Fuel distribution control system of CFB boiler

Abstract

The invention discloses a fuel distribution control system of a CFB (circulating fluid bed) boiler, which belongs to the technical field of fuel distribution of CFB boilers and comprises a comburent condition analysis module, a load prediction module, a fuel supply and metering module, a combustion environment monitoring module and an abnormity monitoring module. The comburent condition analysis module, the load prediction module, the fuel supply and metering module, the combustion environment monitoring module and the abnormity monitoring module are in communication connection in sequence; the comburent condition analysis module is used for analyzing combustion requirements and analyzing required combustion conditions; according to the fuel distribution control system of the CFB boiler, combustion requirements can be clearly known by analyzing comburent conditions, targeted combustion standards are set according to the combustion requirements, and meanwhile the functions that load prediction is used for planning the fuel consumption in advance and monitoring the combustion condition in real time to adjust the fuel consumption are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of fuel distribution of CFB boilers, and in particular is a fuel distribution control system of a CFB boiler. Background Art

[0002] CFB boiler is a circulating fluidized bed boiler, which is a highly efficient and clean combustion equipment. It is mainly composed of furnace, separator, return device, tail flue and other parts. In the current energy production field, circulating fluidized bed (CFB) boilers are widely used due to their high efficiency and low pollution. CFB boilers can burn a variety of fuels, including coal, biomass, waste, etc., and have strong fuel adaptability;

[0003] However, during the operation of CFB boilers, fuel distribution control has always been a key issue. Traditional fuel distribution methods often require manual calculations, which may result in certain calculation errors and many disadvantages.

[0004] Traditional fuel distribution does not have the function of predicting load in advance according to weather temperature. Different weather temperatures will require different loads for boilers. It also does not have the function of real-time monitoring of boiler operating conditions and abnormal monitoring. Therefore, a fuel distribution control system for CFB boilers is proposed. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a fuel distribution control system for a CFB boiler, which enables the boiler to automatically and accurately distribute fuel.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A fuel distribution control system for a CFB boiler, comprising a combustion condition analysis module, a load prediction module, a fuel supply and metering module, a combustion environment monitoring module, and an abnormality monitoring module, wherein the combustion condition analysis module, the load prediction module, the fuel supply and metering module, the combustion environment monitoring module, and the abnormality monitoring module are sequentially connected in communication;

[0008] The combustion material condition analysis module analyzes the combustion requirements and the required combustion conditions;

[0009] The load prediction module uses machine learning technology to predict boiler load based on historical operating data and external factors including weather and season, so as to adjust fuel supply in advance and ensure efficient operation of the boiler under different loads;

[0010] The fuel supply and metering module is used to accurately measure the amount of fuel entering the boiler and transfer the fuel from the storage location to the boiler;

[0011] The combustion environment and dynamic adjustment monitoring are used to monitor the oxygen content, temperature and pressure in the boiler, judge the combustion state, and adjust the fuel supply in real time according to the actual combustion situation to ensure sufficient oxygen and stable combustion;

[0012] The abnormal monitoring module, when detecting abnormal combustion, issues an abnormal warning and quickly adjusts the fuel quantity, and at the same time, proposes equipment maintenance and overhaul suggestions based on the results of the combustion condition analysis.

[0013] Furthermore, the combustion condition analysis module analyzes the combustion requirements, and the required combustion condition processing process is as follows:

[0014] The combustion requirements are divided into two types: combustion to produce hot water and combustion to produce steam. The working temperature of the hot water boiler is set at 80℃-150℃, the pressure is set at 0.1-0.5MPa, and the hot water produced is used for heating. The working temperature of the steam boiler is higher than that of the hot water boiler, the temperature is set at 160℃-540℃, the pressure is set at above 1MPa, and the steam produced is used for power generation or industrial processes.

[0015] Different combustion modes have different safety requirements. In view of the different pressure and temperature requirements for hot water heating and steam generation, safety protection measures can be taken in advance to reduce the risk of accidents. In addition, by clarifying the combustion type, the combustion process can be optimized in a targeted manner to improve thermal efficiency.

