Air separation voltage intelligent adsorption pollution discharge method, system and device combined with pressure curve

Abstract

The application discloses a pressure curve combined air separation variable pressure intelligent adsorption blowdown method, system and device, relates to the intelligent blowdown technical field.The pressure curve combined air separation variable pressure intelligent adsorption blowdown method, system and device, comprising the following steps: S1, real-time acquisition operation state data, and pretreatment is carried out to operation state data;S2, operation state data is analyzed to pressure disturbance intensity;S3, through real-time analysis and feature extraction to pressure curve;S4, operation state data is predicted to start-stop risk;S5, comprehensive operation state data, pressure disturbance intensity analysis result and start-stop risk prediction result execute blowdown control instruction.The air compressor and variable pressure adsorption system lack linkage control based on pressure change, blowdown is easy to cause pressure fluctuation and frequent start-stop, which leads to energy consumption increase and equipment wear, and the existing control mode lacks feedforward regulation mechanism.

Description

Technical Field

[0001] This invention relates to the field of intelligent sewage discharge technology, specifically to an air separation pressure swing intelligent adsorption sewage discharge method, system, and device that incorporates pressure curves. Background Technology

[0002] With the widespread application of pressure swing adsorption (PSA) technology in air separation oxygen production, energy consumption control and coordination of wastewater discharge rhythm during system operation have become crucial issues for improving operational efficiency. As the primary air supply equipment, the start-stop frequency and loading status of the air compressor are significantly coupled with the wastewater discharge behavior of the adsorption tower. System pressure curves, as an important data foundation reflecting dynamic changes in operation, are increasingly being incorporated into wastewater control and energy efficiency analysis processes.

[0003] For example, invention patent CN114437846B discloses a computer-based optimization method for natural gas pressure swing adsorption (PSA) denitrification. This method includes: controlling the valve opening degree according to multiple preset flow rates during multiple periods when the valve is not operating; detecting the actual flow rate of the valve during each of these multiple periods; calculating the differences between the preset flow rates and the actual flow rates within a test cycle; calculating valve opening degree compensation and adjustment values ​​for the next test cycle based on these differences; and controlling the valve opening status according to the valve opening degree adjustment values ​​in the next test cycle. This invention can promptly detect frequently switching valve systems and optimize them based on the detection results, ensuring precise valve control and improving the yield and product purity of the natural gas PSA denitrification process.

[0004] For example, invention patent CN114437847B discloses a computer control method and system for a natural gas pressure swing adsorption (PSA) denitrification process. This method includes: a) determining the gas inlet time of the absorber; b) determining the adsorption and desorption times for the first half of the cycle; c) determining the switching time for the first half of the cycle; d) determining the adsorption and desorption times for the second half of the cycle; e) determining the switching time for the second half of the cycle; and f) an operation monitoring and feedback phase. This computer control method for the natural gas PSA denitrification process enables the scientific setting of the switching times for the pressure boosting and depressurizing valves of a group of adsorption towers, thereby improving both production efficiency and the utilization rate of the raw gas.

[0005] Most existing air separation transformer systems still rely on timed blowdowns and fixed threshold triggering, failing to dynamically adjust based on real-time operating conditions. Blowdown processes easily cause system pressure fluctuations, leading to frequent compressor starts and stops, increased energy consumption, and increased equipment wear. Current control methods lack comprehensive analysis of blowdown disturbances, operating trends, and abnormal fluctuations, making it difficult to achieve stable and efficient blowdown management.

[0006] To address the above issues, there is an urgent need for an air separation pressure swing intelligent adsorption wastewater discharge method, system, and device that incorporates pressure curves. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a method, system, and device for air separation pressure swing adsorption (PSA) wastewater discharge that incorporates pressure curves. This solves the problems of the lack of pressure-based linkage control between the air compressor and the PSA system, which leads to pressure fluctuations and frequent start-stops during wastewater discharge, resulting in increased energy consumption and equipment wear. Furthermore, existing control methods lack a feedforward adjustment mechanism.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: an intelligent air separation pressure swing adsorption (ESA) method for wastewater discharge based on pressure curves, comprising the following steps: S1: Real-time acquisition of operating status data and preprocessing of the operating status data; S2: Pressure disturbance intensity analysis of the operating status data, determining the disturbance level of the current wastewater discharge action based on the pressure disturbance intensity analysis results, and selecting the corresponding wastewater discharge control strategy; S3: Real-time analysis and feature extraction of the pressure curve to identify the adsorption cycle, assess the disturbance intensity, and monitor abnormal fluctuations; S4: Start-up and shutdown risk prediction of the operating status data, further determining whether the wastewater discharge behavior will trigger the start-up and shutdown of the air compressor based on the start-up and shutdown risk prediction results, and dynamically adjusting the wastewater discharge rhythm, opening degree, and duration; S5: Execution of wastewater discharge control commands by comprehensively considering the operating status data, pressure disturbance intensity analysis results, and start-up and shutdown risk prediction results, while simultaneously feeding back the wastewater discharge execution results and system response information to the status acquisition and energy efficiency assessment, performing parameter rolling optimization and adaptive updating of the control strategy.

[0011] Further, the specific steps for real-time acquisition and preprocessing of operational status data are as follows: Operational status data includes: time variables, discharge start time, discharge duration, instantaneous discharge pressure, initial pressure, average pressure before discharge, target pressure, baseline power, baseline operating time, optimized power, optimized operating time, baseline fluctuation standard deviation, and optimized fluctuation standard deviation; Time variables are acquired in real-time and timestamped synchronously with other operational status data to form a complete time series; the discharge start time is obtained through the trigger timestamp automatically recorded by the system when the discharge control command is issued; the discharge duration is obtained by recording the opening and closing times of the discharge valve and calculating their time difference; the instantaneous discharge pressure is obtained synchronously and in real-time through pressure sensors; the initial pressure is obtained by real-time acquisition by pressure sensors at the discharge start time; the average pressure before discharge is calculated by continuously acquiring historical instantaneous discharge pressure data from pressure sensors before the discharge start time; the target pressure is obtained by consulting the air compressor equipment manufacturing parameters and calibrating the settings based on long-term system operating experience; and the average pressure is obtained by extracting power acquisition data from historical operating cycles and calculating its average value. The process involves several steps: obtaining a baseline power; obtaining a baseline operating time by extracting time records corresponding to the default discharge strategy in the control system; obtaining optimized power by collecting data in real time from a power acquisition device and calculating the average value during the current control strategy's operation; obtaining optimized operating time by statistically analyzing the start and end time records corresponding to the current control strategy; obtaining the baseline fluctuation standard deviation by calculating the sample standard deviation of the historical power sequence collected within the operating cycle corresponding to the default discharge strategy; obtaining the optimized fluctuation standard deviation by calculating the sample standard deviation of the power data collected within the operating cycle of the current optimized discharge strategy; and preprocessing steps including: removing outliers from the collected operating status data, identifying various data anomalies including sensor value anomalies, sudden signal interference, and continuous sampling interruptions, and filling missing values ​​using interpolation based on the anomaly location, and marking unrecoverable parts as missing; resampling all variables with a uniform time step; normalizing continuous variables to the range of zero to one using maximum and minimum values; standardizing variables used for fluctuation and trend analysis; and extracting characteristic values ​​for each time period from the continuous data using a sliding window of fixed length that moves in steps.

