Method for optimizing combustion of thermal power boiler based on multi-parameter feedback control

Abstract

This application relates to the field of thermal power generation, and discloses a combustion optimization method for thermal power boilers based on multi-parameter feedback control, including the following steps: obtaining key parameters such as oxygen concentration, temperature, pulverized coal concentration, air flow rate, pulverized coal flow rate, and boiler load during boiler operation; establishing a distributed parameter model that describes the dynamic changes of oxygen concentration, temperature, and pulverized coal concentration in the furnace over time and space; extracting key characteristic variables representing the combustion state through multi-scale analysis; dynamically optimizing multi-parameters in different regions of the furnace based on a distributed feedback controller; constructing a global optimization objective function to dynamically adjust the ratio of air flow rate to pulverized coal flow rate; sending the optimized control instructions to the boiler system, and real-time adjusting the combustion parameters through an actuator. The present invention realizes a significant improvement in combustion efficiency, an effective reduction in pollutant emissions, and can adapt to coal quality fluctuations and load changes through distributed modeling, multi-scale analysis, and feedback control.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and particularly to a combustion optimization method for thermal power boilers 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 economy and environmental protection performance of power production. Improving the 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 techniques based on single parameters are usually adopted. For example, the supply of air and fuel is regulated by monitoring the oxygen concentration or flue gas temperature. However, this single-parameter control method has obvious limitations in the complex boiler combustion process. The combustion process involves multiple coupled variables (such as oxygen concentration, temperature field, pulverized coal concentration, etc.), which affect each other and change dynamically. A single parameter cannot fully reflect the actual working conditions of the boiler combustion state.

[0003] Traditional methods respond slowly in the face of complex working conditions such as coal quality fluctuations and load changes, which easily lead to unstable combustion states, resulting in problems such as low combustion efficiency and excessive pollutant emissions. In addition, single-parameter regulation is difficult to achieve global optimization of combustion parameters in the furnace, resulting in possible local hot spots or low-oxygen areas inside the boiler, further exacerbating the risk of decreased fuel utilization rate and pollutant generation. At the same time, the existing technology lacks the ability to monitor and comprehensively control the dynamic changes in the combustion process in real time and cannot meet the requirements of high-efficiency energy conservation and environmental protection emissions of 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] Aiming at the deficiencies of the existing technology, the present invention provides a combustion optimization method for thermal power boilers 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 realized through the following technical solutions: A combustion optimization method for thermal power boilers based on multi-parameter feedback control, including the following steps:

[0006] Obtain multiple key parameters during the boiler combustion process, including oxygen concentration, temperature, pulverized coal concentration, air flow rate, pulverized coal flow rate, and boiler load;

[0007] Establish a distributed parameter model describing the combustion state in the boiler furnace, and the model characterizes the dynamic changes of oxygen concentration, temperature, and pulverized coal concentration over time and space during the combustion process;

[0008] Decompose the time scale and space scale of the combustion process through multi-scale analysis, and extract the key characteristic variables representing the combustion state;

[0009] According to the distributed parameter model and the multi-scale analysis results, realize the multi-parameter dynamic optimization of different regions in the boiler furnace based on the distributed feedback controller;

[0010] On the basis of distributed feedback control, construct a global optimization objective function and dynamically adjust the ratio of air flow to pulverized coal flow;

[0011] Send the optimized control instructions to the boiler system, and adjust the combustion parameters in real time through the actuator.

[0012] 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:

[0013] Oxygen concentration dynamic equation, describing the transport, diffusion and consumption behavior of oxygen, satisfying the following relationship:

[0014]

[0015] Among them, is the oxygen concentration, is the gas flow velocity field, is the oxygen diffusion coefficient, is the oxygen consumption rate coefficient, is the pulverized coal concentration;

[0016] Temperature dynamic equation, describing heat transfer, heat release from combustion and heat loss behavior, satisfying the following relationship:

[0017]

[0018] Among them, is the temperature, is the gas density, is the specific heat capacity, is the thermal conductivity, is the enthalpy change of combustion reaction, is the heat loss coefficient, is the ambient temperature;

[0019] Pulverized coal concentration dynamic equation, describing the pulverized coal distribution and combustion reaction behavior, satisfying the following relationship:

[0020]

[0021] The model constraints include initial conditions and boundary conditions;

[0022] Initial conditions:

[0023]

[0024] Among them, is the initial distribution value of the oxygen concentration at time ; is the initial distribution value of the temperature at time ; is the initial distribution value of the pulverized coal concentration at time ;

[0025] Boundary conditions:

[0026]

[0027] Among them, is the boundary normal vector, is the temperature of the furnace wall.

[0028] Preferably, the step of decomposing the time scale and space scale of the combustion process through multi-scale analysis includes:

[0029] On the time scale, the oxygen concentration and pulverized coal concentration are decomposed into fast variables, and the temperature is decomposed into slow variables;

[0030] On the space scale, based on the regional division of the furnace, the average combustion parameters in each region are extracted, including the regional average value of the oxygen concentration, the regional gradient of the temperature, and the regional change rate of the pulverized coal concentration.

[0031] Preferably, the step of realizing multi-parameter dynamic optimization in different regions of the boiler furnace based on the distributed feedback controller includes:

[0032] A local feedback controller is established separately for each partition, and the local controller calculates the control deviation according to the real-time oxygen concentration, temperature, and pulverized coal concentration data in the partition;

[0033] Using the feedback control law, dynamically adjust the distribution of the air flow and pulverized coal flow in the partition;

[0034] Based on the parameter coordination relationship between regions, optimize the overall combustion performance through the region coupling mechanism.

