Boiler combustion optimization control method and system for coal-fired power plant

Abstract

The invention relates to the technical field of boiler combustion optimization, in particular to a boiler combustion optimization control method and system for a coal-fired power plant, and the method comprises the steps: determining the smoke exhaust heat loss and fuel incomplete combustion loss of boiler combustion based on the operation parameters of boiler combustion, nitrogen oxide emission, sulfur dioxide emission and carbon monoxide emission of boiler combustion are reduced; collecting the electrical load of a power supply area for boiler combustion at each moment every day; the method comprises the following steps: determining an electrical load predicted value at the current moment, constructing an objective function, solving the objective function by using a particle swarm optimization algorithm, determining an optimal operation parameter of boiler combustion, and correcting an inertia weight in the particle swarm optimization algorithm in real time by using a difference between the electrical load predicted value at the current moment and the electrical load predicted value at an adjacent moment. Therefore, the boiler combustion optimization control precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of boiler combustion optimization, and in particular to a boiler combustion optimization control method and system for a coal-fired power plant. Background Art

[0002] With increasing energy consumption and increasingly stringent environmental protection requirements, coal-fired power plants face the enormous challenge of improving energy efficiency and reducing pollution emissions. Key parameters in the boiler combustion process are directly related to improving combustion efficiency and reducing exhaust emissions. By controlling these key parameters during the boiler combustion process, combustion efficiency can be improved and efficient power supply can be achieved.

[0003] However, in real life, electricity demand in different time periods can cause drastic changes in the load of power plant boiler equipment, which in turn affects key parameters in the combustion process. This makes the boiler's combustion efficiency a dynamic optimization problem. The traditional particle swarm optimization algorithm seeks the optimal solution based on the current state. In dealing with a series of chain effects brought about by changes in electricity demand, the response of the power plant boiler has a lag. For example, during the upcoming peak electricity consumption period, the boiler operates at a stable load. After entering the peak period, the power plant boiler will directly enter ultra-high load operation to meet the current electricity demand. The particle swarm optimization algorithm finds it difficult to find the optimal solution for the current combustion efficiency parameters in real time, which in turn affects combustion efficiency and pollutant emissions. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for optimizing boiler combustion control in coal-fired power plants. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a method for optimizing boiler combustion control in a coal-fired power plant, the method comprising the following steps: Determine the flue gas heat loss and incomplete fuel combustion loss of the boiler combustion, as well as the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions of the boiler combustion based on the operating parameters of the boiler combustion; Collecting the power load at each time of the day in the power supply area where the boiler is burning; determining a predicted power load value at the current time based on the actual power load at the current time in the history of each day and the difference between the actual power load and the actual power load at the time adjacent to the current time on the same day; The operating parameters of the boiler combustion are weighted using the current power load forecast value, and the flue gas heat loss and fuel incomplete combustion loss, as well as the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions are determined based on the weighted operating parameters to construct an objective function; The particle swarm optimization algorithm is used to solve the objective function and determine the optimal operating parameters of the boiler combustion. The difference between the power load forecast value at the current moment and the adjacent moments is used to correct the inertia weight in the particle swarm optimization algorithm in real time.

[0005] In one embodiment, the current power load forecast value is determined by calculating: Where, is the predicted value of power load at the current time t, is the kth adjacent moment before moment t on the dth day before the current moment t, is the actual power load at the kth adjacent time, D is the preset number of days before the current time t, K is the preset number of adjacent time points before time t, The weight of the actual power load at the kth adjacent moment is recorded as the first weight, and the first weight is determined by the difference between the actual power load at the kth adjacent moment and the power load forecast value at the adjacent moment before the current moment t.

[0006] In one embodiment, determining the first weight includes: Where, is the electricity consumption deviation at the kth adjacent moment, where , is the time before the current time t The electricity load forecast value at the moment; The first weight is determined based on the power usage deviation.

[0007] In one embodiment, the expression of the first weight is: .

