An intelligent control and low energy consumption FFU group control system

Abstract

The invention discloses an intelligent control low-energy consumption FFU group control system, which belongs to the field of intelligent control technology and comprises core control system software, an online particle counter, an FFU cluster and a network gateway device. The online particle counter, the FFU cluster and the network gateway device are installed inside a workshop. The invention can effectively improve the energy utilization rate of the FFU clean system. The FFU closed-loop control system further processes data, improves the energy utilization rate of the system and improves production efficiency, thereby improving the qualified rate of product production, effectively controlling the quality and cost of products, and realizing the production energy utilization rate.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, in particular to an intelligent control low-energy consumption FFU group control system. Background Art

[0002] Modern industry has extremely strict regulations on the cleanliness of the production workshop environment, and more and more industries have clearer standards for the production environment. Against this background, all companies are measuring their electricity consumption in the production process and studying how to reduce various electricity consumption. Among the electricity consumption components, air purification electricity accounts for a relatively large part. This is because the specified cleanliness level needs to be maintained during the production process, and the clean system equipment needs to be in operation to maintain it. At present, the mainstream clean systems of large-scale high-tech manufacturers in the market use DC FFU, while AC FFU is still used in small clean systems. The working state of FFU is determined by calculating the space volume and the number of ventilation times. The control is relatively rough, and static cleanliness is used as the standard test verification. At the same time, whether the dynamic cleanliness in the production process meets the standard is unknown, or manual detection is required to adjust the working state of FFU, which cannot control the working state of the clean system in real time and accurately.

[0003] In response to production cleanliness requirements, many system solution products are now available on the market, but they are all relatively rough solutions for energy efficiency. Most products do not even consider the issue of energy efficiency. They only guarantee static cleanliness requirements and adjust the FFU wind speed to a fixed point to keep working. Therefore, the energy utilization rate of the clean system is very limited, and it is difficult to meet the production needs of modern industry and the country's carbon peak target. Therefore, how to effectively improve the energy utilization rate in the existing clean system has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent control low-energy consumption FFU group control system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent control low-energy consumption FFU group control system comprises core control system software, an online particle counter, an FFU cluster and a network gateway device. The online particle counter, the FFU cluster and the network gateway device are installed inside a workshop, and are connected to each other through an IP network and then communicate with a PC installed with the core control system software. The PC runs the core control system software, sets the space target level, and the system starts the FFU cluster. Ten minutes after the startup, the control software automatically collects particle data of the online particle counter in the environmental system, compares the collected current particle data with the steady-state value, outputs the deviation and the deviation rate to a FuzzyPID module for calculation and outputs the execution amount, and then converts the execution amount into a PWM signal for controlling the FFU cluster to execute control on the FFU cluster.

[0007] As a further technical solution of the present invention: in the process of controlling the FFU cluster, the online particle counter will collect data every 4 minutes, and the time for each data collection is 1 minute, and the closed-loop control system is repeated. When the system reaches a steady state, the working interval of the online particle counter is modified to 20 minutes or more, so that the system can monitor the changes in particle concentration in the space in real time.

[0008] As a further technical solution of the present invention: the data collection method of the online particle counter is as follows:

[0009] The particle concentration of 0.5μm particles at Class 100 cleanliness is 3520 / m 3 , average 3.52 / L; 0.3um particles are 10200 / m 3 , average 10.2 / L;

[0010] Particle concentration at Class 1000 cleanliness level: 0.5μm particles: 35200 / m 3 , average 23.2 / L; 0.3μm particles 102000 / m 3 , average 102 / L;

[0011] Particle counter performance indicators: 0.3um recognition rate is 50%, 0.5>= recognition rate is 100%, 2.83L / min, data is refreshed once per second, data unit is pcs / L;

[0012] For Class 100 cleanliness, use the average value method to process again;

[0013] X=(x (n+1) +…+x (n+t) ) / (t-2)×1000

[0014] X is the average value within t seconds (excluding the minimum and maximum values). As the t value increases, the X value is closer to the real value of the space. In the dynamic space, timeliness is considered, so the value of t needs to be measured;

[0015] t=t n +t k

[0016] t n Refers to a fixed time period, which is determined according to the characteristics of the particle counter, t k According to the current x n+t Comparison with the X value is determined.

