A method and system for blending and burning coal

Abstract

This application relates to a method and system for blended coal combustion. The method includes: real-time detection and analysis of blended coal used for thermal power generation to obtain initial blending information; based on the initial blending information, obtaining an optimized blending ratio of the blended coal using an intelligent combustion optimization model, wherein the optimized blending ratio is used to maximize the combustion efficiency of the blended coal and minimize the concentration of nitrogen oxides produced; and adjusting the optimized blending ratio using a self-learning algorithm model to obtain the adjusted optimized blending ratio. This application realizes intelligent combustion optimization and a self-learning algorithm, which can optimize the combustion effect of blended coal, improve combustion efficiency, reduce pollutant emissions, and automatically adjust the model according to coal quality and operating conditions during operation, continuously improving the optimization effect of the model, thus solving the problem of how to achieve efficient and environmentally friendly combustion of blended coal in thermal power plants.

Description

Technical Field

[0001] This application relates to the field of thermal power generation technology, and in particular to a method and system for blending and burning coal. Background Technology

[0002] In new power systems dominated by renewable energy generation, coal-fired units play a key role in deep peak shaving. Coal blending technology is an important means to ensure that these coal-fired units can operate efficiently, economically, stably and safely. By optimizing the blending of different types of coal, combustion efficiency can be effectively improved, while reducing the emission of pollutants such as nitrogen oxides and sulfur oxides.

[0003] Traditional blending methods rely heavily on experience-based settings and manual adjustments, resulting in low efficiency and difficulty in responding quickly to real-time changes in coal quality, thus hindering optimal combustion efficiency and environmental performance. Specifically, this method requires operators to adjust combustion based on experience and intuition, often making precise control of the combustion process difficult. This leads to significant fluctuations in combustion efficiency and environmental emissions. Furthermore, the time-consuming manual adjustment process makes it difficult to respond promptly to changes in coal quality, further impacting combustion efficiency and environmental performance. With increasingly stringent environmental regulations and the growing diversity of coal types, achieving efficient and environmentally friendly blended coal combustion has become a critical issue that thermal power plants urgently need to address.

[0004] Currently, no effective solution has been proposed for the problem of how to achieve efficient and environmentally friendly combustion of mixed coal in thermal power plants. Summary of the Invention

[0005] This application provides a method and system for blended coal combustion, which at least solves the problem of how to achieve efficient and environmentally friendly combustion of blended coal in thermal power plants in related technologies.

[0006] In a first aspect, embodiments of this application provide a method for blending and burning coal, the method comprising:

[0007] Real-time detection and analysis of the mixed coal used for thermal power generation are performed to obtain the initial coal blending and combustion information of the mixed coal.

[0008] Based on the initial coal blending information, the optimal blending ratio of the mixed coal is obtained through an intelligent combustion optimization model, wherein the optimal blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced.

[0009] The optimized blending ratio is adjusted by using a self-learning algorithm model to obtain the adjusted optimized blending ratio.

[0010] In some embodiments, based on the initial coal blending information, an optimized blending ratio of the mixed coal is determined through an intelligent combustion optimization model. This optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced, including:

[0011] The objective function for constructing a multi-objective optimization algorithm is F(K) = w1×η(K) - w2×ξ. NOx (K), where F(K) is the objective function for optimization, η(K) is the combustion efficiency of the mixed coal, and ξ NOx (K) represents the concentration of nitrogen oxides produced after combustion, and w1 and w2 are weighting coefficients;

[0012] Based on maximizing the objective function of the multi-objective optimization algorithm, and using the initial coal blending information, the optimal blending ratio of the mixed coal is obtained through an intelligent combustion optimization model.

[0013] In some embodiments, the method includes:

[0014] Construct a functional expression for the combustion efficiency of blended coal. Where η0 is the baseline combustion efficiency, e i The combustion efficiency contributed by the i-th type of coal, f(C) i H i Q i (T) is the weight calculation function, C i H i and Q i These are the carbon content, hydrogen content, and calorific value of the i-th type of coal, respectively, and T is the combustion temperature;

[0015] Construct a functional expression for the concentration of nitrogen oxides produced by the combustion of the mixed coal. Where, ξ NOx,i (K i Let f(O2, N) represent the concentration of nitrogen oxides produced by the combustion of the i-th type of coal. i ) is the weighting calculation function, O2 is the oxygen concentration, and N i Let be the coal quality parameters of the i-th type of coal.