[0008] In one embodiment, the objective function is constructed as follows: Combining the exhaust heat loss and the incomplete combustion loss of the fuel, a first function is determined, specifically: ,in, is the first function, X is the current operating parameter of boiler combustion, is the exhaust heat loss caused by the current operating parameters of boiler combustion, The fuel incomplete combustion loss caused by the current operating parameters of the boiler combustion; Based on the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions from boiler combustion, the second function is determined as follows: Where, is the second function, is the nitrogen oxide emission caused by the current operating parameters of boiler combustion, is the sulfur dioxide emission caused by the current operating parameters of boiler combustion, is the carbon monoxide emissions caused by the current operating parameters of boiler combustion, a, b, and c are the preset weights of nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions, respectively, where a+b+c=1; The first function and the second function are combined to determine an objective function.

[0009] In one embodiment, the objective function is expressed as: Where, is the objective function at the current time t, is the first function, is the second function, is the weight of the operating parameter of boiler combustion, recorded as the second weight, and the second weight is determined by the difference between the power load forecast value at the current moment and the previous moment.

[0010] In one embodiment, determining the second weight includes: The difference between the power load forecast value at the current moment and the previous moment is calculated and recorded as the first difference. The opposite of the first difference is used as the exponent of an exponential function with a natural constant as the base, and the calculation result of the exponential function is used as the second weight.

[0011] In one embodiment, the real-time correction of the inertia weight in the particle swarm optimization algorithm includes: Preset the maximum and minimum values ​​of the inertia weight, calculate the difference between the maximum and minimum values, record it as the second difference, determine the difference between the maximum and minimum values ​​of the power load forecast value at all times before the current moment, record it as the third difference; The inertia weight in the particle swarm optimization algorithm at the current moment is determined by combining the first difference, the second difference, the third difference, and the minimum value of the preset inertia weight.

[0012] In one embodiment, determining the inertia weight in the particle swarm optimization algorithm at the current moment includes: Calculating a sum of the third difference and a preset value greater than 0, determining a ratio of an absolute value of the first difference to the sum, and calculating a product of the second difference and the ratio; The inertia weight in the particle swarm optimization algorithm at the current moment is the sum of the minimum value of the preset inertia weight and the product.

[0013] In a second aspect, an embodiment of the present application also provides a boiler combustion optimization control system for a coal-fired power plant, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0014] This application has at least the following beneficial effects: This application determines the exhaust heat loss and incomplete fuel combustion loss of boiler combustion based on the operating parameters of boiler combustion, and uses this as a control factor for boiler combustion optimization, which helps to accurately optimize the boiler combustion process, improve combustion efficiency, and reduce energy waste; in addition, by effectively controlling the emissions of nitrogen oxides, sulfur dioxide, and carbon monoxide, it reduces environmental pollution and improves the environmental protection level of boiler combustion; further, it uses historical electricity load data and load differences at adjacent times to predict the current electricity load, thereby improving the accuracy of electricity load prediction, thereby making boiler operation more stable and reducing boiler instability caused by load fluctuations. risk; finally, by constructing the objective function and using the particle swarm optimization algorithm, the optimal operating parameters of the boiler combustion are solved and the fuel supply, air distribution and operating status of the burner are adjusted to ensure that the boiler can maintain high combustion efficiency and low emission levels under different loads, and avoid the resource waste and pollution emission problems caused by the sudden change of boiler load due to the response lag of the power supply of the power plant boiler. In addition, by real-time correction of the inertia weight of the particle swarm optimization algorithm, the efficiency and accuracy of solving the optimal operating parameters are improved, the boiler combustion process is finely controlled, the instability of the boiler combustion is reduced, and the safety and reliability of the boiler operation are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 A flowchart of a method for optimizing boiler combustion control in a coal-fired power plant according to an embodiment of the present application; Figure 2 Construct a flowchart for the objective function of the particle swarm optimization algorithm. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the implementation, structure, features, and effectiveness of the boiler combustion optimization control method and system for coal-fired power plants proposed in this application. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the boiler combustion optimization control method and system for coal-fired power plants provided by this application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the steps of a boiler combustion optimization control method for a coal-fired power plant provided by one embodiment of the present application, the method comprising the following steps: S1, based on the operating parameters of the boiler combustion, determine the exhaust heat loss and incomplete combustion loss of the fuel, as well as the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions of the boiler combustion.