[0012] Further, the specific steps for analyzing the pressure disturbance intensity of the operating status data are as follows: Obtain the time variable, discharge start time, discharge duration, instantaneous discharge pressure, and average pressure before discharge; calculate the rate of change of the instantaneous discharge pressure by integrating over the discharge duration, with the integration time interval starting from the discharge start time and continuing until the discharge start time plus the discharge duration. Within this interval, calculate the derivative of the instantaneous discharge pressure with respect to time and take its absolute value, representing the rate of pressure change during the discharge process; this rate is then multiplied by a correction factor, which is the ratio of the difference between the instantaneous discharge pressure and the average pressure before discharge to the average pressure before discharge. The integration result represents the cumulative disturbance value throughout the entire discharge process, and finally, divide by the discharge duration to obtain the average disturbance intensity experienced by the system per unit time, i.e., the pressure disturbance intensity value.

[0013] Further, the specific steps for determining the disturbance level of the current sewage discharge action based on the pressure disturbance intensity analysis results and selecting the corresponding sewage discharge control strategy are as follows: Real-time comparison of the pressure disturbance intensity value with the disturbance intensity threshold, which includes a primary disturbance threshold and a secondary disturbance threshold; When the pressure disturbance intensity value is greater than or equal to the primary disturbance threshold, the sewage discharge operation is flow-limited, the sewage valve opening is controlled within 30%, segmented small-flow release is adopted, and the buffer time is extended. Simultaneously, the gas replenishment and buffering devices are activated, abnormal behavior is recorded and an alarm is triggered. If this occurs continuously, the system switches to a low-frequency sewage discharge mode and prompts for manual review; When the pressure disturbance intensity value is greater than the secondary disturbance threshold but less than the primary disturbance threshold, the sewage discharge is switched to a slow discharge mode, the valve is opened intermittently with a maximum opening of no more than 60%, the discharge time is appropriately extended, and multiple adsorption towers automatically stagger their discharge times. The controller records the disturbance level and feeds it back to the adjustment module; When the pressure disturbance intensity value is less than or equal to the secondary disturbance threshold, the original sewage discharge rhythm and opening are maintained, and the control system does not intervene.

[0014] Furthermore, the specific steps for identifying the adsorption cycle, assessing disturbance intensity, and monitoring abnormal fluctuations through real-time analysis and feature extraction of pressure curves are as follows: Acquire inlet and outlet pressure data of the adsorption tower and process the pressure curves in real time to identify the adsorption, desorption, and pressure equalization stages of the system, providing a cycle reference for the discharge strategy; extract the pressure change rate through a sliding window; analyze the amplitude, decline, and fluctuation characteristics of the curves before and after discharge, extract deviation, slope, and stability indicators, and identify signs of valve jamming and abnormal back pressure loss of control; compare the current cycle with historical typical cycles to determine operational deviations and assist in strategy adjustment; output all analysis results in a structured format for use by the linkage regulation and energy efficiency assessment modules, supporting strategy judgment and parameter optimization.

[0015] Furthermore, the specific steps for predicting start-up and shutdown risks based on operational status data are as follows: Obtain the discharge start time, starting pressure, and target pressure; the calculation process for the predicted start-up and shutdown risk value includes two parts. The first part is to multiply the derivative of the starting pressure with respect to time, i.e., the instantaneous rate of change of the system pressure at the start of discharge, by a rate weighting coefficient. The second part is to calculate the difference between the starting pressure and the target pressure, then divide this difference by the target pressure to obtain the deviation ratio of the starting pressure relative to the target pressure. This ratio is then squared to represent the intensity of the deviation, and finally multiplied by a deviation weighting coefficient. The results of the first two parts are added together to obtain the predicted start-up and shutdown risk value.

[0016] Furthermore, the specific steps for determining whether the discharge behavior triggers the start-up and shutdown of the air compressor based on the start-up and shutdown risk prediction results, and dynamically adjusting the discharge rhythm, opening degree, and duration are as follows: Real-time comparison of the start-up and shutdown risk prediction value with the start-up and shutdown risk threshold, which includes a primary risk threshold and a secondary risk threshold; when the start-up and shutdown risk prediction value is greater than or equal to the primary risk threshold, the current discharge is suspended until the pressure recovers, and the discharge valve opening is limited to within 30%, closed if necessary, and simultaneously activated with air replenishment and high-pressure loading modes. If skipping is not possible, multi-stage low-flow discharge is adopted and set... Fixed-interval buffering; triggering alarms and manual prompts, switching to low-frequency discharge and protection mode when alarms occur continuously; when the predicted value of start-stop risk is greater than the secondary risk threshold but less than the primary risk threshold, the discharge is adjusted to an intermittent slow discharge mode, limiting the valve opening to no more than 60%, appropriately extending the discharge duration, automatically staggering peak discharge when multiple adsorption towers discharge, recording disturbance trajectories and entering dynamic adjustment; when the predicted value of start-stop risk is less than or equal to the secondary risk threshold, the original discharge strategy remains unchanged, the valve is fully open normally, the discharge duration remains at the default, and the control system does not intervene or trigger compensation and protection logic.

[0017] Furthermore, the comprehensive operating status data, pressure disturbance intensity analysis results, and start-stop risk prediction results are used to execute pollution control commands. Simultaneously, the pollution control execution results and system response information are fed back to the status acquisition and energy efficiency assessment system for parameter rolling optimization and adaptive update of the control strategy. The specific steps are as follows: First, obtain the baseline power, baseline operating time, optimized power, optimized operating time, baseline fluctuation standard deviation, and optimized fluctuation standard deviation. Second, multiply the baseline power by the baseline operating time and subtract the optimized power multiplied by the optimized operating time to obtain the absolute magnitude difference of the current energy-saving benefit. Then, divide this difference by the product of the baseline power and the baseline operating time, add a minimum correction term, and construct a normalized ratio. This ratio serves as the input to the hyperbolic tangent function. Third, subtract the optimized fluctuation standard deviation from the baseline fluctuation standard deviation to obtain the fluctuation improvement amount. Divide this difference by the baseline fluctuation standard deviation, add a minimum correction term, and take the square root to obtain the fluctuation improvement factor. Finally, multiply the result of the hyperbolic tangent function by one and add the fluctuation improvement factor to calculate the comprehensive energy-saving stability value. The system compares the overall energy-saving stability value with the energy-saving assessment threshold in real time. The energy-saving assessment threshold includes a primary energy-saving threshold and a secondary energy-saving threshold. When the overall energy-saving stability value is greater than or equal to the primary energy-saving threshold, the current parameter is marked as not recommended. The upper limit of valve opening is automatically reduced, the sewage discharge time is shortened, and the cycle is appropriately extended. If inefficiency continues, the parameters of the previous high-scoring round are restored, and the system can switch to a conservative mode. When the overall energy-saving stability value is greater than the secondary energy-saving threshold but less than the primary energy-saving threshold, the current parameter is temporarily retained. The valve opening adjustment step size is reduced, the duration is controlled more precisely, and the trend is continuously tracked. If the medium-efficiency state continues for two rounds, it is retained as a candidate solution. When the overall energy-saving stability value is less than or equal to the secondary energy-saving threshold, the current parameter is marked as a high-optimal solution, stored in the strategy library, and called with priority. It is continuously reused under normal circumstances. When parallel control conditions are available, the strategy is synchronously applied to other towers and zones.

