Method and system for continuous monitoring of biomass boiler flue gas

Abstract

The application relates to the field of boiler control and environmental protection technology, and discloses a control method for continuously monitoring biomass boiler flue gas, which comprises the following steps: S1, collecting pollutant concentration data in biomass boiler flue gas in real time, wherein the pollutants include carbon monoxide, carbon dioxide, nitrogen oxide and sulfur dioxide; and S2, transmitting the collected pollutant concentration data to a data processing module, and a control system for continuously monitoring biomass boiler flue gas is also provided, which is used for executing the control method for continuously monitoring biomass boiler flue gas, and comprises a data acquisition module used for collecting temperature, pressure and pollutant concentration data in boiler flue gas in real time through a sensor array. The application combines real-time data and an optimization algorithm, uses Kalman filtering denoising and a multi-factor mathematical model to predict pollutant concentration, dynamically adjusts boiler operation parameters, improves energy efficiency, reduces pollutant emission, and solves the problems of inaccurate control and slow response in traditional technologies.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of boiler control and environmental protection technology, specifically to a control method and system for continuously monitoring the flue gas of a biomass boiler. BACKGROUND

[0002] Currently, biomass boilers are facing increasing technical challenges under the increasingly stringent environmental standards and the improvement of energy efficiency requirements. Existing technical solutions usually rely on fixed control strategies, and there are significant deficiencies in the monitoring and adjustment of pollutant concentrations. Traditional boiler control systems mainly rely on pre-set parameters set by experience for operation adjustment, lack flexible response mechanisms, and cannot make timely optimization according to the real-time working 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 cause incomplete combustion and increase pollutant emissions, 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 single mathematical models. These models have obvious limitations in dealing with complex combustion processes and cannot accurately predict the trend of pollutant concentration changes. 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 reaction, resulting in inaccurate prediction results. Even if there are abnormal fluctuations in pollutant concentrations, 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 methods have obvious defects. Traditional techniques 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, leading to a decrease in the accuracy of pollutant concentration data. Therefore, in actual operation, incorrect pollutant concentration monitoring results may be caused by data noise, further affecting subsequent boiler control decisions.

[0005] In summary, the main problems of traditional technology are the lack of dynamic adjustment and real-time optimization capability, inaccurate pollutant concentration prediction, and insufficient data noise removal capability. These technical deficiencies make it difficult for existing biomass boiler systems to achieve the desired environmental protection effect and energy efficiency in actual operation, and innovative solutions are needed to make up for these deficiencies to ensure that the boiler runs efficiently and environmentally. SUMMARY

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

[0007] To achieve the above object, the present application is implemented by the following technical solutions: a control method for continuously monitoring biomass boiler flue gas, comprising the following steps:

[0008] 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;

[0009] S2, transmitting the collected pollutant concentration data to a data processing module, and performing denoising processing on the data by a Kalman filtering algorithm to obtain accurate pollutant concentration data;

[0010] S3, predicting the change trend of the pollutant concentration in the boiler based on real-time data and a mathematical model, the mathematical model including flue gas flow, heat conduction, pollutant diffusion and biomass combustion reaction model;

[0011] S4, minimizing the pollutant concentration by calculating the target function, and adjusting the boiler operating parameters according to the minimization result, the operating parameters including oxygen supply amount, combustion temperature, gasification agent flow and fuel input amount;

[0012] S5, real-time feedback adjustment of the operating state of the boiler according to the adjustment result, continuous optimization of the combustion process to ensure that the pollutant emission meets the environmental protection standard.

[0013] Preferably, the step of real-time collection of pollutant concentration data in biomass boiler flue gas in the S1 step is specifically as follows:

[0014] S1.1, installing a multi-point sensor array at the furnace outlet, middle section of the flue and exhaust port of the boiler for real-time monitoring of the pollutant concentration in the flue gas;

[0015] S1.2, the data collected by the sensor includes the pollutant concentration, temperature, pressure and flow rate information in the flue gas, and the collected data is transmitted in real time to the data processing module through a data transmission interface.

[0016] Preferably, the sensor array in the S1.1 step comprises:

[0017] Fourier transform infrared spectrum sensor for measuring CO2 and NOx gas composition and concentration;

[0018] Laser absorption spectrum sensor for measuring CO and SO2 gas composition and concentration;

[0019] A gas chromatograph for accurately measuring the concentration of pollutants.

[0020] Preferably, the step of transmitting the collected pollutant concentration data to the data processing module in the S2 step is specifically as follows:

[0021] S2.1, transmitting the data of each sensor to the data processing module through wireless or wired communication;

[0022] S2.2, performing preliminary verification on the received data in the data processing module;

[0023] S2.3, using Kalman filter algorithm to denoise the received pollutant concentration data, eliminating the noise interference of the sensor, and obtaining accurate pollutant concentration data;

[0024] S2.4, transmitting the optimized data to the prediction model module for further analysis.

[0025] Preferably, the step of predicting the change trend of the pollutant concentration in the boiler based on real-time data and mathematical model in the S3 step is specifically as follows:

[0026] S3.1, establishing a boiler flue gas flow model according to the real-time collected data, describing the flow of flue gas in the boiler, and using fluid dynamics model to calculate the flow state;

[0027] S3.2, calculating the flue gas temperature distribution according to the flue gas flow model and heat conduction equation;

[0028] S3.3, based on the pollutant diffusion equation and biomass combustion reaction model, combining the pollutant concentration and temperature distribution in the flue gas, predicting the change trend of the pollutant concentration with time, and identifying the potential risk of excessive emission of pollutants.

