Method for efficiently purifying anesthetic waste gas

Abstract

The present application relates to the technical field of anesthetic waste gas treatment, and discloses an efficient purification treatment method for anesthetic waste gas. The method monitors the gas concentration fluctuation data of the output end of the anesthetic waste gas in real time, and the waste gas component distribution characteristics in different anesthetic stages are included. The concentration change rate is used to mark the steady-state operation period and the fluctuation response period. By comparing the concentration differences of the characteristic pollutants in the two periods, the waste gas component types that need to be focused on are screened. For the key components, the purification efficiency factor is calculated by combining the adsorption efficiency data of the steady-state period and the temperature and pressure difference change data of the fluctuation period. Based on the concentration gradient, gas flow rate data and efficiency factor of the fluctuation period, the dynamic balance parameters of the adsorption medium are generated. The purification unit in the best state is selected as the core processing node, the concentration fluctuation data of the fluctuation period is linked to construct a multi-stage purification topology network, and a differentiated waste gas treatment strategy is configured according to the network. The method is suitable for anesthetic waste gas treatment scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of anesthetic waste gas treatment, in particular to an efficient anesthetic waste gas purification treatment method. BACKGROUND

[0002] In medical activities, the emission of anesthetic waste gas has long been a concern. In operating rooms, anesthetic recovery rooms and other scenarios, the anesthetic process continuously releases waste gas containing volatile anesthetics. These waste gases contain sevoflurane, isoflurane, desflurane and other volatile organic compounds. If not effectively treated and directly discharged, not only will it affect indoor air quality, but also may pose potential health risks to medical staff who are exposed to the environment for a long time, and may also cause some pollution to the external environment.

[0003] Traditional anesthetic waste gas treatment methods mostly use fixed mode purification devices, common ones include adsorption method, catalytic oxidation method, etc. Among them, the adsorption method uses activated carbon and other adsorbent materials to treat waste gas, but such devices usually use fixed purification unit combinations and cannot adjust the operation strategy according to the real-time changes in waste gas concentration. In actual anesthetic processes, waste gas concentration is not stable and unchanging, but will fluctuate significantly during different stages of anesthetic induction, maintenance, and recovery. For example, during the anesthetic induction stage, the use of volatile anesthetics increases sharply, and the concentration of waste gas will rise significantly in a short period of time. At this time, the traditional fixed purification unit is prone to adsorption saturation, which leads to a sharp decline in purification efficiency and makes it difficult to cope with the impact of concentration fluctuations.

[0004] The composition distribution of waste gas at different anesthetic stages is significantly different. Traditional treatment methods lack targeted identification and screening of waste gas components, often treating all components uniformly, resulting in inefficient consumption of purification resources on non-key components, causing low purification efficiency and resource waste. In addition, traditional methods rely on a single adsorption efficiency index when evaluating the performance of purification units, ignoring key parameters such as temperature changes and pressure differences during the operation of the purification unit, and cannot fully reflect the real-time state of the purification unit. This makes it difficult to accurately determine whether the purification unit is in the best operating state during the purification process, and also makes it difficult to discover potential performance degradation problems in a timely manner, thus leading to insufficient overall operation stability of the purification system and making it difficult to maintain efficient purification effects for a long time.

[0005] With the improvement of medical level, anesthetic technology is continuously developing, and the amount and complexity of anesthetic waste gas emissions are also increasing. Traditional treatment methods have been unable to meet the current demand for efficient and stable purification of anesthetic waste gas, and there is an urgent need for a purification treatment method that can adapt to fluctuations in waste gas concentration, accurately identify key components, and achieve dynamic regulation. SUMMARY

[0006] The present application aims to provide an efficient anesthesia waste gas purification method to solve the problems in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides an efficient anesthesia waste gas purification method, which comprises:

[0008] Real-time monitoring of the gas concentration fluctuation data of the anesthesia waste gas output end, wherein the gas concentration fluctuation data contains the waste gas component distribution characteristics of different anesthesia stages;

[0009] According to the gas concentration fluctuation data, the period when the concentration change rate is continuously lower than the set threshold is marked as a steady-state operation cycle, and the period when the concentration change rate exceeds the set threshold is marked as a fluctuation response cycle;

[0010] For each type of waste gas component, by comparing the concentration difference values of the characteristic pollutants in the steady-state operation cycle and the fluctuation response cycle, the waste gas component types that need to be treated are screened;

[0011] For each waste gas component type that needs to be treated, the purification unit with active adsorption record and record duration exceeding the preset minimum adsorption window in the steady-state operation cycle is marked as the reference treatment unit, and the purification unit with abnormal concentration in the fluctuation response cycle is marked as the target treatment unit; the adsorption efficiency data of the reference treatment unit in the steady-state operation cycle is obtained, combined with the temperature change data, pressure difference change data and pollutant concentration gradient data of the target treatment unit in the fluctuation response cycle, the weighted fusion result of the adsorption lag effect value and the dynamic adsorption equilibrium coefficient is calculated, and the purification efficiency factor of each purification unit for a specific waste gas component in the steady-state operation cycle is obtained;

[0012] Based on the pollutant concentration gradient and gas flow rate monitoring data of the inlet and outlet of each purification unit in the fluctuation response cycle, combined with the purification efficiency factor, the adsorption medium dynamic balance parameter of each purification unit in the steady-state operation cycle is generated;

[0013] According to the adsorption medium dynamic balance parameter, the purification unit in the best adsorption state is selected as the core processing node, the gas concentration fluctuation data of the same waste gas component type in the fluctuation response cycle is linked, a multi-level purification topology network is constructed, and a differentiated waste gas treatment strategy is configured according to the multi-level purification topology network.

[0014] Preferably, the gas concentration fluctuation data contains volatile organic compound concentration detection values at each sampling time point; the waste gas component distribution characteristics contain halogenated hydrocarbon substance proportion data and nitrogen oxide concentration change curve; and the adsorption efficiency data contains real-time record of the saturation adsorption amount and adsorption rate of the adsorption material in the purification unit for a specific anesthetic gas.

[0015] Preferably, the process of calculating the purification efficiency factor of each purification unit for a specific exhaust component in a steady-state operation period specifically comprises:

[0016] Selecting any exhaust component type that needs to be focused on, marking the purification unit with active adsorption records of the exhaust component type in the steady-state operation period as a reference treatment unit, and marking the purification unit with abnormal concentration of the exhaust component type in the fluctuation response period as a target treatment unit; designating any reference treatment unit as a current reference unit and any target treatment unit as a current target unit;

[0017] Extracting the adsorption material temperature data and pressure difference monitoring values of the current reference unit at each sampling time point in the steady-state operation period, correlating the pollutant concentration gradient data and gas flow rate monitoring data of the current target unit at the corresponding time points in the fluctuation response period, and obtaining the adsorption lag effect value of the current reference unit relative to the current target unit at each sampling time point by calculating the product of the concentration response coefficient and the flow rate response coefficient between the two units;

[0018] Quantifying the similarity index between the adsorption efficiency data of the current reference unit at each adsorption operation period in the steady-state operation period and the adsorption efficiency data of the current target unit at each adsorption operation period in the fluctuation response period, and generating the dynamic adsorption equilibrium coefficient corresponding to each adsorption operation period;

[0019] Using the adsorption lag effect value as a weighting coefficient, performing weighted fusion processing on the dynamic adsorption equilibrium coefficient of the current target unit at each adsorption operation period to obtain the component purification contribution degree of the current reference unit relative to the current target unit, and finally taking the average of the component purification contribution degrees of the current reference unit relative to all target treatment units as the purification efficiency factor of the current reference treatment unit.