[0016] In some embodiments, real-time detection and analysis of the blended coal used for thermal power generation are performed to obtain initial blending information of the blended coal, including:

[0017] The initial blending information includes the initial blending ratio of the blended coals, the coal type information and coal quality information of each type of coal;

[0018] Real-time monitoring of the mixed coal used for thermal power generation yields information on the coal type of each type of coal in the mixed coal.

[0019] Raman spectroscopy analysis was performed on the mixed coal to obtain the initial blending ratio and coal quality information of each type of coal.

[0020] In some embodiments, after real-time detection and analysis of the blended coal used for thermal power generation to obtain initial blending information of the blended coal, the method includes:

[0021] Based on the initial blending ratio of the mixed coal, the coal type information and coal quality information of each type of coal, the functional expression of the mixed coal quality model is constructed as follows:

[0022] [Mixed Coal Quality Model] = [n] * [Cn] * [kn]

[0023] Where n represents the coal type information, Cn represents the coal quality information of the nth type of coal, and kn represents the initial blending ratio.

[0024] In some embodiments, based on the initial coal blending information, the optimized blending ratio of the mixed coal is determined by an intelligent combustion optimization model, including:

[0025] Under the current operating conditions of the thermal power generation equipment, the optimal blending ratio and corresponding comprehensive characteristic index of the mixed coal are obtained by calculating using the intelligent combustion optimization model and the mixed coal quality model. The comprehensive characteristic index is used to evaluate the quality of the optimized blending ratio.

[0026] In some embodiments, the functional expression of the comprehensive characteristic index is:

[0027]

[0028] Where, k n For the optimal blending ratio of the nth type of coal, η n () is a function for calculating the combustion efficiency of the nth type of coal. Let S be the oxygen content of the nth type of coal. n Let T be the sulfur content of the nth type of coal, and T be the temperature of the mixed coal.

[0029] In some embodiments, the optimized blending ratio is adjusted using a self-learning algorithm model to obtain the adjusted optimized blending ratio, including:

[0030] The objective function for constructing the particle swarm optimization algorithm is f(k) = w3 × ε. η +w4×ε ξNOx , where ε η =η1-η2, where η1 is the actual combustion efficiency after optimizing the blending ratio, η2 is the predicted combustion efficiency after optimizing the blending ratio, and ε ξNOx =ξ NOx,1 -ξNOx,2 ξ NOx,1 To optimize the true value of nitrogen oxide concentration after co-firing ratio, ξ NOx,2 To optimize the predicted nitrogen oxide concentration after co-firing ratio, w3 and w4 are weighting coefficients;

[0031] Based on minimizing the objective function of the particle swarm optimization algorithm, the optimized blending ratio is adjusted through a self-learning algorithm model to obtain the adjusted optimized blending ratio.

[0032] In some embodiments, after adjusting the optimized blending ratio using a self-learning algorithm model to obtain the adjusted optimized blending ratio, the method includes:

[0033] It receives the optimized blending ratio derived from the self-learning algorithm model and generates the corresponding optimized blending ratio instruction to automatically blend the raw coal.

[0034] Secondly, embodiments of this application provide a coal blending and combustion system, the system including a coal detection and analysis module, an intelligent combustion optimization module, and a self-learning adjustment module;

[0035] The coal detection and analysis module is used to perform real-time detection and analysis on the mixed coal used for thermal power generation, and to obtain the initial coal blending information.

[0036] The intelligent combustion optimization module is used to determine the optimal blending ratio of the mixed coal based on the initial coal blending information through an intelligent combustion optimization model. The optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced.

[0037] The self-learning adjustment module is used to adjust the optimized blending ratio through a self-learning algorithm model to obtain the adjusted optimized blending ratio.

