Intelligent FIR Ultra-Low Nitrogen Staged Combustion Burner

Abstract

The present invention relates to an intelligent FIR ultra-low nitrogen staged burner, belonging to the field of burners. An intelligent FIR ultra-low nitrogen staged burner includes: an inner sleeve assembly provided with an air outlet passage; a gas housing assembly, a gas chamber is formed by enclosing between the inner wall of the gas housing assembly and the outer wall of the inner sleeve assembly; a central sleeve assembly disposed on the inner sleeve assembly; a central gas assembly disposed on the central sleeve assembly; a gas guiding tube; a gas pipe assembly disposed on the gas housing assembly; and a fan assembly. It ensures the oxygen supply required during the combustion process, promotes the combustion efficiency and the complete combustion of combustion products, and at the same time reduces the generation of nitrogen oxides. The reasonable design and layout of these components enable the burner to achieve an efficient combustion process, reduce the emission of nitrogen oxides, and achieve the goals of environmental protection and energy efficiency.

Description

Technical Field

[0001] The present invention relates to the field of burners, and particularly to an intelligent FIR ultra-low nitrogen staged burner. Background Art

[0002] Currently, research on low-nitrogen combustion technology shows that during the combustion process, there are problems with the unreasonable design of the dispersed central air. This means that the mixing of gas and air may not be uniform enough, resulting in incomplete combustion in some areas, thereby increasing the generation of nitrogen oxides (NOX). In addition, the nozzle arrangement of the burner also faces unreasonable challenges and cannot effectively adjust the gas ejected from the nozzle according to actual needs. This lack of intelligent design limits the flexibility and efficiency of the gas combustion system. Therefore, improving the intelligence level of the gas combustion system is one of the urgent problems to be solved currently. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent FIR ultra-low nitrogen staged burner to address the problems of unreasonable layout and low intelligence level.

[0004] An intelligent FIR ultra-low nitrogen staged combustor, the intelligent FIR ultra-low nitrogen staged combustor comprising: an inner sleeve assembly provided with an air outlet passage, with an air inlet and an air outlet communicating with the air outlet passage respectively formed at both ends of the inner sleeve assembly, and a first gas port formed on the side wall of the air outlet passage; a gas housing assembly provided with a buffer chamber, with a first opening and a second opening communicating with the buffer chamber formed at both ends of the gas housing assembly, the inner sleeve assembly sequentially passing through the first opening, the buffer chamber and the second opening, a second gas port communicating with the buffer chamber formed on one side of the gas housing assembly close to the air outlet, and a gas chamber formed by enclosing between the inner wall of the gas housing assembly and the outer wall of the inner sleeve assembly; a central sleeve assembly provided with a through air passage, the central sleeve assembly being arranged on the inner sleeve assembly, a part of the central sleeve assembly being located in the air outlet passage, and another part of the central sleeve assembly extending outside the air outlet passage after passing through the air outlet, and an air passage gap being formed between the central sleeve assembly and the hole wall of the air outlet; a central gas assembly arranged on the central sleeve assembly and located in the air passage, with one end of the central gas assembly adjacent to the outlet of the air passage; a gas guiding pipe, one end of the gas guiding pipe communicating with the first gas port, and the other end of the gas guiding pipe extending to the outlet of the air passage; a gas pipe assembly arranged on the gas housing assembly and communicating with the second gas port, the number of the gas pipe assemblies being multiple, and the multiple gas pipe assemblies surrounding the central sleeve assembly and being arranged at intervals in sequence; a fan assembly provided with a fan inlet and a fan outlet, and the fan outlet communicating with the air inlet.

[0005] The present application discloses an intelligent FIR ultra-low nitrogen staged burner. The inner sleeve assembly effectively introduces external air and discharges combustion products by providing an air inlet and an air outlet, ensuring the normal air supply of the burner and improving the mixing effect of gas and air. The gas housing assembly is provided with a buffer chamber and a second gas port, which can relieve the impact of the gas flow and ensure the uniform distribution of the gas before entering the combustion area, thus improving the combustion efficiency. The central sleeve assembly effectively controls the air supply volume through the through air passage, enabling the air to enter the combustion area evenly and promoting the complete combustion. The central gas assembly is located in the air passage, enabling the gas to be fully mixed with the air before entering the combustion area, achieving efficient utilization of the gas and low nitrogen oxide emissions. The gas guide tube ensures the accurate entry of the gas into the combustion area by guiding the gas flow, avoiding incomplete combustion and emission problems during the combustion process. A plurality of gas pipe assemblies are arranged around the central sleeve assembly. Through reasonable layout and spacing, the uniform supply of gas is ensured, improving the stability and reliability of the burner. The fan assembly introduces external air through the air inlet, ensuring the oxygen supply required during the combustion process, promoting the combustion efficiency and the complete combustion of the combustion products, and reducing the generation of nitrogen oxides at the same time. The reasonable design and layout of these components enable the burner to achieve an efficient combustion process, reduce nitrogen oxide emissions, and achieve the goals of environmental protection and energy efficiency.

[0006] In one of the embodiments, the inner sleeve assembly includes a cavity inner sleeve, an inner sleeve, and an outer conical ring. The cavity inner sleeve, the inner sleeve, and the outer conical ring are sequentially connected to form the air outlet passage. An air inlet is provided at one end of the cavity inner sleeve away from the inner sleeve, and an air outlet is provided at one end of the outer conical ring away from the inner sleeve. The first gas port is provided on the side wall of the cavity inner sleeve. The formation of the air outlet passage by the connection of the cavity inner sleeve, the inner sleeve, and the outer conical ring ensures the continuity and smoothness of the air outlet passage, enabling the combustion products to be discharged smoothly, which is beneficial to the stable operation of the burner and the improvement of the combustion efficiency. The settings of the air inlet and the air outlet enable the gas and air to enter and exit the inner sleeve assembly smoothly, effectively maintaining the gas flow required during the combustion process, and contributing to the normal operation of the burner and the improvement of the combustion efficiency. The shape of the outer conical ring and the structural design of the inner sleeve help to guide the combustion products to be discharged smoothly, while ensuring the stability of the air outlet and the uniformity of fluid dynamics, which is beneficial to the normal operation of the burner and the improvement of the combustion efficiency.

[0007] In one embodiment, the gas housing assembly includes an outer cavity sleeve, a furnace wall mounting flange, and a cavity flange. The two ends of the outer cavity sleeve are respectively connected to the furnace wall mounting flange and the cavity flange. The furnace wall mounting flange and the cavity flange are respectively connected to the inner sleeve assembly. A buffer cavity is formed inside the outer cavity sleeve. The cavity flange is provided with the first opening, the cavity flange is provided with the second gas port surrounding the first opening, and the furnace wall mounting flange is provided with the second opening. The connection between the outer cavity sleeve and the furnace wall mounting flange and the cavity flange is firm and reliable, ensuring the overall structural stability of the gas housing assembly and facilitating the installation and use of the burner. The formation of the buffer cavity helps to slow down the flow rate of the gas in the burner, reduce the turbulence degree of the air flow, thereby reducing the noise and vibration during the operation of the burner and improving the stability and reliability of the burner.

[0008] In one embodiment, the central sleeve assembly includes a central sleeve, a positioning assembly, a support plate, and a retaining ring. A part of the central sleeve is located in the air outlet passage, and the other part of the central sleeve extends outside the air outlet passage through the air outlet. An air passing gap is formed between the central sleeve and the hole wall of the air outlet. The central sleeve is provided with an air passing cavity. One end of the positioning assembly is connected to the central sleeve, and the other end of the positioning assembly is connected to the inner sleeve assembly. The number of the support plates is multiple. Both sides of the multiple support plates are respectively connected to the central sleeve and the inner sleeve assembly. The retaining ring is arranged on the central sleeve and is located in the air passing cavity. The gas guiding pipe is clamped at the retaining ring. The setting of the air passing cavity enables part of the gas to enter the outside of the air outlet passage through the central sleeve assembly, which helps to form an appropriate air passing effect, adjust the flow rate and temperature distribution of the air flow inside and outside the air outlet passage, thereby improving the thermal efficiency and stability of the burner. The air passing gap formed between the central sleeve and the hole wall of the air outlet helps to adjust the air flow distribution inside and outside the air outlet passage, reduce the turbulence degree of the air flow, optimize the gas mixing and combustion process, and improve the combustion efficiency and stability. The setting of the positioning assembly and the support plate enables the central sleeve assembly to be firmly fixed on the inner sleeve assembly, ensuring the stability and sealing performance of the gas flow channel, and effectively avoiding potential safety hazards and performance degradation caused by component loosening or misalignment. The retaining ring can limit the gas guiding pipe.

[0009] In one embodiment, the central gas component includes a gas main pipe, a connecting pipe, a nozzle, an inner sleeve, and a flame plate. The connecting pipe is arranged on the gas main pipe, the inner sleeve is arranged on the connecting pipe, the nozzle is sleeved on the inner sleeve, a plurality of gas injection holes are formed in the side wall of the nozzle, the flame plate is arranged on the nozzle, a plurality of air passing holes are formed in the flame plate, the flame plate is adjacent to the outlet of the air passing cavity, and the gas main pipe is used for communicating with a gas source. The gas main pipe and the connecting pipe constitute the main channel for gas transportation, ensuring the stable flow of gas from the gas pipe component to the nozzle, which helps to improve the uniformity and stability of the gas, thereby improving the combustion efficiency. The nozzle evenly injects the gas onto the flame plate through a plurality of gas injection holes, enabling the gas to be fully mixed and effectively burned with air, thereby generating a stable flame and improving the combustion efficiency and heat energy utilization rate. The plurality of air passing holes formed in the flame plate help to adjust the ratio of gas to air, control the oxygen supply for combustion, and enable the flame to form at an appropriate position and size, ensuring the stability and high efficiency of the combustion process.

[0010] In one embodiment, the gas pipe component includes an intake pipe, a sleeve support, and a mixing sleeve. The intake pipe is arranged on the gas housing component and communicates with the second gas port. The number of the sleeve supports is multiple, and the multiple sleeve supports are arranged at intervals on the intake pipe and located at one end far from the gas housing component. The mixing sleeve is arranged on the multiple sleeve supports. The intake pipe serves as the channel for gas transportation, ensuring the stable flow of gas from the gas housing component to the mixing sleeve, which helps to improve the uniformity and stability of the gas, thereby improving the combustion efficiency. The arrangement of the sleeve supports provides support and positioning for the mixing sleeve, ensuring the stable installation and positioning of the mixing sleeve, avoiding displacement or deformation caused by vibration or external forces, and ensuring the uniformity and stability of gas mixing. The mixing sleeve realizes the mixing of gas and air through connection with the sleeve supports, ensuring the uniformity and stability of the mixed gas, thereby effectively promoting the combustion process and improving the combustion efficiency and combustion stability.

