A system for adsorbing and catalytically oxidizing foul-smelling gases from poultry manure

Abstract

The present application relates to the technical field of waste gas treatment, and discloses a kind of fowl manure foul gas adsorption and catalytic oxidation treatment system.The system includes three stages of initial evaluation, dynamic regulation and feedback optimization to realize closed-loop control.Initial evaluation stage collects inlet characteristics and calculates its deviation from the benchmark spectrum to trigger regulation preparation.The dynamic regulation stage synchronously evaluates the efficiency of the adsorption and catalytic units, and generates coordinated control instructions through coupled analysis.The feedback optimization stage executes the instructions and verifies the treatment effect to determine whether to restart the evaluation process.This method overcomes the drawbacks of response lag and isolated control in traditional technology by advanced prediction of inlet conditions and coordinated linkage of the two units, improving the system's stability against load fluctuations, while optimizing energy consumption and extending equipment life.

Description

Technical Field

[0001] This invention relates to the field of waste gas treatment technology, specifically to a system for the adsorption and catalytic oxidation of odorous gases from poultry manure. Background Technology

[0002] The malodorous gases generated during poultry manure treatment are complex in composition, and their concentration and components fluctuate significantly due to factors such as manure cleaning frequency and ambient temperature and humidity. While the widely used "adsorption + catalytic oxidation" combined process can achieve emission standards, its control strategy is inadequate in the face of fluctuating intake air. Conventional systems often employ fixed parameter operation or simple feedback control based on end-point concentration monitoring. This control mode is inherently lagging, only passively adjusting operating parameters after the treatment effect has been affected or even exceeded, making it difficult to cope with instantaneous high-concentration shock loads, resulting in poor emission stability.

[0003] Existing technologies suffer from control lag and unit isolation. The system lacks proactive assessment of the intake air conditions, failing to predict the potential impact of pollutants before they enter the core treatment unit. When the adsorbent rapidly saturates due to a sudden increase in load, high-concentration pollutants can directly penetrate the catalytic oxidation unit, forcing it to drastically increase the reaction temperature to maintain removal efficiency. These frequent temperature fluctuations not only lead to increased energy consumption but also accelerate catalyst thermal aging and failure. The passive nature of the control mode keeps the system in a constant "impact-remediation" cycle, making it difficult to optimize equipment lifespan and operating costs.

[0004] The adsorption and catalytic oxidation units are typically controlled independently, with a lack of coordinated adjustment strategies. The system does not treat the two units as an integrated whole for coordinated optimization. This isolated control results in low overall system energy efficiency, making it impossible to dynamically optimize to the lowest energy consumption and equipment wear range based on real-time operating conditions while ensuring treatment effectiveness. This limits further improvements in the economic efficiency and adaptability of the treatment system. Summary of the Invention

[0005] The purpose of this invention is to provide a poultry manure odor gas adsorption and catalytic oxidation treatment system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a poultry manure odor gas adsorption and catalytic oxidation treatment system, the system comprising:

[0007] In the initial assessment phase, multi-dimensional features of the input poultry manure odor gas are collected to generate a gas feature spectrum. The gas state deviation is calculated by combining the pre-stored benchmark feature spectrum and the system control preparation command is triggered based on the gas state deviation calculation result.

[0008] In the dynamic control phase, in response to the system control preparation command, the adsorption efficiency evaluation process for the working state of the adsorption unit and the catalytic efficiency evaluation process for the working state of the catalytic oxidation unit are started simultaneously. Based on the output results of the adsorption efficiency evaluation process and the output results of the catalytic efficiency evaluation process, a coupled analysis is performed to generate a coordinated control command that includes the adjustment amount of adsorption parameters and the adjustment amount of catalytic oxidation parameters.

[0009] During the feedback optimization phase, coordinated control commands are executed to adjust system operating parameters. After adjustment, verification feature acquisition of the treated gas is initiated. The verification feature acquisition results are compared with the preset target range. Based on the comparison results, it is decided whether to initiate a new round of initial evaluation phase to form closed-loop control.

[0010] Preferably, the multi-dimensional feature acquisition in the initial evaluation phase specifically includes:

[0011] The concentration, temperature, and humidity data of the odorous gas from poultry manure are continuously acquired by a sensor array arranged in the gas input pipeline.

[0012] The concentration, temperature, and humidity data acquired during the continuous monitoring period were used to form concentration time-series curves, temperature time-series curves, and humidity time-series curves, respectively.

[0013] Retrieve the baseline concentration curve, baseline temperature curve, and baseline humidity curve under standard conditions from the system database;

[0014] The concentration time-series curve is compared with the baseline concentration curve point by point, and then integrated to obtain the concentration deviation integral value.

[0015] The temperature time series curve is compared with the reference temperature curve point by point and then integrated to obtain the temperature deviation integral value.

[0016] The humidity time series curve is compared with the reference humidity curve point by point and then integrated to obtain the humidity deviation integral value.

[0017] The gas state deviation is obtained by weighting and fusing the integral values ​​of concentration deviation, temperature deviation, and humidity deviation.

[0018] Preferably, the adsorption efficiency evaluation process in the dynamic control stage specifically includes:

[0019] Monitor the real-time saturation of the adsorbent in the adsorption unit and plot the trajectory of saturation change;

[0020] Acquire the pressure difference data at the inlet and outlet of the adsorption unit and generate a pressure difference change curve;

[0021] Retrieve the ideal saturation change trajectory and ideal pressure difference change curve of the adsorbent under standard operating conditions from the system database;

[0022] Calculate the degree of overlap between the real-time saturation change trajectory and the ideal saturation change trajectory;

[0023] Calculate the curve similarity between the real-time differential pressure change curve and the ideal differential pressure change curve;

[0024] Adsorption efficiency index is calculated based on trajectory overlap and curve similarity.

[0025] Preferably, the catalytic efficiency evaluation process in the dynamic regulation stage specifically includes:

[0026] Monitor the temperature distribution in the reaction zone of the catalytic oxidation unit and generate a temperature field cloud map;

[0027] Monitor the concentration of specific odorous components at the outlet of the catalytic oxidation unit and generate component degradation curves;

[0028] Retrieve the ideal temperature field cloud map and ideal component degradation curve of the catalytic oxidation unit under standard operating conditions from the system database;

[0029] The real-time temperature field cloud map is compared with the ideal temperature field cloud map by gridding, and the temperature field consistency coefficient is calculated.

[0030] Correlation analysis was performed between the real-time component degradation curve and the ideal component degradation curve to calculate the component degradation efficiency coefficient.

[0031] The catalytic efficiency index is calculated based on the temperature field consistency coefficient and the component degradation efficiency coefficient.

[0032] Preferably, the coupling analysis in the dynamic control stage specifically includes:

[0033] Establish a decision matrix with adsorption efficiency index and catalytic efficiency index as inputs;

[0034] The system queries the preset control strategy mapping table in the system database. The control strategy mapping table defines the adjustment amount of adsorption parameters and catalytic oxidation parameters corresponding to different combinations of adsorption efficiency index and catalytic efficiency index.

