Blended coal blending combustion method and system

Abstract

The invention relates to a mixed coal blending combustion method and system.The method comprises the steps that mixed coal used for thermal power generation is detected and analyzed in real time, and initial mixed coal blending combustion information of the mixed coal is obtained; based on the initial mixed coal blending combustion information, the optimized blending combustion proportion of the mixed coal is obtained through an intelligent combustion optimization model, and the optimized blending combustion proportion is used for maximizing the combustion efficiency of the mixed coal and minimizing the concentration of generated nitric oxide; and the optimized blending combustion proportion is adjusted through the self-learning algorithm model, and the adjusted optimized blending combustion proportion is obtained. Through the application, intelligent combustion optimization and a self-learning algorithm are realized, the combustion effect of the mixed coal can be optimized, the combustion efficiency is improved, the pollutant emission is reduced, the model is automatically adjusted according to the coal quality and the working condition in the operation process, the optimization effect of the model is continuously improved, and the operation efficiency is improved. The problem of how to achieve efficient and environment-friendly combustion of mixed coal in a thermal power station is solved.

Description

Technical Field

[0001] The present application relates to the technical field of thermal power generation, and in particular to a mixed coal combustion method and system. Background Art

[0002] In the new power system dominated by renewable energy generation, coal-fired units play a key role in deep peak regulation, and 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, the combustion efficiency can be effectively improved, while reducing the emissions of pollutants such as nitrogen oxides and sulfur oxides.

[0003] Traditional blending methods mainly rely on experience settings and manual adjustments, which are inefficient and difficult to respond quickly to real-time changes in coal quality, resulting in difficulty in achieving optimal combustion efficiency and environmental performance. Specifically, this method requires operators to make combustion adjustments based on experience and intuition, and it is often difficult to accurately control the combustion process, resulting in large fluctuations in combustion efficiency and environmental emission indicators. At the same time, the manual adjustment process is time-consuming and difficult to respond to changes in coal quality in a timely manner, further affecting combustion efficiency and environmental performance. With increasingly stringent environmental regulations and increasing diversity of coal types, how to achieve efficient and environmentally friendly combustion of mixed coal has become a key issue that thermal power plants urgently need to solve.

[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 in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a mixed coal combustion method and system to at least solve the problem of how to achieve efficient and environmentally friendly combustion of mixed coal in a thermal power plant in the related art.

[0006] In a first aspect, an embodiment of the present application provides a method for mixed coal combustion, the method comprising:

[0007] Performing real-time detection and analysis on mixed coal used for thermal power generation to obtain initial mixed coal combustion information of the mixed coal;

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

[0009] The optimized blending and combustion ratio is adjusted through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

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

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

[0012] On the basis of maximizing the objective function of the multi-objective optimization algorithm, based on the initial mixed coal blending information, the optimized blending ratio of the mixed coal is obtained through an intelligent combustion optimization model.

[0013] In some embodiments, the method comprises:

[0014] Constructing a functional expression for the combustion efficiency of mixed coal Among them, η0 is the benchmark combustion efficiency, e i The combustion efficiency contributed by the i-th coal, f(C i ,H i ,Q i ,T) is the weight calculation function, C i , H i and Q i are the carbon content, hydrogen content and calorific value of the i-th coal, respectively, and T is the combustion temperature;

[0015] Construct a functional expression for the concentration of nitrogen oxides produced by the mixed coal after combustion Among them, ξ NOx,i (K i ) is the concentration of nitrogen oxides produced after the combustion of the i-th type of coal, f(O2,N i ) is the weight calculation function, O2 is the oxygen concentration, N i is the coal quality parameter of the i-th coal.

[0016] In some embodiments, real-time detection and analysis of mixed coal for thermal power generation is performed to obtain initial mixed coal combustion information of the mixed coal, including:

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

[0018] Performing real-time detection on mixed coal used for thermal power generation to obtain coal type information of each type of coal in the mixed coal;

[0019] The mixed coal is subjected to Raman spectroscopy analysis to obtain the initial blending ratio of the mixed coal and the coal quality information of each type of coal.

[0020] In some embodiments, after real-time detection and analysis of mixed coal for thermal power generation is performed to obtain initial mixed coal combustion information of the mixed 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 coal, a function expression of the mixed coal quality model is constructed:

[0022] [Mixed coal quality model] = [n] * [Cn] * [kn]

[0023] Among them, n is the coal flow and coal type information, Cn is the coal quality information of the nth type of coal, and kn is the initial blending ratio.

