Air pollution waste gas purification method and system

Abstract

The invention relates to the technical field of waste gas treatment, in particular to an atmospheric pollution waste gas purification method and system, and the purification effect is improved through concentration sequence analysis and gas-liquid efficiency fitting, dynamic adjustment of a liquid amount strategy, backtracking of a residual concentration generation evaluation window and matching of a thermal response material. According to the invention, through linkage analysis of pollutant concentration data at the outlet of the spray tower and physical parameters such as molecular polarity, spray liquid viscosity and the like, a gas-liquid contact efficiency offset evaluation mechanism based on a time sequence is established, and the ability of sensing the dynamic change of the purification performance is effectively improved. The concentration change trend is refined, so that different pollution fluctuation states are accurately divided, a multi-dimensional slope judgment method is introduced to realize classified induction of the contact efficiency trend, and scientific support is provided for subsequent liquid flow regulation and control. According to the gas-liquid efficiency change characteristics under different fluctuation sections, a dynamic adaptation strategy is set for the liquid amount adjustment direction and amplitude, and regulation and control logic highly coupled with the pollution trend is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of waste gas treatment, and in particular to a method and system for purifying waste gas from air pollution. Background Art

[0002] The technical field of waste gas treatment includes relevant methods, devices and systems for purifying and controlling harmful gases emitted during industrial production processes. Its core content involves using physical, chemical or biological means to collect, separate and transform the generated waste gas, so as to reduce the pollutant emissions and minimize the impact on the environment. This technical field systematically covers multiple directions such as particulate matter capture, gaseous pollutant adsorption and transformation, catalytic decomposition of harmful components, biological purification, and low-temperature plasma treatment, and is applicable to the treatment of various types of waste gases in industries such as chemical engineering, power, metallurgy, pharmaceuticals, and spraying.

[0003] Among them, the method and system for purifying waste gas from air pollution refer to technical solutions for purifying waste gas containing pollutants such as particulate matter, nitrogen oxides, sulfur oxides, and volatile organic compounds generated from industrial or urban emission sources, by means of adsorption, absorption, catalytic oxidation or biodegradation, including using high-surface-area materials for gas adsorption and separation, capturing water-soluble gases through liquid spraying and absorption methods, decomposing chemical pollutants in waste gas using specific catalysts at specific temperatures, or biodegrading organic pollutants in waste gas by introducing a specific microbial population with specific metabolic functions.

[0004] Based on traditional purification systems that handle waste gas through fixed adsorption, absorption or catalytic methods, the gas-liquid contact process is conventionally set with static parameters. When dealing with sudden changes in emission concentration, it is difficult to correct the efficiency through real-time feedback, resulting in a more significant fluctuation in purification performance. During operation, the liquid flow rate often depends on manual adjustment within a set range, lacking a dynamic linkage mechanism for different pollution trends, which poses risks of over-adjustment or lag in flow rate allocation, leading to uneven system load and imbalance in treatment capacity. The residual level of pollutants is usually evaluated based on average values or instantaneous numerical values, ignoring the role of concentration changes in periodic trends, which limits the accurate identification of fluctuations in treatment effects and results in one-sided evaluation conclusions. The material selection is mostly based on conventional thermal conductivity or empirical data, without a thermal response matching mechanism under actual operating conditions, making it difficult to ensure the stable performance of adsorption packing in high-temperature or frequently fluctuating gas environments. For example, when dealing with high-temperature organic waste gas, the change trend of heat flux in terms of material response intensity is not considered, which may cause a sudden drop in adsorption efficiency or material denaturation problems, reducing the reliability of the system during long-term operation. The above defects are superimposed, resulting in a decline in the purification efficiency of existing systems in environments with significant pollution changes or unstable parameters, an increase in maintenance frequency, and it is difficult for adjustment strategies to accurately adapt to the changing rhythm of the emission end. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose a method and system for purifying waste gas from air pollution.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for purifying waste gas from air pollution, comprising the following steps:

[0007] S1: Obtain the pollutant concentration sequence at the outlet of the spray tower within a specified time, calculate the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the viscosity of the spray liquid, mark the offset state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtain the gas-liquid ratio offset degree data;

[0008] S2: According to the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data, fit it into an efficiency change trend curve at the sampling time interval, judge the curve trend characteristics, and generate a concentration fluctuation partition label sequence;

[0009] S3: Combine the current section type and the liquid phase flow rate record in the concentration fluctuation partition label sequence, set the liquid volume adjustment mechanism for each section, and generate the recommended adjusted liquid flow rate information;

[0010] S4: Refer to the recommended adjusted liquid flow rate information, trace back the residual concentration in the continuous cycle of the corresponding spray section, and set the section within the specified cycle as the analysis interval to generate a purification evaluation time window;

[0011] S5: Based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, screen the responsive and stable materials by combining the material thermal parameters, sort and select according to the response rate, and obtain the recommended air pollution purification materials.

[0012] The improvement of the present invention is that the gas-liquid ratio offset degree data includes a contact efficiency sequence, an offset identification label, and an offset degree value. The concentration fluctuation partition label sequence is specifically a change trend classification identifier, a trend section boundary index, and a trend change amplitude threshold. The recommended adjusted liquid flow rate information includes an adjustment direction determination, a proportional supplement value, and an accumulated flow rate recommendation value. The purification evaluation time window includes a cycle anchor time, a median time coordinate, and a symmetric evaluation interval range. The recommended air pollution purification materials are specifically target packing types, response rate rankings, and thermosensitivity adaptation materials.

[0013] The improvement of the present invention is that the specific steps of obtaining the pollutant concentration sequence at the outlet of the spray tower within a specified time, calculating the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the viscosity of the spray liquid, and marking the offset state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark to obtain the gas-liquid ratio offset degree data are as follows:

[0014] S101: Obtain the pollutant concentration sequence data at the outlet of the spray tower within a specified time period, extract the concentration values and sampling timestamps corresponding to adjacent time points, calculate the concentration change amount and time interval within each time period respectively based on the difference between adjacent time points, and generate a concentration change rate sequence;

[0015] S102: Based on the rate corresponding to each time period in the concentration change rate sequence, combine the pollutant molecular polarity data and the spray liquid viscosity parameter, calculate the gas-liquid contact efficiency for each time period, and generate the gas-liquid contact efficiency calculation result;

[0016] S103: According to the gas-liquid contact efficiency in the gas-liquid contact efficiency calculation result, call the equipment mass transfer efficiency reference value and the gas-liquid contact offset tolerance interval, calculate the difference and determine whether it exceeds the offset tolerance interval. If it exceeds, mark it as the offset state and record the corresponding time period information, and generate the gas-liquid ratio offset degree data.

[0017] The improvement of the present invention is that, according to the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data, it is fitted into an efficiency change trend curve according to the sampling time interval, the curve trend characteristics are judged, and the specific steps for generating the concentration fluctuation partition label sequence are as follows:

[0018] S201: Based on the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data, extract the time series and efficiency series according to the sampling time interval corresponding to each group of adjacent efficiency values, and construct a two-dimensional point set structure in chronological order to fit the efficiency change trend curve;

[0019] S202: Based on the efficiency change trend curve, extract equally spaced time points within a specified time period, calculate the average rate of change of the contact efficiency according to the curve slope at adjacent time points, and generate the average slope information of the gas-liquid efficiency change;

[0020] S203: Based on the average slope information of the gas-liquid efficiency change and the fluctuation range, call the decline zone threshold, the amplitude limit of the fluctuation zone, and the growth zone threshold in the trend determination criterion, classify the current slope mean value into intervals, determine the trend type, and mark the interval index corresponding to each section to generate the concentration fluctuation partition label sequence.