[0021] During the operation phase of the boiler in a coal-fired power plant, various operating parameter data of the boiler are obtained through sensors on the boiler equipment, including boiler load, air-coal ratio, combustion temperature, pressure, oxygen concentration, flue gas composition, coal feed rate, primary air volume, secondary air volume, excess air coefficient, etc.

[0022] Existing boiler combustion optimization methods use intelligent optimization algorithms to construct the boiler combustion objective function and then adjust various operating parameters to find the optimal boiler combustion state. In real life, electricity demand at different times causes the load of power plant boiler equipment to fluctuate, which in turn affects the operating parameters during the combustion process, making boiler combustion efficiency a dynamic optimization problem. Traditional optimization algorithms seek the optimal solution based on the current state, resulting in a lag in the power plant boiler's response to the chain reactions caused by changes in electricity demand.

[0023] To address the above problems, this application models the boiler combustion optimization problem under different electricity consumption time periods through an improved particle swarm optimization algorithm based on dynamic time-varying prediction constraints, constructs objective functions for various operating parameters, designs the objective function of boiler combustion by predicting electricity demand at future moments as an additional constraint, and designs time-varying dynamic weights according to changes in the predicted value of electricity demand, thereby enhancing the ability of the particle swarm optimization algorithm to search for the optimal solution and avoiding the waste of resources and pollution emissions caused by drastic changes in boiler load due to the response lag of power plant boiler power supply.

[0024] Specifically, the boiler combustion optimization problem under different power consumption time periods is transformed into a multi-objective optimization problem. It is necessary to combine various operating parameters of boiler combustion to optimize the combustion efficiency and exhaust emissions at the same time. Assume that the solution vector required by the particle swarm optimization algorithm in this embodiment is , which contains n operating parameters of boiler combustion, Indicates the first operating parameter of boiler combustion. Indicates the second operating parameter of boiler combustion. Indicates the nth operating parameter of boiler combustion.

[0025] First, consider the combustion efficiency of boiler combustion. Boiler combustion efficiency is used to measure the amount of heat energy conversion in the combustion system. The operating parameters of boiler combustion jointly determine whether the fuel is fully burned and the effective utilization of the heat energy released by combustion. Combining the exhaust heat loss of boiler combustion and the loss of incomplete fuel combustion, the first function is constructed. The specific expression is: ,in, is the first function, X is the current operating parameter of boiler combustion, is the exhaust heat loss caused by the current operating parameters of boiler combustion, The calculation of exhaust heat loss and incomplete fuel combustion loss is a well-known technique, and the specific process will not be described in detail here.

[0026] Secondly, consider the issue of exhaust gas emissions from boiler combustion. Boiler exhaust gas emissions mainly depend on factors such as fuel characteristics, combustion temperature, excess air coefficient, air-to-coal ratio, and pulverized coal combustion completeness. These factors jointly affect the generation and emission concentration of pollutants in flue gas, especially The formation of is highly sensitive to furnace temperature and oxygen concentration. Emissions are closely related to the sulfur content in coal and combustion conditions. Emissions often reflect incomplete combustion. By rationally optimizing the combustion process, harmful gas and smoke emissions can be effectively reduced to meet environmental protection requirements. The second function is constructed, and the specific expression is: Where, is the second function, is the nitrogen oxide emission caused by the current operating parameters of boiler combustion, is the sulfur dioxide emission caused by the current operating parameters of boiler combustion, is the carbon monoxide emissions caused by the current operating parameters of boiler combustion, and a, b, and c are the preset weights for nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions, respectively. Here, a + b + c = 1. In this embodiment, a = 0.55, b = 0.3, and c = 0.15. Implementers can set these values ​​based on actual conditions, and this embodiment does not impose any restrictions. The calculation of nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions is well-known technology, and the specific process is not detailed here.

