Method, device and equipment for optimizing cow dung blending combustion proportion of coal-fired power plant and medium

Abstract

The invention relates to an optimization method, device and equipment for a cow dung blending combustion ratio of a coal-fired power plant and a medium. The method comprises the following steps: acquiring hearth design parameters of the coal-fired boiler, operation data of the coal-fired boiler, cow dung quality detection data, coal quality analysis data and combustion emission data; determining an objective function based on a preset relation model of combustion efficiency and emission level, and determining constraint conditions based on hearth design parameters of the coal-fired boiler, operation data of the coal-fired boiler, cow dung quality detection data, coal quality analysis data and combustion emission data; and based on the objective function and the constraint condition, constructing a Lagrange function, and based on the Lagrange function, calculating the current optimal cow dung blending combustion proportion. Therefore, the cow dung blending combustion proportion is dynamically adjusted according to the boiler hearth condition, the cow dung quality and the historical operation data, the problem that in the prior art, the combustion efficiency is low due to the fact that the cow dung blending combustion proportion is fixed is solved, and efficient, environment-friendly and stable combustion operation is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of coal-fired power generation, and in particular to a method, device, equipment and medium for optimizing the proportion of cow dung blending in coal-fired power plants. Background Art

[0002] Cow dung is a common agricultural waste. Disposal methods such as composting or direct discharge can cause environmental pollution. However, cow dung has a certain calorific value. Combusting it with coal can convert it into energy, reducing coal usage and lowering fuel costs.

[0003] In the related art, additives and water are added to a mixture of loose coal and cow dung, and the mixture is compressed and molded to obtain composite coal, wherein the cow dung accounts for 2% to 10% of the mass of the loose coal.

[0004] However, the ratio of cow dung to be burned in this method is fixed and cannot be adjusted dynamically, resulting in low combustion efficiency, which needs to be solved urgently. Summary of the Invention

[0005] The present application provides a method, device, equipment and medium for optimizing the cow dung blending ratio in a coal-fired power plant, so as to solve the problem of low combustion efficiency caused by the fixed cow dung blending ratio in the prior art, and achieve efficient, environmentally friendly and stable combustion operation.

[0006] To achieve the above objectives, the first embodiment of the present application proposes a method for optimizing the ratio of cow dung co-combustion in a coal-fired power plant, comprising the following steps:

[0007] Obtaining furnace design parameters of a coal-fired boiler, operating data of the coal-fired boiler, cow dung quality test data, coal quality analysis data, and combustion emission data;

[0008] An objective function is determined based on a preset relationship model between combustion efficiency and emission level, and constraints are determined based on furnace design parameters of the coal-fired boiler, operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data, wherein the preset relationship model between combustion efficiency and emission level is trained by the furnace design parameters of the coal-fired boiler, a historical operating data set of the coal-fired boiler, a historical cow dung quality detection data set, a historical coal quality analysis data set, and a historical combustion emission data set;

[0009] Based on the objective function and the constraint conditions, a Lagrangian function is constructed, and the current optimal cow dung blending ratio is calculated based on the Lagrangian function.

[0010] According to one embodiment of the present application, before determining the objective function based on the preset relationship model between combustion efficiency and emission level, the method further includes:

[0011] Acquire a historical operation dataset of the coal-fired boiler, a historical cow dung quality detection dataset, a historical coal quality analysis dataset, and a historical combustion emission dataset;

[0012] Calculating an initial cow dung blending ratio using a calorific value balance strategy based on the historical cow dung quality detection data set and the historical coal quality analysis data set;

[0013] Based on the initial cow dung blending ratio, performing a cow dung blending process according to a preset incremental gradient, obtaining multiple cow dung blending ratios and combustion efficiencies and emission levels corresponding to each cow dung blending ratio;

[0014] The multiple cow dung blending ratios, the historical operation data set of the coal-fired boiler, the historical cow dung quality detection data set and the historical coal quality analysis data set are used as input parameters of a preset neural network, the combustion efficiency and emission level corresponding to each cow dung blending ratio are used as output variables of the preset neural network, and the preset kernel function is used to perform model training on the preset neural network to obtain the preset relationship model between the combustion efficiency and the emission level.

