Control method and system for continuously monitoring flue gas of biomass boiler

Abstract

The invention relates to the technical field of boiler control and environmental protection, and discloses a control method for continuously monitoring biomass boiler flue gas, which comprises the following steps: S1, collecting the concentration data of pollutants in the biomass boiler flue gas in real time, the pollutants including carbon monoxide, carbon dioxide, nitrogen oxide and sulfur dioxide; s2, the collected pollutant concentration data are transmitted to a data processing module, the control system for continuously monitoring the biomass boiler flue gas is further provided and used for executing the control method for continuously monitoring the biomass boiler flue gas, and the control system comprises a data collection module, a data processing module and a data processing module, the sensor array is used for collecting temperature, pressure and pollutant concentration data in boiler flue gas in real time through the sensor array. According to the method, real-time data and an optimization algorithm are combined, the pollutant concentration is predicted through Kalman filtering denoising and a multi-factor mathematical model, boiler operation parameters are dynamically adjusted, the energy efficiency is improved, pollutant emission is reduced, and the problems of inaccurate control and slow response in the traditional technology are solved.

Description

Technical Field

[0001] The invention relates to the technical field of boiler control and environmental protection, and in particular to a control method and system for continuously monitoring flue gas from a biomass boiler. Background Art

[0002] At present, biomass boilers are facing more and more technical challenges under the increasingly stringent environmental protection standards and the improvement of energy efficiency requirements. Existing technical solutions usually rely on fixed control strategies and have significant deficiencies in monitoring and regulating pollutant concentrations. Traditional boiler control systems mainly rely on preset parameters set by experience for operation adjustments, lack flexible response mechanisms, and cannot make timely optimizations based on the real-time operating conditions of the boiler. Especially when the boiler load fluctuates, the fuel type changes, and the environmental conditions change, the existing system cannot automatically adapt, which may lead to incomplete combustion, resulting in increased pollutant emissions, and ultimately affecting the energy efficiency and environmental performance of the boiler.

[0003] In addition, existing pollutant concentration prediction methods often rely on simple statistical analysis or a single mathematical model. These models have obvious limitations when dealing with complex combustion processes and cannot accurately predict the changing trend of pollutant concentrations. For example, some traditional monitoring systems may use basic statistical methods to estimate pollutant emissions, but these methods ignore the interaction of multiple factors such as flue gas flow, heat conduction, and combustion reactions, resulting in inaccurate prediction results. Even if the pollutant concentration fluctuates abnormally, the system cannot adjust the working state of the boiler in real time to avoid excessive pollutant emissions.

[0004] In addition, existing pollutant data denoising processing methods have obvious defects. Traditional technologies usually use simple filtering algorithms (such as average filtering or median filtering) to eliminate noise in measurement data. However, the processing effect of these methods is limited, especially when the sensor data noise is large, the filtering effect is often insufficient, resulting in a decrease in the accuracy of pollutant concentration data. Therefore, in actual operation, the influence of data noise may lead to erroneous pollutant concentration monitoring results, further affecting subsequent boiler control decisions.

[0005] In summary, the main problems of traditional technologies are the lack of dynamic adjustment and real-time optimization capabilities, inaccurate prediction of pollutant concentrations, and insufficient data noise removal capabilities. These technical deficiencies make it difficult for existing biomass boiler systems to achieve the expected environmental protection effects and energy efficiency in actual operation. Innovative solutions are urgently needed to make up for these deficiencies and ensure that boiler operation is both efficient and environmentally friendly. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a control method and system for continuously monitoring the flue gas of a biomass boiler, which solves the deficiencies of the prior art in pollutant emission control, real-time optimization and regulation, and data processing accuracy of the biomass boiler.

[0007] To achieve the above objectives, the present invention is implemented by the following technical solutions: A control method for continuously monitoring flue gas from a biomass boiler comprises the following steps: S1. Real-time collection of pollutant concentration data in biomass boiler flue gas, the pollutants including carbon monoxide, carbon dioxide, nitrogen oxides and sulfur dioxide; S2, transmitting the collected pollutant concentration data to the data processing module, performing denoising on the data through the Kalman filter algorithm to obtain accurate pollutant concentration data; S3. Predicting the changing trend of pollutant concentration in the boiler based on real-time data and mathematical models, wherein the mathematical models include flue gas flow, heat conduction, pollutant diffusion, and biomass combustion reaction models; S4, minimizing the pollutant concentration by calculating the objective function, and adjusting the boiler operating parameters according to the minimization result, wherein the operating parameters include oxygen supply, combustion temperature, gasification agent flow rate and fuel input; S5. Based on the adjustment results, real-time feedback is provided to adjust the operating status of the boiler and continuously optimize the combustion process to ensure that pollutant emissions meet environmental protection standards.

[0008] Preferably, the step of collecting pollutant concentration data in the flue gas of the biomass boiler in real time in step S1 is specifically as follows: S1.1. Install a multi-point sensor array at the furnace outlet, the middle section of the flue and the exhaust port of the boiler to monitor the pollutant concentration in the flue gas in real time; S1.2. The data collected by the sensor include information on pollutant concentration, temperature, pressure and flow rate in the flue gas, and the collected data is transmitted to the data processing module in real time through the data transmission interface.

[0009] Preferably, the sensor array in step S1.1 comprises: Fourier Transform Infrared Spectroscopy Sensor for Measuring CO 2 and NOx gas composition and concentration; Laser absorption spectroscopy sensor for measuring CO and SO 2 Gas composition and concentration; Gas chromatographs for precise measurement of pollutant concentrations.

[0010] Preferably, the step of transmitting the collected pollutant concentration data to the data processing module in step S2 is specifically as follows: S2.1, transmitting the data of each sensor to the data processing module via wireless or wired communication; S2.2, performing preliminary verification on the received data in the data processing module; S2.3. Use the Kalman filter algorithm to denoise the received pollutant concentration data, eliminate the noise interference of the sensor, and obtain accurate pollutant concentration data; S2.4. Pass the optimized data to the prediction model module for further analysis.

