Laboratory exhaust cabinet pollution control performance management method and system

Abstract

The embodiment of the invention provides a pollution control performance management method and system for a laboratory exhaust cabinet. The method comprises the steps that real-time operation parameters and environment data of the laboratory exhaust cabinet are acquired; based on the operation parameters and the environment data, calculating an optimal configuration scheme through a preset model; operating parameters of the exhaust cabinet are adjusted to execute the optimal configuration scheme; and monitoring the operation state of the optimized exhaust cabinet and feeding back an adjustment result. Through the scheme of the embodiment of the invention, the dual problems of high energy consumption and poor exhaust effect can be solved by performing optimal configuration on the operating parameters of the exhaust cabinet.

Description

Technical Field

[0001] The present application relates to the technical field of laboratory fume hood pollution control, and in particular to a laboratory fume hood pollution control performance management method and system. Background Art

[0002] The pollution control performance management of laboratory fume hoods aims to ensure that the operating parameters of laboratory fume hoods are effectively controlled while achieving the goal of energy conservation and environmental protection through systematic monitoring and optimal configuration of the operating parameters of laboratory fume hoods. However, in practical applications, there is the problem of how to optimize the configuration of these operating parameters to simultaneously solve the two major problems of high energy consumption and poor exhaust effect. Since fume hoods need to maintain an efficient gas exchange rate to ensure the safety and health of laboratory personnel, but this process is usually accompanied by high energy consumption, if the parameters cannot be accurately adjusted, it may lead to high energy consumption and unsatisfactory exhaust effect. Therefore, how to balance the relationship between the two has become an important issue that needs to be solved urgently. Summary of the invention

[0003] In view of this, an embodiment of the present disclosure provides a method for managing the pollution control performance of a laboratory fume hood, which at least partially solves the problems existing in the prior art.

[0004] A laboratory fume hood pollution control performance management method of the present application includes:

[0005] Obtain real-time operating parameters and environmental data of laboratory fume hoods;

[0006] Based on the operating parameters and environmental data, an optimized configuration scheme is calculated through a preset model;

[0007] Adjusting the operating parameters of the fume hood to implement the optimized configuration scheme;

[0008] Monitor the operating status of the optimized fume hood and provide feedback on the adjustment results.

[0009] Preferably, the step of calculating the optimization configuration scheme through a preset model based on the operating parameters and environmental data includes:

[0010] Collect key operating parameters of the fume hood, such as air volume, air pressure and temperature;

[0011] Collect environmental data such as indoor temperature, humidity and air quality in the laboratory;

[0012] Based on the collected data, the energy consumption and ventilation effect prediction model is used to analyze and generate an optimized configuration plan;

[0013] Determine the optimal configuration parameters to ensure improved ventilation while reducing energy consumption.

[0014] Preferably, the step of analyzing the collected data through a prediction model of energy consumption and ventilation effect to generate an optimized configuration plan includes:

[0015] Based on the collected air volume Q and air pressure P, calculate the energy consumption E per unit air volume as E = Q×P / η, where E represents the energy consumption per unit air volume, Q represents the air volume, P represents the air pressure, and η represents the efficiency;

[0016] Evaluate the change trend of the indoor air quality AQI, and calculate the fluctuation range ΔAQI of the air quality index;

[0017] Combine the fluctuation range ΔAQI of the air quality index and the energy consumption E per unit air volume to determine the optimization goal;

[0018] If the fluctuation range ΔAQI of the air quality index is greater than the threshold A, give priority to adjusting the air volume Q to ensure that ΔAQI is less than or equal to A; otherwise, give priority to adjusting the air pressure P to ensure that E is minimized.

[0019] Preferably, the step of combining the fluctuation range ΔAQI of the air quality index and the energy consumption E per unit air volume to determine the optimization goal includes:

[0020] Obtain the current air quality index AQI and energy consumption E;

[0021] Calculate the adjusted air volume Q1 and air pressure P1, and generate a new configuration plan;

[0022] Compare the changes in the air quality index and energy consumption before and after adjustment, and determine the optimal configuration;

[0023] If the new air quality index AQI1 - AQI < D and the energy consumption E1 - E < F per unit air volume, then adopt the new configuration plan; otherwise, maintain the original plan, where AQI1 represents the new air quality index, D represents the threshold of the decline range of the air quality index, E1 represents the new energy consumption per unit air volume, and F represents the threshold of the decline range of the energy consumption.

[0024] Preferably, the step of comparing the changes in the air quality index and energy consumption before and after adjustment to determine the optimal configuration includes:

[0025] Calculate the change in energy consumption ΔE1 = E1 - E before and after adjusting the air volume Q, and the change in the air quality index ΔAQI1 = AQI1 - AQI;

[0026] Calculate the change in energy consumption ΔE2 = E2 - E before and after adjusting the air pressure P, and the change in the air quality index ΔAQI2 = AQI2 - AQI;

[0027] Compare the effects of different adjustment methods, and select the optimal configuration plan;

[0028] If ΔAQI1≥0 and ΔE1<0 or ΔAQI1>ΔAQI2 and ΔE1≤ΔE2, the air volume Q adjustment scheme is selected; otherwise, the wind pressure P adjustment scheme is selected, where E2 and AQI2 represent the energy consumption per unit air volume and the air quality index after the wind pressure P is adjusted, respectively.

