Intelligent air compressor optimization control method and system based on IOE control system

Abstract

The invention relates to the technical field of air compressor optimization, and discloses an intelligent air compressor optimization control method and system based on an IOE control system.The intelligent air compressor optimization control system comprises a collection unit, a preprocessing unit, an analysis unit and an optimization unit, rich operation data such as air inlet pressure, flow, exhaust pressure and temperature are comprehensively collected in real time, denoising is conducted through a moving average method, and data accuracy is ensured; normal, overload and abnormal fluctuation modes are clearly divided, accurate judgment is performed by means of a preset interval and reasonable calculation, and powerful support is provided for control decision making; in a normal mode, intelligent regulation is performed according to a parameter average value, and operation efficiency and stability are improved; the valve opening degree is adjusted according to the deviation degree in the overload mode, the load is effectively reduced, and equipment is protected; in the abnormal fluctuation mode, a PID control algorithm is used, the opening degree, the cooling amount and the like of the valve are flexibly adjusted according to deviation and the change rate, and parameters are rapidly recovered to be stable. The fault rate and the maintenance cost can be greatly reduced, and the working efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air compressor optimization, and in particular to an intelligent air compressor optimization control method and system based on an IOE control system. Background Art

[0002] Traditional air compressor control systems generally have problems such as single control strategy and low level of intelligence.

[0003] On the one hand, existing air compressor systems often lack comprehensive monitoring of key operating parameters during operation. Some systems only monitor a few parameters, such as exhaust pressure, and are unable to obtain multi-dimensional data such as intake pressure and flow, motor winding temperature, and lubricant oil pressure and temperature in real time, making it difficult to comprehensively and accurately assess the compressor's operating status. This results in the inability to promptly detect potential equipment anomalies, which can lead to equipment failures and disrupt production continuity.

[0004] On the other hand, traditional control strategies often rely on fixed parameter control or simple threshold control. This extensive control approach cannot achieve adaptive adjustment when the air compressor is operating under complex conditions or with fluctuating loads. For example, during normal operation, it is difficult to optimize parameters such as output pressure and speed based on real-time operating conditions, resulting in energy waste. In the event of overload or abnormal fluctuations in operating parameters, targeted control measures cannot be taken quickly and effectively, which can easily lead to equipment damage and shorten service life. It also increases maintenance costs and downtime, reducing production efficiency.

[0005] With the development of industrial automation and intelligence, the market has put forward higher requirements for the operating efficiency, reliability and energy saving of air compressors. Therefore, there is an urgent need for an intelligent air compressor optimization control method and system based on advanced control systems. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent air compressor optimization control method and system based on an IOE control system, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The intelligent air compressor optimization control method based on the IOE control system includes the following steps: Data collection: Various types of sensors are installed at key parts of the air compressor to collect the operating data of the air compressor in real time; Data preprocessing: denoising the operating data of the air compressor; Data analysis: Based on the pre-set normal operating data interval, it is compared with the real-time collected air compressor operating data to determine the operating status of the air compressor; Optimization control: According to the operating status of the air compressor, data control is performed on the air compressor in different operating states.

[0008] As a further solution of the present invention: the operating data includes: The pressure sensor and flow sensor installed at the air inlet monitor the air pressure and air flow entering the air compressor in real time, and record them as P and in and Q in ; The exhaust pressure and exhaust temperature of the air compressor are monitored in real time by the pressure sensor and temperature sensor installed at the exhaust port, and are recorded as P out and T out ; The temperature of the motor winding is monitored in real time by a temperature sensor installed at the motor winding, and is recorded as the winding temperature T win ; The lubricating oil pressure and lubricating oil temperature are monitored in real time by the pressure sensor and temperature sensor installed in the lubricating oil pipeline, and are recorded as P and lu and T lu .

[0009] As a further solution of the present invention: the denoising process adopts a sliding average method; The formula is as follows: Where X0 represents the running data after denoising, X t Represents the running data at the current moment, and n is the sliding window size.

