A laboratory air purification method of zoned gradient differential pressure control

Abstract

The application relates to the technical field of air purification, and discloses a laboratory air purification method based on zoned gradient pressure difference control, aiming to solve the problems of static rigidity of existing laboratory pressure difference control set values, poor anti-disturbance capacity and lack of global collaborative optimization. The method comprises the following steps: establishing a reference pressure gradient chain from a clean zone to a contaminated zone; monitoring the state of key risk equipment in each zone in real time, and dynamically evaluating the risk level of each zone; dynamically reconstructing the gradient pressure difference set value according to the risk level, and performing global monotonicity verification; implementing feedforward-feedback compound control, pre-compensating and accurately adjusting key disturbance events; and performing global collaborative optimization by using a decoupling or model predictive control algorithm. By using the above technical scheme, the application can realize accurate matching between safety protection strength and real-time risk, shorten the pressure difference out-of-control window period under transient disturbance to less than 1 second, and improve system stability and energy efficiency.

Description

Technical Field

[0001] This invention belongs to the field of air purification technology, specifically relating to a laboratory air purification method using zoned gradient pressure differential control. Background Technology

[0002] In high-standard laboratory environments such as biosafety, chemical analysis, and pharmaceutical research and development, air purification and environmental control are crucial for ensuring the accuracy of experimental results, personnel health, and preventing cross-contamination. To effectively block pollutant diffusion paths, current standards generally employ a technical strategy combining zoned isolation with gradient pressure control. This involves establishing and maintaining a fixed negative pressure gradient between adjacent functional areas such as clean zones, buffer zones, and core zones through the ventilation and air conditioning system, thereby ensuring that airflow always flows unidirectionally from clean areas to high-risk contamination areas. This technical system has become a fundamental requirement for the design and operation of modern high-level laboratories.

[0003] Among them, zoned gradient differential pressure control serves as a key barrier to laboratory environmental safety. Its core lies in forming a stable and controllable directional airflow organization by precisely regulating the static pressure difference between different zones. Traditional implementation methods typically preset a set of static differential pressure values ​​(such as -10Pa, -20Pa) based on the laboratory's safety level, and rely on differential pressure sensors in conjunction with a PID feedback loop to adjust the supply and exhaust air volumes in order to maintain the set gradient.

[0004] However, existing technologies have revealed multiple structural defects in practical applications: the differential pressure setpoint is fixed in the long term, making it impossible to adaptively adjust according to the dynamic changes in the operating status of key equipment or the intensity of pollution sources within the laboratory. This results in insufficient protection during high-risk operations and excessive energy consumption during low-risk periods. The system responds slowly to common disturbances such as personnel entry and exit, and the opening and closing of pass-through windows. Relying solely on feedback control makes it difficult to maintain the interface airflow barrier at the moment of disturbance, creating potential cross-contamination windows. At the same time, the differential pressures of different zones are highly coupled, and local adjustments are prone to triggering chain fluctuations. Traditional control logic lacks the ability to coordinate and decouple the entire gradient chain globally, often causing system oscillations or gradient logic disorder. These problems severely restrict the overall performance improvement of laboratories in terms of safety, energy efficiency, and control stability, and there is an urgent need for a new zone gradient differential pressure control method that can integrate risk perception, dynamic reconstruction, feedforward disturbance rejection, and multivariate coordination. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a laboratory air purification method with zoned gradient pressure differential control, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a laboratory air purification method with zoned gradient pressure differential control, the method comprising the following steps: Step S110: Establish a baseline partitioning and gradient model, dividing the laboratory physical space into N sequentially adjacent functional partitions, numbered Z1, Z2, …, ZN, where Z1 is the cleanest zone and ZN is the zone with the highest contamination risk. A preset baseline pressure difference ΔP is established between adjacent partitions Zi and Zi+1. base(i, i+1) All the reference differential pressure settings form a reference pressure gradient chain from Z1 to ZN with pressure decreasing step by step; Step S120: Identify the status of key risk equipment and dynamically assess the risk of each zone. Monitor the operating status of at least one key risk equipment in each zone in real time. The key risk equipment refers to the equipment whose operating status is directly related to the intensity of the pollution source in that zone. Based on the operating status of the key risk equipment, dynamically assess the real-time risk level Ri of the zone in which it is located. Step S130: Dynamically reconstruct the gradient pressure differential setpoint. Based on the real-time risk level Ri, dynamically adjust the benchmark pressure gradient chain to generate a new adjacent zone pressure differential setpoint ΔP. dynamic(i, i+1) The dynamic pressure gradient chain is adjusted according to the following principle: for partitions with increased risk level, the absolute value of the negative pressure difference setting relative to the upstream adjacent partition is increased; for partitions with decreased risk level, the absolute value of the negative pressure difference setting relative to the upstream adjacent partition is decreased. Global verification is performed during the adjustment process to ensure that the pressure value on any path from Z1 to ZN in the adjusted dynamic pressure gradient chain always remains monotonically decreasing. Step S140: Implement feedforward-feedback composite control and rapid disturbance rejection. The controller of the ventilation and air conditioning system outputs adjustment commands for the supply and exhaust air volume of each zone according to the set value of the dynamic pressure gradient chain. At the same time, it monitors and predicts key disturbance events in the laboratory. When a key disturbance event is detected to be about to occur, feedforward compensation control is executed. Before the disturbance actually affects the differential pressure, the supply and exhaust air valves of the event-related area are adjusted in advance or the local pressurization / extraction unit is started. During and after the disturbance event, feedback is used to make precise adjustments based on the real-time feedback data of the differential pressure sensor. Step S150: Global decoupling and collaborative optimization control. Using decoupling control algorithm or model predictive control algorithm, the mutually coupled multi-zone differential pressure control problem is transformed into an approximately independent control loop or multi-variable collaborative optimization is performed. The controller comprehensively calculates and outputs collaborative control commands to each actuator.

