Nitrogen oxide incineration equipment control method for drying and carbonizing

Abstract

The invention provides a nitrogen oxide incineration equipment control method for drying carbonization, which comprises the following steps: acquiring combustion characteristic data of high-humidity sludge blocks in a fluidized bed incinerator, and extracting nitrogen oxide peak intensity, occurrence time, duration and peak shape steep degree to obtain a parameter set; real-time load data of the induced draft fan are obtained, the hearth negative pressure stability fluctuation range is analyzed in combination with the adjustment value, and if fluctuation exceeds a preset safety range, the load adjustment amplitude of the induced draft fan is calculated, and a load adjustment curve is generated; a multi-actuator cooperative control time sequence model is constructed based on the load adjustment curve and the adjustment value, the nitrogen oxide peak generation time and the duration are fused, the cooperative adjustment time sequence of secondary air supply and the draught fan load is calculated, and a control instruction is generated; and a control instruction is transmitted to a fluidized bed incinerator executing mechanism, the opening state of the tuyere and the air volume ratio are adjusted, the load of the induced draft fan is updated, and the adjusted hearth operation parameters are continuously monitored.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a control method for drying and carbonizing nitrogen oxide incineration equipment. Background Art

[0002] Fluidized bed incineration is a key technology for treating high-moisture sludge. It efficiently recovers heat and effectively controls pollutants, making it crucial for reducing solid waste and maximizing its resource utilization. However, existing technologies for controlling nitrogen oxides (NOx) generated during the incineration of high-moisture sludge clumps often rely on single control devices. Due to the slow heat transfer within the clumps, these technologies struggle to adapt to the complex NOx release patterns encountered during incineration. For example, NOx concentrations can spike suddenly, often occurring later than the peak temperature of the furnace bed. This makes it difficult to consistently meet NOx emissions standards within the furnace, impacting the environmental performance of the incineration process. Accurately predicting the intensity, onset, duration, and steepness of NOx emission peaks, and using this predicted information to coordinate the control of multiple actuators, such as the secondary air supply and induced draft fan, presents a complex problem. This coordinated control requires precise, layered control of the secondary air supply, dynamic adjustment of the induced draft fan load to match incineration demand, and rapid response to dramatic, nonlinear fluctuations in NOx concentration peaks. However, there is currently a lack of a predictive model specifically tailored to the incineration characteristics of high-humidity sludge clumps, nor is there a way to effectively combine this prediction with the coordinated control of multiple regulating devices. This lack directly leads to improper control timing and inaccurate control of the adjustment amplitude, making it difficult to effectively reduce the peak emission of nitrogen oxides. Therefore, how to dynamically generate and optimize the coordinated adjustment plan for stratified secondary air supply and induced draft fan load based on accurate predictions of the peak characteristics of nitrogen oxide release, such as the adjustment of the air outlet timing and amplitude, has become the core technical bottleneck for effectively controlling nitrogen oxide emissions in fluidized bed incinerators when treating high-humidity sludge clumps. Summary of the Invention

[0003] The present invention provides a control method for drying and carbonizing nitrogen oxide incineration equipment, which mainly includes:

[0004] Obtain the combustion characteristic data of high-humidity sludge agglomerates in a fluidized bed incinerator, extract the nitrogen oxide peak intensity, occurrence time, duration, and peak steepness, and obtain a parameter set;

[0005] The peak bed temperature data in the fluidized bed incinerator is collected. Based on the parameter set, the time series analysis method is used to calculate the hysteresis relationship between the occurrence time of the nitrogen oxide peak and the bed temperature peak. The change trend characteristics of the release peak are determined by combining the peak steepness and duration.

[0006] According to the changing trend characteristics and based on the furnace airflow dynamics, a stratified secondary air supply adjustment model was constructed. The peak intensity and peak steepness of nitrogen oxides were input into the stratified secondary air supply adjustment model to calculate the opening timing of tuyere at different heights and the air volume distribution ratio, and adjust the stratified air volume;

[0007] Based on the airflow distribution simulation, the airflow distribution in the furnace is obtained. Combined with the adjusted stratified air volume, the suppression effect of the secondary air supply on the nitrogen oxide release peak is analyzed. This is reflected by the emission concentration reduction index. If the emission concentration reduction index is less than the preset threshold, the air outlet timing and air volume ratio are optimized to obtain the adjustment value.

[0008] Obtain real-time induced draft fan load data, combine it with the adjustment value, and analyze the fluctuation range of furnace negative pressure stability. If the fluctuation exceeds the preset safety range, calculate the induced draft fan load adjustment range and generate a load adjustment curve;

[0009] A multi-actuator coordinated control timing model is constructed based on the load adjustment curve and adjustment value. The NOx peak occurrence time and duration are integrated to calculate the coordinated adjustment timing of the secondary air supply and the induced draft fan load, and generate control instructions.

[0010] Transmit control instructions to the fluidized bed incinerator actuator, adjust the air inlet opening status and air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operating parameters.

[0011] Furthermore, data on the combustion characteristics of high-humidity sludge agglomerates in a fluidized bed incinerator were collected to extract the peak intensity, onset time, duration, and peak steepness of nitrogen oxides (NOx). This parameter set was obtained. This included: collecting sludge agglomerate surface temperature data using an infrared imaging system; obtaining the surface reflectance spectrum of the sludge agglomerates using an infrared spectrometer; and calculating the sludge moisture content and particle size distribution parameters based on the spectral absorption characteristic curve. Real-time combustion data was collected using a temperature sensor array and a pressure sensor array arranged at the bottom of the fluidized bed. The temperature and pressure data were subjected to noise reduction processing using a Kalman filter algorithm to obtain a set of sludge agglomerate combustion state parameters. Combustion gas was collected from sampling probes arranged at different heights within the fluidized bed. The oxygen concentration curve in the combustion gas was measured using an oxygen content analyzer, and a real-time reaction activity index was calculated based on the combustion reaction stoichiometry. On-line analysis of NOx in the sampled gas was performed using a gas chromatography-mass spectrometer to obtain time-series NOx concentration data. The periodic characteristics of the concentration fluctuations were extracted using a Fourier transform, and the peak intensity value was calculated. Based on time-series data on nitrogen oxide concentrations, a wavelet transform was used to perform multi-scale decomposition of the concentration curve, extracting peak shape characteristic parameters. A quantitative indicator of steepness was obtained by calculating the maximum value of the curve's first-order derivative. Based on temperature variation data and nitrogen oxide generation data from sludge agglomerate combustion, a temperature-nitrogen oxide correlation model was constructed using a long-short-term memory network. Input features included temperature, pressure, moisture content, and reaction activity indicators, and the output was a set of nitrogen oxide characteristic parameters.

[0012] Furthermore, peak bed temperature data from the fluidized bed incinerator is collected. Based on a set of parameters, a time series analysis method is used to calculate the lag between the NOx peak occurrence time and the bed temperature peak. The trend characteristics of the release peak are determined by combining the peak steepness and duration. This includes: collecting bed temperature data from the fluidized bed incinerator using a thermocouple sensor array, removing noise from the temperature data using a bandpass filter, extracting the bed temperature peak point from the filtered temperature curve, and obtaining the bed temperature peak time and peak intensity data. The temperature gradient and heat accumulation values between adjacent measurement points are calculated based on the bed temperature time series data. The temperature gradient data is smoothed using a median filter to obtain a bed temperature change rate curve. NOx concentration data is recorded using an online gas monitoring device, and the concentration data is decomposed into five layers using a wavelet transform. The NOx peak time and peak intensity are extracted from the wavelet coefficients. A fixed time window is set to perform cross-correlation analysis on the bed temperature peak and the NOx peak. The lag time between the two is determined by the time difference corresponding to the maximum value of the cross-correlation function. The first-order derivative of the NOx concentration curve is calculated to obtain the instantaneous release rate, and the second-order derivative is calculated to obtain the inflection point of the curve. The fluctuation period of the NOx release process is determined by combining the wavelet transform coefficients. Peak offset and duration characteristics are extracted from the NOx release curve. Combined with the bed temperature change rate and heat accumulation value, support vector regression is used to establish a mapping relationship between temperature characteristics and NOx release characteristics. Input features include temperature peak intensity, temperature change rate, and heat accumulation value, while output features include peak offset, release rate, and fluctuation amplitude.