[0010] As a further solution of the present invention: wherein, the operating state of the air compressor is divided into a normal mode, an overload mode, and an abnormal fluctuation mode; Among them, the normal operating data range includes: intake pressure range [P in（a） , P in（b） ]、Intake flow range [Q in（a） , Q in（b） ]、Exhaust pressure range [P out（a） , P out（b）]、 Exhaust temperature range [T out（a） , T out（b） ]、Winding temperature range [T win（a） , T win（b） ]、Lubricating oil pressure range [P lu（a） , P lu（b） ]、Lubricating oil temperature range [T lu（a） , T lu（b） ]. As a further solution of the present invention: the normal operating mode is determined as follows: Within a specified unit time, multiple operating data of the air compressor are obtained, and then the average value is calculated; the end point of the specified unit time is the current time node; When P in The average value ∈[P in（a） , P in（b） ]、Q in The average value ∈[Q in（a） , Q in（b） ]、P out The average value ∈[P out（a） , P out（b）]、Tout The average value ∈[T out（a） , T out（b）]、Twin The average value ∈[T win（a），Twin（b） ]、P lu The average value ∈[P lu（a），Plu（b） ]、T lu The average value ∈[T lu（a） , T lu（b） ], it is determined that the air compressor is in normal operation mode.

[0011] As a further solution of the present invention: the overload operation mode is determined as follows: When T win The average value of >T win（b） At the same time, P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If at least one comparison formula is established, it is determined that the air compressor is in overload operation mode.

[0012] As a further solution of the present invention: the abnormal fluctuation operation mode is determined as follows: In the specified unit time, the maximum and minimum values ​​of intake pressure, intake flow, exhaust pressure and exhaust temperature are extracted respectively; Then pass: Calculate the fluctuation range variable ZB of intake pressure, intake flow, exhaust pressure and exhaust temperature; in: ZB={P in (B), Q in (B), P out (B), T out (B)}, P in (B), Q in (B), P out (B), T out(B) refers to the fluctuation amplitude of intake pressure, intake flow, exhaust pressure and exhaust temperature respectively; Z max ={P in（max） , Q in（max） 、P out（max） 、T out（max）}, P in（max） , Q in（max） 、P out（max） 、T out（max） They respectively refer to the maximum values ​​of intake pressure, intake flow, exhaust pressure and exhaust temperature per unit time; Z min ={P in（min） , Q in（min） 、P out（min） 、T out（min）}, P in（min） , Q in（min） 、P out（min） 、T out（min） Respectively refers to the maximum values ​​of intake pressure, intake flow, exhaust pressure, and exhaust temperature per unit time Z （a） ={P in（a） , Q in（a） 、P out（a） 、T out（a）}; Z （b） ={P in（b） , Q in（b） 、P out（b） 、T out（b）}; Then extract the preset fluctuation amplitude threshold P corresponding to the intake pressure, intake flow, exhaust pressure and exhaust temperature in (B y )、Q in (B y )、P out (B y )、T out (B y ); When P in (B)>P in (B y )、Q in (B)>Q in (B y )、P out (B)>P out (B y )、T out (B)>T out (B y ), if at least two comparison formulas are established, it is determined that the air compressor is in abnormal fluctuation operation mode.

[0013] As a further solution of the present invention: the data control method in the normal operating mode is as follows: Starting from the current time node, continuously monitor the intake flow rate and exhaust pressure in the next unit time, and calculate their average values ​​respectively; when < ,and < , then increase the output pressure of the air compressor; Where Q in (P) and P out (P) are the average values ​​of intake flow and exhaust pressure in the unit time respectively; when < ,and < , it indicates that the temperature of the air compressor is slightly high, then stop the air compressor or reduce the speed of the air compressor; Where, T win (P) and T lu (P) are the average values ​​of winding temperature and lubricating oil temperature in the unit time respectively; when < ,and < , it indicates that the heat dissipation capacity of the lubricating oil is insufficient, and the cooling water flow rate is increased to cool the lubricating oil; Where, P lu (P) is the average value of the lubricating oil pressure per unit time.