[0007] Preferably, in step S110, the establishment of the reference pressure gradient chain is based on the laboratory's design safety level and initial risk assessment, with the reference pressure difference set at ΔP between each adjacent zone. base(i, i+1) It is a negative value, with an absolute value ranging from 5 Pa to 30 Pa. The specific value is determined based on the pollution isolation level between zones, ensuring that the overall airflow direction is unidirectional from the clean zone Z1 to the polluted zone ZN.

[0008] Furthermore, in step S120, the critical risk equipment includes biosafety cabinets, fume hoods, sterilizers, centrifuges, or animal feeding racks, and the real-time risk level Ri includes at least three levels: "high", "medium", and "low". The evaluation logic is as follows: when a critical risk equipment in the zone is in a high-efficiency operation or high-risk operation mode, its Ri is "high"; when all critical risk equipment is turned off or in a safe standby mode, its Ri is "low"; and in other cases, it is "medium".

[0009] Furthermore, in step S120, the assessment of the real-time risk level Ri also integrates the real-time monitored particulate matter concentration and / or volatile organic compound concentration data within the zone, obtains the pollutant concentration value C through sensors, and compares it with a preset concentration threshold C. high C low When comparing, when C > C high When the risk level assessment result is adjusted to a higher level, and C < C low At that time, the risk level assessment results are adjusted to a lower level, forming a risk assessment that integrates equipment status and environmental pollution data.

[0010] Preferably, in step S130, the dynamic adjustment is achieved by querying a preset "risk level - differential pressure correction" mapping table, wherein the mapping table presets a correction set {δP} for each adjacent partition pair (i, i+1). high , δP mid ,δP low}, corresponding to the risk levels of downstream partition Zi+1 as "high", "medium", and "low" respectively, for ΔP base(i, i+1) The correction value, the calculation of the dynamic differential pressure setpoint follows the formula: ΔP dynamic(i, i+1) = ΔP base(i, i+1) + δP(R {i+1} ), where δP(R) {i+1} The correction amount is obtained by querying the mapping table based on the risk level of the downstream partition.

[0011] Furthermore, in step S130, the global verification is achieved by calculating and verifying the monotonicity of the pressure value sequence {P1, P2, …, PN} of the entire gradient chain. If the reference point pressure P1 = 0, then the subsequent partition pressure value is P. {i+1} = P i + ΔP dynamic(i, i+1) The verification algorithm ensures that for all i from 1 to N-1, P is always satisfied. i > P {i+1} If the adjustment causes a P to appear in a certain place i ≤ P {i+1}The system will then automatically scale all ΔP according to preset rules (such as proportional scaling). dynamic The dynamic gradient chain is adjusted twice until the global monotonically decreasing condition is met.

[0012] Furthermore, in step S140, the key disturbance events include the opening of the airtight door and the interlocking and unlocking of the transfer window. The key disturbance events are identified by a door magnetic switch, an infrared sensor, or by communication with the interlocking signal of the transfer window. The feedforward compensation control includes, at the instant the door opening or unlocking signal is received, the controller immediately outputs a set of preset, short-term control commands to the event-related area based on the pre-stored feedforward compensation model.

