Denitration catalyst microwave regeneration optimization method based on machine learning

Abstract

The invention belongs to the cross technical field of artificial intelligence and environmental protection engineering, particularly relates to a denitration catalyst microwave regeneration optimization method based on machine learning, and aims to solve the problems of inaccurate energy matching, large structural damage and low efficiency of a traditional regeneration method. The method comprises the following steps: constructing a catalyst multi-dimensional feature database, establishing a microwave regeneration multi-physics field coupling simulation model, and training an integrated learning prediction model to associate input features with a regeneration effect; in the regeneration process, infrared thermal imaging, microwave reflection and gas sensing equipment are integrated, temperature field, energy absorption and tail gas data are collected in real time, and microwave power, frequency and atmosphere parameters are dynamically regulated and controlled. Accurate energy release and closed-loop intelligent regulation and control are achieved, the activity recovery rate after regeneration exceeds 92%, the specific surface area retention rate exceeds 85%, the regeneration period is shortened to one third of that of a traditional method, energy consumption is reduced by 40%, full-life-cycle intelligent management of the catalyst is supported, and economical efficiency and sustainability of environmental protection equipment are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of artificial intelligence and environmental engineering, specifically relating to a microwave regeneration optimization method for denitrification catalysts based on machine learning. Background Technology

[0002] With increasingly stringent requirements for industrial flue gas treatment, denitrification catalysts, as core materials for nitrogen oxide emission reduction, directly impact the operating costs and sustainability of environmental protection equipment due to their regeneration efficiency after performance degradation. While traditional thermal regeneration and chemical cleaning methods can partially restore catalyst activity, they generally suffer from high energy consumption, long regeneration cycles, and significant loss of active components, failing to meet the demands of modern industry for efficient, low-carbon, and precise regeneration processes. Especially under complex operating conditions, the uneven distribution of poisoned substances on the catalyst surface and varying degrees of damage to the pore structure place higher demands on the precise coordinated control of the temperature field, energy field, and reaction kinetics during the regeneration process.

[0003] However, existing microwave regeneration technologies largely rely on empirical parameter settings, lacking the ability to dynamically model the relationship between the catalyst's microscopic state and regeneration response. This results in a low match between energy input and activity recovery, easily leading to localized overheating or incomplete regeneration. Simultaneously, the complex coupling effects of multiple physical fields (electromagnetic, thermal, and chemical fields) during regeneration make it difficult for traditional control strategies to achieve adaptive adjustment of key parameters, failing to balance regeneration efficiency and structural integrity. Furthermore, the determination of the regeneration endpoint relies on offline detection, lacking a real-time feedback mechanism, resulting in an ambiguous process window and difficulty in ensuring batch consistency.

[0004] Therefore, a machine learning-based optimization method for microwave regeneration of denitrification catalysts is desired. Summary of the Invention

[0005] The purpose of this invention is to provide a microwave regeneration optimization method for denitrification catalysts based on machine learning, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for optimizing microwave regeneration of denitration catalysts based on machine learning includes the following specific steps: Step (1) Constructing a catalyst state feature database: Collecting physicochemical parameters of multiple batches of deactivated denitration catalysts, including specific surface area, pore volume, pore size distribution, active component content, types and concentrations of poisoned elements, and surface morphology images, and combining them with their corresponding historical operating condition data to form a structured feature dataset; Step (2) Establishing a multi-physics field coupled simulation model for the microwave regeneration process: Based on electromagnetic wave propagation theory, heat conduction equation and chemical reaction kinetics, constructing a mapping relationship model between microwave power, frequency, action time and the internal temperature field, electromagnetic field intensity distribution and chemical reaction rate of the catalyst; Step (3) Training a machine learning model for predicting regeneration effect: Using the feature dataset in step (1) as input and the simulation model in step (2) as input. The regeneration activity recovery rate, structural integrity index and energy consumption data are used as labels, and the regeneration effect prediction model is trained by ensemble learning algorithm; Step (4) Implement online sensing and parameter adaptive control: In the microwave regeneration equipment, an infrared thermal imager, a microwave reflection power sensor and a gas composition analyzer are integrated to collect the temperature distribution, energy absorption efficiency and tail gas composition change data in real time during the regeneration process, and input them into the prediction model trained in step (3) to dynamically adjust the microwave power, frequency and gas atmosphere parameters; Step (5) Determine the regeneration endpoint and output optimized process parameters: Based on the real-time predicted activity recovery rate change trend and structural damage risk index, when the activity recovery rate tends to be stable and the structural damage risk is lower than the preset threshold, the regeneration process is determined to be over, and the optimal process parameter combination for this regeneration is recorded for updating the prediction model and process knowledge base.

[0007] Preferably, in step (1), the physicochemical parameters are determined by nitrogen adsorption-desorption method to measure specific surface area and pore structure, by X-ray fluorescence spectroscopy to determine the content of active components and poisoning elements, by scanning electron microscopy to obtain surface morphology images, and historical operating condition data including flue gas temperature, flow rate, initial concentration of nitrogen oxides, running time and shutdown frequency.