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

[0025] Under the current operating conditions of the thermal power generation equipment, the optimized blending ratio of the mixed coal and the corresponding comprehensive characteristic index are calculated through the intelligent combustion optimization model and the mixed coal quality model, wherein the comprehensive characteristic index is used to evaluate the advantages and disadvantages of the optimized blending ratio.

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

[0027]

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

[0029] In some embodiments, the optimized blending ratio is adjusted by a self-learning algorithm model, and the adjusted optimized blending ratio includes:

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

[0031] On the basis of minimizing the objective function of the particle swarm algorithm, the optimized blending and combustion ratio is adjusted through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

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

[0033] The optimized blending ratio obtained from the self-learning algorithm model is received, and a corresponding optimized blending ratio instruction is generated to automatically mix the raw coal.

[0034] In a second aspect, an embodiment of the present application provides a mixed coal combustion system, the system comprising 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 to obtain initial mixed coal combustion information of the mixed coal;

[0036] The intelligent combustion optimization module is used to obtain an optimized blending ratio of the mixed coal through an intelligent combustion optimization model according to the initial mixed coal blending information, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides generated;

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

[0038] Compared with the related art, the embodiment of the present application provides a mixed coal blending method and system, wherein the method obtains initial mixed coal blending information of the mixed coal by real-time detection and analysis of the mixed coal used for thermal power generation; based on the initial mixed coal blending information, the optimized blending ratio of the mixed coal is obtained through an intelligent combustion optimization model, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides generated; the optimized blending ratio is adjusted through a self-learning algorithm model to obtain an adjusted optimized blending ratio, thereby realizing intelligent combustion optimization and self-learning algorithms, being able to optimize the combustion effect of the mixed coal, improve combustion efficiency, and reduce pollutant emissions, and automatically adjusting the model according to coal quality and operating conditions during operation, continuously improving the optimization effect of the model, and solving the problem of how to achieve efficient and environmentally friendly combustion of mixed coal in thermal power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 is a flow chart of the steps of the mixed coal combustion method according to an embodiment of the present application;

[0041] Figure 2 is a schematic flow chart of a mixed coal combustion method according to an embodiment of the present application;

[0042] Figure 3 is a structural schematic diagram of a mixed coal combustion system according to an embodiment of the present application;

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

[0044] Attached figure symbols: 31. Coal detection and analysis module; 32. Intelligent combustion optimization module; 33. Self-learning adjustment module. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0046] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0047] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0048] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; 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 that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0049] The present application provides a method for mixed coal combustion. Figure 1 is a flow chart of the steps of the mixed coal combustion method according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0050] Step S102, performing real-time detection and analysis on the mixed coal used for thermal power generation to obtain initial mixed coal combustion information of the mixed coal;

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

[0052] Step S1021, performing real-time detection on 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, performing Raman spectroscopy analysis 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 is a schematic diagram of a process for the mixed coal combustion method according to an embodiment of the present application, such as Figure 2 As shown, in step S102, preferably, the coal flow signal is identified by a coal flow tracing device to obtain the coal type information of various single coals in the mixed coal, and the coal quality parameters (coal quality information) of various single coals and the initial blending ratio are detected online by a Raman coal quality online detection device; based on the initial blending ratio of the mixed coal, the coal type information and coal quality information of each coal, a function expression of the mixed coal quality model is constructed:

[0055] [Mixed coal quality model] = [n] * [Cn] * [kn]

[0056] Among them, n is the coal type information of the coal flow, Cn is the coal quality information of the nth type of coal, and kn is the initial blending ratio. Specifically, the mixed coal information of the coal in stock of the power plant 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 mixed coal blending information, an optimized blending ratio of the mixed coal is obtained through an intelligent combustion optimization model, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of generated nitrogen oxides;

[0060] In some preferred embodiments, Figure 2 As shown, step S104 preferably calculates the optimized blending ratio and the corresponding comprehensive characteristic index of the mixed coal under the current operating conditions of the thermal power generation equipment (including parameters such as O2, CO and load) through the intelligent combustion optimization model and the mixed coal quality model, wherein the comprehensive characteristic index is used to evaluate the advantages and disadvantages of the optimized blending ratio, and the function expression of the comprehensive characteristic index is:

[0061]

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

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

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

[0065] Step S1042: construct a functional expression for the concentration of nitrogen oxides generated by the mixed coal after combustion. Among them, ξ NOx,i (K i ) is the concentration of nitrogen oxides produced after the combustion of the i-th type of coal, f(O2,N i ) is the weight calculation function, O2 is the oxygen concentration, N i is the coal quality parameter of the i-th coal.

