Efficient and energy-saving industrial air purification system and method

Abstract

The invention discloses an efficient and energy-saving industrial air purification system and method, and particularly relates to the technical field of air purification. The burden of the subsequent stage is reduced by introducing the primary treatment of the low-resistance filter; a dynamic plasma technology and a nano-porous adsorption material are combined, so that deep purification of complex pollutants is realized; by utilizing an intelligent monitoring and control technology, equipment operation parameters are adjusted in real time so as to optimize the purification efficiency; waste heat is recovered through a heat exchange technology, and energy consumption stability is evaluated; according to the energy consumption condition, the operation intensity is dynamically adjusted or clean gas is recycled and discharged, the removal efficiency of pollutants in high-temperature and high-speed airflow is remarkably improved, the service life of equipment is prolonged, the energy consumption of the system is reduced, and long-term stable and efficient operation of the industrial air purification system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of air purification, and in particular to a highly efficient and energy-saving industrial air purification system and method. Background Art

[0002] With the acceleration of industrialization, particulate matter, volatile organic compounds (VOCs), acidic gases and other harmful pollutants generated in industrial production processes seriously threaten the environment and human health. Therefore, industrial air purification technology has become a key area to ensure air quality. Existing technologies mostly use multi-stage filtration, activated carbon adsorption, electrostatic capture and other methods to treat pollutants. However, these methods usually have problems such as high energy consumption, large equipment footprint, and complex operation and maintenance. In addition, with the increasingly stringent global environmental protection regulations, how to significantly reduce energy consumption while improving air purification efficiency has become a technical problem that needs to be solved urgently in the industrial field.

[0003] The prior art has the following deficiencies:

[0004] In the existing industrial air purification technology, there are still great challenges in the simultaneous and efficient removal of fine particulate matter (such as submicron particles) and gaseous pollutants with weak adsorption (such as low-concentration VOCs) in high-temperature airflow. Especially in the case of high flow rate and complex composition of airflow, traditional filter materials are prone to failure due to temperature and chemical corrosion, and the capture efficiency of electric field purification devices is significantly reduced due to changes in gas conductivity. In addition, this equipment performance attenuation problem caused by high temperature and high-speed airflow not only seriously affects the purification effect, but also leads to a significant increase in system energy consumption, becoming a bottleneck for the long-term and efficient operation of industrial air purification systems. Summary of the invention

[0005] The purpose of the present invention is to provide an efficient and energy-saving industrial air purification system and method to solve the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: an efficient and energy-saving industrial air purification method, comprising the following steps:

[0007] S1: Introducing pollutant-laden industrial gas streams into the primary treatment stage, removing large particles and some gaseous pollutants through low-resistance filters to reduce the burden on subsequent treatment stages;

[0008] S2: Multifunctional deep purification of primary treated air streams, including the use of dynamic plasma technology to generate high-energy active particles to decompose volatile organic compounds and acid gases through electrochemical reactions; the use of nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions;

[0009] S3: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the trend of the change of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results;

[0010] S4: The airflow is introduced into the terminal treatment stage, and the waste heat generated during the purification process is recovered through heat exchange technology and the heat recovery rate is calculated. The stability of the overall energy consumption is evaluated based on the fluctuation of the heat recovery rate over several time periods;

[0011] S5: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

[0012] Preferably, in S2, according to the change of the particle size spectrum of the pollutant distribution and the fluctuation amplitude of the redox potential of the pollutant, a data prediction model is constructed to predict the trend of the change of the pollutant concentration, and the particle size spectrum change anomaly index is generated after analyzing the change of the particle size spectrum of the pollutant distribution. The method for obtaining the particle size spectrum change anomaly index is:

[0013] It is assumed that the particle size spectrum is measured at t time points, and each time the particle size spectrum is measured, it is divided into n particle size ranges, and the matrix is ​​constructed , where each row represents the particle size distribution at a time point: ; It represents the concentration within the particle size range j at time point t. The data is standardized. The standardization formula is: ; is the standardized data, is the mean value of particle size range j, is the standard deviation of particle size range j, and the matrix is ​​obtained after standardization , calculate the covariance matrix C of the standardized data matrix Z, the covariance matrix formula is: ; In the formula, , the covariance matrix between the particle size ranges, t is the number of samples, T is the matrix transpose, and the eigenvalues ​​and eigenvectors of the covariance matrix are calculated using the formula: ; is the eigenvector corresponding to the i-th principal component, is the eigenvalue of the i-th principal component, indicating the variance explained by the principal component. The eigenvalues ​​are arranged in descending order, and the first k principal components are taken to construct the principal component matrix , project the standardized data Z into the principal component space and obtain the projection matrix P, the formula is: ; Use the principal component projection results and the principal component matrix to reconstruct the particle size spectrum data. The reconstruction formula is: ; The standardized data reconstructed in the principal component space is used to calculate the error between the original standardized data and the reconstructed data as the particle size spectrum abnormality index. The expression is: ; Where HK is the particle size spectrum abnormality index, To reconstruct the data.

[0014] Preferably, in S2, the ORP value fluctuation index is generated after analyzing the fluctuation amplitude of the redox potential of the pollutant. The method for obtaining the ORP value fluctuation index is:

[0015] The ORP value is sorted into a set of data points according to the time series, and the measured ORP value is set to , the corresponding time point is ; Determine the sliding window length L, which represents the time interval used for analysis, set the sliding step S, which represents the interval time of window sliding, and the calculation expression of the total number of windows k is: ; Where m is the total number of ORP values, for each window , calculate the window mean , the expression is: ; For Window The jth ORP value in the window standard deviation , the expression is: ; Maximum change range within the window , the expression is: ; Indicates the extreme fluctuation range of ORP value within the window and calculates the abnormal value of ORP , the expression is: ; Calculate the ORP value fluctuation index, the expression is: ; GH is the ORP value fluctuation index, is the smoothing factor.

