Deep fusion air pressure intelligent optimization control method and system

Abstract

The invention discloses a deep fusion air pressure intelligent optimization control method and system. The method comprises the steps that the air load of a user side is obtained; calculating plan parameters of the air compressor by adopting a feed-forward intelligent control strategy based on the air load; calculating target parameters of the air compressor by adopting a PLC feedback control strategy based on the planned parameters of the air compressor and the real-time pressure of the system; the air compressor plan parameters comprise the opening number of the air compressors, the opening degree of an inlet adjusting valve, the closing degree of an emptying adjusting valve and the operation frequency; the target parameters of the air compressor comprise the opening number, the operation frequency and the opening degree of an inlet adjusting valve of the air compressor; accurate control and adjustment of the operation process of the air compression system are achieved, the problems that due to manual control, the output pressure fluctuation of the air compression system is large, stable operation of the system cannot be guaranteed, and the energy consumption and the air supply cost of the air compression system are high are solved, energy efficiency fluctuation is accurately analyzed, potential energy consumption problems are predicted, and the energy efficiency of the air compression system is improved. The overall operation efficiency is improved, and the optimal energy efficiency is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control of air compressor systems, and specifically relates to a deeply integrated air compressor intelligent optimization control method and system. Background Art

[0002] At present, some domestic air compressor stations are still controlled manually. Manual control requires staff to manually start and stop the unit, set unit parameters and adjust loads based on experience, and manually record operating data, resulting in a large workload for managers, complex work processes, and inability to achieve precise control and real-time analysis. When the system is subject to certain complex interferences, it may not be able to respond effectively and automatically adjust, thereby increasing the number of starts and stops of the air compressor, resulting in large fluctuations in the output pressure of the air compressor system, and being unable to ensure the stable operation of the system, resulting in high energy consumption and gas supply costs for the air compressor system. Summary of the invention

[0003] The purpose of the present invention is to provide a deeply integrated air compression intelligent optimization control method and system to reduce the energy consumption of the air compression system and ensure the smooth operation of the air compression system.

[0004] The present invention adopts the following technical solutions:

[0005] A deep fusion air compressor intelligent optimization control method comprises the following steps:

[0006] Obtain the gas load at the user end;

[0007] Based on the gas load, the feedforward intelligent control strategy is used to calculate the air compressor planning parameters;

[0008] Based on the planned parameters of the air compressor and the real-time pressure of the system, the PLC feedback control strategy is used to calculate the target parameters of the air compressor;

[0009] The planned parameters of the air compressor include the number of air compressor openings, the opening of the inlet regulating valve, the closing of the vent regulating valve and the operating frequency;

[0010] The target parameters of the air compressor include the number of times the air compressor is opened, the operating frequency and the opening of the inlet regulating valve.

[0011] Furthermore, obtaining the gas load at the user end includes:

[0012] Build an air compression load prediction model to predict the gas load at the user end.

[0013] Furthermore, the air compression load prediction model is established based on the BP neural network, and the historical gas load data of the user end is used to train the model to predict the gas load of the user end.

[0014] Furthermore, the feedforward intelligent control strategy is used to calculate the planned parameters of the air compressor, including:

[0015] Taking the gas load or gas production cost at the user end as the constraint and the full-day operating cost as the target, calculate the number of air compressor openings, the opening degree of the inlet regulating valve, the closing degree of the vent regulating valve and the operating frequency.

[0016] Furthermore, the air compressor includes a centrifugal air compressor, a fixed-frequency screw air compressor and a variable-frequency screw air compressor.

[0017] Furthermore, based on the planned parameters of the air compressor and the real-time pressure of the system, the PLC feedback control strategy is used to calculate the target parameters of the air compressor, including:

[0018] When the real-time system pressure is lower than the working pressure range, increase the number of air compressors opened until the real-time system pressure reaches the working pressure range;

[0019] When the real-time system pressure is between the working pressure bands, if the load proportion of the centrifugal air compressor is lower than the first threshold, the fixed-frequency screw air compressor is unloaded, and the load proportion is adjusted by the inlet regulating valve of the centrifugal air compressor and the frequency of the variable-frequency screw air compressor until the load proportion of the centrifugal air compressor is higher than the first threshold;

[0020] When the load proportion of the centrifugal air compressor is greater than the second threshold or the load proportions of the centrifugal air compressor and the variable frequency screw air compressor are both greater than the first threshold, adjust the outlet pressure setting value of the air compressor until the load proportion of the centrifugal air compressor is lower than the second threshold or the load proportions of the centrifugal air compressor and the variable frequency screw air compressor are lower than the first threshold; wherein the first threshold is less than the second threshold;

[0021] When the real-time system pressure is greater than the working pressure band, reduce the number of air compressor openings until the real-time system pressure reaches the working pressure band.

