Waste gas treatment method and system based on model optimization

Abstract

The invention relates to the technical field of industrial waste gas treatment, in particular to a waste gas treatment method and system based on model optimization. The method comprises the following steps: carrying out multi-dimensional spectrum acquisition on the semiconductor production line waste gas to obtain VOCs molecular spectrum data; performing target VOCs molecule selection based on the VOCs molecule spectrum data to obtain target VOCs molecule positioning data; performing plasma energy field activation on the target VOCs molecules according to the target VOCs molecule positioning data to obtain VOCs molecule energy conversion data; and according to the VOCs molecule energy conversion data, carrying out selective molecular bond cleavage on the target VOCs molecule to obtain VOCs molecule degradable structure data. The VOCs in semiconductor production can be accurately identified and treated, and the pertinence and efficiency of waste gas treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial waste gas treatment, and particularly to a waste gas treatment method and system based on model optimization. Background Art

[0002] In the semiconductor (display panel) industry, industrial waste gas treatment is an important environmental protection issue. The waste gas generated during the production process in this industry has complex components, mainly including a large amount of volatile organic compounds (VOCs, i.e., Volatile Organic Compounds) volatilized from organic solvents used in processes such as lithography, cleaning, and degluing, such as acetone, isopropyl alcohol, and amines. These organic waste gases not only pose a threat to the environment but also are harmful to human health. However, existing waste gas treatment technologies have significant technical problems in dealing with the waste gas components unique to the semiconductor industry. First, the VOCs formed after the volatilization of various organic solvents used in the lithography process are diverse in types and concentrations, making it difficult for a single waste gas treatment technology to achieve comprehensive purification. For example, the VOCs generated during the lithography process account for about 70% - 90% of the total generation, while the VOCs generated during the cleaning process account for about 10% - 20%. This uneven emission characteristic requires that the waste gas treatment technology must have high adaptability and selectivity. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a waste gas treatment method and system based on model optimization to solve at least one of the above technical problems.

[0004] To achieve the above object, a waste gas treatment method based on model optimization includes the following steps:

[0005] Step S1: Perform multi-dimensional spectral collection on the waste gas of the semiconductor production line to obtain VOCs molecular spectral data; based on the VOCs molecular spectral data, select target VOCs molecules to obtain target VOCs molecular positioning data;

[0006] Step S2: Activate the target VOCs molecules with a plasma energy field according to the target VOCs molecular positioning data to obtain VOCs molecular energy conversion data; perform selective molecular bond cleavage on the target VOCs molecules according to the VOCs molecular energy conversion data to obtain VOCs molecular degradable structure data;

[0007] Step S3: Perform directional adsorption on the nano-catalytic material according to the VOCs molecular degradable structure data to obtain VOCs molecular catalytic conversion intermediate product data; perform catalytic degradation on the intermediate product based on the VOCs molecular catalytic conversion intermediate product data to obtain VOCs molecular degradation concentration control data;

[0008] Step S4: Adjust the parameters of the preset gradient deep oxidation system according to the VOCs molecular degradation concentration control data to obtain an optimized model for the VOCs molecular oxidation reaction; perform cascade processing on the target VOCs molecules based on the optimized model for the VOCs molecular oxidation reaction to obtain deep conversion data of the VOCs molecules;

[0009] Step S5: Perform real-time spectral monitoring on the treated exhaust gas according to the deep conversion data of the VOCs molecules to obtain molecular conversion data during the exhaust gas treatment process; dynamically adjust the catalyst activity based on the molecular conversion data during the exhaust gas treatment process to obtain a fully controllable exhaust gas treatment plan.

[0010] Through multi-dimensional spectral acquisition and the selection of target VOCs molecules, the present invention can accurately identify and treat various VOCs generated during the semiconductor production process, including organic compounds with different concentrations and various types, thereby improving the adaptability and selectivity of the exhaust gas treatment technology. By activating VOCs molecules using a plasma energy field and selectively breaking molecular bonds, the VOCs molecules can be effectively transformed into a degradable structure, improving the degradation efficiency while reducing the harm to the environment and human health. Through the directional adsorption and catalytic conversion of nano-catalytic materials, the VOCs molecules can be more effectively transformed into intermediate products and further degraded, improving the catalytic efficiency and selectivity and reducing the consumption of the catalyst. Through parameter adjustment and cascade processing of the gradient deep oxidation system, the deep oxidation of VOCs molecules can be achieved, further reducing the content of harmful substances in the exhaust gas and enhancing the treatment effect. Through real-time spectral monitoring of the treated exhaust gas and dynamic adjustment of the catalyst activity, the exhaust gas treatment process can be monitored in real time, and the treatment strategy can be adjusted in a timely manner to ensure the stability and reliability of the treatment effect.

[0011] Preferably, the present invention also provides an exhaust gas treatment system based on model optimization for performing the exhaust gas treatment method based on model optimization as described above. The exhaust gas treatment system based on model optimization includes:

[0012] A spectral acquisition module for performing multi-dimensional spectral acquisition on the exhaust gas of the semiconductor production line to obtain VOCs molecular spectral data; selecting target VOCs molecules based on the VOCs molecular spectral data to obtain target VOCs molecular positioning data;

[0013] A plasma treatment module for activating the target VOCs molecules using a plasma energy field according to the target VOCs molecular positioning data to obtain VOCs molecular energy conversion data; performing selective molecular bond breaking on the target VOCs molecules according to the VOCs molecular energy conversion data to obtain VOCs molecular degradable structure data;

[0014] A catalytic conversion module, which is used to perform directional adsorption on a nano-catalytic material according to the biodegradable structure data of VOCs molecules to obtain data on intermediate products of VOCs molecule catalytic conversion; and perform catalytic degradation on the intermediate products based on the data on intermediate products of VOCs molecule catalytic conversion to obtain data on VOCs molecule degradation concentration control;

[0015] An oxidation optimization module, which is used to adjust the parameters of a preset gradient depth oxidation system according to the data on VOCs molecule degradation concentration control to obtain an optimized model for VOCs molecule oxidation reaction; and perform cascade processing on target VOCs molecules based on the optimized model for VOCs molecule oxidation reaction to obtain data on deep conversion of VOCs molecules;

[0016] A monitoring and regulation module, which is used to perform real-time spectral monitoring on the treated exhaust gas according to the data on deep conversion of VOCs molecules to obtain data on molecular conversion during the exhaust gas treatment process; and dynamically regulate the activity of the catalyst based on the data on molecular conversion during the exhaust gas treatment process to obtain a fully controllable exhaust gas treatment plan.

[0017] Through the spectral acquisition module, the present invention can accurately identify and process VOCs in semiconductor production, improving the pertinence and efficiency of exhaust gas treatment. Through the plasma treatment module, VOCs molecules can be efficiently activated, promoting the breaking of molecular bonds and providing conditions for subsequent degradation. Through the catalytic conversion module, directional adsorption and catalytic degradation of VOCs molecules are achieved, improving the degradation efficiency and reducing the generation of harmful intermediate products. The oxidation optimization module realizes the deep oxidation of VOCs molecules through parameter regulation, further improving the treatment effect and ensuring the thorough purification of the exhaust gas. The monitoring and regulation module can monitor the exhaust gas treatment process in real time and dynamically adjust the activity of the catalyst, ensuring the continuity and stability of the treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description with reference to the accompanying drawings:

[0019] Figure 1 The schematic diagram of the step flow of the exhaust gas treatment method based on model optimization in an embodiment is shown.

[0020] Figure 2 The detailed step flow schematic diagram of step S16 in an embodiment is shown.

[0021] Figure 3 The detailed step flow schematic diagram of step S26 in an embodiment is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus the repeated description of them will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0025] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a waste gas treatment method based on model optimization, including the following steps:

[0026] Step S1: Perform multi-dimensional spectral acquisition on the waste gas of the semiconductor production line to obtain VOCs molecular spectral data; select target VOCs molecules based on the VOCs molecular spectral data to obtain target VOCs molecular localization data;

[0027] Step S2: Activate the target VOCs molecules with a plasma energy field according to the target VOCs molecular localization data to obtain VOCs molecular energy conversion data; perform selective molecular bond cleavage on the target VOCs molecules according to the VOCs molecular energy conversion data to obtain VOCs molecular degradable structure data;

[0028] Step S3: Perform directional adsorption on the nano-catalytic material according to the VOCs molecular degradable structure data to obtain VOCs molecular catalytic conversion intermediate product data; perform catalytic degradation on the intermediate product based on the VOCs molecular catalytic conversion intermediate product data to obtain VOCs molecular degradation concentration control data;

[0029] Step S4: Adjust the parameters of the preset gradient deep oxidation system according to the VOCs molecule degradation concentration control data to obtain an optimized model for the VOCs molecule oxidation reaction; perform cascade processing on the target VOCs molecules based on the optimized model for the VOCs molecule oxidation reaction to obtain the VOCs molecule deep conversion data;

[0030] Step S5: Perform real-time spectral monitoring on the treated exhaust gas according to the VOCs molecule deep conversion data to obtain the molecular conversion data during the exhaust gas treatment process; dynamically adjust the catalyst activity based on the molecular conversion data during the exhaust gas treatment process to obtain a fully controllable exhaust gas treatment plan.

[0031] In this embodiment, a multi-dimensional spectrometer (such as the Thermo Scientific Nicolet iS50 FTIR spectrometer) is used to collect multi-dimensional spectra of the exhaust gas from the semiconductor production line to obtain the spectral data of VOCs molecules. The scanning range of the spectrometer is set to 4000 - 400 cm -1 , and the resolution is 4 cm -1, to ensure the collection of detailed molecular vibration information. The spectral data is analyzed using cheminformatics software (such as JChem Suite from ChemAxon), and target VOCs molecules are selected based on molecular fingerprint feature extraction technology, and the localization data of the target molecules is obtained. Then, the waste gas is introduced into a plasma reactor (such as a customized radio frequency plasma reactor with a working frequency of 13.56 MHz), and the target VOCs molecules are activated by a plasma energy field under controlled atmospheres and conditions (such as a mixed gas of oxygen and argon, a pressure of 100 Pa, and a power of 100 W) to break molecular bonds and obtain energy conversion data. The waste gas after plasma treatment is analyzed using a mass spectrometer (such as Thermo Scientific LTQ Orbitrap XL) to selectively break specific chemical bonds of the target VOCs molecules and obtain degradable structure data. Then, these degradable VOCs molecules are introduced into a fixed-bed reactor filled with a nano-catalytic material (such as a Pd / Al2O3 catalyst), and the reaction temperature is controlled at 300 °C to promote the catalytic conversion of VOCs molecules and obtain intermediate product data. A gas chromatograph (GC, such as Agilent 7890B) is used to monitor the concentration of intermediate products during the catalytic conversion process, and based on these data, the intermediate products are further catalytically degraded to obtain VOCs molecule degradation concentration control data. Then, according to these data, the parameters of the gradient depth oxidation system (such as a customized multi-stage oxidation tower) are adjusted to optimize the oxidation reaction conditions of VOCs molecules to obtain an optimized model for the oxidation reaction of VOCs molecules. Finally, based on the optimized model, cascade treatment is performed on the target VOCs molecules to achieve deep conversion. An online monitoring system (such as an FTIR spectrometer) is used to monitor the molecular conversion data of the waste gas after treatment in real time, and the activity of the catalyst is controlled by dynamically adjusting the dosage of the catalyst or replacing the catalyst (such as by changing the ratio of Pd / Al2O3 or adding a promoter) to obtain a fully controllable waste gas treatment solution.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: Preheat and calibrate the waste gas collection system of the semiconductor production line to obtain waste gas collection system calibration data;

[0034] Specifically, a preheating calibration device can be used, such as an automatic calibrator with the model number AC-2024. Set the preheating temperature to 40°C and continuously preheat for 30 minutes to ensure that the system reaches a stable working state. During the preheating process, use a temperature sensor (model: TS-100, accuracy ±0.1°C) to monitor and record the actual temperature of the system to ensure that the preheating process meets the expected parameters. After preheating is completed, input a known VOCs concentration standard gas sample, such as a mixed gas of acetone and isopropanol, with concentrations of 100 ppm and 50 ppm respectively, through the calibration software. The system will automatically adjust the sensor response to match the actual concentration of the standard gas, thereby obtaining the calibration data of the exhaust gas collection system.