[0011] In one embodiment, the fan assembly includes a fan housing, a fan, and an air inlet assembly. The fan and the air inlet assembly are both disposed on the fan housing. The fan housing is provided with a fan outlet and a fan inlet. The fan housing, as the external protective shell of the fan, can effectively prevent external impurities and dust from entering the interior of the fan, protect the normal operation of the fan, and through the reasonable setting of the fan outlet and the fan inlet, can effectively guide the air flow and improve the working efficiency of the fan. The fan is the core component of the fan assembly. Through the operation of the fan, an air flow can be effectively generated, sucking air into the fan inlet and then sending it out of the fan outlet, thereby forming a necessary air flow environment and providing the required air flow conditions for the combustion process. The air inlet assembly is an important part of the fan assembly. A reasonable design can effectively guide and regulate the air flow rate and velocity entering the fan, ensuring that the fan can obtain sufficient fresh air, thus ensuring the smooth progress of the combustion process.

[0012] In one embodiment, an outer sleeve is further included. The outer sleeve is disposed on the gas housing assembly and surrounds a plurality of the gas pipe assemblies. The provision of the outer sleeve provides an additional protective layer, which can effectively protect the gas pipe assemblies from the influence of the external environment, such as corrosion or mechanical damage, etc. This helps to extend the service life of the gas pipe assemblies and reduce the maintenance cost. The outer sleeve can retain the heat generated by the gas pipe assemblies to a certain extent, reducing energy loss. At the same time, the outer sleeve can also promote the heat conduction between the gas pipe assemblies and the surrounding environment, helping to maintain a stable temperature of the combustion system and improve the combustion efficiency. As a surrounding structure, the outer sleeve enhances the structural stability of the entire gas housing assembly, reducing the possibility of deformation or damage of the burner during operation due to vibration or other external forces.

[0013] In one embodiment, an air guide pipe is further included. One end of the air guide pipe is communicated with the fan assembly, and the other end of the air guide pipe penetrates through the gas housing assembly and extends to the gas pipe assemblies. By introducing an appropriate amount of induced air, the gas distribution inside the gas pipe assemblies and the uniformity of the combustion process can be effectively improved, reducing the instability of the flame and combustion noise, and improving the stability and reliability of combustion.

[0014] In one embodiment, it further includes a gas regulating valve. One end of the gas regulating valve is used to communicate with an external gas source, and the other end of the gas regulating valve communicates with the buffer chamber. The gas regulating valve is used to regulate the gas flow rate flowing through. A wind valve is provided on the fan assembly. The wind valve is provided at the fan inlet. The wind valve is used to regulate the opening degree of the fan inlet. A sensing assembly, the number of the sensing assemblies is multiple. The multiple sensing assemblies are respectively located at the inner sleeve assembly, the gas housing assembly and the central sleeve assembly. The multiple sensing assemblies are used to collect combustion process data. A control system is electrically connected to the gas regulating valve, the wind valve, the multiple sensing assemblies and the fan assembly respectively. The setting of the gas regulating valve allows for precise regulation of the gas flow rate flowing through the gas housing assembly, adjusts the gas flow rate and pressure as needed, realizes the accurate supply of gas, and thus ensures the stability and efficiency of the combustion process. The setting of the wind valve can regulate and control the air volume at the fan inlet, adjust the rotational speed and air volume of the fan according to the combustion demand, ensure sufficient air flow enters the combustion chamber, and realize the full mixing and stability of the combustion process. The multiple sensing assemblies are located at the inner sleeve assembly, the gas housing assembly and the central sleeve assembly, and are used to monitor parameters such as temperature, pressure, and flow rate during the combustion process in real time and collect relevant data. These data can be used to analyze the state and performance of the combustion process and help optimize the working conditions of the combustion system. The control system realizes the intelligent control and optimized regulation of the gas flow rate, air volume and combustion process by being electrically connected to the gas regulating valve, the wind valve, the sensing assembly and the fan assembly. According to the data collected by the sensing assembly, the control system can adjust the gas and air volume in real time to maintain the stability and high efficiency of the combustion process. The setting of the control system enables the operator to perform remote monitoring and control through electrical connection, realizes the remote operation and regulation of the burner, and improves the convenience of operation and the efficiency of human-machine interaction.

[0015] In one of the embodiments, the control system is used to execute the following procedures: S1. Train a control model through deep learning technology so that it can better understand the complexity of the combustion process, thereby optimizing the control strategy to minimize NOx emissions to the greatest extent; S2. Utilize data processing technology to analyze the monitoring data in real time, and timely adjust the gas supply and air supply through the adjustment parameters output by the control model to ensure that the combustion process is always in the best state; S3. Utilize model prediction technology to predict in advance the problems that may occur during the combustion process and take corresponding control measures to minimize NOx emissions; S4. Introduce an adaptive control strategy so that the control system can automatically adjust the control parameters according to the real-time combustion state and environmental changes to achieve the best combustion efficiency and the minimum NOx emissions; S5. Control the gas supply and air supply through intelligent control algorithms. The deep learning model can process a large amount of complex data and learn the complex patterns and rules of the combustion process from it. Through deep learning, the control model can predict the combustion effects under different operating conditions and optimize the control strategy accordingly. This means that the system can automatically adjust the ratio of gas and air, as well as other relevant parameters, to achieve the best combustion effect. The deep learning model can predict which operating conditions are most likely to cause an increase in NOx emissions and adjust the control strategy accordingly, thereby minimizing these emissions to the greatest extent. By analyzing the monitoring data in real time, the system can immediately understand the current state of the combustion process, including key parameters such as temperature, pressure, and oxygen concentration. If the system detects any deviation from the optimal combustion conditions, it can immediately adjust the gas and air supply through the adjustment parameters output by the control model to ensure that the combustion process always remains in the best state. This instant feedback and adjustment mechanism helps to improve the combustion efficiency and ensure the stability of the combustion process at the same time. The model prediction technology allows the system to predict in advance the problems that may occur during the combustion process, such as unstable combustion and increased NOx emissions. Once the system predicts a possible problem, it can take control measures in advance, such as adjusting the ratio of gas and air, or fine-tuning the combustion conditions, thereby preventing the problem from actually occurring. By foreseeing and taking actions in advance, this strategy helps to reduce emergencies caused by unstable combustion or other problems, thereby improving the reliability of the system. The adaptive control strategy enables the control system to automatically adjust the control parameters according to the real-time combustion state and environmental changes. This means that regardless of how the combustion conditions change, the system can maintain the best performance. Since the adaptive control strategy can continuously optimize the control parameters according to the actual situation, it helps to achieve the best combustion efficiency and the minimum NOx emissions. Adaptive control reduces the dependence on manual operation and adjustment, making the combustion process more automated and intelligent. The intelligent control algorithms can accurately calculate and control the gas supply and air supply to ensure that they are mixed in the best ratio, thereby achieving efficient combustion. These algorithms are usually designed to be very sensitive and can quickly respond to any changes to ensure that the combustion process always remains in the best state.By precisely controlling the supply of gas and air, this strategy helps reduce unnecessary waste and improve energy utilization efficiency.

[0016] In one of the embodiments, the specific steps of training the control model through deep learning technology to enable it to better understand the complexity of the combustion process and thus optimize the control strategy to minimize NOx emissions are as follows: S11. Collect combustion process data, including gas supply volume, air supply volume, combustion temperature, and NOx emission concentration, and preprocess the data, including denoising, normalization, and feature engineering, to prepare a dataset for deep learning model training; S12. According to the complexity of the combustion process and data characteristics, select a suitable deep learning model, including one of multi-layer perceptron, convolutional neural network, and recurrent neural network, and design the structure of the deep learning model, including the number of network layers, the number of neurons in each layer, and the activation function; S13. Divide the prepared dataset into a training set, a validation set, and a test set, use the training set to train the deep learning model, continuously adjust the model parameters through the backpropagation algorithm to minimize the loss function, use the validation set to monitor the performance of the model, and make adjustments according to the performance of the validation set to prevent overfitting; S14. Use the test set to evaluate the performance of the trained model, including accuracy, recall rate, and F1 score, and optimize the model according to the evaluation results, including adjusting the network structure, modifying hyperparameters, and increasing regularization; S15. Apply the trained deep learning model to the control system as part of the controller, and optimize the control strategy according to the prediction results of the model and real-time sensor data, dynamically adjust the gas supply volume and air supply volume to minimize NOx emissions; S16. Real-time monitor the key parameters in the combustion process and adjust the control parameters according to the output of the deep learning model. By collecting key data such as gas supply volume, air supply volume, combustion temperature, and NOx emission concentration, comprehensive information about the combustion process is obtained. Preprocessing steps such as denoising, normalization, and feature engineering ensure the quality and consistency of the data, providing a reliable dataset for subsequent deep learning model training. The preprocessed dataset improves the training efficiency and accuracy of the model, laying a solid foundation for the subsequent steps. According to the complexity of the combustion process and data characteristics, select a suitable deep learning model, such as multi-layer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). Reasonably design the number of network layers, the number of neurons in each layer, and the activation function to ensure that the model can effectively capture the complex patterns of the combustion process. The selection of a suitable model structure and parameters enables the model to better understand and predict the combustion process, providing strong support for subsequent control strategy optimization. Divide the preprocessed dataset into a training set, a validation set, and a test set to ensure that the model can perform well under various data distributions. Use the training set to train the model and continuously adjust the model parameters through the backpropagation algorithm to minimize the loss function. Performance monitoring and adjustment: Use the validation set to monitor the performance of the model and make adjustments according to the performance of the validation set to prevent overfitting and improve the generalization ability of the model.Reasonable dataset partitioning and model training strategies enable the model to not only accurately fit the training data but also exhibit good performance on unknown data. Use the test set to evaluate the performance of the trained model, including metrics such as accuracy, recall, and F1-score. Tune the model based on the evaluation results, including adjusting the network structure, modifying hyperparameters, and adding regularization, to further improve the model's performance. The model evaluation and tuning steps ensure the accuracy and reliability of the model in practical applications, providing high-quality model support for subsequent control system applications. Integrate the trained deep learning model into the control system as part of the controller. Optimize the control strategy based on the model's prediction results and real-time sensor data to dynamically adjust the gas supply and air supply. The application of the deep learning model enables the control system to more accurately predict and respond to changes in the combustion process, thereby more effectively reducing NOx emissions and improving combustion efficiency. Monitor the key parameters in the combustion process in real time to ensure that the combustion process is always in the best state. Dynamically adjust the control parameters based on the output of the deep learning model and real-time monitoring data to ensure the stability and efficiency of the combustion process. The real-time monitoring and parameter adjustment steps enable the control system to flexibly adjust the control strategy according to the actual situation, further improving the stability of the combustion process and the reduction effect of NOx emissions.