[0035] Based on the adsorption efficiency index and catalytic efficiency index obtained in real time, a matching query is performed in the control strategy mapping table to output the corresponding adsorption parameter adjustment amount and catalytic oxidation parameter adjustment amount.

[0036] The adjustment amount of the adsorption parameter is encapsulated as the adsorption unit control instruction, and the adjustment amount of the catalytic oxidation parameter is encapsulated as the catalytic oxidation unit control instruction, together forming a synergistic control instruction.

[0037] Preferably, the verification feature collection in the feedback optimization phase specifically includes:

[0038] A set of verification sensors was installed at the final exhaust port of the system to collect processed gas samples.

[0039] Perform full component analysis on gas samples to obtain residual concentration data of key odor indicators;

[0040] The residual concentration data of key odor indicators are compared with the preset emission standard thresholds;

[0041] If the residual concentration of all key odor indicators is lower than the corresponding emission standard threshold, a treatment compliance signal will be generated.

[0042] If the residual concentration of any key odor indicator is higher than or equal to the corresponding emission standard threshold, a failure to meet the treatment standard signal will be generated.

[0043] Preferably, the comparison result decision in the feedback optimization stage specifically includes:

[0044] When a signal indicating that the processing has met the standards is received, the current system operating parameters are maintained, and the deviation of the gas state is continuously monitored.

[0045] When the deviation of the gas state exceeds the stability threshold, the initial evaluation phase is retried;

[0046] When a signal indicating that the processing has not met the standards is received, a new round of initial evaluation is immediately triggered. In the new dynamic control phase, the weight allocation of the adsorption efficiency evaluation process and the catalytic efficiency evaluation process is adaptively adjusted.

[0047] Preferably, a sensor calibration stage is provided before the initial evaluation stage, specifically including:

[0048] A standard gas of known concentration is periodically introduced into the gas inlet pipeline;

[0049] The response readings of a standard gas are obtained through a sensor array;

[0050] The response readings are compared with the known concentration values ​​of the standard gas to calculate the measurement deviation of each sensor;

[0051] The measurement data of the sensor array is compensated in real time based on the measurement deviation to generate calibrated sensor data;

[0052] The calibrated sensor data will be used for subsequent multi-dimensional feature acquisition.

[0053] Preferably, an adsorbent regeneration trigger judgment process is set after the adsorption efficiency evaluation process, specifically including:

[0054] When the adsorption efficiency index is lower than the preset threshold, the adsorbent regeneration judgment is initiated.

[0055] Monitor for sudden increases in gas concentration at the outlet of the adsorption unit;

[0056] Calculate the remaining lifespan of the adsorbent by combining the total amount of gas treated by the adsorbent and the operating time;

[0057] When the adsorption efficiency index is below the threshold, a sudden increase in concentration is detected, and the remaining lifetime is greater than the critical value, an adsorbent regeneration command is generated.

[0058] The regeneration program of the adsorption unit is started according to the regeneration command, and the adsorbent is regenerated by heating or depressurization.

[0059] Preferably, a catalyst activity maintenance process is provided after the catalytic performance evaluation process, specifically including:

[0060] When the catalytic efficiency index decreases continuously multiple times, the catalyst activity diagnosis is initiated.

[0061] Analyze the changing trend of the temperature field uniformity coefficient and the decay rate of the component degradation efficiency coefficient;

[0062] The cause of catalyst deactivation is determined based on the diagnostic results, and classified as either temporary poisoning or permanent deactivation.

[0063] In the case of temporary poisoning, the thermal regeneration procedure of the catalytic oxidation unit is initiated to restore the catalyst activity through high-temperature baking;

[0064] In the case of permanent deactivation, a catalyst replacement warning signal is generated, and the deactivation mode is recorded to the system database.

[0065] Compared with the prior art, the beneficial effects of the present invention are:

[0066] By collecting multi-dimensional features of the input poultry manure odor gas and generating a gas characteristic spectrum, which is then compared with a pre-stored benchmark characteristic spectrum, the gas state deviation is calculated. This mechanism allows the system to identify deviations in key characteristics such as concentration and composition from standard operating conditions before the gas enters the core processing unit. Based on the deviation-triggered control preparation, the system's control mode shifts from reactive remediation to proactive intervention. The treatment system thus gains the ability to anticipate load fluctuations, enabling it to initiate internal parameter adjustment processes in advance when external conditions change but have not yet affected the final treatment effect. This enhances the system's stability against shock loads and prevents excessive emissions at the outlet due to inlet gas fluctuations from the outset.

[0067] Simultaneously, independent performance evaluations of the adsorption and catalytic oxidation units were conducted, followed by coupled analysis of the two evaluation results. This process established a correlation model between the performance of the two treatment units, analyzed the cascading effects of changes in adsorption performance on the catalytic oxidation unit, and examined the constraints of the catalytic oxidation unit's capacity boundary on the adsorption unit's adjustment strategy. Based on this analysis, coordinated control instructions were generated that simultaneously included adjustments to adsorption and catalytic oxidation parameters, ensuring the overall integrity of the adjustment strategy. This coordinated control between units enables the system to make decisions with global energy efficiency as the goal, ensuring treatment effectiveness while reducing overall system energy consumption, delaying catalyst aging, and achieving a balance between treatment efficiency and operational economy. Attached Figure Description

[0068] Figure 1 This is a schematic diagram illustrating the working principle of the poultry manure odor gas adsorption and catalytic oxidation treatment system described in this invention.

[0069] Figure 2 This is a flowchart for evaluating adsorption efficiency;

[0070] Figure 3 A flowchart for evaluating catalytic performance;

[0071] Figure 4 This is a graph showing the trend of catalyst activity change with the regeneration cycle during the catalyst activity maintenance stage.

[0072] Figure 5 This is a comparison chart of the residual concentration of key odor indicators and the standard threshold during the feedback and optimization phase. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0074] Please see Figure 1This invention provides a poultry manure odor gas adsorption and catalytic oxidation treatment system. The system includes: multi-dimensional feature acquisition of the input poultry manure odor gas, which is completed by a sensor array installed in the gas input pipeline. The acquired gas features include parameters such as concentration, temperature, and humidity. These real-time data are processed to generate a gas feature spectrum. The system database stores a reference feature spectrum measured under standard operating conditions. By comparing the real-time gas feature spectrum with the reference feature spectrum, the gas state deviation is calculated. This deviation is a quantitative indicator that reflects the degree of difference between the current intake state and the ideal state. When the gas state deviation exceeds a preset trigger threshold, the system automatically generates a system control preparation command, indicating that the system is about to enter the parameter adjustment process. The dynamic control phase begins upon receiving the system control preparation command. This phase executes two evaluation processes in parallel: one assessing the adsorption efficiency of the adsorption unit and the other assessing the catalytic efficiency of the catalytic oxidation unit. The adsorption efficiency evaluation outputs an adsorption efficiency index, and the catalytic efficiency evaluation outputs a catalytic efficiency index. The system couples these two indices for analysis based on preset decision rules, ultimately generating a coordinated control command. This command explicitly includes the required adjustment amounts for both the adsorption and catalytic oxidation parameters. The feedback optimization phase executes the coordinated control command. The system adjusts the operating parameters of the adsorption and catalytic oxidation units according to the command, such as the adsorbent regeneration cycle and catalytic reaction temperature. After parameter adjustment, the system initiates verification feature acquisition at the final exhaust port to detect the treated gas. The detection results are compared with the preset target treatment standard range. If the comparison results meet the requirements, the system maintains the current parameters and continues monitoring. If the comparison results do not meet the requirements, the system immediately triggers a new round of initial evaluation, thus forming a self-correcting closed-loop control system to ensure the continuous stability of the treatment effect.