[0008] Preferably, in step (2), the multiphysics coupling simulation model uses the finite element method to solve Maxwell's equations and Fourier heat conduction equations. The chemical reaction kinetics module considers the decomposition activation energy changes of typical poisoning substances such as sulfates and arsenides under microwave fields. The model mesh division accuracy is not less than 0.1 mm, and the time step is set to 0.5 seconds.

[0009] Preferably, in step (3), the ensemble learning algorithm adopts a weighted fusion strategy of gradient boosting decision tree and random forest, the input feature dimension is not less than 32, the model training adopts five-fold cross-validation, the root mean square error of the predicted activity recovery rate is less than 3%, and the structural integrity index includes pore collapse rate and specific surface area retention rate.

[0010] Preferably, in step (4), the infrared thermal imager has a spatial resolution of 128 x 128 pixels, a temperature measurement range of 25 to 800 degrees Celsius, and an accuracy of ±2 degrees Celsius; the microwave reflective power sensor has a sampling frequency of 10 Hz; and the gas composition analyzer monitors the concentrations of ammonia, nitrogen oxides, and water vapor in real time with a response time of less than 1 second.

[0011] Preferably, the dynamic adjustment strategy in step (4) includes: when the local temperature gradient exceeds 50 degrees Celsius per centimeter, reducing the microwave power and switching to pulse mode; when the microwave energy absorption efficiency is less than 70%, adjusting the microwave frequency to the frequency band corresponding to the peak value of catalyst dielectric loss; when arsine or hydrogen sulfide is detected in the exhaust gas, introducing an oxygen-containing carrier gas to promote oxidative decomposition.

[0012] Preferably, the determination of the regeneration endpoint in step (5) adopts a dual threshold mechanism: the change in the activity recovery rate is less than 0.5% for 30 consecutive seconds, and the structural damage risk index is less than 0.2; the structural damage risk index is calculated by weighting the duct collapse probability and the thermal stress concentration coefficient, and the weight coefficient is determined by inverting historical regeneration failure cases.

[0013] Preferably, it also includes establishing a regeneration process knowledge base: storing the input characteristics, control parameters, real-time sensor data and final performance indicators of each regeneration process into the database, with the number of database records being greater than or equal to 5,000, and supporting retrieval and cluster analysis by dimensions such as catalyst type, poisoning degree, and runtime.

[0014] Preferably, it also includes an online model update mechanism: when the number of new regeneration batches reaches 100, the incremental learning of the prediction model is triggered, and a sliding window strategy is used to retain the most recent 2000 sets of high-quality data, and to remove abnormal or low-confidence samples, so as to ensure that the model continuously adapts to the catalyst aging trend and changes in operating conditions.

[0015] Preferably, the method is integrated into a fully automated microwave regeneration system, which is equipped with a robotic arm for automatically loading and unloading catalyst modules with a positioning accuracy of ±0.5 mm. The regeneration chamber has a dual atmosphere switching function of nitrogen and air, and the microwave source output power ranges from 0 to 5 kW and the frequency is adjustable from 0.9 to 2.45 GHz.

[0016] Compared with the prior art, the present invention has the following beneficial effects: Achieving precise energy matching and process control By constructing a dynamic mapping model between the catalyst's microstate and regeneration response, the mismatch between energy input and activity recovery caused by traditional empirical parameter settings is overcome. The machine learning model can predict the optimal microwave parameter combination based on the individual differences of each batch of catalyst, ensuring that energy is precisely applied to the poisoned area, avoiding localized overheating or insufficient regeneration. Experiments show that the activity recovery rate after regeneration remains stable above 92%, an improvement of 15 percentage points compared to the traditional fixed-parameter method, with a batch-to-batch standard deviation of less than 3%.

[0017] Ensure the integrity of the catalyst structure The combination of multiphysics coupled simulation and real-time sensing feedback achieves synergistic suppression of thermal stress and chemical erosion. By dynamically controlling the microwave pulse mode and gas atmosphere, the temperature gradient and reaction rate within the pores are effectively controlled, significantly reducing the risk of pore collapse and sintering of active components. The catalyst regenerated by this invention retains more than 85% of its specific surface area, while traditional thermal regeneration methods typically retain less than 70%.

[0018] Improve recycling efficiency and intelligence level The online sensing and adaptive control mechanism eliminates process lag caused by offline detection, shortening the regeneration cycle to one-third of that of traditional methods. The online update mechanism of the process knowledge base and model enables the system to continuously learn, adapting to catalysts from different sources and with different poisoning types, demonstrating strong versatility. Energy consumption per regeneration is reduced by 40%, and no chemical reagents are required, meeting the requirements of green and low-carbon manufacturing.

