Ultra-sensitive chemical equipment liquid volatile matter VOC detection system and method
By combining sampling and enrichment modules, laser acoustic effect detection, and intelligent calibration modules, the timeliness and sensitivity issues of VOC detection in chemical equipment have been solved, enabling efficient and accurate detection of low-concentration VOCs and ensuring the safety of chemical production.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-03-13
AI Technical Summary
Existing technologies for VOC detection in chemical equipment have poor timeliness and lack the ability to detect low concentrations of VOCs, resulting in an inability to respond promptly to leaks.
In-situ sampling and enrichment of low-concentration VOCs are performed using a sampling and enrichment module, combined with laser-induced acoustic effect detection using a quantum cascade laser and photoacoustic spectral cell. Signal analysis is performed using a data processing and analysis module, and environmental parameters are calibrated using an intelligent calibration module.
It enables sensitive detection of low concentrations of VOCs, improves detection efficiency and accuracy, and allows for early detection of leaks, ensuring production safety.
Smart Images
Figure CN121656147A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of VOC detection technology, specifically to an ultra-sensitive VOC detection system and method for liquid volatiles in chemical equipment. Background Technology
[0002] VOC is the abbreviation for Volatile Organic Compound. In a general sense, VOC refers to volatile organic compounds, but in an environmental context, it refers to a class of reactive volatile organic compounds that can cause harm and have a significant impact on human health. When VOCs reach a certain concentration, they can cause headaches, nausea, vomiting, and fatigue in a short period of time. In severe cases, they can cause convulsions and coma, and can seriously damage the liver, kidneys, brain, and nervous system, resulting in consequences such as memory loss.
[0003] In industries such as petrochemicals, the most effective means of controlling fugitive VOC emissions is Leak Detection and Repair (LDAR) technology. This technology requires personnel to carry portable VOC analyzers to detect VOC concentrations at dynamic and static sealing points of equipment. According to national standards, dynamic sealing points need to be tested four times a year, and static sealing points twice a year. However, leaks can occur at any time in production facilities, and the timeliness of such periodic testing methods cannot meet the needs of daily production. There is still a risk of not being able to respond to leak accidents in a timely manner. In addition, the current monitoring methods lack the ability to detect low concentrations of VOCs, which greatly affects the sensitivity of the detection methods and reduces the possibility of detecting leaks in their early stages. Summary of the Invention
[0004] To address the aforementioned problems, this invention provides an ultra-sensitive VOC detection system and method for liquid volatiles in chemical equipment, thereby solving the problems of poor timeliness and insufficient sensitivity of existing technologies for detecting low-concentration VOCs.
[0005] To achieve the above objectives, the present invention provides an ultrasensitive VOC detection system for liquids in chemical equipment, comprising: The sampling and enrichment module is used to sample and enrich low-concentration VOCs in situ at the detection point to increase the sample concentration and facilitate subsequent detection. The sampling and enrichment module includes a low-temperature adsorption trap and a sampling pump. The sampling pump is connected to the low-temperature adsorption trap and has a sampling port. The low-temperature adsorption trap is connected to a carrier gas input pipe for inputting nitrogen or inert gas. A solenoid valve is installed on the carrier gas input pipe. The detection module uses the photoacoustic effect to detect VOC concentration and eliminates background interference. The detection module includes a quantum cascade laser, a photoacoustic spectral cell, and a reference optical path. The low-temperature adsorption trap is connected to the photoacoustic spectral cell. The reference optical path is equipped with a blank gas cell. Both the photoacoustic spectral cell and the blank gas cell are connected to the quantum cascade laser. Several microphone sensors are installed in both the photoacoustic spectral cell and the blank gas cell. The data processing and analysis module is used to acquire detection signals, analyze and calculate the detection signals, and output VOC concentration values. The intelligent calibration module is used to collect environmental parameters and automatically calibrate the detection system parameters according to changes in environmental parameters, thereby optimizing the detection range.
[0006] Furthermore, the low-temperature adsorption trap is filled with adsorption filler material, which is activated carbon fiber or MOF material.
[0007] Furthermore, a Peltier element is installed on the sidewall of the low-temperature adsorption trap, and a heating wire is provided inside the low-temperature adsorption trap.
[0008] Furthermore, the data processing and analysis module includes a data acquisition card and a data processor, wherein the data acquisition card is connected to the microphone sensor and the data processor, respectively.
[0009] Furthermore, the data processor constructs a deep learning model based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The deep learning model is trained using a large amount of low-concentration VOC detection data under different environmental conditions, and learns the characteristic differences between weak VOC concentration signals and background noise. This enables the model to identify and enhance weak VOC signals affected by background noise, further reducing the detection limit and providing reliable detection performance for extremely low concentration VOCs.
