Air compression station energy-saving system based on digital twinning and control method

Abstract

The invention belongs to the technical field of compressed air control, and particularly relates to an air compression station energy-saving system based on digital twinning and a control method. The air compressor unit and the drying unit are connected through an air supply header pipe; the system further comprises a sensor module, a control system and a digital twin platform. The sensor module comprises a first sensor group, a second sensor group and a third sensor group; the first sensor set is correspondingly installed at the position of the air compressor set. The second sensor group is correspondingly mounted at the position of the drying unit; the third sensor group is correspondingly mounted at the air supply header pipe; the first sensor group, the second sensor group and the third sensor group are all connected with the control system. The control system is connected with the digital twin platform; reverse optimization control over key equipment such as an air compressor and a drying machine is achieved, the system is convenient to operate, the operation data value can be effectively mined, the optimal energy-saving strategy is generated and executed, the energy consumption of the compressed air system is remarkably reduced, and the production cost is saved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of compressed air control, and particularly relates to an air compression station energy-saving system and a control method based on digital twinning. BACKGROUND

[0002] The air compression station is a major energy consumer of an industrial enterprise. Traditional monitoring methods usually realize plane display of on-site device running states (such as start-stop, pressure, etc.) on a control cabinet or a SCADA system, and lack deep visualization and analysis of key elements such as device running efficiency (such as specific power), pipe network state, dew point precise control and multi-unit cooperative optimization. The existing systems are mostly two-dimensional displays, and it is difficult to intuitively reflect the complex three-dimensional spatial layout of the air compression station and the dynamic correlation between devices, and the energy consumption analysis function is weak, resulting in low running efficiency of the air compression system and energy waste.

[0003] Therefore, an air compression station energy-saving system capable of improving the running efficiency of the air compression system and avoiding energy waste is urgently needed. SUMMARY

[0004] In view of the above problems, the application provides an air compression station energy-saving system based on digital twinning, which comprises an air compression unit, a drying unit and a gas supply main pipe. The air compression unit and the drying unit are connected through the gas supply main pipe. The application further comprises a sensor module, a control system and a digital twinning platform. The sensor module comprises a first sensor group, a second sensor group and a third sensor group. The first sensor group is installed at the position of the air compression unit. The second sensor group is installed at the position of the drying unit. The third sensor group is installed at the position of the gas supply main pipe. The first sensor group, the second sensor group and the third sensor group are connected with the control system. The control system is connected with the digital twinning platform. The digital twinning platform is internally provided with an air compression station three-dimensional model constructed based on 3ds Max, and is integrated with the sensor module and the air compression station three-dimensional model to monitor the device running state, generate energy efficiency optimization strategies and perform reverse control.

[0005] Further, the application further comprises a centralized control room and a power distribution room, and the centralized control room is connected with the power distribution room. The control system is installed in the power distribution room. The digital twinning platform is installed in the centralized control room.

[0006] Further, the first sensor group comprises N exhaust pressure sensors, N temperature sensors and N current sensors.

[0007] Further, the second sensor group comprises M leak point sensors.

[0008] Further, the third sensor group includes a supply air pressure sensor and a supply air flow meter.

[0009] The application provides an air compression station energy-saving control method based on digital twinning, and an air compression station energy-saving system based on the method, which comprises the following steps: The sensor module is used to collect the operation parameters of the supply air main pipe, the air compressor unit and the drying unit in the air compression station in real time and transmit the operation parameters to the control system. The control system processes the received operation signals and transmits the operation signals to the digital twinning platform. The digital twinning platform integrates and correlates the received operation signals with the three-dimensional model of the air compression station constructed based on 3ds Max. The digital twinning platform analyzes the energy efficiency indicators of the air compression station system in real time based on the integrated signals and the three-dimensional model, and generates an energy efficiency optimization strategy. Based on the energy efficiency optimization strategy, reverse control is performed.

[0010] Further, the real-time analysis of the energy efficiency indicators of the air compression station system and the generation of the energy efficiency optimization strategy comprise the following steps: Air compressor optimization: according to the supply air main pipe pressure and flow demand, in combination with the energy efficiency characteristics of each air compressor, the real-time specific power and efficiency of each air compressor are calculated, the unit combination and load distribution are determined, and the start / stop, loading / unloading and operation frequency control instructions of the air compressor are generated.

[0011] Further, the real-time analysis of the energy efficiency indicators of the air compression station system and the generation of the energy efficiency optimization strategy further comprise the following steps: Drying machine optimization: according to the dew point set value, the air consumption load and the air compressor operation state, the compressed air dew point data are analyzed, and the start / stop, operation mode switching and regeneration cycle adjustment control instructions of the drying machine are generated.