[0016] Furthermore, the load forecasting module uses machine learning technology to forecast boiler load based on historical operating data and external factors including weather and season, so as to adjust fuel supply in advance and ensure efficient operation of the boiler under different loads. The process is as follows:

[0017] Collect historical boiler operation data, including load changes, fuel consumption, and hot water or steam output; obtain data on external factors related to boiler operation, including weather data and seasonal information;

[0018] Preprocess the data, including handling missing values, outliers, and duplicate data;

[0019] Generate seasonal labels based on the month: winter: 12, 1, 2; spring: 3, 4, 5; summer: 6, 7, 8; autumn: 9, 10, 11. Use historical data to calculate the interactive features of temperature and create interactive features of temperature and fuel consumption to capture the relationship between the two.

[0020] The dataset is divided into a training set and a test set, and a decision tree is used for load forecasting. The selected machine learning algorithm is trained using the training set, and the performance of the model is evaluated on the test set. Finally, the trained model is used to predict the boiler load for a specific time period in the future and generate a fuel demand forecast.

[0021] According to the prediction results, the fuel supply and operating parameters are adjusted in advance to ensure efficient operation of the boiler under different loads.

[0022] By accurately predicting the load, the fuel supply can be precisely adjusted when the boiler is running, avoiding fuel waste and improving fuel utilization.

[0023] Furthermore, the data set is divided into a training set and a test set, and a decision tree is used for load forecasting; the selected machine learning algorithm is trained using the training set, and the performance of the model is evaluated on the test set. Finally, the trained model is used to forecast the boiler load in a specific time period in the future, and the fuel demand forecasting process is generated as follows:

[0024] The original data set is D, and the data set is divided into training set D1 and test set D2, of which 80% is used for training and 20% is used for testing;

[0025] The decision tree model is trained using the training set D1, and the target variable is the boiler load L. After the training is completed, the test set D2 is used for prediction. For the input feature X1, the predicted load L1 is generated. The specific formula is: L1 = f(X1), where f represents the trained decision tree model;

[0026] After model training and testing are completed, the mean square error is used to evaluate the difference between the predicted value and the true value;

[0027] Use the trained model to predict a specific time period in the future. Assuming the future feature data is X2, the predicted boiler load L2 is:

[0028] L2=f(X2);

[0029] Using the data points of boiler load and fuel consumption in the historical data, a straight line is fitted by linear regression, the formula is:

[0030] Y = kX + C;

[0031] Among them, Y represents fuel consumption, that is, the fuel consumption per unit load, X is the boiler load, C is the intercept, and the fuel consumption coefficient k is obtained according to this formula;

[0032] Then, the fuel consumption coefficient k and the predicted boiler load are used to generate the formula for fuel demand:

[0033] F1=k·L2

[0034] Among them, F1 represents the future fuel demand.

[0035] Furthermore, the fuel supply and metering module is used to accurately measure the amount of fuel entering the boiler and transfer the fuel from the storage location to the boiler. The process is as follows:

[0036] Determine the amount of fuel required by the boiler based on the output of the boiler load forecasting module;

[0037] Use high-precision sensors and flow meters to monitor the amount of fuel entering the boiler in real time and record the data;

[0038] Measuring equipment needs to be calibrated regularly. Regular calibration of measuring equipment can reduce errors caused by equipment changes.

[0039] The fuel is transported from the storage location to the boiler via a conveyor belt.

[0040] Furthermore, the combustion environment and dynamic adjustment monitoring are used to monitor the oxygen content, temperature and pressure in the boiler, judge the combustion state, and adjust the fuel supply in real time according to the actual combustion situation to ensure sufficient oxygen and stable combustion. The processing process is as follows:

[0041] Collect sensor data, monitor the oxygen content, temperature and pressure of the boiler in real time, analyze the current combustion status, establish an algorithm model based on the current combustion status, and determine the adjustment strategy of fuel supply;

[0042] Sensors and related equipment need to be regularly maintained and calibrated to ensure the accuracy of monitoring data.

[0043] Adjust the fuel supply in real time according to the analysis results to ensure sufficient oxygen content in the boiler and stable combustion state;

[0044] Feedback the adjusted supply volume to the monitoring system to form a closed-loop control and track the adjustment effect;

[0045] Through continuous data analysis, the fuel supply strategy can be continuously optimized to improve combustion efficiency and stability.