[0020] Preferably, the process of calculating the adsorption lag effect value of the current reference unit relative to the current target unit at each sampling time point specifically comprises:

[0021] Selecting an adsorption operation period on any time sequence as a target processing period;

[0022] Determining the running state deviation between the two units through the Euclidean distance calculation of the running data of the current reference unit in the temperature dimension and the pressure difference dimension and the running data of the current target unit in the same dimension;

[0023] Taking the ratio of the pollutant concentration gradient value of the current target unit in the target processing period to the pollutant concentration gradient value of the current reference unit in the same period as the concentration response coefficient, and taking the ratio of the running state deviation to the gas flow rate monitoring value of the current target unit in the target processing period as the flow rate response coefficient;

[0024] The product of the concentration response coefficient and the flow rate response coefficient is taken as the adsorption hysteresis effect value of the current reference unit relative to the current target unit in the target processing period.

[0025] Preferably, the specific operation of generating the adsorption medium dynamic balance parameter of each purification unit in the steady-state operation period based on the pollutant concentration gradient and the gas flow rate monitoring data of the inlet and outlet of each purification unit in the fluctuation response period, and combining the purification efficiency factor includes:

[0026] According to the saturation adsorption capacity change data and the adsorption material degradation coefficient of the reference processing unit in each adsorption operation period in the steady-state operation period, the medium activity retention rate of the reference processing unit is calculated.

[0027] The product of the purification efficiency factor and the medium activity retention rate of the reference processing unit is normalized, and the obtained value is taken as the adsorption medium dynamic balance parameter of the reference processing unit.

[0028] Preferably, the process of calculating the medium activity retention rate of the reference processing unit specifically includes:

[0029] The ratio of the saturation adsorption capacity change value and the adsorption rate real-time record of the reference processing unit in each adsorption operation period in the steady-state operation period is calculated, and the ratio is taken as the unit adsorption efficiency index. The arithmetic mean value of the adsorption efficiency index in all adsorption operation periods in the steady-state operation period is taken. The product of the arithmetic mean value and the adsorption material degradation coefficient is taken as the medium activity retention rate of the reference processing unit.

[0030] Preferably, the specific operation of screening the purification unit in the best adsorption state as the core processing node according to the adsorption medium dynamic balance parameter, and linking the gas concentration fluctuation data of the same waste gas component type in the fluctuation response period to construct the multi-stage purification topology network includes:

[0031] The reference processing unit with the adsorption medium dynamic balance parameter exceeding the preset balance threshold value is set as the core processing node of each sub-network in the multi-stage purification topology network.

[0032] The medium activity retention rate of each target processing unit in the fluctuation response period is obtained, the adsorption hysteresis effect value between each target processing unit and the corresponding reference processing unit of the core processing node is calculated, and the adsorption hysteresis effect value is multiplied by the medium activity retention rate of the target processing unit to obtain the purification compliance index of the target processing unit relative to the core processing node.

[0033] For any core processing node, the hierarchical purification structure is constructed according to the purification compliance index of the target processing unit relative to the core processing node from high to low. The target processing units at the same level in the hierarchical purification structure have equal purification compliance indexes.

[0034] The hierarchical purification structures of all core processing nodes jointly constitute a multi-stage purification topology network.

[0035] Preferably, the specific operation of configuring the differentiated waste gas treatment strategy according to the multi-stage purification topology network comprises:

[0036] The layer level at which the target processing units first shared by different core processing nodes in the multi-stage purification topology network is recorded as a primary purification layer; and the layer level containing the most target processing units after the primary purification layer is recorded as a secondary purification layer.

[0037] The low-temperature catalytic oxidation treatment strategy is adopted for the units between the core processing nodes and the primary purification layer; the activated carbon fiber adsorption and regeneration strategy is adopted for the units between the primary purification layer and the secondary purification layer; and the biological enzyme degradation treatment strategy is adopted for the units after the secondary purification layer.

[0038] Preferably, the specific operation of dividing the steady-state operation period and the fluctuation response period according to the gas concentration fluctuation data comprises:

[0039] For any waste gas component type, the volatile organic compound concentration detection values in a continuous time sequence are collected to form a concentration time sequence curve, a first-order difference sequence of the concentration time sequence curve is calculated, and the time point at which the difference value is greater than zero is screened and recorded as a concentration rising time point.

[0040] The concentration rising time points are arranged in ascending order of corresponding difference values to form a difference sequence, a second-order difference value of the difference sequence is calculated, and the concentration rising time point corresponding to the maximum second-order difference value is recorded as a concentration transition time point; a period before the concentration transition time point is defined as a steady-state operation period, and a period after the concentration transition time point is defined as a fluctuation response period.

[0041] Preferably, the specific operation of screening the waste gas component type requiring key processing comprises:

[0042] For any waste gas component type, the average value of the volatile organic compound concentration detection values in the steady-state operation period is obtained and recorded as a reference concentration average value, and the average value of the volatile organic compound concentration detection values in the fluctuation response period is obtained and recorded as a response concentration average value; the absolute difference value between the response concentration average value and the reference concentration average value is calculated, the absolute difference value is normalized to obtain a concentration deviation coefficient, and if the concentration deviation coefficient is greater than a preset deviation threshold, the waste gas component type is marked as a waste gas component type requiring key processing.

[0043] Compared with the prior art, the present application has the following beneficial effects:

[0044] The method provides a more adaptive solution for anesthesia waste gas purification by constructing a dynamic monitoring and differential regulation system. By monitoring the concentration fluctuation data at the output end of the waste gas in real time, the method can accurately capture the change characteristics of the waste gas components at different anesthesia stages, changing the problem of response lag of traditional purification methods to waste gas fluctuations. Using the concentration change rate as the basis for period division, the definition of steady-state operation period and fluctuation response period is more clear, laying a foundation for subsequent targeted processing, allowing the purification system to adjust the operation mode according to the characteristics of waste gas in different periods, avoiding the inefficiency of fixed purification mode when the concentration suddenly rises or the component suddenly changes.

[0045] By comparing the concentration differences of characteristic pollutants in the steady-state and fluctuation periods, the method can accurately lock the components that need to be processed, allowing the purification resources to focus on key pollutants and reducing ineffective investment in low-impact components. This targeted processing mode avoids the waste of resources caused by the average allocation of purification resources to all components in traditional methods, allowing the limited purification capacity to be used more efficiently.

[0046] In terms of purification efficiency evaluation, the method combines the adsorption efficiency data of the steady-state period with the temperature and pressure difference change data of the fluctuation period to calculate the purification efficiency factor, which can comprehensively reflect the actual performance of the purification unit in different operating states. This multi-parameter comprehensive evaluation method breaks through the limitations of traditional single-index evaluation and can more accurately grasp the purification capacity of each purification unit for specific components, providing a scientific reference for the optimal configuration of purification units.

[0047] The dynamic balance parameters generated based on concentration gradient, gas flow rate, and purification efficiency factor can reflect the adsorption state of the purification unit in real time, allowing the system to adjust the operating parameters of each purification unit in time to ensure its good adsorption performance in both steady-state and fluctuation periods. This dynamic balance regulation mechanism effectively reduces the probability of adsorption saturation of the purification unit when the concentration fluctuates, prolonging the effective operation time of the purification unit.