[0013] Furthermore, based on the changing trend characteristics and furnace airflow dynamics, a stratified secondary air supply adjustment model was constructed. The NOx peak intensity and peak steepness were input into the stratified secondary air supply adjustment model to calculate the opening sequence of tuyere openings at different heights and the air volume distribution ratio, thereby adjusting the stratified air volume. This approach involved using a Doppler flowmeter to collect airflow velocity data at different furnace heights, using a velocity probe array to obtain three-dimensional velocity components, and solving the airflow dynamics equations using the finite difference method to obtain a velocity field distribution map within the furnace. The velocity field distribution map was then used to divide the furnace airflow into zones. Pressure data from each zone was collected using an array of pressure sensors, and combined with temperature distribution data from temperature sensors to establish a furnace airflow characteristic dataset. NOx concentration data was collected using an online gas monitoring device, and peak intensity and peak steepness features were extracted from the concentration curve. An air volume distribution predictor was established using a deep neural network. The input features included airflow velocity, pressure, and temperature data, and the output was the tuyere opening value for each layer. Based on the tuyere opening values for each layer, computational fluid dynamics methods were used to calculate the degree of mixing between the secondary air and the furnace gas, and the mixing intensity index was quantified using the tracer gas concentration decay rate. The optimal air volume distribution scheme was solved using a particle swarm optimization algorithm. The optimization objective function included mixing intensity and nitrogen oxide emissions, and the constraints included the maximum opening limit of the tuyere and the requirement for total air volume balance. Dynamic response features were extracted from the pressure variation data of the tuyere at each floor, and a support vector machine predictor was used to establish a tuyere opening timing planner. The input features included the frequency and amplitude of pressure fluctuations, and the output parameters were the tuyere opening time and duration.

[0014] Furthermore, based on airflow distribution simulation, the airflow distribution within the furnace is determined. Combined with the adjusted stratified air volume, the effect of secondary air supply on suppressing the NOx release peak is analyzed. This is reflected by an emission concentration reduction index. If the emission concentration reduction index is less than a preset threshold, the tuyere timing and air volume ratio are optimized to obtain the adjustment value. This includes: Based on the furnace airflow velocity data collected by a Doppler flow meter and the tuyere pressure data recorded by a pressure sensor, computational fluid dynamics simulation is used to obtain the furnace airflow field distribution data. The airflow velocity vector field and Reynolds stress tensor are extracted from the airflow field distribution data. The Reynolds stress tensor is used to calculate the airflow turbulence intensity index. The furnace airflow mixing regions are divided according to the turbulence intensity index, and the gas mixing characteristic parameters of each region are obtained. The NOx emission concentration data before and after the adjustment are recorded using an online gas monitoring device. The concentration curve is denoised using a wavelet transform, and the emission concentration reduction index is calculated based on the peak concentration ratio. The emission concentration reduction index is compared with the preset baseline value. If the reduction index is less than the preset baseline value, the tuyere parameter optimization process is initiated. A genetic algorithm is used to optimize the vent opening sequence and air volume distribution ratio. The optimization objective function includes gas mixing characteristic parameters and emission concentration reduction indicators. Constraints include the maximum vent opening limit and the total air volume balance requirement. Based on the optimized parameters solved by the genetic algorithm, a neural network predictor is used to establish a vent adjustment plan. The input features include airflow field distribution data and gas mixing characteristic parameters, and the output features include the vent opening sequence and opening curve.

[0015] Furthermore, real-time airflow velocity and direction data from the combustion zone and secondary air inlet area of the furnace are collected. Turbulence dynamics simulation is performed on this data based on fluid dynamics to simulate airflow distribution characteristics. Simulation results of the airflow velocity and direction fields are output. These simulation results are then calibrated with actual furnace operating parameters to obtain simulated furnace airflow distribution data. This involves placing a sensor grid in the combustion zone and secondary air inlet area using Doppler flowmeters to collect real-time three-dimensional airflow velocity data. A Kalman filter is then used to denoise the raw velocity data to obtain filtered velocity field data. Frequency domain processing of the velocity field data is performed using a bandpass filter to extract velocity pulsation characteristics. The velocity pulsation intensity and the angle between the main flow direction are calculated to obtain characteristic airflow motion parameters. Temperature field data is collected from a thermocouple array in the combustion zone, and pressure distribution data from a pressure sensor array are used to obtain secondary air inlet pressure distribution data. Combined with real-time oxygen content data recorded by an oxygen sensor, a set of furnace operating state parameters is constructed. A turbulence dynamics predictor is developed using a deep neural network. The input layer contains velocity field data, temperature field data, and pressure distribution data. The hidden layer uses a residual structure to extract features. The output layer generates a turbulent stress tensor. The turbulent stress tensor is used to calculate the airflow mixing intensity, and the pressure gradient in the wake region is used to determine the airflow swirl characteristics, thereby obtaining a prediction of the airflow trajectory. The prediction results are analyzed for errors based on the measured operating parameters, and the least squares method is used to calculate the correction coefficient. The prediction results are then calibrated and compensated to obtain the corrected airflow field distribution data.

[0016] Furthermore, the real-time load data of the induced draft fan is obtained, and combined with the adjustment value, the fluctuation range of the furnace negative pressure stability is analyzed. If the fluctuation exceeds the preset safety range, the adjustment amplitude of the induced draft fan load is calculated, and a load adjustment curve is generated, including: collecting speed sensor data and power sensor data through the induced draft fan load monitoring device, recording the furnace negative pressure value using the pressure sensor array, and smoothing the negative pressure data using a Butterworth low-pass filter to obtain the furnace negative pressure time series data. The standard deviation and change rate of the negative pressure are calculated based on the negative pressure time series data, and the operating state feature vector is constructed in combination with the induced draft fan speed and power data, and the induced draft fan load condition parameters are recorded. The short-time Fourier transform is used to perform time-frequency analysis on the negative pressure time series data, and the time window width is set to calculate the negative pressure fluctuation spectrum characteristics to obtain the fluctuation amplitude and frequency distribution. The negative pressure fluctuation amplitude is compared with the preset pressure threshold. If the fluctuation amplitude exceeds the preset pressure threshold, the load adjustment calculation process is triggered. A support vector regression algorithm was used to establish an induced draft fan load regulation predictor. Input features included the negative pressure fluctuation spectrum, induced draft fan speed, and power parameters, and the outputs were the induced draft fan speed and power regulation values. A load regulation sequence was generated based on the induced draft fan speed and power regulation values, and the regulation sequence was dynamically smoothed using an adaptive Kalman filter. The smoothed load regulation sequence was corrected using the negative pressure response characteristics, and the regulation slope limit was determined based on the pressure response time constant to obtain the induced draft fan load regulation curve.

[0017] Furthermore, a multi-actuator coordinated control timing model was constructed based on the load adjustment curve and adjustment value. This model integrates the NOx peak occurrence time and duration, calculates the coordinated adjustment timing of the secondary air supply and the induced draft fan load, and generates control instructions. This model includes: using a multi-channel data acquisition device to record the secondary air damper opening and induced draft fan speed values, performing a four-layer decomposition of the actuator dynamic response data using wavelet transform, and obtaining the actuator adjustment rate curve and response delay curve. Dynamic characteristic parameters, including response rise time, adjustment deadband, and overshoot amplitude, were extracted from the actuator adjustment rate curve to establish an actuator dynamic characteristic dataset. A gas analyzer was used to record NOx concentration data, and a peak detection algorithm was used to identify concentration mutation points. The peak type was determined based on the slope of the data before and after the mutation point, and the peak occurrence time and duration were determined. A concentration predictor was established based on the NOx peak characteristics, and a long short-term memory neural network was used to predict concentration trends, outputting a concentration change sequence for future time periods. Based on the actuator dynamic characteristics and the concentration prediction sequence, a multi-actuator coordinated controller was established. A recursive least squares method was used to calculate the actuator adjustment gains and generate the coordinated adjustment timing. A fuzzy rule base is constructed based on the coordinated regulation sequence. The regulation variables are fuzzy processed using membership functions, and the actuator regulation instructions are defuzzified using the center of gravity method. Combined with the actuator response delay curve, the regulation instructions are feedforward compensated to generate a control instruction sequence that takes dynamic characteristics into account.

