Method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curve

Abstract

The application discloses a dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves, and belongs to the technical field of energy-saving control of industrial air compression systems. The minimum value, maximum value, average value and mode of the end pressure are counted, the dynamic range of gas demand is accurately represented, the pressure setting deviation caused by a single average value is avoided, an air compressor energy efficiency curve library is arranged, the optimal continuous pressure interval is screened instead of a single point optimal, the operation stability and energy efficiency advantages are considered, and finally, the target pressure of each air compressor is distributed through a multi-objective optimization algorithm, energy waste caused by pressure overlap is avoided, global energy efficiency optimization is realized, and the problems of energy waste caused by frequent loading and unloading of air compressors of an existing air compression station, low end gas pressure demand and excessively high pressure setting value redundancy when multiple air compressors are operated in parallel are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy-saving control of industrial air compression systems, and particularly relates to a dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves. BACKGROUND

[0002] The air compression station is a power center for providing clean, dry and stable compressed air for industrial production. The core function is to increase the air pressure through the compressor unit, and after purification, the air is delivered to the gas-consuming equipment through the pipeline network. The air compressor unit, the mother pipe and the gas-consuming terminal form the core delivery link of the compressed air system. Multiple air compressor units are connected in parallel to one end of the mother pipe and prevent backflow through the check valve. The other end of the mother pipe is connected to multiple gas-consuming terminals.

[0003] The traditional air compression station adopts fixed pressure control, which easily leads to the situation of "overpressure gas supply" or "underpressure shutdown", causing energy waste. SUMMARY

[0004] To solve the problems raised in the background art, the application provides a dynamic target pressure generation method based on multi-source pressure characteristics and energy efficiency curves to solve the problem of energy waste caused by the situation of "overpressure gas supply" or "underpressure shutdown" caused by the traditional air compression station adopting fixed pressure control.

[0005] To achieve the above-mentioned purpose, the application provides the following technical scheme:

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

[0007] S1: Collecting the pressure data of the mother pipe and the gas pressure data of each terminal in the air compression station, and establishing an energy efficiency curve library including the energy efficiency curves of each air compressor;

[0008] S2: Extracting the characteristic values according to the minimum value and the maximum value of the gas pressure data of all terminals, and the average value and the mode of the gas pressure data of all terminals;

[0009] S3: Based on the characteristic values extracted in S2, constructing the pressure demand curve of the terminal gas, which is used to predict the total terminal gas pressure change trend in the future period;

[0010] S4: For each air compressor, combining the mother pipe pressure data, the energy efficiency curve and the pressure points of the pressure demand curve, screening the optimal continuous pressure interval with the minimum specific power;

[0011] S5: Combining the optimal continuous pressure interval of each air compressor, distributing the controller of each air compressor through a multi-objective optimization algorithm to make the target pressure with the minimum system total energy consumption;

[0012] S6: Real-time monitoring of the deviation of the actual pressure of each air compressor from the target pressure, and if the deviation exceeds the threshold, adjusting the frequency of the air compressor through the controller.

[0013] Compared with the prior art, the beneficial effects of the present application are:

[0014] The present application accurately characterizes the dynamic range of gas demand by statistically analyzing the minimum value, maximum value, average value and mode of the end pressure, avoids the pressure setting deviation caused by a single average value, and sets an air compressor efficiency curve library to select the optimal continuous pressure interval instead of a single point, taking into account the operation stability and energy efficiency advantage. Finally, the present application distributes the target pressure of each air compressor through a multi-objective optimization algorithm to avoid energy waste caused by pressure overlap, and realizes global energy efficiency optimization. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The flowchart of the present application is shown. DETAILED DESCRIPTION

[0016] In order to facilitate those skilled in the art to understand the technical content of the present application, the present application will be further described in detail in combination with the drawings and specific examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0017] Example 1

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

[0019] S1: Collecting pressure data of the mother pipe in the air compression station and gas pressure data of each end, and establishing an energy efficiency curve library including the energy efficiency curve of each air compressor, wherein the gas pressure data of the end is represented as P end,i (t), wherein i=1,2,…,N, N is the number of ends, t is the time, the pressure data of the station house mother pipe is P main (t), and the air compressor energy efficiency curve is represented as C j (P,f)=η j (P,f), wherein j=1,2,…,M, M is the number of air compressors, η j (P,f) is the specific power of the jth air compressor at pressure P and frequency f, with the unit of kW / (m³ / min).