[0016] Preferably, the particle size spectrum change anomaly index and the ORP value fluctuation index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the pollutant concentration change trend value label as the prediction target, and takes minimizing the sum of prediction errors for all pollutant concentration change trend value labels as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The pollutant concentration change trend value is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0017] Preferably, the obtained pollutant concentration change trend value is compared with a reference threshold value of the pollutant concentration change trend value set according to historical data. If the pollutant concentration change trend value is greater than or equal to the reference threshold value of the pollutant concentration change trend value set according to historical data, it indicates that the pollutant concentration change trend is large. At this time, a warning signal is generated and the operating parameters of the purification equipment are adjusted; if the pollutant concentration change trend value is less than the reference threshold value of the pollutant concentration change trend value set according to historical data, it indicates that the pollutant concentration change trend is small. At this time, no warning signal is generated and no additional adjustment is required.

[0018] Preferably, in S4, the airflow is introduced into the terminal treatment stage, the waste heat generated in the purification process is recovered by heat exchange technology and the heat recovery rate is calculated, and the stability of the overall energy consumption is evaluated according to the fluctuation of the heat recovery rate in several time periods, specifically:

[0019] The heat recovery rate η is used to measure the heat recovery efficiency and is defined as the ratio of recovered heat to total waste heat: ; is the heat recovered by the heat exchanger; Total waste heat released during the purification process; heat recovery The calculation expression is: ; In the formula, is the mass flow rate of the processed airflow per unit time; is the specific heat capacity of the airflow; is the temperature change of the airflow after passing through the heat exchanger, the total waste heat The calculation expression is: ; In the formula, The overall temperature change of the airflow before and after purification;

[0020] Heat recovery rate fluctuation refers to the change in heat recovery rate η in different time periods. The heat recovery rate sequence of several time periods is recorded. ; Calculate the mean and standard deviation of the heat recovery rate series, and after analyzing it, evaluate the stability of the overall energy consumption.

[0021] Preferably, if the mean value of the heat recovery rate in the data set is greater than or equal to the reference threshold value of the mean value of the heat recovery rate, and the standard deviation of the heat recovery rate is less than the reference threshold value of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is high and the fluctuation is small, the overall energy consumption is stable, and the purification equipment is operating in an ideal state. There is no need to adjust the operating parameters. At this time, the operating intensity of the existing equipment is maintained;

[0022] If the mean value of the heat recovery rate is greater than or equal to the reference threshold value of the mean value of the heat recovery rate, and the standard deviation of the heat recovery rate is greater than or equal to the reference threshold value of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is high, but the fluctuation is large. At this time, check whether the heat exchange device has intermittent operation problems, and optimize the control logic to reduce the fluctuation range;

[0023] If the mean value of heat recovery rate is less than the reference threshold value of heat recovery rate, and the standard deviation of heat recovery rate is greater than or equal to the reference threshold value of heat recovery rate standard deviation, it means that the heat recovery efficiency of the system is low, with large fluctuations, high energy consumption and insufficient recovered heat. Check the operating status of the heat recovery equipment and adjust the operating parameters.

[0024] If the mean heat recovery rate is less than the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is less than the reference threshold of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is low, but the operation is stable. The equipment operating performance has not reached the ideal state, but has not shown instability. Improve the operating intensity of the heat recovery equipment, optimize the equipment performance, and improve the overall heat recovery efficiency.

[0025] The present invention also provides an efficient and energy-saving industrial air purification system, comprising a pre-treatment module, a deep purification module, a monitoring and control module, a heat exchange and energy evaluation module and a clean gas processing module;

[0026] Pretreatment module: introduces pollutant-containing industrial airflow into the primary treatment stage, removes large particles and some gaseous pollutants through low-resistance filters to reduce the burden of subsequent treatment stages;

[0027] Deep purification module: Multifunctional deep purification of the primary treated airflow, including the use of dynamic plasma technology to generate high-energy active particles, decomposing volatile organic compounds and acid gases through electrochemical reactions; using nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions;

[0028] Monitoring and control module: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the trend of the change of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results;

[0029] Heat exchange and energy assessment module: directs the airflow into the terminal treatment stage, recovers the waste heat generated during the purification process through heat exchange technology and calculates the heat recovery rate. Based on the fluctuation of the heat recovery rate over several time periods, the stability of the overall energy consumption is assessed;

[0030] Clean gas processing module: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

[0031] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0032] 1. Through optimized design, the system achieves efficient purification in each treatment stage: the primary treatment module reduces the burden on subsequent equipment, the deep purification module combines dynamic plasma technology and nano-adsorption materials to achieve efficient removal of complex pollutants, intelligent monitoring and dynamic adjustment optimize operating parameters, and terminal heat recovery technology significantly improves energy utilization efficiency, ensuring long-term operation stability and economy. At the same time, advanced machine learning models are used to predict the trend of pollutant concentration changes, and operating parameters are dynamically adjusted through data-driven optimization strategies, which significantly improves the system's adaptability to complex working conditions.

[0033] 2. The present invention not only significantly improves the pollutant removal efficiency under high temperature, high flow rate and complex airflow conditions, but also effectively reduces the overall energy consumption through heat recovery and energy consumption monitoring. The unique intelligent and data analysis functions enable the system to identify the operating status in real time and generate early warning signals, ensuring that the equipment maintains operational stability while being highly efficient and energy-saving. Overall, the present invention provides a technical solution for industrial air purification that integrates efficient purification, intelligent optimization, energy conservation and environmental protection, and has important industrial application value and promotion significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0035] Figure 1 The present invention is a flow chart of the method.