[0014] As a further solution of the present invention: the data control method in the overload operation mode is as follows: When the air compressor is determined to be in overload operation mode, it means that the air compressor of the equipment is too large, and the load is reduced accordingly; The load reduction methods are: When P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If only one comparison formula is true, reduce the valve opening by 5%; When P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If there are two contrasting equations, the valve opening will be reduced by 10%; When P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If only three comparison formulas are true, reduce the valve opening by 15%. As a further solution of the present invention, the data control method in the abnormal fluctuation operation mode is as follows: First, the intake pressure, intake flow, exhaust pressure, and exhaust temperature are collected at a preset sampling period, and the collected data are stored in a buffer area to form time series data, which are marked as P in（t） , Q in（t） 、P out（t） 、T out（t） ; Next, the set values ​​predetermined based on intake pressure, intake flow, exhaust pressure, and exhaust temperature are extracted and marked as P in (S), Q in (S), P out (S), T out (S); Then, by: Calculate the deviation eP of intake pressure, intake flow, exhaust pressure and exhaust temperature respectively in 、eQ in 、eP out 、eT out ; Then, by: Calculate the change rate of the corresponding deviation of intake pressure, intake flow, exhaust pressure and exhaust temperature e1P respectively in 、e1Q in 、e1P out 、e1T out ; Where t0 is the duration of the sampling period; Afterwards, the PID control algorithm is used to calculate the control quantities of intake pressure, intake flow, exhaust pressure, and exhaust temperature; The calculation formula is: Where K p is the proportional coefficient, used to quickly respond to deviations; K i is the integral coefficient, used to eliminate steady-state error; K d is the differential coefficient, which is used to suppress the deviation change rate and improve the system stability; p , K i , Kd Set appropriately according to the actual operating characteristics and debugging experience of the air compressor; UP in 、UQ in UP out UT out The control quantities are intake pressure, intake flow, exhaust pressure and exhaust temperature respectively; UP in 、UQ in Used to adjust the opening of the intake valve; UP out Used to adjust the opening of the exhaust valve; UT out Used to adjust the cooling water volume or cooling fan speed of the cooling system.

[0015] An intelligent air compressor optimization control system based on an IOE control system is used to implement an intelligent air compressor optimization control method based on an IOE control system. The system includes: The acquisition unit is used to collect the operating data of the air compressor in real time through various sensors deployed at key parts of the air compressor; A pre-processing unit, used for denoising the operating data of the air compressor; An analysis unit is used to compare the pre-set normal operating data interval with the real-time collected operating data of the air compressor to determine the operating status of the air compressor; The optimization unit is used to perform data control on the air compressor in different operating states according to the operating state of the air compressor.

[0016] Beneficial effects of the present invention: The present invention installs various types of sensors at key locations of the air compressor, such as the air inlet, exhaust port, motor winding, and lubricating oil pipeline, to collect operating data such as intake pressure, intake flow, exhaust pressure, exhaust temperature, winding temperature, lubricating oil pressure, and lubricating oil temperature in real time and comprehensively, providing a rich and accurate data basis for subsequent analysis and control, making the judgment of the operating status of the air compressor more accurate.

[0017] The present invention uses a sliding average method to denoise the collected operating data, which can effectively remove noise interference in the data and improve the quality of the data, thereby making subsequent data-based analysis and judgment results more reliable and reducing misjudgments caused by noise.

[0018] This invention clearly defines three operating states for an air compressor: normal mode, overload mode, and abnormal fluctuation mode, and provides a detailed and reasonable method for determining these states. Based on a pre-defined range of normal operating data, by calculating the average value of different operating data and determining specific conditions, the compressor's operating state can be accurately determined, providing a reliable basis for implementing targeted control measures.