[0011] Preferably, the step of predicting the change trend of pollutant concentration in the boiler based on real-time data and mathematical models in step S3 is as follows: S3.1. Establish a boiler flue gas flow model based on real-time collected data to describe the flow of flue gas inside the boiler and use a fluid dynamics model to calculate the flow state; S3.2. Calculate the flue gas temperature distribution based on the flue gas flow model and heat conduction equation; S3.3. Based on the pollutant diffusion equation and biomass combustion reaction model, combined with the pollutant concentration and temperature distribution in the flue gas, predict the changing trend of pollutant concentration over time and identify potential risks of excessive pollutant emissions.

[0012] Preferably, the step of minimizing the pollutant concentration by calculating the objective function in step S4 is specifically as follows: S4.1. Define an objective function based on real-time data and prediction results, wherein the objective function is a weighted square sum of pollutant concentrations; S4.2. Calculate optimal boiler control parameters for minimizing pollutant concentrations based on the objective function, including oxygen supply, combustion temperature, gasification agent flow rate, and fuel input; S4.3. Use optimization algorithm to solve the optimal control parameters.

[0013] Preferably, the objective function in step S4.1 is: Among them, C i (t) is the concentration of the i-th pollutant, α i is the weight coefficient of the pollutant, u(t) is the control parameter of the boiler, u optimal is the optimal control parameter, β is the penalty coefficient, and t is the time variable, which is used to represent the change of the objective function at different times. is the square of the pollutant concentration, and J(t) is the objective function value.

[0014] Preferably, the step of adjusting the operating state of the boiler in real time according to the adjustment result in step S5 is specifically as follows: S5.1. According to the optimal operating parameters calculated by the optimization control algorithm, the oxygen supply, combustion temperature, gasification agent flow rate and fuel input of the boiler are adjusted in real time; S5.2. Monitor the actual operating status of the boiler and compare it with the target value, and correct and adjust the parameters in real time to ensure the optimization of the boiler's combustion efficiency and pollutant emissions; S5.3. Feedback sensors are installed at various key locations of the boiler to monitor the temperature and pollutant concentration data during the combustion process in real time to ensure that the boiler continues to operate in the optimal state.

[0015] The present invention also provides a control system for continuously monitoring the flue gas of a biomass boiler, which is used to execute the above-mentioned control method for continuously monitoring the flue gas of a biomass boiler, comprising: The data acquisition module is used to collect temperature, pressure and pollutant concentration data in boiler flue gas in real time through the sensor array; the data processing module is used to remove noise and optimize the collected data and output accurate pollutant concentration data; the prediction model module is used to predict the change trend of pollutant concentration based on real-time data and mathematical models; An optimization control module is used to calculate the objective function and adjust the operating parameters of the boiler according to the pollutant concentration prediction results; The feedback regulation module is used to adjust the operating status of the boiler in real time according to the output results of the optimization control module to ensure the minimization of pollutant emissions and improve the energy efficiency of the boiler.

[0016] The present invention provides a control method and system for continuously monitoring flue gas from a biomass boiler. It has the following beneficial effects: 1. The present invention realizes dynamic feedback adjustment of boiler operating parameters by combining real-time monitoring data with optimization algorithms. The technical solution can automatically adjust the oxygen supply, combustion temperature, gasifier flow rate and fuel input according to real-time pollutant concentration, temperature, pressure and other data, thereby continuously optimizing the combustion process. Compared with the prior art that only relies on preset parameters or simple control systems, the intelligent optimization control system of the present invention can cope with complex factors such as boiler load fluctuations and changes in fuel composition, ensuring that the boiler can maintain the optimal operating state under various operating conditions, thereby greatly improving the energy utilization efficiency of the boiler and significantly reducing pollutant emissions.

[0017] 2. The present invention adopts the Kalman filtering algorithm in the pollutant concentration data processing link to effectively remove data errors caused by sensor noise or environmental interference, so that the real-time data of pollutant concentration is more accurate. This precise data processing method has significant advantages over the simple filtering technology commonly used in the prior art. The prior art mostly relies on traditional average filtering or median filtering. These methods are not ideal when processing large noise. Kalman filtering can make accurate estimates based on noise characteristics when the system changes dynamically, thereby providing more reliable data support for subsequent pollutant concentration prediction and boiler control.

[0018] 3. The present invention realizes the accurate prediction of the change of pollutant concentration in the boiler by integrating the mathematical model of flue gas flow, heat conduction, pollutant diffusion and biomass combustion reaction. The model combines the interaction of multiple factors and takes into account the complex physical and chemical processes inside and outside the boiler. The existing technology usually adopts a relatively simplified static model or a single factor model, which cannot fully consider the dynamic changes of the boiler operating conditions. By comprehensively analyzing these factors, the present invention can provide a more accurate prediction of pollutant concentration, provide a scientific basis for the real-time optimization control of the boiler, and solve the shortcomings of traditional technology in dealing with complex and changeable operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Please see attached Figure 1 The embodiment of the present invention provides a control method for continuously monitoring flue gas from a biomass boiler, comprising the following steps: S1. Real-time collection of pollutant concentration data in biomass boiler flue gas, the pollutants including carbon monoxide, carbon dioxide, nitrogen oxides and sulfur dioxide; In the present invention, the goal of step S1 is to collect pollutant concentration data in biomass boiler flue gas in real time. The pollutants collected include carbon monoxide, carbon dioxide, nitrogen oxides and sulfur dioxide. These pollutant concentration data are the basis for subsequent steps (such as data processing, pollutant concentration prediction, boiler operation control, etc.). By obtaining the pollutant concentration of boiler flue gas in real time, the emission status of the boiler can be accurately evaluated, and based on this, the operating status of the boiler can be optimized to ensure compliance with environmental protection standards.

[0022] In this embodiment, multi-point sensor arrays are installed at different locations of the boiler for real-time monitoring. These sensor arrays are equipped with different types of sensors, which can simultaneously measure the concentration of multiple pollutants and other key gas parameters (such as temperature, pressure, flow rate, etc.). The data collected by the sensors will be transmitted to the data processing module through the data transmission interface for subsequent processing and analysis. Specifically, the sensors include the following types: Fourier Transform Infrared Spectroscopy (FTIR) is a widely used technique for gas analysis and is particularly suited to measuring the absorption properties of some gases. Each gas molecule has its own specific vibration frequency, which corresponds to specific absorption wavelengths in the infrared spectrum. FTIR sensors take advantage of this property and quantitatively measure the concentration of a gas by analyzing its absorption properties at different wavelengths.

