Intelligent monitoring method for argon recovery system

Abstract

The invention discloses an intelligent monitoring method for an argon recovery system, particularly relates to the technical field of gas purification adsorbent state monitoring, and is used for solving the problem of adsorbent recessive failure early warning lag caused by dependence on tail gas average concentration monitoring of an existing argon recovery system. The method comprises the following steps: collecting regeneration tail gas concentration time sequence data in real time, and extracting a slope change rate of a desorption rate rising section and an area integral value of a desorption peak attenuation section; constructing an area integral value sequence and calculating a symmetric deviation degree; identifying a pressure pulse disturbance time period to obtain effective time period data; calculating the fluctuation abnormality of the slope change rate offset and the symmetry deviation degree based on the effective data; when the two regeneration periods simultaneously exceed the threshold value and three continuous regeneration periods meet the condition, an adsorbent replacement early warning signal is generated, and early-stage accurate early warning of adsorbent performance degradation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas purification adsorbent state monitoring, and more particularly to an intelligent monitoring method for an argon recovery system. Background Art

[0002] In metal powder production systems, argon recovery systems circulate and purify impure gases (such as oxygen, nitrogen, and water vapor) through adsorption towers to maintain argon purity and recovery efficiency. Existing technologies use average concentration monitoring or peak threshold alarms in regenerated exhaust gas to determine adsorbent status. This method relies on macroscopic statistical values ​​of gas composition and cannot detect progressive deterioration of the adsorbent's microstructure (such as molecular sieve pore blockage and activated alumina crack growth), resulting in delayed maintenance decisions.

[0003] Existing monitoring methods have defects in identifying the degradation of adsorbent performance: the dynamic time series characteristics in the regenerated exhaust gas that are strongly correlated with the physical state of the adsorbent (such as desorption rate fluctuations and concentration waveform tailing effects) are not effectively extracted and utilized. This causes the system to trigger an alarm only when the argon purity drops significantly, and it is unable to provide early warning of hidden failure of the adsorbent, resulting in sudden shutdown and waste of argon. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent monitoring method for an argon recovery system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: An intelligent monitoring method for an argon recovery system comprises the following steps: S1. During the regeneration stage of the adsorption tower, real-time collection of concentration time series data of the regenerated tail gas; S2. Extracting the slope change rate of the desorption rate rising segment and the area integral value of the desorption peak decay segment from the concentration time series data; S3, continuously extracting the area integral values ​​of the current regeneration cycle and a preset number of previous regeneration cycles to construct an area integral value sequence; S4. Calculate the symmetric deviation between the area integral value of the current regeneration cycle and the mean of the area integral value sequence; S5. Identify the pressure pulse disturbance period in the regeneration phase, remove the slope change rate and area integral value in the pressure pulse disturbance period, and obtain effective period data; S6. Calculate the slope change rate offset and the fluctuation anomaly of the symmetric deviation based on the valid period data; S7. When the slope change rate offset exceeds a first threshold and the fluctuation abnormality exceeds a second threshold, an adsorbent replacement warning signal is generated.

[0006] Furthermore, during the regeneration phase of the adsorption tower, the concentration time series data of the regenerated tail gas is collected in real time, including: A mid-infrared laser gas analyzer is used to obtain the original signal of the regenerated exhaust gas concentration at a sampling frequency of not less than once per second; Synchronously receive the metal dust concentration detection results and pressure sensor data of the regeneration pipeline; Perform sliding mean filtering on the original signal of regenerated exhaust gas concentration to eliminate instantaneous noise interference; The filtered concentration signal, metal dust concentration detection results and pressure sensor data are associated with timestamps and stored as concentration time series data.

[0007] Furthermore, the slope change rate of the desorption rate rising section and the area integral value of the desorption peak decay section are extracted from the concentration time series data, including: Identify the starting and ending points of the desorption rate increase segment in the concentration time series data; The desorption rate rising section is divided into multiple sub-intervals at fixed time intervals, and the linear regression slope of the concentration change in each sub-interval is calculated. The standard deviation of the slopes of all sub-intervals is used as the slope change rate; Determine the start and end time of the desorption peak decay segment; The concentration values ​​and time intervals of all adjacent sampling points in the desorption peak attenuation section are accumulated in a trapezoidal manner, and the accumulated results are used as the area integral value.

[0008] Furthermore, the starting point is the moment when the concentration value exceeds the average concentration value for the first time, and the end point is the moment when the concentration value reaches the peak concentration for the first time; the starting time is the moment corresponding to the peak concentration, and the ending time is the moment when the concentration value falls back to the average concentration value.

[0009] Furthermore, the area integral values ​​of the current regeneration cycle and the previous preset number of regeneration cycles are continuously extracted to construct an area integral value sequence, including: Get the end time of the current regeneration cycle and the historical end time of the previous preset number of regeneration cycles; Retrieving the area integral value corresponding to the end time of each regeneration cycle from the stored concentration time series database; Verify the integrity of the area integral value, and arrange the qualified area integral values ​​in chronological order of the end time of the regeneration cycle to form an area integral value sequence.

[0010] Furthermore, the integrity of the area integral value is verified as follows: if the area integral value of the current regeneration cycle does not contain pressure pulse disturbance period data, and the area integral values ​​of the historical regeneration cycles are continuous and complete, then it is determined to be qualified data.

[0011] Furthermore, the symmetric deviation between the area integral value of the current regeneration cycle and the mean value of the area integral value sequence is calculated, including: Calculate the arithmetic mean of the area integral value sequence as the mean of the area integral value sequence; Determine the absolute difference between the area integral value of the current regeneration cycle and the mean of the area integral value sequence; Obtain the standard deviation of the area integral value sequence as the dispersion benchmark; Divide the absolute difference by the dispersion benchmark to obtain the preliminary deviation; The time decay factor of the cumulative running time of the adsorbent was introduced to correct the initial deviation and obtain a symmetrical deviation.

[0012] Furthermore, the pressure pulse disturbance period in the regeneration phase is identified, and the slope change rate and area integral value in the pressure pulse disturbance period are eliminated to obtain the effective period data, including: Obtaining the time series of pressure sensor data during the regeneration phase; Calculate the pressure change rate time series of pressure sensor data; identifying consecutive periods in the pressure change rate time series where the pressure change rate threshold is exceeded and the pressure sensor data exceeds the pressure absolute value threshold; When the duration of a continuous period is greater than the minimum duration threshold, it is marked as a pressure pulse disturbance period; In the time interval of the desorption rate rising section and the desorption peak decay section, the slope change rate and area integral value of the period overlapping with the pressure pulse disturbance period are eliminated; The uneliminated slope change rate and the uneliminated area integral value are combined into the effective period data.

[0013] Furthermore, the slope change rate offset and the fluctuation anomaly of the symmetric deviation are calculated based on the valid period data, including: Extract the slope change rate of the current regeneration cycle from the valid period data; Obtain the moving average of the slope change rate under historical normal operating conditions as a reference value for historical normal operating conditions; Calculate the absolute difference between the slope change rate of the current regeneration cycle and the historical normal operating condition reference value, and divide it by the historical normal operating condition reference value to obtain the slope change rate offset; Continuously extracting the symmetry deviation of the current regeneration cycle and the previous preset number of regeneration cycles; Calculate the standard deviation of the symmetric deviation sequence for a preset number of consecutive regeneration cycles; The standard deviation is divided by the arithmetic mean of the symmetric deviation degree sequence of a preset number of consecutive regeneration cycles to obtain the fluctuation abnormality degree of the symmetric deviation degree.