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

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

[0068] It should be noted that Table 2 is an example table of optimized blending ratios for a dual-coal mixed coal scheme obtained through step S104 based on Table 1, and Table 3 is an example table of optimized blending ratios for a multi-coal mixed coal scheme obtained through step S104 based on Table 1.

[0069] Table 2

[0070] Coal type number Ratio(%) 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 Ratio(%) 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, adjusting the optimized blending and combustion ratio through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

[0074] Step S106 specifically includes the following steps:

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

[0076] Step S1062, on the basis of minimizing the objective function of the particle swarm algorithm, adjusting the optimized blending and combustion ratio through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

[0077] In some preferred embodiments, step S1062 preferably updates the speed and position of the particle with the goal of minimizing the objective function of the particle swarm algorithm:

[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 historical optimal value f(pi) of the particle, 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 best particle. If the fitness value f(k) of a particle is better than the global optimal position f(g), 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: The particle swarm updates according to the individual optimal position p i And the global optimal position g to adjust the speed of each particle. 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, which is used to maintain the inertia of the current velocity of the particle. c1 and c2 are acceleration constants, which control the influence of individuals and groups on the particle position update. r1 and r2 are random factors, which are used to introduce a certain randomness to prevent particles from falling into local optimality.

[0085] Position update: Update the particle position according to the updated speed (i.e. the adjusted optimized blending ratio):

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

[0087] The blending ratio and speed of each particle are gradually updated until the convergence condition is reached (such as the maximum number of iterations or the fitness function value changes very little). In each iteration, the particle gradually approaches the optimal solution through error feedback information. The global optimal position g represents the current optimal coal blending ratio, that is, the final optimized blending ratio x is obtained after the iteration is completed. i (t+1).

[0088] It should be noted that Table 4 is an example table of optimized blending ratios for a dual-coal mixed coal scheme obtained through step S106 based on Table 1, and Table 5 is an example table of optimized blending ratios for a multi-coal mixed coal scheme obtained through step S106 based on Table 1.

[0089] Table 4

[0090]

[0091] Table 5

[0092]

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

[0094] The optimized blending ratio obtained from the self-learning algorithm model is received, and a corresponding optimized blending ratio instruction is generated to automatically mix the raw coal.

[0095] Through the process steps in the above 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 improve the optimization effect of the model, and solve 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 the flowchart in the accompanying drawings 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 can be executed in an order different from that shown here.

[0097] The embodiment of the present application provides a mixed coal combustion system. Figure 3 is a structural schematic diagram of a mixed coal combustion system according to an embodiment of the present application, such as 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 initial mixed coal combustion information of the mixed coal;

[0099] An intelligent combustion optimization module 32 is used to obtain an optimized blending ratio of the mixed coal through an intelligent combustion optimization model according to the initial mixed coal blending information, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides generated;

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

[0101] Through the coal detection and analysis module 31, the intelligent combustion optimization module 32 and the self-learning adjustment module 33 in this embodiment, the 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 the coal quality and operating conditions during operation, continuously improve the optimization effect of the model, and solve 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 by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0103] This embodiment further 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 execute the steps in any one 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 implementation modes, and this embodiment will not be described in detail here.

[0106] In addition, in combination with the mixed coal combustion method in the above embodiment, the embodiment of the present application can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the mixed coal combustion methods in the above embodiment is implemented.

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

[0108] In one embodiment, Figure 4 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 4 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 4 As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system and the computer program, the computer program is executed by the processor to implement a mixed coal combustion method, and the database is used to store data.

[0109] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of 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 a different arrangement of components.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0111] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are 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 above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A mixed coal combustion method, characterized in that: The method comprises: Performing real-time detection and analysis on mixed coal used for thermal power generation to obtain initial mixed coal combustion information of the mixed coal; Based on the initial mixed coal blending information, an optimized blending ratio of the mixed coal is obtained through an intelligent combustion optimization model, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides generated; The optimized blending and combustion ratio is adjusted through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