[0015] According to one embodiment of the present application, the combustion efficiency corresponding to each cow dung blending ratio is:

[0016]

[0017] Among them, E output (x) is the effective energy converted into heat after combustion, E input (x) is the total energy of the input fuel, and x is the proportion of cow dung mixed with combustion.

[0018] According to one embodiment of the present application, the emission level corresponding to each cow dung burning ratio is:

[0019] C(x)=α·CO2(x)+β·SO2(x)+γ·NO x (x);

[0020] Among them, α, β, and γ are model parameters, CO2(x) is the emission concentration of CO2, SO2(x) is the emission concentration of SO2, and NO x (x) is the emission concentration of NOx.

[0021] According to one embodiment of the present application, the calorific value balance strategy is:

[0022]

[0023] Among them, x0 is the initial cow dung burning ratio, Q coal is the coal calorific value in the coal quality analysis data, Qcoal is the calorific value of cow dung in the cow dung quality detection data, Q min The minimum calorific value required for furnace combustion.

[0024] According to one embodiment of the present application, the objective function is to maximize combustion efficiency and minimize emission levels.

[0025] According to the optimization method for the cow dung blending ratio of a coal-fired power plant proposed in the embodiment of the present application, the objective function can be determined based on a preset relationship model between combustion efficiency and emission level, and the constraints can be determined based on the furnace design parameters of the coal-fired boiler, the operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data; then, based on the objective function and the constraints, a Lagrangian function is constructed, and the current optimal cow dung blending ratio is calculated based on the Lagrangian function. Thus, by dynamically adjusting the cow dung blending ratio according to the boiler furnace condition, cow dung quality, and historical operating data, the problem of low combustion efficiency caused by the fixed cow dung blending ratio in the prior art is solved, and efficient, environmentally friendly, and stable combustion operation is achieved.

[0026] To achieve the above-mentioned objectives, a second embodiment of the present application proposes a device for optimizing the ratio of cow dung to be burned in a coal-fired power plant, comprising:

[0027] An acquisition module, configured to acquire furnace design parameters of a coal-fired boiler, operating data of the coal-fired boiler, cow dung quality detection data, coal quality analysis data, and combustion emission data;

[0028] a determination module, configured to determine an objective function based on a preset relationship model between combustion efficiency and emission level, and determine constraint conditions based on furnace design parameters of the coal-fired boiler, operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data, wherein the preset relationship model between combustion efficiency and emission level is trained by the furnace design parameters of the coal-fired boiler, a historical operating data set of the coal-fired boiler, a historical cow dung quality detection data set, a historical coal quality analysis data set, and a historical combustion emission data set;

[0029] A calculation module is used to construct a Lagrangian function based on the objective function and the constraint conditions, and calculate the current optimal cow dung blending ratio based on the Lagrangian function.

[0030] According to one embodiment of the present application, before determining the objective function based on the preset relationship model between combustion efficiency and emission level, the determination module is further configured to:

[0031] Acquire a historical operation dataset of the coal-fired boiler, a historical cow dung quality detection dataset, a historical coal quality analysis dataset, and a historical combustion emission dataset;

[0032] Based on the initial cow dung blending ratio, performing a cow dung blending process according to a preset incremental gradient, obtaining multiple cow dung blending ratios and combustion efficiencies and emission levels corresponding to each cow dung blending ratio;

[0033] The multiple cow dung blending ratios, the historical operation data set of the coal-fired boiler, the historical cow dung quality detection data set and the historical coal quality analysis data set are used as input parameters of a preset neural network, the combustion efficiency and emission level corresponding to each cow dung blending ratio are used as output variables of the preset neural network, and the preset kernel function is used to perform model training on the preset neural network to obtain the preset relationship model between the combustion efficiency and the emission level.