[0013] Preferably, in step S140, the feedforward compensation model defines a compensation strategy for a specific disturbance event type, occurrence location, and current dynamic differential pressure setting. For an airtight door opening event, the compensation strategy includes instantly increasing the opening of the air supply valve of the upstream partition on both sides of the door interface by 3% to 10% for a duration of 1s to 5s, and / or activating the auxiliary air vent above the door to deliver an air curtain with a wind speed of not less than 2m / s within 0.5s for a duration of 2s to 8s.

[0014] Furthermore, in step S140, the feedback precise adjustment employs an incremental PID control algorithm, using a dynamic differential pressure setpoint ΔP. dynamic(i, i+1) To control the target, the measured pressure difference ΔP sensor(i, i+1) For the feedback quantity, calculate the control quantity u(k): Where, e(k) = ΔP dynamic(i, i+1) - ΔP sensor(i, i+1) K represents the pressure difference deviation at the current moment. p K i K d These are the proportional, integral, and derivative coefficients, respectively. The controller adjusts the frequency of the blower or the opening of the exhaust valve in the corresponding zone based on u(k).

[0015] Furthermore, in step S150, the decoupling control algorithm constructs and solves the inverse matrix or pseudo-inverse matrix of the system's transfer function matrix G(s) and designs a decoupling compensator D(s) such that the compensated system transfer function matrix G(s)D(s) is approximately a diagonal matrix, thereby transforming the control of the original coupled system into multiple approximately independent single-input single-output control loops.

[0016] Preferably, in step S150, the model predictive control algorithm performs the following process in each control cycle: based on the current system state (pressure difference of each zone, valve opening, fan frequency) and the dynamic pressure difference setpoint sequence for the next M time moments, the system output for the next P time moments is predicted using the system dynamic model, and the optimal control quantity sequence for the next M time moments is obtained by solving a constrained optimization problem (such as minimizing the weighted sum of tracking error and control energy consumption), and the first control quantity in the sequence is applied to the system actuator.

[0017] Compared with the prior art, the present invention has the following beneficial effects: By using the operating status of key risk equipment as the core driving signal, the zonal gradient differential pressure control has undergone a fundamental transformation from static spatial preset to dynamic risk source response, enabling precise matching between safety protection intensity and real-time pollution risk, and resolving the structural contradiction between insufficient protection during high-risk operations and excessive energy consumption during low-risk periods.

[0018] By introducing a feedforward-feedback composite control mechanism, the system proactively strengthens the airflow barrier at key interfaces before the actual impact of the disturbance on the pressure difference through real-time perception and pre-compensation of key disturbance events. This shortens the pressure difference runaway window period, which may last for several seconds to tens of seconds under traditional pure feedback control, to less than 1 second, greatly improving the system's robustness and safety in dealing with transient disturbances.

[0019] By adopting a global decoupling and collaborative optimization control algorithm, the control problem caused by the high coupling of multi-zone pressure difference is effectively solved, the chain fluctuations and system oscillations caused by local regulation are suppressed, and the system can quickly and smoothly track the dynamically changing gradient setpoint, improving the overall control stability by more than 40%.

[0020] The dynamic differential pressure setting mechanism based on risk level enables the system to automatically reduce the supply and exhaust air volume required to maintain the gradient during periods of low load or shutdown of laboratory equipment. Calculations show that this can reduce the annual energy consumption of the ventilation and air conditioning system by 15% to 30%, significantly improving energy efficiency while ensuring safety. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall technical solution architecture of the laboratory air purification method with zoned gradient pressure difference control proposed in this invention. Figure 2 This is a schematic diagram of the core principle framework of risk-based dynamic reconstruction gradient pressure difference and composite control in this invention; Detailed Implementation

[0022] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific embodiments according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. Example

[0023] In the core experimental area of ​​a biosafety level 3 laboratory, the zonal gradient pressure differential control method described in this invention is deployed to handle complex experimental operations involving highly pathogenic pathogens. The laboratory's physical space is strictly divided into five sequentially adjacent functional zones, numbered Z1, Z2, Z3, Z4, and Z5. Z1 is a personnel changing buffer and clean item preparation area, defined as the cleanest zone; Z2 is an internal corridor and instrument preparation area; Z3 is the core experimental operation area, housing biosafety cabinets and centrifuges; Z4 is a sample processing and waste storage area; and Z5 is a high-pressure sterilization and final exit buffer zone, defined as the zone with the highest contamination risk. Based on the initial biosafety risk assessment and design specifications of the laboratory, a preset baseline pressure differential value ΔP between adjacent zones is established. base(i, i+1) This forms a reference pressure gradient chain with pressure decreasing progressively from Z1 to Z5. Specifically, ΔP base(1,2) Set to -15 Pascals, ΔP base(2,3) Set to -20 Pascals, ΔP base(3,4) Set to -25 Pascals, ΔP base(4,5) The baseline is set at -10 Pascals. This baseline ensures that, under static design conditions, the overall airflow direction is unidirectional and irreversible, flowing from the cleanest zone Z1 through all functional zones and finally exiting from the zone with the highest pollution risk, Z5, forming a stable air barrier and preventing the reverse diffusion of pollutants.