[0035] Preferably, the global optimization objective function is constructed based on the optimization objectives of maximizing combustion efficiency, minimizing pollutant emissions, and improving combustion uniformity, and specifically includes:

[0036] Combustion efficiency objective: Maximize the combustion efficiency by improving the combustion sufficiency of fuel and oxygen in the furnace. The combustion efficiency objective function is defined as:

[0037]

[0038] Among them, Represents the combustion efficiency function;

[0039] Pollutant emission target, minimizing pollutant emissions by reducing the emissions of nitrogen oxides and sulfur dioxide generated during the combustion process. The pollutant emission target function is defined as:

[0040]

[0041] Wherein, Represents the amount of NOx generated, Represents the amount of SOx generated;

[0042] Combustion uniformity target, improving combustion uniformity by reducing the gradient distribution of the temperature field in the furnace. The combustion uniformity target function is defined as:

[0043]

[0044] Wherein, Represents the square of the gradient of the temperature field;

[0045] Global optimization target function, constructing the global target function by weighted combination of sub-target functions, defined as:

[0046]

[0047] Wherein, , , Are weight coefficients.

[0048] Preferably, the weight coefficients of the global optimization target function are dynamically adjusted according to the operating conditions of the boiler to adapt to the coal quality fluctuations and boiler load changes.

[0049] Preferably, the feedback controller dynamically adjusts the air flow rate and pulverized coal flow rate through the following control law:

[0050] Calculating the adjustment command according to the combustion state deviation in each region;

[0051] Sending the adjustment results of the air flow rate and pulverized coal flow rate to the actuators, including the air damper and the pulverized coal feeder.

[0052] The present invention also provides a combustion optimization system for a thermal power boiler based on multi-parameter feedback control, including:

[0053] Data acquisition unit, used to collect dynamic data of oxygen concentration, temperature, pulverized coal concentration in the boiler furnace and external input parameters in real time;

[0054] Distributed parameter modeling unit, used to establish a distributed parameter model of the combustion state in the boiler furnace;

[0055] A multi-scale analysis unit for extracting key combustion characteristic variables under time scale and space scale;

[0056] A distributed feedback control unit for adjusting boiler operation parameters based on real-time data, including air flow rate and pulverized coal flow rate;

[0057] An optimization execution unit for executing optimization instructions.

[0058] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method as described above is implemented.

[0059] 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 as described above is implemented.

[0060] The present invention provides a combustion optimization method for thermal power boilers based on multi-parameter feedback control. It has the following beneficial effects:

[0061] 1. Through multi-parameter feedback control and distributed optimization methods, the present invention accurately adjusts the ratio of air to pulverized coal in the boiler furnace, optimizes the combustion reaction process of fuel and oxygen, and significantly improves the combustion efficiency. By dynamically adjusting the boiler operation parameters in real time, it can minimize fuel waste and reduce the energy consumption cost to the greatest extent.

[0062] 2. The present invention constructs a global optimization function aiming at reducing the generation of pollutants such as NOx and SOx, combines the dynamic regulation of oxygen concentration and temperature, inhibits the generation of NOx in high-temperature areas, and at the same time improves the uniformity of pulverized coal supply to reduce the SOx emission.

[0063] 3. Through the regional collaborative optimization of the distributed feedback controller, the present invention reduces the non-uniformity of parameters in the furnace (such as oxygen concentration, temperature, and pulverized coal concentration), avoids the occurrence of local hot spots or insufficient combustion phenomena. The optimized combustion uniformity improves the heat transfer efficiency of the boiler, reduces the operation risk caused by parameter fluctuations at the same time, and improves the overall stability and reliability of the system.

[0064] 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 working conditions such as boiler load fluctuations, coal quality changes, 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, extends the service life of equipment and improves the economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of the method of the present invention;

[0066] Figure 2 Schematic diagram of the system structure of the present invention;

[0067] Figure 3 Schematic diagram of the computer device structure of the present invention.

[0068] 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 device; 41, processor; 42, memory; 43, storage medium. Specific embodiments

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0070] Please refer to the attached Figure 1 , the present invention provides a combustion optimization method for thermal power boilers based on multi-parameter feedback control, which realizes the dynamic optimization of the boiler combustion process by obtaining multiple key parameters, distributed parameter modeling, multi-scale analysis, distributed feedback control, and the construction of a global optimization objective function.

[0071] As Figure 1 shown, the combustion optimization method for thermal power boilers based on multi-parameter feedback control may include the following steps:

[0072] S1. Obtain multiple key parameters during the boiler combustion process;

[0073] S2. Establish a distributed parameter model describing the combustion state in the boiler furnace;

[0074] S3. Extract key characteristic variables of the combustion state through multi-scale analysis;

[0075] S4. Achieve multi-parameter dynamic optimization based on a distributed feedback controller;

[0076] S5. Construct a global optimization objective function and dynamically adjust the parameter ratio;

[0077] S6. Execute the optimization control instruction.

[0078] The following will elaborate on each step of the method of the present invention in detail.

[0079] For step S1, in this embodiment, in order to accurately characterize various operating state parameters during the boiler combustion process and ensure the real-time and comprehensiveness of the combustion optimization process, high-precision sensors and a data acquisition system are used to obtain multiple key parameters during boiler operation, and real-time transmission and processing are achieved through industrial communication protocols.