[0075] Example 1: Multi-dimensional feature acquisition is achieved through a sensor array arranged in the gas input pipeline. The sensor array consists of multiple functionally independent sensor units. These sensor units continuously acquire concentration, temperature, and humidity data of poultry manure odor gas. The system records the readings of all sensors at preset fixed time intervals and concatenates the concentration, temperature, and humidity data sequences acquired during the continuous monitoring period to form time-series curves reflecting the dynamic changes of the parameters: concentration, temperature, and humidity. The system database retrieves the baseline concentration, temperature, and humidity curves under standard conditions. These baseline curves were pre-determined and stored during the system debugging phase by measuring gas parameters under standard operating conditions, serving as reference standards for subsequent calculations. In practical implementation, calculating the gas state deviation is a core step. The calculation process involves point-by-point difference calculation between the real-time collected concentration time-series curve and the reference concentration curve. The difference at each time point is then integrated to obtain the concentration deviation integral value. Using the same mathematical method, point-by-point difference calculation is performed between the temperature time-series curve and the reference temperature curve, and the humidity time-series curve is also performed between the temperature time-series curve and the reference humidity curve to obtain the humidity deviation integral value. Different weighting coefficients are assigned to the concentration deviation integral value, temperature deviation integral value, and humidity deviation integral value. The magnitude of the weighting coefficients is determined based on the sensitivity of each parameter to the subsequent processing technology. Finally, a weighted fusion algorithm combines the three deviation integral values ​​into a comprehensive numerical index, namely the gas state deviation.

[0076] In practical implementation, the sensor calibration phase is a crucial step preceding the initial evaluation phase. This phase is executed automatically and periodically, with the frequency adjustable based on sensor accuracy and operating environment. During calibration, the system controls a calibration gas generator to introduce a standard gas of known concentration into the gas input pipeline. The composition and concentration of the standard gas are pre-calibrated. A sensor array positioned within the pipeline measures the standard gas and generates response readings. The system records the output value of each sensor unit. Subsequently, the system compares the response reading of each sensor unit with the known concentration value of the standard gas, calculating the current measurement deviation value for each sensor unit. This deviation value is the difference between the response reading and the known concentration value. These deviation values ​​are input into the system's real-time compensation model, which generates correction parameters for each sensor unit. During actual operation, the raw measurement data acquired by the sensor array is immediately corrected using these correction parameters, generating calibrated sensor data. Only this calibrated sensor data is allowed to be used in all calculation steps of the subsequent multi-dimensional feature acquisition process, including the generation of time-series curves and the calculation of gas state deviation.

[0077] It is understandable that the continuous and stable operation of the sensor array is the foundation of the entire system's reliability. Sensor arrays typically include various types, such as electrochemical sensors, semiconductor sensors, and thermal conductivity sensors, to cover the measurement needs of different physicochemical parameters. These sensors convert physical quantities such as gas concentration, temperature, and humidity into electrical signals, which are then acquired by the central processing unit (CPU) after passing through an analog-to-digital converter. The CPU records each set of data according to strict timestamps, ensuring the temporal synchronization of concentration, temperature, and humidity data—a necessary condition for generating accurate time-series curves. The system database not only stores the baseline curve but also historical calibration data, sensor performance profiles, and other information, providing data support for deviation calculation and compensation models. The weighted fusion algorithm can employ linear weighted summation or more complex nonlinear fusion strategies. Its core purpose is to integrate deviation information from multiple dimensions into a scalar with clear physical meaning, facilitating unified judgment and decision-making by the system.

[0078] Understandably, automating the calibration process is key to reducing human intervention and improving system robustness. The calibration gas generator typically consists of a high-pressure gas cylinder, a pressure reducing valve, a mass flow controller, and a solenoid valve, with the system program precisely controlling the timing and flow rate of the standard gas injection. Measurement deviation calculations include not only zero-point drift correction but may also include range drift correction to ensure the sensor's linear response across its entire range. The real-time compensation model can be a simple offset compensation or a complex algorithm model incorporating temperature compensation and nonlinear correction, with its parameters dynamically updated based on the results of each calibration. This closed-loop calibration mechanism effectively combats the natural degradation of sensor performance over time and measurement errors caused by changes in environmental factors, thus maintaining the high accuracy of the multi-dimensional feature acquisition system over the long term.

[0079] In practice, the calculation of gas state deviation is executed by a dedicated algorithm module in the system's central processing unit. The algorithm module reads real-time acquired and calibrated sensor data sequences from memory, and simultaneously reads the corresponding baseline curve data from the system's non-volatile memory. Point-by-point difference calculation involves subtracting the baseline data value from the real-time data value at each identical time point to obtain a series of instantaneous deviation values. Integration calculation involves numerically integrating this series of instantaneous deviation values ​​over a specified time window, typically by summing the absolute values ​​of the deviations or integrating the squares of the deviations. The integration result reflects the cumulative degree of deviation of the parameter from the baseline state over the entire period. Concentration deviation integral values, temperature deviation integral values, and humidity deviation integral values ​​quantify the abnormal conditions of the three parameters, respectively. The weighted fusion process assigns a weight to each deviation integral value. The weighting coefficient is usually determined based on experiments and experience; for example, concentration changes may have the most significant impact on subsequent adsorption and catalytic processes, so the weight of the concentration deviation integral value is often set to the highest. The fused gas state deviation is a dimensionless value, and its magnitude directly determines the triggering conditions for the system's control preparation command.

[0080] In some embodiments, the layout of the sensor array needs to consider the uniformity of the gas flow field. The sensor probe should be installed on a straight section of the gas input pipeline, avoiding bends, valves, and other areas that may generate eddies or uneven concentration distribution, to ensure the representativeness of the collected data. The data acquisition frequency should be matched to the system's response speed; too high a acquisition frequency will increase the data processing burden, while too low a frequency may result in the loss of important dynamic characteristics. The baseline curves are established during the initial system commissioning by running the system under the designed standard intake conditions for an extended period and recording the parameter curves under steady-state conditions. These baseline curves essentially represent the intake condition template when the system's processing capacity is optimal.