[0017] As a further technical solution of the present invention: the control method of the FFU group is closed-loop control. This closed-loop control is based on controlling the FFU group to output purified air through a high-efficiency filter and then monitoring the particle concentration through a particle counter to output feedback for correction of the FFU group control method. When the actual particle concentration in the measured space deviates from the preset particle concentration, it is corrected according to the quota or standard.

[0018] As a further technical solution of the present invention: closed-loop control is implemented by core control system software, including a FuzzyPID module, a PID controller function, a conversion drive module, a data processing module and a particle sensor.

[0019] As a further technical solution of the present invention: the FuzzyPID module includes a fuzzy control calculation unit, which obtains real-time proportional, integral and differential control parameters respectively (K p , K i , K d );PID controller receives the deviation source through proportion, integration, and differentiation. At the same time, the proportion, integration, and differentiation control parameters of the receiving module are (K p , K i , K d ) to merge and output the execution volume;

[0020] PID controller function: Where, u(t) represents the output value of the PID controller, K P represents the proportional parameter, e(t) represents the difference between the target value and the measured value, T t represents the integration time constant, T D represents the differential time constant.

[0021] As a further technical solution of the present invention: the function can be transformed in a discrete manner:

[0022]

[0023] Extract the coefficients and transform the formula:

[0024]

[0025] As a further technical solution of the present invention: the system sets a preset value and receives the system feedback result at the same time, counts the deviation and deviation rate between the actual and the preset, counts the parameters that need to be controlled and executed for the fuzzy PID module, converts the parameters into the speed of the FFU fan that needs to be controlled, the particle sensor monitors the actual particle concentration, and processes the data as a closed-loop control system for the system feedback value.

[0026] As a further technical solution of the present invention: fuzzy PID control includes fuzzification, determination of fuzzy rules, defuzzification and other components. The system collects spatial particle concentration information through sensors, determines the deviation E of the current particle concentration and the change rate E of the current deviation and the previous deviation c , fuzzy reasoning is performed according to the given fuzzy rules, and finally the fuzzy parameters are defuzzified to output the PID control parameters.

[0027] As a further technical solution of the present invention: the particle concentration information data processing process is as follows: first run the FFU for 10 minutes before starting detection, and then monitor for 1 minute every 4 minutes. When the system reaches a steady state, the monitoring time interval is lengthened. When the system concentration is detected to deviate from the specified range, the switch monitoring time interval is set to allow the closed-loop control system to reach a steady state.

[0028] Compared with the prior art, the beneficial effects of the present invention are: the present invention can effectively improve the energy utilization rate of the FFU clean system, the FFU closed-loop control system further processes data, improves the energy utilization rate of the system and improves production efficiency, thereby improving the qualified rate of product production, effectively controlling product quality and cost, and achieving production energy utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a graph of time and particle concentration data;

[0030] Figure 2 It is a schematic diagram of the experimental verification environment;

[0031] Figure 3 It is the overall closed-loop control structure diagram of the system;

[0032] Figure 4 It is the fuzzy PID control principle diagram;

[0033] Figure 5 It is a software automatic control flow chart. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1-5 An intelligent control low-energy consumption FFU group control system includes core control system software, online particle counters, FFU clusters and network gateway devices. The online particle counters, FFU clusters and network gateway devices are installed inside a workshop and are connected to each other through an IP network and then communicate with a PC installed with the core control system software. The PC runs the core control system software, sets the space target level, and the system starts the FFU cluster. Ten minutes after the startup, the control software automatically collects particle data from the online particle counter in the environmental system, compares the collected current particle data with the steady-state value, and outputs the deviation and deviation rate to the FuzzyPID module for calculation and output of the execution amount, and then converts the execution amount into a PWM signal for controlling the FFU cluster to control the FFU cluster.