[0018] The second aspect of this invention provides an intelligent air separation pressure swing adsorption (PSA) system for wastewater discharge that incorporates pressure curves, comprising: a status acquisition module, a wastewater discharge control module, a curve analysis module, a linkage adjustment module, and an energy efficiency assessment module. The system is characterized in that: the status acquisition module is used to acquire operational status data in real time and preprocess the data; the wastewater discharge control module is used to perform pressure disturbance intensity analysis on the operational status data, determine the disturbance level of the current wastewater discharge action based on the analysis results, and select a corresponding wastewater discharge control strategy; the curve analysis module is used to identify the adsorption cycle, assess disturbance intensity, and monitor abnormal fluctuations through real-time analysis and feature extraction of the pressure curve; the linkage adjustment module is used to predict start-up and shutdown risks based on the operational status data, further determine whether the wastewater discharge behavior will trigger the start-up and shutdown of the air compressor based on the prediction results, and dynamically adjust the wastewater discharge rhythm, opening degree, and duration; the energy efficiency assessment module is used to execute wastewater discharge control commands by comprehensively considering the operational status data, pressure disturbance intensity analysis results, and start-up and shutdown risk prediction results, while simultaneously feeding back the wastewater discharge execution results and system response information to the status acquisition and energy efficiency assessment modules for rolling parameter optimization and adaptive updating of the control strategy.

[0019] A third aspect of this invention provides an intelligent air separation pressure swing adsorption (PSA) wastewater discharge device incorporating pressure curves, comprising: a status acquisition and preprocessing unit, a disturbance intensity analysis unit, a curve feature recognition unit, a start-stop risk prediction unit, and a wastewater discharge control and strategy optimization unit. The status acquisition and preprocessing unit is used to acquire data related to the adsorption tower, air compressor, and wastewater discharge, and to perform alignment, cleaning, and standardization processing. The disturbance intensity analysis unit is used to calculate the pressure disturbance intensity during wastewater discharge and determine the disturbance level to trigger corresponding control strategies. The curve feature recognition unit is used to analyze the pressure curve, identify the adsorption stage, extract key change indicators, and monitor abnormal fluctuations. The start-stop risk prediction unit is used to assess the impact of wastewater discharge on the operation of the air compressor, generate a risk score, and adjust wastewater discharge parameters. The wastewater discharge control and strategy optimization unit is used to execute wastewater discharge control, provide feedback on the operating results, update control parameters, and optimize strategies.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This invention establishes a standardized time series data structure by uniformly collecting and preprocessing the operating status data, which solves the problems of messy data sources and difficulty in variable alignment in traditional systems, and provides a unified and computable input basis for subsequent analysis modules, thereby improving the accuracy and availability of control strategy response.

[0023] (2) By designing a pressure disturbance intensity analysis formula, this invention combines the pressure derivative and disturbance deviation during the sewage discharge period into a composite index, thereby realizing the quantitative classification of disturbance levels and selecting the corresponding control strategy based on the threshold. This solves the problems of existing sewage discharge control being unable to identify disturbance intensity and having a single response mode, and significantly enhances the dynamic adjustment capability and anti-disturbance performance of the system.

[0024] (3) In this invention, a period identification and fluctuation monitoring mechanism based on pressure curve is introduced. By extracting curve feature indicators through a sliding window, the current adsorption stage and abnormal fluctuation behavior are identified. This solves the problem that traditional sewage control cannot identify the operating cycle and cannot identify abnormal fluctuations in advance, and enhances the coupling between sewage control and system status and the feedforward adjustment capability.

[0025] (4) This invention uses start-stop risk prediction and energy-saving stability assessment indicators to link disturbance intensity, operation trend and energy efficiency performance, realize closed-loop adjustment of control parameters and strategy optimization, solve the problem of no feedback mechanism and inability to update parameters in existing sewage control, and significantly improve the system's energy-saving level and the adaptive capability of operation strategy.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a flowchart of the air separation pressure swing intelligent adsorption sewage discharge method based on pressure curves according to the present invention.

[0028] Figure 2 This is a structural diagram of the air separation pressure swing intelligent adsorption sewage discharge system based on the pressure curve of the present invention;

[0029] Figure 3 This is a distribution diagram of the energy-saving and stable comprehensive value of the present invention;

[0030] Figure 4 This is a comparison chart of pressure curves under typical sewage disturbance conditions according to the present invention. Detailed Implementation

[0031] 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.

[0032] Please see Figures 1-4This invention provides a technical solution: an intelligent air separation pressure swing adsorption method for wastewater discharge based on pressure curves, comprising the following steps: S1: Real-time acquisition of operating status data and preprocessing of the operating status data; S2: Pressure disturbance intensity analysis of the operating status data, determining the disturbance level of the current wastewater discharge action based on the pressure disturbance intensity analysis results, and selecting the corresponding wastewater discharge control strategy; S3: Real-time analysis and feature extraction of the pressure curve to identify the adsorption cycle, assess the disturbance intensity, and monitor abnormal fluctuations; S4: Start-up and shutdown risk prediction of the operating status data, further determining whether the wastewater discharge behavior will trigger the start-up and shutdown of the air compressor based on the start-up and shutdown risk prediction results, and dynamically adjusting the wastewater discharge rhythm, opening degree, and duration; S5: Execution of wastewater discharge control commands by comprehensively considering the operating status data, pressure disturbance intensity analysis results, and start-up and shutdown risk prediction results, while simultaneously feeding back the wastewater discharge execution results and system response information to the status acquisition and energy efficiency assessment, performing parameter rolling optimization and adaptive updating of the control strategy.

[0033] Specifically, the real-time acquisition and preprocessing of operational status data involves the following steps: Operational status data includes time variables, discharge start time, discharge duration, instantaneous discharge pressure, initial pressure, average pressure before discharge, target pressure, baseline power, baseline operating time, optimized power, and optimized operating time. These data constitute the key inputs for system operation, providing support for subsequent disturbance identification, risk assessment, and energy efficiency determination. Time variables are timestamped synchronously by the acquisition equipment to form a structured time series; the discharge start time is determined by the timestamp recorded by the system when the control command is triggered; the discharge duration is calculated from the opening and closing times of the discharge valve; the instantaneous discharge pressure is acquired in real time by pressure sensors; the initial pressure is the pressure value measured by the system at the start of discharge; the average pressure before discharge is calculated by backtracking historical data from a short period before discharge; the target pressure is set based on air compressor parameters and experience; the baseline power and baseline operating time are calculated from historical default strategy cycles; and the optimized power and optimized operating time are obtained from real-time sampling and statistical results during the current strategy execution period. The preprocessing steps include: outlier removal, interpolation and labeling of missing data; uniform resampling of all variables and alignment by time step; normalization of continuous variables to the [0,1] interval, and standardization of trend variables; and extraction of average values, volatility and other statistical characteristics of each time period from continuous data by setting a fixed-length sliding time window to provide input support for subsequent disturbance analysis and strategy optimization.

[0034] In this implementation plan, this step constructs a complete and standardized state data sequence, providing high-quality and highly comparable basic data support for subsequent disturbance intensity analysis, cycle identification, start-up and shutdown prediction, energy efficiency assessment, and other modules of the system. By collecting and preprocessing key operating variables in real time, it ensures a high degree of synchronization between sewage discharge behavior and system status, improving the accuracy and responsiveness of data-driven control strategies, and helping to achieve intelligent linkage regulation and energy-saving optimization control objectives based on pressure changes.