[0081] In some embodiments, setting the calibration cycle is a trade-off. Shorter calibration cycles maintain higher measurement accuracy but consume more standard gas and may increase system downtime; longer calibration cycles may introduce errors due to sensor drift. The system can dynamically adjust the calibration cycle based on historical drift data or environmental conditions (such as the magnitude of ambient temperature fluctuations). The selection of standard gas is also critical; its composition should be as close as possible to the odorous poultry manure gas actually being treated, but it must also be stable and have a known concentration. Standard gases provided by standard gas preparation units certified by metrology institutions are typically used.

[0082] Optionally, for concentration data acquisition, sensors based on multiple principles can be used for cross-validation. For example, for hydrogen sulfide gas, an electrochemical sensor and a photoionization detector can be deployed simultaneously. If significant differences remain between the readings of the two sensors after calibration, the system can issue a sensor fault warning, prompting manual inspection or maintenance. This redundant design further enhances the reliability of the multi-dimensional feature acquisition system. The calculation of gas state deviation can also incorporate more complex dynamic time warping algorithms to handle slight scaling or offset between the real-time time series curve and the baseline curve on the time axis. This makes the deviation assessment focus more on shape similarity rather than strict time point alignment. This can, to some extent, overcome the impact of time series curve distortion caused by small fluctuations in inlet flow rate on the evaluation results, allowing the gas state deviation to more accurately reflect the essential changes in the gas state.

[0083] Example 2: See Figure 2 The adsorption efficiency evaluation process is achieved by monitoring the real-time saturation of the adsorbent in the adsorption unit and plotting the saturation change trajectory. Saturation data can be indirectly reflected by resistive or capacitive humidity sensors installed in the adsorption bed, or theoretically estimated by accumulating the total amount of gas flowing through the adsorption unit and combining it with the initial adsorption capacity. The system records saturation values ​​at fixed time intervals, forming a saturation change trajectory over time. Simultaneously, the adsorption efficiency evaluation process acquires pressure sensor readings at the inlet and outlet of the adsorption unit, calculates and records the real-time pressure difference, forming another pressure difference change curve. The system database pre-stores the ideal saturation change trajectory and ideal pressure difference change curve of the adsorbent under standard operating conditions. These ideal curves are standard reference data obtained under laboratory conditions using standard components and concentrations of poultry manure odor gas, at set optimal flow rates and temperatures, to determine the performance of the new adsorbent. During evaluation, the system calculates the overlap between the real-time saturation change trajectory and the ideal saturation change trajectory. The overlap reflects the degree of dynamic agreement between the actual adsorption process and the ideal adsorption process.

[0084] In practical implementation, trajectory overlap can be calculated using various mathematical methods. For example, the correlation coefficient between the two trajectories at each sampling time point can be calculated, or the root mean square error (RMSE) between the two trajectories can be calculated and normalized. The similarity calculation between the pressure difference change curve and the ideal pressure difference change curve follows a similar principle. Dynamic Time Warping (DTW) can be used to overcome the time axis scaling problem caused by small airflow fluctuations, thus more accurately assessing the similarity of curve shapes. The system combines the calculated trajectory overlap and curve similarity values ​​through a predefined fusion function, ultimately outputting a quantified adsorption efficiency index. This fusion function can be a weighted average model, with weight coefficients learned from historical data to reflect the different degrees of influence of saturation and pressure difference changes on the overall efficiency of the adsorption unit. The adsorption efficiency index is a normalized value; the closer its value is to 1, the closer the performance of the current adsorption unit is to the ideal state.

[0085] The adsorbent regeneration trigger judgment process is executed after the adsorption efficiency assessment process. Its activation condition is that the adsorption efficiency index is lower than a preset efficiency threshold. When this condition is met, the judgment process is activated. The judgment process first monitors the gas concentration at the adsorption unit outlet, analyzing whether there is a sudden increase in concentration. A sudden increase in concentration is usually manifested as a significant rise in the concentration of the target pollutant in the outlet gas within a short period of time, which is a typical sign that the adsorbent is approaching saturation and its adsorption capacity is decreasing. The system combines the total amount of gas already treated by the adsorbent and the total operating time to calculate the remaining lifetime of the adsorbent using a built-in adsorbent lifetime prediction model. The adsorbent lifetime prediction model can consider various factors such as the number of adsorption-regeneration cycles, the average concentration of the treated gas, and the operating temperature. The generation of the adsorbent regeneration command requires three conditions to be met simultaneously: first, the adsorption efficiency index is lower than the preset efficiency threshold; second, the system detects a sudden increase in gas concentration at the adsorption unit outlet; and third, the remaining lifetime of the adsorbent calculated by the lifetime prediction model is greater than a set critical value. This critical value means that the adsorbent still has a certain mechanical strength and chemical stability, making it worthwhile to restore its activity through regeneration. Only when all three conditions are met simultaneously will the system generate an adsorbent regeneration command. If the adsorption efficiency index is low and a sudden increase in concentration is detected, but the calculated remaining lifespan of the adsorbent is already below the critical value, the system will generate an adsorbent replacement warning signal, indicating that a new adsorbent material needs to be replaced.

[0086] Understandably, the accuracy of the saturation change trajectory is crucial. The method of estimating saturation through the cumulative total gas volume requires precise flow metering and inlet concentration monitoring; its calculation formula can be expressed as:

[0087]

[0088] in: This indicates the real-time saturation of the adsorbent at time t. This indicates the volumetric flow rate of the gas entering the adsorption unit. This indicates the concentration of the target pollutant at the inlet of the adsorption unit. This indicates the saturated adsorption capacity of the adsorbent for the target pollutant. The pressure difference curve is directly measured by high-precision differential pressure transmitters installed before and after the adsorption bed. An abnormal increase in pressure difference may indicate local blockage of the adsorbent bed or uneven airflow distribution.

[0089] It is understandable that the multi-condition simultaneous fulfillment mechanism in the adsorbent regeneration triggering logic can effectively avoid false triggering. For example, if the adsorption efficiency index is only briefly lower than the threshold due to a momentary fluctuation in the inlet gas concentration, but without a sudden increase in the outlet concentration, the system will not initiate the regeneration procedure, thus preventing unnecessary downtime and production interruption. The critical value of the remaining adsorbent lifetime is an empirical parameter, which depends on the material and structure of the adsorbent itself, as well as the severity of the actual operating conditions. It needs to be calibrated during the system commissioning phase or set based on data provided by the supplier.

[0090] In some embodiments, the criteria for determining a sudden increase in outlet gas concentration can be further refined. The system can set a threshold for the rate of concentration change; for example, if the concentration increases by more than a certain percentage per minute, it is considered a sudden increase. Alternatively, a sliding window statistical method can be used, comparing the difference between the recent average concentration and the historical average concentration. If the difference exceeds a set range and is statistically significant, it is considered a sudden increase. These methods can enhance adaptability to different scenarios such as slow or rapid penetration.