[0021] The improvement of the present invention is that, combined with the current section type and the liquid phase flow record in the concentration fluctuation partition label sequence, a liquid volume adjustment mechanism is set for each section, and the specific steps for generating the recommended adjusted liquid flow information are as follows:

[0022] S301: Combine the current section type in the concentration fluctuation partition label sequence with the liquid flow rate record. Based on the section label, determine whether it belongs to the decreasing type. If it is a decreasing section, record the original liquid flow data and set it as static retention. If it is not the decreasing type, mark it as a dynamically adjustable section, and generate a liquid flow maintenance or adjustment section identification result;

[0023] S302: According to the fluctuation section information in the liquid flow maintenance or adjustment section identification result, extract the gas-liquid contact efficiency change rate sequences of the current and the previous section, compare the change patterns and rate directions of the two sections, determine the level to which the liquid flow regulation range belongs, and set an adjustment amplitude label based on the current flow rate to establish the liquid flow change information for the fluctuation section;

[0024] S303: Based on the liquid flow change information for the fluctuation section, identify the growth rate label corresponding to the growth type section. Combine the existing flow rate records with the corresponding adjustment amplitude label for each section, and collate the output of the recommended liquid flow rate to be configured for each section to generate the recommended adjusted liquid flow rate information.

[0025] The improvement of the present invention is that for determining the level to which the liquid flow regulation range belongs, the formula is used:

[0026]

[0027] Calculate the liquid flow regulation level L, and set the liquid flow regulation range according to the liquid flow regulation level;

[0028] where, ΔR represents the average slope difference of the gas-liquid contact efficiency change between the current section and the previous section, R current represents the average efficiency change slope of the current section, R prev represents the average efficiency change slope of the previous adjacent section of the current section, R max represents the maximum slope of the efficiency change during operation, Q current represents the liquid phase flow rate of the current fluctuation section, Q prev represents the liquid flow rate of the previous section, Q ref represents the reference liquid flow rate.

[0029] The improvement of the present invention is that referring to the recommended adjusted liquid flow rate information, trace back the residual concentration within consecutive cycles in the corresponding spray section, and set the section within the specified cycle as the analysis interval. The specific steps for generating the purification evaluation time window are as follows:

[0030] S401: Based on the recommended adjusted liquid flow rate information, trace back the residual concentration records within consecutive cycles in the corresponding spray section, extract the residual concentration sequence within the cycle, and identify the minimum value position of the concentration sequence within each cycle, record the time node corresponding to the minimum residual concentration in each cycle, and generate the minimum residual time node sequence;

[0031] S402: Calculate the time differences between cycles respectively according to the time intervals between adjacent time points in the minimum residual time node sequence, compare each group of time differences with the liquid volume change response time scale item by item, screen out all time point pairs that satisfy being less than or equal to the response time scale, extract the intermediate position time points, and generate a median response time point sequence;

[0032] S403: Take each time point in the median response time point sequence as the center point, intercept a fixed-length time period interval based on the set symmetric time interval range from the residual concentration record, and summarize all interval data to form a unified set of evaluation time period intervals, generating a purification evaluation time window.

[0033] The improvements of the present invention are as follows. Based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, combined with the material thermal parameters, screen the response-stable materials, sort and select according to the response rate, and the specific steps for obtaining the recommended air pollution purification materials are as follows:

[0034] S501: Based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, call the thermal resistance and specific heat capacity parameters of the candidate materials, sequentially obtain the thermal response data of the materials in the corresponding time periods, judge by associating the temperature change rate, the thermal conductivity change trend with the material thermal physical properties parameters, extract the performance strength of the heat flux response in the time dimension, and perform merging processing on the performance strength to generate a material thermal response strength sequence;

[0035] S502: According to the material thermal response strength sequence, obtain the response strength list of each material in the full cycle, respectively count the number of times of switching of the continuous change direction in each group of lists, extract all fluctuation frequency information, and compare it with the set response frequency threshold, screen out the material names with frequencies higher than the response frequency threshold, and generate a set of materials with effective response frequencies;

[0036] S503: Based on the thermal response strength change rate corresponding to each material in the set of materials with effective response frequencies, sort according to the magnitude of the rate, extract the material numbers with the top ranking positions from the sorting results, and combine with the replacement requirements of the target spray section for heat-sensitive gas absorption fillers, and select the replaceable materials after matching judgment to generate a list of recommended air pollution purification materials.

[0037] The improvements of the present invention are as follows. For judging the performance strength of the heat flux response in the time dimension, the formula is used:

[0038]

[0039] Calculate the candidate material at time point tf The normalized heat response intensity Q on f , and judge the performance strength of the heat flux response in the time dimension according to the heat response intensity;

[0040] Among them, R λ represents the thermal resistance of the material, and λ g (t f ) represents the gas thermal conductivity of the target waste gas at time point t f , λ ref represents the reference thermal conductivity for normalization, dT / dt represents the waste gas temperature change rate at the current moment, and (dT / dt) ref represents the normalization reference value of the temperature change rate, C p represents the specific heat capacity of the current candidate material, and C p,ref is the reference specific heat capacity.

[0041] An air pollution waste gas purification system, the system includes:

[0042] The gas-liquid contact offset evaluation module obtains the pollutant concentration sequence at the outlet of the spray tower within a specified time, combines the pollutant molecular polarity data and the spray liquid viscosity to calculate the gas-liquid contact efficiency, marks the offset state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtains the gas-liquid ratio offset degree data;

[0043] The concentration trend partition identification module fits the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data into an efficiency change trend curve according to the sampling time interval, judges the curve trend characteristics, and generates a concentration fluctuation partition label sequence;

[0044] The liquid flow adjustment strategy generation module combines the current section type and the liquid phase flow rate record in the concentration fluctuation partition label sequence, sets the liquid volume adjustment mechanism for each section, and generates the recommended adjusted liquid flow rate information;

[0045] The purification period evaluation window construction module refers to the recommended adjusted liquid flow rate information, traces back the residual concentration in the continuous cycle of the corresponding spray section, and sets the section within the specified cycle as the analysis interval to generate a purification evaluation time window;

[0046] The heat response material screening and recommendation module selects stable response materials based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, combines the material thermal parameters, sorts and selects according to the response rate, and obtains the recommended air pollution purification materials.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by linking the pollutant concentration data at the outlet of the spray tower with physical parameters such as molecular polarity and spray liquid viscosity, a gas-liquid contact efficiency offset evaluation mechanism based on time series is established, which effectively improves the perception of dynamic changes in purification performance. The refinement of the concentration change trend enables the precise division of different pollution fluctuation states, and introduces a multi-dimensional slope judgment method to achieve the classification and induction of the contact efficiency trend, providing scientific support for subsequent liquid flow control. According to the gas-liquid efficiency change characteristics under different fluctuation sections, a dynamic adaptation strategy is set for the liquid volume adjustment direction and amplitude, forming a control logic that is highly coupled with the pollution trend, reducing the uncertainty of artificially set parameters. The periodic backtracking of the residual concentration constructs a purification evaluation time window with the help of center point anchoring and symmetric intervals to enhance the timeliness and resolution of purification level monitoring. The response stability of the response materials is sorted in combination with the thermal physical parameters, so that the material selection has pertinence and data support, and the response efficiency and thermal sensitivity adaptation performance of the replacement of high-temperature exhaust gas treatment materials are improved. The overall logic consists of five interconnected levels: pollution change perception, trend division, liquid flow regulation, cycle backtracking and material response. Through a continuous feedback mechanism and multi-parameter fusion judgment, a closed-loop optimization structure is constructed to achieve the evolution of purification strategies from static settings to dynamic responses, effectively enhancing the ability to respond to changeable pollution environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 This is a detailed flow chart of step S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of step S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of step S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of step S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of step S5 of the present invention;