[0027] S2, collecting the power load at each time of the day in the power supply area where the boiler is burning; determining the power load forecast value at the current time based on the actual power load at the current time in the history of each day, and the difference between the actual power load and the actual power load at the adjacent time of the current time on the same day.

[0028] In coal-fired power plants, boiler load is a core parameter that is dynamically adjusted based on the grid's electricity demand. In the power system's comprehensive load curve, the electricity load exhibits different variations during different time periods and can be divided into peak load, valley load, mid-load, and base load. These different load segments reflect the demand for electricity load. Peak load periods represent periods of peak system electricity load, and coal-fired power plant boilers must increase their operating load to meet high power output. During valley periods, electricity load drops to its lowest level of the day, and boiler operating load must decrease accordingly to avoid energy waste. During mid-load and base load periods, electricity load is stable or fluctuates slightly, and boilers must maintain stable load operation.

[0029] Based on the above analysis, this embodiment collects the electricity load at each time of the day in the power supply area where the boiler is burning, with one day's data as a cycle and a time interval of 15 minutes between adjacent times. The daily electricity load curve of the power supply area is drawn based on the collected electricity load, which facilitates the subsequent analysis and prediction of the daily electricity consumption in the power supply area. The implementer can limit the sampling time interval according to actual conditions, and this embodiment does not impose any restrictions on this.

[0030] Since the change of electricity load determines the change of boiler load, the boiler needs to adjust the operating parameters to meet the change of electricity demand and keep the combustion efficiency and exhaust gas emissions within the optimal range. 2 days of historical electricity load data to build dynamic forecast constraints , used to indicate the current The power load forecast value at the moment, The specific expression is: Where, is the predicted value of power load at the current time t, is the kth adjacent moment before moment t on the dth day before the current moment t, is the actual power load at the kth adjacent time, D is the preset number of days before the current time t, K is the preset number of adjacent time points before time t, The weight of the actual power load at the kth adjacent moment is recorded as the first weight, and the first weight is determined by the difference between the actual power load at the kth adjacent moment and the power load forecast value at the adjacent moment before the current moment t.

[0031] It should be noted that, in this embodiment, D=30, the unit is day, K=5, no unit, when k=0, it means the time t of the dth day before the current time t, the values ​​of D and K can be set by the implementer according to actual conditions, and this embodiment does not impose any restrictions on this.

[0032] The first weight is calculated as follows: Where, is the electricity consumption deviation at the kth adjacent moment, where , is the time before the current time t The power load forecast value at the moment. The expression of the first weight is: .

[0033] It should be understood that when Historical electricity load data at the nearest time and When the gap is large, it indicates that the historical electricity load data If the error between the predicted load and The smaller, the Weight when forecasting electricity load The smaller; on the contrary, and When the gap is small, it indicates that the historical electricity load data The error with the predicted load is small. The bigger, the Weight when forecasting electricity load The larger the value, the larger the dynamic prediction constraint is designed to give prediction weights to the actual load values ​​of each historical moment and its neighboring moments in a short period of time during the prediction. , the load forecast result is more accurate. When the boiler is in the starting state, there is no need to constrain the optimization of boiler combustion parameters by predicting the power demand. .

[0034] S3, using the current power load forecast value to weight the boiler combustion operating parameters, and constructing an objective function based on the flue gas heat loss and fuel incomplete combustion loss, as well as the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions determined based on the weighted operating parameters.