[0048] By selecting core processing nodes and constructing a multi-level purification topology network, the method realizes the coordinated operation of different purification units. In the face of complex waste gas components and concentration fluctuations, the multi-level topology network can flexibly adjust the working mode of each purification unit according to real-time data to form a differentiated processing strategy. This networked collaborative processing mode enhances the adaptability of the system to changes in waste gas at different anesthesia stages, making the purification process more stable and reliable, and enabling the system to continuously purify anesthesia waste gas under various working conditions. At the same time, the adaptability of the method to different components and different concentration fluctuations also allows it to be applied to various anesthesia scenarios, improving the overall level of anesthesia waste gas treatment. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The working principle diagram of the anesthetic waste gas efficient purification treatment method of the present application;

[0050] Figure 2 The calculation flow chart of the purification efficiency factor in the steady state operation period;

[0051] Figure 3 The generation flow chart of the adsorption medium dynamic balance parameter;

[0052] Figure 4 The configuration flow chart of the differentiated waste gas treatment strategy. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0054] Please refer to Figure 1 The present application provides an anesthetic waste gas efficient purification treatment method, which comprises:

[0055] Based on the gas concentration fluctuation data, the concentration change rate is analyzed. A threshold value is set in the data processing system, which is a pre-configured fixed value or a dynamically adjusted value. The concentration change rate is calculated by an algorithm, and when the concentration change rate continuously falls below the set threshold value, the corresponding period is marked as a steady-state operation period. When the concentration change rate exceeds the set threshold value, the corresponding period is marked as a fluctuation response period. The marking operation is completed by using a timestamp segmentation algorithm. For each type of exhaust gas component, the processing system performs a comparison analysis. The comparison process focuses on the concentration difference value of the characteristic pollutants in the steady-state operation period and the fluctuation response period. The concentration difference value is achieved by calculating the average value difference of the same component concentration in the two periods. Based on the difference calculation result, the part where the difference value exceeds the preset range is identified as the screening condition, and after screening, the exhaust gas component type that needs to be focused on is determined. For each screened exhaust gas component type, data correlation operation is implemented. The adsorption efficiency data of each purification unit in the steady-state operation period is obtained. The adsorption efficiency data includes numerical records. Combined with the temperature change data and pressure difference change data of the same purification unit in the fluctuation response period, the temperature change data is collected from the thermocouple sensor, and the pressure difference change data is monitored by the pressure difference transmitter. The processing system calculates the purification efficiency factor of each purification unit for a specific exhaust gas component in the steady-state operation period. The calculation method involves integrating the relationship between the mathematical formula and the external parameters. Based on the pollutant concentration gradient and gas flow rate monitoring data of the inlet and outlet of each purification unit in the fluctuation response period, the pollutant concentration gradient is obtained by concentration difference calculation, and the gas flow rate monitoring data is collected from the flow meter. Combined with the purification efficiency factor, the system generates the adsorption medium dynamic balance parameter of each purification unit in the steady-state operation period. The generation process uses a parameterized model. According to the adsorption medium dynamic balance parameter, the system performs a screening logic.

[0056] The screening condition is that the parameter value meets the preset optimal adsorption state range, and the identified purification unit is set as the core processing node. The system links the gas concentration fluctuation data of the same exhaust gas component type in the fluctuation response period, and the linkage process uses a data transmission protocol. Based on the data, a multi-stage purification topology network is constructed. The construction process involves mathematical modeling of node connection relationships. Finally, according to the multi-stage purification topology network, the system configures a differentiated exhaust gas treatment strategy. The strategy configuration includes a parameter adjustment mechanism.

[0057] Embodiment 1: see Figure 2The embodiment specifically describes the monitoring method of gas concentration fluctuation data and the calculation method of purification efficiency factor in the process of anesthetic waste gas treatment. When monitoring the gas concentration fluctuation data of the anesthetic waste gas output end in real time, a gas chromatograph-mass spectrometer is used as the main detection equipment to capture the volatile organic compound concentration detection value at each time point with a millisecond-level sampling frequency. The detection value is stored in the form of a floating-point array and includes a time stamp identifier. The extraction of waste gas component distribution characteristics is achieved through double data analysis: for halogenated hydrocarbons, the real-time percentage of the halogenated hydrocarbons in the total gas concentration is calculated, and the percentage data is generated into a two-dimensional matrix through division operation. The matrix rows correspond to the sampling time points, and the columns correspond to the substance types; for nitrogen oxides, the concentration detection values of the continuous time sequence are collected, and a smooth curve is fitted through a cubic spline interpolation method, and the curve data is stored in a ring buffer in the form of a discrete coordinate point set.

[0058] The definition of adsorption efficiency data includes two parallel recording dimensions: the saturation adsorption amount of the adsorbent material to a specific anesthetic gas, which is in milligrams per gram of adsorbent, and the data is derived from a preset material property reference table, which is indexed and queried using a hash table structure; the adsorption rate record is the concentration change per minute, which is obtained through differential calculation of the concentration sensor and stored as a signed floating-point number. After filtering the waste gas component types that need to be processed, the system starts the purification efficiency factor calculation process. When calculating the purification efficiency factor, the adsorption efficiency data of the steady-state period and the temperature and pressure difference data of the fluctuation period need to be associated in dimension through the Euclidean distance algorithm to eliminate the time deviation of different period data and ensure data correspondence. When selecting the target waste gas component type, traverse all the component identifier list and sort them according to the pollution index priority. The marker of the reference processing unit needs to meet two conditions: there is an adsorption rate record greater than zero in the steady-state operation period, and the record duration is longer than the preset minimum adsorption window (for example, ≥5 minutes). The marker operation updates the purification unit state register using a binary identifier, and the units that do not meet the conditions are set to 0, and the units that meet the conditions are set to 1. The judgment logic of the target processing unit is: in the fluctuation response period, the waste gas component concentration value exceeds three times the standard deviation range of the historical concentration mean value of the component, which triggers the marker, and the abnormal state is written into the alarm log synchronously. The reference processing unit needs to meet the conditions of continuous running time ≥5 minutes and adsorption efficiency ≥80% in the steady-state operation period (i.e., having active adsorption records), and the target processing unit needs to meet the conditions of concentration gradient exceeding the steady-state period mean value ±30% (i.e., concentration anomaly) in the fluctuation response period to avoid ambiguity in unit selection. When extracting the sampling data of the reference unit and the target unit, the time stamp of the reference unit is used as the reference, and linear interpolation is performed on the target unit data—when the time deviation is ≤5 seconds, it is directly matched, and when the deviation is >5 seconds, it is supplemented by interpolation of the adjacent 3 data points to ensure the accuracy of the corresponding time point data.

[0059] The process of assigning the current reference unit and the current target unit adopts a dynamic matching algorithm: the system creates independent processing queues for each exhaust component type, the reference processing unit queue is arranged in descending order of adsorption efficiency, the target processing unit queue is arranged in ascending order of concentration anomaly value, and a set of units is selected from the heads of the two queues for pairing each time. The temperature data of the adsorption material is collected using a PT100 platinum resistance temperature sensor, and the data is stored as a floating point number in Celsius; the differential pressure monitoring value is obtained by a differential pressure transmitter, with a unit of pascal, and the data is stored in a time series database after Kalman filtering. The calculation of the pollutant concentration gradient data is defined as the instantaneous difference between the outlet concentration detection value and the inlet concentration detection value of the purification unit, and the gradient value is updated every second and stored with a sign (positive value indicates purification effect, negative value indicates failure state).

