A method for controlling a carbonized nitrogen oxide incineration apparatus

Abstract

The application provides a kind of for drying carbonized nitrogen oxide incineration equipment control method, comprising: obtaining the combustion characteristic data of high-wet sludge lump in fluidized bed incinerator, extracting nitrogen oxide peak intensity, occurrence time, duration and peak shape steepness, obtain parameter set;Real-time data of induced draft fan load is obtained, combined with adjustment value, analyze the fluctuation range of furnace negative pressure stability, if the fluctuation exceeds the preset safety range, then calculate the adjustment range of induced draft fan load, generate load adjustment curve;Based on the load adjustment curve and adjustment value, a multi-actuator collaborative control time sequence model is constructed, the nitrogen oxide peak occurrence time and duration are fused, the collaborative adjustment time sequence of secondary air supply and induced draft fan load is calculated, and the control instruction is generated;The control instruction is transmitted to the fluidized bed incinerator actuator, the air opening state and air volume ratio are adjusted, the induced draft fan load is updated, and the adjusted furnace operating parameters are continuously monitored.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a control method for a nitrogen oxide incineration equipment used for drying and carbonization. Background Technology

[0002] Fluidized bed incineration technology is a key technology for treating high-moisture sludge, enabling efficient heat recovery and effective pollutant control, which is crucial for reducing solid waste and its resource utilization. However, current technologies for controlling nitrogen oxides (NOx) emissions during the incineration of high-moisture sludge clumps mostly rely on individual regulating devices. Due to the slow heat transfer within high-moisture sludge clumps, it is difficult to adapt to the complex NOx release patterns during incineration. For example, NOx concentrations can suddenly spike, and this peak often occurs later than the highest temperature in the furnace bed. This makes it difficult to consistently meet NOx emission standards, affecting the environmental performance of the incineration process. Accurately predicting the intensity, timing, duration, and steepness of NOx release peaks, and then using this prediction information to coordinate the control of multiple actuators such as secondary air supply and induced draft fans, is highly complex. This complexity lies in the need for precise, tiered regulation of secondary air supply, dynamic adjustment of induced draft fan load to match incineration demands, and rapid response to drastic and nonlinear changes in NOx concentration peaks. However, there is currently a lack of predictive models specifically for the incineration characteristics of high-moisture sludge flocs, and such predictions cannot be effectively integrated with the coordinated control of multiple regulating devices. This deficiency directly leads to inappropriate timing of control and inaccurate control of adjustment magnitude, making it difficult to effectively suppress peak nitrogen oxide emissions. Therefore, how to dynamically generate and optimize the coordinated adjustment scheme of stratified secondary air supply and induced draft fan load based on accurate prediction of peak nitrogen oxide release characteristics, such as the timing and magnitude of air outlet adjustments, has become the core technical bottleneck for effectively controlling nitrogen oxide emissions in fluidized bed incinerators when treating high-moisture sludge flocs. Summary of the Invention

[0003] This invention provides a control method for nitrogen oxide incineration equipment used for drying and carbonization, mainly comprising:

[0004] The combustion characteristics data of high-moisture sludge lumps in a fluidized bed incinerator were obtained, and the peak intensity, occurrence time, duration and peak steepness of nitrogen oxides were extracted to obtain a parameter set.

[0005] Peak bed temperature data were collected in the fluidized bed incinerator. Based on the parameter set, time series analysis was used to calculate the lag relationship between the peak occurrence time of nitrogen oxides and the peak bed temperature. Combined with the steepness of the peak shape and the duration, the trend characteristics of the release peak were determined.

[0006] Based on the changing trend characteristics and the construction of a layered secondary air supply adjustment model based on furnace airflow dynamics, the peak intensity and steepness of nitrogen oxides are input into the layered secondary air supply adjustment model to calculate the opening sequence and air volume distribution ratio of air outlets at different heights, and adjust the layered 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 secondary air supply on the nitrogen oxide release peak is analyzed. The effect is reflected by the emission concentration reduction index. If the emission concentration reduction index is less than the preset threshold, the tuyer timing and air volume ratio are optimized to obtain the adjustment value.

[0008] The system acquires real-time data on the induced draft fan load, combines it with adjustment values, analyzes the fluctuation range of furnace negative pressure stability, and calculates the adjustment range of the induced draft fan load if the fluctuation exceeds the preset safety range, generating a load adjustment curve.

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

[0010] The control commands are transmitted to the actuators of the fluidized bed incinerator to adjust the opening status of the tuyeres and the air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operating parameters.

[0011] Furthermore, combustion characteristic data of high-moisture sludge clumps in a fluidized bed incinerator were obtained. Peak intensity, occurrence time, duration, and peak steepness of nitrogen oxides were extracted to obtain a parameter set, including: sludge clump surface temperature data acquired via an infrared imaging system; surface reflectance spectra obtained via an infrared spectrometer; and sludge moisture content and particle size distribution parameters calculated based on spectral absorption characteristic curves. Real-time combustion data was acquired using temperature and pressure sensor arrays arranged at the bottom of the fluidized bed. Noise reduction of temperature and pressure data was performed using a Kalman filter algorithm to obtain a set of sludge clump combustion state parameters. Combustion gases were acquired from sampling probes arranged at different heights of the fluidized bed. Oxygen concentration variation curves in the combustion gases were measured using an oxygen content analyzer, and real-time reaction activity indicators were calculated based on the stoichiometric relationship of the combustion reaction. Online analysis of nitrogen oxides in the sampled gases was performed using gas chromatography-mass spectrometry (GC-MS) to obtain time-series data of nitrogen oxide concentration. The concentration fluctuation periodicity characteristics were extracted using Fourier transform, and peak intensity values ​​were calculated. Based on time-series data of nitrogen oxide concentration, wavelet transform was used to decompose the concentration curve at multiple scales, extracting peak shape characteristic parameters. The steepness of the curve was quantitatively obtained by calculating the maximum value of the first derivative of the curve. Based on temperature change data and nitrogen oxide generation data during sludge clumping combustion, a temperature-nitrogen oxide correlation model was constructed using a long short-term memory network. The input features included temperature, pressure, moisture content, and reactivity indicators, and the output was a set of nitrogen oxide characteristic parameters.

[0012] Furthermore, peak bed temperature data within the fluidized bed incinerator were collected. Based on the parameter set, time series analysis was used to calculate the lag relationship between the peak occurrence time of nitrogen oxides and the peak bed temperature. Combining the steepness and duration of the peak shape, the changing trend characteristics of the release peak were determined. This included: collecting bed temperature data within the fluidized bed incinerator using a thermocouple sensor array; removing noise from the temperature data using a bandpass filter; extracting the peak bed temperature point from the filtered temperature curve to obtain the peak time and intensity data; calculating the temperature gradient and heat accumulation between adjacent measuring points based on the bed temperature time series data; smoothing the temperature gradient data using median filtering to obtain the bed temperature change rate curve; recording nitrogen oxide concentration data using an online gas monitoring device; performing a five-level decomposition of the concentration data using wavelet transform; and extracting the peak time and intensity of nitrogen oxides from the wavelet coefficients; and performing cross-correlation analysis on the peak bed temperature and peak nitrogen oxide values ​​within a fixed time window, determining the lag time between them by the time difference corresponding to the maximum value of the cross-correlation function. The instantaneous release rate is obtained by calculating the first derivative of the nitrogen oxide concentration curve, and the inflection point of the curve is obtained by calculating the second derivative. The fluctuation period of the nitrogen oxide release process is determined by combining the wavelet transform coefficients. Peak offset and duration features are extracted from the nitrogen oxide 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 features and nitrogen oxide release features. The input features include temperature peak intensity, temperature change rate, and heat accumulation value, and the output features include peak offset, release rate, and fluctuation amplitude.