[0014] Furthermore, when the slope change rate offset exceeds a first threshold and the fluctuation abnormality exceeds a second threshold, an adsorbent replacement warning signal is generated, including: Get the current values ​​of the slope change rate offset and fluctuation anomaly; Reading a preset first threshold and a second threshold; Determine whether the slope change rate offset is greater than a first threshold, and simultaneously determine whether the fluctuation abnormality is greater than a second threshold; When the slope change rate offset is greater than the first threshold and the fluctuation abnormality is greater than the second threshold, the warning judgment condition is triggered; Repeated detection of warning determination conditions within three consecutive regeneration cycles; If the warning judgment conditions are met for three consecutive regeneration cycles, an adsorbent replacement warning signal is generated; The adsorbent replacement warning signal is associated with the end time of the current regeneration cycle and stored.

[0015] Compared with the prior art, the present invention has the following beneficial effects: First, by deeply analyzing the dynamic time series characteristics of the regenerated exhaust, early detection of adsorbent microstructural degradation is achieved. Extracting the slope change rate of the desorption rate rise segment can sensitively reflect desorption kinetic anomalies caused by molecular sieve micropore blockage. Calculating the area integral of the desorption peak decay segment can quantify the concentration decay hysteresis effect caused by crack propagation in the activated alumina. These two microscopic characteristics synergistically construct a two-dimensional assessment system for the physical state of the adsorbent, breaking through the perception bottleneck of traditional macroscopic statistical monitoring. Characteristic sequence analysis based on continuous regeneration cycles further reveals the gradual change trajectory of the adsorbent performance, enabling the system to identify hidden failure risks before the argon purity has significantly decreased. Second, the elimination of pressure pulse disturbances ensures the purity of characteristic data and avoids misjudgment caused by process fluctuations; the slope change rate offset reveals the degree of deviation of the current adsorption kinetics from the historical benchmark, and the fluctuation anomaly of the symmetry deviation characterizes the consistency degradation trend of the regeneration process; the combined trigger conditions of the two combined with continuous cycle verification can not only avoid single abnormal false alarms, but also capture the cumulative effect of progressive failures; the adsorbent replacement warning can be advanced to the critical performance attenuation period, effectively preventing sudden shutdowns, and optimizing the balance between argon recovery efficiency and adsorbent service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of an intelligent monitoring method for an argon recovery system. DETAILED DESCRIPTION

[0017] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example: Figure 1 The present invention provides an intelligent monitoring method for an argon recovery system, which includes the following steps: S1. During the regeneration stage of the adsorption tower, real-time collection of concentration time series data of the regenerated tail gas; S2. Extracting the slope change rate of the desorption rate rising segment and the area integral value of the desorption peak decay segment from the concentration time series data; S3, continuously extracting the area integral values ​​of the current regeneration cycle and a preset number of previous regeneration cycles to construct an area integral value sequence; S4. Calculate the symmetric deviation between the area integral value of the current regeneration cycle and the mean of the area integral value sequence; S5. Identify the pressure pulse disturbance period in the regeneration phase, remove the slope change rate and area integral value in the pressure pulse disturbance period, and obtain effective period data; S6. Calculate the slope change rate offset and the fluctuation anomaly of the symmetric deviation based on the valid period data; S7. When the slope change rate offset exceeds a first threshold and the fluctuation abnormality exceeds a second threshold, an adsorbent replacement warning signal is generated.

[0019] When performing concentration time series data collection during the regeneration phase of the adsorption tower, a mid-infrared laser gas analyzer is used to continuously acquire the original signal of the regenerated exhaust gas concentration at a sampling frequency of twice per second. The mid-infrared laser gas analyzer transmits a laser beam of a specific wavelength through the regeneration pipe to detect the absorption intensity of the laser by impurity gas molecules in the argon gas. The laser wavelength is set to 4.26 microns to match the characteristic absorption peak of carbon dioxide, and is equipped with an automatic gain control circuit to ensure stable light intensity reception in a metal dust environment. During each sampling, the photodetector inside the analyzer converts the optical signal into a voltage signal, which is then converted into a digital concentration original signal by a 16-bit analog-to-digital converter. This process meets the performance standard requirements of the IEC 61207 gas analyzer.

[0020] Two auxiliary parameters from the regeneration duct are received synchronously via the Modbus communication protocol: metal dust concentration is measured by a beta-ray dust monitor installed on the duct sidewall. This monitor calculates dust concentration by detecting the attenuation of beta-rays as they pass through the airflow; and pressure sensor data is collected by a piezoresistive sensor with a measurement range of 0 to 500 kPa and an accuracy level of 0.5. The dust monitor outputs its results every 200 milliseconds, and the pressure sensor refreshes its data every 100 milliseconds. Both timestamps are synchronized to the central processor's system clock.

[0021] When performing sliding mean filtering on the raw signal of regenerated exhaust gas concentration, the sliding window width is set to 10 sampling points. The specific processing process is as follows: the concentration values ​​of the current moment and the previous nine consecutive sampling points are taken to form a data window. The arithmetic mean of all concentration values ​​within this window is calculated, and this mean is used as the filtered concentration value at the current moment. Each time a new sampling point arrives, the oldest data point in the window is removed, the new point is added, and the arithmetic mean is recalculated. This continuous filtering process effectively suppresses transient impulse noise caused by metal powder splashing. For example, when a single sampling value deviates abnormally from the adjacent value by more than 30%, the deviation can be reduced to less than 5% after filtering.

[0022] The processed concentration signal, metal dust concentration test results, and pressure sensor data are linked and stored with millisecond-level time accuracy. This storage utilizes the hierarchical data structure of a time series database: the absolute timestamp is the primary key, and each record contains three fields: filtered concentration value (floating point, unit: ppm), dust concentration value (floating point, unit: mg / m³), and pressure value (floating point, unit: kPa). Timestamp synchronization accuracy is controlled within ±10 milliseconds, ensuring strict time alignment among the three data types. This concentration time series data is written to solid-state storage in real time, retaining the last 30 days of complete data for subsequent analysis.

[0023] Metal dust concentration measurement results are obtained as follows: The radioactive source built into the beta-ray dust monitor emits an electron beam. A Geiger counter on the opposite side of the pipe measures the beam's intensity decay and calculates the dust mass concentration based on the Beer-Lambert law. If the dust concentration exceeds 50 mg / m³, a compressed air backflush cleaning process is automatically triggered to prevent dust accumulation from affecting the laser analyzer's accuracy. Pressure sensor data is collected using a temperature compensation algorithm. A built-in thermistor monitors the sensor's temperature in real time. If the temperature changes by more than 5°C, the pressure reading is automatically corrected by 0.05% / °C to eliminate thermal drift errors.