[0055] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0058] Please refer to Figure 1 , the present invention provides a technical solution: an air pollution waste gas purification method, including the following steps:

[0059] S1: Obtain the pollutant concentration sequence at the outlet of the spray tower within a specified time, calculate the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the viscosity of the spray liquid, mark the offset state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtain the gas-liquid ratio offset degree data;

[0060] S2: According to the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data, fit it into an efficiency change trend curve according to the sampling time interval, judge the curve trend characteristics, and generate a concentration fluctuation partition label sequence;

[0061] S3: Combine the current section type and the liquid phase flow rate record in the concentration fluctuation partition label sequence, set the liquid volume adjustment mechanism for each section, and generate the recommended adjusted liquid flow rate information;

[0062] S4: Refer to the recommended adjusted liquid flow rate information, trace back the residual concentration in the continuous cycle of the corresponding spray section, and set the section within the specified cycle as the analysis interval to generate a purification evaluation time window;

[0063] S5: Based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, screen the response-stable materials by combining the material thermal parameters, sort and select according to the response rate, and obtain the recommended air pollution purification materials;

[0064] The gas-liquid ratio offset degree data includes the contact efficiency sequence, offset identification label, and offset degree value. The concentration fluctuation partition label sequence is specifically the change trend classification identification, trend section boundary index, and trend change amplitude threshold. The recommended adjusted liquid flow rate information includes the adjustment direction determination, proportional supplement value, and cumulative flow rate recommended value. The purification evaluation time window includes the cycle anchor time, median time coordinate, and symmetric evaluation interval range. The recommended air pollution purification materials are specifically the target packing type, response rate ranking, and heat sensitivity adaptation material.

[0065] Please refer toFigure 2 , obtain the pollutant concentration sequence at the outlet of the spray tower within a specified time, calculate the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the viscosity of the spray liquid, mark the deviation state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and the specific steps to obtain the gas-liquid ratio deviation degree data are as follows:

[0066] S101: Obtain the pollutant concentration sequence data at the outlet of the spray tower within a specified time, extract the concentration values and sampling timestamps corresponding to adjacent time points, and calculate the concentration change amount and time interval within each time period respectively according to the difference between adjacent time points to generate a concentration change rate sequence;

[0067] The acquisition of the pollutant concentration at the outlet of the spray tower is based on the continuous emission monitoring system (such as CEMS) to automatically record the pollutant concentration (taking SO2 as an example) per minute within a specified time period. The sampling frequency is usually 1 time / minute, and the corresponding time series can be set as t i , the pollutant concentration sequence is set as C i , where i = 1, 2,..., n. Taking a certain example from 12:00:00 to 12:09:00 on April 10, 2025, 10 groups of data are sampled, and the corresponding concentration sequence is: C = {48, 50, 53, 55, 58, 60, 63, 65, 67, 70} (mg / m 3 ); the time series is once every 60 seconds, that is, Δt = t i+1 -t i = 60s, and the concentration change difference is calculated as: ΔC i = C i+1 -C i ; then calculate the concentration change rate sequence according to the following formula: where, R i : The pollutant concentration change rate in the i-th time period, with the unit of mg / (m 3 ·s), ΔC i : The concentration difference in the i-th time period, with the unit of mg / m 3 , Δt: The time interval between adjacent sampling points, with the unit of second (s). For example, the first segment of data is: ΔC1 = 50 - 48 = 2 (mg / m 3 ), Δt = 60s; And so on, to obtain the complete rate sequence: R = {0.0333, 0.05, 0.0333, 0.05, 0.0333, 0.05, 0.0333, 0.0333, 0.05}; each segment of rate data corresponds to its time period, forming the following structure: {12:00–12:01:0.0333, 12:01–12:02:0.05,..., 12:08–12:09:0.05}.

[0068] S102: Calculate the gas-liquid contact efficiency for each time period based on the rate corresponding to each time period in the concentration change rate sequence, and combine the pollutant molecular polarity data and the spray liquid viscosity parameter to generate the calculation result of the gas-liquid contact efficiency;

[0069] When calculating the gas-liquid contact efficiency of each concentration change rate, the influence of the polarity parameter of the pollutant molecule and the viscosity of the spray liquid on the mass transfer process needs to be considered. The gas-liquid contact efficiency is expressed as: where η c,i : The gas-liquid contact efficiency (dimensionless) in the i-th time period, R i : The concentration change rate in the i-th time period, with the unit of mg / (m 3 ·s), μ: The electric dipole moment (polarity coefficient) of the pollutant molecule, with the unit of Debye (D). For example, the μ of the SO2 molecule is 1.6 D, η: The viscosity of the spray liquid, with the unit of millipascal-second (mPa·s). For example, the viscosity η of the 5% NaOH solution at 25 °C is 1.02 mPa·s. Taking the rate R1 = 0.0333 in the first segment as an example and substituting it: Repeat the above calculation. Suppose the obtained efficiency sequence is: η c = {0.0523, 0.0784, 0.0523, 0.0784, 0.0523, 0.0784, 0.0523, 0.0523, 0.0784}; Each segment of contact efficiency corresponds to the sampling time period, such as {12:00–12:01: 0.0523,..., 12:08–12:09: 0.0784}, forming a complete efficiency calculation data structure.

[0070] S103: According to the gas-liquid contact efficiency in the calculation result of the gas-liquid contact efficiency, call the benchmark value of the equipment mass transfer efficiency and the gas-liquid contact offset tolerance interval, calculate the difference and judge whether it exceeds the offset tolerance interval. If it exceeds, mark it as the offset state and record the corresponding time period information to generate the gas-liquid ratio offset degree data;

[0071] Compare the above contact efficiency sequence with the equipment design standard. Define the benchmark value of the mass transfer efficiency as η0 = 0.065, and the allowable offset tolerance interval of the gas-liquid contact efficiency is ±0.01, that is, the normal interval is: [η0 - 0.01, η0 + 0.01] = [0.055, 0.075]; The offset value of each segment is calculated as follows: Δη i = η c,i - η0; where Δη i : The contact efficiency offset value (dimensionless) in the i-th time period, η c,i : The actually calculated gas-liquid contact efficiency, η0: The benchmark value of the equipment reference mass transfer efficiency. The judgment basis is: if |Δη iIf it is > 0.01, it exceeds the tolerance and is marked as "offset state"; if it does not exceed, it is marked as "normal". For example, the first paragraph: The second paragraph: The judgment results are summarized as: state sequence = {offset, offset, offset, offset, offset, offset, offset, offset, offset}; each paragraph is bound to a time period to form a structure: {12:00–12:01: offset, 12:01–12:02: offset,..., 12:08–12:09: offset}; this data structure constitutes the basis for the complete time-series analysis of the offset degree for use in subsequent processes.