[0080] During the operation of the boiler, the combustion state is closely related to multiple core parameters. In the present invention, various sensors and data acquisition devices distributed in the boiler furnace are used to collect these key parameters. The key parameters include but are not limited to:

[0081] Oxygen concentration : Oxygen concentration is one of the most important feedback parameters during the combustion process, reflecting the degree of oxidation-reduction during the combustion process. In this embodiment, oxygen sensors arranged in different regions of the furnace are used to monitor the oxygen concentration distribution in real time. The oxygen sensors use high-precision chemical sensing elements and can respond to changes in oxygen content as low as one part per million (ppm).

[0082] The real-time change of oxygen concentration satisfies the following relationship:

[0083]

[0084] Where:

[0085] is the dynamic relationship between the air flow input and the combustion area;

[0086] is the oxygen consumption, which is related to the pulverized coal concentration and the combustion reaction rate.

[0087] The change trend of oxygen concentration is directly used for subsequent air-fuel ratio adjustment.

[0088] Temperature : Temperature reflects the heat release and transfer during the combustion process and is an important parameter for combustion efficiency. In this embodiment, multiple high-temperature sensors arranged on the furnace wall and inside are used to measure the temperature distribution at different positions in the furnace in real time.

[0089] To ensure the accuracy of data acquisition, the high-temperature sensors cover a temperature range of 2000 °C and can effectively characterize the differences between high-temperature and low-temperature regions during the combustion process.

[0090] The dynamic change of the furnace temperature is represented by the following equation:

[0091]

[0092] Where:

[0093] is the gas density in the furnace;

[0094] is the specific heat capacity;

[0095] is the air flow velocity;

[0096] is the thermal conductivity;

[0097] is the heat released by combustion;

[0098] is the heat loss.

[0099] The temperature distribution collected through sensor data is used for subsequent distributed modeling and temperature field optimization.

[0100] Coal powder concentration : The coal powder concentration is an important parameter for fuel supply, and its uniformity directly affects the combustion efficiency and the amount of pollutants generated. In this embodiment, a coal powder concentration sensor is used to monitor the spatial distribution after the fuel is injected into the furnace to ensure uniform fuel supply.

[0101] The dynamic change of the coal powder concentration satisfies the following relationship:

[0102]

[0103] Where:

[0104] is the conveying speed of the coal powder;

[0105] is the reaction rate coefficient of oxygen and coal powder.

[0106] The coal powder concentration data is used as the input for the subsequent distributed feedback controller.

[0107] Air flow rate : The air flow rate determines the supply amount of oxygen and is the key control parameter to achieve sufficient combustion. In this embodiment, a high-precision flow meter in the air duct is used to measure the air flow rate entering the furnace in real time.

[0108] The input relationship between the air flow rate and the oxygen concentration satisfies the following conditions:

[0109]

[0110] Where, is the opening of the control valve for air flow, is the adjustment instruction of the controller for the air flow rate.

[0111] The measurement of the air flow rate provides a direct basis for subsequent oxygen concentration adjustment.

[0112] Coal powder flow rate :The pulverized coal flow rate is the total control parameter of fuel supply, and its real-time measurement determines the heat output of combustion. In this embodiment, an on-line pulverized coal flow meter is used to measure the dynamic flow rate of the fuel at the outlet of the coal feeder.

[0113] The relationship between the pulverized coal flow rate and the load demand satisfies the following conditions:

[0114]

[0115] Where:

[0116] is the current heat load demand of the boiler;

[0117] is the combustion efficiency correction coefficient.

[0118] The monitoring result of the pulverized coal flow rate is directly used for the fuel supply adjustment under the condition of load change.

[0119] Boiler load : The boiler load is an important input condition for the combustion process and reflects the external heat output demand. In this embodiment, the boiler control system is used to collect the load signal in real time, and the combustion parameters are dynamically adjusted in combination with the current air flow rate and pulverized coal flow rate.

[0120] The boiler load is used to guide the global optimization of the subsequent combustion parameters.

[0121] In order to ensure the accuracy and real-time performance of the above parameter acquisition, in this embodiment, an industrial communication protocol (such as Modbus, Profibus) is used to transmit the parameter data to the central data processing unit, and the data is processed and stored in real time.

[0122] The data provides the basic input conditions for the subsequent distributed modeling, multi-scale analysis and feedback controller, and realizes the comprehensive monitoring and optimization of the combustion process.

[0123] 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 pulverized coal concentration over time and space. This model fully considers the transportation, diffusion and reaction behaviors of oxygen, heat and fuel in the combustion process, and mathematically describes the combustion state in the furnace through a set of partial differential equations (PDE).

[0124] The distributed parameter model specifically includes:

[0125] Oxygen concentration dynamic model:

[0126] The oxygen concentration is jointly affected by gas flow transmission, molecular diffusion and chemical reaction consumption during the combustion process, and its dynamic change is described by the following equation:

[0127]

[0128] Among them, represents the oxygen concentration; represents time; represents the air flow velocity field, whose 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 pulverized coal; represents the pulverized coal concentration.

[0129] The above dynamic equation of oxygen concentration reflects the relationship between the supply and consumption of oxygen, and its distribution state has a decisive impact on combustion efficiency and pollutant generation. In the model, represents the consumption rate of oxygen in the combustion reaction.

[0130] Temperature dynamic model:

[0131] The temperature distribution in the furnace is a core parameter of the combustion process, 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:

[0132]

[0133] Among them, is the 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; represents the ambient temperature outside the furnace.