[0029] Preferably, the step of minimizing the pollutant concentration by calculating the objective function in the S4 step is specifically as follows:

[0030] S4.1, defining the objective function according to the real-time data and the prediction result, wherein the objective function is the weighted sum of squares of the pollutant concentration; S4.2, calculating the optimal boiler control parameters for minimizing the pollutant concentration based on the objective function, including oxygen supply amount, combustion temperature, gasifying agent flow and fuel input amount;

[0031] S4.3, using optimization algorithm to solve the optimal control parameters.

[0032] Preferably, the objective function in the S4.1 step is:

[0033]

[0034] wherein, Ci (t) is the concentration of the ith pollutant, a 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, t is the time variable, used to represent the change of the objective function at different times, is the square of the pollutant concentration, J(t) is the value of the objective function.

[0035] Preferably, the step of adjusting the operation state of the boiler in real time according to the adjustment result in the S5 step is specifically as follows:

[0036] S5.1, adjusting the oxygen supply amount, the combustion temperature, the gasifying agent flow and the fuel input amount of the boiler in real time according to the optimal operation parameters calculated by the optimal control algorithm;

[0037] S5.2, monitoring the actual operation state of the boiler and comparing it with the target value, and correcting the adjustment parameters in real time to ensure that the combustion efficiency and the pollutant emission of the boiler are optimized;

[0038] S5.3, installing feedback sensors at key positions of the boiler to monitor the temperature and the pollutant concentration data in the combustion process in real time, and ensuring that the operation of the boiler continues in the optimal state.

[0039] The application also provides a control system for continuously monitoring the flue gas of a biomass boiler, which is used to execute the control method for continuously monitoring the flue gas of a biomass boiler.

[0040] A data acquisition module is configured to acquire temperature, pressure and pollutant concentration data in the flue gas of the boiler in real time through a sensor array; a data processing module is configured to remove noise and optimize the acquired data, and output accurate pollutant concentration data; and a prediction model module is configured to predict the change trend of the pollutant concentration based on real-time data and a mathematical model.

[0041] An optimal control module is configured to calculate an objective function and adjust the operation parameters of the boiler according to the prediction result of the pollutant concentration.

[0042] A feedback adjustment module is configured to adjust the operation state of the boiler in real time according to the output result of the optimal control module, so as to minimize the pollutant emission and improve the energy efficiency of the boiler.

[0043] The application provides a control method and system for continuously monitoring the flue gas of a biomass boiler, which has the following beneficial effects:

[0044] 1. The present application realizes the 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, gasifying agent flow 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 which only relies on preset parameters or simple control systems, the intelligent optimization control system of the present application can cope with complex factors such as boiler load fluctuations and fuel composition changes, ensuring that the boiler can maintain an optimal operating state under various operating conditions, thereby significantly improving the energy utilization efficiency of the boiler and significantly reducing pollutant emissions.

[0045] 2. The present application adopts Kalman filtering algorithm in the pollutant concentration data processing link, effectively removes the data errors caused by sensor noise or environmental interference, and makes the real-time data of pollutant concentration more accurate. This accurate data processing method has significant advantages compared with the simple filtering techniques commonly used in the prior art, which rely on traditional average filtering or median filtering. These methods are not ideal when dealing with large noise, while Kalman filtering can accurately estimate in the case of system dynamic changes, combined with noise characteristics, thereby providing more reliable data support for subsequent pollutant concentration prediction and boiler control.

[0046] 3. The present application realizes the accurate prediction of the change of pollutant concentration in the boiler by synthesizing the mathematical models of flue gas flow, heat conduction, pollutant diffusion and biomass combustion reaction. The model combines the interaction of multiple factors and considers the complex physical and chemical processes inside and outside the boiler. The prior art usually uses a 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 application can provide more accurate prediction of pollutant concentration, providing a scientific basis for real-time optimization control of the boiler and solving the shortcomings of traditional technology in dealing with complex and variable operating conditions. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a method flowchart of the present application;

[0048] Figure 2 is a system framework diagram of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] Please refer to the drawings of the present application Figure 1The embodiment of the present application provides a control method for continuously monitoring biomass boiler flue gas, comprising the following steps:

[0051] S1, collecting pollutant concentration data in biomass boiler flue gas in real time, wherein the pollutants include carbon monoxide, carbon dioxide, nitrogen oxides and sulfur dioxide;

[0052] In the present application, the target of step S1 is to collect pollutant concentration data in biomass boiler flue gas in real time. The collected pollutants 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 the boiler flue gas in real time, the emission status of the boiler can be accurately evaluated, and the operation status of the boiler can be optimized based on this to ensure compliance with environmental protection standards.

[0053] In the present embodiment, multi-point sensor arrays are installed at different positions of the boiler for real-time monitoring. These sensor arrays are configured with different types of sensors that can simultaneously measure the concentrations 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:

[0054] Fourier Transform Infrared Spectroscopy (FTIR) is a widely used technology for gas analysis, especially suitable for measuring the absorption characteristics of some gases. Each gas molecule has its specific vibration frequency, which corresponds to a specific absorption wavelength in the infrared spectrum. FTIR sensors utilize this characteristic to quantitatively measure the concentration of gases by analyzing their absorption characteristics at different wavelengths.

[0055] CO2: Carbon dioxide has clear absorption peaks in the infrared spectrum, especially in the 4.2 μm and 15 μm wavelength regions. FTIR can accurately measure these absorption peaks to identify and quantify the concentration of CO2 gas.

[0056] NOx (including NO and NO2): Nitrogen oxides (NO and NO2) also have specific absorption characteristics in the infrared spectrum, and FTIR sensors can measure the concentrations of NO and NO2 simultaneously by detecting the absorption at these specific wavelengths.

