• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
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  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    Method for detecting leaks (12) in a gas network (1), comprising: - sources (6); - consumers (7); sensors (9a, 9b, 9d); characterized in that the method comprises the following phases: - an estimation phase (15) in which a physical model is determined between the measurements of a first group and a second group of sensors (9a, 9b, 9c, 9d); - an operational phase (18) in which the physical model between the measurements of the first group and the second group of sensors (9a, 9b, 9c, 9d) is used to predict leaks (12) in the gas network (1); wherein the operational phase (18) comprises the following steps: - based on the readings of the first group of sensors (9a, 9b, 9c, 9d), the value of a second group of sensors (9a, 9b, 9c, 9d) calculate using the physical model; - determining the difference between the calculated value and the read-out values of the second group of sensors (9a, 9b, 9c, 9d); - Determine whether a leak (12) is present on the basis of a residual value analysis.
    公开号:BE1026852B1
    申请号:E20195838
    申请日:2019-11-26
    公开日:2020-09-28
    发明作者:Philippe Geuens；Ebrahim Louarroudi
    申请人:Atlas Copco Airpower Nv；
    IPC主号:
    专利说明:

    Method for detecting leaks in a gas network 9 under pressure or under vacuum and gas network. The present invention relates to a method for detecting leaks in a gas network under pressure or under vacuum. More specifically, the invention is intended to be able to quantify leaks occurring in a gas network. For example, "gas" here means, but not necessarily, air, but nitrogen or natural gas is also possible. Methods are already known for monitoring or controlling a gas network under pressure, these methods being set up for long and straight pipelines. , where the incoming flow is not necessarily equal to the outgoing flow due to the compressibility of the gas concerned. These methods are based on a number of assumptions, such as, for example, very long pipelines, straight pipelines, which are not suitable for complex pressurized gas networks where one or more compressor installations supply pressurized gas to a complex network of consumers. Methods such as described in US are also already known
    7,031,850 Bé and US 6,711,502 B2, to detect leaks in
    | 2 pneumatic components GE tools of the 9 end users themselves. One end user can use both a | individual end consumers are considered to be a combined 9 consumer zone or group of individual 9 & end consumers. Vox methods for estimating the total leakage rate on the source side are already known from, for example, DE
    20.2008,013.127 Ul and DE 20.2010.015.450 UL,
    TC Such Known methods therefore have the disadvantage that they do not allow to enter a complex network of lines between the source and the consumers or. consumer zone leaks. In addition, the L5 network of pipes of a gas or vacuum network constitutes a source of leaks not to be underestimated. The present invention aims to provide a solution to this, = 0 The present invention has as object for detecting and quantifying leaks in a gas network under pressure, the gas network comprising: - one or more sources of compressed gas or of vacuum: - one or more consumers, consumer zone of Compressed gas or leakage areas of vacuum; - Pipes or a network of pipes to transport the gas or vacuum from the sources to the consumers, consumer zone or applications:
    | - several sensors which determine one or more [ysic] parameters of the gas at different
    | times and locations in the gas network; 9 characterized in that the gas network is optionally further 9 3 provided with additional sensors which display the position (tv. [On / off} of the sources, consumers, consumer zones or applications and that the method comprises the following stages:
    - a possible start-up phase in which the aforementioned sensors are calibrated before use;
    - a traininos or estimation phase in which a physical model or mathematical relationship is determined between the measurements of a first group of sensors and a second green of sensors based on physical laws with a range of estimation algorithms;
    “An operational phase in which the established physical model or mathematical relationship between the measurements of the first group of sensors and the second green of sensors is used to predict leaks in the gas network; where the operational phase includes the following steps:
    “Choosing the first group and second group of sensors;
    - on the basis of the readings of the first group of sensors, calculate or determine the value of a second green Sensors using the physical model or mathematical relationship;
    - comparing the calculated or determined values of the second group of sensors with the read-out values of the second group of sensors and determining the difference between them; : * Based on a residual value analysis, determine whether | a leak is present in the gas network; 9 5 = generating an alarm and / or generating a leak flow rate and / or generating the associated costs if a leak is detected, 9 An advantage is that such a method will allow leaks to occur in the cas network itself learn, detect and also start to quantify, In other words, the leaks that are detected by the method are not limited to leaks in the hums or consumers of compressed gas, ie in the compressor devices and pneumatic tools, but can also concerns in the pipes of the gas network itself.