[0019] The present invention, under normal operating mode, adopts different control measures such as increasing output pressure, pausing or reducing rotational speed, and increasing cooling water flow rate according to the average values ​​of different operating parameters, thereby further optimizing the operating performance of the air compressor and improving its operating efficiency, while ensuring the safe and stable operation of the equipment and avoiding damage to the equipment caused by problems such as excessive temperature and insufficient heat dissipation of lubricating oil.

[0020] The present invention, when determining that the air compressor is in an overload operation mode, reduces the load by reducing the valve opening to different degrees according to different deviation conditions, which can timely and effectively reduce the workload of the air compressor, avoid damage to the equipment due to overload operation, and extend the service life of the equipment.

[0021] The present invention adopts a PID control algorithm in an abnormal fluctuation operation mode to calculate the deviation and deviation change rate of each operating parameter based on the collected data, and then calculates the corresponding control quantity to adjust the intake valve opening, exhaust valve opening, cooling water volume of the cooling system or cooling fan speed, etc., which can quickly and effectively restore the operating parameters of the air compressor to a normal and stable state, thereby improving the stability and reliability of the air compressor operation.

[0022] The present invention, through precise control of the air compressor under different operating states, can enable the system to better adapt to different working conditions and operating environments, improve the overall performance and work efficiency of the air compressor, and at the same time reduce the failure rate and maintenance cost of the equipment, with high economic benefits and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 It is a system block diagram of the intelligent air compressor optimization control system based on the IOE control system of the present invention.