[0012] Further, based on the energy efficiency optimization strategy, the reverse control comprises the following steps: The digital twinning platform generates control instructions from the energy efficiency optimization strategy through an optimization algorithm, transmits the control instructions to the control system, and controls the operation of the air compressor unit and the drying unit.

[0013] Further, the energy efficiency indicators of the air compression station system include the operation efficiency of the air compression station, the supply air state and the energy consumption.

[0014] Advantages The application has the following advantages over the prior art: 1.The application builds the physical distribution, real-time running state and energy efficiency level of the entire air compression station system through pipe network topology construction and device three-dimensional modeling technology. It presents the device entity relationship and energy flow intuitively in a three-dimensional virtual reality environment, realizes reverse optimization control of key devices such as air compressors and dryers, and is convenient to operate. It can effectively tap the value of operation data, generate and execute the optimal energy-saving strategy, significantly reduce the energy consumption of the compressed air system, and save production costs.

[0015] 2.The application builds a high-fidelity air compression station environment model through a customized three-dimensional digital twin platform, realizes real-time visualization, deep analysis and operation guidance of device running parameters and energy consumption data. Using the high-fidelity simulation and optimization calculation capability of the digital twin model, the system energy efficiency is analyzed in real time, the actual running data and the optimization target are compared, and the running parameters of the physical system are automatically generated and controlled in reverse.

[0016] 3.The application monitors the key running parameters of the air compressor in real time and transmits signals to the control system, so as to control the start / stop, loading / unloading state and frequency converter running frequency of the air compressor in the digital twin platform according to the total gas pipe pressure and flow demand, combining with the energy efficiency characteristics of each air compressor, to realize the optimal combination and load distribution of the unit.

[0017] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and obtained by the structure indicated in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0019] Figure 1 The system structure schematic diagram in the embodiment of the present application is shown.

[0020] Figure 2 The method flowchart in the embodiment of the present application is shown.

[0021] In the figure, 1 is a control room, 2 is a power distribution room, 3 is a digital twin platform, 4 is a control system, 5 is an air compressor unit, 6 is a dryer unit, 7 is a gas supply main pipe, 8 is a first sensor group, 9 is a second sensor group, and 10 is a third sensor group. DETAILED DESCRIPTION ​

[0022] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0023] The present application provides an air compression station energy-saving system based on digital twinning, referring to Figure 1 , comprising an air compressor unit 5, a drying unit 6 and a gas supply main pipe 7; The air compressor unit 5 and the drying unit 6 are connected through the gas supply main pipe 7; It also includes a sensor module, a control system 4 and a digital twinning platform 3; The sensor module includes a first sensor group 8, a second sensor group 9 and a third sensor group 10; the first sensor group 8 is installed at the position of the air compressor unit 5; the second sensor group 9 is installed at the position of the drying unit 6; the third sensor group 10 is installed at the position of the gas supply main pipe 7; The first sensor group 8, the second sensor group 9 and the third sensor group 10 are connected with the control system 4; the control system 4 is connected with the digital twinning platform 3; The digital twinning platform 3 is built with an air compression station three-dimensional model based on 3ds Max, and integrates the sensor module and the air compression station three-dimensional model to associate, to perform equipment running state monitoring, energy efficiency optimization strategy generation and reverse control execution.

[0024] The digital twinning reverse control process is as follows: First, the sensor module is connected to the air compression station control system 4, and the control system 4 is a PLC control system 4. The digital twinning platform 3 located in the centralized control room 1 or the energy management center reads the signals in the PLC control system 4 in real time, visualizes the display in the three-dimensional model environment, and evaluates the running state of the air compression station by using the built-in energy efficiency analysis model and optimization algorithm; after the digital twinning platform 3 calculates the optimal running strategy (such as starting the air compressor and loading to 70%, stopping the air compressor; starting the drying machine and switching to energy-saving mode), the control instruction is sent to the PLC control system 4, and the PLC control system 4 drives the field execution mechanism (frequency converter, start-stop cabinet, valve, etc.) to execute the operation, so as to realize the reverse control and optimize the system energy efficiency.