[0029] Preferably, the step of comparing the effects of different adjustment methods and selecting the most optimized configuration solution includes:

[0030] Evaluate the combined scores of the two adjustment options;

[0031] Comprehensively consider the changes in air quality and energy consumption before and after the adjustment, and give the optimization weights W1 and W2;

[0032] Select the best solution based on the comprehensive score;

[0033] If (W1×ΔAQI1+W2×ΔE1)≥(W1×ΔAQI2+W2×ΔE2), the air volume Q adjustment scheme is selected; otherwise, the wind pressure P adjustment scheme is selected, where W1 represents the weight of the air quality change and W2 represents the weight of the energy consumption change.

[0034] In addition, the present application also provides a laboratory fume hood pollution control performance management system, which is characterized by comprising:

[0035] Sensors to obtain real-time operating parameters and environmental data of laboratory fume hoods;

[0036] A calculation unit, based on the operating parameters and environmental data, calculates an optimization configuration scheme through a preset model;

[0037] A control unit, adjusting operating parameters of the fume hood to implement the optimized configuration scheme; and

[0038] The monitoring unit monitors the operating status of the optimized fume hood and provides feedback on the adjustment results.

[0039] Preferably, the step of calculating the optimization configuration scheme through a preset model based on the operating parameters and environmental data includes:

[0040] Collect key operating parameters of the fume hood, such as air volume, air pressure and temperature;

[0041] Collect environmental data such as indoor temperature, humidity and air quality in the laboratory;

[0042] Based on the collected data, the energy consumption and ventilation effect prediction model is used to analyze and generate an optimized configuration plan;

[0043] Determine the optimal configuration parameters to ensure improved ventilation while reducing energy consumption.

[0044] Preferably, the step of generating an optimized configuration plan by analyzing the collected data through a prediction model of energy consumption and ventilation effect includes:

[0045] Based on the collected air volume Q and air pressure P, calculate the energy consumption E per unit air volume as E = Q×P / η, where E represents the energy consumption per unit air volume, Q represents the air volume, P represents the air pressure, and η represents the efficiency;

[0046] Evaluate the changing trend of the indoor air quality AQI and calculate the fluctuation range ΔAQI of the air quality index;

[0047] Combine the air quality index fluctuation range ΔAQI and the energy consumption E per unit air volume to determine the optimization objective;

[0048] If the air quality index fluctuation range ΔAQI is greater than the threshold A, give priority to adjusting the air volume Q to ensure that ΔAQI is less than or equal to A; otherwise, give priority to adjusting the air pressure P to ensure the minimum E.

[0049] Preferably, the step of combining the air quality index fluctuation range ΔAQI and the energy consumption E per unit air volume to determine the optimization objective includes:

[0050] Obtain the current air quality index AQI and energy consumption E;

[0051] Calculate the adjusted air volume Q1 and air pressure P1 to generate a new configuration plan;

[0052] Compare the changes in air quality index and energy consumption before and after adjustment to determine the optimal configuration;

[0053] If the new air quality index AQI1 - AQI < D and the energy consumption E1 - E per unit air volume < F, then adopt the new configuration plan; otherwise, maintain the original plan, where AQI1 represents the new air quality index, D represents the threshold of the decline range of the air quality index, E1 represents the new energy consumption per unit air volume, and F represents the threshold of the decline range of the energy consumption.

[0054] The embodiments of the present disclosure provide a method for managing the pollution control performance of a laboratory fume hood, including: obtaining the real-time operation parameters and environmental data of the laboratory fume hood; calculating an optimized configuration plan through a preset model based on the operation parameters and environmental data; adjusting the operation parameters of the fume hood to execute the optimized configuration plan; monitoring the operation state of the optimized fume hood and feeding back the adjustment result. Through the solution of the embodiments of the present disclosure, the problem of how to optimize the configuration of the operation parameters of the fume hood to solve the dual problems of high energy consumption and poor exhaust effect can be solved. Description of the Drawings

[0055] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0056] Figure 1 It is a flow chart of a laboratory fume hood pollution control performance management method of the present application;

[0057] Figure 2 It is a flowchart that calculates the optimal configuration scheme based on operating parameters and environmental data through a preset model;

[0058] Figure 3 It is based on the collected data, through the energy consumption and ventilation effect prediction model to analyze and generate a flowchart of the optimized configuration plan;

[0059] Figure 4 It is a flow chart that determines the optimization target by combining the fluctuation range of the air quality index ΔAQI and the energy consumption per unit air volume E;

[0060] Figure 5 It is a flow chart that compares the changes in air quality index and energy consumption before and after adjustment to determine the most optimized configuration;

[0061] Figure 6 It is a flowchart for comparing the effects of different adjustment methods and selecting the most optimized configuration solution;

[0062] Figure 7 It is a block diagram of a laboratory fume hood pollution control performance management system of the present application. DETAILED DESCRIPTION

[0063] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0064] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0065] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0066] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0067] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0068] Next, a method for managing pollution control performance of a laboratory fume hood according to the present invention will be described with reference to the accompanying drawings.

[0069] like Figure 1 As shown, the laboratory fume hood pollution control performance management method of the present application includes:

[0070] S101: Obtain real-time operating parameters and environmental data of laboratory fume hoods;

[0071] S102: Based on the operating parameters and environmental data, an optimization configuration scheme is calculated by a preset model;

[0072] S103: adjusting the operating parameters of the fume hood to execute the optimized configuration scheme;

[0073] S104: Monitor the optimized operating status of the fume hood and provide feedback on the adjustment results.