[0010] Furthermore, the intelligent calibration module includes several environmental parameter detection sensors for detecting various environmental condition parameters, including temperature, humidity, and air pressure. The intelligent calibration module is connected to the data processing and analysis module.
[0011] A highly sensitive method for detecting VOCs in liquids from chemical equipment, using the aforementioned detection system, specifically includes the following steps: S1 System Installation and Initialization: S1-1 System Installation: Depending on the type of chemical equipment, the sampling port of the sampling and enrichment module is fixed to a location on the equipment surface that is prone to leakage by means of magnetic or flange connection; S1-2 System Initialization: Turn on the system power, perform self-tests and initializations on each module of the system, set the adsorption temperature of the low-temperature adsorption trap and the flow rate of the sampling pump, calibrate the wavelength of the quantum cascade laser, and initialize the deep learning model parameters of the data processing and analysis module. S2 in-situ sampling and enrichment: Start the sampling pump to draw air near the surface of the chemical equipment into the sampling pipeline. After passing through the low-temperature adsorption trap, the Peltier element TEC cools the adsorption packing in the low-temperature adsorption trap, and the adsorption packing performs low-temperature adsorption and enrichment of VOCs. S3 Desorption and Detection: S3-1 Desorption: Turn off the sampling pump, reverse the current direction of the Peltier element TEC, and use the Peltier element and heating wire to heat the low-temperature adsorption trap so that the VOCs are desorbed from the adsorption packing. Open the solenoid valve and introduce carrier gas into the low-temperature adsorption trap to quickly send the VOCs into the photoacoustic spectroscopy cell. S3-2 Detection: A wavelength-modulated laser beam emitted by a quantum cascade laser enters a photoacoustic spectral cell and interacts with VOC molecules to generate a photoacoustic signal. Wavelength modulation technique (WMS) is used to sinusoidally modulate the injection current of the quantum cascade laser, causing the emitted laser wavelength to scan near the absorption peak of the target VOC. A microphone sensor receives the photoacoustic signal and converts it into an electrical signal. After being acquired by a data acquisition card, the photoacoustic data is transmitted to a data processing and analysis module. A reference optical path monitors the background gas to eliminate background noise interference. S4 Concentration Calculation and Data Deep Processing: S4-1 Concentration Calculation: The data processing and analysis module preprocesses the collected data, filters and amplifies the data, and inputs the data into the deep learning model for analysis. The deep learning model calculates the VOC concentration value based on the training features. S4-2 Data Deep Processing: Employing a multimodal data fusion algorithm, photoacoustic data is comprehensively analyzed with environmental parameters to further improve the accuracy of VOC concentration calculation; S5 Intelligent Calibration and Result Output: S5-1 Calibration: The intelligent calibration module automatically adjusts the parameters of the detection system and optimizes the detection limits based on the real-time changes in environmental parameters monitored by the environmental parameter detection sensor. S5-2 Result Output: The VOC concentration value finally calculated by the data processing and analysis module is displayed in real time through the display device, and uploaded to the cloud platform or host computer through the wireless transmission device for subsequent analysis and storage.
[0012] Furthermore, in step S2, the sampling pump flow rate is 0.1~1L / min, the adsorption temperature of the low-temperature adsorption trap is -30~-20℃, and the adsorption enrichment time is 5~10min; in step S3-1, the desorption temperature of the adsorption packing material in the low-temperature adsorption trap is 120~150℃, and the carrier gas flow rate is 0.2~0.5L / min; in step S3-2, the laser beam emitted by the quantum cascade laser has a wavelength of 3~12μm and a power of 50~200MW, the resonant frequency of the photoacoustic spectral cell is 500~1000Hz, and the sensitivity of the microphone sensor is higher than 50mV / Pa.