2. The method according to claim 1, characterized in that: Based on the initial mixed coal combustion information, an optimized blending ratio of the mixed coal is obtained through an intelligent combustion optimization model, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides generated, and includes: Construct the objective function of the multi-objective optimization algorithm F(K) = w1×η(K)-w2×ξ NOx (K), where F(K) is the optimization objective function, η(K) is the combustion efficiency of the mixed coal, ξ NOx (K) is the concentration of nitrogen oxides produced after combustion, w1 and w2 are weight coefficients; On the basis of maximizing the objective function of the multi-objective optimization algorithm, based on the initial mixed coal blending information, the optimized 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 comprises: Constructing a functional expression for the combustion efficiency of mixed coal Among them, η0 is the benchmark combustion efficiency, e i The combustion efficiency contributed by the i-th coal, f(C i ,H i ,Q i ,T) is the weight calculation function, C i , H i and Q i are the carbon content, hydrogen content and calorific value of the i-th coal, respectively, and T is the combustion temperature; Construct a functional expression for the concentration of nitrogen oxides produced by the mixed coal after combustion Among them, ξ NOx,i (K i ) is the concentration of nitrogen oxides produced after the combustion of the i-th type of coal, f(O2,N i ) is the weight calculation function, O2 is the oxygen concentration, N i is the coal quality parameter of the i-th coal.

4. The method according to claim 1, characterized in that: Real-time detection and analysis of mixed coal used for thermal power generation is performed to obtain initial mixed coal combustion information of the mixed coal, including: The initial mixed coal combustion information includes the initial blending ratio of the mixed coal, coal type information and coal quality information of each type of coal; Performing real-time detection on mixed coal used for thermal power generation to obtain coal type information of each type of coal in the mixed coal; The mixed coal is subjected to Raman spectroscopy analysis to obtain the initial blending ratio of the mixed coal and the coal quality information of each type of coal.

5. The method according to claim 4, characterized in that After real-time detection and analysis of mixed coal for thermal power generation is performed to obtain initial mixed coal combustion information of the mixed coal, the method includes: Based on the initial blending ratio of the mixed coal, the coal type information and coal quality information of each coal, a function expression of the mixed coal quality model is constructed: [Mixed coal quality model] = [n] * [Cn] * [kn] Among them, n is the coal flow and coal type information, Cn is the coal quality information of the nth type of coal, and kn is the initial blending ratio.

6. The method according to claim 5, characterized in that Based on the initial mixed coal combustion information, the optimized blending ratio of the mixed coal is obtained through the intelligent combustion optimization model, including: Under the current operating conditions of the thermal power generation equipment, the optimized blending ratio of the mixed coal and the corresponding comprehensive characteristic index are calculated through the intelligent combustion optimization model and the mixed coal quality model, wherein the comprehensive characteristic index is used to evaluate the advantages and disadvantages of the optimized blending ratio.

7. The method according to claim 6, characterized in that The functional expression of the comprehensive characteristic index is: Among them, k n is the optimal blending ratio of the nth coal, η n () is the function for calculating the combustion efficiency of the nth type of coal, is the oxygen content of the nth type of coal, S n is the sulfur content of the nth type of coal, and T is the mixed coal temperature.

8. The method according to claim 1, characterized in that The optimized blending ratio is adjusted by a self-learning algorithm model, and the adjusted optimized blending ratio includes: Constructing the objective function of the particle swarm algorithm Among them, ε η =η1-η2, η1 is the actual value of the combustion efficiency after optimizing the blending ratio, η2 is the predicted value of the combustion efficiency after optimizing the blending ratio, ξ NOx,1 To optimize the true value of nitrogen oxide concentration after the blending ratio, ξ NOx,2 To optimize the predicted value of nitrogen oxide concentration after the blending ratio, w3 and w4 are weight coefficients; On the basis of minimizing the objective function of the particle swarm algorithm, the optimized blending and combustion ratio is adjusted through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

9. The method according to claim 1, characterized in that: After the optimized blending and combustion ratio is adjusted by the self-learning algorithm model to obtain the adjusted optimized blending and combustion ratio, the method includes: The optimized blending ratio obtained from the self-learning algorithm model is received, and a corresponding optimized blending ratio instruction is generated to automatically mix the raw coal.

10. A mixed coal combustion system, characterized in that: 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 to obtain initial mixed coal combustion information of the mixed coal; The intelligent combustion optimization module is used to obtain an optimized blending ratio of the mixed coal through an intelligent combustion optimization model according to the initial mixed coal blending information, wherein the optimized blending ratio is used to maximize the combustion efficiency of the mixed coal and minimize the concentration of nitrogen oxides generated; The self-learning adjustment module is used to adjust the optimized blending and combustion ratio through a self-learning algorithm model to obtain an adjusted optimized blending and combustion ratio.

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