[0024] The sliding mean filter's window width is set based on analyzing the noise spectrum characteristics of historical data, determining that the primary interference frequency is in the 5 Hz to 20 Hz range. Based on Shannon's sampling theorem, a 10-point window width (corresponding to a 0.5-second window width) effectively filters out noise in this frequency range. During system initialization, a noise baseline test is automatically performed: a 30-second background signal is collected without regeneration gas flow, the standard deviation of the background noise is calculated, and the window width is dynamically adjusted to cover at least three times the standard deviation.

[0025] Concentration time series data is stored using a ring buffer mechanism, allocating 200 megabytes of memory space. When the data volume reaches the storage limit, the oldest data block is automatically overwritten, and data older than 24 hours is compressed and transferred to the hard disk. Network time protocol synchronization is used for data association. The main controller synchronizes with the timing server every 10 minutes to ensure that the timestamp error between multiple devices is less than 1 millisecond. A timeout retransmission mechanism is set for the reception of dust concentration and pressure data: if no new data is received within 500 milliseconds, a retransmission request is automatically sent to the sensor, and the data for that period is marked as pending in the database.

[0026] The calibration process for the mid-infrared laser gas analyzer includes daily automated zero and span calibrations. Zero calibration involves introducing high-purity nitrogen into the optical path to reset the measured value to zero. Span calibration involves introducing a 1000 ppm concentration of carbon dioxide standard gas, and adjusting the gain factor to ensure the reading error is within ±2% of the reference value. Calibration data is stored separately in a calibration log. When light intensity attenuation exceeds 30% of the initial value, an optical window cleaning reminder is generated. The dust monitor's beta-ray source intensity is automatically checked monthly, triggering a warning to replace the radiation source when the source intensity drops below 80% of the initial value.

[0027] The pressure sensor is installed in the middle straight section of the regeneration pipeline, at least five pipe diameters away from the elbow to ensure a stable flow field. The sensor's pressure tapping hole has a diameter of 2 mm, with rounded edges to reduce eddy current interference. The axis of the pressure tapping hole is perpendicular to the airflow direction. Pressure is transmitted to the sensing diaphragm via a capillary tube filled with silicone oil as a pressure transmission medium to prevent dust from entering the sensing cavity. The sensor automatically performs zero drift calibration every 24 hours: the zero pressure value is recorded when the regeneration pipeline valve is closed, and this offset is deducted from subsequent measurements.

[0028] After obtaining the concentration time series data, the arithmetic mean of the exhaust gas concentration during the entire regeneration phase is first calculated as the baseline average concentration value. This average concentration value is calculated by extracting the concentration values ​​of all sampling points from the start to the end of the regeneration phase, adding these values, and dividing by the total number of sampling points. For example, if a regeneration phase lasts 1200 seconds and the sampling frequency is twice per second, the number of sampling points involved in the calculation is 2400. The calculated average concentration value serves as the baseline threshold for subsequent feature extraction.

[0029] When identifying the starting point of the desorption rate rise segment, the concentration time series data is scanned point by point in chronological order starting from the starting time point of the regeneration phase. The starting point judgment condition is: when the concentration value of a certain sampling point exceeds the benchmark average concentration value for the first time, and the concentration values ​​of the two subsequent consecutive sampling points remain above the benchmark average concentration value, the point is confirmed as the starting moment. This continuous verification mechanism can avoid misidentification caused by transient noise. The end point identification continues to scan backward from the starting moment. When it is detected that the concentration value reaches the global maximum value, and the subsequent three consecutive sampling points show a monotonically decreasing trend, the moment corresponding to the peak point is determined as the end point. The judgment range of the global maximum value is the data of the entire regeneration phase, which is achieved by comparing the concentration values ​​of all sampling points.

[0030] Within the determined desorption rate ramp-up period, multiple subintervals are divided at fixed time intervals. The time interval is set to 10 seconds, a value determined based on the adsorbent's desorption kinetics. Analysis of desorption rate test data for various molecular sieve materials indicates that a 10-second interval captures the main reaction trends while minimizing noise interference. Each subinterval contains 20 sampling points (calculated at a sampling frequency of two per second). A linear regression is performed on the concentration data within each subinterval, using time as the independent variable and concentration as the dependent variable, using the least squares principle to fit a straight line. The calculation process involves calculating the covariance of the time series and concentration series, dividing it by the variance of the time series, to obtain the linear regression slope for that subinterval. After calculating the slopes for all subintervals, the standard deviation of these slopes is calculated. The standard deviation is calculated using an unbiased estimator: first, sum the squares of the differences between each slope and the average slope, divide by the number of subintervals minus one, and then take the square root. This standard deviation represents the slope change rate, which indicates the stability of the desorption process.

[0031] When determining the desorption peak decay phase, the starting time is directly the time corresponding to the global maximum concentration. The end time is determined by scanning backward from the peak point. The end time is determined when the concentration value first falls below the baseline average concentration value, and when two subsequent sampling points are both below the baseline average concentration value. This continuous verification mechanism prevents premature termination due to data fluctuations.

[0032] When performing area integration on the desorption peak attenuation section, the trapezoidal method is used for cumulative calculation. The specific process is: traverse all adjacent pairs of sampling points in the section in chronological order. For any two adjacent points, calculate the area of ​​the trapezoid they constitute: take the average of the concentration values ​​of the two points, and multiply it by the time interval between the two points. For example, if the time of two adjacent points is t1 and t2, and the concentrations are c1 and c2, then the infinitesimal area is [(c1+c2) / 2]×(t2-t1). The infinitesimal areas of all adjacent point pairs in the attenuation section are accumulated to obtain the total area integral value. The time interval is the actual sampling interval. When the sampling frequency is twice per second, the fixed time interval is 0.5 seconds. Compared with the rectangular integration method, this trapezoidal integration method can more accurately reflect the nonlinear attenuation characteristics of concentration.

[0033] During feature extraction, an abnormal data handling mechanism is implemented: when missing concentration values ​​are detected within a subinterval, the concentration mean of the two preceding and following sampling points is automatically interpolated; if more than three consecutive sampling points are missing, the slope calculation for that subinterval is discarded. For the area integral of the decay segment, if the number of sampling points within the start and end time interval is less than 10, a data validity check is automatically triggered: the total concentration change for that segment is calculated. If the change is less than 20% of the average concentration value, the decay segment is deemed invalid, the area integral value extraction is abandoned, and a data anomaly log is generated.

[0034] The linear regression slope calculation includes a data validity check: if the standard deviation of the concentration values ​​within a subinterval is less than 1% of the average concentration value, the concentration change in that segment is considered insignificant, and the slope calculation is abandoned. The subinterval length is automatically extended to 20 seconds and the calculation is repeated. If the condition is still not met, the output slope is zero and the segment is marked as low activity. Baseline correction is added to the area integral calculation: the baseline average concentration value is deducted before integration. This means that the baseline average concentration value is subtracted from the concentration value of each sampling point before the area calculation is included in the calculation to eliminate the influence of background concentration.