[0091] In some embodiments, the adsorbent regeneration procedure itself is also configurable. Depending on the properties of the adsorbent and the type of pollutant adsorbed, the regeneration method can be thermal regeneration, depressurization regeneration, steam purging, or a combination of these methods. The adsorbent regeneration command generated by the system can include not only a command to trigger regeneration but also specific parameter settings for regeneration, such as the regeneration temperature profile, regeneration gas flow rate, and regeneration duration. These parameters can be found in the system database and are associated with the current adsorbent type and the characteristics of the adsorbed pollutants.

[0092] Optionally, for large adsorption units or multiple adsorption beds connected in parallel, saturation monitoring and differential pressure monitoring can be performed in sections. For example, multiple saturation monitoring points can be set at different locations at the top, middle, and bottom of the adsorption bed, and differential pressure can be measured at different cross-sections of the bed. This allows for a more precise assessment of the movement of the adsorption front and the blockage of the bed, providing more accurate guidance for local regeneration or maintenance. The adsorbent regeneration trigger judgment process can also be correlated with the gas state deviation during the initial assessment stage. When the system detects a drastic change in the inlet conditions and a significant increase in the gas state deviation, it can be foreseen that the adsorption unit will face a shock load. At this time, the system can temporarily adjust the judgment threshold of the adsorption efficiency index or prepare for regeneration in advance, demonstrating a certain degree of proactive adjustment capability.

[0093] Example 3: See Figure 3 The catalytic efficiency evaluation process is achieved by monitoring the temperature distribution in the reaction zone of the catalytic oxidation unit and generating a temperature field cloud map. Temperature distribution monitoring is accomplished by multiple thermocouples or resistance temperature sensors uniformly arranged inside the reactor of the catalytic oxidation unit. These sensors form a sensor network. The system periodically collects readings from all temperature sensors and uses a spatial interpolation algorithm to generate a real-time temperature field cloud map reflecting the temperature distribution across the entire reaction cross-section. Simultaneously, the catalytic efficiency evaluation process monitors the concentration of specific odorous components at the outlet of the catalytic oxidation unit. These specific odorous components typically include key pollutants such as ammonia, hydrogen sulfide, and methanethiol. Concentration monitoring is performed using an online gas analyzer installed on the exhaust pipe. The system records the change in outlet concentration over time and generates component degradation curves. The system database pre-stores ideal temperature field cloud maps and ideal component degradation curves for the catalytic oxidation unit under standard operating conditions. These ideal data are benchmarks obtained through extensive experiments when the catalyst activity is optimal and the inlet conditions are stable.

[0094] In practical implementation, the quantification of temperature field consistency is achieved by comparing the real-time temperature field cloud map with the ideal temperature field cloud map in a gridded manner. The system divides the entire cloud map region into uniformly sized grids, calculates the absolute or relative deviation between the real-time temperature value and the ideal temperature value within each corresponding grid, performs statistical processing on the deviations of all grids (e.g., calculating the mean absolute deviation or root mean square deviation), and then performs normalization to finally obtain a temperature field consistency coefficient. The evaluation of component degradation efficiency involves correlation analysis between the real-time component degradation curve and the ideal component degradation curve. The Pearson correlation coefficient can be used to measure the similarity of the two curve shapes. Simultaneously, the ratio of the percentage decrease in concentration reflected by the real-time curve to the percentage decrease in concentration reflected by the ideal curve within the same reaction residence time is calculated to obtain a degradation efficiency ratio. The temperature field consistency coefficient and degradation efficiency ratio are finally used to calculate the catalytic efficiency index using a synthetic algorithm. The catalytic efficiency index reflects the overall performance status of the current catalytic oxidation unit.

[0095] The catalyst activity maintenance process is initiated when the catalytic performance evaluation process detects a continuous decline in performance. The initiation condition is that the catalytic performance index shows a downward trend over multiple consecutive evaluation periods. Once initiated, the catalyst activity diagnostic program begins running. This program analyzes the changing trend of the temperature field consistency coefficient, for example, checking for localized high-temperature or low-temperature regions in the temperature field cloud map, and analyzing the expansion rate and temperature gradient changes of these abnormal regions. Simultaneously, the program analyzes the decay rate of the component degradation efficiency ratio to determine the rate of catalyst activity decline. Based on the combined pattern of the temperature field consistency coefficient change trend and the component degradation efficiency ratio decay rate, the system attempts to determine the main cause of catalyst deactivation. The diagnostic logic classifies deactivation causes into temporary poisoning and permanent deactivation. Temporary poisoning is usually caused by substances such as sulfides, halides, and carbon deposits covering the catalyst active sites; this type of deactivation is often reversible. Permanent deactivation is usually caused by sintering or loss of the catalyst active components or damage to the support structure; this type of deactivation is irreversible.

[0096] Understandably, the accuracy of temperature field contour maps depends on the number and layout of temperature sensors. The sensor network needs to be dense enough to capture any potential temperature inhomogeneities within the reactor. Spatial interpolation algorithms, such as inverse distance weighting or Kriging interpolation, are used to construct a continuous temperature distribution field based on temperature measurements at discrete points. The accuracy of the component degradation curve depends on the response time and measurement accuracy of the online gas analyzer. An ideal temperature field contour map typically exhibits a relatively uniform temperature distribution with a moderate central temperature, while an ideal component degradation curve shows a rapid, exponential decrease in pollutant concentration over reaction time.

[0097] Understandably, the automatic diagnosis of catalyst deactivation causes is a complex pattern recognition process. The system database may store a library of typical changes in the temperature field consistency coefficient and component degradation efficiency ratio under different deactivation modes. The diagnostic program matches the real-time monitored trends with the patterns in the feature library to determine the most likely cause of deactivation. For example, temporary poisoning may manifest as a slow decrease in the temperature field consistency coefficient, while the component degradation efficiency ratio decreases rapidly, and localized cold spots may appear in the temperature field; while permanent deactivation may manifest as a continuous and irreversible linear decrease in both the temperature field consistency coefficient and the component degradation efficiency ratio.

[0098] In cases determined to be temporary poisoning, the system automatically initiates the thermal regeneration program for the catalytic oxidation unit. The thermal regeneration program controls the heating element to introduce high-temperature air into the reaction zone of the catalytic oxidation unit or raises and maintains the temperature of the reaction zone within a preset high-temperature range (e.g., 400-500 degrees Celsius) for a period of time. This high-temperature baking decomposes or volatilizes the sulfides and halides covering the catalyst surface, burns off carbon deposits, and thus restores the temporarily poisoned active sites. The temperature profile and duration of the thermal regeneration are set according to the assessment of the degree of poisoning.