[0020] S2: Extracting the minimum value, maximum value of all end gas pressure data, and the average value and mode of all end gas pressure data through a sliding window to extract the characteristic value, extracting the characteristic value of the end gas pressure data for characterizing the dynamic range of gas demand, which is specifically expressed as:

[0021] ;

[0022] wherein, P end,min (t), P end,max (t), P end,avg (t) and P end,mode (t) represent the minimum value, the maximum value, the average value and the mode of all end pressure data, respectively, and g represents the frequency distribution density.

[0023] S3: Based on the feature values extracted in S2, a pressure demand curve of end pressure is constructed, and the pressure demand curve is used to predict the overall end pressure change trend in a future period of time (such as 1 hour in the future). Specifically, a 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] wherein, τ is the prediction time step, and P req (t) is the end pressure prediction value at the current time.

[0026] S4: For each air compressor, the optimal continuous pressure interval with the minimum specific power is selected by combining the mother pipe pressure data, the own energy efficiency curve and the pressure points of the pressure demand curve, which specifically includes the following steps:

[0027] S4.1: Determine the pressure search range; combine the mother pipe pressure P main (t) and the end pressure data, set the upper limit P max and the lower limit P min of the air compressor operating pressure, which are expressed as:

[0028] ;

[0029] wherein, 0.05 is a safety redundancy value, is the minimum safety pressure of the air compressor;

[0030] S4.2: Traverse the energy efficiency curve; in the range of [P min , P max ], traverse by a step size ΔP=0.01 MPa, calculate the specific power ηj(P k , f k ) of each air compressor j at each pressure point of the pressure demand curve, P k =P min +kΔP, k=0,1,…,n;

[0031] S4.3: Screening the optimal continuous pressure interval; selecting the minimum specific power of the continuous 3 pressure points (smooth processing) as the optimal continuous pressure interval of air compressor j , and recording the center pressure of the interval , as the optimal target pressure of the air compressor under this working condition.

[0032] S5: In order to avoid "air grabbing" or "idling" caused by the pressure overlap of multiple air compressors, the optimal continuous pressure interval of each air compressor is combined, and the controller of each air compressor is distributed through a multi-objective optimization algorithm to make the target pressure of the system total energy consumption minimum. The objective function and constraint condition of the multi-objective optimization algorithm are as follows:

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

[0034] ;

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

[0036] Constraint condition:

[0037] ;

[0038] Where, 1.05 is the pipe network leakage coefficient, indicates that the target pressure does not exceed the upper limit of the mother pipe pressure P main (t).

[0039] S6: Real-time monitoring of the deviation of the actual pressure and the target pressure of each air compressor, if the deviation exceeds the threshold, adjust the frequency of the air compressor through the controller, specifically:

[0040] Real-time monitoring of the deviation of the actual pressure and the target pressure:

[0041] ;

[0042] If the deviation exceeds the threshold, adjust the air compressor frequency f j through the PID controller to realize closed-loop control:

[0043] ;

[0044] wherein K p , K i , K d are PID controller parameters.

[0045] In the embodiment, the application accurately characterizes the dynamic range of gas demand by counting the minimum value, maximum value, average value and mode of the end pressure, avoids the pressure setting deviation caused by a single average value, sets an air compressor energy efficiency curve library to select the optimal continuous pressure interval instead of a single point, takes into account the operation stability and energy efficiency advantage, finally allocates the target pressure of each air compressor through a multi-objective optimization algorithm to avoid energy waste caused by pressure overlap, realizes the global energy efficiency optimization, and solves the problems of energy waste caused by frequent loading and unloading of air compressors in the existing air compression station, the end gas pressure demand lower than the air compressor set value, and the pressure set value redundancy too high when multiple air compressors are operated in parallel.