[0035] Specifically, the steps for analyzing the pressure disturbance intensity of the operating status data are as follows: Obtain the time variables, discharge start time, discharge duration, instantaneous discharge pressure, and average pressure before discharge to form the minimum input set required for disturbance assessment. These variables accurately characterize the dynamic impact of discharge behavior on system pressure. The pressure fluctuation of the system during discharge is quantified by integrating the rate of change of the instantaneous discharge pressure over the discharge duration. The integration interval starts from the discharge start time and extends to the sum of the start time and discharge duration, fully covering the entire discharge process. Within this time interval, the derivative of the instantaneous discharge pressure with respect to time is calculated, and its absolute value is taken as the instantaneous disturbance rate experienced by the system. This disturbance rate is then multiplied by a correction factor, which is the ratio of the difference between the instantaneous discharge pressure and the average pressure before discharge to the average pressure. This correction factor reflects the degree of deviation of the disturbance amplitude from the steady-state pressure baseline. Finally, the corrected rate value is integrated over the time interval to obtain the cumulative disturbance during the entire sewage discharge period. This cumulative disturbance is then divided by the sewage discharge duration to obtain the average disturbance intensity experienced by the system per unit time, defined as the pressure disturbance intensity value. This value can be used to determine the sewage discharge disturbance level and as the triggering basis for policy-linked control.

[0036] The specific calculation method for the pressure disturbance intensity value is as follows:

[0037]

[0038] In the formula, This indicates the intensity of the pressure disturbance. Represents a time variable. Indicates the start time of sewage discharge. Indicates the duration of sewage discharge. Indicates the instantaneous pressure of sewage discharge. This indicates the average pressure before sewage discharge.

[0039] In this implementation plan, this step quantifies the dynamic fluctuation amplitude and rate of change of system pressure during the sewage discharge process by constructing a pressure disturbance intensity value, effectively reflecting the actual impact of sewage discharge behavior on system stability. This value not only possesses the characteristics of high sensitivity and timely response, but also serves as an important basis for disturbance level judgment and control strategy selection, providing reliable support for achieving coordinated adjustment of sewage discharge rhythm, valve opening, and other parameters, thus helping to improve the controllability of sewage discharge behavior and the stability of system operation.

[0040] Specifically, based on the pressure disturbance intensity analysis results, the disturbance level of the current sewage discharge action is determined, and the corresponding sewage discharge control strategy is selected. The specific steps are as follows: The pressure disturbance intensity value is compared in real time with the set disturbance intensity threshold. The disturbance intensity threshold includes a primary disturbance threshold and a secondary disturbance threshold, corresponding to high, medium, and low disturbance level classification standards, respectively. This judgment mechanism enables rapid response and hierarchical management of pressure fluctuations caused by sewage discharge. When the pressure disturbance intensity value is greater than or equal to the primary disturbance threshold, it is identified as a high disturbance state. The sewage discharge operation is executed in a flow-limiting manner, the maximum opening of the sewage discharge valve is controlled within 30%, and a segmented small-flow release method is adopted to reduce instantaneous pressure impact. At the same time, the exhaust buffer time is extended, and the air replenishment device and buffer module are coordinated to improve the system back pressure stability. Abnormal sewage discharge behavior will be recorded and trigger alarm events. If high disturbance situations occur repeatedly, the system will automatically switch to a low-frequency sewage discharge safety mode and prompt manual review and intervention. When the pressure disturbance intensity value is between the first and second level thresholds, it is determined to be a medium disturbance state. The sewage discharge strategy is adjusted to a slow discharge mode, the sewage discharge valve is opened intermittently with a maximum opening degree not exceeding 60%, and the discharge duration is appropriately extended to reduce the disturbance rate. If multiple adsorption towers are to be discharged, they are automatically staggered to avoid disturbance superposition, and the disturbance level information is fed back to the regulation module. If the pressure disturbance intensity value is less than or equal to the second level disturbance threshold, it is considered that the current sewage discharge behavior has little impact on the pressure. The original sewage discharge rhythm and valve opening remain unchanged, the control system does not trigger any intervention measures, and maintains a highly efficient and stable discharge state.

[0041] In this implementation plan, this step establishes a disturbance intensity-based control mechanism to dynamically identify and adjust responses to system pressure fluctuations caused by wastewater discharge. Based on the disturbance intensity value, different levels are assigned to match different wastewater control strategies. This ensures timely flow restriction and buffering under high disturbance conditions, optimized staggered discharge under medium disturbance conditions, and maintenance of the original strategy under low disturbance conditions. This mechanism significantly improves the flexibility and accuracy of wastewater control, effectively suppresses system pressure instability caused by wastewater discharge, and provides strategic support for stable system operation and energy-saving control.

[0042] Specifically, by real-time analysis and feature extraction of the pressure curve, adsorption cycle identification, disturbance intensity assessment, and abnormal fluctuation monitoring are performed. The specific steps are as follows: First, the inlet and outlet pressure data of the adsorption tower are acquired, and the pressure curve is processed in real time to identify the current operating stage of the system, including adsorption, desorption, and pressure equalization processes, providing cycle position information support for subsequent wastewater discharge strategies. Second, the pressure change rate is extracted by setting a fixed-length sliding window to capture local change trends, forming a key input for disturbance analysis. Third, the change amplitude, slope, and fluctuation intensity of the pressure curve before and after wastewater discharge are further analyzed to extract deviation values, curve slopes, pressure stability intervals, and other characteristic indicators to determine whether the system exhibits abnormal fluctuation behavior, such as valve jamming, abnormal back pressure, and other potential fault symptoms. Fourth, the pressure curve of the current cycle is compared with multiple historical typical cycles to identify the degree of deviation in the operating state, assisting in adaptive correction of the system strategy. All analysis results are output in structured data form and uniformly transmitted to the linkage regulation module and energy efficiency assessment module to support the dynamic determination of control strategies and the rolling optimization of parameters.

[0043] like Figure 4 As shown, this is a comparison of pressure curves under typical sewage discharge disturbance scenarios provided in this embodiment. The high-disturbance curve rises rapidly to a maximum pressure of approximately 7.59 near t=15, then drops significantly to a minimum of approximately 6.38 at t=48, exhibiting large overall fluctuations and severe disturbance characteristics. The medium-disturbance curve reaches a peak of approximately 7.40 at t=15, then gradually declines, dropping to approximately 6.60 at t=48, with relatively mild fluctuations. In contrast, the low-disturbance curve maintains a relatively stable pressure level throughout, with a maximum value of approximately 7.22 and a minimum value of 6.81, demonstrating high system stability. The significant differences in the trends of these three types of curves near the start of sewage discharge at t=20 reveal the dynamic impact of different disturbance levels on system operation. Multiple inflection points and turning points in the figure visually demonstrate the pressure fluctuation response caused by sewage discharge behavior, providing a basis for disturbance intensity identification and strategy classification.

[0044] In this implementation plan, this step accurately identifies the current adsorption cycle stage of the system by real-time analysis and feature extraction of the operating pressure curve, assesses the intensity of disturbances caused by the discharge behavior, and promptly detects potential abnormal fluctuations, effectively improving the timeliness and accuracy of the control strategy. By transforming the curve trend characteristics into structured data results, it achieves coordinated support for discharge rhythm, energy-saving control, and fault early warning, providing a key basis for intelligent system adjustment and stable operation.

[0045] Specifically, the start-up and shutdown risk prediction based on operational status data involves the following steps: Obtain three key parameters—the start time of the discharge, the initial pressure, and the target pressure—to construct a start-up and shutdown risk score. The calculation process for the predicted start-up and shutdown risk value comprises two core parts, used to assess the impact of the discharge behavior on the operational stability of the air compressor. The first part calculates the derivative of the system pressure with respect to time at the start of the discharge, i.e., the instantaneous rate of change, reflecting the drastic nature of the current pressure change trend. This is then multiplied by a set rate weighting coefficient to quantify its contribution to the system's start-up and shutdown risk. The rate weighting coefficient is obtained through statistical correlation analysis of the first-order differential change amplitude of the pressure sensor output signal and the start-up and shutdown frequency of the air compressor; the value typically ranges from 0.3 to 0.7. The second part calculates the difference between the initial pressure and the target pressure, divides this difference by the target pressure to obtain the pressure deviation ratio, and then squares this ratio to enhance the sensitivity of the deviation intensity. This ratio is then multiplied by a deviation weighting coefficient to form an influence factor on the system's bias state. The deviation weighting coefficient is obtained by correlation statistical analysis of the deviation between the initial pressure and the target pressure before discharge and abnormal start-up and shutdown records of the air compressor; the value typically ranges from 0.5 to 0.9. Finally, the calculation results from the two parts are added together to obtain a comprehensive start-up and shutdown risk prediction value, providing a decision-making basis for whether the system should perform discharge, adjust valve control, and other subsequent strategies.