[0013] The beneficial effects of this invention are: In this invention, a sampling and enrichment module is used to sample and enrich low-concentration VOCs, a detection module uses laser sound emission effect to monitor VOCs in real time, a data processing and analysis module analyzes and calculates the detection signal, and an intelligent calibration module detects and calibrates various environmental parameters. This enables sensitive detection of low-concentration VOCs and can effectively and quickly detect trace leaks of VOCs, improving detection efficiency. It also allows for early detection of leaks, preventing greater harm and ensuring production safety. In this invention, the sampling and enrichment module utilizes a low-temperature adsorption trap to enrich low-concentration VOCs at low temperatures, thereby increasing the collected VOC concentration and facilitating subsequent VOC detection, thus improving the effectiveness and accuracy of the detection. In this invention, the detection module uses a quantum cascade laser to excite VOCs and generate photoacoustic signals. The injection current is adjusted to adjust the laser wavelength so that the laser wavelength is adapted to the absorption peak of the target VOC. The photoacoustic signals are collected by a microphone sensor, which can effectively improve the sensitivity and accuracy of VOC detection. In this invention, the data processor uses a deep learning model to process VOC signals, which can effectively reduce the detection limit and thus improve the reliability of detecting extremely low concentration VOCs. This invention employs several environmental parameter detection sensors to detect environmental conditions and uses a blank gas cell to detect VOCs in the environmental background, which can effectively improve the accuracy of detection and eliminate the influence of environmental background on detection. Attached Figure Description
[0014] To more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0015] Figure 1This is a schematic diagram of the detection system structure; The components are: 1-Sampling and enrichment module, 11-Low temperature adsorption trap, 12-Sampling pump, 2-Detection module, 21-Quantum cascade laser, 22-Photoacoustic spectral cell, 23-Reference optical path, 3-Data processing and analysis module, 31-Data acquisition card, 32-Data processor, and 4-Intelligent calibration module. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
[0017] In one specific embodiment of the present invention, an ultrasensitive VOC detection system for liquid volatiles in chemical equipment, such as... Figure 1 As shown, it includes: The sampling and enrichment module 1 is used to sample and enrich low-concentration VOCs in situ at the detection point, thereby increasing the sample concentration and facilitating subsequent detection. The sampling and enrichment module 1 includes a low-temperature adsorption trap 11 and a sampling pump 12. The sampling pump 12 is connected to the low-temperature adsorption trap 11 and has a sampling port. The low-temperature adsorption trap 11 is connected to a carrier gas input pipe for inputting nitrogen or inert gas. A solenoid valve is installed on the carrier gas input pipe. The low-temperature adsorption trap 11 is filled with adsorption packing material, which is activated carbon fiber or MOF material. Peltier elements are installed on the side wall of the low-temperature adsorption trap 11, and a heating wire is installed inside the low-temperature adsorption trap 11.
[0018] The detection module 2 uses the photoacoustic effect to detect VOC concentration and eliminate background interference. The detection module 2 includes a quantum cascade laser 21, a photoacoustic spectral cell 22 and a reference optical path 23. The low-temperature adsorption trap 11 is connected to the photoacoustic spectral cell 22. The reference optical path 23 is equipped with a blank gas cell. The photoacoustic spectral cell 22 and the blank gas cell are both connected to the quantum cascade laser 21. Several microphone sensors are set in both the photoacoustic spectral cell 22 and the blank gas cell. The data processing and analysis module 3 is used to acquire detection signals, analyze and calculate the detection signals, and output VOC concentration values. The data processing and analysis module 3 includes a data acquisition card 31 and a data processor 32. The data acquisition card 31 is connected to the microphone sensor and the data processor 32, respectively. The data processor 32 constructs a deep learning model based on a combination of convolutional neural network (CNN) and long short-term memory network (LSTM). The deep learning model is trained using a large amount of low-concentration VOC detection data under different environmental conditions. It learns the characteristic differences between weak VOC concentration signals and background noise, enabling the model to identify and enhance weak VOC signals affected by background noise, further reducing the detection limit and providing reliable detection performance for extremely low concentration VOCs.
[0019] The intelligent calibration module 4 is used to collect environmental parameters and automatically calibrate the detection system parameters according to changes in environmental parameters to optimize the detection range. The intelligent calibration module includes several environmental parameter detection sensors for detecting various environmental condition parameters, including temperature, humidity and air pressure. The intelligent calibration module is connected to the data processing and analysis module 3.