[0035] Step S12: Collect the production process of the semiconductor production line to obtain the process characteristic data of the semiconductor production line;

[0036] Specifically, a data acquisition system (model: DP-3000) can be used. This system can monitor the key process parameters on the production line in real time, such as temperature, pressure, flow rate, and chemical concentration. In the lithography process, set the data acquisition system to record data every 5 seconds. For example, record that the temperature during the photoresist coating process is controlled at 23°C ± 1°C, the exposure time is set to 30 seconds, and the ultraviolet intensity is 5 mW / cm². At the same time, use a gas chromatograph (model: GC-5000) to analyze the solvents used in the cleaning process, such as the concentrations of acetone and isopropanol, to determine their emission characteristics. Through these data, the process characteristic data of the semiconductor production line can be obtained, including the emission amounts and types of VOCs in different processes, as well as their change trends during the production process.

[0037] Step S13: Configure the parameters of the exhaust gas collection device according to the calibration data of the exhaust gas collection system and the process characteristic data of the semiconductor production line to obtain the multi-dimensional spectral acquisition parameter configuration data;

[0038] Specifically, the calibration data of the exhaust gas collection system can be extracted from the database, including the standard gas concentrations of acetone and isopropanol obtained in step S11. According to the process characteristic data of the semiconductor production line, such as the type of photoresist used and the exposure conditions during the lithography process, select appropriate exhaust gas collection parameters. Use a configuration software and input the following parameters: the sampling flow rate is set to 2 liters per minute, the sampling time is set to automatically switch samples every 30 seconds to adapt to the emission characteristics of different processes. The spectral acquisition range is set to 400 - 4000 wavenumbers (cm⁻¹) to cover the main absorption bands of acetone and isopropanol. Through these parameter configurations, the exhaust gas collection device can accurately capture the spectral characteristics of VOCs molecules, thereby obtaining the multi-dimensional spectral acquisition parameter configuration data.

[0039] Step S14: Perform preliminary concentration treatment on the exhaust gas sample, and based on the multi-dimensional spectral acquisition parameter configuration data, conduct a preliminary infrared spectrum scan on the concentrated exhaust gas sample to obtain the primary infrared spectrum data of VOCs molecules;

[0040] Specifically, an exhaust gas preconcentration machine (model: Conc-450) can be used to concentrate the exhaust gas sample through a cold trap (temperature set at -20°C) to increase the concentration of VOCs. The concentration ratio is set at 100:1, which is the ratio of the original exhaust gas volume to the concentrated volume. After concentration, a Fourier transform infrared spectrometer (FTIR, model: FTIR-6500) is used to conduct a preliminary scan on the concentrated sample. According to the multi-dimensional spectral acquisition parameter configuration data, the working parameters of the FTIR are set: the scanning range is 400 - 4000 wavenumbers (cm-1), the resolution is 4 wavenumbers, and the number of scans is 32 times. Through these operations, the primary infrared spectrum data of VOCs molecules can be obtained.

[0041] Step S15: Conduct gas chromatography pretreatment based on the primary infrared spectrum data of VOCs molecules to obtain the leading data for gas chromatography separation of VOCs molecules;

[0042] Specifically, a gas chromatograph (GC, model: 7890B) can be used to analyze the primary infrared spectrum data of VOCs molecules. A capillary column with a polar stationary phase is selected (such as DB-WAX, length: 30 meters, inner diameter: 0.25 millimeters, film thickness: 0.25 micrometers). The initial temperature of the chromatograph is set at 40°C and heated at a rate of 10°C per minute to 200°C, and then held for 5 minutes to ensure the effective separation of VOCs. The carrier gas is high-purity helium (purity 99.999%), and the flow rate is set at 1 milliliter per minute. The inlet temperature is set at 250°C to avoid sample decomposition. Program temperature control is used to adapt to VOCs molecules with different boiling points. The sample injection volume is set at 1 microliter, and the split injection mode is adopted with a split ratio of 10:1. Through these parameter settings, the gas chromatograph can effectively separate VOCs molecules and record the chromatogram. Then, chromatographic data processing software (such as ChemStation) is used to perform integration processing on the chromatogram to obtain the peak areas and retention times of each VOCs molecule, and these data are the leading data for gas chromatography separation of VOCs molecules.

[0043] Step S16: Conduct spectral analysis on the leading data for gas chromatography separation of VOCs molecules to obtain the spectral data of VOCs molecules; based on the spectral data of VOCs molecules, select the target VOCs molecules to obtain the positioning data of the target VOCs molecules.

[0044] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of Step S16.

[0045] The present invention preheats and calibrates the exhaust gas collection system, ensuring the accuracy and reliability of the data. By collecting the process characteristic data of the semiconductor production line, customized exhaust gas treatment can be carried out, improving the pertinence and efficiency of the treatment. By combining the calibration data of the exhaust gas collection system with the process characteristic data, parameter configuration of the exhaust gas collection equipment is performed, making the collection process more accurate and effectively capturing the characteristics of VOCs molecules. By performing preliminary concentration treatment on the exhaust gas samples, the representativeness of the samples is enhanced, contributing to more accurate identification of VOCs. The primary infrared spectral data of VOCs molecules is quickly obtained through preliminary infrared spectral scanning. The pretreatment of gas chromatography data helps to remove interference factors and improve the accuracy and efficiency of chromatographic analysis. By performing spectral analysis on the leading data of gas chromatography separation, VOCs molecules can be identified and analyzed in detail. Based on the spectral data of VOCs molecules, target VOCs molecules are selected, achieving precise positioning of specific VOCs molecules and providing targets for plasma treatment and catalytic conversion.

[0046] Preferably, step S16 includes the following steps:

[0047] Step S161: Perform combined detection on the leading data of gas chromatography separation of VOCs molecules to obtain the original spectral data of VOCs molecules;

[0048] Specifically, a gas chromatography - mass spectrometry (GC - MS, model: Agilent 7890B - 5977A) can be used to combine the gas chromatograph with the mass spectrometer for mass spectrometry analysis while performing chromatographic separation. In GC - MS analysis, a suitable chromatographic column (such as HP - 5MS, length: 30 m, inner diameter: 0.25 mm, film thickness: 0.25 μm) and mass spectrometry parameters (such as electron ionization energy 70 eV, scanning range 50 - 500 m / z) are selected. Through chromatographic separation, VOCs molecules are sent into the mass spectrometer one by one according to their different boiling points. The mass spectrometer records the ionization signals of each molecule to generate a mass spectrum. By comparing with a standard mass spectrum library (such as the NIST spectrum library), the identity of VOCs molecules is determined. At the same time, the time - of - flight (TOF) mode of the mass spectrometer can provide the exact mass of the molecule to further confirm the molecular structure. Through combined detection, the original spectral data of VOCs molecules can be obtained, including information such as the retention time, mass - to - charge ratio, and relative abundance of the molecules.

[0049] Step S162: Enhance the signals of the original spectral data of VOCs molecules to obtain the spectral data of VOCs molecules;

[0050] Specifically, signal processing software (such as MassHunter) can be used to process the original data of VOCs molecular spectra. Apply baseline correction algorithms, such as the Savitzky-Golay smoothing filter, to eliminate noise and baseline drift in the mass spectra. Set the window size of the filter to 15 data points and the polynomial order to 2. Then, integrate the mass spectral peaks of each VOCs molecule to calculate its relative abundance. Next, apply a signal enhancement algorithm, such as local maximum detection. Set the detection threshold to 0.8 to identify signals that are 80% higher than the background noise. Through these steps, the spectral data of VOCs molecules can be obtained.

[0051] Step S163: Extract the molecular fingerprint features from the VOCs molecular spectral data to obtain the VOCs molecular fingerprint feature vectors;

[0052] Specifically, chemometric software (such as the Chemometrics Toolbox of MATLAB) can be used to import the VOCs molecular spectral data obtained from GC-MS. Select principal component analysis (PCA) as the method for fingerprint feature extraction. Set the cumulative contribution rate threshold of PCA to 95%. Preprocess the spectral data, including centering and scaling, to eliminate the influence of dimensions between different samples. Run the PCA algorithm, and the software will output the loading matrix of each VOCs molecule, which contains the scores of the main components of the molecule. Extract the first five principal components from the loading matrix to form the VOCs molecular fingerprint feature vectors. These vectors can represent the spectral characteristics of each VOCs molecule.

[0053] Step S164: Conduct a preliminary screening of the target VOCs molecules based on the VOCs molecular fingerprint feature vectors to obtain the VOCs molecular candidate set;

[0054] Specifically, chemometric software can be used with the VOCs molecular fingerprint feature vectors as the input. Set a screening threshold. For example, select molecules with scores of the first five principal components all higher than 0.5 as candidate molecules. The software will screen out the eligible molecules from all the analyzed VOCs molecules according to this criterion to form the VOCs molecular candidate set. For example, if there are 100 VOCs molecules in the database, 20 candidate molecules can be obtained after screening.

[0055] Step S165: Conduct statistics on the concentration and distribution of the VOCs molecular candidate set to obtain the VOCs molecular distribution concentration mapping data;

[0056] Specifically, a Laboratory Information Management System (LIMS, such as LabVantage) can be used to import the gas chromatography - mass spectrometry (GC - MS) data of the VOCs molecular candidate set. Through the data analysis module of the LIMS software, the chromatographic peak area of each candidate VOC molecule is integrated to determine its concentration. Integral parameters are set, for example, the slope (S) is 10 and the intercept (C) is 5 to adapt to the shape of the chromatographic peak. Using Geographic Information System (GIS) software (such as ArcGIS), the concentration data of each VOC molecule is combined with its sampling point location on the semiconductor production line to generate a distribution map of VOC molecules. The grid size is set to 10 meters × 10 meters in GIS to cover the entire production line, and the average concentration of VOC molecules in each grid is calculated. Finally, a mapping data graph containing the concentration and distribution information of VOC molecules is obtained, which shows the concentration distribution of different VOC molecules across the entire production line.

[0057] Step S166: Select target VOC molecules based on the VOC molecule distribution concentration mapping data to obtain target VOC molecule location data.

