An energy-saving laboratory air purification method and system based on adaptive control

Abstract

This invention relates to the technical field of energy-saving air purification in laboratories, specifically to a method and system for energy-saving air purification in laboratories based on adaptive control. The method includes the following steps: acquiring event information indicating that an automated high-throughput processing device has stopped releasing pollutants; estimating the concentration decay trend of pollutants in the laboratory space based on the event information, preset environmental parameters, and the characteristics of the pollution source; adjusting the operating intensity of the purification equipment according to the concentration decay trend to reduce the purification intensity; acquiring environmental pollutant concentration information and comparing it with the concentration decay trend to correct the adjustment process of the purification intensity; and switching the purification equipment to a low-power operation mode when the environmental pollutant concentration reaches a preset safety level. This application achieves a balance between purification effect and energy saving.

Description

Technical Field

[0001] This invention relates to the technical field of energy-saving air purification in laboratories, and specifically to an energy-saving air purification method and system for laboratories based on adaptive control. Background Technology

[0002] In modern laboratory environments, air purification systems are crucial for ensuring experimental safety and accurate results. However, with the introduction of automated, high-throughput processing equipment, the patterns of pollutant release within laboratories have changed significantly. Traditional air purification systems typically operate with fixed parameters or are designed and optimized based on slow, continuous pollutant volatilization patterns. This design makes it difficult for traditional systems to achieve a balance between purification effectiveness and energy consumption in rapidly changing pollution environments. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by proposing an energy-saving laboratory air purification method and system based on adaptive control.

[0004] The present invention adopts the following technical solution: An energy-saving laboratory air purification method based on adaptive control, comprising the following steps: Obtain event information indicating that automated high-throughput processing equipment has stopped releasing pollutants; Based on event information, preset environmental parameters, and the characteristics of the pollution source, estimate the concentration decay trend of pollutants in the laboratory space; Based on the concentration decay trend, adjust the operating intensity of the purification equipment to reduce the purification intensity; Information on the concentration of environmental pollutants is obtained and compared with the concentration decay trend to correct the adjustment process of purification intensity. When the concentration of environmental pollutants reaches the preset safety level, the purification equipment will switch to a low-power operation mode.

[0005] Through this technical solution, this application can dynamically adjust the purification intensity according to pollutant release events and decay trends, avoid over-purification, effectively solve the problems of slow response and energy waste in traditional systems, and achieve a balance between purification effect and energy saving.

[0006] This application also discloses a laboratory energy-saving air purification system based on adaptive control, which applies the above-mentioned laboratory energy-saving air purification method based on adaptive control. The system includes: The acquisition module acquires event information about the automated high-throughput processing equipment ceasing to release pollutants; The estimation module estimates the concentration decay trend of pollutants in the laboratory space based on event information, preset environmental parameters, and the characteristics of the pollution source. The adjustment module adjusts the operating intensity of the purification equipment based on the concentration decay trend to reduce the purification intensity. The comparison module acquires environmental pollutant concentration information and compares it with the concentration decay trend to correct the adjustment process of purification intensity. The switching module switches the purification equipment to a low-power operation mode when the concentration of environmental pollutants reaches a preset safety level.

[0007] This application provides a hardware or software system capable of implementing the above-mentioned method, providing a concrete carrier for the implementation of the method and ensuring the effective operation of the method and the achievement of energy-saving goals.

[0008] Based on the attenuation trend, this application's system can adaptively adjust the operating intensity of the purification equipment, thereby promptly reducing the purification intensity as pollutant concentration decreases. Furthermore, by continuously acquiring environmental pollutant concentration information and comparing it with the estimated attenuation trend, the system can correct the purification intensity adjustment process in real time, ensuring the accuracy and timeliness of the purification effect. When the environmental pollutant concentration reaches a preset safety level, the purification equipment can quickly switch to a low-power operation mode, further achieving energy-saving goals. Simultaneously, through precise purification intensity adjustment and low-power mode switching, unnecessary power consumption is significantly reduced, achieving an optimal balance between purification effect and energy consumption, providing a safer, more efficient, and more energy-saving air purification solution for modern laboratory environments.

[0009] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0010] Figure 1 This is a flowchart of a laboratory energy-saving air purification method based on adaptive control according to the present invention. Figure 2 This is a schematic diagram of the structure of an energy-saving laboratory air purification system based on adaptive control according to the present invention. Detailed Implementation

[0011] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0012] This embodiment provides a laboratory energy-saving air purification method and system based on adaptive control, combined with... Figure 1 and Figure 2 As shown.

[0013] refer to Figure 1 An energy-saving laboratory air purification method based on adaptive control, comprising the following steps: Obtain event information indicating that automated high-throughput processing equipment has stopped releasing pollutants; Based on event information, preset environmental parameters, and the characteristics of the pollution source, estimate the concentration decay trend of pollutants in the laboratory space; Based on the concentration decay trend, adjust the operating intensity of the purification equipment to reduce the purification intensity; Information on the concentration of environmental pollutants is obtained and compared with the concentration decay trend to correct the adjustment process of purification intensity. When the concentration of environmental pollutants reaches the preset safety level, the purification equipment will switch to a low-power operation mode.

[0014] "Automated high-throughput processing equipment" refers to automated instruments capable of large-scale, rapid sample processing in a laboratory environment, such as automated liquid handling workstations and high-throughput screening systems. These devices may release various pollutants during operation, such as volatile organic compounds (VOCs) and aerosols. "The concentration decay trend of pollutants in the laboratory space" refers to the natural decrease in the concentration of pollutants in the air over time after release, influenced by factors such as diffusion, sedimentation, and ventilation. "The operating intensity of purification equipment" typically refers to parameters such as the operating power, fan speed, and filtration efficiency of purification equipment (e.g., air purifiers, fume hoods), which directly affect purification effectiveness and energy consumption. "Low-power operation mode" refers to the mode in which purification equipment operates with minimal energy consumption while meeting basic air quality requirements.

[0015] Specifically, the implementation of this application can be described as follows: First, regarding the acquisition of event information when automated high-throughput treatment equipment stops releasing pollutants, this information can be obtained in several ways. For example, sensors can be installed on the automated high-throughput treatment equipment to monitor its operating status in real time. When the equipment completes the pollutant release operation and enters standby or shutdown mode, the sensor will trigger a signal, which serves as the event information. Another example is by connecting to the automated high-throughput treatment equipment's control system via a data interface, directly reading the command to stop pollutant release or status changes from the equipment's control log. Additionally, manual input can be used, with laboratory operators manually entering the corresponding event information on the purification system's control interface after the equipment stops releasing pollutants.

[0016] Secondly, based on event information, preset environmental parameters, and the characteristics of the pollution source, the system estimates the concentration decay trend of pollutants within the laboratory space. After acquiring event information, the system combines preset environmental parameters and the characteristics of the pollution source to estimate the concentration decay trend of pollutants. For example, preset environmental parameters may include the laboratory's volume, temperature, humidity, and ventilation rate. The characteristics of the pollution source may include the type of pollutant, initial release concentration, volatility, and particle size distribution. The estimation process can employ physical or empirical models. For instance, a mathematical model can be established based on the law of conservation of mass and the diffusion equation; after inputting the aforementioned parameters, the curve of pollutant concentration changing over time can be calculated. Alternatively, a predictive model can be trained based on historical data and machine learning algorithms; inputting current event information, environmental parameters, and pollution source characteristics, it can predict future concentration decay trends.

[0017] Secondly, the operating intensity of the purification equipment is adjusted based on the concentration decay trend to reduce the purification intensity. After estimating the pollutant concentration decay trend, the purification system adjusts the operating intensity of the purification equipment accordingly. For example, if the estimation shows that the pollutant concentration will decay rapidly, the system can immediately reduce the main fan speed of the purification equipment, thereby reducing the purification intensity. Alternatively, if the estimation shows that the pollutant concentration decays slowly, the system will maintain a higher purification intensity but gradually reduce it according to the decay trend. This adjustment can be linear or segmented; for example, when the concentration decays to a certain threshold, the purification intensity is adjusted from a high level to a medium level, and then to a low level.

[0018] Next, the system acquires environmental pollutant concentration information and compares it with concentration decay trends to adjust the purification intensity. To ensure the accuracy of the purification effect, the system continuously acquires environmental pollutant concentration information. For example, multiple pollutant sensors can be deployed in a laboratory space to monitor air pollutant concentrations in real time. These measured concentrations are then compared with previously estimated concentration decay trends. If the measured concentration is higher than the estimated trend, it indicates that the estimation may be too optimistic, and the system will increase the purification intensity accordingly; if the measured concentration is lower than the estimated trend, it indicates that the estimation may be too conservative, and the system will further reduce the purification intensity. This comparison and correction process is dynamic, ensuring that the purification intensity always matches the actual pollution level.

