Clean room energy-saving pressure control method and system based on dynamic pipe network and model predictive control

Abstract

The invention relates to the technical field of clean environment control, in particular to a dynamic pipe network and model predictive control clean room energy-saving pressure control method and system. Obtaining current pipe network impedance according to the pipe network impedance curve; calculating the current valve impedance according to the valve opening; calculating the total pressure drop of the current pipe network; constructing a fan dynamic model based on a fan similarity law to calculate the air volume at the next moment; a clean room pressure difference dynamic model is constructed, and the clean room pressure difference at the next moment is calculated; constructing a state space model and a target function of a model prediction controller; and when the pressure difference of the clean room is unstable, predicting the change trend of the pressure difference of the clean room in the next time period, and reversely solving the optimal fan frequency and valve opening of the discretization state space model by using the model prediction controller so as to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The control precision and robustness of the pressure difference of the clean room are remarkably improved, and meanwhile energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of clean environment control, and in particular to a clean room energy-saving pressure control method and system based on a dynamic pipe network and model predictive control. Background Art

[0002] As an important infrastructure supporting high-tech industries, the clean industry is entering a golden period of rapid development. How to improve the energy efficiency of air-conditioning systems while maintaining high cleanliness has become a difficult problem that the clean industry urgently needs to solve.

[0003] Cleanroom pressure difference refers to the pressure difference between the cleanroom and the adjacent area or the external environment. It is an important parameter for cleanroom environmental control and plays a key role in maintaining the cleanliness of the cleanroom and preventing external pollutants from entering. Existing cleanroom pressure difference control strategies include: multi-fan combined frequency conversion technology, coordinated demand control ventilation strategy, variable residual air volume tracking method, zoned air conditioning and independent control method, and heat recovery system combined with variable air volume control method.

[0004] The multi-fan joint frequency conversion technology builds a pressure difference control system model based on the system air volume balance, and adds fresh air and exhaust air frequency conversion control. However, the coordinated control of multiple fans is difficult, and the fan states affect each other. Once an abnormality occurs, it is easy to cause pressure imbalance and oscillation, making it difficult to stabilize the pressure difference. The coordinated demand control ventilation strategy coordinates the outdoor and supply ventilation systems to achieve energy savings. However, this strategy may cause over-ventilation to maintain the pressure difference, increasing energy consumption. If the control strategy is not good, it will continue to operate at high energy consumption, reducing the overall energy efficiency of the system. The variable residual air volume tracking method maintains the clean room air leakage and pressure gradient through a data relationship model under variable air volume conditions. However, this method lacks direct monitoring of pressure fluctuations and cannot maintain pressure differential stability in time in the face of external disturbances such as door opening and equipment failure. The zoned air conditioning and independent control method is divided into zones according to function and pollution degree, and equipped with independent air conditioning and pressure difference control system. However, this greatly increases the complexity of system design, requires consideration of many factors, and increases the difficulty and cost of equipment maintenance. The heat recovery system combines the variable air volume control method with a heat recovery device to recover the heat and cold of the exhaust air to pre-treat the fresh air. However, this method increases the complexity of the system and the initial cost, the control relies on multi-variable coordination, the dynamic response is slow, the maintenance is difficult, and the adaptability is limited in some specific scenarios.

[0005] Although the above method can achieve basic pressure difference stability, it still has core problems such as insufficient control accuracy, limited energy consumption optimization, high system complexity, weak dynamic response capability and poor adaptability. It is difficult to meet the stringent requirements of pressure difference stability and energy-saving efficiency in high-tech fields such as biomedicine and semiconductors. Summary of the invention

[0006] To this end, the technical problem to be solved by the present invention is to overcome the problems of insufficient control accuracy, limited energy consumption optimization, high system complexity, weak dynamic response ability and poor adaptability in the prior art.

[0007] To solve the above technical problems, the present invention provides a clean room energy-saving pressure control method for a dynamic pipe network and model predictive control, including: Obtain the current fan speed according to the current fan frequency, and then calculate the current wind pressure according to the current fan speed and the current air volume. Obtain the pipe network impedance at different air volumes through numerical simulation, and construct a pipe network impedance curve; according to the pipe network impedance curve, obtain the current pipe network impedance from the current air volume; calculate the current valve impedance according to the valve opening; calculate the current total pipe network pressure drop according to the current valve impedance, the current pipe network impedance and the air handling equipment impedance. Construct a fan dynamic model based on the fan similarity law, which is used to calculate the air volume at the next moment according to the current air volume, the current total pipe network pressure drop and the current wind pressure; among them, the air volume of the exhaust fan is the exhaust air volume, and the air volume of the supply fan is the supply air volume. Calculate the valve pressure drop according to the valve opening and the current valve impedance, and then calculate the valve output flow. Construct a clean room differential pressure dynamic model, which is used to calculate the clean room differential pressure at the next moment according to the current clean room differential pressure, the exhaust air volume and the supply air volume at the next moment. Construct a discretized state space model, where the fan frequency and the valve opening are control inputs, the exhaust air volume, the supply air volume and the valve output flow are used as system state variables, and the clean room differential pressure is used as the output; construct an objective function of the model predictive controller with the goal of strong stability of the clean room differential pressure, which is used to minimize the differential pressure tracking error and the valve opening penalty term. Real-time monitor the clean room differential pressure. When the clean room differential pressure is unstable, use the clean room differential pressure dynamic model to predict the change trend of the clean room differential pressure in the next time period, and then use the model predictive controller to reverse solve the optimal valve opening of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency; adjust the fan frequency and the valve opening in the next time period with the optimal valve opening and fan frequency.

[0008] Preferably, calculate the current wind pressure according to the current fan speed and the current air volume, and the formula is: ; Wherein, is the current wind pressure, is the rated wind pressure, and are the current fan speed and the rated fan speed respectively, and are the current air volume and the rated air volume respectively, 、 , and are fitting coefficients.

[0009] Preferably, the current valve impedance is calculated according to the valve opening degree, and the formula is: ; where represents the valve angle corresponding to the valve opening degree, represents the valve impedance, represents the valve flow area, represents the air density, , and represent fitting coefficients; The current total pressure drop of the pipe network is calculated according to the current valve impedance, the current pipe network impedance and the impedance of the air handling equipment, and the formula is: ; where represents the total pressure drop of the pipe network, represents the pipe network impedance, represents the valve impedance, represents the impedance of the clean room air handling equipment, represents the air volume; When the valve opening degree is controlled above 85%, the total pressure drop of the pipe network is the smallest.

[0010] Preferably, the formula of the fan dynamic model is: ; where is the current air volume, is the air volume at the next moment, is the air volume response time constant, is the fan gain coefficient, is the fan frequency, is the functional relationship between the fan speed and the fan frequency, is the current wind pressure, is the total pressure difference; Total pressure difference where is the clean room pressure, is the total pressure drop of the pipe network.

[0011] Preferably, the valve dynamic model is used to adjust the valve opening degree in the next time period, and the formula is expressed as: ; where is the current valve opening degree, is the valve opening degree at the next moment, is the valve opening control signal, is the valve response time constant, is the valve gain coefficient.