[0019] Establish a closed-loop optimized regeneration system From data acquisition, model prediction, and real-time control to endpoint determination and knowledge accumulation, a complete data-driven regeneration closed loop has been formed. This system not only improves the quality of each regeneration cycle but also continuously optimizes future regeneration strategies through the accumulation of historical data, providing technical support for the full life-cycle management of denitrification catalysts and significantly reducing the long-term operating costs of environmental protection equipment. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention. Detailed Implementation

[0021] Example 1 Please refer to Figure 1 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0022] Currently, the regeneration process of denitrification catalysts faces several technical challenges, including low regeneration efficiency, high risk of structural damage, and poor generalizability of process parameters due to significant differences in catalyst deactivation states, complex relationships between microwave energy input and activity recovery response, and a lack of real-time feedback control mechanisms. To address these issues, this invention proposes a machine learning-based optimization method for microwave regeneration of denitrification catalysts, and applies it to this machine learning-based microwave regeneration optimization method for denitrification catalysts.

[0023] In the above-mentioned optimization method for microwave regeneration of denitration catalysts based on machine learning, step (1) involves constructing a catalyst state characteristic database: collecting physicochemical parameters of multiple batches of deactivated denitration catalysts, including specific surface area, pore volume, pore size distribution, active component content, types and concentrations of poisoned elements, and surface morphology images, and combining them with their corresponding historical operating condition data to form a structured feature dataset. Specifically, in step (1), the physicochemical parameters are determined by nitrogen adsorption-desorption method to measure specific surface area and pore structure. This method is based on the international standard ISO 9277, using the static volumetric method to measure the amount of nitrogen adsorbed on the catalyst surface, calculating the specific surface area using the BET equation, and analyzing the pore size distribution using the BJH model. The test temperature is the liquid nitrogen temperature (77K), and the pressure range is 0.05 to 0.99P. a At least 30 data collection points are required to ensure complete characterization of the pore structure; the pore volume is obtained by subtracting the micropore volume from the total adsorption volume, with an accuracy superior to [previous value]. The pore size distribution is represented by a cumulative pore volume versus pore size curve with a resolution of not less than 0.5 nm. The content of active components and the types and concentrations of poisoning elements were determined by X-ray fluorescence spectrometry using a wavelength dispersive XRF device. The excitation source was an Rh target X-ray tube, with a voltage of 40 kV and a current of 40 mA. Sample preparation employed a pellet method with a pressing pressure of 10 MPa and a thickness controlled within 1 mm. The detected elemental range covered common catalytic active components and poisoning elements such as V, W, Mo, S, As, Se, Pb, Na, K, Ca, and Mg, with a detection limit below 1 ppm and a relative standard deviation of less than 5%. Surface morphology images were acquired using a scanning electron microscope (SEM) with an accelerating voltage of 15 kV, a working distance of 8 mm, a magnification range of 50 to 50,000x, and an image resolution of 1024 × 768 pixels. The sampling area covered the catalyst cross-section and typical surface regions. At least five images with different fields of view were collected for each batch for subsequent image segmentation and pore morphology identification. Historical operating data includes flue gas temperature, flow rate, initial nitrogen oxide concentration, operating time, and shutdown frequency. Flue gas temperature is measured using thermocouples, with a measurement range of 150 to 450 degrees Celsius and an accuracy of ±1 degree Celsius. Flow rate is measured using a combination of a Pitot tube and a differential pressure transmitter, with a range of 1 to 15 m / s and an accuracy of ±0.1 m / s. Initial nitrogen oxide concentration is determined using non-dispersive infrared absorption, with a detection range of 10 to 1000 ppm and a response time of less than 1 second. Operating time records the cumulative operating hours, and shutdown frequency is calculated based on the number of shutdowns per month. Data is collected hourly and stored in an industrial-grade PLC system. Data integrity is verified using the CRC-32 algorithm to ensure error-free data transmission. All collected physicochemical parameters and historical operating condition data are integrated through a unified data interface protocol to form a structured feature dataset containing 32 or more feature vectors. The data format is CSV, and the field naming follows the ISO 19115 metadata standard. Each sample corresponds to an independent catalyst batch number. The total number of datasets is no less than 5,000, which will be used for subsequent model training and validation.

[0024] In the above-mentioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, step (2) establishes a multi-physics coupling simulation model of the microwave regeneration process: based on electromagnetic wave propagation theory, heat conduction equations and chemical reaction kinetics, a mapping relationship model is constructed between microwave power, frequency, action time and the internal temperature field, electromagnetic field intensity distribution and chemical reaction rate of the catalyst. Specifically, in step (2), the multi-physics coupling simulation model uses the finite element method to solve Maxwell's equations and Fourier heat conduction equations, where Maxwell's equations are discretized in the time domain as follows:

[0025] in, The magnetic field strength, For free current density, It is the electric displacement vector. For charge density, For electric field strength, represents the magnetic flux density. Within the catalyst material region, the dielectric constant... With permeability It is a frequency-dependent function, and the dielectric loss factor is considered. The influence of this leads to a power density of microwave energy converted into heat energy of [value missing]. ,in For electrical conductivity, Angular frequency, This represents the imaginary part of the dielectric constant. The heat conduction equation is:

[0026] in, For material density, For specific heat capacity, For temperature, Thermal conductivity, The term represents the heat source, i.e., the power density converted from the electromagnetic field. The chemical reaction kinetics module considers the activation energy changes of typical poisoning substances such as sulfates and arsenides under a microwave field, and uses the Arrhenius equation to describe the reaction rate constant. ,in Pre-exponential factor, For activation energy, The gas constant is This is the absolute temperature. Under the influence of a microwave field, The value decreases as the local electric field strength increases; a correction factor is introduced. This makes the effective activation energy [missing information]. ,in The electric field intensity dependence function was obtained through experimental calibration. The model mesh was generated with a precision of no less than 0.1 mm, using a hexahedral dominant mesh structure, with the boundary layer mesh refined to 0.05 mm to ensure accurate capture of the electromagnetic field and temperature gradient in the near-wall region. The time step was set to 0.5 seconds to satisfy the Courant-Friedrichs-Lewy stability condition and ensure numerical solution convergence. The simulation boundary conditions were set as follows: the outer wall of the regeneration chamber was an adiabatic boundary, the catalyst surface was a radiative boundary, the microwave incident port was a port excitation boundary, the initial temperature was set to room temperature (25 degrees Celsius), and the initial electric field intensity was zero. The model output included the three-dimensional temperature field distribution inside the catalyst, the electromagnetic field intensity distribution, the decomposition rate of each poisoned substance, and the predicted values ​​of the activity recovery rate, which served as label data for subsequent machine learning models.

[0027] In the above-mentioned optimization method for microwave regeneration of denitrification catalyst based on machine learning, step (3) trains a machine learning model for predicting regeneration effect: using the feature dataset in step (1) as input, and the activity recovery rate, structural integrity index and energy consumption data output by the simulation model in step (2) as labels, an ensemble learning algorithm is used to train the model for predicting regeneration effect. Specifically, in step (3), the ensemble learning algorithm adopts a weighted fusion strategy of gradient boosting decision tree and random forest. The gradient boosting decision tree adopts the XGBoost framework, with a tree depth limit of 10, a learning rate of 0.1, and regularization parameters λ=1 and α=0.1 to prevent overfitting. The random forest is implemented using Scikit-learn, with 100 trees, a maximum depth of 15, a minimum number of leaf samples of 5, and a random subspace method for feature selection. The input features must be at least 32-dimensional, including specific surface area, pore volume, mean and standard deviation of pore size distribution, active component content, concentration of poisoning elements, surface roughness, pore connectivity index, historical operating time, shutdown frequency, average flue gas temperature, and initial concentration of nitrogen oxides. All features are standardized, with the mean normalized to 0 and the standard deviation normalized to 1. Model training employs five-fold cross-validation, dividing the dataset into five mutually exclusive subsets, which alternately serve as the validation set, with the remainder used as the training set. This process is repeated five times, and the average performance index is used as the final evaluation result. The root mean square error of the predicted activity recovery rate is less than 3%. Structural integrity indices include pore collapse rate and specific surface area retention rate. The pore collapse rate is defined as the percentage decrease in the proportion of micropores with a diameter less than 2 nanometers, and the specific surface area retention rate is defined as the ratio of the regenerated specific surface area to the original value. Both are calculated using the simulation model output. The model output is a continuous value, with the activity recovery rate ranging from 0 to 100%, the structural integrity index ranging from 0 to 1, and energy consumption data in kilowatt-hours per kilogram of catalyst. After the model is trained, it is saved as a PMML format file and deployed on edge computing nodes to support real-time inference calls.

[0028] In the above-mentioned optimization method for microwave regeneration of denitrification catalyst based on machine learning, step (4) implements online sensing and parameter adaptive control: an infrared thermal imager, a microwave reflection power sensor, and a gas composition analyzer are integrated into the microwave regeneration equipment to collect data on temperature distribution, energy absorption efficiency, and tail gas composition changes during the regeneration process in real time, and input them into the prediction model trained in step (3) to dynamically adjust the microwave power, frequency, and gas atmosphere parameters. Specifically, in step (4), the infrared thermal imager has a spatial resolution of 128 x 128 pixels, a temperature measurement range of 25 to 800 degrees Celsius, and an accuracy of ±2 degrees Celsius. It uses an uncooled microbolometer array with a frame rate of 30 Hz and an image acquisition frequency of 10 Hz. Temperature calibration is performed using a blackbody calibration device, and the calibration error is less than ±1 degree Celsius. The microwave reflection power sensor has a sampling frequency of 10 Hz, a measurement range of 0 to 100 watts, and an accuracy of ±0.5 watts. It is installed at the end of the microwave transmission line to calculate the energy absorption efficiency. ,in For reflected power, The incident power is used. The gas composition analyzer monitors the concentrations of ammonia, nitrogen oxides, and water vapor in real time, employing a combination of electrochemical and infrared absorption sensors. The ammonia detection range is 0 to 100 ppm with a response time of less than 1 second; the nitrogen oxide detection range is 0 to 500 ppm; and the water vapor detection range is 0 to 100% RH with a response time of less than 1 second. All sensor signals are transmitted to the central controller after analog-to-digital conversion. The real-time acquired data is synchronized to the prediction model input via a CAN bus. The model updates its prediction results every second, outputting the optimal microwave power, frequency, and gas atmosphere parameters. The dynamic adjustment strategy includes: when the local temperature gradient exceeds 50 degrees Celsius per centimeter, reducing the microwave power and switching to pulse mode, with a pulse width set to 10 milliseconds and a duty cycle of 50%, to suppress thermal stress concentration; when the microwave energy absorption efficiency is below 70%, adjusting the microwave frequency to the frequency band corresponding to the peak dielectric loss of the catalyst, querying the target frequency through a preset dielectric constant database, with a frequency adjustment step of 0.1 GHz; when arsine or hydrogen sulfide is detected in the exhaust gas, introducing oxygen-containing carrier gas to promote oxidative decomposition, with an oxygen flow rate set to 5 liters / minute and a duration of no less than 30 seconds, to ensure complete oxidation of toxic gases.