[0074] First, obtain the real-time operating parameters and environmental data of the laboratory fume hood. This step means collecting key information about the current usage status and various environmental factors in the laboratory, such as temperature, humidity, pollutant concentration, and exhaust flow rate. These indicators can directly reflect whether the exhaust system is operating normally or not. For example, in one embodiment, technicians can install a sensor network in the laboratory to continuously track the airflow rate in the fume hood and the concentration of particles in the air to ensure that the information obtained truly and effectively reflects the actual status and provides strong support for subsequent analysis.

[0075] Then, based on the obtained operating parameters and environmental data, optimization is performed through a pre-set algorithm or prediction framework. The so-called model calculation refers to the use of pre-designed algorithms such as intelligent algorithms, genetic algorithms or other mathematical models to perform complex calculations to find the most ideal configuration parameters. For example, after mastering a series of correlation functions such as fan speed and indoor air quality, the best combination can be determined by computing power to make the fan work in the most energy-saving mode while still ensuring excellent results, thereby achieving energy saving and improving efficiency. In this process, the model must not only consider the current parameters but also fully combine historical performance records and future potential possibilities to ensure the comprehensiveness and foresight of the plan.

[0076] Then, adjust the operating parameters of the exhaust cabinet to adapt it to the ideal conditions obtained previously. Here, it is emphasized that the various hardware facilities should be controlled according to the results obtained in the early stage to achieve the established values. For example, adjusting the damper angle, changing the motor power, etc. are all possible change measures. Based on this, the system is allowed to perform operations according to the optimal path, so as to maximize its due effectiveness, reduce resource consumption and improve work efficiency. If some key parts cannot be set manually, the software automatically updates the control command to send instructions to the CPU or DSP inside the device, so that it can complete the automatic correction task and keep the entire system always running in the best state of high performance and low energy consumption.

[0077] The mouth is around the room, and the status of the exhaust cabinet after the transformation is detected and the latest actual operation status after adjustment is fed back to the initial decision-making system to re-verify the rationality of the plan and provide reference data for further improvement. This operation requires the installation of online monitoring equipment to keep an eye on all dynamic factors. For example, new gas quality reports will be collected at regular intervals to check whether there are any unexpected new trends, timely evaluate the actual performance of the current optimization measures, and prompt managers whether a new round of adjustment process needs to be started to ensure continuous promotion of overall performance upgrades and form a virtuous cycle mechanism, thereby achieving dynamic management and real-time response capabilities.

[0078] For example, by observing the new parameters running for a period of time, if they do bring significant improvements, such as a significant decrease in power consumption or the content of toxic substances in the air is below the safety threshold, the setting plan will be formally adopted. On the contrary, if the problem is not solved or even a negative impact occurs, the previous assumptions will be quickly withdrawn and reviewed, and methods in different dimensions will be tried until the problem is finally solved.

[0079] Next, refer to Figure 2 , describing the steps of the present invention of calculating the optimal configuration solution through a preset model based on operating parameters and environmental data.

[0080] S201: Collect the key operating parameters of the fume hood, such as air volume, air pressure and temperature. The key parameters of the fume hood directly determine the operation effect and safety of the ventilation system. Air volume refers to the volume of air passing through the fume hood per unit time, and the unit is generally cubic meters per hour (m3 / hour). 3 / h); its value must be related to the number and layout of laboratory equipment to ensure that harmful substances do not escape into the environment. Wind pressure reflects the pressure difference overcome by the fume hood during the exhaust process, and the value depends on the fan power and pipeline layout; while temperature parameters mainly affect the exhaust efficiency and its heat energy consumption. Too high operating ambient temperature will increase the system cooling burden. These data are important basic information for subsequent analysis to ensure a comprehensive understanding of the working status of the exhaust device. For example, when a chemical laboratory needs to update the fume hood, the technician first records the actual current working parameters of the fume hood and makes statistics.

[0081] S202: Collect the laboratory's indoor temperature, humidity, air quality and other environmental data. This step is to understand the overall condition of the entire indoor environment. The indoor temperature usually ranges from 18-24 degrees Celsius, and may deviate in some cases, such as in highly exothermic reactions; the air humidity is generally controlled between 40% and 60%. Too high or too low humidity affects the accuracy and safety of the results of chemical experiments; air quality involves monitoring of parameters such as carbon dioxide concentration. In one embodiment, laboratory managers use sensors to regularly record changes in key factors such as indoor temperature and humidity, VOCs (volatile organic compounds) concentration, etc., to determine the true value of environmental data.

[0082] S203: Based on the collected data, the energy consumption and ventilation effect prediction model is used for analysis to generate an optimized configuration scheme. Based on all the above data, the energy consumption and ventilation effect prediction model is used for analysis to generate an optimized configuration scheme. In one embodiment, the formula P (optimized_configuration) = αE + βP can be used to evaluate the effect scores of different schemes, where α and β are weight coefficients corresponding to the energy saving benefit E and the ventilation performance score P, respectively, and both values ​​are between 0 and 1. When the goal focuses on reducing energy consumption, the α value is appropriately increased; if more attention is paid to efficient ventilation and exhaust, the β value setting is increased. Specifically, if it is found that the current setting makes E = 5 points and P = 3 points, in order to take into account the needs of both, the ratio can be adjusted to achieve a balance, such as setting α = 0.6, β = 0.4, and recalculating and solving the optimal parameter set. At the same time, according to the specific environmental conditions, the importance ratio of the two factors is adjusted to obtain a more reasonable optimal solution to achieve coordination and unity between economic benefits and functional guarantees.

