Intelligent combustion precise air distribution online monitoring system for boiler

Abstract

The invention relates to the technical field of boiler combustion control, in particular to a boiler intelligent combustion precise air distribution online monitoring system which comprises a combustion state sensing module, a combustion state sensing module, an execution adjusting module, an online monitoring feedback module and a data storage and visualization module. According to the boiler intelligent combustion precise air distribution online monitoring system, through fusion of multi-dimensional combustion state perception and a deep learning algorithm, intelligent self-adaptive optimization of an air distribution scheme is achieved, a traditional manual experience adjustment and single parameter control mode is abandoned, and the intelligent combustion precise air distribution online monitoring system is high in practicability. Key information in the combustion process is comprehensively captured by multiple types of sensors such as infrared thermal imaging and flue gas analysis, air volume, air speed and air temperature adjusting instructions of primary air, secondary air and tertiary air are accurately output through data preprocessing and fusion model operation, and complex working conditions such as fuel type change and load fluctuation can be dynamically adapted; real-time decision making and regulation can be completed without manual intervention, and the intelligent level of boiler combustion control is improved.

Description

Technical Field

[0001] This invention relates to the field of boiler combustion control technology, specifically to an online monitoring system for intelligent combustion and precise air distribution in boilers. Background Technology

[0002] Boilers, as core equipment for energy conversion, are widely used in industrial sectors such as power, chemical, and metallurgy, as well as in residential applications such as heating and steam supply. Combustion efficiency and pollutant emissions are key indicators for boiler operation, and the rationality of the air distribution scheme directly affects the combustion effect: insufficient air distribution leads to incomplete fuel combustion, which not only reduces energy utilization but also increases emissions of pollutants such as CO; excessive air distribution causes heat loss and exacerbates combustion. generate. Existing boiler air distribution control mostly relies on manual experience-based adjustment or traditional PID control methods, which have the following technical drawbacks: First, manual adjustment depends on the operator's experience, resulting in a slow response and difficulty in adapting to dynamic operating conditions such as changes in fuel type and load fluctuations; Second, traditional PID control adjusts air volume based solely on a single oxygen content parameter, ignoring multi-dimensional combustion status information such as temperature distribution and flue gas composition, leading to low air distribution accuracy; Third, the lack of a real-time online monitoring and feedback mechanism makes it impossible to detect combustion anomalies and air distribution deviations in a timely manner, resulting in consistently low combustion efficiency and a high risk of exceeding pollutant emission standards. With increasingly stringent environmental protection requirements and the popularization of energy conservation concepts, there is an urgent need to develop a ventilation system with intelligent decision-making, precise control and real-time monitoring functions to address the shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide an online monitoring system for intelligent combustion and precise air distribution in boilers, in order to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an online monitoring system for intelligent combustion and precise air distribution in boilers, including a combustion status sensing module for real-time acquisition of data on temperature distribution, flue gas composition, oxygen content, furnace pressure, and fuel feed rate within the boiler furnace; The intelligent air distribution decision module communicates with the combustion state sensing module, has a built-in deep learning model, constructs a combustion efficiency-air distribution parameter correlation model based on the collected combustion parameters, and outputs accurate air distribution commands. The execution adjustment module is communicatively connected to the intelligent air distribution decision module and includes several variable frequency fans, adjustable dampers and flow sensors, used to adjust the air volume, air speed and air temperature of each air path according to the air distribution command; The online monitoring and feedback module is communicatively connected to the combustion state sensing module and the execution adjustment module, respectively, to monitor combustion efficiency, pollutant emission concentration and air distribution execution accuracy in real time, and feeds the monitoring data back to the intelligent air distribution decision module to achieve closed-loop optimization. The data storage and visualization module is used to store historical combustion data, air distribution schemes and monitoring results, and displays real-time operating status and trend analysis charts through a visualization interface.