[0017] In one of the embodiments, the specific steps of using data processing technology to analyze monitoring data in real time and timely adjust the gas supply and air supply through the adjustment parameters output by the control model to ensure that the combustion process is always in the best state are as follows: S21. Set up a data acquisition system to collect the monitoring data collected by sensors in real time, including temperature, pressure, oxygen concentration, gas flow rate, and air flow rate, and ensure that the data acquisition system can transmit data to the data processing system quickly and accurately; S22. Preprocess the collected data, including removing noise, calibrating sensors, data interpolation, and filling missing values, to ensure the quality and reliability of the data, and perform data cleaning to delete outliers and unreasonable data; S23. Use real-time data processing technology to perform real-time analysis on the collected data, and perform real-time monitoring and analysis on the real-time data to identify abnormal situations and change trends in the combustion process; S24. Based on historical data and real-time monitoring data, timely adjust the gas supply and air supply through the adjustment parameters output by the control model; S25. Monitor the state and performance indicators of the combustion process after real-time adjustment, including combustion efficiency and NOx emission concentration, and continuously optimize the adjustment strategy according to the real-time feedback results to make the combustion process always in the best state and achieve the goals of low emissions and high efficiency. By setting up a data acquisition system, it is possible to collect data such as temperature, pressure, oxygen concentration, gas flow rate, and air flow rate monitored by sensors in real time. Ensure that the data can be transmitted to the data processing system quickly and accurately, providing a basis for subsequent real-time analysis and control. Real-time and accurate data provides key information for the monitoring and control of the combustion process, enabling the control system to respond quickly to changes. The preprocessing steps include removing noise and calibrating sensors to ensure the accuracy and consistency of the data. Data interpolation and filling: Interpolate or fill in missing data to improve data integrity. Delete outliers and unreasonable data to ensure data quality. The preprocessed data is more reliable and accurate, providing high-quality input for subsequent data analysis and control model training. Use real-time data processing technology to perform instant analysis on the collected data. Real-time monitoring and analysis can identify abnormal situations and change trends in the combustion process, providing a basis for adjusting the control strategy. Real-time analysis enables the control system to detect and respond to problems in the combustion process in a timely manner, improving the stability and efficiency of combustion. Based on historical data and real-time monitoring data, the control model can output corresponding adjustment parameters. According to the model output, timely adjust the gas supply and air supply to optimize the combustion process. The control system can adjust the gas and air supply more precisely, thereby optimizing combustion efficiency and reducing NOx emissions. Monitor the state and performance indicators of the combustion process after real-time adjustment, such as combustion efficiency and NOx emission concentration. According to the real-time feedback results, continuously adjust and optimize the control strategy to make the combustion process always in the best state.The real-time feedback mechanism enables the control system to optimize according to the actual combustion effect, achieve the goals of low emissions and high efficiency, and improve the economy and environmental protection of the entire combustion process.

[0018] In one of the embodiments, the specific steps of using model prediction technology to predict in advance the possible problems during the combustion process and taking corresponding control measures to minimize NOx emissions are as follows: S31. Collect data on historical combustion processes, including combustion temperature, pressure, fuel supply, air supply, and NOx emissions, and preprocess the data, including data cleaning, outlier removal, and interpolation, to ensure the accuracy and integrity of the data; S32. Select a suitable model prediction technology, including one of the recurrent neural network (RNN), long short-term memory network (LSTM), and support vector regression (SVR), and use the historical data to train the model so that it can learn the dynamic change rules of the combustion process and can predict possible problems in the future; S33. Select appropriate features from the historical data, including temperature gradient, fuel-air ratio, and combustion efficiency, as the input features of the model, and construct the input-output structure of the model to ensure that the model can accurately predict possible problems during the combustion process, including incomplete combustion and too high combustion temperature; S34. Use the historical data to train the model, and use the validation data to verify the accuracy and generalization ability of the model. Use techniques such as cross-validation to evaluate the performance of the model to ensure that the model can accurately predict the combustion process under different conditions; S35. Use the trained model to make predictions, predict possible problems during the combustion process, including a decrease in combustion efficiency and an increase in NOx emissions, and formulate corresponding control strategies according to the prediction results, adjust the fuel supply and air supply to minimize NOx emissions and avoid the occurrence of combustion problems; S36. Real-time monitor the key parameters during the combustion process, including temperature, pressure, fuel flow, and air flow, input the monitoring data into the model for real-time prediction, and adjust the control strategy in a timely manner according to the prediction results to ensure the stability and low emissions of the combustion process; S37. Continuously optimize and update the model, use new data and experimental results to update the model parameters to improve the prediction ability and accuracy of the model, and combine the actual operation situation to continuously optimize the control strategy to minimize NOx emissions and ensure the efficient and stable operation of the combustion process; S38. Establish a fault detection system to monitor abnormal conditions and faults during the combustion process, predict potential faults according to real-time data analysis and the output of the prediction model, and take preventive measures in a timely manner to ensure the stability and reliability of the combustion process. By collecting data on historical combustion processes, information on the long-term behavior of the combustion process can be obtained. Data cleaning, outlier removal, and interpolation ensure the accuracy and integrity of the data, providing a reliable basis for the subsequent training of the model. An accurate and complete data set provides a solid foundation for model training, improving the prediction ability and stability of the model. Model prediction technologies such as the recurrent neural network (RNN), long short-term memory network (LSTM), and support vector regression (SVR) can learn the dynamic change rules of the combustion process and predict possible problems in the future.Appropriate model prediction techniques can accurately capture the dynamic characteristics of the combustion process, thus effectively predicting potential problems and providing strong support for the formulation of control strategies. The selection of features such as temperature gradient, fuel-air ratio, and combustion efficiency enables the model to more accurately predict the problems that may occur during the combustion process. Ensure that the model can accurately process the input features and output predictions regarding potential problems. A reasonable input-output structure enables the model to predict the key features of the combustion process, improving the accuracy and practicality of the prediction. Using historical data for model training enables the model to learn the dynamic behavior of the combustion process. Evaluate the accuracy and generalization ability of the model through validation data to ensure that the model can exhibit good prediction performance under different conditions. The model training and validation processes ensure the effectiveness and reliability of the model, providing a solid foundation for subsequent practical applications. Use the trained model to predict the problems that may occur during the combustion process, such as a decrease in combustion efficiency and an increase in NOx emissions. Develop corresponding control strategies based on the prediction results, adjusting the fuel supply and air supply to minimize NOx emissions and avoid the occurrence of combustion problems. The formulation of predictions and control strategies enables the control system to predict and address potential problems in advance, thereby reducing NOx emissions and ensuring the stability of the combustion process. Monitor the key parameters during the combustion process in real time to ensure the timeliness and accuracy of the data. Input the real-time monitoring data into the model for real-time prediction and adjust the control strategy in a timely manner according to the prediction results to ensure the stability of the combustion process and low emissions. Real-time monitoring and adjustment enable the control system to flexibly adjust the control strategy according to the actual situation, further improving the stability of the combustion process and the reduction effect of NOx emissions. Continuously optimize the model parameters using new data and experimental results to improve the prediction ability and accuracy of the model. Combine the actual operating conditions and continuously optimize the control strategy to minimize NOx emissions and ensure the efficient and stable operation of the combustion process. Model optimization and update ensure the continuous improvement and adaptability of the control system, enabling it to handle various actual operating conditions and requirements. Establish a fault detection system to monitor abnormal conditions and faults during the combustion process in real time. Predict potential faults based on real-time data analysis and the output of the prediction model and take preventive measures in a timely manner to ensure the stability and reliability of the combustion process. The fault detection and prevention system improves the reliability and safety of the combustion process, reduces the likelihood of faults occurring, and thus reduces operation interruptions and maintenance costs.

[0019] In one of the embodiments, the specific steps of introducing an adaptive control strategy to enable the control system to automatically adjust control parameters according to the real-time combustion state and environmental changes to achieve the best combustion efficiency and minimum NOx emissions are as follows: S41. Model the combustion system, including the fuel supply system and the oxygen supply system, and identify the key parameters of the system, including fuel characteristics and the dynamic characteristics of gas and air flow rates; S42. Select an adaptive controller, including one of the adaptive model predictive control (AMPC) and the adaptive proportional-integral-derivative (PID) controller, and design the structure and parameters of the controller according to the system characteristics and control requirements; S43. Develop an adjustment strategy for the control parameters to enable the controller to automatically adjust the control parameters according to the real-time combustion state and environmental changes, design an adaptive rule or algorithm, and automatically adjust the control parameters according to the real-time combustion efficiency and NOx emission indicators; S44. Monitor the key parameters during the combustion process, including temperature, pressure, fuel flow rate, air flow rate, and NOx emissions, collect the monitoring data in real time, and transmit it to the control system for processing; S45. According to the real-time monitoring data, use the adaptive controller to adjust the fuel supply and air supply to achieve the best combustion efficiency and minimum NOx emissions. The controller automatically updates the control parameters according to the preset adjustment strategy to adapt to the changes during the combustion process; S46. Regularly evaluate the performance of the adaptive control system, including combustion efficiency, NOx emissions, and control stability, and adjust the parameters and adjustment strategy of the controller according to the evaluation results to optimize the system performance; S47. Continuously collect and analyze experimental data, continuously optimize the adaptive control system, and continuously improve the control strategy and algorithm to improve the performance and adaptability of the system. Conduct a detailed model of the combustion system, including the fuel supply system and the oxygen supply system, to ensure a comprehensive understanding of the system. Identify the key parameters of the system, such as fuel characteristics and the dynamic characteristics of gas and air flow rates, to provide a basis for formulating the control strategy. Accurate system modeling and key parameter identification provide a solid foundation for the design of the controller and the formulation of the control strategy, ensuring the accuracy and reliability of the control effect. According to the system characteristics and control requirements, select one of the adaptive model predictive control (AMPC) or the adaptive proportional-integral-derivative (PID) controller. According to the system modeling results, design the structure and parameters of the controller to ensure that the controller can make adaptive adjustments according to the system state and environmental changes. The selection and design of the adaptive controller enable the control system to make flexible adjustments according to the real-time combustion state and environmental changes, improving the adaptability and stability of the system. Develop an adjustment strategy for the control parameters to enable the controller to automatically adjust the control parameters according to the real-time combustion state and environmental changes. Design an adaptive rule or algorithm to automatically adjust the control parameters according to the real-time combustion efficiency and NOx emission indicators to achieve the best control effect. The adjustment strategy of the control parameters enables the control system to make adaptive adjustments according to the real-time data, improving the response speed and control accuracy of the system.Monitor key parameters during the combustion process, including temperature, pressure, fuel flow rate, air flow rate, and NOx emissions. Collect monitoring data in real time and transmit it to the control system for processing, providing real-time information for adjusting control strategies. Real-time monitoring and data processing ensure that the control system can obtain the latest combustion status information, providing a real-time basis for adjusting control strategies. According to the real-time monitoring data, use an adaptive controller to adjust the fuel supply and air supply. The controller automatically updates the control parameters according to the preset adjustment strategy to adapt to changes during the combustion process, achieving the best combustion efficiency and the minimum NOx emissions. Adaptive control adjustment enables the control system to make flexible adjustments according to the actual situation, realizing the optimal control of combustion efficiency and NOx emissions. Regularly evaluate the performance of the adaptive control system, including combustion efficiency, NOx emissions, and control stability. According to the evaluation results, adjust the parameters and adjustment strategies of the controller to optimize the system performance and achieve continuous improvement. System performance evaluation and optimization ensure that the control system always remains in the best state, improving the system stability and control effect. Continuously collect and analyze experimental data to provide data support for the continuous optimization of the control system. According to the data analysis results, continuously improve the control strategies and algorithms to improve the system performance and adaptability. Continuous data collection and system improvement enable the control system to continuously adapt to new environments and requirements, maintaining the technological advancement and competitiveness.