    It should be noted here that with a gas network under pressure, the leaks will occur to the outside, whereby gas will escape to the surrounding area. With a gas network under vacuum, leaks will occur "inwards", which means that ambient air will enter the gas network.
    During the (training phase, on the basis of known physical laws and using the measurements of the different sensors, a mathematical relationship is established. Luszen this group of sensors,
    à An estimation | is used here algorithm or estimator algorithm, It is assumed that in the first instance; There are 5 leaks in the gas network, with others | words assume a normal situation of the | gas network or a so-called "baseline", | In this way, a physical model, or mathematical model, can be established that reflects the relationship between the various parameters measured by the sensors.
    This model can then be used to immediately detect irregularities in future measurements of the sensors by comparing the results of the model and the measurements of the sensors. In this way, leaks can be detected very quickly and, upon detection a leak, intervene and stop the leak.
    Preferably, at some point, the operational phase is temporarily interrupted or stopped, after which the training phase is resumed in the physical mode! or mathematical relationship between the measurements of different sensors, before restarting the operational phase.
    It should also be noted that the process, i.e. the gas network with sources, consumers and pipes, is not
    | BE2019 / 5838 | 6 9 is stopped, but only the working method. In other words: when the operational phase becomes temporary | 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 relationship is updated or updated.
    As a result, it will be possible to take into account the time-varying behavior of the gas network or the system, so that the detection of leaks does not depend on the varying behavior of the gas network. This is the case, for example, when a leak has been detected and treated after an energy audit, extensions to the gas network and / or introduced blockages in the network. In this case, the detection system starts from a new "baseline" or zero.
    The invention also relates to a gas network under pressure or under vacuum, which gas network is at least provided with: - one or more sources of compressed gas or vacuum; - pen or more consumers or consumer zones of compressed gas or applications of vacuum; “Pipes or network of pipes to transport the gas or vacuum from the sources to the consumers, consumption zones or applications;
    | - multiple sensors which measure one or more physical parameters of the gas on different | Times and locations in the gas network;
    9 characterized in that the gas network is further provided with: 9 5 - optionally one or more sensors which indicate the state 9 off of one or more sources, 9 consumers, consumer zones or applications | view; - pen data acquisition control unit for the LD collecting data from the sensors; - a calculation unit for carrying out the method according to the invention.
    Such a device can be used to apply a method according to the invention. With the insight of demonstrating the features of the invention better, Le, some preferred variants of a method and gas network according to the invention are described below, by way of example without any limiting character. , with reference to the accompanying drawings, in which: figure 21 schematically shows a device according to the invention; figure 2 shows a schematic flow diagram of the method according to the invention, the gas network 21 of figure 1 mainly comprises a source side 2, a consumer side 3 and a network 4 of pipes 5 Loops both.
    The gas network in this case is a gas network 1 under | pressure, ie, there is a pressure higher than the atmospheric pressure. | The gas can be air, oxygen, nitrogen or some other | 3 bi] preferably non-toxic and / or hazardous gas or mixture; of gases, | The source side 2 includes a number of compressors &, in this case 9, which generate compressed air, 20 It is also possible that the compressors 6 contain compressed air dryers. The consumer side 3 comprises a number of compressed air consumers 7 and in this case also three .
    It cannot be ruled out that the compressors 6 may also be located upstream of the gas network 1, so-called “boost compressors” are available. ai The compressed air is led via the network 4 of pipes 5 of the compressors & to the consumers 7.
    This network à is in most cases a very complex network of pipes 5. Figure 1 shows this network 4 in a very schematic and simplified manner.
    Also, associated shut-off and 38 bypass taps in the gas network 1 are not explicitly shown in order to preserve simplicity in figure 1.
    N BE2019 / 5838> 9 In most real situations, the network 4 of 9 lines 5 consists of very numerous lines 5 connecting the # consumers 7 in series and in parallel with each other and with the 9 compressors 6. It cannot be ruled out that a 9S part of the network 4 assumes or includes a ring structure.9 This is because the gas network 1 is often expanded over time with additional consumers 7 or compressors 6, with new pipes 5 already existing pipes 5 must be laid, which leads to a tangle of pipes 5.