[0005] Preferably, the combustion state sensing module includes: An infrared thermal imaging sensor is used to acquire two-dimensional images of the temperature distribution inside the furnace, with a temperature measurement range of 0-1800℃ and a measurement accuracy of ±1%. Flue gas analysis sensor group, including CO sensor, sensor, Sensors and The sensors all achieve a detection accuracy at the ppm level. Pressure and flow sensors are used to collect data on furnace pressure and fuel feed flow rate, respectively, with a measurement error of ≤±2%.

[0006] Preferably, the intelligent air distribution decision module includes: The data preprocessing unit is used to perform noise reduction, normalization, and outlier removal on the collected raw data. The model training unit, based on a fusion model of BP neural network and genetic algorithm, trains an air distribution parameter optimization model with the objective functions of maximizing combustion efficiency and minimizing pollutant emissions. The real-time decision-making unit, based on the preprocessed combustion state data, calls the trained model to output the air volume ratio and wind speed adjustment instructions for primary, secondary, and tertiary air, with a decision response time of ≤1s.

[0007] Preferably, the execution adjustment module includes: Several variable frequency centrifugal fans are respectively assigned to the primary air, secondary air, and tertiary air supply pipelines. The fan frequency adjustment range is 20-50Hz, and the air volume adjustment accuracy is ±3%. Electric adjustable dampers are installed on each air duct, with an opening range of 0-100° and an adjustment response time of ≤0.5s; The air temperature control unit adjusts the air supply temperature through electric heating or waste heat recovery devices, with an adjustment range of 30-200℃ and a temperature control accuracy of ±5℃.

[0008] Preferably, the online monitoring feedback module includes: The combustion efficiency calculation unit calculates the combustion efficiency in real time based on flue gas composition, oxygen content, and fuel data using the heat balance method, with a calculation accuracy of ≤±1%. The pollutant monitoring unit collects CO and other pollutant data in the flue gas in real time. , Emission concentration data will trigger an alarm when the concentration exceeds a preset threshold. The air distribution accuracy detection unit compares the air distribution command with the actual execution parameters, calculates the adjustment error, and when the error exceeds ±5%, it feeds back to the intelligent air distribution decision module for secondary adjustment.

[0009] Preferably, the intelligent combustion and precise air distribution online monitoring system for boilers also includes a remote control module. The remote control module establishes a connection with each module of the system through 5G, Ethernet or LoRa wireless communication protocols, supports remote parameter setting, real-time status monitoring, air distribution scheme retrieval and historical data backtracking on multiple terminals such as mobile terminals and PC terminals, and has a hierarchical management function for operation permissions, with different permission users corresponding to different operation ranges.

[0010] Preferably, the boiler intelligent combustion precision air distribution online monitoring system also includes a remote control module and a fault diagnosis and early warning module. The fault diagnosis and early warning module analyzes the operating parameters of each module, the stability of sensor data, and the trend of air distribution adjustment error to identify abnormal operating conditions such as sensor failure, actuator jamming, and communication interruption. Based on the fault type, it generates graded early warning signals and pushes corresponding fault troubleshooting suggestions.

[0011] Preferably, the boiler intelligent combustion precision air distribution online monitoring system also includes a remote control module and a fault diagnosis and early warning module. The fault diagnosis and early warning module analyzes the operating parameters of each module, the stability of sensor data, and the trend of air distribution adjustment error to identify abnormal operating conditions such as sensor failure, actuator jamming, and communication interruption. Based on the fault type, it generates graded early warning signals and pushes corresponding fault troubleshooting suggestions.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. This intelligent combustion precision air distribution online monitoring system for boilers achieves intelligent adaptive optimization of the air distribution scheme through the fusion of multi-dimensional combustion state perception and deep learning algorithms. It abandons the traditional manual experience adjustment and single-parameter control mode, and relies on multiple types of sensors such as infrared thermal imaging and flue gas analysis to comprehensively capture key information of the combustion process. After data preprocessing and fusion model calculation, it accurately outputs the air volume, air speed and air temperature adjustment commands for primary air, secondary air and tertiary air. It can dynamically adapt to complex operating conditions such as changes in fuel type and load fluctuations, and can complete real-time decision-making and control without manual intervention. It improves the intelligence level of boiler combustion control, solves the technical pain points of traditional air distribution with lag response and poor adaptability, ensures that the combustion process is always in the optimal state, and provides core support for the efficient operation of the boiler.