[0034] According to one embodiment of the present application, the combustion efficiency corresponding to each cow dung blending ratio is:

[0035]

[0036] Among them, E output (x) is the effective energy converted into heat after combustion, E input (x) is the total energy of the input fuel, and x is the proportion of cow dung mixed with combustion.

[0037] According to one embodiment of the present application, the emission level corresponding to each cow dung burning ratio is:

[0038] C(x)=α·CO2(x)+β·SO2(x)+γ·NO x (x);

[0039] Among them, α, β, and γ are model parameters, CO2(x) is the emission concentration of CO2, SO2(x) is the emission concentration of SO2, and NO x (x) is the emission concentration of NOx.

[0040] According to one embodiment of the present application, the calorific value balance strategy is:

[0041]

[0042] Among them, x0 is the initial cow dung burning ratio, Q coal is the coal calorific value in the coal quality analysis data, Q coal is the calorific value of cow dung in the cow dung quality detection data, Q min The minimum calorific value required for furnace combustion.

[0043] According to one embodiment of the present application, the objective function is to maximize combustion efficiency and minimize emission levels.

[0044] According to the device for optimizing the proportion of cow dung in a coal-fired power plant proposed in the embodiment of the present application, the objective function can be determined based on a preset relationship model between combustion efficiency and emission level, and the constraints can be determined based on the furnace design parameters of the coal-fired boiler, the operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data; then, based on the objective function and the constraints, a Lagrangian function is constructed, and the current optimal cow dung proportion is calculated based on the Lagrangian function. Thus, by dynamically adjusting the cow dung proportion according to the boiler furnace condition, cow dung quality, and historical operating data, the problem of low combustion efficiency caused by the fixed cow dung proportion in the prior art is solved, and efficient, environmentally friendly, and stable combustion operation is achieved.

[0045] To achieve the above-mentioned objectives, the third aspect of the present application proposes an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing the proportion of cow dung co-combustion in a coal-fired power plant as described in the above-mentioned embodiment.

[0046] To achieve the above-mentioned objectives, the fourth embodiment of the present application proposes a computer-readable storage medium on which a computer program is stored, which is executed by a processor to implement the method for optimizing the proportion of cow dung blending in a coal-fired power plant as described in the above embodiment.

[0047] To achieve the above-mentioned objectives, the fifth embodiment of the present application proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the method for optimizing the proportion of cow dung co-combustion in a coal-fired power plant as described in the above embodiment.

[0048] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0050] Figure 1 This is a flow chart of a method for optimizing the proportion of cow dung blending in a coal-fired power plant according to an embodiment of the present application;

[0051] Figure 2 A flow chart of another method for optimizing the proportion of cow dung in coal-fired power plants according to an embodiment of the present application;

[0052] Figure 3 Schematic diagram of a block diagram of a device for optimizing the ratio of cow dung to be burned in a coal-fired power plant according to an embodiment of the present application;

[0053] Figure 4 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0055] The following describes, with reference to the accompanying drawings, a method, device, equipment, and medium for optimizing the ratio of cow dung to be burned in a coal-fired power plant according to an embodiment of the present application.

[0056] Figure 1 This is a flow chart of a method for optimizing the proportion of cow dung in coal-fired power plants according to one embodiment of the present application.

[0057] For example, Figure 1 As shown, the method for optimizing the proportion of cow dung in the combustion of coal-fired power plants includes the following steps:

[0058] In step S101, furnace design parameters of a coal-fired boiler, operation data of the coal-fired boiler, cow dung quality detection data, coal quality analysis data, and combustion emission data are obtained.