[0038] Compared to related technologies, the present application provides a method and system for blended coal combustion. This method obtains initial blending information by real-time detection and analysis of the blended coal used for thermal power generation. Based on this initial information, an optimized blending ratio is derived using an intelligent combustion optimization model. This optimized ratio maximizes the combustion efficiency of the blended coal and minimizes the concentration of nitrogen oxides. The optimized blending ratio is then adjusted using a self-learning algorithm model, resulting in a revised optimized blending ratio. This achieves intelligent combustion optimization and a self-learning algorithm, optimizing the combustion effect of the blended coal, improving combustion efficiency, and reducing pollutant emissions. Furthermore, the model is automatically adjusted based on coal quality and operating conditions during operation, continuously improving its optimization effect. This solves the problem of achieving efficient and environmentally friendly combustion of blended coal in thermal power plants. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0040] Figure 1 This is a flowchart of the steps of the coal blending and combustion method according to the embodiments of this application;

[0041] Figure 2 This is a schematic flowchart of a coal blending and combustion method according to an embodiment of this application;

[0042] Figure 3 This is a schematic diagram of the structure of a coal blending and combustion system according to an embodiment of this application;

[0043] Figure 4 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application.

[0044] The attached diagram is labeled as follows: 31. Coal detection and analysis module; 32. Intelligent combustion optimization module; 33. Self-learning adjustment module. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0046] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0047] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0048] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0049] This application provides a method for blending and burning coal. Figure 1 This is a flowchart of the coal blending and combustion method according to an embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps:

[0050] Step S102: Real-time detection and analysis of the mixed coal used for thermal power generation to obtain the initial coal blending information.

[0051] The initial coal blending information in step S102 includes the initial blending ratio of the blended coals, the coal type information and coal quality information of each type of coal, and specifically includes the following steps:

[0052] Step S1021: Real-time detection of the mixed coal used for thermal power generation to obtain coal type information of each type of coal in the mixed coal.

[0053] Step S1022: Raman spectroscopy analysis is performed on the mixed coal to obtain the initial blending ratio of the mixed coal and the coal quality information of each type of coal.

[0054] In some preferred embodiments, taking a 350MW unit of a power plant as an example... Figure 2 This is a schematic flowchart of the coal blending and combustion method according to an embodiment of this application, as shown below. Figure 2 As shown, in step S102, preferably, the coal flow signal is identified by a coal flow tracer to obtain coal type information of various individual coals in the mixed coal; the coal quality parameters (coal quality information) of various individual coals are detected online by a Raman online coal quality detection device, as well as the initial blending ratio; based on the initial blending ratio of the mixed coal, the coal type information and coal quality information of each coal, a functional expression for the mixed coal quality model is constructed:

[0055] [Mixed Coal Quality Model] = [n] * [Cn] * [kn]

[0056] Where n represents the coal type information, Cn represents the coal quality information of the nth type of coal, and kn represents the initial blending ratio. Specifically, the mixed coal information of the power plant's coal inventory is shown in Table 1.

[0057] Table 1

[0058] Coal type number <![CDATA[Calorific value (MJ / kg -1 )]]> Volatile matter (%) Sulfur content (%) Moisture (%) Ash content (%) M01 19.13 32.72 0.55 13.05 5.27 M02 20.54 29.15 0.75 5.59 7.65 M03 17.37 32.50 0.95 12.18 13.81 M04 19.02 32.92 0.82 13.09 7.11 M05 17.90 35.14 0.44 15.94 4.50

[0059] Step S104: Based on the initial coal blending information, the optimal blending ratio of the blended coal is obtained through the intelligent combustion optimization model. The optimal blending ratio is used to maximize the combustion efficiency of the blended coal and minimize the concentration of nitrogen oxides produced.

[0060] In some preferred embodiments, such as Figure 2 As shown, in step S104, preferably, under the current operating conditions of the thermal power generation equipment (including parameters such as O2, CO, and load), the optimal blending ratio of the mixed coal and the corresponding comprehensive characteristic index are calculated using an intelligent combustion optimization model and a mixed coal quality model. The comprehensive characteristic index is used to evaluate the quality of the optimized blending ratio, and its functional expression is as follows:

[0061]

[0062] Where, k n For the optimal blending ratio of the nth type of coal, η n () is a function for calculating the combustion efficiency of the nth type of coal. Let S be the oxygen content of the nth type of coal. n Let T be the sulfur content of the nth type of coal, and T be the temperature of the mixed coal.