[0046] Furthermore, the process of establishing an algorithm model based on the current combustion state to determine the adjustment strategy of fuel supply is as follows:

[0047] Extract the features related to the combustion state from the preprocessed data, specifically: the rate of change of oxygen content and the fluctuation range of temperature, and use these features as the input of the algorithm model to describe the current combustion state;

[0048] A neural network is used to establish a relationship model between the combustion state and the fuel supply adjustment amount. The specific formula is:

[0049] Output of the hidden layer: H = f(W h ·X a +b h );

[0050] Among them, W h is the weight matrix from the input layer to the hidden layer, X a Input feature vector, which represents the parameters of the current combustion state, including o2 is oxygen, T is temperature, and P is pressure;

[0051] Output of the output layer: Y a =W O ·H+b o ;

[0052] Among them, W O is the weight matrix from the hidden layer to the output layer, b o is the output layer bias, and the loss function used in the training process is the mean square error;

[0053] After training, given a new combustion state input X b , the fuel supply adjustment amount Y can be calculated through the model b :

[0054] Y b =W o ·f(W h ·X+b o ).

[0055] Furthermore, the abnormal monitoring module, when detecting abnormal combustion, issues an abnormal warning and quickly adjusts the fuel quantity. At the same time, according to the results of the combustion condition analysis, it proposes equipment maintenance and overhaul suggestions. The processing process is as follows:

[0056] There are preset thresholds for abnormal temperature, air pressure, and oxygen concentration. When abnormal sensor data is detected, an abnormal warning will be triggered. The warning information will be sent to the operator through sound and light alarms, SMS notifications, and system interface prompts. At the same time as the warning is issued, the abnormal monitoring module will automatically adjust the fuel supply to try to correct the abnormal situation.

[0057] Comprehensively evaluate the composition, temperature, pressure, flow rate and other parameters of the combustion products to diagnose specific abnormalities;

[0058] Based on the results of the combustion condition analysis, corresponding equipment maintenance and overhaul recommendations are generated.

[0059] These suggestions include cleaning carbon deposits in the combustion chamber, replacing clogged nozzles, adjusting the fuel-air mixture ratio, and checking the tightness of the air supply system.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] By setting up the combustion condition analysis module, the required combustion conditions can be analyzed according to specific combustion requirements, so that the combustion conditions can be set specifically to optimize the combustion process;

[0062] By setting up a load forecasting module, the load of the boiler can be forecasted based on the collected historical data and weather and seasonal data, and the fuel supply can be adjusted in advance according to the load forecast results to ensure that the boiler can still maintain efficient operation under different weather temperatures. This advance planning of fuel supply can ensure sufficient fuel supply at different production stages, thereby improving the overall efficiency of the production process;

[0063] By setting the combustion environment and dynamically adjusting the monitoring, the combustion state can be judged according to the real-time monitoring of the boiler oxygen content, temperature and pressure, and an algorithm model can be established according to the current combustion state to adjust the combustion state and adjust the fuel strategy according to the combustion state to ensure the combustion efficiency and combustion stability;

[0064] By setting up an abnormal monitoring module, judgments can be made based on real-time monitored data. When abnormal data is received, an alarm will be automatically sounded. At the same time, it can also automatically analyze the combustion conditions and generate corresponding suggestions, which can effectively help reduce downtime and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The present invention is a block diagram of a fuel distribution control system for a CFB boiler. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] like Figure 1 As shown, a fuel distribution control system for a CFB boiler includes a combustion condition analysis module, a load prediction module, a fuel supply and metering module, a combustion environment monitoring module, and an abnormality monitoring module, wherein the combustion condition analysis module, the load prediction module, the fuel supply and metering module, the combustion environment monitoring module, and the abnormality monitoring module are sequentially connected in communication;

[0068] The combustion conditions analysis module analyzes the combustion requirements and the required combustion conditions;

[0069] In this embodiment, the combustion material condition analysis module analyzes the combustion requirements, and the required combustion condition processing process is as follows:

[0070] The combustion requirements are divided into two types: combustion to produce hot water and combustion to produce steam. The working temperature of the hot water boiler is set at 80℃-150℃, the pressure is set at 0.1-0.5MPa, and the hot water produced is used for heating. The working temperature of the steam boiler is higher than that of the hot water boiler, the temperature is set at 160℃-540℃, the pressure is set at above 1MPa, and the steam produced is used for power generation or industrial processes.