[0013] 2. This intelligent combustion precision air distribution online monitoring system for the boiler constructs a closed-loop control system for the entire process, enhancing the environmental performance and stability of the combustion process. The online monitoring and feedback module tracks combustion efficiency, pollutant emission concentration, and air distribution accuracy in real time. It accurately assesses combustion performance using the heat balance method and promptly triggers alarms and secondary adjustments for abnormal situations such as excessive pollutants and air distribution deviations. This suppresses pollutant generation caused by incomplete combustion and excessive air distribution, while avoiding equipment operation risks caused by abnormal combustion conditions. It meets increasingly stringent environmental requirements and ensures the long-term stable operation of the boiler.

[0014] 3. This intelligent combustion precision air distribution online monitoring system for boilers can be flexibly adapted to various combustion equipment such as power plant boilers, industrial boilers, and civil boilers. It is compatible with multiple fuel types and can be installed and deployed without large-scale modifications to the boiler body. The data storage and visualization module displays real-time operating status, historical trends, and report data through an intuitive interface. With the remote control function, it supports multi-terminal access and remote operation and maintenance, which greatly reduces the workload of operators. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0019] Please see Figure 1 This invention provides a technical solution: an online monitoring system for intelligent combustion and precise air distribution in boilers, comprising: 1. Combustion status sensing module, used to collect real-time data on temperature distribution, flue gas composition, oxygen content, furnace pressure and fuel feed rate in the boiler furnace; Key parameters in the boiler combustion process are comprehensively captured by multiple types of sensors: infrared thermal imaging sensors capture the flame morphology and temperature distribution inside the furnace in real time, generating a two-dimensional temperature field image to provide a basis for judging combustion uniformity; the flue gas analysis sensor group simultaneously detects the flue gas at the furnace outlet and inside the flue. CO , Concentration accurately reflects combustion completeness and pollutant generation; pressure and flow sensors respectively collect furnace pressure and fuel feed flow to ensure the air distribution scheme matches the fuel load. All sensor data are sampled at a frequency ≥10Hz to ensure real-time and continuous data.

[0020] Sensor deployment and calibration: Infrared thermal imaging sensors are evenly installed around the furnace (6 for power plant boilers and 4 for industrial boilers). Flue gas analysis sensor groups are set at the furnace outlet and the middle section of the flue. Pressure sensors are placed in key areas of the furnace inner wall, and flow sensors are connected in series with the fuel feed pipeline. After installation, the sensors are calibrated using a standard signal source to ensure that the initial error of the sensors meets the requirements (temperature ±1%, pressure ≤ ±2%, flow ≤ ±2%). Multi-parameter synchronous acquisition: Start the sensor acquisition program, set the sampling frequency to ≥10Hz, and synchronously acquire the following data: Infrared thermal imaging sensors capture temperature distribution data inside the furnace and generate a two-dimensional pixel temperature matrix. , For horizontal pixel indexing, Vertical pixel index; Flue gas analysis sensor group collects CO , The concentration data are denoted as follows: , , , (Unit: ppm); The pressure sensor collects the real-time pressure P (unit: kPa) inside the furnace. The flow sensor collects the instantaneous flow rate of fuel feed. (Unit: kg / s). Preliminary data verification: The validity of the collected raw data is judged, and invalid values ​​that are outside the sensor's measurement range (such as data with a temperature >1800℃ or <0℃) are removed, while valid data is retained for subsequent transmission.