[0083] S204: Determine the optimal configuration parameters to ensure that the exhaust effect is improved while reducing energy consumption. In this process, it is necessary to repeatedly test and calibrate various parameters to find the best working point. For example, a research institute conducted a fine-tuning experiment on the established ventilation system and obtained the optimal parameter set: the exhaust fan speed was set to v_optimal = about 700r / min, and the air inlet opening and closing angle A_angle was about 40°. This not only maintained a good air exchange efficiency, but also increased the energy utilization rate to a near-ideal state, ensuring that the staff was in a safe and healthy experimental environment. In this way, not only the effective management of energy consumption and the purpose of reducing emissions and increasing efficiency are achieved, but also the quality of the laboratory environment is greatly improved.

[0084] Next, refer to Figure 3 , describing the steps of the present invention of generating an optimized configuration plan based on the collected data through analysis of the prediction model of energy consumption and ventilation effect.

[0085] S301: Based on the collected air volume Q and wind pressure P, calculate the energy consumption per unit air volume E=Q×P / η, where E represents the energy consumption per unit air volume, Q represents the air volume, P represents the wind pressure, and η represents the efficiency;

[0086] S302: Evaluate the change trend of the indoor air quality AQI and calculate the fluctuation range of the air quality index ΔAQI;

[0087] S303: Determine an optimization target based on the air quality index fluctuation range ΔAQI and the energy consumption per unit air volume E;

[0088] S304: If the air quality index fluctuation range ΔAQI is greater than the threshold A, the air volume Q is adjusted first to ensure that ΔAQI is less than or equal to A; otherwise, the wind pressure P is adjusted first to ensure that E is minimum.

[0089] First, the energy consumption per unit air volume E is calculated based on the measured values ​​during the operation of the laboratory fume hood. The specific method is to use the air volume Q and the wind pressure P, combined with the working efficiency η of the fan system, and calculate it using the formula E=Q×P / η. Among them, Q is the flow rate of air discharged through the fume hood, generally in cubic meters / hour, and the value depends on the needs of the experimental equipment and space conditions; P is the pressure difference required to promote air movement to overcome resistance, in Pascals, mainly caused by factors such as pipeline layout, local components and filter elements, and fluctuates over time; and η refers to the working efficiency of the fan, which usually ranges from 0 to 1. High efficiency means lower energy consumption, and ideally it is close to 1. However, in practice, it is difficult to maintain the maximum value for a long time due to various factors. The purpose of this formula is to quantify how much electricity is consumed per cubic unit volume of air to guide subsequent adjustment operations.

[0090] Then, pay attention to the air quality assessment link, arrange appropriate sensors indoors to monitor AQI, so as to obtain the changing trend of the index over a period of time, and record the fluctuation range of AQI ΔAQI. This step can directly reflect the quality and stability of the air inside the laboratory. For example, during a certain experimental project, if it is found that the emission of volatile organic compounds causes a sharp increase in AQI within an hour, for example, from 80 to 230, and the fluctuation range ΔAQI is 150, it can be confirmed that the cleaning efficiency of the current ventilation system is poor, and corresponding measures must be taken to improve the pollution removal capacity or increase the exhaust speed to ensure the health and safety of employees while maintaining a good experimental environment.

[0091] Finally, according to the previously acquired data, the optimization goal is set and the corresponding parameter changes are made. When the degree of air quality fluctuation during the observation period exceeds the predetermined threshold value A, the air volume Q should be adjusted to an appropriate level first so that ΔAQI decreases until it meets the limit condition; suppose that in a certain test, it is found that the normal air quality has deteriorated significantly and exceeded the tolerance limit A due to the sudden release of a large number of toxic substances. At this time, it is necessary to increase the ventilation volume to remove pollutants as soon as possible to restore cleanliness. On the contrary, when ensuring that the air quality index is always stable at a better state and the change does not exceed the specified threshold, energy conservation should be considered as the priority, and the wind pressure P should be reduced to ensure that the overall energy consumption is minimized. This can effectively control costs and meet the social requirements of environmental protection and energy conservation. For example, for the normal state in daily operation, if the high-quality air quality level has been achieved and there is no risk of deterioration, moderately lowering the static pressure will help reduce power consumption and optimize the resource allocation plan.

[0092] The above steps coordinate with each other to jointly achieve the goal of efficient pollution control and achieve the effect of energy conservation and emission reduction.

[0093] Next, refer to Figure 4 to describe the steps of determining the optimization goal by combining the fluctuation range ΔAQI of the air quality index and the energy consumption E per unit air volume of the present invention.

[0094] S401: Obtain the current air quality index AQI and energy consumption E;

[0095] S402: Calculate the adjusted air volume Q1 and air pressure P1, and generate a new configuration plan;

[0096] S403: Compare the changes in the air quality index and energy consumption before and after adjustment, and determine the optimal configuration;

[0097] S404: If the new air quality index AQI1 - AQI < D and the energy consumption per unit air volume E1 - E < F, then adopt the new configuration plan, otherwise maintain the original plan, where AQI1 represents the new air quality index, D represents the threshold of the decline range of the air quality index, E1 represents the new energy consumption per unit air volume, and F represents the threshold of the decline range of the energy consumption.

[0098] First, obtain the current air quality index AQI and energy consumption E, which means reading the data of air quality and energy use in the existing state through sensors and energy monitoring devices in the laboratory. For example, in a certain experimental environment, the measured AQI is 150 (range: 0 - 500), indicating the pollution level at this time; at the same time, the monitored energy consumption E is 8 kWh / cubic meter.

[0099] After that, calculate the adjusted air volume Q1 and air pressure P1, and generate a new configuration plan. In this stage, a computer program is used to simulate different ventilation mode parameters and compare them with the original conditions for improvement. Specifically, the energy consumption value is reduced as much as possible while maintaining good pollutant removal ability. If a simulation shows that reducing a certain proportion of the wind force still maintains the necessary cleaning standard, then Q1 and P1 will be set accordingly to achieve this goal, which requires an accurate algorithm to achieve dynamic balance.