[0025] The application connects signals into the system by deploying pressure, temperature, current, flow, dew point, etc. sensors at key positions such as air compressors, dryers, gas supply manifolds 7, etc. Through the customized three-dimensional digital twin platform 3, a high-fidelity air compression station environment model is constructed to realize real-time visualization, deep analysis and operation guidance of equipment operation parameters and energy consumption data. Using the high-fidelity simulation and optimization calculation capability of the digital twin model, the system energy efficiency is analyzed in real time, the actual operation data is compared with the optimization target, and the operation parameters of the physical system are automatically generated and controlled in reverse. For example, when the gas load changes, the digital twin platform 3 can automatically optimize which air compressor to start and stop, adjust the loading rate and operating frequency of some air compressors, and coordinate the operation mode of the dryer, so that the entire air compression station system always operates in the optimal energy efficiency range to achieve significant energy saving and consumption reduction; Real-time data-based fine energy-saving control strategies (such as loading rate adjustment and dryer linkage control) are realized.

[0026] In an embodiment of the application, a control room 1 and a power distribution room 2 are also included, and the control room 1 is connected with the power distribution room 2; the control system 4 is installed in the power distribution room 2; and the digital twin platform 3 is installed in the control room 1.

[0027] The PLC control system 4 is installed in the air compression station power distribution room 2, the digital twin platform 3 reads all pressure, temperature, current, flow, dew point sensor signals from the PLC control system 4 and calculates system energy efficiency indicators (such as total power consumption, specific power, gas supply efficiency) in real time, analyzes the pipe network state, and generates control instructions based on optimization algorithms to control the operation of air compressors and dryers in reverse.

[0028] The digital twin platform 3 is installed in the control room 1, and the signals are integrated into the digital twin platform 3 by reading from the PLC control system 4, and the signals are associated with the 3ds Max-based air compression station three-dimensional model (accurately including air compressors, dryers, gas storage tanks, filters, valves, pipe networks, etc. and their spatial relationship) constructed on the digital twin platform 3, thereby realizing real-time three-dimensional visualization feedback of equipment operation state, historical energy consumption analysis, energy efficiency bottleneck diagnosis, predictive maintenance prompts, and execution of reverse optimization control strategies.

[0029] In an embodiment of the application, the first sensor group 8 includes N exhaust pressure sensors, N temperature sensors and N current sensors.

[0030] N exhaust pressure sensors, temperature sensors and current sensors are installed at the corresponding numbered air compressors respectively for real-time monitoring of the key operating parameters of the air compressors and transmitting signals to the signal acquisition system in the control system 4, so that according to the pressure and flow demand of the air supply main pipe 7, combined with the energy efficiency characteristics of each air compressor, the start-stop, loading / unloading state and frequency converter operating frequency of the air compressor are reversely controlled in the digital twin platform 3 to realize the optimal combination and load distribution of the unit.

[0031] In an embodiment of the application, the second sensor group 9 includes M leak point sensors.

[0032] M leakage sensors are installed at the outlet of the corresponding numbered drying machines respectively for real-time monitoring of the dew point of compressed air and transmitting signals to the signal acquisition system in the control system 4, so that according to the dew point set value, gas load and air compressor operating state, the start-stop, operating mode (such as heating regeneration, air blowing regeneration switching) and regeneration cycle of the drying machine are reversely controlled in the digital twin platform 3 to reduce the energy consumption of the drying machine under the premise of ensuring the dew point to meet the standard.

[0033] In an embodiment of the application, the third sensor group 10 includes a gas supply pressure sensor and a gas supply flowmeter.

[0034] Reference Figure 2 The application provides an air compression station energy-saving control method based on digital twinning, based on the above-mentioned air compression station energy-saving system based on digital twinning, comprising the following steps: The operating parameters of the air supply main pipe 7, the air compressor unit 5 and the drying machine unit 6 in the air compression station are collected in real time by the sensor module and transmitted to the control system 4; The control system 4 processes the received operating signals and transmits them to the digital twin platform 3; The digital twin platform 3 integrates and correlates the received operating signals with the 3ds Max-based three-dimensional model of the air compression station; The digital twin platform 3 analyzes the energy efficiency indicators of the air compression station system in real time based on the integrated signals and three-dimensional model, and generates energy efficiency optimization strategies; Based on the energy efficiency optimization strategies, reverse control is performed.

[0035] In an embodiment of the application, the energy efficiency indicators of the air compression station system are analyzed in real time, and the energy efficiency optimization strategies are generated, comprising: Optimization of air compressors: according to the pressure and flow demand of the air supply main pipe 7, combined with the energy efficiency characteristics of each air compressor, the real-time specific power and efficiency of each air compressor are calculated, the optimal unit combination and load distribution are determined, and the start-stop, loading / unloading and operating frequency control instructions of the air compressor are generated.