[0060] The calculation process of the adsorption lag effect value is executed in three steps: first, extract the temperature curve point set and the differential pressure sequence array of the current reference unit within the selected adsorption operation period (the system defaults to 30 seconds as an operation period), synchronously obtain the running data of the current target unit with the same dimension, calculate the Euclidean distance after mapping the two groups of data into n-dimensional vectors, and record the result as the running state deviation; second, read the pollutant concentration gradient value A of the current target unit within the target processing period and the same period gradient value B of the current reference unit, calculate the concentration response coefficient C=A / B, the concentration response coefficient is the ratio of the pollutant concentration gradient of the target purification unit in the fluctuation period to that of the reference purification unit in the steady state period, the flow rate response coefficient is the ratio of the running state deviation (the calculation result of the temperature and differential pressure Euclidean distance) to the gas flow rate of the target unit, and the adsorption lag effect value is the product of the two; third, obtain the gas flow rate monitoring value V (cubic meters per minute) of the current target unit in the target processing period, calculate the flow rate response coefficient D=running state deviation / V; finally, the adsorption lag effect value E=CxD, the result is saved as a double-precision floating point array, and the array index corresponds to the timestamp. The running state deviation is calculated by the temperature data collected by the PT100 platinum resistance sensor and the differential pressure data collected by the differential pressure transmitter, the gas flow rate monitoring value comes from the turbine flowmeter (range 0-10 m³ / h, accuracy ±2%), and the pollutant concentration gradient is obtained by the difference between the inlet and outlet infrared spectrum analyzer detection values, all parameters need to be processed by Kalman filtering for noise reduction.

[0061] The generation of the dynamic adsorption balance coefficient involves similarity matching: for each adsorption operation period (5-minute fixed window divided by the system), the adsorption efficiency dataset S of the current reference unit in the steady state period and the adsorption efficiency dataset T of the current target unit in the fluctuation response period are extracted respectively. The Pearson correlation coefficient P of the two datasets is calculated, and the formula is realized by calculating the ratio of the covariance to the standard deviation product, and the calculation process calls the mathematical coprocessor instruction. The correlation coefficient P is normalized and linearly transformed to the interval [0, 1], and the result is used as the dynamic adsorption balance coefficient F. The coefficient sequence is stored in a relational database. The dynamic adsorption balance coefficient calculates the adsorption efficiency similarity of the steady state period reference unit and the fluctuation period target unit through the Pearson correlation coefficient, and is normalized to the interval [0, 1], which is used to quantify the performance matching degree of different purification units.

[0062] The component purification contribution degree needs to be obtained by weighted fusion: taking the adsorption hysteresis effect value E corresponding to the current adsorption operation period as the weight coefficient, multiplying the dynamic adsorption balance coefficient F of the same operation period by E to obtain the weighted value G, and after traversing all adsorption operation periods, the weighted value sequence is obtained, and the geometric mean value of the sequence is taken as the component purification contribution degree H of the current reference unit relative to the current target unit. The determination of the final purification efficiency factor is determined by hierarchical aggregation: all target processing units are looped, the component purification contribution degree of the current reference unit to each target unit is calculated, a contribution degree set is formed, and the arithmetic mean I of the set is taken. The value I is used as the purification efficiency factor of the current reference unit, and is written into the purification unit attribute table persistent storage. The whole calculation process runs under the task scheduling management of the real-time operating system, and each exhaust gas component type calculation task is executed by an independent thread. Critical data is exchanged between threads through shared memory, and data consistency is guaranteed by the semaphore mechanism. The system reserves a serial communication interface, which can output intermediate calculation results to the monitoring terminal through the RS-485 bus.

[0063] Embodiment 2: see Figure 3 This embodiment describes the calculation implementation process of the adsorption hysteresis effect value and the dynamic balance parameter of the adsorption medium. When determining the adsorption hysteresis effect value, the system selects any continuous adsorption operation period on the time axis as the target processing period. The period selection is realized by a sliding window algorithm, the window width is fixed at 10 minutes, and the window step is set to 1 minute, ensuring that there is data overlap between adjacent periods. The temperature dimension running data of the current reference unit is stored in time series format, and the data points are taken from the PT100 platinum resistance temperature sensor with a sampling frequency of 1 Hz. After Kalman filtering, the data is stored in a ring buffer. The differential pressure dimension running data is collected by a differential pressure transmitter with a range of 0-10 kPa. The data is compressed by minute average and saved as an array structure.

[0064] The corresponding dimensional data of the current target unit is synchronously called, and the time alignment of the data of the two units is achieved by accurate matching of the time stamp. The calculation of the running state deviation is based on multi-dimensional space mapping: the temperature curve point set and the pressure difference sequence array are combined into a two-dimensional vector, and each vector element corresponds to the data point at the same sampling time. The Euclidean distance of the vector group of the two units within the target processing period is calculated point by point, the distance value is processed by square root, and the arithmetic average of each point distance is taken as the final running state deviation. The value is stored as a double-precision floating-point number, and the numerical range is limited to 0 to 1000 dimensionless units.

[0065] The calling of the pollutant concentration gradient data includes the inlet and outlet concentration values of the current target unit within the target processing period. The concentration value is detected in real time by an infrared spectrum analyzer, and the data is analyzed as the absolute value of the concentration difference. When calculating the concentration response coefficient, the system retrieves the gradient value A of the current target unit in the target period and the gradient value B of the current reference unit in the same period, and performs the division operation of A / B. The operation result is kept to three decimal places, and the coefficient is less than 1, which is marked as inhibition state, and greater than 1, which is marked as enhancement state. The gas flow rate monitoring data is obtained from the turbine flow meter, and the unit of measurement is converted to cubic meters per hour, and the value is updated to the register every second. The calculation of the flow rate response coefficient divides the running state deviation by the average flow rate V of the current target unit in the target period, and the division operation uses fixed-point number processing to avoid floating-point error.

[0066] The generation of the adsorption lag effect value performs a multiplication operation: the system inputs the concentration response coefficient and the flow rate response coefficient as multipliers into the multiplier module, and the product result is written into the time series table of the real-time database. The table structure includes five columns: time stamp, current reference unit ID, current target unit ID, target processing period start and end time, and lag effect value. The database is stored in tables according to the type of exhaust gas components, and the maximum storage capacity of a single table is set to 100,000 records.

[0067] In the generation of the adsorption medium dynamic balance parameter stage, the saturated adsorption capacity change data of the reference processing unit is derived from the historical record database. The data is stored as a discrete point column according to the time axis, and each adsorption operation period (the system defaults to 30 minutes) corresponds to a record unit. The degradation coefficient of the adsorption material is defined as a fixed constant or a dynamic variable: for activated carbon adsorbent, the coefficient is preset to 0.02 per day; for molecular sieve material, the system dynamically calculates the material aging rate according to the temperature history data. The calculation process of the medium activity retention rate is divided into three steps: first, the saturated adsorption capacity change value ΔQ of the reference processing unit in each adsorption operation period is counted; second, the adsorption rate real-time record R of the same period is extracted; and the unit adsorption efficiency index K = ΔQ / R is calculated.