[0058] Specifically, GIS software (ArcGIS) can be used to combine the VOC molecule distribution concentration mapping data and the process flow chart of the production line to determine the VOC molecules that need to be processed preferentially. For example, a threshold is set, and VOC molecules with a concentration exceeding 100 ppm and distributed in key areas of the production line (such as the lithography area and the cleaning area) are selected as target molecules. In GIS, using the buffer analysis tool, with each key area as the center, the buffer radius is set to 50 meters to identify the VOC molecules with a concentration exceeding the threshold within this range. Then, using the hot spot analysis tool, the areas with high VOC molecule concentration are identified, and these areas are the key points for pollution control. Finally, a set of location data of target VOC molecules is obtained, including their specific locations and concentration levels.

[0059] The present invention obtains the spectral raw data of VOC molecules through a combined detection technology, improving the detection accuracy and data integrity. The signal - to - noise ratio is enhanced through signal enhancement processing, making the characteristics of VOC molecules more obvious. The extraction of molecular fingerprint features provides a unique identification mark for VOC molecules, enhancing the accuracy and reliability of the identification process. The preliminary screening based on the molecular fingerprint feature vector improves the screening efficiency and quickly narrows down the range of target molecules. By performing concentration and distribution statistics on the VOCs molecular candidate set, the target VOC molecules can be identified and located more accurately. Selecting target molecules according to the VOC molecule distribution concentration mapping data ensures the pertinence of the treatment process and improves the treatment efficiency and effect.

[0060] Preferably, step S2 includes the following steps:

[0061] Step S21: Collect the geometric structure of the plasma reaction chamber to obtain the plasma chamber structure parameters;

[0062] Specifically, a three-dimensional laser scanner (such as Faro Focus3D X 130 with an accuracy of 0.05 mm) can be used to scan the internal structure of the plasma reaction chamber. During the scanning process, set the working distance of the scanner to 0.5 m and the scanning speed to 10 frames per second. Through scanning, a three-dimensional model of the reaction chamber can be obtained, including the length, width, height of the chamber, and the dimensions of internal features. Use computer-aided design software (CAD, such as AutoCAD) to draw a detailed geometric structure diagram of the plasma reaction chamber based on the scanning data. In the CAD software, set the tolerance range to ±0.1 mm, and finally obtain the structure parameters of the plasma chamber, including the volume, surface area, and key dimensions of the chamber.

[0063] Step S22: Configure a low-temperature non-equilibrium plasma excitation device based on the plasma chamber structure parameters to obtain a plasma excitation system;

[0064] Specifically, a low-temperature non-equilibrium plasma excitation device can be designed according to the plasma chamber structure parameters. Select a high-frequency power supply (such as 40 kHz with a power of 5 kW) as the excitation source, and use electromagnetic simulation software (such as ANSYS HFSS) to simulate the electromagnetic field distribution during the plasma excitation process. In the simulation, set the working frequency to 40 kHz, the chamber material to stainless steel, and the electrode material to molybdenum. According to the simulation results, adjust the distance between the electrodes to 5 cm to obtain a uniform electromagnetic field distribution. Then, use machining equipment (such as a CNC machine tool, model: HAAS UMC-750) to manufacture the electrodes and other components. Finally, assemble the high-frequency power supply, electrodes, and other components into a complete plasma excitation system and install and debug it in the plasma reaction chamber. Through these steps, a low-temperature non-equilibrium plasma excitation system customized according to the plasma chamber structure parameters can be obtained.

[0065] Step S23: Determine the plasma excitation parameters according to the target VOCs molecular localization data to obtain the excitation parameter configuration data;

[0066] Specifically, chemical kinetics simulation software (such as Chemkin-Pro) can be used to simulate the decomposition reactions of target VOCs molecules (such as acetone and isopropanol) in the plasma. According to the simulation results, determine the excitation parameters, such as electron temperature and density, to optimize the decomposition efficiency of VOCs molecules. For example, set the electron temperature to 3 eV and the electron density to 10 15 cm 3, then, use a plasma diagnostic device (such as a Langmuir probe, model: PBD-5) to measure the actual plasma parameters and compare them with the simulation results to adjust the excitation parameters. By adjusting the power output of the high-frequency power supply, for example, gradually increasing the power from 3 kW to 5 kW, observe the change in the readings of the Langmuir probe until the optimal excitation parameters are reached. Finally, obtain a set of excitation parameter configuration data, including power, frequency, gas flow rate, etc.

[0067] Step S24: Regulate the electromagnetic field intensity of the plasma excitation system to obtain the electromagnetic field control data of the excitation system;

[0068] Specifically, an electromagnetic field simulation software (such as CST Studio Suite) can be used to simulate the electromagnetic field distribution of the plasma excitation system. According to the simulation results, select a suitable electromagnetic field intensity distribution. For example, the electromagnetic field intensity in the central region of the plasma reaction chamber should reach 100 mT. Then, use an electromagnetic field intensity regulator (such as a matching network composed of adjustable inductors and capacitors, model: MN-300) to adjust the electromagnetic field intensity of the plasma excitation system. By changing the values of the inductor and capacitor, for example, adjusting the inductor from 100 μH to 200 μH and the capacitor from 1 nF to 2 nF, to optimize the electromagnetic field intensity. At the same time, use an electromagnetic field intensity measuring device (such as a flux density meter, model: Hirstron HT-1000) to monitor the electromagnetic field intensity in real time and feedback it to the control system for closed-loop control. Through these operations, the electromagnetic field control data of the excitation system can be obtained, including electromagnetic field intensity, frequency, phase, etc.

[0069] Step S25: Based on the excitation parameter configuration data and the electromagnetic field control data of the excitation system, perform directional energy activation on the target VOCs molecules to obtain the VOCs molecular energy conversion data;

[0070] Specifically, the operating conditions of the plasma excitation system can be set according to the excitation parameter configuration data and the electromagnetic field control data of the excitation system. For example, set the power output of the high-frequency power supply (model: PAC-40K, frequency: 40 kHz) to 3 kW. At the same time, adjust the electromagnetic field intensity regulator (model: MN-300) to ensure that the electromagnetic field intensity in the central region of the plasma reaction chamber reaches 100 mT. Introduce the target VOCs molecules (such as acetone and isopropanol) into the plasma reaction chamber, and control the gas flow rate to be 500 sccm (standard cubic centimeters per minute). Use a plasma diagnostic device (such as an optical emission spectrometer, model: Ocean Optics HR4000) to monitor the light emission at specific wavelengths in the plasma in real time. These wavelengths correspond to specific energy level transitions of the VOCs molecules, so as to obtain real-time data on the energy conversion of the VOCs molecules. By analyzing these data, the energy conversion efficiency of the VOCs molecules in the plasma can be determined. For example, the transition intensity of acetone molecules at the 3.4 eV energy level and the transition intensity of isopropanol molecules at the 4.5 eV energy level. Finally, a set of VOCs molecule energy conversion data is obtained, including the energy absorption, excitation, and conversion efficiencies of different molecules.

[0071] Step S26: Perform molecular bond energy analysis based on the VOCs molecule energy conversion data to obtain a VOCs molecule bond breakage prediction model;

[0072] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of step S26.

[0073] Step S27: Reconstruct the molecular energy levels of the target VOCs molecules based on the VOCs molecule bond breakage prediction model to obtain fine VOCs molecule energy conversion data, and select a directional molecular bond breakage path for the target VOCs molecules based on the fine VOCs molecule energy conversion data to obtain a VOCs molecule bond breakage strategy;

[0074] Specifically, chemical calculation software (such as Gaussian 16) and molecular dynamics simulation software (such as Materials Studio) can be used for molecular energy level reconstruction. Import the data of the VOCs molecular bond cleavage prediction model, including the bond cleavage energy threshold and the bond cleavage path mapping data of the molecule. Select appropriate calculation methods, such as using the B3LYP functional and the 6-31G(d) basis set, which can provide an accurate description of the molecular energy level structure. Then, perform molecular orbital calculations to determine the HOMO and LUMO energy levels of the molecule and calculate the energy gap between them. Set the software to perform electronic transition calculations, such as using the TD-DFT method, to simulate the excited state behavior of the molecule after absorbing energy. Through these calculations, fine data on the energy conversion of VOCs molecules can be obtained, including the electronic state density, energy level distribution, and transition probability of the molecule. Use chemical informatics software (such as the JChem Suite of ChemAxon) to select the molecular bond cleavage path. Based on the fine energy conversion data, identify the bond cleavage path with the lowest energy, which corresponds to the most likely bond cleavage reaction of the molecule after absorbing energy. For example, if the calculation results show that the energy required for the cleavage of the C-C bond is lower than that of the C-H bond, then the C-C bond will be preferentially considered as the cleavage target. Finally, integrate the results of molecular energy level reconstruction and the fine energy conversion data to formulate a VOCs molecular bond cleavage strategy. This strategy will include the molecular bonds to be cleaved directionally, the expected energy input, and the products after cleavage. For example, the strategy indicates that under the excitation of light with a specific wavelength, the C-C bond of acetone molecules is preferentially cleaved to generate specific radical intermediates. Through these steps, a VOCs molecular bond cleavage strategy can be obtained.

[0075] Step S28: Intervene in the target VOCs molecules according to the VOCs molecular bond cleavage strategy to obtain the degradable structure data of the VOCs molecules.

[0076] Specifically, according to the VOCs molecular bond cleavage strategy, a specific intervention method can be selected. For example, if the strategy indicates that the C-C bond of acetone molecules needs to be cleaved, laser photolysis technology is used for intervention. Select a suitable laser system (such as an Excimer laser, model: XeCl, wavelength: 308 nm), and this wavelength corresponds to an absorption peak of acetone molecules and can effectively excite and cleave the C-C bond. Set the operating parameters of the laser, such as the pulse width is 20 ns and the energy density is 50 mJ / cm 2, a gas sample containing acetone is introduced into the reaction chamber, and the sample is irradiated using a laser system. By adjusting the focus of the laser and the sample flow rate, the reaction conditions are controlled to ensure that acetone molecules can effectively absorb the laser energy and break. A mass spectrometer (such as a quadrupole mass spectrometer, model: Thermo Scientific TSQ 9000) is used to monitor the fragments and intermediates generated during the reaction. The scanning range of the mass spectrometer is set to 1 - 200 m / z to cover the fragmentation products. By analyzing the mass spectrum, the radicals and molecular fragments generated after the break are identified and quantified. For example, the radicals CH 3 CO and CH 3 . Finally, the data of all products generated after the break are collected and analyzed to obtain the degradable structure data of VOCs molecules. These data include the mass-to-charge ratio, relative abundance, and energy distribution of the fragmentation products.

[0077] In the present invention, by collecting the geometric structure of the plasma reaction chamber and configuring a low-temperature non-equilibrium plasma excitation device, precise control of the plasma excitation system is achieved, the excitation parameter configuration is optimized, and the excitation process is made more in line with the actual requirements. Through the precise regulation of the electromagnetic field intensity, the uniformity and controllability of energy activation are further enhanced, and the directional energy activation improves the selectivity and efficiency of molecular bond breakage. The mechanism of molecular bond breakage is understood through molecular bond energy analysis. Through molecular energy level reconstruction, the molecular energy conversion becomes more refined, and the precision of molecular bond breakage is improved. The selective molecular bond breakage path and the effective intervention on the target VOCs molecules improve the efficiency and selectivity of the conversion of target molecules.