[0134] In this model, represents the heat released by the combustion reaction, represents the heat loss. Through this model, the temperature field change at different positions in the furnace can be accurately described, providing support for subsequent optimization of combustion uniformity.

[0135] Pulverized coal concentration dynamic model:

[0136] The distribution of pulverized coal concentration is a direct reflection of fuel supply and combustion reaction, and its dynamic change is described by the following equation:

[0137]

[0138] Among them, represents the pulverized coal concentration; represents the consumption rate of the pulverized coal combustion reaction.

[0139] By considering the transportation and combustion reaction of pulverized coal, this model accurately reflects the supply and consumption status of fuel, providing a basis for subsequent optimization of fuel distribution.

[0140] Boundary conditions and initial conditions:

[0141] To make the model have practical applicability, boundary conditions and initial conditions are further introduced in this embodiment to constrain the initial values and distribution ranges of oxygen concentration, temperature, and pulverized coal concentration.

[0142] The initial conditions are:

[0143]

[0144] Among them, , , are the initial distribution values of oxygen concentration, temperature, and pulverized coal concentration respectively.

[0145] The boundary conditions are:

[0146]

[0147] Among them, represents the boundary no-flux condition of oxygen, is the boundary normal vector; represents the boundary heat transfer condition of temperature, is the furnace wall temperature; represents the boundary no-flux condition of pulverized coal.

[0148] In this embodiment, based on the above distributed parameter model, the model is discretely solved by numerical calculation methods (such as the finite difference method or the finite element method) to realize the real-time simulation and dynamic tracking of oxygen concentration, temperature, and pulverized coal concentration in the furnace.

[0149] In the specific implementation process, the distribution data of oxygen concentration, temperature, and pulverized coal concentration are obtained in real time through an industrial data acquisition system as the input conditions of the model; by solving the above equations, the dynamic distribution of each parameter in the furnace is obtained, providing basic support for subsequent control and optimization.

[0150] The running time of the model is synchronized with the sensor-collected data to ensure that the distributed parameter model can be updated in real time and reflect the boiler operation status.

[0151] The distributed parameter model established in this embodiment can accurately describe the dynamic change laws of oxygen concentration, temperature, and pulverized coal concentration during the boiler combustion process, providing a scientific basis for subsequent optimal control and the construction of the global objective function.

[0152] For step S3, in this embodiment, in order to accurately extract the key characteristic variables in the boiler combustion process to better characterize the variation laws of core parameters such as oxygen concentration, temperature, and pulverized coal concentration at different time and space scales, a multi-scale analysis method is used to decompose and quantify the combustion process.

[0153] The core idea of multi-scale analysis is to divide the complex combustion process into two dimensions of time and space, extract key characteristics from the two levels of fast and slow variables and regional distribution, establish a simplified and efficient expression of characteristic variables, and provide support for subsequent controller design and optimization.

[0154] Time scale analysis:

[0155] There are significant time scale differences in different physical parameters during the combustion process. For example, the combustion reaction rate changes rapidly, while the temperature transfer changes relatively slowly. Therefore, in this embodiment, a time scale decomposition method is adopted to separate the fast variables from the slow variables to improve the description accuracy of the model.

[0156] Oxygen concentration and pulverized coal concentration are regarded as fast variables, and their change rates are significantly affected by the combustion reaction rate. The change of fast variables can be formally described as follows:

[0157]

[0158] Where:

[0159] represents the reaction consumption rate of oxygen;

[0160] represents the reaction consumption rate of pulverized coal;

[0161] is the combustion reaction rate constant.

[0162] Temperature 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:

[0163]

[0164] Where:

[0165] represents the heat released by the combustion reaction;

[0166] represents the heat loss.

[0167] Through time scale decomposition, this embodiment extracts the change rate of oxygen concentration , the change rate of pulverized coal concentration and the time variation trend of temperature , and these characteristic quantities are directly used as input parameters for controller design.

[0168] Spatial scale analysis:

[0169] The combustion process inside the boiler furnace has significant spatial distribution characteristics. There may be significant differences in oxygen concentration, temperature, and pulverized coal concentration in different regions. This spatial non-uniformity will directly affect combustion efficiency and pollutant generation. Therefore, in this embodiment, the regional distribution characteristics of combustion parameters are quantified through spatial scale decomposition.

[0170] For ease of analysis, the furnace region is divided into multiple sub-regions , and the combustion parameters within each sub-region are considered to be relatively uniformly distributed. The following characteristic quantities are extracted for each sub-region:

[0171]

[0172] Where:

[0173] represents the volume of the sub-region ;

[0174] are the average values of oxygen concentration, temperature, and pulverized coal concentration within the sub-region, respectively.

[0175] In addition, to further characterize the non-uniformity of parameters between regions, the gradient characteristics between adjacent regions are calculated:

[0176]

[0177] Where:

[0178] is the geometric center distance between adjacent regions and ;

[0179] , , are the spatial gradients of oxygen concentration, temperature, and pulverized coal concentration, respectively.

[0180] Through the above spatial scale analysis, this embodiment extracts the average parameter values of each sub-region and the gradient characteristics of adjacent regions, which are used to evaluate combustion uniformity and parameter distribution status.

[0181] During the actual operation, in this embodiment, the industrial data acquisition system is used to obtain the distribution data of oxygen concentration, temperature, and pulverized coal concentration in real time, and the key feature quantities are extracted based on the above multi-scale analysis method. The extraction results are input into the controller in real time to guide the optimization of regional parameters and the improvement of combustion uniformity.