[0021] 2. The intelligent air distribution decision module is connected to the combustion state sensing module. It has a built-in deep learning model and builds a combustion efficiency-air distribution parameter correlation model based on the collected combustion parameters, and outputs accurate air distribution instructions. A three-layer architecture of data preprocessing, model training, and real-time decision-making is adopted: The data preprocessing unit uses the Kalman filter algorithm to reduce noise, employs Z-Score standardization to process data, and combines the 3σ criterion to remove outliers, ensuring data quality; The model training unit builds a basic model based on a BP neural network and uses a genetic algorithm to optimize the initial weights and thresholds of the neural network, solving the problem that BP neural networks are prone to getting trapped in local optima. The objective function is set as: max (combustion efficiency) - λ × min (pollutant emission concentration), where λ is a weight coefficient that can be adjusted according to actual operating conditions; The real-time decision-making unit receives the preprocessed combustion parameters, calls the trained model, and outputs the air volume ratio, air speed, and air temperature adjustment instructions for primary air (used for fuel drying and transportation), secondary air (used for combustion support), and tertiary air (used for supplementary combustion). The decision response time is ≤1s, meeting the requirements of dynamic operating conditions.

[0022] Data preprocessing: Noise reduction: The Kalman filter algorithm is used to reduce noise in time-series data such as pressure and flow rate. The filtering formula is as follows:

[0023] in, The value measured at time k is... The original measurement value at time k. For Kalman gain, Let k be the error covariance. To measure the noise variance, For the observation matrix, It is an identity matrix.

[0024] Normalization: Z-Score standardization is used to normalize all feature parameters, using the following formula: ,in, The normalized value. The original data, This parameter represents the mean of the historical dataset. The standard deviation is denoted as .

[0025] Outlier removal: Outliers are identified based on the 3σ criterion. When this data point is reached, it is marked as an outlier and removed. It is then replaced with linear interpolation of valid data from adjacent time points. The interpolation formula is as follows: ,in, The time corresponding to the outlier. , The time of the valid data adjacent to the outlier.

[0026] Model training: Constructing a fusion model: Based on a BP neural network, the number of nodes in the input layer is set to... (Equal to the number of preprocessed feature parameters, such as 12), the number of hidden layer nodes is set to 20, and the number of output layer nodes is set to 6 (corresponding to the wind volume and wind speed of the primary, secondary, and tertiary winds); the initial weights of the BP neural network are optimized using a genetic algorithm. and threshold The optimization objective is to minimize the model prediction error.

[0027] Define the objective function: With maximizing combustion efficiency and minimizing pollutant emissions as the dual objectives, construct a weighted objective function: ,in, Combustion efficiency (%) This is a weighting coefficient (with a value range of 0.1-0.5, which can be adjusted according to environmental protection requirements and energy-saving needs). The formula for calculating the overall pollutant concentration is as follows: , , , These are pollutant weighting coefficients, set according to national emission standards, such as... =0.5、 , =0.2.

[0028] Model training iteration: Historical combustion data (including characteristic parameters and optimal air distribution parameters under different fuel types and load conditions) are divided into a training set (80%) and a validation set (20%), which are then input into the fusion model for training. The genetic algorithm population size is set to 50, and the number of iterations is set to 100. Training is stopped when the validation set error is ≤3%, and the optimal model parameters are saved.

[0029] Real-time decision-making: Data input: Input the preprocessed real-time combustion parameters into the trained model; Air distribution parameter calculation: The model outputs the primary air volume based on the input data. Secondary air volume Three winds wind speed Primary wind speed Primary wind speed Target value for wind temperature regulation Decision response time ≤ 1s; Command output: Convert the calculated air distribution parameters into control commands (such as fan frequency, damper opening, etc.) that can be recognized by the control module.

[0030] 3. The execution adjustment module is communicatively connected to the intelligent air distribution decision module, including several variable frequency fans, adjustable dampers and flow sensors, used to adjust the air volume, air speed and air temperature of each air path according to the air distribution command; The intelligent air distribution decision module precisely executes air distribution operations: the variable frequency centrifugal fan adjusts its speed by regulating the power supply frequency to achieve continuous air volume regulation, with a frequency regulation range of 20-50Hz and a corresponding air volume regulation range of 0-100%, with an adjustment accuracy of ±3%; the electric adjustable damper is driven by a stepper motor, with an opening adjustment range of 0-100° and a response time of ≤0.5s, which can quickly adjust the air path resistance; the air temperature regulation unit adjusts the air supply temperature according to combustion requirements by using an electric heating device to increase the air supply temperature or using the waste heat recovery device at the tail of the boiler to preheat the cold air, with an adjustment range of 30-200℃ and a temperature control accuracy of ±5℃, ensuring that the air supply temperature is compatible with the combustion state.