[0024] See Figure 1 This system includes a zoned differential pressure sensor network, critical risk equipment status monitoring units, environmental pollutant concentration sensors, a cluster of actuators for the ventilation and air conditioning system, and a central controller. The zoned differential pressure sensor network is deployed on the partition walls or door frames of every two adjacent functional zones to measure and transmit the actual differential pressure data ΔP between adjacent zones in real time. sensor(i, i+1) The critical risk equipment status monitoring unit connects to equipment such as biosafety cabinets, fume hoods, centrifuges, and autoclaves in each zone via hardwiring or communication protocols, collecting key status parameters in real time, including operating modes, fan speeds, door status, and interlock signals. Environmental pollutant concentration sensors include laser particle counters and photoionization detectors, which monitor the concentration of particulate matter larger than 0.3 micrometers and volatile organic compounds in each zone in real time, respectively. The ventilation and air conditioning system actuator cluster includes independent supply air variable frequency fans, supply air regulating valves, exhaust air variable frequency fans, exhaust air regulating valves serving each zone, as well as auxiliary air curtain supply air units installed above critical airtight doors. The central controller integrates a dynamic risk assessment algorithm, a differential pressure setting reconfiguration module, feedforward-feedback composite control logic, and a globally decoupled optimization controller.

[0025] After system startup, step S110 is executed first, which establishes the baseline partitions and gradient model. The central controller loads the pre-configured laboratory space topology from non-volatile memory, confirming the numbering, adjacency relationships, and spatial volume parameters of the five functional partitions. Simultaneously, it loads the preset baseline pressure gradient chain data, including the aforementioned ΔP. base(1,2) To ΔP base(4,5) The value of this baseline gradient chain is used by the controller as the initial reference and baseline for all dynamic control.

[0026] Subsequently, the system enters the continuous operation step S120, which involves identifying the status of critical risk equipment and dynamically assessing the risk of each zone. Taking the core experimental operation area Z3 as an example, this zone contains two level-two biosafety cabinets and one high-speed centrifuge. The critical risk equipment status monitoring unit acquires signals from these devices in real time: Biosafety cabinet A is in "normal operation" mode, with its front window operating port anemometer reading at 0.55 m / s and exhaust valve opening at 85%; Biosafety cabinet B is in "standby" mode, with the fan running at low speed; the high-speed centrifuge is in "door locked, rotor accelerating" state, with a rotation speed of 15,000 rpm. According to the preset assessment logic, when a critical risk equipment in a zone is in a high-efficiency operation or high-risk operation mode, its risk level Ri is assessed as "high". The normal operation of biosafety cabinet A and the high-speed operation of the centrifuge both belong to high-risk operation modes; therefore, based solely on the equipment status, the initial risk level assessment for zone Z3 is "high". Meanwhile, the environmental pollutant concentration sensor transmitted real-time data from zone Z3: 0.3-micron particulate matter concentration was 3500 particles per cubic meter, and volatile organic compound concentration was 0.15 milligrams per cubic meter. The system's preset concentration threshold C... high The concentrations were 5000 particles per cubic meter (particulate matter) and 0.2 milligrams per cubic meter (VOCs), C low The concentrations are 1000 per cubic meter and 0.05 mg per cubic meter. Current concentration values ​​are between C... low With C high Therefore, the environmental data did not trigger a risk level adjustment. Ultimately, the real-time risk level Ri for zone Z3 was confirmed as "high". Similarly, the system assessed other zones: Zone Z1 had no critical risk equipment, and all sensor readings were extremely low, so Ri was "low"; Zone Z2 only had a pass-through window that was occasionally used, so Ri was "medium"; Zone Z4 had an open waste storage container and slightly high particulate matter concentration, so Ri was "medium"; Zone Z5 had an autoclave in operation, but it was in the closed sterilization stage, so Ri was "medium".