[0036] The online particle counter data acquisition method adopted by the present invention is as follows:

[0037] 1. The particle concentration of 0.5μm particles at Class 100 cleanliness is 3520 / m 3 , average 3.52 / L; 0.3um particles are 10200 / m 3 , average 10.2 / L;

[0038] 2. Particle concentration at Class 1000 cleanliness level: 0.5μm particles: 35200 / m 3 , average 23.2 / L; 0.3μm particles 102000 / m 3 , average 102 / L;

[0039] 3. Particle counter performance indicators: 0.3um recognition rate is 50%, 0.5>= recognition rate is 100%, 2.83L / min, data is refreshed once per second, data unit pcs / L;

[0040] Conventional data collection cannot accurately measure Class 100 cleanliness. According to the data collection rules of the particle counter, the average value method can be used for further processing.

[0041] X=(x (n+1) +…+x (n+t) ) / (t-2)×1000

[0042] X is the average value within t seconds (excluding the minimum and maximum values). When the t value increases, the X value is closer to the real value of the space. In order to consider the timeliness in the dynamic space, it is necessary to measure the value of t.

[0043] t=t n +t k

[0044] t n Refers to a fixed period of time, which is determined by the characteristics of the particle counter.

[0045] t k According to the current x n+t Comparison with the X value is determined.

[0046] The method of closed-loop control of FFU based on particle concentration is as follows:

[0047] Clean space cold start (clean system is shut down, system restarts), in today's vast majority of clean production workshops, the FFU fleet is running at a certain speed, and its speed will not change without changing the static cleanliness level, that is, the wind speed is constant. In this case, the time it takes for the clean space to reach the predetermined cleanliness after a cold start is relatively longer than the time it takes to increase the speed (i.e., wind speed). It takes a long time to reach the preset cleanliness after a cold start, and it does not affect production. The FFU fleet needs to be started in advance and the cleanliness needs to be monitored at the same time (or the time required for a cold start to reach the predetermined cleanliness is obtained through experience from previous times). To avoid this repeated operation process, some manufacturers run the clean system 24 hours a day. The overall energy consumption of the clean system will increase, and the energy efficiency is not high. To improve the efficiency and rapidity of reaching the preset cleanliness during a clean space cold start and provide energy efficiency, the present invention provides the following method.

[0048] Since the online particle counter can collect the particle concentration of the clean space in a relatively real-time manner, the system can dynamically adjust the rotation speed (i.e. wind speed) of the FFU group according to the particle concentration, so as to achieve the preset cleanliness level of the clean space with optimal efficiency.

[0049] This closed-loop control is based on controlling the FFU to output purified air through a high-efficiency filter and then monitoring the particle concentration through a particle counter to output feedback for correction of the FFU control method. When the actual particle concentration in the measured space deviates from the preset one, it is corrected according to the quota or standard.

[0050] The experimental verification environment is as follows Figure 2 As shown,

[0051] The experimental equipment includes: external clean room, internal clean room, 4X4 ECFFU, particle counter, network switch, and PC.

[0052] External clean room size: 2.0m X 2.0m X 3.0m;

[0053] Internal clean room dimensions: 1.2m x 1.2m x 2.2m;

[0054] FFU parameters: total pressure 170 (Pa), air volume 1814m 3 / h, power 142W, noise 48dB(A), stepless speed regulation (PWM), filtration efficiency HEPA 0.3um 99.97%;

[0055] Particle counter parameters: 6 channels (0.3, 0.5, 1.0, 3.0, 5.0, 10.0 μm), flow rate 2.83 L / min, 50％@ 0.3um, 100% @>0.45um, particle concentration limit 8000000 / ft3;

[0056] Gateway: IP network to RS485;

[0057] PC: control program algorithm;

[0058] The overall closed-loop control structure of the system is as follows: Figure 3 As shown:

[0059] The system sets a preset value and receives system feedback results at the same time, counts the deviation and deviation rate between the actual and preset values, counts the parameters that need to be controlled for the fuzzy PID module, converts the parameters into the speed of the FFU fan that needs to be controlled, and the particle sensor monitors the actual particle concentration and processes the data as a closed-loop control system for the system's feedback value.