Claims

1. A method for generating dynamic target pressure based on multi-source pressure characteristics and energy efficiency curve, characterized in that, Comprising the following steps: S1: Collecting pressure data P of the main pipe in the air compression station main (t) and gas pressure data P of each terminal end,i (t), where i=1, 2, …, N, N is the number of terminals, t is time, and an energy efficiency curve library including the energy efficiency curves of each air compressor is established, and the energy efficiency curve of the air compressor is represented as C j (P,f)=η j (P,f), where j=1, 2, …, M, M is the number of air compressors, and η j (P,f) is the specific power of the jth air compressor at pressure P and frequency f, with the unit of kW / (m³ / min). S2: Extracting eigenvalues from the minimum value P end,min (t) of all end-use pressure data end,max (t), and the average value and mode of all end-use pressure data S3: Based on the feature value extracted in S2, a pressure demand curve of end-use gas is constructed, and the pressure demand curve is used to predict the total end-use gas pressure trend in the future period; S4: For each air compressor, combine the mother pipe pressure data, the own energy efficiency curve and the pressure points of the pressure demand curve to screen the optimal continuous pressure interval with the minimum specific power, specifically comprising the following steps: S4.1: Determine pressure search range; combine with mother pipe pressure data P main (t) with end-use pressure data P end,i (t), set upper limit P max and lower limit P min , expressed as: ； wherein 0.05 is a safety redundancy value, for the minimum safety pressure of the air compressor; S4.2: traverse the energy efficiency curve; in the range of [P min ,P max ], traverse by step ΔP=0.01 MPa, calculate the specific power ηj(P k ,f k ) of each air compressor j at each pressure point of the pressure demand curve, P k =P min +kΔP, k=0,1,…,n; S4.3: Screening the optimal continuous pressure interval; selecting the minimum specific work of the continuous 3 pressure points as the optimal continuous pressure interval of air compressor j , and recording the center pressure of the interval , as the optimal target pressure of the air compressor under this working condition; S5: Combine the optimal continuous pressure interval of each air compressor, and distribute the controller of each air compressor through a multi-objective optimization algorithm to make the system total energy consumption minimum target pressure, and the objective function and constraint condition of the multi-objective optimization algorithm are as follows: Objective function: ； wherein, Qj is the discharge volume of the air compressor j at the target pressure in m 3 / min, f j is the operating frequency of the air compressor j, T is the control period, Q j is the air compressor performance curve, Q j (P) = k j x P x f j ; Constraint condition: ； where 1.05 is the pipe network leakage coefficient, P req (t + τ) is the pressure demand curve; S6: Real-time monitor the deviation of the actual pressure of each air compressor from the target pressure, and if the deviation exceeds the threshold, adjust the frequency of the air compressor through the controller.

2. The method of claim 1, wherein, S2 extracts the feature value of the end-use gas pressure data through a sliding window statistics, specifically expressed as: ； where P end,avg (t) and P end,mode (t) are the mean and mode, respectively, of all end-of-line gas pressure data, and g is the frequency distribution density.

3. The method of claim 2, wherein, In S3, a time series model LSTM is used to construct the pressure demand curve, expressed as: P req (t+τ) = LSTM(P req (t), P req (t−1), …, P end,avg (t)) where τ is the prediction time step, P req (t) is the end pressure prediction value at the current time instant.

4. The method of claim 1, wherein, S6 is specifically: Real-time monitoring of actual pressure Deviation from target pressure: ； If the deviation exceeds the threshold value, the frequency f of the air compressor is adjusted by a PID controller j , implementing a closed-loop control: ； where K p , K i , K d are PID controller parameters.

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