[0019] Finally, when the concentration of environmental pollutants reaches a preset safety level, the purification equipment switches to a low-power operation mode. After the above adjustments and corrections, when the concentration of environmental pollutants consistently and stably reaches the preset safety level, the purification system will switch the purification equipment to a low-power operation mode. For example, the safety level can be set to the pollutant concentration limit specified by national or industry standards. In low-power operation mode, the main fan speed of the purification equipment will be reduced to a minimum, maintaining only basic air circulation and filtration, thereby maximizing energy savings.

[0020] This application proposes an energy-saving laboratory air purification method based on adaptive control. Its core innovation lies in introducing an estimation and real-time correction mechanism for the pollutant concentration decay trend. Traditional purification systems typically rely on lagging sensor data for passive response. This results in a slow system response after a sudden high concentration of pollutants is released, failing to promptly increase purification intensity and exposing laboratory personnel to high-concentration exposure risks. Furthermore, after the actual pollutant concentration decreases, the system maintains high-power operation due to sensor delays, leading to energy waste.

[0021] This application, by acquiring event information indicating that an automated high-throughput treatment device has stopped releasing pollutants, can predict the pollutant decay process. Therefore, even before the pollutant concentration is fully reflected by sensors, the system can proactively and forward-lookingly adjust the operating intensity of the purification equipment based on the estimated decay trend. For example, when the automated high-throughput treatment device stops spraying pollutants, the system immediately acquires this event information and, combined with laboratory environmental parameters and pollution source characteristics, estimates that the pollutant concentration will rapidly decay. Based on this estimation, the system can quickly reduce the operating intensity of the purification equipment, avoiding the drawback of traditional systems that continue to operate at high power due to sensor lag.

[0022] Furthermore, this application introduces a real-time correction mechanism, which continuously acquires environmental pollutant concentration information and compares it with the estimated concentration decay trend. This comparison mechanism can compensate for potential errors in the estimation model, ensuring that the adjustment of the purification intensity always remains consistent with the actual pollution level. For example, if the estimation model predicts a rapid concentration decay, but actual sensor data shows a slow decrease, the system will immediately correct and appropriately increase the purification intensity to ensure safety. Conversely, if the actual concentration decreases faster than estimated, the system will further reduce the purification intensity to achieve better energy-saving effects.

[0023] The above method further considers the influence of liquid aerosols and includes the following steps: Based on event information, preset environmental parameters, characteristics of pollution sources, and airflow pattern parameters within the laboratory space, calculate the sedimentation probability and resuspension probability of liquid aerosols in a specific area within the laboratory space. When there is a high probability of liquid aerosol settling and resuspension in a specific area, a local turbulence strategy is activated. This involves periodically increasing the speed of the main fan of the purification equipment or briefly activating the local exhaust vents in the laboratory space to cause the settled liquid aerosols to resuspension. During the process of adjusting the purification intensity, environmental pollutant concentration information is continuously acquired and compared with the concentration decay trend to correct the purification intensity adjustment process. Before switching the purification equipment to a low-power operating mode, check the implementation of the local turbulence strategy and the predicted risk of liquid aerosol residue. When the concentration of environmental pollutants reaches the preset safety level, the local turbulence strategy has been implemented, and the risk of liquid aerosol residue is reduced, the purification equipment will be switched to a low-power operation mode.

[0024] Calculating the settling and resuspension probabilities of liquid aerosols in specific areas of a laboratory space can be understood as using fluid dynamics models and particulate dynamics principles, combined with environmental parameters such as the laboratory's geometry, temperature, humidity, and airflow velocity distribution, as well as the characteristics of the pollution source (e.g., the type and particle size distribution of the solvent sprayed by automated high-throughput treatment equipment), to predict the deposition rate and adhesion strength of liquid aerosol particles on specific surfaces (such as workbenches, walls, and floors) under the influence of gravity, Brownian motion, and turbulent diffusion, and the probability of them detaching from the surface and re-entering the air under conditions such as airflow disturbance and temperature changes. For example, computational fluid dynamics (CFD) simulations can be used to obtain detailed airflow pattern parameters within the laboratory space, and calculations can be performed using particulate settling models (such as Stokes' law of settling) and resuspension models (such as shear-force-based resuspension models). The aim is to accurately assess the dynamic behavior of liquid aerosols and avoid purification blind spots or secondary pollution caused by their settling and resuspension characteristics.

[0025] In practical applications, when there is a high probability of liquid aerosol sedimentation and resuspension in a specific area, activating a local turbulence strategy involves actively intervening to reintroduce settled liquid aerosols into the airflow so that purification equipment can capture and remove them. Specifically, periodically increasing the main fan speed of the purification equipment can be understood as temporarily increasing the fan's output power, thereby increasing the air circulation speed and turbulence intensity throughout the laboratory space. This causes liquid aerosol particles adhering to surfaces to detach due to increased airflow shear force. Briefly activating local exhaust vents in the laboratory space, for example, involves setting up a local exhaust system in a specific area (such as near a pollution source). This system is briefly opened when needed, creating local negative pressure and strong airflow to draw sediment into the exhaust system or promote its resuspension. The aim is to proactively address the sedimentation characteristics of liquid aerosols, ensuring their effective capture and preventing residue.

[0026] Furthermore, during the purification intensity adjustment process, environmental pollutant concentration information is continuously acquired and compared with the concentration decay trend to correct the purification intensity adjustment process. This is similar to the correction process for general pollutants in the above method, but in this context, the focus of the correction is whether the actual concentration change of liquid aerosols conforms to the predicted decay trend. Especially after the local turbulence strategy is activated, it is necessary to monitor whether it effectively promotes the resuspension and removal of liquid aerosols.

[0027] Furthermore, before switching the purification equipment to low-power operation mode, the implementation of local turbulence strategies and the predicted risk of liquid aerosol residue should be checked. This can be understood as follows: before deciding to enter energy-saving mode, the system needs to confirm that all turbulence measures aimed at removing liquid aerosols have been executed as planned, and assess whether the residual amount of liquid aerosols in the current environment has been reduced to an acceptable risk level based on model predictions or actual monitoring data. For example, sampling and analysis of specific areas can be performed using additional sensor arrays, or a more refined predictive model can be used to assess the residual risk. The purpose is to ensure that liquid aerosols have been adequately removed when switching to low-power mode, avoiding potential secondary pollution risks.

[0028] Finally, when the concentration of environmental pollutants reaches the preset safety level, the local turbulence strategy has been implemented, and the risk of residual liquid aerosols is reduced, the purification equipment will switch to a low-power operation mode. This means that the system will only enter energy-saving mode when all conditions are met, including that the concentration of conventional pollutants meets the standards, special treatment measures for liquid aerosols have been completed and the risks are controllable, thus ensuring the thoroughness of purification and the safety of operation.

[0029] This application's solution, by introducing the calculation of the sedimentation and resuspension probabilities of liquid aerosols, enables a more comprehensive assessment of the pollution status within laboratory spaces. Traditional concentration decay models may fail to accurately predict the concentration rebound caused by the resuspension of liquid aerosols after sedimentation, potentially leading to the purification equipment prematurely entering a low-power operation mode and leaving potential pollution hazards. By calculating the sedimentation and resuspension probabilities, the system can identify areas where liquid aerosols may persist or recur. When a high probability of sedimentation and resuspension risk is identified, a local turbulence strategy is activated, such as periodically increasing the main fan speed of the purification equipment or briefly activating local exhaust vents. This proactive approach actively reintroduces the settled liquid aerosols into the airflow, allowing them to be captured and removed by the purification equipment. This proactive intervention mechanism overcomes the shortcomings of traditional passive purification modes, ensuring the complete removal of liquid aerosols. Meanwhile, during the purification intensity adjustment process, the system continuously acquires environmental pollutant concentration information and compares it with the attenuation trend. In addition, before switching to the low power consumption mode, it checks the implementation of the local turbulence strategy and the risk of liquid aerosol residue. This further enhances the system's adaptability and safety, ensuring that the energy-saving mode is switched only after all pollutants (including liquid aerosols) have reached a safe level.

[0030] In some preferred embodiments, suppose a laboratory uses an automated high-throughput processing device for drug screening, which sprays liquid aerosols containing specific chemicals during operation. When the device stops spraying the contaminants, the system acquires event information.