[0022] A deeply integrated air compression intelligent optimization control system, used to execute any of the above methods, comprising: an air compressor unit, the air outlet of the air compressor unit is connected to the air inlet of the air storage tank, the air outlet of the air storage tank is connected to the air inlet of the pressure sensor, the air outlet of the pressure sensor is connected to the air inlet of the flow meter, and the air outlet of the flow meter is connected to the user end;

[0023] The controller is electrically connected to the air compressor unit, the air storage tank, the pressure sensor and the flow meter respectively.

[0024] Furthermore, the controller includes a feedforward intelligent controller and a PLC feedback controller.

[0025] The beneficial effects of the present invention are as follows: the present invention uses a feedforward intelligent control strategy to calculate the opening quantity, inlet regulating valve opening, vent regulating valve closing degree and operating frequency of the air compressor based on the gas load at the user end, and then uses a PLC feedback control strategy to calculate the opening quantity, operating frequency and inlet regulating valve opening of the air compressor by comparing the real-time pressure of the system and the size of the working pressure band. The present invention realizes precise control and regulation of the operation process of the air compression system, reduces the fluctuation of the output pressure, improves the operating efficiency, and ensures the smooth operation of the air compression system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of utilizing the feedforward-based air compressor intelligent control in the present invention to make the air compressor system reach the planned working condition;

[0027] Figure 2 This is a structural schematic diagram of a deeply integrated air compressor intelligent optimization control system of the present invention;

[0028] Figure 3 A schematic diagram of the structure of a deeply integrated air compressor intelligent optimization control system provided in an embodiment of the present invention;

[0029] Figure 4 The following is a gas load prediction curve diagram for a certain day obtained by a workshop using the method of the present invention.

[0030] In the figure, 1. air compressor unit, 2. air storage tank, 3. pressure sensor, 4. flow meter, 5. user end, 6. controller, 7. drying unit, 8. controller, 9. air compressor flow meter, 10. cooling unit, 11. cooling water pump, 12. cooling tower, 13. air compressor station outlet main pipe, 14. cooling water return pipe, 15. cooling water inlet pipe. DETAILED DESCRIPTION

[0031] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] The present invention is based on the current working condition of the air compressor system and utilizes feedforward air compressor intelligent control to enable the air compressor system to reach the planned working condition. Based on the planned working condition, the present invention utilizes PLC feedback control to enable the air compressor system to reach the target working condition.

[0033] Specifically:

[0034] A deeply integrated air compressor intelligent optimization control method, such as Figure 1 As shown, the following steps are included:

[0035] S110, obtaining the gas load of the user end.

[0036] Specifically:

[0037] S111, build equipment energy efficiency model: The equipment energy efficiency adopts the "white box + black box" modeling method. On the basis of the theoretical model (mathematical modeling), the actual operation data on site is used to fit the correction parameters (functions). The energy efficiency data model of the unit is established through the operating parameters of the air compressor, dryer, cooling water pump, and cooling tower. Specifically:

[0038] Air compressor:

[0039] Fixed frequency screw air compressor:

[0040] Functional relationship between operating pressure p and power P: loading: P = f(p), unloading: P = 0.3f(p);

[0041] Functional relationship between operating pressure p and gas production Q: loading: Q = f(p), unloading: Q = 0;

[0042] Variable frequency screw air compressor:

[0043] The functional relationship between power P and frequency f is: P = f(f);

[0044] The functional relationship between gas production Q and frequency f is: Q = f(f);

[0045] Centrifugal air compressor:

[0046] Functional relationship between inlet regulating valve (IGV) opening and power P: P = f (IGV opening);

[0047] The functional relationship between IGV opening, vent control valve (BOV) closing and gas production Q is: Q = f (IGV opening) - f (BOV closing).

[0048] Dryer:

[0049] The data relationship between air consumption and air processing volume Q: ΔQ = Q 产气 -Q 用气 .