[0035] The identification of the rising desorption rate phase is protected by a time boundary: if the starting point is within 5 seconds of the start of the regeneration phase, or the end point is within 5 seconds of the end, the time range is automatically expanded and the identification is repeated. If the boundary is still reached after the expansion, a backup identification algorithm is activated: a sequence of concentration change rates is calculated, starting with the moment the rate first exceeds 0.5 ppm / second and ending with the moment the rate falls below 0.2 ppm / second. This mechanism ensures that valid features can be obtained even when concentration data is abnormal.

[0036] Before outputting the slope rate of change, normalization is performed: the standard deviation is divided by the average slope value of the rising segment to convert it into a dimensionless parameter. The area integral value undergoes a dimensionless conversion: the integral result is multiplied by the sampling frequency to make the results comparable at different sampling rates. All eigenvalues ​​are stored with associated quality flags, including the number of subintervals for calculating the slope standard deviation of the rising segment, the number of sampling points for the decay segment integration, and an abnormal data handling flag. If the quality flag indicates that the percentage of valid data is less than 80%, the regeneration cycle eigenvalue is marked as suspect.

[0037] The feature extraction process monitors the computational load in real time. If the processing time of a single regeneration cycle exceeds 10 seconds, a simplified mode is automatically initiated: the subinterval length is extended to 20 seconds, and the trapezoidal integration sampling interval is increased to 1 second. Simplified mode triggers are recorded in the system log, and the feature values ​​are marked as fast calculation mode results. The system automatically generates a daily feature extraction quality report, which summarizes the slope change rate calculation failure rate and the area integral value abnormality rate. If the failure rate exceeds 15% for three consecutive days, a sensor calibration reminder is triggered.

[0038] Global peak concentration detection utilizes a local maximum validation method: a valid peak is identified when the concentration at a sampling point is higher than both the previous and next points, and the difference between this value and the previous point is greater than three times the noise threshold. The noise threshold is calculated using 1.5 times the standard deviation of the concentration during the 30 seconds before regeneration. This mechanism prevents small fluctuations from being mistaken for peaks. In the presence of multiple peaks, the highest peak is selected as the starting point of the desorption peak decay phase, and the remaining peaks are recorded as auxiliary features and stored in the database.

[0039] The time interval parameter optimization mechanism involves automatically analyzing historical data quarterly and calculating characteristic stability indicators at different time intervals. This stability indicator is defined as the coefficient of variation of the characteristic values ​​over 10 consecutive cycles for the same adsorption tower. The interval that minimizes the coefficient of variation of the slope change rate is selected as the new parameter, and the update range is limited to between 5 and 30 seconds. The interval for area integration is fixed and tied to the sampling frequency and is not dynamically adjusted. All parameter changes are recorded in the system configuration file, and the last set of characteristic values ​​before the change is saved as a baseline reference.

[0040] When obtaining the end time of the current regeneration cycle, read the regeneration end signal trigger timestamp recorded by the adsorption tower controller. The timestamp accuracy is in milliseconds and is generated by the programmable logic controller when the regeneration valve closing action is detected. The historical end time of the previous preset number of regeneration cycles is obtained by querying the historical operation log. The initial default value of the preset number is 8 cycles. This value is determined based on the typical aging cycle of the adsorbent: through statistical analysis of the continuous operation data of the adsorption tower, it is found that when the adsorbent performance decays, the area integral value usually shows a trend change within 8 consecutive cycles. The preset number can be dynamically adjusted in the system parameters, and the adjustment range is 5 to 12 cycles. It is automatically matched according to the adsorbent type: molecular sieve adsorbents are set to 10 cycles, and activated carbon adsorbents are set to 6 cycles.

[0041] When retrieving area integral values ​​from a concentration time series database, a timestamp-based exact matching mechanism is established. The concentration time series database uses a time-partitioned storage structure, with each regeneration cycle's data stored independently in a data partition named by the end time. The search first locates the target partition, then searches within that partition for data records tagged "area integral value." Each record contains three key fields: integral value (floating point), calculation status flag (integer), and time interval start and end timestamps (8 bytes each). When multiple records with the same tag are retrieved, the record with the most recent timestamp is automatically selected as the valid value.

[0042] A two-level check is performed when verifying the integrity of the area integral value. The first level checks the current regeneration cycle data: checks the "pressure pulse disturbance mark" associated with the area integral value of the cycle. The mark is generated by step S5 and stored in the database. A mark value of 0 indicates that the pressure pulse disturbance period data is not included, and a mark value of 1 indicates that disturbance data is included. The check is passed when the mark value is 0. The second level checks the historical data: the continuity of the area integral value within a preset number of cycles before the check. The continuity judgment standard is: the interval between the end times of two adjacent regeneration cycles is within the range of plus or minus 15% of the standard regeneration cycle length, and there are valid area integral value records for all cycles. The standard regeneration cycle length is the average regeneration time of the last 30 cycles. If the current cycle passes the first level check and the historical cycle passes the second level check, the area integral value is judged to be qualified data.

[0043] When data integrity requirements are not met, an automatic compensation mechanism is activated. If the current cycle is deemed unqualified due to the presence of disturbed data, the data is traced back one cycle: the current value is replaced with the area integral value of the previous cycle and marked as replacement data in the sequence. If the historical data is discontinuous, gap filling is performed: the moving average of the area integral values ​​of the two cycles before and after the gap is calculated as the filling value. If the gap exceeds three cycles, the sequence construction is terminated and a data anomaly alert is generated.

[0044] When constructing the area integral value sequence, qualified area integral values ​​are sorted by the end time of their regeneration cycles. Sorting is in ascending order, with the area integral value corresponding to the earliest end time placed at the beginning of the sequence. The sequence is stored as a dynamic array structure containing the following metadata: sequence generation timestamp, number of regeneration cycles included, and a mapping between the end time of each cycle and the area integral value. The sequence length dynamically adapts to the preset number and is automatically reconstructed when the preset number changes.

[0045] Quality monitoring metrics are set during the sequence construction process. Key metrics include data integrity (the ratio of qualified data to total data volume) and historical data continuity (the longest consecutive valid cycles). When the data integrity rate falls below 90%, the preset number of qualified data is automatically reduced to the current qualified data volume, but with a minimum of five cycles. When the historical data continuity falls below 80% of the preset number, a data continuity check command is triggered: the operation logs of the previous 30 cycles are scanned, abnormal downtime events are identified, and associated tags are generated.

[0046] Sequence data storage utilizes a dual-backup mechanism. A primary copy is stored in an in-memory database, retaining the 10 most recently constructed sequences for real-time analysis. A secondary copy is stored in a disk-based database, archiving historical sequence data on a daily basis. Sequences in the in-memory database are persisted every 10 minutes to prevent data loss due to system power outages. The sequence access interface provides time range queries, allowing retrieval of historical sequences by the end of a regeneration cycle.

[0047] The optimization mechanism for the preset number of cycles is as follows: the system automatically analyzes the stability of the sequence monthly. The coefficient of variation (standard deviation divided by mean) of the sequence is calculated for different preset numbers, and the value with the lowest coefficient of variation is selected as the new preset value. The optimization process is constrained by the requirement that the preset number of cycles must not be less than 5 and not more than 20. The optimization results are recorded in the system configuration log, and the last set of sequences before the optimization is saved as a baseline.