[0023] CO 2 : Carbon dioxide has clear absorption peaks in the infrared spectrum, especially in the 4.2μm and 15μm wavelength regions. FTIR can accurately measure these absorption peaks, identify and quantify CO 2 The concentration of the gas.

[0024] NOx (including NO and NO 2 ): Nitrogen oxides (NO and NO 2 ) also have specific absorption characteristics in the infrared spectrum, and FTIR sensors are able to measure both NO and NO simultaneously by detecting the absorption at these specific wavelengths. 2 concentration.

[0025] The advantage of FTIR sensors is that they can measure multiple gas components simultaneously, making them particularly suitable for detecting CO 2 and NOx and other gases with unique absorption characteristics.

[0026] Laser absorption spectroscopy (LAS) is a highly sensitive gas analysis technique that uses laser as a light source to detect the concentration of a gas by measuring the absorption of a specific wavelength of laser light by the gas. The laser light source can accurately select a specific wavelength, making it highly selective and sensitive. Laser absorption spectroscopy sensors are suitable for measuring the absorption characteristics of some gases at specific wavelengths.

[0027] CO: Carbon monoxide has very unique absorption characteristics, especially in the 4.6μm and 5.2μm bands. The laser absorption spectroscopy sensor can selectively excite and detect these absorption peaks, so it can measure the concentration of CO with high sensitivity.

[0028] SO 2 :Sulfur dioxide also has unique absorption characteristics, especially in the band of about 7.3μm. Laser absorption spectroscopy can accurately measure SO 2 concentration, and by selecting lasers of different wavelengths the interference can be reduced and the accuracy of detection can be improved.

[0029] The advantage of laser absorption spectroscopy sensor is that it has very high sensitivity, selectivity and real-time response capability, and is suitable for low-concentration gases (such as CO and SO 2 ) detection.

[0030] Gas Chromatograph (GC): GC analyzer can accurately analyze gas composition, especially can accurately measure the concentration of trace pollutants in flue gas, and is suitable for the analysis of complex gas systems.

[0031] The sensor array is installed at key locations of the boiler, such as the furnace outlet, the middle section of the flue, the exhaust port, etc. The arrangement of the sensor array takes into account the actual operating conditions and monitoring requirements of the boiler. By reasonably selecting the sensor type and location, the concentration data of various pollutants in the boiler can be efficiently obtained.

[0032] Specifically, during different working stages of the boiler, such as when there are large changes in load, the sensor array can monitor the temperature, pressure, flow rate and pollutant concentration of the flue gas in real time. The collected data will be transmitted to the data processing module via wireless or wired communication for subsequent processing and analysis.

[0033] As an option, in multiple operating modes of the boiler, the position of the sensor array can be dynamically adjusted according to the specific operating conditions. For example, in the case of large load fluctuations, the system can focus on monitoring the pollutant concentration near the exhaust port to better reflect the emissions of the boiler under high and low loads. Through this flexible sensor arrangement, the system can fully grasp the operating status of the boiler and optimize the emission control strategy.

[0034] The collection of pollutant concentration data includes the following key parameters: C CO (t): Carbon monoxide concentration, in mol / m 3 , which indicates the concentration of CO in boiler flue gas.

[0035] C CO2 (t): Carbon dioxide concentration, in mol / m 3, indicating the CO in boiler flue gas 2 concentration.

[0036] C NOx (t): Nitrogen oxide concentration, unit is mol / m 3 , which indicates the concentration of nitrogen oxides in boiler flue gas.

[0037] C SO2 (t): sulfur dioxide concentration, in mol / m 3 , which indicates the concentration of sulfur dioxide in boiler flue gas.

[0038] T gas (t): The temperature of the flue gas, in K, represents the instantaneous temperature of the flue gas.

[0039] P gas (t): Flue gas pressure, in Pa, indicating the instantaneous pressure of flue gas.

[0040] V gas (t): Flue gas velocity, in m / s, indicating the flow speed of flue gas.

[0041] After the above pollutant concentration data is collected, it will be transmitted to the data processing module in real time. Each sensor module will independently monitor and record the concentration of the corresponding pollutant according to its type and function. By collecting and transmitting this data, the system can understand the operation of the boiler in real time, providing a basis for subsequent control and optimization.

[0042] Pollutant concentration formula: Pollutant concentration is usually measured by sensors. Based on the working principle of the sensor, the concentration of pollutants can be expressed as: Where: C i (t) is the concentration of the i-th pollutant, in mol / m 3 , A i V is the absorbance or signal intensity of the i-th pollutant at the sensor detection point. The unit depends on the working principle of the sensor. gas (t) is the flue gas velocity in m / s, T gas (t) is the flue gas temperature, in K, P gas (t) is the flue gas pressure, in Pa.

[0043] Through the above formula, the data measured by the sensor will be converted according to the actual flue gas conditions to obtain the accurate pollutant concentration.

[0044] Data transmission and processing: The pollutant concentration data collected by the sensor is transmitted to the data processing module in real time through wireless or wired communication. There will be no obvious loss of data during the transmission process. The data processing module will verify and optimize the transmitted data.

[0045] By collecting pollutant concentration data in biomass boiler flue gas in real time, the present invention can provide accurate real-time data support for pollutant concentration prediction and control in subsequent steps. Due to the combined use of multiple sensors, the comprehensiveness and accuracy of the data can be ensured, especially under high load or complex working conditions, the emission level of the boiler can be accurately reflected. Combined with the pollutant concentration data, the control system can timely adjust the operating parameters of the boiler, optimize the combustion process, reduce pollutant emissions, and improve the energy efficiency of the boiler.

[0046] In one embodiment, the data acquisition module can flexibly increase or decrease monitoring points to adapt to different operating stages or load changes of the boiler. This dynamic adaptive design enhances the adaptability and accuracy of the system, especially in the case of drastic load changes or complex emission characteristics, ensuring the accuracy of monitoring data.

[0047] S2, transmitting the collected pollutant concentration data to the data processing module, performing denoising on the data through the Kalman filter algorithm to obtain accurate pollutant concentration data; In the present invention, the purpose of step S2 is to denoise the collected pollutant concentration data to ensure the accuracy of the pollutant concentration data. Since the real-time data collected by the sensor may be interfered by noise, affecting the quality of the data, it is necessary to optimize the data through a suitable algorithm to eliminate the noise. This step uses the Kalman filter algorithm to denoise the data. Kalman filtering is a statistical method that can effectively extract the optimal estimation result from the noise data and is widely used in the processing and optimization of real-time data.