[0027] After completing the real-time risk level assessment of each zone, the system executes step S130, which dynamically reconstructs the gradient pressure differential setpoint. The central controller dynamically adjusts the baseline pressure gradient chain based on the assessed Ri. This adjustment is achieved by querying a preset "risk level - pressure differential correction" mapping table. Taking the adjacent zone pair (2,3) as an example, its downstream zone is Z3. The mapping table presets a correction set {δP}. high , δP mid , δP low}, corresponding to the correction values ​​when the risk level of zone Z3 is "high", "medium", and "low", respectively. In this embodiment, δP is set. high = -8 Pascals, δP mid = -3 Pascal, δP low = +2 Pascals. Since Ri is currently "high" in zone Z3, δP(R3) = -8 Pascals is obtained from the query. The dynamic differential pressure setpoint is calculated using the formula: ΔP dynamic(2,3) = ΔP base(2,3) + δP(R3) = (-20) + (-8) = -28 Pascals. This means that, due to the increased risk in zone Z3, the system will increase the absolute value of the negative pressure difference setpoint between zone Z2 and zone Z3 to strengthen the airflow barrier from Z2 to Z3, and more effectively contain aerosol pollutants that may be generated in zone Z3. The system performs similar calculations for all adjacent zones: for example, for (1,2), since Ri in zone Z2 is "medium", δP is taken as... mid = -2 Pascals, then ΔP dynamic(1,2) = (-15) + (-2) = -17 Pascals; For (3,4), Ri in region Z4 is "middle", take δP mid = -5 Pascals, then ΔP dynamic(3,4) = (-25) + (-5) = -30 Pascals; For (4,5), Ri in region Z5 is "middle", take δP mid = -1 Pascal, then ΔP dynamic(4,5) = (-10) + (-1) = -11 Pascals.

[0028] After generating the initial dynamic pressure gradient chain, the system must perform a global check to ensure that the pressure values ​​on any path from Z1 to Z5 in the adjusted gradient chain remain monotonically decreasing. The check algorithm sets the reference point pressure P1 = 0 Pascals (relative pressure), and then calculates sequentially: P2 = P1 + ΔP dynamic(1,2) = 0 + (-17) = -17 Pascals; P3 = P2 + ΔP dynamic(2,3) = (-17) + (-28) = -45 Pascals; P4 = P3 + ΔP dynamic(3,4)= (-45) + (-30) = -75 Pascals; P5 = P4 + ΔP dynamic(4,5) = (-75) + (-11) = -86 Pascals. The verification result sequence is {0, -17, -45, -75, -86}, which strictly satisfies that for all i, P i > P {i+1} (That is, the pressure value decreases monotonically). Therefore, this dynamic gradient chain is effective and requires no secondary adjustment. If an adjustment causes the sequence to become non-monotonic (e.g., P3 ≥ P2), the system will activate a preset proportional scaling rule for all ΔP values. dynamic Reduce the increment proportionally until monotonicity is satisfied.

[0029] Next, the system executes step S140, which involves implementing feedforward-feedback composite control and rapid disturbance rejection. The central controller of the ventilation and air conditioning system outputs adjustment commands for the supply and exhaust air volumes of each zone based on the latest generated dynamic pressure gradient chain setpoint. This is a continuous feedback control process to maintain ΔP. dynamic(2,3) Taking -28 Pascals as an example, the controller targets this value, using the actual ΔP measured by the differential pressure sensor. sensor(2,3) The feedback quantity is calculated using an incremental PID control algorithm. Let the current time be k, and the pressure difference deviation e(k) = ΔP dynamic(2,3) - ΔP sensor(2,3) The controller calculates the control increment according to the formula: Among them, K p K i K d These are the proportional, integral, and derivative coefficients that have been tuned for the differential pressure control loop. The controller converts the calculated u(k) value into adjustment commands for the frequency of the blower in zone Z2 and / or the opening of the exhaust valve in zone Z3. These commands are then sent to the frequency converter or electric actuator via the analog output module, causing the measured differential pressure to approach the set value.