[0078] Preferably, step S26 includes the following steps:

[0079] Step S261: Perform chemical calculation preprocessing on the VOCs molecule energy conversion data to obtain the VOCs molecule chemical calculation preprocessing data;

[0080] Specifically, chemical calculation software (such as Gaussian 16) can be used to import the VOCs molecule energy conversion data obtained from the plasma excitation system. These data include the electronic state distribution and energy level transition information of the molecules. In the software, appropriate basis sets and methods are selected for calculation. For example, the 6-31G(d) basis set and the B3LYP functional are used. These selections can balance the calculation accuracy and the required calculation resources. For example, all energy values are converted to electron volt (eV) units and normalized to the highest occupied molecular orbital (HOMO) energy of the molecule. Then, a self-consistent field (SCF) calculation is performed to optimize the geometric structure of the molecule and obtain the electronic structure information of the molecule. Finally, the preprocessed VOCs molecule chemical calculation data are output, including the optimized geometric structure, energy level distribution, and electron density distribution of the molecule, etc.

[0081] Step S262: Based on the preprocessed data of VOCs molecular chemical calculations, construct the molecular orbital transition states to obtain the VOCs molecular orbital structure mapping data;

[0082] Specifically, a chemical calculation software (Gaussian 16) can be used to import the preprocessed data of VOCs molecular chemical calculations. Set the software to perform time-dependent density functional theory (TD-DFT) calculations, which is a method for simulating electronic transitions and excited states. Select a suitable TD-DFT functional, such as B3LYP, and set the number of excited states to be calculated, such as calculating the first 5 excited states. Run the TD-DFT calculation, and the software will output the excitation energy, transition probability, and transition properties of each excited state. Use visualization software (such as GaussView) to view and analyze the spatial distribution and shape of the molecular orbitals, as well as the transition paths between different orbitals. Finally, output the VOCs molecular orbital structure mapping data, including the energy, shape, and transition information of the molecular orbitals.

[0083] Step S263: Calculate the valence electron state density of the molecular bonds based on the VOCs molecular orbital structure mapping data to obtain the VOCs molecular electron state distribution data;

[0084] Specifically, a chemical software (such as Gaussian 16) can be used for molecular orbital analysis. Import the VOCs molecular orbital structure mapping data, including the ground state and excited state information of the molecule. Select a suitable calculation method, such as using the B3LYP functional and the 6-31G(d) basis set, which can provide an accurate description of the valence electron state density. Then, perform natural orbital occupancy number (NOON) analysis, which is a method for calculating the electron density in molecular orbitals. Set the software to calculate the electron occupancy of each orbital, such as calculating the electron density of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). Through these calculations, the VOCs molecular electron state distribution data can be obtained, including the electron density and energy level of each orbital.

[0085] Step S264: Estimate the energy transition probability of the VOCs molecular electron state distribution data to obtain the VOCs molecular energy level transition conversion rate;

[0086] Specifically, chemical software (Gaussian 16) can be used for transition analysis. Import the electronic state distribution data of VOCs molecules, including the electron density and energy levels of the HOMO and LUMO orbitals. Set the software to perform time-dependent density functional theory (TD-DFT) calculations. For example, select the B3LYP functional and the 6-31G(d) basis set for the calculations, and set the number of excited states to be calculated. For example, calculate the first 5 excited states. The software will output the excitation energy, transition probability, and transition properties of each excited state. By analyzing these data, the transition probability between different energy levels can be estimated. For example, the transition probability from HOMO to LUMO can be estimated, and using these transition probability data, the conversion rate of the energy level transition of VOCs molecules can be calculated. For example, calculate the conversion rate of the transition from the ground state to the first excited state.

[0087] Step S265: Based on the conversion rate of the energy level transition of VOCs molecules, construct a molecular bond cleavage prediction model for the target VOCs molecules to obtain a VOCs molecular bond cleavage prediction model.

[0088] Specifically, for the detailed implementation process of this embodiment, please refer to the sub-steps of step S265.

[0089] Through the chemical calculation preprocessing of the VOCs molecular energy conversion data, the present invention ensures the accuracy and reliability of subsequent calculations, provides basic data for the construction of the molecular orbital transition state, and thus deeply understands the internal structure and electronic behavior of VOCs molecules. Through the calculation of the molecular bond valence electron state density, detailed information on the electronic state distribution of VOCs molecules can be obtained. By estimating the energy transition probability, the prediction ability of the conversion rate of the energy level transition of VOCs molecules is improved. Through the optimized molecular bond cleavage prediction model, unnecessary energy input is reduced, energy consumption and processing costs are lowered, and the economy of the entire waste gas treatment process is improved.

[0090] Preferably, step S265 includes the following steps:

[0091] Step S2651: Based on the conversion rate of the energy level transition of VOCs molecules, perform a molecular bond resonance state simulation on the target VOCs molecules to obtain VOCs molecular resonance state kinetic data;

[0092] Specifically, molecular dynamics simulation software (such as Materials Studio) can be used to perform molecular bond resonance state simulation and import the data of the conversion rate of VOCs molecular energy level transitions. Select appropriate force fields (such as UFF or MM4), which can simulate the vibration and resonance behavior of molecules. Set the simulation parameters, such as the initial temperature of 300K, the pressure of 1 atmosphere, and the simulation time step of 1 fs (femtosecond), and the total simulation time of 10 ps (picosecond). Run the molecular dynamics simulation, and the software will calculate the dynamic behavior of molecules during different energy level transitions, including the vibration frequency and amplitude of molecular bonds. By analyzing these data, the dynamic data of the resonance state of VOCs molecules can be obtained, such as the vibration mode and energy distribution of molecular bonds.

[0093] Step S2652: Calculate the bond energy distribution of molecular bonds based on the dynamic data of the resonance state of VOCs molecules to obtain the energy spectrum of VOCs molecular bonds;

[0094] Specifically, molecular dynamics simulation software (Materials Studio) can be used to calculate the bond energy distribution of molecular bonds and import the dynamic data of the resonance state of VOCs molecules. Select energy distribution analysis tools, such as the Vibronic Coupling Analysis (VCA) tool, which can analyze the coupling and energy distribution of molecular vibration energy levels. Set the parameters of the VCA tool, such as the coupling strength threshold of 0.1 eV and the number of vibration energy levels. Run the VCA analysis, and the software will calculate the bond energy distribution of molecular bonds, including the energy and intensity of each vibration energy level. Through these calculations, the energy spectrum of VOCs molecular bonds can be obtained, such as the energy distribution of C-H bonds, C-C bonds, and C=O bonds in the molecule.

[0095] Step S2653: Extract the statistical characteristics of the energy spectrum of VOCs molecular bonds to obtain the energy distribution data of VOCs molecules;

[0096] Specifically, data processing and statistical analysis software (such as Python in combination with the SciPy and NumPy libraries) can be used to extract statistical features and import the energy spectrum data of VOCs molecular bonds. Peak fitting and distribution analysis are selected to extract the key features in the energy spectrum. Set the peak fitting algorithm, such as Gaussian fitting or Lorentz fitting, to identify and quantify the peaks in the energy spectrum. For example, set the standard deviation of the fitting function to 0.2 eV and the position parameter to the bond energy value. Through fitting, extract the central position (representing the bond energy size), width (representing the broadening of the energy distribution), and height (representing the peak intensity) of each peak, and use descriptive statistical methods to calculate statistical parameters such as the mean, median, and standard deviation of the energy spectrum to comprehensively describe the energy distribution characteristics of VOCs molecules. Finally, obtain a set of VOCs molecular energy distribution data, including the energy characteristics and statistical parameters of each molecular bond.

[0097] Step S2654: Predict the critical energy for molecular bond cleavage of the target VOC molecules based on the VOC molecular energy distribution data to obtain the VOC molecular cleavage energy threshold;

[0098] Specifically, data processing and statistical analysis software (Python in combination with the SciPy and NumPy libraries) can be used to predict the critical energy for molecular bond cleavage, and import the VOC molecular energy distribution data. Select an energy threshold prediction model based on statistical learning to predict the critical energy for molecular bond cleavage. Set the model parameters. For example, select a support vector machine (SVM) classifier with a radial basis function (RBF) kernel, a regularization parameter C of 1.0, and a kernel function parameter γ of 0.1. Use the training dataset (including known molecular bond cleavage energies and corresponding energy distribution data) to train the model, and then use the test dataset to verify the prediction ability of the model. Through model prediction, obtain the critical energy for cleavage of each molecular bond. For example, predict that the critical energy for cleavage of the C-H bond is 3.8 eV and the critical energy for cleavage of the C-C bond is 3.0 eV. Finally, obtain a set of VOC molecular cleavage energy thresholds.

[0099] Step S2655: Map the molecular bond cleavage paths of the target VOC molecules based on the VOC molecular cleavage energy thresholds to obtain the VOC molecular cleavage path mapping data;

[0100] Specifically, chemical informatics software (such as JChem Suite from ChemAxon) can be used to map the molecular bond cleavage paths and import the data of the VOCs molecular cleavage energy thresholds. Select a suitable molecular editing tool, such as MarvinSketch, to construct the structure of the target VOCs molecule and mark the cleavage bonds. Set the software to perform molecular dynamics simulations, such as using the GROMOS 54A7 force field, to simulate the process of molecular bond cleavage at a specific energy threshold. Through the simulation, observe and record the cleavage behavior of the molecule under different energy inputs, especially paying attention to the bonds whose energy is close to or exceeds the cleavage energy threshold. Use a path analysis tool, such as Reaction Pathway Explorer, to map the molecular bond cleavage paths, including the order of cleavage and the intermediates. Finally, obtain a set of VOCs molecular cleavage path mapping data, which details the potential paths and intermediate products of molecular bond cleavage at a specific energy threshold.

[0101] Step S2656: Based on the VOCs molecular cleavage energy thresholds and the VOCs molecular cleavage path mapping data, perform kinetic collaborative reconstruction to obtain a VOCs molecular bond cleavage prediction model.

[0102] Specifically, molecular dynamics simulation software (such as Gaussian 16) can be used to perform kinetic collaborative reconstruction, import the VOCs molecular cleavage path mapping data and the cleavage energy threshold data. Select a suitable calculation method, such as using the M06-2X functional and the 6-311++G(2d,p) basis set, which can provide an accurate description of the molecular dynamics process. Set the software to perform reaction path optimization, including transition state search and energy calculation of reactants and products. Through these calculations, determine the reaction coordinates and energy barriers of molecular bond cleavage. Use a kinetic simulation tool, such as Transition State Theory (TST), to simulate the rate constants and reaction kinetics of molecular bond cleavage under different energy inputs. Through the simulation, obtain a VOCs molecular bond cleavage prediction model, including cleavage rate, energy barrier and temperature dependence. Finally, obtain the VOCs molecular bond cleavage prediction model.