    The gas network 1 is optionally also provided with a pressure vessel 8, whereby all compressors & discharge on this pressure vessel 3. It is not excluded that one or more pressure vessels B are located downstream of the gas network 1. In addition, components 19, such as filters, separators, nebulizers, and / or regulators, are provided in the gas network 1. These components 12 can exist in various combinations and can be located in the vicinity of the pressure vessel 8 as well as close to the individual consumers 7. in the example shown, these components 15 are provided after the buffer vessel 8 and near the individual consumers 7,
    | In the network 4 there are furthermore a number of sensors Sa, 9b, Sc | and Sa, which are located at different locations in the | network €, 9 5 In this case, one flow sensor Sa is placed, just after {the aforementioned pressure vessel &, which will measure the total flow 3, 9 supplied by all compressors 6. It is not excluded that the individual flow rates of # the compressors 6 are measured themselves.
    Furthermore, the figure shows four pressure sensors 9b, which measure the pressure at different locations in the network 4. Preferably, a pressure sensor Sb is also provided to measure the pressure in the pressure vessel 8 in order to measure the “mass-in-mass”. - off ”principle to correct for large, concentrated volumes, It is clear that more, or less, than four pressure sensors Sb can provide wine. The number of flow sensors 2a is also not limiting for the invention. In addition to the flow sensors Sa or the pressure sensors Sb, it is possible to additionally, or alternatively, use sensors Sa, Sb that determine one or more of the following physical caramelers of the gas: pressure difference, cartridge speed, temperature or humidity,
    | Furthermore, in addition to the aforementioned sensors Ja and 9b, which measure 9 physical parameters of the gas, there are possibly also a | number of sensors Sc, cf "state sensors So" provided, | which are placed in the vicinity of the compressors 6, the consumers 7 9 5 or consumer zones.
    Preferably make these | sensors Sc are part of the consumers 7 themselves, 9 then speak of smarter consumers,
    9 These sensors Sc then determine the position or condition, 9 12 for example on or off, of the compressors 6, the 9 consumers 7 or consumer zones.
    As released later, by using these Current Sensors Sc, the noise sensitivity of the estimation algorithms can be reduced so that these estimation algorithms become more reliable.
    Levens cannot rule out the possibility that at least some of the sensors Yes, Sb, Jc together with a source 6 and / or consumer 7 are integrated in one module. This is referred to as so-called 'smart connected pneumatic devices'. It is also possible to use sensors Ja, 9b which measure the pressure or flow rate of the gas at the location of the consumers 7 or consumer zones.
    It is also possible to use sensors which measure the temperature of the gas at the location of the consumers 7 or consumer zone.
    The aforementioned differential pressure sensors 93d, coming from the group of additional or alternative sensors Sa, So, are preferably connected via filter, separator, nebulizer, and / or
    | BE2019 / 5838: 12 reguiatcr = components 19 installed. It goes without saying that the number of differential pressure sensors 52 may differ from what is shown in Figure 1.
    The aforementioned humidity and temperature sensors coming from the group of additional or alternative sensors Sa, 5b are preferably mounted at the inlet and / or outlet of the compressors 6 and the consumers 7.
    In the example shown, the aforementioned additional or alternative sensors Sa, 9b are not all included in the gas network 1, but it goes without saying that this is also possible. Certainly in more extensive and complex gas networks 1, such sensors Yes, 9b can be used, as well as in networks 1 where only the volumetric flow is measured instead of the mass flow.
    According to the invention, the gas network 1 is further provided with a data acquisition control unit 10 for collecting 220 data from the aforementioned sensors Ja, Sp, Jc and Sd.
    In other words, the sensors Ja, 3b, Yo, Sd determine or measure the physical parameters of the gas and the condition of the compressors 6, consumers 7 and / or consumer zone and send this data to the data acquisition control unit 10, according to In the invention, the gas network 1 is further provided with a computing unit 11 for processing the data
    + BE2019 / 5838 of the sensors Sa, 9b, Sc, 90, whereby the computing unit 11 9 will be able to perform the method according to the invention for detecting and quantifying bubbles 12 in the gas network 1, as explained below. | The aforementioned calculation unit 11 can be a physical module which is a physical part of the gas network 1. It is not excluded that the calculation unit 11 is not a physical module, but a so-called cloud-based calculation unit 11, which may or may not be wireless with the gas network 1. connected.