[0036] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1, please refer to Figure 1 As shown, the efficient and energy-saving industrial air purification method described in this embodiment includes the following steps:

[0039] S1: Introducing pollutant-laden industrial gas streams into the primary treatment stage, removing large particles and some gaseous pollutants through low-resistance filters to reduce the burden on subsequent treatment stages;

[0040] S2: Multifunctional deep purification of primary treated air streams, including the use of dynamic plasma technology to generate high-energy active particles to decompose volatile organic compounds and acid gases through electrochemical reactions; the use of nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions;

[0041] S3: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the trend of the change of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results;

[0042] S4: The airflow is introduced into the terminal treatment stage, and the waste heat generated during the purification process is recovered through heat exchange technology and the heat recovery rate is calculated. The stability of the overall energy consumption is evaluated based on the fluctuation of the heat recovery rate over several time periods;

[0043] S5: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

[0044] Among them, in S1, the industrial air flow containing pollutants is introduced into the primary treatment stage, and large particles and some gaseous pollutants are removed through low-resistance filters to reduce the burden of subsequent treatment stages, specifically:

[0045] The primary treatment stage is the first step in an energy-efficient industrial air purification method. Its main purpose is to remove large particles and some gaseous pollutants from industrial airflow. Through this stage of treatment, the load of subsequent deep purification units can be effectively reduced, and the operating efficiency and service life of the entire purification system can be improved. At the same time, this stage can also prevent large particles from causing wear or blockage on the equipment, reducing system maintenance costs.

[0046] The use of large flux, low resistance filter materials, such as non-woven fabrics or multi-layer folded metal mesh, ensures that the pressure drop is minimized when the airflow passes through, while being able to capture larger particles (such as dust, smoke and fibrous impurities). The filter can intercept large particles with a particle size of more than 10 microns, which are the most common primary pollutants in industrial airflow, such as dust generated in production and processing or residual particles from incomplete combustion.

[0047] Pre-coating technology is introduced into the design of the filter, and chemically active coatings (such as aluminum oxide or activated carbon) are used to adsorb or decompose some gaseous pollutants in the airflow. For acidic gases (such as SO2, HCl, etc.) or some volatile organic compounds (VOCs), their concentration is reduced through adsorption and surface catalytic reactions, which reduces the burden on subsequent processing links. By controlling the air intake speed, it is ensured that large particles have enough time to be captured and will not pass through the filter due to high-speed airflow. The low flow rate design can also reduce the impact of airflow turbulence on filtration performance. A mechanical vibration device or a pulse airflow system is integrated on the surface of the filter to regularly remove accumulated particles to avoid blockage that affects the smoothness of the airflow and the filtration effect.

[0048] The primary treatment stage can efficiently capture more than 80% of large particles in industrial airflow, which not only reduces the operating burden of subsequent equipment, but also reduces the wear of particles on the core processing unit. The low-resistance filter reduces the power consumption of the fan and makes the system more energy-efficient by optimizing the balance between airflow resistance and filtration efficiency. After the initial removal of large particles and some gaseous pollutants, the purification system can focus more resources on treating pollutants that are more difficult to remove, such as submicron particles and low-concentration VOCs, thereby significantly improving the overall purification efficiency. Through the protection of the primary filter, subsequent deep purification units (such as dynamic plasma modules and adsorption modules) are not easy to clog or damage, extending the service life of the equipment and reducing maintenance frequency and costs.

[0049] S2: Multifunctional deep purification of the primary treated airflow, including the use of dynamic plasma technology to generate high-energy active particles, decomposing volatile organic compounds and acid gases through electrochemical reactions; and the use of nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions.

[0050] The multifunctional deep purification stage is designed to further treat pollutants in industrial gas streams that have not been completely removed by primary treatment, especially hard-to-capture volatile organic compounds (VOCs), acid gases and low-concentration pollutants. This stage uses the synergistic effect of dynamic plasma technology and nano-porous adsorption materials to efficiently remove pollutants while minimizing energy consumption.

[0051] Dynamic plasma technology excites molecules and atoms in the airflow through a high-voltage electric field to produce high-energy active particles (such as free radicals, electrons, ions and ozone). These active particles react electrochemically with gaseous pollutants and decompose them into harmless small molecules (such as carbon dioxide and water).

[0052] A dynamic plasma device is configured in the air flow channel to use a non-thermal equilibrium plasma field to excite active particles. The operating voltage is in the range of several thousand volts to ensure the balance between the generation rate of high-energy particles and energy consumption. Treatment object: Volatile organic compounds (VOCs): Active particles destroy the chemical bonds of VOCs molecules through chain reactions and decompose them into harmless products, such as formaldehyde being oxidized into CO2 and water. Acidic gas: Dynamic plasma effectively removes acidic components such as SO2 and HCl through oxidation and neutralization reactions. The non-thermal treatment process is suitable for working conditions of various temperatures and humidity in the air flow; the dynamic control technology can adjust the electric field strength according to the concentration of air pollutants to reduce energy consumption.