[0012] Preferably, calculate the valve pressure drop according to the valve opening and the current valve impedance, and then calculate the valve output flow rate, including: Calculate the valve pressure drop according to the valve opening and the current valve impedance, and the formula is: ; where, is the valve pressure drop, represents the valve impedance, represents the air volume passing through the valve; The calculation formula for the valve output flow rate is: ; where, is the current valve output flow rate, is the valve output flow rate at the next moment, is the valve flow response time constant, is the valve flow gain coefficient, is the current valve opening.

[0013] Preferably, the formula for the clean room differential pressure dynamic model is: ; where, is the current differential pressure of the clean room, is the differential pressure of the clean room at the next moment, is the air capacitance constant of the clean room, and are the supply air volume and the exhaust air volume of the clean room area e respectively, is the clean room leakage constant.

[0014] Preferably, construct a discretized state space model, and the formula is expressed as: ; ; where, is the system state vector at time k, is the system state vector at time k+1, is the control input vector at time k, is the output vector at time k, is the system matrix, is the input matrix, is the output matrix, is the direct transfer matrix; Construct the objective function of the model predictive controller, and the formula is expressed as: ; wherein, is the first objective function, is the second objective function, is the prediction horizon length, is the differential pressure of the clean room at time k, is the preset value of the differential pressure of the clean room, is the valve opening at time k, is the slack variable at time k, is the soft constraint of the valve opening at time k, and are the minimum and maximum values of the differential pressure of the clean room respectively, , , , and are all weight coefficients.

[0015] Preferably, when it is detected that the door of the clean room is opened, the fan frequency is increased to the preset frequency, the change trend of the differential pressure of the clean room in the next time period is predicted by using the dynamic model of the differential pressure of the clean room, and then the optimal valve opening is inversely solved by using the state space model to adjust the valve opening in the next time period.

[0016] The present invention also provides a clean room energy-saving pressure control system for a dynamic pipe network and model predictive control, including: A sensor module, including: A differential pressure sensor, installed between the clean room and the adjacent area, and at the front and rear ends of the pipe network and the fan, for real-time detecting the differential pressure of the clean room and the pipe network impedance; An air volume sensor, installed at the rear end of the supply air valve, the front end of the exhaust air valve, and the front end of the fan, for obtaining the supply air volume and the exhaust air volume; A valve opening sensor, installed on the supply air valve and the exhaust air valve, for obtaining the valve opening; A control module, communicatively connected to the sensor module, is configured to obtain the current fan speed according to the current fan frequency, and then calculate the current air pressure according to the current fan speed and the current air volume; obtain the pipe network impedance at different air volumes through numerical simulation, and construct a pipe network impedance curve; according to the pipe network impedance curve, obtain the current pipe network impedance from the current air volume; calculate the current valve impedance according to the valve opening; calculate the current total pipe network pressure drop according to the current valve impedance, the current pipe network impedance and the air handling equipment impedance; construct a fan dynamic model based on the fan similarity law, which is used to calculate the air volume at the next moment according to the current air volume, the current total pipe network pressure drop and the current air pressure; wherein, the air volume of the exhaust fan is the exhaust air volume, and the air volume of the supply fan is the supply air volume; calculate the valve pressure drop according to the valve opening and the current valve impedance, and then calculate the valve output flow; construct a clean room differential pressure dynamic model, which is used to calculate the clean room differential pressure at the next moment according to the current clean room differential pressure, the exhaust air volume and the supply air volume at the next moment; construct a discretized state space model, wherein the fan frequency and the valve opening are control inputs, the exhaust air volume, the supply air volume and the valve output flow are used as system state variables, and the clean room differential pressure is used as the output; construct an objective function of the model predictive controller with the goal of stable clean room differential pressure strength, which is used to minimize the differential pressure tracking error and the valve opening penalty term; monitor the clean room differential pressure in real time. When the clean room differential pressure is unstable, use the clean room differential pressure dynamic model to predict the change trend of the clean room differential pressure in the next time period, and then use the model predictive controller to inversely solve the optimal valve opening of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency; An execution module is configured to adjust the fan frequency and the valve opening in the next time period with the optimal valve opening and fan frequency.

[0017] The above technical solution of the present invention has the following beneficial effects compared with the prior art: The clean room energy-saving pressure control method for a dynamic pipe network and model prediction control according to the present invention takes into account the pipe network impedance and the valve impedance. By constructing a pipe network impedance curve to dynamically calculate the pipe network impedance and calculating the valve impedance according to the valve opening, the total pipe network pressure drop can be calculated; then construct a fan dynamic model based on the fan similarity law, optimize the air volume in the next time period according to the air volume and the total pipe network pressure drop, and dynamically predict the clean room differential pressure in the next time period according to the optimized air volume; finally, construct a state space model including the fan, the pipe network, the valve and the clean room differential pressure, and use the model predictive controller to inversely solve the optimal valve opening of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The state space model realizes multi-variable cooperative control by coupling multiple variables, breaking through the trade-off problem between differential pressure stability and energy consumption in the prior art. The present invention combines the pipe network impedance and the valve opening for joint optimization, can perform dynamic response in advance according to the predicted clean room differential pressure, significantly improves the control accuracy and robustness of the clean room differential pressure, reduces energy consumption at the same time, and is applicable to high-requirement scenarios such as semiconductors. Brief Description of the Drawings

[0018] In order to make the content of the present invention easier to be clearly understood, the following further describes the present invention in detail according to specific embodiments of the present invention in combination with the drawings, where: Figure 1 is a flowchart of an energy-saving pressure control method for a clean room with a dynamic pipe network and model predictive control according to the present invention; Figure 2 is a main circulation flowchart of an energy-saving pressure control system for a clean room with a dynamic pipe network and model predictive control in an embodiment of the present invention; Figure 3 is an operation flowchart of an energy-saving pressure control system for a clean room with a dynamic pipe network and model predictive control in an embodiment of the present invention; Figure 4 is a prediction and optimization flowchart of a control module of an energy-saving pressure control system for a clean room with a dynamic pipe network and model predictive control in an embodiment of the present invention; Figure 5 is an example structure diagram of an energy-saving pressure control system for a clean room with a dynamic pipe network and model predictive control in an embodiment of the present invention; Figure 6 is a schematic diagram of the control module issuing a control input; Explanation of the reference numerals in the drawings: 1. Return air speed sensor in the return air section of the clean room; 2. Return air pressure sensor in the return air section of the clean room; 3. Return air valve opening sensor in the return air section of the clean room; 4. Supply air speed sensor; 5. Supply air pressure sensor; 6. Supply air speed sensor in the supply air section of the return air clean room; 7. Supply air pressure sensor in the supply air section of the return air clean room; 8. Supply air valve opening sensor in the supply air section of the return air clean room; 9. Supply air speed sensor in the supply air section of the exhaust air clean room; 10. Supply air pressure sensor in the supply air section of the exhaust air clean room; 11. Supply air valve opening sensor in the supply air section of the exhaust air clean room; 12. Pressure sensor in the return air clean room; 13. Pressure sensor in the exhaust air clean room; 14. Exhaust air speed sensor in the exhaust air section of the exhaust air clean room; 15. Exhaust air pressure sensor in the exhaust air section of the exhaust air clean room; 16. Exhaust air valve opening sensor in the exhaust air section of the exhaust air clean room; 17. Exhaust air speed sensor; 18. Exhaust air pressure sensor; 19. Door magnetic sensor in the return air clean room; 20. Door magnetic sensor in the exhaust air clean room; 21. Clean room system; 22. CSV data; 23. Control module; 24. Fresh air valve; 25. Air handling equipment; 26. Supply air fan; 27. Return air valve of the return air clean room; 28. Supply air valve of the return air clean room; 29. Return air clean room; 30. Supply air valve of the exhaust air clean room; 31. Exhaust air clean room; 32. Exhaust air valve of the exhaust air clean room; 33. Exhaust air fan. Detailed Description of the Preferred Embodiments

[0019] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it, but the specific embodiments cited do not limit the present invention.