[0071] It should be noted that hot water boilers have relatively low requirements for oxygen, and only need to ensure sufficient combustion. Steam boilers require higher heat output and combustion efficiency. Steam boilers have more stringent requirements for oxygen supply and usually need to operate at a higher air to fuel ratio to achieve complete combustion.

[0072] The load forecasting module uses machine learning technology to predict boiler load based on historical operating data and external factors including weather and season, so as to adjust fuel supply in advance and ensure efficient operation of the boiler under different loads;

[0073] In this embodiment, the load prediction module uses machine learning technology to predict the boiler load based on historical operation data and external factors including weather and season, so as to adjust the fuel supply in advance to ensure the efficient operation of the boiler under different loads. The process is as follows:

[0074] Collect historical boiler operation data, including load changes, fuel consumption, and hot water or steam output; obtain data on external factors related to boiler operation, including weather data and seasonal information;

[0075] Preprocess the data, including handling missing values, outliers, and duplicate data;

[0076] Generate seasonal labels based on the month: winter: 12, 1, 2; spring: 3, 4, 5; summer: 6, 7, 8; autumn: 9, 10, 11. Use historical data to calculate the interactive features of temperature and create interactive features of temperature and fuel consumption to capture the relationship between the two.

[0077] The dataset is divided into a training set and a test set, and a decision tree is used for load forecasting. The selected machine learning algorithm is trained using the training set, and the performance of the model is evaluated on the test set. Finally, the trained model is used to predict the boiler load for a specific time period in the future and generate a fuel demand forecast.

[0078] According to the prediction results, the fuel supply and operating parameters are adjusted in advance to ensure efficient operation of the boiler under different loads.

[0079] It should be noted that using machine learning technology to predict boiler load based on historical operating data and external factors can significantly improve energy efficiency. At the same time, by predicting demand fluctuations, adjustments can be made in advance to maintain stable boiler operation.

[0080] In this embodiment, the data set is divided into a training set and a test set, and a decision tree is used for load forecasting. The selected machine learning algorithm is trained using the training set, and the performance of the model is evaluated on the test set. Finally, the trained model is used to predict the boiler load in a specific time period in the future. The fuel demand forecasting process is generated as follows:

[0081] The original data set is D, and the data set is divided into training set D1 and test set D2, of which 80% is used for training and 20% is used for testing;

[0082] The decision tree model is trained using the training set D1, and the target variable is the boiler load L. After the training is completed, the test set D2 is used for prediction. For the input feature X1, the predicted load L1 is generated. The specific formula is: L1 = f(X1), where f represents the trained decision tree model;

[0083] After model training and testing are completed, the mean square error is used to evaluate the difference between the predicted value and the true value;

[0084] Use the trained model to predict a specific time period in the future. Assuming the future feature data is X2, the predicted boiler load L2 is:

[0085] L2=f(X2);

[0086] Using the data points of boiler load and fuel consumption in the historical data, a straight line is fitted by linear regression, the formula is:

[0087] Y = kX + C;

[0088] Among them, Y represents fuel consumption, that is, the fuel consumption per unit load, X is the boiler load, C is the intercept, and the fuel consumption coefficient k is obtained according to this formula;

[0089] Then, the fuel consumption coefficient k and the predicted boiler load are used to generate the formula for fuel demand:

[0090] F1=k·L2

[0091] Among them, F1 represents the future fuel demand.

[0092] Fuel supply and metering modules to accurately measure the amount of fuel entering the boiler and transfer fuel from the storage location to the boiler;

[0093] In this embodiment, the fuel supply and metering module is used to accurately measure the amount of fuel entering the boiler and transfer the fuel from the storage location to the boiler. The process is as follows:

[0094] Determine the amount of fuel required by the boiler based on the output of the boiler load forecasting module;

[0095] Use high-precision sensors and flow meters to monitor the amount of fuel entering the boiler in real time and record the data;

[0096] The fuel is transported from the storage location to the boiler via a conveyor belt.