[0031] First, the system calculates the settling and resuspension probabilities of liquid aerosols in specific areas such as workbenches and floors based on the event information, preset environmental parameters such as laboratory temperature, humidity, and ventilation rate, as well as the pollution source characteristics such as the volatility and particle size distribution of the solvent used, combined with the laboratory's CAD model and airflow pattern parameters obtained through pre-calculated CFD simulations. For example, the calculation results show that near workbenches, due to the lower airflow velocity, the settling probability of liquid aerosols is higher, and the resuspension probability is also higher when people walk or equipment vibrates.

[0032] Given this high probability of sedimentation and resuspension, the system will activate a localized turbulence strategy. Specifically, the main fan speed of the purification equipment will be periodically increased, for example, every 10 minutes the speed will be increased from the usual 500 RPM to 800 RPM and maintained for 30 seconds to generate pulsed airflow to wash the workbench surface. At the same time, if the laboratory is equipped with local exhaust vents, such as exhaust hoods installed above the workbench, these vents will also be briefly activated to enhance localized airflow turbulence, causing settled liquid aerosols to be resuspended in the air.

[0033] Throughout the purification intensity adjustment process, environmental pollutant concentration sensors continuously monitor the pollutant concentration in the laboratory and compare the real-time data with the concentration predicted based on the initial decay trend model. If the actual concentration decreases more slowly than expected, or if there is a brief rebound after turbulence, the system will adjust the operating intensity of the purification equipment, such as appropriately extending the high-intensity operation time or increasing the turbulence frequency.

[0034] Before switching the purification equipment to low-power operation mode, the system performs a final check. First, it confirms that all preset local turbulence strategies (such as increasing the main fan speed and activating local exhaust vents) have been executed as planned. Second, the system predicts the residual risk of liquid aerosols in the current environment based on the latest environmental pollutant concentration data and liquid aerosol sedimentation / resuspension models. For example, the system will only determine that the switching conditions are met if the prediction model shows that the residual risk has dropped to less than one ten-thousandth and the environmental pollutant concentration sensor shows that the concentration is below the safety threshold.

[0035] Ultimately, the purification equipment will be safely switched to a low-power operation mode only when the concentration of environmental pollutants reaches the preset safety level, the local turbulence strategy has been implemented, and the risk of liquid aerosol residue is assessed as an acceptablely low level, thereby achieving energy saving while ensuring thorough purification.

[0036] The method also includes the following steps: Based on event information, preset environmental parameters, characteristics of pollution sources, and airflow pattern parameters within the laboratory space, calculate the duration of liquid aerosol in a specific area within the laboratory space. Based on event information, the characteristics of the pollution source, the sedimentation probability, resuspension probability, and duration of liquid aerosols in a specific area of ​​the laboratory space, the preset main fan speed under low power operation mode, and the capture efficiency curve of the filter screen in the purification equipment, the rate of continuous accumulation of particles of a specific size in the filter screen and the potential clogging risk are calculated. When the rate at which particles of a specific size accumulate in the filter and the potential risk of clogging indicate a high risk of particle accumulation of that specific size, the filter enters a protective operating mode. In the filter protection mode, the main fan speed of the purification equipment is maintained at the set level to ensure that the filter maintains sufficient capture efficiency for particles of a specific size. The main fan speed of the purification equipment is periodically increased to generate pulsed airflow to remove loosely attached particles from the surface of the filter screen. Monitor the differential pressure data of the filter screen; When the differential pressure data of the filter indicates an increased risk of filter clogging, a filter replacement warning is issued. When the risk of particle accumulation of a specific size decreases and the differential pressure data of the filter screen stabilizes, switch the purification equipment to a low-power operation mode.

[0037] Specifically, calculating the duration of liquid aerosols in a specific area of ​​the laboratory space refers to accurately predicting the length of time that liquid aerosols will remain suspended in the air and potentially be captured by the filter, by comprehensively considering the release characteristics of pollutants, laboratory ventilation conditions, temperature, humidity, and other environmental factors, as well as airflow organization. This duration is a crucial input parameter for assessing the cumulative risk of the filter. Calculating the rate of continuous accumulation of particles of a specific size in the filter and the potential clogging risk can be understood as establishing a dynamic model based on the aforementioned duration, the sedimentation probability and resuspension probability of the liquid aerosols, the main fan speed of the purification equipment, and the filter's capture efficiency curve. This model predicts the deposition rate and accumulation amount of particles of different sizes on the filter, thereby assessing the degree of clogging and risk. For example, mass balance equations or particle dynamics models can be used for calculation. In practical applications, when calculation results indicate a high risk of accumulation of particles of a specific size, the system will automatically enter a filter protection operation mode. In this mode, the main fan speed of the purification equipment will be maintained at a preset level that ensures the efficiency of the filter screen to avoid a decrease in capture efficiency due to excessively low wind speed, while providing a stable airflow basis for subsequent cleaning operations.

[0038] Furthermore, the main fan speed of the purification equipment is periodically increased to generate pulsed airflow to remove loosely attached particles from the filter surface. The purpose is to use short-duration, high-intensity airflow in either the reverse or forward direction to blow off particles that are not yet firmly bonded to the filter surface, thereby extending the filter's lifespan and maintaining its capture efficiency. In addition, the differential pressure data of the filter is monitored in real time by differential pressure sensors installed on both sides of the filter. This data is a direct indicator of the degree of filter clogging. When the differential pressure data continues to rise and reaches a preset threshold, it indicates an increased risk of filter clogging. At this point, the system will issue a filter replacement warning, reminding maintenance personnel to intervene. Finally, when the risk of accumulated particles of a specific size is effectively controlled through measures such as pulsed airflow scouring, and the differential pressure data of the filter returns to a stable and safe range, the purification equipment will switch back to a low-power operation mode to achieve energy-saving goals.

[0039] This application's solution effectively addresses the issue of filter efficiency degradation or clogging due to long-term operation in the aforementioned basic solutions by introducing a dynamic assessment and proactive management mechanism for the risk of particulate accumulation on the filter. Specifically, by calculating the duration of liquid aerosols, the particulate accumulation rate, and the potential clogging risk, the system can predict the filter's health status. When a high risk is detected, a protective operating mode for the filter is immediately activated, maintaining the main fan speed to ensure capture efficiency and periodically removing loose particles through pulsed airflow. This is equivalent to "self-cleaning" the filter, thus delaying clogging. Simultaneously, real-time monitoring of the filter's differential pressure data provides a scientific basis for filter replacement, avoiding over-cleaning or under-cleaning. Therefore, the entire purification system can more intelligently manage the filter's lifecycle, ensuring energy savings while maintaining high-efficiency air purification performance.

[0040] In some preferred embodiments, it is assumed that the automated high-throughput treatment equipment stops releasing pollutants after completing a single spraying experiment. The system first calculates the duration of the liquid aerosol generated during the experiment in a specific area of ​​the laboratory based on event information, laboratory environmental parameters, characteristics of the pollution source, and airflow pattern parameters. Then, combining this information, the preset main fan speed in low-power operation mode, and the capture efficiency curve of the filter in the purification equipment, the system predicts the accumulation rate of particles of a specific size on the filter and the potential risk of clogging. If the calculation indicates a high risk of particle accumulation, the system immediately enters a filter protection operation mode. In this mode, the main fan speed of the purification equipment is maintained at, for example, 800 rpm to ensure continuous particle capture. Simultaneously, the system periodically increases the main fan speed to 1200 rpm for 10 seconds every 30 minutes to generate a pulsed airflow to remove loosely attached particles from the filter surface. During this process, the system continuously monitors the differential pressure data of the filter. If the differential pressure continues to rise after flushing, for example, by more than 5 Pa per hour, and reaches the preset replacement threshold (e.g., 150 Pa), the system will issue a filter replacement warning. Conversely, if the risk of particulate accumulation decreases after multiple pulsed airflow flushes, and the differential pressure of the filter stabilizes within a safe range (e.g., below 100 Pa), the system will switch the purification equipment back to a low-power operation mode, for example, reducing the main fan speed to 500 rpm to continue energy-efficient operation.

[0041] The steps described above for removing loosely attached particles from the filter surface by generating pulsed airflow include: Obtain information on the solvent type and spray volume of the automated high-throughput processing equipment in this spraying operation; Based on solvent type information, spray volume information, preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters, calculate the pulse airflow scouring intensity and duration required to remove particles; Adjust the increase in the main fan speed and the duration of the pulsed airflow scouring based on the intensity and duration of the pulsed airflow scouring. Obtain information on the rate of change of pressure difference on the filter screen; Adjust the pulsed airflow scouring frequency based on the pressure difference change rate information of the filter screen.

