Dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curve

Abstract

The invention discloses a dynamic target pressure generation method based on multi-source pressure characteristics and an energy efficiency curve, and belongs to the technical field of energy-saving control of industrial air compression systems. According to the method, the minimum value, the maximum value, the average value and the mode of tail end pressure are counted, the dynamic range of gas consumption requirements is accurately represented, and the dynamic target pressure is generated. The air compressor energy efficiency curve library is set, the optimal continuous pressure interval is screened instead of single-point optimization, the operation stability and the energy efficiency advantage are considered, finally, the target pressure of each air compressor is distributed through the multi-target optimization algorithm, energy consumption waste caused by pressure overlapping is avoided, and the energy efficiency of the air compressor is improved. The global energy efficiency is optimal, and the problems that due to frequent loading and unloading of the air compressors of an existing air compression station, energy consumption is wasted, the tail end air pressure requirement is lower than the air compressor set value, and the redundancy of the pressure set value is too high when the multiple air compressors operate in parallel are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy-saving control of industrial air compression systems, and in particular relates to a method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves. Background Art

[0002] An air compressor station is an air source power center that provides clean, dry, and stable compressed air for industrial production. Its core function is to pressurize the air through a compressor unit, and after purification, it is transported to gas-consuming equipment through a pipeline network. The air compressor unit, the main pipe, and the gas-consuming terminal constitute the core transmission link of the compressed air system. Multiple air compressor units are connected in parallel to one end of the main pipe, and a check valve is used to prevent backflow of airflow. The other end of the main pipe is connected to multiple gas-consuming terminals.

[0003] Traditional air compressor stations mostly use fixed pressure control, which can easily lead to "over-pressure gas supply" or "under-pressure shutdown", resulting in energy waste. Summary of the Invention

[0004] In order to solve the problems raised in the above background technology, the present invention provides a dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves to solve the problem that traditional air compression stations use fixed pressure control, which easily leads to "over-pressure gas supply" or "under-pressure shutdown", resulting in energy waste.

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

[0006] A dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves includes the following steps:

[0007] S1: Collect the pressure data of the main pipe in the air compressor station and the gas pressure data of each terminal, and establish an energy efficiency curve library including the energy efficiency curve of each air compressor;

[0008] S2: Extract feature values based on the minimum and maximum values of the gas pressure data of all terminals, as well as the average and mode of the gas pressure data of all terminals;

[0009] S3: Based on the characteristic values extracted in S2, a pressure demand curve for terminal gas consumption is constructed. The pressure demand curve is used to predict the trend of total terminal gas pressure changes over a period of time in the future.

[0010] S4: For each air compressor, the optimal continuous pressure range with the minimum specific power is selected by combining the main pipe pressure data, its own energy efficiency curve and each pressure point of the pressure demand curve;

[0011] S5: Based on the optimal continuous pressure range of each air compressor, the controller of each air compressor is assigned a target pressure that minimizes the total energy consumption of the system through a multi-objective optimization algorithm;

[0012] S6: Monitor the deviation between the actual pressure and the target pressure of each air compressor in real time. If the deviation exceeds a threshold, adjust the frequency of the air compressor through the controller.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] This application accurately characterizes the dynamic range of gas demand by statistically calculating the minimum, maximum, average and mode of the terminal pressure, avoiding the pressure setting deviation caused by a single mean value. This application also sets up an air compressor energy efficiency curve library to screen the optimal continuous pressure range rather than the single point optimality, taking into account both operational stability and energy efficiency advantages. Finally, this application allocates the target pressure of each air compressor through a multi-objective optimization algorithm to avoid energy waste caused by pressure overlap and achieve global energy efficiency optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of the application. DETAILED DESCRIPTION

[0016] To facilitate those skilled in the art to understand the technical content of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] Example 1

[0018] like Figure 1 As shown, the dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves includes the following steps:

[0019] S1: Collect the pressure data of the main pipe in the air compressor station and the gas pressure data of each terminal, and establish an energy efficiency curve library including the energy efficiency curve of each air compressor. The gas pressure data of the terminal is represented by P end,i (t), where i=1,2,…,N, N is the number of terminals, t is time, and the pressure data of the main pipe in the station is P main (t), the air compressor energy efficiency curve is expressed as C j (P,f)=η j (P,f), where j=1,2,…,M, M is the number of air compressors, η j (P,f) is the specific power of the j-th air compressor at pressure P and frequency f, in kW / (m³ / min).