[0100] Then, compare the changes in the air quality index and energy consumption before and after adjustment, and determine the optimal configuration. This step involves analyzing and evaluating the new air quality index AQI1 after the implementation of the adjustment plan and the initial situation AQI, as well as evaluating the change in the energy consumption E1 under the new unit air volume. In one embodiment, if the new configuration can reduce the AQI in the room to an ideal level close to 50 while the energy consumption only slightly increases to about 10.5 kWh / m 3 , it shows that the change has a good effect. However, it is necessary to further consider whether it is applicable in a wider scenario and the long-term operation stability.

[0101] Finally, decide whether to adopt or discard the new configuration according to specific criteria: such as AQI1 - AQI < D (the threshold of air quality index change, usually preferably less than 20) and E1 - E < F (the range of energy consumption change under unit air volume, for example, not exceeding 5%). Such a setting is to ensure that the improvement can significantly improve air quality without causing excessive additional expenses. If the comparison between the new and old systems in the above example meets these two constraints, select the new plan; otherwise, maintain the original operation process unchanged.

[0102] In a specific embodiment, for example, the current air quality index AQI is 150, and the new air quality index AQI1 after adjusting the air volume Q1 and air pressure P1 is 50. The threshold D is set to 20, then AQI1 - AQI = - 150 < 20. In addition, the current energy consumption E is 5 kWh / m 3 , after adjusting the air volume Q1 and air pressure P1, the new energy consumption E1 is 10.5 kWh / m 3 , for example, F is 3 kWh / m 3 , then E1 - E = 10.5 - 5 = 5.5 kWh / m 3 > F, which indicates that the increase in energy consumption is too large. At this time, maintain the original plan. If the new energy consumption E1 is 6.5 kWh / m 3 , then E1 - E = 6.5 - 5 = 1.5 kWh / m 3 < F, at this time, adopt the new configuration plan.

[0103] In summary, these meticulous and rigorous operation links aim to effectively improve the pollution control efficiency of the fume hood and save resource consumption as much as possible. The whole process fully considers the complexity of the relationship between different factors and the importance of weighing the pros and cons, ensuring the optimal control of the laboratory environment.

[0104] Next, refer to Figure 5 , describe the steps of determining the optimal configuration by comparing the changes in air quality index and energy consumption before and after adjustment of the present invention.

[0105] S501: Calculate the energy consumption change ΔE1 = E1 - E and the air quality index change ΔAQI1 = AQI1 - AQI before and after adjusting the air volume Q;

[0106] S502: Calculate the energy consumption change ΔE2 = E2 - E and the air quality index change ΔAQI2 = AQI2 - AQI before and after adjusting the air pressure P;

[0107] S503: Compare the effects of different adjustment methods and select the optimal configuration plan;

[0108] S504: If ΔAQI1≥0 and ΔE1<0 or ΔAQI1>ΔAQI2 and ΔE1≤ΔE2, select the air volume Q adjustment scheme; otherwise, select the wind pressure P adjustment scheme, where E2 and AQI2 represent the energy consumption per unit air volume and the air quality index after the wind pressure P is adjusted, respectively.

[0109] First, after the air volume Q and wind pressure P are adjusted respectively, the corresponding unit air volume energy consumption (E, E1, E2) is recorded. Here E refers to the initial value before adjustment, while E1 and E2 are the new energy consumption data points obtained after adjusting the air volume Q and adjusting the wind pressure P, respectively. For the air quality index (AQI, AQI1, AQI2) under different conditions before and after adjustment, the relevant parameter values ​​are also collected. AQI represents the initial benchmark measurement result; AQI1 is the monitoring value after changing the air volume; AQI2 is generated by changing the wind pressure. By comparing ΔE1=E1-E and ΔE2=E2-E, two different energy consumption change differences can be obtained.

[0110] Next, calculate the change in the air quality index. Specifically, the impact of changes in air volume Q on air quality is expressed as ΔAQI1=AQI1-AQI, which reveals that after the air volume is adjusted, the concentration of particulate matter or harmful gases in the laboratory air has improved or deteriorated relative to the initial state. The results of the adjustment of wind pressure P show the same comparison effect using the formula ΔAQI2=AQI2-AQI. The smaller the ΔAQI value, the more the air quality is shifting to a better level; on the contrary, it means that the air quality has deteriorated to varying degrees.

[0111] Then, the process of comparing and selecting the effects of different adjustment methods is entered, with the goal of determining the optimal configuration method. If the air volume Q adjustment can bring no more than the original energy consumption while achieving or exceeding the original air quality and the improvement brought by the change of wind pressure P (ΔAQI1≥0 and ΔE1<0, or ΔAQI1>ΔAQI2 and ΔE1≤ΔE2), this method will be given priority; otherwise, the method of adjusting wind pressure will be used for optimization.