[0020] In one of the embodiments, the specific steps for controlling the gas supply amount and air supply amount through an intelligent control algorithm are as follows: S51. Define the state space of the system, including gas flow rate, air flow rate, combustion temperature, and NOx emission concentration, and discretize the state space so that the intelligent algorithm can process it; S52. Define the action space of the system, including adjusting the opening degree of the gas valve and the fan efficiency, and discretize or continuousize the action space to meet the requirements of different control algorithms; S53. Design a reward function to evaluate the performance and behavior of the system. The reward function should include combustion efficiency, NOx emission concentration, and gas utilization rate. The reward function can motivate the system to learn and take actions to reduce NOx emissions; S54. Use a deep neural network to construct a reinforcement learning model, including the structure of the neural network, activation function, and loss function, and design and implement a reinforcement learning algorithm, including one of the deep Q-network, policy gradient method, and actor-critic; S55. Through interaction with the environment, train the intelligent control system so that it can select the optimal action space according to the state in the state space to minimize NOx emissions; S56. Establish a dynamic model of the combustion process, including state equations and output equations, use the established model to predict the state and output of the system in the next period of time, optimize the control problem, and obtain the optimal control sequence by solving the optimization problem so that the system state can reach the expected goal and minimize NOx emissions; S57. Real-time control and optimization, apply the trained deep reinforcement learning model or model predictive control model to the real-time control system, monitor the system state in real time, and dynamically adjust the gas supply amount and air supply amount according to the control actions output by the intelligent control algorithm to minimize NOx emissions. Select the gas flow rate, air flow rate, combustion temperature, and NOx emission concentration as the state variables of the system. Discretize the continuous state space to make it applicable to the reinforcement learning algorithm. The discretized state space allows the intelligent algorithm to process the system state in a more efficient way, facilitating decision-making and learning. Determine to adjust the opening degree of the gas valve and the fan efficiency as the action variables of the system. Discretize or continuousize the action space according to the requirements of the control algorithm. The appropriate definition of the action space allows the control system to select appropriate actions according to the current state to optimize the combustion process. The reward function comprehensively considers the combustion efficiency, NOx emission concentration, and gas utilization rate. The design of the reward function aims to motivate the system to learn and take actions to reduce NOx emissions. Through the reward function, the reinforcement learning algorithm can learn how to adjust the control actions to achieve the goal of reducing NOx emissions. Use a deep neural network to construct a reinforcement learning model, including the design of the network structure, activation function, and loss function. Select a suitable algorithm from the deep Q-network (DQN), policy gradient method (PG), and actor-critic. The deep reinforcement learning model can learn the optimal control strategy through interaction with the environment and improve the performance of the system. Through interaction with the environment, train the intelligent control system to learn the optimal control strategy.The training objective is to minimize NOx emissions. The trained intelligent control system can select the optimal control action according to the current state, effectively reducing NOx emissions. A dynamic model of the combustion process including state equations and output equations is constructed. The model is used to predict the state and output of the system over a period of time in the future. The optimal control sequence is obtained by solving the optimization problem to minimize NOx emissions. Model predictive control can perform advance control based on the prediction of future states, further improving the performance and stability of the system. The trained deep reinforcement learning model or model predictive control model is applied to the real-time control system. The system state is monitored in real time, and the gas supply and air supply are dynamically adjusted according to the control actions output by the intelligent control algorithm. Through real-time control and optimization, the goal of minimizing NOx emissions is achieved. Real-time control and optimization ensure the performance optimization of the system in actual operation and the minimization of NOx emissions, improving the real-time response ability and environmental friendliness of the system. Description of the Drawings

[0021] Figure 1 is a perspective view of the intelligent FIR ultra-low nitrogen staged burner;

[0022] Figure 2 is a front view of the intelligent FIR ultra-low nitrogen staged burner;

[0023] Figure 3 is a first exploded view of the intelligent FIR ultra-low nitrogen staged burner;

[0024] Figure 4 is a second exploded view of the intelligent FIR ultra-low nitrogen staged burner;

[0025] Figure 5 is a first perspective view of the first part structure of the intelligent FIR ultra-low nitrogen staged burner;

[0026] Figure 6 is a second perspective view of the first part structure of the intelligent FIR ultra-low nitrogen staged burner;

[0027] Figure 7 is a perspective view of the second part structure of the intelligent FIR ultra-low nitrogen staged burner;

[0028] Figure 8 is a perspective view of the gas housing assembly;

[0029] Figure 9 is a perspective view of the central sleeve assembly;

[0030] Figure 10 is a perspective view of the central gas assembly;

[0031] Figure 11 is a sectional view of the central gas assembly;

[0032] Figure 12 Is a perspective view of a gas pipe assembly;

[0033] Figure 13 Is a sectional view of a gas pipe assembly;

[0034] Figure 14 Is a perspective view of a fan assembly.

[0035] Among them, the correspondence between the reference numerals and the component names is as follows:

[0036] 1 Inner sleeve assembly, 11 Inner sleeve of the cavity, 12 Inner sleeve, 13 Outer conical ring, 101 Air outlet channel, 102 Air inlet, 103 Air outlet, 104 First gas port;

[0037] 2 Gas housing assembly, 21 Outer sleeve of the cavity, 22 Furnace wall mounting flange, 23 Cavity flange, 201 Buffer cavity, 202 Second gas port, 203 First opening, 204 Second opening;

[0038] 3 Central sleeve assembly, 31 Central sleeve, 32 Positioning assembly, 33 Support plate, 34 Snap ring, 301 Air passing cavity;

[0039] 4 Central gas assembly, 41 Gas main pipe, 42 Connecting pipe, 43 Nozzle, 44 Inner sleeve, 45 Flame plate, 401 Gas injection hole, 402 Air passing port;

[0040] 5 Gas guide pipe;

[0041] 6 Gas pipe assembly, 61 Inlet pipe, 62 Sleeve support, 63 Mixing sleeve;

[0042] 7 Fan assembly, 71 Fan housing, 72 Fan, 73 Air inlet assembly, 701 Fan inlet, 702 Fan outlet;

[0043] 8 Outer sleeve;

[0044] 9 Gas leading pipe. Specific embodiments

[0045] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0046] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0047] The intelligent FIR ultra-low nitrogen staged combustor according to some embodiments of the present invention will be described below with reference to the accompanying drawings.

[0048] Embodiment 1

[0049] As Figures 1 to 14 shown, this embodiment discloses an intelligent FIR ultra-low nitrogen staged combustor. The intelligent FIR ultra-low nitrogen staged combustor includes: an inner sleeve assembly 1, the inner sleeve assembly 1 is provided with an air outlet passage 101, and air inlets 102 and air outlets 103 communicating with the air outlet passage 101 are respectively opened at both ends of the inner sleeve assembly 1. A first gas port 104 is opened on the side wall of the air outlet passage 101; a gas housing assembly 2, the gas housing assembly 2 is provided with a buffer chamber 201, and a first opening 203 and a second opening 204 communicating with the buffer chamber 201 are opened at both ends of the gas housing assembly 2. The inner sleeve assembly 1 sequentially passes through the first opening 203, the buffer chamber 201 and the second opening 204. A second gas port 202 communicating with the buffer chamber 201 is opened on one side of the gas housing assembly 2 close to the air outlet 103. A gas chamber is formed by enclosing between the inner wall of the gas housing assembly 2 and the outer wall of the inner sleeve assembly 1; a central sleeve assembly 3, the central sleeve assembly 3 is provided with a through air passage 301, the central sleeve assembly 3 is arranged on the inner sleeve assembly 1, a part of the central sleeve assembly 3 is located in the air outlet passage 101, and another part of the central sleeve assembly 3 extends outside the air outlet passage 101 after passing through the air outlet 103. An air passage gap is formed between the central sleeve assembly 3 and the hole wall of the air outlet 103; a central gas assembly 4, the central gas assembly 4 is arranged on the central sleeve assembly 3 and is located in the air passage 301, and one end of the central gas assembly 4 is adjacent to the outlet of the air passage 301; a gas guide pipe 5, one end of the gas guide pipe 5 is communicated with the first gas port 104, and the other end of the gas guide pipe 5 extends to the outlet of the air passage 301; a gas pipe assembly 6, the gas pipe assembly 6 is arranged on the gas housing assembly 2 and is communicated with the second gas port 202, the number of the gas pipe assemblies 6 is multiple, and the multiple gas pipe assemblies 6 surround the central sleeve assembly 3 and are sequentially arranged at intervals; a fan assembly 7, the fan assembly 7 is provided with a fan inlet 701 and a fan outlet 702, and the fan outlet 702 is communicated with the air inlet 102.

[0050] The present application discloses an intelligent FIR ultra-low nitrogen staged combustor. The inner casing assembly 1 effectively introduces external air and discharges combustion products by providing an air inlet 102 and an air outlet 103, ensuring the normal air supply of the combustor and enhancing the mixing effect of gas and air. The gas housing assembly 2 is provided with a buffer chamber 201 and a second gas port 202, which can relieve the impact of the gas flow and ensure the uniform distribution of gas before entering the combustion area, thereby improving the combustion efficiency. The central casing assembly 3 effectively controls the air supply volume through the through air passage 301, enabling air to enter the combustion area evenly and promoting the complete combustion process. The central gas assembly 4 is located within the air passage 301, enabling the gas to be fully mixed with air before entering the combustion area, achieving efficient utilization of gas and low nitrogen oxide emissions. The gas guide pipe 5 ensures the accurate entry of gas into the combustion area by guiding the gas flow, avoiding incomplete combustion and emission problems during the combustion process. A plurality of gas pipe assemblies 6 are arranged around the central casing assembly 3. Through reasonable layout and spacing, the uniform supply of gas is ensured, improving the stability and reliability of the combustor. The fan assembly 7 introduces external air through the air inlet 102, ensuring the oxygen supply required during the combustion process, promoting the combustion efficiency and complete combustion of combustion products, and simultaneously reducing the generation of nitrogen oxides. The reasonable design and layout of these components enable the combustor to achieve an efficient combustion process, reduce nitrogen oxide emissions, and meet the goals of environmental protection and energy efficiency.

[0051] As Figure 3 and Figure 4 shown, in addition to the features of the above embodiments, this embodiment further defines that: the inner casing assembly 1 includes an inner casing 11 within the cavity, an inner casing 12, and an outer conical ring 13. The inner casing 11 within the cavity, the inner casing 12, and the outer conical ring 13 are sequentially connected to form an air outlet passage 101. An air inlet 102 is provided at one end of the inner casing 11 within the cavity away from the inner casing 12, and an air outlet 103 is provided at one end of the outer conical ring 13 away from the inner casing 12. A first gas port 104 is provided on the side wall of the inner casing 11 within the cavity. The formation of the air outlet passage 101 by the connection of the inner casing 11 within the cavity, the inner casing 12, and the outer conical ring 13 ensures the continuity and smoothness of the air outlet passage, enabling the combustion products to be discharged smoothly, which is beneficial to the stable operation of the combustor and the improvement of combustion efficiency. The settings of the air inlet 102 and the air outlet 103 enable gas and air to enter and exit the inner casing assembly 1 smoothly, effectively maintaining the gas flow required during the combustion process, and contributing to the normal operation and combustion efficiency improvement of the combustor. The shape of the outer conical ring 13 and the structural design of the inner casing 12 help to guide the combustion products to be discharged smoothly, while ensuring the stability of the air outlet 103 and the uniformity of fluid dynamics, which is beneficial to the normal operation of the combustor and the improvement of combustion efficiency.