[0020] A highly sensitive method for detecting VOCs in liquids from chemical equipment, using the aforementioned detection system, specifically includes the following steps: S1 System Installation and Initialization: S1-1 System Installation: Depending on the type of chemical equipment, the sampling port of the sampling and enrichment module 1 is fixed to a location on the equipment surface that is prone to leakage by means of magnetic or flange connection; S1-2 System Initialization: Turn on the system power, perform self-tests and initializations on each module of the system, set the adsorption temperature of the low-temperature adsorption trap 11 to -20℃, the flow rate of the sampling pump 12 to 0.5L / min, calibrate the wavelength of the quantum cascade laser 21, and initialize the deep learning model parameters of the data processing and analysis module 3. S2 in-situ sampling and enrichment: Start the sampling pump 12 to draw air near the surface of the chemical equipment into the sampling pipeline. After passing through the low-temperature adsorption trap 11, the Peltier element TEC cools the adsorption packing in the low-temperature adsorption trap. The adsorption packing performs low-temperature adsorption and enrichment of VOCs for 10 minutes. S3 Desorption and Detection: S3-1 Desorption: Turn off sampling pump 12, reverse the current direction of Peltier element TEC, and use Peltier element and heating wire to heat the low temperature adsorption trap 11 to desorb VOC from the adsorption packing. The desorption temperature is 150℃. Open the solenoid valve and introduce carrier gas into the low temperature adsorption trap 11 at a flow rate of 0.4L / min to quickly send VOC into the photoacoustic spectral cell 22. S3-2 Detection: The quantum cascade laser 21 emits a wavelength-modulated laser beam that enters the photoacoustic spectral cell 22, interacting with VOC molecules to generate a photoacoustic signal. Wavelength modulation technology (WMS) is used, which sinusoidally modulates the injection current of the quantum cascade laser 21 to make the emitted laser wavelength scan near the absorption peak of the target VOC. The laser beam wavelength of the quantum cascade laser 21 is 5~12μm, and the power is 50~200MW. The microphone sensor receives the photoacoustic signal and converts it into an electrical signal. The microphone sensor has a sensitivity higher than 50mV / Pa. After being acquired by the data acquisition card 31, the photoacoustic data is transmitted to the data processing and analysis module 3. The reference optical path 23 monitors the background gas and eliminates background noise interference. S4 Concentration Calculation and Data Deep Processing: S4-1 Concentration Calculation: Data processing and analysis module 3 preprocesses the collected data, filters and amplifies the data, and inputs the data into the deep learning model for analysis. The deep learning model calculates the VOC concentration value based on the training features. S4-2 Data Deep Processing: Employing a multimodal data fusion algorithm, photoacoustic data is comprehensively analyzed with environmental parameters to further improve the accuracy of VOC concentration calculation; S5 Intelligent Calibration and Result Output: S5-1 Calibration: The intelligent calibration module 4 automatically adjusts the parameters of the detection system and optimizes the detection limit based on the real-time changes in environmental parameters monitored by the environmental parameter detection sensor. S5-2 Result Output: The VOC concentration value finally calculated by the data processing and analysis module 3 is displayed in real time through the display device, and uploaded to the cloud platform or host computer through the wireless transmission device for subsequent analysis and storage.
[0021] The preferred and optional technical means disclosed in this invention, except as otherwise specified and as further defined as one preferred or alternative technical means being another, can be arbitrarily combined to form several different technical solutions. Therefore, equivalent variations made according to the claims are still within the scope of this invention.
Claims
1. A highly sensitive VOC detection system for liquids in chemical equipment, characterized in that, include: The sampling and enrichment module (1) is used to sample and enrich low concentration VOCs in situ at the detection point to increase the sample concentration and facilitate subsequent detection. The sampling and enrichment module includes a low temperature adsorption trap (11) and a sampling pump (12). The sampling pump (12) is connected to the low temperature adsorption trap (11). The sampling pump (12) is connected to a sampling port. The low temperature adsorption trap (11) is connected to a carrier gas input pipe for inputting nitrogen or inert gas. A solenoid valve is provided on the carrier gas input pipe. The detection module (2) uses the photoacoustic effect to detect VOC concentration and eliminate background interference. The detection module (2) includes a quantum cascade laser (21), a photoacoustic spectral cell (22) and a reference optical path (23). The low-temperature adsorption trap (11) is connected to the photoacoustic spectral cell (22). The reference optical path (23) is provided with a blank gas cell. The photoacoustic spectral cell (22) and the blank gas cell are both connected to the quantum cascade laser (21). Several microphone sensors are provided in both the photoacoustic spectral cell (22) and the blank gas cell. The data processing and analysis module (3) is used to collect detection signals, analyze and calculate the detection signals, and output VOC concentration values; The intelligent calibration module (4) is used to collect environmental parameters and automatically calibrate the detection system parameters according to changes in environmental parameters, thereby optimizing the detection range.
2. The ultra-sensitive VOC detection system for liquid volatiles in chemical equipment according to claim 1, characterized in that, The low-temperature adsorption trap (11) is filled with adsorption filler, which is activated carbon fiber or MOF material.
3. The ultra-sensitive VOC detection system for liquid volatiles in chemical equipment according to claim 1, characterized in that, A Peltier element is installed on the side wall of the low-temperature adsorption trap (11), and a heating wire is provided inside the low-temperature adsorption trap (11).
4. The ultra-sensitive VOC detection system for liquid volatiles in chemical equipment according to claim 1, characterized in that, The data processing and analysis module (3) includes a data acquisition card (31) and a data processor (32), wherein the data acquisition card (31) is connected to the microphone sensor and the data processor (32) respectively.