[0112] For example, in one embodiment, assuming that the operating parameters of the existing exhaust system in a laboratory show that the current air volume is Q, corresponding to the initial energy consumption E and the initially monitored AQI, two modes are performed according to the actual test requirements: one is to simply reduce Q to reduce unnecessary power consumption without affecting the health requirements of the working environment; the other is to adapt to a higher pressure to achieve the effect of rapid ventilation and purification of the indoor atmosphere. Experimental data show that the former can reduce the working efficiency of the equipment to the new E1, which is slightly lower than the previous cost. At the same time, due to the appropriate slowdown of the exhaust air flow rate, the pollutant flow rate also tends to be stable and slow, and the final AQI value is increased from the original AQI to the more ideal AQI1. After increasing the pressure setting, it is found that its overall performance index is indeed better than the original E, that is, it reaches E2, but due to the accelerated air flow, the local turbulence may increase the additional energy consumption and some pollutants are redistributed, so that the AQI changes to a slightly lower AQI2, which is still higher than the original value. According to the principles set above, it is obviously recommended to adopt the first strategy in this case to continue to improve the efficiency configuration of the exhaust system.

[0113] In a specific embodiment, for example, the initial energy consumption E is adjusted to 5 kWh / m 3 After increasing the air volume, the energy consumption E1 after adjusting the air volume is 8.5kWh / m 3 , and the air quality index changes from 150 to 50, then ΔE1=E1-E=3.5kWh / m 3 , and ΔAQI1=AQI1-AQI=50-150=-100.

[0114] On the other hand, for example, the energy consumption E is adjusted to 5 kWh / m 3 After increasing the wind pressure, that is, adjusting the wind pressure P, the energy consumption E2 is 9.5kWh / m 3 , and the air quality index changes from 150 to 40, then ΔE2=E2-E=4.5kWh / m 3 , and ΔAQI2=AQI2-AQI=40-150=-110.

[0115] At this time, ΔAQI1=-100<0, ΔE1>0, and ΔAQI1=-100>ΔAQI2=-110, and ΔE1=3.5≤ΔE2=4.5. At this time, the wind pressure P adjustment scheme is selected.

[0116] Specifically, the logic behind these formulas is to meet the indoor air quality standards while pursuing the highest possible resource utilization. Here, the two factors ΔE and ΔAQI are given equal importance to evaluate the effectiveness and rationality of the adjustment, because even if the cost of excessive ΔAQI can significantly reduce the potential harm of ΔE to human health, it may not be the most economical and feasible option; conversely, excessive additional energy consumption investment is unwise even in exchange for slightly better quality returns. Only by weighing these factors can we find a balance, which is beneficial to environmental protection and can maintain scientific management measures with low operating costs.

[0117] Next, refer to Figure 6 , describing the steps of comparing the effects of different adjustment methods and selecting the most optimized configuration solution of the present invention.

[0118] S601: Evaluate the comprehensive score of the two adjustment options;

[0119] S602: Comprehensively consider the changes in air quality and energy consumption before and after the adjustment, and give optimization weights W1 and W2;

[0120] S603: Select the best solution based on the comprehensive score;

[0121] S604: If (W1×ΔAQI1+W2×ΔE1)≥(W1×ΔAQI2+W2×ΔE2), select the air volume Q adjustment scheme; otherwise, select the wind pressure P adjustment scheme, where W1 represents the weight of the air quality change and W2 represents the weight of the energy consumption change.

[0122] Evaluate the comprehensive scores of the two adjustment schemes. This step requires a detailed evaluation and scoring of two different configuration adjustment modes, such as adjusting air volume and air pressure. The factors considered in the comprehensive score usually include but are not limited to the efficiency after adjustment, the stability of the system, and other relevant performance parameters, in order to establish a preliminary basis for comparison. A specific example includes, if a laboratory needs to test the effect of exhaust hoods on pollution control under specific conditions, then the initial score can be based on the historical performance data under similar settings in the past.

[0123] Next, the changes in air quality and energy consumption before and after the adjustment are comprehensively considered, and the corresponding optimization weights W1 and W2 are given. Among them, W1 (range 0 to 1) represents the importance of air quality improvement, and W2 reflects the degree of demand for reducing energy consumption. These two parameters can be set to optimal values ​​according to the actual situation such as the importance and policy orientation of the specific application scenario. For example, if the location of the laboratory has stricter requirements for energy conservation and emission reduction, or the user is more concerned about energy efficiency rather than the absolute best air quality purification ability, W2 can take a higher value; conversely, if the emphasis is on the quality of pollution discharge, a greater weight can be given to W1. This setting allows flexibility to adapt to different usage scenarios.

[0124] After setting the optimization weights according to the above principles, the final solution is further selected based on the comprehensive score. This involves calculating the two results of (W1×ΔAQI1+W2×ΔE1) and (W1×ΔAQI2+W2×ΔE2), and comparing the sizes to determine which is the best choice. In terms of symbolic explanation, ΔAQI represents the change in the concentration of pollutants in the air caused by the adjustment, and ΔE reflects the change in energy consumption under the corresponding configuration. Through this formula, the system can objectively evaluate the overall benefits brought about by different adjustment schemes.

[0125] Also refer to the above Figure 5 As an example, ΔE1=E1-E=3.5kWh / m 3 , ΔAQI1=AQI1-AQI=50-150=-100, and ΔE2=E2-E=4.5kWh / m 3 , ΔAQI2=AQI2-AQI=40-150=-110.

[0126] At this time, assuming that the weights are W1=0.6 and W2=0.4 respectively, W1×ΔAQI1+W2×ΔE1=0.6×(-100)+0.4×3.5=-58.6, W1×ΔAQI2+W2×ΔE2=0.6×(-110)+0.4×4.5=-64.2, then (W1×ΔAQI1+W2×ΔE1)≥(W1×ΔAQI2+W2×ΔE2) is satisfied, and the air volume Q adjustment plan is selected at this time.