[0020] Embodiment 1:

[0021] Referring to Figure 1 as shown, the present invention provides a clean room energy-saving pressure control method for a dynamic pipe network and model predictive control, including: S1: Obtain the current fan speed according to the current fan frequency, and then calculate the current wind pressure according to the current fan speed and the current air volume.

[0022] To achieve accurate monitoring of the fan operation status, in this embodiment, air volume and wind pressure sensors are installed at key parts of the air duct system, and at the same time, a functional relationship between the fan frequency and the fan speed is constructed through experiments: ; wherein, is the fan speed, is the fan frequency, is the functional relationship between the fan speed and the fan frequency.

[0023] According to the fan similarity law, the air volume of the fan at different speeds is proportional to the speed, and the wind pressure is proportional to the square of the speed. Calculate the current wind pressure according to the current fan speed and the current air volume, and the formula is: ; wherein, is the current wind pressure, is the rated wind pressure, and are the current fan speed and the rated fan speed respectively, and are the current air volume and the rated air volume respectively, , , and are fitting coefficients.

[0024] In practical applications, the speed of the fan in the initial stable operation state can be calculated through the corresponding relationship between the fan frequency and the speed. When the fan frequency changes, the speed corresponding to the new frequency . Then, according to the above formula, the corresponding wind pressure at different speeds can be accurately calculated.

[0025] S2: Obtain the pipe network impedance under different air volumes through numerical simulation, and construct a pipe network impedance curve; according to the pipe network impedance curve, obtain the current pipe network impedance from the current air volume; calculate the current valve impedance according to the valve opening; calculate the current total pressure drop of the pipe network according to the current valve impedance, the current pipe network impedance, and the air handling equipment impedance.

[0026] For the complex pipe network structure of the clean room, in the design stage, use computational fluid dynamics (CFD) software to simulate and analyze the air flow in the pipe network. By inputting detailed parameters such as the pipe diameter, length, roughness, number and angle of elbows of the pipe network, combined with actual gas physical property data, and the performance parameters of the unique air handling equipment in the clean room (such as high-efficiency air filters, fan coil units, modular air handling units, etc.), the pressure distribution and flow rate in the pipe network under different working conditions are obtained through simulation. During the actual operation process, the working conditions of the clean room will change continuously, which poses challenges to the air flow stability in the pipe network. In order to achieve precise regulation of the air flow in the pipe network under different working conditions, it is necessary to understand the impedance characteristics of the pipe network, so it is very necessary to design a pipe network impedance curve. On this basis, in this embodiment, the pipe network impedance formula is defined as: ; where, represents the pressure drop caused by the pipe network, represents the pipe network impedance, represents the air volume.

[0027] Calculate a series of pipe network impedance values by simulating and outputting the corresponding wind pressure data at different flow rates. At the same time, establish a refined valve model. Through experimental tests, obtain the flow rate and pressure drop at different valve openings at different openings, and construct the relationship between the valve impedance and the valve opening, which can be approximately expressed as: ; where, represents the valve angle corresponding to the valve opening, represents the valve impedance, represents the valve flow area, represents the air density, 、 and represent fitting coefficients.

[0028] Calculate the current total pressure drop of the pipe network according to the current valve impedance, the current pipe network impedance, and the air handling equipment impedance. The formula is: ; where, represents the total pressure drop of the pipe network, represents the pipe network impedance, represents the valve impedance, Represents the impedance of the cleanroom air handling equipment, represents the air volume.

[0029] The above formula indicates that to ensure the minimization of the pipe network impedance, the valve opening is controlled above 85%, the overall resistance coefficient of the pipe network is minimized, and the total pressure drop of the pipe network is minimized. At this time, the actual working air volume and air pressure of the fan are reduced, thereby realizing the reduction of the fan frequency to save energy.

[0030] S3: Construct a fan dynamic model based on the fan similarity law for calculating the air volume at the next moment according to the current air volume, the current total pressure drop of the pipe network, and the current air pressure; among them, the air volume of the exhaust fan is the exhaust air volume, and the air volume of the supply fan is the supply air volume.

[0031] Based on the above fan similarity law (static relationship), the fan dynamic model is established as a first-order inertia, and the formula is expressed as: ; where, is the current air volume, is the air volume at the next moment, is the air volume response time constant, is the fan gain coefficient, is the fan frequency, is the functional relationship between the fan speed and the fan frequency, is the current air pressure, is the total pressure difference.

[0032] Total pressure difference , where is the cleanroom pressure, is the total pressure drop of the pipe network.

[0033] By experimentally measuring the air volume response time constant and the fan gain coefficient, with the input being the fan control signal (such as the frequency of the frequency converter) and the output being the fan air volume, it is driven by the square root term of the total pressure drop of the pipe network.

[0034] Furthermore, the pipe network model of the present invention adopts the static impedance distribution method, and the air volume of the branch with a valve is expressed as: ; where, represents the air volume of the i-th branch with a valve, represents the valve angle corresponding to the valve opening of the i-th branch with a valve, is the pipe network impedance of this branch, is the pressure drop caused by this branch of the pipe network.

[0035] The air volume of the branch without a valve is expressed as: ; where, represents the air volume of the j-th branch without a valve is the pipe network impedance of this branch is the pressure drop caused by the pipe network of this branch. Model the pipe network impedance of the branch without a valve as a fixed impedance without dynamic adjustment

[0036] The calculated air volume needs to satisfy the total pipe network flow balance, that is, the air volume output by the fan must be equal to the sum of the air volumes of all branches. The formula is expressed as: .