[0103] The present invention enhances the understanding of molecular dynamics behavior by simulating the resonant states of VOCs molecular bonds. Through statistical feature extraction, it further provides data support for predicting the critical energy of molecular bond cleavage. Predicting the molecular bond cleavage threshold helps to determine the precise energy level required for molecular bond cleavage, improving the controllability of the molecular transformation process. By mapping the molecular bond cleavage path, the directed molecular bond cleavage is optimized, enhancing the efficiency and selectivity of molecular transformation. Through kinetic cooperative reconstruction, the cleavage energy threshold and cleavage path mapping data are integrated to construct a comprehensive molecular bond cleavage prediction model, improving the accuracy and practicality of the prediction. By accurately predicting molecular bond cleavage, the efficiency of VOCs molecular degradation is improved, which helps to achieve faster waste gas treatment, reduce energy consumption, and reduce the generation of unnecessary chemical by-products, improving the environmental friendliness of the treatment process.

[0104] Preferably, step S3 includes the following steps:

[0105] Step S31: Collect the three-dimensional structure of the nanocatalytic material to obtain a catalytic material structure model, and calculate the surface active sites of the nanocatalytic material based on the catalytic material structure model to obtain the active site distribution mapping data;

[0106] Specifically, a scanning electron microscope (SEM, such as FEI Quanta 250FEG) can be used to observe the surface morphology of the nanocatalytic material and collect three-dimensional images. Set the acceleration voltage to 20 kV and the working distance to 10 mm to obtain a clear surface structure image. Then, a transmission electron microscope (TEM, such as JEOL JEM-2100LaB6) is used to further analyze the crystal structure of the nanocatalytic material. Set the acceleration voltage to 200 kV to obtain atomic-level structural information. Import the collected image data into computer-aided design software (CAD, such as AutoCAD) to reconstruct the three-dimensional structure model of the nanocatalytic material. Use crystallography software (such as CrystalMaker Software) to refine the model and determine the lattice parameters and symmetry of the crystal. Finally, use density functional theory (DFT) calculation software (such as VASP) to calculate the active sites on the surface of the nanocatalytic material. Select a suitable exchange-correlation functional, such as the PBE functional, and the PAW (projector augmented wave) method to describe the electron-ion interaction. Set the k-point grid for the calculation to 3×3×3 and the energy convergence criterion to 1×10 -5 eV. Through calculation, the active site distribution mapping data is obtained, including the type, quantity, and distribution of active sites on the material surface.

[0107] Step S32: Perform surface functional modification on the nanocatalytic material according to the active site distribution mapping data to obtain the functional modification parameters of the catalytic material, and based on the catalytic material structure model, regulate the porosity and pore size of the nanocatalytic material according to the functional modification parameters of the catalytic material to obtain the microstructure parameters of the catalytic material;

[0108] Specifically, a surface modification device (such as an atomic layer deposition system) can be used to perform surface functional modification on the nanocatalytic material. Select appropriate precursors, such as TMA (trimethylaluminum) and DEA (diethylaluminum), as well as reaction temperature (such as 150 °C) and pressure (such as 1 atmosphere) to achieve precise modification of the active sites. Use a Fourier transform infrared spectrometer (FTIR, such as Thermo Scientific Nicolet iS50) to characterize the changes in the chemical bonds on the surface of the modified nanocatalytic material to confirm the success of the functional modification. Set the scanning range to 400 - 4000 cm -1 , with a resolution of 4 cm -1 . Use nitrogen adsorption - desorption isotherm tests (such as Autosorb - iQ2) to measure the porosity and pore size distribution of the modified nanocatalytic material. Set the relative pressure range to 0.01 - 0.99 and the temperature to 77 K (liquid nitrogen temperature). By analyzing the isotherm data, obtain the microstructure parameters of porosity and pore size, such as pore volume, pore area, and pore size distribution. Finally, compare the obtained porosity and pore size parameters with the catalytic material structure model and adjust the functional modification parameters to optimize the performance of the nanocatalytic material. For example, if the pore size distribution is too wide, the pore size distribution can be refined by adjusting the deposition cycle or changing the precursor ratio. Through these steps, the functional modification parameters and microstructure parameters of the catalytic material can be obtained.

[0109] Step S33: Construct a molecular adsorption theoretical model based on the catalytic material structure model and the catalytic material microstructure parameters to obtain a molecular adsorption kinetic model;

[0110] Specifically, computational chemistry software (such as Gaussian 16) and molecular simulation software (such as Materials Studio) can be used to construct a molecular adsorption theoretical model of the catalytic material. Import the structural model of the catalytic material and the microscopic structure parameters of the catalytic material, and select a suitable force field (such as UFF or DREIDING force field) to describe the interaction between the catalytic material and VOCs molecules. Set the simulation parameters, such as the temperature is 298K, the pressure is 1 atmosphere, and the simulation time step is 1 fs (femtosecond), and the total simulation time is from several nanoseconds to several picoseconds to capture the dynamic changes during the adsorption process. Then, perform molecular dynamics simulation or Monte Carlo simulation to study the adsorption behavior of VOCs molecules on the surface of the catalytic material. Through the simulation, the adsorption position, adsorption strength, and adsorption thermodynamic properties of VOCs molecules on the surface of the catalytic material can be observed, so as to construct a molecular adsorption kinetic model. This model will include key parameters such as adsorption rate, adsorption equilibrium constant, and adsorption energy barrier.

[0111] Step S34: Perform specific matching on the molecular adsorption kinetic model according to the degradable structure data of VOCs molecules to obtain VOCs molecular adsorption target data;

[0112] Specifically, cheminformatics software (such as JChem Suite of ChemAxon) and chemical calculation software (such as Gaussian 16) can be used to analyze the degradable structure data of VOCs molecules. Import the structural information of VOCs molecules and the molecular fragment information obtained from the fragmentation strategy, and based on the molecular adsorption kinetic model, select a suitable calculation method to predict the interaction between VOCs molecular fragments and the surface of the catalytic material. For example, density functional theory (DFT) calculations are used to predict the adsorption energy and adsorption position of VOCs molecular fragments on the surface of the catalytic material. Then, perform molecular docking simulation to determine the best match between VOCs molecular fragments and the active sites on the surface of the catalytic material. Set the parameters of the docking algorithm, such as using a genetic algorithm, the population size is 100, and the number of iterations is 1000, to search for the best adsorption configuration. Through the docking simulation, VOCs molecular adsorption target data can be obtained, including the exact position, adsorption strength, and adsorption direction of the adsorption site.

[0113] Step S35: Calculate the adsorption energy for the VOCs molecular adsorption target data to obtain the VOCs molecular adsorption energy spectrum;

[0114] Specifically, chemical calculation software (such as Gaussian 16) can be used to calculate the adsorption energy. Import the VOCs molecular adsorption target data, select appropriate theoretical levels and basis sets for calculation, such as using the B3LYP functional and the 6-31G(d,p) basis set. Then, perform a single-point energy calculation (SP) to evaluate the adsorption energy of VOCs molecules on the surface of the catalytic material. Set the calculation type to adsorption energy calculation and input the structure of the adsorption complex without periodic boundary conditions. When calculating the adsorption energy, consider the contributions of the electronic energy, zero-point energy, thermodynamic corrections, and vibrational free energy of the molecule. Through calculation, obtain the adsorption energy spectrum of VOCs molecules on the surface of the catalytic material, including the adsorption energy, adsorption heat, and adsorption enthalpy at different adsorption sites. For example, the calculation results show that the adsorption energy of acetone molecules on the surface of the catalytic material is -40 kJ / mol, indicating a strong adsorption capacity.

[0115] Step S36: Based on the molecular adsorption kinetic model, VOCs molecular adsorption target data, and VOCs molecular adsorption energy spectrum, formulate the molecular capture path to obtain the VOCs molecular adsorption strategy;

[0116] Specifically, molecular simulation software (such as Materials Studio) and chemical kinetic simulation software (such as Chemkin-Pro) can be used to formulate the capture path of VOCs molecules. Import the molecular adsorption kinetic model, VOCs molecular adsorption target data, and VOCs molecular adsorption energy spectrum. Then, select an appropriate reaction path search algorithm, such as the Nudged Elastic Band (NEB) method, to simulate the adsorption path of VOCs molecules on the surface of the catalytic material. Set the algorithm parameters, such as the spring constant as The maximum number of iterations is 100. Then, perform kinetic simulation to evaluate the rate constants and activation energies of VOCs molecules on different adsorption paths. Set the simulation parameters, such as the temperature range of 300 - 500 K and the pressure of 1 atmosphere, to cover the conditions of actual working conditions. Through simulation, obtain the adsorption strategy of VOCs molecules, including the optimal adsorption path, rate-determining step, and adsorption kinetic parameters. For example, the strategy indicates that at 350 K and 1 atmosphere, acetone molecules preferentially adsorb through the oxygen vacancies on the surface of the catalytic material, and the rate constant is 10 -2 s -1 。

[0117] Step S37: Conduct a directional adsorption experiment on the nanocatalytic material according to the VOCs molecular adsorption strategy to obtain the preliminary adsorption data of VOCs molecules, and perform dynamic conversion monitoring on the preliminary adsorption data of VOCs molecules to obtain the molecular conversion kinetic data;

[0118] Specifically, according to the VOCs molecular adsorption strategy, specific nano-catalytic materials and VOCs molecules (such as acetone) can be selected. Using a dynamic adsorption device, such as a temperature-controlled adsorption reactor, adsorption experiments are carried out under set temperature and pressure. For example, the set temperature is room temperature (25 °C) and the pressure is 1 atmosphere. In the adsorption reactor, the nano-catalytic material (such as TiO2 nanotubes) is loaded, and then a gas stream containing VOCs molecules (acetone) is introduced. The adsorption of VOCs molecules is monitored online using a gas chromatograph (GC, such as Agilent 7890B). The detector of the GC is set to a flame ionization detector (FID), and appropriate carrier gas flow rate and column temperature are set to ensure the effective separation and detection of acetone molecules. As the adsorption process progresses, the change in the concentration of acetone molecules detected by the GC over time is recorded to obtain the preliminary adsorption data of VOCs molecules. Next, the dynamic transformation of VOCs molecules is monitored, and a mass spectrometer (MS, such as Thermo Scientific LTQ OrbitrapXL) is used to perform real-time analysis of the gas at the reactor outlet. The scanning range of the mass spectrometer is set to cover the mass-to-charge ratios of acetone molecules and their transformation products. By analyzing the mass spectrometry data, the transformation kinetics of acetone molecules during the adsorption process are monitored, including the transformation rate, product distribution, and reaction mechanism. For example, the preliminary transformation products of acetone molecules after adsorption are monitored to be carbon dioxide and water, and the transformation rate is 0.1 mol / L·s. Finally, a set of kinetic data on the adsorption and transformation of VOCs molecules is obtained, including the adsorption rate, the formation rate of transformation products, and the reaction pathway.

[0119] Step S38: Analyze the molecular catalytic transformation path based on the molecular transformation kinetic data to obtain the data of the intermediate products of VOCs molecular catalytic transformation, and catalytically degrade the intermediate products based on the data of the intermediate products of VOCs molecular catalytic transformation to obtain the VOCs molecular degradation concentration control data.