    This means that the calculation unit 11 or the software of the calculation unit li is located in the "cloud". In this case, the gas network 1 is further provided with monitor 13 for displaying or signaling leaks 12 detected by means of the method. The operation of the gas network 1 and the method according to the invention is very simple and as follows,
    Figure 2 schematically represents the method for detecting and quantifying leaks 12 in the gas network 1 of Figure 1, in a first phase 14, the start-up phase 14, the sensors Sa, Jh, Jo, Dd are calibrated, if necessary, before use. realize that if there are other sensors, they can also be calibrated before use.
    This happens once when the sensors 9% a, 3b, Se, Sd are placed in the gas network 1. Obviously it is not
    {excluded that the sensors Yes, Sb, Sc, 3d after expiry of | time to be recalibrated. Preferably, the sensors Sa, 9b, Jc, 9d are calibrated in operation or by means of an in-situ self-calibration.
    This keeps | in that the sensors Yes, 9b, Jc, 9d in the gas network 1, ie, 9 after they have been placed, are calibrated, By “in: operation” or “in situ” is meant: calibration without the 9 relevant sensor 9a, Sb , Sc, Id to have to disassemble from 9 10 the network 1. In this way one can be sure that the placement of the sensors 9a, Sb, Sc, 9d themselves does not influence their measurements, because one can only take after placement of the sensors 9a, 9b, Doc, 9d will do the calibration, Then starts the second phase 15, or the training phase 15, In this phase a physical model or mathematical relationship is determined between the measurements of a first group of sensors Sa, Sb , Sc, Bd and a Second group of Sensors Yes, Sb, Ic, Id cp based on physical laws using estimation algorithms.
    By taking into account additional state sensors Sc (e.g. on / off) of the compressors 6, consumers 7 or consumer zones, the noise sensitivity of the estimation algorithms can be reduced, so that the estimation algorithms become more reliable.
    == BE2019 / 5838 | On the basis of known physical laws, a model | cogethered between a first green of sensors 9a, 3b, Sc, 9d and a second group of sensors 9a, 9b, dc, Sa. 9 5 This first group of sensors Yes, Sb, Sc, Sd preferably all 9 measure the same {physical parameter of the gas, [for example pressure © and / or pressure difference do, at different [locations in the gas network 1. The second group of sensors Sa, Sb, Sc, Bd measure bi preferably also all the same in physical parameter of the gas, for example the flow co. The model consists, for example, of a mathematical relationship such as, for example, a matrix or the like, in which there are a number of parameters or constants. 15 These parameters or constants can be determined by Reading the appropriate sensors Yes, Sb, Zo, Dd and using estimation algorithms. This is based on a kind of baseline situation or a normal situation of the gas network 1 without leaks 12. Also, when determining the mathematical model, it is assumed that the resistance of the pipes 5 is not distant and that the topology of the network 4 is fixed.
    The data acquisition control unit 10 will select the sensors Da, Sp, So, 9d and send these data to the computing unit 11, where the necessary calculations will be performed to determine the aforementioned parameters or constants.
    {- BE2019 / 5838 | Once the parameters or constants have been determined, it is | physical model determined, in the form of a mathematical: relationship between the iwee groups of sensors Sa, Sb, Sc, Od, # 5 In the example shown, a cerest situation is 16 | shown on the right half of figure 2, wherein the second group comprises one flow sensor Sa as shown in figure 9 and a second situation 17 on the left half of figure 2, wherein the second group may comprise a plurality of flow sensors LD Sa.
    For the second situation 17, several flow sensors Yes will be placed in the metwork à, such as, for example, close to the consumers 7 or consumer zones, which form the second group of sensors Za.
    The first group of sensors 9a, 9b, Jc, Sd in both cases comprises different pressure sensors 9b and / or differential pressure sensors Sd at different locations in the gas network 1 and optionally one or more flow sensors Ga.
    It is important to note here that the flow sensor (en; 9a of the second group are different from the flow sensors Sa of the first group.
    The only condition is therefore that the cross-section of the two groups of sensors Sa, Sb, Sc, Sd must be empty.
    In both situations 16, 17, the method for determining the physical model is almost identical.