[0059] It is understandable that the furnace design parameters of coal-fired boilers include furnace volume, burner layout and heat load range. The operating data of coal-fired boilers include real-time operating data and historical operating data. Real-time operating data include combustion temperature, primary air / secondary air flow, and load; historical operating data covers the slagging frequency and fly ash carbon content under different loads in the past three months or more. Cow dung quality detection data include moisture content, calorific value and ash content. Coal quality analysis data include calorific value, sulfur content, chlorine content and other indicators. Combustion emission data include carbon dioxide (CO2) concentration, sulfur dioxide (SO2) concentration, nitrogen oxides (NO x ) concentration, etc., can be collected in real time by the CEMS (Continuous Emission Monitoring System).

[0060] In step S102, the objective function is determined based on a preset relationship model between combustion efficiency and emission level, and the constraint conditions are determined based on the furnace design parameters of the coal-fired boiler, the operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data and the combustion emission data, wherein the preset relationship model between combustion efficiency and emission level is trained by the furnace design parameters of the coal-fired boiler, the historical operating data of the coal-fired boiler, the historical cow dung quality detection data, the historical coal quality analysis data and the historical combustion emission data.

[0061] In some embodiments, the objective function is to maximize combustion efficiency and minimize emission levels.

[0062] Specifically, in the process of optimizing the ratio of cow dung to be burned, an objective function can be constructed first. This objective function can achieve the maximum combustion efficiency and the minimum emission level, which can be determined by a preset relationship model between combustion efficiency and emission level.

[0063] Optionally, in some embodiments, the combustion efficiency corresponding to each cow dung blending ratio is:

[0064]

[0065] Among them, E output (x) is the effective energy converted into heat after combustion, E input (x) is the total energy of the input fuel.

[0066] Alternatively, in other embodiments, the emission level corresponding to each cow dung blending ratio is:

[0067] C(x)=α·CO2(x)+β·SO2(x)+γ·NO x (x); (2)

[0068] Among them, α, β, and γ are model parameters, CO2(x) is the emission concentration of CO2, SO2(x) is the emission concentration of SO2, and NO x (x) is NO x emission concentration.

[0069] Therefore, the objective functions are maxη(x) and minC(x), which can be obtained by equations (1) and (2) respectively.

[0070] In order to ensure stable combustion in the furnace, a series of constraints can also be considered, as shown in Equations (3) to (7). These constraints can be determined based on the furnace design parameters of the coal-fired boiler obtained in step S101, the operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data.

[0071] Combustion temperature constraints:

[0072] T min ≤T(x)≤T max ; (3)

[0073] Pollutant emission limits:

[0074]

[0075] Blending ratio constraints:

[0076] 0≤x≤x max ; (7)

[0077] Furthermore, to develop an accurate model for the relationship between combustion efficiency and emissions (i.e., a pre-defined model), training can be performed using historical data. This data includes furnace design parameters of coal-fired boilers, historical operation data of coal-fired boilers, historical cow dung quality testing data, historical coal quality analysis data, and historical combustion emissions data. By analyzing this historical data, a more accurate model can be developed to guide the optimization of cow dung blending ratios in practical applications, thereby achieving energy conservation and emission reduction.

[0078] For ease of understanding, the following describes in detail how to obtain a preset relationship model between combustion efficiency and emission levels.

[0079] As a possible implementation method, in some embodiments, before determining the objective function based on the preset relationship model between combustion efficiency and emission level, it also includes: obtaining the historical operation data set of the coal-fired boiler, the historical cow dung quality detection data set, the historical coal quality analysis data set and the historical combustion emission data set; calculating the initial cow dung blending ratio according to the historical cow dung quality detection data set and the historical coal quality analysis data set using the calorific value balance strategy; based on the initial cow dung blending ratio, performing the cow dung blending process according to the preset increasing gradient to obtain multiple cow dung blending ratios and the combustion efficiency and emission level corresponding to each cow dung blending ratio; using the multiple cow dung blending ratios, the historical operation data set of the coal-fired boiler, the historical cow dung quality detection data set and the historical coal quality analysis data set as the input parameters of the preset neural network, and the combustion efficiency and emission level corresponding to each cow dung blending ratio as the output variables of the preset neural network, and using the preset kernel function to train the preset neural network to obtain the preset relationship model between combustion efficiency and emission level.