[0182] In this embodiment, the characteristic variables extracted through multi-scale analysis include the oxygen concentration change rate, the pulverized coal concentration change rate, the temperature change trend, the regional average value, and the gradient between regions. These characteristic variables accurately characterize the dynamic characteristics and spatial distribution characteristics of the combustion state, providing support for subsequent distributed feedback control and the construction of the global optimization objective function.

[0183] For step S4, in this embodiment, in order to precisely and dynamically optimize the combustion process in the boiler furnace, a distributed feedback controller is used to independently adjust the key parameters such as oxygen concentration, temperature, and pulverized coal concentration in different regions of the furnace, and the global combustion performance is improved through collaborative optimization between regions.

[0184] The design of the distributed feedback controller is based on the previously established distributed parameter model and the multi-scale analysis results. Its goal is to enable each region of the boiler to reach an ideal combustion state through real-time monitoring and dynamic adjustment of multiple parameters, thereby improving the combustion efficiency, reducing pollutant emissions, and optimizing the combustion uniformity.

[0185] Structure of the local feedback controller:

[0186] In this embodiment, the furnace is divided into multiple regions, and each region is independently equipped with a local feedback controller for real-time adjustment of the combustion parameters in this region.

[0187] The control law of the local feedback controller is as follows:

[0188]

[0189] Where:

[0190] is the control input for region , including the air flow adjustment amount and the pulverized coal flow adjustment amount ;

[0191] is the state vector of region , including oxygen concentration, temperature, and pulverized coal concentration;

[0192] is the target state vector of region , determined according to the optimization objective;

[0193] is the regional feedback gain matrix, which determines the response intensity of the controller to the deviation;

[0194] is the region and the coupling weight with the adjacent region is used to coordinate the parameter distribution between adjacent regions;

[0195] is the set of regions adjacent to the region adjacent.

[0196] This control law consists of two parts: the first item is the local feedback regulation term within the region, which directly calculates the adjustment instruction according to the combustion parameter deviation of this region; the second item is the coupling regulation term between regions, which optimizes the collaborative relationship between regions through the gradient information of parameters.

[0197] Dynamic calculation of control input:

[0198] According to the control law, the controller calculates the adjustment amounts of the air flow and the pulverized coal flow in real time:

[0199]

[0200]

[0201] Among them, and are the adjustment functions of the air and pulverized coal flows calculated based on the control law, which are specifically related to the deviation amount and the difference of the adjacent regions related.

[0202] The dynamic adjustment of the air flow directly affects the supply of oxygen concentration, and the dynamic adjustment of the pulverized coal flow affects the heat released by combustion. The coordinated action of the two ensures that the combustion process reaches an ideal state.

[0203] Setting of the regional target state:

[0204] The target state is set based on the global optimization objective function to ensure that the combustion parameters of each region meet the following conditions:

[0205] Oxygen concentration : within the range corresponding to the ideal air-fuel ratio, avoiding over-oxygen or oxygen-deficient phenomena;

[0206] Temperature : within the optimal range of combustion thermal efficiency, while reducing the high-temperature region as much as possible to inhibit the generation of NOx;

[0207] Pulverized coal concentration : ensure uniform feeding, avoiding local excess or deficiency.

[0208] The dynamic adjustment of the target state is updated in real time through the online optimization module based on the changes in the boiler operation load and coal quality conditions.

[0209] Cooperative optimization between regions:

[0210] The distributed feedback controller in this embodiment realizes the cooperative optimization among multiple regions through the regional coupling term. Specifically, it is manifested as:

[0211]

[0212] This item adjusts the control input according to the parameter differences between adjacent regions, making the oxygen concentration, temperature, and pulverized coal concentration gradually tend to be balanced throughout the furnace.

[0213] For example, when there is an obvious temperature gradient between adjacent regions, the controller will increase the fuel supply in the low-temperature region and reduce the fuel supply in the high-temperature region at the same time to reduce the temperature difference and improve the combustion uniformity.

[0214] In actual operation, the distributed feedback controller realizes dynamic optimization through the following steps:

[0215] Receive the oxygen concentration, temperature, and pulverized coal concentration data in the region in real time;

[0216] Calculate the deviation between the current state and the target state, and calculate the adjustment amount in combination with the parameter differences of adjacent regions;

[0217] Generate adjustment instructions for the air flow and pulverized coal flow, and send them to the actuators (such as air dampers and coal feeders);

[0218] Continuously update the control input, adjust the control strategy according to the real-time data, and ensure the continuous optimization of the combustion process.

[0219] Through the dynamic adjustment of the distributed feedback controller in this embodiment, the multi-parameter optimization of different regions in the boiler furnace is realized. The controller can quickly respond to the changes in the oxygen concentration, temperature, and pulverized coal concentration, making the combustion states of each region tend to be ideal. Further, through the cooperative optimization between regions, the parameter gradient in the furnace is reduced, and the combustion uniformity is significantly improved.

[0220] For step S5, in this embodiment, in order to maximize the combustion efficiency, minimize the pollutant emissions, and improve the combustion uniformity during the boiler combustion process, based on the optimization results of the aforementioned distributed feedback control, a global optimization objective function is further constructed to guide the dynamic ratio adjustment of the air flow and pulverized coal flow, so as to optimize the combustion performance of the boiler globally.