[0018] Furthermore, control instructions are transmitted to the fluidized bed incinerator actuator to adjust the tuyere opening status and air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operating parameters. This involves transmitting control instructions to the actuator via a fieldbus network, decoding the instructions using a communication protocol parser, generating actuator control electrical signals, collecting actuator position feedback signals, and constructing an actuator action status table. The actuator action status table is used to determine the execution response. A differential pressure transmitter is used to collect pressure signals at each tuyere level, and a mass flow meter is used to measure duct air volume to calculate the air volume distribution ratio parameter. A thermocouple array is used to collect furnace temperature field data, an oxygen analyzer is used to measure flue gas oxygen content, and a Doppler flowmeter is used to monitor air flow velocity. This generates a furnace operation status table. The furnace operation status table is compared with preset parameter ranges to determine whether the temperature field, oxygen content, and air flow velocity exceed thresholds, generating a parameter deviation index. The parameter deviation index is used to evaluate the actuator's responsiveness, calculate the actuator's adjustment deadband and response delay, and generate an actuator characteristic parameter table. A parameter predictor is constructed using a long short-term memory network. Its input features include temperature field, oxygen content, and airflow velocity data, and it outputs a sequence of predicted parameters. Based on this sequence of predicted parameters, actuator optimization instructions are generated. A closed-loop controller modifies the actuator control signals and updates the actuator action state table.

[0019] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0020] The present invention discloses a control method for nitrogen oxide incineration equipment for drying and carbonization. The method acquires combustion characteristic data, analyzes the hysteresis relationship between the nitrogen oxide release peak and the bed temperature peak, and constructs a layered secondary air supply adjustment model. According to the peak characteristics of nitrogen oxides, the opening timing and air volume distribution of air vents at different heights are calculated, and the suppression effect is analyzed in combination with the air flow distribution simulation. At the same time, the induced draft fan load data is acquired, the furnace negative pressure stability is analyzed, and a load adjustment curve is generated. Finally, a multi-actuator collaborative control timing model is constructed, the collaborative adjustment timing of the secondary air supply and the induced draft fan load is calculated, and the control instructions are generated and transmitted to the actuator. The present invention effectively suppresses the nitrogen oxide release peak by precisely controlling the secondary air supply and the induced draft fan load, thereby achieving low nitrogen emissions during the incineration of high-humidity sludge. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a control method for drying and carbonizing nitrogen oxide incineration equipment.

[0022] Figure 2 This is a schematic diagram of a control method for drying and carbonizing nitrogen oxide incineration equipment according to the present invention. DETAILED DESCRIPTION

[0023] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] In fluidized bed incineration systems, the combustion characteristics of high-moisture sludge agglomerates are complex, and nitrogen oxide emissions exhibit nonlinear variations. The control end collects combustion characteristic data, extracts nitrogen oxide emission characteristics, and coordinates these characteristics with furnace airflow dynamics and induced draft fan load to achieve emission control. This embodiment of the present invention addresses the lack of control accuracy in existing technologies through precise prediction and dynamic adjustment.

[0025] Figure 1 The implementation process of the control method for drying and carbonizing nitrogen oxide incineration equipment provided by the embodiment of the present invention is shown, and is detailed as follows:

[0026] S101 obtains the combustion characteristic data of high-humidity sludge agglomerates in a fluidized bed incinerator, extracts nitrogen oxide release characteristics and generates a parameter set.

[0027] In an embodiment of the present invention, the control terminal has an incineration control function. This function can be integrated into the control system of the fluidized bed incinerator or implemented by an independent control device. The specific implementation method can be determined according to the actual application scenario. The incineration control function can be activated by the user through the operation interface, such as through a touch screen or remote command, or it can be automatically activated according to preset conditions, such as automatically starting when the furnace temperature reaches a set threshold.

[0028] like Figure 2 S1011 collects surface temperature distribution data of high-humidity sludge agglomerates through infrared imaging system, obtains surface reflection spectrum by infrared spectrum analyzer, analyzes spectral absorption characteristics, and calculates moisture content and particle size distribution parameters of sludge agglomerates.

[0029] To obtain combustion characteristic data, an infrared imaging system scans the surface radiated energy of the sludge agglomerates, covering a wavelength range of 2000 to 4000 nanometers, to extract temperature distribution characteristics. Simultaneously, an infrared spectrometer acquires reflectance spectrum data and calculates moisture content based on absorption peak intensities, such as the water absorption peak at 3400 nanometers. As the moisture content decreases from 35% to 20%, the absorption peak intensity gradually decreases. Using a pre-set curve corresponding to absorption peaks and moisture content, the moisture content parameter can be accurately determined. Particle size distribution parameters are determined by analyzing spectral scattering characteristics, reflecting the physical properties of the sludge agglomerates.

[0030] S1012 uses a temperature sensor array and a pressure sensor array to collect real-time combustion data, processes the data through a Kalman filter algorithm to obtain the combustion state parameters of the sludge agglomerates, and uses an oxygen content analyzer to measure the oxygen concentration in the combustion gas and calculate the reaction activity index.

[0031] A 5×5 matrix array of temperature sensors is placed at the bottom of the fluidized bed, with adjacent sensors spaced 200 mm apart and a sampling frequency of 10 Hz. This array collects real-time data on the combustion temperature of the sludge agglomerates, rapidly increasing from 80°C to 850°C at a rate of 200°C per minute. A Kalman filter algorithm is used to reduce noise in the temperature and pressure data, filtering out random noise and producing a smoothed set of combustion state parameters. Combustion gases are collected using sampling probes at different heights, and oxygen concentrations are measured using zirconium oxide sensors—for example, 8%, 6%, and 4% at the bottom, middle, and top, respectively. The reaction activity index is calculated based on the relationship between oxygen content and reaction activity. When the oxygen content falls below 5%, the reaction activity index drops to 0.6, indicating decreased combustion efficiency.

[0032] S1013 uses gas chromatography-mass spectrometry to perform online analysis of nitrogen oxides in combustion gases, extracts the characteristics of the nitrogen oxide concentration curve through wavelet transform, and generates a set of parameters including peak intensity, occurrence time, duration and steepness.

[0033] A gas chromatograph-mass spectrometer equipped with a DB5MS capillary column, helium carrier gas, a column temperature of 180 degrees Celsius, and a sampling interval of 30 seconds was used to obtain time-series data on nitrogen oxide concentrations. Fourier transform was used to extract the periodic characteristics of concentration fluctuations, and a significant spectral peak was found at 0.05 Hz, corresponding to a nitrogen oxide generation period of approximately 20 minutes. The concentration curve was decomposed into five layers using the db4 wavelet basis function. The peak occurrence time was extracted 15 minutes after feeding, the duration was 25 minutes, and the peak intensity was 200 ppm. By calculating the maximum value of the first-order derivative of the concentration curve, the steepness index was obtained as 15 ppm per minute. Based on temperature, pressure, moisture content, and reaction activity indicators, a temperature-nitrogen oxide association model was constructed using a long short-term memory network. The input contains 4 feature nodes, the hidden layer has 128 neurons, and the output is peak intensity, duration, and steepness. The model prediction error is less than 10%.

[0034] In this embodiment of the present invention, multidimensional data collection and analysis can accurately extract NOx emission characteristics, generating a parameter set encompassing peak intensity, onset time, duration, and steepness, providing a reliable basis for subsequent regulation. Compared to traditional single-device control, this method significantly improves the accuracy and stability of feature extraction through multi-sensor collaboration and advanced algorithm processing, laying the foundation for NOx emission control.

[0035] If the parameter set is generated, in step S102 , the correlation characteristics between the NOx release peak and the bed temperature peak are analyzed based on the parameter set and the furnace bed temperature data to determine the release trend.

