Method, device, equipment, medium and product for determining optimal oxygen concentration at inlet of high-temperature economizer of CFB (circulating fluid bed) boiler

Abstract

The invention discloses a method, device and equipment for determining the optimal oxygen concentration of an inlet of a high-temperature economizer of a CFB boiler, a medium and a product, and relates to the field of CFB boiler parameter optimization. The historical data is preprocessed; constructing a machine learning model; training the machine learning model based on the preprocessed historical data; inputting historical data into the trained machine learning model to obtain a predicted value of thermal efficiency; constructing a target function based on the predicted value of the thermal efficiency; according to the method, the optimal oxygen concentration of the inlet of the high-temperature economizer of the CFB boiler can be determined, sufficient combustion of fuel is guaranteed, and emission of harmful substances is reduced.

Description

Technical Field

[0001] The present application relates to the field of CFB boiler parameter optimization, and in particular to a method, device, equipment, medium and product for determining the optimal oxygen concentration at the inlet of a high-temperature economizer of a CFB boiler. Background Art

[0002] Circulating fluidized bed boiler is an advanced boiler technology that burns coal, oil or other solid fuels. Compared with traditional combustion technology, CFB boiler uses fluidized bed as combustion medium, which has the advantages of high combustion efficiency, low emissions, strong flexibility, etc., and has important application value in energy utilization and environmental protection. Parameter optimization of CFB boiler to achieve energy saving and consumption reduction and improve efficiency is an important research direction in the current engineering technology field.

[0003] During the operation of CFB boilers, the oxygen concentration at the inlet of the high-temperature economizer has an important impact on combustion efficiency and environmental emissions. Too high oxygen concentration will lead to excessive combustion, fuel waste and increased harmful emissions, while too low oxygen concentration will lead to incomplete combustion and the generation of toxic gases, thereby reducing boiler performance, increasing environmental pollution and safety risks. In traditional methods, operators set oxygen target values ​​based on their experience and understanding of boiler operation. This method usually determines the oxygen set value based on the current operating conditions of the boiler, such as load, coal volume, primary air volume, secondary air volume and other operating parameters, which makes it difficult to achieve efficient combustion. Therefore, seeking a technical solution to determine the optimal oxygen set value using data acquisition and machine learning methods has become the key to improving boiler performance and environmental benefits. Summary of the invention

[0004] The purpose of this application is to provide a method, device, equipment, medium and product for determining the optimal oxygen concentration at the inlet of a CFB boiler high-temperature economizer, which can determine the optimal oxygen concentration at the inlet of a CFB boiler high-temperature economizer, ensure sufficient combustion of the fuel, and reduce the emission of harmful substances.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for determining an optimal oxygen concentration at an inlet of a high-temperature economizer of a CFB boiler, comprising:

[0007] Get historical data;

[0008] Preprocessing the historical data;

[0009] Build machine learning models;

[0010] Training the machine learning model based on the preprocessed historical data;

[0011] Input historical data into the trained machine learning model to obtain the predicted value of thermal efficiency;

[0012] constructing an objective function based on the predicted value of the thermal efficiency;

[0013] The objective function is solved to obtain the optimal oxygen concentration.

[0014] Optionally, the historical data include: coal feed flow, main steam flow, feed water pressure, feed water temperature, main steam temperature, main steam pressure, high temperature economizer inlet O2 concentration, total primary air volume, total secondary air volume, secondary fan outlet air pressure, upper combustion chamber pressure and middle combustion chamber pressure.

[0015] Optionally, preprocessing the historical data specifically includes: performing data screening, missing value processing, outlier detection and processing, and smoothing processing on the historical data.

[0016] Optionally, the machine learning model is one of CatBoost, LSTM and Transformer, and the structure of the LSTM includes: an input gate, a forget gate and an output gate, and an update of a cell state;

[0017] The specific steps include:

[0018] Calculate the forget gate and select the information to be forgotten;

[0019] Calculate the memory gate and select the information to be memorized;

[0020] Calculate the cell state at the current moment;

[0021] Calculate the output gate and the current hidden layer state.