[0072] Please refer to Figure 3 , according to the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data, fit it into an efficiency change trend curve according to the sampling time interval, and judge the curve trend characteristics. The specific steps for generating the concentration fluctuation partition label sequence are as follows:

[0073] S201: Based on the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio offset degree data, extract the time series and efficiency series according to the sampling time interval corresponding to each group of adjacent efficiency values, and construct a two-dimensional point set structure in chronological order to fit the efficiency change trend curve;

[0074] First, pair the sequence according to the time stamps corresponding to the data acquisition to form a time series and an efficiency series. For example, in the spray tower monitoring system, the recorded time stamps are {12:00, 12:01, 12:02, 12:03, 12:04, 12:05}, and the corresponding efficiency series is {0.0523, 0.0784, 0.0523, 0.0784, 0.0523, 0.0784}. The sampling interval corresponding to each group of adjacent data is 60 seconds. After sorting the two columns of data through Excel and importing them into MATLAB, use MATLAB to establish a two-dimensional point set (x i1 , y i1 ), where x i1 is time (uniformly represented in seconds conversion such as 0, 60, 120...), and y i1 is the gas-liquid contact efficiency value. Use the spline(x, y) function in the command window to generate a cubic spline interpolation function, and use the plot function to visualize the overall change trend by plotting the point set and the interpolation result. You can also call the polyfit(x, y, n) function to perform polynomial fitting on the data points, where n = 2 is a quadratic fit. After generating the fitting coefficient vector, use the polyval function to plot the efficiency change trend curve, compare the fitting residuals at different orders, and finally select the trend curve with a better fitting degree as the expression of the efficiency change trend.

[0075] S202: Based on the efficiency change trend curve, extract equally spaced time points within a specified time period. According to the curve slopes at adjacent time points, calculate the average rate of change of the contact efficiency, and generate the average slope information of the gas-liquid efficiency change;

[0076] Based on the efficiency change trend curve obtained by MATLAB fitting, select an analysis time period such as from 12:00 to 12:10, set the sampling step size to 60 seconds, and use the linspace function in MATLAB to generate equally spaced time points within this time period, such as {0, 60, 120, …, 600}. Obtain the fitted efficiency values at each time point through the fitting function expression, and then calculate the slope according to any two adjacent points. For example, if the efficiency values at the 60-second and 120-second points after fitting are 0.0784 and 0.0523, then calculate the slope of this section as (0.0523 - 0.0784) / (120 - 60) = -0.000435. Process all adjacent time period data in sequence, write a loop or vectorized operation in MATLAB to complete the batch calculation of multiple slope sections, and take the average of all calculated slope values as the average slope of the gas-liquid contact efficiency change within this time period. For example, if the 10 slope values are {-0.000435, 0.000435, -0.000435, …}, then use the mean function to obtain its mean value as the output parameter.

[0077] S203: Based on the average slope information and fluctuation range of the gas-liquid efficiency change, call the decline zone threshold, fluctuation zone amplitude limit, and growth zone threshold in the trend determination criteria to classify the current slope mean value into intervals, determine the trend type, and label the interval index corresponding to each section, generating a concentration fluctuation partition label sequence;

[0078] After obtaining the average slope value, set the slope threshold for judging the trend interval. The threshold for the decline area is set to -0.0004, the threshold for the growth area is set to +0.0004, and the threshold for the fluctuation area is set to be between -0.0004 and +0.0004. All threshold settings are based on the historical monitoring and statistical data of the change in gas-liquid contact efficiency. In this system, when the absolute value of the change rate is less than 0.0004, the system response change is not obvious and is classified into the fluctuation area; when the slope is continuously greater than 0.0004, it is observed that the efficiency continues to rise; when it is less than -0.0004, it is monitored as a downward trend. This threshold is obtained from the average value and the 1.5-fold standard deviation interval of the 10-minute concentration change range data fitting under normal spray absorption conditions. After setting, the trend is judged based on whether the slope value exceeds this threshold interval. For example, if the current average slope is -0.000435, its value is less than the decline area threshold of -0.0004, and it is classified into the decline area trend and marked with an index of 0. If the average slope is 0.0002 and falls within the fluctuation interval, it is marked with an index of 1. If it is 0.0005, it is classified into the growth area and marked with an index of 2. The trend numbers of all time periods form a label sequence in chronological order, such as {0, 1, 2, 0, 0, 1}. Finally, in MATLAB, this label sequence is bound to the time period and exported to form a complete concentration fluctuation partition label data structure.

[0079] Please refer to Figure 4 , combined with the current section type and liquid phase flow rate record in the concentration fluctuation partition label sequence, set the liquid volume adjustment mechanism for each section, and the specific steps to generate the recommended adjusted liquid flow rate information are as follows:

[0080] S301: Combine the current section type in the concentration fluctuation partition label sequence with the liquid phase flow rate record, and judge whether it belongs to the decline type according to the section mark. If it is a decline section, record the original liquid flow data and set it as static retention. If it is not a decline type, mark it as a dynamic adjustment section, and generate the liquid flow maintenance or adjustment section identification result;

[0081] Combined with the current section type in the concentration fluctuation partition tag sequence and the liquid flow rate record, first extract the corresponding type information of each section from the trend tag sequence. For example, if the tag sequence is {0, 1, 2, 0, 1, 1}, it is defined that a value of 0 represents a "descending" type section, and values of 1 and 2 represent "fluctuating" and "growing" type sections respectively. Establish an index mapping structure between the tags and time periods. Taking 6 consecutive sections as an example, the corresponding times of the sections are 12:00–12:01, 12:01–12:02, 12:02–12:03, 12:03–12:04, 12:04–12:05, 12:05–12:06, and the corresponding liquid flow rate records are {22.5, 22.5, 22.5, 23.0, 23.5, 23.5} (unit: L / min). After judging the section type, mark the first and fourth sections as the descending type, and set their liquid flow values of 22.5 L / min and 23.0 L / min as static retention according to the rules, which do not participate in the subsequent adjustment calculation and are marked as "static sections". The remaining second, third, fifth, and sixth sections are identified as non-descending type sections, that is, sections that can participate in the adjustment analysis, and are marked as "dynamic sections". Finally, establish a maintenance and adjustment state structure sequence as {static, dynamic, dynamic, static, dynamic, dynamic}, and each item corresponds to the start and end times of its section and the current flow value, constituting the identification result of the liquid flow maintenance or adjustment section.

[0082] S302: According to the fluctuation section information in the identification result of the liquid flow maintenance or adjustment section, extract the change rate sequences of the gas-liquid contact efficiency of the current and the previous section, compare the change patterns and rate directions of the two sections, judge the level of the liquid flow adjustment range, and set an adjustment amplitude tag based on the current flow rate to establish the flow rate change information of the fluctuation section;

[0083] Extract the change rate sequences of the gas-liquid contact efficiency of the current fluctuation section and its previous section. Let the current processing object be the fifth section, and its previous section be the fourth section. The average contact efficiency change slope values recorded in the system are R current = 0.0002 s -1 for the fifth section and R prev = -0.0005 s -1 for the fourth section. Calculate the slope difference between the two as: ΔR = R current - R prev = 0.0002 - (-0.0005) = 0.0007 s -1 ; To unify different physical quantity units, a dimensionless adjustment index L is constructed using a standardization processing method to judge the level of the liquid flow adjustment range. The formula is as follows:

[0084]

[0085] Among them, L represents the liquid flow regulation level, which is a dimensionless quantity used to measure the comprehensive change range of the current fluctuation section compared with the previous section in terms of contact efficiency and flow rate; ΔR represents the average slope difference of the gas-liquid contact efficiency change between the current section and the previous section, with the unit of s -1 , which is used to reflect the mutation degree of the efficiency change rate; R current represents the average efficiency change slope of the current section, with the unit of s -1 , that is, the speed of efficiency change per unit time; R prev represents the average efficiency change slope of the previous adjacent section of the current section, with the unit of s -1 ; R max represents the known maximum slope value of the efficiency change during the operation of the entire system, with the unit of s -1 , which is used to standardize the slope-related parameters; Q current represents the liquid flow rate value of the current fluctuation section, with the unit of L / min, that is, the volume of liquid sprayed in per minute; Q prev represents the liquid flow rate value of the previous section, with the same unit of L / min; Q ref represents the reference liquid flow rate value set in the system design or configuration, with the unit of L / min. Usually, the target flow rate value under the rated working condition is taken, which is used to make a normalized comparison of the flow rate change degrees of different sections.