[0036] In this embodiment of the present invention, once the parameter set is generated, the collected furnace bed temperature data and NOx release characteristics are used to accurately identify the dynamic correlation between NOx peak values and bed temperature peak values through time series analysis. Release trend characteristics are then extracted to provide a basis for subsequent control. Release trend characteristics include peak offset, rate of change, and fluctuation amplitude, and are intended to reflect the dynamic changes in NOx release. Through multi-dimensional data analysis and model construction, this embodiment of the present invention ensures the accuracy of trend characteristics, laying the foundation for optimizing incineration control.

[0037] S1021 collects bed temperature data in the fluidized bed incinerator through a thermocouple sensor array, uses a bandpass filter to preprocess the data, extracts the peak time and intensity of the bed temperature, and calculates the temperature gradient and heat accumulation value.

[0038] A grid-shaped array of thermocouple sensors is used, with a vertical spacing of 150 mm and a horizontal spacing of 200 mm. The sampling frequency is 2 Hz, covering a temperature range of 200 to 1200 degrees Celsius, and real-time bed temperature data is collected. The bandpass filter is set with a cutoff frequency of 0.1 to 10 Hz to effectively filter out high-frequency noise and low-frequency drift, ensuring data smoothness. The bed temperature peak usually occurs 10 to 15 minutes after feeding, with a peak intensity range of 850 to 950 degrees Celsius. The temperature gradient is calculated from the temperature data of adjacent measuring points to reflect the heat transfer characteristics. When the temperature difference between the upper and lower measuring points exceeds 100 degrees Celsius, it indicates that the heat distribution of the bed is uneven. The heat accumulation value is calculated by integrating the temperature data. The accumulation rate in the stable combustion stage is about 4000 kilojoules per minute, providing a basis for heat transfer for subsequent analysis.

[0039] S1022 uses a gas online monitoring device to collect nitrogen oxide concentration data, extracts peak features through wavelet transform decomposition, and performs cross-correlation analysis in combination with temperature data to determine the lag relationship between the nitrogen oxide peak value and the bed temperature peak value.

[0040] A chemiluminescence gas monitoring device records nitrogen oxide concentrations in real time, with a minimum detection limit of 5 ppm, a linear range of 0 to 500 ppm, and a response time of less than 30 seconds, providing high-precision concentration time series data. The concentration data is decomposed into five layers using the db4 wavelet basis function, reconstructing the signal with 99% energy retention. Peak intensities between 150 and 200 ppm, peak occurrence times, and durations are extracted. Combined with bed temperature data, cross-correlation analysis is performed using a 60-minute time window with a 1-minute sliding step. The cross-correlation coefficients at different time delays are calculated, revealing that the nitrogen oxide peak lags behind the bed temperature peak by 5 to 8 minutes, with a cross-correlation coefficient of 0.85, indicating a high correlation between the two. The release rate is calculated using the first-order derivative of the concentration curve, with a slope of approximately 20 ppm / minute during the ascending phase and approximately 4 ppm / minute during the descending phase, with a fluctuation period of 35 to 40 minutes.

[0041] S1023 constructs a support vector regression model based on the bed temperature change rate, heat accumulation value and nitrogen oxide peak characteristics, maps temperature characteristics to nitrogen oxide release trends, and outputs peak offset, release rate and fluctuation amplitude.

[0042] A support vector regression model was used to establish a mapping relationship between temperature characteristics and NOx release trends. Input features included peak temperature intensity, temperature change rate, and heat accumulation, while output features included peak offset, release rate, and fluctuation amplitude. The model employed a radial basis kernel function, optimizing kernel parameters and penalty factors through cross-validation. The training dataset consisted of 200 samples with peak temperatures ranging from 850 to 950 degrees Celsius, a temperature change rate of 150 to 250 degrees Celsius per minute, and heat accumulation values of 80,000 to 120,000 kilojoules. Output features included a peak offset of 4 to 10 minutes, a release rate of 15 to 25 ppm per minute, and a fluctuation amplitude of 30 to 50 ppm. The model achieved a prediction accuracy of 90%, ensuring the reliability of trend feature extraction. The temperature change rate curve exhibited 30-minute periodic fluctuations. Combined with the heat accumulation and NOx concentration curve characteristics, release trends could be accurately determined, providing data support for subsequent regulatory actions.

[0043] In this embodiment of the present invention, multi-level data processing and model analysis accurately capture the dynamic correlation between NOx release and bed temperature peaks, generating trend characteristics including peak offset, release rate, and fluctuation amplitude. Compared with traditional single-data analysis methods, this method significantly improves the accuracy and robustness of trend prediction by combining cross-correlation analysis and wavelet transform with support vector regression, providing a reliable basis for the coordinated control of stratified secondary air supply and induced draft fan load.

[0044] S103 builds a stratified secondary air supply adjustment plan based on the furnace airflow dynamics model, and optimizes the air outlet timing and air volume distribution.

[0045] In this embodiment of the present invention, once the NOx emission trend is characterized, the furnace airflow dynamics, combined with the NOx peak intensity and peak steepness, generate a stratified secondary air supply adjustment plan. This plan effectively suppresses NOx emissions by dynamically optimizing the tuyere opening sequence and air volume distribution ratio. This embodiment of the present invention utilizes multi-dimensional airflow data analysis and intelligent optimization algorithms to ensure precise control of secondary air supply, improving the environmental performance of the incineration process.

[0046] S1031 collects airflow velocity data in the furnace through a Doppler flow meter, combines it with an array of air pressure and temperature sensors to construct an airflow characteristic data set and divide the furnace airflow area.

[0047] A Doppler flowmeter equipped with a three-dimensional probe array was used. Measurement points were arranged every 500 mm along the furnace height, with eight points per circumferentially located on each layer. The measured velocity range was 0 to 50 meters per second (m / s) with a sampling frequency of 100 Hz, generating a three-dimensional velocity vector distribution. Airflow velocity in the lower furnace was approximately 15 to 20 m / s, decreasing to 10 to 15 m / s in the middle, and stabilizing at 8 to 12 m / s at the top. The finite difference method was used to solve the airflow dynamics equations to generate a velocity field distribution map, which was then used to divide the furnace into lower, middle, and top airflow zones. An array of piezoresistive pressure sensors with a range of 0 to 10 kPa and an accuracy of 0.1 kPa were placed at each tuyere to record the secondary air supply pressure. The pressure at the bottom tuyere was approximately 6 kPa, 4 kPa in the middle, and 2 kPa at the top. A platinum RTD temperature sensor with a measurement range of 0 to 1200°C was used to capture the stratified characteristics of the temperature field and construct a data set of airflow characteristics including velocity, pressure, and temperature, providing a foundation for subsequent control.

[0048] S1032 uses gas monitoring devices to collect nitrogen oxide concentration data, predicts air inlet openings through deep neural networks, and uses particle swarm optimization algorithms to generate the optimal air volume distribution plan.

[0049] A chemiluminescence gas monitoring device collects nitrogen oxide concentration data in real time, with a detection range of 0 to 500 ppm and an accuracy of 5 ppm. Peak intensity and steepness characteristics are extracted from the concentration curve. A deep neural network is used to construct an air volume distribution predictor. The network consists of a five-layer structure, with 15 nodes in the input layer covering airflow velocity, pressure, and temperature characteristics, 256 neurons in the hidden layer, and an output layer that generates the opening values of each layer of tuyere, ranging from 0 to 100%. The training dataset contains 2,000 sets of operation records, 1,600 for training and 400 for validation, with a prediction accuracy of 95%. Based on the tuyere opening values, a particle swarm optimization algorithm is used with a population size of 100 and 1,000 iterations to optimize the objective function of the comprehensive mixing intensity index and nitrogen oxide emission index, with weights of 0.4 and 0.6, respectively. The mixing intensity is quantified by the tracer gas concentration decay rate. With an initial concentration of 500 ppm, the decay rate of the bottom tuyere is 80%, the middle tuyere is 60%, and the top tuyere is 40%. The constraints include an upper limit of 95% for the air outlet opening and a total air volume deviation of less than 2%. After optimization, the air outlet openings at the bottom layer, the middle layer, and the top layer are 75%, 60%, and 45%, respectively, achieving efficient airflow mixing and emission control.