[0053] Nano-scale porous adsorption materials can efficiently capture and adsorb pollutants at the molecular level through their large specific surface area and uniform pore size distribution. Materials can be combined with chemical modification to achieve selective adsorption of specific pollutants (such as low-concentration VOCs). Use porous activated carbon, molecular sieves, metal organic frameworks (MOFs) and other materials with pore sizes ranging from 0.2–5 nanometers, which can effectively capture tiny pollutant molecules. Coat the surface of the material with functional modifiers (such as metal oxides or amino groups) to enhance its adsorption capacity for specific pollutants. Use a heating device to keep the material within the optimal operating temperature range to avoid a decrease in adsorption efficiency due to low temperature. By adjusting the humidity, excessive moisture is prevented from occupying the active sites of the adsorption material, thereby affecting the adsorption effect of pollutants. After adsorption saturation, the adsorption material can be regenerated by low-temperature heating or airflow backblowing to restore its adsorption capacity and reduce operating costs.

[0054] S3: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the changing trend of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results.

[0055] The core goal of intelligent monitoring and control technology is to dynamically adjust the operating status of purification equipment by real-time acquisition and analysis of key parameters of airflow (such as pollutant concentration, temperature, flow rate, etc.) to ensure the best balance between pollutant removal efficiency and system energy consumption. At the same time, by analyzing the changing trend of airflow pollutant composition, the optimal operating parameters in the future are predicted to improve the operating stability and adaptability of the system.

[0056] Use laser particle counters, optical sensors or chemical sensors to accurately detect the concentration of particulate matter (such as PM2.5, PM10) and gaseous pollutants (such as VOCs, SO2) in the airflow. Equipped with thermistors and flow rate sensors, real-time data on airflow temperature and flow rate are collected to provide a reference for the subsequent adjustment of operating parameters of purification equipment. Sensors are arranged at key locations where the airflow enters and leaves each processing unit to ensure that the treatment effect of each stage can be monitored in real time. Data is transmitted to the control system in real time via wireless transmission or industrial bus protocols (such as MODBUS).

[0057] According to the changes in the particle size spectrum of pollutant distribution and the fluctuation amplitude of the redox potential of pollutants, a data prediction model is constructed to predict the trend of pollutant concentration changes, specifically:

[0058] After analyzing the changes in the particle size spectrum of pollutant distribution, the particle size spectrum change anomaly index is generated. The method for obtaining the particle size spectrum change anomaly index is as follows:

[0059] It is assumed that the particle size spectrum is measured at t time points, and each time the particle size spectrum is measured, it is divided into n particle size ranges, and the matrix is ​​constructed , where each row represents the particle size distribution at a time point: ; It represents the concentration within the particle size range j at time point t. Since the concentrations in different particle size ranges may have different magnitudes, the data needs to be standardized. The standardization formula is: ; is the standardized data, is the mean value of particle size range j, is the standard deviation of particle size range j, and the matrix is ​​obtained after standardization , calculate the covariance matrix C of the standardized data matrix Z, the covariance matrix formula is: ; In the formula, , the covariance matrix between the particle size ranges, t is the number of samples (number of time points), and T is the matrix transpose. The eigenvalues ​​and eigenvectors of the covariance matrix are calculated by eigendecomposition or singular value decomposition (SVD), and the formula is: ; is the eigenvector corresponding to the i-th principal component, is the eigenvalue of the i-th principal component, indicating the variance explained by the principal component. The eigenvalues ​​are arranged in descending order, and the first k principal components are taken to construct the principal component matrix . Project the standardized data Z into the principal component space to obtain the projection matrix P, the formula is: ; Use the principal component projection results and the principal component matrix to reconstruct the particle size spectrum data. The reconstruction formula is: ; The standardized data reconstructed in the principal component space is used to calculate the error between the original standardized data and the reconstructed data as the particle size spectrum abnormality index. The expression is: ; Where HK is the particle size spectrum abnormality index, To reconstruct the data.

[0060] When the particle size spectrum change anomaly index is large, it means that the particle size distribution at the current moment deviates significantly from the normal state, reflecting the drastic change in pollutant concentration in the particle size range. This may mean a sudden release of pollution sources, such as industrial equipment failure, a sharp change in external environmental conditions (such as wind speed or temperature), or the introduction of new pollutant components. For example, if the concentration of particulate matter in a certain particle size range increases significantly, while other ranges remain unchanged, this uneven change will cause the anomaly index to increase. At this time, the particle size range corresponding to the anomaly index should be analyzed in detail to identify the composition characteristics of the pollutants so that targeted control measures can be taken.

[0061] When the abnormal index of particle size spectrum change is small, it means that the particle size distribution at the current moment is basically consistent with that in the normal state, indicating that the change trend of pollutant concentration in time and particle size range is relatively stable. This usually means that the operation of industrial equipment is stable, the external environmental conditions do not fluctuate significantly, and the release process of pollution sources is relatively uniform. In this case, the purification equipment can be processed according to the set operating parameters without additional adjustments. However, it should be noted that although a small abnormal index indicates that the current state is stable, a long-term stable state may also mask some chronic changes. Therefore, it is still necessary to combine long-term monitoring data to evaluate the overall efficiency of the system and the effect of pollutant removal.

[0062] The ORP value fluctuation index is generated by analyzing the fluctuation range of the redox potential of the pollutants. The method for obtaining the ORP value fluctuation index is as follows:

[0063] The ORP values ​​are organized into a set of data points in time series. Assume that the measured ORP value is , the corresponding time point is ; Determine the sliding window length L, which represents the time interval used for analysis (for example, 5 minutes, 10 minutes, or 1 hour). Set the sliding step size S, which represents the interval time of window sliding (usually the data sampling interval, such as 1 minute). The calculation expression of the total number of windows k is: ; Where m is the total number of ORP values, for each window (Contains L data points), calculate the window mean , the expression is: ; For Window The jth ORP value in the window standard deviation , the expression is: ; Maximum change range within the window , the expression is: ; Indicates the extreme fluctuation range of the ORP value within the window. Based on the statistical characteristics of each window, calculate the abnormal value of ORP , the expression is: ; Calculate the ORP value fluctuation index, the expression is: ; GH is the ORP value fluctuation index, is the smoothing factor (usually between 0.1 and 0.3).