[0013] Furthermore, based on the changing trend characteristics and a stratified secondary air supply adjustment model constructed based on furnace airflow dynamics, the peak intensity and peak steepness of nitrogen oxides are input into the stratified secondary air supply adjustment model to calculate the opening sequence of tuyeres at different heights and the air volume distribution ratio, adjusting the stratified air volume. This includes: using a Doppler current meter to collect airflow velocity data at different heights in the furnace, using a velocity probe array to obtain three-dimensional velocity components, solving the airflow dynamics equations using the finite difference method to obtain a velocity field distribution map within the furnace. Based on the velocity field distribution map, the furnace airflow regions are divided, and pressure data for each region is collected using a pressure sensor array. Combined with temperature distribution data obtained from a temperature sensor, a furnace airflow characteristic dataset is established. Nitrogen oxide concentration data is collected using an online gas monitoring device, and peak intensity and peak steepness features are extracted from the concentration curve. A deep neural network is used to establish an air volume distribution predictor, with input features including airflow velocity, pressure, and temperature data, and outputting the tuyer opening values ​​for each layer. Based on the tuyer opening values ​​for each layer, computational fluid dynamics methods are used to calculate the mixing degree between the secondary air and the furnace gas, and the mixing intensity index is quantified by the tracer gas concentration decay rate. The optimal airflow allocation scheme is solved using the particle swarm optimization algorithm. The objective function includes mixing intensity and nitrogen oxide emission indicators, while the constraints include the maximum opening limit of the air vents and the overall airflow balance requirement. Dynamic response features are extracted from the air pressure change data of each air vent. A support vector machine predictor is used to build an air vent opening timing planner. The input features include the air pressure fluctuation frequency and amplitude, and the output parameters are the air vent opening time and duration.

[0014] Furthermore, based on airflow distribution simulation, the airflow distribution within the furnace is obtained. Combined with the adjusted stratified airflow, the suppression effect of secondary air supply on the nitrogen oxide release peak is analyzed, reflected by the emission concentration reduction index. If the emission concentration reduction index is less than a preset threshold, the tuyer timing and airflow ratio are optimized to obtain an adjustment value. This includes: obtaining furnace airflow field distribution data through computational fluid dynamics simulation based on furnace airflow velocity data collected by a Doppler velocimeter and tuyer pressure data recorded by a pressure sensor; extracting the airflow velocity vector field and Reynolds stress tensor from the airflow field distribution data; calculating the airflow turbulence intensity index using the Reynolds stress tensor; dividing the furnace airflow mixing region based on the turbulence intensity index; and obtaining gas mixing characteristic parameters for each region. A gas online monitoring device records nitrogen oxide emission concentration data before and after adjustment; wavelet transform is used to denoise the concentration curves; and the emission concentration reduction index is calculated using the peak concentration ratio. Comparing the emission concentration reduction index with a preset benchmark value, if the reduction index is less than the preset benchmark value, the tuyer parameter optimization process begins. A genetic algorithm is used to optimize the vent opening timing and airflow distribution ratio. The objective function includes gas mixing characteristic parameters and emission concentration reduction indicators, while the constraints include the maximum vent opening limit and total airflow balance requirements. Based on the optimization parameters obtained from the genetic algorithm, a neural network predictor is used to establish a vent regulation scheme. The input features include airflow field distribution data and gas mixing characteristic parameters, and the output features include the vent opening timing and opening curve.

[0015] Furthermore, real-time airflow velocity and direction data in the combustion zone and secondary air inlet area of ​​the furnace are collected. Turbulent dynamics simulation is performed on the collected data based on fluid dynamics to simulate airflow distribution characteristics, outputting the simulation results of airflow velocity and direction fields. The simulation results are then calibrated using actual furnace operating parameters to obtain simulated airflow distribution data within the furnace. This includes: using a Doppler current meter to deploy a sensor grid in the combustion zone and secondary air inlet area to collect real-time three-dimensional airflow velocity data; using a Kalman filter to denoise the raw velocity data to obtain filtered velocity field data; using a bandpass filter to perform frequency domain processing on the velocity field data to extract velocity fluctuation features; calculating the angle between the velocity fluctuation intensity and the mainstream direction to obtain airflow motion characteristic parameters; collecting temperature field data from a thermocouple array in the combustion zone; obtaining pressure distribution data at the secondary air inlet using a pressure sensor array; and combining this with real-time oxygen content data recorded by an oxygen sensor to construct a furnace operating state parameter set. A turbulent dynamics predictor is established 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; and the output layer generates a turbulent stress tensor. The mixing intensity of the airflow is calculated using the turbulent stress tensor, and the swirling characteristics of the airflow are solved by the pressure gradient in the wake region to obtain the predicted airflow trajectory. Error analysis is performed on the predicted results based on measured operating parameters, and correction coefficients are calculated using the least squares method to calibrate and compensate for the predicted results, resulting in corrected airflow field distribution data.

[0016] Furthermore, real-time load data of the induced draft fan is acquired, and combined with adjustment values, the fluctuation range of 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. This includes: collecting speed sensor data and power sensor data through the induced draft fan load monitoring device; recording furnace negative pressure values ​​using a pressure sensor array; smoothing the negative pressure data using a Butterworth low-pass filter to obtain furnace negative pressure time-series data; calculating the negative pressure standard deviation and rate of change based on the negative pressure time-series data; constructing an operating status feature vector by combining induced draft fan speed and power data; and recording the induced draft fan load operating parameters. Short-time Fourier transform is used to perform time-frequency analysis on the negative pressure time-series data, setting the time window width, calculating the negative pressure fluctuation spectrum characteristics, and obtaining the fluctuation amplitude and frequency distribution. The negative pressure fluctuation amplitude is compared with a preset pressure threshold; if the fluctuation amplitude exceeds the preset pressure threshold, the load adjustment calculation process is triggered. A support vector regression algorithm is used to establish a load regulation predictor for the induced draft fan. Input features include the negative pressure fluctuation spectrum, fan speed, and power parameters. Outputs are the fan speed and power regulation values. A load regulation sequence is generated based on these values ​​and dynamically smoothed using an adaptive Kalman filter. The smoothed load regulation sequence is then corrected using negative pressure response characteristics. A slope limit is determined based on the pressure response time constant, resulting in the induced draft fan load regulation curve.