[0048] Standardized unit processing for area integral values: Dimensional normalization is automatically performed during the sequence construction phase. Raw integral values ​​(unit: ppm × second) are converted to a percentage scale by taking the maximum area integral value from the last 100 cycles as a baseline, dividing the current value by the baseline value and multiplying it by 100. The converted values ​​range from 0 to 100, eliminating magnitude differences between adsorption towers. The conversion factor is stored in the device configuration file. If the area integral value exceeds the baseline value for 10 consecutive cycles, the baseline value is automatically updated and the historical data is reconverted.

[0049] After receiving a sequence of area integral values, the arithmetic mean of the sequence is calculated as the mean. This calculation is performed by summing all area integral values ​​in the sequence and dividing the resulting sum by the number of regeneration cycles in the sequence. For example, if the sequence contains data for eight cycles, the sum of the eight area integral values ​​is divided by 8. The arithmetic mean is calculated using double-precision floating-point arithmetic, and intermediate results are rounded to 16 significant digits to avoid roundoff errors. If the sequence is empty, the calculation terminates immediately and returns a null error code.

[0050] Strict data type checking is performed when determining the absolute difference between the area integral value of the current regeneration cycle and the mean of the area integral value sequence. The current area integral value is obtained from the most recent record generated in step S2, and the sequence mean is obtained using the above calculation. The absolute difference is the absolute value of the sequence mean minus the current value, ensuring that the result is always non-negative. Overflow protection is set during the calculation: when the absolute value of the difference exceeds 10 times the maximum value of the sequence, the raw data review process is automatically triggered to verify the dimensional consistency of the area integral value.

[0051] A statistically unbiased estimation method is used to obtain the standard deviation of a series of area integral values. The specific process is to first calculate the square of the difference between each area integral value in the series and the series mean, sum these squared values, divide them by the number of series periods minus one, and finally take the square root of the quotient. The standard deviation indicates the degree of dispersion of the series data and serves as a benchmark for subsequent normalization. To enhance stability in the calculation process, when the number of series periods is less than 5, the standard deviation is fixed at an empirical value of 0.15, which is derived from the 90th percentile of the distribution of the minimum standard deviation in historical data analysis.

[0052] Divide the absolute difference by the dispersion benchmark to obtain the preliminary deviation. Before this division, perform a non-zero divisor check: if the standard deviation is less than 0.0001, reset the dispersion benchmark to 5% of the series mean to avoid division by zero errors. The preliminary deviation is a dimensionless parameter, and the calculation result is rounded to four decimal places. If the preliminary deviation exceeds 10.0, it is automatically marked as an extreme outlier, triggering the data traceability mechanism: recheck the original data calculated for the area integral value of that period.

[0053] When introducing a time decay factor to correct for initial deviations, the cumulative adsorbent operating time is first determined. This operating time is obtained by reading the total operating hours of the adsorption tower from the equipment control system, accurate to the nearest tenth of an hour. The time decay factor is calculated using an inverse proportional function model: the decay factor is equal to 1 divided by (1 plus the cumulative operating time multiplied by the decay factor). The decay factor is initially set to 0.00015 per hour. This factor is based on the aging rate of the molecular sieve adsorbent: accelerated life testing has shown that when the adsorbent capacity decays by 20%, the deviation tolerance needs to be increased by a factor of 1.5.

[0054] The correction calculation process involves multiplying the initial deviation by the time decay factor to obtain the symmetric deviation. This calculation model implements an aging compensation effect: as the operating time increases, the same initial deviation will produce a smaller symmetric deviation, reflecting the expected impact of natural adsorbent performance degradation. The symmetric deviation result is stored as a percentage value ranging from 0 to 100, and automatically resets to the limit value if it exceeds the range.

[0055] The dynamic adjustment mechanism for the time decay factor involves performing a coefficient calibration every six months. This calibration method involves selecting symmetrical deviation samples from the same adsorption tower in both new and retired states, establishing a curve between operating time and deviation, and updating the decay factor based on the slope of the curve. The calibration process limits the decay factor change to within ±30% of the original value to prevent sudden changes in the parameter.

[0056] The calculation process includes multiple data validations. Before obtaining the sequence mean, the time validity of each area integral value in the sequence is verified: the corresponding regeneration cycle end time is checked to ensure that it is before the current system time, preventing future data from being mixed in. When calculating the standard deviation, outliers that deviate from the mean by more than three standard deviations are automatically excluded and replaced by the moving average of the two preceding and following cycles.

[0057] Before outputting the symmetric deviation, smoothing is performed: the moving average of the three most recent calculations is used as the final output. If a single calculation result differs from the previous by more than 20%, a recalculation is initiated: the original sequence data is reacquired, and a full recalculation log is recorded. The final result is associated with the following metadata: calculation timestamp, number of sequence cycles used, time decay factor version number, and data verification status flag.

[0058] An exception handling mechanism is configured for obtaining the accumulated adsorbent runtime. If a read failure occurs from the device control system, a backup timer is activated: the accumulated runtime is equal to the average duration of the last 200 regeneration cycles multiplied by the total number of completed regeneration cycles. The backup timer synchronizes with the main system every 24 hours, triggering a clock synchronization alarm if the error exceeds 2 hours. The runtime is stored as an unsigned long integer in units of 0.1 hours, with a maximum recordable time of 1 million hours.

[0059] Historical data storage for symmetric deviation utilizes differential compression technology. A full value is stored for every 10 cycles, while only the difference between the cycles and the previous one is stored for the intermediate cycles. During reading, the data sequence is reconstructed by accumulating the differences, saving over 50% of storage space. A temperature compensation factor is also associated with storage: when the ambient temperature exceeds 40°C, the symmetric deviation value is automatically reduced by 0.1% / °C to compensate for the temperature effect on adsorption performance.

[0060] When acquiring the time series of pressure sensor data during the regeneration phase, a data stream labeled "pressure value" is extracted from the concentration time series database established in step S1. The pressure value is in kilopascals, the sampling frequency is 10 times per second, and the timestamp accuracy is at the millisecond level. The data extraction range is limited to the time interval from the start signal to the end signal of the regeneration phase. The start signal is the moment when the regeneration valve opening instruction is issued, and the end signal is the moment when the regeneration valve closing is confirmed. When continuous missing pressure data is detected, the cubic spline interpolation method is automatically used to complete the data. The maximum allowable completion time is 2 seconds. If this time is exceeded, the pressure data of the regeneration cycle is marked as invalid.

[0061] When calculating the pressure rate of change time series, a central difference method is used for real-time processing. The specific calculation method is: for the i-th sampling point in the pressure sequence, the pressure rate of change is equal to the pressure value at the i+1-th sampling point minus the pressure value at the i-1-th sampling point, divided by twice the time interval. The time interval is fixed at 0.1 seconds (corresponding to a sampling frequency of 10 Hz), and the calculated result is expressed in kilopascals per second. At the 1-second boundary between the first and last points of the sequence, forward or backward differencing is used instead: the first point is calculated by subtracting the first point from the second point divided by the time interval, and the last point is calculated by subtracting the second-to-last point from the last point divided by the time interval. All rate of change values ​​are stored as double-precision floating-point sequences, strictly aligned with the original pressure value timestamps.