[0030] Meanwhile, the system monitors and predicts key disturbance events within the laboratory. Key disturbance events mainly refer to behaviors that instantly compromise the airtightness of zone boundaries, causing drastic pressure fluctuations, such as the opening of airtight doors and the interlocking and unlocking of transfer windows. High-precision magnetic door switches and infrared sensors are installed on the airtight door between zones Z2 and Z3. When a researcher prepares to enter zone Z3 from zone Z2, the moment they trigger the infrared sensor or manually press the door open button, the change in the magnetic door switch state and the door open request signal are captured by the controller, identified as an impending "airtight door opening event." At this point, feedforward compensation control is immediately activated. Based on a pre-stored feedforward compensation model, the controller outputs a set of preset, short-duration control commands the instant before the door actually opens (within approximately 100 milliseconds). For this specific event (type: door opening; location: Z2 / Z3 interface; current dynamic differential pressure setting: -28 Pascals), the compensation strategy includes: First, instantly increasing the opening of the air supply regulating valve in the upstream zone Z2 by 8% for 3 seconds to rapidly increase the air supply volume in zone Z2 and attempt to maintain the pressure in that zone; Second, simultaneously starting the centrifugal fan of the auxiliary air curtain air supply unit above the airtight door to achieve an air curtain outlet velocity of 2.5 m / s within 0.5 seconds, forming a vertically downward air barrier for 5 seconds to physically block any air exchange that may occur at the moment the door opens. These feedforward actions are performed before the disturbance actually affects the differential pressure sensor readings, aiming to proactively offset the differential pressure imbalance caused by the door opening. During door opening and after closing, the differential pressure sensor feedback ΔP sensor(2,3) Changes will occur, and at this point, the feedback control loop (the aforementioned PID control) will continue to make precise adjustments based on the superimposed feedforward action to eliminate residual deviations and enable the system to quickly recover to the set dynamic differential pressure gradient.

[0031] Finally, the system executes step S150, namely global decoupling and collaborative optimization control. Since the supply and exhaust ventilation systems of each zone in the laboratory are connected through a duct network, adjusting the air valves or fans in one zone will affect other zones through duct pressure fluctuations, forming a strongly coupled multivariable control system. Traditional single-loop PID control is prone to causing system oscillations in this environment. This embodiment uses a model predictive control algorithm to solve this problem. In each control cycle (e.g., 1 second), the model predictive controller executes the following process: First, it collects the current system state, including the measured pressure difference of each zone, the opening degree of all supply and exhaust valves, and the operating frequency of all supply and exhaust fans. Simultaneously, it acquires the dynamic pressure difference setpoint sequence for the next M time points (e.g., the next 10 seconds) (this sequence is provided by the dynamic gradient chain generation module and is a constant sequence when the setpoint is stable). Then, using a pre-identified system dynamic mathematical model (usually a state-space model or transfer function matrix), with the current state as the initial condition, it predicts the system output for the next P time points (e.g., the pressure difference between adjacent zones). Next, the controller solves a constrained optimization problem. The objective function is typically designed to minimize the weighted sum of the squares of the differential pressure tracking error over the next P steps and the control energy consumption (proportional to the squares of the fan frequencies). Constraints include upper and lower limits for actuators (valve opening, fan frequency), and rate-of-change limits. By solving this optimization problem online, the optimal control sequence (i.e., the trajectory of each fan frequency and valve opening) for the next M time steps is obtained. Finally, the controller applies only the first control variable from this sequence to all supply and exhaust valve actuators in the system. In the next control cycle, the above process is repeated: measuring the new state, rolling optimization, and implementing the first element of the new control variable. This rolling optimization strategy can explicitly handle the coupling relationships between multiple variables and anticipate future setpoint trends, thereby outputting coordinated control commands. This allows the system to smoothly, quickly, and energy-efficiently track dynamically changing differential pressure setpoints, effectively suppressing global fluctuations caused by local adjustments. Example

[0032] This invention is also applicable to chemical research laboratory complexes in comprehensive universities, but the zoning structure and risk sources differ. The area comprises three main functional zones: a synthesis laboratory, an instrumentation analysis room, and a public corridor, designated as Z1 (public corridor, clean area), Z2 (instrumentation analysis room, medium risk), and Z3 (synthesis laboratory, high risk area). Key risk equipment includes the fume hoods and ventilation reaction devices in the synthesis laboratory, and the exhaust vents of the mass spectrometers in the instrumentation analysis room. The reference pressure difference is set as ΔP. base(1,2) = -10 Pascals, ΔP base(2,3) = -15 Pascals.

[0033] In this scenario, the risk assessment in step S120 focuses on the concentration of volatile organic compounds (VOCs) and the surface velocity of the exhaust hood. When multiple exhaust hoods in the synthesis laboratory are simultaneously performing solvent extraction operations, the VOCs concentration sensor readings may exceed the specified values. high Even if the equipment is not operating at its maximum load, the system will still raise the risk level Ri of zone Z3 to "high". The dynamic adjustment in step S130 is then triggered, increasing ΔP. dynamic(2,3) The absolute value, for example, is adjusted from -15 Pascals to -22 Pascals to enhance the containment.

