[0085] For step S5, in this embodiment, in order to maximize combustion efficiency, minimize pollutant emissions and improve combustion uniformity during boiler combustion, a global optimization objective function is further constructed based on the optimization results of the aforementioned distributed feedback control to guide the dynamic ratio adjustment of air flow and coal powder flow, thereby optimizing the combustion performance of the boiler on a global scale.

[0086] The construction of the global optimization objective function combines the three core optimization objectives of combustion efficiency, pollutant emissions and combustion uniformity. Through the multi-objective weighted method, the performance indicators of these three aspects are quantified and integrated into an objective function to ensure that the optimization process can dynamically balance the weights of different objectives under complex working conditions.

[0087] Construction of global optimization objective function: The global objective function constructed in this embodiment is in the following form: in: Combustion efficiency optimization goal; Pollutant emission optimization objectives; Combustion uniformity optimization objectives; , , : They are the weight coefficients of combustion efficiency, pollutant emission and combustion uniformity respectively. The weight coefficients can be dynamically adjusted according to the boiler operating conditions.

[0088] Combustion efficiency is an important indicator of boiler operating performance, and its optimization goal is defined as maximizing the full combustion of fuel and air. The combustion efficiency optimization objective function is expressed as: in: is a function of combustion efficiency, which depends on the oxygen concentration , coal powder concentration and temperature ; The spatial area representing the furnace; To optimize the time range.

[0089] The combustion efficiency function is improved through the coordinated optimization of multiple parameters to ensure that the supply of fuel and oxygen matches the combustion reaction rate and avoid incomplete combustion.

[0090] The goal of pollutant emission optimization is to reduce the generation of major pollutants such as nitrogen oxides (NOx) and sulfur dioxide (SOx). This goal is defined as minimizing pollutant emissions, specifically expressed as: in: Indicates the amount of nitrogen oxides produced, which depends on oxygen concentration and temperature distribution; Indicates the amount of sulfur dioxide generated, which depends on the coal powder concentration and the sulfur content of the coal.

[0091] By adjusting the distribution of oxygen concentration and temperature, the generation of NOx under high temperature conditions can be suppressed. At the same time, by optimizing the uniformity of coal powder flow, the SOx generated by excessive combustion in local areas can be reduced.

[0092] Combustion uniformity is a key factor in boiler operation stability. Its optimization goal is defined as minimizing the gradient distribution of the temperature field in the furnace, which can be specifically expressed as: in: represents the square of the temperature gradient; The smaller the square of the gradient, the more uniform the temperature field distribution.

[0093] By optimizing the regional distribution of air flow and pulverized coal flow, local hot spots and low-temperature areas in the temperature field are reduced, and the overall combustion uniformity is improved.

[0094] In this embodiment, the weight coefficient , , The value can be adjusted in real time according to the boiler operating conditions to meet different combustion optimization requirements. For example: In case of large load fluctuations, The weight of the combustion efficiency is given priority; Under working conditions with strict environmental requirements, increase weights to reduce pollutant emissions; When the coal quality fluctuates significantly, the weights to maintain combustion uniformity and operating stability.

[0095] The dynamic weight adjustment mechanism is implemented through an online optimization algorithm, which calculates the impact of each objective function on the global performance in real time and dynamically updates the weight value.

[0096] Dynamic adjustment of air flow and coal powder flow: Through the optimization results of the above objective function, the air flow is calculated in real time and coal powder flow The adjustment instruction is generated as follows: in: and They are the basic supply of air and pulverized coal, respectively, which are set according to the boiler load demand; and They are respectively the dynamic adjustment amounts calculated based on the optimization objective function.

[0097] The adjustments are calculated by a feedback controller to ensure that the supply of fuel and air always matches the combustion requirements.

[0098] In actual operation, the global optimization objective function is calculated in real time by the boiler control system, and the optimization results are directly used to generate control instructions for air and coal flow. The optimization process works in conjunction with the distributed feedback controller to ensure the consistency of global objectives and local regulation.