[0221] The construction of the global optimization objective function combines three core optimization objectives: combustion efficiency, pollutant emissions, and combustion uniformity. Through the method of multi-objective weighting, the performance indicators in these three aspects are quantified and integrated into one objective function to ensure that the optimization process can dynamically balance the weights of different objectives under complex working conditions.

[0222] Construction of the global optimization objective function:

[0223] The form of the global objective function constructed in this embodiment is as follows:

[0224]

[0225] Where:

[0226] Optimization objective of combustion efficiency;

[0227] Optimization objective of pollutant emissions;

[0228] Optimization objective of combustion uniformity;

[0229] , , : are the weight coefficients of combustion efficiency, pollutant emissions, and combustion uniformity respectively, and the weight coefficients can be dynamically adjusted according to the boiler operating conditions.

[0230]

[0231] Combustion efficiency is an important indicator of boiler operation performance, and its optimization objective is defined as maximizing the full combustion of fuel and air. The optimization objective function of combustion efficiency is expressed as:

[0232]

[0233] Where:

[0234] is the combustion efficiency function, which depends on the oxygen concentration , pulverized coal concentration and temperature ;

[0235] represents the spatial area of the furnace;

[0236] is the optimization time range.

[0237] The combustion efficiency function is improved through the collaborative optimization of multiple parameters to ensure that the supply of fuel and oxygen matches the combustion reaction rate and avoid incomplete combustion.

[0238]

[0239] The optimization goal of pollutant emissions 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 and is specifically expressed as:

[0240]

[0241] Where:

[0242] represents the generation amount of nitrogen oxides, which depends on the oxygen concentration and temperature distribution;

[0243] represents the generation amount of sulfur dioxide, which depends on the pulverized coal concentration and the sulfur content of the coal quality.

[0244] By adjusting the distribution of oxygen concentration and temperature, the generation of NOx under high-temperature conditions is inhibited. At the same time, by optimizing the uniformity of pulverized coal flow rate, the SOx generated by excessive combustion in local areas is reduced.

[0245]

[0246] Combustion uniformity is a key factor in the stability of boiler operation. Its optimization goal is defined as minimizing the gradient distribution of the temperature field in the furnace and is specifically expressed as:

[0247]

[0248] Where:

[0249] represents the square of the temperature gradient;

[0250] The smaller the square of the gradient, the more uniform the temperature field distribution indicates.

[0251] By optimizing the regional distribution of air flow rate and pulverized coal flow rate, local hot spots and low-temperature areas in the temperature field are reduced, and the overall combustion uniformity is improved.

[0252] In this embodiment, the weight coefficients , , can be adjusted in real time according to the boiler operation conditions to meet different combustion optimization requirements. For example:

[0253] In the case of large fluctuations in load, increase the weight of to give priority to ensuring combustion efficiency;

[0254] In the case of strict environmental protection requirements, increase the weight of to reduce pollutant emissions;

[0255] When the coal quality fluctuates significantly, enhance The weights are used to maintain the combustion uniformity and operation stability.

[0256] 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 values.

[0257] Dynamic adjustment of air flow rate and pulverized coal flow rate:

[0258] Based on the optimization results of the above objective functions, the air flow rate is calculated in real time and the pulverized coal flow rate of the adjustment amount. The adjustment instruction is generated in the following way:

[0259]

[0260]

[0261] Where:

[0262] and are the basic supply amounts of air and pulverized coal respectively, which are set according to the boiler load demand;

[0263] and are the dynamic adjustment amounts calculated based on the optimization objective function respectively.

[0264] The adjustment amount is calculated by a feedback controller to ensure that the supply of fuel and air always matches the combustion demand.

[0265] 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 the control instructions for the air and pulverized coal flow rates. The optimization process works in coordination with the distributed feedback controller to ensure the consistency of the global objective and local regulation.

[0266] In this embodiment, by constructing the global optimization objective function and dynamically adjusting the ratio of the air flow rate to the pulverized coal 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 operating conditions and ensures that the combustion efficiency, pollutant emissions, and combustion uniformity reach the optimal balance.

[0267] 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 the dynamic optimization and closed-loop control of the combustion process. This step is based on the above optimization results and combines the actual boiler operation conditions to accurately transmit the adjustment instructions of the air flow rate and the pulverized coal flow rate to the actuator device to achieve the real-time adjustment of the furnace combustion state.

[0268] The control instruction sending and execution mechanism of this embodiment includes three core links: optimized instruction generation, instruction transmission and distribution, and actuator response and feedback.

[0269] Optimized instruction generation:

[0270] The optimized instruction is jointly generated by a distributed feedback controller and a global optimization objective function. The instruction content includes the air flow adjustment amount and the pulverized coal flow adjustment amount. . The generation of the optimized instruction is based on the following formula:

[0271]

[0272]

[0273] Where:

[0274] and are the optimized air flow and pulverized coal flow respectively;

[0275] and are the basic supply amounts of air and pulverized coal, which are set according to the boiler load and fuel characteristics;

[0276] and are the dynamic adjustment amounts calculated based on the control law, which are derived from the calculation results of the aforementioned distributed controller.

[0277] The generation of the optimized instruction ensures that the dynamic adjustment of the air flow and pulverized coal flow matches the real-time combustion demand, and at the same time ensures that the combustion states in different areas of the furnace reach the optimization goal.