[0017] Furthermore, a multi-actuator collaborative control timing model is constructed based on the load adjustment curve and adjustment value. This model integrates the peak occurrence time and duration of nitrogen oxides (NOx) to calculate the collaborative adjustment timing of secondary air supply and induced draft fan load, generating control commands. These commands include: recording the secondary damper opening and induced draft fan speed using a multi-channel data acquisition device; performing a four-level decomposition of the actuator dynamic response data using wavelet transform to obtain the actuator regulation rate curve and response delay curve; extracting dynamic characteristic parameters from the actuator regulation rate curve, including response rise time, regulation dead zone, and overshoot amplitude, to establish an actuator dynamic characteristic dataset; recording NOx concentration data using a gas analyzer; identifying concentration abrupt change points using a peak detection algorithm; determining the peak type based on the data slope before and after the abrupt change point; and obtaining the peak occurrence time and duration. A concentration predictor is established based on the NOx peak characteristics, using a long short-term memory neural network to predict concentration change trends and output a concentration change sequence for future periods. Based on the actuator dynamic characteristics and the concentration prediction sequence, a multi-actuator collaborative controller is established, using recursive least squares to calculate the actuator regulation gain and generate a collaborative adjustment timing sequence. A fuzzy rule base is constructed based on the coordinated adjustment time sequence. The adjustment quantity is fuzzy-processed using a membership function, and the actuator adjustment command is obtained by defuzzification using the centroid method. Combined with the actuator response delay curve, feedforward compensation is applied to the adjustment command to generate a control command sequence that considers dynamic characteristics.

[0018] Furthermore, control commands are transmitted to the actuators of the fluidized bed incinerator to adjust the tuyer opening status and air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operating parameters. This includes: transmitting control commands to the actuators via a fieldbus network, decoding the commands using a communication protocol parser to generate actuator control electrical signals, collecting actuator position feedback signals, and constructing an actuator action status table. The execution response is determined based on the actuator action status table. Pressure signals are collected at each tuyer using a differential pressure transmitter, and the air volume in the duct is measured using a mass flow meter to calculate the air volume distribution ratio parameters. Furnace temperature field data is collected using a thermocouple array, flue gas oxygen content is measured using an oxygen analyzer, and airflow velocity is monitored using a Doppler flow meter to generate a furnace operating status table. The furnace operating status table is compared with preset parameter ranges to determine whether the temperature field, oxygen content, and airflow velocity exceed thresholds, generating parameter deviation indicators. The actuator responsiveness is evaluated based on the parameter deviation indicators, the actuator dead zone and response delay are calculated, and an actuator characteristic parameter table is generated. A parameter predictor is constructed using a Long Short-Term Memory (LSTM) network. Input features include temperature field, oxygen content, and airflow velocity data, and the output is a sequence of predicted parameters. Based on this sequence, actuator optimization instructions are generated. A closed-loop controller then corrects the actuator control signals and updates the actuator's action state table.

[0019] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0020] This invention discloses a control method for nitrogen oxide incineration equipment used in 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. Based on the nitrogen oxide peak characteristics, the opening sequence and air volume distribution of air inlets at different heights are calculated, and the suppression effect is analyzed by combining airflow distribution simulation. Simultaneously, induced draft fan load data is acquired, furnace negative pressure stability is analyzed, and a load adjustment curve is generated. Finally, a multi-actuator collaborative control timing model is constructed to calculate the collaborative adjustment timing of secondary air supply and induced draft fan load, generating and transmitting control commands to the actuators. This invention effectively suppresses the nitrogen oxide release peak by precisely controlling the secondary air supply and induced draft fan load, achieving low nitrogen emissions during the high-moisture sludge incineration process. Attached Figure Description

[0021] Figure 1 This is a flowchart of a nitrogen oxide incineration equipment control method for drying and carbonization according to the present invention.

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

[0023] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In fluidized bed incineration systems, the combustion characteristics of high-moisture sludge clumps are complex, and nitrogen oxide (NOx) release exhibits nonlinear changes. The control system collects combustion characteristic data, extracts NOx release features, and coordinates these with furnace airflow dynamics and induced draft fan load to achieve emission control. This invention addresses the problem of insufficient control precision in existing technologies through accurate prediction and dynamic adjustment.

[0025] Figure 1 The implementation flow of the nitrogen oxide incineration equipment control method for drying and carbonization provided in the embodiment of the present invention is illustrated below:

[0026] S101 acquires combustion characteristic data of high-moisture sludge lumps in a fluidized bed incinerator, extracts nitrogen oxide release characteristics, and generates a parameter set.

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

[0028] like Figure 2 S1011 collects surface temperature distribution data of high-moisture sludge clumps through an infrared imaging system, and uses an infrared spectrometer to obtain surface reflectance spectra, analyze spectral absorption characteristics, and calculate the moisture content and particle size distribution parameters of the sludge clumps.

[0029] When acquiring combustion characteristic data, an infrared imaging system was used to scan the surface radiation energy of the sludge flocs, covering a wavelength range of 2000 to 4000 nanometers, to extract temperature distribution characteristics. Simultaneously, reflectance spectral data was acquired using an infrared spectrometer, and the moisture content was calculated based on the absorption peak intensity, such as the moisture absorption peak at 3400 nanometers. As the moisture content decreased from 35% to 20%, the absorption peak intensity gradually weakened; the moisture content parameter was accurately determined using a pre-defined absorption peak-moisture content correlation curve. Particle size distribution parameters were obtained through spectral scattering characteristic analysis, reflecting the physical properties of the sludge flocs.

[0030] The S1012 uses a temperature sensor array and a pressure sensor array to collect real-time combustion data. The data is processed by a Kalman filter algorithm to obtain the combustion state parameters of the sludge clumps. An oxygen content analyzer is used to measure the oxygen concentration in the combustion gas and calculate the reactivity index.

[0031] A 5×5 matrix temperature sensor array was arranged at the bottom of the fluidized bed, with adjacent sensors spaced 200 mm apart and a sampling frequency of 10 Hz. Real-time data on the combustion temperature of the sludge clumps was collected, rapidly increasing the temperature from 80°C to 850°C at a rate of 200°C per minute. Kalman filtering was used to denoise the temperature and pressure data, removing random noise and obtaining a smooth set of combustion state parameters. Combustion gases were collected using sampling probes at different heights, and oxygen concentrations were measured using zirconia sensors. For example, the concentrations at the bottom, middle, and top were 8%, 6%, and 4%, respectively. The reactivity index was calculated based on the relationship between oxygen content and reactivity. When the oxygen content fell below 5%, the reactivity index dropped to 0.6, indicating a decrease in combustion efficiency.

[0032] The S1013 uses gas chromatography-mass spectrometry to perform online analysis of nitrogen oxides in combustion gases. It 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] Gas chromatography-mass spectrometry (GC-MS) with a DB5MS capillary column, helium as carrier gas, column temperature 180°C, and sampling interval of 30 seconds was used to acquire time-series data of nitrogen oxide (NOx) concentration. Fourier transform was used to extract the periodic characteristics of concentration fluctuations, revealing a significant peak at 0.05 Hz, corresponding to a NOx formation cycle of approximately 20 minutes. The concentration curve was decomposed into five layers using the db4 wavelet basis function, extracting the peak occurrence time as 15 minutes after feed, a duration of 25 minutes, and a peak intensity of 200 ppm. The steepness index was calculated to be 15 ppm per minute by determining the maximum value of the first derivative of the concentration curve. Based on temperature, pressure, moisture content, and reactivity indicators, a long short-term memory (LSTM) network was used to construct a temperature-NOx correlation model. The input contained four feature nodes and 128 hidden neurons, and the outputs were peak intensity, duration, and steepness. The model's prediction error was less than 10%.

[0034] In this embodiment of the invention, through multi-dimensional data acquisition and analysis, nitrogen oxide release characteristics can be accurately extracted, forming a parameter set including peak intensity, occurrence time, duration, and steepness, providing a reliable basis for subsequent regulation. Compared with 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 nitrogen oxide emission control.