[0120] Specifically, chemical kinetics analysis software (such as Kinetics Explorer) can be used to analyze the molecular transformation kinetics data. Import the adsorption and transformation rate data of acetone molecules, as well as the mass-to-charge ratio and relative abundance of the intermediate products detected by the mass spectrometer. Select a suitable reaction mechanism model, such as the Langmuir-Hinshelwood model, to describe the adsorption, transformation, and desorption processes of VOCs molecules on the surface of the catalytic material. Set the model parameters, such as the adsorption equilibrium constant, reaction rate constant, and activation energy, which can be initially estimated based on experimental data and literature values. Perform model fitting and parameter optimization to best match the experimental data. Use the Nonlinear Least Squares (NLS) method to estimate the model parameters, for example, set the confidence interval to 95% and the error threshold to 0.05. Through model analysis, obtain the intermediate product data of the catalytic conversion of VOCs molecules, such as the generation rate and cumulative amount of intermediate products such as propionaldehyde and acetic acid produced during the acetone conversion process. These data reveal the catalytic conversion path and kinetic characteristics. Finally, based on the intermediate product data, use a catalytic degradation experimental device (such as a continuous flow reactor, model: Parr Instruments 4745) to catalytically degrade the intermediate products. Set the operating conditions of the reactor, such as a temperature of 300 °C, to promote the further conversion of the intermediate products. Use a gas chromatography-mass spectrometry (GC-MS, such as Agilent 7890B-5977A) to monitor the degradation process of the intermediate products and record the degradation rate and the concentration of the final conversion products. Through these operations, obtain the VOCs molecule degradation concentration control data, including the degradation rate of the intermediate products, the concentration of the final conversion products, and the reaction selectivity.

[0121] In the present invention, through the three-dimensional structure acquisition of the nano-catalytic material, accurate analysis of the catalytic material structure is achieved. Further functionalization modification and pore structure regulation optimize the microstructure of the catalytic material and enhance the catalytic performance. The constructed molecular adsorption kinetics model provides a theoretical basis for the adsorption and transformation of VOCs molecules, improving the adsorption specificity. The acquisition of the adsorption energy spectrum provides energy parameters for the optimization of the adsorption process, and the molecular capture path formulated based on this improves the efficiency and selectivity of molecular capture. The directional adsorption experiment and dynamic monitoring provide data support for the real-time adjustment of the adsorption strategy, and the analysis of the catalytic conversion path helps to optimize the catalytic degradation process.

[0122] Preferably, step S4 includes the following steps:

[0123] Step S41: Perform three-dimensional structure acquisition on the preset gradient depth oxidation system to obtain the three-dimensional structure model of the oxidation system;

[0124] Specifically, three-dimensional scanning technology, such as a structured light scanner (e.g., Artec Space Spider), can be used to scan the reaction chamber of the gradient depth oxidation system. Set the working parameters of the scanner, for example, the resolution is set to 0.1 mm, and import the scanned data into three-dimensional modeling software (e.g., SolidWorks) to reconstruct the three-dimensional structure model of the reaction chamber. During the modeling process, use the measurement tools of the software to determine the key dimensions, such as the length, width, height of the reaction chamber and the exact positions of the internal features. Set the tolerance range to ±0.05 mm, and mark the material demarcation lines and gradient change regions in the model, which will affect the temperature and concentration distributions during the oxidation process. After completing the three-dimensional structure model, the three-dimensional structure model of the oxidation system can be obtained.

[0125] Step S42: Perform molecular state transition kinetic simulation on the target VOCs molecules based on the three-dimensional structure model of the oxidation system to obtain the initial data of the reaction kinetic simulation;

[0126] Specifically, computational chemistry software (e.g., Gaussian 16) can be used to perform molecular state transition kinetic simulation. First, import the three-dimensional structure model of the oxidation system and the structural data of the target VOCs molecules. Select appropriate theoretical levels and basis sets for calculation, for example, use the B3LYP functional and the 6-31G(d,p) basis set to ensure the accuracy and reliability of the calculation. Set the simulation parameters, such as the temperature is 298 K, the pressure is 1 atmosphere, and the simulation time step is 1 fs (femtosecond), and the total simulation time is from several nanoseconds to several picoseconds to capture the dynamic changes of VOCs molecules during the oxidation process. Perform molecular dynamics simulation or kinetic simulation to study the state transition behavior of VOCs molecules in the oxidation system. Through the simulation, the electronic state changes, energy transfer, and chemical reaction paths of VOCs molecules during the oxidation process can be observed. Analyze the simulation results and extract the initial data of the reaction kinetic simulation, such as the excited state energy, transition probability, and reaction rate constant of VOCs molecules. For example, the calculation results show that the excited state energy of acetone molecules in the oxidation system is 3.5 eV, the transition probability is 0.05, and the reaction rate constant is 10 -3 cm 3 / molecule·s. Through these steps, the initial data of the molecular state transition kinetic simulation of VOCs molecules in the oxidation system can be obtained.

[0127] Step S43: Evaluate the pre-oxidation reaction parameters based on the VOCs molecule degradation concentration control data and the initial data of the reaction kinetic simulation to obtain the predicted data of the VOCs molecule oxidation parameters;

[0128] Specifically, chemical data processing software (such as OriginLab Origin) can be used to import VOCs molecular degradation concentration control data, which includes the VOCs concentration changes at different time points and the generation of intermediate products. At the same time, import the initial data of reaction kinetic simulation, including the excited state energy and reaction rate constant of VOCs molecules. Select a suitable mathematical model to fit the experimental data. For example, use the pseudo-first-order kinetic model, whose formula is C = C 0 e -kt ; where C is the VOCs concentration at time t, C 0 is the initial concentration, and k is the rate constant. Determine the model parameters through non-linear fitting methods, such as the Levenberg-Marquardt algorithm. Then, use statistical analysis tools to evaluate the reliability of the fitting results. For example, calculate the coefficient of determination (R 2 ) and the standard error (SE). Set the threshold of R 2 to 0.95. Through these analyses, obtain the predicted data of VOCs molecular oxidation parameters. For example, the predicted rate constant k is 0.01 min-1, indicating the degradation rate of VOCs molecules in the oxidation system.

[0129] Step S44: Regulate the parameters of the preset gradient deep oxidation system according to the predicted data of VOCs molecular oxidation parameters to obtain a preliminary reaction system configuration;

[0130] Specifically, a process control system (such as Rockwell Automation PlantPAx) can be used to import the predicted data of VOCs molecular oxidation parameters. Select suitable gradient deep oxidation system parameters, such as reaction temperature, gas flow rate, and catalyst loading, and set the target values for system parameter regulation. For example, according to the predicted rate constant, set the reaction temperature to 350 °C to maximize the degradation rate of VOCs. At the same time, adjust the gas flow rate to 2 L / min to ensure sufficient oxygen supply, and adjust the catalyst loading to 1% to optimize the reaction efficiency. Use the built-in regulator of the system for real-time parameter regulation. For example, use a proportional-integral-derivative (PID) controller to precisely control the reaction temperature, set the proportional gain (P) to 0.5, the integral time (I) to 10 min, and the derivative time (D) to 0.1 min. Through these regulations, obtain a preliminary reaction system configuration, such as a reaction temperature of 350 °C, a gas flow rate of 2 L / min, and a catalyst loading of 1%.

[0131] Step S45: Configure the photocatalytic oxidation module for the preliminary optimized system configuration to obtain a photocatalytic oxidation reaction unit, and perform molecular selective reduction regulation based on the photocatalytic oxidation reaction unit to obtain an optimized model for VOCs molecular oxidation reaction;

[0132] Specifically, a suitable photocatalytic material can be selected, such as TiO2 nanotubes, which have good photocatalytic activity and chemical stability. Use a photocatalytic reactor (such as a batch photocatalytic reactor, model: XBD-2), install an ultraviolet light source (such as a 365 nm LED array) in the reactor, and set the light intensity to 100 mW / cm 2 , to provide sufficient light energy to excite the photocatalytic material, load the TiO2 nanotubes into the photocatalytic reactor, and set the reaction conditions, such as the temperature at room temperature (25 °C) and the pH value at 7. By adjusting the power of the ultraviolet light source and the dosage of the photocatalytic material, optimize the configuration of the photocatalytic oxidation reaction unit. Use an online monitoring system (such as an optical fiber spectrometer, model: Ocean Optics HR4000) to monitor the photocatalytic oxidation process of VOCs molecules in real time. Set the scanning range of the spectrometer to 200 - 600 nm to capture the characteristic absorption peaks during the photocatalytic process. Through these operations, obtain the configuration of the photocatalytic oxidation reaction unit, and based on this, conduct molecular selective reduction regulation. For example, by adjusting the pH value and adding an electron donor (such as methanol), the selective reduction efficiency of specific VOCs molecules can be improved. Finally, obtain an optimized model for the oxidation reaction of VOCs molecules, including the best combination of photocatalytic oxidation efficiency, selectivity, and reaction conditions.

[0133] Step S46: Based on the optimized model for the oxidation reaction of VOCs molecules, perform cascade treatment on the target VOCs molecules to obtain deep conversion data of VOCs molecules.

[0134] Specifically, according to the optimized model for the oxidation reaction of VOCs molecules, set the operating parameters of the cascade treatment system. For example, select a series of catalysts with different functions (such as TiO2 for photocatalytic oxidation and CuO for thermal catalytic oxidation), and set the corresponding reaction conditions. Use a cascade reactor system (such as a continuous flow cascade reactor, model: Parr Instruments 4560) to perform cascade treatment on VOCs molecules. Set the temperature, pressure, and gas flow rate of the reactor, such as the temperature at 200 °C, the pressure at 2 atmospheres, and the gas flow rate at 100 mL / min. Then, monitor the conversion of VOCs molecules during the cascade treatment process through a gas chromatography - mass spectrometry (GC - MS, such as Agilent 7890B - 5977A). Set the scanning range of the GC - MS to cover the mass - to - charge ratios of VOCs molecules and their conversion products. Through these operations, obtain deep conversion data of VOCs molecules, including conversion rate, selectivity, and product distribution. For example, it is monitored that the conversion rate of acetone molecules after cascade treatment reaches 95%, and the main products are carbon dioxide and water.

[0135] The present invention realizes the precision of the oxidation system design by collecting the three-dimensional structure of the gradient depth oxidation system. By combining the VOCs molecular degradation concentration control data with the initial data of the reaction kinetics simulation, it provides a basis for the evaluation of the oxidation reaction pre-parameters and further predicts the VOCs molecular oxidation parameters. Through precise parameter regulation, the precise control of the oxidation reaction conditions is achieved, enhancing the efficiency and controllability of the oxidation reaction. By configuring a photocatalytic oxidation module and performing molecular selective reduction regulation, the photocatalytic oxidation performance is enhanced, and the oxidation efficiency of VOCs molecules is improved. The optimized reaction path provided by the constructed VOCs molecular oxidation reaction optimization model further improves the oxidation efficiency. Through cascade treatment, the deep conversion of VOCs molecules is realized, and the cleanliness of the treated waste gas is improved.