[0025] Figure 2 It is a flow chart of the intelligent air compressor optimization control method based on the IOE control system of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Example 1 See also Figure 1 and Figure 2As shown, the present invention is an intelligent air compressor optimization control method based on the IOE control system, comprising the following steps: Data collection: Various types of sensors are installed at key parts of the air compressor to collect the operating data of the air compressor in real time; In this embodiment, key locations such as the air inlet, exhaust port, motor windings, lubricating oil pipelines, etc. Operational data includes: The pressure sensor and flow sensor installed at the air inlet monitor the air pressure and air flow entering the air compressor in real time, and record them as P and in and Q in ; The exhaust pressure and exhaust temperature of the air compressor are monitored in real time by the pressure sensor and temperature sensor installed at the exhaust port, and are recorded as P out and T out ; The temperature of the motor winding is monitored in real time by a temperature sensor installed at the motor winding, and is recorded as the winding temperature T win ; The lubricating oil pressure and lubricating oil temperature are monitored in real time by the pressure sensor and temperature sensor installed in the lubricating oil pipeline, and are recorded as P and lu and T lu ; Data analysis: Based on the pre-set normal operating data interval, it is compared with the real-time collected air compressor operating data to determine the operating status of the air compressor; Among them, the operating status of the air compressor is divided into normal mode, overload mode, and abnormal fluctuation mode; Normal operating data range includes: intake pressure range [P in（a） , P in（b） ]、Intake flow range [Q in（a） , Q in（b） ]、Exhaust pressure range [P out（a） , P out（b） ]、Exhaust temperature range [T out（a） , T out（b） ]、Winding temperature range [T win（a） , T win（b） ]、Lubricating oil pressure range [P lu（a） , P lu（b） ]、Lubricating oil temperature range [T lu（a） , T lu（b） ]; Normal operating mode is determined as follows: Within a specified unit time, multiple operating data of the air compressor are obtained, and then the average value is calculated; the end point of the specified unit time is the current time node; When Pin The average value ∈[P in（a） , P in（b） ]、Q in The average value ∈[Q in（a） , Q in（b） ]、P out The average value ∈[P out（a） , P out（b） ]、T out The average value ∈[T out（a） , T out（b） ]、T win The average value ∈[T win（a） , T win（b） ]、P lu The average value ∈[P lu（a） , P lu（b） ]、T lu The average value ∈[T lu（a） , T lu（b） ], it is determined that the air compressor is in normal operating mode; In this embodiment, in the normal operating mode, the compression process of the air compressor is stable, the energy conversion efficiency is high, the wear of each component of the equipment is at a normal level, and the equipment can continuously and reliably provide stable compressed air for production; The overload operation mode is determined as follows: When T win The average value of >T win（b） At the same time, P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If at least one comparison formula is established, it is determined that the air compressor is in overload operation mode; In this embodiment, the overload operation mode is when multiple parameters related to power and load exceed the normal range; overload operation will accelerate the wear of the equipment, reduce the service life of the motor, and may even cause serious faults such as motor burnout, so timely measures need to be taken to adjust or shut down for maintenance; The abnormal fluctuation operation mode is determined as follows: In the specified unit time, the maximum and minimum values ​​of intake pressure, intake flow, exhaust pressure and exhaust temperature are extracted respectively; Then pass: Calculate the fluctuation range variable ZB of intake pressure, intake flow, exhaust pressure and exhaust temperature; in: ZB={P in (B), Q in (B), Pout (B), T out (B)}, P in (B), Q in (B), P out (B), T out (B) refers to the fluctuation amplitude of intake pressure, intake flow, exhaust pressure and exhaust temperature respectively; Z max ={P in（max） , Q in（max） 、P out（max） 、T out（max）}, P in（max） , Q in（max） 、P out（max） 、T out（max） They respectively refer to the maximum values ​​of intake pressure, intake flow, exhaust pressure and exhaust temperature per unit time; Z min ={P in（min） , Q in（min） 、P out（min） 、T out（min）}, P in（min） , Q in（min） 、P out（min） 、T out（min） Respectively refers to the maximum values ​​of intake pressure, intake flow, exhaust pressure, and exhaust temperature per unit time Z （a） ={P in（a） , Q in（a） 、P out（a） 、T out（a）}; Z （b） ={P in（b） , Q in（b） 、P out（b） 、T out（b）}; Then extract the preset fluctuation amplitude threshold P corresponding to the intake pressure, intake flow, exhaust pressure and exhaust temperature in (B y )、Q in (B y )、P out (B y )、T out (B y ); When P in (B)>P in (B y )、Q in (B)>Q in (B y )、P out (B)>P out (By )、T out (B)>T out (B y ), if at least two contrasting expressions hold true, the air compressor is judged to be in abnormal fluctuation operation mode; In this embodiment, abnormal fluctuating operation will cause additional impact and wear to various components of the equipment, increasing the risk of equipment failure, and requiring immediate inspection and maintenance of relevant components; Example 1 proposes an intelligent air compressor optimization control method based on an IOE control system. This method collects operating data in real time by installing multiple sensors at key locations on the air compressor, and then compares it with a preset normal operating data interval to determine the operating status. This method can accurately monitor the operating status of the air compressor. For example, during normal operation, it can ensure a stable compression process, high energy conversion efficiency, and normal equipment wear. It can also promptly identify overloads and abnormal fluctuation patterns, facilitating the early detection of potential faults and providing a basis for subsequent adjustment or maintenance measures, ensuring that the air compressor continuously and reliably provides stable compressed air for production.