[0080] Specifically, in order to deeply study the relationship between combustion efficiency and emission levels under different cow dung blending ratios, we can first collect characteristic data related to combustion efficiency and emission levels, including historical operation data sets of coal-fired boilers (such as combustion temperature, air flow, load rate), historical cow dung quality detection data sets (such as moisture content, calorific value, ash content, volatile matter), historical coal quality analysis data sets (such as moisture content, calorific value, ash content, volatile matter), and historical combustion emission data sets (such as CO2 concentration, SO2 concentration, NO x Next, based on the historical cow dung quality detection dataset and the historical coal quality analysis dataset, the calorific value balance strategy was used to calculate the initial cow dung blending ratio.

[0081] Optionally, in some embodiments, the calorific value balance strategy is:

[0082]

[0083] Among them, x0 is the initial cow dung burning ratio, Q coal is the calorific value of coal in the coal quality analysis data, Q coal is the calorific value of cow dung in the cow dung quality detection data, Q min The minimum calorific value required for furnace combustion.

[0084] The initial cow dung blending ratio is determined based on the equilibrium point of the calorific value of cow dung and coal to ensure that the thermal efficiency of the combustion process is maximized.

[0085] After obtaining the initial cow dung blending ratio, the combustion efficiency and emission levels under different blending ratios can be modeled, that is, the cow dung blending ratio is increased according to a preset incremental gradient (such as 1%, 3%, 5%, 7%, 9%, etc.) to conduct experiments on the cow dung blending process, and the combustion efficiency and emission data under different blending ratios are recorded at the same time. Among them, the step size of the increasing blending ratio can be adjusted according to actual conditions, and no specific limitation is made here.

[0086] Finally, by using the support vector machine model as the preset neural network, multiple cow dung blending ratios, historical operation data sets of coal-fired boilers, historical cow dung quality detection data sets and historical coal quality analysis data sets are used as the input parameters of the preset neural network for model training, and the target output variables are the combustion efficiency and emission level corresponding to each cow dung blending ratio. During the model training process, the radial basis function (RBF) can be used as the preset kernel function. By adjusting the parameters of the support vector machine model, the model fitting is achieved, and finally a model that can accurately describe the relationship between combustion efficiency and emission level is obtained, that is, a preset relationship model between combustion efficiency and emission level. The preset relationship model between combustion efficiency and emission level can realize the prediction of combustion efficiency η and pollutant emission level C(x) for a given blending ratio x and furnace operation parameters.

[0087] In step S103, a Lagrangian function is constructed based on the objective function and the constraints, and the current optimal cow dung blending ratio is calculated based on the Lagrangian function.

[0088] Specifically, after determining the objective function and constraints, the KKT (Karush-Kuhn-Tucker) conditions can be used to solve the current optimal cow dung blending ratio x based on the objective function and constraints. KKT conditions are a set of necessary conditions for nonlinear programming problems. They extend the Lagrangian multiplier method and are applicable to situations with equality and inequality constraints. By constructing the Lagrangian function, as shown in Equation 9, the original problem (i.e., solving the current optimal cow dung blending ratio) can be transformed into an unconstrained problem, and the optimal solution to the original problem can be found by solving the extreme value of the Lagrangian function.

[0089]

[0090] Among them, λ is the Lagrange multiplier, g i (x) is the constraint condition.

[0091] Based on the calculated current optimal cow dung blending ratio, the result can be fed back to the combustion control system to adjust and control the cow dung and coal feed ratio in real time.

[0092] It should be noted that in order to adapt to different operating conditions and improve combustion efficiency, the cow dung blending ratio can be updated regularly to achieve dynamic optimization control.

