Patent Grant
12135033
This application is a United States National Phase Application of International Application PCT/EP2019/061210, filed May 2, 2019, and claims the benefit of priority under 35 U.S.C. § 119 of European Application Ser. No. 18/171,929.5, filed May 11, 2018, the entire contents of which are incorporated herein by reference.
The present disclosure relates generally to monitoring modules and methods for identifying an operating scenario in a wastewater pumping station. In particular, such an operating scenario may be a faulty operation, such as pump fault or clogging, pipe clogging or leakage.
Sewage or wastewater collection systems for wastewater treatment plants typically comprise one or more wastewater pits, wells or sumps for temporarily collecting and buffering wastewater. Typically, wastewater flows into such pits passively under gravity flow and/or actively driven through a force main. One, two or more pumps are usually installed in or at each pit to pump wastewater out of the pit. If the inflow of wastewater is larger than the outflow for a certain period of time, the wastewater pit or sump will eventually overflow. Such overflows should be prevented as much as possible in order to avoid environmental impact. Therefore, any pump fault or clogging, pipe clogging, leakage or other type of faulty operating scenario should be identified as quickly as possible for maintenance staff to take according action, like cleaning, repairing or replacing as quickly as possible.
U.S. Pat. No. 8,594,851 B1 describes a wastewater treatment system and a method for reducing energy used in operation of a wastewater treatment facility.
It is a challenge for known wastewater pumping station management systems to reliably identify the cause for a certain problem in order to give an operator or maintenance staff a clear indication for the appropriate action, e.g. where or what needs to be cleaned, repaired or replaced.
In contrast to known systems, embodiments of the present disclosure provide a monitoring module and method for identifying an operating scenario with more specific and more reliable information.
In accordance with a first aspect of the present disclosure, a monitoring module for identifying an operating scenario in a wastewater pumping station is provided, with at least one pump arranged for pumping wastewater out of a wastewater pit into a pipe, wherein the monitoring module is configured to process at least one load-dependent pump variable indicative of how the at least one pump operates and at least one model-based pipe parameter indicative of how the wastewater flows through the pipe and/or the at least one pump, and wherein the monitoring module is configured to identify an operating scenario in the wastewater pumping station by selecting an operating scenario from a group of predefined operating scenarios dependent on at least one first criterion that is based on the at least one load-dependent pump variable and at least one second criterion that is based on the at least one model-based pipe parameter.
The group of predefined operating scenarios may include faulty and/or non-faulty operating scenarios. For example, faulty operating scenarios may be a clogging of the pipe downstream of the pump(s), a clogging in one or more of the at least one pump(s), a leak in a non-return valve for one or more of the at least one pump(s), and/or a leak in a connection between one or more of the at least one pump(s) and the pipe. The combination of at least two criteria, the first one of which is based on the at least one load-dependent pump variable and the second one of which is based on the at least one model-based pipe parameter, may be interpreted by the monitoring module as a “scenario signature”.
Optionally, the group of operating scenarios may be predefined in a selection matrix unambiguously associating each operating scenario with a unique combination of the at least one first criterion and the at least one second criterion. For instance, in case of a wastewater pumping station with only one pump, three different operating scenarios may be identified based on the combination of the two criteria as follows:
In case of a wastewater pumping station with two or more pumps, a first criterion for each pump may be used to more finely distinguish between operating scenarios in which a specific pump is clogged or pump connection is leaking, for example, three different operating scenarios may be identified based on the combination of the two criteria as follows:
In case of a wastewater pumping station with two or more pumps, only one pump is typically running at a time as long as one pump suffices for pumping enough wastewater out of the wastewater pit into the pipe. In order to evenly distribute the operating hours and wear, the pumps may be running in turns. In contrast to operating all or several pumps simultaneously, the overall operating hours, and thus wear, and the overall energy consumption may be reduced by this. Only in case more pump power is needed during times of high inflow, e.g. at heavy rain incidents, all or several pumps may run simultaneously in order to prevent an overflow. For the alternating normal operation of only one pump at a time, non-return valves may be installed for each pump to prevent the active pump from pumping wastewater through the passive pump(s) back into the wastewater pit. A leak in such a non-return valve of a passive pump may have a different scenario signature than a leak in the pump connection of the active pump if, for example, a further second criterion is used based on another model-based pipe parameter as follows:
Optionally, the at least one load-dependent pump variable may comprise a specific energy consumption Esp of the at least one pump. There are different ways to determine the specific energy consumption Esp of the at least one pump. For example, the specific energy consumption Esp may be defined by Esp=E/V, wherein E is an average energy consumed by the at least one pump during a defined time period and V is the volume of wastewater pumped during said defined time period by the at least one pump. The average energy consumption may be determined by integrating or summing the current power consumption P(t) over the time t between an end of a delay period after pump start and pump stop:
Analogously, the pumped wastewater volume may be determined by integrating or summing the current flow q(t) over the same time period:
The delay period may be useful to skip an initial period of high fluctuations after start-up of the pump(s). The monitoring module may be signal connected wirelessly or via a cable with the pump(s) to receive a signal indicative of the power or energy consumption. Furthermore, the monitoring module may be signal connected wirelessly or via a cable with a flow sensor to receive a signal indicative of the flow through the pipe.