[0037] S4: Calculate the valve pressure drop according to the valve opening and the current valve impedance, and then calculate the valve output flow

[0038] Adjust the valve opening for the next time period using the valve dynamic model. The formula is expressed as: ; where is the current valve opening is the valve opening at the next moment is the valve opening control signal is the valve response time constant is the valve gain coefficient

[0039] Calculate the valve pressure drop according to the valve opening and the current valve impedance as: ; where is the valve pressure drop represents the valve impedance represents the air volume passing through the valve

[0040] The calculation formula for the valve output flow is: ; where is the current valve output flow is the valve output flow at the next moment is the valve flow response time constant is the valve flow gain coefficient is the current valve opening

[0041] S5: Build a dynamic model of the cleanroom pressure difference to calculate the cleanroom pressure difference at the next moment based on the current cleanroom pressure difference, the exhaust air volume and the supply air volume at the next moment

[0042] The formula for the dynamic model of the cleanroom pressure difference is: ; Among them, is the current differential pressure of the clean room, is the differential pressure of the clean room at the next moment, is the air capacitance constant of the clean room, and are the supply air volume and exhaust air volume of area e of the clean room respectively, is the leakage constant of the clean room.

[0043] S6: Construct a discretized state space model, where the fan frequency and valve opening are the control inputs, the exhaust air volume, supply air volume, and valve output flow are used as system state variables, and the differential pressure of the clean room is used as the output; construct the objective function of the model predictive controller with the goal of stabilizing the differential pressure of the clean room to minimize the differential pressure tracking error and the valve opening penalty term.

[0044] To achieve multi-variable dynamic cooperative control, this embodiment introduces a model predictive controller and constructs a state space model of a system including a fan, a pipe network, a valve, and the differential pressure of the clean room, which is expressed by the formula: ; ; Among them, is the system state vector at time k, is the derivative of the system state vector at time k + 1, is the control input vector at time k, is the output vector at time k, is the system matrix, is the input matrix, is the output matrix, is the direct transfer matrix.

[0045] Set the objective function of the model predictive controller for the valve opening of 85% and the goal of stabilizing the differential pressure to minimize the differential pressure tracking error and the valve opening penalty term, which is expressed by the formula: ; Among them, is the first objective function, is the second objective function, is the prediction horizon length, is the differential pressure of the clean room at time k, is the preset value of the differential pressure of the clean room, is the valve opening at time k, is the slack variable at time k, allowing short-term violation of the differential pressure constraint; is the soft constraint of the valve opening at time k, allowing the valve opening to be lower than 85% in the short term; and are the minimum and maximum values of the differential pressure of the clean room respectively, , , , and are all weighting coefficients.

[0046] Through theoretical derivation and experimental verification, the present invention proposes an operation criterion for minimizing the pipe network resistance when the valve opening degree ≥ 85%, breaking through the limitations of traditional fixed opening degree or single-variable regulation. The objective function can preferentially ensure the stability of the pressure difference, and at the same time limit the energy consumption through the valve opening degree penalty term, ensuring that the pressure difference fluctuation ≤ 0.5 Pa and it recovers within 2 seconds.

[0047] S7: Real-time monitor the pressure difference in the clean room. When the pressure difference in the clean room is unstable, use the dynamic model of the clean room pressure difference to predict the change trend of the clean room pressure difference in the next time period, and then use the model predictive controller to inversely solve the optimal valve opening degree of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency; adjust the fan frequency and valve opening degree in the next time period with the optimal valve opening degree and fan frequency.

[0048] Preferably, the opening of the clean room door can be used as an event source, and a high-precision door magnetic sensor is installed on the clean room door frame. When the door is opened, the door magnetic sensor quickly captures the state change and sends a trigger signal to the control system to increase the fan frequency to the preset frequency. Use the dynamic model of the clean room pressure difference to predict the change trend of the clean room pressure difference in the next time period, and then inversely solve the optimal valve opening degree using the state space model to adjust the valve opening degree in the next time period. This can quickly supplement the air volume and reduce the pressure difference disturbance. When the clean room system door is closed, the door magnetic sensor sends a signal again, and the control system adjusts the fan frequency and valve opening degree according to the steps of S1-S7.

[0049] In summary, for the energy-saving pressure control method of the clean room with a dynamic pipe network and model predictive control described in the present invention, considering the pipe network impedance and valve impedance, the pipe network impedance curve is constructed to dynamically calculate the pipe network impedance, and the valve impedance is calculated based on the valve opening degree, and the total pressure drop of the pipe network can be calculated; then a fan dynamic model is constructed based on the fan similarity law, the air volume in the next time period is optimized according to the air volume and the total pressure drop of the pipe network, and the clean room pressure difference in the next time period is dynamically predicted according to the optimized air volume; finally, a state space model including the fan, pipe network, valve, and clean room pressure difference is constructed, and the model predictive controller is used to inversely solve the optimal valve opening degree of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The state space model realizes multi-variable collaborative control by coupling multiple variables, breaking through the trade-off problem between pressure difference stability and energy consumption in the prior art. The present invention combines the pipe network impedance and valve opening degree for joint optimization, can make a dynamic response in advance according to the predicted clean room pressure difference, significantly improves the control accuracy and robustness of the clean room pressure difference, reduces energy consumption at the same time, and is applicable to high-requirement scenarios such as semiconductors.

[0050] The applicable fields of the present invention include: the pharmaceutical and biopharmaceutical industries, such as GMP-certified clean workshops and biosafety laboratories; the electronics manufacturing and semiconductor industries, such as semiconductor clean rooms and precision optical component production; the medical device and food industries, such as the production of sterile medical devices and clean workshops for food and dairy products; intelligent building and renovation projects, such as hospital operating rooms and ICUs, and energy-saving renovation of existing clean rooms; nuclear facilities and radioactive laboratories; animal disease isolation centers; cultural relic restoration laboratories.

[0051] Embodiment 2:

[0052] Based on the clean room energy-saving pressure control method of a dynamic pipe network and model predictive control described in Embodiment 1, this embodiment provides a clean room energy-saving pressure control system of a dynamic pipe network and model predictive control, including: A sensor module, including a differential pressure sensor, an air volume sensor, and a valve opening sensor; A control module, configured to obtain the current fan speed according to the current fan frequency, and then calculate the current air pressure according to the current fan speed and the current air volume; obtain the pipe network impedance at different air volumes through numerical simulation, and construct a pipe network impedance curve; according to the pipe network impedance curve, obtain the current pipe network impedance from the current air volume; calculate the current valve impedance according to the valve opening; calculate the current total pipe network pressure drop according to the current valve impedance, the current pipe network impedance, and the air handling equipment impedance; construct a fan dynamic model based on the fan similarity law, for calculating the air volume at the next moment according to the current air volume, the current total pipe network pressure drop, and the current air pressure; where the air volume of the exhaust fan is the exhaust air volume, and the air volume of the supply fan is the supply air volume; construct a clean room differential pressure dynamic model, for calculating the clean room differential pressure at the next moment according to the current clean room differential pressure, the exhaust air volume and the supply air volume at the next moment; construct a state space model, where the fan frequency and the valve opening are control inputs, the exhaust air volume and the supply air volume are used as system state variables, and the clean room differential pressure is used as the output; construct an objective function of the state space model with the goal of stable clean room differential pressure strength, for minimizing the differential pressure tracking error and the valve opening penalty term; monitor the clean room differential pressure in real time, when the clean room differential pressure is unstable, use the clean room differential pressure dynamic model to predict the change trend of the clean room differential pressure in the next time period, and then use the state space model to inversely solve the optimal fan frequency and valve opening; An execution module, configured to adjust the fan frequency and the valve opening in the next time period; A communication module, responsible for data transmission and communication between the sensor module, the control module, and the execution module. Adopt a reliable communication protocol to ensure the accurate transmission of data and the stable operation of the system.