[0064] When the ORP value fluctuation index is large, it indicates that the redox characteristics of the pollutant have changed significantly in a short period of time, which usually corresponds to a sharp fluctuation in the concentration of the pollutant or a sudden change in the composition. For example, if volatile organic compounds (VOCs) are suddenly released in large quantities, or if strong oxidizing or reducing chemicals (such as ozone or sulfide) are introduced into the external environment, the ORP value will fluctuate greatly. Such drastic changes may indicate abnormal release of pollution sources or unstable operation of industrial processes. At this time, the specific composition of the pollutants and the reasons for the changes should be analyzed in time to quickly adjust the operating parameters of the purification equipment.

[0065] When the ORP value fluctuation index is small, it means that the redox characteristics of the pollutants remain within a relatively stable range, reflecting that the trend of pollutant concentration changes is relatively gentle. This shows that the emission characteristics of the pollution source tend to be stable, the operation of industrial equipment is in a normal state, and the external environmental conditions (such as temperature and humidity) have little impact on the pollutants. In this case, the purification equipment of the system can operate according to the set optimization parameters without frequent adjustments. However, it is still necessary to pay attention to long-term trends to ensure that the equipment can continue to adapt to slight changes in composition to maintain purification efficiency.

[0066] The particle size spectrum change anomaly index and ORP value fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model takes the pollutant concentration change trend value label predicted by each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of prediction errors for all pollutant concentration change trend value labels as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The pollutant concentration change trend value is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0067] The method for obtaining the pollutant concentration change trend value is: obtain the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: ; In the formula, is the output function of the model, HK is the particle size spectrum abnormality index, GH is the ORP value fluctuation index, is the pollutant concentration change trend value.

[0068] The obtained pollutant concentration change trend value is compared with the reference threshold of the pollutant concentration change trend value set according to historical data. If the pollutant concentration change trend value is greater than or equal to the reference threshold of the pollutant concentration change trend value set according to historical data, it means that the pollutant concentration change trend is large. At this time, a warning signal is generated and the operating parameters of the purification equipment are adjusted; if the pollutant concentration change trend value is less than the reference threshold of the pollutant concentration change trend value set according to historical data, it means that the pollutant concentration change trend is small. At this time, no warning signal is generated and no additional adjustment is required.

[0069] When the pollutant concentration change trend value is large, a warning signal is generated and the operating parameters are adjusted. Specifically: when the concentration change trend value is greater than or equal to the reference threshold, it means that the pollutant concentration has changed significantly, and the equipment parameters need to be optimized and adjusted to ensure purification efficiency and equipment stability.

[0070] Adjust the fan speed and airflow to improve the equipment's ability to handle high pollution loads and avoid equipment overload. Increase the fan speed or airflow to quickly handle large amounts of high-concentration airflow and prevent pollutants from accumulating in the pipeline. If high wind speed may reduce purification efficiency (such as adsorption devices are sensitive to airflow residence time), the balance point between wind speed and residence time can be appropriately adjusted.

[0071] Dynamically enhance the intensity of the dynamic plasma electric field, accelerate the redox reaction, and improve the decomposition efficiency of volatile organic compounds (VOCs) and acidic gases. Increase the operating voltage or frequency of the plasma electric field to produce more high-energy active particles. When the trend value is too large, enable the standby plasma unit to share the processing load.

[0072] Optimize the temperature and humidity control of the adsorption device to enhance the removal efficiency of low-concentration pollutants, especially in the high-load state that occurs during the rising trend value. Increase the operating temperature of the adsorption material to increase the adsorption rate; adjust the dehumidification equipment to reduce the humidity of the air flow to prevent moisture from occupying the adsorption site and reduce the risk of decreased adsorption efficiency.

[0073] Optimize the operation of the heat recovery device: reduce energy consumption during high-load operation to ensure long-term stability of the equipment. Increase the heat recovery rate and recover excess heat to the primary treatment stage to reduce the additional burden on the fan or heater. When the equipment operating pressure increases, extend the working time of the heat exchanger to relieve energy consumption pressure.

[0074] When the pollutant concentration change trend value is small, no warning signal is generated and no additional adjustment is required. When the concentration change trend value is less than the reference threshold, it indicates that the system is operating in a relatively stable state. At this time, no major adjustments are required. It is only necessary to maintain the optimized parameter operating state of the equipment. If necessary, energy consumption can be appropriately reduced to extend the life of the equipment.

[0075] Reduce fan speed and air volume: Reduce energy consumption while maintaining adequate purification capacity. According to the real-time pollutant concentration, adjust the fan speed to the lower limit of the optimization range to ensure purification efficiency while reducing operating costs.

[0076] Reduce the plasma electric field strength: Save electricity and avoid equipment wear due to excessive processing. Reduce the electric field voltage or operating frequency to keep the plasma device at the minimum necessary strength. If the trend value is at a low level for a long time, some plasma units can be shut down in stages to extend their service life.

[0077] Reduce the frequency of temperature and humidity control of the adsorption material: save energy and extend the regeneration cycle of the adsorption material. When the air flow humidity is low, appropriately reduce the operating intensity of the dehumidification equipment; reduce the working temperature of the adsorption material, delay material aging, and reduce the replacement frequency. Reduce the load of the heat recovery equipment: reduce unnecessary heat circulation in the system and further reduce energy consumption. Adjust the heat recovery device to a low-intensity operation mode and only recover basic heat for the system's own use.