[0063] In some preferred embodiments, step S104 preferably includes the following steps:

[0064] Step S1041: Construct a functional expression for the combustion efficiency of the mixed coal. Where η0 is the baseline combustion efficiency, e i The combustion efficiency contributed by the i-th type of coal, f(C) i H i Q i (T) is the weight calculation function, C i H i and Q i These are the carbon content, hydrogen content, and calorific value of the i-th type of coal, respectively, and T is the combustion temperature;

[0065] Step S1042: Construct a functional expression for the concentration of nitrogen oxides produced by the combustion of mixed coal. Where, ξ NOx,i (K i Let f(O2, N) represent the concentration of nitrogen oxides produced by the combustion of the i-th type of coal. i ) is the weighting calculation function, O2 is the oxygen concentration, and N i Let be the coal quality parameters of the i-th type of coal.

[0066] Step S1043: Construct the objective function of the multi-objective optimization algorithm: F(K) = w1×η(K) - w2×ξ NOx (K), where F(K) is the objective function for optimization, η(K) is the combustion efficiency of the mixed coal, and ξ NOx (K) represents the concentration of nitrogen oxides produced after combustion, and w1 and w2 are weighting coefficients;

[0067] Step S1044: Based on maximizing the objective function of the multi-objective optimization algorithm, and based on the initial coal blending information, the optimal blending ratio of the mixed coal is obtained through the intelligent combustion optimization model.

[0068] For example, Table 2 is an example table of optimized blending ratios for a dual-coal blending scheme obtained from Table 1 through step S104, and Table 3 is an example table of optimized blending ratios for a multi-coal blending scheme obtained from Table 1 through step S104.

[0069] Table 2

[0070] Coal type number Proportion (%) M01 and M03 68:32 M02 and M04 70:30 M03 and M05 68:32 M01 and M05 67:33

[0071] Table 3

[0072] Coal type number Proportion (%) M01, M03, M05 40:33:27 M01, M02, M04 20:50:30 M02, M03, M05 25:35:40 M03, M04, M05 17:25:58

[0073] Step S106: Adjust the optimized blending ratio using a self-learning algorithm model to obtain the adjusted optimized blending ratio.

[0074] Step S106 specifically includes the following steps:

[0075] Step S1061: Construct the objective function of the particle swarm optimization algorithm: f(k) = w3 × ε η +w4×ε ξNOx , where ε η =η1-η2, where η1 is the actual combustion efficiency after optimizing the blending ratio, η2 is the predicted combustion efficiency after optimizing the blending ratio, and ε ξNOx =ξ NOx,1 -ξ NOx,2 ξ NOx,1 To optimize the true value of nitrogen oxide concentration after co-firing ratio, ξ NOx,2 To optimize the predicted nitrogen oxide concentration after co-firing ratio, w3 and w4 are weighting coefficients;

[0076] Step S1062: Based on minimizing the objective function of the particle swarm optimization algorithm, the optimized blending ratio is adjusted through a self-learning algorithm model to obtain the adjusted optimized blending ratio.

[0077] In some preferred embodiments, step S1062 preferably involves updating the particle velocity and position with the objective function of the particle swarm optimization algorithm as the goal:

[0078] Individual optimal position update: Calculate the fitness value f(k) of each particle. If the current fitness value f(k) is better than the particle's historical best value f(pi), then update the individual optimal position p. i The function expression is as follows:

[0079] p i (t+1)=argmin{f(p i (t)),f(k)}

[0080] Global optimal position update: Compare the fitness values ​​of all particles to find the optimal particle. If the fitness value f(k) of a particle is better than the global optimal position f(g), then the function expression for updating the global optimal position g is as follows:

[0081] g(t+1) = argmin{f(g(t)),f(k)}

[0082] Speed ​​update: Particle swarm updates based on the optimal position p of each individual particle. i The velocity of each particle is adjusted based on the global optimal position g. The specific formula is as follows:

[0083] v i (t+1)=wvi (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t))

[0084] w is the inertia weight, used to maintain the inertia of the particle's current velocity; c1 and c2 are acceleration constants, controlling the influence of individuals and groups on the particle's position update; r1 and r2 are random factors, used to introduce a certain degree of randomness to prevent particles from getting trapped in local optima.

