System and method for rapidly analyzing and detecting multiple components of mixed gas

Abstract

The invention discloses a system and a method for rapidly analyzing and detecting multiple components of mixed gas. The system comprises a gas sampling module, a gas preprocessing module, a multi-dimensional detection module, a signal processing module, a component analysis module, a result output module, a system control module and a data security and storage module, through the gas sampling module and the pretreatment module, the representativeness and the purity of a collected sample are ensured, and a high-quality gas sample is provided for subsequent detection. The multi-dimensional detection module adopts optical detection and electrochemical sensing dual-technology fusion, realizes synchronous acquisition of spectral characteristics and electrochemical response characteristics of mixed gas, and forms a complementary data set.

Description

Technical Field

[0001] This invention relates to the field of gas detection and analysis technology, specifically to a rapid analysis and detection system and method for multi-component mixed gases. Background Technology

[0002] In industrial production and environmental management, the multi-component concentration detection of mixed gases (such as industrial waste gas containing CO, NO2, SO2, VOCs, etc.) is a crucial step in ensuring production safety and controlling pollution emissions. While gas chromatography can achieve multi-component separation and detection, its long analysis cycle (typically 5-30 minutes) makes it difficult to meet real-time monitoring needs. Single-sensor array detection is susceptible to cross-interference from gas components, resulting in low detection accuracy. Although some optical detection technologies are fast, they lack sensitivity for low-concentration components and cannot simultaneously detect both polar and non-polar gas components.

[0003] The aforementioned technical limitations make it difficult for existing solutions to simultaneously achieve both "rapid detection" and "precise multi-component analysis," especially in scenarios such as real-time monitoring of industrial waste gas and emergency detection of sudden pollution incidents. These limitations can easily lead to decision-making delays due to detection lag or insufficient accuracy. Therefore, there is an urgent need for a rapid multi-component analysis and detection system and method for mixed gases that can simultaneously address the problems of "slow speed, high interference, and low accuracy." Summary of the Invention

[0004] The purpose of this invention is to provide a rapid analysis and detection system and method for multi-component mixed gases to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a rapid analysis and detection system for multi-component mixed gas, comprising a gas sampling module, a gas preprocessing module, a multi-dimensional detection module, a signal processing module, a component analysis module, a result output module, a system control module, and a data security and storage module;

[0006] The gas sampling module is responsible for collecting representative mixed gas samples, controlling the sampling flow rate and temperature, and avoiding sample loss or condensation.

[0007] The gas pretreatment module removes impurities and dehydrates the collected raw gas, eliminating the interference of particulate matter and moisture on subsequent detection.

[0008] The multi-dimensional detection module integrates optical detection and electrochemical sensing technologies to simultaneously acquire the spectral characteristics and electrochemical response characteristics of the mixed gas, thereby achieving multi-dimensional data complementarity.

[0009] The signal processing module amplifies, filters, and standardizes the weak signal output by the detection module to generate a feature signal that can be used for analysis.

[0010] The component analysis module is based on a quantitative analysis model and combines multi-dimensional feature signals to calculate the concentration of each gas component and output accurate analysis results.

[0011] The result output module displays the test results in a visual format and supports data export and alarm for exceeding the standard.

[0012] The system control module coordinates the synchronous operation of each module to achieve parameter configuration, process control and status monitoring;

[0013] The data security and storage module encrypts and backs up the test data and calibration parameters, and sets access control to ensure data security.

[0014] Preferably, the gas sampling module includes a sampling probe (anti-clogging design, made of corrosion-resistant polytetrafluoroethylene), a flow regulation unit (controlling the sampling flow rate via a mass flow controller, ranging from 0.5-2 L / min, with an accuracy of ±2%), a temperature control unit (using a semiconductor temperature controller to stabilize the sampling gas temperature at 25±2℃ to prevent water vapor condensation), and a sampling pipeline (made of smooth-walled quartz material to reduce gas component adsorption loss); during sampling, samples are collected by probe insertion or online extraction, and the flow rate and temperature parameters are adjusted in real time by the system control module.

[0015] Preferably, the gas pretreatment module includes a three-stage processing unit:

[0016] Primary filtration unit: Uses a polytetrafluoroethylene filter membrane to filter out particulate impurities in the gas; the filter membrane can be replaced periodically.