[0078] S4: The airflow is introduced into the terminal treatment stage, and the waste heat generated during the purification process is recovered through heat exchange technology and the heat recovery rate is calculated. The stability of the overall energy consumption is evaluated based on the fluctuation of the heat recovery rate over several time periods.

[0079] During the air purification process, the equipment will generate a certain amount of waste heat (such as heat energy released by high-temperature airflow or processing units). Heat exchange technology transfers waste heat to other systems that require heat, such as heating units in the pretreatment stage, through heat exchangers (such as plate heat exchangers or rotary heat recovery devices), reducing additional energy consumption.

[0080] The heat recovery rate η is used to measure the heat recovery efficiency and is defined as the ratio of recovered heat to total waste heat: ; is the heat recovered by the heat exchanger (unit: J or kJ); is the total waste heat released during the purification process (unit: J or kJ).

[0081] Heat recovery The calculation expression is: ; In the formula, is the mass flow rate of the processed airflow per unit time (kg / s); is the specific heat capacity of the airflow (J / kg·K); is the temperature change of the airflow after passing through the heat exchanger (K). Total waste heat The calculation expression is: ; In the formula, Overall temperature change of the airflow before and after purification (K).

[0082] Heat recovery rate fluctuation refers to the change in the heat recovery rate η in different time periods. The time period division can be set according to the actual operation (such as calculating once every hour or every working cycle). Record the heat recovery rate sequence of several time periods ; Calculate the mean and standard deviation of the heat recovery rate series, and after analyzing it, evaluate the stability of the overall energy consumption.

[0083] If the mean heat recovery rate in the data set is greater than or equal to the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is less than the reference threshold of the standard deviation of the heat recovery rate, it means that the system's heat recovery efficiency is high and the fluctuation is small, and the overall energy consumption is stable. The purification equipment is operating in an ideal state and there is no need to adjust the operating parameters. At this time, the existing equipment operation intensity is maintained, and the energy consumption allocation strategy can be appropriately optimized to further improve the economy.

[0084] If the mean heat recovery rate is greater than or equal to the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is greater than or equal to the reference threshold of the standard deviation of the heat recovery rate, it means that the system's heat recovery efficiency is high, but the fluctuation is large, and there may be fluctuations caused by unstable operation or changes in environmental conditions. The overall energy consumption is high, but the instability may bring potential risks to the long-term operation of the equipment. At this time, check whether the heat exchange device has intermittent operation problems, and optimize the control logic to reduce the fluctuation range (such as adjusting the air flow temperature or flow).

[0085] If the mean heat recovery rate is less than the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is greater than or equal to the reference threshold of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is low and fluctuates greatly, which may be caused by equipment aging, mismatch of operating parameters or unstable operating conditions. The energy consumption is high and the recovered heat is insufficient, and the equipment operating efficiency is obviously low. Check the operating status of the heat recovery equipment (such as blockage of the heat exchanger or degradation of the heat transfer medium performance), and adjust the operating parameters (such as reducing the air flow rate or replacing the heat exchanger material). If the fluctuation problem persists, equipment maintenance or upgrade should be considered.

[0086] If the mean heat recovery rate is less than the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is less than the reference threshold of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is low, but the operation is relatively stable, which may be caused by low equipment parameter settings or insufficient process design. The equipment operating performance has not reached the ideal state, but has not shown significant instability. Improve the operating intensity of the heat recovery equipment (such as increasing the heat exchange area or increasing the air flow rate), optimize the equipment performance, and improve the overall heat recovery efficiency.

[0087] S5: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

[0088] When the overall energy consumption is stable, it means that the purification system is operating in an optimized state and there is no need to adjust the operating intensity of the equipment. The main goal at this time is to efficiently utilize clean gas resources and optimize resource recycling or safe discharge. The clean gas is tested to meet environmental emission standards (such as particulate matter concentration and VOC content below the specified limit). The emission environment can safely accommodate the treated gas without the risk of secondary pollution. The exhaust channel is enabled to directly discharge the gas to the external environment. Monitor the composition and temperature of the gas at the exhaust port to ensure environmental compliance.

[0089] Oxygen or other components in clean gas have recycling value, such as for production processes (such as welding, combustion support) or other industrial processes. Guide the clean gas into the production circulation pipeline, and ensure that the reflux volume matches the production demand by adjusting the gas distributor. Dynamically adjust the gas recovery rate according to production demand to avoid excessive pressure or reduced operating efficiency due to excessive recovery.

[0090] When the overall energy consumption is unstable, the purification equipment may face the risk of overload operation or reduced efficiency. The main goals at this time are: reduce energy consumption and ensure the stability of equipment operation. Reduce the fan speed to reduce the airflow processing volume and reduce the operating power of the equipment. Adjust the operating voltage and frequency of the plasma device to reduce the generation of high-energy active particles to reduce power consumption. Reduce the operating temperature of the adsorption material and the power of the dehumidification equipment to extend the service life of the adsorption material.

[0091] By adjusting the air duct resistance or the air valve opening, the air flow velocity entering the purification equipment is reduced, and the gas residence time in the purification equipment is prolonged to improve the processing efficiency. If the processing speed is nonlinearly related to the purification efficiency, the optimal speed point is selected for adjustment to ensure energy consumption optimization while maintaining the purification effect. Reduce the time of high-load operation and reduce the impact of energy consumption fluctuations on the system. In the case of reduced air flow, by extending the air flow residence time, ensure that the pollutant removal efficiency is not significantly affected.