[0099] In this embodiment, by constructing a global optimization objective function and dynamically adjusting the ratio of air flow rate to coal powder flow rate, the comprehensive optimization of the boiler combustion process is achieved. The dynamic weight adjustment mechanism of the objective function enables the optimization process to adapt to different working conditions and ensure that the combustion efficiency, pollutant emissions and combustion uniformity are optimally balanced.

[0100] For step S6, in this embodiment, in order to apply the optimized control instructions to the boiler system, the combustion parameters are adjusted in real time through the actuator to achieve dynamic optimization and closed-loop control of the combustion process. This step is based on the above optimization results and combined with the actual boiler operation conditions to accurately transmit the adjustment instructions of the air flow and the pulverized coal flow to the actuator to achieve real-time adjustment of the furnace combustion state.

[0101] The control instruction sending and execution mechanism of this embodiment includes three core links: optimizing instruction generation, instruction transmission and distribution, and actuator response and feedback.

[0102] Optimizing instruction generation: The optimization instructions are generated by the distributed feedback controller and the global optimization objective function. The instructions include the air flow adjustment amount And pulverized coal flow adjustment The generation of optimization instructions is based on the following formula: in: and are the optimized air flow rate and pulverized coal flow rate respectively; and It is the basic supply of air and pulverized coal, which is set according to boiler load and fuel characteristics; and It is the dynamic adjustment quantity calculated based on the control law, which comes from the calculation result of the aforementioned distributed controller.

[0103] The generation of optimization instructions ensures that the dynamic adjustment of air flow and coal powder flow matches the real-time combustion demand, while ensuring that the combustion status of different areas in the furnace reaches the optimization target.

[0104] Instruction transmission and distribution: The generated optimization instructions are transmitted to the execution module in the boiler control system through the industrial communication network. During the transmission process, the system distributes the instructions to the following two types of actuators: 1. Air flow control module: It includes a damper valve, a blower and an actuator for adjusting the damper; After the command is transmitted to the damper valve controller, the air flow rate sent to the furnace is adjusted in real time to ensure that the oxygen concentration meets the combustion requirements.

[0105] 2. Pulverized coal flow control module: It includes a pulverizer feeder, pulverized coal nozzle and a control unit for a burner; After the command is transmitted to the powder feeder, the amount of pulverized coal supplied to the furnace and the injection flow rate are dynamically adjusted to ensure that the fuel is evenly distributed and matches the air flow.

[0106] In order to improve the real-time response performance of the system, a high-bandwidth, low-latency industrial communication protocol is adopted in this embodiment to ensure the stability and accuracy of instruction transmission.

[0107] Actuator response and adjustment: After the optimization instruction is transmitted to the actuator, each actuator adjusts the supply parameters of air and pulverized coal according to the instruction content. The specific adjustment process is as follows: The air flow is adjusted by adjusting the air door opening, and the dynamic adjustment formula is: in: is the actual damper opening; is the basic damper opening; The opening increment calculated for the optimization instruction.

[0108] The adjustment of pulverized coal flow is achieved by controlling the rotation speed of the pulverizer and the injection speed of the nozzle. The dynamic adjustment formula is: in: It is an optimization function for coal powder supply and is updated in real time according to the combustion reaction rate.

[0109] The actuator executes the command immediately after receiving it, and simultaneously feeds back the adjustment result to the control system to ensure the closed loop completion of the adjustment process.

[0110] Feedback and closed-loop control: In order to ensure the continuity and real-time performance of the combustion optimization process, this embodiment adopts a closed-loop control mechanism, which specifically includes: Real-time sensors monitor changes in oxygen concentration, temperature and coal powder concentration, and feed the monitoring data back to the control system. After the feedback signal is compared with the target value, the calculation results of the distributed feedback controller and the global optimization module are updated in real time.

[0111] The execution process of feedback closed-loop control is as follows: Real-time collected parameter data includes oxygen concentration ,temperature , coal powder concentration ; Parameter deviation As input to the distributed controller, it is used to adjust the optimization instructions; The actuator performs the next round of dynamic adjustment after receiving the new optimization instruction.

[0112] Through closed-loop control, it is ensured that the combustion state is always kept within the optimal range and responds quickly to changes in operating conditions (such as load fluctuations or changes in coal quality).