[0068] The index K forms a time series in the steady-state operation period, and the system takes the arithmetic mean of the K values of all adsorption operation periods. The mean value is calculated by weighted processing, and the time period is used as the weight factor. The final mean value is multiplied by the degradation coefficient of the adsorption material, and the product is recorded as the medium activity retention rate M. This value is displayed in the system in percentage form. The purification performance factor I calls the calculation results of Example 1, and the system performs the multiplication operation of I x M. The product is normalized: set the maximum theoretical product value of the reference processing unit to 200, and convert the actual product value to the 0-1 interval through a linear scaling formula. The scaling result is the adsorption medium dynamic balance parameter P. Parameter P is updated to the purification unit state monitoring interface in hourly granularity, and the interface uses a color coding mechanism: P > 0.8 displays green, 0.6 < P ≤ 0.8 displays yellow, and P ≤ 0.6 displays red alarm.

[0069] All numerical operations are completed in the digital signal processor (DSP) of the embedded system. The DSP model is TMS320C6748 with a main frequency of 456 MHz. The original sensor data is input to the PLC analog module through a 4-20 mA current loop. The module has a resolution of 16 bits and a sampling rate of 1 kS / s. Intermediate calculation results are temporarily stored in a DDR3 memory buffer, and the final parameters are written to an SPIFlash memory for persistent storage. The system is configured with a watchdog timer to monitor the calculation process, and the timeout threshold is set to 2 seconds. When the timeout threshold is exceeded, the hardware is reset and the process is restarted.

[0070] Example 3: This example describes in detail the calculation process of the medium activity retention rate and the construction method of the multi-stage purification topology network. In calculating the medium activity retention rate of the reference processing unit, the system first counts the saturated adsorption capacity change value of each adsorption operation period in the steady-state operation period. The change value is defined as the difference between the saturated adsorption capacities at adjacent sampling time points. The data is obtained from the adsorption material characteristic database and stored in the form of a signed floating-point number array. Each adsorption operation period is divided into 30-minute intervals by default, and each interval contains 1800 sampling points (1 sample per second).

[0071] The unit adsorption efficiency index is calculated using the following formula:

[0072]

[0073] wherein, represents the unit adsorption efficiency index of the i-th adsorption operation period, represents the saturated adsorption capacity change value (unit: mg / g) of the i-th period, represents the saturated adsorption capacity change value (unit: mg / g) of the i-th period, represents the saturated adsorption capacity change value (unit: mg / g) of the i-th period, represents the saturated adsorption capacity change value (unit: mg / g) of the i-th period, Adsorption rate real-time record (unit: mg / g·min) of each period. Calculation results Save as time series array, array index corresponds to period number. The system calculates the average value of all adsorption operation periods The calculation process uses a sliding window average algorithm, and the window size is set to 10 consecutive periods.

[0074] Degradation coefficient of adsorption material The setting is dynamically adjusted according to the material type: for activated carbon adsorbent, The initial value is 0.015 per day, and it is updated every 24 hours according to the operating temperature. The update rule is that for every 10°C increase in temperature, Increase 0.002; for molecular sieve material, Based on the cumulative adsorption amount, increase linearly, every 100mg / g of pollutants, Increase 0.001.

[0075] When constructing a multi-stage purification topology network, the system first selects the reference treatment unit of the adsorption medium dynamic equilibrium parameter Exceeds the preset equilibrium threshold The threshold value Is set to 0.7, which can be dynamically adjusted according to the type of waste gas components, with an adjustment range of ±0.1. The selected unit is marked as the core processing node, and the node information is written into the network configuration table. The table structure includes four columns of node ID, belonging to waste gas component type, Value, physical location coordinates.

[0076] When the medium activity retention rate of the target processing unit in the fluctuation response period is obtained The system performs the same calculation process as the reference treatment unit, but the data source is changed to the monitoring database of the fluctuation response period. When the adsorption lag effect value between the target processing unit and the core processing node is calculated Reuse the algorithm in Embodiment 2, but add time alignment verification: when the timestamp deviation between the target processing unit and the reference treatment unit exceeds 5 seconds, automatically interpolate and fill in the data points.

[0077] The calculation formula of the purification compliance index is:

[0078]

[0079] Among them, Indicates the adsorption lag effect value, Indicates the medium activity retention rate of the target processing unit. The calculation results ​Normalized to the range of 0-1, as the node correlation weight value. The system establishes an independent hierarchical purification structure for each core processing node:

[0080] The target processing unit of is divided into the first level; The unit of is divided into the second level; The unit of is marked as a node to be optimized.

[0081] The hierarchical relationship is stored through a directed graph data structure, and the edge weight of the graph is value. After merging the hierarchical structures of all core processing nodes, the system performs loop detection and elimination: when cross-references are found between target processing units of different core nodes, the correlation edge with a higher value is retained, and the lower value edge is deleted. The final generated topology network is stored in the form of an adjacency list, and each list entry records the source node ID, target node ID, level number, value four data.

[0082] The network visualization module maps the topology structure into a three-dimensional solid graph: the core processing node is displayed as a red cube, the first level unit is displayed as a yellow sphere, the second level is displayed as a green prism, and the third level is displayed as a blue cylinder. The thickness of the connection line between nodes is proportional to the value, and the rendering engine updates the graphics data every 5 seconds. Network configuration data is written synchronously to the SQLite database, supporting historical version backtracking and comparative analysis.

[0083] In terms of computing resource allocation, the medium activity retention rate calculation task runs in a real-time operating system priority thread (priority 90) with a thread cycle of 1 minute. The topology network construction task runs in a normal thread (priority 60), and the triggering condition is the adsorption medium dynamic balance parameter update event. The system memory is divided into three areas: sensor data buffer area (256KB), intermediate calculation result area (512KB), and network configuration storage area (1MB). The watchdog timer monitors the thread running state, and the calculation task timeout threshold is set to 500ms. After timeout, the calculation environment is automatically reinitialized.

[0084] The communication interface uses the ModbusTCP protocol, port number 502, and supports simultaneous connection of 32 client terminals. When the network configuration table is changed, the system notifies all monitoring terminals through UDP multicast, multicast address 224.0.0.100, TTL value set to 5. The error handling mechanism records data verification errors during the calculation process, and the error codes are divided into three categories: sensor data out-of-range (E101), timestamp out-of-sync (E102), and memory overflow (E103). Error logs are forwarded to the log server through the Syslog protocol.

[0085] Example 4: Refer to Figure 4 This example describes the specific configuration method of differentiated waste gas treatment strategy in multi-level purification topology network. After the system identifies the primary purification layer and the secondary purification layer, a purification unit hierarchical relationship mapping table 1 needs to be established before strategy allocation. This table records the key attributes of all nodes in the topology network. The following is an example data segment.

[0086] Table 1: Purification unit hierarchical relationship mapping table.

[0087] Node ID Belonging hierarchy Exhaust gas component type Medium activity retention rate (%) Purification compliance Treatment strategy number U-102 Primary purification layer Halogenated hydrocarbons 82.3 0.91 ST-201 U-215 Secondary purification layer Nitrogen oxides 76.8 0.73 ST-302 U-307 Primary purification layer Volatile organic compounds 85.1 0.89 ST-201 U-411 Tertiary purification layer Halogenated hydrocarbons 63.4 0.52 ST-403

[0088] The system first analyzes the structural characteristics of the multi-level purification topology network. The determination standard of the primary purification layer is: the layer where the target processing unit is first called by different core processing nodes. The network traversal algorithm starts from each core processing node, performs breadth-first search along the connection edge, and records the node set where the first intersection path appears. The determination of the secondary purification layer uses the number statistics method: the system counts the number of nodes in each level after the primary purification layer, and marks the level containing the most target processing units as the secondary purification layer. When the number is the same, prefer to select the level closer to the core processing node.