[0099] For cases deemed permanently deactivated, the system generates a catalyst replacement warning signal. This signal indicates that the catalyst's activity cannot be effectively restored through conventional regeneration methods, necessitating a shutdown and replacement. Simultaneously, the system records the diagnosed catalyst deactivation mode, including the initial onset time of deactivation, temperature field and degradation efficiency changes, and the final diagnostic conclusion, in its database. This historical data provides valuable reference for future assessments of the lifespan of similar catalysts, optimization of operating conditions, and prevention of deactivation. The catalytic efficiency index can be calculated as follows:

[0100]

[0101] in: Represents the catalytic efficiency index. This represents the calculated temperature field consistency coefficient. This represents the calculated ratio of component degradation efficiency. and These are the weighting coefficients assigned to the temperature field uniformity coefficient and the component degradation efficiency ratio, respectively, and satisfying the following conditions: .

[0102] In some embodiments, the calculation of the temperature field consistency coefficient can be further refined, for example, by assigning different weights to the central and peripheral regions of the reactor, since the temperature in the central region typically has a more critical impact on reaction efficiency. The evaluation of component degradation efficiency can also be performed separately for multiple key odor components, and then the weighted average or lowest value is taken as the overall degradation efficiency ratio to ensure that all controlled pollutants are effectively treated. The specific parameters of the thermal regeneration process can be more intelligent. The system can select a preset regeneration temperature curve from a database based on the magnitude of the decrease in the catalytic efficiency index and the diagnosed type of poisoning. For example, for severe carbon buildup-induced poisoning, a higher regeneration temperature and a longer regeneration time may be required; while for mild sulfur poisoning, relatively mild regeneration conditions can be used.

[0103] Optionally, the catalyst activity maintenance process can also include a regeneration effect verification step. After the thermal regeneration procedure, the system does not immediately enter normal operation. Instead, a low concentration of standard gas is introduced to briefly run the catalytic oxidation unit and reassess the catalytic efficiency index to confirm whether the activity has been effectively restored. If the restoration effect is not ideal, the system can trigger secondary regeneration or upgrade the warning level. For the determination of permanent deactivation, the system can be set up with a more cautious multi-level warning mechanism. For example, when permanent deactivation is initially determined, a primary warning is issued, suggesting that an inspection be arranged; when the catalytic efficiency index further declines to a critical level, an emergency warning requiring replacement is issued. This mechanism provides a buffer time for manual intervention.

[0104] See Figure 4 This figure visualizes the core monitoring data of the catalyst activity maintenance process, focusing on the performance degradation pattern of the catalyst after multiple regenerations. The horizontal axis represents the regeneration cycle, and the vertical axis represents catalyst activity; the curves clearly show the trend of activity change with the number of regenerations. This figure is a key basis for catalyst activity diagnosis: the system determines the type of deactivation based on the activity decay rate. If the activity remains below the threshold and does not recover, a catalyst replacement warning is triggered. This data not only verifies the effectiveness of the thermal regeneration procedure but also provides support for catalyst lifetime prediction, making it one of the core monitoring indicators for ensuring the stable performance of the catalytic oxidation unit.

[0105] Example 4: The coupling analysis process uses the adsorption efficiency index output from the adsorption efficiency evaluation process and the catalytic efficiency index output from the catalytic efficiency evaluation process as input parameters. The system internally constructs a logical decision matrix to define the system operating conditions corresponding to different combinations of efficiency indices. The system database pre-stores a control strategy mapping table optimized from a large amount of experimental data and historical operating data; this table is the core decision basis for the coupling analysis process. The control strategy mapping table uses the discretized interval of the adsorption efficiency index as the row index and the discretized interval of the catalytic efficiency index as the column index, forming a two-dimensional query structure. Each cell in the table stores the specific numerical instructions for the system's recommended adjustment of adsorption parameters and catalytic oxidation parameters under a specific combination of adsorption efficiency and catalytic efficiency indices.

[0106] In practical implementation, referring to Table 1, the coupling analysis process first discretizes the adsorption efficiency index and catalytic efficiency index obtained in real time, classifying them into the corresponding row and column intervals in the control strategy mapping table. The system uses a search algorithm to perform a matching query in the mapping table, locating a unique cell. The queried adsorption parameter adjustment amounts may involve changes to the adsorption unit regeneration cycle, inlet flow rate, or operating temperature; the catalytic oxidation parameter adjustment amounts may involve adjustments to the reaction zone temperature, auxiliary fuel supply rate, or space velocity. The system encapsulates the queried adsorption parameter adjustment amounts into adsorption unit control instructions and catalytic oxidation parameter adjustment amounts into catalytic oxidation unit control instructions, together forming a complete coordinated control instruction and sending it to the actuator.

[0107] Table 1: Mapping Table of Control Strategies

[0108]

[0109] It is understandable that the construction of the regulation strategy mapping table is based on a deep understanding of the coupling relationship between adsorption and catalytic oxidation units. The contents of the mapping table determine the system's strategy for dealing with different combinations of performance states. For example, when the adsorption efficiency index is low but the catalytic efficiency index is good, the strategy focuses on optimizing the adsorption unit to protect the catalytic unit; when the catalytic efficiency index is low, the strategy focuses on restoring catalytic activity. This query mechanism based on the preset mapping table ensures the immediacy and determinism of the regulation response.

[0110] In some embodiments, to achieve smoother and more precise control, an interpolation method can be introduced into the calculation of the parameter adjustment for performance index values ​​within the range. The formula for calculating the adjustment Δ can be expressed as:

[0111]

[0112] in: This represents the final parameter adjustment amount that needs to be calculated. It is a proportionality coefficient. It is the normalized efficiency index.

[0113] In some embodiments, the control strategy mapping table can be designed to support dynamic updates. System administrators can fine-tune the baseline adjustment α or strategy within specific cells based on long-term operational data, enabling the system control strategy to possess a certain degree of adaptive optimization capability. The coupling analysis process can incorporate a command security verification step. Before issuing coordinated control commands, the system verifies whether the parameter adjustment amount is within the equipment's safe operating threshold. If it exceeds the limit, it automatically clamps to the safety boundary to prevent misoperation. For abnormal situations where performance does not improve after continuous control, the system can set escalation strategies, such as triggering a high-level alarm to prompt manual intervention, avoiding ineffective cycles.

[0114] Example 5: Feedback Optimization Phase. The collaborative control command generated during the dynamic control phase is executed and initiated to verify the actual effect of parameter adjustments. Verification feature acquisition is performed using a verification sensor group located at the system's final exhaust port. This sensor group consists of high-precision gas analysis instruments, such as Fourier transform infrared spectrometers or proton transfer reaction mass spectrometers, to sample and perform full-component analysis on gas samples processed by the adsorption and catalytic oxidation units. This obtains residual concentration data for key odor indicators, including but not limited to ammonia, hydrogen sulfide, methanethiol, and volatile organic compounds. The system has preset strict emission standard thresholds, set according to national or local environmental regulations and even stricter internal control standards. The verification process compares the residual concentration data of each key odor indicator collected with its corresponding emission standard threshold.