[0048] In this embodiment, the data acquisition module collects pollutant concentration data in the boiler flue gas through the sensor array, and the data transmission module transmits the collected pollutant concentration data to the data processing module. Since the pollutant concentration data may be affected by factors such as sensor error and environmental noise, the data provided by the sensor usually contains noise, and directly using this data may lead to inaccurate subsequent processing and analysis. Therefore, in the data processing module, the collected data is denoised by using the Kalman filter algorithm to ensure that the processed pollutant concentration data has a high accuracy.

[0049] Specifically, the Kalman filter algorithm uses a recursive method to combine the physical model and noise characteristics of the system to optimally estimate real-time data. The basic idea is to obtain the optimal state estimate through the weighted average of historical data and current measurement values. The basic form of Kalman filtering can be expressed as: in: represents the estimate of the system state at time k, and the unit is the same as the system state (for example, the unit of pollutant concentration is mol / m 3 ); K represents the state estimation based on the data at the previous moment at time k; k represents the Kalman gain, which is a coefficient calculated based on the measurement error and prediction error, and its unit is dimensionless; k Represents the pollutant concentration data collected at time k, in mol / m 3 ;H k Represents the observation matrix, which transforms the state of the system into measurable output, usually the identity matrix.

[0050] During the Kalman filter processing, the system will continuously update the state estimate based on the sensor data and noise characteristics, and iteratively solve the optimal result. This process can effectively eliminate the impact of sensor noise on the data and provide more accurate pollutant concentration data.

[0051] As an option, different Kalman filters can be set in the data processing module according to the characteristics of different pollutants. For low-concentration pollutants such as nitrogen oxides (NOx) and carbon monoxide (CO), the Kalman filter can be optimized for the signal-to-noise ratio at low concentrations, thereby further improving the measurement accuracy.

[0052] In one possible implementation, for more complex noise characteristics, the Kalman filter can also be combined with other noise filtering methods to form a multiple filtering mechanism. In this way, the system's adaptability and accuracy to pollutant concentrations in high-noise environments can be further improved. For example, it can be combined with an adaptive filtering algorithm to dynamically adjust the filtering parameters during data acquisition to adapt to the fluctuations in pollutant concentrations during boiler operation.

[0053] The core algorithm of Kalman filtering contains two important update steps: prediction and update. In step S2, we mainly focus on the calculation of Kalman gain and prediction error in the data update process.

[0054] Prediction Steps: in: represents the predicted state value, estimated at time k based on the data at time k-1; A k represents the state transfer matrix, describing the change of system state from time k-1 to k; B k represents the control input matrix, which is used to describe the impact of external input on the system state; u k Indicates control input.

[0055] Update steps: in: K k represents the Kalman gain, which represents the weight of new observations in data update; z k Indicates the measured value, which indicates the pollutant concentration collected in real time; H k Represents the observation matrix, which converts the state quantity into a measurable quantity.

[0056] The optimization process of Kalman filtering will continuously adjust the filter gain and error covariance matrix through recursive calculation to ensure the accuracy of pollutant concentration data.

[0057] By adopting the Kalman filter algorithm, this step can effectively remove the interference of sensor noise on the data and improve the accuracy and reliability of pollutant concentration data. The data processed by the Kalman filter will be passed to subsequent steps, such as pollutant concentration prediction and boiler control, to ensure that the system's control algorithm can be accurately optimized based on high-quality data. In addition, the application of the Kalman filter algorithm provides higher robustness and adaptability for the real-time monitoring system, especially in the face of boiler load fluctuations and complex operating conditions, and can more accurately reflect the actual situation of flue gas pollutant concentration.

[0058] S3. Predicting the changing trend of pollutant concentration in the boiler based on real-time data and mathematical models, wherein the mathematical models include flue gas flow, heat conduction, pollutant diffusion, and biomass combustion reaction models; In the present invention, the main goal of step S3 is to predict the changing trend of pollutant concentration in the boiler based on real-time data and mathematical models. Since the pollutant concentration in the boiler is affected by many factors, such as boiler load, combustion conditions, gas flow, temperature distribution, etc., the use of mathematical models can predict these changes in advance and provide accurate decision-making basis for subsequent steps. Specifically, by introducing a comprehensive mathematical model including flue gas flow, heat conduction, pollutant diffusion and biomass combustion reaction, the system can more accurately evaluate the dynamic changes of pollutant concentration, identify potential emission risks, and adjust boiler operating parameters to optimize emission control and improve energy efficiency.

[0059] In this embodiment, in order to achieve accurate prediction of pollutant concentration, the present invention combines multiple physical models to simulate the generation, diffusion, flow and other processes of pollutants in the boiler. These models include: The flue gas flow model is used to describe the flow characteristics of the flue gas inside the boiler.

[0060] The heat conduction equation is used to describe the temperature distribution of flue gas in the boiler.

[0061] The pollutant diffusion equation is used to simulate the diffusion process of pollutants in flue gas flow.

[0062] Biomass combustion reaction model is used to describe the generation rate of pollutants during biomass combustion.

[0063] Through the combination of these mathematical models, the system can comprehensively predict the changing trends of pollutant concentrations in the boiler from different dimensions, thereby providing a scientific basis for subsequent control and optimization.

[0064] Specifically, in this step, the real-time pollutant concentration data is first collected, combined with the actual operation of the boiler (such as temperature, pressure, flow rate, etc.), and the fluid dynamics model is used to simulate the flow of flue gas. Then, the temperature distribution in the boiler is calculated by the heat conduction equation, and the diffusion and reaction process of the pollutants are considered. Finally, the reaction rate model is combined to predict the change trend of the pollutant concentration over time. This process obtains a dynamic change prediction of the pollutant concentration in the future by solving the numerical solution of the mathematical model.

[0065] As an option, in this step, the model parameters can also be dynamically adjusted according to different operating conditions of the boiler (such as load changes, gasification agent flow changes, etc.) to improve the prediction accuracy. At this time, by comparing the real-time monitoring data with the model results, the model parameters are optimized and adjusted to ensure the timeliness and accuracy of the model.

