CN115219106B - Compressed air pipe network leakage dynamic measurement method based on cloud computing - Google Patents
Compressed air pipe network leakage dynamic measurement method based on cloud computing Download PDFInfo
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Abstract
The invention relates to a dynamic measurement method of compressed air pipe network leakage based on cloud computing, which is characterized in that the monitoring method uploads field-level flow real-time data through the technology of a narrow-band internet of things, and the data are stored and analyzed and processed through a cloud server, so that the defects that the flow data distortion rate is high and the leakage quantity of a pipeline cannot be checked under the working condition that a large-scale manufacturing enterprise continuously runs for a long time at an air compression station can be effectively overcome. The method aims at quantitatively monitoring the leakage of the compressed air pipe network by combining a cloud computing technology with an industrial field monitoring technology, and helping a user to better arrange a field maintenance plan so as to realize energy conservation and emission reduction of the compressed air system.
Description
Technical Field
The invention relates to the technical field of industrial Internet of things, in particular to a method for dynamically measuring leakage of a compressed air pipe network based on cloud computing.
Background
The air compression station is used as a large user for producing and molding enterprises, and the energy consumption ratio of the air compression station is usually more than 30% of the total energy consumption of the enterprises, and the energy-saving measures aiming at the air compression station are all the important factors of the manufacturing enterprises. Under the current energy-saving strategy of the air compression station, the compressed air industry focuses more on the coordinated control optimization of the air compression station room, and the scheme aims at further improving the energy-saving space of the air compression station by a monitoring means of a compressed air pipe network on the basis of energy saving of the whole station. When the manufacturing enterprises face the compressed air pipe network, the pipeline is aged increasingly along with the use of the air pipe network, and a large number of leakage phenomena occur. The conventional method is used for quantitatively monitoring the leakage quantity, and the air compression station is required to stop, and the method is realized by a gas storage volume pressure drop time measuring method or an air compressor loading and unloading ratio measuring and calculating method under the no-load condition. These methods of measurement are not practical for large industrial users who are continuously generating and cannot stop, and the leakage amount of large industrial gas is not neglected.
Disclosure of Invention
The processing method aims at solving the problem of measuring the leakage quantity of the pipe network in real time under the condition of no stop, and calculating the number of possible leakage points. And the leakage quantity is qualitatively and quantitatively measured, and meanwhile, a user is guided to carry out on-site detection and maintenance. The cloud computing technology of the energy management integrated platform is utilized, the investment of field-level equipment is reduced, and a more light deployment mode, scientific automatic analysis, more accurate measurement and calculation and visual data analysis and statistics are realized.
The invention aims to solve the technical defect that the demand of large-scale manufacturing enterprises for pipe network leakage measurement contradicts synchronous production, and provides a compressed air pipe network leakage dynamic measurement method based on cloud computing.
A leakage dynamic measurement dividing step:
Simplifying the physical model of the compressed air pipe network: according to the actual field situation, a metering point of the main pipe in the pipe network, which can reflect the flow change most, and a metering point of the main branch pipe, which can reflect the flow change at the corresponding tail end most, are found out. Simplifying a compressed air pipe network into a physical model of only one air supply port and a plurality of air utilization ports;
the method comprises the steps of collecting and uploading field flow aiming at a simplified pipe network: and a gas flowmeter is additionally arranged on the field-level pipeline to realize flow measurement of the gas supply side and the gas utilization side, and each flowmeter is provided with an NB-Iot DTU wireless terminal. The flowmeter sends the data of the instantaneous value and the accumulated value of the flow to the NB-IotDTU wireless terminal in real time through the RS485 serial port, and the communication with the cloud server is realized by adopting the narrowband Internet of things technology. The DTU wireless terminal packages serial port data and sends the serial port data to a server erected in a cloud end of the system, and the server completes data storage in real time;
Establishing a cloud data ledger: and obtaining the total pipe volume flow W Total (S) and the branch pipe volume flow W i reported in real time on site according to the instantaneous flow reported by the on-site flowmeter. Because the gas density in the pipe network is almost consistent, the volume flow under ideal conditions also meets the requirement of uniform distribution, and the approximate mass flow loss value Q Leakage valve of the leaked gas can be obtained according to the difference value of the mass flow measured by the flowmeter by conversion of an equation.