[0053] The sensor module includes: Differential pressure sensors are installed at the front and rear ends of the clean room and adjacent areas, as well as in front of and behind the pipe network and the fan. They monitor the differential pressure of the clean room and the pipe network impedance in real time and transmit the differential pressure data to the control module. The sensors are characterized by high precision and fast response, and can capture small changes in differential pressure in a timely manner. The accuracy of the sensors is ±0.1 Pa, and the data transmission frequency is 100 ms / time. Air volume sensors are installed at the rear end of the supply air valve, the front end of the exhaust air valve, and the front end of the fan respectively to measure the supply air volume and the exhaust air volume. By monitoring the air volume in real time, accurate air flow information is provided to the control module for precise control and adjustment. The measured wind speed range of the sensors is 0 - 20 m / s, and the accuracy of calculating the air volume in combination with the pipe diameter is ±2%. Valve opening sensors, using magnetostrictive displacement sensors, are installed on the supply air valve and the exhaust air valve to feedback the opening state of the valves in real time. The control module can calculate the impedance of the valves according to the data of the valve opening sensors and conduct comprehensive analysis in combination with the pipe network impedance model. The sensors detect the valve opening range of 0 - 100%, and the response time is <50 ms.

[0054] Preferably, the sensor module further includes a door magnetic sensor installed on the door frame of the clean room to detect the opening and closing state of the door, with the highest trigger priority.

[0055] The control module includes: A pipe network impedance calculation sub-module is used to input pipe network parameters (pipe diameter, roughness, number of elbows, etc.) and output the pipe network impedance curve at different flow rates. A valve model library is used to store the opening-impedance relationships (such as Kv value tables) of different valve types (butterfly valve, ball valve). A real-time impedance calculation module is used to dynamically update the total pipe network impedance according to the current valve opening and air volume. A model predictive controller, as the core control unit of the system, constructs a state space model according to the data collected by the sensor module. The fan frequency and valve opening are used as control inputs, the supply air volume, exhaust air volume, etc. are used as system state variables, and the differential pressure of the clean room is used as the system output. The model predictive controller sets an objective function, which includes a differential pressure tracking error and a valve opening penalty term. By minimizing the objective function, while ensuring the stability of the differential pressure in the clean room, the valve opening is optimized, the pipe network resistance is reduced, and energy-saving operation is achieved. In each control cycle, the model predictive controller predicts the future state of the system and solves the optimization problem according to the prediction results to dynamically adjust the fan frequency and valve opening.

[0056] The execution module includes: The fan is a variable-frequency fan that adjusts its rotational speed according to the frequency signal output by the control module, thereby changing the air supply volume. The performance parameters of the fan are obtained by experimentally fitting the relationship between frequency and rotational speed and dynamically calculated in combination with the fan similarity law. The variable-frequency fan drives the output of a 0 - 50 Hz frequency signal with a frequency adjustment accuracy of ±0.1 Hz. The valves include an air supply valve and an exhaust valve, which adjust their opening degrees according to the instructions of the control module. A quantitative relationship is established between the opening degree of the valve and the impedance. By adjusting the opening degree of the valve in real time, the pipe network impedance is optimized. The electric valve control outputs a 4 - 20 mA signal with an opening degree resolution of 0.1%.

[0057] Each module of the clean room energy-saving pressure control system with a dynamic pipe network and model predictive control works in coordination to achieve precise control of the pressure difference in the clean room and energy-saving operation.

[0058] The specific working process of the clean room energy-saving pressure control system with a dynamic pipe network and model predictive control provided by the present invention is as follows: Step 1: Initialization.

[0059] After the system is started, initialization operations are performed, including loading pipe network parameters, sensor calibration, initial parameter setting, etc. Among them, the initial control parameter sets the fan frequency to 30 Hz and the valve opening degree to 90%.

[0060] After initialization is completed, it enters the main loop. The process of the main loop refers to Figure 2 as shown, which includes model predictive controller prediction and optimization, multi-mode selection and instruction execution, disturbance response detection and logging, and at the same time, adding disturbance detection to the main loop to emphasize its control priority. Figure 3 It is the operation flow chart of the clean room energy-saving pressure control system with a dynamic pipe network and model predictive control in the embodiment of the present invention.

[0061] Step 2: Data acquisition.

[0062] The sensor module real-time collects data such as the pressure difference, air supply volume, exhaust air volume, and valve opening degree in the clean room, and conducts data validity verification on the collected data.

[0063] When the data is invalid, such as data with large fluctuations compared with the previous moment, the abnormal data is marked and ignored.

[0064] When the data is valid, data filtering is performed to reduce the influence of noise on the data, and this data is used for real-time pipe network impedance calculation, and then working condition identification is carried out. The working condition identification mainly relies on the dynamic data of the pipe network, and then the identified working condition mode is transmitted to the control module so that it can perform different optimization calculations according to the working condition.

[0065] Step 3: Working condition identification.

[0066] The system adopts an adaptive control strategy. Based on factors such as the personnel activities and equipment operation status in the clean room, it automatically identifies different operating condition modes according to the personnel density and equipment load, such as normal mode, intensive mode, high-load mode, etc.

[0067] Preferably, an infrared sensor is used to identify the personnel density, and current monitoring is used to identify the equipment load.

[0068] The normal mode aims to optimize energy consumption. On the basis of stabilizing the differential pressure in the clean room, the objective function is optimized, and the output low fan frequency and valve opening are limited to about 85% to optimize energy consumption. The intensive mode mainly targets scenarios with frequent personnel activities (such as peak production periods, experimental operation periods). At this time, the control module will reduce the priority of optimizing energy consumption, give priority to ensuring the differential pressure stability, increase the fan frequency, and allow the valve opening to be lower than 85% for a short time to optimize the overall air flow distribution. The load mode mainly targets changes in equipment operation compliance, such as the increase in pipeline impedance caused by filter blockage in air handling equipment or differential pressure disturbances caused by detecting events such as door opening. The system will switch to the load mode. At this time, the control module will no longer consider optimizing energy consumption. The only control purpose is to control the differential pressure in the clean room. The control strategy includes outputting the rated fan frequency + dynamic adjustment of the valve opening, no longer taking the valve opening as a constraint condition, and giving priority to adjusting the differential pressure in the clean room.

[0069] The intensive mode is the response mode under the condition that the clean room door is opened. When the door magnetic sensor detects a door opening event, the operating condition mode is identified as the load mode for control calculation, that is, the fan frequency is set to 50Hz, and the valve opening is dynamically optimized; when the door is closed, it returns to the normal mode, and the control module is used to optimize both the fan frequency and the valve opening at the same time to continue the main loop.