[0060] The various module subsystems in the system structure are as follows:

[0061] FuzzyPID module:

[0062] This module includes a fuzzy control calculation unit to obtain real-time proportional, integral, and differential control parameters (K p , K i , K d );PID controller receives the deviation source through proportion, integration, and differentiation. At the same time, the proportion, integration, and differentiation control parameters of the receiving module are (K p , K i , K d ) to merge and output the execution amount.

[0063] PID controller function:

[0064]

[0065] u(t) represents the output value of PID controller, KP represents the proportional parameter, e(t) represents the difference between the target value and the measured value, T t represents the integration time constant, T D represents the differential time constant;

[0066] The first term is the proportional term, the second term is the integral term, and the third term is the differential term.

[0067] In this control system, the function can be transformed in a discrete manner:

[0068]

[0069] Extract the coefficients and transform the formula:

[0070]

[0071] Fuzzy PID control uses fuzzy logic and certain fuzzy rules to optimize PID parameters in real time to overcome the disadvantage that traditional PID parameters cannot adjust PID parameters in real time. Fuzzy PID control includes fuzzification, determination of fuzzy rules, and defuzzification. The system collects spatial particle concentration information through sensors, determines the deviation E of the current particle concentration, and the rate of change E of the current deviation and the previous deviation. c , fuzzy reasoning is performed according to the given fuzzy rules, and finally the fuzzy parameters are defuzzified to output the PID control parameters.

[0072] like Figure 4 As shown in the figure, e represents the difference, Derivative represents the differential value, Fuzzy Logic Controller represents the fuzzy control module, and the function is used to calculate the three parameters of PID through the membership function. Goto represents the output proportional gain parameter Kp, Goto1 represents the output integral gain parameter Ki, Goto2 represents the output differential gain parameter Kd, From corresponds to Goto, From1 corresponds to Goto1, From2 corresponds to Goto2, Integrator represents the error integral, Derivative1 represents the error differential, Product represents the proportional module, calculates the proportional gain value, Product1 represents the integral module, calculates the integral gain value, Product2 represents the differential module, calculates the rounds gain value, and u represents the output value.

[0073] Taking the preset cleanliness level of 1000 as an example, we generate a parameter matrix and use the Fuzzy LogicDesigner module of MATLAB to design a parameter model. The cleanliness level of 1000 mainly refers to 0.5um particles, whose concentration range is 3520~35200 / m 3In order to better control the 1000 level, the threshold is set to 20000 / m 3 In this way, there is room for control, and the error range is set within 5000 / m 3 ~35000 / m 3 The membership function type uses Gaussian distribution, and the defuzzification uses the centroid method. Conversion driver:

[0074] The difference that needs to be controlled is converted into the parameter that controls the fan speed to increase or decrease; according to different wind speeds, it can be converted into the number of ventilation times, that is, the number of times the air is filtered to count its cleanliness (considering the balance between the dust source and the filter). Before starting the FFU, the air particle concentration is 7000000 / m 3 , after starting the FFU, the dust source u(t).

[0075] The total amount of dust per cubic meter after the air in the space is ventilated k times is:

[0076]

[0077]

[0078] S K ≈u(t)

[0079] S represents the initial particle concentration [particles / m 3 ];

[0080] S k represents the particle concentration after k ventilation times [particles / m 3 ];

[0081] u(t) represents the current particle concentration [particles / m 3 ];

[0082] Represents the filter coefficient.

[0083] In the system, it is necessary to control the emission source so that the concentration of dust particles in the space can be well controlled. The dust source is generally a relatively stable dust source in all stages of the system.