[0035] S102 If the parameter set is generated, analyze the correlation between the peak value of nitrogen oxide release and the peak value of bed temperature based on the parameter set and the furnace bed temperature data, and determine the release trend.

[0036] In this embodiment of the invention, after the parameter set is generated, the collected furnace bed temperature data and nitrogen oxide release characteristics are used to accurately identify the dynamic correlation between the peak nitrogen oxide concentration and the peak bed temperature through time series analysis, and release trend characteristics are extracted to provide a basis for subsequent regulation. Release trend characteristics include peak shift, rate of change, and fluctuation amplitude, aiming to reflect the dynamic changes in nitrogen oxide release. This embodiment of the invention ensures the accuracy of trend characteristics through multi-dimensional data analysis and model construction, laying the foundation for optimized incineration control.

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

[0038] A grid-like array of thermocouple sensors, with a vertical spacing of 150 mm and a horizontal spacing of 200 mm, is used to collect bed temperature data in real time, covering a temperature range of 200 to 1200 degrees Celsius. A bandpass filter with a cutoff frequency of 0.1 to 10 Hz effectively filters out high-frequency noise and low-frequency drift, ensuring data smoothness. The peak bed temperature typically appears 10 to 15 minutes after feeding, with a peak intensity ranging from 850 to 950 degrees Celsius. The temperature gradient is calculated by analyzing the temperature data from adjacent measuring points to reflect heat transfer characteristics. A temperature difference exceeding 100 degrees Celsius between measuring points indicates uneven heat distribution within the bed. The cumulative heat value is calculated by integrating the temperature data; the cumulative rate during the stable combustion phase is approximately 4000 kJ / min, providing a basis for subsequent analysis of heat transfer.

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

[0040] Nitrogen oxide (NOx) concentrations were recorded in real time using a chemiluminescence gas monitoring device, 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, acquiring high-precision concentration time-series data. The concentration data was decomposed into five levels using the db4 wavelet basis function, reconstructing the signal while retaining 99% of the energy, extracting peak intensities of 150 to 200 ppm, peak occurrence time, and duration. Combined with bed temperature data, cross-correlation analysis was performed over a 60-minute time window with a 1-minute sliding step, calculating the cross-correlation coefficient under different time delays. It was found that the NOx peak lagged 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 was calculated using the first derivative of the concentration curve, with an upward slope of approximately 20 ppm per minute and a downward slope of approximately 4 ppm per minute, 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, mapping temperature characteristics to nitrogen oxide release trends, and outputting peak offset, release rate, and fluctuation amplitude.

[0042] A support vector regression model was used to establish a mapping relationship between temperature characteristics and nitrogen oxide (NOx) release trends. Input features included peak temperature intensity, temperature change rate, and cumulative heat value. Output features included peak offset, release rate, and fluctuation amplitude. The model employed a radial basis function kernel, optimizing kernel parameters and penalty factors through cross-validation. The training dataset contained 200 samples, with peak temperatures ranging from 850 to 950 degrees Celsius, temperature change rates from 150 to 250 degrees Celsius per minute, and cumulative heat values ​​from 80,000 to 120,000 kJ. Output features included peak offsets ranging from 4 to 10 minutes, release rates from 15 to 25 ppm per minute, and fluctuation amplitudes from 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 a 30-minute periodic fluctuation. Combined with the cumulative heat value and NOx concentration curve features, the release trend could be accurately determined, providing data support for subsequent regulation.

[0043] In this embodiment of the invention, through multi-level data processing and model analysis, the dynamic correlation between nitrogen oxide release and bed temperature peak can be accurately captured, generating trend features including peak shift, release rate, and fluctuation amplitude. Compared with traditional single data analysis methods, this method, through cross-correlation analysis and wavelet transform combined with support vector regression, significantly improves the accuracy and robustness of trend prediction, providing a reliable basis for the coordinated control of stratified secondary air supply and induced draft fan load.

[0044] Based on the furnace airflow dynamics model, S103 constructs a layered secondary air supply adjustment scheme and optimizes the tuyer timing and air volume distribution.

[0045] In this embodiment of the invention, once the nitrogen oxide release trend characteristics are determined, a stratified secondary air supply adjustment scheme is generated by utilizing the furnace airflow dynamics characteristics, combined with the peak intensity and steepness of the nitrogen oxide peak. By dynamically optimizing the tuyer opening timing and airflow distribution ratio, effective suppression of nitrogen oxide emissions is achieved. This embodiment of the invention ensures precise control of the secondary air supply through multi-dimensional airflow data analysis and intelligent optimization algorithms, thereby improving the environmental performance of the incineration process.

[0046] The S1031 collects airflow velocity data inside the furnace using a Doppler current meter, and combines this with an array of pressure and temperature sensors to construct an airflow characteristic dataset and divide the furnace airflow regions.

[0047] A Doppler current meter equipped with a three-dimensional probe array was used, with measuring points arranged every 500 mm along the furnace height, eight measuring points circumferentially per layer. The measured velocity range was 0 to 50 m / s, with a sampling frequency of 100 Hz, generating a three-dimensional velocity vector distribution. The airflow velocity in the lower part of the 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 airflow dynamics equations were solved using the finite difference method to generate a velocity field distribution map, which was used to divide the furnace into lower, middle, and top airflow regions. A piezoresistive pressure sensor array, with a range of 0 to 10 kPa and an accuracy of 0.1 kPa, was placed at each layer of tuyeres to record the secondary air supply pressure. The pressure at the bottom tuyeres was approximately 6 kPa, 4 kPa in the middle, and 2 kPa at the top. A platinum resistance thermometer was used for temperature measurement, with a range of 0 to 1200 degrees Celsius, to obtain the stratified temperature field characteristics and construct an airflow characteristic dataset including velocity, pressure, and temperature, providing a basis for subsequent control.

[0048] S1032 uses a gas monitoring device to collect nitrogen oxide concentration data, predicts the vent opening through a deep neural network, and uses a particle swarm optimization algorithm to generate the optimal air volume distribution scheme.

[0049] Nitrogen oxide (NOx) concentration data was acquired in real time using a chemiluminescence gas monitoring device, with a detection range of 0 to 500 ppm and an accuracy of 5 ppm. Peak intensity and steepness features were extracted from the concentration curve. A deep neural network was used to construct an airflow distribution predictor. The network has a 5-layer structure: the input layer has 15 nodes covering airflow velocity, pressure, and temperature characteristics; the hidden layer has 256 neurons; and the output layer generates the vent opening values ​​for each layer, ranging from 0 to 100%. The training dataset contains 2000 sets of running records, with 1600 sets used for training and 400 sets used for validation, achieving a prediction accuracy of 95%. Based on the vent opening values, a particle swarm optimization algorithm was used, with a population size of 100 and 1000 iterations. The objective function was optimized to integrate the mixing intensity index and the NOx emission index, with weights of 0.4 and 0.6, respectively. The mixing intensity was quantified by the attenuation rate of the tracer gas concentration. With an initial concentration of 500 ppm, the attenuation rate was 80% for the bottom layer, 60% for the middle layer, and 40% for the top layer. The constraints include an upper limit of 95% for air vent opening and a total air volume deviation of less than 2%. After optimization, the air vent opening is 75% for the bottom layer, 60% for the middle layer, and 45% for the top layer, achieving efficient airflow mixing and emission control.