[0046] The specific calculation method for the start-stop risk prediction value is as follows:

[0047]

[0048] In the formula, This indicates the predicted value of start-up and shutdown risks. Represents the rate weighting coefficient. This represents the deviation weighting coefficient. Indicates the initial pressure. This indicates target pressure.

[0049] In this implementation plan, this step constructs a start-stop risk prediction value, comprehensively assesses the deviation between the pressure change trend at the start of sewage discharge and the system's target pressure, and quantifies the impact of sewage discharge behavior on the start-stop risk of the air compressor. This value serves as a crucial basis for coordinated regulation, enabling early prediction of potential start-stop shocks. It supports the system in dynamically adjusting the sewage discharge rhythm, valve opening, and execution logic when facing instability risks, thereby improving operational stability and the synergy of energy consumption control.

[0050] Specifically, based on the start-stop risk prediction results, it is further determined whether the sewage discharge behavior triggers the start-stop of the air compressor, and the sewage discharge rhythm, opening degree, and duration are dynamically adjusted. The specific steps are as follows: The start-stop risk prediction value is compared in real time with the set start-stop risk threshold, which is divided into two levels: Level 1 risk threshold and Level 2 risk threshold, to achieve graded response control. When the start-stop risk prediction value is greater than or equal to the Level 1 risk threshold, it is identified as a high-risk state, and the current sewage discharge operation must be immediately suspended until the system pressure returns to stability before it can be resumed. The opening degree of the sewage discharge valve is limited to within 30%, and if necessary, the sewage discharge valve is closed to avoid system fluctuations. At the same time, the air supply device is started and the air compressor is kept in a high-pressure loading state. If the sewage discharge cannot be skipped, it is changed to intermittent discharge with multiple small flow rates and a buffer time is set. This behavior is marked as a high-risk event, and an alarm is automatically triggered. If the alarm is triggered continuously, it switches to the low-frequency sewage discharge protection mode. If the predicted start-up / shutdown risk value is between the Level 1 and Level 2 thresholds, it is classified as a medium-risk state. The sewage discharge mode is adjusted to intermittent slow discharge, the maximum valve opening is limited to no more than 60%, and the discharge time is appropriately extended to reduce the disturbance intensity. If multiple adsorption towers discharge sewage, peak staggering is automatically implemented to avoid disturbance superposition. At the same time, the disturbance trajectory is recorded and a dynamic adjustment state is entered, continuously monitoring the pressure recovery. If the predicted start-up / shutdown risk value is less than or equal to the Level 2 risk threshold, it is classified as a low-risk state. The sewage discharge rhythm and control logic remain unchanged, the valves are fully open normally, the discharge time is not adjusted, the control system does not trigger intervention or activate protection logic, ensuring efficient and stable sewage discharge.

[0051] In this implementation plan, this step achieves dynamic identification and graded response to the potential start-up and shutdown risks of air compressors caused by sewage discharge by comparing the predicted start-up and shutdown risks with preset thresholds in real time. The sewage discharge rhythm, valve opening, and discharge duration are automatically adjusted according to the risk level. In high-risk situations, proactive suspension and flow restriction are implemented; in medium-risk situations, the rhythm is optimized and staggered execution is carried out; and in low-risk situations, the original strategy remains unchanged. This mechanism effectively avoids frequent start-up and shutdown of air compressors caused by sewage discharge operations, improving the stability of system operation and the safety of sewage discharge control.

[0052] Specifically, the system executes pollution control commands by integrating operational status data, pressure disturbance intensity analysis results, and start-up / shutdown risk prediction results. Simultaneously, the pollution control execution results and system response information are fed back to the status acquisition and energy efficiency assessment module for parameter rolling optimization and adaptive updating of the control strategy. The specific steps are as follows: First, key performance indicators such as baseline power, baseline operating time, optimized power, optimized operating time, baseline fluctuation standard deviation, and optimized fluctuation standard deviation are obtained to quantify the energy-saving effect and operational stability of the control strategy. The first part multiplies the baseline power by the baseline operating time, subtracts the product of the optimized power and optimized operating time, and obtains the energy-saving benefit of the current strategy compared to the default strategy. This is then divided by the product of the baseline power and operating time, plus a minimum correction term, to construct a normalized index as the input to the hyperbolic tangent function, used to control the nonlinear gain. The minimum correction term is used to prevent calculation anomalies caused by the denominator being zero or close to zero, ensuring formula stability, and is set as a constant. Even smaller, adjusted according to the system's numerical range. The second part subtracts the optimized fluctuation standard deviation from the baseline fluctuation standard deviation to obtain the system fluctuation improvement amount, which is further normalized and the square root is taken to form the fluctuation enhancement factor. Combining the two, an energy-saving and stability comprehensive value is calculated to measure the overall performance of the current strategy in terms of energy saving and stability. This value is compared in real time with energy-saving assessment thresholds, including primary and secondary energy-saving thresholds. When the overall energy-saving stability value is greater than or equal to the first-level threshold, it is judged as an inefficient state. The current parameter group is marked as not recommended, and the upper limit of the sewage valve opening is automatically tightened, the discharge time is shortened, and the sewage cycle is appropriately extended. If inefficiency persists, the parameters that performed better in the previous round are restored, and a conservative strategy mode can be entered. When the overall energy-saving stability value is between the second-level and first-level thresholds, it is judged as a medium-efficiency state. The current parameter group is temporarily retained, while the valve opening adjustment step size is reduced, the duration control logic is refined, and subsequent score changes are continuously tracked. If medium efficiency is maintained for two consecutive cycles, the parameter is retained as a candidate solution. When the overall energy-saving stability value is less than or equal to the second-level energy-saving threshold, the current strategy is judged to have both excellent energy-saving effect and operational stability. The parameter group is marked as a high-optimal solution and stored in the strategy library, set as a priority call configuration, and can be reused continuously under the premise of stable system operation. When multi-tower control capability is available, the strategy can also be applied to other towers and zones simultaneously to improve overall operational efficiency.

[0053] The specific calculation method for the comprehensive energy-saving stability value is as follows:

[0054]

[0055] In the formula, This represents the overall energy-saving and stable value. Indicates the reference power. Indicates the baseline running time. Indicates optimized power. This indicates optimized runtime. Indicates a minimal correction term. Indicates the standard deviation of the benchmark fluctuation. This represents the optimized standard deviation of fluctuation.