[0028] Example 2 See also Figure 1 and Figure 2 As shown, as the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of this embodiment is different from that of the first embodiment only in that this embodiment further includes the steps: Optimization control: Based on the results of data analysis, the air compressor is controlled by data; the data control method is as follows: Optimized operation in normal operating mode: Starting from the current time node, continuously monitor the intake flow rate and exhaust pressure in the next unit time, and calculate their average values ​​respectively; when < ,and < , then increase the output pressure of the air compressor; Where Q in (P) and P out (P) are the average values ​​of intake flow and exhaust pressure in the unit time respectively; when < ,and < , it indicates that the temperature of the air compressor is slightly high, then stop the air compressor or reduce the speed of the air compressor; Where, T win (P) and T lu (P) are the average values ​​of winding temperature and lubricating oil temperature in the unit time respectively; when < ,and < , it indicates that the heat dissipation capacity of the lubricating oil is insufficient, and the cooling water flow rate is increased to cool the lubricating oil; Where, P lu (P) is the average value of the lubricating oil pressure in the unit time; Optimized operation in overload mode: When the air compressor is determined to be in overload operation mode, it means that the air compressor of the equipment is too large, and the load is reduced accordingly; The load reduction methods are: When P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If only one comparison formula is true, reduce the valve opening by 5%; When P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If there are two contrasting equations, the valve opening will be reduced by 10%; When P in The average value of <P in（a） 、P out The average value of >P out（b） and P lu The average value of <P lu（a） If only three contrasting equations are established, the valve opening will be reduced by 15%; Optimized operation under abnormal fluctuation operation mode: First, the intake pressure, intake flow, exhaust pressure, and exhaust temperature are collected at a preset sampling period, and the collected data are stored in a buffer area to form time series data, which are marked as P in（t） , Q in（t） 、P out（t） 、T out（t） ; Next, the set values ​​predetermined based on intake pressure, intake flow, exhaust pressure, and exhaust temperature are extracted and marked as P in (S), Q in (S), P out (S), T out (S); Then, by: Calculate the deviation eP of intake pressure, intake flow, exhaust pressure and exhaust temperature respectively in 、eQ in 、eP out 、eT out ; Then, by: Calculate the change rate of the corresponding deviation of intake pressure, intake flow, exhaust pressure and exhaust temperature e1P respectively in 、e1Q in 、e1P out 、e1T out ; Where t0 is the duration of the sampling period; Afterwards, the PID control algorithm is used to calculate the control quantities of intake pressure, intake flow, exhaust pressure, and exhaust temperature; The calculation formula is: Where K p is the proportional coefficient, used to quickly respond to deviations; K i is the integral coefficient, used to eliminate steady-state error; K d is the differential coefficient, which is used to suppress the deviation change rate and improve the system stability; p , K i , K d According to the actual operating characteristics of the air compressor and debugging experience, it is set appropriately. In this embodiment, K p =0.5, K i =0.1, K d =0.2;UP in 、UQ in UP out UT out The control quantities are intake pressure, intake flow, exhaust pressure and exhaust temperature respectively; UP in 、UQ in Used to adjust the opening of the intake valve; UP out Used to adjust the opening of the exhaust valve; UT out Used to adjust the cooling water volume or cooling fan speed of the cooling system.

[0029] Example 2 adds an optimization control step based on Example 1, and performs targeted control of the air compressor in different operating modes based on the data analysis results. In normal operating mode, by monitoring the average value of relevant data in the subsequent unit time, the output pressure can be increased, the speed can be adjusted, or the cooling lubricant oil can be adjusted according to the specific situation, further improving operating efficiency and stability. In overload operating mode, the valve opening is adjusted to reduce the load based on the establishment of different comparison formulas, which can reduce equipment wear and extend the service life of the motor. In abnormal fluctuation operating mode, the PID control algorithm is used to calculate the control quantity and adjust the relevant parameters, which can effectively reduce the risk of equipment failure and ensure normal operation of the equipment.

[0030] Example 3 See also Figure 1 and Figure 2 As shown, as the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments. The technical solution of this embodiment is different from the first and second embodiments only in that this embodiment further includes the steps: Data preprocessing: denoising the operating data of the air compressor; The denoising method uses the sliding average method; The formula is as follows: Where X0 represents the running data after denoising, X t Represents the running data at the current moment, and n is the sliding window size.

[0031] Example 3 adds a data preprocessing step based on Examples 1 and 2, using a sliding average method to denoise the air compressor operating data. This helps remove noise interference from the data, making subsequent data analysis and operating status judgment more accurate, thereby improving the effectiveness of optimization control and making the operation of the entire intelligent air compressor optimization control system more stable and reliable.