    # The physical model in the form of a mathematical relationship | between the measurements of the first group and the second croeg | of sensors Sa, 9b, Sc, Sd can in an operational phase 18: be used to detect and quantify leaks 12 in the gas network 1, 9 3 The operational phase 18 is carried out during the operation 9 of the gas network 1, this will be when the | compressors 6 deliver compressed gas via the network 4 from pipes 5 to the various consumers 7. It is during the operation of the gas network 1 that it will be important to be able to detect and quantify tasty 12.
    The operational phase 18 is similar for both aforementioned situations and is as follows: reading the first group of sensors 9a, Sb, Sa; - on the basis of the readings of the first green from sensors Da, Db, Do, calculate or pevest the value of a second group of sensors Sa, Sb, Jc using the physical model or mathematical relationship; - the determined or calculated value of the second group of sensors Sa, Sb, Jo, Sd is compared with the read-out value of the second group of sensors Ja, Sb, Jc, Bd and the difference between them is determined;
    9 «Based on a residual value analysis and possibly the | condition sensors Sc calculate whether there is a leak 12 9 in the system; | = generating an alarm if a leak 12 is detected with possibly the associated lex flow 9 and / or leakage. Here, too, the data acquisition control unit 10 will collect the various data from the sensors 9a, Sb, Sc and the computing unit 11 will perform the necessary calculations using the physical model established in the previous phase 15. These steps of the operational phases 18 are preferably repeated sequentially at a predetermined time interval. As a result, leaks 12 can be detected and traced during the entire operational period of the gas network 1 and, for example, not only once or shortly after the start-up of the gas network 1. The aforementioned time interval can be selected and set depending on the gas network 1.
    In the previous first situation 16, as shown on the right half of figure 2, the operational phase 18 comprises the following juices: reading the first group of sensors Da, 45, Sa, Bd;
    1%
    9 = Based on the readings from the first
    | carried of sensures Sa, Jb, Sc, Sd the value of the
    9 pre-entered debist sensor Da of second green of
    9 sensors Yes, calculate or determine Sb, Sc, Sd with
    9 5 using the physical model or mathematical relationship:
    9 = the calculated GE determined value of this flow sensor Yes compare with the read value of this flow sensor Sa and cp based on a residual value analysis calculate whether there is a leak 12 in
    16 the gas network 1. This method has the advantage that only one flow sensor Sa is required, both in the training phase 15 and in the operational phase 18,
    A flow sensor Sa is generally more difficult to realize technically, more complex and more expensive than a pressure sensor Sb and / or ser pressure differential sensor gd.
    By minimizing the number of flow sensors to one, the system is cheaper. in order to determine a leak 12 in the gas network 1, in the last step the determined or calculated value of the flow rate q will be compared with the read-out value of the debillet sensor Ga, taking into account any information coming from the condition sensors Sc.
    When the difference between the loops exceeds a certain threshold value, this indicates a leak 12 in the gas network 1.
    | BE2019 / 5838 20 {This threshold value can be set or selected in advance:. 9 When a leak 12 is detected, an alarm: 3 will be generated. In this case, this is done with the help of {the monitor 13 on which the alarm is displayed, 9 The user of the gas network 1 will notice this alarm and # be able to take the appropriate steps, in the aforementioned second situation 17 as shown in the left half of figure 2, the operational phase 18 comprises the following steps: - reading out the first group of sensors Ja, 35 µb, Sc, Ba; - on the basis of the readings of the first group of sensors Da, Sb, Sc, Sd, calculate or determine the value of the aforementioned flow sensors 9a of the second group of zensors Da, Sb, 90, 3d using the physical model, or mathematical relationship; = compare the calculated or determined value of these flow sensors 9a with the read-out value of the flow sensors 9a and determine on the basis of a residual value analysis whether there is a leak 12 in the gas network i.
    If one of the differences, or its derivative such as a weighted sum, exceeds a certain threshold value, this will indicate a leak 12 in the gas network 1 and will indicate
    | Li BE2019 / 5838 21 | similar way as in the first situation an alarm: can be generated.
    9 In this case too, one or more threshold values can be set or selected at 9 3 in advance.
    9 Starting from several flow sensors Sa, the method 9 in the second situation 17 has the advantage that the leaks 12 are easier to locate.