[0092] Monitor key indicators of energy consumption and purification efficiency (such as fan power consumption, purification efficiency, airflow pressure loss, etc.). Input real-time data into the machine learning model to predict the optimal parameter combination for the next adjustment. Combine actual data with the predicted results to dynamically adjust operating parameters (such as wind speed, adsorption temperature, etc.) to achieve the best balance between energy consumption and efficiency. Evaluate the adjustment effect based on historical operation records and continuously optimize the equipment operation logic.

[0093] In this embodiment, first, the industrial airflow enters the primary treatment stage, and a low-resistance filter is used to remove large particles and some gaseous pollutants to reduce the burden of subsequent treatment; then, the airflow passes through a multifunctional deep purification stage, and dynamic plasma technology is used to generate high-energy active particles to decompose volatile organic compounds and acidic gases, while nano-scale porous adsorption materials are used to capture low-concentration pollutants under controllable temperature and humidity conditions; then, the pollutant concentration, temperature, flow rate and other parameters of the airflow are monitored in real time through intelligent monitoring and control technology, the trend of pollutant composition changes is analyzed and the equipment operation parameters are dynamically optimized; entering the terminal treatment stage, the waste heat generated during the purification process is recovered through heat exchange technology, the heat recovery rate is calculated and the stability of the overall energy consumption is evaluated based on its fluctuations; finally, if the energy consumption is stable, the purified clean gas is directly discharged or recycled; if it is unstable, energy consumption is reduced by reducing the equipment operation intensity and airflow processing speed to achieve efficient and energy-saving industrial air purification.

[0094] Example 2, please refer to Figure 2 As shown, the energy-efficient industrial air purification system described in this embodiment includes a pretreatment module, a deep purification module, a monitoring and control module, a heat exchange and energy evaluation module, and a clean gas processing module;

[0095] Pretreatment module: introduces pollutant-containing industrial airflow into the primary treatment stage, removes large particles and some gaseous pollutants through low-resistance filters to reduce the burden of subsequent treatment stages;

[0096] Deep purification module: Multifunctional deep purification of the primary treated airflow, including the use of dynamic plasma technology to generate high-energy active particles, decomposing volatile organic compounds and acid gases through electrochemical reactions; using nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions;

[0097] Monitoring and control module: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the trend of the change of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results;

[0098] Heat exchange and energy assessment module: directs the airflow into the terminal treatment stage, recovers the waste heat generated during the purification process through heat exchange technology and calculates the heat recovery rate. Based on the fluctuation of the heat recovery rate over several time periods, the stability of the overall energy consumption is assessed;

[0099] Clean gas processing module: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

[0100] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0101] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0102] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0103] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0104] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0105] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An efficient and energy-saving industrial air purification method, characterized by: The following steps are involved: S1: Introducing pollutant-laden industrial gas streams into the primary treatment stage, removing large particles and some gaseous pollutants through low-resistance filters to reduce the burden on subsequent treatment stages; S2: Multifunctional deep purification of primary treated air streams, including the use of dynamic plasma technology to generate high-energy active particles to decompose volatile organic compounds and acid gases through electrochemical reactions; the use of nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions; S3: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the trend of the change of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results; S4: The airflow is introduced into the terminal treatment stage, and the waste heat generated during the purification process is recovered through heat exchange technology and the heat recovery rate is calculated. The stability of the overall energy consumption is evaluated based on the fluctuation of the heat recovery rate over several time periods; S5: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

2. The high-efficiency and energy-saving industrial air purification method according to claim 1, characterized in that: In S2, according to the change of particle size spectrum of pollutant distribution and the fluctuation amplitude of redox potential of pollutants, a data prediction model is constructed to predict the trend of pollutant concentration change. After analyzing the change of particle size spectrum of pollutant distribution, the particle size spectrum change abnormality index is generated. The method for obtaining the particle size spectrum change abnormality index is as follows: It is assumed that the particle size spectrum is measured at t time points, and each time the particle size spectrum is measured, it is divided into n particle size ranges, and the matrix is ​​constructed , where each row represents the particle size distribution at a time point: ; It represents the concentration within the particle size range j at time point t. The data is standardized. The standardization formula is: ; is the standardized data, is the mean value of particle size range j, is the standard deviation of particle size range j, and the matrix is ​​obtained after standardization , calculate the covariance matrix C of the standardized data matrix Z, the covariance matrix formula is: ; In the formula, , the covariance matrix between the particle size ranges, t is the number of samples, T is the matrix transpose, and the eigenvalues ​​and eigenvectors of the covariance matrix are calculated using the formula: ; is the eigenvector corresponding to the i-th principal component, is the eigenvalue of the i-th principal component, indicating the variance explained by the principal component. The eigenvalues ​​are arranged in descending order, and the first k principal components are taken to construct the principal component matrix , project the standardized data Z into the principal component space and obtain the projection matrix P, the formula is: ; The particle size spectrum data is reconstructed using the principal component projection results and the principal component matrix. The reconstruction formula is: ; The standardized data reconstructed in the principal component space is used to calculate the error between the original standardized data and the reconstructed data as the particle size spectrum abnormality index. The expression is: ; Where HK is the particle size spectrum abnormality index, To reconstruct the data.