[0032] Example 4 See also Figure 1 and Figure 2 As shown, as the fourth embodiment of the present invention, when this application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine the solutions of the above-mentioned first, second, third and fourth embodiments.

[0033] Example 4 combines the solutions of Examples 1, 2, and 3, organically integrating data collection, data analysis, optimization control, and data preprocessing. Through comprehensive system design, it not only ensures the accurate collection and analysis of air compressor operating data, but also enables effective optimization control based on the analysis results, while also removing data noise interference, thereby maximizing the operating efficiency, stability, and reliability of the air compressor, reducing equipment failures and maintenance costs, and providing a higher-quality compressed air supply for production.

[0034] The present invention also proposes: an intelligent air compressor optimization control system based on an IOE control system, which is used to implement an intelligent air compressor optimization control method based on an IOE control system, and the system includes: The acquisition unit is used to collect the operating data of the air compressor in real time through various sensors deployed at key parts of the air compressor; A pre-processing unit, used for denoising the operating data of the air compressor; An analysis unit is used to compare the pre-set normal operating data interval with the real-time collected operating data of the air compressor to determine the operating status of the air compressor; The optimization unit is used to perform data control on the air compressor in different operating states according to the operating state of the air compressor.

[0035] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0036] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The intelligent air compressor optimization control method based on IOE control system is characterized by: The following steps are involved: Data collection: Various types of sensors are installed at key parts of the air compressor to collect the operating data of the air compressor in real time; Data analysis: Based on the pre-set normal operating data interval, it is compared with the real-time collected air compressor operating data to determine the operating status of the air compressor; Optimization control: According to the operating status of the air compressor, data control is performed on the air compressor in different operating states.

2. The intelligent air compressor optimization control method based on IOE control system according to claim 1 is characterized in that: Operational data includes: The pressure sensor and flow sensor installed at the air inlet monitor the air pressure and air flow entering the air compressor in real time, and record them as P and in and Q in ; The exhaust pressure and exhaust temperature of the air compressor are monitored in real time by the pressure sensor and temperature sensor installed at the exhaust port, and are recorded as P out and T out ; The temperature of the motor winding is monitored in real time by a temperature sensor installed at the motor winding, and is recorded as the winding temperature T win ; The lubricating oil pressure and lubricating oil temperature are monitored in real time by the pressure sensor and temperature sensor installed in the lubricating oil pipeline, and are recorded as P and lu and T lu .

3. The intelligent air compressor optimization control method based on IOE control system according to claim 2 is characterized in that: in, The operating status of the air compressor is divided into normal mode, overload mode, and abnormal fluctuation mode; Among them, the normal operating data range includes: intake pressure range [P in(a) , P in(b) ]、Intake flow range [Q in(a) , Q in(b) ]、Exhaust pressure range [P out(a) , P out(b) ]、Exhaust temperature range [T out(a) , T out(b) ]、Winding temperature range [T win(a) , T win(b) ]、Lubricating oil pressure range [P lu(a) , P lu(b) ]、Lubricating oil temperature range [T lu(a) , T lu(b) ].

4. The intelligent air compressor optimization control method based on IOE control system according to claim 3 is characterized in that: Normal operating mode is determined as follows: Within a specified unit time, multiple operating data of the air compressor are obtained, and then the average value is calculated; the end point of the specified unit time is the current time node; When P in , Q in 、P out 、T out 、T win 、P lu 、T lu If the average values ​​of the values ​​are all within the corresponding preset normal operating data interval, it is determined that the air compressor is in normal operating mode.

5. The intelligent air compressor optimization control method based on IOE control system according to claim 3 is characterized in that: The overload operation mode is determined as follows: When T win The average value of >T win(b) At the same time, P in The average value of <P in(a) 、P out The average value of >P out(b) and P lu The average value of <P lu(a)中 If at least one comparison formula is established, it is determined that the air compressor is in overload operation mode.