[0070] Through the adaptive control strategy, the system can automatically adjust the control parameters and strategies for different operating condition modes. For example, in the intensive personnel mode, the fan frequency is appropriately increased and the valve opening is enlarged to ensure sufficient ventilation volume and differential pressure stability. When the equipment is at low load and there are few personnel, the fan frequency is reduced and the valve opening is decreased to achieve energy-saving operation.

[0071] Step Four: Model calculation and optimization.

[0072] Refer to Figure 4 As shown, the control module constructs a state space model based on the collected data, calculates the current system state and future predicted state; at the same time, it conducts constraint condition checks. Different constraint conditions characterized by different operating condition modes will enable the control module to determine the optimal fan frequency and valve opening according to different constraint conditions. For example, for the quadratic programming problem solved in the normal mode, the control input with the minimum energy consumption while obtaining a stable differential pressure is obtained.

[0073] Step Five: Control Instruction Output: The control module sends the calculated fan frequency and valve opening instructions to the execution module.

[0074] Step Six: Execution of Regulation: The fan adjusts its speed according to the frequency instruction, and the valve adjusts its opening according to the opening instruction to achieve precise control of the differential pressure in the clean room.

[0075] Step Seven: Loop Monitoring and Adjustment: The system continuously loops through steps such as data acquisition, model calculation, control instruction output, and execution of regulation, and monitors and adjusts the differential pressure and energy consumption in the clean room in real time to ensure that the system is always in an optimal operating state.

[0076] In this embodiment, through dynamic pipe network impedance modeling and real-time control technology, the problem that the traditional method cannot actively optimize the system resistance is solved, achieving a 20%-30% reduction in pipe network resistance and a 15%-25% reduction in fan energy consumption; adopting a multi-variable cooperative control structure driven by a model predictive controller can improve the differential pressure control accuracy to ±0.5 Pa, and shorten the disturbance recovery time to within 2 seconds; an optimization algorithm based on the fan similarity law synchronously optimizes the fan frequency and valve opening, increasing the fan operation efficiency by 40%; a multi-mode intelligent switching mechanism realizes fine energy consumption optimization, with energy savings of 15%-20% in typical scenarios; an event-driven compensation mechanism shortens the differential pressure recovery time after the door is opened to within 2 seconds; a modular distributed architecture improves the system reliability and scalability. These innovations jointly break through the trade-off problem between differential pressure stability and energy consumption, forming a self-consistent intelligent control system, and significantly improving the energy efficiency ratio and robustness of differential pressure control in the clean room.

[0077] Embodiment Three:

[0078] This embodiment takes two clean rooms with two different differential pressure control strategies as an example to provide an example of an energy-saving differential pressure control system for a clean room with dynamic pipe network and model predictive control, and its structure is as shown in Figure 5 as shown.

[0079] This system includes clean rooms that control differential pressure with two different strategies. Among them, the return air clean room 29 controls the differential pressure with a return air strategy, and the exhaust air clean room 31 controls the differential pressure with an exhaust air strategy. In the clean room ventilation system, the supply air pipe network is respectively equipped with a supply air section wind speed sensor 4 and a supply air section pressure sensor 5 in the main ventilation section to detect the air volume and air pressure in the main pipeline.

[0080] The supply air valve 28, supply air section air velocity sensor 6, supply air section pressure sensor 7, and supply air valve opening degree sensor 8 of the return air clean room form the supply air VAV system of the return air clean room 29. The return air clean room 29 adopts a return air strategy to control the differential pressure, so a return air section air velocity sensor 1, return air section pressure sensor 2, return air valve opening degree sensor 3, and return air valve 27 of the return air clean room are installed on the return air duct. The pressure sensor 12 of the return air clean room can be assembled at any position in the return air clean room 29, but it is recommended to be at least two air outlet diameters away from the ventilation opening and the exhaust opening to ensure that the collected clean room pressure data is less polluted.

[0081] The exhaust air clean room 31 controls the differential pressure with an exhaust air strategy. The supply air section air velocity sensor 9, supply air section pressure sensor 10, supply air valve opening degree sensor 11, and supply air valve 30 of the exhaust air clean room form the VAV system of the exhaust air clean room 31. The pressure sensor 13 of the exhaust air clean room 31 is used to detect the pressure of the exhaust air clean room 31. The exhaust air section air velocity sensor 14, exhaust air section pressure sensor 15, exhaust air valve opening degree sensor 16, and exhaust air valve 32 of the exhaust air clean room form the exhaust air VAV system of the exhaust air clean room 31. The power source of the exhaust air system is provided by the exhaust air fan 33. The exhaust air section air velocity sensor 17 and the exhaust air section pressure sensor 18 are used to detect the air volume and air pressure of the exhaust air fan.

[0082] Specifically, a supply air VAV system is installed on the supply air outlet of the return air clean room 29, while a return air system is installed on the side close to the fresh air valve 24, and a return air VAV system is installed on the return air system. The return air and the fresh air together form the supply air volume of the return air clean room 29 and the exhaust air clean room 31. At the same time, the supply air volume is controlled by the supply air VAV systems of the two clean rooms respectively. The exhaust air clean room 31 is equipped with an exhaust air duct, and an exhaust air VAV system, an exhaust air fan 33, an exhaust air section air velocity sensor 17, and an exhaust air section pressure sensor 18 are installed on the exhaust air duct.

[0083] Specifically, a wireless communication module is installed on the control module 23 to collect the operation data of the air return section air velocity sensor 1 in the return air clean room, the air return section pressure sensor 2 in the return air clean room, the return air valve opening sensor 3 in the return air clean room, the air supply section air velocity sensor 4, the air supply section pressure sensor 5, the air supply section air velocity sensor 6 in the return air clean room, the air supply section pressure sensor 7 in the return air clean room, the return air valve opening sensor 8 in the return air clean room, the pressure sensor 12 in the return air clean room, the air supply section air velocity sensor 9 in the exhaust air clean room, the air supply section air pressure sensor 10 in the exhaust air clean room, the return air valve opening sensor 11 in the exhaust air clean room, the pressure sensor 13 in the exhaust air clean room, the exhaust air section air velocity sensor 14 in the exhaust air clean room, the exhaust air section pressure sensor 15 in the exhaust air clean room, the exhaust air valve opening sensor 16 in the exhaust air clean room, the exhaust air section air velocity sensor 17, the exhaust air section pressure sensor 18, the door magnetic sensor 19 in the return air clean room, and the door magnetic sensor 20 in the exhaust air clean room, and generate CSV data 22.

[0084] The control module monitors the air volume and air pressure in the main pipeline according to the data of the air supply section air velocity sensor 4 and the air supply section pressure sensor 5, and detects the air volume and air pressure in the exhaust pipeline according to the data of the exhaust air section air velocity sensor 17 and the exhaust air section pressure sensor 18.