[0062] When identifying pressure pulse disturbance periods, triple threshold conditions are applied simultaneously. The pressure change rate threshold is set to 50 kPa per second, which is determined based on statistical analysis of historical disturbance events: 80% of the minimum change rate in 100 typical disturbance events is taken as the safety threshold. The absolute pressure value threshold is set to 300 kPa, corresponding to 75% of the design pressure of the regeneration pipeline. The minimum duration threshold is set to 100 milliseconds to filter out transient interference. The identification process uses a sliding window mechanism: when it is detected that the pressure change rate of three consecutive sampling points exceeds the threshold and the absolute pressure value exceeds the threshold, it is marked as the starting point of the potential disturbance period; when two subsequent consecutive sampling points do not meet the conditions, it is marked as the end point. The period between the starting point and the end point is recorded as the candidate disturbance period.

[0063] The candidate disturbance period is validated. If the duration of the candidate period exceeds the minimum duration threshold, the pressure variation characteristics within the period are further examined: the integral value of the pressure change rate within the period is calculated. If the integral value exceeds 500 kPa, it is confirmed as a valid pressure pulse disturbance period. The disturbance period information is stored as a two-tuple data (start timestamp, end timestamp) and associated with the disturbance intensity index (the ratio of the maximum pressure change rate to the average pressure within the period).

[0064] Data removal is performed within the time interval of the desorption rate rising segment and the desorption peak decay segment. First, the precise time boundaries of the two characteristic segments are obtained from step S2: the desorption rate rising segment is from the starting moment to the end moment, and the desorption peak decay segment is from the starting moment to the end moment. A time interval overlap detection algorithm is established: for each characteristic segment and pressure pulse disturbance period, the duration of the time intersection of the two is calculated. When the intersection duration is greater than 10 milliseconds, it is determined that there is an overlap. In the overlapping period, the slope change rate and area integral value removal operation is performed: if the desorption rate rising segment overlaps with the disturbance period, the corresponding slope change rate in the rising segment is marked as invalid; if the desorption peak decay segment overlaps with the disturbance period, the area integral value of the decay segment is marked as invalid.

[0065] The unremoved slope rate of change and unremoved area integral values ​​are combined to form valid period data. This valid period data is stored in a structured format and contains three core fields: valid slope rate of change (floating point), valid area integral value (floating point), and characteristic segment status flag (integer). The status flag encoding rule is: 00 indicates both characteristic segments are valid, 01 indicates the rising segment is invalid, 10 indicates the decaying segment is invalid, and 11 indicates both segments are invalid. When status 11 occurs, the supplemental data collection mechanism is automatically triggered: high-precision monitoring is prioritized during the next regeneration cycle.

[0066] The dynamic optimization mechanism for pressure threshold parameters involves monthly statistical analysis of pressure pulse disturbance events. The maximum pressure value, maximum rate of change, and duration of each event are recorded, and the 95th percentile of these parameters is used as the new threshold. Constraints are set during the optimization process: the pressure rate of change threshold must be adjusted within ±20% of the original value, and the absolute pressure threshold must not exceed 85% of the pipeline design pressure.

[0067] A safeguard is implemented during the data removal process: When the percentage of a single characteristic segment removed exceeds 50%, a segment retention mechanism is activated. This characteristic segment is divided into 10 equal-length subintervals. Only subintervals that completely overlap with the disturbance period are removed, while data from partially overlapping subintervals is retained. For example, for a rising desorption rate segment, the average slope of the non-overlapping subintervals is calculated as a surrogate value.

[0068] Added noise filtering for pressure rate of change calculations: When pressure jumps exceeding 100 kPa between adjacent sampling points are detected, median filtering preprocessing is automatically enabled. A three-point sliding window median filter is used, taking the median value between the current point and the points before and after it as the effective pressure value. This filtering process is recorded and marked for subsequent data analysis.

[0069] After valid period data is generated, integrity verification is performed. Verification rules include: the valid slope change rate must be within the historical normal range (0.1 to 5.0 ppm / second), and the valid area integral value must be greater than 50 ppm / second. If data exceeds the range, it is automatically linked to the metal dust concentration data for that regeneration cycle. If the dust concentration exceeds the threshold, it is marked as a dust interference event; otherwise, it is marked as a device abnormality.

[0070] When extracting the slope change rate for the current regeneration cycle from the valid period data, access the structured data record generated in step S5. The valid data source is selected based on the feature segment status flag: when the flag is 00 or 10, the valid slope change rate field is read directly; when the flag is 01 or 11, the backup data acquisition mechanism is enabled - retrieving the moving average of the slope change rate for the last three undisturbed regeneration cycles as an alternative value. A unit consistency check is performed on the extracted data: the slope change rate unit is ensured to be ppm / second. If a dimensional discrepancy is detected, it is automatically converted to the standard unit of ppm / minute by multiplying by 60, and the conversion operation is recorded in the data log.

[0071] When obtaining the historical normal operating condition reference value, the historical normal operating condition is defined as the set of cycles in the first 100 regeneration cycles with a symmetrical deviation less than 1.0. The moving average calculation uses a rolling window mechanism: the window size is fixed at 20 cycles, and the calculation is recalculated each time it rolls forward one cycle. The specific calculation process is: within the normal operating condition cycle set, the slope change rate of the latest 20 cycles is taken in reverse chronological order, and the arithmetic mean is calculated as the reference value. The reference value is updated once an hour, and the data of the cycle being regenerated is excluded during the update. When the number of normal operating condition cycles is less than 20, the window size is automatically reduced to the actual number but not less than 5 cycles. If it is less than 5, the historical data expansion alarm is triggered.

[0072] When calculating the slope change rate offset, a three-step calculation process is performed. First, the absolute difference between the current slope change rate and the historical normal operating condition reference value is calculated, and the absolute value is taken to ensure that the result is non-negative. Secondly, divisor protection is performed: when the historical normal operating condition reference value is less than 0.01, the reference value is reset to 1.1 times the historical minimum slope change rate of the adsorption tower. Finally, the absolute difference is divided by the historical normal operating condition reference value, and the result is expressed as a percentage. For example, if the current value is 0.5ppm / second and the reference value is 0.45ppm / second, the offset is (|0.5-0.45| / 0.45)×100=11.11%. The calculation result range is limited to 0 to 1000%. If it exceeds, it will be automatically truncated and marked as an extreme value.

[0073] When extracting symmetric deviations continuously, the preset number of cycles is consistent with the sequence construction parameters in step S3. The search scope is limited to the current regeneration cycle and the preset number of cycles preceding it, with the cycle status marked as "Complete Calculation." A temporal sequence check is performed during extraction: the end times of each symmetric deviation corresponding to the regeneration cycle are checked to ensure strict continuity. If an anomaly in the time interval is detected, the system automatically traces back to the most recent consecutive cycle group to ensure that the extracted symmetric deviation sequence meets the temporal continuity requirements.

[0074] To calculate the standard deviation of a symmetric deviation series, a two-stage data processing process is employed. The first stage eliminates outliers: the initial mean and standard deviation of the series are calculated, and data points that deviate from the mean by more than three standard deviations are removed. The second stage recalculates the standard deviation on the cleaned series using an unbiased estimation formula: the sum of the squares of the differences between each symmetric deviation and the mean, divided by the number of data points minus one, and then taking the square root. To ensure computational stability, when the series has fewer than three periods, the standard deviation output is zero and flagged as a low-confidence result.