[0127] In another embodiment, assume that there are two different types of exhaust devices A and B in the laboratory, which respectively increase the air volume (Q adjustment) and increase the wind pressure (P adjustment) to try to improve the indoor air quality. After testing, it was recorded that device A can effectively reduce the AQI index from 50 to 30, that is, ΔAQI1 = -20, but its operation period increased by about 8 degrees of electricity, that is, ΔE1 = 8; in comparison, device B reduces the AQI index to 32 while only adding 6 degrees of electricity load, that is, ΔAQI2 = -18, ΔE2 = 6. Based on the fact that the local environmental regulatory authorities attach more importance to air quality than cost savings, the weights are set to W1 = 0.6 and W2 = 0.4 respectively. At this time, the calculation shows that (W1×ΔAQI1+W2×ΔE1)=(0.6×(-20)+0.4×8)=-11.2, and (W1×ΔAQI2+W2×ΔE2)=0.6×(-18)+0.4×6=-8.4, which shows that (W1×ΔAQI1+W2×ΔE1)<(W1×ΔAQI2+W2×ΔE2). Therefore, according to the setting logic, the P adjustment scheme of device B will be preferred. This method not only helps to accurately locate the best configuration mode, but also ensures that resource allocation is scientific, reasonable and in line with expectations.

[0128] The laboratory fume hood pollution control performance management method of the present application has been described above with reference to the accompanying drawings. Next, the laboratory fume hood pollution control performance management system of the present application will be described.

[0129] like Figure 7 As shown, a laboratory fume hood pollution control performance management system 700 of the present application includes:

[0130] Sensor 701, to obtain real-time operating parameters and environmental data of the laboratory fume hood;

[0131] A calculation unit 702 calculates an optimization configuration scheme through a preset model based on the operating parameters and environmental data;

[0132] A control unit 703 adjusts the operating parameters of the fume hood to implement the optimized configuration scheme; and

[0133] The monitoring unit 704 monitors the optimized operating status of the fume hood and feeds back the adjustment result.

[0134] In one embodiment, the step of calculating the optimization configuration scheme through a preset model based on the operating parameters and environmental data includes:

[0135] Collect key operating parameters of the fume hood, such as air volume, air pressure and temperature;

[0136] Collect environmental data such as indoor temperature, humidity and air quality in the laboratory;

[0137] Based on the collected data, analyze through the prediction models of energy consumption and ventilation effect to generate an optimized configuration plan;

[0138] Determine the optimal configuration parameters to ensure improving the exhaust effect while reducing energy consumption.

[0139] In one embodiment, the steps of analyzing through the prediction models of energy consumption and ventilation effect based on the collected data to generate an optimized configuration plan include:

[0140] Based on the collected air volume Q and air pressure P, calculate the energy consumption E per unit air volume = Q×P / η, where E represents the energy consumption per unit air volume, Q represents the air volume, P represents the air pressure, and η represents the efficiency;

[0141] Evaluate the change trend of the indoor air quality AQI and calculate the fluctuation range ΔAQI of the air quality index;

[0142] Combine the air quality index fluctuation range ΔAQI and the energy consumption E per unit air volume to determine the optimization goal;

[0143] If the air quality index fluctuation range ΔAQI is greater than the threshold A, preferentially adjust the air volume Q to ensure that ΔAQI is less than or equal to A; otherwise, preferentially adjust the air pressure P to ensure that E is minimized.

[0144] In one embodiment, the steps of combining the air quality index fluctuation range ΔAQI and the energy consumption E per unit air volume to determine the optimization goal include:

[0145] Obtain the current air quality index AQI and energy consumption E;

[0146] Calculate the adjusted air volume Q1 and air pressure P1 to generate a new configuration plan;

[0147] Compare the changes in air quality index and energy consumption before and after adjustment to determine the optimal configuration;

[0148] If the new air quality index AQI1 - AQI < D and the energy consumption E1 - E < F per unit air volume, then adopt the new configuration plan; otherwise, maintain the original plan, where AQI1 represents the new air quality index, D represents the threshold of the decline range of the air quality index, E1 represents the new energy consumption per unit air volume, and F represents the threshold of the decline range of the energy consumption.

[0149] The functions of the laboratory fume hood pollution control performance management system 700 of the present application have been described above with reference to the method embodiments and will not be elaborated here.

[0150] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present disclosure. It should be understood that the above description is only the specific implementation method of the embodiments of the present disclosure and is not intended to limit the protection scope of the embodiments of the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the protection scope of the embodiments of the present disclosure.

Claims

1. A method for managing the pollution control performance of a laboratory fume hood, characterized in that: Including: Obtain the real-time operating parameters and environmental data of the laboratory fume hood; Based on the operating parameters and environmental data, calculate the optimized configuration plan through a preset model; Adjust the operating parameters of the fume hood to execute the optimized configuration plan; Monitor the operating status of the optimized fume hood and feedback the adjustment results.

2. A laboratory fume hood pollution control performance management method according to claim 1, characterized in that: The step of calculating the optimized configuration plan through the preset model based on the operating parameters and environmental data includes: Collect key operating parameters such as the air volume, air pressure, and temperature of the fume hood; Collect environmental data such as the indoor temperature, humidity, and air quality of the laboratory; Based on the collected data, analyze through a prediction model of energy consumption and ventilation effect to generate an optimized configuration plan; Determine the optimal configuration parameters to ensure improved exhaust effect while reducing energy consumption.