[0022] Optionally, the forget gate is calculated to select information to be forgotten, specifically using the following formula:

[0023] f t =σ(W f ·[h t-1 ,x t ]+b f );

[0024] Among them, f t represents the output of the forget gate, indicating how much of the cell state needs to be forgotten at the current moment, σ represents the Sigmoid activation function, W f represents the weight matrix of the forget gate, h t-1 represents the hidden state of the previous moment, x t represents the input at the current moment, b f represents the bias of the forget gate;

[0025] Calculate the memory gate and select the information to be memorized. The following formula is used

[0026] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0027]

[0028] Among them, i t represents the output of the memory gate, indicating how much new information will be written into the cell state at the current moment; W i represents the weight matrix of the memory gate, which is used to weight the hidden state of the previous moment and the current input; b i Represents the bias of the memory gate, which is used to adjust the output of the memory gate; represents the candidate cell state, represents the new candidate state generated based on the current input and past information, and W c The weight matrix representing the candidate cell state is used to weight the hidden state at the previous moment and the current input; b c Represents the bias of the candidate cell state, which is used to adjust the output of the candidate cell state;

[0029] Calculate the current cell state using the following formula

[0030]

[0031] Among them, C t Indicates the current cell state, used for long-term storage of information, C t-1 Indicates the cell state at the previous moment, indicating the information stored by the LSTM unit at the previous moment;

[0032] Calculate the output gate and the current hidden layer state using the following formula:

[0033] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0034] h t =o t tanh(C t )

[0035] Among them, O t Represents the output of the output gate, which determines which part of the current cell state will be output as the hidden state h t ; W orepresents the weight matrix of the output gate, which is used to weight the hidden state of the previous moment and the current input; b o Represents the bias of the output gate, which is used to adjust the output of the output gate, h t Indicates the hidden state at the current moment.

[0036] Optionally, the objective function is expressed as follows:

[0037] j=w p (1-y p (k)) 2 +w k k 2

[0038] Among them, j represents the value of the objective function, w p is the prediction weight, w k is the control weight, y p is the thermal efficiency prediction result, k is the oxygen content set value, and j is minimized by traversing the k value interval.

[0039] In a second aspect, the present application provides a device for determining an optimal oxygen concentration at an inlet of a high-temperature economizer of a boiler, the device comprising:

[0040] A historical data acquisition module is used to acquire historical data;

[0041] A preprocessing module, used for preprocessing the historical data;

[0042] Machine learning model building module, used to build machine learning models;

[0043] A training module, used for training the machine learning model based on the preprocessed historical data;

[0044] Thermal efficiency prediction module, which is used to input historical data into the trained machine learning model to obtain the predicted value of thermal efficiency;

[0045] An objective function building module, used for building an objective function based on the predicted value of the thermal efficiency;

[0046] The objective function solving module is used to solve the objective function to obtain the optimal oxygen concentration.

[0047] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer as described in any one of the above.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining the optimal oxygen concentration at the inlet of a high-temperature economizer of a CFB boiler as described in any one of the above.

[0049] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for determining the optimal oxygen concentration at the inlet of a high-temperature economizer of a CFB boiler as described in any one of the above.

[0050] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0051] The present application provides a method, device, equipment, medium and product for determining the optimal oxygen concentration at the inlet of a high-temperature economizer of a CFB boiler, by acquiring historical data; preprocessing the historical data; constructing a machine learning model; training the machine learning model based on the preprocessed historical data; inputting the historical data into the trained machine learning model to obtain a predicted value of thermal efficiency; constructing an objective function based on the predicted value of thermal efficiency; solving the objective function to obtain the optimal oxygen concentration. It can be seen that the present application can respond to changes in the operating state of the CFB boiler in real time through a machine learning algorithm, thereby maximizing combustion efficiency and reducing environmental emissions, and has higher adaptability and optimization effects. Compared with traditional experience-based methods, it can more effectively improve boiler performance and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 This is an application environment diagram of a method for determining an optimal oxygen concentration at an inlet of a high-temperature economizer of a CFB boiler in one embodiment of the present application;

[0054] Figure 2 A flow chart of a method for determining an optimal oxygen concentration at an inlet of a high-temperature economizer of a CFB boiler provided in one embodiment of the present application;

[0055] Figure 3 A schematic diagram of the hidden layer structure of the LSTM provided in one embodiment of the present application;

[0056] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0059] The method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the data to be processed to the server 104. After the server 104 receives the data to be processed, for the data to be processed, the server 104 obtains historical data; pre-processes the historical data; builds a machine learning model; trains the machine learning model based on the pre-processed historical data; inputs the historical data into the trained machine learning model to obtain a predicted value of thermal efficiency; constructs an objective function based on the predicted value of thermal efficiency; solves the objective function to obtain the optimal oxygen concentration. The server 104 can feedback the obtained optimal oxygen concentration to the terminal 102. In addition, in some embodiments, the method for determining the optimal oxygen concentration at the inlet of the high-temperature economizer of a CFB boiler can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the data to be processed, or the server 104 can obtain the data to be processed from the data storage system and process the data to be processed.