[0089] The configuration of the low-temperature catalytic oxidation treatment strategy is aimed at the units between the core processing node and the primary purification layer. The strategy parameters are stored in the strategy library, and the record numbered ST-201 contains: the catalyst type is set to platinum-palladium bimetallic supported type, and the carrier is γ-alumina; the temperature control interval is set to 45-50°C, and the temperature sensor sampling period is 10 seconds; the oxygen concentration maintenance range is set to 18-21%, and the air supply rate is adjusted by the mass flow controller; the catalyst regeneration period is fixed at 72 hours, and 350°C hot nitrogen gas is introduced for 30 minutes during regeneration. When the system activates the strategy, it issues the parameter set to the PLC controller of the corresponding purification unit, and the controller executes the temperature PID control and gas proportioning adjustment according to the preset program.

[0090] The activated carbon fiber adsorption regeneration strategy is suitable for the units between the primary purification layer and the secondary purification layer. The parameter configuration of strategy number ST-302 includes: the adsorption material is specified as polyacrylonitrile-based activated carbon fiber, and the specific surface area is set to 1200-1500㎡ / g; the inlet air humidity during the adsorption stage is controlled below 40%RH, which is achieved by pre-feed control of the dehumidifier; the desorption temperature is set to 120°C, which is heated by steam, and the desorption duration is 20 minutes; the pressure loss alarm threshold is set to 2.5kPa, and the automatic switching of the standby adsorption tank is triggered when the threshold is exceeded. When the strategy is executed, the system monitors the pressure difference sensor data of the adsorption unit, and when the preset desorption conditions are met, the inlet valve is automatically closed and the heating program is started, and the desorption waste gas is introduced into the secondary processing unit.

[0091] The biological enzyme degradation treatment strategy is configured to the unit after the secondary purification layer. The parameters of strategy No. ST-403 include: enzyme preparation selection laccase and peroxidase complex formula, dosage concentration maintained at 5-8 mg / L; reaction pH value controlled in the range of 6.5-7.5, automatically adjusted by sodium bicarbonate buffer; dissolved oxygen level not less than 4 mg / L, maintained by using a microporous aeration device; reaction temperature maintained at 25-30°C, regulated by a heat exchanger circulating water system. The system monitors the oxidation-reduction potential of the reaction tank in real time, and automatically adds enzyme preparation dosage when the potential value is lower than 200 mV, with an additional amount of 30% of the baseline amount.

[0092] The strategy switching logic is based on level boundary detection. When the purification unit performance decreases due to adsorption saturation or catalyst deactivation during operation, the system detects the real-time change of the medium activity retention rate. If the value decreases by more than 15% within 2 hours, the level reevaluation process is started: the purification compliance index of the unit is recalculated, and when its value crosses the level threshold value (such as from 0.62 to 0.58), the unit is automatically moved to the next level and the corresponding treatment strategy is switched. The strategy switching command is transmitted through the OPCUA protocol, and the execution process includes three steps: first, stop all actuators of the current strategy, second, write the new strategy parameters to the control register, and third, activate the new strategy related equipment according to the start sequence.

[0093] The strategy adaptation of waste gas component type is realized by dynamic query. The system maintains a strategy-component association matrix, with the matrix rows representing strategy numbers and the matrix columns representing waste gas component types, and the matrix element values being adaptation coefficients (0-1). When a level contains multiple waste gas component type treatment units, the system calculates the weighted average value of the adaptation coefficient of each strategy for the current component, and selects the strategy with the highest value as the default scheme. For example, the adaptation coefficient of halogenated hydrocarbons in ST-201 strategy is 0.92, and in ST-302 it is 0.85, so ST-201 strategy is preferred.

[0094] Historical strategy execution data is recorded in the operation log library, and each record contains fields such as strategy number, start timestamp, execution duration, target waste gas component, consumable usage, energy efficiency index, etc. The system generates a strategy performance analysis report every week, which reports statistics such as the average running time of each strategy in different levels, the medium activity retention rate decay slope, and the pollutant removal rate. The operator can adjust the strategy parameter preset value through the human-machine interface, and modify the record by inputting the change reason and passing double password authentication.

[0095] At the hardware control level, the heating element of the low-temperature catalytic oxidation strategy is controlled by an SSR solid-state relay, and the on-off ratio is adjusted by a PWM signal. The switching valve of the activated carbon fiber adsorption strategy is a pneumatic V-type ball valve, with a response time of less than 1 second. The dosing pump of the biological enzyme degradation strategy is a peristaltic pump, with a flow accuracy control within ±3% error range. All actuator status signals are connected to the DI module through hardwiring, with a sampling rate of 100 ms / second. The safety interlocking system operates independently of the main control PLC. When an abnormality in strategy execution is detected (such as catalytic bed over-temperature or biological reaction tank overflow), an emergency shutdown sequence is triggered immediately and the on-site alarm light column is lit.

[0096] The network communication adopts a layered architecture: strategy parameters are issued through real-time Ethernet (Profinet RT) with a cycle of 1 ms; sensor data collection is carried out through industrial wireless network (802.11ac) with a data packet interval of 500 ms; alarm signal transmission is carried out through a hardwired safety loop. The system reserves 4-20 mA analog interfaces, supporting connection to third-party devices. Data storage adopts a dual backup mechanism: real-time data is written to an in-memory database, synchronized to a disk array every hour; historical data is compressed and archived daily, with a retention period of three years.

[0097] The strategy effect evaluation module runs on the edge computing gateway, and calculates the key performance indicators of the strategy every 6 hours: including pollutant removal efficiency stability, energy consumption ratio, operating cost, etc. The evaluation results are displayed in color coding on the topology network visualization interface: green indicates that the indicators are better than the benchmark value, yellow indicates normal fluctuation range, and red indicates the need for manual intervention. The evaluation algorithm uses moving average filtering to process the original data, with a window width of 24 data points (6-hour cycle).

[0098] Example 5: This example describes the period division method of gas concentration fluctuation data and the screening process of key waste gas component types. In dividing the steady-state operation period and the fluctuation response period, the system first collects volatile organic compound concentration detection values on a continuous time series. The detection values come from an online gas chromatograph, with a sampling interval of 5 seconds. The data is stored in the form of time stamp-concentration value in a circular buffer. The buffer capacity is set to 8640 records, corresponding to 12 hours of continuous monitoring data. The system pre-processes the original data, including removing sensor outliers (such as sudden data exceeding the range) and smoothing filtering (using a sliding average algorithm with a window width of 7 data points).

[0099] The interpolation method is used to process the missing data in the construction of the concentration-time curve. When the interval between two valid data points exceeds 15 seconds, the system automatically performs linear interpolation to supplement the intermediate points. The interpolated data sequence forms a complete concentration-time curve, and the curve data are stored in the time series database, with each data point containing a timestamp, a concentration value, and a data source marker. The calculation of the first-order difference sequence is realized by the backward difference method, i.e., subtracting the concentration value at the previous time from the current time, and the result is saved as a new time series. Positive values in the difference sequence represent an upward trend in concentration, negative values represent a downward trend, and zero values represent stable concentration.