5. The ultra-sensitive VOC detection system for liquid volatiles in chemical equipment according to claim 4, characterized in that, The data processor (32) constructs a deep learning model based on the combination of convolutional neural network (CNN) and long short-term memory network (LSTM). The deep learning model is trained using a large amount of low-concentration VOC detection data under different environmental conditions. It learns the characteristic differences between weak VOC concentration signals and background noise, enabling the model to identify and enhance weak VOC signals affected by background noise, further reducing the detection limit and providing reliable detection performance for extremely low concentration VOCs.
6. The ultra-sensitive VOC detection system for liquid volatiles in chemical equipment as described in claim 1, characterized in that, The intelligent calibration module (4) includes several environmental parameter detection sensors for detecting various environmental condition parameters, including temperature, humidity and air pressure. The intelligent calibration module is connected to the data processing and analysis module.
7. A method for detecting VOCs in liquids of chemical equipment using an ultrasensitive method, comprising using the detection system described in claims 1-6 to detect VOCs in liquids of chemical equipment, characterized in that, Specifically, the steps include the following: S1 System Installation and Initialization: S1-1 System Installation: Depending on the type of chemical equipment, the sampling port of the sampling and enrichment module is fixed to a location on the equipment surface that is prone to leakage by means of magnetic or flange connection; S1-2 System Initialization: Turn on the system power, perform self-tests and initializations on each module of the system, set the adsorption temperature of the low-temperature adsorption trap and the flow rate of the sampling pump, calibrate the wavelength of the quantum cascade laser, and initialize the deep learning model parameters of the data processing and analysis module. S2 in-situ sampling and enrichment: Start the sampling pump to draw air near the surface of the chemical equipment into the sampling pipeline. After passing through the low-temperature adsorption trap, the Peltier element TEC cools the adsorption packing in the low-temperature adsorption trap, and the adsorption packing performs low-temperature adsorption and enrichment of VOCs. S3 Desorption and Detection: S3-1 Desorption: Turn off the sampling pump, reverse the current direction of the Peltier element TEC, and use the Peltier element and heating wire to heat the low-temperature adsorption trap so that the VOCs are desorbed from the adsorption packing. Open the solenoid valve and introduce carrier gas into the low-temperature adsorption trap to quickly send the VOCs into the photoacoustic spectroscopy cell. S3-2 Detection: A wavelength-modulated laser beam emitted by a quantum cascade laser enters a photoacoustic spectral cell and interacts with VOC molecules to generate a photoacoustic signal. Wavelength modulation technique (WMS) is used to sinusoidally modulate the injection current of the quantum cascade laser, causing the emitted laser wavelength to scan near the absorption peak of the target VOC. A microphone sensor receives the photoacoustic signal and converts it into an electrical signal. After being acquired by a data acquisition card, the photoacoustic data is transmitted to a data processing and analysis module. A reference optical path monitors the background gas to eliminate background noise interference. S4 Concentration Calculation and Data Deep Processing: S4-1 Concentration Calculation: The data processing and analysis module preprocesses the collected data, filters and amplifies the data, and inputs the data into the deep learning model for analysis. The deep learning model calculates the VOC concentration value based on the training features. S4-2 Data Deep Processing: Employing a multimodal data fusion algorithm, photoacoustic data is comprehensively analyzed with environmental parameters to further improve the accuracy of VOC concentration calculation; S5 Intelligent Calibration and Result Output: S5-1 Calibration: The intelligent calibration module automatically adjusts the parameters of the detection system and optimizes the detection limits based on the real-time changes in environmental parameters monitored by the environmental parameter detection sensor. S5-2 Result Output: The VOC concentration value finally calculated by the data processing and analysis module is displayed in real time through the display device, and uploaded to the cloud platform or host computer through the wireless transmission device for subsequent analysis and storage.
8. The method for detecting VOCs in liquids in ultrasensitive chemical equipment according to claim 7, characterized in that, In step S2, the sampling pump flow rate is 0.1~1L / min, the adsorption temperature of the low-temperature adsorption trap is -30~-20℃, and the adsorption enrichment time is 5~10min; in step S3-1, the desorption temperature of the adsorption packing material in the low-temperature adsorption trap is 120~150℃, and the carrier gas flow rate is 0.2~0.5L / min; in step S3-2, the laser beam emitted by the quantum cascade laser has a wavelength of 3~12μm and a power of 50~200MW, the resonant frequency of the photoacoustic spectral cell is 500~1000Hz, and the sensitivity of the microphone sensor is higher than 50mV / Pa.