[0084] In a unidirectional clean space, a uniform model can be used, and its dust concentration is stable:

[0085]

[0086] g represents the dust emission per unit time [particles / (m 3 .min)]

[0087] k represents the ventilation rate [times / h];

[0088] t represents the current time [min];

[0089] From the above formula we can get

[0090] The relationship between the FFU rotation speed and wind speed can be described by a linear proportional function:

[0091] V = ar + b;

[0092] b represents a constant, a represents a coefficient, v represents wind speed, and r represents rotation speed;

[0093] The FFU is controlled by PWM, and the percentage of PWM pulse width ratio is used to control the fan speed. The PWM control speed is in a linear proportional relationship:

[0094] r=maxR×β

[0095] β represents the PWM (pulse width modulation) percentage;

[0096] maxR represents the maximum speed of the FFU fan;

[0097] r represents the controllable speed.

[0098] Conversion formula between particle concentration and ventilation frequency:

[0099]

[0100] S k represents the particle concentration in the space, Δk represents the ventilation times, represents the particle filtration coefficient, λ represents a constant, and P represents the expected particle concentration.

[0101] In the control process, the particle concentration control range is converted into the number of ventilation times per unit time. The required speed is calculated based on the volume of the space, and then the PWM pulse width percentage required to control the fan is derived. Therefore, the control conversion drive control quantity is as follows:

[0102]

[0103] β represents PWM, Δk represents the number of ventilation times, h represents the space length, w represents the space width, d represents the space height, n represents the number of FFUs, h′ represents the FFU cross-sectional length, and represents the FFU cross-sectional width.

[0104] Particle concentration data acquisition and data processing:

[0105] Since the particle concentration collected by the particle counter in the system may drift, the particle data needs to be filtered. The specific method is to continuously observe the particle concentration trend in the system. The amount of data with a single mutation needs to be processed to make the system data relatively smooth. The collection time of the particle counter needs to be considered in the design. When the system starts running, the FFU can be run for 10 minutes before starting the detection, and then monitor for 1 minute every 4 minutes (the monitoring time varies according to different cleanliness levels). When the system reaches a steady state, the monitoring time interval is lengthened. When the concentration of the system deviates from the specified range, the switch monitoring time interval is switched to achieve a steady state of the closed-loop control system.

[0106] The module PID converges faster than the simple PID control, and the stable difference after convergence is smaller than the PID control. It can save the workload of adjusting the PID parameters the most.

[0107] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0108] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. An intelligent control low energy consumption FFU group control system, including core control system software, online particle counter, FFU cluster and network gateway equipment, characterized in that: The online particle counter, FFU cluster and network gateway device are installed inside the workshop, and are connected to each other through an IP network and then communicate with a PC installed with the core control system software. The PC runs the core control system software, sets the space target level, and the system starts the FFU cluster. Ten minutes after the startup, the core control system software automatically collects the particle data of the online particle counter in the environmental system, compares the collected current particle data with the steady-state value, and outputs the deviation and deviation rate to the FuzzyPID module for calculation and output of the execution amount, and then converts the execution amount into a PWM signal for controlling the FFU cluster to execute control on the FFU cluster; Conversion formula between particle concentration and ventilation frequency: S k represents the spatial particle concentration, Δk represents the ventilation times, Φ represents the particle filtration coefficient, λ represents a constant, and P represents the expected particle concentration; In the control process, the particle concentration control range is converted into the number of ventilation times required per unit time. The required speed is calculated based on the space volume, and then the PWM pulse width percentage required to control the fan is derived; therefore, the control conversion drive control quantity is as follows: β represents PWM, Δk represents the number of ventilation times, h represents the length of the space, w represents the width of the space, d represents the height of the space, n represents the number of FFUs, h′ represents the length of the FFU cross section, and w ’ It represents the cross-sectional width of FFU, maxR represents the maximum speed of FFU fan, b represents the constant, and a represents the coefficient.