[0093] In order to facilitate those skilled in the art to further understand the optimization method of the ratio of cow dung mixed combustion in coal-fired power plants proposed in the embodiment of the present application, the following is combined with Figure 2 For further explanation.

[0094] like Figure 2 As shown, the method for optimizing the proportion of cow dung in the combustion of coal-fired power plants may include the following steps:

[0095] Step S201, data collection and analysis, collects furnace design, operation, cow dung quality, coal quality and emission data, and analyzes combustion performance under different loads.

[0096] Step S202: Calculate the initial blending ratio using the calorific value of coal and cow dung according to the calorific value balance method.

[0097] Step S203: extract features and use a support vector machine model to establish a combustion efficiency and emission level model.

[0098] Step S204 , combining the combustion efficiency and emission level models, setting the objective function and constraints, and solving the optimal blending ratio using the KKT method.

[0099] Step S205: Feedback the optimal blending ratio to the control system to dynamically adjust the feed ratio of cow dung and coal.

[0100] It can be seen that the embodiment of the present application can maximize energy output by dynamically optimizing the blending ratio of cow dung and coal and combining it with the support vector machine algorithm to predict combustion efficiency. Compared with the fixed-ratio blending method, the present application can adjust the blending ratio in real time according to the furnace operating status and fuel characteristics, significantly improving combustion efficiency and reducing energy waste. At the same time, it can reduce manual intervention, improve operating efficiency, and reduce operating costs. In addition, by optimizing the blending ratio, the stability of furnace operation can be ensured, avoiding problems such as slagging and combustion fluctuations caused by improper blending ratio, and extending the service life of the equipment.

[0101] According to the optimization method for the cow dung blending ratio of a coal-fired power plant proposed in the embodiment of the present application, the objective function can be determined based on a preset relationship model between combustion efficiency and emission level, and the constraints can be determined based on the furnace design parameters of the coal-fired boiler, the operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data; then, based on the objective function and the constraints, a Lagrangian function is constructed, and the current optimal cow dung blending ratio is calculated based on the Lagrangian function. Thus, by dynamically adjusting the cow dung blending ratio according to the boiler furnace condition, cow dung quality, and historical operating data, the problem of low combustion efficiency caused by the fixed cow dung blending ratio in the prior art is solved, and efficient, environmentally friendly, and stable combustion operation is achieved.

[0102] Next, a device for optimizing the ratio of cow dung to be burned in a coal-fired power plant according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0103] Figure 3 It is a block diagram of a device for optimizing the ratio of cow dung to be burned in a coal-fired power plant according to one embodiment of the present application.

[0104] like Figure 3 As shown, the device 10 for optimizing the proportion of cow dung in the combustion of coal-fired power plants includes: an acquisition module 100 , a determination module 200 and a calculation module 300 .

[0105] The acquisition module 100 is used to obtain the furnace design parameters of the coal-fired boiler, the operation data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data and the combustion emission data;

[0106] a determination module 200 for determining an objective function based on a preset relationship model between combustion efficiency and emission levels, and determining constraints based on furnace design parameters of the coal-fired boiler, operating data of the coal-fired boiler, cow dung quality test data, coal quality analysis data, and combustion emission data, wherein the preset relationship model between combustion efficiency and emission levels is trained using the furnace design parameters of the coal-fired boiler, a historical operating data set of the coal-fired boiler, a historical cow dung quality test data set, a historical coal quality analysis data set, and a historical combustion emission data set;

[0107] The calculation module 300 is used to construct a Lagrangian function based on the objective function and the constraint conditions, and calculate the current optimal cow dung blending ratio based on the Lagrangian function.