[0031] Command parsing: Receives control commands output by the intelligent air distribution decision module and parses them to obtain the target air volume for each air path. Target wind speed Target wind temperature .

[0032] Air volume regulation (based on variable frequency fan) Calculate the target frequency of the fan based on the target air volume. The following formula is used to fit the performance curve of the wind turbine: ,in, This refers to the actual air volume. For the frequency of the fan, For wind resistance loss, , , This is the inherent coefficient of the wind turbine (calibrated experimentally); it is obtained by reverse calculation using this formula. The frequency range is 20-50Hz.

[0033] Start the frequency converter and adjust the fan frequency to... Real-time acquisition of actual air volume from flow sensors Calculate the adjustment error ,when Repeat the frequency adjustment steps described above until the error meets the requirements. Wind speed regulation (based on electrically adjustable damper) Calculate the target opening of the damper based on the target wind speed. The formula relating wind speed and damper opening is as follows: ,in This refers to the actual wind speed. This represents the maximum wind speed along the wind path. For the damper opening, The flow resistance coefficient (calibrated experimentally); obtained by reverse calculation Adjustable range: 0-100°.

[0034] Control the stepper motor to drive the damper to adjust to The response time is ≤0.5s, and the actual wind speed is fed back by the wind speed sensor. If the error exceeds the limit, a second fine-tuning is performed.

[0035] Air temperature control If the target wind temperature Current air supply temperature Start the electric heating device, heating power Calculate using the following formula: ,in The specific heat capacity of air ( ), The density of air is 1.2 kg / m³. The efficiency of the electric heating device is taken as 0.95.

[0036] If the target wind temperature Current air supply temperature Switch to waste heat recovery mode, adjust the opening of waste heat recovery valve, and reduce the supply air temperature through waste heat exchange; monitor the feedback data from the air temperature sensor in real time to ensure temperature control accuracy of ±5℃. Status feedback: The actual operating parameters of each actuator (fan frequency, damper opening, actual air temperature, etc.) are fed back to the online monitoring feedback module.

[0037] IV. The online monitoring and feedback module is connected to the combustion state sensing module and the execution adjustment module respectively, and monitors the combustion efficiency, pollutant emission concentration and air distribution execution accuracy in real time. The monitoring data is fed back to the intelligent air distribution decision module to achieve closed-loop optimization. The combustion efficiency calculation unit calculates combustion efficiency using the heat balance method based on flue gas composition, oxygen content, and fuel calorific value data. The pollutant monitoring unit tracks CO and other pollutants in real time. , When the emission concentration exceeds the national emission standard or preset threshold, the operator is alerted by an audible and visual alarm, and the information is automatically fed back to the intelligent air distribution decision module. The air distribution accuracy detection unit compares the air distribution command with the actual parameters collected by the flow sensor and pressure sensor, calculates the adjustment error, and triggers a secondary adjustment command when the error exceeds ±5% to ensure air distribution accuracy.

[0038] Combustion efficiency calculation Collect fuel calorific value (Unit: kJ / kg, pre-input via fuel analysis or real-time detection), flue gas temperature Excessive smoke exhaust air coefficient (based on Concentration calculation: .

[0039] Combustion efficiency is calculated using the heat balance method, and the formula is: ,in (Total heat input from fuel) The total heat loss is calculated as follows: In the formula: Flue gas heat loss: , For the specific heat capacity of flue gas, For the density of the flue gas, For smoke emission volume, Ambient temperature; Heat loss from incomplete combustion (12636 is the heat of combustion of CO in kJ / kg); Heat loss from incomplete combustion of solids (32860 is the heat of combustion of carbon, kJ / kg) As fuel ash, (This refers to the unburned carbon content in the ash); the accuracy of combustion efficiency calculation is ≤ ±1%.

[0040] Pollutant monitoring and alarm Real-time reading of monitoring data from the flue gas analysis sensor array , and preset threshold (Based on national environmental protection standards) A comparison was made.