[0035] During the operation of coal-fired power plants, the fluctuation of power demand in the power grid directly affects the combustion state of the boiler. During peak hours, the power load will surge in a short period of time. At this time, in order to meet the huge instantaneous power demand of the power grid, the boiler enters a high-load operation state and increases the coal supply and air supply. However, coal-fired boilers are constrained by the physical and chemical characteristics of the combustion process. After the pulverized coal enters the furnace, it takes a certain amount of time from heating to complete combustion. A sudden and substantial increase in the coal supply results in the inability of the pulverized coal to burn fully, and the actual oxygen consumption in the boiler is lower than expected. In a high-temperature, high-oxygen environment, excessive oxygen is generated. 、 At the same time, part of the heat is not effectively utilized, the exhaust heat loss increases, and the boiler combustion efficiency decreases.

[0036] During periods of low electricity demand, electricity demand drops significantly. At this time, the boiler's target load is simultaneously lowered, and both the coal and air feeds need to be reduced, which should lead to a corresponding decrease in furnace temperature. However, due to the significant thermal inertia of boiler combustion, it is impossible for the furnace temperature, pressure, and flue gas flow to drop significantly and instantaneously. If the boiler immediately adjusts parameters based on the instantaneous load state, reducing the coal and air feeds too quickly can cause the furnace temperature to drop too sharply, leading to unstable combustion, increased CO emissions, and even the risk of flameout. Failure to predict and adjust in advance can easily lead to fuel waste and compromise combustion efficiency.

[0037] Therefore, in order to avoid a series of combustion problems caused by changes in electricity load, electricity load forecasting is used to identify load trend changes in advance, and parameters such as coal supply and air supply are gradually adjusted to achieve a smooth transition in the boiler combustion process, thereby ensuring that the combustion efficiency and exhaust gas emission indicators are in the optimal state.

[0038] Based on the above and dynamic prediction constraints , the objective function of boiler combustion is: ; ; Where, is the weight of the boiler combustion operating parameters, recorded as the second weight, e is a natural constant, is the predicted value of power load at the current time t, is the power load forecast value at the moment before the current moment t, is the objective function at the current time t, is the first function, is the second function. Recorded as the first difference. The objective function construction flow chart of the particle swarm optimization algorithm is as follows Figure 2 shown.

[0039] The objective function constructed in this embodiment is to run the parameter vector and the power load forecast value at different times as input, with combustion efficiency Maximization of exhaust emissions The goal is to minimize the constraint terms and The difference between the two values ​​is used as an additional constraint to control the change of boiler decision variables and compensate for the change of boiler load caused by the drastic change of electricity consumption. The operating parameters cannot achieve the optimal boiler combustion state due to the lag in boiler response. When The power demand suddenly increases at any moment, and the operating parameters before adding dynamic prediction constraints It is difficult to meet the optimal combustion control when the boiler load suddenly increases. The purpose is to increase the value of the operating parameters in advance to meet the needs of high-load combustion of the boiler and the combustion optimization under high load, so as to avoid the difficulty of adjusting the parameter variables of the current optimal boiler combustion problem due to the response lag when the power demand and boiler load suddenly increase; when When the electricity demand decreases, , reduce and adjust the operating parameters of the boiler in advance to avoid the problem of incomplete combustion of fuel caused by a sudden reduction in the operating parameters of the boiler, resulting in reduced combustion efficiency and increased exhaust emissions.

[0040] At this point, the construction of the mathematical model for optimal control of boiler combustion problems is completed.

[0041] S4, using a particle swarm optimization algorithm to solve the objective function and determine the optimal operating parameters for boiler combustion, wherein the inertia weight in the particle swarm optimization algorithm is corrected in real time by utilizing the difference between the power load forecast values ​​at the current moment and the power load forecast values ​​at the adjacent moments.

[0042] The particle swarm optimization algorithm is used to solve the objective function at the current time t to maximize the objective function. During the initialization of the operating parameters in the solution vector X and in each subsequent iteration of the solution vector X, it is ensured that the values ​​of the operating parameters in the solution vector X are within their normal value ranges, that is, meaningless boiler combustion operating parameters are avoided.