[0056] Table 1 shows the comprehensive energy-saving and stable value data provided in the embodiments of this application. In this embodiment, the baseline power of Scheme 1 is set to 100, the baseline operating time is set to 2.0, the optimized power is set to 88, the optimized operating time is set to 2.0, the baseline fluctuation standard deviation is set to 2.2, and the optimized fluctuation standard deviation is set to 3.5; the baseline power of Scheme 2 is set to 100, the baseline operating time is set to 2.0, the optimized power is set to 84, the optimized operating time is set to 2.0, the baseline fluctuation standard deviation is set to 3.1, and the optimized fluctuation standard deviation is set to 3.8; the baseline power of Scheme 3 .... The baseline power is set to 2.0, the optimized power is set to 82, the optimized running time is set to 2.0, the baseline fluctuation standard deviation is set to 2.4, and the optimized fluctuation standard deviation is set to 3.9. For Scheme 4, the baseline power is set to 100, the baseline running time is set to 2.0, the optimized power is set to 77, the optimized running time is set to 2.0, the baseline fluctuation standard deviation is set to 3.5, and the optimized fluctuation standard deviation is set to 4.0. For Scheme 5, the baseline power is set to 100, the baseline running time is set to 2.0, the optimized power is set to 70, the optimized running time is set to 2.0, the baseline fluctuation standard deviation is set to 3.9, and the optimized fluctuation standard deviation is set to 4.2.

[0057] Table 1. Comprehensive Energy Saving and Stability Value Data Table

[0058]

[0059] like Figure 3 The figure shows the distribution of the comprehensive energy-saving stability value provided in the embodiments of this application. According to the data in the image and table, the set primary energy-saving threshold is 0.4, and the secondary energy-saving threshold is 0.2. The comprehensive energy-saving stability value fluctuates significantly among the five schemes, ranging from 0.3442 to 0.5645. Scheme 3 has the highest comprehensive value at 0.5645, exceeding the primary energy-saving threshold, indicating that this scheme has low system energy efficiency and high volatility under the pollution control strategy. Scheme 1's Ec value also exceeds the primary threshold at 0.4913, similarly falling within the low-efficiency range. Schemes 2, 4, and 5 have Ec values ​​of 0.3442, 0.3550, and 0.3714, respectively, falling between the primary and secondary thresholds, reflecting moderate energy-saving and stability performance. This figure visually demonstrates the differences in energy efficiency among different control schemes during system operation, providing a quantitative basis for the dynamic optimization and selection of pollution control strategies.

[0060] In this implementation plan, this step calculates a comprehensive energy-saving stability value to evaluate the effectiveness of the current wastewater control strategy in terms of energy consumption and system fluctuations. Based on tiered thresholds, it achieves dynamic evaluation and adaptive updates of the strategy. The system automatically identifies high-efficiency, medium-efficiency, and low-efficiency strategy states based on the evaluation results, thereby adjusting wastewater parameters, optimizing control logic, and enabling the rolling retention and cross-tower reuse of high-performing strategies. This mechanism significantly enhances the closed-loop optimization capability of the control strategy, improving the overall energy-saving level and operational stability of the system.

[0061] like Figure 2 The diagram shown is a structural schematic of the air-separation pressure swing intelligent adsorption sewage discharge system based on pressure curves provided in this embodiment. The air-separation pressure swing intelligent adsorption sewage discharge system based on pressure curves provided in this embodiment of the application includes: a status acquisition module, a sewage discharge control module, a curve analysis module, a linkage adjustment module, and an energy efficiency assessment module. The system comprises four modules: a status acquisition module and a discharge control module. The former collects multi-dimensional status data in real-time, including pressure, valve status, and discharge timing. It performs preprocessing operations such as data cleaning, alignment, and normalization to ensure the accuracy and timeliness of subsequent analysis. The latter analyzes pressure disturbance intensity based on operational data, constructs disturbance intensity values, compares them with set thresholds, determines the discharge level, and matches corresponding control strategies to achieve graded response regulation. The former analyzes pressure curve trends in real-time, identifies the system's adsorption, desorption, and equalization stages, and extracts pressure change rates, fluctuation characteristics, and other key indicators to monitor abnormal behavior and assist in strategy adjustment. The latter, based on start-stop risk prediction, assesses the potential impact of discharge actions on the air compressor's start-stop stability, automatically adjusts the discharge rhythm, valve opening, and execution mode according to risk level to prevent amplified system fluctuations. The latter integrates system energy consumption data and operational fluctuations to calculate a comprehensive energy-saving and stable value, determines the strategy's effectiveness, and feeds the results back to the acquisition and regulation modules. This supports parameter rolling optimization and adaptive strategy updates, forming a complete closed-loop control logic.

[0062] In this implementation scheme, the system architecture, through a clearly defined modular design, achieves closed-loop control throughout the entire process, from data acquisition, disturbance identification, and risk prediction to strategy evaluation and optimization. The modules work collaboratively to ensure that pollution discharge behavior achieves energy-saving goals while maintaining system stability. The status acquisition and preprocessing module provides high-quality basic data, the curve analysis and linkage adjustment module enables dynamic identification and precise response, the pollution discharge control module implements tiered strategy matching, and the energy efficiency assessment module is responsible for continuous optimization and intelligent updates, thus comprehensively improving the system's intelligence level and control precision.

[0063] The air separation pressure swing intelligent adsorption sewage discharge system provided in this application embodiment applies an air separation pressure swing intelligent adsorption sewage discharge method based on pressure curves, including: a state acquisition and preprocessing unit, a disturbance intensity analysis unit, a curve feature recognition unit, a start-stop risk prediction unit, and a sewage discharge control and strategy optimization unit. The system comprises several key components: a status acquisition and preprocessing unit, a status acquisition and preprocessing unit, and a wastewater discharge execution path. The former acquires various operational data from the adsorption tower inlet and outlet, air compressor outlet, and wastewater discharge execution path, performing time synchronization, outlier removal, and normalization to provide a unified input structure for subsequent analysis. The latter, a disturbance intensity analysis unit, calculates disturbance intensity values ​​in real time based on the pressure change rate and amplitude fluctuations during wastewater discharge, classifies disturbance levels according to threshold levels, and triggers corresponding control logic. The former performs real-time analysis based on continuous pressure curves, identifies the current adsorption cycle stage, and extracts key indicators such as slope, fluctuation, deviation, and other critical indicators to detect abnormal fluctuations. The latter predicts start-stop risks by jointly modeling the pressure trend at the start of wastewater discharge and the target pressure deviation, outputting a start-stop risk score, and dynamically adjusting the wastewater discharge rhythm, valve opening, and execution method based on the score. The latter executes the corresponding control strategy after receiving the analysis unit's results, while simultaneously recording the system response in real time and feeding the execution results back to the parameter optimization mechanism. This enables continuous adjustment of control parameters and rolling updates of the strategy library, constructing a data-driven adaptive wastewater discharge control process.

[0064] In this implementation plan, the structure divides functions into five core units, constructing a logically clear and collaboratively efficient intelligent sewage discharge control process. Each unit forms a closed loop between data acquisition, disturbance identification, pressure analysis, risk prediction, and control execution, ensuring high-precision data-driven decision-making during system operation. Through joint determination of disturbance intensity and start-up / shutdown risk, the system can not only cope with different sewage discharge disturbances but also continuously optimize control parameters, achieving dynamic adjustment of sewage discharge behavior and adaptive achievement of energy-saving goals.

[0065] 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.