[0097] It should be noted that the conveyor belt is equipped with a safety valve and a monitoring system to prevent oversupply and conveying failure. When an abnormality occurs during the conveying or metering process, an alarm will be issued in time and the emergency treatment procedure will be initiated;

[0098] During fuel delivery, every step of the fuel supply will be recorded, and a complete operation log will be formed based on the recorded steps. This operation log can be easily analyzed and reviewed later.

[0099] Combustion environment and dynamic adjustment monitoring, used to monitor the oxygen content, temperature and pressure in the boiler, judge the combustion state, and adjust the fuel supply in real time according to the actual combustion situation to ensure sufficient oxygen and stable combustion;

[0100] In this embodiment, the combustion environment and dynamic adjustment monitoring are used to monitor the oxygen content, temperature and pressure in the boiler, judge the combustion state, and adjust the fuel supply in real time according to the actual combustion situation to ensure sufficient oxygen and stable combustion. The processing process is as follows:

[0101] Collect sensor data, monitor the oxygen content, temperature and pressure of the boiler in real time, analyze the current combustion status, establish an algorithm model based on the current combustion status, and determine the adjustment strategy of fuel supply;

[0102] Adjust the fuel supply in real time according to the analysis results to ensure sufficient oxygen content in the boiler and stable combustion state;

[0103] Feedback the adjusted supply volume to the monitoring system to form a closed-loop control and track the adjustment effect;

[0104] It should be noted that by analyzing the current combustion state and establishing a corresponding algorithm model to determine the fuel supply adjustment strategy, the combustion efficiency can be significantly improved and fuel waste can be reduced. In addition, this automated management method can reduce manual intervention and improve operation efficiency.

[0105] In this embodiment, an algorithm model based on the current combustion state is established to determine the fuel supply adjustment strategy processing process as follows:

[0106] Extract the features related to the combustion state from the preprocessed data, specifically: the rate of change of oxygen content and the fluctuation range of temperature, and use these features as the input of the algorithm model to describe the current combustion state;

[0107] A neural network is used to establish a relationship model between the combustion state and the fuel supply adjustment amount. The specific formula is:

[0108] Output of the hidden layer: H = f(W h ·X a +b h );

[0109] Among them, W h is the weight matrix from the input layer to the hidden layer, X a Input feature vector, which represents the parameters of the current combustion state, including o2 is oxygen, T is temperature, and P is pressure;

[0110] Output of the output layer: Y a =W O ·H+b o ;

[0111] Among them, W O is the weight matrix from the hidden layer to the output layer, b o is the output layer bias, and the loss function used in the training process is the mean square error;

[0112] The mean squared error (MSE) as a loss function has several advantages in neural network training: it has a simple and easy-to-understand mathematical form, and its continuous differentiability allows the gradient descent optimization algorithm to be effectively applied and can ensure that the global optimum is reached in simple linear regression; due to its square form, MSE can amplify the prediction error, making the model pay more attention to large errors and improving the overall prediction accuracy.

[0113] After training, given a new combustion state input X b , the fuel supply adjustment amount Y can be calculated through the model b :

[0114] Y b =W o ·f(W h ·X+b o ).

[0115] The abnormality monitoring module issues an abnormality warning when abnormal combustion is detected, and quickly adjusts the fuel quantity. At the same time, it makes suggestions for equipment maintenance and repair based on the results of the combustion condition analysis;

[0116] In this embodiment, the abnormality monitoring module, when detecting abnormal combustion, issues an abnormality warning and quickly adjusts the fuel quantity. At the same time, according to the results of the combustion condition analysis, it proposes equipment maintenance and overhaul suggestions. The processing process is as follows:

[0117] There are preset thresholds for abnormal temperature, air pressure, and oxygen concentration. When abnormal sensor data is detected, an abnormal warning will be triggered. The warning information will be sent to the operator through sound and light alarms, SMS notifications, and system interface prompts. At the same time as the warning is issued, the abnormal monitoring module will automatically adjust the fuel supply to try to correct the abnormal situation.