[0050] Cooling pump, cooling tower fan:

[0051] Functional relationship between heat dissipation and power P, frequency f, and temperature t: Q 散热 =f(P,f,t).

[0052] S112, build an air compression load prediction model: use BP neural network to build an air compression load prediction model, and use the user's historical gas load data to train the model, and use the historical gas load data to predict the gas load every 5 minutes in the next 24 hours.

[0053] Load forecasting, as the basic support for feedforward control operation, is a prerequisite for achieving economic scheduling of various equipment in the air compression system and intelligent optimization of the system. The accuracy of the forecast directly determines the energy efficiency improvement and economic gain.

[0054] S120, based on the gas load, a feedforward intelligent control strategy is used to calculate the planned parameters of the air compressor; the planned parameters of the air compressor include the number of air compressor openings, the opening degree of the inlet regulating valve, the closing degree of the vent regulating valve and the operating frequency.

[0055] Specifically:

[0056] S121, determine the number of air compressors to be turned on and their operating status: based on the gas load every 5 minutes in the next 24 hours, with the goal of minimizing the air compressor system's full-day operating cost / lowest gas production cost, comprehensively consider the unit's operating energy consumption and startup energy consumption, and use the baseline operating restrictions and terminal gas supply requirements as constraints to allocate the air compressor operating status.

[0057] The number of air compressors shall be at least 3, including centrifugal air compressors, fixed-frequency screw air compressors and variable-frequency screw air compressors.

[0058] The air compressor operating status includes the start / stop status, IGV opening and BOV opening of the centrifugal air compressor, the start / stop status, loading status and unloading status of the fixed-frequency screw compressor, and the start / stop status and operating frequency of the variable-frequency screw compressor as decision variables.

[0059] S122, determine the number of drying units and cooling units that are turned on and their operating status: calculate the energy consumption of each air compressor based on the operating status of the air compressor, and obtain the operating status of the air compressor every 5 minutes in the next 24 hours and the status of the decision variable; obtain the heat dissipation of the system based on the operating status of the air compressor, optimize the distribution of the cooling system, and determine the parameters such as the number of cooling pumps and cooling towers that are turned on and the frequency of operating units; then determine the number of dryers that are turned on and their operating status based on the load forecast results.

[0060] S123, the control system operates the air compression system according to the determined number of air compressors, drying units and cooling units that are turned on and their operating states until the air compression system reaches the planned operating condition.

[0061] S130, based on the planned parameters of the air compressor and the real-time pressure of the system, the PLC feedback control strategy is used to calculate the target parameters of the air compressor; the target parameters of the air compressor include the opening quantity, operating frequency and opening degree of the inlet regulating valve of the air compressor.

[0062] Specifically:

[0063] The pressure feedback data is obtained from the pressure sensor installed in the gas storage tank and used as the basis for the control and adjustment of each unit. The system pressure P is used as the control target (P1≤P≤P2) and the working pressure range (P1, P2) is set;

[0064] The control system compares the collected real-time system pressure value with the working pressure range and performs constant pressure interval control;

[0065] When P<P1, after a delay of 5 seconds, the control system controls and starts one more air compressor (the air compressor goes from starting, loading, to constant pressure adjustment). The starting and loading sequence is variable frequency screw air compressor, fixed frequency screw air compressor, until the real-time system pressure reaches the working pressure range;

[0066] When P1≤P≤P2, if the load proportion of the centrifugal air compressor is less than 75%, a running fixed-frequency screw air compressor will be unloaded first, and then the load proportion will be adjusted by the IGV of the centrifugal air compressor and the frequency of the variable-frequency screw air compressor; when the load proportion of the centrifugal air compressor is greater than 85% or the load proportion of the centrifugal air compressor and the variable-frequency screw air compressor is greater than 75%, self-regulation will be performed by adjusting the outlet pressure setting value of the air compressor;

[0067] When P>P2, after a delay of 5 seconds, the control system needs to reduce the load on the running air compressor, and the unloading and shutdown order is fixed-frequency screw air compressor, variable-frequency screw air compressor, until the real-time system pressure reaches the working pressure range.

[0068] If the system pressure or load fluctuates greatly, the air compressor intelligent rolling update monitors the deviation between the real-time load and the predicted load. If the deviation exceeds 5%, that is, the actual gas load deviates from the predicted load by more than 5%, intraday rolling optimization is performed, the optimization strategy is regenerated, and sent to the PLC feedback controller for execution.