[0066] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart adsorption-based wastewater discharge method using pressure curves in conjunction with air separation pressure swing analysis, characterized in that: The method comprises the following steps: S1: collecting running state data in real time and preprocessing the running state data; S2: analyzing the pressure disturbance intensity of the running state data, judging the disturbance level of the current blowdown action according to the analysis result of the pressure disturbance intensity, and selecting a corresponding blowdown control strategy; The specific steps of analyzing the pressure disturbance intensity of the running state data are as follows: obtaining a time variable, a blowdown starting time, a blowdown duration, a blowdown instantaneous pressure and an average pressure before blowdown; by integrating the change rate of the blowdown instantaneous pressure within the blowdown duration, the time interval of integration starts from the blowdown starting time and lasts until the blowdown starting time plus the blowdown duration, within the interval, the derivative of the blowdown instantaneous pressure with respect to time is calculated and its absolute value is taken, which represents the rate of pressure change during the blowdown process; the rate is multiplied by a correction factor, which is composed of the ratio of the difference between the blowdown instantaneous pressure and the average pressure before blowdown to the average pressure before blowdown, the integral result represents the disturbance cumulative value within the entire blowdown process, and finally divided by the blowdown duration, the average disturbance intensity per unit time that the system receives, i.e. the pressure disturbance intensity value, is obtained; The specific steps of judging the disturbance level of the current blowdown action according to the analysis result of the pressure disturbance intensity and selecting a corresponding blowdown control strategy are as follows: real-time comparison of the pressure disturbance intensity value and the disturbance intensity threshold, the disturbance intensity threshold including a first-level disturbance threshold and a second-level disturbance threshold; when the pressure disturbance intensity value is greater than or equal to the first-level disturbance threshold, the blowdown operation is limited to execute, the blowdown valve opening is controlled within 30%, a segmented small flow release is adopted, the buffer time is prolonged, the air supplement and buffer device are enabled, the abnormal behavior is recorded and an alarm is triggered, and when the abnormal behavior occurs continuously, the blowdown mode is switched to a low-frequency mode and a manual review is prompted; when the pressure disturbance intensity value is greater than the second-level disturbance threshold and less than the first-level disturbance threshold, the blowdown is switched to a slow blowdown mode, the valve is intermittently opened and the maximum opening is not more than 60%, the emission time is appropriately prolonged, the automatic peak shifting is executed when multiple adsorption towers blowdown, the controller records the disturbance level and feeds back to the adjustment module; when the pressure disturbance intensity value is less than or equal to the second-level disturbance threshold, the original rhythm and opening of the blowdown are maintained, and the control system does not intervene; S3: identifying the adsorption cycle, evaluating the disturbance intensity and monitoring the abnormal fluctuation by real-time analysis and feature extraction of the pressure curve; The specific steps of identifying the adsorption cycle, evaluating the disturbance intensity and monitoring the abnormal fluctuation by real-time analysis and feature extraction of the pressure curve are as follows: obtaining the adsorption tower inlet and outlet pressure data and processing the pressure curve in real time, identifying the adsorption, desorption and equalization stages of the system, providing cycle reference for the blowdown strategy, extracting the pressure change rate through a sliding window, analyzing the amplitude, backfall and fluctuation characteristics of the curve before and after blowdown, extracting the deviation, slope and stability index, identifying the valve jam, back pressure abnormal out-of-control signs, comparing the current cycle with the historical typical cycle, judging the running deviation and assisting the strategy adjustment, and structuring all analysis results for use by the linkage adjustment and energy efficiency evaluation modules to support strategy judgment and parameter optimization. S4: Perform start-stop risk prediction on the operating state data, and further determine whether the blowdown behavior triggers the start-stop of the air compressor according to the start-stop risk prediction result, and dynamically adjust the blowdown rhythm, opening degree and duration; S5: Execute the blowdown control instruction based on the operating state data, the pressure disturbance intensity analysis result and the start-stop risk prediction result, and feed back the blowdown execution result and system response information to the state acquisition and energy efficiency evaluation, and perform parameter rolling optimization and adaptive update of the control strategy.

2. The pressure curve integrated air separation pressure swing intelligent adsorption purging method according to claim 1, characterized in that: The real-time acquisition of the operating state data and the preprocessing of the operating state data are specifically as follows: The operating state data includes time variable, blowdown start time, blowdown duration, blowdown instantaneous pressure, start pressure, average pressure before blowdown, target pressure, baseline power, baseline operation time, optimized power, optimized operation time, baseline fluctuation standard deviation and optimized fluctuation standard deviation; The time variable is acquired in real time and is time-stamped synchronously with other operating state data to form a complete time sequence; the blowdown start time is obtained through the trigger time stamp recorded automatically by the system when the blowdown control instruction is issued; the blowdown duration is obtained by recording the opening and closing time of the blowdown valve and calculating the time difference; the blowdown instantaneous pressure is obtained by synchronous real-time acquisition through the pressure sensor; the start pressure is obtained by real-time acquisition through the pressure sensor at the blowdown start time; the average pressure before blowdown is obtained by calculating the historical blowdown instantaneous pressure data continuously acquired by the pressure sensor before the blowdown start time; the target pressure is obtained by checking the air compressor equipment manufacturing parameters and calibrating and setting according to the long-term operation experience of the system; the baseline power is obtained by extracting the power acquisition data in the historical operation period and calculating the average value; the baseline operation time is obtained by extracting the time record corresponding to the period of the default blowdown strategy in the control system; the optimized power is obtained by real-time acquisition of data through the power acquisition device during the operation of the current control strategy and calculating the average value; the optimized operation time is obtained by counting the start and end time records corresponding to the current control strategy; the baseline fluctuation standard deviation is obtained by calculating the sample standard deviation of the power history sequence acquired in the operation period corresponding to the default blowdown strategy; and the optimized fluctuation standard deviation is obtained by calculating the sample standard deviation of the power data acquired in the operation period of the current blowdown optimization strategy; The preprocessing steps include: removing outliers from the acquired operating state data, identifying various data abnormal conditions including sensor value abnormality, sudden signal interference and continuous sampling interruption, and filling in the missing values by interpolation according to the abnormal position, and performing missing mark processing on the unrecoverable part; all variables are resampled by aligning with a uniform time step; continuous variables are normalized to the range of zero to one through maximum and minimum values; variables for fluctuation and trend analysis are subjected to standardization processing; and the feature values of each time period are extracted from the continuous data by setting a sliding window with a fixed length and moving by a step.

3. The pressure curve integrated air separation pressure swing intelligent adsorption purging method according to claim 1, characterized in that: The start-stop risk prediction on the operating state data is specifically as follows: The blowdown start time, start pressure and target pressure are obtained; The calculation process of the start-stop risk prediction value includes two parts. The first part is the derivative of the starting pressure with respect to time, that is, the instantaneous change rate of the system pressure at the start time of the blowdown, multiplied by the rate weight coefficient. The second part is to calculate the difference between the starting pressure and the target pressure, and then divide the difference by the target pressure to obtain the deviation proportion of the starting pressure with respect to the target pressure. Then, the proportion is squared to represent the strength of the deviation, and then multiplied by the deviation weight coefficient. The calculation results of the first two parts are added to obtain the start-stop risk prediction value.

4. The pressure curve integrated air separation pressure swing intelligent adsorption purging method according to claim 1, characterized in that: The specific steps of further judging whether the blowdown behavior triggers the start-stop of the air compressor according to the start-stop risk prediction result and dynamically adjusting the blowdown rhythm, opening degree and duration are as follows: Real-time comparison of the start-stop risk prediction value and the start-stop risk threshold, wherein the start-stop risk threshold includes a first-level risk threshold and a second-level risk threshold; When the start-stop risk prediction value is greater than or equal to the first-level risk threshold, the current blowdown is suspended, and after the pressure is restored, the blowdown valve opening degree is limited to within 30%, and the air supplement and high-pressure loading mode are enabled when necessary. If it cannot be skipped, multi-segment low-flow discharge is adopted with interval buffering; an alarm is triggered and manual prompting is performed, and when it occurs continuously, it is switched to a low-frequency blowdown and protection mode; When the start-stop risk prediction value is greater than the second-level risk threshold and less than the first-level risk threshold, the blowdown is adjusted to an intermittent slow blowdown mode, the valve opening degree is limited to not more than 60%, the discharge time is moderately extended, and when multiple adsorption towers are blowdown, automatic peak shifting is performed, the disturbance trajectory is recorded and dynamic adjustment is entered; When the start-stop risk prediction value is less than or equal to the second-level risk threshold, the original blowdown strategy remains unchanged, the valve is normally fully opened, the discharge duration is maintained by default, and the control system does not intervene or trigger compensation and protection logic.