[0017] Deep dehydration unit: adopts hollow fiber membrane dehumidification technology, realizes water penetration through the humidity difference on both sides of the membrane, reduces the gas humidity to ≤5%RH (relative humidity), and has a dehydration efficiency of ≥95%;

[0018] Interference adsorption unit: filled with targeted adsorbents (such as molecular sieves to adsorb acidic impurities and activated carbon to adsorb organic interfering substances). The type of adsorbent can be changed according to the target detection component to avoid cross-interference of non-target components with the detection signal.

[0019] The cleanliness of the pretreated gas meets the input requirements of the multi-dimensional detection module, and the recovery rate of the target component is ≥98%.

[0020] Preferably, the multi-dimensional detection module includes an optical detection submodule and an electrochemical sensor array submodule, which operate synchronously.

[0021] Optical detection submodule: It adopts a near-infrared spectroscopy detector, which receives the near-infrared absorption spectrum of the gas through an optical fiber probe to obtain the characteristic absorption peak signals of each component (corresponding to the molecular vibrational energy level transitions of different components).

[0022] Electrochemical sensor array submodule: contains at least 4 electrochemical sensors, respectively targeting CO, NO2, SO2 and VOCs, and generates current signals through the reaction of gas diffusion electrodes with electrolytes;

[0023] Data from both modules is synchronously transmitted to the signal processing module to form a dual-dimensional feature dataset of "spectral and electrochemical" characteristics, reducing the detection blind spots of a single technology.

[0024] Preferably, the signal processing module includes:

[0025] Signal amplification unit: Employs a differential amplifier circuit to amplify the weak current signal output by the electrochemical sensor by 1000-10000 times;

[0026] Noise filtering unit: Employs Kalman filtering algorithm (dynamically adjusts filtering coefficients) to filter out high-frequency noise in the spectral signal and the amplified electrochemical signal;

[0027] Signal normalization unit: Converts the spectral signal to absorbance value after baseline correction, and performs electrochemical signal normalization according to the formula... Normalization is performed, where X is the original current signal, X min X max These are the minimum and maximum response values ​​of the sensor, respectively, to eliminate the influence of individual device differences and environmental fluctuations.

[0028] Preferably, the component analysis module incorporates a multi-component quantitative analysis model based on partial least squares regression (PLSR), which calculates the concentration of each target component using multi-dimensional feature signals. The core formula is:

[0029] C i =a 0i +∑(a ji ×X ji )+E i

[0030] Among them, C i Let a be the concentration of the i-th target gas component; 0i The regression intercept for the i-th component; j is the index of the feature variable, j = 1, 2, ..., n, where n is the total number of feature variables, including but not limited to spectral absorbance values ​​and electrochemically normalized signals, n ≥ 8; a ji X is the regression coefficient of the j-th characteristic variable on the i-th component; ji E is the standardized value of the j-th characteristic variable, i.e., the absorbance value or normalized electrochemical signal output by the signal processing module; i The detection error of the i-th component is controlled by the system accuracy, |E i |≤5%×C i To ensure the reliability of test results;

[0031] The module also includes a built-in standard gas calibration library (containing parameters for 5-10 standard mixed gases of different concentrations), supporting periodic automatic calibration and updates. 0i With a ji Parameters are set to avoid accuracy degradation caused by component aging.

[0032] Preferably, the result output module includes:

[0033] Visualization display unit: It adopts a touch LCD screen to display the concentration values ​​of each component, detection time, detection error, and system operating status in real time;

[0034] Data export unit: Supports data export via USB interface or wireless (Bluetooth / Wi-Fi), in formats including CSV and Excel, and can be connected to industrial control system DCS or environmental monitoring platform;

[0035] Exceedance alarm unit: When the concentration of a certain component exceeds a preset threshold (the threshold can be customized through the system control module, such as CO ≤ 30 mg / m³), an alarm is triggered. 3 NO2 ≤ 10 mg / m³ 3 When the alarm is triggered (sound pressure level ≥ 85dB, alarm light flashing red), an audio-visual alarm will be activated, and a text message notification will be sent to the preset administrator's mobile phone.

[0036] Preferably, the system control module is based on an embedded microprocessor and communicates with each module via RS485 / Ethernet to achieve:

[0037] Parameter configuration: Set sampling flow rate, pretreatment temperature, detection cycle, calibration cycle, and alarm threshold;

[0038] Process control: Automating the process of sampling, preprocessing, detection, and analysis;

[0039] Status monitoring: Real-time collection of operating parameters of each module (such as whether the sampling flow is stable, whether the sensor is faulty), and generation of fault codes and prompts when a module is abnormal.