[0075] To calculate the volatility anomaly, we first calculate the arithmetic mean of the symmetric deviation series: sum all series values ​​and divide by the number of periods. A divisor check is then performed: if the arithmetic mean is less than 0.01, it is reset to 0.01 to prevent division by zero errors. The final volatility anomaly is equal to the standard deviation divided by the arithmetic mean. This dimensionless parameter represents the relative intensity of volatility. The result is converted to a percentage and stored; for example, a raw value of 0.15 represents a 15% volatility level.

[0076] The historical normal operating reference value update mechanism includes a self-learning function. A monthly reference baseline assessment is performed: the current reference value is compared with the distribution of the actual slope change rate over the last 30 cycles. When the median of the actual value deviates from the reference value by more than 20%, a reference value recalibration is initiated: the normal operating condition filter is expanded to the previous 200 cycles and the moving average is recalculated. This calibration process lasts for up to three rounds; if convergence is still not achieved, an adsorption characteristic drift warning is generated.

[0077] The symmetry deviation sequence is stored using incremental encoding. Each regeneration cycle stores only the difference from the previous cycle, while the absolute value is stored for the initial cycle. During readout, the complete sequence is reconstructed by accumulating the differences, saving 70% of storage space. A temperature compensation factor is associated with the stored value: when the ambient temperature exceeds 25°C, the symmetry deviation is corrected by 0.5% per degree Celsius to eliminate the effects of temperature on adsorption equilibrium.

[0078] Dynamic smoothing has been added to the calculation of volatility anomalies. A weighted average of the three most recent calculations is taken: 50% for the current period, 30% for the previous week, and 20% for the previous two weeks. If a single result differs from the previous by more than 30%, a data review is initiated: the original symmetric deviation sequence is re-extracted, and the review process is fully documented. The final result output includes a confidence level calculated based on the number of data periods used and quality markers. A confidence level below 60% triggers additional data collection.

[0079] The slope change rate offset adaptively adjusts its range by compiling historical offset distribution data and updating the displayed range quarterly. For example, when the historical maximum offset is 150%, the displayed range is set from 0% to 200%. If a value exceeds 200%, the range is automatically expanded to 300%. This mechanism ensures data visibility at different aging stages.

[0080] When obtaining the current values ​​of the slope change rate offset and fluctuation anomaly, a data validity verification mechanism is established. The slope change rate offset extracts the latest record from the output cache of step S6, and the fluctuation anomaly obtains real-time calculation results from the symmetric deviation analysis module. Before extraction, the data timestamp is verified to ensure that both correspond to the end of the same regeneration cycle and that the time deviation does not exceed 5% of the average regeneration cycle duration. If a value out of bounds is detected (offset > 1000% or anomaly > 100%), the original data review process is automatically triggered: the calculation in step S6 is re-executed and the review log is recorded.

[0081] When reading the preset first and second thresholds, the dynamic configuration table in the system parameter database is accessed. The initial value of the first threshold is 25%, which is set based on the adsorbent performance attenuation model: determined through laboratory accelerated aging tests, when the adsorbent capacity decreases by 15%, the typical offset is 25±3%. The initial value of the second threshold is 30%, based on the statistical distribution of historical normal operating condition fluctuation anomalies: the 90th percentile of the maximum fluctuation anomaly value for the first 100 cycles is taken. The threshold storage format is floating point and supports runtime modification: after modification, the operator information and modification time are automatically recorded.

[0082] When judging dual-threshold conditions, atomic transactions are used to ensure logical integrity. First, the current slope change rate offset value is locked, and the first threshold comparison is performed. If the offset is greater than 25%, condition A is marked as true. Then, the current fluctuation anomaly value is locked, and the second threshold comparison is performed. If the anomaly is greater than 30%, condition B is marked as true. The dual-condition judgment is completed within 10 milliseconds, avoiding inconsistent states caused by data updates. When both conditions A and B are met, a warning trigger event is generated. Event information includes the trigger time, the actual offset value, and the actual anomaly value.

[0083] The continuous cycle verification mechanism is initiated after the early warning judgment condition is triggered. A cycle counter is created with an initial value of 1. At the end of each subsequent regeneration cycle, the dual threshold judgment is repeated: if the condition is met again, the counter is incremented; if the condition is not met, the counter is reset to zero. A state retention function is set during the continuous verification period: if a cycle cannot be judged due to invalid data (such as pressure pulse disturbance), the counting state of the previous cycle is retained. When the counter reaches 3, an adsorbent replacement warning signal is generated and the counter is reset.

[0084] A multi-level warning system is defined when generating an adsorbent replacement warning signal. The basic signal contains three fields: warning level (enumeration type: mild / moderate / severe), trigger time (the end of the regeneration cycle), and diagnostic summary (text type). Level determination rules: When the offset is between 25% and 35% and the abnormality is between 30% and 40%, it is mild; when the offset is between 35% and 50% or the abnormality is between 40% and 60%, it is moderate; and the rest are severe. The diagnostic summary is automatically generated by splicing key parameter values ​​and trigger condition descriptions, for example, "Offset 28.6% > 25%, abnormality 33.2% > 30%, and the standards have been met for three consecutive cycles."

[0085] Signal storage utilizes a spatiotemporal correlation architecture. The time dimension converts the regeneration cycle end time into a UNIX timestamp (accurate to the millisecond) as the primary key. The spatial dimension associates the adsorption tower number with the adsorbent batch number. Storage implements a dual-write operation: real-time data is written to an in-memory database for access by the monitoring system and asynchronously persisted to a disk database. Records are retained in the in-memory database for seven days, while the disk database stores them permanently. Environmental context is also included during storage, including auxiliary parameters such as the average temperature during the regeneration phase and peak metal dust concentration.

[0086] The dynamic threshold calibration mechanism is automatically performed quarterly. Calibration data sources are historical warning records and adsorbent test reports. The minimum offset / anomaly value during actual adsorbent replacement cycles is calculated and used as the new threshold baseline. The first threshold update rule: 80% of the baseline value is used as the new threshold (no less than 20%). The second threshold update rule: 90% of the baseline value is used as the new threshold (no less than 25%). Changes exceeding 10% of the original value require manual confirmation.

[0087] Continuous cycle verification settings are interrupted. If valid data cannot be obtained for two consecutive cycles (e.g., equipment downtime for maintenance), verification is suspended and an interruption log is generated. After the system resumes operation, the count restarts from the most recent valid cycle. If the cumulative interruption exceeds five cycles, the verification process is forced to reset.

[0088] The generation of an early warning signal triggers a series of actions. A severe warning automatically freezes adsorption tower operating parameters to prevent misadjustments, activates a backup adsorption tower for parallel operation, and sends SMS and email alerts to the maintenance team. A moderate warning triggers the generation of a weekly report summarizing trend charts for 10 consecutive cycles. A minor warning only generates a log for subsequent trend analysis.