[0057] The advantage of FTIR sensors is that they can simultaneously measure multiple gas components, especially suitable for detecting gases with unique absorption characteristics such as CO2 and NOx.

[0058] Laser Absorption Spectroscopy (LAS) is a highly sensitive gas analysis technique that uses a laser as the light source to detect the concentration of a gas by measuring its absorption of specific wavelengths of laser light. The laser light source can be precisely selected to specific wavelengths, making it highly selective and sensitive. Laser Absorption Spectroscopy sensors are suitable for measuring the absorption characteristics of certain gases at specific wavelengths.

[0059] CO: Carbon monoxide has very unique absorption characteristics, especially in the 4.6 μm and 5.2 μm wavelength bands. Laser Absorption Spectroscopy sensors can selectively excite and detect these absorption peaks, allowing for highly sensitive measurement of CO concentration.

[0060] SO2: Sulfur dioxide also has unique absorption characteristics, especially in the wavelength band around 7.3 μm. Laser Absorption Spectroscopy technology can accurately measure the concentration of SO2, and can reduce interference and improve detection accuracy by selecting different wavelengths of laser light.

[0061] The advantage of Laser Absorption Spectroscopy sensors is that they have very high sensitivity, selectivity and real-time response capability, making them suitable for the detection of low-concentration gases such as CO and SO2.

[0062] Gas Chromatography Analyzer (GC): GC analyzers can accurately analyze gas components, especially the concentration of trace pollutants in flue gas, and are suitable for the analysis of complex gas systems.

[0063] Sensor arrays are installed at key locations of the boiler, such as the furnace outlet, the middle section of the flue, and the exhaust port. The arrangement of the sensor array takes into account the actual operating conditions and monitoring requirements of the boiler. By reasonably selecting the type and location of the sensors, the concentration data of various pollutants in the boiler can be efficiently obtained.

[0064] Specifically, during different operating stages of the boiler, such as large load fluctuations, 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 through wireless or wired communication for subsequent processing and analysis.

[0065] As an option, the position of the sensor array can be dynamically adjusted according to the specific operating conditions in different operating modes of the boiler. 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 state of the boiler and optimize the emission control strategy.

[0066] The collection of pollutant concentration data includes the following key parameters:

[0067] C CO(t): Carbon monoxide concentration, unit: mol / m 3 , represents the concentration of CO in the boiler flue gas.

[0068] C CO2 (t): Carbon dioxide concentration, unit: mol / m 3 , represents the concentration of CO2 in the boiler flue gas.

[0069] C NOx (t): Nitrogen oxide concentration, unit: mol / m 3 , represents the concentration of nitrogen oxides in the boiler flue gas.

[0070] C SO2 (t): Sulfur dioxide concentration, unit: mol / m 3 , represents the concentration of sulfur dioxide in the boiler flue gas.

[0071] T gas (t): Flue gas temperature, unit: K, represents the instantaneous temperature of the flue gas.

[0072] P gas (t): Flue gas pressure, unit: Pa, represents the instantaneous pressure of the flue gas.

[0073] V gas (t): Flue gas flow rate, unit: m / s, represents the flow velocity of the flue gas.

[0074] After collecting the above pollutant concentration data, it will be transmitted to the data processing module in real time. Each sensor module will monitor and record the concentration of the corresponding pollutant according to its type and function. By collecting and transmitting these data, the system can understand the operation of the boiler in real time, providing a basis for subsequent control and optimization.

[0075] Pollutant concentration formula: The concentration of pollutants is usually measured by sensors. Based on the working principle of the sensor, the concentration of the pollutant can be expressed as:

[0076]

[0077] Where: C i (t) is the concentration of the i-th pollutant, unit: mol / m 3 , A i is the absorbance or signal intensity of the i-th pollutant at the sensor detection point, unit depends on the working principle of the sensor, V gas (t) is the flue gas flow rate, unit: m / s, T gas (t) is the flue gas temperature, unit: K, P gas (t) is the flue gas pressure, unit: Pa.

[0078] Through the above formula, the data measured by the sensor is converted according to the actual flue gas conditions, so as to obtain the accurate pollutant concentration.

[0079] 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, and there is no obvious loss in the transmission process. The data processing module will check and optimize the transmitted data.

[0080] By collecting the pollutant concentration data in the biomass boiler flue gas in real time, the present application can provide accurate real-time data support for subsequent steps of pollutant concentration prediction and control. Due to the combination of multiple sensors, the comprehensiveness and accuracy of the data can be ensured, especially in 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 the emission of pollutants, and improve the energy utilization efficiency of the boiler.

[0081] In one embodiment, the data acquisition module can flexibly increase or decrease the 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 severe load changes or complex emission characteristics, to ensure the accuracy of the monitoring data.

[0082] S2, the collected pollutant concentration data is transmitted to the data processing module, and the data is denoised by Kalman filtering algorithm to obtain accurate pollutant concentration data;

[0083] In the present application, 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 disturbed by noise, affecting the quality of the data, it is necessary to optimize the data by suitable algorithm to eliminate these noises. This step uses Kalman filtering algorithm for data denoising. Kalman filtering is a statistical method that can effectively extract the optimal estimation result from noisy data and is widely used in real-time data processing and optimization.

[0084] In this embodiment, the data acquisition module collects the 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 sensor errors, environmental noise and other factors, the data provided by the sensor usually contains noise, and direct use of these data may lead to inaccurate subsequent processing and analysis. Therefore, in the data processing module, the collected data is denoised by using Kalman filtering algorithm to ensure that the processed pollutant concentration data has high accuracy.