[0040] Preferably, the data security and storage module includes:

[0041] Data encryption unit: The AES-256 symmetric encryption algorithm is used to encrypt the storage and transmission of test data and calibration parameters to prevent data tampering or leakage;

[0042] Data backup unit: Supports local SD card backup and cloud synchronization backup;

[0043] Access control: Set three levels of permissions: administrator, operator, and viewer. Administrators can modify parameters and delete data, operators can only start the detection, and viewers can only view the results.

[0044] Operation log unit: Records the parameters, results, operators, and system status for each test.

[0045] A detection method for a rapid multi-component analysis and detection system for mixed gases includes the following steps:

[0046] Step 1: System Initialization and Parameter Configuration

[0047] The administrator logs in through the system control module and completes system initialization: ① Calibrate the multi-dimensional detection module; ② Set sampling parameters, preprocessing parameters, detection cycle, and alarm thresholds for each component; ③ Initialize the encryption key and backup strategy for the data security and storage module.

[0048] Step 2, Sampling of the mixed gas:

[0049] The gas sampling module collects mixed gas samples through a sampling probe (inserted into an industrial exhaust outlet or environmental monitoring point). The flow regulation unit stabilizes the sampling flow rate at 1L / min, and the temperature control unit controls the gas temperature at 25℃ to prevent water vapor condensation. The sampling pipeline delivers the gas to the gas pretreatment module.

[0050] Step 3, Gas Pretreatment:

[0051] The pretreatment module processes the gas in the following order: "primary filtration → deep dehydration → adsorption of interfering substances": ① Polytetrafluoroethylene filter membrane filters out particulate matter with a particle size ≥1μm; ② Hollow fiber membrane reduces the gas humidity to below 5%RH; ③ Molecular sieve adsorbs acidic impurities, and the pretreated clean gas is delivered to the multi-dimensional detection module.

[0052] Step 4: Multi-dimensional signal detection:

[0053] The multi-dimensional detection module simultaneously starts two sub-modules: ① The optical detection sub-module acquires near-infrared spectral signals in the wavelength range of 900-1700nm (to obtain characteristic absorption peaks of components such as CO and NO2); ② The electrochemical sensor array sub-module acquires the current response signals of each component (such as the current signal corresponding to VOCs), and the two signals are transmitted to the signal processing module in real time.

[0054] Step 5: Signal Processing

[0055] The signal processing module processes the signal as follows: ① The differential amplifier circuit amplifies the electrochemical current signal by 1000 times; ② The Kalman filter algorithm filters out high-frequency noise; ③ The spectral signal is converted into absorbance values, and the electrochemical signal is normalized to X. norm A standardized set of feature variables (n=10, containing 6 spectral features + 4 electrochemical features) is generated and transferred to the component analysis module.

[0056] Step 6, Component Concentration Analysis:

[0057] The component analysis module calls the PLSR quantitative model and substitutes the standardized characteristic variable set into formula C. i =a 0i +∑(a ji ×X ji )+E i Calculate the concentration of each component and verify the detection error |E i |≤5%×C i Generate a component analysis report;

[0058] Step 7, Result Output and Alarm:

[0059] The results output module displays the concentration of each component, detection time, and error range on the touch screen, and plots the concentration change curve over the past 2 hours; at the same time, it stores the data in CSV format and synchronizes it to the industrial DCS system; since the concentration of all components is below the alarm threshold, there is no need to trigger an alarm.

[0060] Step 8: Data Storage and System Monitoring

[0061] The data security and storage module uses AES-256 encryption to store detection data and analysis reports, and simultaneously uploads them to the cloud for backup; the system control module monitors the operating status of each module in real time (such as stable sampling flow and no sensor failure); the data security module performs daily vulnerability checks, and records operation logs if no abnormalities are found.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] This invention ensures the representativeness and purity of collected samples through a gas sampling module and a preprocessing module, providing high-quality gas samples for subsequent detection. The multi-dimensional detection module integrates optical detection and electrochemical sensing technologies, achieving simultaneous acquisition of the spectral and electrochemical response characteristics of the mixed gas, forming a complementary dataset. This dual-dimensional detection method not only broadens the detection range but also improves the sensitivity and accuracy of detection, effectively solving the detection blind spots and cross-interference problems inherent in single technologies. The signal processing module and component analysis module further enhance the reliability of the detection results. The signal processing module amplifies, filters, and standardizes weak signals, eliminating the influence of equipment differences and environmental fluctuations. The component analysis module, based on a partial least squares regression (PLSR) multi-component quantitative analysis model, accurately calculates the concentration of each component through multi-dimensional feature signals, ensuring the accuracy and stability of the detection results. Attached Figure Description