Filtering the calculated mass flow loss value Q Leakage valve to remove distortion data:
The leak amount change rate k obtained by the adjacent measurement time is calculated.
And adopting a vergence algorithm to gather and scatter a central point N of the change rate k.
The discrete distance DIS n for each rate of change k is calculated using a second order Minkowski distance calculation.
The threshold value G tv is calculated from the set DIS of discrete distances.
Selecting and removing distorted data according to the threshold value, and calculating to obtain more accurate average leakage mass flow q Leakage valve in the system sampling time;
calculating the leakage area of the compressed air pipe network, and calculating the number of leakage points:
and obtaining the calculation expansion of the leakage area S Leakage valve according to the calculation equation of the Bernoulli equation under the subcritical flow rate.
And calculating the number range N epsilon [ N min,Nmax ] of the leakage points of the pipe network according to the leakage area range [ S min,Smax ] of each leakage point in the normal condition.
The invention has the following beneficial effects:
1. aiming at the gas consumption of large industrial compressed air, the method is gradually prolonged along with construction time, and the leakage loss has great influence on the energy consumption waste of the whole station.
2. Other flowmeters are very high in installation requirement, the influence of the pipeline conditions and transformation conditions of the old pipeline on the installation position can influence the flow data, the data is filtered through a clustering algorithm, the error influence of distortion data on cloud computing is reduced, and more accurate leakage data are obtained.
3. The final purpose of quantitative measurement of leakage is to prompt a user to perform field detection and maintenance on the compressed pipe network in time. According to the method, for measuring and calculating the number of the leakage points, a user can arrange the maintenance time better in advance, the number of the leakage points found by comparison detection is checked in advance, the maintenance effect is checked in advance, and the user is helped to optimize the management mode to solve the problem of pipe network leakage more efficiently.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a system diagram of a compressed air pipe network field device of the present invention;
FIG. 2 is a schematic view of eliminating discrete points of flow data for a period of time according to the present invention;
FIG. 3 is a graph of a sample leak gas mass flow for a period of time in accordance with the present invention;
FIG. 4 is a graph of a filtered sample of the leak gas mass flow for a period of time in accordance with the present invention;
Detailed Description
The invention is described below with reference to the drawings and the formula calculation.
1-4, The design of the invention is a method for dynamically measuring the leakage of a compressed air pipe network by cloud computing, which comprises the following steps:
1. because the composition of the compressed air pipe network corresponding to the actual air compression station is generally complex, the installation of the flowmeter needs to consider the conditions of economical practicability and compatible end air statistics of the installation point. The first step is implemented to simplify the physical model of the compressed air pipe network, and according to the actual field situation, the metering point of the main pipe in the pipe network, which can reflect the flow change most, and the metering point of the main branch pipe, which can reflect the flow change at the corresponding end most, are found out. For the same air pressure of the tip branch pipe, the air consumption points with no obvious difference in air consumption can be selected to be measured in the main branch pipe. The compressed air pipe network is simplified into a physical model with only one air supply port and a plurality of main air ports, and the flow measurement of the air supply side and the air utilization side is realized by adding an air flowmeter to a field-level pipeline, as shown in figure 1.
The flowmeter sends the data of the instantaneous value and the accumulated value of the gas volume flow to the NB-IotDTU wireless terminal in real time through the RS485 serial port, and the communication with the cloud server is realized by adopting the narrowband Internet of things technology. The DTU wireless terminal packages serial data and sends the serial data to a server erected in the cloud of the system, and the server completes data storage in real time.
2. According to the instantaneous flow reported by the on-site flowmeter, a cloud data ledger is established, and the volume flow W Total (S) of the main pipe and the volume flow W i of each branch pipe can be obtained. Since the mass flow distribution is uniform throughout the air network, the total mass flow Q Total (S) is always equal to the cumulative value of the mass flow Q i for each branch under ideal conditions. Because the gas density in the pipe network is almost consistent, the volume flow under ideal conditions also meets the requirement of uniform distribution, and the approximate mass flow loss value Q Leakage valve of the leaked gas can be obtained according to the difference value of the mass flow measured by the flowmeter by conversion of an equation.