[0060] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0061] In an exemplary embodiment, Figure 2As shown, a method for determining the optimal oxygen concentration at the inlet of a high-temperature economizer of a CFB boiler is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 207. Among them:

[0062] Step 201: Obtain historical data.

[0063] Among them, data is collected by collecting massive historical data in the MIS system of each thermal power plant. The historical data include: coal feed flow, main steam flow, feed water pressure, feed water temperature, main steam temperature, main steam pressure, high temperature economizer inlet O2 concentration, total primary air volume, total secondary air volume, secondary fan outlet air pressure, combustion chamber upper pressure and combustion chamber middle pressure.

[0064] The data collection interval must be less than 1 minute at most to avoid the data interval being too large and losing important information during modeling. The data collection interval is at least 1 month and should be the most recent time.

[0065] Step 202: Preprocess the historical data.

[0066] Specifically include:

[0067] Perform data screening, missing value processing, outlier detection and processing, smoothing processing, and normalization processing on the data.

[0068] And calculate the "thermal efficiency" column as follows:

[0069]

[0070] In addition, a single boiler generally has multiple coal feeders, and each coal feeder has a coal feed flow measurement point. The total coal feed is the sum of the coal feed flows of all coal feeders of the single boiler. The boiler evaporation volume is the main steam flow of the boiler. The steam enthalpy value and feed water enthalpy value are calculated through the main steam pressure, main steam temperature, feed water pressure, and feed water temperature. The low calorific value of the fuel comes from on-site sampling.

[0071] Step 203: Build a machine learning model.

[0072] The specific models used include: CatBoost\LSTM\Transformer, etc.

[0073] Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are two important variants of Recurrent Neural Network (RNN). It solves the long-range dependency problem of traditional RNN by introducing gating mechanism and cell state. LSTM can transmit long-distance information in the network, thereby capturing the temporal dependency in the sequence. The core of LSTM is the cell state, which runs through the entire sequence and can transmit long-distance information in the network. The structure of LSTM includes input gate, forget gate and output gate, as well as the update of cell state.

[0074] The hidden layer structure of LSTM is as follows Figure 3 As shown, it includes: input gate, forget gate and output gate, as well as the update of cell state.

[0075] Step 204: Train the machine learning model based on the preprocessed historical data.

[0076] The specific steps include:

[0077] Taking the historical data column as input and the “thermal efficiency” column as output, a machine learning model is constructed, which can be achieved through data preprocessing, feature engineering, and model parameter optimization.

[0078] ①Data preprocessing:

[0079] Data cleaning: handle missing values ​​and outliers, use interpolation to fill missing data, or replace missing values ​​with mean or median.

[0080] Feature selection: Select the features that are most relevant to the target variable and use the SelectKBest method to select the top K best features.

[0081] ②Feature Engineering:

[0082] Feature enhancement: Create new features or transform existing features to enhance model performance, logarithmic transformation, normalization, polynomial feature generation.

[0083] Dimensionality reduction: Reduce the number of features to remove redundant or noisy features, using principal component analysis (PCA) to reduce features to a lower dimension.

[0084] ③Model parameter optimization:

[0085] Hyperparameter tuning: Adjust the model’s hyperparameters to find the best configuration, using grid search or random search to optimize the hyperparameters.

[0086] Adjust model architecture: Modify the structure of a model to improve performance, such as increasing or decreasing the number of hidden layers in a neural network.

[0087] Optimization algorithm selection: Choose a suitable optimization algorithm to speed up convergence. Choose Adam optimizer instead of SGD in deep learning.

[0088] Regularization: Use regularization techniques to prevent overfitting, use L2 regularization in regression models or use Dropout layers in neural networks.

[0089] Finally, keep the pkl or hdf5 file after the model training is completed.

[0090] Step 205: Input the historical data into the trained machine learning model to obtain the predicted value of thermal efficiency.

[0091] The specific steps include:

[0092] Calculate the forget gate and select the information to be forgotten;

[0093] Input the hidden state h of the previous moment t-1 , the current input word is x t , output the value of the forget gate f t , as can be seen from the lower right corner of the structure diagram.

[0094] Calculate the memory gate and select the information to be memorized;

[0095] Input the hidden state h of the previous moment t-1 , the current input word is x t , output the value of memory gate i t and temporary cell states

[0096] Calculate the cell state at the current moment;

[0097] Input the value i of the memory gate t , the value of the forget gate f t , temporary cell state The cell state at the last moment is C t-1 , output the current cell state C t .

[0098] Calculate the output gate and the current hidden layer state.

[0099] Input the hidden state h of the previous moment t-1 , the current input word is x t , the current cell state C t . Output the value of the output gate o t and the hidden state h t .