[0085] Specifically, the Kalman filter algorithm uses a recursive approach, combining the physical model of the system and the noise characteristics, to perform optimal estimation on real-time data. The basic idea is to obtain the optimal state estimation value through the weighted average of historical data and current measurement values. The basic form of Kalman filtering can be represented as:

[0086]

[0087] where: represents the estimation of the system state at time k, with the same unit as the system state (for example, the unit of pollutant concentration is mol / m 3 ); represents the state estimation based on the data at the previous time k; K k represents the Kalman gain, which is a coefficient calculated based on measurement error and prediction error, dimensionless; z k represents the pollutant concentration data collected at time k, with the unit of mol / m 3 ; H k represents the observation matrix, which converts the state of the system into measurable output, usually a unit matrix.

[0088] In the processing process of Kalman filtering, the system will continuously update the state estimation according to the sensor data and noise characteristics, iteratively solving the optimal result. This process can effectively eliminate the influence of sensor noise on data and provide more accurate pollutant concentration data.

[0089] As an option, in the data processing module, different Kalman filters can be set 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 under low concentration, thereby further improving the measurement accuracy.

[0090] 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 adaptability and accuracy of the system to the concentration of pollutants in a high-noise environment 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 concentration during boiler operation.

[0091] The core algorithm of Kalman filtering includes two important update steps: prediction and update. In step S2, we mainly focus on the calculation of the Kalman gain and the prediction error of the data update process.

[0092] Prediction step:

[0093]

[0094] wherein:

[0095] represents the predicted state value, estimated based on the data at time k-1 at time k;

[0096] A k represents the state transition matrix, describing the change of system state from time k-1 to k;

[0097] B k represents the control input matrix, used to describe the influence of external input on system state;

[0098] u k represents the control input.

[0099] Update step:

[0100]

[0101] wherein:

[0102] K k represents the Kalman gain, representing the weight of new observation value in data update;

[0103] z k represents the measurement value, representing the real-time collected pollutant concentration;

[0104] H k represents the observation matrix, converting state quantity into measurable quantity.

[0105] The optimization process of Kalman filter will continuously adjust the gain and error covariance matrix of the filter through recursive calculation, so as to ensure the accuracy of pollutant concentration data.

[0106] By adopting Kalman filter algorithm, this step can effectively remove the interference of sensor noise on data, improve the precision and reliability of pollutant concentration data. The data processed by Kalman filter will be passed to subsequent steps such as pollutant concentration prediction and boiler control, to ensure that the control algorithm of the system can be based on high-quality data for accurate optimization. In addition, the application of Kalman filter algorithm provides higher robustness and adaptability for real-time monitoring system, especially in the face of boiler load fluctuation and complex operating conditions, it can more accurately reflect the actual situation of flue gas pollutant concentration.

[0107] S3, based on real-time data and mathematical model, predict the trend of pollutant concentration in the boiler, the mathematical model includes flue gas flow, heat conduction, pollutant diffusion and biomass combustion reaction model;

[0108] In the present invention, the main objective of step S3 is to predict the trend of pollutant concentration changes within the boiler based on real-time data and mathematical models. Since the pollutant concentration within the boiler is influenced by multiple factors such as boiler load, combustion conditions, gas flow, temperature distribution, etc., using 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 assess the dynamic changes of pollutant concentration, identify potential emission risks, and adjust the boiler operating parameters to optimize emission control and improve energy efficiency.

[0109] 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, etc. of pollutants within the boiler. These models include:

[0110] Flue gas flow model, used to describe the flow characteristics of flue gas inside the boiler.

[0111] Heat conduction equation, used to describe the temperature distribution of flue gas within the boiler.

[0112] Pollutant diffusion equation, used to simulate the diffusion process of pollutants in flue gas flow.

[0113] Biomass combustion reaction model, used to describe the generation rate of pollutants during biomass combustion.

[0114] Through the combination of these mathematical models, the system can comprehensively predict the trend of changes in pollutant concentration within the boiler from different dimensions, thereby providing scientific basis for subsequent control and optimization.

[0115] Specifically, in this step, first, through real-time collection of pollutant concentration data, combined with the actual operation of the boiler (such as temperature, pressure, flow rate, etc.), fluid dynamics model is used to simulate the flow of flue gas. Then, through the heat conduction equation, the temperature distribution within the boiler is calculated, and the diffusion and reaction process of pollutants is considered, and finally the trend of change of pollutant concentration with time is predicted by combining the reaction rate model. This process obtains the dynamic change prediction of pollutant concentration in the future period of time by solving the numerical solution of the mathematical model.

[0116] As an option, model parameters can also be dynamically adjusted according to different operating states of the boiler (such as load change, gasification agent flow change, etc.) in this step to improve the accuracy of prediction. At this time, by comparing the real-time monitored data with the model results, the model parameters are optimized and adjusted to ensure the timeliness and accuracy of the model.

[0117] Flue gas flow model (Navier-Stokes equation):

[0118] The flue gas flow is described by the Navier-Stokes equation, which takes into account the inertial forces, pressure gradient forces, viscous forces and external forces of the fluid. The flue gas flow equation can be expressed as:

[0119]

[0120] where: ρ is the density of the fluid [kg / m 3 ], represents the mass density of the flue gas, v is the velocity field of the flue gas [m / s], represents the flow rate and flow direction of the flue gas, t is the time [s], used to describe the time variation in the flue gas flow process, p is the pressure [Pa], represents the pressure of the flue gas, μ is the dynamic viscosity [Pa·s], represents the viscous characteristics of the flue gas, f is the body force (such as gravity) [N / m 3 ], represents the external force acting on the flue gas, 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 ), represents the influence of non-uniform flow of the fluid in space on the velocity, is the pressure gradient (unit: Pa / m), represents the rate of change of pressure in space, is the Laplace operator of the velocity field (unit: m / s 2 ), describes the spatial diffusion effect of the velocity field.