Qi＝ρ*Wi
Ρ is the density of compressed air in the corresponding system, kg/m 3;
because the accuracy of flow measurement, the accuracy of an on-site flowmeter, the installation condition and the actual working condition are all affected, the leaked volume flow can also form a certain accumulated error after mathematical operation. Therefore, the cloud server needs to screen the reported data, and filters and removes the data with obviously distorted partial flow, so as to obtain a more stable average value of the leakage mass flow of the air pipe network. For large air pipe networks, the trend of the change of each uploaded parameter point is slow by adopting a high-frequency flow measurement method. When a distortion signal is generated, the change rate of the parameter has a distortion phenomenon relative to the normal signal, and whether the measured value is distorted or not can be obtained through analysis of the calculated change rate of the leakage volume flow. As shown in fig. 2, continuous sampling is performed for a short period of time, and the times t1 and t2 are filtered out due to the abnormality of the leak mass flow rate change rates at the times t1 and t 2. The generation of the entire data curve is not affected due to the continuous high frequency sampling over a period of time.
The filter algorithm is described as follows:
According to the data segment reported by the flowmeter of the air-pressure pipe network, calculating a data set of mass flow of leakage quantity: q Leakage valve ＝{Q1,Q2,Q3……Qn, obtaining a constant t of sampling time according to the report frequency of the message set by the NB-Iot DTU field level.
The adjacent measurement time results in a leak amount change rate k of:
From the measurement, a data set k= { k 2,k3……kn } of the leak amount change rate can be obtained. After the collection of the leakage rate is obtained, a second-order Minkowski distance calculation mode is adopted to calculate a discrete distance DIS n so as to measure the discrete relation degree of the data. When the clustering algorithm is used for data statistics, the smaller the distance between the clustering center and the scattering center is, the higher the continuity is, and conversely, the larger the distance is, the higher the discrete type is.
The gather-scatter center point N of the change rate k is:
calculating the distance between the leakage rate and the dispersion center point by using a second-order Minkowski distance method:
After the discrete distance DIS n is obtained, parameters which do not meet the set continuous attribute in the data set are removed by setting the gather-scatter distance threshold value G tv. The setting of the threshold value is very important, the magnitude of the safety coefficient needs to be considered properly, and the threshold value is calculated according to the median of the discrete distance in the time period. When the DIS n>Gtv is performed, the mass flow data Q n of the sampling point n is judged to be distorted, and rejection is performed. When DIS n≤Gtv is performed, the mass flow data Q n at the sampling point n is determined to be valid, and the calculation is kept.
From the set dis= { DIS 1,DIS2……DISn } of discrete distances, the threshold G tv is calculated:
Gtv＝g*median(DIS)
g is a safety coefficient set up according to system attributes;
media () is a median evaluation function;
More accurate average leakage mass flow q Leakage valve in the system sampling time:
The rejection coefficient is a n:
at DIS n>Gtv, a n＝0,DISn≤Gtv, a n =1;
The sample spectrum of the mass flow data Q Leakage valve for the sample calculated leakage is shown in fig. 3. There is a large amount of obvious distortion data before processing, and the mass flow spectrum chart after filtering by the vergence algorithm is shown in figure 4. The data obtained through processing are smoother, so that more accurate average leakage mass flow q Leakage valve is obtained, the influence of flow measurement errors on the follow-up leakage point position quantity deduction is reduced, and the accuracy of the whole cloud computing is improved.
3. According to the measured leakage volume flow, a leakage area equivalent value S Leakage valve which is instantaneously approximate can be obtained according to the cloud computing mode. Since air satisfies the ideal gas equation, a computational expansion of the leakage area S Leakage valve can be obtained in combination with the computational equation of the Bernoulli equation at subcritical flow rate.
Leakage area S Leakage valve calculation formula:
Y is the leakage coefficient;
Cd is a gas leakage coefficient, and the value of the gas medium through the gap is preferably 0.9;
P Air flow is the pressure value of the compressed air in the actual air pipeline;
m is the molar mass of the gas, 28.96g/mol;
R is a gas constant, 8.314 x 10 3 J/(kmol x K);
t is the temperature value of the compressed air in the actual air pipeline, K;
k is an insulation coefficient, and the ideal value of air is about 1.4;
leakage coefficient Y calculation formula:
P atm is ambient pressure, typically 0.1MPa;
from the range of the leakage area of each leakage point measured outside, the range of the leakage area of each leakage point under empirical estimation can be obtained [ S min,Smax ]. According to the obtained leakage area S Leakage valve , the number range N epsilon [ N min,Nmax ] of the leakage points of the pipe network can be obtained.