[0278] Instruction transmission and distribution:

[0279] The generated optimized instruction is transmitted to the execution module in the boiler control system through an industrial communication network. During the transmission process, the system distributes the instruction to the following two types of actuators:

[0280] 1. Air flow control module:

[0281] It includes actuators such as air dampers, forced draft fans, and regulating dampers;

[0282] After the instruction is transmitted to the air damper controller, it adjusts the air flow into the furnace in real time to ensure that the oxygen concentration meets the combustion demand.

[0283] 2. Pulverized coal flow control module:

[0284] It includes control units such as coal feeders, pulverized coal nozzles, and burners;

[0285] After the instruction is transmitted to the coal feeder, the coal powder amount supplied to the furnace and the injection flow rate are dynamically adjusted to ensure uniform distribution of the fuel and matching with the air flow rate.

[0286] In order to improve the real-time response performance of the system, in this embodiment, an industrial communication protocol with high bandwidth and low latency is adopted to ensure the stability and accuracy of instruction transmission.

[0287] Actuator response and adjustment:

[0288] After optimizing the instruction transmission to the actuator, each actuator adjusts the supply parameters of air and coal powder according to the instruction content. The specific adjustment process is as follows:

[0289] The adjustment of the air flow rate is achieved by adjusting the damper opening, and its dynamic adjustment formula is:

[0290]

[0291] Where:

[0292] is the actual damper opening;

[0293] is the basic damper opening;

[0294] is the opening increment calculated from the optimized instruction.

[0295] The adjustment of the coal powder flow rate is achieved by controlling the rotation speed of the coal feeder and the injection speed of the nozzle, and its dynamic adjustment formula is:

[0296]

[0297] Where:

[0298] is the optimization function for coal powder supply, which is updated in real time according to the combustion reaction rate.

[0299] The actuator executes immediately after receiving the instruction and synchronously feeds back the adjustment result to the control system to ensure the completion of the closed loop of the adjustment process.

[0300] Feedback and closed-loop control:

[0301] In order to ensure the continuity and real-time nature of the combustion optimization process, this embodiment adopts a closed-loop control mechanism. Specifically, it includes:

[0302] Real-time sensors monitor the changes in oxygen concentration, temperature, and coal powder concentration, and feed the monitoring data back to the control system. After comparing the feedback signal with the target value, the calculation results of the distributed feedback controller and the global optimization module are updated in real time.

[0303] The execution flow of the feedback closed-loop control is as follows:

[0304] The parameter data collected in real time includes oxygen concentration , temperature , pulverized coal concentration ;

[0305] Parameter deviation is used as the input of the distributed controller to adjust the optimization instruction;

[0306] After receiving the new optimization instruction, the actuator performs the next round of dynamic adjustment.

[0307] Through closed-loop control, ensure that the combustion state is always maintained within the optimal range and quickly respond to changes in working conditions (such as load fluctuations or coal quality changes).

[0308] The process of sending and executing the optimization instruction in this embodiment is realized through the following steps:

[0309] After the optimization instruction is generated, it is transmitted to each execution module through the industrial communication network;

[0310] The actuator adjusts the air flow rate and pulverized coal flow rate according to the instruction and performs real-time feedback through closed-loop control;

[0311] The control system processes the feedback data to generate a new optimization instruction to ensure the dynamic optimization of the combustion process.

[0312] In this embodiment, the optimized control instruction acts on the boiler system in real time, realizing the dynamic regulation of the combustion process. The real-time adjustment of the air flow rate and pulverized coal flow rate ensures the precise matching of the oxygen concentration and fuel supply, effectively improving the combustion efficiency and significantly reducing pollutant emissions. Through the closed-loop control mechanism, the sustainability and working condition adaptability of combustion optimization are ensured.

[0313] Generally speaking, the present invention obtains multiple key parameters such as oxygen concentration, temperature, pulverized coal concentration, air flow rate, pulverized coal flow rate, and boiler load during the operation of the boiler, establishes a distributed parameter model to describe the combustion state, extracts key characteristic variables through multi-scale analysis, designs a distributed feedback controller to perform multi-parameter dynamic optimization on different regions in the furnace, and dynamically adjusts the ratio of the air flow rate and pulverized coal flow rate by constructing a global optimization objective function. Finally, the actuator performs real-time adjustment on the combustion parameters.

[0314] The thermal power boiler combustion optimization system based on multi-parameter feedback control described below can be referred to correspondingly with the thermal power boiler combustion optimization method based on multi-parameter feedback control described above.

[0315] 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, including:

[0316] A data acquisition unit 100 is used to collect dynamic data of the oxygen concentration, temperature, pulverized coal concentration in the boiler furnace and external input parameters in real time;

[0317] A distributed parameter modeling unit 200 is used to establish a distributed parameter model of the combustion state in the boiler furnace;

[0318] A multi-scale analysis unit 300 is used to extract key combustion characteristic variables under time scale and space scale;

[0319] A distributed feedback control unit 400 is used to adjust the boiler operation parameters based on real-time data, including air flow and pulverized coal flow;

[0320] An optimization execution unit 500 is used to execute optimization instructions.

[0321] The system of this embodiment can be used to execute the above method embodiment, and its principle and technical effect are similar, which will not be elaborated here.

[0322] Please refer to the appendix Figure 3 The present invention also provides a computer device 40, including: a processor 41 and a memory 42. The memory 42 stores a computer program executable by the processor. When the computer program is executed by the processor, it executes the above method.

[0323] The present invention also provides a storage medium 43. A computer program is stored on the storage medium 43. When the computer program is run by the processor 41, it executes the above method.