[0108] Optionally, in some embodiments, before determining the objective function based on the preset relationship model between combustion efficiency and emission level, the determination module 200 is further configured to:

[0109] Acquire a historical operation dataset of the coal-fired boiler, a historical cow dung quality detection dataset, a historical coal quality analysis dataset, and a historical combustion emission dataset;

[0110] Based on the historical cow dung quality detection data set and the historical coal quality analysis data set, the initial cow dung blending ratio was calculated using the calorific value balance strategy.

[0111] Based on the initial cow dung blending ratio, the cow dung blending process is performed according to a preset incremental gradient to obtain multiple cow dung blending ratios and the combustion efficiency and emission level corresponding to each cow dung blending ratio;

[0112] Multiple cow dung blending ratios, historical operation data sets of coal-fired boilers, historical cow dung quality detection data sets and historical coal quality analysis data sets are used as input parameters of the preset neural network. The combustion efficiency and emission level corresponding to each cow dung blending ratio are used as output variables of the preset neural network. The preset kernel function is used to train the preset neural network model to obtain a preset relationship model between combustion efficiency and emission level.

[0113] Optionally, in some embodiments, the combustion efficiency corresponding to each cow dung blending ratio is:

[0114]

[0115] Among them, E output (x) is the effective energy converted into heat after combustion, E input (x) is the total energy of the input fuel, and x is the proportion of cow dung mixed with combustion.

[0116] Optionally, in some embodiments, the emission level corresponding to each cow dung co-combustion ratio is:

[0117] C(x)=α·CO2(x)+β·SO2(x)+γ·NO x (x);

[0118] Among them, α, β, and γ are model parameters, CO2(x) is the emission concentration of CO2, SO2(x) is the emission concentration of SO2, and NO x (x) is NO x emission concentration.

[0119] Optionally, in some embodiments, the calorific value balance strategy is:

[0120]

[0121] Among them, x0 is the initial cow dung burning ratio, Q coal is the calorific value of coal in the coal quality analysis data, Q coal is the calorific value of cow dung in the cow dung quality detection data, Q min The minimum calorific value required for furnace combustion.

[0122] Alternatively, in some embodiments, the objective function is to maximize combustion efficiency and minimize emission levels.

[0123] It should be noted that the above explanation of the embodiment of the method for optimizing the proportion of cow dung in the combustion of coal-fired power plants is also applicable to the device for optimizing the proportion of cow dung in the combustion of coal-fired power plants in this embodiment, and will not be repeated here.

[0124] According to the device for optimizing the proportion of cow dung in a coal-fired power plant proposed in the embodiment of the present application, the objective function can be determined based on a preset relationship model between combustion efficiency and emission level, and the constraints can be determined based on the furnace design parameters of the coal-fired boiler, the operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data; then, based on the objective function and the constraints, a Lagrangian function is constructed, and the current optimal cow dung proportion is calculated based on the Lagrangian function. Thus, by dynamically adjusting the cow dung proportion according to the boiler furnace condition, cow dung quality, and historical operating data, the problem of low combustion efficiency caused by the fixed cow dung proportion in the prior art is solved, and efficient, environmentally friendly, and stable combustion operation is achieved.

[0125] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0126] Memory 401 , processor 402 , and computer programs stored in the memory 401 and executable on the processor 402 .

[0127] When the processor 402 executes the program, the method for optimizing the proportion of cow dung in the combustion of coal-fired power plants provided in the above embodiment is implemented.

[0128] Furthermore, the electronic device further includes:

[0129] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0130] The memory 401 is used to store computer programs that can be run on the processor 402 .

[0131] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0132] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0133] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0134] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0135] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the proportion of cow dung in the combustion of coal-fired power plants.

[0136] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above method for optimizing the proportion of cow dung in the combustion of coal-fired power plants.