    16 As already mentioned, these steps of the operation phase will be repeated sequentially, cyclically. In a preferred variant of the invention, at certain times, the operational phase 18 will be temporarily interrupted or stopped, after which the training phase 15 is resumed to re-establish the physical model or mathematical relationship between the measurements of different sensors, before the operational phase. phase 18 is restarted. "At certain times" should be interpreted here as moments which are pre-set, for example not once a week, per month or per year or as moments which can be chosen by the user as it suits the user best. This will make it physically fashion! be updated to take into account any time-varying behavior of the system,
    | BE2019 / 5838; 22 For example, leaks 12 in: the network 4 which are due to replacement of the relevant parts or seals which are being closed, additional 9 3 blockages in the network 4 or changes in the # topology of the network 4d. Although in the example of figure 1 it concerns a gas network 1 under pressure, it can also be a gas network 1 under vacutm.
    The source side Z then contains a number of sources of vacuum, i.e. vacuum pumps or the like.
    In this case, the consumers 7 have been replaced by applications that require vacuum. Otherwise the method is the same, whereby it must of course be taken into account that leaks now introduce ambient air into the gas network 1. Thus, preferably other suitable threshold values will be used. The present invention is in no way limited to the embodiments described by way of example and shown in the figures, but such a method and gas network according to the invention can be realized in various variants without departing from the scope of the invention. ,
    权利要求:
    Claims (1)
    [1]
    | 23 BE2019 / 5838 Conclusions. : Li.
    Leak detection and quantification method {5 {12}; in a gas network {1} under pressure or vacuum, the 9 gas network {1} comprising: 9 - one or more sources (6) of compressed gas or of | vacuum;
    - one or more consumers (7), consumer zones of
    29 compressed gas or vacuum applications;
    - pipes (5) or network {4} of pipes (53 to transport the gas or vacuum from the hums {6} to the consumers (7), consumer zones or applications;
    - several sensors (Sa, Sb, Sd) which determine one or more physical parameters of the gas 02 different times and locations in the gas network {1};
    characterized in that the gas network {1} is further optionally provided with one or more sensors (Qc) which can register the position or state of one or more sources (6), consumers (7), consumer zones or applications and that the method follows the following stages includes:
    - a possible start-up phase (14) in which the aforementioned sensors {9a, Jb, Sc, Sd} are calibrated before use;
    - a training or estimation phase {15} in which a physical model or mathematical relationship is determined between the measurements of a first group of sensors (Sa, 9b, 4c, 9d) and a second group of
    | 5 à BE2019 / 5838 9 sensors (da, Sb, Sc, 9d) based on physical 9 laws using estimation algorithms; # = an operational phase ([18} in which the established 9 physical model or mathematical relationship between the 9 5 measurements of the series of sensors (Ya, Sb, | Sc, Sd} and the second group of sensors {9a, 9b, Sc, Bd} is used to predict leaks (12) in the gas network {1}, the operational phase {18} comprising the following steps: = reading the first group of sensors (Sa, Sh, 236, Odi; = on the basis of the readings of the first group of sensors (Sa, Sb, So, Sd), calculate or determine the value of 35 sen second Jroen sensors (Da, gb, dc, Ad) using the physical model or mathematically relationship; - compare the calculated or determined values of the second group of sensors (Za, Sb, Sc, 3d) with the read-out values of the second group of sensors (Da, 3b, ge, Sd) and determine the difference between them; - on the basis of a residual value analysis determining whether there is a leak (12) in the gas network {li}; - generating an alarm and / or generating a leak flow rate and / or the generation of the associated leakage if a lex {12} is detected,
    2. " Method according to claim 1, characterized in that the first group of sensors (Za, 9b, Do, 90) are different
    N BE2019 / 5838 25 pressure and / or differential pressure sensors (2D, Sc} includes = at 9 different locations in the gas network (1), one or more bleed sensors (93) and possibly several sensors 9 {8c} that of the sources (6), consumers {7}, 9 can determine 5 consumer zones or applications, and the | second group of sensors {Sa, 9b, Sc, 9d} comprises a flow sensor # {9a}, where the operational phase (18 ) comprises the following steps: - reading out the first group of sensors (Sa, Go, 9c, Sd); - based on the readings of the first group of var sensors (Da, Sb, 2c, Sd}, the value Calculate or determine the flow sensor (Ba) of the I second group of sensors (9a, Jb, Zo, Sd) using the physical model or mathematical relationship; - the calculated or determined value of the flow sensor {Sa} of the second group of sensors (9a, Sb, Sc, Sd} with the read-out value of the flow sensor {Sa} of the second group of sensors (Sa, 9b, Sc, Sd} and based on the The difference between the two, or its derivative, determine whether there is a Leak (12) in the gas network {1}.