[0324] 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 (abbreviation: SRAM), electrically erasable programmable read-only memory (abbreviation: EEPROM), erasable programmable read-only memory (abbreviation: EPROM), programmable read-only memory (abbreviation: PROM), read-only memory (abbreviation: ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0325] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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 It includes the following steps: Obtain multiple key parameters during the boiler combustion process, including oxygen concentration, temperature, pulverized coal concentration, air flow rate, pulverized coal flow rate, and boiler load; Establish a distributed parameter model describing the combustion state in the boiler furnace, and the model characterizes the dynamic changes of oxygen concentration, temperature, and pulverized coal concentration over time and space during the combustion process; Decompose the time scale and space scale of the combustion process through multi-scale analysis, and extract key characteristic variables characterizing the combustion state; Based on the distributed parameter model and the results of multi-scale analysis, realize the multi-parameter dynamic optimization of different regions in the boiler furnace based on a distributed feedback controller; On the basis of distributed feedback control, construct a global optimization objective function and dynamically adjust the ratio of air flow rate to pulverized coal flow rate; Send the optimized control instructions to the boiler system, and adjust the combustion parameters in real time through the actuator; The distributed parameter model characterizes the dynamic changes of oxygen concentration, temperature, and pulverized coal concentration over time and space in the boiler furnace, and specifically includes the following dynamic equations: Oxygen concentration dynamic equation, which describes the transport, diffusion, and consumption behavior of oxygen and satisfies the following relationship: Among them, is the oxygen concentration, represents time, is the air velocity field, is the oxygen diffusion coefficient, is the oxygen consumption rate coefficient, is the pulverized coal concentration; Temperature dynamic equation, which describes heat transfer, heat release during combustion, and heat loss behavior and satisfies the following relationship: Among them, is the temperature, is the gas density, is the specific heat capacity, is the thermal conductivity, is the enthalpy change of combustion reaction, is the heat loss coefficient, is the ambient temperature; Pulverized coal concentration dynamic equation, which describes the pulverized coal distribution and combustion reaction behavior and satisfies the following relationship: The model constraint conditions include initial conditions and boundary conditions; Initial conditions: wherein, is the initial distribution value of the oxygen concentration at time ; is the initial distribution value of the temperature at time ; is the initial distribution value of the pulverized coal concentration at time ; Boundary conditions: Among them, is the boundary normal vector, is the furnace wall temperature; The step of decomposing the time scale and space scale of the combustion process through multi-scale analysis includes: On the time scale, decompose the oxygen concentration and pulverized coal concentration into fast variables, and decompose the temperature into slow variables; On the space scale, extract the average combustion parameters in each region 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 pulverized coal concentration; The step of realizing the multi-parameter dynamic optimization of different regions in the boiler furnace based on a distributed feedback controller includes: Establish a local feedback controller for each partition separately. The local controller calculates the control deviation according to the real-time oxygen concentration, temperature, and pulverized coal concentration data in the partition; Use the feedback control law to dynamically adjust the distribution of air flow rate and pulverized coal flow rate in the partition; Based on the parameter coordination relationship between regions, optimize the overall combustion performance through the regional coupling mechanism.

2. The combustion optimization method for 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 objectives of maximizing combustion efficiency, minimizing pollutant emissions, and improving combustion uniformity, and specifically includes: Combustion efficiency objective, maximize the combustion efficiency by improving the combustion sufficiency of fuel and oxygen in the furnace, and the combustion efficiency objective function is defined as: Among them, represents the combustion efficiency function; Pollutant emission objective, minimize the pollutant emissions by reducing the emissions of nitrogen oxides and sulfur dioxide generated during the combustion process, and the pollutant emission objective function is defined as: Among them, represents the NOx generation amount, represents the SOx generation amount; Combustion uniformity objective, improve the combustion uniformity by reducing the gradient distribution of the temperature field in the furnace, and the combustion uniformity objective function is defined as: Among them, represents the square of the gradient of the temperature field; Global optimization objective function, construct the global objective function by weighted combination of sub-objective functions, and is defined as: Among them, , , are weight coefficients.

3. The method for optimizing the combustion of a thermal power boiler based on multi-parameter feedback control according to claim 2, wherein The weight coefficients of the global optimization objective function are dynamically adjusted according to the boiler operating conditions to adapt to coal quality fluctuations and boiler load changes.

4. The combustion optimization method for 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 the adjustment command according to the combustion state deviation in each area; Send the adjustment results of the air flow rate and the pulverized coal flow rate to the actuators, including the air damper and the pulverized coal feeder.

5. A combustion optimization system for thermal power boilers based on multi-parameter feedback control, which is used to execute the combustion optimization method for thermal power boilers based on multi-parameter feedback control according to any one of claims 1-4, characterized in that It includes: A data acquisition unit for real-time acquisition of dynamic data of the oxygen concentration, temperature, pulverized coal concentration in the boiler furnace and external input parameters; A distributed parameter modeling unit for establishing a distributed parameter model of the combustion state in the boiler furnace; A multi-scale analysis unit for extracting key combustion characteristic variables under the time scale and the space scale; A distributed feedback control unit for adjusting the boiler operation parameters based on real-time data, including the air flow rate and the pulverized coal flow rate; An optimization execution unit for executing the optimization command.

6. A computer device, comprising a memory, a processor, and a computer program stored on 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 according to any one of claims 1-4.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the thermal power boiler combustion optimization method based on multi-parameter feedback control according to any one of claims 1-4.

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