[0036] The optimization control process of the air compressor unit 5 is as follows: By calculating the current total specific power, then generating all possible unit combinations, seeking the optimal solution, checking whether it meets the gas demand and reserving the margin, calculating the expected specific power, and finally generating control instructions; when the gas load decreases, causing the supply manifold 7 pressure to rise, the supply manifold 7 pressure sensor signal is transmitted to the PLC control system 4. The digital twin platform 3 reads the signal and all air compressor exhaust pressure, temperature, current signals, calculates the real-time specific power and efficiency of each air compressor. After the platform analyzes, it may issue instructions: reduce the operating frequency of the air compressor with relatively low efficiency (reduce its gas production), or let the air compressor run unloaded, maintain the total pipe pressure stable, make the average specific power of the running unit the lowest, and achieve energy saving. The instructions are executed by the PLC control system 4 to control the field devices in reverse.

[0037] In an embodiment of the present application, real-time analysis of air compression station system energy efficiency indicators generates energy efficiency optimization strategies, which also include: Optimization of the drying machine: According to the dew point set value, gas load and air compressor operating state, analyze the compressed air dew point data, generate drying machine start-stop, running mode switching and regeneration cycle adjustment control instructions.

[0038] The drying machine linkage control process is as follows: First, read the key data, set the control parameters, and perform corresponding processing when the dew point is not up to standard, and perform corresponding processing when the dew point is up to standard; the digital twin platform 3 monitors the compressed air dew point according to the drying machine dew point sensor signal. When the dew point value is stable and lower than the set value and the gas load is low, the platform analyzes and issues instructions: switch the drying machine from high-energy heating regeneration mode to low-energy air blowing regeneration mode, or appropriately extend its regeneration cycle. At the same time, combined with the air compressor operating state, the platform can instruct to stop the corresponding drying machine. These instructions are executed by the PLC control system 4 to control the drying machine in reverse, reducing the energy consumption of the drying machine.

[0039] In an embodiment of the present application, based on the energy efficiency optimization strategy, reverse control is performed, including: The digital twin platform 3 generates control instructions for the air compressor unit 5 and the drying machine unit 6 through optimization algorithms based on the energy efficiency optimization strategy.

[0040] In an embodiment of the present application, the air compression station system energy efficiency indicators include air compression station operating efficiency, air supply state and energy consumption. Specifically, it includes total power consumption, specific power, and air supply efficiency.

[0041] Supply pressure and flow optimization control program segment: # Calculate the current total specific power current_spec_power = sum(c.power / (c.flow+0.001) for c in compressorsif c.running) # Generate all possible combinations of compressors possible_combinations = self._generate_combinations(compressors) # Find the best combination best_combo = None best_power = float('inf') for combo in possible_combinations: # Check if the air demand is met total_flow = sum(c.max_flow for c in combo if c.running) if total_flow<air_demand 1.1:# Keep a 10% margin continue # Calculate the projected power projected_power = self._calculate_combo_power(combo, air_demand) if projected_power<best_power: best_power = projected_power best_combo = combo # Generate the control commands commands = [] for comp in compressors: target_state = comp in best_combo if comp.running!= target_state: commands.append(ControlCommand( device=comp.id, command="START" if target_state else "STOP" )) Dryer dew point control and linkage program segment: def dryer_dewpoint_control(): #Read key data dewpoints = read_all_dewpoints() dryers = get_dryers_status() system_load = get_system_load() #Control parameter settings DEWPOINT_TARGET = 3# °C DEWPOINT_THRESHOLD = -10# °C (Energy Saving Mode Trigger Point) LOW_LOAD_THRESHOLD = 40# % (System load threshold) actions = [] For dryer in dryers: current_dewpoint = dewpoints[dryer.id] #Handling of Dew Point Failure if current_dewpoint>DEWPOINT_TARGET: if not dryer.is_in_heated_mode: actions.append(dryer.switch_mode('HEATED')) log_action(f"Dryer {dryer.id} switched to heating mode") if dryer.can_increase_regeneration: actions.append(dryer.increase_regeneration_frequency()) log_action(f"Increase regeneration frequency of dryer {dryer.id}") #Dew point meets standard and has sufficient margin - Check energy-saving mode elif current_dewpoint<= DEWPOINT_THRESHOLD: if dryer.is_in_heated_mode and dryer.supports_blower_mode: actions.append(dryer.switch_mode('BLOWER')) log_action(f"Dryer {dryer.id} switched to blower energy-saving mode") if dryer.can_reduce_regeneration: actions.append(dryer.reduce_regeneration_frequency()) log_action(f"Reducing the regeneration frequency of the dryer {dryer.id}") Digital twin reverse control program segment: / / / <summary> / / Display model / / < / summary> public void ShowBody() { body?.SetActive(true); } / / / <summary> / / positioning object / / < / summary> / / / <param name="name"> Object Name / / / <param name="distance"> Distance between camera and object / / / <param name="time"> time / / / Inspection route / / / / / / <param name="patrolEntity"> public void SetPatrolLine(WebrtcServer user, PatrolEntitypatrolEntity) { _user = user; m_Entity = patrolEntity; if (patrolEntity==null||patrolEntity.ListPatrolItems==null||patrolEntity.ListPatrolItems.Count<= 0) { Logger.LogWarning(user, "No patrol route configured!", 3, true); return; } if (m_Entity != null&&m_Entity.BlShowLine) DrawPatrolLine(); DrawPatroPOI(); } Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital-twin-based air compression station energy-saving system, characterized in that, It comprises an air compressor unit (5), a drying unit (6) and a gas supply main (7); The air compressor unit (5) and the drying unit (6) are connected through the gas supply main (7); It also comprises a sensor module, a control system (4) and a digital twin platform (3); The sensor module comprises a first sensor group (8), a second sensor group (9) and a third sensor group (10); the first sensor group (8) is installed at the position of the air compressor unit (5); the second sensor group (9) is installed at the position of the drying unit (6); and the third sensor group (10) is installed at the position of the gas supply main (7); The first sensor group (8), the second sensor group (9) and the third sensor group (10) are connected with the control system (4); and the control system (4) is connected with the digital twin platform (3); The digital twin platform (3) is built-in with an air compression station three-dimensional model based on 3ds Max, and integrates the sensor module with the air compression station three-dimensional model to monitor the equipment running state, generate energy efficiency optimization strategy and perform reverse control.