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

[0051] Fluctuation frequency and amplitude characteristics are extracted from the air pressure data of each tuyer, with a frequency range of 0.1 to 1 Hz and an amplitude range of 0.5 to 2 kPa. A support vector machine predictor employs a radial basis function kernel function, optimizing kernel parameters through cross-validation. Inputting air pressure fluctuation characteristics, it outputs a tuyer opening advance of 2 to 5 seconds and a duration of 15 to 30 seconds. By analyzing the dynamic air pressure response, the predictor ensures synchronization between tuyer opening and furnace airflow changes, improving real-time control. This embodiment of the invention significantly optimizes the response speed of secondary air supply through dynamic timing planning, reducing peak nitrogen oxide emissions.

[0052] In this embodiment of the invention, the combination of deep neural networks and particle swarm optimization algorithms enables accurate prediction of duct opening and optimization of airflow distribution. Combined with support vector machines for dynamic planning of the opening sequence, this ensures a high degree of matching between secondary air supply and furnace airflow conditions. Compared to traditional fixed airflow regulation, this method, through multi-dimensional data-driven and intelligent optimization, significantly improves the accuracy and stability of nitrogen oxide emission control, providing an efficient solution for environmentally friendly incineration.

[0053] S104 verifies the adjustment effect through airflow distribution simulation and generates coordinated control commands by combining the induced draft fan load data.

[0054] In this embodiment of the invention, after the stratified secondary air supply adjustment scheme is generated, the airflow distribution inside the furnace is simulated using computational fluid dynamics simulation to analyze the suppression effect of secondary air supply on nitrogen oxide emissions. Furthermore, the duct parameters are optimized based on induced draft fan load data to ensure an effective reduction in emission concentration. This invention, through multi-dimensional data processing and intelligent optimization algorithms, dynamically adjusts the air volume distribution and timing, significantly improving the environmental friendliness and stability of the incineration process.

[0055] S1041 uses a Doppler current meter and sensor array 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] A Doppler current meter was used, with a 5×5×4 three-dimensional measurement grid arranged at a vertical spacing of 300 mm and a horizontal spacing of 400 mm. The sampling frequency was 100 Hz, and the measured airflow velocity range was 0 to 60 m / s, generating a three-dimensional velocity vector field. The airflow velocity at the bottom of the furnace was approximately 25 to 30 m / s, decreasing to 15 to 20 m / s in the middle, and maintaining 10 to 15 m / s at the top. The raw velocity data was noise-reduced using a Kalman filter, reducing the fluctuation amplitude from 15% to 3%. It was then processed by a bandpass filter with a cutoff frequency of 0.5 to 20 Hz to extract velocity pulsation characteristics. Combined with temperature field data measured by a platinum-rhodium thermocouple array (range 0 to 1600 degrees Celsius, combustion zone temperature 950 to 1100 degrees Celsius) and pressure data measured by a piezoresistive pressure sensor array (range 0 to 10 kPa, secondary air inlet pressure 4 to 6 kPa), a set of operating status parameters was constructed. Computational fluid dynamics simulations were used to solve the Navier-Stokes equations, generating airflow field distribution data, calculating the Reynolds stress tensor, and extracting turbulence intensity indices. Turbulence intensity reached 35% in the bottom region, 25% in the middle, and 15% at the top, thus dividing the region into strongly mixed, transitional, and weakly mixed zones. Gas mixing characteristics were quantified using tracer gas concentration decay rates. With an initial concentration of 500 ppm, the decay rate was 80% in the strongly mixed zone and decreased to 40% in the weakly mixed zone, providing a basis for airflow optimization.

[0057] S1042 collects nitrogen oxide concentration data through a gas monitoring device, analyzes the emission suppression effect, and uses a genetic algorithm to optimize vent parameters and 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. Nitrogen oxide concentration data before and after adjustment were recorded. A four-level decomposition denoising method using the db4 wavelet basis function was employed, reconstructing the signal while retaining 98% of its energy. Peak concentration features were extracted; the peak value before adjustment was approximately 180 ppm, which decreased to 120 ppm after adjustment, representing a 33% reduction. A preset baseline value of 40% was used; if the reduction fell below this value, the optimization process was triggered. A genetic algorithm was used to optimize the vent opening timing and airflow distribution, with a population size of 200, 500 iterations, a crossover probability of 0.8, and a mutation probability of 0.1. The objective function was optimized with a weighting of 0.4 for gas mixing characteristics and 0.6 for emission reduction. Constraints included a maximum vent opening of 90% and a total airflow fluctuation of ±5%. The optimization results show that the bottom layer air vents are opened at 80% with a 2-second delay and last for 25 seconds; the middle layer air vents are opened at 65% with a 3-second delay and last for 20 seconds; and the top layer air vents are opened at 50% with a 5-second delay and last for 15 seconds, forming a coordinated air distribution mode.

[0059] The S1043 uses a deep neural network predictor to combine airflow field data with induced draft fan load to generate coordinated control commands, ensuring the dynamic stability of emission control.

[0060] A deep neural network predictor was constructed using an 8-layer residual structure. The input layer has 75 nodes, containing velocity, temperature, and pressure field data. The number of hidden layer units is halved layer by layer from 512. The output layer generates the opening timing and opening curves of six air outlets. The training dataset contains 2000 sets of operating condition records. Batch normalization and dropout are used to control the prediction error within 5%. Combined with induced draft fan load data, the power change of the induced draft fan is monitored in real time, ranging from 50 to 150 kW. The air volume distribution is optimized to balance the furnace negative pressure and maintain it within -50 to -100 Pa. The predictor takes the airflow field distribution and mixing characteristics as input and outputs the air outlet adjustment scheme. The mixing intensity is calculated using the turbulent stress tensor. The index is greater than 0.8 in the strong mixing zone and decreases to 0.4 in the weak mixing zone. The airflow trajectory is analyzed by the pressure gradient of the wake zone. The swirl number reaches 1.2 at the inlet and decreases to 0.6 upstream. The least squares method was used to analyze the error between the measured and predicted values. The error was 8% for the velocity field, 5% for the temperature field, and 10% for the pressure field. After correction, the average error was reduced to 4%, ensuring the accuracy of the control commands.

[0061] In this embodiment of the invention, dynamic suppression of nitrogen oxide emissions through multi-level simulation and optimization significantly reduces peak concentrations. Compared to traditional static regulation, this method utilizes a combination of airflow dynamics simulation and intelligent algorithms to precisely optimize vent parameters and coordinate the control of the induced draft fan, improving the real-time performance and stability of emission control and providing a reliable guarantee for the environmental performance of high-moisture sludge incineration.

[0062] S105 acquires real-time load data of the induced draft fan and combines it with the duct adjustment value to analyze the furnace negative pressure fluctuation characteristics. If the fluctuation exceeds the safe range, the induced draft fan operating parameters are optimized through the load adjustment algorithm to generate a smooth load adjustment curve.

[0063] In this embodiment of the invention, after the coordinated control command is generated, the impact of negative pressure fluctuations on the stability of the incineration process is analyzed by real-time monitoring of the induced draft fan load data and the furnace negative pressure status, combined with the tuyeres adjustment parameters. Furthermore, the induced draft fan speed and power are dynamically optimized through intelligent algorithms to ensure that the furnace negative pressure is maintained within a safe range. This method improves the accuracy and response speed of induced draft fan load control through multi-dimensional data analysis and adaptive adjustment, providing a guarantee for the stable operation of high-moisture 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 Butterworth low-pass filter to smooth the data, generate negative pressure time series data and extract fluctuation characteristics.