[0100] Among them, the forget gate is calculated and the information to be forgotten is selected. The specific formula is as follows:

[0101] f t =σ(W f ·[h t-1 ,x t ]+b f );

[0102] Among them, f t is the output of the forget gate, indicating how much of the cell state needs to be forgotten at the current moment, and its value range is between 0 and 1. The closer the value is to 0, the more forgotten it is. σ is the Sigmoid activation function, which compresses the result of the linear combination to the range of 0 to 1, and is used to determine whether information is retained or forgotten. W f is the weight matrix of the forget gate, which is used to weight the hidden state of the previous moment and the current input; h t-1 is the hidden state at the previous moment, which is the output of the LSTM unit at the previous moment and contains the compressed representation of the past time series information; t is the input at the current moment, indicating the external input data at the current moment; b f It is the bias of the forget gate, which is used to adjust the output of the forget gate.

[0103] Calculate the memory gate and select the information to be memorized. The following formula is used:

[0104] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0105]

[0106] Among them, i t is the output of the memory gate, indicating how much new information will be written into the cell state at the current moment. The value range is between 0 and 1; W i is the weight matrix of the memory gate, which is used to weight the hidden state of the previous moment and the current input; b i It is the bias of the memory gate, used to adjust the output of the memory gate; is the candidate cell state, which represents the new candidate state generated based on the current input and past information, and its value range is between -1 and 1; tanh is the hyperbolic tangent activation function, which is used to compress the linear combination result into the range of -1 to 1, so that LSTM can better handle nonlinear relationships; W c is the weight matrix of the candidate cell state, which is used to weight the hidden state of the previous moment and the current input; b cis the bias of the candidate cell state, which is used to adjust the output of the candidate cell state.

[0107] Calculate the current cell state using the following formula:

[0108]

[0109] Among them, C t Represents the current cell state, which is the most important part of LSTM and is used to store information for a long time. It combines the past cell state C t-1 and the candidate state at the current moment C t-1 Indicates the cell state at the previous moment, indicating the information stored by the LSTM unit at the previous moment;

[0110] Calculate the output gate and the current hidden layer state using the following formula:

[0111] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0112] h t =o t tanh(C t )

[0113] Among them, O t is the output of the output gate, which determines which part of the current cell state will be output as the hidden state ht; W o is the weight matrix of the output gate, which is used to weight the hidden state of the previous moment and the current input; b o is the bias of the output gate, which is used to adjust the output of the output gate; h t is the hidden state at the current moment, which is the output of the LSTM unit and can also be used as the input of the next moment. It is the cell state C t The result of filtering and outputting part of the information.

[0114] Step 206: constructing an objective function based on the predicted value of the thermal efficiency.

[0115] Based on the constructed model, historical data is used as fixed input and oxygen content is used as a variable to traverse and output all prediction results, that is, the thermal efficiency values ​​predicted and outputted at different oxygen content under the current working conditions. The expression of the optimized objective function is as follows:

[0116] j=w p (1-y p (k)) 2 +w k k2

[0117] Among them, j represents the value of the objective function, w p is the prediction weight, w k is the control weight, y p is the thermal efficiency prediction result, k is the oxygen content set value, and j is minimized by traversing the k value interval.

[0118] When the predicted output j is the smallest, the oxygen content setting value input at this time is the optimal value and can be used as the oxygen content setting value under the current working conditions.

[0119] Step 207: Solve the objective function to obtain the optimal oxygen concentration.

[0120] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer for implementing the above-mentioned method for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer provided below can refer to the limitations of the method for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer above, and will not be repeated here.

[0121] In an exemplary embodiment, a device for determining an optimal oxygen concentration at an inlet of a high-temperature economizer of a boiler is provided, comprising:

[0122] A historical data acquisition module is used to acquire historical data;

[0123] A preprocessing module, used for preprocessing the historical data;

[0124] Machine learning model building module, used to build machine learning models;

[0125] A training module, used for training the machine learning model based on the preprocessed historical data;

[0126] Thermal efficiency prediction module, which is used to input historical data into the trained machine learning model to obtain the predicted value of thermal efficiency;

[0127] An objective function building module, used for building an objective function based on the predicted value of the thermal efficiency;

[0128] The objective function solving module is used to solve the objective function to obtain the optimal oxygen concentration.

[0129] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data for determining the optimal oxygen concentration at the inlet of a CFB boiler high-temperature economizer. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the optimal oxygen concentration at the inlet of a CFB boiler high-temperature economizer is implemented.

[0130] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0131] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0132] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0133] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0135] Those of ordinary skill 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0136] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0137] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above 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.