A current specific energy consumption Esp(t) of the at least one pump may be defined by Esp(t)=P(t)/q(t), wherein P(t) is a current power consumption of the at least one pump and q(t) is a current flow of wastewater pumped by the at least one pump. The current specific energy consumption Esp(t) may be monitored as the at least one load-dependent pump variable as an alternative to the averaged specific energy consumption Esp as defined above. If the current specific energy consumption Esp(t) fluctuates too much to the at least one first criterion on it, a low-pass filtering may be applied as explained later herein. Even in case of a specific energy consumption Esp that is averaged for each pump cycle, it can fluctuate between the pump cycles so much that a low-pass filtering may be advantageous.
As a flow meter may be quite expensive and may require regular maintenance, it may be preferable to estimate the outflow q of wastewater through the pump(s) based on a measured pressure differential Δp and power consumption P. For instance, the outflow q of wastewater through the pump(s) may be estimated by
wherein s is the number of running pumps, ω is the pump speed (e.g. constant), Δp is the measured pressure differential, P is the power consumption of the running pump(s), and λ0, λ1, λ2 and λ3 are pump parameters that may be known from the pump manufacturer or determined by calibration. Accordingly, the monitoring module may be signal connected wirelessly or via a cable with a pressure sensor, which is located at or downstream of the pump(s), to receive a signal indicative of the pressure differential Δp. So, optionally, the monitoring module may be configured to receive a measured pressure pm at or downstream of an outlet of the at least pump. Alternatively or in addition, the monitoring module may be configured to receive a measured flow qm through the pipe or to process an estimated wastewater flow qe through the pump.
It is important to note that the “scenario signature” may depend on whether a flow q through the pipe is measured or a flow q through the pump(s) is estimated. For instance, a leak in a pump connection or in a non-return valve may result in a rising specific energy consumption Esp when the flow q through the pipe is measured. However, if a flow q through the pump(s) is estimated, the specific energy consumption Esp may turn out to be falling. Therefore, the monitoring module may be configured to apply one of at least two predefined selection matrices dependent on whether a flow q through the pipe is measured or a flow q through the pump(s) is estimated. Each of the at least two selection matrices unambiguously associate each operating scenario with a unique combination of the at least one first criterion and the at least one second criterion.
Optionally, one of the at least one model-based pipe parameter may be a pipe clogging parameter A in a pipe model polynomial p=Aq2+B, wherein p is a pressure at or downstream of an outlet of the at least pump, q is a wastewater flow through the pipe and/or the at least one pump, and B is a zero-flow offset parameter. The zero-flow offset parameter B may be a second one of at least two model-based pipe parameters, wherein the pipe clogging parameter A may be a first one of the at least two model-based pipe parameters.