[0113] The optimization instruction sending and execution process of this embodiment is implemented by the following steps: After the optimization instructions are generated, they are transmitted to each execution module using the industrial communication network; The actuator adjusts the air flow and coal powder flow according to the instructions and provides real-time feedback through closed-loop control; The control system processes the feedback data and generates new optimization instructions to ensure dynamic optimization of the combustion process.

[0114] This embodiment uses the optimized control instructions to act on the boiler system in real time, realizing dynamic regulation of the combustion process. The real-time adjustment of air flow and coal powder flow ensures the precise matching of oxygen concentration and fuel supply, effectively improves combustion efficiency, and significantly reduces pollutant emissions. Through the closed-loop control mechanism, the continuity of combustion optimization and adaptability to working conditions are guaranteed.

[0115] In general, the present invention obtains multiple key parameters such as oxygen concentration, temperature, coal powder concentration, air flow, coal powder flow and boiler load during boiler operation, establishes a distributed parameter model to describe the combustion state, extracts key characteristic variables in combination with multi-scale analysis, and designs a distributed feedback controller to perform multi-parameter dynamic optimization of different areas in the furnace. The ratio of air flow and coal powder flow is dynamically adjusted by constructing a global optimization objective function, and finally the combustion parameters are adjusted in real time through the actuator.

[0116] The thermal power boiler combustion optimization system based on multi-parameter feedback control described below and the thermal power boiler combustion optimization method based on multi-parameter feedback control described above can be referred to each other.

[0117] Please refer to the attached Figure 2 The present invention also provides a thermal power boiler combustion optimization system based on multi-parameter feedback control, comprising: The data acquisition unit 100 is used to collect dynamic data of oxygen concentration, temperature, pulverized coal concentration and external input parameters in the boiler furnace in real time; A distributed parameter modeling unit 200 is used to establish a distributed parameter model of the combustion state in the boiler furnace; A multi-scale analysis unit 300, for extracting key combustion characteristic variables at time scales and spatial scales; A distributed feedback control unit 400, for adjusting boiler operating parameters, including air flow and pulverized coal flow, based on real-time data; The optimization execution unit 500 is used to execute the optimization instructions.

[0118] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0119] Please refer to the attached Figure 3 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is executed.

[0120] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41, the above method is executed.

[0121] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0122] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A combustion optimization method for thermal power boilers based on multi-parameter feedback control, characterized in that: The following steps are involved: Obtain multiple key parameters during boiler combustion, including oxygen concentration, temperature, pulverized coal concentration, air flow, pulverized coal flow and boiler load; A distributed parameter model is established to describe the combustion state in the boiler furnace, wherein the model represents the dynamic changes of oxygen concentration, temperature and pulverized coal concentration over time and space during the combustion process; Through multi-scale analysis, the time and space scales of the combustion process are decomposed to extract key characteristic variables that characterize the combustion state; According to the distributed parameter model and multi-scale analysis results, the multi-parameter dynamic optimization of different areas in the boiler furnace is realized based on the distributed feedback controller; Based on distributed feedback control, a global optimization objective function is constructed to dynamically adjust the ratio of air flow and coal powder flow. The optimized control instructions are sent to the boiler system, and the combustion parameters are adjusted in real time through the actuator.

2. The method for optimizing combustion of thermal power boilers based on multi-parameter feedback control according to claim 1, characterized in that: The distributed parameter model characterizes the dynamic changes of oxygen concentration, temperature and pulverized coal concentration in the boiler furnace over time and space, and specifically includes the following dynamic equations: The oxygen concentration dynamic equation describes the transmission, diffusion and consumption behavior of oxygen and satisfies the following relationship: in, is the oxygen concentration, Indicates time, is the air flow velocity field, is the oxygen diffusion coefficient, is the oxygen consumption rate coefficient, is the coal powder concentration; The temperature dynamic equation describes the behavior of heat transfer, combustion heat release and heat loss, and satisfies the following relationship: in, is the temperature, is the gas density, is the specific heat capacity, is the thermal conductivity, is the enthalpy change of the combustion reaction, is the heat loss coefficient, is the ambient temperature; The dynamic equation of pulverized coal concentration describes the distribution of pulverized coal and the combustion reaction behavior and satisfies the following relationship: Model constraints include initial conditions and boundary conditions; Initial conditions: in, is the oxygen concentration at time The initial distribution value at is the temperature at time The initial distribution value at is the coal powder concentration at time The initial distribution value at time ; Boundary conditions: in, is the boundary normal vector, is the furnace wall temperature.