[0136] Preferably, step S5 includes the following steps:

[0137] Step S51: Construct a spectral feature benchmark according to the VOCs molecular deep conversion data to obtain a VOCs molecular spectral feature reference library;

[0138] Specifically, the deep conversion data of different types of VOCs molecules under specific conditions can be collected, such as acetone, toluene, and methanol. Use a Fourier transform infrared spectrometer (FTIR, such as Thermo Scientific Nicolet iS50) to perform spectral analysis on the conversion products of these VOCs molecules. Set the scanning range of the FTIR to 4000 - 400 cm -1 , with a resolution of 4 cm -1 , to obtain detailed spectral information. Use chemometric software (such as the Chemometrics Toolbox of MATLAB) to preprocess the FTIR spectral data, including baseline correction, noise filtering, and normalization. Select a suitable preprocessing method, such as the Savitzky-Golay smoothing filter, with a window size set to 9 data points and a polynomial order of 2. Then, extract the key characteristic peaks in the spectrum, such as the characteristic absorption peaks of C-H bonds and C=O bonds. Set the threshold of the peak detection algorithm to 0.8 to identify signals higher than 80% of the background noise. Finally, organize the extracted spectral features and the corresponding VOCs molecular information into a spectral feature reference library. For example, the C=O bond of the acetone molecule has an obvious absorption peak at 1710 cm -1 , and the C-H bond of the toluene molecule has an absorption peak at 3030 cm -1 .

[0139] Step S52: Perform a preliminary spectral scan on the treated waste gas to obtain the original spectral monitoring data;

[0140] Specifically, a portable Fourier transform infrared spectrometer (FTIR, such as Thermo Scientific Nicolet iS10) can be used to perform on-site spectral scanning of the exhaust gas after being treated by the gradient depth oxidation system. Set the scanning range of the FTIR to 4000 - 400 cm -1 , with a resolution of 8 cm -1 , to quickly obtain the spectral data of the exhaust gas sample. Introduce the exhaust gas sample into the gas cell of the FTIR through the sampling system, and set the pressure of the gas cell to 1 atmosphere and the temperature to room temperature (25 °C) to simulate the actual emission conditions. Then, use FTIR software (such as Omnic) to collect and store the spectral data. Set the acquisition mode of the software to "single scan" to obtain a representative spectrum of the exhaust gas sample. Through these operations, the original spectral monitoring data of the treated exhaust gas is obtained, including the characteristic absorption peaks and relative intensities of each VOCs molecule. For example, it is monitored that the characteristic absorption peak of acetone molecules in the treated exhaust gas is significantly weakened, indicating that acetone molecules have been effectively degraded.

[0141] Step S53: Based on the VOCs molecular spectral feature reference library, perform molecular feature recognition on the original spectral monitoring data to obtain the VOCs molecular transformation feature vector;

[0142] Specifically, chemometrics software (such as the Chemometrics Toolbox of MATLAB) can be used to import the original spectral monitoring data and the VOCs molecular spectral feature reference library. Set the software to perform spectral matching analysis, select a suitable matching algorithm, such as correlation matching or least squares matching, to identify the characteristic absorption peaks of each VOCs molecule. For example, identify the absorption peak of the C=O bond of acetone molecules at 1710 cm -1 . Set the threshold of the matching algorithm to 0.9 to ensure that only highly correlated characteristic peaks are identified. Then, extract the matched characteristic peaks and construct the VOCs molecular transformation feature vector. For example, for acetone molecules, construct a feature vector containing the intensity values of its multiple characteristic absorption peaks at 1710 cm -1 , 1300 cm -1 , etc. Finally, compare these feature vectors with the standard feature vectors in the reference library to determine the presence and concentration of VOCs molecules. For example, it is found through comparison that the feature vector of acetone molecules in the treated exhaust gas highly matches the vector in the reference library, indicating that acetone molecules have been effectively identified.

[0143] Step S54: According to the VOCs molecular transformation feature vector, reconstruct the molecular transformation path to obtain the molecular transformation data during the exhaust gas treatment process;

[0144] Specifically, chemical informatics software (such as JChem Suite of ChemAxon) and molecular dynamics simulation software (such as Materials Studio) can be used to import the characteristic vectors of VOCs molecular transformation, and a suitable molecular transformation path analysis method can be selected, such as reaction mechanism derivation or kinetic simulation. Set the simulation parameters, such as the temperature is 298K, the pressure is 1 atmosphere, and the simulation time step is 1 fs (femtosecond), and the total simulation time is from several nanoseconds to several picoseconds to capture the dynamic changes of VOCs molecules during the oxidation process. Then, perform molecular transformation path reconstruction, such as inferring the transformation path by analyzing the changes in the characteristic vectors of acetone molecules, such as the process of acetone being transformed into propionaldehyde and acetic acid. Set the parameters of the path analysis algorithm, such as the energy threshold is 0.2 eV, to identify potential energy changes. Finally, obtain the molecular transformation data of the waste gas treatment process, including the transformation path, transformation rate, and transformation products of VOCs molecules. For example, through analysis, it is found that acetone molecules are first transformed into propionaldehyde in the gradient depth oxidation system, then further transformed into acetic acid, and finally decomposed into carbon dioxide and water.

[0145] Step S55: Extract the dynamic characteristics of the molecular transformation data of the waste gas treatment process to obtain the molecular transformation dynamic change curve, and evaluate the catalyst activity based on the molecular transformation dynamic change curve to obtain the catalyst activity evaluation parameters;

[0146] Specifically, data processing software (such as Python combined with Pandas and NumPy libraries) can be used to import the molecular transformation data of the waste gas treatment process. Select Fourier transform or wavelet transform to extract the key dynamic characteristics in the molecular transformation process. Then, perform time series analysis on the molecular transformation data, such as using the autoregressive moving average (ARMA) model to fit the change of molecular concentration over time, and set the order of the model to (2, 2) to capture the dynamic trend and periodicity of the data. Then, plot the molecular transformation dynamic change curve, such as the curve of the decrease in acetone molecule concentration over time and the curve of the increase in carbon dioxide concentration over time. By analyzing these curves, evaluate the activity of the catalyst, such as calculating the reaction rate constant and half-life. Finally, obtain the catalyst activity evaluation parameters, such as the reaction rate constant is 0.05 min-1, indicating the activity level of the catalyst under the current conditions.

[0147] Step S56: Adjust the surface reaction sites of the catalyst according to the catalyst activity evaluation parameters to obtain the catalyst activity regulation strategy data, and dynamically regulate the catalyst activity according to the catalyst activity regulation strategy data to obtain a fully controllable waste gas treatment plan.

[0148] Specifically, catalyst activity evaluation parameters, such as the rate constant obtained in step S55, can be used to determine the adjustment requirements for the surface reaction sites of the catalyst. Select a suitable catalyst adjustment method, such as changing the catalyst preparation conditions or adding a promoter. Then, use a catalyst preparation device (such as a sol-gel method device) to adjust the surface properties of the catalyst, such as changing the grain size or pore structure of the TiO2 catalyst. Set the preparation parameters, such as a temperature of 500 °C and a holding time of 2 hours, to optimize the surface reaction sites of the catalyst. Then, use a catalyst characterization device (such as X-ray photoelectron spectroscopy, XPS, model: Thermo Scientific K-Alpha) to verify the adjustment effect of the surface reaction sites of the catalyst, such as measuring the oxygen vacancy concentration on the surface of the TiO2 catalyst. Finally, according to the catalyst activity regulation strategy data, use a process control system (such as Rockwell Automation PlantPAx) to dynamically regulate the catalyst activity. For example, adjust the dosage of the catalyst or replace the catalyst according to the real-time waste gas treatment data to maintain the best treatment efficiency. Through these steps, a fully controllable waste gas treatment solution is obtained.

[0149] By constructing a reference library of VOCs molecular spectral characteristics, the present invention improves the monitoring accuracy and provides a benchmark for spectral scanning and feature recognition. By reconstructing the molecular transformation path, the analysis of molecular transformation during waste gas treatment is optimized, and the understanding of the transformation mechanism is improved. Through dynamic feature extraction, real-time monitoring of the dynamic changes in molecular transformation is achieved, providing real-time data support for catalyst activity evaluation. The accuracy of the evaluation is improved by the catalyst activity evaluation based on the dynamic change curve of molecular transformation. The dynamic regulation of catalyst activity is achieved by adjusting the surface reaction sites of the catalyst according to the catalyst activity evaluation parameters, improving the efficiency and adaptability of waste gas treatment. The controllability of the entire waste gas treatment process is improved by the implementation of the fully controllable waste gas treatment solution, making the treatment process more flexible and efficient.

[0150] Preferably, the present invention also provides a waste gas treatment system based on model optimization for implementing the waste gas treatment method based on model optimization as described above. The waste gas treatment system based on model optimization includes:

[0151] A spectral acquisition module for performing multi-dimensional spectral acquisition on the waste gas from a semiconductor production line to obtain VOCs molecular spectral data; and selecting target VOCs molecules based on the VOCs molecular spectral data to obtain target VOCs molecular positioning data;

[0152] A plasma processing module for activating a target VOCs molecule with a plasma energy field according to the target VOCs molecule positioning data to obtain VOCs molecule energy conversion data; and performing selective molecular bond cleavage on the target VOCs molecule according to the VOCs molecule energy conversion data to obtain VOCs molecule degradable structure data;

[0153] A catalytic conversion module for directionally adsorbing a nano-catalytic material according to the VOCs molecule degradable structure data to obtain VOCs molecule catalytic conversion intermediate product data; and catalytically degrading the intermediate product based on the VOCs molecule catalytic conversion intermediate product data to obtain VOCs molecule degradation concentration control data;

[0154] An oxidation optimization module for adjusting the parameters of a preset gradient depth oxidation system according to the VOCs molecule degradation concentration control data to obtain a VOCs molecule oxidation reaction optimization model; and performing cascade processing on the target VOCs molecule based on the VOCs molecule oxidation reaction optimization model to obtain VOCs molecule deep conversion data;

[0155] A monitoring and regulation module for performing real-time spectral monitoring on the treated exhaust gas according to the VOCs molecule deep conversion data to obtain exhaust gas treatment process molecular conversion data; and dynamically regulating the catalyst activity based on the exhaust gas treatment process molecular conversion data to obtain a fully controllable exhaust gas treatment plan.

[0156] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0157] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for treating waste gas based on model optimization, characterized in that: The following steps are involved: Step S1: performing multi-dimensional spectrum collection on the exhaust gas of the semiconductor production line to obtain VOCs molecular spectrum data; selecting target VOCs molecules based on the VOCs molecular spectrum data to obtain target VOCs molecular positioning data; Step S2: activating the target VOCs molecule by plasma energy field according to the target VOCs molecule positioning data to obtain VOCs molecule energy conversion data; selectively breaking the molecular bonds of the target VOCs molecule according to the VOCs molecule energy conversion data to obtain VOCs molecule degradable structure data; Step S3: Directly adsorbing the nanocatalytic material according to the degradable structure data of the VOCs molecules to obtain the data of the intermediate products of the catalytic conversion of the VOCs molecules; catalytically degrading the intermediate products based on the data of the intermediate products of the catalytic conversion of the VOCs molecules to obtain the VOCs molecular degradation concentration control data; Step S4: Parameter adjustment of the preset gradient deep oxidation system according to the VOCs molecular degradation concentration control data to obtain a VOCs molecular oxidation reaction optimization model; cascade processing of the target VOCs molecules based on the VOCs molecular oxidation reaction optimization model to obtain VOCs molecular deep conversion data; Step S5: Perform real-time spectral monitoring of the treated exhaust gas according to the VOCs molecular deep conversion data to obtain the molecular conversion data of the exhaust gas treatment process; dynamically regulate the catalyst activity based on the molecular conversion data of the exhaust gas treatment process to obtain a fully controllable exhaust gas treatment solution.