[0121] 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 calculation.

[0122] Heat conduction equation:

[0123] 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 its specific form is:

[0124]

[0125] where: T is the temperature [K], represents the temperature of the flue gas in the boiler, v is the flue gas flow rate [m / s], affects the transfer of heat, α is the thermal diffusivity [m 2 / s], represents the rate of heat diffusion, q is the heat source intensity per unit volume [W / m 3 ], represents the heat source situation in the boiler, t is the time [s], used to describe the time variation of the temperature, is the rate of change of temperature with time (unit: K / s), represents the change of temperature field over time, is the Laplace operator of the temperature field (unit: K / m 2represents the spatial diffusion of temperature, describing how heat transfers from high-temperature regions to low-temperature regions in space, is the convection term of the temperature field (unit: K / s), describing the influence of the fluid's velocity field on the temperature distribution. The flow of fluid can bring heat from high-temperature regions to low-temperature regions.

[0126] By solving this equation, the temperature distribution at each position in the boiler can be obtained, providing temperature data support for the subsequent calculation of the pollutant diffusion equation.

[0127] Pollutant diffusion equation:

[0128] The diffusion process of pollutants in flue gas is modeled by the diffusion equation. The diffusion equation considers the change of pollutant concentration with time and space, which is expressed as:

[0129]

[0130] where C is the pollutant concentration [mol / m 3 ], representing the concentration of pollutants in the boiler flue gas, t is the time [s] used to describe the change of pollutant concentration with time, 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 with time (unit: mol / m 3 ·s), representing the change of pollutant concentration per unit volume with time.

[0131] R(T,C) is the reaction rate [mol / m 3 ·s], which depends on temperature T and pollutant concentration C, representing the generation rate of pollutants in the biomass combustion process.

[0132] is the Laplacian operator, representing the change of pollutant concentration in space.

[0133] This equation further obtains the concentration distribution of pollutants by solving the diffusion process of pollutants in the boiler.

[0134] Biomass combustion reaction model (Arrhenius equation):

[0135] The reaction rate of biomass combustion process is described by the Arrhenius equation, which considers the influence of temperature and reactant concentration on the reaction rate, and the specific form is:

[0136]

[0137] where:

[0138] r is the reaction rate [mol / m 3·s] represents the rate of pollutant generation.

[0139] A is a pre-exponential factor [mol / m 3 ·s] represents the constant of reaction rate.

[0140] E is the activation energy [J / mol], which describes the temperature dependence of the reaction.

[0141] R is the gas constant [8.314 J / mol·K], which is a constant.

[0142] T is the temperature [K], which represents the temperature of flue gas in the boiler.

[0143] C is the reactant concentration [mol / m 3 ], which represents the concentration of reactants in the biomass combustion process;

[0144] is an exponential term, which represents the influence of temperature on reaction rate.

[0145] Through the biomass combustion reaction model, the system can calculate the generation rate of pollutants in the combustion process, and further calculate the change of pollutant concentration in the boiler.

[0146] By combining these mathematical models, the present application can accurately predict the trend of change of pollutant concentration in the boiler. The joint use of flow model, heat conduction equation, diffusion equation and combustion reaction model makes the prediction results fully reflect the dynamic behavior of pollutants in the boiler. Under actual working conditions such as change of boiler load and change of combustion temperature, the model can timely adjust and provide accurate prediction of the change of pollutant concentration, thereby providing reliable data support for the optimization control of the boiler.

[0147] In one embodiment, by dynamically adjusting the model parameters, the system can cope with different boiler operating conditions and external conditions (such as climate change, fuel quality, etc.), ensuring the accuracy and real-time of the prediction. This makes the system have strong adaptability and robustness, which can optimize the operating parameters of the boiler in real time, reduce pollutant emissions and improve the operating efficiency of the boiler.

[0148] S4, minimizing the pollutant concentration by calculating the objective function, and adjusting the boiler operating parameters according to the minimization result, the operating parameters including oxygen supply amount, combustion temperature, gasification agent flow and fuel input amount;

[0149] The purpose of step S4 is to minimize the pollutant concentration by calculating the objective function and adjusting the operating parameters of the boiler based on the minimization result, further optimizing the combustion efficiency and pollutant emission of the boiler. The core of this step is to calculate the optimal boiler operating parameters (such as oxygen supply, combustion temperature, gasifying agent flow, fuel input, etc.) based on real-time data and the predicted pollutant concentration trend in the previous steps through an optimization algorithm. The adjustment of these optimal parameters aims to maximize the energy utilization efficiency of the boiler while ensuring that the pollutant emission is minimized and meets the environmental emission standards.

[0150] 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 previous step S3, thereby achieving the minimum emission of pollutants and optimizing the combustion efficiency.

[0151] In this embodiment, this step is implemented by constructing an objective function to minimize the pollutant concentration and adjusting the control parameters of the boiler based on the optimization result. The objective function is used to measure the relationship between the pollutant concentration and the boiler control parameters, and the optimization goal is to minimize the pollutant concentration and make the boiler operating parameters (such as oxygen supply, combustion temperature, gasifying agent flow, and fuel input) optimal.