[0041] When the concentration of any pollutant meets When the alarm is triggered, an audible and visual alarm is activated, and the alarm time, pollutant type, and concentration value are recorded. The alarm signal is then fed back to the intelligent air distribution decision module.

[0042] Air distribution accuracy testing The actual parameters fed back by the collection and execution adjustment module are collected. ) and the target parameters of the intelligent air distribution decision module ( Compare these figures and calculate the adjustment errors for each item:

[0043] when or or When the air distribution reaches 5%, a secondary adjustment command is generated and fed back to the intelligent air distribution decision module to initiate a new round of air distribution optimization.

[0044] V. Data storage and visualization module, used to store historical combustion data, air distribution schemes and monitoring results, and to display real-time operating status and trend analysis charts through a visualization interface.

[0045] The system uses an industrial database to store historical combustion data, air distribution schemes, monitoring results, and other information. The storage capacity is expandable and supports data backtracking and trend analysis. The visualization interface is developed based on the web and displays real-time data such as furnace temperature distribution, flue gas composition, combustion efficiency, pollutant emission concentration, and air distribution parameters in the form of charts. It supports historical data query, trend comparison, and report generation, making it easy for operators to understand the boiler's operating status and providing data support for operation and maintenance management.

[0046] Data classification and storage Establish a data storage structure, which is divided into four categories: real-time data table, historical data table, alarm data table, and air distribution scheme table; The real-time data table stores current combustion parameters, air distribution parameters, and monitoring results at a frequency of 10Hz, and the data is retained for 24 hours. Historical data tables are stored using time sharding, summarizing data hourly (average, maximum, and minimum values) and storing it in an InfluxDB database. The retention period is configurable (default 1 year). The storage formula (using combustion efficiency as an example) is as follows:

[0047] in, The number of sampling points in 1 hour ( =36000), For the first Combustion efficiency at each sampling point.

[0048] The alarm data table stores the alarm times. Pollutant type, alarm concentration Information such as processing status; The air distribution scheme table stores the optimal air distribution parameters and corresponding combustion effect data under different operating conditions.

[0049] Data visualization processing Real-time data display: Real-time trend charts are generated through a web interface, using line graphs to show changes in parameters such as combustion efficiency, pollutant concentration, and air volume, and using heat maps to show furnace temperature distribution, based on an infrared thermal imaging temperature matrix. ; Historical data query: Supports querying historical data by time range (hour / day / week / month), generating trend comparison charts, and calculating statistical indicators (such as average combustion efficiency, average pollutant emissions). Statistical formulas:

[0050] in, To query the number of hours within a time range, The average combustion efficiency in the h-th hour. The average pollutant concentration is given in hour h.

[0051] Report generation: Automatically generates daily / weekly / monthly operation reports, including key parameter statistics, alarm records, and suggestions for optimizing air distribution schemes.

[0052] In addition, the system is equipped with a remote control module, which enables remote access via 5G or Ethernet. Operators can set parameters, monitor status, and diagnose faults on mobile devices or PCs, improving the convenience of operation and maintenance.

[0053] In addition, the system is equipped with a remote control module, which enables remote access via 5G or Ethernet. Operators can set parameters, monitor status, and diagnose faults on mobile devices or PCs, improving the convenience of operation and maintenance.

[0054] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart combustion and precise air distribution online monitoring system for boilers, characterized in that: It includes a combustion status sensing module, which is used to collect data on temperature distribution, flue gas composition, oxygen content, furnace pressure and fuel feed rate in the boiler furnace in real time; The intelligent air distribution decision module communicates with the combustion state sensing module, has a built-in deep learning model, constructs a combustion efficiency-air distribution parameter correlation model based on the collected combustion parameters, and outputs accurate air distribution commands. The execution adjustment module is communicatively connected to the intelligent air distribution decision module and includes several variable frequency fans, adjustable dampers and flow sensors, used to adjust the air volume, air speed and air temperature of each air path according to the air distribution command; The online monitoring and feedback module is communicatively connected to the combustion state sensing module and the execution adjustment module, respectively, to monitor combustion efficiency, pollutant emission concentration and air distribution execution accuracy in real time, and feeds the monitoring data back to the intelligent air distribution decision module to achieve closed-loop optimization. The data storage and visualization module is used to store historical combustion data, air distribution schemes and monitoring results, and displays real-time operating status and trend analysis charts through a visualization interface.

2. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 1, characterized in that: The combustion state sensing module includes: An infrared thermal imaging sensor is used to acquire two-dimensional images of the temperature distribution inside the furnace, with a temperature measurement range of 0-1800℃ and a measurement accuracy of ±1%. Flue gas analysis sensor group, including CO sensor, sensor, Sensors and The sensors all achieve a detection accuracy at the ppm level. Pressure and flow sensors are used to collect data on furnace pressure and fuel feed flow rate, respectively, with a measurement error of ≤±2%.

3. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 1, characterized in that: The intelligent air distribution decision module includes: The data preprocessing unit is used to perform noise reduction, normalization, and outlier removal on the collected raw data. The model training unit, based on a fusion model of BP neural network and genetic algorithm, trains an air distribution parameter optimization model with the objective functions of maximizing combustion efficiency and minimizing pollutant emissions. The real-time decision-making unit, based on the preprocessed combustion state data, calls the trained model to output the air volume ratio and wind speed adjustment instructions for primary, secondary, and tertiary air, with a decision response time of ≤1s.

4. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 1, characterized in that: The execution adjustment module includes: Several variable frequency centrifugal fans are respectively assigned to the primary air, secondary air, and tertiary air supply pipelines. The fan frequency adjustment range is 20-50Hz, and the air volume adjustment accuracy is ±3%. Electric adjustable dampers are installed on each air duct, with an opening range of 0-100° and an adjustment response time of ≤0.5s; The air temperature control unit adjusts the air supply temperature through electric heating or waste heat recovery devices, with an adjustment range of 30-200℃ and a temperature control accuracy of ±5℃.

5. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 1, characterized in that: The online monitoring feedback module includes: The combustion efficiency calculation unit calculates the combustion efficiency in real time based on flue gas composition, oxygen content, and fuel data using the heat balance method, with a calculation accuracy of ≤±1%. The pollutant monitoring unit collects CO and other pollutant data in the flue gas in real time. , Emission concentration data will trigger an alarm when the concentration exceeds a preset threshold. The air distribution accuracy detection unit compares the air distribution command with the actual execution parameters, calculates the adjustment error, and when the error exceeds ±5%, it feeds back to the intelligent air distribution decision module for secondary adjustment.

6. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 1, characterized in that: The intelligent combustion and precise air distribution online monitoring system for boilers also includes a remote control module. The remote control module establishes a connection with each module of the system through 5G, Ethernet or LoRa wireless communication protocols, supports remote parameter setting, real-time status monitoring, air distribution scheme retrieval and historical data backtracking on multiple terminals such as mobile terminals and PC terminals, and has a hierarchical management function for operation permissions, with different permissions corresponding to different operation ranges.

7. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 1, characterized in that: The intelligent combustion and precise air distribution online monitoring system for boilers also includes a remote control module and a fault diagnosis and early warning module. The fault diagnosis and early warning module analyzes the operating parameters of each module, the stability of sensor data, and the trend of air distribution adjustment error to identify abnormal operating conditions such as sensor failure, actuator jamming, and communication interruption. Based on the fault type, it generates graded early warning signals and pushes corresponding fault troubleshooting suggestions.

8. The intelligent combustion and precise air distribution online monitoring system for boilers according to claim 2, characterized in that: The combustion state sensing module is also equipped with an adaptive calibration unit, which periodically calls the standard calibration signal to verify the accuracy of each sensor. When the sensor measurement deviation exceeds the preset threshold, the calibration program is automatically started to correct the measurement parameters. For the infrared thermal imaging sensor, the measurement accuracy of the temperature field image is corrected in real time through the preset temperature calibration target point in the furnace, so as to ensure the accuracy of data acquisition during long-term operation.

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