[0086] For example, let ΔR be the slope difference (s -1 ), Q current = 23.5 L / min, Q prev = 23.0 L / min be the liquid flow rates of the current and the previous sections respectively, and Q ref = 25 L / min be the system-rated reference flow rate,

[0087] R max = 0.0015 s -1 be the maximum slope value monitored in the system for normalization processing, and the specific calculation is as follows:

[0088] Slope difference normalization term:

[0089] Flow rate change normalization term:

[0090] Slope product normalization term:

[0091] The total is: L = 0.4667 + 0.02 + 0.0444 = 0.5311.

[0092] Set according to the adjustment level classification standard: the low level is L < 0.3, the medium level is 0.3 ≤ L < 0.6, and the high level is L ≥ 0.6. Classify the current result L = 0.5311 into the medium-level adjustment range, and set the corresponding adjustment range to ±6%. Based on the current liquid flow rate of 23.5 L / min, calculate the adjustment range as 23.5 × 0.06 = 1.41 L / min. Organize the flow rate change information for this section as follows: time period 12:04–12:05, current liquid flow 23.5 L / min, flow rate in the previous section 23.0 L / min, slope difference 0.0007, adjustment level medium level, recommended adjustment range ±6%, recommended adjustment amount ±1.41 L / min. This structure is used as the flow rate change information for the fluctuation section for subsequent use.

[0093] S303: Based on the flow rate change information of the fluctuation section, identify the growth rate labels corresponding to the growth type sections. Combine the existing flow rate records with the adjustment range labels corresponding to each section under each section, and organize the output of the recommended liquid flow rate to be configured for each section to generate the recommended adjusted liquid flow rate information.

[0094] Screen all sections with a label value of 2 from the concentration fluctuation partition label sequence as the processing objects for the growth type. For example, the 3rd and 6th sections are identified as growth sections, and their corresponding times are 12:02–12:03 and 12:05–12:06 respectively. The system records their average slopes as 0.0006 and 0.0009 respectively. According to the same standard in paragraph 2, classify 0.0006 as the medium-level growth label and 0.0009 as the high-level growth label. The basic liquid flow rate of the 3rd section is 22.5 L / min, and the corresponding adjustment range is ±6%. Calculate the recommended adjustment amount as 22.5 × 0.06 = 1.35 L / min, and configure the recommended value as 22.5 + 0.5 × 1.35 = 23.175 L / min by taking the median value of the adjustment range. The basic liquid flow of the 6th section is 23.5 L / min, and the corresponding high-level adjustment range is ±10%. The recommended adjustment amount is 23.5 × 0.10 = 2.35 L / min, and the recommended value is 23.5 + 0.5 × 2.35 = 24.675 L / min. Combine the recommended results of all growth sections to form the final recommended liquid flow rate output structure as follows: for the 3rd section, the time is 12:02–12:03, the basic flow rate is 22.5 L / min, the growth level is medium, and the recommended flow rate is 23.18 L / min; for the 6th section, the time is 12:05–12:06, the basic flow rate is 23.5 L / min, the growth level is high, and the recommended flow rate is 24.68 L / min. Finally, summarize and output the recommended adjusted liquid flow rate information sequence for use in device flow control strategy configuration or execution call.

[0095] Please refer to Figure 5, referring to the adjusted liquid flow rate information recommended, trace back the residual concentration within consecutive cycles in the corresponding spray section, and set the section within a specified cycle as the analysis interval. The specific steps for generating the purification evaluation time window are as follows:

[0096] S401: Based on the adjusted liquid flow rate information recommended, trace back the residual concentration records within consecutive cycles in the corresponding spray section, extract the residual concentration sequence within the cycle, identify the position of the minimum value in the concentration sequence for each cycle, and record the time nodes corresponding to the minimum residual concentration in each cycle to generate a sequence of minimum residual time nodes;

[0097] First, locate the spray section corresponding to the recommended flow rate by binding with the time index, read the sequence of residual concentration records of this spray section in the system within consecutive monitoring cycles, and intercept the concentration time series containing several sampling points with the start and end times of this section as the index. In a specific implementation, for example, if the recommended adjusted flow rate acts on the time period from 12:00 to 12:06, and the residual concentration sampling period is once every 30 seconds, then the concentration sequence such as {14.3, 13.9, 13.5, 12.8, 13.2, 13.7, 14.0} can be extracted within this section, and the corresponding time series is {12:00:00, 12:00:30, 12:01:00,..., 12:03:00}. For the concentration sequence within each cycle, use the minimum value identification method. The min function in MATLAB can be used to locate the residual concentration array, or the numpy.argmin() function in Python can be used to determine the minimum value index, so as to obtain the time point where the minimum value is located. For example, the minimum concentration value in the above sequence is 12.8, and the corresponding time is 12:01:30. Take this as the minimum residual time point of the current cycle. Repeat the above operations to process all the cycle periods of the recommended flow rate section. Extract the concentration sequence of each section one by one through a loop structure and identify the time where the minimum value is located. Finally, form a complete sequence of minimum residual time nodes, such as {12:01:30, 12:04:00, 12:06:30}. This sequence is used as the input set for the subsequent response time extraction operation.

[0098] S402: According to the time intervals between adjacent time points in the sequence of minimum residual time nodes, calculate the time differences between cycles respectively, and compare each group of time differences with the liquid volume change response time scale item by item. Screen out all the time point pairs that satisfy being less than or equal to the response time scale, extract the intermediate position time points, and generate a sequence of median response time points;

[0099] According to the time intervals between adjacent time points in the minimum residue time node sequence, calculate the time differences between all cycles respectively and compare them with the liquid volume change response time scale defined by the system. In specific operations, assume the minimum residue time node sequence is {12:01:30, 12:04:00, 12:06:30}. First, convert each time stamp to seconds, that is, {7290, 7440, 7590} seconds. Calculate the differences between adjacent time points in turn: t2 - t1 = 7440 - 7290 = 150 seconds, t3 - t2 = 7590 - 7440 = 150 seconds. Define the liquid volume change response time scale as 180 seconds. This time threshold can be set according to the physical response lag time of the system or obtained by statistical analysis of historical data. For example, calculate the average value plus one standard deviation of the concentration response time after multiple liquid flow regulation processes to obtain a reasonable threshold. After setting, compare each time difference with 180 seconds in turn to determine whether it is less than or equal to the threshold. The time pairs that meet the conditions are selected. Extract the middle time point from the time pairs that meet the conditions, that is, the median time point is t m =(t i +t i+1 ) / 2. For example, for the time pair between 12:01:30 and 12:04:00, the median time is 12:02:45. Similarly, take the midpoint between 12:04:00 and 12:06:30 as 12:05:15. Combine the middle time points calculated from all time pairs that meet the conditions to form a median response time point sequence, such as {12:02:45, 12:05:15}. This structure is used to locate the center position of the evaluation window.