6. The intelligent air compressor optimization control method based on IOE control system according to claim 3 is characterized in that: The abnormal fluctuation operation mode is determined as follows: In the specified unit time, the maximum and minimum values ​​of intake pressure, intake flow, exhaust pressure and exhaust temperature are extracted respectively; Then pass: Calculate the fluctuation range variable ZB of intake pressure, intake flow, exhaust pressure and exhaust temperature; Where Z max Z is the maximum value of intake pressure, intake flow, exhaust pressure and exhaust temperature per unit time; min Z is the maximum value of intake pressure, intake flow, exhaust pressure and exhaust temperature per unit time; (a) and Z (b) are the lower and upper limits of the normal operating data interval respectively; Then, the preset fluctuation amplitude threshold corresponding to the intake pressure, intake flow, exhaust pressure and exhaust temperature is extracted; When the fluctuation amplitudes of at least two parameters exceed the corresponding fluctuation amplitude thresholds, it is determined that the air compressor is in an abnormal fluctuation operation mode.

7. The intelligent air compressor optimization control method based on IOE control system according to claim 4 is characterized in that: Data control in normal operating mode is as follows: Starting from the current time node, continuously monitor the intake flow rate and exhaust pressure in the next unit time, and calculate their average values ​​respectively; when and Then increase the output pressure of the air compressor; where Q in (P) and P out (P) are the average values ​​of intake flow rate and exhaust pressure in the unit time; when and Then stop the air compressor or reduce the speed of the air compressor; where T win (P) and T lu (P) are the average values ​​of winding temperature and lubricating oil temperature in the unit time; when and Then increase the cooling water flow rate to cool the lubricating oil; where, P lu (P) is the average value of the lubricating oil pressure per unit time.

8. The intelligent air compressor optimization control method based on IOE control system according to claim 5 is characterized in that: The data control method in overload operation mode is as follows: When the air compressor is determined to be in overload operation mode, it means that the air compressor of the equipment is too large, and the load is reduced accordingly. The load reduction method is: When P in The average value of <P in(a) 、P out The average value of >P out(b) and P lu The average value of <P lu(a) If only one comparison formula is true, reduce the valve opening by 5%; When P in The average value of <P in(a) 、P out The average value of >P out(b) and P lu The average value of <P lu(a) If there are two contrasting equations, the valve opening is reduced by 10%; When P in The average value of <P in(a) 、P out The average value of >P out(b) and P lu The average value of <P lu(a) If only three comparison equations are established, the valve opening will be reduced by 15%.

9. The intelligent air compressor optimization control method based on IOE control system according to claim 6 is characterized in that: The data control method in abnormal fluctuation operation mode is as follows: The intake pressure is selected and the intake pressure data is collected at a preset sampling period. The collected data is then stored in a buffer area to form time series data, which is marked as P in(t) , extract the set value predetermined by the intake pressure and mark it as P in (S); By: eP in =P in(t) -P in (S), calculate the intake pressure deviation eP in ; Also through: Calculate the change rate of intake pressure corresponding to the deviation e1P in ;Where, t0 is the duration of the sampling period; Using PID control algorithm: Calculate the control amount UP of the intake pressure in Where K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient; According to the intake pressure, the control quantities of intake flow, exhaust pressure and exhaust temperature are determined.

10. An intelligent air compressor optimization control system based on an IOE control system, the system being used to implement the intelligent air compressor optimization control method based on an IOE control system according to any one of claims 1 to 9, characterized in that: The system includes: The acquisition unit is used to collect the operating data of the air compressor in real time through various sensors deployed at key parts of the air compressor; A pre-processing unit, used for denoising the operating data of the air compressor; An analysis unit is used to compare the pre-set normal operating data interval with the real-time collected operating data of the air compressor to determine the operating status of the air compressor; The optimization unit is used to perform data control on the air compressor in different operating states according to the operating state of the air compressor.

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