WO2020136475A1 - Method for detecting leaks in a gas network under pressure or under vacuum and gas network - Google Patents
Method for detecting leaks in a gas network under pressure or under vacuum and gas network Download PDFInfo
- Publication number
- WO2020136475A1 WO2020136475A1 PCT/IB2019/060290 IB2019060290W WO2020136475A1 WO 2020136475 A1 WO2020136475 A1 WO 2020136475A1 IB 2019060290 W IB2019060290 W IB 2019060290W WO 2020136475 A1 WO2020136475 A1 WO 2020136475A1
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- WIPO (PCT)
- Prior art keywords
- sensors
- gas
- gas network
- network
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- Prior art date
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Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
Definitions
- the invention is intended to be able to quantify leaks that occur in a gas network.
- a final consumer may be an individual final consumer or include a so-called consumer area or a group of individual final consumers .
- Methods for estimating the total leakage rate on the source side are also known from e.g. DE 20.2008.013.127 Ul and
- the current invention relates to a method for detecting and quantifying leaks in a pressurized gas network; the gas network comprising:
- a training or estimation phase in which a physical model or mathematical relationship is determined between the measurements of a first set of sensors and a second set of sensors based on physical laws using estimation algorithms;
- an operational phase where the established physical model or mathematical relationship between the measurements of the first set of sensors and the second set of sensors is used to predict leaks in the gas network; wherein the operational phase comprises the following steps:
- the leaks detected and quantified by the method are not limited to leaks in the sources or consumers of compressed gas, i.e. in the compressor plants and pneumatic tools or components, but may also concern leaks in the pipelines of the gas network itself.
- the operational phase is temporarily interrupted or stopped, after which the training phase is resumed in order to redefine the physical model or mathematical relationship between the measurements of different sensors, before the operational phase is restarted.
- the process i.e. the gas network with sources, pipelines and consumers, is not shut down, but only the method. In other words, if the operational phase is temporarily interrupted or stopped, the sources will still supply gas or vacuum to the consumers .
- Interrupting the operational phase and resuming the training phase has the advantage that the physical model or the mathematical relation is updated.
- the invention also concerns a gas network under pressure or under vacuum; the gas network is at least provided with: one or more sources of compressed gas or vacuum; one or more consumers or consumer areas of compressed gas or vacuum applications;
- pipelines or a network of pipelines to transport the gas or vacuum from the sources to the consumers, consumer areas or applications ;
- figure 1 schematically shows an arrangement in accordance with the invention
- figure 2 shows a schematic flowchart of the method in accordance with the invention.
- the gas network 1 in figure 1 comprises mainly a source side 2, a consumer side 3 and a network 4 of pipelines 5 between the two.
- the gas network 1 in this case is a gas network 1 under pressure, i.e. there is a pressure higher than the atmospheric pressure.
- the source side 2 comprises a number of compressors 6, in this case three, which generate compressed air.
- the compressors 6 contain compressed air dryers .
- the consumer side 3 contains a number of consumers 7 of compressed air and .in this case also three.
- compressors 6 can also be located downstream of the gas network 1. This is referred to as "boost compressors”.
- the compressed air is routed through the network 4 of pipelines 5 from the compressors 6 to the consumers 7.
- This network 4 is in most cases a very complex network of pipelines 5.
- Figure 1 shows this network 4 in a very schematic and simplified way.
- the associated shut-off and bypass valves in the gas network 1 are not explicitly indicated in order to maintain the simplicity in figure 1.
- the gas network 1 may also be provided with a pressure vessel 8, with all compressors 6 in front of this pressure vessel
- components 19 such as filters, separators, atomizers and/or regulators, can also be provided in the gas network 1. These components 19 can be found in various combinations and can be found both near the pressure vessel 8 and close to the individual consumers 7.
- Network 4 also includes a number of sensors 9a, 9b, 9c and 9d, which are located at different locations in network 4.