[0069] In addition, if the number of air compressors of various types in the air compression system is greater than 2, they need to be rotated according to the length of operation time of the units to ensure that the operation time of each air compressor is balanced.

[0070] The control system calculates the number of air compressor openings, operating frequency and opening of the inlet regulating valve, and then makes adjustments based on the calculation results.

[0071] The present invention provides a deeply integrated intelligent optimization control method for air compression, which establishes energy efficiency models of air compressors, dryers, and cooling equipment, grasps the operating status and various parameters of the air compression system in real time, generates a day-ahead optimization scheduling strategy and an intraday rolling optimization scheduling strategy based on real-time changes in load forecasts, and accurately adjusts and controls the air compression system, which can effectively reduce the energy consumption and gas supply costs of the air compression station.

[0072] A deeply integrated air compressor intelligent optimization control system, such as Figure 2As shown, it includes: an air compressor unit 1, the air outlet end of the air compressor unit 1 is connected to the air inlet end of the air storage tank 2, the air outlet end of the air storage tank 2 is connected to the air inlet end of the pressure sensor 3, the air outlet end of the pressure sensor 3 is connected to the air inlet end of the flow meter 4, and the air outlet end of the flow meter 4 is connected to the user end 5;

[0073] The controller 6 is electrically connected to the air compressor unit 1 , the air storage tank 2 , the pressure sensor 3 and the flow meter 4 respectively.

[0074] The controller 8 includes a feedforward intelligent controller and a PLC feedback controller.

[0075] The air compressor unit 1 includes a plurality of centrifugal air compressors, fixed-frequency screw air compressors and variable-frequency screw air compressors.

[0076] The present invention is further described in detail below in conjunction with embodiments.

[0077] like Figure 3 As shown, the air compression system includes an air compressor unit 1, the air outlet end of the air compressor unit 1 is connected to the air inlet end of the air compressor flow meter 9, the air outlet end of the air compressor flow meter 9 is connected to the air inlet end of the air storage tank 2, the air outlet end of the air storage tank 2 is connected to the air inlet end of the pressure sensor 3, the air outlet end of the pressure sensor 3 is connected to the air inlet end of the drying unit 7, the air outlet end of the drying unit 7 is connected to the air inlet end of the air supply pressure sensor 8, the air outlet end of the air supply pressure sensor 8 is connected to the air inlet end of the flow meter 4, the air supply pressure sensor 8 and the flow meter 4 are both arranged on the air compression station outlet mother pipe 13, the water outlet of the cooling unit 10 is connected to the water inlet of the air compressor unit 1 and the water inlet of the drying unit 7 through the cooling water inlet pipe 15, and the water return port of the cooling unit 10 is connected to the water outlet of the air compressor unit 1 and the water outlet of the drying unit 7 through the cooling water return pipe 14.

[0078] The controller 6 is electrically connected to the air compressor unit 1, the air compressor flow meter 9, the air storage tank 2, the pressure sensor 3, the drying unit 7, the air supply pressure sensor 8, the flow meter 4 and the cooling unit 10 respectively.

[0079] The cooling unit 10 includes a cooling water pump 11, a cooling tower 12 and a fan.

[0080] The air compressor unit 1 includes a plurality of centrifugal air compressors, fixed-frequency screw air compressors and variable-frequency screw air compressors.

[0081] The drying unit 7 includes an adsorption dryer, a refrigeration dryer and a combined dryer.

[0082] The pressure feedback data is obtained from the pressure sensor 3 installed on the gas tank as the basis for the control and adjustment of each unit. The air supply pressure of the air compression system is obtained by the air supply pressure sensor 8 installed at the end of the station building to ensure safe and stable air supply; the air supply load is obtained from the air compressor flow meter 9 at the outlet of the air compressor unit, and the air load is obtained from the flow meter 4 on the outlet mother pipe 13 of the air compression station at the rear end of the drying unit, which is used to calculate the operating status of the air compressor, dryer, cooling water pump and cooling tower in the air compression system.

[0083] The controller 6 includes a feedforward intelligent controller and a PLC feedback controller.