[0085] The control module obtains the return air volume of the return air clean room according to the return air section air velocity sensor 1 in the return air clean room, obtains the return air pressure of the return air clean room according to the return air section pressure sensor 2 in the return air clean room, obtains the opening degree of the return air valve in the return air clean room according to the return air valve opening sensor 3 in the return air clean room, and then calculates the return air volume of the return air clean room at the next moment by using the fan dynamic model; obtains the supply air volume of the return air clean room according to the supply air section air velocity sensor 6 in the return air clean room, obtains the supply air pressure of the return air clean room according to the supply air section pressure sensor 7 in the return air clean room, obtains the opening degree of the supply air valve in the return air clean room according to the supply air valve opening sensor 8 in the return air clean room, and then calculates the supply air volume of the return air clean room at the next moment by using the fan dynamic model. Obtains the differential pressure of the return air clean room according to the pressure sensor 12 in the return air clean room, and then uses the clean room differential pressure dynamic model to predict the differential pressure change of the return air clean room in the next time period based on the differential pressure of the return air clean room, the return air volume and the supply air volume of the return air clean room at the next moment. Using the state space model, optimize the valve opening degrees of the return air valve 27 and the supply air valve 28 in the return air clean room based on the differential pressure change of the return air clean room in the next time period, the return air volume and the supply air volume of the return air clean room, so as to adjust the return air volume and the supply air volume.

[0086] The control module obtains the air supply volume of the exhaust purification chamber according to the air supply section wind speed sensor 9 of the exhaust purification chamber, obtains the air supply wind pressure of the exhaust purification chamber according to the air supply section wind pressure sensor 10 of the exhaust purification chamber, obtains the opening degree of the air supply valve of the exhaust purification chamber according to the air supply valve opening degree sensor 11 of the exhaust purification chamber, and then calculates the air supply volume of the exhaust purification chamber at the next moment by using the fan dynamic model; obtains the exhaust volume of the exhaust purification chamber according to the exhaust section wind speed sensor 14 of the exhaust purification chamber, obtains the exhaust wind pressure of the exhaust purification chamber according to the exhaust section pressure sensor 15 of the exhaust purification chamber, obtains the opening degree of the air supply valve of the exhaust purification chamber according to the exhaust valve opening degree sensor 16 of the exhaust purification chamber, and then calculates the exhaust volume of the exhaust purification chamber at the next moment by using the fan dynamic model. Obtains the differential pressure of the exhaust purification chamber according to the pressure sensor 13 of the exhaust purification chamber, and then uses the differential pressure dynamic model of the purification chamber to predict the change of the differential pressure of the exhaust purification chamber in the next time period based on the differential pressure of the exhaust purification chamber, the air supply volume and the exhaust volume of the exhaust purification chamber at the next moment. Uses the state space model to optimize the opening degrees of the air supply valve 30 and the exhaust valve 32 of the exhaust purification chamber based on the change of the differential pressure of the exhaust purification chamber, the air supply volume and the exhaust volume of the exhaust purification chamber in the next time period, so as to adjust the air supply volume and the exhaust volume.

[0087] When the differential pressure of the return air purification chamber or the exhaust purification chamber becomes unstable due to the occurrence of the door opening and closing event, the fan frequencies of the air supply fan 26 and the exhaust fan 33 will be increased to the preset frequency; after the differential pressure is stable, the state space model is used to optimize the fan frequencies of the air supply fan 26 and the exhaust fan 33, as well as the opening degrees of the fresh air valve 24, the air supply valve 28 of the return air purification chamber, the air supply valve 30 of the exhaust purification chamber, and the exhaust valve 32 of the exhaust purification chamber.

[0088] In this embodiment, constructing the discretized state space model includes: The state vector is selected as: ; is the air supply fan flow rate at time k, is the exhaust fan air volume at time k, is the output flow rate of the air supply valve of the return air purification chamber at time k, is the output flow rate of the return air valve of the return air purification chamber at time k, is the differential pressure of the return air purification chamber at time k, is the output flow rate of the air supply valve of the exhaust purification chamber at time k, is the output flow rate of the exhaust valve of the exhaust purification chamber at time k, is the differential pressure of the exhaust purification chamber at time k.

[0089] The input variables are selected as: ; is the air supply fan frequency at time k, is the exhaust fan frequency at time k, is the opening of the supply air valve in the return air clean room at time k, is the opening of the return air valve in the return air clean room at time k, is the opening of the supply air valve in the exhaust air clean room at time k, is the opening of the exhaust air valve in the exhaust air clean room at time k.

[0090] Then the system matrix is: ; Among them, is the discretized time step, is the response time of the supply air fan, is the response time of the exhaust air fan, is the valve flow response constant of the supply air valve in the return air clean room, is the valve flow response constant of the return air valve in the return air clean room, is the air capacitance constant of the return air clean room, is the air capacitance constant of the exhaust air clean room, is the valve flow response constant of the supply air valve in the exhaust air clean room, is the valve flow response constant of the exhaust air valve in the exhaust air clean room.

[0091] The input matrix is: ; Among them, and are the rated air volume and rated frequency of the supply air fan respectively, and are the rated air volume and rated frequency of the exhaust air fan respectively, is the gain coefficient of the supply air fan, is the gain coefficient of the exhaust air fan, is the gain coefficient of the supply air valve in the return air clean room, is the gain coefficient of the return air valve in the return air clean room, is the gain coefficient of the supply air valve in the exhaust air clean room, is the gain coefficient of the exhaust air valve in the exhaust air clean room, is the pressure drop of the supply air valve in the return air clean room, is the pressure drop of the return air valve in the return air clean room, is the pressure drop of the supply air valve in the exhaust air clean room, is the pressure drop of the exhaust air valve in the exhaust air clean room.

[0092] The output matrix and the direct transfer matrix are: ; ; Refer to the schematic diagram of the control input issued by the control module Figure 6 as shown. The control module 23 transmits the calculated control inputs to each actuator through the communication module, including the fresh air valve 24, the return air valve of the return air clean room 27, the supply air fan 26, the supply air valve of the return air clean room 28, the supply air valve of the exhaust air clean room 30, the exhaust air valve of the exhaust air clean room 32, and the exhaust air fan 33.

[0093] Obviously, the above embodiments are only examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A clean room energy-saving and pressure control method based on dynamic pipe network and model predictive control, characterized in that: include: The current fan speed is obtained according to the current fan frequency, and the current wind pressure is calculated according to the current fan speed and the current wind volume; Through numerical simulation, the pipe network impedance under different air volumes is obtained, and the pipe network impedance curve is constructed; According to the pipe network impedance curve, the current pipe network impedance is obtained from the current air volume; the current valve impedance is calculated according to the valve opening; the current pipe network total pressure drop is calculated according to the current valve impedance, the current pipe network impedance and the air handling equipment impedance; A fan dynamic model is constructed based on the fan similarity law to calculate the next moment's air volume according to the current air volume, the current total pressure drop of the pipe network and the current wind pressure; the air volume of the exhaust fan is the exhaust air volume, and the air volume of the supply fan is the supply air volume; Calculate the valve pressure drop based on the valve opening and the current valve impedance, and then calculate the valve output flow; Construct a dynamic model of clean room pressure difference, which is used to calculate the clean room pressure difference at the next moment based on the current clean room pressure difference, the exhaust air volume and the supply air volume at the next moment; A discretized state space model is constructed, in which the fan frequency and valve opening are control inputs, the exhaust volume, supply volume and valve output flow rate are system state variables, and the clean room pressure difference is output; the objective function of the model predictive controller is constructed with the strong stability of the clean room pressure difference as the goal, which is used to minimize the pressure difference tracking error and the valve opening penalty term; The clean room pressure difference is monitored in real time. When the clean room pressure difference is unstable, the clean room pressure difference dynamic model is used to predict the change trend of the clean room pressure difference in the next time period. Then the model prediction controller is used to reversely solve the optimal valve opening of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency; the fan frequency and valve opening of the next time period are adjusted with the optimal valve opening and fan frequency.