Alternatively or in addition, one of the at least one model-based pipe parameter may be a residual r=pm−pe=pm−Aq2−B between a measured pressure pm at or downstream of an outlet of the at least pump and an estimated pressure pe according to a pipe model polynomial pe=Aq2+B, wherein A is a pipe clogging parameter of the pipe, q is a wastewater flow through the pipe and/or the at least one pump and B is a zero-flow offset parameter. The residual r may be considered as a pipe model testing parameter. If the residual r deviates from zero by more than a certain threshold, e.g. 100 Pa, one of the at least one second criterion may be fulfilled, otherwise not. Such a fulfilled second criterion may mean a “model mismatch”, indicating a pipe clogging, whereas a non-fulfilled second criterion may mean a “model match”, indicating a pump problem rather than a pipe clogging. As described above, a leak in a pump connection or in a non-return valve may show a model mismatch when the flow through the pump(s) is estimated, but a model match if a flow q through the pipe is measured.
Optionally, the monitoring module may be configured to apply a low-pass filtering to the at least one load-dependent pump variable and/or the at least one model-based pipe parameter before selecting an operating scenario dependent on the at least one first criterion and/or second criterion, respectively. This may be very helpful to cope with fluctuations of the load-dependent pump variable, e.g. the specific energy consumption Esp, and/or the pipe parameter, e.g. the pipe clogging parameter A or the residual r.
For instance, the monitoring module may be configured to sequentially process a multitude of samples of the at least one load-dependent pump variable, wherein the at least one first criterion is based on whether a cumulative sum of deviations between the actual sample and an average of past samples of the at least one load-dependent pump variable exceeds a predetermined maximum or falls below a predetermined minimum. Such a low-pass filtering may follow a so called iterative CUSUM (cumulative sum) algorithm such as:
Sup(i+1)=max[0,Sup(i)+Gup(x−nσ)]
Sdown(i+1)=max[0,Sdown(i)−Gdown(x−nσ)],
wherein Sup and Sdown are decision variables summing up deviations using a test variable x. The test variable x may, for instance, be defined as the deviation of the specific energy consumption in the i-th pump cycle from an average specific energy consumption Esp, i.e. x=Esp−Ēsp. The average specific energy consumption Esp may be a predefined value or a value statistically determined over several previous pump cycles during normal faultless operation. For instance, it may be useful to identify non-faulty operating scenarios to statistically determine an average specific energy consumption Ēsp. Dependent on the variance of x, the decision variables may be tuned by gain parameters Gup and Gdown. Fluctuations below a certain number n, e.g. n=1, 2 or 3, of standard deviations a may be suppressed for the decision variables. Similar to the average specific energy consumption Ēsp, the standard deviation a may be statistically determined over several previous pump cycles during normal faultless operation.
A first one of the at least one first criterion based on the specific energy consumption Esp may be whether the decision variable Sup is above or below an alarm threshold indicating that the specific energy consumption Esp is rising. A second one of the at least one first criterion based on the specific energy consumption Esp may be whether the decision variable Sdown is above or below an alarm threshold indicating that the specific energy consumption Esp is falling. An estimation of the flow through the pump based on pressure and power consumption of the pump(s) has, compared to a flow measured by a flow meter, not only the advantage that a flow meter can be spared with, but also that the scenario signature is different in cases of a leakage of a pump connection or a non-return valve. In those cases, the specific energy consumption Esp would appear as falling if the flow through the pump is estimated. If the flow through pipe is measured, the specific energy consumption Esp would be rising in case of pipe clogging, pump fault/clogging and leakage of a pump connection or a non-return valve. In case of a wastewater pumping station with m≥2 pumps, there may be two first criteria per pump, i. e. 2 times m first criteria to identify the operating scenario.
A similar low-pass filtering may be applied to the at least one model-based pipe parameter before selecting an operating scenario dependent on the at least one second criterion. So, optionally, the monitoring module may be configured to sequentially process a multitude of samples of the at least one model-based pipe parameter, wherein the at least one second criterion is based on whether a cumulative sum of deviations between the actual sample and an average of past samples of the at least one model-based pipe parameter exceeds a predetermined maximum or falls below a predetermined minimum.