[0118] Comprehensively evaluate the composition, temperature, pressure, flow rate and other parameters of the combustion products to diagnose specific abnormalities;

[0119] Based on the results of the combustion condition analysis, corresponding equipment maintenance and overhaul recommendations are generated.

[0120] It should be noted that this module can receive abnormal data of the combustion process in real time, issue early warnings in a timely manner and quickly adjust the amount of fuel, thereby effectively preventing accidents and ensuring the safe operation of the equipment. At the same time, by analyzing the combustion conditions, it can provide professional equipment maintenance and repair suggestions to help reduce downtime and maintenance costs.

[0121] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0122] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A fuel distribution control system for a CFB boiler, characterized in that: It includes a combustion material condition analysis module, a load prediction module, a fuel supply and metering module, a combustion environment monitoring module, and an abnormality monitoring module, wherein the combustion material condition analysis module, the load prediction module, the fuel supply and metering module, the combustion environment monitoring module, and the abnormality monitoring module are sequentially connected in communication; The combustion material condition analysis module analyzes the combustion requirements and the required combustion conditions; The load prediction module uses machine learning technology to predict boiler load based on historical operating data and external factors including weather and season, so as to adjust fuel supply in advance and ensure efficient operation of the boiler under different loads; The fuel supply and metering module is used to accurately measure the amount of fuel entering the boiler and transfer the fuel from the storage location to the boiler; The combustion environment and dynamic adjustment monitoring are used to monitor the oxygen content, temperature and pressure in the boiler, judge the combustion state, and adjust the fuel supply in real time according to the actual combustion situation to ensure sufficient oxygen and stable combustion; The abnormal monitoring module, when detecting abnormal combustion, issues an abnormal warning and quickly adjusts the fuel quantity, and at the same time, proposes equipment maintenance and overhaul suggestions based on the results of the combustion condition analysis.

2. A fuel distribution control system for a CFB boiler according to claim 1, characterized in that: The combustion condition analysis module analyzes the combustion requirements, and the required combustion condition processing process is as follows: The combustion requirements are divided into two types: combustion to produce hot water and combustion to produce steam. The working temperature of the hot water boiler is set at 80℃-150℃, the pressure is set at 0.1-0.5MPa, and the hot water produced is used for heating. The working temperature of the steam boiler is higher than that of the hot water boiler, the temperature is set at 160℃-540℃, the pressure is set at above 1MPa, and the steam produced is used for power generation or industrial processes.

3. The fuel distribution control system for a CFB boiler according to claim 1, characterized in that: The load forecasting module uses machine learning technology to predict boiler load based on historical operating data and external factors including weather and season, so as to adjust fuel supply in advance and ensure efficient operation of the boiler under different loads. The process is as follows: Collect historical boiler operation data, including load changes, fuel consumption, and hot water or steam output; obtain data on external factors related to boiler operation, including weather data and seasonal information; Preprocess the data, including handling missing values, outliers, and duplicate data; Generate seasonal labels based on the month, winter: 12, 1, 2; Spring: 3, 4, 5; Summer: 6, 7, 8; Autumn: 9, 10, 11, using historical data to calculate the interactive features of temperature, and create interactive features of temperature and fuel consumption to capture the relationship between the two; The dataset is divided into a training set and a test set, and a decision tree is used for load forecasting. The selected machine learning algorithm is trained using the training set, and the performance of the model is evaluated on the test set. Finally, the trained model is used to predict the boiler load for a specific time period in the future and generate a fuel demand forecast. According to the prediction results, the fuel supply and operating parameters are adjusted in advance to ensure efficient operation of the boiler under different loads.