[0042] Specifically, solvent type information refers to the types of chemical reagents used by the automated high-throughput processing equipment during the experiment, such as water, ethanol, acetone, or other organic solvents. Spray volume information refers to the total amount of solvent released by the equipment within a specific operating cycle. This information can be obtained through data interface communication with the control system of the automated high-throughput processing equipment or manually entered by the operator. Its purpose is to provide basic data for subsequent particulate characteristic analysis, as different solvents produce particles with different physicochemical properties after drying. Among these, particulate adhesion characteristic parameters can be understood as the magnitude of the adhesion force between the particles and the filter surface, such as van der Waals forces, electrostatic forces, or capillary forces. These parameters are usually related to the solvent type and the chemical composition of the particles. Agglomeration tendency parameters refer to the tendency of particles to form clumps on the filter surface, which affects the difficulty of removal. These preset parameters can be stored based on experimental data, theoretical models, or empirical values. By integrating this information, the required airflow impact force (intensity) and duration can be accurately calculated to effectively and gently peel the particles from the filter surface. In practical applications, the increase in the main fan speed of the purification equipment directly determines the intensity of the pulsed airflow rinsing, while the duration of the pulsed airflow rinsing controls the length of time the airflow acts on the filter. These adjustment parameters are sent to the control unit of the purification equipment to precisely execute the calculated rinsing strategy. The purpose is to ensure that the actual rinsing operation matches the calculation results, thereby achieving customized cleaning effects. The filter differential pressure change rate information refers to the rate of change of the pressure difference across the filter over a period of time. This information can be collected in real time by differential pressure sensors installed on both sides of the filter and processed and analyzed by the control system. Its purpose is to reflect the dynamic changes in the degree of filter clogging and particle accumulation in real time, providing a basis for dynamically adjusting the rinsing frequency. Specifically, if the differential pressure change rate is fast, it indicates a fast particle accumulation rate, requiring more frequent pulsed airflow rinsing to prevent premature filter clogging. Conversely, if the differential pressure change rate is slow, the rinsing frequency can be appropriately reduced to save energy and extend equipment life. The purpose is to achieve adaptive adjustment of the rinsing frequency to cope with different pollution loads.

[0043] This application's solution, by acquiring information on the solvent type and spray volume of the automated high-throughput processing equipment, accurately grasps the characteristics of the pollution source. Furthermore, by combining preset particle adhesion and aggregation tendency parameters, it conducts an in-depth analysis of the physicochemical properties of the particles on the filter screen surface. This allows for precise calculation of the pulsed airflow rinsing intensity and duration required to remove these specific particles, avoiding the insufficient or excessive rinsing problems that may occur with fixed-parameter rinsing in traditional solutions. Moreover, by acquiring real-time information on the filter screen's differential pressure change rate, the system can dynamically sense the filter screen's clogging status and adaptively adjust the pulsed airflow rinsing frequency accordingly. This adaptive control mechanism based on multi-dimensional information feedback enables the pulsed airflow rinsing process to precisely match the actual pollution status and particle characteristics of the filter screen, thereby ensuring cleaning effectiveness while minimizing filter screen wear and energy consumption.

[0044] In some preferred embodiments, it is assumed that the automated high-throughput processing equipment sprays a large amount of solvent containing viscous polymers in an experiment, resulting in the formation of highly adhesive particle clumps on the filter surface. The system first acquires information about the solvent type and the spray volume. Based on preset particle adhesion characteristic parameters and preset agglomeration tendency parameters, the system calculates that a higher pulsed airflow rinsing intensity and a longer duration are required to effectively remove these particles. For example, the main fan speed of the purification equipment is increased by 80% of its maximum value for a duration of 5 seconds. During the rinsing process, if the differential pressure sensor detects that the rate of pressure change on the filter screen is still relatively fast, indicating that particle removal is incomplete or that new particles are accumulating rapidly, the system will immediately adjust the rinsing frequency, for example, shortening the rinsing interval from the original 30 minutes to 15 minutes, to perform cleaning more frequently. Conversely, if the equipment sprays a volatile, non-sticky solvent, resulting in less adhesive particles, the system will calculate a lower flushing intensity and shorter duration. For example, the main fan speed of the purification equipment will be increased to 40% of its maximum value for 2 seconds. Furthermore, when the rate of pressure change is low, the flushing frequency will be adjusted to longer intervals, such as once every 60 minutes. This dynamic adjustment ensures that the filter receives optimal cleaning under different pollution conditions, thereby achieving energy-efficient operation.

[0045] The steps described above for calculating the required pulsed airflow intensity and duration for particulate removal include: Obtain surface vibration response data of the filter screen; The surface vibration response data of the filter screen is compared with the preset filter screen state mode; When the surface vibration response data of the filter screen differs from the preset filter screen state mode, it is determined that the surface physicochemical properties of the filter screen have changed. Based on the changes in the physicochemical properties of the filter screen surface, adjust the solvent type information, spray volume information, preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters; Based on the adjusted solvent type information, spray volume information, preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters, the pulsed airflow scouring intensity and duration required to remove particles are calculated.

[0046] Specifically, acquiring surface vibration response data of the filter screen refers to collecting vibration signals of the filter screen in real time during operation using sensors installed on the screen. These vibration signals reflect the mass distribution, stiffness changes, and characteristics of surface deposits on the filter screen. The preset filter screen state mode can be understood as a baseline of vibration characteristics when the filter screen is in a clean state or a known specific contamination state, such as its natural frequency and amplitude. Comparing the acquired surface vibration response data with this preset mode aims to identify the deviation between the current state of the filter screen and the baseline state.

[0047] When the comparison results show a difference between the filter's surface vibration response data and the preset filter state mode, it indicates that the surface physicochemical properties of the filter may have changed. Such changes may include increased mass due to particle accumulation, altered surface roughness, enhanced particle adhesion, or aging of the filter material itself. Based on these assessments, the solvent type, spray volume, preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters used to calculate the rinsing intensity can be adjusted. For example, if it is determined that there is strong particle accumulation with high adhesion, the solvent type can be adjusted or the spray volume increased; if it is determined that the particle agglomeration tendency is enhanced, the agglomeration tendency parameter can be adjusted to optimize the rinsing strategy. Therefore, based on the adjusted parameters, the pulsed airflow rinsing intensity and duration required to remove particles are recalculated to ensure that the rinsing parameters more accurately match the current actual state of the filter.

[0048] This application's solution achieves dynamic sensing of changes in the physicochemical properties of the filter screen surface by introducing real-time monitoring and analysis of the filter screen surface vibration response data. When the mass, stiffness, or adhesion characteristics of the filter screen surface change due to particle accumulation or material changes, its vibration response data will show detectable changes accordingly. By comparing these real-time data with preset filter screen state modes, the system can accurately determine whether and how the physicochemical properties of the filter screen surface have changed. It is precisely because of this real-time state feedback that the system can adaptively adjust the solvent type information, spray volume information, preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters required for particle removal according to the actual condition of the filter screen. This adjustment ensures that the subsequently calculated pulsed airflow scouring intensity and duration can accurately act on the filter screen, thereby effectively removing attached particles and avoiding insufficient or excessive cleaning due to parameter mismatch.

[0049] In some preferred embodiments, it is assumed that after long-term operation, the filter screen in the laboratory purification system accumulates a large number of dried, solid, sticky particles on its surface due to the treatment of experimental waste gas containing highly viscous liquid aerosols. These particles increase the surface mass of the filter screen, change its local stiffness, and enhance the tendency for particle aggregation. At this time, the system acquires the surface vibration response data of the filter screen. By performing spectral analysis on this data, it is found that its natural frequency has shifted significantly downward, and the amplitude of certain high-frequency vibration modes has significantly increased, which is significantly different from the preset clean filter screen state mode. Based on this, the system determines that the surface physicochemical properties of the filter screen have changed, specifically manifested as increased surface mass and enhanced adhesion. Based on this determination, the system adjusts the relevant rinsing parameters. For example, the solvent type information may be adjusted to a more soluble solvent, the spray volume information may be increased, the preset particle adhesion characteristic parameters may be corrected to a higher adhesion strength value, and the preset aggregation tendency parameters may also be adjusted to adapt to the new particle morphology. Subsequently, the system recalculates the pulsed airflow rinsing intensity and duration required to remove the particles based on these adjusted parameters. For example, calculations might indicate the need for a higher fan speed and a longer flushing duration to generate a stronger, more sustained pulsed airflow that effectively strips away and removes these stubborn, sticky particles. This adaptive adjustment ensures the filter is thoroughly cleaned and its proper capture efficiency is restored.