[0137] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0138] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0139] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing the proportion of cow dung in coal-fired power plants, characterized in that: The following steps are involved: Obtaining furnace design parameters of a coal-fired boiler, operating data of the coal-fired boiler, cow dung quality test data, coal quality analysis data, and combustion emission data; An objective function is determined based on a preset relationship model between combustion efficiency and emission level, and constraints are determined based on furnace design parameters of the coal-fired boiler, operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data, wherein the preset relationship model between combustion efficiency and emission level is obtained by the furnace design parameters of the coal-fired boiler, a historical operating data set of the coal-fired boiler, a historical cow dung quality detection data set, a historical coal quality analysis data set, and a historical combustion emission data training set; Based on the objective function and the constraint conditions, a Lagrangian function is constructed, and the current optimal cow dung blending ratio is calculated based on the Lagrangian function.

2. The method according to claim 1, characterized in that Before determining the objective function based on the preset relationship model between combustion efficiency and emission level, the method further includes: Acquire a historical operation dataset of the coal-fired boiler, a historical cow dung quality detection dataset, a historical coal quality analysis dataset, and a historical combustion emission dataset; Calculating an initial cow dung blending ratio using a calorific value balance strategy based on the historical cow dung quality detection data set and the historical coal quality analysis data set; Based on the initial cow dung blending ratio, performing a cow dung blending process according to a preset incremental gradient, obtaining multiple cow dung blending ratios and combustion efficiencies and emission levels corresponding to each cow dung blending ratio; The multiple cow dung blending ratios, the historical operation data set of the coal-fired boiler, the historical cow dung quality detection data set and the historical coal quality analysis data set are used as input parameters of a preset neural network, the combustion efficiency and emission level corresponding to each cow dung blending ratio are used as output variables of the preset neural network, and the preset kernel function is used to perform model training on the preset neural network to obtain the preset relationship model between the combustion efficiency and the emission level.

3. The method according to claim 2, characterized in that The combustion efficiency corresponding to each cow dung blending ratio is: Among them, E output (x) is the effective energy converted into heat after combustion, E input (x) is the total energy of the input fuel, and x is the proportion of cow dung mixed with combustion.

4. The method according to claim 2, characterized in that The emission levels corresponding to each cow dung burning ratio are: C(x)=α·CO2(x)+β·SO2(x)+γ·NO x (x); Among them, α, β, and γ are model parameters, CO2(x) is the emission concentration of CO2, SO2(x) is the emission concentration of SO2, and NO x (x) is NO x emission concentration.

5. The method according to claim 2, characterized in that The calorific value balance strategy is: Among them, x0 is the initial cow dung burning ratio, Q coal is the coal calorific value in the coal quality analysis data, Q coal is the calorific value of cow dung in the cow dung quality detection data, Q min The minimum calorific value required for furnace combustion.

6. The method according to claim 1, characterized in that The objective function is to maximize combustion efficiency and minimize emission levels.

7. A device for optimizing the proportion of cow dung in coal-fired power plants, characterized in that: include: An acquisition module, configured to acquire furnace design parameters of a coal-fired boiler, operating data of the coal-fired boiler, cow dung quality detection data, coal quality analysis data, and combustion emission data; a determination module, configured to determine an objective function based on a preset relationship model between combustion efficiency and emission level, and determine constraint conditions based on furnace design parameters of the coal-fired boiler, operating data of the coal-fired boiler, the cow dung quality detection data, the coal quality analysis data, and the combustion emission data, wherein the preset relationship model between combustion efficiency and emission level is trained by the furnace design parameters of the coal-fired boiler, a historical operating data set of the coal-fired boiler, a historical cow dung quality detection data set, a historical coal quality analysis data set, and a historical combustion emission data set; A calculation module is used to construct a Lagrangian function based on the objective function and the constraint conditions, and calculate the current optimal cow dung blending ratio based on the Lagrangian function.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing the proportion of cow dung blending in a coal-fired power plant as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for optimizing the proportion of cow dung in the combustion of coal-fired power plants as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, is used to implement the method for optimizing the proportion of cow dung blending in a coal-fired power plant according to any one of claims 1 to 6.

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