[0064] Figure 1 This is a system schematic diagram of the present invention;

[0065] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0066] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0067] Please see Figure 1-2 This invention provides a rapid analysis and detection system for multi-component mixed gases, including a gas sampling module, a gas preprocessing module, a multi-dimensional detection module, a signal processing module, a component analysis module, a result output module, a system control module, and a data security and storage module;

[0068] The gas sampling module is responsible for collecting representative mixed gas samples, controlling the sampling flow rate and temperature, and avoiding sample loss or condensation.

[0069] The gas pretreatment module removes impurities and dehydrates the collected raw gas, eliminating the interference of particulate matter and moisture on subsequent detection.

[0070] The multi-dimensional detection module integrates optical detection and electrochemical sensing technologies to simultaneously acquire the spectral characteristics and electrochemical response characteristics of the mixed gas, achieving multi-dimensional data complementarity.

[0071] The signal processing module amplifies, filters, and standardizes the weak signal output by the detection module to generate a feature signal that can be used for analysis.

[0072] The component analysis module is based on a quantitative analysis model and combines multi-dimensional characteristic signals to calculate the concentration of each gas component and output accurate analysis results.

[0073] The results output module displays the test results in a visual format and supports data export and alarm for exceeding the limit.

[0074] The system control module coordinates the synchronous operation of each module to achieve parameter configuration, process control and status monitoring;

[0075] The data security and storage module encrypts and backs up test data and calibration parameters, and sets access control to ensure data security.

[0076] The gas sampling module includes a sampling probe (anti-clogging design, made of corrosion-resistant polytetrafluoroethylene), a flow regulation unit (controlling the sampling flow rate via a mass flow controller, ranging from 0.5-2 L / min with an accuracy of ±2%), a temperature control unit (using a semiconductor temperature controller to stabilize the sampled gas temperature at 25±2℃ to prevent water vapor condensation), and a sampling pipeline (made of smooth-walled quartz to reduce gas component adsorption loss). During sampling, samples are collected via probe insertion or online extraction, and the flow rate and temperature parameters are adjusted in real time by the system control module.

[0077] The gas pretreatment module includes three processing units:

[0078] Primary filtration unit: Uses a polytetrafluoroethylene filter membrane to filter out particulate impurities in the gas; the filter membrane can be replaced periodically.

[0079] Deep dehydration unit: adopts hollow fiber membrane dehumidification technology, realizes water penetration through the humidity difference on both sides of the membrane, reduces the gas humidity to ≤5%RH (relative humidity), and has a dehydration efficiency of ≥95%;

[0080] Interference adsorption unit: filled with targeted adsorbents (such as molecular sieves to adsorb acidic impurities and activated carbon to adsorb organic interfering substances). The type of adsorbent can be changed according to the target detection component to avoid cross-interference of non-target components with the detection signal.

[0081] The cleanliness of the pretreated gas meets the input requirements of the multi-dimensional detection module, and the recovery rate of the target component is ≥98%.

[0082] The multi-dimensional detection module includes an optical detection submodule and an electrochemical sensor array submodule, which work synchronously:

[0083] Optical detection submodule: It adopts a near-infrared spectroscopy detector, which receives the near-infrared absorption spectrum of the gas through an optical fiber probe to obtain the characteristic absorption peak signals of each component (corresponding to the molecular vibrational energy level transitions of different components).

[0084] Electrochemical sensor array submodule: contains at least 4 electrochemical sensors, respectively targeting CO, NO2, SO2 and VOCs, and generates current signals through the reaction of gas diffusion electrodes with electrolytes;

[0085] Data from both modules is synchronously transmitted to the signal processing module to form a dual-dimensional feature dataset of "spectral and electrochemical" characteristics, reducing the detection blind spots of a single technology.