[0065] A photoelectric speed sensor, measuring from 0 to 1500 rpm at a sampling frequency of 100 Hz, records the induced draft fan speed data. A power transmitter, measuring from 0 to 500 kW, acquires real-time power data. Furnace negative pressure is monitored by 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. The negative pressure data is processed by a Butterworth low-pass filter with a cutoff frequency of 2 Hz to filter out 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 per minute. If the standard deviation exceeds 100 Pa or the rate of change exceeds 300 Pa per minute, it indicates increased negative pressure fluctuations, requiring the triggering of an adjustment process. Short-time Fourier transform analysis of negative pressure time series data was performed using a Hanning window with a window width of 60 seconds and an overlap rate of 50%. Fluctuation spectrum features were extracted, with the main frequency distribution between 0.1 and 0.5 Hz. The normal fluctuation amplitude was about 200 Pa, and the preset safety threshold was 300 Pa. If the threshold was exceeded, optimization calculation was initiated.

[0066] S1052 uses the support vector regression algorithm to construct an induced draft fan load regulation predictor. Combining negative pressure fluctuation characteristics with induced draft fan operating data, it calculates the speed and power regulation amounts and generates a load regulation sequence.

[0067] A support vector regression algorithm was used to construct a load regulation predictor for the induced draft fan. Input features included the negative pressure fluctuation spectrum, rotational speed, power, and negative pressure change rate. Outputs were the rotational speed and power regulation amounts. The algorithm used a radial basis function kernel, and the kernel parameters were optimized through cross-validation. The training dataset contained 1000 sets of operating records. The rotational speed regulation range was limited to ±100 rpm, and the power regulation range was ±50 kW. The model's prediction accuracy reached 90%. The amplitude and frequency distribution of negative pressure fluctuations reflect the stability of the furnace airflow. Based on a typical operating condition of 1200 rpm and 400 kW, the predictor calculated a rotational 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 according to the intensity 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 this embodiment of the invention, the induced draft fan load adjustment curve is generated by modifying the adjustment parameters based on the smoothed load adjustment sequence and the negative pressure response characteristics, ensuring stable furnace negative pressure. The negative pressure response exhibits a first-order inertial element with a time constant of approximately 15 seconds, and the upper limit of the adjustment slope is set at 40 revolutions per minute. The final load adjustment curve exhibits an S-shaped characteristic, with an upward slope of 30 revolutions per minute, a plateau lasting 120 seconds, and a downward slope of 20 revolutions per minute, for a total adjustment time of approximately 300 seconds. After adjustment, the induced draft fan speed is reduced from 1200 revolutions per minute to 1150 revolutions per minute, 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, significantly improving the stability of furnace negative pressure. This method, through a combination of time-frequency analysis and adaptive filtering, dynamically optimizes the induced draft fan operating parameters, significantly reducing the negative pressure fluctuation amplitude compared to traditional fixed adjustment methods, providing reliable support for the environmental protection and stable operation of the incineration process.

[0069] Based on the load adjustment curve and the vent adjustment value, and incorporating the nitrogen oxide release characteristics, S106 constructs a multi-actuator collaborative control timing model to generate a control command sequence.

[0070] In this embodiment of the invention, after the induced draft fan load adjustment curve is generated, the dynamic coordination between the secondary air damper and the induced draft fan is optimized by combining the secondary air supply adjustment value and the peak characteristics of nitrogen oxides through a multi-actuator collaborative control timing model. This generates a control command sequence to ensure effective suppression of nitrogen oxide emissions. This method, through dynamic characteristic analysis and intelligent prediction, significantly improves the accuracy and real-time performance of multi-actuator collaborative control, providing a highly efficient guarantee for the environmental performance of high-moisture sludge incineration.

[0071] S1061 acquires dynamic response data of the secondary damper and induced draft fan actuator through a multi-channel data acquisition unit, and uses wavelet transform to extract the adjustment rate and response delay features to construct a dynamic characteristic dataset of the actuator.

[0072] A multi-channel data acquisition system monitors the secondary damper opening (range 0-100%) and the induced draft fan speed (range 0-1500 rpm) at a sampling frequency of 50 Hz. Dynamic response data is decomposed into four levels using the db4 wavelet basis function, retaining the 0.1-10 Hz frequency band characteristics while filtering out high-frequency noise and low-frequency drift, generating regulation rate and response delay curves. The regulation rate curve reflects the actuator's action speed: the secondary damper response rise time is approximately 3 seconds, the regulation dead zone is 2%, and the overshoot is controlled within 5%; the induced draft fan response rise time is approximately 5 seconds, the regulation dead zone is 20 rpm, and the overshoot is 3%. The response delay curve quantifies the actuator's action lag: the secondary damper delay is approximately 2 seconds, and the induced draft fan delay is approximately 3 seconds. The dynamic characteristic dataset is updated every second, including response rise time, regulation dead zone, and overshoot, providing accurate input for coordinated control.

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

[0074] A chemiluminescence gas analyzer with a range of 0 to 500 ppm and a resolution of 0.1 ppm was used to record nitrogen oxide concentrations in real time. The peak detection algorithm, based on a three-point comparison method, identifies a peak when the concentration at the center point exceeds that of the two adjacent points by a difference greater than 20 ppm. The rising slope is approximately 10 ppm per second, and the falling slope is approximately 2 ppm per second, accurately identifying the timing and duration of the peak. A long short-term memory neural network was used to construct the concentration predictor, employing 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 trend for the next 30 seconds. The network was trained using 1000 historical records, and the validation set error was 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 invention, based on the dynamic characteristics of the actuators and the concentration prediction sequence, a multi-actuator collaborative controller is constructed using recursive least squares and a fuzzy control strategy to generate a control command sequence. The collaborative controller calculates the adjustment gain using recursive least squares, with an initial gain of 0.8 for the secondary damper, 0.5 for the induced draft fan, and a forgetting factor of 0.95. The gain matrix is ​​iteratively updated with a sampling interval of 1 second and a prediction time domain of 60 seconds. The fuzzy rule base contains 49 rules, with the input variables being concentration deviation and rate of change, and the output being the actuator adjustment amount. A triangular membership function is used, divided into five levels: negative large, negative small, zero, positive small, and positive large. The inference uses the minimum-maximum method and the centroid method to solve the fuzzy conditions and generate the adjustment commands. Feedforward compensation is adjusted according to the response delay curve, with the secondary damper compensated for 2 seconds and the induced draft fan compensated for 3 seconds. The command sequence update cycle is 1 second. When an upward trend in nitrogen oxide concentration is detected, the command sequence instructs the secondary damper to open 2 seconds in advance, increasing the opening by 20%, and the induced draft fan to increase its speed by 100 revolutions per minute 3 seconds in advance, achieving precise collaborative suppression of the peak value. This method, by combining dynamic characteristic analysis with predictive control, significantly improves the response speed and emission control effect of multi-actuator collaboration, providing strong support for the stability of the incineration process.

[0076] S107 transmits control command sequences to the fluidized bed incinerator actuators via the fieldbus network, adjusts the tuyer status and air volume ratio, updates the induced draft fan load, and continuously monitors the furnace operating parameters.