[0100] S403: Take each time point in the median response time point sequence as the center point. According to the set symmetric time interval range, intercept a fixed-length time period interval centered on the center point from the residual concentration record, and summarize all interval data to form a unified set of evaluation time period intervals, generating a purification evaluation time window;

[0101] Taking each time point in the median response time point sequence as the center point, according to the set symmetric time interval range, a fixed-length time period interval centered on this center point is intercepted from the residual concentration record. If the symmetric interval length is set to 4 minutes, then each midpoint extends 2 minutes forward and backward to form an evaluation time section. For example, the midpoint 12:02:45 corresponds to the evaluation section from 12:00:45 to 12:04:45, and 12:05:15 corresponds to the section from 12:03:15 to 12:07:15. In specific operations, it can be converted to a second-level interval through timestamp offset addition and subtraction and then matched and intercepted with the original time series of residual concentration. The index slicing method in MATLAB or the interval slicing operation based on the datetime module in Python is used to extract all residual concentration values within this time period. If the residual concentration sampling interval is 30 seconds, each section should contain approximately 9 sample points. A concentration subsequence for each evaluation interval is constructed and its corresponding time index information is bound. Finally, the concentration data of all sections are merged into a unified structure to form a set of purification evaluation time windows, and the output is {Window 1: [12:00:45–12:04:45], Window 2: [12:03:15–12:07:15]}.

[0102] Please refer to Figure 6 , based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, combined with the material thermal parameters, screen for materials with stable responses, sort and select according to the response rate. The specific steps to obtain the recommended air pollution purification materials are as follows:

[0103] S501: Based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, call the thermal resistance and specific heat capacity parameters of the candidate materials, and sequentially obtain the thermal response data of the materials within the corresponding time period. By correlating the temperature change rate, the trend of thermal conductivity change, and the material thermal physical properties parameters, judge the strength of the heat flux response in the time dimension, and perform a merging process on the strength, generating a material thermal response intensity sequence;

[0104] First, extract the gas temperature sampling data second by second within each evaluation window, organize it into a time–temperature comparison table and perform a continuous difference operation to obtain the temperature change rate sequence. At the same time, read the target waste gas thermal conductivity curve data stored in the system to form the gas phase thermal conductivity value corresponding to each second. Then, sequentially read the thermal resistance values and specific heat capacity parameters of each material in the candidate material database, and construct a thermal response calculation structure for each material. For each material and each time point, respectively correlate the gas temperature change rate and the thermal conductivity curve value at that moment, and calculate its thermal response intensity index in combination with the material thermal physical properties parameters. The thermal response intensity index is expressed in the following dimensionless form:

[0105]

[0106] Among them, Q f represents the normalized thermal response intensity of the candidate material at time point t f , dimensionless; R λ represents the thermal resistance of the material, λ g (t f ) represents the gas thermal conductivity of the target exhaust gas at time point t f , with the unit of W / m·K; λ ref represents the reference thermal conductivity for normalization, generally taking the thermal conductivity value of normal-temperature air, and the unit is also W / m·K; dT / dt represents the change rate of the exhaust gas temperature at the current moment, with the unit of K / s, and the calculation method is the difference between two adjacent temperature sampling points divided by the sampling time interval; (dT / dt) ref represents the normalized reference value of the temperature change rate, generally determined by the mean value of historical data, and the unit is also K / s; C p represents the specific heat capacity at constant pressure of the current candidate material, with the unit of J / kg·K; C p,ref is the reference specific heat capacity value, with the unit of J / kg·K, and usually takes the specific heat capacity value of standard media such as water or air. After calculating each material at each time point through the above formula, a set of thermal response intensity sequences are generated. Further, the intensity sequences of each material are merged and evaluated according to the mean value, range or change trend. For example, in the time period of a certain material, the thermal response intensity sequence is obtained as {1.21, 1.35, 1.40, 1.47}, then it is recorded as the response performance of this material under the current evaluation window, and finally a material thermal response intensity sequence is generated for horizontal comparison and subsequent screening. Specifically, taking material A as an example, its thermal resistance is R λ = 0.003 K·m 2 / W, the specific heat capacity is C p = 900 J / kg·K, the reference thermal conductivity is λ ref = 0.026 W / m·K, the reference temperature change rate is (dT / dt) ref = 0.12 K / s, the reference specific heat capacity is C p,ref = 1000 J / kg·K, the exhaust gas thermal conductivity at a certain current time point is λ g (t f ) = 0.052 W / m·K, the temperature change rate is dT / dt = 0.15 K / s. According to the formula, the calculations are as follows: the first item is 1 / 0.003 = 333.33, the second item is 0.052 / 0.026 = 2.0, the third item is 0.15 / 0.12 = 1.25, the fourth item is 1000 / 900 ≈ 1.111, and the final thermal response intensity is Q f= 333.33 · 2.0 · 1.25 · 1.111 ≈ 925.9. Compare this value with the thermal response intensities of other materials in the same time window. For example, the thermal response intensity of Material B is 534.6 and that of Material C is 789.3. Then, Material A has the strongest thermal response and has the ability to transfer heat preferentially and follow gas-phase thermal disturbances within this time window. All calculation results are used to establish the "thermal response intensity sequence" structure and serve as an important input basis for subsequent judgment of response fluctuation frequencies and selection of thermosensitive adsorption packing materials. This thermal response intensity will also be used as the dominant quantity in steps such as identifying and sorting materials with continuous fluctuation behaviors.

[0107] S502: According to the thermal response intensity sequence of materials, obtain the list of response intensities of each material within the full cycle, respectively count the number of times the continuous change direction switches in each group of lists, extract all fluctuation frequency information, and compare it with the set response frequency threshold. Screen out the material names with frequencies higher than the response frequency threshold to generate a set of materials with effective response frequencies.

[0108] First, obtain the thermal response intensity values of each candidate material arranged in a time series in seconds within the purification evaluation time window from the output of the previous stage, and construct a one-to-one mapping structure between the material numbers and the thermal response lists. For example, the intensity sequence corresponding to Material A is 875.4, 902.2, 925.9, 915.7, 901.3, Material B is 519.1, 527.6, 534.6, 530.2, 524.8, and Material C is 763.4, 773.1, 789.3, 785.2, 772.9. For each material, calculate the number of times the continuous change direction switches in its response intensity sequence in turn. The specific judgment method is to construct two adjacent differences from any three adjacent data and determine whether the signs of these two differences are opposite. If they are opposite, it indicates that a direction reversal behavior occurs. For example, in Material A, the first to the third values gradually increase, while the fourth value drops from 925.9 to 915.7, showing a decrease and a direction reversal. Then, the further drop from 915.7 to 901.3 belongs to continuous decrease and does not constitute a new reversal. Therefore, only one reversal is recorded in this section, denoted as frequency 1. The whole process of Material B is a small increase and decrease with stable changes, and the frequency statistic is 0. For Material C, the third value rises to the peak of 789.3 and then drops to 785.2, constituting one reversal. The fifth value continues to drop to 772.9. Therefore, the frequency is still 1. Arrange the direction change frequencies of all materials in turn, Material A is 1 time, Material B is 0 times, and Material C is 1 time. Then compare with the preset response frequency threshold. The threshold is generally set according to the average fluctuation times and standard deviation of materials in historical test data. Here, the qualified standard is set to not exceed 2 times. Any material with the number of change direction switches greater than this threshold is regarded as having unstable thermal response behavior and is excluded. For example, if the response frequency of Material D is 4, it should be excluded, and this material will no longer participate in subsequent analysis. Finally, retain the material numbers of all materials that meet the frequency requirements to form a set of materials with effective response frequencies. This set will be bound to the original thermal response intensity sequence, and only the materials in the set are allowed to enter the next stage of the thermal response change rate sorting and spray section adsorption filler replacement matching process.