[0086] The signal processing module includes:

[0087] Signal amplification unit: Employs a differential amplifier circuit to amplify the weak current signal output by the electrochemical sensor by 1000-10000 times;

[0088] Noise filtering unit: Employs Kalman filtering algorithm (dynamically adjusts filtering coefficients) to filter out high-frequency noise in the spectral signal and the amplified electrochemical signal;

[0089] Signal normalization unit: Converts the spectral signal to absorbance value after baseline correction, and performs electrochemical signal normalization according to the formula... Normalization is performed, where X is the original current signal, X min X max These are the minimum and maximum response values ​​of the sensor, respectively, to eliminate the influence of individual device differences and environmental fluctuations.

[0090] The component analysis module incorporates a multi-component quantitative analysis model based on partial least squares regression (PLSR). It calculates the concentration of each target component using multi-dimensional characteristic signals. The core formula is:

[0091] C i =a 0i +∑(a ji ×X ji )+E i

[0092] Among them, C i Let a be the concentration of the i-th target gas component; 0i The regression intercept for the i-th component; j is the index of the feature variable, j = 1, 2, ..., n, where n is the total number of feature variables, including but not limited to spectral absorbance values ​​and electrochemically normalized signals, n ≥ 8; a ji X is the regression coefficient of the j-th characteristic variable on the i-th component; ji E is the standardized value of the j-th characteristic variable, i.e., the absorbance value or normalized electrochemical signal output by the signal processing module; i The detection error of the i-th component is controlled by the system accuracy, |E i |≤5%×C i To ensure the reliability of test results;

[0093] The module also includes a built-in standard gas calibration library (containing parameters for 5-10 standard mixed gases of different concentrations), supporting periodic automatic calibration and updates. 0i With a ji Parameters are set to avoid accuracy degradation caused by component aging.

[0094] The results output module includes:

[0095] Visualization display unit: It adopts a touch LCD screen to display the concentration values ​​of each component, detection time, detection error, and system operating status in real time;

[0096] Data export unit: Supports data export via USB interface or wireless (Bluetooth / Wi-Fi), in formats including CSV and Excel, and can be connected to industrial control system DCS or environmental monitoring platform;

[0097] Exceedance alarm unit: When the concentration of a certain component exceeds a preset threshold (the threshold can be customized through the system control module, such as CO ≤ 30 mg / m³), an alarm is triggered. 3 NO2 ≤ 10 mg / m³ 3 When the alarm is triggered (sound pressure level ≥ 85dB, alarm light flashing red), an audio-visual alarm will be activated, and a text message notification will be sent to the preset administrator's mobile phone.

[0098] Preferably, the system control module is based on an embedded microprocessor and communicates with each module via RS485 / Ethernet to achieve:

[0099] Parameter configuration: Set sampling flow rate, pretreatment temperature, detection cycle, calibration cycle, and alarm threshold;

[0100] Process control: Automating the process of sampling, preprocessing, detection, and analysis;

[0101] Status monitoring: Real-time collection of operating parameters of each module (such as whether the sampling flow is stable, whether the sensor is faulty), and generation of fault codes and prompts when a module is abnormal.

[0102] The data security and storage module includes:

[0103] Data encryption unit: The AES-256 symmetric encryption algorithm is used to encrypt the storage and transmission of test data and calibration parameters to prevent data tampering or leakage;

[0104] Data backup unit: Supports local SD card backup and cloud synchronization backup;

[0105] Access control: Set three levels of permissions: administrator, operator, and viewer. Administrators can modify parameters and delete data, operators can only start the detection, and viewers can only view the results.

[0106] Operation log unit: Records the parameters, results, operators, and system status for each test.

[0107] A detection method for a rapid multi-component analysis and detection system for mixed gases includes the following steps:

[0108] Step 1: System Initialization and Parameter Configuration

[0109] The administrator logs in through the system control module and completes system initialization: ① Calibrate the multi-dimensional detection module; ② Set sampling parameters, preprocessing parameters, detection cycle, and alarm thresholds for each component; ③ Initialize the encryption key and backup strategy for the data security and storage module.

[0110] Step 2, Sampling of the mixed gas:

[0111] The gas sampling module collects mixed gas samples through a sampling probe (inserted into an industrial exhaust outlet or environmental monitoring point). The flow regulation unit stabilizes the sampling flow rate at 1L / min, and the temperature control unit controls the gas temperature at 25℃ to prevent water vapor condensation. The sampling pipeline delivers the gas to the gas pretreatment module.