[0084] The present invention adopts a control mode of intelligent control (front-end type) + automatic control (feedback type). The controller includes two parts: an intelligent optimization controller and a PLC feedback controller. The intelligent optimization controller generates a full-time node control strategy according to the load and production plan predicted by the air compressor. The PLC feedback controller performs feedback adjustment based on the system operating parameters. The advantage is that at the beginning of the operation cycle, the actual control command set generated by the intelligent control is first issued to adjust the system operating conditions to the planned conditions. During the operation cycle, the target conditions are approximated and regulated according to the feedback parameters.

[0085] Take 6 air compressors as an example, the gas output of the centrifugal air compressor is 100m 3 / min, fixed frequency screw compressor gas output 70m 3 / min, the gas output of the variable frequency screw compressor is 40m 3 / min.

[0086] The air compression system is operated by using feedforward-based intelligent air compression control to determine the number of air compressors, drying units and cooling units to be turned on and their operating status until the air compression system reaches the planned operating conditions.

[0087] Figure 4 This is the gas load prediction curve for a workshop on a certain day. The reinforcement learning algorithm is used to calculate the operating status of its 24-hour air compressor equipment with the lowest gas production cost as the optimization goal. The hourly operating results are as follows.

[0088] Table 1 24-hour operation status of air compressor equipment

[0089]

[0090]

[0091] Based on the planned operating conditions, PLC feedback control is used to make the air compression system reach the target operating conditions.

[0092] In each period, the pressure fluctuation of the system is feedback controlled by the PLC feedback controller, and the control method is as follows:

[0093] The pressure feedback data is obtained from the pressure sensor 3 installed on the gas storage tank, which is used as the control and adjustment basis for each unit. The system pressure P is used as the control target (P1≤P≤P2), and the working pressure range (P1, P2) is set;

[0094] The controller compares the collected real-time system pressure value with the working pressure range and performs constant pressure interval control;

[0095] When P<P1, after a delay of 5 seconds, the control system controls and starts one more air compressor (the air compressor goes from starting, loading, to constant pressure adjustment). The starting and loading sequence is variable frequency screw air compressor, fixed frequency screw air compressor, until the real-time system pressure reaches the working pressure range;

[0096] When P1≤P≤P2, (1) when the load ratio of the 1# and 2# centrifugal air compressor units is less than 75%, delay 20 minutes and start the load reduction operation (prioritize the fixed-frequency screw air compressor; after stopping all fixed-frequency screw air compressors, continue to judge by the load ratio. If the load ratio of the centrifugal air compressor unit is still less than 75%, continue to stop the variable-frequency screw air compressor); (2) when the load ratio of the centrifugal air compressor is greater than 75% and less than 85%, and the load ratio of the variable-frequency screw air compressor is less than 85%, delay 20 minutes and start the load reduction operation (stop one fixed-frequency screw air compressor, and continue to judge by the load ratio after stopping); (3) when the load ratio of the centrifugal air compressor is greater than 85%, keep the existing compressor running without adjustment. (4) when the load ratio of the centrifugal air compressor is greater than 75% and less than 85%, and the load ratio of the variable-frequency screw air compressor is greater than 85%, keep the existing compressor running without adjustment.

[0097] When P>P2, after a delay of 5 seconds, the control system needs to reduce the load on the running air compressor, and the unloading and shutdown sequence is 1# and 2# fixed-frequency screw air compressors, and 1# and 2# variable-frequency screw air compressors.

[0098] If the system pressure or load fluctuates greatly, the air compressor intelligent rolling update monitors the deviation between the real-time load and the predicted load. If the deviation exceeds 5%, that is, the actual gas load deviates from the predicted load by more than 5%, intraday rolling optimization is performed, the optimization strategy is regenerated, and sent to the PLC feedback controller for execution.

[0099] In addition, if the number of air compressors of various types in the air compression system is greater than 2, they need to be rotated according to the running time of the units to ensure that the running time of each air compressor is balanced.

[0100] As mentioned above, the current number of running units is ≤4 and the number of running air compressors of each type is ≥1, which is a necessary condition for rotation.

[0101] The prerequisite is that P1≤P≤P2 is met. When the running time of air compressor No. 1 is ≥ the rotation time, a rotation request is issued after a delay of 10 seconds.

[0102] After the rotation start command is issued, the 3# air compressor starts and loads normally; after a delay of 20 seconds, the 1# air compressor automatically unloads and stops.

[0103] When the running time of 2# air compressor is ≥ the rotation time, the next standby machine, i.e. 4# air compressor, is started in the same way.