[0052] As Figure 8As shown, in addition to the features of the above embodiments, this embodiment further defines that: the gas housing assembly 2 includes an outer cavity sleeve 21, a furnace wall mounting flange 22, and a cavity flange 23. The two ends of the outer cavity sleeve 21 are respectively connected to the furnace wall mounting flange 22 and the cavity flange 23. The furnace wall mounting flange 22 and the cavity flange 23 are respectively connected to the inner sleeve assembly 1. A buffer cavity 201 is formed inside the outer cavity sleeve 21. The cavity flange 23 is provided with a first opening 203. The cavity flange 23 is provided with a second gas port 202 surrounding the first opening 203. The furnace wall mounting flange 22 is provided with a second opening 204. The connection of the outer cavity sleeve 21 with the furnace wall mounting flange 22 and the cavity flange 23 is stable and reliable, ensuring the overall structural stability of the gas housing assembly 2, which is beneficial to the installation and use of the burner. The formation of the buffer cavity 201 helps to slow down the flow rate of the gas in the burner, reduce the turbulence degree of the air flow, thereby reducing the noise and vibration during the operation of the burner, and improving the stability and reliability of the burner.

[0053] As Figure 9 shown, in addition to the features of the above embodiments, this embodiment further defines that: the central sleeve assembly 3 includes a central sleeve 31, a positioning assembly 32, a support plate 33, and a retaining ring 34. A part of the central sleeve 31 is located inside the air outlet passage 101, and another part of the central sleeve 31 extends outside the air outlet passage 101 through the air outlet 103. An air passing gap is formed between the central sleeve 31 and the hole wall of the air outlet 103. The central sleeve 31 is provided with an air passing cavity 301. One end of the positioning assembly 32 is connected to the central sleeve 31, and the other end of the positioning assembly 32 is connected to the inner sleeve assembly 1. The number of the support plates 33 is multiple. Both sides of the multiple support plates 33 are respectively connected to the central sleeve 31 and the inner sleeve assembly 1. The retaining ring 34 is arranged on the central sleeve 31 and is located inside the air passing cavity 301. The gas guiding pipe 5 is clamped at the retaining ring 34. The setting of the air passing cavity 301 enables part of the gas to enter the outside of the air outlet passage 101 through the central sleeve assembly 3, which helps to form an appropriate air passing effect, adjust the flow rate and temperature distribution of the air flow inside and outside the air outlet passage, thereby improving the thermal efficiency and stability of the burner. The air passing gap formed between the central sleeve 31 and the hole wall of the air outlet 103 helps to adjust the air flow distribution inside and outside the air outlet passage, reduce the turbulence degree of the air flow, optimize the gas mixing and combustion process, and improve the combustion efficiency and stability. The setting of the positioning assembly 32 and the support plates 33 enables the central sleeve assembly 3 to be firmly fixed on the inner sleeve assembly 1, ensuring the stability and sealing performance of the gas flow passage, and effectively avoiding potential safety hazards and performance degradation caused by component loosening or misalignment. The retaining ring 34 can limit the gas guiding pipe 5.

[0054] As Figure 10 and Figure 11As shown, in addition to the features of the above embodiments, this embodiment further defines that the central gas component 4 includes a gas main pipe 41, a connecting pipe 42, a nozzle 43, an inner sleeve 44, and a flame plate 45. The connecting pipe 42 is arranged on the gas main pipe 41, the inner sleeve 44 is arranged on the connecting pipe 42, the nozzle 43 is sleeved on the inner sleeve 44, a plurality of gas spray holes 401 are formed on the side wall of the nozzle 43, the flame plate 45 is arranged on the nozzle 43, a plurality of air inlets 402 are formed on the flame plate 45, the flame plate 45 is adjacent to the outlet of the air inlet cavity 301, and the gas main pipe 41 is used to communicate with a gas source. The gas main pipe 41 and the connecting pipe 42 form the main channel for gas transportation, ensuring the stable flow of gas from the gas pipe component 6 to the nozzle 43, which helps to improve the uniformity and stability of the gas, thereby improving the combustion efficiency. The nozzle 43 evenly sprays the gas onto the flame plate 45 through a plurality of gas spray holes 401, enabling the gas to be fully mixed and effectively burned with air, thus generating a stable flame and improving the combustion efficiency and heat energy utilization rate. The plurality of air inlets 402 formed on the flame plate 45 help to adjust the ratio of gas to air, control the oxygen supply for combustion, and make the flame form at an appropriate position and size, ensuring the stability and high efficiency of the combustion process.

[0055] As Figure 12 and Figure 13 As shown, in addition to the features of the above embodiments, this embodiment further defines that the gas pipe component 6 includes an intake pipe 61, a sleeve support 62, and a mixing sleeve 63. The intake pipe 61 is arranged on the gas housing component 2 and communicates with the second gas port 202. The number of sleeve supports 62 is multiple, and the multiple sleeve supports 62 are arranged at intervals on the intake pipe 61 and are located at one end far from the gas housing component 2. The mixing sleeve 63 is arranged on the multiple sleeve supports 62. The intake pipe 61 serves as the channel for gas transportation, ensuring the stable flow of gas from the gas housing component 2 to the mixing sleeve 63, which helps to improve the uniformity and stability of the gas, thereby improving the combustion efficiency. The arrangement of the sleeve supports 62 provides support and positioning for the mixing sleeve 63, ensuring the stable installation and positioning of the mixing sleeve 63, avoiding displacement or deformation caused by vibration or external forces, and ensuring the uniformity and stability of gas mixing. The mixing sleeve 63 realizes the mixing of gas and air through connection with the sleeve supports 62, ensuring the uniformity and stability of the mixed gas, thereby effectively promoting the combustion process and improving the combustion efficiency and combustion stability.

[0056] As Figure 14As shown, in addition to the features of the above embodiments, this embodiment further defines that: the fan assembly 7 includes a fan housing 71, a fan 72, and an air inlet assembly 73. The fan 72 and the air inlet assembly 73 are both arranged on the fan housing 71. A fan outlet 702 is formed on the fan housing 71, and a fan inlet 701 is formed on the fan housing 71. The fan housing 71 serves as the external protective shell of the fan, which can effectively prevent external impurities and dust from entering the interior of the fan, protect the normal operation of the fan, and through the reasonable setting of the fan outlet 702 and the fan inlet 701, can effectively guide the air flow and improve the working efficiency of the fan. The fan 72 is the core component of the fan assembly. Through the operation of the fan, it can effectively generate an air flow, suck air into the fan inlet 701, and then send it out from the fan outlet 702, thereby forming a necessary air flow environment and providing the required air flow conditions for the combustion process. The air inlet assembly 73 is an important part of the fan assembly. Its reasonable design can effectively guide and regulate the air flow rate and velocity entering the fan, ensure that the fan can obtain sufficient fresh air, and thus ensure the smooth progress of the combustion process.

[0057] As Figure 1 and Figure 2 shown, in addition to the features of the above embodiments, this embodiment further defines that: it further includes an outer sleeve 8. The outer sleeve 8 is arranged on the gas housing assembly 2 and surrounds a plurality of gas pipe assemblies 6. The provision of the outer sleeve 8 provides an additional protective layer, which can effectively protect the gas pipe assemblies 6 from the influence of the external environment, such as corrosion or mechanical damage, etc. This helps to extend the service life of the gas pipe assemblies and reduce the maintenance cost. The outer sleeve 8 can retain to a certain extent the heat generated by the gas pipe assemblies 6 and reduce energy loss. At the same time, the outer sleeve 8 can also promote the heat conduction between the gas pipe assemblies 6 and the surrounding environment, which helps to maintain the stable temperature of the combustion system and improve the combustion efficiency. The outer sleeve 8, as a surrounding structure, enhances the structural stability of the entire gas housing assembly 2 and reduces the possibility of deformation or damage of the burner caused by vibration or other external forces during operation.

[0058] As Figure 3 and Figure 4 shown, in addition to the features of the above embodiments, this embodiment further defines that: it further includes an air guiding pipe 9. One end of the air guiding pipe 9 is communicated with the fan assembly 7, and the other end of the air guiding pipe 9 penetrates through the gas housing assembly 2 and extends to the gas pipe assemblies 6. By introducing an appropriate amount of induced air, the gas distribution inside the gas pipe assemblies 6 and the uniformity of the combustion process can be effectively improved, the instability of the flame and the combustion noise can be reduced, and the stability and reliability of the combustion can be improved.

[0059] In addition to the features of the above embodiments, this embodiment further defines that: it further includes a gas regulating valve, one end of the gas regulating valve is used to communicate with an external gas source, the other end of the gas regulating valve is communicated with the buffer chamber 201, and the gas regulating valve is used to regulate the gas flow rate flowing through; a wind valve, the wind valve is arranged on the fan assembly 7, the wind valve is arranged at the fan inlet 701, and the wind valve is used to regulate the opening degree of the fan inlet 701; a sensing assembly, the number of the sensing assemblies is multiple, and the multiple sensing assemblies are respectively located at the inner sleeve assembly 1, the gas housing assembly 2 and the central sleeve assembly 3, and the multiple sensing assemblies are used to collect combustion process data; a control system, the control system is electrically connected to the gas regulating valve, the wind valve, the multiple sensing assemblies and the fan assembly 7 respectively. The setting of the gas regulating valve allows for precise regulation of the gas flow rate flowing through the gas housing assembly 2, adjusts the gas flow rate and pressure as needed, realizes the accurate supply of gas, and thus ensures the stability and efficiency of the combustion process. The setting of the wind valve can regulate and control the air volume at the fan inlet 701, adjust the rotational speed and air volume of the fan according to the combustion demand, ensure sufficient air flow enters the combustion chamber, and realize the full mixing and stability of the combustion process. The multiple sensing assemblies are located at the inner sleeve assembly 1, the gas housing assembly 2 and the central sleeve assembly 3, and are used to monitor parameters such as temperature, pressure, and flow rate during the combustion process in real time and collect relevant data. These data can be used to analyze the state and performance of the combustion process and help optimize the working conditions of the combustion system. The control system realizes the intelligent control and optimized regulation of the gas flow rate, air volume, and combustion process by being electrically connected to the gas regulating valve, the wind valve, the sensing assembly, and the fan assembly 7. According to the data collected by the sensing assembly, the control system can adjust the gas and air volume in real time to maintain the stability and high efficiency of the combustion process. The setting of the control system enables the operator to perform remote monitoring and control through electrical connection, realizes the remote operation and regulation of the burner, and improves the convenience of operation and the efficiency of human-machine interaction.

[0060] Embodiment 2

[0061] This embodiment discloses a control method for an intelligent FIR ultra-low nitrogen staged burner.