[0050] Based on the dynamic response characteristics of air pressure, S1033 uses a support vector machine predictor to plan the opening timing of air vents to ensure the dynamic accuracy of air volume distribution.

[0051] The fluctuation frequency and amplitude characteristics are extracted from the air pressure data of each layer of the tuyere, with a frequency range of 0.1 to 1 Hz and an amplitude of 0.5 to 2 kPa. The support vector machine predictor uses a radial basis kernel function, optimizes the kernel parameters through cross-validation, inputs the air pressure fluctuation characteristics, and outputs the tuyere opening time advance of 2 to 5 seconds and the duration of 15 to 30 seconds. The predictor ensures that the opening of the tuyere is synchronized with the change of the furnace airflow by analyzing the dynamic response of the air pressure, thereby improving the real-time control. The embodiment of the present invention significantly optimizes the response speed of the secondary air supply and reduces the peak value of nitrogen oxide emissions through dynamic timing planning.

[0052] In this embodiment of the present invention, a deep neural network combined with a particle swarm optimization algorithm accurately predicts tuyere openings and optimizes air volume distribution. This, combined with a support vector machine to dynamically program the opening sequence, ensures that secondary air supply closely matches furnace airflow conditions. Compared to traditional fixed air volume control, this method, driven by multidimensional data and intelligent optimization, significantly improves the accuracy and stability of nitrogen oxide emission control, providing a highly efficient solution for environmentally friendly incineration.

[0053] S104 verifies the adjustment effect through airflow distribution simulation and generates coordinated control instructions based on the induced draft fan load data.

[0054] In this embodiment of the present invention, once a stratified secondary air supply adjustment plan is generated, computational fluid dynamics simulation is used to simulate the airflow distribution within the furnace, analyze the effect of secondary air supply on nitrogen oxide emissions, and optimize air inlet parameters based on induced draft fan load data to ensure effective reductions in emission concentrations. This invention utilizes multidimensional data processing and intelligent optimization algorithms to dynamically adjust air volume distribution and timing, significantly improving the environmental friendliness and stability of the incineration process.

[0055] S1041 uses Doppler flowmeters and sensor arrays to collect furnace airflow, pressure, and temperature data, generates airflow field distribution through computational fluid dynamics simulation, and extracts turbulence intensity and gas mixing characteristics.

[0056] Doppler flowmeters were used, arranged in a 5×5×4 three-dimensional measurement grid with 300 mm vertical spacing and 400 mm horizontal spacing, and a sampling frequency of 100 Hz. Airflow velocities were measured over a range of 0 to 60 meters per second (m / s), generating a three-dimensional velocity vector field. Airflow velocities at the furnace bottom were approximately 25 to 30 m / s, decreasing to 15 to 20 m / s in the middle, and maintaining a constant 10 to 15 m / s at the top. The raw velocity data were denoised using a Kalman filter, reducing the fluctuation amplitude from 15% to 3%. The data were then processed using a bandpass filter with a cutoff frequency of 0.5 to 20 Hz to extract velocity pulsation characteristics. The operating status parameter set was constructed by combining temperature data measured by a platinum-rhodium thermocouple array (ranging from 0 to 1600°C, combustion zone temperatures from 950 to 1100°C), with pressure data measured by a piezoresistive pressure sensor array (ranging from 0 to 10 kPa, with secondary air inlet pressures from 4 to 6 kPa). Computational fluid dynamics simulations were used to solve the Navier-Stokes equations, generate airflow distribution data, calculate the Reynolds stress tensor, and extract turbulence intensity indicators. The turbulence intensity in the bottom region reached 35%, the middle region 25%, and the top region 15%. Based on this, the airflow was divided into strong mixing, transition, and weak mixing zones. The gas mixing characteristics were quantified using the tracer gas concentration decay rate. At an initial concentration of 500 ppm, the decay rate in the strong mixing zone was 80%, while in the weak mixing zone it dropped to 40%, providing a basis for air volume optimization.

[0057] S1042 collects nitrogen oxide concentration data through gas monitoring devices, analyzes the emission suppression effect, and uses genetic algorithms to optimize air outlet parameters to generate adjustment plans.

[0058] A chemiluminescence gas monitoring device was used with a detection range of 0 to 500 ppm, a minimum detection limit of 2 ppm, and a response time of less than 20 seconds. The nitrogen oxide concentration data before and after adjustment were recorded. The db4 wavelet basis function was used for four-layer decomposition and denoising. The reconstructed signal retained 98% of the energy and the peak concentration characteristics were extracted. The peak value before adjustment was about 180 ppm, and after adjustment it dropped to 120 ppm, a concentration drop of 33%. The preset baseline value is 40%. If the drop is lower than this value, the optimization process is triggered. A genetic algorithm is used to optimize the vent opening timing and air volume distribution, with a population size of 200, a number of iterations of 500, a crossover probability of 0.8, and a mutation probability of 0.1. The optimization objective function has a comprehensive gas mixing characteristic weight of 0.4 and an emission reduction weight of 0.6. The constraints include an upper limit of 90% for the vent opening and a total air volume fluctuation of plus or minus 5%. The optimization results show that the bottom-level air vents are opened at 80%, opened 2 seconds in advance, and last for 25 seconds; the middle-level air vents are opened at 65%, delayed by 3 seconds, and last for 20 seconds; the top-level air vents are opened at 50%, delayed by 5 seconds, and last for 15 seconds, forming a coordinated air distribution mode.

[0059] S1043 uses a deep neural network predictor to combine airflow field data and induced draft fan load to generate coordinated control instructions to ensure the dynamic stability of emission control.

[0060] A deep neural network predictor was constructed, employing an 8-layer residual architecture with 75 nodes in the input layer, containing velocity, temperature, and pressure field data. The number of hidden units in the layer was halved from 512 at each layer, and the output layer generated the opening timing and opening curves for six tuyere outlets. The training dataset consisted of 2,000 operating condition records, and batch normalization and dropout were used to maintain prediction errors within 5%. Combined with induced draft fan load data, the power variation of the induced draft fan was monitored in real time, ranging from 50 to 150 kilowatts. Air volume distribution was optimized to balance the furnace negative pressure, maintaining it between -50 and -100 Pa. The predictor inputs the airflow field distribution and mixing characteristics, and outputs a tuyere adjustment scheme. The mixing intensity was calculated using the turbulent stress tensor, with an index exceeding 0.8 in the strong mixing zone and decreasing to 0.4 in the weak mixing zone. The airflow trajectory was analyzed using the pressure gradient in the wake zone, indicating a swirl number of 1.2 at the inlet and decaying to 0.6 upstream. The least squares method is used to analyze the measured and predicted errors. The error in the velocity field is 8%, the temperature field is 5%, and the pressure field is 10%. After correction, the average error is reduced to 4%, ensuring the accuracy of the control instructions.

[0061] In this embodiment of the present invention, dynamic suppression of nitrogen oxide emissions is achieved through multi-level simulation and optimization, significantly reducing peak concentrations. Compared to traditional static regulation, this method combines airflow dynamics simulation with intelligent algorithms to precisely optimize air inlet parameters and coordinate control with the induced draft fan. This improves the real-time and stability of emission control, providing reliable protection for the environmental performance of high-humidity sludge incineration.

[0062] S105 obtains the real-time load data of the induced draft fan and combines it with the air inlet adjustment value to analyze the fluctuation characteristics of the furnace negative pressure. If the fluctuation exceeds the safe range, the load adjustment algorithm is used to optimize the operating parameters of the induced draft fan to generate a smooth load adjustment curve.

[0063] In this embodiment of the present invention, once a coordinated control instruction is generated, real-time monitoring of induced draft fan load data and furnace negative pressure is used, combined with tuyere adjustment parameters, to analyze the impact of negative pressure fluctuations on the stability of the incineration process. Intelligent algorithms are then used to dynamically optimize the induced draft fan speed and power to ensure that the furnace negative pressure remains within a safe range. This method, through multidimensional data analysis and adaptive adjustment, improves the accuracy and response speed of induced draft fan load control, ensuring the stable operation of high-humidity sludge incineration.