[0029] In the above-mentioned optimization method for microwave regeneration of denitrification catalyst based on machine learning, step (5) determines the regeneration endpoint and outputs optimized process parameters: based on the real-time predicted trend of activity recovery rate and structural damage risk index, when the activity recovery rate tends to stabilize and the structural damage risk is lower than the preset threshold, the regeneration process is determined to be over, and the optimal combination of process parameters for this regeneration is recorded for updating the prediction model and process knowledge base. Specifically, the determination of the regeneration endpoint in step (5) adopts a dual threshold mechanism: the change in activity recovery rate is less than 0.5% for 30 consecutive seconds, and the structural damage risk index is lower than 0.2; the structural damage risk index is calculated by weighting the pore collapse probability and the thermal stress concentration coefficient, and the weight coefficient is determined by inversion of historical regeneration failure cases. The pore collapse probability is calculated by the correlation function between the micropore volume loss rate predicted by the simulation model and the temperature gradient, and the thermal stress concentration coefficient is calculated by the ratio of the maximum principal stress to the material yield strength obtained by finite element analysis, with weights of 0.6 and 0.4, respectively, and is optimized from 500 failure cases by Bayesian inversion method. When the dual threshold conditions are met, the system automatically stops microwave output, shuts off gas supply, and initiates a cooling program. The cooling rate is controlled within 5 degrees Celsius per minute to avoid thermal shock. Simultaneously, the optimal combination of process parameters for this regeneration is recorded, including microwave power, frequency, action time, gas atmosphere composition, and pulse mode parameters, and stored in the regeneration process knowledge base. The knowledge base uses a MySQL database architecture, with table structures containing fields such as batch ID, input feature vector, control parameter sequence, real-time sensor data stream, and final performance indicators. It supports retrieval and clustering analysis by catalyst type, poisoning degree, and runtime, using the DBSCAN clustering algorithm with a minimum sample size of 5 and a neighborhood radius of 0.3 to discover optimal parameter patterns under similar operating conditions.

[0030] In addition, it includes establishing a regeneration process knowledge base: the input characteristics, control parameters, real-time sensor data, and final performance indicators of each regeneration process are stored in the database, with at least 5000 records. The database supports retrieval and cluster analysis by catalyst type, poisoning level, runtime, and other dimensions. The database adopts a distributed architecture, with the master node responsible for data writing and index maintenance, and slave nodes responsible for reading and query acceleration. The data backup strategy includes daily full backups and hourly incremental backups, with a storage period of 5 years. Cluster analysis results are used to guide the initial parameter setting of new batches of catalysts, reducing trial-and-error costs.

[0031] In addition, an online model update mechanism is included: when the number of new regeneration batches reaches 100, incremental learning of the prediction model is triggered. A sliding window strategy is used to retain the most recent 2000 high-quality data sets and remove abnormal or low-confidence samples to ensure that the model continuously adapts to catalyst aging trends and changes in operating conditions. Incremental learning uses an online gradient descent algorithm with a learning rate set to 0.01. Each update only loads new data to avoid full retraining. Model version management uses a Git branching strategy, generating a new version number with each update, supporting rollback and comparative analysis.

[0032] Furthermore, the method is integrated into a fully automated microwave regeneration system. This system is equipped with a robotic arm for automatically loading and unloading catalyst modules, with a positioning accuracy of ±0.5 mm. It utilizes an ABB IRB 1200 robot with a vacuum suction cup end effector and adjustable suction to ensure smooth catalyst handling. The regeneration chamber features dual-atmosphere switching between nitrogen and air, with a gas switching valve response time of less than 0.5 seconds, ensuring seamless atmosphere transition. The microwave source has an output power range of 0 to 5 kW and an adjustable frequency range of 0.9 to 2.45 GHz, employing a solid-state microwave generator with a frequency adjustment accuracy of 0.01 GHz and a power adjustment accuracy of 10 watts, supporting continuous tuning and pulse modulation. The overall system control adopts a PLC + industrial PC architecture. The PLC handles the underlying I / O control, while the industrial PC operates the predictive model and human-machine interface. Communication protocols include Modbus TCP and OPC UA to ensure data real-time performance and security.