3. The high-efficiency and energy-saving industrial air purification method according to claim 2 is characterized in that: In S2, the ORP value fluctuation index is generated after analyzing the fluctuation amplitude of the redox potential of the pollutant. The method for obtaining the ORP value fluctuation index is as follows: The ORP value is sorted into a set of data points according to the time series, and the measured ORP value is set to , the corresponding time point is ; Determine the sliding window length L, which represents the time interval used for analysis, set the sliding step S, which represents the interval time of window sliding, and the calculation expression of the total number of windows k is: ; Where m is the total number of ORP values, for each window , calculate the window mean , the expression is: ; For Window The jth ORP value in the window standard deviation , the expression is: ; Maximum change range within the window , the expression is: ; Indicates the extreme fluctuation range of ORP value within the window and calculates the abnormal value of ORP , the expression is: ; Calculate the ORP value fluctuation index, the expression is: ; GH is the ORP value fluctuation index, is the smoothing factor.

4. The high-efficiency and energy-saving industrial air purification method according to claim 3 is characterized in that: The particle size spectrum change anomaly index and ORP value fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model takes the pollutant concentration change trend value label predicted by each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of prediction errors for all pollutant concentration change trend value labels as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The pollutant concentration change trend value is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. The high-efficiency and energy-saving industrial air purification method according to claim 4 is characterized in that: The obtained pollutant concentration change trend value is compared with the reference threshold of the pollutant concentration change trend value set according to historical data. If the pollutant concentration change trend value is greater than or equal to the reference threshold of the pollutant concentration change trend value set according to historical data, it means that the pollutant concentration change trend is large. At this time, a warning signal is generated and the operating parameters of the purification equipment are adjusted; if the pollutant concentration change trend value is less than the reference threshold of the pollutant concentration change trend value set according to historical data, it means that the pollutant concentration change trend is small. At this time, no warning signal is generated and no additional adjustment is required.

6. The high-efficiency and energy-saving industrial air purification method according to claim 5, characterized in that: In S4, the airflow is introduced into the terminal treatment stage, and the waste heat generated during the purification process is recovered through heat exchange technology and the heat recovery rate is calculated. According to the fluctuation of the heat recovery rate in several time periods, the stability of the overall energy consumption is evaluated, specifically: The heat recovery rate η is used to measure the heat recovery efficiency and is defined as the ratio of recovered heat to total waste heat: ; is the heat recovered by the heat exchanger; Total waste heat released during the purification process; heat recovery The calculation expression is: ; In the formula, is the mass flow rate of the processed airflow per unit time; is the specific heat capacity of the airflow; is the temperature change of the airflow after passing through the heat exchanger, the total waste heat The calculation expression is: ; In the formula, The overall temperature change of the airflow before and after purification; Heat recovery rate fluctuation refers to the change in heat recovery rate η in different time periods. The heat recovery rate sequence of several time periods is recorded. ; Calculate the mean and standard deviation of the heat recovery rate series, and after analyzing it, evaluate the stability of the overall energy consumption.

7. The high-efficiency and energy-saving industrial air purification method according to claim 6, characterized in that: If the mean heat recovery rate in the data set is greater than or equal to the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is less than the reference threshold of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is high and the fluctuation is small, the overall energy consumption is stable, and the purification equipment is operating in an ideal state. There is no need to adjust the operating parameters. At this time, the existing equipment operation intensity is maintained; If the mean value of the heat recovery rate is greater than or equal to the reference threshold value of the mean value of the heat recovery rate, and the standard deviation of the heat recovery rate is greater than or equal to the reference threshold value of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is high, but the fluctuation is large. At this time, check whether the heat exchange device has intermittent operation problems, and optimize the control logic to reduce the fluctuation range; If the mean value of heat recovery rate is less than the reference threshold value of heat recovery rate, and the standard deviation of heat recovery rate is greater than or equal to the reference threshold value of heat recovery rate standard deviation, it means that the heat recovery efficiency of the system is low, with large fluctuations, high energy consumption and insufficient recovered heat. Check the operating status of the heat recovery equipment and adjust the operating parameters. If the mean heat recovery rate is less than the reference threshold of the mean heat recovery rate, and the standard deviation of the heat recovery rate is less than the reference threshold of the standard deviation of the heat recovery rate, it means that the heat recovery efficiency of the system is low, but the operation is stable. The equipment operating performance has not reached the ideal state, but has not shown instability. Improve the operating intensity of the heat recovery equipment, optimize the equipment performance, and improve the overall heat recovery efficiency.

8. An efficient and energy-saving industrial air purification system, used to implement the efficient and energy-saving industrial air purification method according to any one of claims 1 to 7, characterized in that: It includes pre-treatment module, deep purification module, monitoring and control module, heat exchange and energy evaluation module and clean gas processing module; Pretreatment module: introduces pollutant-containing industrial airflow into the primary treatment stage, removes large particles and some gaseous pollutants through low-resistance filters to reduce the burden of subsequent treatment stages; Deep purification module: Multifunctional deep purification of the primary treated airflow, including the use of dynamic plasma technology to generate high-energy active particles, decomposing volatile organic compounds and acid gases through electrochemical reactions; using nano-scale porous adsorption materials to capture low-concentration pollutants under controllable temperature and humidity conditions; Monitoring and control module: Through intelligent monitoring and control technology, the pollutant concentration, temperature and flow rate of the airflow are monitored in real time, the trend of the change of the airflow pollutant composition is analyzed, and the operating parameters of the purification equipment are dynamically adjusted according to the analysis results; Heat exchange and energy assessment module: directs the airflow into the terminal treatment stage, recovers the waste heat generated during the purification process through heat exchange technology and calculates the heat recovery rate. Based on the fluctuation of the heat recovery rate over several time periods, the stability of the overall energy consumption is assessed; Clean gas processing module: If the overall energy consumption is stable, the purified clean gas will be directly discharged into the external environment, or transported back to the industrial production system for recycling. If the overall energy consumption is unstable, the equipment operation intensity and airflow processing speed will be reduced to reduce energy consumption.

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