3. A laboratory fume hood pollution control performance management method according to claim 2, characterized in that: The step of analyzing through a prediction model of energy consumption and ventilation effect based on the collected data to generate an optimized configuration plan includes: Based on the collected air volume Q and air pressure P, calculate the energy consumption per unit air volume E = Q×P / η, where E represents the energy consumption per unit air volume, Q represents the air volume, P represents the air pressure, and η represents the efficiency; Evaluate the change trend of the indoor air quality AQI and calculate the fluctuation range ΔAQI of the air quality index; Combine the air quality index fluctuation range ΔAQI and the energy consumption per unit air volume E to determine the optimization goal; If the air quality index fluctuation range ΔAQI is greater than the threshold A, give priority to adjusting the air volume Q to ensure that ΔAQI is less than or equal to A; otherwise, give priority to adjusting the air pressure P to ensure that E is minimized.

4. A laboratory fume hood pollution control performance management method according to claim 3, characterized in that: The step of combining the air quality index fluctuation range ΔAQI and the energy consumption per unit air volume E to determine the optimization goal includes: Obtain the current air quality index AQI and energy consumption E; Calculate the adjusted air volume Q1 and air pressure P1 to generate a new configuration plan; Compare the changes in air quality index and energy consumption before and after adjustment to determine the optimal configuration; If the new air quality index AQI1 - AQI < D and the energy consumption per unit air volume E1 - E < F, then adopt the new configuration plan; otherwise, keep the original plan, where AQI1 represents the new air quality index, D represents the threshold of the decline range of the air quality index, E1 represents the new energy consumption per unit air volume, and F represents the threshold of the decline range of energy consumption.

5. A laboratory fume hood pollution control performance management method according to claim 4, characterized in that: The step of comparing the changes in air quality index and energy consumption before and after adjustment to determine the optimal configuration includes: Calculate the change in energy consumption ΔE1 = E1 - E and the change in air quality index ΔAQI1 = AQI1 - AQI before and after adjusting the air volume Q; Calculate the change in energy consumption ΔE2 = E2 - E and the change in air quality index ΔAQI2 = AQI2 - AQI before and after adjusting the air pressure P; Compare the effects of different adjustment methods and select the optimal configuration plan; If ΔAQI1 ≥ 0 and ΔE1 < 0 or ΔAQI1 > ΔAQI2 and ΔE1 ≤ ΔE2, then select the air volume Q adjustment plan; otherwise, select the air pressure P adjustment plan, where E2 and AQI2 respectively represent the energy consumption per unit air volume and the air quality index after adjusting the air pressure P.

6. A laboratory fume hood pollution control performance management method according to claim 5, characterized in that: The step of comparing the effects of different adjustment methods and selecting the optimal configuration plan includes: Evaluate the comprehensive scores of the two adjustment plans; Comprehensively consider the changes in air quality and energy consumption before and after adjustment, and give the optimization weights W1 and W2; Select the optimal solution according to the comprehensive score; If (W1×ΔAQI1 + W2×ΔE1) ≥ (W1×ΔAQI2 + W2×ΔE2), then select the air volume Q adjustment plan; otherwise, select the air pressure P adjustment plan, where W1 represents the weight of air quality change and W2 represents the weight of energy consumption change.

7. A laboratory fume hood pollution control performance management system, characterized in that: Including: A sensor to obtain the real-time operation parameters and environmental data of the laboratory fume hood; A calculation unit to calculate the optimization configuration plan through a preset model based on the operation parameters and environmental data; A control unit to adjust the operation parameters of the fume hood to execute the optimization configuration plan; And A monitoring unit to monitor the operation status of the optimized fume hood and feedback the adjustment result.

8. A laboratory fume hood pollution control performance management system according to claim 7, characterized in that: The step of calculating the optimization configuration plan through a preset model based on the operation parameters and environmental data includes: Collect key operation parameters such as the air volume, air pressure, and temperature of the fume hood; Collect environmental data such as the indoor temperature, humidity, and air quality of the laboratory; Based on the collected data, analyze through the prediction models of energy consumption and ventilation effect to generate an optimization configuration plan; Determine the optimal configuration parameters to ensure improved exhaust effect while reducing energy consumption.

9. A laboratory fume hood pollution control performance management system according to claim 7, characterized in that: The step of analyzing through the prediction models of energy consumption and ventilation effect based on the collected data to generate an optimization configuration plan includes: Based on the collected air volume Q and air pressure P, calculate the energy consumption per unit air volume E = Q×P / η, where E represents the energy consumption per unit air volume, Q represents the air volume, P represents the air pressure, and η represents the efficiency; Evaluate the change trend of the indoor air quality AQI and calculate the fluctuation range ΔAQI of the air quality index; Combine the air quality index fluctuation range ΔAQI and the energy consumption per unit air volume E to determine the optimization goal; If the air quality index fluctuation range ΔAQI is greater than the threshold A, give priority to adjusting the air volume Q to ensure that ΔAQI is less than or equal to A; otherwise, give priority to adjusting the air pressure P to ensure the minimum E.

10. A laboratory fume hood pollution control performance management system according to claim 9, characterized in that: The step of combining the air quality index fluctuation range ΔAQI and the energy consumption per unit air volume E to determine the optimization goal includes: Obtain the current air quality index AQI and energy consumption E; Calculate the adjusted air volume Q1 and air pressure P1 to generate a new configuration plan; Compare the changes in air quality index and energy consumption before and after adjustment to determine the most optimized configuration; If the new air quality index AQI1 - AQI < D and the energy consumption per unit air volume E1 - E < F, then adopt the new configuration plan; otherwise, keep the original plan, where AQI1 represents the new air quality index, D represents the threshold of the decline range of the air quality index, E1 represents the new energy consumption per unit air volume, and F represents the threshold of the decline range of the energy consumption.

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