[0112] Step 3, Gas Pretreatment:

[0113] The pretreatment module processes the gas in the following order: "primary filtration → deep dehydration → adsorption of interfering substances": ① Polytetrafluoroethylene filter membrane filters out particulate matter with a particle size ≥1μm; ② Hollow fiber membrane reduces the gas humidity to below 5%RH; ③ Molecular sieve adsorbs acidic impurities, and the pretreated clean gas is delivered to the multi-dimensional detection module.

[0114] Step 4: Multi-dimensional signal detection:

[0115] The multi-dimensional detection module simultaneously starts two sub-modules: ① The optical detection sub-module acquires near-infrared spectral signals in the wavelength range of 900-1700nm (to obtain characteristic absorption peaks of components such as CO and NO2); ② The electrochemical sensor array sub-module acquires the current response signals of each component (such as the current signal corresponding to VOCs), and the two signals are transmitted to the signal processing module in real time.

[0116] Step 5: Signal Processing

[0117] The signal processing module processes the signal as follows: ① The differential amplifier circuit amplifies the electrochemical current signal by 1000 times; ② The Kalman filter algorithm filters out high-frequency noise; ③ The spectral signal is converted into absorbance values, and the electrochemical signal is normalized to X. norm A standardized set of feature variables (n=10, containing 6 spectral features + 4 electrochemical features) is generated and transferred to the component analysis module.

[0118] Step 6, Component Concentration Analysis:

[0119] The component analysis module calls the PLSR quantitative model and substitutes the standardized characteristic variable set into formula C. i =a 0i +∑(a ji ×X ji )+E i Calculate the concentration of each component and verify the detection error |E i |≤5%×C i Generate a component analysis report;

[0120] Step 7, Result Output and Alarm:

[0121] The results output module displays the concentration of each component, detection time, and error range on the touch screen, and plots the concentration change curve over the past 2 hours; at the same time, it stores the data in CSV format and synchronizes it to the industrial DCS system; since the concentration of all components is below the alarm threshold, there is no need to trigger an alarm.

[0122] Step 8: Data Storage and System Monitoring

[0123] The data security and storage module uses AES-256 encryption to store detection data and analysis reports, and simultaneously uploads them to the cloud for backup; the system control module monitors the operating status of each module in real time (such as stable sampling flow and no sensor failure); the data security module performs daily vulnerability checks, and records operation logs if no abnormalities are found.

[0124] Example:

[0125] Taking "multi-component detection of industrial waste gas" as the application scenario, the target detection components are CO, NO2, SO2, and VOCs. The specific implementation is as follows:

[0126] System deployment: The sampling probe is installed at the exhaust outlet after the factory's exhaust gas treatment, and the multi-dimensional detection module, signal processing module, etc. are integrated in the detection cabinet. It is connected to the factory's DCS system via Ethernet.

[0127] Calibration process: Introduce a standard mixed gas (CO = 30 mg / m³). 3 NO2 = 10 mg / m³ 3 SO2 = 12 mg / m³ 3 VOCs = 20mg / m³ 3 The system automatically collects feature signals, trains the PLSR model, and determines a. 0i (CO = 0.05, NO2 = 0.03, SO2 = 0.04, VOCs = 0.06) and a ji (For example, the regression coefficient of spectral feature 1 for CO is 0.8, and the regression coefficient of electrochemical feature 1 is 0.3).

[0128] Actual test: The test was performed according to steps 1-8. Each test took 2.5 minutes, and the result was CO = 28 mg / m³. 3 (Error = 0.9 mg / m 3 ≤1.4mg / m 3 NO2 = 9 mg / m³ 3 (Error = 0.3 mg / m 3 ≤0.45mg / m 3 SO2 = 11 mg / m³ 3 (Error = 0.4 mg / m 3 ≤0.55mg / m 3VOCs = 19 mg / m³ 3 (Error = 0.6 mg / m 3 ≤0.95mg / m 3 All values ​​were below the alarm threshold, and the data was synchronized to the DCS system and backed up in the cloud.

[0129] Anomaly Handling: When the SO2 concentration in a certain test is 15 mg / m³ 3 (exceeding the threshold of 12 mg / m³) 3 The output module triggers an audible and visual alarm and simultaneously sends a text message to the administrator (content: "SO2 concentration at XX factory exceeds the standard, currently 15 mg / m³"). 3 The system control module records the exception log (time XX:XX) for easy tracing later.