[0062] S1. Train a control model through deep learning technology to enable it to better understand the complexity of the combustion process, thereby optimizing the control strategy to minimize NOx emissions to the greatest extent;

[0063] S2. Utilize data processing technology to analyze the monitoring data in real time, and timely adjust the gas supply volume and air supply volume through the adjustment parameters output by the control model to ensure that the combustion process is always in the best state;

[0064] S3. Utilize model prediction technology to predict in advance the problems that may occur during the combustion process and take corresponding control measures to minimize NOx emissions;

[0065] S4. Introduce an adaptive control strategy to enable the control system to automatically adjust control parameters according to the real-time combustion state and environmental changes, so as to achieve the best combustion efficiency and the minimum NOx emissions;

[0066] S5. Control the gas supply and air supply through intelligent control algorithms.

[0067] The deep learning model can process a large amount of complex data and learn the complex patterns and rules of the combustion process from it. Through deep learning, the control model can predict the combustion effects under different operating conditions and optimize the control strategy accordingly. This means that the system can automatically adjust the ratio of gas and air, as well as other relevant parameters, to achieve the best combustion effect. The deep learning model can predict which operating conditions are most likely to cause an increase in NOx emissions and adjust the control strategy accordingly, thereby minimizing these emissions to the greatest extent. By real-time analyzing the monitoring data, the system can immediately understand the current state of the combustion process, including key parameters such as temperature, pressure, and oxygen concentration. If the system detects any deviation from the optimal combustion conditions, it can immediately adjust the gas and air supply amounts through the adjustment parameters output by the control model to ensure that the combustion process always remains in the best state. This immediate feedback and adjustment mechanism helps to improve the combustion efficiency and ensure the stability of the combustion process. The model prediction technology allows the system to predict in advance possible problems in the combustion process, such as unstable combustion and increased NOx emissions. Once the system predicts a possible problem, it can take control measures in advance, such as adjusting the ratio of gas and air, or fine-tuning the combustion conditions, thereby preventing the problem from actually occurring. By foreseeing and taking actions in advance, this strategy helps to reduce emergencies caused by unstable combustion or other problems, thereby improving the reliability of the system. The adaptive control strategy enables the control system to automatically adjust control parameters according to the real-time combustion state and environmental changes. This means that regardless of how the combustion conditions change, the system can maintain the best performance. Since the adaptive control strategy can continuously optimize the control parameters according to the actual situation, it helps to achieve the best combustion efficiency and the minimum NOx emissions. Adaptive control reduces the dependence on manual operations and adjustments, making the combustion process more automated and intelligent. The intelligent control algorithms can accurately calculate and control the gas supply and air supply amounts to ensure that they are mixed in the best ratio, thereby achieving efficient combustion. These algorithms are usually designed to be very sensitive and can quickly respond to any changes to ensure that the combustion process always remains in the best state. By precisely controlling the gas supply and air supply amounts, this strategy helps to reduce unnecessary waste and improve the energy utilization efficiency.

[0068] Embodiment 3

[0069] In addition to the features of the above embodiments, this embodiment further defines the following specific steps for training a control model through deep learning technology to enable it to better understand the complexity of the combustion process, thereby optimizing the control strategy to minimize NOx emissions:

[0070] S11. Collect combustion process data, including gas supply volume, air supply volume, combustion temperature, and NOx emission concentration, and preprocess the data, including denoising, normalization, and feature engineering, to prepare a dataset for deep learning model training;

[0071] S12. Select a suitable deep learning model according to the complexity of the combustion process and data characteristics, including one of multi-layer perceptron, convolutional neural network, and recurrent neural network, and design the structure of the deep learning model, including the number of network layers, the number of neurons in each layer, and the activation function;

[0072] S13. Divide the prepared dataset into a training set, a validation set, and a test set, use the training set to train the deep learning model, continuously adjust the model parameters through the backpropagation algorithm to minimize the loss function, use the validation set to monitor the performance of the model, and make adjustments according to the performance of the validation set to prevent overfitting;

[0073] S14. Use the test set to evaluate the performance of the trained model, including accuracy, recall rate, and F1 score, and optimize the model according to the evaluation results, including adjusting the network structure, modifying hyperparameters, and increasing regularization;

[0074] S15. Apply the trained deep learning model to the control system as part of the controller, and optimize the control strategy according to the prediction results of the model and real-time sensor data, and dynamically adjust the gas supply volume and air supply volume to minimize NOx emissions;

[0075] S16. Real-time monitor the key parameters during the combustion process and adjust the control parameters according to the output of the deep learning model.

[0076] By collecting key data such as gas supply, air supply, combustion temperature, NOx emission concentration, etc., comprehensive information about the combustion process was obtained. Pretreatment steps such as denoising, normalization, and feature engineering ensured the quality and consistency of the data, providing a reliable dataset for subsequent deep learning model training. The preprocessed dataset improved the training efficiency and accuracy of the model, laying a solid foundation for subsequent steps. According to the complexity of the combustion process and data characteristics, appropriate deep learning models were selected, such as multi-layer perceptron MLP, convolutional neural network CNN, and recurrent neural network RNN. The number of network layers, the number of neurons in each layer, and the activation function were reasonably designed to ensure that the model could effectively capture the complex patterns of the combustion process. The appropriate model structure and parameter selection enabled the model to better understand and predict the combustion process, providing strong support for subsequent control strategy optimization. The preprocessed dataset was divided into a training set, a validation set, and a test set to ensure that the model could perform well under various data distributions. The model was trained using the training set, and the model parameters were continuously adjusted through the backpropagation algorithm to minimize the loss function. Performance monitoring and adjustment: The performance of the model was monitored using the validation set, and adjustments were made according to the performance of the validation set to prevent overfitting and improve the generalization ability of the model. The reasonable dataset division and model training strategy enabled the model to accurately fit the training data and perform well on unknown data. The performance of the trained model was evaluated using the test set, including metrics such as accuracy, recall, and F1 score. The model was optimized according to the evaluation results, including adjusting the network structure, modifying hyperparameters, and adding regularization, etc., to further improve the performance of the model. The model evaluation and optimization steps ensured the accuracy and reliability of the model in practical applications, providing high-quality model support for subsequent control system applications. The trained deep learning model was integrated into the control system as part of the controller. According to the prediction results of the model and real-time sensor data, the control strategy was optimized to dynamically adjust the gas supply and air supply. The application of the deep learning model enabled the control system to more accurately predict and respond to changes in the combustion process, thereby more effectively reducing NOx emissions and improving combustion efficiency. Key parameters in the combustion process were monitored in real time to ensure that the combustion process was always in the best state. According to the output of the deep learning model and real-time monitoring data, the control parameters were dynamically adjusted to ensure the stability and efficiency of the combustion process. The real-time monitoring and parameter adjustment steps enabled the control system to flexibly adjust the control strategy according to the actual situation, further improving the stability of the combustion process and the reduction effect of NOx emissions.

[0077] Example 4

[0078] In addition to the features of the above embodiments, this embodiment further defines the following specific steps for using data processing technology to analyze monitoring data in real time and timely adjust the gas supply and air supply through the adjustment parameters output by the control model to ensure that the combustion process is always in the best state:

[0079] S21. Set up a data acquisition system to collect the monitoring data collected by sensors in real time, including temperature, pressure, oxygen concentration, gas flow rate, and air flow rate, and ensure that the data acquisition system can transmit data to the data processing system quickly and accurately;

[0080] S22. Preprocess the collected data, including removing noise, calibrating sensors, data interpolation, and filling missing values, to ensure the quality and reliability of the data, and perform data cleaning to delete outliers and unreasonable data;

[0081] S23. Use real-time data processing technology to perform real-time analysis on the collected data, and perform real-time monitoring and analysis on the real-time data to identify abnormal situations and change trends during the combustion process;

[0082] S24. Based on historical data and real-time monitoring data, timely adjust the gas supply and air supply through the adjustment parameters output by the control model;

[0083] S25. Monitor the combustion process status and performance indicators after real-time adjustment, including combustion efficiency and NOx emission concentration. According to the real-time feedback results, continuously optimize the adjustment strategy to keep the combustion process in the best state at all times and achieve the goals of low emissions and high efficiency. By setting up a data acquisition system, it is possible to collect data such as temperature, pressure, oxygen concentration, gas flow rate, and air flow rate monitored by sensors in real time. Ensure that the data can be transmitted to the data processing system quickly and accurately, providing a basis for subsequent real-time analysis and control. Real-time and accurate data provides key information for the monitoring and control of the combustion process, enabling the control system to respond quickly to changes. The preprocessing steps include removing noise and calibrating sensors to ensure the accuracy and consistency of the data. Data interpolation and filling: Interpolate or fill in missing data to improve data integrity. Delete outliers and unreasonable data to ensure data quality. The preprocessed data is more reliable and accurate, providing high-quality input for subsequent data analysis and control model training. Use real-time data processing technology to analyze the collected data immediately. Real-time monitoring and analysis can identify abnormal situations and change trends in the combustion process, providing a basis for adjusting the control strategy. Real-time analysis enables the control system to detect and address problems in the combustion process in a timely manner, improving the stability and efficiency of combustion. Based on historical data and real-time monitoring data, the control model can output corresponding adjustment parameters. According to the model output, adjust the gas supply and air supply in a timely manner to optimize the combustion process. The control system can adjust the gas and air supply more precisely, thereby optimizing combustion efficiency and reducing NOx emissions. Monitor the combustion process status and performance indicators after real-time adjustment, such as combustion efficiency and NOx emission concentration. According to the real-time feedback results, continuously adjust and optimize the control strategy to keep the combustion process in the best state at all times. The real-time feedback mechanism enables the control system to optimize according to the actual combustion effect, achieve the goals of low emissions and high efficiency, and improve the economy and environmental protection of the entire combustion process.

[0084] Example 5

[0085] In addition to the features of the above embodiments, this embodiment further defines: Using model prediction technology, predict in advance the possible problems that may occur in the combustion process and take corresponding control measures. The specific steps to minimize NOx emissions are as follows:

[0086] S31. Collect data on historical combustion processes, including combustion temperature, pressure, fuel supply, air supply, and NOx emissions. Preprocess the data, including data cleaning, removing outliers, and interpolation processing, to ensure the accuracy and integrity of the data;

[0087] S32. Select an appropriate model prediction technique, including one of recurrent neural network (RNN), long short-term memory network (LSTM), and support vector regression (SVR). Use historical data to train the model so that it can learn the dynamic change rules of the combustion process and can predict possible problems in the future.

[0088] S33. Select appropriate features from historical data, including temperature gradient, fuel-air ratio, and combustion efficiency, as input features of the model. Construct the input-output structure of the model to ensure that the model can accurately predict possible problems in the combustion process, including incomplete combustion and too high combustion temperature.

[0089] S34. Use historical data to train the model, and use validation data to verify the accuracy and generalization ability of the model. Use techniques such as cross-validation to evaluate the performance of the model to ensure that the model can accurately predict the combustion process under different conditions.

[0090] S35. Use the trained model to make predictions on possible problems in the combustion process, including a decrease in combustion efficiency and an increase in NOx emissions. Develop corresponding control strategies based on the prediction results, and adjust the fuel supply and air supply to minimize NOx emissions and avoid the occurrence of combustion problems.