[0064] S1051 collects speed, power and negative pressure data through the induced draft fan load monitoring device and pressure sensor array, and uses a Butterworth low-pass filter for smoothing to generate negative pressure time series data and extract fluctuation characteristics.

[0065] A photoelectric speed sensor with a measurement range of 0 to 1500 rpm and a sampling frequency of 100 Hz records induced draft fan speed data. A power transmitter with a measurement range of 0 to 500 kW acquires real-time power data. Furnace negative pressure is monitored using a differential pressure sensor array with a range of -5000 to 0 Pa, an accuracy of 0.1%, and a sampling frequency of 100 Hz. Negative pressure data is processed using a Butterworth low-pass filter with a cutoff frequency of 2 Hz to remove high-frequency noise and generate smooth negative pressure time series data. The standard deviation and rate of change of the negative pressure are calculated. Under normal operating conditions, the standard deviation remains within 50 Pa, and the rate of change is less than 200 Pa / minute. If the standard deviation exceeds 100 Pa or the rate of change exceeds 300 Pa / minute, it indicates that the negative pressure fluctuation is increasing and the adjustment process needs to be triggered. The negative pressure time series data was analyzed by short-time Fourier transform. A Hanning window with a window width of 60 seconds and an overlap rate of 50% was used to extract the fluctuation spectrum characteristics. The main frequency distribution was between 0.1 and 0.5 Hz, the normal fluctuation amplitude was about 200 Pa, and the preset safety threshold was 300 Pa. When exceeded, the optimization calculation was initiated.

[0066] S1052 uses the support vector regression algorithm to build an induced draft fan load regulation predictor, combines the negative pressure fluctuation characteristics with the induced draft fan operation data, calculates the speed and power adjustment amount, and generates a load regulation sequence.

[0067] A support vector regression algorithm was used to construct an induced draft fan load regulation predictor. Input features included the negative pressure fluctuation spectrum, speed, power, and negative pressure change rate, and the outputs were speed and power regulation. The algorithm used a radial basis kernel function, with kernel parameters optimized through cross-validation. The training dataset consisted of 1,000 operating records, with the speed regulation range limited to ±100 rpm and the power regulation range to ±50 kW. The model achieved a prediction accuracy of 90%. The amplitude and frequency distribution of negative pressure fluctuations reflect furnace airflow stability. Based on typical operating conditions of 1,200 rpm and 400 kW, the predictor calculated a speed regulation of approximately -50 rpm and a power regulation of approximately -30 kW. The generated load regulation sequence was smoothed using an adaptive Kalman filter. The filter dynamically adjusted the noise covariance matrix based on the strength of the negative pressure fluctuations, prioritizing predicted values for larger fluctuations and measured values for smaller fluctuations to ensure sequence smoothness. The maximum regulation rate was controlled at 60 rpm.

[0068] In an embodiment of the present invention, the adjustment parameters are corrected according to the smoothed load adjustment sequence in combination with the negative pressure response characteristics to generate an induced draft fan load adjustment curve to ensure the stability of the furnace negative pressure. The negative pressure response is manifested as a first-order inertia link with a time constant of approximately 15 seconds, and the upper limit of the adjustment slope is set to 40 rpm. The final load adjustment curve has an S-shaped characteristic, with a rising slope of 30 rpm, a platform section lasting 120 seconds, a falling slope of 20 rpm, and a total adjustment time of approximately 300 seconds. After adjustment, the induced draft fan speed is adjusted from 1200 rpm to 1150 rpm, the power is reduced from 400 kW to 370 kW, the negative pressure fluctuation amplitude is reduced from 350 Pa to 180 Pa, and the standard deviation is reduced from 120 Pa to 45 Pa, which significantly improves the stability of the furnace negative pressure. This method dynamically optimizes the induced draft fan operating parameters by combining time-frequency analysis with adaptive filtering. Compared with the traditional fixed adjustment method, it significantly reduces the negative pressure fluctuation amplitude, providing reliable support for the environmental protection and stable operation of the incineration process.

[0069] Based on the load adjustment curve and the air outlet adjustment value, S106 integrates the nitrogen oxide release characteristics, builds a multi-actuator collaborative control timing model, and generates a control instruction sequence.

[0070] In this embodiment of the present invention, once the induced draft fan load regulation curve is generated, the secondary air supply adjustment value and the NOx peak characteristics are combined to optimize the dynamic coordination between the secondary air damper and the induced draft fan through a multi-actuator coordinated control timing model. This generates a control instruction sequence to ensure effective NOx emission suppression. Through dynamic characteristic analysis and intelligent prediction, this method significantly improves the precision and real-time performance of multi-actuator coordinated control, providing a highly effective guarantee for the environmental performance of high-humidity sludge incineration.

[0071] S1061 acquires the dynamic response data of the secondary air door and induced draft fan actuator through a multi-channel data collector, extracts the adjustment rate and response delay characteristics using wavelet transform, and constructs the actuator dynamic characteristic data set.

[0072] A multi-channel data acquisition system monitors the secondary damper opening (ranging from 0 to 100%) and the induced draft fan speed (ranging from 0 to 1500 rpm) at a 50 Hz sampling frequency. Dynamic response data is decomposed into four layers using the db4 wavelet basis function, preserving the 0.1 to 10 Hz frequency band while filtering out high-frequency noise and low-frequency drift. This generates a regulation rate curve and a response delay curve. The regulation rate curve reflects actuator actuation speed, with a response rise time of approximately 3 seconds for the secondary damper, a 2% regulation deadband, and overshoot within 5%. The response rise time for the induced draft fan is approximately 5 seconds, with a 20 rpm regulation deadband and an overshoot of 3%. The response delay curve quantifies actuator actuation lag, with a delay of approximately 2 seconds for the secondary damper and approximately 3 seconds for the induced draft fan. The dynamic characteristic data set, updated every second, includes response rise time, regulation deadband, and overshoot, providing precise input for coordinated control.

[0073] S1062 uses a gas analyzer to collect nitrogen oxide concentration data, predicts concentration change trends through peak detection and long short-term memory neural network, and generates a concentration prediction sequence.

[0074] A chemiluminescence gas analyzer with a range of 0 to 500 ppm and a resolution of 0.1 ppm is used to record nitrogen oxide concentrations in real time. The peak detection algorithm is based on a three-point comparison method. When the concentration at the center point exceeds that of the two side points and the difference is greater than 20 ppm, it is determined to be a peak point. The rising edge slope is approximately 10 ppm per second, and the falling edge slope is approximately 2 ppm per second, accurately identifying the time when the peak occurs and its duration. A long short-term memory neural network is used to construct a concentration predictor with a three-layer structure. The input layer contains concentration data from the past 60 seconds, the hidden layer has 128 units, and the output layer predicts the concentration change trend in the next 30 seconds. The network training uses 1,000 sets of historical records, and the error of the validation set is controlled within 5%, ensuring that the predicted sequence accurately reflects the dynamics of nitrogen oxide release and provides a reliable basis for coordinated control.

[0075] In this embodiment of the present invention, a multi-actuator collaborative controller is constructed based on the dynamic characteristics of the actuators and the concentration prediction sequence, employing recursive least squares and fuzzy control strategies to generate a control command sequence. The collaborative controller uses recursive least squares to calculate control gains, with initial gains of 0.8 for the secondary air damper and 0.5 for the induced draft fan. A forgetting factor of 0.95 is used, and the gain matrix is iteratively updated with a sampling interval of 1 second and a prediction horizon of 60 seconds. The fuzzy rule base contains 49 rules. The input variables are concentration deviation and rate of change, and the output is the actuator control variable. A triangular membership function is used, with five levels: negative, negative, zero, positive, and positive. Inference is performed using the min-max method, and the center of gravity method is used for defuzzification to generate control commands. Feedforward compensation is adjusted based on the response delay curve, with 2 seconds for the secondary air damper and 3 seconds for the induced draft fan. The command sequence update period is 1 second. When an upward trend in nitrogen oxide concentration is detected, the command sequence instructs the secondary air damper to open 2 seconds earlier, increasing its opening by 20%, and the induced draft fan to increase its speed by 100 rpm 3 seconds earlier, achieving precise collaborative peak suppression. This method significantly improves the response speed and emission control effect of multi-actuator collaboration by combining dynamic characteristic analysis with predictive control, providing strong support for the stability of the incineration process.