[0109] S503: Based on the thermal response intensity change rate corresponding to each material in the set of materials with effective response frequencies, sort according to the magnitude of the rate, and extract the material numbers with forward sorting positions from the sorting results. Combine with the replacement requirements of the target spray section for the heat-sensitive gas absorption filler, and select the replaceable materials after matching judgment to generate a recommended list of air pollution purification materials;

[0110] First, extract the initial and final thermal response intensity values and the time span of each material within the purification evaluation time window, and calculate its average change rate per unit time as the basis for subsequent sorting. Specifically, taking Material A as an example, its initial response value is 875.4, the final response value is 901.3, and the window length is 5 seconds. Therefore, the average change rate is the difference divided by the time, that is, increasing by 5.18 units per second. Material B increases from 519.1 to 524.8, with an average change of 1.14 units per second. Material C increases from 763.4 to 772.9, with an average change of 1.9 units per second. Compose a correspondence table of material numbers and rates from the above rate results, sort them from high to low, and the order is Material A, Material C, Material B. The system is set to preferentially extract the top two as candidate materials for subsequent high-sensitivity responses, namely Material A and Material C. Then, combined with the actual absorption performance requirements of the target spray section, compare the technical parameters of these two materials to determine whether they meet technical thresholds such as the minimum thermal conductivity, the maximum working temperature, and the minimum structural strength. For example, looking up Material A in the table, its thermal conductivity is 0.23, with the unit of watt per meter per Kelvin, the upper limit of the working temperature is 210 degrees Celsius, and the structural impact resistance is 6.2 kilojoules per square meter, all of which meet the requirements. However, the thermal conductivity of Material C is 0.19, which is lower than the limit standard of 0.21, so it is not passed. Only Material A is retained as the thermosensitive response material and enters the recommendation list. This recommendation result will be bound to the complete parameters in the system material database for subsequent control systems to call or feedback and confirm in the material replacement task of the spray section.

[0111] Please refer to Figure 7 , an air pollution waste gas purification system, the system includes:

[0112] The gas-liquid contact deviation evaluation module obtains the pollutant concentration sequence at the outlet of the spray tower within a specified time, combines the pollutant molecular polarity data and the spray liquid viscosity to calculate the gas-liquid contact efficiency, marks the deviation state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtains the gas-liquid ratio deviation degree data;

[0113] The concentration trend partition identification module fits the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio deviation degree data into an efficiency change trend curve according to the sampling time interval, judges the trend characteristics of the curve, and generates a concentration fluctuation partition label sequence;

[0114] The liquid flow adjustment strategy generation module combines the current section type and the liquid phase flow rate record in the concentration fluctuation partition label sequence, sets the liquid volume adjustment mechanism for each section, and generates the recommended adjusted liquid flow rate information;

[0115] The purification period evaluation window construction module refers to the recommended adjusted liquid flow rate information, traces back the residual concentration in the continuous cycle of the corresponding spray section, and sets the section within the specified cycle as the analysis interval to generate the purification evaluation time window;

[0116] The heat-responsive material screening and recommendation module screens for materials with stable responses based on the gas temperature change sequence and the target waste gas thermal conductivity curve in the purification evaluation time window, combines the material thermal parameters, sorts and selects according to the response rate, and obtains the recommended air pollution purification materials.

[0117] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as they do not depart from the technical solution content of the present invention, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for purifying air pollution waste gas, characterized in that: The following steps are involved: S1: Obtain the pollutant concentration sequence at the spray tower outlet within a specified time, calculate the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the spray liquid viscosity, mark the deviation state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtain the gas-liquid ratio deviation degree data; S2: fitting the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio deviation degree data into an efficiency change trend curve according to the sampling time interval, determining the trend characteristics of the curve, and generating a concentration fluctuation partition label sequence; S3: combining the current segment type and the liquid phase flow record in the concentration fluctuation partition label sequence, setting a liquid volume adjustment mechanism under each segment, and generating recommended adjusted liquid flow information; S4: referring to the liquid flow information of the recommended adjustment, tracing back the residual concentration in the continuous cycle in the corresponding spray section, and setting the section in the specified cycle as the analysis interval to generate the purification evaluation time window; S5: Based on the gas temperature change sequence in the purification evaluation time window and the target exhaust gas thermal conductivity curve, the response-stable materials are screened in combination with the material thermal parameters, and the response rates are sorted and selected to obtain recommended air pollution purification materials.

2. The method for purifying air polluted waste gas according to claim 1, characterized in that: The gas-liquid ratio deviation data includes a contact efficiency sequence, a deviation identification label, and a deviation degree value; the concentration fluctuation partition label sequence specifically includes a change trend classification identifier, a trend segment boundary index, and a trend change amplitude threshold; the recommended adjusted liquid flow information includes an adjustment direction determination, a proportional supplementary value, and a cumulative flow recommendation value; the purification evaluation time window includes a cycle anchor point time, a median time coordinate, and a symmetrical evaluation interval range; the recommended atmospheric pollution purification materials specifically include target filler types, response rate rankings, and thermosensitive adaptation materials.

3. The method for purifying air polluted waste gas according to claim 1, characterized in that: The specific steps for obtaining the pollutant concentration sequence at the spray tower outlet within a specified time, calculating the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the spray liquid viscosity, marking the deviation state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtaining the gas-liquid ratio deviation degree data are as follows: S101: Obtain pollutant concentration sequence data at the spray tower outlet within a specified time, extract concentration values ​​and sampling timestamps corresponding to adjacent time points, calculate the concentration change and time interval in each time period according to the difference between adjacent time points, and generate a concentration change rate sequence; S102: Based on the rate corresponding to each time period in the concentration change rate sequence, combined with the pollutant molecule polarity data and the spray liquid viscosity parameter, the gas-liquid contact efficiency of each time period is calculated to generate a gas-liquid contact efficiency calculation result; S103: According to the gas-liquid contact efficiency in the gas-liquid contact efficiency calculation result, call the equipment mass transfer efficiency baseline value and the gas-liquid contact deviation tolerance interval, calculate the difference and determine whether it exceeds the deviation tolerance interval. If it exceeds, mark it as a deviation state and record the corresponding time period information to generate gas-liquid ratio deviation degree data.