[0115] In practice, the comparison logic is rigorous and sequential. The system checks the residual concentration data of all monitored key odor indicators. Only when the residual concentration data of all key odor indicators are below their respective emission standard thresholds will the system generate a compliance signal. A compliance signal indicates that the execution of the coordinated control command has achieved the expected results, and the current operating parameters of the system are appropriate and effective. Conversely, during the comparison process, if the residual concentration data of any key odor indicator is found to be higher than or equal to its corresponding emission standard threshold, the system will immediately generate a non-compliance signal. A non-compliance signal indicates that despite parameter adjustments, pollutants in the final emitted gas have not been effectively purified, and the current operating parameters need further optimization.

[0116] The comparison result decision module executes different preset logics based on the type of received signal. When the comparison result decision module receives a processing compliance signal, the decision logic is to maintain the current system operating parameters unchanged. The system controls each actuator to maintain its existing working state. However, the feedback optimization phase does not signify the end of monitoring; the system continues to run the gas state deviation calculation process from the initial evaluation phase, continuously monitoring the intake conditions. The system sets a gas state deviation stability threshold, which is typically lower than the threshold that triggers the initial evaluation phase. As long as the continuously monitored and calculated gas state deviation value does not exceed the gas state deviation stability threshold, the system maintains its current steady-state operation mode. Once the gas state deviation exceeds the gas state deviation stability threshold, it indicates a sufficiently significant change in the intake state, and the current operating parameters may soon become inapplicable. At this point, the system automatically re-triggers the initial evaluation phase, initiating a new round of evaluation, control, and verification closed loop.

[0117] When the comparison result decision module receives a signal indicating that the processing has not met the standards, the decision logic immediately triggers a new round of initial evaluation. The system does not wait for the gas state deviation to exceed the limit but starts the loop directly from the beginning, indicating that the system's response to processing failures is of the highest priority. Furthermore, in the subsequent new dynamic control phase, the system adaptively adjusts the weight allocation of the adsorption efficiency evaluation process and the catalytic efficiency evaluation process during the coupled analysis. The weight adjustment is based on clues revealed by the results of confirmatory feature acquisition. For example, if the residual excessive components in the treated gas are mainly substances that should have been completely decomposed in the catalytic oxidation unit (such as methanethiol), while the adsorption unit's treatment effect on the components (such as some VOCs) is acceptable, the system may temporarily increase the weight factor of the catalytic efficiency index in the subsequent coupled analysis, making the generated synergistic control instructions more inclined to optimize the operating parameters of the catalytic oxidation unit. This adaptive weight allocation adjustment mechanism gives the system a certain learning ability, enabling it to more effectively address the main contradictions it currently faces. The mathematical expression of the decision logic can be reflected in the dynamic calculation of the weight factors, as shown in the following formula:

[0118]

[0119] in: This represents a temporary weighting factor assigned to the catalytic efficiency evaluation process during the new round of dynamic regulation phase. The residual concentration data of the i-th key odor indicator obtained from confirmatory feature collection is represented. The emission standard threshold representing the i-th key odor indicator. It is an indicator factor, with a value of 1 when the main removal mechanism of the i-th odor indicator is determined to be catalytic oxidation, and a value of 0 otherwise. The summation symbol is applied to all n key odor indicators.

[0120] It is understandable that verifying the accuracy and reliability of the sensor array is crucial; its performance should be superior to that of the sensor array on the gas input pipeline, as it bears the responsibility of ultimately verifying product quality (i.e., the purified gas). Regular calibration and maintenance of the sensor array are fundamental to ensuring the accuracy of feedback information. The setting of emission standard thresholds needs to include an appropriate safety margin to accommodate sensor measurement errors and instantaneous gas fluctuations. A mechanism that immediately triggers a new round of initial evaluation ensures the system's rapid response to processing failures, enabling it to quickly try new parameter combinations to avoid prolonged non-compliant emissions. Adaptive adjustment of weight allocation reflects the intelligence of the system's control strategy; it no longer mechanically executes fixed coupling analysis rules but fine-tunes decision-making tendencies based on feedback from the actual effects of the previous control, gradually approaching the optimal operating point.

[0121] In some embodiments, the handling of non-compliant signals can be further categorized. For example, based on the number of exceeding factors and the magnitude of the exceeding, non-compliant signals can be divided into "slight exceeding" and "severe exceeding." For "slight exceeding," the system may only adjust the weights and then start a new cycle; for "severe exceeding," in addition to immediately starting a new cycle, the system may also issue an audible and visual alarm to alert operators to potential system malfunctions. The setting of the gas state deviation stability threshold can be dynamic. The system can use historical operating data to statistically analyze the normal fluctuation range of gas state deviation during periods of compliant and stable operation, and dynamically update the stability threshold based on this, making the triggering conditions more closely match the actual operating characteristics of the system.

[0122] Optionally, a short-term historical memory function can be introduced during the feedback optimization phase. The system can record the content of the most recent coordinated control commands and their corresponding verification results. When encountering similar combinations of gas state deviation and efficiency index again, the system can prioritize trying control commands that have been verified as effective in the past, or avoid repeating commands that have been verified as ineffective, thereby accelerating convergence to suitable parameters. For extreme cases where non-compliant signals are continuously received and processed, the system can set a maximum loop count limit. When the number of times the initial evaluation phase is triggered consecutively within the limited time exceeds the preset value, the system determines that adaptive adjustment cannot solve the problem, triggers the highest level fault alarm, and suggests switching to safe mode operation or shutting down for inspection to prevent infinite loops.

[0123] See Figure 5 This graph visualizes the core verification data from the feedback optimization phase, focusing on the compliance of key odor indicators in the treated gas. The horizontal axis represents the four core odor indicators, and the vertical axis represents concentration. Two types of bars are used to compare residual concentrations with standard thresholds. This graph corresponds to the results of the verification feature acquisition phase, and its function is to determine the effectiveness of the coordinated control commands: if all residual concentrations are below the threshold, a treatment compliance signal is generated, and the system maintains the current parameters; if they exceed the threshold, a new round of initial assessment is triggered. This graph intuitively demonstrates the verification capability of the system's closed-loop control and provides crucial data support for ensuring emission stability.