[0138] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for determining the optimal oxygen concentration at the inlet of a high-temperature economizer of a CFB boiler, characterized in that: The method for determining the optimal oxygen concentration at the inlet of the high-temperature economizer of the CFB boiler includes: Get historical data; Preprocessing the historical data; Build machine learning models; Training the machine learning model based on the preprocessed historical data; Input historical data into the trained machine learning model to obtain the predicted value of thermal efficiency; constructing an objective function based on the predicted value of the thermal efficiency; The objective function is solved to obtain the optimal oxygen concentration.

2. The method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer according to claim 1, characterized in that: The historical data include: coal feed flow, main steam flow, feed water pressure, feed water temperature, main steam temperature, main steam pressure, high temperature economizer inlet O2 concentration, total primary air volume, total secondary air volume, secondary fan outlet air pressure, upper combustion chamber pressure and middle combustion chamber pressure.

3. The method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer according to claim 1, characterized in that: The preprocessing of the historical data specifically includes: data screening, missing value processing, outlier detection and processing, and smoothing processing of the historical data.

4. The method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer according to claim 1, characterized in that: The machine learning model is one of CatBoost, LSTM and Transformer, and the structure of the LSTM includes: an input gate, a forget gate and an output gate, and an update of a cell state; The specific steps include: Calculate the forget gate and select the information to be forgotten; Calculate the memory gate and select the information to be memorized; Calculate the cell state at the current moment; Calculate the output gate and the current hidden layer state.

5. The method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer according to claim 1, characterized in that: The calculation of the forget gate selects the information to be forgotten, and specifically adopts the following formula: f t =σ(W f ·[h t-1 ,x t ]+b f ); Among them, f t represents the output of the forget gate, indicating how much of the cell state needs to be forgotten at the current moment, σ represents the Sigmoid activation function, W f represents the weight matrix of the forget gate, h t-1 represents the hidden state of the previous moment, x t represents the input at the current moment, b f represents the bias of the forget gate; Calculate the memory gate and select the information to be memorized. The following formula is used i t =σ(W i ·[h t-1 ,x t ]+b i ) Among them, i t represents the output of the memory gate, indicating how much new information will be written into the cell state at the current moment; W i represents the weight matrix of the memory gate, which is used to weight the hidden state of the previous moment and the current input; b i Represents the bias of the memory gate, which is used to adjust the output of the memory gate; represents the candidate cell state, represents the new candidate state generated based on the current input and past information, and W c The weight matrix representing the candidate cell state is used to weight the hidden state at the previous moment and the current input; b c Represents the bias of the candidate cell state, which is used to adjust the output of the candidate cell state; Calculate the current cell state using the following formula Among them, C t Indicates the current cell state and is used to store information for a long time. t-1 Indicates the cell state at the previous moment, indicating the information stored by the LSTM unit at the previous moment; Calculate the output gate and the current hidden layer state using the following formula: the t =σ(W o ·[h t-1 ,x t ]+b o ) h t =o t ·tanh(C t ) Among them, O t Represents the output of the output gate, which determines which part of the current cell state will be output as the hidden state h t ; W o represents the weight matrix of the output gate, which is used to weight the hidden state of the previous moment and the current input; b o Represents the bias of the output gate, which is used to adjust the output of the output gate, h t Indicates the hidden state at the current moment.

6. The method for determining the optimal oxygen concentration at the inlet of a CFB boiler high temperature economizer according to claim 1, characterized in that: The expression of the objective function is as follows: j=w p (1-y p (k)) 2 +w k k 2 Among them, j represents the value of the objective function, w p is the prediction weight, w k is the control weight, y p is the thermal efficiency prediction result, k is the oxygen content set value, and j is minimized by traversing the k value interval.

7. A device for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer, characterized in that: The device for determining the optimal oxygen concentration at the inlet of the boiler high-temperature economizer comprises: A historical data acquisition module is used to acquire historical data; A preprocessing module, used for preprocessing the historical data; Machine learning model building module, used to build machine learning models; A training module, used for training the machine learning model based on the preprocessed historical data; Thermal efficiency prediction module, which is used to input historical data into the trained machine learning model to obtain the predicted value of thermal efficiency; An objective function building module, used for building an objective function based on the predicted value of the thermal efficiency; The objective function solving module is used to solve the objective function to obtain the optimal oxygen concentration.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for determining the optimal oxygen concentration at the inlet of the boiler high-temperature economizer according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for determining the optimal oxygen concentration at the inlet of a boiler high-temperature economizer as described in any one of claims 1 to 6 are implemented.

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