[0152] Definition of the objective function:

[0153] First, define an objective function J(t) that aims to minimize the concentration of pollutants in the boiler flue gas while keeping the boiler operating at an optimal state. The mathematical form of the objective function is:

[0154]

[0155] where C i (t) is the concentration of the i-th pollutant, a 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, t is the time variable, which represents the change of the objective function at different times, is the square of the pollutant concentration, and J(t) is the value of the objective function.

[0156] This objective function combines the relationship between the pollutant concentration and the boiler control parameters. The square term of the pollutant concentration emphasizes the impact of pollutants with higher concentrations, while the deviation term between the control parameter and the optimal control parameter reflects the optimization direction of the boiler operating state.

[0157] Optimization of the objective function:

[0158] To minimize the objective function, optimization algorithms (such as gradient descent, optimal control algorithms, etc.) are used to calculate the optimal boiler control parameters. Through an iterative optimization process, the optimization algorithm continuously adjusts the boiler's operating parameters to minimize the objective function J(t). Specifically, the optimal control parameters are obtained by solving the following equation:

[0159]

[0160] where:

[0161] is the partial derivative of the objective function with respect to the control parameter u(t), representing the trend of the objective function under the current control parameter;

[0162] u(t) is the control parameter of the boiler, with units related to the control target (such as m / s, °C). 3

[0163] After solving this equation, the optimal control parameters can be obtained, thereby minimizing the pollutant concentration and allowing the boiler to operate in an optimal state.

[0164] Boiler operating parameter adjustment:

[0165] After obtaining the optimal control parameters calculated in the optimization process, the system adjusts the boiler's operating state in real time based on these optimal parameters, with the following specific adjustments:

[0166] Oxygen supply amount (u O2 ): Properly adjust the oxygen supply amount to ensure sufficient oxygen during the combustion process, thereby improving combustion efficiency and reducing pollutant emissions. The adjustment of oxygen supply amount 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 that combustion is carried out within the optimal temperature range, promoting complete combustion and reducing the generation of harmful gases. By adjusting the boiler's burner and combustion temperature sensor, the temperature is adjusted in real time;

[0167] Gasification agent flow (u gas ): According to the needs of the combustion process, adjust the flow of gasification agents (such as steam, air, etc.) to control the oxygen supply and reaction rate in the gasification reaction. Adjusting the gasification agent flow helps reduce the generation of pollutants;

[0168] Fuel input amount (u fuel ): Adjust the input amount of fuel according to the boiler load and operating state. Timely increase or decrease the fuel flow, which helps maintain the stability of the boiler operation and ensures that the emission of pollutants is maintained within the specified standards.

[0169] ​With these optimized boiler operating parameters, the boiler can maintain high efficiency under different loads and operating conditions, and reduce pollutant emissions.

[0170] Dynamic adjustment of the objective function:

[0171] As an option, if the system faces multiple optimization objectives, such as both reducing pollutant concentration and improving energy utilization efficiency, the objective function can be further extended to a multi-objective optimization problem. In this case, the weighting coefficients (such as α i and β) in the objective function can be adjusted according to different operating conditions and optimization requirements, to balance the needs of pollutant concentration control and boiler performance optimization.

[0172] The extended objective function can also have the following form:

[0173]

[0174] Where:

[0175] E eff (t) is the energy efficiency of the boiler, representing the energy utilization efficiency of the boiler under the current control parameters;

[0176] β1 and β2 are the weighting coefficients of pollutant concentration and energy efficiency, respectively;

[0177] J multi (t) is the objective function value of multi-objective optimization;

[0178] is the weighted sum of pollutant concentration terms, which is the first part of the objective function, representing the contribution of each pollutant concentration C i (t) to the objective function. α i is the weight coefficient of each pollutant concentration, used to represent the influence degree of different pollutants on the objective function. By squaring C i (t), the influence of high concentration pollutants on the optimization objective can be emphasized;

[0179] β1(u(t)-u optimal ) 2 is the square term of the boiler control parameter deviation (unit: dimensionless). This part reflects the contribution of the difference between the current control parameter u(t) of the boiler and its optimal value u optimal to the objective function. β1 is the weight coefficient, the greater the deviation of the control parameter, the greater the penalty to the objective function, thus encouraging the system to adjust the parameter to reduce this deviation;

[0180] β2E eff (t) is the boiler energy efficiency term (unit: dimensionless). E eff(t) represents the real-time energy efficiency of the boiler, and β2 is its weight coefficient, indicating the influence of energy efficiency on the objective function. A higher energy efficiency value will reduce the value of the objective function, promoting the optimization of the boiler operation.

[0181] By adjusting the weight coefficient, the system can dynamically optimize according to different target requirements, achieving the dual optimization goal of minimizing pollutant emissions and maximizing energy efficiency.

[0182] Through the above optimization process, this step can optimize the combustion efficiency of the boiler while ensuring that the pollutant emissions meet environmental protection standards. The system can not only accurately control the pollutant concentration by real-time calculation and adjustment of the boiler's operating parameters, but also improve the energy utilization efficiency of the boiler and reduce fuel consumption. Under different working conditions, the optimization algorithm can ensure the continuous optimization of the boiler's operating state, thereby realizing a long-term stable and efficient combustion process.

[0183] In one possible implementation, through real-time monitoring and optimization control, the system can respond to fluctuations in boiler load and external climate changes, ensuring that the boiler always operates in an optimal state. In addition, through the dynamic adjustment mechanism of the objective function, the system can flexibly adjust the optimization target according to different requirements of pollutant emissions and energy utilization efficiency, thereby realizing a more efficient control strategy.

[0184] S5, based on the adjustment result, real-time feedback adjusts the operating state of the boiler, continuously optimizes the combustion process, and ensures that the pollutant emissions meet environmental protection standards.