[0020] S2: The minimum and maximum values of the gas pressure data of all terminals, as well as the average and mode of the gas pressure data of all terminals are counted through a sliding window to extract feature values. The feature values used to characterize the dynamic range of gas demand are extracted from the gas pressure data of the terminals. The specific expression is:

[0021] ;

[0022] Among them, P end,min (t), P end,max (t), P end,avg (t) and P end,mode (t) represents the minimum, maximum, average and mode of all terminal gas pressure data, and g represents the frequency distribution density.

[0023] S3: Based on the feature values extracted in S2, a pressure demand curve for terminal gas consumption is constructed. The pressure demand curve is used to predict the trend of total terminal gas pressure changes over a period of time in the future (e.g., the next hour). Specifically, the time series model LSTM is used to construct the pressure demand curve, which is expressed as:

[0024] P req (t+τ)=LSTM(P req (t),P req (t−1),…,P end,avg (t));

[0025] Among them, τ is the prediction time step, P req (t) is the predicted value of the terminal pressure at the current moment.

[0026] S4: For each air compressor, the optimal continuous pressure range with the minimum specific power is selected by combining the main pipe pressure data, the compressor's own energy efficiency curve, and each pressure point of the pressure demand curve. The specific steps include:

[0027] S4.1: Determine the pressure search range; combined with the main pipe pressure P main (t) and the terminal gas pressure data, set the upper limit of the air compressor operating pressure P max and the lower limit P min , expressed as:

[0028] ;

[0029] Among them, 0.05 is the safety redundancy value, It is the minimum safety pressure of the air compressor;

[0030] S4.2: Traverse the energy efficiency curve; in [P min ,P max ] range, traverse with a step size of ΔP = 0.01MPa, and calculate the specific power ηj (P k ,f k ), P k =P min +kΔP, k=0,1,…,n;

[0031] S4.3: Screen the optimal continuous pressure range; select the three consecutive pressure points with the smallest specific power (smoothing process) as the optimal continuous pressure range for air compressor j , and record the central pressure of the interval , It is the optimal target pressure of the air compressor under this working condition.

[0032] S5: To avoid "gas grabbing" or "idling" caused by overlapping pressures of multiple air compressors, a multi-objective optimization algorithm is used to assign a target pressure to each air compressor controller that minimizes the total energy consumption of the system, based on the optimal continuous pressure range of each air compressor. The objective function and constraints of the multi-objective optimization algorithm are as follows:

[0033] Objective function (minimum total energy consumption):

[0034] ;

[0035] in, The target pressure of the air compressor j The exhaust volume under the 3 / min, f i is the operating frequency of air compressor i, t is the control period, Q j is the performance curve of the air compressor, Q j (P)=k j ×P×f j ;

[0036] Constraints:

[0037] ;

[0038] Among them, 1.05 is the pipe network leakage coefficient, Indicates target pressure Not exceeding the upper limit of the main pipe pressure P main (t).

[0039] S6: Monitor the deviation between the actual pressure and target pressure of each air compressor in real time. If the deviation exceeds the threshold, the controller will adjust the frequency of the air compressor. Specifically:

[0040] Real-time monitoring of actual pressure Deviation from target pressure:

[0041] ;

[0042] If the deviation exceeds the threshold, the air compressor frequency f is adjusted by the PID controller j , to achieve closed-loop control:

[0043] ;

[0044] Among them, K p , K i , K d are the PID controller parameters.

[0045] In this embodiment, the present application accurately characterizes the dynamic range of gas demand by statistically analyzing the minimum, maximum, average and mode of the terminal pressure, avoiding the pressure setting deviation caused by a single mean value, and the present application sets up an air compressor energy efficiency curve library to screen the optimal continuous pressure range rather than the single point optimality, taking into account both operational stability and energy efficiency advantages. Finally, the present application allocates the target pressure of each air compressor through a multi-objective optimization algorithm to avoid energy waste caused by pressure overlap, achieve global energy efficiency optimization, and solve the problems of energy waste caused by frequent loading and unloading of air compressors in existing air compression stations, the terminal gas pressure demand is lower than the air compressor set value, and the pressure setting value redundancy is too high when multiple air compressors are operated in parallel.