[0050] Specifically, the steps for obtaining the surface vibration response data of the filter screen may include the following operations: Multiple vibration sensors are installed on the filter screen frame, and at least one environmental vibration sensor is installed in the laboratory space away from the filter screen. During the operation of the main fan of the purification equipment, response data from all vibration sensors and environmental vibration sensors are collected simultaneously. Spectral analysis was performed on the response data of the vibration sensor to identify the vibration components related to the operating frequency of the main fan of the purification equipment, and the vibration response signal of the filter screen was extracted. Spectral analysis was performed on the response data of the environmental vibration sensor to identify the background vibration components related to the operation of the non-clean equipment, and the vibration signals of the background vibration components were extracted. The surface vibration response data of the filter screen is obtained by subtracting the background vibration component from the vibration response signal of the filter screen.

[0051] Specifically, vibration sensors, such as piezoelectric sensors, accelerometers, or laser Doppler vibrometers, are positioned at various locations on the filter frame, such as the edges, center, or support structure, to comprehensively capture the overall and local vibration characteristics of the filter. Environmental vibration sensors are placed within the laboratory space, but at a certain distance from the cleanroom equipment, to effectively collect background vibrations generated by non-cleanroom equipment operation, such as vibrations from other laboratory equipment, HVAC systems, or the external environment. Synchronous acquisition means acquiring data from all vibration sensors and environmental vibration sensors within the same time window, at the same sampling frequency and accuracy. This ensures time alignment between filter vibration and background vibration during subsequent data processing, thereby improving the accuracy of background noise cancellation.

[0052] Furthermore, spectral analysis can employ methods such as Fast Fourier Transform (FFT) to convert time-domain signals into frequency-domain signals. The main fan of the purification equipment generates specific vibration frequencies and their harmonics during operation. These frequency components are typically closely related to the forced vibration of the filter screen. By identifying these components related to the main fan's operating frequency, the filter screen's own response can be effectively distinguished from random noise. Similarly, spectral analysis of the response data from environmental vibration sensors can identify inherent vibration frequencies in the laboratory environment or vibration frequencies caused by other equipment. These vibration components, not related to the operation of the purification equipment, constitute background noise and need to be accurately identified and extracted. Finally, by subtracting the extracted background vibration signal from the filter screen's vibration response signal, the interference of environmental noise can be effectively eliminated, resulting in purer and more accurate vibration response data of the filter screen surface. This differential processing helps to highlight subtle vibration changes in the filter screen caused by factors such as particle accumulation and material variations, providing a reliable basis for subsequent physicochemical property judgments.

[0053] This application's solution achieves synchronous, multi-point acquisition of both the filter's own vibration and ambient background vibration by installing multiple vibration sensors on the filter frame and at least one environmental vibration sensor in a laboratory space away from the filter. During the operation of the main fan of the purification equipment, the response data of all sensors are collected synchronously, ensuring data temporal consistency. Subsequently, by performing spectral analysis on the vibration sensor response data, the filter vibration response signal related to the main fan's operating frequency can be identified and extracted, reflecting the dynamic characteristics of the filter under working conditions. Simultaneously, spectral analysis of the ambient vibration sensor response data can identify and extract background vibration signals not related to the operation of the purification equipment. Finally, by subtracting the background vibration component from the filter's vibration response signal, interference from environmental noise is effectively eliminated, resulting in purer and more accurate filter surface vibration response data. This processing method allows for the clear capture of minute vibration changes in the filter, providing reliable input data for subsequent assessment of changes in the filter's surface physicochemical properties.

[0054] This application further proposes a step for determining changes in the surface physicochemical properties of the filter screen, which includes: Spectral analysis was performed on the surface vibration response data of the filter screen to identify the vibration components related to the inherent resonant frequency of the filter screen material. Identify vibrational components associated with changes in surface quality and stiffness resulting from the accumulation of particles of specific sizes; By comparing the amplitude and frequency shift of the vibration components related to the inherent resonant frequency of the filter material, and the amplitude and frequency shift of the vibration components related to the changes in surface quality and stiffness caused by the accumulation of particles of a specific size, it can be determined whether the changes in the surface physicochemical properties of the filter are caused by the accumulation of particles of a specific size or by material aging or microscopic damage. When the changes in the surface physicochemical properties of the filter screen are determined to be caused by the accumulation of particles of a specific size, the spectral features related to the accumulation of particles of a specific size are extracted to reflect the surface physicochemical properties of the filter screen.

[0055] Specifically, spectral analysis of the surface vibration response data of a filter screen involves using signal processing techniques such as Fourier transform to convert the time-domain vibration signal into a frequency-domain spectrum, thereby revealing the various frequency components contained in the vibration signal and their corresponding energy or amplitude. Among these, the vibration components related to the inherent resonant frequencies of the filter screen material refer to the inherent vibration characteristics of the material itself when it is not affected by external loads or contamination. These characteristics are typically related to physical parameters such as the material's elastic modulus, density, and geometry. Identifying these inherent resonant frequency components can serve as a benchmark for judging the condition of the filter screen substrate.

[0056] Furthermore, identifying vibrational components related to changes in surface mass and stiffness caused by the accumulation of particles of a specific size refers to inferring whether particles are attached to the filter surface by analyzing new vibrational peaks or shifts in existing peaks in the spectrum. When particles accumulate on the filter surface, they increase the mass of the filter and may change its local stiffness, thereby causing changes in the overall vibrational characteristics of the filter, such as a decrease in the natural resonant frequency or the emergence of new vibrational modes.

[0057] Based on this, by comparing the amplitude and frequency shifts of vibration components related to the inherent resonant frequency of the filter material, and the amplitude and frequency shifts of vibration components related to changes in surface quality and stiffness caused by the accumulation of particles of a specific size, the specific causes of changes in the physicochemical properties of the filter surface can be determined. For example, if the amplitude and frequency shifts of the inherent resonant frequency components are significant, and the vibration components related to particle accumulation are not obvious, it may indicate material aging or microscopic damage; conversely, if the vibration components related to particle accumulation are significant, it indicates that the main problem is particle accumulation. This comparative analysis aims to distinguish vibration characteristics caused by different reasons, thereby achieving a refined diagnosis of the filter's condition.

[0058] When changes in the surface physicochemical properties of a filter are determined to be caused by the accumulation of particles of a specific size, it is necessary to extract the spectral characteristics associated with this accumulation. These characteristics can be energy distribution, peak frequency, peak amplitude, or bandwidth within a specific frequency range. They can quantitatively reflect the degree, type, or morphology of particle accumulation, providing data support for subsequent cleaning strategies.

[0059] This application's solution, through refined spectral analysis of the surface vibration response data of the filter screen, can attribute changes in the physicochemical properties of the filter screen surface to specific causes. Specifically, by identifying and distinguishing vibrational components related to the inherent resonant frequencies of the filter screen material and vibrational components related to changes in surface quality and stiffness caused by the accumulation of particles of specific sizes, the system can establish a multi-dimensional diagnostic model. Changes in the inherent resonant frequency components mainly reflect the structural integrity and aging degree of the filter screen substrate, while vibrational components related to particle accumulation directly indicate the adhesion of contaminants. It is precisely because of this differentiated spectral analysis and comparison mechanism that the system can accurately determine whether changes in the physicochemical properties of the filter screen surface are caused by particle accumulation or by material aging or microscopic damage. This precise attribution capability avoids potential misjudgments in traditional methods, thus providing a reliable basis for subsequent adaptive cleaning strategies.

[0060] In some preferred embodiments, it is assumed that after a period of operation, the surface vibration response data of the filter screen in the laboratory purification system differs from a preset health state mode. The system first performs spectral analysis on the collected filter screen surface vibration response data. In the spectral graph, two key components are identified: one part is the vibrational component related to the inherent resonant frequencies of the filter screen material (e.g., at 100Hz and 250Hz), and the other part is the vibrational component related to changes in surface quality and stiffness caused by the accumulation of particles of a specific size (e.g., the appearance of new vibrational peaks at 50Hz and 150Hz or a significant increase in the amplitude of existing peaks). Subsequently, the system compares and analyzes these identified vibrational components. For example, if the amplitude and frequency shift of the inherent resonant frequency component are relatively small, but the amplitude of the vibrational component related to particle accumulation increases significantly and the frequency shift is obvious, the system determines that the change in the physicochemical properties of the filter screen surface is mainly caused by the accumulation of particles of a specific size. In this case, the system extracts these spectral features related to particle accumulation, such as the peak amplitude and frequency shift at 50Hz and 150Hz, as a basis for reflecting the degree and type of particle accumulation. Conversely, if the amplitude and frequency shift of the inherent resonant frequency components are large, while the changes in the vibrational components related to particle accumulation are not significant, the system may determine that the change is caused by material aging or microscopic damage, and may trigger different processing procedures, such as issuing a replacement warning instead of a cleaning instruction. In this way, the solution of this application can accurately diagnose the actual problems of the filter, thereby guiding subsequent maintenance operations.