    Method according to one of the preceding claims, characterized in that the sensors (Sa, 320, Jo, Sd) are calibrated by means of an in-situ self-calibration.
    Method according to any one of the preceding claims, characterized in that said sensors (Sa, 3b, Dd) include one or more of the following physical parameters of the gas.
    | BE2019 / 5838 26 | can measure: pressure, differential pressure, temperature, gas velocity, | flow rate, humidity. Method according to any of the preceding claims, characterized in that at certain moments, the operational phase (18) is temporarily interrupted or stopped, after which the training phase (15) is resumed on the physical model or mathematical relationship. between the measurements 9 from different sensors (Da, Sb Sc, 3d} can be determined again 9 10, before the operational phase (16) is restarted,
    Method according to any one of the preceding claims, characterized in that the steps of the operational phase (18) are repeated sequentially at a specified time interval. 7, - Gas network under pressure or under vacuum, which gas network {1} is at least provided with : - one or more sources (6) of pressurized cas or of vacutim; - one or more consumers (7), compressed gas consumer zones or vacuum applications; - pipes 45} or network (4) of pipes {5} to gas or vacuum from the sources {6} to the consumers (7), consumer zones or applications; - several sensors {Sa, Sb, Sd} which determine one or more physical parameters of the compressed cas at different times and locations in the gas network {1};
    | BE2019 / 5838 27 | characterized in that the gas network (1) is further provided | is of: - possibly one or more sensors (9c) which can register the position or state of one or more sources (6) # 5 consumers (7), consumer zones or Applications 9, - a = data acquisition control unit {10} for collecting data from the sensors 9 (9a, 9b, Jo, 90}; - a computing unit {11} for performing the method according to any one of the preceding claims E. Gas network according to claim 9 7, characterized in that at least some of the sensors (Sa, 9b, Sc, Sd) are integrated in one module together with a source {6}, consumer (7), consumer zone or application,
    Gas network according to claim 7 or B, characterized in that the gas network {1} is further provided with a monitor (13) for displaying or signaling leaks {12}, leak rates, leakage costs and any location.
    Gas network according to any one of the preceding claims 7 to 9, characterized in that the computing unit (11) is a cloud-based accounting unit {11}, which is connected to the gas network (1}, whether or not wirelessly.
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    JP2022517504A|2022-03-09|Gas networks and methods for detecting failures in gas networks under pressure or vacuum
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    JP2002267562A|2002-09-18|Gas leakage determining device and gas leakage determining method
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    同族专利:
    公开号 | 公开日
    US20220057048A1|2022-02-24|
    BE1026852A1|2020-07-03|
    JP2022516479A|2022-02-28|
    CN113227642A|2021-08-06|
    KR20210107748A|2021-09-01|
    EP3903018A1|2021-11-03|
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    法律状态:
    2020-10-23| FG| Patent granted|Effective date: 20200928 |
    优先权:
    申请号 | 申请日 | 专利标题
    US201862785254P| true| 2018-12-27|2018-12-27|PCT/IB2019/060290| WO2020136475A1|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|2018-12-27|2019-11-28|Method for detecting leaks in a gas network under pressure or under vacuum and gas network|
    JP2021537856A| JP2022516479A|2018-12-27|2019-11-28|Methods for detecting leaks in gas networks under pressure or vacuum, and gas networks|
    CN201980085833.3A| CN113227642A|2018-12-27|2019-11-28|Method for detecting a leak in a gas network under pressure or vacuum, and gas network|
    EP19832194.5A| EP3903018A1|2018-12-27|2019-11-28|Method for detecting leaks in a gas network under pressure or under vacuum and gas network|
    KR1020217022771A| KR20210107748A|2018-12-27|2019-11-28|Methods and gas networks for detecting leaks in gas networks that are under pressure or under vacuum|
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