[0066] Smoke flow model (Navier-Stokes equations): The smoke flow is described by the Navier-Stokes equation, which takes into account the inertial force, pressure gradient force, viscosity force and external force of the fluid. The smoke flow equation can be expressed as: Where: ρ is the density of the fluid [kg / m 3], which represents the mass density of smoke, v is the velocity field of smoke [m / s], which represents the flow rate and flow direction of smoke, t is time [s], which is used to describe the time change of smoke flow, p is pressure [Pa], which represents the pressure of smoke, μ is dynamic viscosity [Pa·s], which represents the viscosity characteristics of smoke, and f is volume force (such as gravity) [N / m 3 ], represents the external force acting on the smoke, is the time rate of change of the velocity field (unit: m / s 2 ), is the convection term of the velocity field (unit: m / s 2 ), which represents the effect of non-uniform flow of fluid in space on velocity, is the pressure gradient (unit: Pa / m), which indicates the rate of change of pressure in space. is the Laplace operator of the velocity field (unit: m / s 2 ), describing the spatial diffusion effect of the velocity field.

[0067] By solving the Navier-Stokes equation, the flow state of the flue gas in the boiler can be obtained, providing a basis for subsequent temperature distribution and pollutant concentration calculations.

[0068] Heat conduction equation: In order to simulate the temperature distribution in the boiler, the heat conduction equation is used. This equation describes the heat transfer process in the flue gas flow, and the specific form is: Where: T is temperature [K], indicating the temperature of the flue gas in the boiler, v is the flue gas velocity [m / s], which affects the heat transfer, and α is the thermal diffusion coefficient [m 2 / s], represents the rate of heat diffusion, q is the intensity of heat source per unit volume [W / m 3 ], represents the heat source in the boiler, t is time [s], used to describe the time change of temperature, is the rate of change of temperature over time (unit: K / s), indicating the change of temperature field over time. is the Laplace operator of the temperature field (unit: K / m 2 ), represents the spatial diffusion of temperature and describes how heat is transferred from high-temperature areas to low-temperature areas in space. It is the convection term of the temperature field (unit: K / s), which describes the effect of the velocity field of the fluid on the temperature distribution. The flow of the fluid will bring heat from the high temperature area to the low temperature area.

[0069] By solving this equation, the temperature distribution at various locations in the boiler can be obtained, providing temperature data support for the subsequent calculation of the pollutant diffusion equation.

[0070] Pollutant diffusion equation: The diffusion process of pollutants in flue gas is modeled by the diffusion equation. The diffusion equation takes into account the variation of pollutant concentration over time and space, which is specifically expressed as: Where: C is the pollutant concentration [mol / m 3 ], represents the concentration of pollutants in boiler flue gas, t is time [s], used to describe the time variation of pollutant concentration, D is the diffusion coefficient of pollutants [m 2 / s], describing the diffusion rate of pollutants in the flue gas flow, is the rate of change of pollutant concentration over time (unit: mol / m 3 ·s), which represents the change of pollutant concentration in unit volume over time.

[0071] R(T,C) is the reaction rate [mol / m 3 ·s], which depends on the temperature T and the pollutant concentration C, and represents the generation rate of pollutants during biomass combustion.

[0072] is the Laplace operator, which represents the spatial variation of pollutant concentration.

[0073] The equation further obtains the concentration distribution of pollutants by solving the diffusion process of pollutants in the boiler.

[0074] Biomass combustion reaction model (Arrhenius equation): The reaction rate of the biomass combustion process is described by the Arrhenius equation, which takes into account the effects of temperature and reactant concentration on the reaction rate. The specific form is: in: r is the reaction rate [mol / m 3 ·s], which represents the rate at which pollutants are generated.

[0075] A is the prefactor [mol / m 3 ·s], which represents the reaction rate constant.

[0076] E is the activation energy [J / mol], describing the temperature dependence of the reaction.

[0077] R is the gas constant [8.314 J / mol·K], which is a constant.

[0078] T is temperature [K], which indicates the temperature of the flue gas in the boiler.

[0079] C is the concentration of reactants [mol / m 3], which represents the concentration of reactants during biomass combustion; is an exponential term that represents the effect of temperature on the reaction rate.

[0080] Through the biomass combustion reaction model, the system can calculate the generation rate of pollutants during the combustion process, and then infer the changes in pollutant concentrations in the boiler.

[0081] By combining these mathematical models, the present invention can accurately predict the changing trend of pollutant concentration in the boiler. The combined use of flow model, heat conduction equation, diffusion equation and combustion reaction model enables the prediction results to fully reflect the dynamic behavior of pollutants in the boiler. Under actual operating conditions such as boiler load changes and combustion temperature changes, the model can be adjusted in time and provide accurate prediction of pollutant concentration changes, thereby providing reliable data support for the optimization control of the boiler.

[0082] In one embodiment, by dynamically adjusting the model parameters, the system can respond to different boiler operating conditions and external conditions (such as climate change, fuel quality, etc.), ensuring the accuracy and real-time nature of the prediction. This makes the system highly adaptable and robust, and can optimize the operating parameters of the boiler in real time, reduce pollutant emissions, and improve the operating efficiency of the boiler.

[0083] S4, minimizing the pollutant concentration by calculating the objective function, and adjusting the boiler operating parameters according to the minimization result, wherein the operating parameters include oxygen supply, combustion temperature, gasification agent flow rate and fuel input; The purpose of step S4 is to minimize the pollutant concentration by calculating the objective function, and adjust the boiler's operating parameters according to the minimization result to further optimize the boiler's combustion efficiency and pollutant emissions. The core of this step is to calculate the optimal boiler operating parameters (such as oxygen supply, combustion temperature, gasification agent flow, fuel input, etc.) through an optimization algorithm based on real-time data and the pollutant concentration change trend predicted in the previous steps. The adjustment of these optimal parameters is intended to maximize the energy efficiency of the boiler while ensuring that pollutant emissions are minimized and meet environmental emission standards.

[0084] Step S4 determines the optimal operating conditions of the boiler by optimizing the objective function based on the pollutant concentration prediction data obtained in the aforementioned step S3, thereby minimizing pollutant emissions and optimizing combustion efficiency.

[0085] In this embodiment, this step is achieved by constructing an objective function to minimize the pollutant concentration and adjusting the boiler control parameters according to the optimization results. The objective function is used to measure the relationship between the pollutant concentration and the boiler control parameters. The optimization goal is to minimize the pollutant concentration and optimize the boiler operating parameters (such as oxygen supply, combustion temperature, gasification agent flow rate and fuel input).