[0185] In the present application, the core purpose of step S5 is to adjust the operating state of the boiler in real time based on the optimization results 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 maintain optimal performance under varying working conditions, reduce pollutant emissions, and improve energy utilization efficiency by real-time monitoring of the boiler's operating state, adjusting key control parameters, and optimizing the combustion process.

[0186] Specifically, this step adjusts the key parameters of the boiler (such as oxygen supply, combustion temperature, gasifying agent flow, and fuel input) according to real-time data through a feedback control mechanism, ensuring that the boiler can maintain optimal combustion conditions and the lowest pollutant emissions under any working conditions. This process involves real-time monitoring, data analysis, parameter adjustment, and optimization.

[0187] Technical implementation:

[0188] 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 the best combustion efficiency and the lowest pollutant emissions.

[0189] Real-time monitoring and data feedback:

[0190] Generally, during the operation of the boiler, various sensors are used to collect real-time information about the composition of flue gas (such as pollutant concentration), temperature, pressure, and flow rate, etc. 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, CO2, NOx, etc.), pressure sensors, and flow sensors. Sensor data provides dynamic information of various key parameters in the boiler.

[0191] As an option, sensor arrays are installed at various key locations of the boiler (such as the combustion chamber, the furnace outlet, the exhaust port, etc.) to ensure real-time acquisition of data such as pollutant concentration in flue gas, combustion temperature, flow rate, etc. These data not only reflect the working state of the boiler, but also reflect the emission level of pollutants. Whenever the concentration of pollutants is detected to be excessive or the combustion conditions are not ideal, the system will make corresponding adjustments through the feedback mechanism.

[0192] Real-time feedback and control parameter adjustment:

[0193] Specifically, after receiving the sensor data, the system will adjust the control parameters of the boiler according to the comparison between the real-time data and the target value. The adjustment of control parameters includes but is not limited to oxygen supply, combustion temperature, gasification agent flow, and fuel input. The goal of adjusting each parameter is to improve combustion efficiency, ensure complete combustion, and minimize the generation of pollutants.

[0194] Oxygen supply (u O2 ): By adjusting the air flow to control the oxygen supply, it ensures sufficient oxygen during the combustion process. At low oxygen concentration, the boiler may experience incomplete combustion, which increases pollutant emissions, especially carbon monoxide (CO) and incompletely burned carbon particles. Real-time adjustment of oxygen flow to adapt to changes in boiler load ensures complete combustion.

[0195] Combustion temperature (u T ): Temperature has an important influence on the combustion process. Under high temperature conditions, the combustion efficiency is higher, but excessively high temperature will lead to the generation of nitrogen oxides (NOx). The system will adjust the input of the burner according to 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.

[0196] Gasification agent flow rate (u gas ): The flow rate of the gasification agent (such as steam or air) directly affects the reaction rate and the concentration of the generated gas during the gasification process. By adjusting the flow rate of the gasification agent in real time, the boiler can maintain the optimal gasification reaction, ensuring that the reaction is complete and optimizing the emission of pollutants.

[0197] Fuel input quantity (u fuel ): The fuel input quantity directly affects the thermal load and emission level of the boiler. According to the load requirements of the boiler and the real-time control target, the system will adjust the amount of fuel supplied to avoid incomplete combustion or energy waste caused by excessive or insufficient fuel input.

[0198] Real-time optimization and feedback of the adjustment process:

[0199] 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 expected target, the system will continue to adjust the parameters until the requirements are met. For example, after adjusting the oxygen supply quantity, the system will monitor the concentrations of CO, CO2, and NOx. If the concentrations still do not reach the ideal level, the system will further increase or decrease the oxygen flow until the optimal balance is achieved.

[0200] In one possible implementation, the feedback adjustment process is completed through a control algorithm (such as a PID controller or a fuzzy controller) that automatically corrects any deviations. The system calculates the deviation in real time based on target values such as target pollutant concentrations, combustion temperature, etc., 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.

[0201] System optimization and adaptive adjustment:

[0202] Specifically, as the boiler operates in different stages and under varying loads, the feedback adjustment system can dynamically adjust the priority of the control parameters. For example, in low-load conditions, the fuel input quantity may not need to be adjusted too much, while in high-load or cold-start stages, the supply of fuel and oxygen may need to be adjusted first. The system can adaptively adjust the control algorithm based on factors such as boiler load, external environment, etc.

[0203] As an option, the system can also learn from historical data to establish an adaptive control mechanism that automatically adjusts the optimal parameters. For example, the system can optimize the weight coefficients of the objective function through machine learning algorithms based on the operating data of the boiler under different loads, to achieve more accurate control.

[0204] Through the feedback adjustment mechanism in this step, the system can continuously optimize the operation process of the boiler according to real-time data and adjustment results, ensuring that the boiler maintains efficient combustion and reduces pollutant emissions under various working conditions. Combined with the target function minimization process in step S4, the feedback adjustment mechanism can dynamically respond to external factors such as boiler load fluctuations, fuel quality changes, and climate condition changes, so that the boiler always maintains optimal combustion conditions.

[0205] Generally, this real-time feedback control can significantly improve the energy utilization efficiency of the boiler and effectively reduce pollutant emissions, ensuring 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, achieving intelligent, precise, and efficient combustion of the boiler.

[0206] In one possible implementation, combined with real-time data and model prediction, the system can predict the operation trend of the boiler in advance, avoid incomplete combustion or excessive pollutant emissions due to sudden events or load changes, and further improve the adaptability and reliability of the boiler.