Claims

1. A dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves, characterized in that: The following steps are involved: S1: Collect the pressure data of the main pipe in the air compressor station and the gas pressure data of each terminal, and establish an energy efficiency curve library including the energy efficiency curve of each air compressor; S2: Extract feature values based on the minimum and maximum values of the gas pressure data of all terminals, as well as the average and mode of the gas pressure data of all terminals; S3: Based on the characteristic values extracted in S2, a pressure demand curve for terminal gas consumption is constructed. The pressure demand curve is used to predict the trend of total terminal gas pressure changes over a period of time in the future. S4: For each air compressor, the optimal continuous pressure range with the minimum specific power is selected by combining the main pipe pressure data, its own energy efficiency curve and each pressure point of the pressure demand curve; S5: Based on the optimal continuous pressure range of each air compressor, the controller of each air compressor is assigned a target pressure that minimizes the total energy consumption of the system through a multi-objective optimization algorithm; S6: Monitor the deviation between the actual pressure and the target pressure of each air compressor in real time. If the deviation exceeds a threshold, adjust the frequency of the air compressor through the controller.

2. The method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves according to claim 1, characterized in that: In S1, the terminal gas pressure data is expressed as P end,i (t), where i=1,2,…,N, N is the number of terminals, t is time, and the pressure data of the main pipe in the station is P main (t), the air compressor energy efficiency curve is expressed as C j (P,f)=η j (P,f), where j=1,2,…,M, M is the number of air compressors, η j (P,f) is the specific power of the j-th air compressor at pressure P and frequency f, in kW / (m³ / min).

3. The method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves according to claim 2, characterized in that: S2 extracts the characteristic value of the terminal gas pressure data through sliding window statistics, which is specifically expressed as: ； Among them, P end,min (t), P end,max (t), P end,avg (t) and P end,mode (t) represents the minimum, maximum, average and mode of all terminal gas pressure data, and g represents the frequency distribution density.

4. The method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves according to claim 3, characterized in that: In S3, the time series model LSTM is used to construct the pressure demand curve, which is expressed as: P req (t+τ)=LSTM(P req (t),P req (t−1),…,P end,avg (t)); Among them, τ is the prediction time step, P req (t) is the predicted value of the terminal pressure at the current moment.

5. The method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves according to claim 4, characterized in that: S4 includes the following steps: S4.1: Determine the pressure search range; combined with the main pipe pressure P main (t) and the terminal gas pressure data, set the upper limit of the air compressor operating pressure P max and the lower limit P min , expressed as: ； Among them, 0.05 is the safety redundancy value, It is the minimum safety pressure of the air compressor; S4.2: Traverse the energy efficiency curve; in [P min ,P max ] range, traverse with a step size of ΔP = 0.01MPa, and calculate the specific power ηj (P k ,f k ), P k =P min +kΔP, k=0,1,…,n; S4.3: Screen the optimal continuous pressure range; select the three consecutive pressure points with the smallest specific power as the optimal continuous pressure range for air compressor j , and record the central pressure of the interval , It is the optimal target pressure of the air compressor under this working condition.

6. The method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves according to claim 5, characterized in that: The objective function and constraints of the multi-objective optimization algorithm are as follows: Objective function: ； in, The target pressure of the air compressor j The exhaust volume under the 3 / min, f i is the operating frequency of air compressor i, T is the control period, Q j is the performance curve of the air compressor, Q j (P)=k j ×P×f j ; Constraints: ； Among them, 1.05 is the pipeline leakage coefficient.

7. The method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curves according to claim 6, characterized in that: S6 specifically: Real-time monitoring of actual pressure Deviation from target pressure: ； If the deviation exceeds the threshold, the air compressor frequency f is adjusted by the PID controller j , to achieve closed-loop control: ； Among them, K p , K i , K d are the PID controller parameters.

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