[0086] Definition of the objective function: First, define an objective function J(t), which aims to minimize the concentration of pollutants in the boiler flue gas while keeping the boiler running at the best state. The mathematical form of the objective function is: Among them, C i (t) is the concentration of the i-th pollutant, α i is the weight coefficient of the pollutant, u(t) is the control parameter of the boiler, u optimal is the optimal control parameter, β is the penalty coefficient, and t is the time variable, which is used to represent the change of the objective function at different times. is the square of the pollutant concentration, and J(t) is the objective function value.

[0087] The objective function combines the relationship between pollutant concentration and boiler control parameters. The square term of pollutant concentration The influence of pollutants with higher concentrations is emphasized, while the deviation term between the control parameters and the optimal control parameters reflects the optimization direction of the boiler operating status.

[0088] Optimization objective function: In order to minimize the objective function, optimization algorithms (such as gradient descent method, optimal control algorithm, etc.) are used to calculate the optimal boiler control parameters. Through the iterative optimization process, the optimization algorithm continuously adjusts the operating parameters of the boiler to minimize the objective function J(t). Specifically, the optimal control parameters are obtained by solving the following equations: in: is the partial derivative of the objective function with respect to the control parameter u(t), indicating the changing trend of the objective function under the current control parameters; u(t) is the control parameter of the boiler, and its unit is the physical unit related to the control target (such as m 3 / s, ℃).

[0089] After solving this equation, the optimal control parameters can be obtained to minimize the pollutant concentration and make the boiler operate in the optimal state.

[0090] Boiler operating parameters adjustment: After the optimal control parameters are calculated during the optimization process, the system adjusts the operating status of the boiler in real time according to these optimal parameters. The specific adjustments are as follows: Oxygen supply (u O2 ): Properly adjust the oxygen supply to ensure sufficient oxygen during the combustion process, thereby improving combustion efficiency and reducing pollutant emissions. The oxygen supply can be adjusted through the air valve in the boiler control system; combustion temperature (u T ): Adjust the combustion temperature in the boiler to ensure combustion within the optimal temperature range, promote complete combustion and reduce the generation of harmful gases. The temperature is adjusted in real time by adjusting the boiler's burner and combustion temperature sensor; Gasifying agent flow rate (u gas ): According to the needs of the combustion process, the flow rate of the gasifying agent (such as steam, air, etc.) is adjusted to control the oxygen supply and reaction rate in the gasification reaction. The regulation of the gasifying agent flow rate helps to reduce the generation of pollutants; Fuel input (u fuel ): Adjust the fuel input according to the boiler load and operating status. Timely increase or decrease of fuel flow helps maintain the stability of boiler operation and ensure that pollutant emissions are maintained within the prescribed standards.

[0091] Through these optimized boiler operating parameters, the boiler can maintain efficient operation under different loads and operating conditions and reduce pollutant emissions.

[0092] Dynamic adjustment of objective function: As an option, if the system faces multiple optimization goals, such as reducing pollutant concentrations and improving energy efficiency, the objective function can be further expanded to a multi-objective optimization problem. In this case, the weighting coefficients of each item in the objective function (such as α i and β) can be adjusted according to different operating conditions and optimization requirements to balance the needs of pollutant concentration control and boiler performance optimization.

[0093] The extended objective function may also have the following form: in: E eff (t) is the energy efficiency of the boiler, which indicates the energy utilization efficiency of the boiler under the current control parameters; β 1 and β 2 are the weighting coefficients of pollutant concentration and energy efficiency, respectively; J multi (t) is the objective function value of multi-objective optimization; is the weighted sum of squares of the pollutant concentration term, which is the first part of the objective function and represents the concentration C of each pollutant in the boiler. i (t) Contribution to the objective function. α i is the weight coefficient of each pollutant concentration, which is used to indicate the influence of different pollutants on the objective function. i (t), the impact of high concentrations of pollutants on the optimization target can be emphasized; β 1 (u(t)-u optimal ) 2 is the square term of the boiler control parameter deviation (unit: dimensionless). This part reflects the difference between the current boiler control parameter u(t) and its optimal value u optimal The contribution of the gap between β and β to the objective function. 1 is the weight coefficient. The greater the deviation of the control parameter, the greater the penalty on the objective function, thus encouraging the system to adjust the parameters to reduce this deviation. β 2 E eff (t) is the boiler energy efficiency term (unit: dimensionless). eff (t) represents the real-time energy efficiency of the boiler, β 2 is its weight coefficient, which indicates the impact of energy efficiency on the objective function. A higher energy efficiency value will reduce the value of the objective function and promote the optimization of boiler operation.

[0094] By adjusting the weighting coefficients, the system can be dynamically optimized according to different target requirements, thereby achieving the dual optimization goals of minimizing pollutant emissions and maximizing energy efficiency.

[0095] Through the above optimization process, this step can optimize the boiler's combustion efficiency while ensuring that pollutant emissions meet environmental standards. By calculating and adjusting the boiler's operating parameters in real time, the system can not only accurately control the pollutant concentration, but also improve the boiler's energy efficiency and reduce fuel consumption. Under different working conditions, the optimization algorithm can ensure the continuous optimization of the boiler's operating status, thereby achieving a long-term stable and efficient combustion process.

[0096] In one possible implementation, through real-time monitoring and optimization control, the system can cope with fluctuations in boiler load and external climate change, ensuring that the boiler always operates in the optimal state. In addition, through the dynamic adjustment mechanism of the objective function, the system can flexibly adjust the optimization target according to the different requirements of pollutant emissions and energy utilization efficiency, thereby achieving a more efficient control strategy.

[0097] S5. Based on the adjustment results, real-time feedback is provided to adjust the operating status of the boiler and continuously optimize the combustion process to ensure that pollutant emissions meet environmental protection standards.

[0098] In the present invention, the core purpose of step S5 is to adjust the operating state of the boiler in real time based on the optimization result obtained in step S4, and continuously optimize the combustion process to ensure that the pollutant emissions of the boiler always meet environmental protection standards. This step ensures that the boiler can always maintain optimal performance under changing working conditions, reduce pollutant emissions and improve energy efficiency by real-time monitoring of the operating state of the boiler, adjusting key control parameters and optimizing the combustion process.