[0100] The selection of the concentration rising time is based on the sign of the first-order difference sequence. The system scans the entire difference sequence and records all time points where the difference value is greater than zero, forming a list of concentration rising times. Each entry in the list contains a timestamp and the corresponding difference value, arranged in chronological order. The ascending order arrangement of the difference sequence is realized using the quicksort algorithm, and the sorted sequence is used for second-order difference calculation. The calculation of the second-order difference value also uses the backward difference method, i.e., performing difference operation on the sorted first-order difference sequence again. The second-order difference result reflects the acceleration characteristics of the concentration change rate.

[0101] The determination of the concentration transition time is achieved by analyzing the extreme points of the second-order difference sequence. The system scans the second-order difference sequence and finds the maximum value point, which corresponds to the concentration transition time. The determination of the transition time must satisfy two additional conditions: the maximum value of the second-order difference must exceed the preset sensitivity threshold (the default value is 0.5 ppm / s²), and the second-order difference values of the five data points before and after this point must present a single peak distribution characteristic. When a transition time that meets the conditions is detected, the system divides the entire time sequence into two periods with this time as the dividing point: all data points before the transition time belong to the steady-state operation period, and data points after the transition time belong to the fluctuation response period.

[0102] In the screening of waste gas component types that need to be focused on, the system independently performs statistical analysis for each component. The calculation of the baseline concentration mean uses all valid concentration values in the steady-state operation period, and outliers are removed before calculation. The calculation range of the response concentration mean is limited to the fluctuation response period, and also undergoes outlier filtering processing. The absolute difference is directly taken as the arithmetic difference between the two means, and the result is rounded to two decimal places. The normalization process uses the maximum and minimum value scaling method to convert the absolute difference to the range of 0-1. The system's preset offset threshold is 0.35, and when the concentration offset coefficient of a component exceeds this threshold, it is automatically marked as a focus processing object.

[0103] The marking operation updates the status flag in the exhaust component management table. This table records the attribute information of all known exhaust components, including component ID, name, baseline concentration mean, response concentration mean, offset coefficient, processing priority, etc. The component marked for key processing automatically promotes its processing priority field to the highest level and generates a corresponding processing task in the system task queue. Task information includes component ID, marking time, recommended processing method, and other parameters, which are distributed to each processing unit through the message queue.

[0104] The verification of the cycle division result uses the cross-validation method. The system retains the records of the last 10 divisions, and when there is a significant difference between the new division result and the historical pattern (such as a transition time position deviation of more than 30 minutes), an automatic manual review process is triggered. The review interface displays the superimposed graphics of the original concentration curve, the first-order difference sequence, and the second-order difference sequence, and the operator can adjust the transition time position through drag-and-drop. The modified result needs to input the review opinion and sign for confirmation.

[0105] The real-time monitoring module continuously tracks the cycle state changes. When the system detects the occurrence of a new concentration transition time, it automatically updates the cycle division result and recalculates the relevant statistics. The update process uses a transaction processing mechanism to ensure data consistency. Historical cycle records are archived in chronological order, supporting query and retrieval by date, exhaust component type, and other conditions. Each cycle update generates a log record containing old and new values, change time, operator, and other information.

[0106] The dynamic adjustment of exhaust component screening results is based on a sliding window mechanism. The system recalculates the concentration offset coefficients of each component every 15 minutes, and when the coefficient of a previously non-key component exceeds the threshold for three consecutive times, it is automatically added to the key processing list; conversely, when the coefficient of a previously key component is below the threshold for six consecutive times, it is removed from the list. The adjustment operation follows the conservative principle, and the components removed from the list continue to be observed for 24 hours, during which they are still processed according to the original strategy.

[0107] The data acquisition hardware uses a modular design and supports hot swapping. The concentration monitoring module includes a sampling probe, a preprocessing unit, and an analyzer, which communicate with the main controller through a CAN bus. Each module has a unique hardware identification code, and the system automatically identifies and loads the corresponding driver configuration. The sampling flow rate is controlled within the range of 0.5-1.0 L / min, and is accurately adjusted by a mass flow meter. The preprocessing unit includes a dust filter, a dehumidifier, and a constant temperature device to ensure that the gas entering the analyzer meets the detection requirements.

[0108] System maintenance functions include automatic calibration and diagnostic tests. Zero-point calibration is performed every day at 2 a.m. using high-purity nitrogen gas as the standard gas; range calibration is performed every Sunday using a standard concentration of methane gas. Calibration data are recorded in the calibration log, including calibration time, standard value, measured value, deviation percentage, and other information. Diagnostic tests are automatically run once a month, and test items include sensor response time, repeatability error, linearity, and other indicators, and the results generate a test report for archiving.

[0109] The alarm management module monitors key parameter abnormalities. When the concentration detection value exceeds 90% of the range for 5 minutes or the differential value exceeds 3 times the standard deviation range for 10 consecutive times, a secondary alarm is triggered; when the device communication is interrupted for more than 3 minutes or the calibration deviation exceeds 5%, a primary alarm is triggered. Alarm information is sent simultaneously through the sound and light alarm, SMS notification, and email reminder, until it is manually confirmed and released. Alarm history records are saved for one year, and can be viewed by filtering by time, device, alarm level, and other conditions.

[0110] User permission management uses a role-based mechanism. Operators can only view real-time data and perform routine operations; engineers can modify parameter settings and view diagnostic information; administrators have all permissions, including system configuration and user management. Each operation requires login authentication, and key operations require dual authentication (password + dynamic token). Operation logs record user ID, operation time, executed action, parameter modification, and other information, and log files are encrypted and stored for a period of three years.

[0111] The system supports data export and report generation functions. Export formats include CSV, Excel, PDF, etc., and time ranges and data types can be selected. Daily reports include concentration statistics of each exhaust gas component, cycle division results, and key component lists; weekly reports add device operation status evaluation and maintenance recommendations; monthly reports provide trend analysis and performance index summary. Report templates can be customized to support the addition of company logos and notes.

[0112] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0113] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for highly efficient purification and treatment of anesthetic waste gas, characterized in that, The method comprises the following steps: Real-time monitoring of gas concentration fluctuation data of anesthetic waste gas output end, which contains waste gas component distribution characteristics in different anesthesia stages; Marking the period when the concentration change rate in the gas concentration fluctuation data is continuously below the set threshold as a steady-state operation cycle, and marking the period when the concentration change rate exceeds the set threshold as a fluctuation response cycle; For each waste gas component type, by comparing the concentration difference values of the characteristic pollutants in the steady-state operation cycle and the fluctuation response cycle, the waste gas component types that need to be treated are screened out; For each waste gas component type that needs to be treated, mark the purification unit with active adsorption record and record duration longer than the preset minimum adsorption window in the steady-state operation cycle as the reference treatment unit, and mark the purification unit with concentration anomaly in the fluctuation response cycle as the target treatment unit; Obtain the adsorption efficiency data of the reference treatment unit in the steady-state operation cycle, combine the temperature change data, pressure difference change data and pollutant concentration gradient data of the target treatment unit in the fluctuation response cycle, calculate the weighted fusion result of the adsorption lag effect value and the dynamic adsorption equilibrium coefficient, and obtain the purification efficiency factor of each purification unit for a specific waste gas component in the steady-state operation cycle; Based on the pollutant concentration gradient and gas flow rate monitoring data of the inlet and outlet of each purification unit in the fluctuation response cycle, and combined with the purification efficiency factor, the adsorption medium dynamic balance parameters of each purification unit in the steady-state operation cycle are generated; According to the adsorption medium dynamic balance parameters, the purification unit in the best adsorption state is selected as the core processing node, the gas concentration fluctuation data of the same waste gas component type in the fluctuation response cycle is linked, a multi-level purification topology network is constructed, and a differentiated waste gas treatment strategy is configured according to the multi-level purification topology network.