- the figure shows four pressure sensors 9b, which measure the pressure at different locations in the network
- a pressure sensor 9b to measure the pressure in the pressure vessel 8 is also recommended to correct the "mass in - mass out" principle for large, concentrated volumes.
- These sensors 9c then determine the state or status, for example on or off, of the compressors 6, the consumers 7 or consumer areas. As explained later, by using these state sensors 9c, the cross-sensitivity of the estimation algorithms can be reduced, so that these estimation algorithms become more reliable.
- sensors 9a, 9b, 9c together with a source 6 and/or consumer 7 are integrated in one module. This is referred to as 'smart connected pneumatic devices 1 .
- sensors 9a, 9b which measure the pressure or flow of the gas at the consumers 7 or consumer areas. It is also possible to use sensors that measure the temperature of the gas at the consumers 7 or in the consumer area.
- differential pressure sensors 9d coming from the group of additional or alternative sensors 9a, 9b are preferably placed over filter, separator, atomizer, and/or regulator components 19. It goes without saying that the number of differential pressure sensors 9d may differ from what is shown in figure 1.
- the aforementioned humidity and temperature sensors coming from the group of additional or alternative sensors 9a, 9b are preferably mounted at the inlet and/or outlet of the compressors 6 and the consumers 7.
- the aforementioned additional or alternative sensors 9a, 9b are not all included in the gas network 1, but it goes without saying that this is also possible.
- sensors 9a, 9b can be used, as well as in networks 1 where only the volumetric flow rate is measured instead of the mass flow rate.
- the gas network 1 is further provided with a data acquisition control unit 10 to collect data from the aforementioned sensors 9a, 9b, 9c, 9d.
- sensors 9a, 9b, 9c, 9d determine or measure the physical parameters of the gas and the state of the compressors 6, consumers 7 and/or consumer area and send this data to the data acquisition control unit 10.
- the gas network 1 is further provided with a computing unit 11 for processing the data from sensors 9a, 9b, 9c, 9d, whereby the computing unit 1.1 will be able to carry out the method for detecting and quantifying leaks 12 in the gas network 1 in accordance with the invention, as explained below.
- the aforementioned computing unit 11 can be a physical module which is a physical part of the gas network 1. It cannot be excluded that the computing unit 11 is not a physical module, but a so-called cloud-based computing unit 11, which may or may not be connected wirelessly to the gas network 1. This means that the computing unit 11 or the software of the computing unit 11 is located in the 'cloud'.
- gas network 1 The operation of gas network 1 and the method in accordance with the invention is very simple and as follows .
- Figure 2 schematically illustrates the method for detecting and quantifying leaks 12 in the gas network 1 of figure 1.
- the model consists of a mathematical relation such as a matrix or the like, in which there are still a number of parameters or constants .
- the mathematical model is also based on the assumption that the resistance of the pipelines 5 does not change and that the topology of the network 4 is fixed.
- the data acquisition control unit 10 will read out the sensors 9a, 9b, 9c, 9d and send these data to the computing unit 11, where the necessary calculations will be performed to determine the aforementioned parameters or constants.
- the first group of sensors 9a, 9b, 9c, 9d in both cases includes different pressure sensors 9b and/or differential pressure sensors 9d at different locations in the gas network 1 and possibly one or a plurality of flow sensors 9a. It is important to note that the flow sensor (s) 9a of the second group are different from the flow sensors 9a of the first group . The only condition is therefore that the cross-section of the two groups of sensors 9a, 9b, 9c, 9d must be empty.
- the physical model in the form of a mathematical relationship between the measurements of the first group and the second group of sensors 9a, 9b, 9c, 9d can be used in an operational phase 18 to detect and quantify leaks 12 in the gas network
- the data acquisition control unit 10 will collect the different data from sensors 9a, 9b, 9c and the computing unit 11 will perform the necessary calculations using the physical model established in the previous phase 15.