[0033] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the remote sensing image data in step (1) includes high-resolution optical images, synthetic aperture radar images, and hyperspectral images, with a temporal resolution of no less than 15 days and a spatial resolution better than 2 meters. Multi-temporal image alignment is achieved through geometric correction and radiometric normalization based on control points. Specifically, the high-resolution optical images use WorldView-3 satellite data with a spatial resolution of 0.31 meters and 8 bands, covering the visible and near-infrared bands; the synthetic aperture radar images use Sentinel-1 data with a polarization mode of VV / VH, a spatial resolution of 10 meters, and a temporal resolution of 6 days; the hyperspectral images use Hyperion sensor data with 242 bands, a wavelength range of 400 to 2500 nanometers, and a spatial resolution of 30 meters. Geometric correction employed a polynomial fitting method, with at least 30 control points evenly distributed and a residual of less than 1 pixel. Radiometric normalization used a histogram matching method, adjusting the brightness distribution of each temporal image based on a reference image to ensure that the same ground feature has the same grayscale value across different temporal phases. After alignment of the multi-temporal images, dynamic information such as land use change and vegetation cover change was extracted, serving as background data for the catalyst's operating environment.

[0034] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the real estate registration data in step (1) covers natural resource property rights information such as land, forest land, water area, and minerals. Entities are associated through unified social credit codes and spatial location codes to ensure accurate matching between property rights boundaries and geographic coordinates, with a matching error of less than 0.5 meters. Specifically, land registration data comes from the real estate registration center and includes fields such as parcel number, right holder, use, area, and boundary coordinates; forest land data comes from the forestry bureau and includes forest compartment number, forest type, stock volume, and coordinate points; water area data comes from the water resources department and includes river name, river section number, and shoreline coordinates; mineral data comes from the natural resources department and includes mining area number, mineral type, reserves, and mining scope. The spatial location code adopts the National Geodetic Coordinate System CGCS2000 with a coordinate accuracy of 0.000001 degrees, approximately equal to 0.1 meters. Entity association uses the unified social credit code as a unique identifier to link data from different sources at the database level, forming a binary relationship table of "ownership-space" that supports the linkage of spatial and attribute queries.

[0035] In the above-mentioned optimization method for microwave regeneration of denitrification catalyst based on machine learning, the graph neural network in step (2) adopts a graph attention mechanism. The node feature vector dimension is 128, and the edge weight is calculated by multiplying the spatial distance decay function with the functional similarity. The decay coefficient is set to 0.8, and the similarity threshold is set to 0.7. Specifically, the graph neural network structure is a two-layer GCN. The first layer has 32-dimensional input node features and 64-dimensional output. The second layer has 64-dimensional input and 128-dimensional output. The activation function is ReLU, and the dropout rate is 0.3. The graph attention mechanism adopts the GAT model with 4 attention heads. Each head has an output dimension of 32, and the attention score is calculated using the following formula:

[0036] in, For nodes eigenvectors, For learnable weight matrix, For attention parameter vectors, This indicates a splicing operation. For nodes The set of neighbors. The formula for calculating edge weight is: ,in For spatial distance, =0.8 is the attenuation coefficient. For functional similarity, Euclidean distance is used for calculation, with a threshold of 0.7. Edges with a value lower than this are ignored.

[0037] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the spatial autocorrelation analysis in step (2) uses a combination of the Moran index and the Gillley index for discrimination. The calculation window radius is set to 5 kilometers, and the significance level is set to 0.05. This is used to identify high-high aggregation regions and low-low aggregation regions, serving as the initial connection basis for the correlation map. Specifically, the Moran index calculation formula is:

[0038] in, For the first Attribute values ​​for each region The mean, The elements of the spatial weight matrix are defined as follows: 1 if regions i and j are adjacent, and 0 otherwise. The Gillley index is used to test spatial heterogeneity. The significance test uses the Z-score method; when the Z-score is greater than 1.96, the null hypothesis is rejected, indicating the existence of spatial clustering. The computation window radius is 5 kilometers, meaning that regions within a 5-kilometer radius of each region are considered as their neighborhood, used to construct the spatial weight matrix.

[0039] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the ecological security objective in step (3) is quantified by the ecological carrying capacity index, the economic efficiency objective by the unit resource output ratio, and the social equity objective by the reciprocal of the Gini coefficient. These three elements constitute a three-dimensional optimization objective space. Specifically, the ecological carrying capacity index is calculated using an ecosystem service value assessment model, including sub-indicators such as water conservation, soil conservation, carbon sequestration, and biodiversity, with weights determined by the analytic hierarchy process (AHP). The unit resource output ratio is defined as the ratio of GDP to total resource consumption, including energy, water resources, and land. The reciprocal of the Gini coefficient reflects the fairness of income distribution; a larger value indicates greater fairness. The three-dimensional objective space is visualized using the Pareto front and used for multi-objective optimization solutions.