[0076] S107 transmits the control instruction sequence to the fluidized bed incinerator actuator through the fieldbus network, adjusts the air inlet status and air volume ratio, updates the induced draft fan load, and continuously monitors the furnace operating parameters.

[0077] In this embodiment of the present invention, once a control instruction sequence is generated, it is transmitted to the actuator via a fieldbus network. This dynamically adjusts the secondary air damper opening and induced draft fan speed, while also monitoring key parameters such as furnace temperature, oxygen concentration, and airflow velocity in real time to ensure the stability of the incineration process and control nitrogen oxide emissions. Through precise data acquisition, prediction, and closed-loop control, this method significantly improves the actuator's response accuracy and system stability, providing reliable assurance for the environmental performance of high-moisture sludge incineration.

[0078] S1071 uses the fieldbus network to transmit control instructions, parses and generates actuator control signals, and collects air outlet pressure and air volume data through differential pressure transmitters and mass flow meters to build an actuator action status table.

[0079] Control instructions are transmitted to the actuator using the MODBUS RTU protocol, with a baud rate of 9600 bits per second, 8 data bits, and 1 stop bit. A communication protocol parser decodes the instructions and generates a control electrical signal containing the opening setpoint and an action enable signal. The actuator position feedback signal has a resolution of 0.1% and is updated every 100 milliseconds. The deviation between the actual and target positions is recorded and an actuator action status table is constructed. Differential pressure transmitters are placed at the air outlets on each floor, with a range of 0 to 10 kPa, an accuracy of 0.1%, and an output of 4 to 20 mA signals to monitor changes in air outlet pressure. A vortex flowmeter with a range of 0 to 1000 cubic meters per hour is used to calculate the air volume distribution ratio, with the bottom floor air outlets accounting for approximately 55%, the middle floor 30%, and the top floor 15%. The action status table reflects the real-time response of the actuator, ensuring that the air volume distribution is consistent with the instructions and providing a data foundation for subsequent optimization.

[0080] S1072 collects furnace operating parameters through thermocouple arrays, oxygen analyzers, and Doppler flowmeters, constructs an operating status table, and evaluates parameter deviations to generate an actuator characteristic parameter table.

[0081] A K-type thermocouple array with a temperature measurement range of 0 to 1200°C is used. Each layer is arranged every 500 mm vertically along the furnace, with eight circumferential measurement points on each layer. Temperature field data is collected, and during normal operation, the temperature is maintained between 900 and 950°C. The oxygen analyzer uses a zirconium oxygen probe with a range of 0 to 25%, monitoring the flue gas oxygen content, which remains stable at 6% to 7%. A Doppler flowmeter measures airflow velocity with a range of 0 to 40 meters per second, a recording frequency of 1 Hz, and a velocity maintained between 15 and 18 meters per second. The operating status table is updated every second, comparing against preset thresholds: temperature fluctuations of ±50°C, oxygen content of ±1%, and velocity of ±5 meters per second. Deviation indicators are calculated using the RMS temperature value, the maximum deviation in oxygen content, and the standard deviation of velocity. Deviation reports are generated when thresholds are exceeded. The actuator responsiveness evaluation is based on the deviation index. The damper actuator adjustment dead zone is about 2%, and the response delay is 3 seconds. The induced draft fan adjustment dead zone is 20 revolutions per minute, and the response delay is 5 seconds. The characteristic parameter table is updated every second, recording the dynamic characteristic trend to provide a basis for optimized control.

[0082] In this embodiment of the present invention, a parameter predictor is constructed using a long short-term memory network to generate optimization instructions. Closed-loop control then corrects actuator signals and updates the action state table. The predictor employs a four-layer structure: an input layer containing temperature, oxygen content, and airflow velocity data from the past 60 seconds, a hidden layer with 256 units, and an output layer that predicts parameter trends for the next 30 seconds. The training data consists of 2,000 records, with prediction errors kept within 5% and a confidence interval of plus or minus 3%. The prediction sequence guides the generation of actuator optimization instructions, and a closed-loop controller calculates corrections based on deviation indicators. The damper opening correction range is plus or minus 5%, the induced draft fan speed correction is plus or minus 50 revolutions per minute, and the correction cycle is 2 seconds. After optimization, the temperature field stabilizes at 900 to 950 degrees Celsius, the oxygen content remains between 6% and 7%, and the airflow velocity is between 15 and 18 meters per second. This significantly reduces parameter fluctuations and improves the stability of the incineration process. Through dynamic prediction and closed-loop correction, this method ensures a close match between actuator action and furnace conditions, optimizing nitrogen oxide emission control.

[0083] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the concept of this application. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A control method for drying and carbonizing nitrogen oxide incineration equipment, characterized in that: The method comprises: Obtain the combustion characteristic data of high-humidity sludge agglomerates in a fluidized bed incinerator, extract the nitrogen oxide peak intensity, occurrence time, duration, and peak steepness, and obtain a parameter set; The peak bed temperature data in the fluidized bed incinerator is collected. Based on the parameter set, the time series analysis method is used to calculate the hysteresis relationship between the occurrence time of the nitrogen oxide peak and the bed temperature peak. The change trend characteristics of the release peak are determined by combining the peak steepness and duration. According to the changing trend characteristics and based on the furnace airflow dynamics, a stratified secondary air supply adjustment model was constructed. The peak intensity and peak steepness of nitrogen oxides were input into the stratified secondary air supply adjustment model to calculate the opening timing of tuyere at different heights and the air volume distribution ratio, and adjust the stratified air volume; Based on the airflow distribution simulation, the airflow distribution in the furnace is obtained. Combined with the adjusted stratified air volume, the suppression effect of the secondary air supply on the nitrogen oxide release peak is analyzed. This is reflected by the emission concentration reduction index. If the emission concentration reduction index is less than the preset threshold, the air outlet timing and air volume ratio are optimized to obtain the adjustment value. Obtain real-time induced draft fan load data, combine it with the adjustment value, and analyze the fluctuation range of furnace negative pressure stability. If the fluctuation exceeds the preset safety range, calculate the induced draft fan load adjustment range and generate a load adjustment curve; A multi-actuator coordinated control timing model is constructed based on the load adjustment curve and adjustment value. The NOx peak occurrence time and duration are integrated to calculate the coordinated adjustment timing of the secondary air supply and the induced draft fan load, and generate control instructions. Transmit control instructions to the fluidized bed incinerator actuator, adjust the air inlet opening status and air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operating parameters.

2. A control method for drying and carbonizing nitrogen oxide incineration equipment according to claim 1, characterized in that: The combustion characteristic data of high-humidity sludge agglomerates in a fluidized bed incinerator are obtained, and the peak intensity, occurrence time, duration, and peak steepness of nitrogen oxides are extracted to obtain a parameter set, including: Acquiring surface temperature data and reflectance spectrum data of the sludge agglomerates, and calculating moisture content parameters and particle size distribution parameters of the sludge agglomerates based on an absorption characteristic curve of the reflectance spectrum data; According to the moisture content parameter and the particle size distribution parameter, a temperature sensor array and a pressure sensor array are used to collect real-time combustion data of the sludge agglomerate, and the combustion state parameters of the sludge agglomerate are obtained through a Kalman filter algorithm; The combustion gas corresponding to the combustion state parameters is measured using an oxygen content analyzer, and the reaction activity index is calculated based on the oxygen concentration change curve in the combustion gas; A gas chromatography-mass spectrometer is used to perform online analysis of nitrogen oxides in combustion gases. The nitrogen oxide concentration curve is decomposed at multiple scales using wavelet transform. A temperature-nitrogen oxide correlation model is established based on the reaction activity index and the combustion state parameters. The input features include temperature, pressure, moisture content, and reaction activity index, and the output is a set of nitrogen oxide characteristic parameters.