[0077] In this embodiment of the invention, after the control command sequence is generated, the commands are transmitted to the actuator via a fieldbus network to dynamically adjust the secondary air damper opening and the induced draft fan speed. Key parameters such as furnace temperature, oxygen concentration, and airflow velocity are monitored in real time to ensure the stability of the incineration process and the control of nitrogen oxide emissions. This method, through precise data acquisition, prediction, and closed-loop control, significantly improves the actuator's response accuracy and the system's operational stability, providing a reliable guarantee for the environmental performance of high-moisture sludge incineration.

[0078] The S1071 uses a fieldbus network to transmit control commands, parses and generates actuator control signals, and collects air outlet pressure and air volume data through a differential pressure transmitter and a mass flow meter to construct an actuator action status table.

[0079] The MODBUSRTU protocol, with a baud rate of 9600 bits per second, 8 data bits, and 1 stop bit, is used to transmit control commands to the actuators. A communication protocol parser decodes the commands, generating control electrical signals containing opening setpoints and action enable signals. The actuator position feedback signal has a resolution of 0.1% and is updated every 100 milliseconds, recording the deviation between the actual and target positions to construct an actuator action status table. Differential pressure transmitters are placed at the air vents on each floor, with a range of 0 to 10 kPa, an accuracy of 0.1%, and an output signal of 4 to 20 mA, monitoring changes in air vent pressure. Vortex flow meters are used, with a range of 0 to 1000 cubic meters per hour, calculating the airflow distribution ratio: approximately 55% for the bottom floor vents, 30% for the middle floors, and 15% for the top floor. The action status table reflects the real-time response of the actuators, ensuring that the airflow distribution is consistent with the commands and providing a data foundation for subsequent optimization.

[0080] The S1072 collects furnace operating parameters through a thermocouple array, oxygen analyzer, and Doppler flow meter, constructs an operating status table, and evaluates parameter deviations to generate an actuator characteristic parameter table.

[0081] A K-type thermocouple array is used, with a temperature measurement range of 0 to 1200 degrees Celsius. A layer is arranged every 500 mm along the vertical direction of the furnace, with 8 measuring points circumferentially per layer, to collect temperature field data. During normal operation, the temperature is maintained between 900 and 950 degrees Celsius. An oxygen analyzer uses a zirconium oxygen probe with a range of 0 to 25%, monitoring the oxygen content of the flue gas and stabilizing it at 6% to 7%. A Doppler velocimeter measures the airflow velocity, ranging from 0 to 40 meters per second, recording at a frequency of 1 Hz, with the velocity maintained between 15 and 18 meters per second. The operating status table is updated every second, comparing to preset thresholds: temperature fluctuation ±50 degrees Celsius, oxygen content ±1%, and velocity ±5 meters per second. Deviation indicators are calculated using the root mean square value of temperature, the maximum deviation of oxygen content, and the standard deviation of velocity. A deviation report is generated when the threshold is exceeded. The actuator responsiveness evaluation is based on the deviation index. The damper actuator has an adjustment dead zone of about 2% and a response delay of 3 seconds; the induced draft fan has an adjustment dead zone of 20 revolutions per minute and a response delay of 5 seconds. The characteristic parameter table is updated every second to record the dynamic characteristic trend and provide a basis for optimizing control.

[0082] In this embodiment of the invention, a parameter predictor is constructed using a Long Short-Term Memory (LSTM) network to generate optimization instructions. Closed-loop control is then used to correct actuator signals and update the action status table. The predictor employs a four-layer structure: the input layer contains temperature, oxygen content, and airflow velocity data from the past 60 seconds; the hidden layer has 256 units; and the output layer predicts parameter trends for the next 30 seconds. The training data includes 2000 records, with prediction errors controlled within 5% and a confidence interval of ±3%. The predicted sequence guides the generation of actuator optimization instructions. The closed-loop controller calculates corrections based on deviation indicators: the damper opening correction range is ±5%, the induced draft fan speed correction is ±50 rpm, and the correction cycle is 2 seconds. After optimization, the temperature field stabilizes at 900-950 degrees Celsius, the oxygen content remains at 6%-7%, and the airflow velocity is 15-18 meters per second, significantly reducing parameter fluctuations and improving the stability of the combustion process. This method, through dynamic prediction and closed-loop correction, ensures a high degree of matching between actuator actions and furnace conditions, thus optimizing the nitrogen oxide emission control effect.

[0083] The above description is merely a preferred embodiment of this application and an explanation 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 technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A control method for a nitrogen oxide incineration apparatus for drying carbonization, characterized by, The method comprises: Obtain the combustion characteristic data of high-moisture sludge lumps in a fluidized bed incinerator, extract the nitrogen oxide peak intensity, occurrence time, duration and peak shape steepness, and obtain a parameter set; Collect the bed temperature peak value data in the fluidized bed incinerator, calculate the time lag between the nitrogen oxide peak occurrence time and the bed temperature peak value by using a time series analysis method according to the parameter set, determine the change trend characteristics of the release peak by combining the peak shape steepness and the duration, and adjust the layered air volume based on the change trend characteristics and a layered secondary air supply adjustment model constructed based on the furnace gas flow dynamics, input the nitrogen oxide peak intensity and the peak shape steepness into the layered secondary air supply adjustment model, calculate the opening time sequence and air volume distribution ratio of different height air ports, and adjust the layered air volume; Based on the gas flow distribution simulation, obtain the gas flow distribution in the furnace, analyze the inhibition effect of the secondary air supply on the nitrogen oxide release peak by combining the adjusted layered air volume, and reflect the inhibition effect by a discharge concentration drop amplitude index, if the discharge concentration drop amplitude index is less than a preset threshold value, optimize the air port time sequence and air volume ratio to obtain an adjustment value; Obtain the real-time data of the induced draft fan load, analyze the fluctuation range of the furnace negative pressure stability by combining the adjustment value, if the fluctuation exceeds a preset safety range, calculate the induced draft fan load adjustment amplitude, and generate a load adjustment curve; Based on the load adjustment curve and the adjustment value, construct a multi-actuator cooperative control time sequence model, fuse the nitrogen oxide peak occurrence time and the duration, calculate the cooperative adjustment time sequence of the secondary air supply and the induced draft fan load, and generate a control instruction; Transmit the control instruction to the execution mechanism of the fluidized bed incinerator, adjust the air port opening state and the air volume ratio, update the induced draft fan load, and continuously monitor the adjusted furnace operation parameters. The method comprises:

2. The control method for a nitrogen oxide incineration apparatus for drying carbonization according to claim 1, characterized by, Obtain the combustion characteristic data of high-moisture sludge lumps in a fluidized bed incinerator, extract the nitrogen oxide peak intensity, occurrence time, duration and peak shape steepness, and obtain a parameter set; Obtain the surface temperature data and the reflectance spectrum data of the sludge lumps, calculate the moisture content parameter and the particle size distribution parameter of the sludge lumps according to the absorption characteristic curve of the reflectance spectrum data; According to the moisture content parameter and the particle size distribution parameter, use a temperature sensor array and a pressure sensor array to collect real-time combustion data of the sludge lumps, and obtain the combustion state parameters of the sludge lumps by using a Kalman filtering algorithm; Determine the combustion gas corresponding to the combustion state parameters by using an oxygen content analyzer, and calculate the reaction activity index according to the oxygen concentration change curve of the combustion gas; Perform online analysis on the nitrogen oxides in the combustion gas by using a gas chromatograph-mass spectrometer, perform multi-scale decomposition on the nitrogen oxide concentration curve by using a wavelet transform, establish a temperature and nitrogen oxide correlation model according to the reaction activity index and the combustion state parameters, input the characteristics including temperature, pressure, moisture content and reaction activity index, and output a nitrogen oxide characteristic parameter set.