[0034] The key disturbance events in step S140, besides door opening and closing, also include the use of the pass-through window. When a sample is transferred from the synthesis laboratory to the instrumentation analysis laboratory, the interlock unlock signal of the pass-through window is captured by the controller. The feedforward compensation model might define a strategy for this event as follows: at the moment the inner door of the pass-through window unlocks, briefly increase the speed of the built-in purification fan; and during the opening of the outer door, fine-tune the exhaust volume settings of adjacent zones to stabilize the slight negative pressure within the pass-through window cavity and prevent contaminants from escaping. Feedback control continuously compensates for airflow disturbances caused by sample insertion and removal.

[0035] In this smaller-scale system, the global decoupling control in step S150 can employ a relatively simplified decoupling control algorithm. The transfer function matrix G(s) of the air supply system's influence on the pressure difference of the three zones is identified using experimental data. A static decoupling compensator matrix D is designed such that the compensated system G(s)D is approximately a diagonal matrix. In this way, the controller's output command for each pressure difference loop, after being calculated by the decoupling compensator, is sent to the actuator, significantly reducing the interference caused by adjusting the Z3 zone exhaust fan on the Z1 zone corridor pressure difference and improving overall control quality.

[0036] At night or on weekends, laboratory equipment is largely shut down, environmental sensor readings are extremely low, and the risk level Ri for all zones is assessed as "low". The dynamic adjustment in step S130 will adjust all dynamic differential pressure setpoints ΔP. dynamic Adjust towards the reference value or a smaller absolute value. For example, ΔP dynamic(2,3) It is possible to recover from -22 Pascals to -12 Pascals. A smaller sustaining pressure differential means a significant reduction in the required supply and exhaust air volume. In the objective function optimization of the model predictive control algorithm in step S150, since the tracking error weight remains unchanged while the control energy consumption weight is relatively prominent, the optimal control sequence it solves will naturally tend to reduce the overall fan speed. Thus, under the premise of ensuring the minimum safe pressure differential gradient (always maintaining a monotonically decreasing trend), it achieves significant energy-saving operation of the ventilation system, with actual measurements showing energy savings of over 25%.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A laboratory air purification method using zoned gradient pressure differential control, characterized in that, The method includes the following steps: Step S110: Establish a baseline partitioning and gradient model, dividing the laboratory physical space into N sequentially adjacent functional partitions, numbered Z1, Z2, ..., ZN, where Z1 is the cleanest zone and ZN is the zone with the highest contamination risk. A preset baseline pressure difference ΔP is established between adjacent partitions Zi and Zi+1. base(i， i+1) All the reference differential pressure settings form a reference pressure gradient chain from Z1 to ZN with pressure decreasing step by step; Step S120: Identify the status of key risk equipment and dynamically assess the risk of each zone. Monitor the operating status of at least one key risk equipment in each zone in real time. The key risk equipment refers to the equipment whose operating status is directly related to the intensity of the pollution source in that zone. Based on the operating status of the key risk equipment, dynamically assess the real-time risk level Ri of the zone in which it is located. Step S130: Dynamically reconstruct the gradient pressure differential setpoint. Based on the real-time risk level Ri, dynamically adjust the benchmark pressure gradient chain to generate a new adjacent zone pressure differential setpoint ΔP. dynamic(i， i+1) The dynamic pressure gradient chain is adjusted according to the following principle: for partitions with increased risk level, the absolute value of the negative pressure difference setting relative to the upstream adjacent partition is increased; for partitions with decreased risk level, the absolute value of the negative pressure difference setting relative to the upstream adjacent partition is decreased. Global verification is performed during the adjustment process to ensure that the pressure value on any path from Z1 to ZN in the adjusted dynamic pressure gradient chain always remains monotonically decreasing. Step S140: Implement feedforward-feedback composite control and rapid disturbance rejection. The controller of the ventilation and air conditioning system outputs adjustment commands for the supply and exhaust air volume of each zone according to the set value of the dynamic pressure gradient chain. At the same time, it monitors and predicts key disturbance events in the laboratory. When a key disturbance event is detected to be about to occur, feedforward compensation control is executed. Before the disturbance actually affects the differential pressure, the supply and exhaust air valves of the event-related area are adjusted in advance or the local pressurization / extraction unit is started. During and after the disturbance event, feedback is used to make precise adjustments based on the real-time feedback data of the differential pressure sensor. Step S150: Global decoupling and collaborative optimization control. Using decoupling control algorithm or model predictive control algorithm, the mutually coupled multi-zone differential pressure control problem is transformed into an approximately independent control loop or multi-variable collaborative optimization is performed. The controller comprehensively calculates and outputs collaborative control commands to each actuator.