[0207] The control system for continuously monitoring the flue gas of a biomass boiler described below can be mutually referred to the control method for continuously monitoring the flue gas of a biomass boiler described above.

[0208] Please refer to the attached Figure 2 The control system for continuously monitoring the flue gas of a biomass boiler is used to execute the control method for continuously monitoring the flue gas of a biomass boiler described above, comprising:

[0209] A data acquisition module is used to acquire temperature, pressure, and pollutant concentration data in the flue gas of the boiler in real time through a sensor array; a data processing module is used to remove noise and optimize the collected data, outputting accurate pollutant concentration data; a prediction model module is used to predict the change trend of pollutant concentration based on real-time data and mathematical models;

[0210] An optimization control module is used to calculate a target function and adjust the operating parameters of the boiler according to the prediction results of the pollutant concentration;

[0211] A feedback adjustment module is used to adjust the operating state of the boiler in real time according to the output results of the optimization control module, ensuring the minimization of pollutant emissions and improving the energy efficiency of the boiler

[0212] The system of the embodiment can be used to execute the above-mentioned method embodiments, and the principles and technical effects are similar, which will not be repeated here.

[0213] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for continuously monitoring and controlling flue gas from a biomass boiler, characterized in that, Includes the following steps: S1. Real-time acquisition of pollutant concentration data in the flue gas of biomass boilers, wherein the pollutants include carbon monoxide, carbon dioxide, nitrogen oxides and sulfur dioxide; S2. The collected pollutant concentration data is transmitted to the data processing module, and the data is denoised using the Kalman filter algorithm to obtain accurate pollutant concentration data. S3. Based on real-time data and mathematical models, predict the changing trend of pollutant concentration in the boiler. The mathematical models include flue gas flow, heat conduction, pollutant diffusion, and biomass combustion reaction models. The specific steps in step S3, which involve predicting the changing trend of pollutant concentrations within the boiler based on real-time data and mathematical models, 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 flue gas, predict the trend of pollutant concentration over time and identify potential risks of excessive pollutant emissions. S4. Minimize the pollutant concentration by calculating the objective function, and adjust the boiler operating parameters according to the minimization result. The operating parameters include oxygen supply, combustion temperature, gasifying agent flow rate and fuel input. S5. Based on the adjustment results, provide real-time feedback on the boiler's operating status and continuously optimize the combustion process to ensure that pollutant emissions meet environmental protection standards.

2. The control method for continuous monitoring of biomass boiler flue gas according to claim 1, characterized in that, The specific steps for real-time acquisition of pollutant concentration data in the flue gas of the biomass boiler 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 flue gas outlet of the boiler to monitor the concentration of pollutants in the flue gas in real time. S1.2 The data collected by the sensor includes 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.

3. The control method for continuous monitoring of biomass boiler flue gas according to claim 2, characterized in that, The sensor array in step S1.1 includes: Fourier transform infrared spectroscopy sensor for measuring the composition and concentration of CO2 and NOx gases; Laser absorption spectroscopy sensor for measuring the composition and concentration of CO and SO2 gases; Gas chromatography analyzers are used to accurately measure the concentration of pollutants.

4. The control method for continuous monitoring of biomass boiler flue gas according to claim 1, characterized in that, The specific steps in step S2 of transmitting the collected pollutant concentration data to the data processing module are as follows: S2.1 Transmit the data from each sensor to the data processing module via wireless or wired communication; S2.2 Perform 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 sensor noise interference, and obtain accurate pollutant concentration data. S2.

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

5. The control method for continuous monitoring of biomass boiler flue gas according to claim 1, characterized in that, The step in S4, which involves calculating the objective function to minimize the pollutant concentration, is as follows: S4.

1. Based on real-time data and prediction results, define an objective function, where the objective function is the weighted sum of squares of pollutant concentrations; S4.2 Calculate the optimal boiler control parameters that minimize pollutant concentration based on the objective function, including oxygen supply, combustion temperature, gasifying agent flow rate and fuel input. S4.

3. Use optimization algorithms to solve for the optimal control parameters.

6. The control method for continuous monitoring of biomass boiler flue gas according to claim 5, 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 Here, u(t) is the weighting coefficient for pollutants, and u(t) is the boiler control parameter. optimal β is the optimal control parameter, t is the penalty coefficient, and t is the time variable used to represent the change of the objective function at different times. Let J(t) be the square of the pollutant concentration, and J(t) be the objective function value.

7. The control method for continuous monitoring of biomass boiler flue gas according to claim 1, characterized in that, The step in S5, which involves real-time feedback and adjustment of the boiler's operating status based on the adjustment results, is as follows: S5.

1. Based on the optimal operating parameters calculated by the optimization control algorithm, adjust the oxygen supply, combustion temperature, gasifying agent flow rate and fuel input of the boiler in real time. S5.2 Monitor the actual operating status of the boiler and compare it with the target value. Correct and adjust the parameters in real time to ensure the optimal combustion efficiency and pollutant emissions of the boiler. S5.3 Install feedback sensors at various key locations in the boiler to monitor temperature and pollutant concentration data in real time during the combustion process, ensuring that the boiler operates continuously in the optimal state.

8. 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 1-7 includes: The data acquisition module is used to collect real-time data on temperature, pressure, and pollutant concentration in boiler flue gas through a 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 changing trends of pollutant concentrations based on real-time data and mathematical models. The optimized control module is used to calculate the objective function and adjust the boiler's operating parameters based on the predicted pollutant concentrations. The feedback adjustment module is used to adjust the boiler's operating status in real time based on the output of the optimization control module, ensuring that pollutant emissions are minimized and the boiler's energy efficiency is improved.

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