2. According to claim 1, an intelligent control low energy consumption FFU group control system is characterized in that: During the control process of the FFU cluster, the online particle counter will collect data every 4 minutes, with each data collection time of 1 minute, and repeated closed-loop control. When the system reaches a steady state, the working interval of the online particle counter is modified to 20 minutes or more, so that the system can monitor the changes in particle concentration in the space in real time.

3. According to claim 1, an intelligent control low energy consumption FFU group control system is characterized in that: The data collection method of the online particle counter is as follows: The particle concentration of 0.5μm particles at Class 100 cleanliness is 3520 / m 3 , an average of 3.52 / L; 0.3μm particles are 10200 / m 3 , average 10.2 / L; Particle concentration at Class 1000 cleanliness level: 0.5μm particles: 35200 / m 3 , average 23.2 / L; 0.3μm particles 102000 / m 3 , average 102 / L; Particle counter performance indicators: 0.3μm recognition rate is 50%, when it is greater than or equal to 0.5, the recognition rate is 100%, 2.83L / min, data is refreshed once per second, data unit pcs / L; For Class 100 cleanliness, use the average value method to process again; X=(x (n+1) +…+x (n+t) ) / (t-2)×1000 X is the average value of the space within t seconds, excluding the minimum and maximum values. The larger the t value is, the closer the X value is to the real value of the space. In the dynamic space, timeliness is considered, so the value of t needs to be measured. t=t n +t k t n Refers to a fixed time period, which is determined according to the characteristics of the online particle counter, t k According to the current x (n+t) The comparison with the X value determines the time period.

4. According to claim 1, an intelligent control low energy consumption FFU group control system is characterized in that: The control method of the FFU cluster is closed-loop control. This closed-loop control is achieved by controlling the FFU cluster to output purified air through a high-efficiency filter and then monitoring the particle concentration output feedback through an online particle counter for correction. When the actual particle concentration in the measured space deviates from the preset one, correction is made according to the quota or standard.

5. The intelligent control low energy consumption FFU group control system according to claim 1 is characterized in that: Closed-loop control is achieved through the core control system software, including FuzzyPID module, PID controller function, conversion drive module, data processing module and online particle counter.

6. The intelligent control low energy consumption FFU group control system according to claim 5 is characterized in that: The FuzzyPID module includes a fuzzy control calculation unit to obtain real-time proportional, integral, and differential control parameters, respectively. p , K i , K d PID controller receives the deviation source and controls the parameters K through proportional, integral and differential control p , K i , K d Merge and output the execution volume; PID controller function: Where u(t) represents the output value of the PID controller, K P represents the proportional parameter, e(t) represents the difference between the target value and the measured value, T t represents the integration time constant, T D represents the differential time constant.

7. The intelligent control low energy consumption FFU group control system according to claim 6 is characterized in that: The function can be transformed in a discrete way: Extract the coefficients and transform the formula:

8. The intelligent control low energy consumption FFU group control system according to claim 7 is characterized in that: The system sets a preset value and receives system feedback results at the same time, calculates the deviation and deviation rate between the actual and preset values, counts the parameters that need to be controlled for the fuzzy PID module, converts the parameters into the FFU fan speed that needs to be controlled, and uses the online particle counter to monitor the actual particle concentration. The data is processed as the system feedback value.

9. The intelligent control low energy consumption FFU group control system according to claim 8 is characterized in that: Fuzzy PID control includes fuzzification, determining fuzzy rules, and defuzzification. The system collects spatial particle concentration information through sensors, determines the deviation E of the current particle concentration, and the rate of change E of the current deviation and the previous deviation. c , fuzzy reasoning is performed according to the given fuzzy rules, and finally the fuzzy parameters are defuzzified to output the PID control parameters.

10. The intelligent control low energy consumption FFU group control system according to claim 9 is characterized in that: The particle concentration information collection data processing process is as follows: first run the FFU for 10 minutes before starting the detection, and then monitor for 1 minute every 4 minutes. When the system reaches a steady state, the monitoring time interval is lengthened. When the system concentration is detected to deviate from the specified range, the monitoring time interval is switched to allow the closed-loop control system to reach a steady state.

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