4. A fuel distribution control system for a CFB boiler according to claim 3, characterized in that: The data set is divided into a training set and a test set, and a decision tree is used for load forecasting; the selected machine learning algorithm is trained using the training set, and the performance of the model is evaluated on the test set. Finally, the trained model is used to forecast the boiler load in a specific time period in the future, and the fuel demand forecasting process is generated as follows: The original data set is D, and the data set is divided into training set D1 and test set D2, of which 80% is used for training and 20% is used for testing; The decision tree model is trained using the training set D1, and the target variable is the boiler load L. After the training is completed, the test set D2 is used for prediction. For the input feature X1, the predicted load L1 is generated. The specific formula is: L1 = f(X1), where f represents the trained decision tree model; After model training and testing are completed, the mean square error is used to evaluate the difference between the predicted value and the true value; Use the trained model to predict a specific time period in the future. Assuming the future feature data is X2, the predicted boiler load L2 is: L2=f(X2); Using the data points of boiler load and fuel consumption in the historical data, a straight line is fitted by linear regression, the formula is: Y = kX + C; Among them, Y represents fuel consumption, that is, the fuel consumption per unit load, X is the boiler load, C is the intercept, and the fuel consumption coefficient k is obtained according to this formula; Then, the fuel consumption coefficient k and the predicted boiler load are used to generate the formula for fuel demand: F1=k·L2 Among them, F1 represents the future fuel demand.

5. The fuel distribution control system for a CFB boiler according to claim 1, characterized in that: The fuel supply and metering module is used to accurately measure the amount of fuel entering the boiler and transfer the fuel from the storage location to the boiler. The process is as follows: Determine the amount of fuel required by the boiler based on the output of the boiler load forecasting module; Use high-precision sensors and flow meters to monitor the amount of fuel entering the boiler in real time and record the data; The fuel is transported from the storage location to the boiler via a conveyor belt.

6. The fuel distribution control system for a CFB boiler according to claim 1, characterized in that: The combustion environment and dynamic adjustment monitoring are used to monitor the oxygen content, temperature and pressure in the boiler, judge the combustion state, and adjust the fuel supply in real time according to the actual combustion situation to ensure sufficient oxygen and stable combustion. The processing process is as follows: Collect sensor data, monitor the oxygen content, temperature and pressure of the boiler in real time, analyze the current combustion status, establish an algorithm model based on the current combustion status, and determine the adjustment strategy of fuel supply; Adjust the fuel supply in real time according to the analysis results to ensure sufficient oxygen content in the boiler and stable combustion state; The adjusted supply volume is fed back to the monitoring system to form a closed-loop control and track the adjustment effect.

7. A fuel distribution control system for a CFB boiler according to claim 6, characterized in that: The process of establishing an algorithm model based on the current combustion state and determining the adjustment strategy of fuel supply is as follows: Extract the features related to the combustion state from the preprocessed data, specifically: the rate of change of oxygen content and the fluctuation range of temperature, and use these features as the input of the algorithm model to describe the current combustion state; A neural network is used to establish a relationship model between the combustion state and the fuel supply adjustment amount. The specific formula is: Output of the hidden layer: H = f(W h ·X a +b h ); Among them, W h is the weight matrix from the input layer to the hidden layer, X a Input feature vector, which represents the parameters of the current combustion state, including o2 is oxygen, T is temperature, and P is pressure; Output of the output layer: Y a =W O ·H+b o ; Among them, W O is the weight matrix from the hidden layer to the output layer, b o is the output layer bias, and the loss function used in the training process is the mean square error; After training, given a new combustion state input X b , the fuel supply adjustment amount Y can be calculated through the model b : Y b =W o ·f(W h ·X+b o )。 8. The fuel distribution control system for a CFB boiler according to claim 1, characterized in that: The abnormal monitoring module, when detecting abnormal combustion, issues an abnormal warning and quickly adjusts the fuel quantity. At the same time, according to the results of the combustion condition analysis, it proposes equipment maintenance and overhaul suggestions. The processing process is as follows: There are preset thresholds for abnormal temperature, air pressure, and oxygen concentration. When abnormal sensor data is detected, an abnormal warning will be triggered. The warning information will be sent to the operator through sound and light alarms, SMS notifications, and system interface prompts. At the same time as the warning is issued, the abnormal monitoring module will automatically adjust the fuel supply to try to correct the abnormal situation. Comprehensively evaluate the composition, temperature, pressure, flow rate and other parameters of the combustion products to diagnose specific abnormalities; Based on the results of the combustion condition analysis, corresponding equipment maintenance and overhaul recommendations are generated.

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