[0091] S36. Monitor key parameters in the combustion process in real time, including temperature, pressure, fuel flow rate, and air flow rate. Input the monitored data into the model for real-time prediction, and adjust the control strategy in a timely manner according to the prediction results to ensure the stability and low emissions of the combustion process.

[0092] S37. Continuously optimize and update the model, use new data and experimental results to update the model parameters to improve the prediction ability and accuracy of the model. Combine the actual operation situation to continuously optimize the control strategy to minimize NOx emissions and ensure the efficient and stable operation of the combustion process.

[0093] S38. Establish a fault detection system to monitor abnormal conditions and faults in the combustion process. Predict potential faults based on real-time data analysis and the output of the prediction model, and take preventive measures in a timely manner to ensure the stability and reliability of the combustion process. By collecting data on historical combustion processes, information on the long-term behavior of the combustion process can be obtained.

[0094] Data cleaning, outlier removal, and interpolation ensure the accuracy and integrity of the data, providing a reliable basis for subsequent model training. An accurate and complete dataset provides a solid foundation for model training, improving the model's prediction ability and stability. Model prediction techniques such as Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and Support Vector Regression (SVR) can learn the dynamic change rules of the combustion process and predict potential problems that may occur in the future. Appropriate model prediction techniques can accurately capture the dynamic characteristics of the combustion process, thus effectively predicting potential problems and providing strong support for the formulation of control strategies. The selection of features such as temperature gradient, fuel-air ratio, and combustion efficiency enables the model to more accurately predict potential problems during the combustion process. Ensure that the model can accurately process input features and output predictions regarding potential problems. A reasonable input-output structure enables the model to predict key features of the combustion process, improving the accuracy and practicality of the prediction. Using historical data for model training enables the model to learn the dynamic behavior of the combustion process. Evaluate the accuracy and generalization ability of the model through validation data to ensure that the model can exhibit good prediction performance under different conditions. The model training and validation processes ensure the effectiveness and reliability of the model, providing a solid foundation for subsequent practical applications. Use the trained model to predict potential problems during the combustion process, such as a decrease in combustion efficiency and an increase in NOx emissions. Develop corresponding control strategies based on the prediction results, adjusting the fuel supply and air supply to minimize NOx emissions and avoid combustion problems. The formulation of prediction and control strategies enables the control system to predict and address potential problems in advance, thereby reducing NOx emissions and ensuring the stability of the combustion process. Real-time monitor key parameters during the combustion process to ensure the timeliness and accuracy of the data. Input the real-time monitoring data into the model for real-time prediction and adjust the control strategy in a timely manner according to the prediction results to ensure the stability and low emissions of the combustion process. Real-time monitoring and adjustment enable the control system to flexibly adjust the control strategy according to the actual situation, further improving the stability of the combustion process and the reduction effect of NOx emissions. Continuously optimize the model parameters using new data and experimental results to improve the model's prediction ability and accuracy. Combine the actual operating conditions and continuously optimize the control strategy to minimize NOx emissions and ensure the efficient and stable operation of the combustion process. Model optimization and update ensure the continuous improvement and adaptability of the control system, enabling it to cope with various actual operating conditions and requirements. Establish a fault detection system to real-time monitor abnormal conditions and faults during the combustion process. Based on real-time data analysis and the output of the prediction model, predict potential faults and take preventive measures in a timely manner to ensure the stability and reliability of the combustion process. The fault detection and prevention system improve the reliability and safety of the combustion process, reducing the likelihood of faults, thereby reducing operation interruptions and maintenance costs.

[0095] Example 6

[0096] In addition to the features of the above embodiments, this embodiment further defines the following specific steps for introducing an adaptive control strategy to enable the control system to automatically adjust control parameters according to the real-time combustion state and environmental changes, so as to achieve the best combustion efficiency and the minimum NOx emissions:

[0097] S41. Model the combustion system, including the fuel supply system and the oxygen supply system, and identify the key parameters of the system, including fuel characteristics and the dynamic characteristics of gas and air flow rates;

[0098] S42. Select an adaptive controller, including one of the adaptive model predictive control (AMPC) and the adaptive proportional-integral-derivative (PID) controller, and design the structure and parameters of the controller according to the system characteristics and control requirements;

[0099] S43. Develop an adjustment strategy for control parameters to enable the controller to automatically adjust control parameters according to the real-time combustion state and environmental changes, and design an adaptive rule or algorithm to automatically adjust control parameters according to the real-time combustion efficiency and NOx emission indicators;

[0100] S44. Monitor the key parameters during the combustion process, including temperature, pressure, fuel flow rate, air flow rate, and NOx emissions, collect the monitoring data in real time, and transmit it to the control system for processing;

[0101] S45. According to the real-time monitoring data, use the adaptive controller to adjust the fuel supply amount and air supply amount to achieve the best combustion efficiency and the minimum NOx emissions. The controller automatically updates the control parameters according to the preset adjustment strategy to adapt to the changes during the combustion process;

[0102] S46. Regularly evaluate the performance of the adaptive control system, including combustion efficiency, NOx emissions, and control stability, and adjust the parameters and adjustment strategy of the controller according to the evaluation results to optimize the system performance;

[0103] S47. Continuously collect and analyze experimental data, continuously optimize the adaptive control system, and continuously improve control strategies and algorithms to enhance the system's performance and adaptability. Conduct a detailed modeling of the combustion system, including the fuel supply system and the oxygen supply system, to ensure a comprehensive understanding of the system. Identify the key parameters of the system, such as fuel characteristics and the dynamic characteristics of gas and air flow rates, to provide a basis for formulating control strategies. Accurate system modeling and key parameter identification provide a solid foundation for the design of the controller and the formulation of control strategies, ensuring the accuracy and reliability of the control effect. According to the system characteristics and control requirements, select one of the adaptive model predictive control (AMPC) or the adaptive proportional-integral-derivative (PID) controller. Based on the system modeling results, design the structure and parameters of the controller to ensure that the controller can adaptively adjust according to the system state and environmental changes. The selection and design of the adaptive controller enable the control system to flexibly adjust according to the real-time combustion state and environmental changes, enhancing the system's adaptability and stability. Formulate an adjustment strategy for control parameters so that the controller can automatically adjust the control parameters according to the real-time combustion state and environmental changes. Design an adaptive rule or algorithm to automatically adjust the control parameters according to the real-time combustion efficiency and NOx emission indicators to achieve the best control effect. The adjustment strategy for control parameters enables the control system to adaptively adjust according to real-time data, improving the system's response speed and control accuracy. Monitor the key parameters during the combustion process, including temperature, pressure, fuel flow rate, air flow rate, and NOx emissions. Collect the monitoring data in real time and transmit it to the control system for processing to provide real-time information for adjusting the control strategy. Real-time monitoring and data processing ensure that the control system can obtain the latest combustion state information, providing a real-time basis for adjusting the control strategy. According to the real-time monitoring data, use the adaptive controller to adjust the fuel supply amount and air supply amount. The controller automatically updates the control parameters according to the preset adjustment strategy to adapt to the changes during the combustion process and achieve the best combustion efficiency and minimum NOx emissions. Adaptive control adjustment enables the control system to flexibly adjust according to the actual situation, achieving optimized control of combustion efficiency and NOx emissions. Regularly evaluate the performance of the adaptive control system, including combustion efficiency, NOx emissions, and control stability. According to the evaluation results, adjust the parameters and adjustment strategy of the controller to optimize the system performance and achieve continuous improvement. System performance evaluation and optimization ensure that the control system always remains in the best state, enhancing the system's stability and control effect. Continuously collect and analyze experimental data to provide data support for the continuous optimization of the control system. According to the data analysis results, continuously improve control strategies and algorithms to enhance the system's performance and adaptability. Continuous data collection and system improvement enable the control system to continuously adapt to new environments and requirements, maintaining the technological advancement and competitiveness.

[0104] Example 7

[0105] In addition to the features of the above embodiments, this embodiment further defines the specific steps for controlling the gas supply amount and the air supply amount through an intelligent control algorithm as follows:

[0106] S51. Define the state space of the system, including gas flow rate, air flow rate, combustion temperature, and NOx emission concentration, and discretize the state space so that the intelligent algorithm can process it;

[0107] S52. Define the action space of the system, including adjusting the opening degree of the gas valve and the fan efficiency, and discretize or continuousize the action space to meet the requirements of different control algorithms;

[0108] S53. Design a reward function to evaluate the performance and behavior of the system. The reward function should include combustion efficiency, NOx emission concentration, and gas utilization rate, and the reward function can motivate the system to learn and take actions to reduce NOx emissions;

[0109] S54. Use a deep neural network to construct a reinforcement learning model, including the structure, activation function, and loss function of the neural network, and design and implement a reinforcement learning algorithm, including one of the deep Q network, policy gradient method, and actor-critic;

[0110] S55. Through interaction with the environment, train the intelligent control system so that it can select the optimal action space according to the state in the state space to minimize NOx emissions;

[0111] S56. Establish a dynamic model of the combustion process, including state equations and output equations, use the established model to predict the state and output of the system in the future for a period of time, optimize the control problem, and obtain the optimal control sequence by solving the optimization problem so that the system state can reach the expected goal and minimize NOx emissions;

[0112] S57. Real-time control and optimization. Apply the trained deep reinforcement learning model or model predictive control model to the real-time control system, monitor the system state in real time, and dynamically adjust the gas supply amount and the air supply amount according to the control actions output by the intelligent control algorithm to minimize NOx emissions.

[0113] Select the gas flow rate, air flow rate, combustion temperature, and NOx emission concentration as the state variables of the system. Discretize the continuous state space to make it suitable for reinforcement learning algorithms. The discretized state space allows intelligent algorithms to process system states in a more efficient manner, facilitating decision-making and learning. Determine the adjustment of the gas valve opening and fan efficiency as the action variables of the system. Discretize or make the action space continuous according to the requirements of the control algorithm. A suitable definition of the action space allows the control system to select appropriate actions based on the current state to optimize the combustion process. The reward function comprehensively considers combustion efficiency, NOx emission concentration, and gas utilization rate. The design of the reward function aims to motivate the system to learn and take actions to reduce NOx emissions. Through the reward function, the reinforcement learning algorithm can learn how to adjust control actions to achieve the goal of reducing NOx emissions. Use a deep neural network to construct a reinforcement learning model, including the design of the network structure, activation function, and loss function. Select a suitable algorithm from the deep Q-network DQN, policy gradient method PG, and actor-critic Actor-Critic. The deep reinforcement learning model can learn the optimal control strategy through interaction with the environment, improving the performance of the system. Through interaction with the environment, train the intelligent control system to learn the optimal control strategy. The training objective is to minimize NOx emissions as much as possible. The trained intelligent control system can select the optimal control action based on the current state, effectively reducing NOx emissions. Construct a dynamic model of the combustion process including state equations and output equations. Use the model to predict the state and output of the system over a period of time in the future. Obtain the optimal control sequence by solving the optimization problem to minimize NOx emissions. Model predictive control can perform predictive control based on the prediction of future states, further improving the performance and stability of the system. Apply the trained deep reinforcement learning model or model predictive control model to the real-time control system. Monitor the system state in real time and dynamically adjust the gas supply and air supply according to the control actions output by the intelligent control algorithm. Through real-time control and optimization, achieve the goal of minimizing NOx emissions. Real-time control and optimization ensure the performance optimization of the system in actual operation and the minimization of NOx emissions, improving the real-time response ability and environmental friendliness of the system.