[0089] The signal storage database is encrypted and integrity-protected. Warning content is encrypted using the AES-256 algorithm, with the key rotated every 72 hours. A SHA-256 hash value is generated when data is written and stored in a separate verification area. Data integrity is automatically verified when data is read. Database access is controlled through three levels of permissions: operators can query only, engineers can modify thresholds, and administrators can modify historical records.

[0090] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0091] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0092] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0093] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0095] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0096] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent monitoring method for an argon recovery system, characterized in that: The steps include: S1. During the regeneration stage of the adsorption tower, real-time collection of concentration time series data of the regenerated tail gas; S2. Extracting the slope change rate of the desorption rate rising segment and the area integral value of the desorption peak decay segment from the concentration time series data; S3, continuously extracting the area integral values ​​of the current regeneration cycle and a preset number of previous regeneration cycles to construct an area integral value sequence; S4. Calculate the symmetric deviation between the area integral value of the current regeneration cycle and the mean of the area integral value sequence; S5. Identify the pressure pulse disturbance period in the regeneration phase, remove the slope change rate and area integral value in the pressure pulse disturbance period, and obtain effective period data; S6. Calculate the slope change rate offset and the fluctuation anomaly of the symmetric deviation based on the valid period data; S7. When the slope change rate offset exceeds a first threshold and the fluctuation abnormality exceeds a second threshold, an adsorbent replacement warning signal is generated.

2. The intelligent monitoring method for an argon recovery system according to claim 1, characterized in that: During the regeneration stage of the adsorption tower, the concentration time series data of the regenerated tail gas is collected in real time, including: A mid-infrared laser gas analyzer is used to obtain the original signal of the regenerated exhaust gas concentration at a sampling frequency of not less than once per second; Synchronously receive the metal dust concentration detection results and pressure sensor data of the regeneration pipeline; Perform sliding mean filtering on the original signal of regenerated exhaust gas concentration to eliminate instantaneous noise interference; The filtered concentration signal, metal dust concentration detection results and pressure sensor data are associated with timestamps and stored as concentration time series data.

3. The intelligent monitoring method for an argon recovery system according to claim 2, characterized in that: The slope change rate of the desorption rate rising section and the area integral value of the desorption peak decay section are extracted from the concentration time series data, including: Identify the starting and ending points of the desorption rate increase segment in the concentration time series data; The desorption rate rising section is divided into multiple sub-intervals at fixed time intervals, and the linear regression slope of the concentration change in each sub-interval is calculated. The standard deviation of the slopes of all sub-intervals is used as the slope change rate; Determine the start and end time of the desorption peak decay segment; The concentration values ​​and time intervals of all adjacent sampling points in the desorption peak attenuation section are accumulated in a trapezoidal manner, and the accumulated results are used as the area integral value.

4. The intelligent monitoring method for an argon recovery system according to claim 3, characterized in that: The starting point is the moment when the concentration value exceeds the average concentration value for the first time, and the end point is the moment when the concentration value reaches the peak concentration for the first time; the starting time is the moment corresponding to the peak concentration, and the ending time is the moment when the concentration value falls back to the average concentration value.

5. The intelligent monitoring method for an argon recovery system according to claim 3, characterized in that: Continuously extract the area integral values ​​of the current regeneration cycle and the previous preset number of regeneration cycles to construct an area integral value sequence, including: Get the end time of the current regeneration cycle and the historical end time of the previous preset number of regeneration cycles; Retrieving the area integral value corresponding to the end time of each regeneration cycle from the stored concentration time series database; Verify the integrity of the area integral value, and arrange the qualified area integral values ​​in chronological order of the end time of the regeneration cycle to form an area integral value sequence.

6. The intelligent monitoring method for an argon recovery system according to claim 5, characterized in that: The integrity of the area integral value is verified as follows: if the area integral value of the current regeneration cycle does not contain the pressure pulse disturbance period data, and the area integral value of the historical regeneration cycle is continuous and complete, it is judged as qualified data.

7. The intelligent monitoring method for an argon recovery system according to claim 5, characterized in that: Calculate the symmetric deviation between the area integral value of the current regeneration cycle and the mean of the area integral value sequence, including: Calculate the arithmetic mean of the area integral value sequence as the mean of the area integral value sequence; Determine the absolute difference between the area integral value of the current regeneration cycle and the mean of the area integral value sequence; Obtain the standard deviation of the area integral value sequence as the dispersion benchmark; Divide the absolute difference by the dispersion benchmark to obtain the preliminary deviation; The time decay factor of the cumulative running time of the adsorbent was introduced to correct the initial deviation and obtain a symmetrical deviation.

8. The intelligent monitoring method for an argon recovery system according to claim 7, characterized in that: Identify the pressure pulse disturbance period in the regeneration phase, remove the slope change rate and area integral value within the pressure pulse disturbance period, and obtain the effective period data, including: Obtaining the time series of pressure sensor data during the regeneration phase; Calculate the pressure change rate time series of pressure sensor data; identifying consecutive periods in the pressure change rate time series where the pressure change rate threshold is exceeded and the pressure sensor data exceeds the pressure absolute value threshold; When the duration of a continuous period is greater than the minimum duration threshold, it is marked as a pressure pulse disturbance period; In the time interval of the desorption rate rising section and the desorption peak decay section, the slope change rate and area integral value of the period overlapping with the pressure pulse disturbance period are eliminated; The uneliminated slope change rate and the uneliminated area integral value are combined into the effective period data.

9. The intelligent monitoring method for an argon recovery system according to claim 8, characterized in that: Calculate the slope change rate offset and symmetry deviation fluctuation anomaly based on the valid period data, including: Extract the slope change rate of the current regeneration cycle from the valid period data; Obtain the moving average of the slope change rate under historical normal operating conditions as a reference value for historical normal operating conditions; Calculate the absolute difference between the slope change rate of the current regeneration cycle and the historical normal operating condition reference value, and divide it by the historical normal operating condition reference value to obtain the slope change rate offset; Continuously extracting the symmetry deviation of the current regeneration cycle and the previous preset number of regeneration cycles; Calculate the standard deviation of the symmetric deviation sequence for a preset number of consecutive regeneration cycles; The standard deviation is divided by the arithmetic mean of the symmetric deviation degree sequence of a preset number of consecutive regeneration cycles to obtain the fluctuation abnormality degree of the symmetric deviation degree.

10. The intelligent monitoring method for an argon recovery system according to claim 9, characterized in that: When the slope change rate offset exceeds a first threshold and the fluctuation abnormality exceeds a second threshold, an adsorbent replacement warning signal is generated, including: Get the current values ​​of the slope change rate offset and fluctuation anomaly; Reading a preset first threshold and a second threshold; Determine whether the slope change rate offset is greater than a first threshold, and simultaneously determine whether the fluctuation abnormality is greater than a second threshold; When the slope change rate offset is greater than the first threshold and the fluctuation abnormality is greater than the second threshold, the warning judgment condition is triggered; Repeated detection of warning determination conditions within three consecutive regeneration cycles; If the warning judgment conditions are met for three consecutive regeneration cycles, an adsorbent replacement warning signal is generated; The adsorbent replacement warning signal is associated with the end time of the current regeneration cycle and stored.

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