[0061] This application further proposes steps for determining whether changes in the surface physicochemical properties of a filter screen are caused by the accumulation of particles of a specific size, including: Differential analysis is performed on the amplitude and frequency shifts of vibration components related to the inherent resonant frequency of the filter material, as well as the amplitude and frequency shifts of vibration components related to changes in surface quality and stiffness caused by the accumulation of particles of a specific size, to determine whether there are specific amplitude ratios or frequency shift patterns. Specific amplitude ratios or frequency shift patterns indicate that material aging or micro-damage occurs simultaneously with particle accumulation. When a specific amplitude ratio or frequency offset mode is determined to exist, the contribution weight of material aging or micro-damage to the amplitude and frequency offset of the vibration component related to the inherent resonant frequency of the filter material is calculated based on the filter's operating time, historical maintenance records, and preset material aging rate parameters. The amplitude and frequency shift of the vibration components related to the inherent resonant frequency of the filter material are obtained by subtracting the amplitude and frequency shift corresponding to the contribution weight from the amplitude and frequency shift of the vibration components related to the inherent resonant frequency of the filter material. The amplitude and frequency shift of the vibrational components related to the inherent resonant frequency of the treated filter material were compared with the amplitude and frequency shift of the vibrational components related to the changes in surface quality and stiffness caused by the accumulation of particles of a specific size. When the amplitude and frequency shift of the vibration component related to the inherent resonant frequency of the treated filter material are less than the amplitude and frequency shift of the vibration component related to the changes in surface quality and stiffness caused by the accumulation of particles of a specific size, it is determined that the change in the surface physicochemical properties of the filter is caused by the accumulation of particles of a specific size.

[0062] Specifically, the aforementioned differential analysis refers to in-depth mathematical and pattern recognition analysis of the amplitude and frequency shifts of the two vibration components. For example, machine learning algorithms can be used to train historical data to identify unique amplitude ratios or frequency shift patterns when material aging or microscopic damage and particle accumulation occur simultaneously. These patterns may manifest as changes in energy distribution within a specific frequency range, enhancement or attenuation of harmonic components, etc. The aim is to improve the accuracy of determining the causes of changes in the physicochemical properties of the filter surface.

[0063] The calculation of the contribution weights of material aging or microscopic damage to the amplitude and frequency shift of vibrational components related to the inherent resonant frequency of the filter material can be understood as establishing a mathematical model based on the filter's operating time, historical maintenance records, and preset material aging rate parameters. For example, the impact of material aging on the inherent resonant frequency and amplitude can be estimated based on the filter's cumulative operating hours, extreme environmental conditions experienced (such as high temperature, high humidity, and exposure to corrosive gases), and known material fatigue curves. Historical maintenance records can provide information on filter replacement, cleaning, or repair, further calibrating the aging model. The preset material aging rate parameters are determined based on experimental data or industry standards for different filter materials. The purpose is to quantify the influence of non-particulate accumulation factors on the vibration signal.

[0064] In practical applications, the amplitude and frequency shifts corresponding to the contribution weights are subtracted from the amplitude and frequency shifts of the vibration components related to the inherent resonant frequency of the filter material to obtain the processed amplitude and frequency shifts of the vibration components related to the inherent resonant frequency of the filter material. This refers to using signal processing techniques to separate the vibration signal components caused by material aging or microscopic damage from the total vibration signal. For example, if aging causes a 5Hz decrease in the inherent frequency and a 10% decrease in amplitude, these changes are subtracted from the current measurements during analysis, resulting in a signal that more purely reflects the effects of particle accumulation. The purpose is to eliminate interference from non-particle accumulation factors, making subsequent comparisons more accurate.

[0065] Comparing the amplitude and frequency shifts of vibrational components related to the inherent resonant frequency of the processed filter material with those related to changes in surface quality and stiffness caused by the accumulation of particles of a specific size involves comparing the remaining vibrational signals after excluding the effects of material aging or microscopic damage. For example, correlation coefficients, Euclidean distances, or other similarity metrics can be calculated. The aim is to determine whether particle accumulation is the dominant factor based on a purer signal.

[0066] When the amplitude and frequency shift of the vibrational components related to the inherent resonant frequency of the treated filter material are less than the amplitude and frequency shift of the vibrational components related to the changes in surface quality and stiffness caused by the accumulation of particles of a specific size, it can be determined that the changes in the surface physicochemical properties of the filter are caused by the accumulation of particles of a specific size. This indicates that after excluding other interfering factors, the effect of particle accumulation on the vibrational characteristics of the filter is more significant, thus allowing for a more reliable determination that particle accumulation is the main cause of changes in the surface physicochemical properties of the filter.

[0067] This application's solution addresses the challenge of accurately determining the primary cause of changes in the physicochemical properties of a filter surface when aging or microscopic damage occurs simultaneously with particle accumulation. Specifically, differential analysis identifies specific vibration modes under the combined influence of multiple factors, providing a basis for subsequent quantitative analysis. Contribution weight calculation, based on the filter's operational history and material properties, quantifies the inherent impact of material aging or microscopic damage on the vibration signal, allowing for precise assessment of the influence of these non-particle accumulation factors. By subtracting the amplitude and frequency shifts corresponding to these contribution weights from the original vibration signal, the signal components caused by material aging or microscopic damage can be effectively extracted, resulting in a purer vibration signal reflecting the impact of particle accumulation. Finally, by comparing the processed inherent resonant frequency-related vibration components with those related to changes in surface quality and stiffness caused by particle accumulation, it is possible to more accurately determine whether particle accumulation is the main cause of changes in the physicochemical properties of the filter surface. This step-by-step extraction and comparison method avoids the confusion and misjudgment that may occur in traditional methods, significantly improving diagnostic accuracy.

[0068] In some preferred embodiments, assuming that the filter in a laboratory purification system has been operating for an extended period, spectral analysis of its surface vibration response data shows that both the amplitude and frequency have changed near the inherent resonant frequency of the filter material. Simultaneously, significant vibrational components were also observed in the frequency range associated with the accumulation of particles of a specific size.

[0069] First, the system performs differential analysis on these vibration components. Through a pre-trained machine learning model, the system identifies a specific amplitude ratio and frequency shift pattern in the current vibration mode, indicating that material aging and particle accumulation may occur simultaneously.

[0070] Next, the system calculates the contribution weight of material aging or microscopic damage to the amplitude and frequency shift of the vibrational components related to the current natural resonant frequency, based on the filter's cumulative operating time (e.g., 20,000 hours), historical maintenance records (e.g., last replacement 1 year ago, with no special cleaning performed during that period), and the filter material's preset aging rate parameters (e.g., a 2Hz decrease in natural frequency and a 5% decrease in amplitude every 10,000 hours). Assuming the calculation results show that material aging leads to a 4Hz decrease in natural frequency and a 10% decrease in amplitude, the system can be used to calculate the contribution weight of material aging or microscopic damage to the amplitude and frequency shift of the vibrational components related to the current natural resonant frequency.

[0071] The system then subtracts the 4Hz frequency drop and 10% amplitude attenuation from the currently measured intrinsic resonant frequency-related vibration component, resulting in a "processed" intrinsic resonant frequency-related vibration component. This processed signal more purely reflects the influence of non-aging factors (i.e., particle accumulation) on the intrinsic resonant frequency.

[0072] Finally, the system compares the amplitude and frequency shift changes of the vibrational components related to the processed intrinsic resonant frequency with the amplitude and frequency shift changes of the vibrational components related to changes in surface quality and stiffness directly caused by the accumulation of particles of a specific size. If the change in the vibrational components related to the processed intrinsic resonant frequency is found to be less than the change in amplitude and frequency shift of the vibrational components related to changes in surface quality and stiffness caused by the accumulation of particles of a specific size, the system determines that the changes in the physicochemical properties of the current filter surface are mainly caused by the accumulation of particles of a specific size. Based on this determination, the system can prioritize initiating a pulsed airflow rinsing and cleaning strategy for the filter, rather than immediately recommending filter replacement, thereby achieving more precise maintenance and a longer filter lifespan.

[0073] When it is determined that changes in the surface physicochemical properties of a filter are caused by the accumulation of particles of a specific size, the steps for recalculating the pulsed airflow intensity and duration required to remove the particles include: By performing high-frequency harmonic analysis on the surface vibration response data of the filter screen, characteristic vibration modes related to solid residues or specific chemical reaction products formed after the liquid aerosol is dried can be identified. Based on the characteristic vibration patterns, determine the type or morphology of the current accumulation of particles of a specific size; Based on the type or morphology of accumulated particles of a specific size, the solvent type information, spray volume information, preset particle adhesion characteristic parameters, and preset aggregation tendency parameters are modified. Based on the corrected solvent type information, spray volume information, preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters, the pulse airflow scouring intensity and duration required to remove particles are recalculated.