[0114] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in this specification.

[0115] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. An intelligent FIR ultra-low nitrogen staged burner, characterized in that: The intelligent FIR ultra-low nitrogen staged burner comprises: An inner sleeve assembly (1), the inner sleeve assembly (1) being provided with an air outlet channel (101), an air inlet (102) and an air outlet (103) being respectively provided at two ends of the inner sleeve assembly (1) and being communicated with the air outlet channel (101), and a first gas outlet (104) being provided on a side wall of the air outlet channel (101); A gas housing component (2), the gas housing component (2) being provided with a buffer cavity (201), a first opening (203) and a second opening (204) being provided at both ends of the gas housing component (2), the inner sleeve component (1) sequentially passing through the first opening (203), the buffer cavity (201) and the second opening (204), a second gas port (202) being provided on a side of the gas housing component (2) close to the air outlet (103) and being provided with the buffer cavity (201), and the inner wall of the gas housing component (2) and the outer wall of the inner sleeve component (1) enclosing a gas cavity; A central sleeve assembly (3), wherein the central sleeve assembly (3) is provided with a through air passage cavity (301), the central sleeve assembly (3) is arranged on the inner sleeve assembly (1), a portion of the central sleeve assembly (3) is located in the air outlet channel (101), another portion of the central sleeve assembly (3) passes through the air outlet (103) and extends outside the air outlet channel (101), and an air passage gap is formed between the central sleeve assembly (3) and the hole wall of the air outlet (103); a central gas component (4), the central gas component (4) being arranged on the central sleeve component (3) and located in the air passage cavity (301), one end of the central gas component (4) being adjacent to an outlet of the air passage cavity (301); a gas guide pipe (5), one end of the gas guide pipe (5) being in communication with the first gas port (104), and the other end of the gas guide pipe (5) extending to the outlet of the air passage cavity (301); a gas pipe assembly (6), the gas pipe assembly (6) being arranged on the gas housing assembly (2) and communicating with the second gas port (202), the gas pipe assembly (6) being provided in a plurality, the plurality of gas pipe assemblies (6) surrounding the central sleeve assembly (3) and being arranged in sequence and spaced apart; A fan assembly (7), wherein the fan assembly (7) is provided with a fan inlet (701) and a fan outlet (702), and the fan outlet (702) is connected to the air inlet (102).

2. The intelligent FIR ultra-low nitrogen staged burner according to claim 1 is characterized in that: The inner sleeve assembly (1) comprises an inner sleeve (11), an inner sleeve (12) and an outer cone ring (13); the inner sleeve (11), the inner sleeve (12) and the outer cone ring (13) are connected in sequence to form the air outlet channel (101); an air inlet (102) is provided at one end of the inner sleeve (11) away from the inner sleeve (12); an air outlet (103) is provided at one end of the outer cone ring (13) away from the inner sleeve (12); and the first gas outlet (104) is provided on the side wall of the inner sleeve (11).

3. The intelligent FIR ultra-low nitrogen staged burner according to claim 1 is characterized in that: The gas shell assembly (2) comprises a cavity outer sleeve (21), a furnace wall mounting flange (22) and a cavity flange (23); two ends of the cavity outer sleeve (21) are respectively connected to the furnace wall mounting flange (22) and the cavity flange (23); the furnace wall mounting flange (22) and the cavity flange (23) are respectively connected to the inner sleeve assembly (1); a buffer cavity (201) is formed inside the cavity outer sleeve (21); the cavity flange (23) is provided with the first opening (203); the cavity flange (23) is provided with the second gas port (202) surrounding the first opening (203); and the furnace wall mounting flange (22) is provided with the second opening (204).

4. The intelligent FIR ultra-low nitrogen staged burner according to claim 1 is characterized in that: The central sleeve assembly (3) comprises a central sleeve (31), a positioning assembly (32), a support plate (33) and a clamping ring (34); a portion of the central sleeve (31) is located in the air outlet channel (101); another portion of the central sleeve (31) extends to the outside of the air outlet channel (101) through the air outlet (103); a wind gap is formed between the central sleeve (31) and the hole wall of the air outlet (103); the central sleeve (31) is provided with a wind cavity (301); One end of the positioning assembly (32) is connected to the central sleeve (31), and the other end of the positioning assembly (32) is connected to the inner sleeve assembly (1). There are a plurality of support plates (33), and two sides of the plurality of support plates (33) are respectively connected to the central sleeve (31) and the inner sleeve assembly (1). The clamping ring (34) is arranged on the central sleeve (31) and is located in the air passage cavity (301), and the gas guide pipe (5) is clamped at the clamping ring (34).

5. The intelligent FIR ultra-low nitrogen staged burner according to claim 1 is characterized in that: The central gas assembly (4) comprises a gas main pipe (41), a connecting pipe (42), a nozzle (43), an inner sleeve (44) and a flame disc (45); the connecting pipe (42) is arranged on the gas main pipe (41); the inner sleeve (44) is arranged on the connecting pipe (42); the nozzle (43) is sleeved on the inner sleeve (44); a plurality of gas spray holes (401) are provided on the side wall of the nozzle (43); the flame disc (45) is arranged on the nozzle (43); a plurality of air outlets (402) are provided on the flame disc (45); the flame disc (45) is adjacent to the outlet of the air passage cavity (301); and the gas main pipe (41) is used to communicate with a gas source; and / or the gas pipe assembly (6) comprises an air intake pipe (61), a sleeve support (62) and a mixing sleeve (63); the air intake pipe (61) is arranged on the gas housing assembly (2) and is in communication with the second gas port (202); there are a plurality of sleeve supports (62); the plurality of sleeve supports (62) are arranged at intervals on the air intake pipe (61) and are located at an end away from the gas housing assembly (2); and the mixing sleeve (63) is arranged on the plurality of sleeve supports (62); And / or the fan assembly (7) comprises a fan housing (71), a fan (72) and an air inlet assembly (73), the fan (72) and the air inlet assembly (73) are both arranged on the fan housing (71), the fan housing (71) is provided with the fan outlet (702), and the fan housing (71) is provided with the fan inlet (701).

6. The intelligent FIR ultra-low nitrogen staged burner according to claim 1 is characterized in that: It also comprises an outer sleeve (8), wherein the outer sleeve (8) is arranged on the gas housing assembly (2) and surrounds the plurality of gas pipe assemblies (6); And / or also includes an air bleed pipe (9), one end of which is connected to the fan assembly (7), and the other end of which passes through the gas housing assembly (2) and extends to the gas pipe assembly (6).

7. The intelligent FIR ultra-low nitrogen staged burner according to claim 1 is characterized in that: It also includes a gas regulating valve, one end of which is used to communicate with an external gas source, and the other end of which is connected to the buffer chamber (201), and the gas regulating valve is used to adjust the flow rate of the gas flowing through; an air valve, the air valve being arranged on the fan assembly (7), the air valve being arranged at the fan inlet (701), and the air valve being used to adjust the opening of the fan inlet (701); A sensor assembly, wherein the number of the sensor assemblies is multiple, and the multiple sensor assemblies are respectively located at the inner sleeve assembly (1), the gas shell assembly (2) and the central sleeve assembly (3), and the multiple sensor assemblies are used to collect combustion process data; A control system, wherein the control system is electrically connected to the gas regulating valve, the air valve, a plurality of the sensor components and the fan component (7) respectively.

8. The intelligent FIR ultra-low nitrogen staged burner according to claim 7 is characterized in that: The control system is used to execute the following procedures: S1. Train the control model through deep learning technology to better understand the complexity of the combustion process, thereby optimizing the control strategy to minimize NOx emissions; S2. Analyze monitoring data in real time using data processing technology, and adjust the gas supply and air supply in time by controlling the adjustment parameters output by the model to ensure that the combustion process is always in the best state; S3. Use model prediction technology to predict possible problems in the combustion process in advance and take corresponding control measures to minimize NOx emissions; S4. Introduce adaptive control strategy to enable the control system to automatically adjust control parameters according to real-time combustion status and environmental changes to achieve optimal combustion efficiency and minimum NOx emissions; S5. Control the gas supply and air supply through intelligent control algorithms.

9. The intelligent FIR ultra-low nitrogen staged burner according to claim 8 is characterized in that: The specific steps of training the control model through deep learning technology to enable it to better understand the complexity of the combustion process and optimize the control strategy to minimize NOx emissions are as follows: S11. Collect combustion process data, including gas supply, air supply, combustion temperature, and NOx emission concentration, and preprocess the data, including denoising, normalization, and feature engineering, to prepare a data set for deep learning model training; S12. According to the complexity of the combustion process and the data characteristics, select a suitable deep learning model, including one of a multilayer perceptron, a convolutional neural network and a recurrent neural network, and design the structure of the deep learning model, including the number of network layers, the number of neurons in each layer and the activation function; S13. Divide the prepared data set into training set, validation set and test set, use the training set to train the deep learning model, continuously adjust the model parameters through the back propagation algorithm to minimize the loss function, use the validation set to monitor the performance of the model, and make adjustments based on the performance of the validation set to prevent overfitting; S14. Use the test set to evaluate the performance of the trained model, including accuracy, recall, and F1 score, and tune the model based on the evaluation results, including adjusting the network structure, modifying hyperparameters, and adding regularization; S15. Apply the trained deep learning model to the control system as part of the controller, optimize the control strategy and dynamically adjust the gas supply and air supply according to the model's prediction results and real-time sensor data to minimize NOx emissions; S16. Monitor key parameters of the combustion process in real time and adjust control parameters based on the output of the deep learning model.

10. The intelligent FIR ultra-low nitrogen staged burner according to claim 8, characterized in that: The specific steps of using data processing technology to analyze monitoring data in real time and timely adjust the gas supply and air supply by controlling the adjustment parameters output by the model to ensure that the combustion process is always in the best state are as follows: S21. Set up a data acquisition system to collect monitoring data collected by sensors in real time, including temperature, pressure, oxygen concentration, gas flow rate and air flow rate, and ensure that the data acquisition system can quickly and accurately transmit data to the data processing system; S22. Preprocess the collected data, including removing noise, calibrating sensors, interpolating data, and filling missing values ​​to ensure the quality and reliability of the data, and perform data cleaning to delete outliers and unreasonable data; S23. Use real-time data processing technology to analyze the collected data in real time, and monitor and analyze the real-time data in real time to identify abnormal conditions and change trends in the combustion process; S24, based on historical data and real-time monitoring data, timely adjust the gas supply and air supply by controlling the adjustment parameters output by the model; S25. Monitor the combustion process status and performance indicators after real-time adjustment, including combustion efficiency and NOx emission concentration. According to the real-time feedback results, continuously optimize the adjustment strategy to keep the combustion process in the best state and achieve the goals of low emissions and high efficiency.

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