2. The air compression station energy-saving system based on digital twinning of claim 1, wherein, It also comprises a control room (1) and a power distribution room (2), and the control room (1) is connected with the power distribution room (2); the control system (4) is installed in the power distribution room (2); and the digital twin platform (3) is installed in the control room (1).

3. The air compression station energy-saving system based on digital twinning of claim 1, wherein, The first sensor group (8) comprises N exhaust pressure sensors, N temperature sensors and N current sensors.

4. The air compression station energy-saving system based on digital twinning of claim 1, wherein, The second sensor group (9) comprises M leak point sensors.

5. The air compression station energy-saving system based on digital twinning of claim 1, wherein, The third sensor group (10) comprises a gas supply pressure sensor and a gas flow meter.

6. An air compression station energy-saving control method based on digital twinning, characterized in that, the method is based on the air compression station energy-saving system based on digital twinning according to any one of claims 1-5, It comprises the following steps: Real-time collection of the running parameters of the gas supply main (7), the air compressor unit (5) and the drying unit (6) in the air compression station through the sensor module, and transmission to the control system (4); Processing of the received running signals by the control system (4), and transmission to the digital twin platform (3); Integration of the received running signals with the air compression station three-dimensional model based on 3ds Max by the digital twin platform (3); Real-time analysis of the energy efficiency indicators of the air compression station system by the digital twin platform (3) based on the integrated signals and three-dimensional model, and generation of energy efficiency optimization strategy; Execution of reverse control based on the energy efficiency optimization strategy.

7. The air compression station energy-saving control method based on digital twinning according to claim 6, characterized in that, Real-time analysis of the energy efficiency indicators of the air compression station system and generation of energy efficiency optimization strategy, comprising: Air compressor optimization: calculation of the real-time specific power and efficiency of each air compressor according to the pressure and flow demand of the gas supply main (7), combination of the energy efficiency characteristics of each air compressor, determination of the unit combination and load distribution, and generation of the start / stop, loading / unloading and running frequency control instructions of the air compressor.

8. The air compression station energy-saving control method based on digital twinning of claim 6, wherein, Real-time analysis of the energy efficiency indicators of the air compression station system and generation of energy efficiency optimization strategy, also comprising: Drying machine optimization: analysis of the compressed air dew point data according to the dew point set value, gas load and air compressor running state, and generation of the start / stop, running mode switching and regeneration cycle adjustment control instructions of the drying machine.

9. The air compression station energy-saving control method based on digital twinning of claim 6, wherein, Execution of reverse control based on the energy efficiency optimization strategy, comprising: The digital twin platform (3) generates control instructions through optimization algorithm based on the energy efficiency optimization strategy, and transmits the control instructions to the control system (4) to control the running of the air compressor unit (5) and the drying unit (6).

10. The energy-saving control method for an air compression station based on digital twinning according to claim 6, characterized in that, The air compression station system energy efficiency index includes air compression station operation efficiency, air supply state and energy consumption.

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