3. The control method for a nitrogen oxide incineration apparatus for drying carbonization according to claim 1, characterized by, The peak value data of the bed temperature in the fluidized bed incinerator is collected, the time series analysis method is used to calculate the lag relationship between the nitrogen oxide peak value occurrence time and the bed temperature peak value according to the parameter set, and the change trend characteristics of the release peak are determined by combining the peak steepness and the duration, including: Collecting bed temperature data, filtering the bed temperature data through a band-pass filter to obtain the bed temperature peak time and peak intensity; For the bed temperature peak time and peak intensity, the temperature gradient value between adjacent measuring points is calculated, the temperature gradient value is smoothed by median filtering, and the bed temperature change rate curve is obtained; The nitrogen oxide concentration data is recorded by the gas online monitoring device, the nitrogen oxide concentration data is decomposed by wavelet transform, and the nitrogen oxide peak time and peak intensity are extracted from the wavelet transform coefficients; According to the bed temperature change rate curve and the nitrogen oxide peak value characteristics, a support vector regression model is established, 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.

4. The control method for a nitrogen oxide incineration apparatus for drying carbonization according to claim 1, characterized by, The change trend characteristics are determined, and a layered secondary air supply adjustment model is constructed based on the furnace gas flow dynamics, the nitrogen oxide peak intensity and the peak steepness are input into the layered secondary air supply adjustment model, the opening time sequence and the air volume distribution ratio of different height air ports are calculated, and the layered air volume is adjusted, including: Obtaining three-dimensional velocity component data of the furnace, the velocity component data is measured at different heights by a velocity probe array; According to the velocity component data, the furnace gas flow region is divided, and the gas flow characteristic data set is obtained by collecting pressure data and temperature data of each gas flow region; Collecting nitrogen oxide concentration data, and outputting the air port opening value according to the gas flow characteristic data set and the nitrogen oxide concentration data by a deep neural network; For the air port opening value, the optimal air volume distribution scheme is calculated based on the gas mixing intensity index and the nitrogen oxide emission index by a particle swarm algorithm, the optimization objective function includes the mixing intensity index and the nitrogen oxide emission index, and the constraint condition includes the maximum opening degree limit of the air port and the total air volume balance requirement, and the mixing intensity index is determined by the tracer gas concentration decay rate; A support vector machine predictor is used to establish an air port opening time sequence planner, the input characteristics include air pressure fluctuation frequency and amplitude, and the output air port opening time and duration parameters.

5. The control method for a nitrogen oxide incineration apparatus for drying carbonization according to claim 1, characterized by, Based on the gas flow distribution simulation, the inhibition effect of the secondary air supply on the nitrogen oxide release peak is analyzed combined with the adjusted layered air volume, which is reflected by the emission concentration drop amplitude index, if the emission concentration drop amplitude index is less than the preset threshold value, the air port time sequence and the air volume ratio are optimized to obtain the adjustment value, including: According to the Doppler flowmeter, the furnace gas flow velocity data and the pressure sensor record the air port pressure data, and the furnace gas flow field distribution data is obtained by computational fluid dynamics simulation; The Reynolds stress tensor is calculated using the gas flow field distribution data to obtain the turbulence intensity index; The gas online monitoring device is used to record the concentration data of nitrogen oxide emission, and the concentration curve peak value is obtained through wavelet transform denoising processing; If the emission concentration decrease amplitude index is less than the preset reference value, a genetic algorithm optimization objective function is established according to the gas mixing characteristic parameters; The air port opening timing and air volume distribution ratio are optimized and calculated, the airflow field distribution data and the gas mixing characteristic parameters are input, and the air port adjustment scheme is obtained.

6. The method of claim 5, wherein, Also includes: Real-time airflow velocity and direction data of the combustion zone and the secondary air inlet area in the furnace are collected, and the collected data are subjected to turbulent flow dynamics simulation according to fluid dynamics, the airflow distribution characteristics are simulated, the airflow velocity field and direction field simulation results are output, the simulation results are calibrated in combination with the actual furnace working condition parameters, and the airflow distribution simulation data in the furnace are obtained, specifically including: The Doppler flow velocity meter is used to obtain the original airflow three-dimensional velocity data, and the original airflow three-dimensional velocity data are processed by the Kalman filter to obtain the filtered velocity field data; The filtered velocity field data are subjected to band-pass filtering processing to obtain velocity fluctuation characteristic data, and the velocity fluctuation characteristic data, temperature field data, pressure distribution data and oxygen content data constitute an operating state parameter set; The operating state parameter set is processed through a deep neural network, and the deep neural network adopts a residual structure to extract features to obtain turbulent stress tensor data; The turbulent stress tensor data are used to calculate the airflow mixing intensity value, and the airflow mixing intensity value and the wake region pressure gradient data are used to determine the airflow motion trajectory prediction result.

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

8. The control method for a nitrogen oxide incineration apparatus for drying carbonization according to claim 1, characterized by, A multi-actuator cooperative control time sequence model is constructed based on the load adjustment curve and the adjustment value, the nitrogen oxide peak value occurrence time and the duration are fused, the cooperative adjustment time sequence of the secondary air supply and the induced draft fan load is calculated, and a control instruction is generated, including: The multi-channel data collector is used to obtain actuator dynamic response data, and the dynamic response data are subjected to four-layer decomposition through wavelet transform to obtain adjustment rate curves and response delay curves; According to the adjusting rate curve, a response rise time and an overshoot amplitude parameter are extracted to constitute an actuator dynamic characteristic data set; Nitrogen oxide concentration data are collected by using a gas analyzer, the concentration data are 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; According to the actuator dynamic characteristic data set and the concentration prediction sequence, a multi-actuator cooperative controller is established, the cooperative controller calculates an actuator adjusting gain by using a recursive least square method, and a control instruction sequence of the actuator is obtained by using a barycenter method to solve ambiguity.

9. The control method for a nitrogen oxide incineration apparatus for drying carbonization according to claim 1, characterized by, The transmission control instruction is transmitted to a fluidized bed incinerator actuator to adjust the opening state and the air volume ratio of the air port, update the load of the induced draft fan, continuously monitor the operating parameters of the adjusted furnace, including: A communication protocol parser is used to receive the control instruction transmitted by the field bus network, and an actuator control electric signal is generated according to the control instruction; An actuator position feedback signal is collected according to the actuator control electric signal, an air port pressure signal is obtained by using a differential pressure transmitter, and an air pipe air volume value is obtained by using a mass flowmeter; A furnace temperature field data is obtained by using a thermocouple array, a flue gas oxygen content value is measured by using an oxygen content analyzer, and a gas flow velocity parameter is obtained by using a Doppler flowmeter; A long short-term memory network predictor is constructed according to the furnace temperature field data, the flue gas oxygen content value and the gas flow velocity parameter, an actuator optimization instruction is generated by using the predictor, and the actuator control electric signal is corrected by using a closed-loop controller.

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