[0074] Specifically, high-frequency harmonic analysis of the surface vibration response data of the filter screen involves using Fourier transform or other signal processing techniques to decompose the acquired vibration signal into harmonic components of different frequencies, with a focus on analyzing the vibration characteristics of the high-frequency components. The aim is to identify weak vibrational modes closely related to the fine structure, density, adhesion, and other physicochemical properties of particle accumulation, such as crystals or thin-film solid residues formed after the drying of liquid aerosols, and products generated on the filter screen surface by specific chemical reactions. These characteristic vibrational modes can serve as fingerprints to distinguish different types of particle accumulation.

[0075] Determining the type or morphology of accumulated particles of a specific size based on characteristic vibration patterns can be understood as comparing the identified vibration patterns with a pre-established database or model. This database or model stores typical vibration patterns corresponding to different types of particle accumulation (e.g., dry salt crystals, polymer films, metal oxide powders, etc.). Through comparison, it is possible to accurately determine the type or morphology of the particles currently accumulated on the filter surface, such as whether they are loose powder, dense crystals, or sticky films.

[0076] In practical applications, the solvent type, spray volume, preset particle adhesion characteristics, and preset agglomeration tendency parameters are adjusted based on the type or morphology of accumulated particles of a specific size. This aims to make the subsequent pulsed airflow rinsing strategy more targeted. For example, for highly viscous solid residues, it may be necessary to increase the solvent spray volume or select a solvent with stronger dissolving power, and adjust the particle adhesion characteristics and agglomeration tendency parameters accordingly to reflect their true state. For easily agglomerated powders, it may be necessary to adjust the rinsing intensity and duration to avoid excessive agglomeration. Therefore, based on the corrected solvent type, spray volume, preset particle adhesion characteristics, and preset agglomeration tendency parameters, the required pulsed airflow rinsing intensity and duration for particle removal are recalculated, ensuring that the rinsing parameters accurately match the actual particle accumulation on the filter surface, thereby achieving efficient and non-destructive cleaning.

[0077] In some preferred embodiments, suppose that a laboratory automated high-throughput processing device uses volatile organic solvents in an experiment, and during the operation of the main fan of the purification device, some of the solvent settles on the filter surface in the form of liquid aerosols. Subsequently, these liquid aerosols gradually dry, forming solid crystalline residues with high adhesion. At this point, if the filter is judged to have accumulated particles based solely on the pressure difference data or general vibration response, and cleaning is performed using conventional pulsed airflow parameters, the special adhesion of the crystalline residues may lead to incomplete cleaning. The solution of this application performs high-frequency harmonic analysis on the surface vibration response data collected by vibration sensors installed on the filter frame. For example, the analysis reveals characteristic vibration frequencies and amplitude patterns associated with specific salt crystals or polymer films. Based on these characteristic vibration patterns, the system determines that the current accumulation of particles of a specific size is "highly adhesive solid crystalline residues". Based on this determination, the system will adjust the solvent type information (e.g., suggest using a solvent with specific solubility), spray volume information (e.g., increase the spray volume to fully wet the crystals), preset particle adhesion characteristic parameters, and preset agglomeration tendency parameters. For example, the particle adhesion characteristic parameter is adjusted to reflect high adhesion, and the aggregation tendency parameter is adjusted to reflect the resistance to crystal aggregation. Subsequently, based on these corrected parameters, the system recalculates the intensity and duration of the pulsed airflow required to remove such crystalline residues. For instance, it might calculate the need for a higher main fan speed to generate a stronger airflow impact and a longer flushing duration to ensure complete crystal removal. In this way, efficient and targeted removal of specific types of stubborn contaminants can be ensured.

[0078] refer to Figure 2 This application proposes an energy-saving laboratory air purification system based on adaptive control, the system comprising: The acquisition module acquires event information about the automated high-throughput processing equipment ceasing to release pollutants; The estimation module estimates the concentration decay trend of pollutants in the laboratory space based on event information, preset environmental parameters, and the characteristics of the pollution source. The adjustment module adjusts the operating intensity of the purification equipment based on the concentration decay trend to reduce the purification intensity. The comparison module acquires environmental pollutant concentration information and compares it with the concentration decay trend to correct the adjustment process of purification intensity. The switching module switches the purification equipment to a low-power operation mode when the concentration of environmental pollutants reaches a preset safety level.

[0079] Specifically, the acquisition module can be understood as receiving and processing signals from automated high-throughput processing equipment or other relevant sensors to identify the precise time point at which pollutant release ceases. For example, this module could be a data interface or communication unit that interacts with the laboratory automation system to obtain equipment operating status, experiment completion indicators, or notifications of pollutant emission cessation. Its purpose is to provide accurate start time information for subsequent pollutant concentration decay estimation.

[0080] The estimation module is configured to receive event information from the acquisition module and, in conjunction with preset environmental parameters (such as laboratory volume, ventilation rate, temperature, and humidity) and the characteristics of the pollution source (such as pollutant type, initial concentration, volatility, and diffusion coefficient), predict the decay trend of pollutant concentration in the laboratory space over time by establishing a mathematical model or utilizing machine learning algorithms. In practical applications, this module can be a processor or computing unit that runs a preset decay model, aiming to provide a theoretical basis for the initial adjustment of purification intensity.

[0081] Furthermore, the adjustment module dynamically adjusts the operating intensity of the purification equipment based on the concentration decay trend provided by the estimation module. For example, when the estimated pollutant concentration decays rapidly, the adjustment module can instruct the main fan of the purification equipment to reduce its speed, reduce the number of operating filter units, or reduce the power of the ultraviolet germicidal lamps, thereby reducing the overall purification intensity. The aim is to maximize energy conservation while ensuring purification effectiveness.

[0082] In addition, the comparison module is configured to continuously acquire environmental pollutant concentration information, for example, by connecting to air quality sensors or online monitoring equipment to monitor pollutant concentrations in the laboratory in real time. This module compares the real-time acquired environmental pollutant concentration information with the concentration decay trend predicted by the estimation module, and corrects the purification intensity adjustment process of the adjustment module based on the comparison results. For example, if the actual concentration decay is slower than predicted, the comparison module will provide feedback to the adjustment module to appropriately increase the purification intensity; conversely, it will further reduce the purification intensity. The aim is to achieve adaptive optimization of the purification process, ensuring purification effectiveness while avoiding over-purification.

[0083] Finally, the switching module is configured to switch the purification equipment to a low-power operation mode when the comparison module confirms that the concentration of environmental pollutants has reached a preset safety level. This safety level can be set according to the specific requirements of the laboratory and the toxicity of the pollutants. In low-power operation mode, the purification equipment maintains only minimal operation; for example, the main fan runs at a very low speed to maintain basic ventilation, or some filter units stop working. The aim is to achieve long-term energy-saving operation while ensuring the safety of the laboratory environment.

[0084] The solution in this application automates and intelligently executes the method by specifying each step in the above-mentioned adaptive control-based laboratory energy-saving air purification method into mutually cooperating modules.

[0085] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A laboratory energy-saving air purification method based on adaptive control, characterized by, The method comprises the following steps: Obtaining event information of the automatic high-throughput processing equipment stopping releasing pollutants; According to the event information, the preset environmental parameters, and the characteristics of the pollution source, estimating the concentration decay trend of the pollutants in the laboratory space; According to the concentration decay trend, adjusting the operation intensity of the purification equipment to reduce the purification intensity; Obtaining the environmental pollutant concentration information, and comparing the environmental pollutant concentration information with the concentration decay trend to correct the adjustment process of the purification intensity; When the environmental pollutant concentration reaches the preset safety level, switching the purification equipment to a low-power consumption operation mode.

2. A laboratory energy-efficient air purification method based on adaptive control according to claim 1, characterized in that, The method further considers the influence of liquid aerosol and further comprises the following steps: According to the event information, the preset environmental parameters, the characteristics of the pollution source, and the airflow mode parameters in the laboratory space, calculating the settling probability and resuspension probability of the liquid aerosol in a specific area in the laboratory space; When there is a high probability of liquid aerosol settling and resuspension in the specific area, starting a local turbulence strategy to promote the resuspension of the settled liquid aerosol by periodically increasing the rotation speed of the main fan of the purification equipment or temporarily activating the local exhaust port of the laboratory space; During the adjustment process of the purification intensity, continuously obtaining the environmental pollutant concentration information, and comparing the environmental pollutant concentration information with the concentration decay trend to correct the adjustment process of the purification intensity; Before switching the purification equipment to the low-power consumption operation mode, checking the execution of the local turbulence strategy and the predicted risk of liquid aerosol residue; When the environmental pollutant concentration reaches the preset safety level, and the local turbulence strategy has been executed, and the risk of liquid aerosol residue is reduced, switching the purification equipment to the low-power consumption operation mode.