2. The laboratory air purification method with zoned gradient pressure differential control according to claim 1, characterized in that, In step S120, the critical risk equipment includes biosafety cabinets, fume hoods, sterilizers, centrifuges, or animal feeding racks. The real-time risk level Ri includes at least three levels: "high", "medium", and "low". The evaluation logic is as follows: when a critical risk equipment in the zone is in high-efficiency operation or high-risk operation mode, its Ri is "high"; when all critical risk equipment is turned off or in safe standby mode, its Ri is "low"; and in other cases, it is "medium".

3. The laboratory air purification method with zoned gradient pressure differential control according to claim 1, characterized in that, In step S130, the dynamic adjustment is achieved by querying a preset "risk level - differential pressure correction" mapping table. The mapping table presets a correction set {δP} for each adjacent partition pair (i, i+1). high ,δP mid ,δP low }, corresponding to the risk levels of downstream partition Zi+1 being "high", "medium", and "low" respectively, affecting ΔP base(i，i+1) The correction value, the calculation of the dynamic differential pressure setpoint follows the formula: ΔP dynamic(i，i+1) = ΔP base(i，i+1) + δP(R {i+1} ), where δP(R) {i+1} The correction amount is obtained by querying the mapping table based on the risk level of the downstream partition.

4. The laboratory air purification method with zoned gradient pressure differential control according to claim 1, characterized in that, In step S140, the feedback precise adjustment adopts an incremental PID control algorithm, with the dynamic differential pressure setpoint ΔP. dynamic(i，i+1) To control the target, the measured pressure difference ΔP sensor(i，i+1) For the feedback quantity, calculate the control quantity u(k): Where, e(k) = ΔP dynamic(i，i+1) - ΔP sensor(i，i+1) K represents the pressure difference deviation at the current moment. p K i K d These are the proportional, integral, and derivative coefficients, respectively. The controller adjusts the frequency of the blower or the opening of the exhaust valve in the corresponding zone based on u(k).

5. The laboratory air purification method with zoned gradient pressure differential control according to claim 1, characterized in that, In step S150, the model predictive control algorithm performs the following process in each control cycle: based on the current system state and the dynamic differential pressure setpoint sequence for the next M time moments, it uses the system dynamic model to predict the system output for the next P time moments, obtains the optimal control quantity sequence for the next M time moments by solving a constrained optimization problem, and applies the first control quantity in the sequence to the system actuator.

6. A laboratory air purification system with zoned gradient pressure differential control, characterized in that, The system includes the following components: The baseline partitioning and gradient model establishment module is used to divide the laboratory physical space into N sequentially adjacent functional partitions, numbered Z1, Z2, …, ZN, where Z1 is the cleanest zone and ZN is the zone with the highest contamination risk. A preset baseline pressure difference ΔP is established between adjacent partitions Zi and Zi+1. base(i，i+1) All the reference differential pressure settings form a reference pressure gradient chain from Z1 to ZN with pressure decreasing step by step; The zone risk dynamic assessment module is used to monitor the operating status of at least one key risk device in each zone in real time. The key risk device refers to the device whose operating status is directly related to the intensity of pollution sources in that zone. Based on the operating status of the key risk device, the module dynamically assesses the real-time risk level Ri of the zone in which it is located. The gradient pressure differential dynamic reconstruction module is used to dynamically adjust the benchmark pressure gradient chain based on the real-time risk level Ri, generating a new adjacent zone pressure differential setpoint ΔP. dynamic(i，i+1) The dynamic pressure gradient chain is adjusted according to the following principle: for partitions with increased risk level, the absolute value of the negative pressure difference setting relative to the upstream adjacent partition is increased; for partitions with decreased risk level, the absolute value of the negative pressure difference setting relative to the upstream adjacent partition is decreased. Global verification is performed during the adjustment process to ensure that the pressure value on any path from Z1 to ZN in the adjusted dynamic pressure gradient chain always remains monotonically decreasing. The feedforward-feedback composite control module is used to enable the controller of the ventilation and air conditioning system to output adjustment commands for the supply and exhaust air volume of each zone according to the set value of the dynamic pressure gradient chain. At the same time, it monitors and predicts key disturbance events in the laboratory. When a key disturbance event is detected to be about to occur, it executes feedforward compensation control to adjust the supply and exhaust air valves of the event-related area or start the local pressurization / extraction unit in advance before the disturbance actually affects the differential pressure. During and after the disturbance event, it combines the real-time feedback data of the differential pressure sensor to perform precise feedback adjustment. The global decoupling and collaborative optimization control module is used to transform the mutually coupled multi-zone differential pressure control problem into an approximately independent control loop or to perform multi-variable collaborative optimization by employing decoupling control algorithms or model predictive control algorithms. The controller comprehensively calculates and outputs collaborative control commands for each actuator.

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