3. The combustion optimization method for thermal power boiler based on multi-parameter feedback control according to claim 1 is characterized in that: The step of decomposing the time scale and space scale of the combustion process by multi-scale analysis comprises: On the time scale, oxygen concentration and coal powder concentration are decomposed into fast variables, and temperature is decomposed into a slow variable; On the spatial scale, the average combustion parameters in each area are extracted based on the regional division of the furnace, including the regional average value of oxygen concentration, the regional gradient of temperature and the regional change rate of coal powder concentration.

4. The method for optimizing combustion of thermal power boilers based on multi-parameter feedback control according to claim 1, characterized in that: The steps of realizing multi-parameter dynamic optimization of different areas in the boiler furnace based on the distributed feedback controller include: A local feedback controller is established for each partition separately, and the local controller calculates the control deviation based on the real-time oxygen concentration, temperature and coal powder concentration data in the partition; Using feedback control law, dynamically adjust the distribution of air flow and pulverized coal flow in the partition; Based on the synergistic relationship between regional parameters, the overall combustion performance is optimized through a regional coupling mechanism.

5. The method for optimizing combustion of thermal power boilers based on multi-parameter feedback control according to claim 1, characterized in that: The global optimization objective function is constructed based on the optimization goals of maximizing combustion efficiency, minimizing pollutant emissions, and improving combustion uniformity, and specifically includes: The combustion efficiency objective is to maximize the combustion efficiency by improving the combustion completeness of fuel and oxygen in the furnace. The combustion efficiency objective function is defined as: in, represents the combustion efficiency function; Pollutant emission target, by reducing the emission of nitrogen oxides and sulfur dioxide generated during the combustion process to minimize pollutant emissions, the pollutant emission objective function is defined as: in, Indicates the amount of NOx generated, Indicates the amount of SOx generated; The combustion uniformity objective is to improve the combustion uniformity by reducing the gradient distribution of the temperature field in the furnace. The combustion uniformity objective function is defined as: in, represents the square of the gradient of the temperature field; The global optimization objective function is a weighted combination of sub-objective functions to construct a global objective function, which is defined as: in, , , is the weight coefficient.

6. The method for optimizing combustion of thermal power boilers based on multi-parameter feedback control according to claim 5, characterized in that: The weight coefficient of the global optimization objective function is dynamically adjusted according to the boiler operating conditions to adapt to coal quality fluctuations and boiler load changes.

7. The method for optimizing combustion of thermal power boilers based on multi-parameter feedback control according to claim 1, characterized in that: The feedback controller dynamically adjusts the air flow rate and the pulverized coal flow rate through the following control law: Calculate adjustment instructions based on combustion state deviations in each zone; The adjustment results of the air flow and the pulverized coal flow are sent to the actuators, including the air damper valve and the pulverized coal feeder.

8. A thermal power boiler combustion optimization system based on multi-parameter feedback control, used to execute the thermal power boiler combustion optimization method based on multi-parameter feedback control as claimed in any one of claims 1 to 7, characterized in that: include: Data acquisition unit, used to collect dynamic data of oxygen concentration, temperature, pulverized coal concentration and external input parameters in the boiler furnace in real time; A distributed parameter modeling unit, used to establish a distributed parameter model of the combustion state in the boiler furnace; Multi-scale analysis unit, used to extract key combustion characteristic variables at both temporal and spatial scales; A distributed feedback control unit, which is used to adjust boiler operating parameters based on real-time data, including air flow and pulverized coal flow; The optimized execution unit is used to execute optimized instructions.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the thermal power boiler combustion optimization method based on multi-parameter feedback control as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing combustion of a thermal power boiler based on multi-parameter feedback control as described in any one of claims 1 to 7 is implemented.

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