3. A laboratory energy-efficient air purification method based on adaptive control according to claim 2, characterized in that, The method further comprises the following steps: According to the event information, the preset environmental parameters, the characteristics of the pollution source, and the airflow mode parameters in the laboratory space, calculating the duration of the liquid aerosol in a specific area in the laboratory space; According to the event information, the characteristics of the pollution source, the settling probability, the resuspension probability, the duration of the liquid aerosol in a specific area in the laboratory space, the preset rotation speed of the main fan in the low-power consumption operation mode, and the capture efficiency curve of the filter screen in the purification equipment, calculating the rate of continuous accumulation of particles of a specific size in the filter screen and the potential risk of clogging; When the rate of continuous accumulation of particles of a specific size in the filter screen and the potential risk of clogging indicate a high risk of accumulation of particles of a specific size, entering a protective operation mode of the filter screen; In the protective operation mode of the filter screen, maintaining the rotation speed of the main fan of the purification equipment at a set gear to ensure that the filter screen maintains sufficient capture efficiency for particles of a specific size; Periodically increasing the rotation speed of the main fan of the purification equipment to generate a pulse airflow to remove loosely attached particles on the surface of the filter screen; Monitoring the differential pressure data of the filter screen; When the differential pressure data of the filter screen indicates that the risk of clogging of the filter screen is increasing, issuing a filter screen replacement warning; When the risk of accumulation of particles of a specific size is reduced and the differential pressure data of the filter screen is stable, switching the purification equipment to the low-power consumption operation mode.

4. A laboratory energy-efficient air purification method based on adaptive control according to claim 3, characterized in that, The step of generating a pulse airflow to remove loosely attached particles on the surface of the filter screen comprises: Acquire the solvent type information and the spraying amount information of the current spraying of the automated high-throughput processing equipment; According to the solvent type information, the spraying amount information, the preset particle adhesion characteristic parameter and the preset agglomeration tendency parameter, calculate the pulse airflow flushing intensity and duration required for removing the particles; According to the pulse airflow flushing intensity and duration, adjust the amplitude of the main fan speed increase and the pulse airflow flushing duration of the purification equipment; Acquire the differential pressure change rate information of the filter screen; According to the differential pressure change rate information of the filter screen, adjust the pulse airflow flushing frequency.

5. A laboratory energy-efficient air purification method based on adaptive control according to claim 4, characterized in that, The step of calculating the pulse airflow flushing intensity and duration required for removing the particles includes: Acquire the surface vibration response data of the filter screen; Compare the surface vibration response data of the filter screen with the preset filter screen state mode; When there is a difference between the surface vibration response data of the filter screen and the preset filter screen state mode, judge that the surface physical and chemical properties of the filter screen have changed; According to the change of the surface physical and chemical properties of the filter screen, adjust the solvent type information, the spraying amount information, the preset particle adhesion characteristic parameter and the preset agglomeration tendency parameter; According to the adjusted solvent type information, the spraying amount information, the preset particle adhesion characteristic parameter and the preset agglomeration tendency parameter, calculate the pulse airflow flushing intensity and duration required for removing the particles.

6. A laboratory energy-efficient air purification method based on adaptive control according to claim 5, characterized in that, The step of acquiring the surface vibration response data of the filter screen includes: Set multiple vibration sensors on the filter screen frame, and at the same time set at least one environmental vibration sensor in the laboratory space away from the filter screen; During the operation of the main fan of the purification equipment, synchronously collect the response data of all vibration sensors and the response data of the environmental vibration sensor; Perform frequency spectrum analysis on the response data of the vibration sensors, identify the vibration components related to the operating frequency of the main fan of the purification equipment, and extract the vibration response signal of the filter screen; Perform frequency spectrum analysis on the response data of the environmental vibration sensor, identify the background vibration components related to the non-purification equipment operation, and extract the vibration signal of the background vibration components; Subtract the vibration signal of the background vibration components from the vibration response signal of the filter screen to obtain the surface vibration response data of the filter screen.

7. A laboratory energy-efficient air cleaning method based on adaptive control according to claim 5, characterized in that, The step of judging the change of the surface physical and chemical properties of the filter screen includes: Perform frequency spectrum analysis on the surface vibration response data of the filter screen to identify the vibration components related to the inherent resonant frequency of the filter screen material; Identify the vibration components related to the changes of surface mass and stiffness caused by the accumulation of particles of a specific particle size; Compare the amplitude and frequency offset of the vibration components related to the inherent resonant frequency of the filter screen material with the amplitude and frequency offset of the vibration components related to the changes of surface mass and stiffness caused by the accumulation of particles of a specific particle size, to judge whether the change of the surface physical and chemical properties of the filter screen is caused by the accumulation of particles of a specific particle size or by material aging or micro-damage; When the change of the surface physical and chemical properties of the filter screen is judged to be caused by the accumulation of particles of a specific particle size, extract the frequency spectrum features related to the accumulation of particles of a specific particle size to reflect the surface physical and chemical properties of the filter screen.

8. A laboratory energy-efficient air purification method based on adaptive control according to claim 7, characterized in that, The step of determining whether the change in the surface physical and chemical properties of the filter screen is caused by the accumulation of the specific particle size particles includes: differential analysis of the amplitude and frequency shift of the vibration component related to the inherent resonance frequency of the filter screen material, and the amplitude and frequency shift of the vibration component related to the change in the surface mass and stiffness caused by the accumulation of the specific particle size particles, to determine whether there is a specific amplitude ratio or frequency shift pattern indicating that material aging or micro-damage occurs simultaneously with the accumulation of particles; when it is determined that there is a specific amplitude ratio or frequency shift pattern, calculating the contribution weight of the material aging or micro-damage to the amplitude and frequency shift of the vibration component related to the inherent resonance frequency of the filter screen material according to the running time of the filter screen, the historical maintenance record, and the preset material aging rate parameter; subtracting the amplitude and frequency shift corresponding to the contribution weight from the amplitude and frequency shift of the vibration component related to the inherent resonance frequency of the filter screen material to obtain the amplitude and frequency shift of the processed vibration component related to the inherent resonance frequency of the filter screen material; comparing the amplitude and frequency shift of the processed vibration component related to the inherent resonance frequency of the filter screen material with the amplitude and frequency shift of the vibration component related to the change in the surface mass and stiffness caused by the accumulation of the specific particle size particles; when the change in the amplitude and frequency shift of the processed vibration component related to the inherent resonance frequency of the filter screen material is less than the change in the amplitude and frequency shift of the vibration component related to the change in the surface mass and stiffness caused by the accumulation of the specific particle size particles, determining that the change in the surface physical and chemical properties of the filter screen is caused by the accumulation of the specific particle size particles.

9. A laboratory energy-efficient air purification method based on adaptive control according to claim 8, characterized in that, When it is determined that the change in the surface physical and chemical properties of the filter screen is caused by the accumulation of the specific particle size particles, the step of recalculating the pulse airflow scouring intensity and duration required to remove the particles includes: identifying a characteristic vibration pattern related to solid residues or specific chemical reaction products formed after the drying of liquid aerosols by performing high-frequency harmonic analysis on the surface vibration response data of the filter screen; determining the type or form of the current accumulation of the specific particle size particles according to the characteristic vibration pattern; correcting the solvent type information, the spraying amount information, the preset particle adhesion characteristic parameters, and the preset agglomeration tendency parameters according to the type or form of the accumulation of the specific particle size particles; recalculating the pulse airflow scouring intensity and duration required to remove the particles according to the corrected solvent type information, the spraying amount information, the preset particle adhesion characteristic parameters, and the preset agglomeration tendency parameters.

10. A laboratory energy-saving air purification system based on adaptive control, applying a laboratory energy-saving air purification method based on adaptive control as claimed in claim 1, characterized in that, The system includes: an acquisition module that acquires event information of an automated high-throughput processing device stopping releasing pollutants; an estimation module that estimates the concentration decay trend of the pollutants in the laboratory space according to the event information, preset environmental parameters, and characteristics of the pollution source; an adjustment module that adjusts the running intensity of the purification device to reduce the purification intensity according to the concentration decay trend; a comparison module that acquires environmental pollutant concentration information and compares the environmental pollutant concentration information with the concentration decay trend to correct the adjustment process of the purification intensity; The switching module switches the purification device to a low-power consumption operation mode when the concentration of the environmental pollutants reaches a preset safety level. The switching module switches the purification device to a low-power consumption operation mode when the concentration of the environmental pollutants reaches a preset safety level.

Images