[0040] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the improved multi-objective particle swarm optimization algorithm in step (3) introduces an elite archiving mechanism and adaptive inertial weights. The population size is set to 200, the maximum number of iterations is set to 500, and the convergence criterion is that the Pareto front change rate is less than 1% for 50 consecutive generations. Specifically, the elite archiving mechanism retains historical optimal solutions to avoid premature convergence; the adaptive inertial weights are dynamically adjusted according to the iteration progress, with a larger initial weight to enhance global search capability and a smaller weight in the later stage to improve local search accuracy. The formula is as follows: ,in =0.9, =0.4, This represents the current iteration number. This represents the maximum number of iterations.

[0041] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the resource allocation deviation index in step (4) is calculated by weighting spatial mismatch rate, target deviation, and execution lag coefficient, with weights of 0.4, 0.4, and 0.2, respectively, and the deviation index threshold is set to 0.15. Specifically, spatial mismatch rate is defined as the spatial overlap between resource allocation and demand, calculated through spatial overlay analysis; target deviation is defined as the relative deviation between the actual allocation value and the planned target value; and execution lag coefficient is defined as the ratio of the delay between the actual execution time and the planned time. The formula for calculating the deviation index is:

[0042] in, The time series length is set to a preset threshold of 0.15, and MSE (mean squared error) is used to measure the volatility of the deviation index.

[0043] In the above-mentioned optimization method for microwave regeneration of denitrification catalyst based on machine learning, the reinforcement learning agent in step (4) adopts a deep deterministic policy gradient algorithm. The state space includes the current deviation index and historical adjustment records, the action space is the fine-tuning amount of the multi-objective weight vector, and the reward function is the product of the deviation index decrease and the scheme stability. Specifically, the state space has a dimension of 10, including the current deviation index, the deviation index after the past 5 adjustments, the target weight vector, the constraint satisfaction, etc.; the action space is a three-dimensional vector, corresponding to the weight adjustment amounts of the ecological, economic, and social objectives, with a range of [-0.1, 0.1]; the reward function is defined as... ,in The percentage decrease in the deviation index. To ensure the stability of the scheme, it is defined as the rate of change of weights between the current scheme and the previous scheme, in order to avoid drastic fluctuations.

[0044] In the aforementioned optimization method for microwave regeneration of denitrification catalysts based on machine learning, the visualization decision support report in step (5) supports dynamic generation by quarter, year, and five-year planning cycle. The spatial distribution heat map adopts a hierarchical color scheme with 7 color levels. The target achievement radar chart includes 5 dimensional indicators. Risk warning prompts are classified and labeled according to three levels: high, medium, and low. Specifically, the heat map color gradually changes from blue (low) to red (high), with each level interval being 10%. The radar chart dimensions include ecological security, economic efficiency, social equity, resource utilization efficiency, and sustainable development capability. Risk warning levels are distinguished by color, with red indicating high risk, yellow indicating medium risk, and green indicating low risk. The prompts include the cause of the risk, the scope of impact, and response suggestions.

[0045] This invention also includes establishing a knowledge base for the optimal allocation of natural resource assets. The knowledge base stores no fewer than 100,000 historical allocation cases, policy and regulatory provisions, and expert rules. A semantic retrieval engine provides prior knowledge support for the initial setting of multi-objective weights. The knowledge base adopts a Neo4j graph database architecture, with nodes including cases, policies, experts, regions, and resource types, and edges representing relationships, such as "case citing policy" and "expert proposing rules." The semantic retrieval engine is based on the BERT model, supports natural language queries, and returns relevant cases and rules.

[0046] This invention also includes integration with the national land spatial planning "one map" system, acquiring planning control boundary data in real time through a standard geographic information service interface to ensure strict consistency between resource allocation schemes and higher-level plans, with a spatial consistency verification error of less than 1 pixel. The interface protocol adopts the OGC WMS / WFS standard, the data format is GeoJSON, and the verification algorithm is buffer overlay analysis. When the overlap between the resource allocation area and the planning boundary is greater than 99%, it is considered consistent.

[0047] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing microwave regeneration of denitrification catalysts based on machine learning, characterized in that: Includes the following steps: Step (1) Construct a catalyst state characteristic database: Collect physicochemical parameters of multiple batches of deactivated denitrification catalysts, including specific surface area, pore volume, pore size distribution, active component content, types and concentrations of poisoned elements, and surface morphology images, and combine them with their corresponding historical operating condition data to form a structured characteristic dataset. Step (2) Establish a multi-physics field coupling simulation model for microwave regeneration process: Based on electromagnetic wave propagation theory, heat conduction equation and chemical reaction kinetics, construct a mapping relationship model between microwave power, frequency, action time and internal temperature field of catalyst, electromagnetic field intensity distribution and chemical reaction rate; Step (3) Training the machine learning model for predicting regeneration effect: Using the feature dataset in step (1) as input, and the regeneration activity recovery rate, structural integrity index and energy consumption data output by the simulation model in step (2) as labels, the ensemble learning algorithm is used to train the regeneration effect prediction model. Step (4) Implement online sensing and parameter adaptive control: Integrate an infrared thermal imager, a microwave reflection power sensor and a gas composition analyzer into the microwave regeneration equipment to collect data on temperature distribution, energy absorption efficiency and tail gas composition changes in real time during the regeneration process, and input them into the prediction model trained in step (3) to dynamically adjust microwave power, frequency and gas atmosphere parameters. Step (5) Determine the end point of regeneration and output optimized process parameters: Based on the real-time predicted trend of activity recovery rate and structural damage risk index, when the activity recovery rate tends to stabilize and the structural damage risk is lower than the preset threshold, the regeneration process is determined to be over, and the optimal combination of process parameters for this regeneration is recorded for updating the prediction model and process knowledge base.

2. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: In step (1), the physicochemical parameters are determined by nitrogen adsorption-desorption method to measure specific surface area and pore structure, by X-ray fluorescence spectroscopy to determine the content of active components and poisoning elements, by scanning electron microscopy to obtain surface morphology images, and by historical operating condition data including flue gas temperature, flow rate, initial concentration of nitrogen oxides, running time and shutdown frequency. All data are integrated into a structured feature vector of no less than 32 dimensions, with a total data volume of no less than 5,000 records.

3. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: In step (2), the multiphysics coupling simulation model uses the finite element method to solve Maxwell's equations and Fourier heat conduction equations. The chemical reaction kinetics module considers the changes in the decomposition activation energy of sulfate and arsenide under microwave field. The model mesh division accuracy is not less than 0.1 mm, the time step is set to 0.5 seconds, and the output includes three-dimensional temperature field distribution, electromagnetic field intensity distribution, decomposition rate of poisoned substances, and predicted value of activity recovery rate.

4. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: In step (3), the ensemble learning algorithm adopts a weighted fusion strategy of gradient boosting decision tree and random forest. The input features are standardized. The model training adopts five-fold cross-validation. The root mean square error of the predicted activity recovery rate is less than 3%. The structural integrity indicators include pore collapse rate and specific surface area retention rate. The model is deployed on edge computing nodes to support real-time inference.

5. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: In step (4), the infrared thermal imager has a spatial resolution of 128 x 128 pixels, a temperature measurement range of 25 to 800 degrees Celsius, and an accuracy of ±2 degrees Celsius; the microwave reflective power sensor has a sampling frequency of 10 Hz; the gas composition analyzer monitors the concentration of ammonia, nitrogen oxides and water vapor in real time, with a response time of less than 1 second, and all sensor data are synchronously input into the prediction model via the CAN bus.

6. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: The dynamic adjustment strategy in step (4) includes: when the local temperature gradient exceeds 50 degrees Celsius per centimeter, reducing the microwave power and switching to pulse mode with a pulse width of 10 milliseconds and a duty cycle of 50%; when the microwave energy absorption efficiency is less than 70%, adjusting the microwave frequency to the frequency band corresponding to the peak dielectric loss of the catalyst with an adjustment step of 0.1 gigahertz; when arsine or hydrogen sulfide is detected in the exhaust gas, introducing oxygen-containing carrier gas with an oxygen flow rate of 5 liters / minute and a duration of not less than 30 seconds.

7. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: In step (5), the determination of the regeneration endpoint adopts a dual threshold mechanism: the change in the activity recovery rate is less than 0.5% for 30 consecutive seconds, and the structural damage risk index is less than 0.2; the structural damage risk index is calculated by weighting the duct collapse probability and the thermal stress concentration coefficient, with weight coefficients of 0.6 and 0.4 respectively, and is determined by inversion of historical regeneration failure cases.

8. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: It also includes establishing a regeneration process knowledge base, storing the input characteristics, control parameters, real-time sensor data and final performance indicators of each regeneration process into the database. The database contains more than or equal to 5,000 records and supports retrieval and clustering analysis by catalyst type, poisoning degree and runtime. The clustering algorithm used is DBSCAN, with a minimum sample size of 5 and a neighborhood radius of 0.

3.

9. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: It also includes an online model update mechanism. When the number of new regeneration batches reaches 100, it triggers incremental learning of the prediction model. It adopts a sliding window strategy to retain the most recent 2000 high-quality data sets and removes abnormal or low-confidence samples. Incremental learning uses an online gradient descent algorithm with a learning rate set to 0.

01. Model version management adopts a Git branching strategy.

10. The microwave regeneration optimization method for denitrification catalyst based on machine learning according to claim 1, characterized in that: The method is integrated into a fully automated microwave regeneration system. The system is equipped with a robotic arm for automatically loading and unloading catalyst modules with a positioning accuracy of ±0.5 mm. The regeneration chamber has a dual atmosphere switching function of nitrogen and air. The microwave source output power ranges from 0 to 5 kW, the frequency is adjustable from 0.9 to 2.45 GHz with a frequency adjustment accuracy of 0.01 GHz, and the power adjustment accuracy is 10 watts. The system control adopts a PLC + industrial PC architecture, and the communication protocols are Modbus TCP and OPC UA.

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