According to the cloud computing method for dynamically measuring the leakage quantity of the compressed air pipe network, the transformation input cost is reduced to the greatest extent by utilizing the advantages of the cloud computing technology under the condition of small input of field-level equipment. Meanwhile, the pipe network leakage quantity is quantitatively analyzed in real time under the condition that the whole station continuously works without stopping, so that possible points of leakage are obtained, a manager is helped to better arrange a manual field detection period, and a standard for measuring the effect after repair is provided.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and the description is provided for clarity only, and those skilled in the art will recognize that the embodiments of the disclosure may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
Claims (6)
1. The method for dynamically measuring the leakage of the compressed air pipe network based on the cloud computing is characterized by comprising the following steps of:
The physical model of the compressed air pipe network is simplified;
Aiming at the simplified pipe network, a gas flowmeter is additionally arranged on the field pipeline to realize the monitoring of the gas supply side and the gas side flow, and a wireless terminal is additionally arranged on each flowmeter for reporting data;
According to the instantaneous flow reported by the on-site flowmeter, a cloud data ledger is established, and according to the volume flow W Total (S) of the main pipe and the volume flow W i of each branch pipe, a mass flow loss value Q Leakage valve is calculated;
ρ is the density of compressed air in the corresponding system, kg/m;
Calculating the leakage area S Leakage valve of the leakage point according to the mass flow loss value;
the calculation formula of the leakage area S Leakage valve is as follows:
Y is the leakage coefficient;
Cd is a gas leakage coefficient, and the value of the gas medium through the gap is preferably 0.9;
the P gas is the pressure value of the compressed air in the actual air pipeline;
m is the molar mass of the gas, 28.96g/mol;
r is a gas constant, 8.314 x 10 3 J/(kmol x K);
t is the temperature value of the compressed air in the actual air pipeline, K;
k is an insulation coefficient, and the ideal value of air is about 1.4;
the leakage coefficient Y is calculated as follows:
P atm is ambient pressure, typically 0.1MPa;
Estimating the leakage area range of each leakage point according to the leakage area range of each leakage point [ S min,Smax ];
according to the obtained leakage area S Leakage valve , the calculation formula of the number range N E [ N min,Nmax],Nmin,Nmax ] of the leakage points of the pipe network is obtained as follows
。
2. The method for dynamically measuring leakage of a compressed air pipe network based on cloud computing according to claim 1, wherein the complicated pipe network system is preferentially simplified into a physical model consisting of a gas end for a main pipe and a gas supply main pipe.
3. The method for dynamically measuring the leakage of the compressed air pipe network based on the cloud computing according to claim 1 or 2, wherein a flowmeter and an NB-Iot DTU wireless terminal are additionally arranged on site according to the point to be detected in the simplified pipe network model.
4. The method for dynamically measuring the leakage of the compressed air pipe network based on the cloud computing according to claim 1, wherein the data of the mass flow of the leakage gas obtained through calculation is classified by utilizing a vergence algorithm and a second-order Minkowski distance calculation mode, and data filtering is carried out according to a set threshold value, so that the influence of on-site measurement data errors and calculated accumulated errors on the whole calculation accuracy is reduced.
5. The method for dynamically measuring the leakage of the compressed air pipe network based on the cloud computing according to claim 1, wherein quantitative calculation of the leakage area is performed by adopting a conversion type of a Bernoulli equation according to the mass flow of average leakage gas obtained by filtering and sorting data.
6. The method for dynamically measuring the leakage of the compressed air pipe network based on the cloud computing according to claim 1, wherein the leakage area of each leakage point is analyzed and tidied by using empirical data, the number of the leakage points of the whole pipe network is calculated, and compared with the direct calculation by using the volume flow of the leakage, the method is capable of avoiding systematic errors caused by the change of parameters of different working pressures, environmental temperatures and environmental pressures of the whole system.
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