[0043] Traditional particle swarm optimization algorithms are mostly static, solving for the optimal parameter combination for a fixed operating state. However, when external conditions, such as electricity demand, suddenly change, such as during the upcoming peak period when demand rises sharply, the thermal inertia of the boiler and the lag in the combustion process determine the optimal parameters at that moment. These parameters may not be suitable for operating conditions with a significant load increase within a short period of time, resulting in large errors and an inability to respond to system changes in a timely manner. This ultimately leads to reduced combustion efficiency and excessive exhaust emissions.

[0044] To solve the above problems, in the process of solving the particle swarm optimization algorithm, this embodiment introduces a time-varying adaptive inertia weight to update the particle speed. Specifically, the power demand forecast constraint at the current moment is set. The optimization algorithm is introduced to dynamically adjust the inertia weight of particles in the particle swarm optimization algorithm by predicting the changes in electricity demand during the upcoming peak or low electricity demand period. The inertia weight is adaptively adjusted as the electricity demand changes.

[0045] Based on the above analysis, the inertia weight in the particle swarm optimization algorithm at the current moment The expression is: Where, is the preset maximum inertia weight, is the preset minimum inertia weight. In this embodiment, , , the implementer can set it according to the actual situation, and this embodiment does not limit it. is the predicted value of power load at the current time t, is the power load forecast value at the moment before the current moment t, is the maximum value of the power load forecast value at all times before the current moment, is the minimum value of the power load forecast value in all moments before the current moment, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment , the implementer can set it according to the actual situation, and this embodiment does not limit it. Recorded as the second difference, Recorded as the third difference.

[0046] The time-varying dynamic weight design based on dynamic forecast constraints will be used to predict the power demand value. The weight update mechanism is introduced to adjust the inertia weight in real time according to the trend of the power load forecast value. If the difference between the power demand forecast value at the current moment and the previous moment is large, it means that the power demand at the previous moment has changed significantly, and the particle optimal solution at the previous moment is difficult to meet the algorithm requirements. It will also increase, enhancing the exploration ability of the particle swarm, helping the algorithm to search for boiler parameters in advance to cope with sudden changes in electricity consumption, and prevent the reduction of combustion efficiency and excessive exhaust emissions due to delayed boiler response; similarly, if the difference between the current moment and the previous moment's electricity load forecast value is small, it means that the electricity demand changes smoothly, and the current operating state of the boiler can meet the electricity demand, and the inertia weight The search is done in the vicinity of the current solution to improve the efficiency of the search. In particular, when the power load forecast values ​​at the previous and next moments are the maximum and minimum values ​​up to the current moment, ,at this time , the ability of particles to explore the solution space reaches its strongest point; when hour, , the particle speed update weight is the smallest, and the current optimal solution is found near the optimal solution of the previous moment to improve the search efficiency. The electricity demand at each moment is predicted and updated accordingly The boiler operating parameters at each moment can be calculated to improve the combustion optimization control of the boiler under dynamic load conditions and better fit the physical characteristics of the boiler combustion system.

[0047] Finally, the particle swarm optimization algorithm is used to solve the optimal operating parameters of the current boiler combustion, which are used to adjust the fuel supply, air distribution and burner operating status to ensure that the boiler can maintain high combustion efficiency and low emission levels under different loads, thereby optimizing the boiler operating status. Among them, the learning factor of the particle swarm optimization algorithm is Set to 2, the number of particle swarms Set to 50, the number of iterations The particle swarm optimization algorithm is a well-known technology, and the specific process is not described in detail.

[0048] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides a boiler combustion optimization control system for a coal-fired power plant, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned boiler combustion optimization control methods for a coal-fired power plant are implemented.