[0130] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rapid analysis and detection system for multiple components of mixed gas, characterized in that: It includes a gas sampling module, a gas preprocessing module, a multi-dimensional detection module, a signal processing module, a component analysis module, a result output module, a system control module, and a data security and storage module; The gas sampling module is responsible for collecting representative mixed gas samples, controlling the sampling flow rate and temperature, and avoiding sample loss or condensation. The gas pretreatment module removes impurities and dehydrates the collected raw gas, eliminating the interference of particulate matter and moisture on subsequent detection. The multi-dimensional detection module integrates optical detection and electrochemical sensing technologies to simultaneously acquire the spectral characteristics and electrochemical response characteristics of the mixed gas, thereby achieving multi-dimensional data complementarity. The signal processing module amplifies, filters, and standardizes the weak signal output by the detection module to generate a feature signal that can be used for analysis. The component analysis module is based on a quantitative analysis model and combines multi-dimensional feature signals to calculate the concentration of each gas component and output accurate analysis results. The result output module displays the test results in a visual format and supports data export and alarm for exceeding the standard. The system control module coordinates the synchronous operation of each module to achieve parameter configuration, process control and status monitoring; The data security and storage module encrypts and backs up the test data and calibration parameters, and sets access control to ensure data security.

2. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The gas sampling module includes a sampling probe, a flow regulation unit, a temperature control unit, and a sampling pipeline. During sampling, samples are collected by probe insertion or online extraction, and the flow rate and temperature parameters are adjusted in real time by the system control module.

3. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The gas pretreatment module includes a three-stage processing unit: Primary filtration unit: Uses a polytetrafluoroethylene filter membrane to filter out particulate impurities in the gas; the filter membrane can be replaced periodically. Deep dehydration unit: adopts hollow fiber membrane dehumidification technology, which realizes water penetration through the humidity difference on both sides of the membrane, reduces the gas humidity to ≤5%RH, and the dehydration efficiency is ≥95%; Interference adsorption unit: filled with targeted adsorbent, the type of adsorbent can be changed according to the target detection component to avoid cross-interference of non-target components with the detection signal; The cleanliness of the pretreated gas meets the input requirements of the multi-dimensional detection module, and the recovery rate of the target component is ≥98%.

4. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The multi-dimensional detection module includes an optical detection submodule and an electrochemical sensor array submodule, which operate synchronously. Optical detection submodule: Employs a near-infrared spectrometer detector, which receives the near-infrared absorption spectrum of the gas through a fiber optic probe to obtain the characteristic absorption peak signals of each component; Electrochemical sensor array submodule: contains at least 4 electrochemical sensors, respectively targeting CO, NO2, SO2 and VOCs, and generates current signals through the reaction of gas diffusion electrodes with electrolytes; Data from both modules is synchronously transmitted to the signal processing module to form a dual-dimensional feature dataset of "spectral and electrochemical" characteristics, reducing the detection blind spots of a single technology.

5. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The signal processing module includes: Signal amplification unit: Employs a differential amplifier circuit to amplify the weak current signal output by the electrochemical sensor by 1000-10000 times; Noise filtering unit: Employs Kalman filtering algorithm to filter out high-frequency noise in the spectral signal and the amplified electrochemical signal; Signal normalization unit: Converts the spectral signal to absorbance value after baseline correction, and performs electrochemical signal normalization according to the formula... Normalization is performed, where X is the original current signal, X min X max These are the minimum and maximum response values ​​of the sensor, respectively, to eliminate the influence of individual device differences and environmental fluctuations.

6. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The component analysis module incorporates a multi-component quantitative analysis model based on partial least squares regression (PLSR), which calculates the concentration of each target component using multi-dimensional feature signals. The core formula is: Among them, C i Let a be the concentration of the i-th target gas component; 0i The regression intercept for the i-th component; j is the index of the feature variable, j = 1, 2, ..., n, where n is the total number of feature variables, including but not limited to spectral absorbance values ​​and electrochemically normalized signals, n ≥ 8; a ji X is the regression coefficient of the j-th characteristic variable on the i-th component; ji E is the standardized value of the j-th characteristic variable, i.e., the absorbance value or normalized electrochemical signal output by the signal processing module; i The detection error of the i-th component is controlled by the system accuracy, |E i |≤5%×C i To ensure the reliability of test results; The module also has a built-in standard gas calibration library, supporting periodic automatic calibration and updates. 0i With a ji Parameters are set to avoid accuracy degradation caused by component aging.

7. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The result output module includes: Visualization display unit: It adopts a touch LCD screen to display the concentration values ​​of each component, detection time, detection error, and system operating status in real time; Data export unit: Supports data export via USB interface or wireless, in formats including CSV and Excel, and can be connected to industrial control system DCS or environmental monitoring platform; Exceedance alarm unit: When the concentration of a certain component exceeds the preset threshold, an audible and visual alarm is triggered, and a text message notification is sent to the preset administrator's mobile phone.

8. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The system control module, based on an embedded microprocessor, communicates with each module via RS485 / Ethernet to achieve the following: Parameter configuration: Set sampling flow rate, pretreatment temperature, detection cycle, calibration cycle, and alarm threshold; Process control: Automating the process of sampling, preprocessing, detection, and analysis; Status monitoring: Real-time collection of operating parameters of each module, and generation of fault codes and prompts when a module malfunctions.

9. The rapid analysis and detection system for multi-component mixed gas according to claim 1, characterized in that: The data security and storage module includes: Data encryption unit: The AES-256 symmetric encryption algorithm is used to encrypt the storage and transmission of test data and calibration parameters to prevent data tampering or leakage; Data backup unit: Supports local SD card backup and cloud synchronization backup; Access control: Set three levels of permissions: administrator, operator, and viewer. Administrators can modify parameters and delete data, operators can only start the detection, and viewers can only view the results. Operation log unit: Records the parameters, results, operators, and system status for each test.

10. The detection method of a rapid analysis and detection system for multi-component mixed gas according to any one of claims 1-9, characterized in that: Includes the following steps: Step 1: System Initialization and Parameter Configuration The administrator logs in through the system control module and completes system initialization: ① Calibrate the multi-dimensional detection module; ② Set sampling parameters, preprocessing parameters, detection cycle, and alarm thresholds for each component; ③ Initialize the encryption key and backup strategy for the data security and storage module. Step 2, Sampling of the mixed gas: The gas sampling module collects mixed gas samples through a sampling probe, the flow regulation unit stabilizes the sampling flow rate at 1L / min, the temperature control unit controls the gas temperature at 25℃ to prevent water vapor condensation, and the sampling pipeline delivers the gas to the gas pretreatment module. Step 3, Gas Pretreatment: The pretreatment module processes the gas in the following order: "primary filtration → deep dehydration → adsorption of interfering substances": ① Polytetrafluoroethylene filter membrane filters out particulate matter with a particle size ≥1μm; ② Hollow fiber membrane reduces the gas humidity to below 5%RH; ③ Molecular sieve adsorbs acidic impurities, and the pretreated clean gas is delivered to the multi-dimensional detection module. Step 4: Multi-dimensional signal detection: The multi-dimensional detection module simultaneously starts two sub-modules: ① The optical detection sub-module acquires near-infrared spectral signals in the wavelength range of 900-1700nm; ② The electrochemical sensor array sub-module acquires the current response signals of each component, and the two signals are transmitted to the signal processing module in real time. Step 5: Signal Processing The signal processing module processes the signal as follows: ① The differential amplifier circuit amplifies the electrochemical current signal by 1000 times; ② The Kalman filter algorithm filters out high-frequency noise; ③ The spectral signal is converted into absorbance values, and the electrochemical signal is normalized to X. norm A standardized set of feature variables is generated and transmitted to the component analysis module. Step 6, Component Concentration Analysis: The component analysis module calls the PLSR quantitative model and substitutes the standardized characteristic variable set into formula C. i =a 0i +∑(a ji ×X ji )+E i Calculate the concentration of each component and verify the detection error |E i |≤5%×C i Generate a component analysis report; Step 7, Result Output and Alarm: The results output module displays the concentration of each component, detection time, and error range on the touch screen, and plots the concentration change curve over the past 2 hours; at the same time, it stores the data in CSV format and synchronizes it to the industrial DCS system; since the concentration of all components is below the alarm threshold, there is no need to trigger an alarm. Step 8: Data Storage and System Monitoring The data security and storage module uses AES-256 encryption to store detection data and analysis reports, and simultaneously uploads them to the cloud for backup; the system control module monitors the operating status of each module in real time, and the data security module performs daily vulnerability checks, recording operation logs if no anomalies are found.

Images