[0099] Specifically, this step uses a feedback control mechanism to adjust the key parameters of the boiler (such as oxygen supply, combustion temperature, gasification agent flow rate and fuel input) according to real-time data to ensure that the boiler can maintain optimal combustion conditions and the lowest pollutant emissions under any operating conditions. This process involves multiple links such as real-time monitoring, data analysis, parameter adjustment and optimization.

[0100] Technical implementation: In this embodiment, after obtaining the optimal control parameters by optimizing the objective function in step S4, the system will adjust the operating state of the boiler in real time according to these optimal parameters. The adjustment process is carried out through a feedback control system, which continuously monitors the actual operating state of the boiler and adjusts the operation of the boiler to ensure optimal combustion efficiency and minimum pollutant emissions.

[0101] Real-time monitoring and data feedback: Generally, during the operation of the boiler, various sensors are used to collect information about flue gas composition (such as pollutant concentration), temperature, pressure and flow rate in real time. These data are transmitted to the control system in real time for subsequent analysis and adjustment. The sensor array includes temperature sensors, gas concentration sensors (such as CO, CO 2 , NOx, etc.), pressure sensors and flow sensors. The sensor data provides the system with dynamic information of various key parameters in the boiler.

[0102] As an option, sensor arrays are installed at various key locations of the boiler (such as the combustion chamber, furnace outlet, smoke exhaust port, etc.) to ensure real-time acquisition of data such as pollutant concentration, combustion temperature, flow rate, etc. in the flue gas. These data can not only reflect the working status of the boiler, but also reflect the emission level of pollutants. Whenever it is detected that the pollutant concentration exceeds the standard or the combustion conditions are not ideal, the system will make corresponding adjustments through the feedback mechanism.

[0103] Real-time feedback and control parameter adjustment: Specifically, after receiving sensor data, the system will adjust the boiler's control parameters based on the comparison between real-time data and target values. The control parameter adjustments include but are not limited to oxygen supply, combustion temperature, gasifier flow rate, and fuel input. The goal of adjusting each parameter is to improve combustion efficiency, ensure complete combustion, and minimize pollutant generation.

[0104] Oxygen supply (u O2 ): Control the oxygen supply by adjusting the air flow to ensure sufficient oxygen during the combustion process. At low oxygen concentrations, the boiler may experience incomplete combustion, which will increase pollutant emissions, especially carbon monoxide (CO) and incompletely burned carbon particles. The oxygen flow is adjusted in real time to adapt to changes in boiler load to ensure complete combustion.

[0105] Combustion temperature (u T ): Temperature has an important influence on the combustion process. Under high temperature conditions, combustion efficiency is higher, but too high a temperature will lead to the generation of nitrogen oxides (NOx). The system will adjust the burner input based on the real-time temperature monitoring data in the boiler to ensure that the temperature is within the optimal range to reduce pollutant generation and improve combustion efficiency.

[0106] Gasifying agent flow rate (u gas ): The flow rate of gasifying agent (such as steam, air) directly affects the reaction rate and the concentration of generated gas during the gasification process. By adjusting the flow rate of gasifying agent in real time, the boiler can maintain the optimal gasification reaction, ensure sufficient reaction and optimize the emission of pollutants.

[0107] Fuel input (u fuel ): The amount of fuel input directly affects the thermal load and emission level of the boiler. According to the boiler's load requirements and real-time control targets, the system will adjust the fuel supply to avoid incomplete combustion or energy waste caused by too much or too little fuel input.

[0108] Real-time optimization and feedback of the regulation process: As an option, after each adjustment of the boiler, the feedback control system will evaluate the effect of the adjustment and feed the results back to the control module. If the current adjustment does not achieve the desired goal, the system will continue to adjust the parameters until the requirements are met. For example, after adjusting the oxygen supply, the system will monitor CO, CO 2 and NOx concentrations. If the concentration still does not reach the ideal level, the system will further increase or decrease the oxygen flow until the optimal balance is reached.

[0109] In one possible implementation, the feedback regulation process is completed through a control algorithm (such as a PID controller or a fuzzy controller) to automatically correct any deviations. The system calculates the deviation in real time based on target values ​​such as target pollutant concentration and combustion temperature, and adjusts the control parameters to compensate. This process ensures that the boiler's combustion process can continue to operate optimally under any operating conditions.

[0110] System optimization and adaptive adjustment: Specifically, the feedback regulation system can dynamically adjust the priority of control parameters as the boiler operates at different stages and loads change. For example, when the load is low, the fuel input may not need to be adjusted too much, while during high load or cold start phases, the fuel and oxygen supply may need to be adjusted first. The system can adaptively adjust the control algorithm based on factors such as boiler load and external environment.

[0111] As an option, the system can also learn from historical data to establish an adaptive control mechanism to automatically adjust the optimal parameters. For example, the system can optimize the weight coefficient of the objective function through a machine learning algorithm based on the operating data of the boiler under different loads to achieve more precise control.

[0112] Through the feedback adjustment mechanism in this step, the system can continuously optimize the operation process of the boiler based on real-time data and adjustment results, ensure that the boiler maintains efficient combustion under various operating conditions, and reduce pollutant emissions. Combined with the objective function minimization process in step S4, the feedback adjustment mechanism can dynamically respond to external factors such as boiler load fluctuations, changes in fuel quality, and changes in climate conditions, so that the boiler always remains under optimal combustion conditions.

[0113] Generally speaking, this real-time feedback control can significantly improve the energy efficiency of the boiler and effectively reduce pollutant emissions to ensure compliance with environmental standards. By continuously optimizing the control parameters of the boiler, the system can provide adaptive and efficient control strategies under different operating environments, making the boiler combustion process intelligent, precise and efficient.

[0114] In one possible implementation, by combining real-time data and model predictions, the system can predict the operating trend of the boiler in advance, avoid incomplete combustion or excessive pollutant emissions due to emergencies or load changes, and further improve the adaptability and reliability of the boiler.

[0115] The control system for continuously monitoring the flue gas of a biomass boiler described below and the control method for continuously monitoring the flue gas of a biomass boiler described above can be referred to in correspondence with each other.