[0049] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0051] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for optimizing boiler combustion control in a coal-fired power plant, characterized in that: The method comprises the following steps: Determine the flue gas heat loss and incomplete fuel combustion loss of the boiler combustion, as well as the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions of the boiler combustion based on the operating parameters of the boiler combustion; Collecting the power load at each time of the day in the power supply area where the boiler is burning; determining a predicted power load value at the current time based on the actual power load at the current time in the history of each day and the difference between the actual power load and the actual power load at the time adjacent to the current time on the same day; The operating parameters of the boiler combustion are weighted using the current power load forecast value, and the flue gas heat loss and fuel incomplete combustion loss, as well as the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions are determined based on the weighted operating parameters to construct an objective function; The particle swarm optimization algorithm is used to solve the objective function and determine the optimal operating parameters of the boiler combustion. The difference between the power load forecast value at the current moment and the adjacent moments is used to correct the inertia weight in the particle swarm optimization algorithm in real time.

2. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 1, wherein: The current power load forecast value is determined by: Where, is the predicted value of power load at the current time t, is the kth adjacent moment before moment t on the dth day before the current moment t, is the actual power load at the kth adjacent time, D is the preset number of days before the current time t, K is the preset number of adjacent time points before time t, The weight of the actual power load at the kth adjacent moment is recorded as the first weight, and the first weight is determined by the difference between the actual power load at the kth adjacent moment and the power load forecast value at the adjacent moment before the current moment t.

3. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 2, wherein: Determining the first weight includes: Where, is the electricity consumption deviation at the kth adjacent moment, where , is the time before the current moment t The electricity load forecast value at the moment; The first weight is determined based on the power usage deviation.

4. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 3, wherein: The expression of the first weight is: .

5. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 1, wherein: The construction of the objective function includes: Combining the exhaust heat loss and the incomplete combustion loss of the fuel, a first function is determined, specifically: ,in, is the first function, X is the current operating parameter of boiler combustion, is the exhaust heat loss caused by the current operating parameters of boiler combustion, The fuel incomplete combustion loss caused by the current operating parameters of the boiler combustion; Based on the nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions from boiler combustion, the second function is determined as follows: Where, is the second function, is the nitrogen oxide emission caused by the current operating parameters of boiler combustion, is the sulfur dioxide emission caused by the current operating parameters of boiler combustion, is the carbon monoxide emissions caused by the current operating parameters of boiler combustion, a, b, and c are the preset weights of nitrogen oxide emissions, sulfur dioxide emissions, and carbon monoxide emissions, respectively, where a+b+c=1; The first function and the second function are combined to determine an objective function.

6. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 5, wherein: The expression of the objective function is: Where, is the objective function at the current time t, is the first function, is the second function, is the weight of the operating parameter of boiler combustion, recorded as the second weight, and the second weight is determined by the difference between the power load forecast value at the current moment and the previous moment.

7. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 6, wherein: Determining the second weight includes: The difference between the power load forecast value at the current moment and the previous moment is calculated and recorded as the first difference. The opposite of the first difference is used as the exponent of an exponential function with a natural constant as the base, and the calculation result of the exponential function is used as the second weight.

8. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 7, wherein: The real-time correction of the inertia weight in the particle swarm optimization algorithm includes: Preset the maximum and minimum values ​​of the inertia weight, calculate the difference between the maximum and minimum values, record it as the second difference, determine the difference between the maximum and minimum values ​​of the power load forecast value at all times before the current moment, record it as the third difference; The inertia weight in the particle swarm optimization algorithm at the current moment is determined by combining the first difference, the second difference, the third difference, and the minimum value of the preset inertia weight.

9. The method for optimizing boiler combustion control in a coal-fired power plant according to claim 8, wherein: Determining the inertia weight in the particle swarm optimization algorithm at the current moment includes: Calculating a sum of the third difference and a preset value greater than 0, determining a ratio of an absolute value of the first difference to the sum, and calculating a product of the second difference and the ratio; The inertia weight in the particle swarm optimization algorithm at the current moment is the sum of the minimum value of the preset inertia weight and the product.

10. A boiler combustion optimization control system for a coal-fired power plant, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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