[0085] Position Update: Based on the updated velocity, update the particle position (i.e., the adjusted optimized co-firing ratio):

[0086] x i (t+1)=x i (t)+v i (t+1)

[0087] The blending ratio and velocity of each particle are updated incrementally until convergence conditions are met (e.g., the maximum number of iterations or a small change in the fitness function value). In each iteration, the particles gradually approach the optimal solution through error feedback. The global optimal position g represents the current best coal blending ratio, i.e., the final optimized blending ratio x is obtained after iteration. i (t+1).

[0088] For example, Table 4 is an example table of optimized blending ratios for a dual-coal blending scheme obtained from Table 1 through step S106, and Table 5 is an example table of optimized blending ratios for a multi-coal blending scheme obtained from Table 1 through step S106.

[0089] Table 4

[0090]

[0091] Table 5

[0092]

[0093] In some specific embodiments, after adjusting the optimized blending ratio using a self-learning algorithm model in step S106 to obtain the adjusted optimized blending ratio, the method further includes:

[0094] It receives the optimized blending ratio derived from the self-learning algorithm model and generates the corresponding optimized blending ratio instruction to automatically blend the raw coal.

[0095] Through the process steps in the above embodiments, intelligent combustion optimization and self-learning algorithms are realized, which can optimize the combustion effect of mixed coal, improve combustion efficiency, reduce pollutant emissions, and automatically adjust the model according to coal quality and operating conditions during operation, continuously improving the optimization effect of the model, thus solving the problem of how to achieve efficient and environmentally friendly combustion of mixed coal in thermal power plants.

[0096] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0097] This application provides a coal blending and combustion system. Figure 3 This is a schematic diagram of the structure of a coal blending and combustion system according to an embodiment of this application, as shown below. Figure 3 As shown, the system includes a coal detection and analysis module 31, an intelligent combustion optimization module 32, and a self-learning adjustment module 33;

[0098] The coal detection and analysis module 31 is used to perform real-time detection and analysis on the mixed coal used for thermal power generation to obtain the initial coal blending and combustion information.

[0099] The intelligent combustion optimization module 32 is used to determine the optimal blending ratio of the mixed coal based on the initial coal blending information through the intelligent combustion optimization model. The optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced.

[0100] The self-learning adjustment module 33 is used to adjust the optimized blending ratio through a self-learning algorithm model to obtain the adjusted optimized blending ratio.

[0101] Through the coal detection and analysis module 31, intelligent combustion optimization module 32, and self-learning adjustment module 33 in this embodiment, intelligent combustion optimization and self-learning algorithms are realized, which can optimize the combustion effect of mixed coal, improve combustion efficiency, reduce pollutant emissions, and automatically adjust the model according to coal quality and operating conditions during operation, continuously improving the optimization effect of the model, thus solving the problem of how to achieve efficient and environmentally friendly combustion of mixed coal in thermal power plants.

[0102] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0103] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0104] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0105] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0106] Furthermore, in conjunction with the coal blending and combustion methods described in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the coal blending and combustion methods described in the above embodiments.

[0107] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for blending coal during combustion. The display screen may be a liquid crystal display (LCD) or an electronic ink display. The input device may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0108] In one embodiment, Figure 4 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 4 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 4 As shown, the electronic device includes a processor, a network interface, internal memory, and non-volatile memory connected via an internal bus. The non-volatile memory stores the operating system, computer programs, and a database. The processor provides computing and control capabilities, the network interface communicates with external terminals via a network, the internal memory provides an environment for the operating system and computer programs to run, the computer programs are executed by the processor to implement a coal blending method, and the database stores data.