[0116] Please see attached Figure 2 , a control system for continuously monitoring the flue gas of a biomass boiler, used to execute the above-mentioned control method for continuously monitoring the flue gas of a biomass boiler, comprising: The data acquisition module is used to collect temperature, pressure and pollutant concentration data in boiler flue gas in real time through the sensor array; the data processing module is used to remove noise and optimize the collected data and output accurate pollutant concentration data; the prediction model module is used to predict the change trend of pollutant concentration based on real-time data and mathematical models; An optimization control module is used to calculate the objective function and adjust the operating parameters of the boiler according to the pollutant concentration prediction results; Feedback regulation module, which is used to adjust the operation status of the boiler in real time according to the output results of the optimization control module, to ensure the minimization of pollutant emissions and improve the energy efficiency of the boiler The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0117] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for continuously monitoring flue gas from a biomass boiler, characterized in that: The following steps are involved: S1. Real-time collection of pollutant concentration data in biomass boiler flue gas, the pollutants including carbon monoxide, carbon dioxide, nitrogen oxides and sulfur dioxide; S2, transmitting the collected pollutant concentration data to the data processing module, and performing denoising on the data through the Kalman filter algorithm to obtain accurate pollutant concentration data; S3. Predicting the changing trend of pollutant concentration in the boiler based on real-time data and mathematical models, wherein the mathematical models include flue gas flow, heat conduction, pollutant diffusion, and biomass combustion reaction models; S4, minimizing the pollutant concentration by calculating the objective function, and adjusting the boiler operating parameters according to the minimization result, wherein the operating parameters include oxygen supply, combustion temperature, gasification agent flow rate and fuel input; S5. Based on the adjustment results, real-time feedback is provided to adjust the operating status of the boiler and continuously optimize the combustion process to ensure that pollutant emissions meet environmental protection standards.

2. The control method for continuously monitoring flue gas from a biomass boiler according to claim 1, characterized in that: The steps of collecting pollutant concentration data in the flue gas of the biomass boiler in real time in step S1 are as follows: S1.

1. Install a multi-point sensor array at the furnace outlet, the middle section of the flue and the exhaust port of the boiler to monitor the pollutant concentration in the flue gas in real time; S1.

2. The data collected by the sensor include information on pollutant concentration, temperature, pressure and flow rate in the flue gas, and the collected data are transmitted to the data processing module in real time through the data transmission interface.

3. The control method for continuously monitoring flue gas from a biomass boiler according to claim 2, characterized in that: The sensor array in step S1.1 includes: Fourier transform infrared spectroscopy sensor, used to measure CO2 and NOx gas composition and concentration; Laser absorption spectrum sensor, used to measure CO and SO2 gas composition and concentration; Gas chromatographs for precise measurement of pollutant concentrations.

4. The control method for continuously monitoring flue gas from a biomass boiler according to claim 1, characterized in that: The specific steps of transmitting the collected pollutant concentration data to the data processing module in step S2 are as follows: S2.1, transmitting the data of each sensor to the data processing module via wireless or wired communication; S2.2, performing preliminary verification on the received data in the data processing module; S2.

3. Use the Kalman filter algorithm to denoise the received pollutant concentration data, eliminate the noise interference of the sensor, and obtain accurate pollutant concentration data; S2.

4. Pass the optimized data to the prediction model module for further analysis.

5. The control method for continuously monitoring flue gas from a biomass boiler according to claim 1, characterized in that: The steps of predicting the changing trend of pollutant concentration in the boiler based on real-time data and mathematical models in step S3 are as follows: S3.

1. Establish a boiler flue gas flow model based on real-time collected data to describe the flow of flue gas inside the boiler and use a fluid dynamics model to calculate the flow state; S3.

2. Calculate the flue gas temperature distribution based on the flue gas flow model and heat conduction equation; S3.

3. Based on the pollutant diffusion equation and biomass combustion reaction model, combined with the pollutant concentration and temperature distribution in the flue gas, predict the changing trend of pollutant concentration over time and identify potential risks of excessive pollutant emissions.

6. The control method for continuously monitoring flue gas from a biomass boiler according to claim 1, characterized in that: The steps of minimizing the pollutant concentration by calculating the objective function in step S4 are as follows: S4.

1. Define an objective function based on real-time data and prediction results, wherein the objective function is a weighted square sum of pollutant concentrations; S4.

2. Calculate optimal boiler control parameters for minimizing pollutant concentrations based on the objective function, including oxygen supply, combustion temperature, gasification agent flow rate, and fuel input; S4.

3. Use optimization algorithm to solve the optimal control parameters.

7. The control method for continuously monitoring flue gas from a biomass boiler according to claim 6, characterized in that: The objective function in step S4.1 is: Among them, C i (t) is the concentration of the i-th pollutant, α i is the weight coefficient of the pollutant, u(t) is the control parameter of the boiler, u optimal is the optimal control parameter, β is the penalty coefficient, and t is the time variable, which is used to represent the change of the objective function at different times. is the square of the pollutant concentration, and J(t) is the objective function value.

8. The control method for continuously monitoring flue gas from a biomass boiler according to claim 1, characterized in that: In step S5, the steps of adjusting the operating state of the boiler in real time according to the adjustment result are specifically as follows: S5.

1. According to the optimal operating parameters calculated by the optimization control algorithm, the oxygen supply, combustion temperature, gasification agent flow rate and fuel input of the boiler are adjusted in real time; S5.

2. Monitor the actual operating status of the boiler and compare it with the target value, and correct and adjust the parameters in real time to ensure the optimization of the boiler's combustion efficiency and pollutant emissions; S5.

3. Feedback sensors are installed at various key locations of the boiler to monitor the temperature and pollutant concentration data during the combustion process in real time to ensure that the boiler continues to operate in the optimal state.

9. A control system for continuously monitoring flue gas from a biomass boiler, characterized in that: The control method for continuously monitoring flue gas from a biomass boiler according to any one of claims 1 to 8 comprises: The data acquisition module is used to collect the temperature, pressure and pollutant concentration data in the boiler flue gas in real time through the sensor array; the data processing module is used to remove noise and optimize the collected data to output accurate pollutant concentration data; Prediction model module, used to predict the changing trend of pollutant concentration based on real-time data and mathematical models; An optimization control module is used to calculate the objective function and adjust the operating parameters of the boiler according to the pollutant concentration prediction results; The feedback regulation module is used to adjust the operating status of the boiler in real time according to the output results of the optimization control module to ensure the minimization of pollutant emissions and improve the energy efficiency of the boiler.

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