- steps of the operational phase 18 are preferably repeated sequentially at a certain time interval. As a result, during the entire operational period of the gas network 1, leaks 12 can be detected and traced, and not just once, for example, during or shortly after the start-up of the gas network 1.
- This method has the advantage that only one flow sensor 9a is needed, both in the training phase 15 and in the operational phase 18.
- a flow sensor 9a is generally technically more difficult to realize, more complex and more expensive than a pressure sensor 9b and/or a differential pressure sensor 9d. By minimizing the number of flow sensors 9a to one, the system is cheaper.
- one or more thresholds can be set or selected in advance.
- the operational phase 18 will be temporarily interrupted or stopped, after which the training phase 15 will be resumed to redefine the physical model or mathematical relationship between the measurements of different sensors, before the operational phase 18 is restarted.
- Source side 2 then comprises a number of sources of vacuum, i.e. vacuum pumps or similar.
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Examining Or Testing Airtightness (AREA)
Abstract
Description
Claims
Priority Applications (9)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FIEP19832194.5T FI3903018T3 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
EP19832194.5A EP3903018B1 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
US17/418,389 US20220057048A1 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
KR1020217022771A KR20210107748A (en) | 2018-12-27 | 2019-11-28 | Methods and gas networks for detecting leaks in gas networks that are under pressure or under vacuum |
CN201980085833.3A CN113227642B (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or vacuum and gas network |
CN202310146841.2A CN115979538A (en) | 2018-12-27 | 2019-11-28 | Method for detecting a leak in a gas network under pressure or vacuum, and gas network |
PL19832194.5T PL3903018T3 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
ES19832194T ES2939692T3 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
JP2021537856A JP7339343B2 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in gas networks under pressure or vacuum and gas networks |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201862785254P | 2018-12-27 | 2018-12-27 | |
US62/785,254 | 2018-12-27 | ||
BE20195838A BE1026852B1 (en) | 2018-12-27 | 2019-11-26 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
BE2019/5838 | 2019-11-26 |
Publications (1)
Publication Number | Publication Date |
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WO2020136475A1 true WO2020136475A1 (en) | 2020-07-02 |
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PCT/IB2019/060290 WO2020136475A1 (en) | 2018-12-27 | 2019-11-28 | Method for detecting leaks in a gas network under pressure or under vacuum and gas network |
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112949817A (en) * | 2020-12-24 | 2021-06-11 | 长江勘测规划设计研究有限责任公司 | Water supply pipe leakage edge equipment detection method based on time convolution neural network |
US20220169228A1 (en) * | 2020-12-02 | 2022-06-02 | Volvo Truck Corporation | Air-actuated vehicle system and a method of detecting leakage in an air-actuated vehicle system |
CN115127036A (en) * | 2022-09-01 | 2022-09-30 | 北京云庐科技有限公司 | Municipal gas pipe network leakage positioning method and system |
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EP3409953A1 (en) * | 2017-05-31 | 2018-12-05 | ABB Schweiz AG | Method in a compressed air system |
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2019
- 2019-11-28 WO PCT/IB2019/060290 patent/WO2020136475A1/en unknown
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US5272646A (en) * | 1991-04-11 | 1993-12-21 | Farmer Edward J | Method for locating leaks in a fluid pipeline and apparatus therefore |
US20030187595A1 (en) * | 2002-03-29 | 2003-10-02 | Hiroshi Koshinaka | Compressed air monitor system for monitoring leakage of compressed air in compressed air circuit |
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
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US20220169228A1 (en) * | 2020-12-02 | 2022-06-02 | Volvo Truck Corporation | Air-actuated vehicle system and a method of detecting leakage in an air-actuated vehicle system |
CN112949817A (en) * | 2020-12-24 | 2021-06-11 | 长江勘测规划设计研究有限责任公司 | Water supply pipe leakage edge equipment detection method based on time convolution neural network |
CN115127036A (en) * | 2022-09-01 | 2022-09-30 | 北京云庐科技有限公司 | Municipal gas pipe network leakage positioning method and system |
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