[0104] The machines run in rotation repeatedly. If one of the air compressors fails, it will be automatically skipped and the next one will be started.

[0105] In summary, the present invention uses a feedforward intelligent control strategy to calculate the opening quantity, inlet regulating valve opening, vent regulating valve closing degree and operating frequency of the air compressor based on the gas load at the user end, and then uses a PLC feedback control strategy to calculate the opening quantity, operating frequency and inlet regulating valve opening of the air compressor by comparing the real-time pressure of the system and the size of the working pressure band. The present invention realizes precise control and regulation of the operation process of the air compression system, reduces the fluctuation of the output pressure, improves the operating efficiency, and ensures the smooth operation of the air compression system.

Claims

1. A deep fusion air compressor intelligent optimization control method, characterized in that: The following steps are involved: Obtain the gas load at the user end; Based on the gas load, a feedforward intelligent control strategy is used to calculate the planned parameters of the air compressor; Based on the planned parameters of the air compressor and the real-time pressure of the system, the PLC feedback control strategy is used to calculate the target parameters of the air compressor; The air compressor planning parameters include the number of air compressor openings, the opening of the inlet regulating valve, the closing of the vent regulating valve and the operating frequency; The target parameters of the air compressor include the opening quantity, operating frequency and opening degree of the inlet regulating valve of the air compressor.

2. According to claim 1, a deep fusion air compressor intelligent optimization control method is characterized in that: Obtaining the gas load at the user end includes: Build an air compression load prediction model to predict the gas load at the user end.

3. The deep fusion air compressor intelligent optimization control method according to claim 2 is characterized in that: The air compression load prediction model is established based on the BP neural network, and the historical gas load data of the user end is used to train the model to predict the gas load of the user end.

4. The deep fusion air compressor intelligent optimization control method according to claim 3 is characterized in that: The feedforward intelligent control strategy is used to calculate the planned parameters of the air compressor, including: Taking the gas load or gas production cost at the user end as the constraint and the full-day operating cost as the target, calculate the number of air compressor openings, the opening degree of the inlet regulating valve, the closing degree of the vent regulating valve and the operating frequency.

5. The deep fusion air compressor intelligent optimization control method according to claim 4 is characterized in that: Air compressors include centrifugal air compressors, fixed-frequency screw air compressors and variable-frequency screw air compressors.

6. A deep fusion air compressor intelligent optimization control method according to claim 5, characterized in that: Based on the planned parameters of the air compressor and the real-time pressure of the system, the PLC feedback control strategy is used to calculate the target parameters of the air compressor, including: When the real-time system pressure is lower than the working pressure range, increase the number of air compressors opened until the real-time system pressure reaches the working pressure range; When the real-time system pressure is between the working pressure bands, if the load proportion of the centrifugal air compressor is lower than the first threshold, the fixed-frequency screw air compressor is unloaded, and the load proportion is adjusted by the inlet regulating valve of the centrifugal air compressor and the frequency of the variable-frequency screw air compressor until the load proportion of the centrifugal air compressor is higher than the first threshold; When the load proportion of the centrifugal air compressor is greater than the second threshold or the load proportions of the centrifugal air compressor and the variable frequency screw air compressor are both greater than the first threshold, adjust the outlet pressure setting value of the air compressor until the load proportion of the centrifugal air compressor is lower than the second threshold or the load proportions of the centrifugal air compressor and the variable frequency screw air compressor are lower than the first threshold; wherein the first threshold is less than the second threshold; When the real-time system pressure is greater than the working pressure band, reduce the number of air compressor openings until the real-time system pressure reaches the working pressure band.

7. A deeply integrated air compressor intelligent optimization control system, characterized in that: Used to perform the method according to any one of claims 1 to 6, comprising: an air compressor unit (1), the air outlet end of the air compressor unit (1) is connected to the air inlet end of the air storage tank (2), the air outlet end of the air storage tank (2) is connected to the air inlet end of the pressure sensor (3), the air outlet end of the pressure sensor (3) is connected to the air inlet end of the flow meter (4), and the air outlet end of the flow meter (4) is connected to the user end (5); The controller (6) is electrically connected to the air compressor unit (1), the air storage tank (2), the pressure sensor (3) and the flow meter (4) respectively.

8. The deep fusion air compressor intelligent optimization control system according to claim 7 is characterized in that: The controller (6) comprises a feedforward intelligent controller and a PLC feedback controller.

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