[0109] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0111] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for blending and burning coal, characterized in that, The method includes: Initial coal blending information includes the initial blending ratio of the blended coals, coal type information, and coal quality information for each type of coal. Real-time monitoring of the mixed coal used for thermal power generation yields information on the coal type of each type of coal in the mixed coal. Raman spectroscopy analysis was performed on the mixed coal to obtain the initial blending ratio and coal quality information of each type of coal. Based on the initial blending ratio of the mixed coal, the coal type information and coal quality information of each type of coal, the functional expression of the mixed coal quality model is constructed as follows: Where n represents the coal type information of the coal flow. Cn For the first n Coal quality information for this type of coal kn This is the initial blending ratio; Under the current operating conditions of the thermal power generation equipment, the optimal blending ratio and corresponding comprehensive characteristic index of the mixed coal are calculated using an intelligent combustion optimization model and the mixed coal quality model. The comprehensive characteristic index is used to evaluate the quality of the optimized blending ratio, which aims to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced. The functional expression of the comprehensive characteristic index is as follows: in, k n For the first n Optimize the blending ratio of different types of coal. η n () is used to calculate the first n A function of coal combustion efficiency. For the first n The oxygen content of this type of coal, S n For the first n The sulfur content of coal, T Temperature of the mixed coal; The optimized blending ratio is adjusted by using a self-learning algorithm model to obtain the adjusted optimized blending ratio.

2. The method according to claim 1, characterized in that, Based on the initial coal blending information, an optimized blending ratio for the mixed coal is determined using an intelligent combustion optimization model. This optimized blending ratio aims to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced, including: Constructing the objective function of a multi-objective optimization algorithm ,in, F ( K Let ) be the objective function for optimization. η ( K () represents the combustion efficiency of the mixed coal. This represents the concentration of nitrogen oxides produced after combustion. w 1 and w 2 represents the weighting coefficient; Based on maximizing the objective function of the multi-objective optimization algorithm, and using the initial coal blending information, the optimal blending ratio of the mixed coal is obtained through an intelligent combustion optimization model.

3. The method according to claim 2, characterized in that, The method includes: Construct a functional expression for the combustion efficiency of blended coal. ,in, η 0 is the baseline combustion efficiency. e i For the first i The combustion efficiency contributed by this type of coal, f ( C i , H i , Q i , T ) is the weight calculation function. C i , H i and Q i They are the first i The carbon content, hydrogen content, and calorific value of this type of coal. T The combustion temperature; Construct a functional expression for the concentration of nitrogen oxides produced by the combustion of the mixed coal. ,in, For the first i The concentration of nitrogen oxides produced after the combustion of this type of coal, f ( O 2, N i ) is the weight calculation function. O 2 represents the oxygen concentration. N i For the first i Coal quality parameters of a type of coal.

4. The method according to claim 1, characterized in that, The optimized blending ratio is adjusted using a self-learning algorithm model, resulting in the following adjusted optimized blending ratio: Constructing the objective function of the particle swarm optimization algorithm ,in, , η 1 represents the actual combustion efficiency after optimizing the blending ratio. η 2 represents the predicted combustion efficiency after optimizing the blending ratio. , To optimize the true value of nitrogen oxide concentration after blending ratio, To optimize the predicted nitrogen oxide concentration after blending ratio, w 3 and w 4 represents the weighting coefficient; Based on minimizing the objective function of the particle swarm optimization algorithm, the optimized blending ratio is adjusted through a self-learning algorithm model to obtain the adjusted optimized blending ratio.

5. The method according to claim 1, characterized in that, After adjusting the optimized blending ratio using a self-learning algorithm model to obtain the adjusted optimized blending ratio, the method includes: It receives the optimized blending ratio derived from the self-learning algorithm model and generates the corresponding optimized blending ratio instruction to automatically blend the raw coal.

6. A coal blending and combustion system, characterized in that, The system is used to perform the method according to any one of claims 1 to 5, and the system includes a coal detection and analysis module, an intelligent combustion optimization module, and a self-learning adjustment module; The coal detection and analysis module is used to perform real-time detection and analysis on the mixed coal used for thermal power generation, and to obtain the initial coal blending information. The intelligent combustion optimization module is used to determine the optimal blending ratio of the mixed coal based on the initial coal blending information through an intelligent combustion optimization model. The optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides produced. The self-learning adjustment module is used to adjust the optimized blending ratio through a self-learning algorithm model to obtain the adjusted optimized blending ratio.

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