2. The method for high-efficiency purification of anesthetic waste gas according to claim 1, characterized in that: The gas concentration fluctuation data contains volatile organic compound concentration detection values at each sampling time point; the waste gas component distribution characteristics contain halogenated hydrocarbon substance proportion data and nitrogen oxide concentration change curve; and the adsorption efficiency data contain real-time records of the saturation adsorption amount and adsorption rate of the adsorption material in the purification unit for a specific anesthetic gas.

3. The method of claim 2, wherein the method comprises: The process of calculating the purification efficiency factor of each purification unit for a specific waste gas component in the steady-state operation cycle specifically includes: Selecting any waste gas component type that needs to be treated, marking the purification unit with active adsorption record of the waste gas component type in the steady-state operation cycle as the reference treatment unit, and marking the purification unit with concentration anomaly of the waste gas component type in the fluctuation response cycle as the target treatment unit; selecting any reference treatment unit as the current reference unit and any target treatment unit as the current target unit; Extracting the adsorption material temperature data and pressure difference monitoring values of the current reference unit at each sampling time point in the steady-state operation cycle, correlating the pollutant concentration gradient data and gas flow rate monitoring data of the current target unit at the corresponding time points in the fluctuation response cycle, and calculating the product of the concentration response coefficient and the flow rate response coefficient between the two units to obtain the adsorption lag effect value of the current reference unit relative to the current target unit at each sampling time point; Quantize the similarity index between the adsorption efficiency data of each adsorption operation period of the current reference unit in the steady-state operation cycle and the adsorption efficiency data of each adsorption operation period of the current target unit in the fluctuation response cycle, and generate the dynamic adsorption equilibrium coefficient corresponding to each adsorption operation period; Take the adsorption hysteresis effect value as the weighting coefficient, and perform weighted fusion processing on the dynamic adsorption equilibrium coefficient of each adsorption operation period of the current target unit to obtain the component purification contribution degree of the current reference unit relative to the current target unit. Finally, the average of the component purification contribution degrees of the current reference unit relative to all target processing units is taken as the purification efficiency factor of the current reference processing unit.

4. The method of claim 3, wherein the method is characterized by, The process of calculating the adsorption hysteresis effect value of the current reference unit relative to the current target unit at each sampling time point specifically includes: Select an adsorption operation period on an arbitrary time sequence as a target processing period; Determine the running state deviation degree between the two units through the Euclidean distance calculation of the running data of the current reference unit in the temperature dimension and the pressure difference dimension and the running data of the current target unit in the same dimension; Take the ratio of the pollutant concentration gradient value of the current target unit in the target processing period to the pollutant concentration gradient value of the current reference unit in the same period as the concentration response coefficient; take the ratio of the running state deviation degree to the gas flow rate monitoring value of the current target unit in the target processing period as the flow rate response coefficient; Take the product of the concentration response coefficient and the flow rate response coefficient as the adsorption hysteresis effect value of the current reference unit relative to the current target unit in the target processing period.

5. The method of claim 3, wherein the method is characterized by, The specific operation of generating the adsorption medium dynamic balance parameter of each purification unit in the steady-state operation cycle based on the pollutant concentration gradient and gas flow rate monitoring data of the inlet and outlet of each purification unit in the fluctuation response cycle, combined with the purification efficiency factor, includes: According to the saturation adsorption amount change data and the adsorption material degradation coefficient of the reference processing unit in each adsorption operation period in the steady-state operation cycle, the medium activity retention rate of the reference processing unit is calculated; The product of the purification efficiency factor and the medium activity retention rate of the reference processing unit is normalized, and the obtained value is taken as the adsorption medium dynamic balance parameter of the reference processing unit.

6. The method for efficiently purifying anesthetic waste gas according to claim 5, wherein The process of calculating the medium activity retention rate of the reference processing unit specifically includes: Take the ratio of the saturation adsorption amount change value and the adsorption rate real-time record of the reference processing unit in each adsorption operation period in the steady-state operation cycle as the unit adsorption efficiency index, and take the arithmetic mean of the adsorption efficiency index in all adsorption operation periods in the steady-state operation cycle. The product of the arithmetic mean and the adsorption material degradation coefficient is taken as the medium activity retention rate of the reference processing unit.

7. The method of claim 5, wherein the method is characterized by, The specific operation of selecting the purification unit in the best adsorption state as the core processing node according to the adsorption medium dynamic balance parameter, and linking the gas concentration fluctuation data under the same waste gas component type in the fluctuation response cycle to construct a multi-stage purification topology network includes: The reference processing units with adsorption medium dynamic balance parameters exceeding the preset balance threshold are set as the core processing nodes of each sub-network in the multi-stage purification topology network, respectively; The medium activity retention rate of each target processing unit in the fluctuation response period is obtained, the adsorption hysteresis effect value between each target processing unit and the reference processing unit corresponding to the core processing node is calculated, and the adsorption hysteresis effect value is multiplied by the medium activity retention rate of the target processing unit to obtain a purification compliance index of the target processing unit relative to the core processing node; For any core processing node, a hierarchical purification structure is constructed in the order of the purification compliance index of the target processing unit relative to the core processing node from high to low, and target processing units at the same level in the hierarchical purification structure have equal purification compliance indexes; The hierarchical purification structures of all core processing nodes together constitute a multi-level purification topology network.

8. The method of claim 7, wherein the method is characterized by, The specific operation of configuring a differentiated waste gas treatment strategy according to the multi-level purification topology network comprises: In the multi-level purification topology network, the level of the target processing unit first shared by different core processing nodes is recorded as a primary purification layer, and the level containing the most target processing units after the primary purification layer is recorded as a secondary purification layer; The units between the core processing node and the primary purification layer are treated by a low-temperature catalytic oxidation treatment strategy, the units between the primary purification layer and the secondary purification layer are treated by an activated carbon fiber adsorption regeneration strategy, and the units after the secondary purification layer are treated by a biological enzyme degradation treatment strategy.

9. The method for efficiently purifying anesthetic waste gas according to claim 1, wherein The specific operation of dividing the steady-state operation period and the fluctuation response period according to the gas concentration fluctuation data comprises: For any waste gas component type, the volatile organic compound concentration detection values in a continuous time sequence are collected to form a concentration time sequence curve, the first-order difference sequence of the concentration time sequence curve is calculated, and the time when the difference value is greater than zero is selected as the concentration rising time; The concentration rising times are arranged in ascending order of corresponding difference values to form a difference sequence, the second-order difference value of the difference sequence is calculated, the concentration rising time corresponding to the maximum second-order difference value is recorded as the concentration transition time, and the period before the concentration transition time is defined as the steady-state operation period, and the period after the concentration transition time is defined as the fluctuation response period.

10. The method of claim 9, wherein the method is characterized by, The specific operation of screening waste gas component types that need to be treated includes: For any waste gas component type, the average value of the volatile organic compound concentration detection values in the steady-state operation period is obtained as a reference concentration average value, and the average value of the volatile organic compound concentration detection values in the fluctuation response period is obtained as a response concentration average value; the absolute difference value between the response concentration average value and the reference concentration average value is calculated, the absolute difference value is normalized to obtain a concentration deviation coefficient, and if the concentration deviation coefficient is greater than a preset deviation threshold, the waste gas component type is marked as a waste gas component type that needs to be treated.

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