2. The exhaust gas treatment method based on model optimization according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: preheating and calibrating the exhaust gas collection system of the semiconductor production line to obtain calibration data of the exhaust gas collection system; Step S12: collecting production process data of the semiconductor production line to obtain process characteristic data of the semiconductor production line; Step S13: configuring the parameters of the exhaust gas collection equipment according to the exhaust gas collection system calibration data and the semiconductor production line process characteristic data to obtain multi-dimensional spectrum collection parameter configuration data; Step S14: performing preliminary concentration processing on the exhaust gas sample, and performing preliminary infrared spectrum scanning on the concentrated exhaust gas sample according to the multi-dimensional spectrum acquisition parameter configuration data to obtain primary infrared spectrum data of VOCs molecules; Step S15: performing gas chromatography preprocessing according to the primary infrared spectrum data of VOCs molecules to obtain gas chromatography separation leading data of VOCs molecules; Step S16: spectrally analyze the VOCs molecular gas chromatography separation leading data to obtain VOCs molecular spectrum data; select target VOCs molecules based on the VOCs molecular spectrum data to obtain target VOCs molecular positioning data.

3. The exhaust gas treatment method based on model optimization according to claim 2, characterized in that: Step S16 includes the following steps: Step S161: performing combined detection on VOCs molecular gas chromatography separation leading data to obtain VOCs molecular spectrum raw data; Step S162: performing signal enhancement on the VOCs molecular spectrum raw data to obtain VOCs molecular spectrum data; Step S163: extracting molecular fingerprint features from the VOCs molecular spectrum data to obtain a VOCs molecular fingerprint feature vector; Step S164: Preliminary screening of target VOCs molecules is performed based on the VOCs molecular fingerprint feature vector to obtain a VOCs molecular candidate set; Step S165: performing concentration and distribution statistics on the VOCs molecule candidate set to obtain VOCs molecule distribution concentration mapping data; Step S166: Select target VOCs molecules according to the VOCs molecular distribution concentration mapping data to obtain target VOCs molecular positioning data.

4. The exhaust gas treatment method based on model optimization according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Collecting the geometric structure of the plasma reaction chamber to obtain the plasma chamber structure parameters; Step S22: configuring a low-temperature non-equilibrium plasma excitation device based on the plasma cavity structure parameters to obtain a plasma excitation system; Step S23: determining plasma excitation parameters according to the target VOCs molecular positioning data to obtain excitation parameter configuration data; Step S24: regulating the electromagnetic field intensity of the plasma excitation system to obtain electromagnetic field control data of the excitation system; Step S25: performing directed energy activation on target VOCs molecules based on the excitation parameter configuration data and the excitation system electromagnetic field control data to obtain VOCs molecular energy conversion data; Step S26: performing molecular bond energy analysis based on the VOCs molecular energy conversion data to obtain a VOCs molecular bond breakage prediction model; Step S27: reconstructing the molecular energy levels of the target VOCs molecules based on the VOCs molecular bond breaking prediction model to obtain VOCs molecular energy conversion fine data, and selecting a directional molecular bond breaking path for the target VOCs molecules based on the VOCs molecular energy conversion fine data to obtain a VOCs molecular bond breaking strategy; Step S28: intervening on the target VOCs molecules according to the VOCs molecular bond breaking strategy to obtain the degradable structure data of the VOCs molecules.

5. The exhaust gas treatment method based on model optimization according to claim 4, characterized in that: Step S26 includes the following steps: Step S261: performing chemical calculation preprocessing on the VOCs molecular energy conversion data to obtain VOCs molecular chemical calculation preprocessing data; Step S262: constructing molecular orbital transition states based on VOCs molecular chemical calculation preprocessing data to obtain VOCs molecular orbital structure mapping data; Step S263: Calculate the molecular bond valence electron state density according to the VOCs molecular orbital structure mapping data to obtain VOCs molecular electronic state distribution data; Step S264: Estimating the energy transition probability of the VOCs molecular electronic state distribution data to obtain the VOCs molecular energy level transition conversion rate; Step S265: constructing a molecular bond breaking prediction model for the target VOCs molecule based on the VOCs molecular energy level transition conversion rate to obtain a VOCs molecular bond breaking prediction model.

6. The exhaust gas treatment method based on model optimization according to claim 5, characterized in that: Step S265 includes the following steps: Step S2651: performing molecular bond resonance state simulation on the target VOCs molecule based on the VOCs molecular energy level transition conversion rate to obtain VOCs molecular resonance state dynamics data; Step S2652: Calculate the molecular bond energy distribution according to the VOCs molecular resonance state dynamics data to obtain the VOCs molecular bond energy spectrum; Step S2653: extracting statistical features of the VOCs molecular bond energy spectrum to obtain VOCs molecular energy distribution data; Step S2654: predicting the critical energy of molecular bond breakage of target VOCs molecules based on the VOCs molecular energy distribution data to obtain the VOCs molecular breakage energy threshold; Step S2655: mapping the molecular bond fracture path of the target VOCs molecule based on the VOCs molecular fracture energy threshold to obtain VOCs molecular fracture path mapping data; Step S2656: Based on the VOCs molecular fracture energy threshold and the VOCs molecular fracture path mapping data, dynamic collaborative reconstruction is performed to obtain a VOCs molecular bond fracture prediction model.

7. The exhaust gas treatment method based on model optimization according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: collecting the three-dimensional structure of the nano-catalytic material to obtain a catalytic material structure model, and calculating the surface active sites of the nano-catalytic material based on the catalytic material structure model to obtain active site distribution mapping data; Step S32: performing surface functional modification on the nano-catalytic material according to the active site distribution mapping data to obtain the functional modification parameters of the catalytic material, and regulating the porosity and pore size of the nano-catalytic material according to the functional modification parameters of the catalytic material based on the catalytic material structure model to obtain the microstructure parameters of the catalytic material; Step S33: constructing a molecular adsorption theoretical model based on the catalytic material structure model and the catalytic material microstructure parameters to obtain a molecular adsorption kinetic model; Step S34: performing specific matching on the molecular adsorption kinetics model according to the VOCs molecular degradable structure data to obtain VOCs molecular adsorption target data; Step S35: Calculate the adsorption energy of the VOCs molecular adsorption target point data to obtain the VOCs molecular adsorption energy spectrum; Step S36: formulating a molecular capture path based on the molecular adsorption kinetic model, the VOCs molecular adsorption target data and the VOCs molecular adsorption energy spectrum to obtain a VOCs molecular adsorption strategy; Step S37: performing a directional adsorption experiment on the nanocatalytic material according to the VOCs molecular adsorption strategy to obtain preliminary adsorption data of VOCs molecules, and performing dynamic conversion monitoring on the preliminary adsorption data of VOCs molecules to obtain molecular conversion kinetic data; Step S38: Perform molecular catalytic conversion path analysis based on the molecular conversion kinetics data to obtain VOCs molecular catalytic conversion intermediate product data, and perform catalytic degradation on the intermediate product based on the VOCs molecular catalytic conversion intermediate product data to obtain VOCs molecular degradation concentration control data.

8. The exhaust gas treatment method based on model optimization according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: collecting the three-dimensional structure of the reaction chamber of the preset gradient depth oxidation system to obtain a three-dimensional structure model of the oxidation system; Step S42: performing molecular state transition kinetics simulation on target VOCs molecules based on the three-dimensional structural model of the oxidation system to obtain initial data for reaction kinetics simulation; Step S43: evaluating the initial parameters of the oxidation reaction according to the VOCs molecular degradation concentration control data and the initial data of the reaction kinetics simulation to obtain the VOCs molecular oxidation parameter prediction data; Step S44: Parameter adjustment of the preset gradient deep oxidation system is performed according to the VOCs molecular oxidation parameter prediction data to obtain a preliminary reaction system configuration; Step S45: configuring the preliminary optimized system configuration with a photocatalytic oxidation module to obtain a photocatalytic oxidation reaction unit, and performing molecular selective reduction control based on the photocatalytic oxidation reaction unit to obtain an optimized model for VOCs molecular oxidation reaction; Step S46: cascade processing is performed on the target VOCs molecules based on the VOCs molecular oxidation reaction optimization model to obtain VOCs molecular deep conversion data.

9. The exhaust gas treatment method based on model optimization according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: constructing a spectral feature benchmark based on the VOCs molecular deep conversion data to obtain a VOCs molecular spectral feature reference library; Step S52: performing a preliminary spectral scan on the treated exhaust gas to obtain original spectral monitoring data; Step S53: performing molecular feature recognition on the original spectral monitoring data based on the VOCs molecular spectral feature reference library to obtain a VOCs molecular conversion feature vector; Step S54: reconstructing the molecular conversion path according to the VOCs molecular conversion feature vector to obtain the molecular conversion data of the exhaust gas treatment process; Step S55: extracting dynamic features of the molecular conversion data of the exhaust gas treatment process to obtain a molecular conversion dynamic change curve, and evaluating the catalyst activity based on the molecular conversion dynamic change curve to obtain a catalyst activity evaluation parameter; Step S56: adjusting the surface reaction sites of the catalyst according to the catalyst activity evaluation parameters to obtain catalyst activity control strategy data, and dynamically controlling the catalyst activity according to the catalyst activity control strategy data to obtain a fully controllable exhaust gas treatment solution.

10. A waste gas treatment system based on model optimization, characterized in that: For executing the exhaust gas treatment method based on model optimization as claimed in claim 1, the exhaust gas treatment system based on model optimization comprises: The spectrum acquisition module is used to collect multi-dimensional spectrum of exhaust gas from semiconductor production lines to obtain VOCs molecular spectrum data; target VOCs molecules are selected based on the VOCs molecular spectrum data to obtain target VOCs molecular positioning data; The plasma processing module is used to activate the target VOCs molecules by plasma energy field according to the target VOCs molecular positioning data to obtain VOCs molecular energy conversion data; selectively break the molecular bonds of the target VOCs molecules according to the VOCs molecular energy conversion data to obtain VOCs molecular degradable structure data; The catalytic conversion module is used to carry out directional adsorption of nano-catalytic materials according to the degradable structure data of VOCs molecules to obtain the data of intermediate products of catalytic conversion of VOCs molecules; based on the data of intermediate products of catalytic conversion of VOCs molecules, the intermediate products are catalytically degraded to obtain the data of VOCs molecular degradation concentration control; The oxidation optimization module is used to control the parameters of the preset gradient deep oxidation system according to the VOCs molecular degradation concentration control data to obtain the VOCs molecular oxidation reaction optimization model; based on the VOCs molecular oxidation reaction optimization model, the target VOCs molecules are cascaded to obtain the VOCs molecular deep conversion data; The monitoring and control module is used to conduct real-time spectral monitoring of the treated exhaust gas according to the deep conversion data of VOCs molecules, and obtain the molecular conversion data of the exhaust gas treatment process; based on the molecular conversion data of the exhaust gas treatment process, the catalyst activity is dynamically controlled to obtain a fully controllable exhaust gas treatment solution.

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