2. A clean room energy-saving and pressure-control method based on a dynamic pipe network and model predictive control according to claim 1, characterized in that: The current wind pressure is calculated based on the current fan speed and current wind volume. The formula is: ； in, is the current wind pressure, is the rated wind pressure, and are the current fan speed and the rated fan speed respectively. and They are the current air volume and the rated air volume respectively. , , and is the fitting coefficient.

3. The clean room energy-saving and pressure-control method based on a dynamic pipe network and model predictive control according to claim 1 is characterized in that: Calculate the current valve impedance based on the valve opening, the formula is: ； in, Indicates the valve angle corresponding to the valve opening, represents the valve impedance, Indicates the valve flow area, represents the air density, , and represents the fitting coefficient; The total pressure drop of the current pipe network is calculated based on the current valve impedance, the current pipe network impedance and the air handling equipment impedance. The formula is: ； in, It represents the total pressure drop of the pipe network. represents the impedance of the pipe network, represents the valve impedance, Represents the impedance of the clean room air handling equipment, Indicates air volume; When the valve opening is controlled above 85%, the total pressure drop in the pipeline network is minimized.

4. The clean room energy-saving and pressure-control method of a dynamic pipe network and model predictive control according to claim 1 is characterized in that: The formula of the fan dynamic model is: ； in, is the current wind volume, is the wind volume at the next moment, is the air volume response time constant, is the fan gain coefficient, is the fan frequency, is the functional relationship between fan speed and fan frequency, is the current wind pressure, is the total pressure difference; Total pressure difference ,in is the clean room pressure, is the total pressure drop of the pipe network.

5. The clean room energy-saving and pressure-control method of a dynamic pipe network and model predictive control according to claim 1, characterized in that: The valve dynamic model is used to adjust the valve opening in the next time period. The formula is: ； in, is the current valve opening, is the valve opening at the next moment, is the valve opening control signal, is the valve response time constant, is the valve gain coefficient.

6. A clean room energy-saving and pressure-control method based on a dynamic pipe network and model predictive control according to claim 1, characterized in that: Calculate the valve pressure drop based on the valve opening and the current valve impedance, and then calculate the valve output flow, including: The valve pressure drop is calculated based on the valve opening and the current valve impedance. The formula is: ； in, is the valve pressure drop, represents the valve impedance, Indicates the air volume passing through the valve; The calculation formula of valve output flow is: ； in, is the current valve output flow, The valve output flow at the next moment, is the valve flow response time constant, is the valve flow gain coefficient, is the current valve opening.

7. A clean room energy-saving and pressure-control method based on a dynamic pipe network and model predictive control according to claim 1, characterized in that: The formula of the clean room pressure difference dynamic model is: ； in, is the current pressure difference in the clean room, is the pressure difference of the clean room at the next moment, is the clean room air volume constant, and are the supply and exhaust air volumes of clean room area e, is the clean room leakage constant.

8. The clean room energy-saving and pressure-control method of a dynamic pipe network and model predictive control according to claim 1, characterized in that: Construct a discretized state space model, the formula is expressed as: ； ； in, is the system state vector at time k, is the system state vector at time k+1, is the control input vector at time k, is the output vector at time k, is the system matrix, is the input matrix, is the output matrix, is a direct transfer matrix; The objective function of the model predictive controller is constructed and the formula is expressed as: ； in, is the first objective function, is the second objective function, To predict the time domain length, is the clean room pressure difference at time k, Preset value for clean room pressure difference, is the valve opening at time k, is the slack variable at time k, is the soft constraint of valve opening at time k, and are the minimum and maximum values ​​of the clean room pressure difference, , , , and are all weight coefficients.

9. The clean room energy-saving and pressure-control method of a dynamic pipe network and model predictive control according to claim 1, characterized in that: When it is detected that the door of the clean room is open, the fan frequency is increased to the preset frequency, and the dynamic model of the clean room pressure difference is used to predict the changing trend of the clean room pressure difference in the next time period. Then, the model prediction controller is used to reversely solve the optimal valve opening of the discretized state space model, and the valve opening for the next time period is adjusted.

10. A clean room energy-saving and pressure control system with dynamic pipe network and model predictive control, characterized in that: include: Sensor module, including: The pressure difference sensor is installed between the clean room and the adjacent area, and at the front and rear ends of the pipe network and the fan, and is used to detect the pressure difference in the clean room and the pipe network impedance in real time; Air volume sensors are installed at the rear end of the air supply valve, the front end of the exhaust valve, and the front end of the fan to obtain the supply and exhaust air volumes; Valve opening sensor, installed on the air supply valve and exhaust valve, used to obtain the valve opening; The control module is connected to the sensor module for communication and is used to obtain the current fan speed according to the current fan frequency, and then calculate the current wind pressure according to the current fan speed and the current air volume; obtain the pipe network impedance under different air volumes through numerical simulation, and construct the pipe network impedance curve; obtain the current pipe network impedance from the current air volume according to the pipe network impedance curve; calculate the current valve impedance according to the valve opening; calculate the current pipe network total pressure drop according to the current valve impedance, the current pipe network impedance and the air handling equipment impedance; construct a fan dynamic model based on the fan similarity law, which is used to calculate the air volume at the next moment according to the current air volume, the current pipe network total pressure drop and the current wind pressure; among which, the air volume of the exhaust fan is the exhaust volume, and the air volume of the supply fan is the supply volume; calculate the valve pressure drop according to the valve opening and the current valve impedance, and then calculate the valve output flow Quantity; construct a dynamic model of clean room pressure difference, which is used to calculate the clean room pressure difference at the next moment based on the current clean room pressure difference, the exhaust volume and the supply volume at the next moment; construct a discretized state space model, in which the fan frequency and valve opening are control inputs, the exhaust volume, the supply volume and the valve output flow are system state variables, and the clean room pressure difference is output; with the strong stability of the clean room pressure difference as the goal, the objective function of the model predictive controller is constructed to minimize the pressure difference tracking error and the valve opening penalty term; monitor the clean room pressure difference in real time. When the clean room pressure difference is unstable, use the clean room pressure difference dynamic model to predict the change trend of the clean room pressure difference in the next time period, and then use the model predictive controller to reversely solve the optimal valve opening of the discretized state space model to achieve the lowest pipe network impedance and obtain the optimal fan frequency; The execution module is used to adjust the fan frequency and valve opening in the next time period with the optimal valve opening and fan frequency.

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