5. The pressure curve integrated air separation pressure swing intelligent adsorption purging method according to claim 1, characterized in that: The specific steps of executing the blowdown control instruction based on the comprehensive operation state data, pressure disturbance intensity analysis result and start-stop risk prediction result, and feeding back the blowdown execution result and system response information to the state acquisition and energy efficiency evaluation for parameter rolling optimization and control strategy adaptive updating are as follows: Obtain the reference power, reference running time, optimized power, optimized running time, reference fluctuation standard deviation and optimized fluctuation standard deviation; The first part is to multiply the reference power by the reference running time, subtract the optimized power multiplied by the optimized running time to obtain the absolute magnitude difference of the current energy saving benefit, and then divide the difference by the product of the reference power and the reference running time plus a small correction term to obtain a normalized ratio. The ratio is used as the input of the hyperbolic tangent function; The second part is to subtract the optimized fluctuation standard deviation from the reference fluctuation standard deviation to obtain the fluctuation improvement amount, divide the difference by the reference fluctuation standard deviation plus a small correction term, and take the square root to obtain the fluctuation improvement factor; Finally, multiply the result of the hyperbolic tangent function part by one plus the fluctuation improvement factor to calculate the energy saving and stability comprehensive value; Real-time comparison of the energy saving and stability comprehensive value and the energy saving evaluation threshold, wherein the energy saving evaluation threshold includes a first-level energy saving threshold and a second-level energy saving threshold; When the start-stop risk prediction value is greater than or equal to the first-level risk threshold, the current blowdown is suspended, and after the pressure is restored, the blowdown valve opening degree is limited to within 30%, and the air supplement and high-pressure loading mode are enabled when necessary. If it cannot be skipped, multi-segment low-flow discharge is adopted with interval buffering; an alarm is triggered and manual prompting is performed, and when it occurs continuously, it is switched to a low-frequency blowdown and protection mode; When the start-stop risk prediction value is greater than the second-level risk threshold and less than the first-level risk threshold, the blowdown is adjusted to an intermittent slow blowdown mode, the valve opening degree is limited to not more than 60%, the discharge time is moderately extended, and when multiple adsorption towers are blowdown, automatic peak shifting is performed, the disturbance trajectory is recorded and dynamic adjustment is entered; When the start-stop risk prediction value is less than or equal to the second-level risk threshold, the original blowdown strategy remains unchanged, the valve is normally fully opened, the discharge duration is maintained by default, and the control system does not intervene or trigger compensation and protection logic. The specific steps of executing the blowdown control instruction based on the comprehensive operation state data, pressure disturbance intensity analysis result and start-stop risk prediction result, and feeding back the blowdown execution result and system response information to the state acquisition and energy efficiency evaluation for parameter rolling optimization and control strategy adaptive updating are as follows: Obtain the reference power, reference running time, optimized power, optimized running time, reference fluctuation standard deviation and optimized fluctuation standard deviation; The first part is to multiply the reference power by the reference running time, subtract the optimized power multiplied by the optimized running time to obtain the absolute magnitude difference of the current energy saving benefit, and then divide the difference by the product of the reference power and the reference running time plus a small correction term to obtain a normalized ratio. The ratio is used as the input of the hyperbolic tangent function; The second part is to subtract the optimized fluctuation standard deviation from the reference fluctuation standard deviation to obtain the fluctuation improvement amount, divide the difference by the reference fluctuation standard deviation plus a small correction term, and take the square root to obtain the fluctuation improvement factor; Finally, multiply the result of the hyperbolic tangent function part by one plus the fluctuation improvement factor to calculate the energy saving and stability comprehensive value; Real-time comparison of the energy saving and stability comprehensive value and the energy saving evaluation threshold, wherein the energy saving evaluation threshold includes a first-level energy saving threshold and a second-level energy saving threshold; When the energy-saving stability comprehensive value is greater than or equal to the first energy-saving threshold value, the current parameter is marked as not recommended, the upper limit of the valve opening degree is automatically reduced, the blowdown duration is shortened, and the period is appropriately extended, if the continuous low efficiency is recovered to the previous round of high score parameter, and the conservative mode can be switched to; When the energy-saving stability comprehensive value is greater than the second energy-saving threshold value and less than the first energy-saving threshold value, the current parameter is temporarily retained, the valve opening degree adjustment step is reduced, the duration control is more fine, the trend is continuously tracked, and if the medium efficiency state lasts for two rounds, it is retained as a candidate scheme; When the energy-saving stability comprehensive value is less than or equal to the second energy-saving threshold value, the current parameter is marked as a high-optimization scheme, stored in the strategy library and preferentially called, continuously reused under normal conditions, and when parallel control conditions are met, the strategy is applied to other tower bodies and partitions.

6. The air separation and pressure transformation intelligent adsorption blowdown system combined with a pressure curve, applying the air separation and pressure transformation intelligent adsorption blowdown method combined with a pressure curve according to any one of claims 1-5, comprising: The state acquisition module, the blowdown control module, the curve analysis module, the linkage adjustment module and the energy efficiency evaluation module are characterized in that: The state acquisition module is used for collecting real-time operation state data and pre-processing the operation state data; The blowdown control module is used for analyzing the pressure disturbance intensity of the operation state data, judging the disturbance level of the current blowdown action according to the pressure disturbance intensity analysis result, and selecting the corresponding blowdown control strategy; The curve analysis module is used for identifying the adsorption period, evaluating the disturbance intensity and monitoring the abnormal fluctuation through real-time analysis and feature extraction of the pressure curve; The linkage adjustment module is used for predicting the start-stop risk of the operation state data, further judging whether the blowdown behavior causes the air compressor to start-stop according to the start-stop risk prediction result, and dynamically adjusting the blowdown rhythm, opening degree and duration; The energy efficiency evaluation module is used for executing the blowdown control instruction by comprehensively considering the operation state data, the pressure disturbance intensity analysis result and the start-stop risk prediction result, and feeding back the blowdown execution result and system response information to the state acquisition and energy efficiency evaluation to perform parameter rolling optimization and control strategy adaptive update.

7. The air separation and pressure transformation intelligent adsorption pollution control device combined with the pressure curve, applying the air separation and pressure transformation intelligent adsorption pollution control method combined with the pressure curve according to any one of claims 1-5, comprising: The state acquisition and preprocessing unit, the disturbance intensity analysis unit, the curve feature identification unit, the start-stop risk prediction unit and the blowdown control and strategy optimization unit are characterized in that: The state acquisition and preprocessing unit is used for collecting adsorption tower, air compressor and blowdown related data, and completing alignment, cleaning and standardization processing; The disturbance intensity analysis unit is used for calculating the pressure disturbance intensity during blowdown and determining the disturbance level to trigger the corresponding control strategy; The curve feature identification unit is used for analyzing the pressure curve, identifying the adsorption stage and extracting the key change index, and monitoring the abnormal fluctuation; The start-stop risk prediction unit is used for evaluating the influence of blowdown on the operation of the air compressor, generating a risk score and adjusting the blowdown parameters; The blowdown control and strategy optimization unit is used for executing the blowdown control and feeding back the operation result, updating the control parameters and optimizing the strategy.

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