3. A control method for drying and carbonizing nitrogen oxide incineration equipment according to claim 1, characterized in that: The bed temperature peak data in the fluidized bed incinerator is collected, and based on the parameter set, a time series analysis method is used to calculate the hysteresis relationship between the occurrence time of the nitrogen oxide peak and the bed temperature peak. The change trend characteristics of the release peak are determined by combining the peak steepness and duration, including: Collecting bed temperature data, filtering the bed temperature data through a bandpass filter to obtain a bed temperature peak time and peak intensity; Calculate the temperature gradient between adjacent measuring points based on the bed temperature peak time and peak intensity, and use median filtering to smooth the temperature gradient to obtain a bed temperature change rate curve; recording nitrogen oxide concentration data by a gas online monitoring device, performing wavelet transform decomposition on the nitrogen oxide concentration data, and extracting the nitrogen oxide peak time and peak intensity from the wavelet transform coefficients; A support vector regression model is established based on the bed temperature change rate curve and the nitrogen oxide peak characteristics, wherein the input characteristics of the support vector regression model include temperature peak intensity, temperature change rate and heat accumulation value, and the output characteristics include peak offset, release rate and fluctuation amplitude, and the output characteristics are used as.

4. A control method for drying and carbonizing nitrogen oxide incineration equipment according to claim 1, characterized in that: According to the change trend characteristics and based on the furnace airflow dynamics, a stratified secondary air supply adjustment model is constructed, the nitrogen oxide peak intensity and peak steepness are input into the stratified secondary air supply adjustment model, the opening timing of the air vents at different heights and the air volume distribution ratio are calculated, and the stratified air volume is adjusted, including: Acquiring three-dimensional velocity component data of the furnace, wherein the velocity component data is measured at different heights by a velocity probe array; Divide the furnace airflow area according to the velocity component data, collect pressure data and temperature data of each airflow area to obtain an airflow characteristic data set; Collecting nitrogen oxide concentration data, the deep neural network outputting an air outlet opening value based on the airflow characteristic data set and the nitrogen oxide concentration data; For the tuyere opening value, the particle swarm algorithm calculates and solves the optimal air volume distribution scheme based on the gas mixing intensity index and the nitrogen oxide emission index. The optimization objective function includes the mixing intensity index and the nitrogen oxide emission index. The constraints include the maximum tuyere opening limit and the total air volume balance requirement. The mixing intensity index is determined by the tracer gas concentration decay rate. The support vector machine predictor is used to establish the vent opening timing planner. The input features include the frequency and amplitude of air pressure fluctuations, and the output parameters are the vent opening time and duration.

5. The method for controlling a drying and carbonizing nitrogen oxide incineration device according to claim 1, characterized in that: The airflow distribution in the furnace is obtained based on the airflow distribution simulation. Combined with the adjusted stratified air volume, the suppression effect of the secondary air supply on the nitrogen oxide release peak is analyzed and reflected by the emission concentration reduction index. If the emission concentration reduction index is less than the preset threshold, the air outlet timing and air volume ratio are optimized to obtain the adjustment value, including: The furnace airflow velocity data is collected by Doppler flow meter and the tuyere pressure data is recorded by pressure sensor, and the furnace airflow field distribution data is obtained through computational fluid dynamics simulation; Calculating the Reynolds stress tensor using the airflow field distribution data to obtain a turbulence intensity index; The nitrogen oxide emission concentration data was recorded using a gas online monitoring device, and the peak value of the concentration curve was obtained through wavelet transform denoising. If the emission concentration reduction index is less than the preset benchmark value, a genetic algorithm is established to optimize the objective function based on the gas mixing characteristic parameters; The tuyere opening timing and air volume distribution ratio are optimized and calculated, and the air flow field distribution data and gas mixing characteristic parameters are input to obtain the tuyere adjustment plan.

6. The method according to claim 5, characterized in that Also includes: Collect real-time airflow velocity and direction data in the combustion zone and secondary air inlet area of the furnace, perform turbulence dynamics simulation on the collected data based on fluid dynamics, simulate the airflow distribution characteristics, output the airflow velocity field and direction field simulation results, and calibrate the simulation results with the actual furnace operating parameters to obtain the airflow distribution simulation data in the furnace, specifically including: A Doppler flowmeter is used to obtain three-dimensional airflow velocity raw data, and the three-dimensional airflow velocity raw data is processed by a Kalman filter to obtain filtered velocity field data; Performing bandpass filtering on the filtered velocity field data to obtain velocity pulsation characteristic data, wherein the velocity pulsation characteristic data, temperature field data, pressure distribution data, and oxygen content data constitute an operating state parameter set; Processing the operating state parameter set through a deep neural network, wherein the deep neural network uses a residual structure to extract features to obtain turbulent stress tensor data; The airflow mixing intensity value is calculated based on the turbulent stress tensor data, and the airflow motion trajectory prediction result is determined by the airflow mixing intensity value and the wake zone pressure gradient data.

7. The method for controlling a drying and carbonizing nitrogen oxide incineration device according to claim 1, characterized in that: The real-time data of the induced draft fan load is obtained, combined with the adjustment value, and the fluctuation range of the furnace negative pressure stability is analyzed. If the fluctuation exceeds the preset safety range, the adjustment range of the induced draft fan load is calculated to generate a load adjustment curve, including: The speed sensor data and power sensor data are collected by the induced draft fan load monitoring device, the furnace negative pressure value is recorded by the pressure sensor array, and the negative pressure value is smoothed by a Butterworth low-pass filter to obtain negative pressure time series data; Performing time-frequency analysis using short-time Fourier transform on the negative pressure time series data to obtain negative pressure fluctuation spectrum characteristics, wherein the negative pressure fluctuation spectrum characteristics include fluctuation amplitude and frequency distribution; A support vector regression algorithm is used to establish an induced draft fan load regulation predictor, and the induced draft fan speed regulation amount and power regulation amount are calculated according to the negative pressure fluctuation spectrum characteristics, the speed sensor data, and the power sensor data; A load adjustment sequence is generated according to the induced draft fan speed adjustment amount and the power adjustment amount, and the load adjustment sequence is dynamically smoothed by an adaptive Kalman filter to obtain an induced draft fan load adjustment curve.

8. The method for controlling a drying and carbonizing nitrogen oxide incineration device according to claim 1, characterized in that: The multi-actuator coordinated control timing model is constructed based on the load adjustment curve and adjustment value, integrating the occurrence time and duration of the nitrogen oxide peak, calculating the coordinated adjustment timing of the secondary air supply and the induced draft fan load, and generating control instructions, including: A multi-channel data collector is used to obtain the dynamic response data of the actuator, and the dynamic response data is decomposed into a regulation rate curve and a response delay curve through a four-layer wavelet transform; Extracting response rise time and overshoot amplitude parameters according to the regulation rate curve to form an actuator dynamic characteristic data set; A gas analyzer is used to collect nitrogen oxide concentration data, the concentration data is processed by a peak detection algorithm to obtain a concentration mutation point sequence, and the mutation point sequence is trained by a long short-term memory neural network to obtain a concentration prediction sequence; A multi-actuator collaborative controller is established based on the actuator dynamic characteristic data set and the concentration prediction sequence. The collaborative controller uses the recursive least squares method to calculate the actuator adjustment gain, and obtains the actuator control instruction sequence through the center of gravity method for defuzzification.

9. The method for controlling a drying and carbonizing nitrogen oxide incineration device according to claim 1, characterized in that: The transmission control command is sent to the fluidized bed incinerator actuator to adjust the air inlet opening state and air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operating parameters, including: Using a communication protocol analyzer to receive control instructions transmitted by a fieldbus network, and generating actuator control electrical signals according to the control instructions; The actuator position feedback signal is collected according to the actuator control electrical signal, the air outlet pressure signal is obtained through the differential pressure transmitter, and the air duct air volume value is obtained using a mass flow meter; The furnace temperature field data is obtained through a thermocouple array, the flue gas oxygen content is measured using an oxygen analyzer, and the air flow velocity parameters are obtained using a Doppler flow meter. A long short-term memory network predictor is constructed according to the furnace temperature field data, the flue gas oxygen content value and the airflow velocity parameter, an actuator optimization instruction is generated through the predictor, and an actuator control electrical signal is corrected using a closed-loop controller.

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