4. The method for purifying air polluted waste gas according to claim 1, characterized in that: According to the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio deviation degree data, the efficiency change trend curve is fitted according to the sampling time interval, the trend characteristics of the curve are judged, and the specific steps of generating the concentration fluctuation partition label sequence are as follows: S201: based on the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio deviation degree data, extract the time series and efficiency series according to the sampling time interval corresponding to each group of adjacent efficiency values, and construct a two-dimensional point set structure in time sequence to fit the efficiency change trend curve; S202: Based on the efficiency change trend curve, equally spaced time points are extracted within a specified period of time, and according to the slopes of the curves at adjacent time points, the average rate of change of the contact efficiency is calculated to generate average slope information of the gas-liquid efficiency change; S203: Based on the average slope information and fluctuation range of the gas-liquid efficiency change, the decreasing zone threshold, the fluctuation zone amplitude limit, and the increasing zone threshold in the trend judgment standard are called to classify the current slope mean into intervals, determine the trend type, and mark the interval index corresponding to each segment to generate a concentration fluctuation partition label sequence.

5. The method for purifying air polluted waste gas according to claim 1, characterized in that: Combined with the current segment type and liquid phase flow record in the concentration fluctuation partition label sequence, the specific steps of setting the liquid volume adjustment mechanism under each segment and generating the recommended adjusted liquid flow information are as follows: S301: combining the current segment type and the liquid phase flow record in the concentration fluctuation partition label sequence, judging whether it belongs to the decline type according to the segment mark, if it is a decline segment, recording the original liquid flow data and setting it as static retention, if it is not a decline type, marking it as a dynamic adjustment segment, and generating a liquid flow maintenance or adjustment segment identification result; S302: extracting the gas-liquid contact efficiency change rate sequence of the current and previous sections according to the fluctuation section information in the liquid flow maintenance or adjustment section identification result, comparing the change mode and rate direction of the two sections, determining the level of the liquid flow regulation range, and setting an adjustment amplitude label based on the current flow to establish the fluctuation section flow change information; S303: Based on the flow change information of the fluctuation section, identify the growth rate label corresponding to the growth type section, combine the existing flow record with the adjustment amplitude label corresponding to the section under each section, organize the output of the recommended liquid flow that should be configured for each section, and generate the recommended adjusted liquid flow information.

6. The method for purifying air polluted waste gas according to claim 5, characterized in that: To determine the level of the liquid flow regulation range, the formula is used: Calculate the liquid flow regulation level L, and set the liquid flow regulation range according to the liquid flow regulation level; Among them, ΔR represents the average slope difference of the gas-liquid contact efficiency between the current section and the previous section, R current Indicates the average efficiency change slope of the current section, R prev Indicates the average efficiency change slope of the previous adjacent segment of the current segment, R max Indicates the maximum slope of efficiency change during operation, Q current Indicates the liquid flow rate in the current fluctuation section, Q prev Indicates the liquid flow rate of the previous section, Q ref Indicates reference liquid flow rate.

7. The method for purifying air polluted waste gas according to claim 1, characterized in that: Referring to the recommended adjusted liquid flow information, tracing back the residual concentration in the continuous cycle in the corresponding spray section, and setting the segment in the specified cycle as the analysis interval, the specific steps of generating the purification evaluation time window are as follows: S401: Based on the recommended adjusted liquid flow information, trace back the residual concentration records in the continuous cycles in the corresponding spray section, extract the residual concentration sequence in the cycle, and identify the minimum position of the concentration sequence in each cycle, record the time node corresponding to the minimum residual concentration in each cycle, and generate a minimum residual time node sequence; S402: according to the time interval between two adjacent time points in the minimum residual time node sequence, respectively calculate the time difference between the cycles, and compare each group of time differences with the liquid volume change response time scale item by item, select all time point pairs that are less than or equal to the response time scale, extract the middle position time point, and generate a median response time point sequence; S403: Taking each time point in the median response time point sequence as the center point, according to the set symmetrical time interval range, a fixed-length time period interval based on the center point is intercepted from the residual concentration record, and all interval data are aggregated to form a unified evaluation time period interval set to generate a purification evaluation time window.

8. The method for purifying air polluted waste gas according to claim 1, characterized in that: Based on the gas temperature change sequence and the target exhaust gas thermal conductivity curve in the purification evaluation time window, the specific steps of screening the response stable materials in combination with the material thermal parameters, sorting and selecting according to the response rate, and obtaining the recommended air pollution purification materials are as follows: S501: Based on the gas temperature change sequence and the target exhaust gas thermal conductivity curve in the purification evaluation time window, the thermal conductivity impedance and specific heat capacity parameters of the candidate materials are called, and the thermal response data of the materials in the corresponding time period are obtained in sequence. By correlating and judging the temperature change rate, the thermal conductivity change trend and the material thermal physical property parameters, the performance strength of the heat flux response in the time dimension is extracted, and the performance strength is merged to generate a material thermal response intensity sequence; S502: According to the material thermal response intensity sequence, a response intensity list of each material in the whole cycle is obtained, the number of times the continuous change direction is switched in each group of lists is counted, all fluctuation frequency information is extracted, and compared with the set response frequency threshold, the material names with frequencies higher than the response frequency threshold are screened out, and a valid response frequency material set is generated; S503: Based on the thermal response intensity change rate corresponding to each material in the effective response frequency material set, sort them according to the size of the rate, and extract the material numbers with the top sorting positions from the sorting results. Combined with the replacement requirements of the heat-sensitive gas absorption filler in the target spray section, select the replaceable materials after matching judgment, and generate a list of recommended air pollution purification materials.

9. The method for purifying air polluted waste gas according to claim 8, characterized in that: To judge the performance of heat flux response in the time dimension, the formula is used: Calculate the candidate materials at time point t f The normalized thermal response intensity Q f , according to the intensity of thermal response, the performance strength of heat flux response in the time dimension is judged; Among them, R λ Represents the thermal conductivity resistance of the material, λ g (t f ) represents the target exhaust gas at time point t f The thermal conductivity of the gas at ref represents the reference thermal conductivity used for normalization, dT / dt represents the rate of change of exhaust gas temperature at the current moment, (dT / dt) ref Represents the normalized reference value of the temperature change rate, C p represents the mass specific heat capacity of the current candidate material, C p,ref is the reference specific heat capacity.

10. An air pollution waste gas purification system, characterized in that: According to any one of claims 1 to 9, the method for purifying air pollution waste gas is implemented, and the system comprises: The gas-liquid contact deviation assessment module obtains the pollutant concentration sequence at the spray tower outlet within a specified time, calculates the gas-liquid contact efficiency by combining the pollutant molecular polarity data and the spray liquid viscosity, marks the deviation state between the gas-liquid contact efficiency and the mass transfer efficiency benchmark, and obtains the gas-liquid ratio deviation degree data; The concentration trend partition identification module fits the continuous gas-liquid contact efficiency recorded in the gas-liquid ratio deviation degree data into an efficiency change trend curve according to the sampling time interval, determines the trend characteristics of the curve, and generates a concentration fluctuation partition label sequence; The liquid flow adjustment strategy generation module combines the current segment type and the liquid phase flow record in the concentration fluctuation partition label sequence, sets the liquid volume adjustment mechanism under each segment, and generates recommended adjusted liquid flow information; The purification period evaluation window construction module refers to the recommended adjusted liquid flow information, traces back the residual concentration in the continuous cycle in the corresponding spray section, and sets the section in the specified cycle as the analysis interval to generate the purification evaluation time window; The thermal response material screening and recommendation module is based on the gas temperature change sequence and the target exhaust gas thermal conductivity curve in the purification evaluation time window, combined with the material thermal parameters to screen the response stable materials, sort and select them according to the response rate, and obtain the recommended air pollution purification materials.

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