[0124] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for adsorbing and catalytically oxidizing foul-smelling gases from poultry manure, characterized by, The method comprises the following processing stages: An initial evaluation stage, in which multi-dimensional features of the input poultry manure malodorous gas are collected, a gas feature spectrum is generated, a gas state deviation is calculated by combining a pre-stored reference feature spectrum, and a system regulation preparation instruction is triggered according to the calculation result of the gas state deviation; A dynamic regulation stage, in which, in response to the system regulation preparation instruction, an adsorption efficiency evaluation process of the working state of the adsorption unit and a catalytic efficiency evaluation process of the working state of the catalytic oxidation unit are synchronously started, coupling analysis is performed based on the output results of the adsorption efficiency evaluation process and the catalytic efficiency evaluation process, and a synergistic regulation instruction containing an adsorption parameter adjustment amount and a catalytic oxidation parameter adjustment amount is generated; A feedback optimization stage, in which the synergistic regulation instruction is executed to adjust the system operation parameters, and after the adjustment, a verification feature collection of the processed gas is started, the verification feature collection result is compared with a preset target range, and according to the comparison result, it is decided whether to start a new initial evaluation stage to form a closed-loop control; The coupling analysis in the dynamic regulation stage specifically comprises: A decision matrix is established with the adsorption efficiency index and the catalytic efficiency index as inputs; A regulation strategy mapping table pre-stored in the system database is queried, and the regulation strategy mapping table defines the adsorption parameter adjustment amount and the catalytic oxidation parameter adjustment amount corresponding to different combinations of the adsorption efficiency index and the catalytic efficiency index; According to the real-time calculated adsorption efficiency index and catalytic efficiency index, a matching query is performed in the regulation strategy mapping table, and the corresponding adsorption parameter adjustment amount and catalytic oxidation parameter adjustment amount are outputted; The adsorption parameter adjustment amount is encapsulated as an adsorption unit regulation instruction, and the catalytic oxidation parameter adjustment amount is encapsulated as a catalytic oxidation unit regulation instruction, which together constitute the synergistic regulation instruction; The verification feature collection in the feedback optimization stage specifically comprises: A verification sensor group is arranged at the system final exhaust outlet to collect the processed gas sample; A full-component analysis is performed on the gas sample to obtain residual concentration data of key malodorous indicators; The residual concentration data of the key malodorous indicators are compared with the pre-set emission standard threshold value; If the residual concentrations of all key malodorous indicators are lower than the corresponding emission standard threshold value, a treatment compliance signal is generated; If the residual concentration of any key malodorous indicator is higher than or equal to the corresponding emission standard threshold value, a treatment non-compliance signal is generated; The comparison result decision in the feedback optimization stage specifically comprises: When the treatment compliance signal is received, the current system operation parameters are maintained, and the gas state deviation is continuously monitored; When the gas state deviation exceeds a stable threshold value, the initial evaluation stage is re-triggered; When the treatment non-compliance signal is received, a new initial evaluation stage is immediately triggered, and in the new dynamic regulation stage, adaptive adjustment is performed on the weight distribution of the adsorption efficiency evaluation process and the catalytic efficiency evaluation process.

2. The system for adsorbing and catalytically oxidizing foul-smelling gases from poultry manure according to claim 1, characterized in that, The multi-dimensional feature collection in the initial evaluation stage specifically comprises: Concentration data, temperature data and humidity data of the poultry manure malodorous gas are continuously acquired by a sensor array arranged in the gas input pipeline; The concentration data, temperature data and humidity data obtained in the continuous monitoring period are formed into a concentration time sequence curve, a temperature time sequence curve and a humidity time sequence curve respectively; The reference concentration curve, the reference temperature curve and the reference humidity curve in the standard state are called from the system database; The concentration time sequence curve and the reference concentration curve are subjected to point-by-point difference operation and integration to obtain a concentration deviation integral value; The temperature time sequence curve and the reference temperature curve are subjected to point-by-point difference operation and integration to obtain a temperature deviation integral value; The humidity time sequence curve and the reference humidity curve are subjected to point-by-point difference operation and integration to obtain a humidity deviation integral value; The concentration deviation integral value, the temperature deviation integral value and the humidity deviation integral value are subjected to weighted fusion to obtain a gas state deviation degree.

3. The system for adsorbing and catalytically oxidizing foul-smelling gases of bird manure according to claim 2, characterized in that, The adsorption efficiency evaluation process in the dynamic regulation stage specifically includes: The real-time saturation degree of the adsorbent in the adsorption unit is monitored, and a saturation degree change trajectory is drawn; The pressure difference data of the adsorption unit inlet and outlet are obtained, and a pressure difference change curve is formed; The ideal saturation degree change trajectory and the ideal pressure difference change curve of the adsorbent under the standard working condition are called from the system database; The trajectory coincidence degree between the real-time saturation degree change trajectory and the ideal saturation degree change trajectory is calculated; The curve similarity between the real-time pressure difference change curve and the ideal pressure difference change curve is calculated; The adsorption efficiency index is calculated based on the trajectory coincidence degree and the curve similarity.

4. The system for adsorbing and catalytically oxidizing foul-smelling gases of bird manure according to claim 3, characterized in that, The catalytic efficiency evaluation process in the dynamic regulation stage specifically includes: The temperature distribution of the reaction zone of the catalytic oxidation unit is monitored, and a temperature field cloud map is generated; The concentration of a specific malodorous component at the outlet of the catalytic oxidation unit is monitored, and a component degradation curve is generated; The ideal temperature field cloud map and the ideal component degradation curve of the catalytic oxidation unit under the standard working condition are called from the system database; The real-time temperature field cloud map and the ideal temperature field cloud map are subjected to grid comparison, and a temperature field consistency coefficient is calculated; The real-time component degradation curve and the ideal component degradation curve are subjected to correlation analysis, and a component degradation efficiency coefficient is calculated; The catalytic efficiency index is calculated based on the temperature field consistency coefficient and the component degradation efficiency coefficient.

5. The system for adsorbing and catalytically oxidizing foul-smelling gases of bird manure according to claim 1, characterized in that, The initial evaluation stage is provided with a sensor calibration stage, specifically including: Periodically introduce standard gas with known concentration into the gas input pipeline; The response readings of the standard gas are obtained through the sensor array; The response readings are compared with the known concentration value of the standard gas to calculate the measurement deviation of each sensor; The measurement data of the sensor array are compensated in real time according to the measurement deviation to generate calibrated sensor data; The calibrated sensor data are used for subsequent multi-dimensional feature acquisition.

6. The system for adsorbing and catalytically oxidizing foul-smelling gases of bird manure according to claim 3, characterized in that, The adsorption efficiency evaluation process is provided with an adsorbent regeneration trigger judgment process, specifically including: When the adsorption efficiency index is lower than a preset threshold, start the adsorbent regeneration judgment; Monitor the sudden increase of gas concentration at the outlet of the adsorption unit; Combine the total amount of adsorbent treated gas and the running time to calculate the remaining life of the adsorbent; When the adsorption efficiency index is lower than the threshold, the concentration sudden increase is detected, and the remaining life is greater than the critical value, generate an adsorbent regeneration instruction; Start the regeneration program of the adsorption unit according to the regeneration instruction, and regenerate the adsorbent by heating or depressurization.

7. The system for adsorbing and catalytically oxidizing foul-smelling gases of bird manure according to claim 4, characterized in that, The catalytic performance evaluation process is followed by a catalyst activity maintenance process, specifically including: When the catalytic performance index decreases continuously for multiple times, start catalyst activity diagnosis; Analyze the change trend of the temperature field consistency coefficient and the decay rate of the component degradation efficiency coefficient; According to the diagnosis result, judge the catalyst deactivation reason, and distinguish between temporary poisoning and permanent deactivation; For temporary poisoning, start the heat regeneration program of the catalytic oxidation unit to restore the catalyst activity through high-temperature baking; For permanent deactivation, generate a catalyst replacement warning signal, and record the deactivation mode to the system database.

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