For instance, the evolvement of the pipe clogging parameter A may be monitored by decision variables Sup and Sdown with a test variable x being defined as the deviation of the pipe clogging parameter A in the i-th pump cycle from an average pipe clogging parameter A, i.e. x=A−Ā. Kalman filters may be applied to calculate the mean and variance of the pipe clogging parameter. As an alternative or in addition, the residual r for testing whether the pipe model still matches with reality may be used as test variable x, i.e. x=r. In this case, a combined decision variable S=Sup+Sdown may be used to indicate a model mismatch, because there is no need to distinguish between upward and downward fluctuations.
Optionally, the monitoring module may be configured to process a first of at least two model-based pipe parameters and a zero-flow offset parameter as a second of the at least two model-based pipe parameters, wherein the negative-flow parameter is indicative of how the wastewater flows through the pipe and/or the at least one pump when the at least one pump is stopped, wherein the monitoring module may be configured to identify an operating scenario in the wastewater pumping station by selecting an operating scenario from a group of predefined operating scenarios further dependent on at least one third criterion that is based on the negative-flow parameter. Optionally, the negative-flow parameter may show as a decay of the zero-flow offset parameter B in a pipe model polynomial p=Aq2+B, wherein p is a pressure at or downstream of an outlet of the at least one pump, q is a wastewater flow through the pipe and/or the at least one pump, and A is a pipe clogging parameter.
Alternatively or in addition, the negative-flow parameter may be a leakage flow through one of the non-return valves or a pump connection, for instance, which will gradually lead to a pressure decay when the at least one pump is stopped. This may be formulated by D{dot over (p)}=−q, wherein D is the cross-sectional area of the pipe,
is the change in pressure at the outlet of a pump over time, and q is the leakage flow. Following Toricelli's law, the leakage flow may be calculated by q=K√{square root over (p−ρgh−Δp0)}, wherein K is a constant, ρ is the density of the wastewater, p is the measured pressure at the pump outlet, h is the wastewater's height above a hydrostatic pressure sensor for level measurement at the bottom of the pit, and Δp0 is a hydrostatic pressure of a difference in geodetic elevation between the pump outlet and the bottom of the pit. This leads to a differential equation as follows: A{dot over (p)}=K√{square root over (p−ρgh−Δp0)}, which may be approximated by discrete test samples i as follows:
so that a decision variable
may be tested as a third criterion for hypotheses H0 and H1, wherein H0: γ=0 and H1: γ≠0. If hypothesis H0 cannot be rejected, there is probably a leak in the non-return-valve. If the decision variable γ is above a threshold value, for instance 0.1, the hypothesis H0 may be rejected. The threshold value for this third criterion may be adjusted to an acceptable compromise between the sensitivity for a leakage and a false alarm rate.
In accordance with a second aspect of the present disclosure and analogous to the monitoring module described above, a method is provided for identifying an operating scenario in a wastewater pumping station with at least one pump arranged for pumping wastewater out of a wastewater pit into a pipe, wherein the method comprises:
Optionally, the group of operating scenarios may be predefined in a selection matrix unambiguously associating each operating scenario with a unique combination of the at least one first criterion and the at least one second criterion.
Optionally, the at least one load-dependent pump variable may be a specific energy consumption Esp of the at least one pump.
Optionally, the specific energy consumption Esp of the at least one pump may be defined by Esp=E/V, wherein E is an average energy consumed during a defined time period and V is the volume of wastewater pumped during said defined time period by the at least one pump.
Optionally, the specific energy consumption Esp of the at least one pump may be defined by Esp=P/q, wherein P is a power consumption and q is a flow of wastewater pumped by the at least one pump.
Optionally, the at least one model-based pipe parameter may be a pipe clogging parameter A in a pipe model polynomial p=Aq2+B, wherein p is a pressure at or downstream of an outlet of the at least pump, q is the wastewater flow through the pipe and/or the at least one pump, and B is a zero-flow offset parameter.
Optionally, the at least one model-based pipe parameter may be a residual r=pm−pe=pm−Aq2−B between a measured pressure pm at or downstream of an outlet of the at least pump and an estimated pressure pe according to a pipe model polynomial pe=Aq2+B, wherein A is a pipe clogging parameter of the pipe, q is the wastewater flow through the pipe and/or the at least one pump and B is a zero-flow offset parameter.
Optionally, the method may further comprise a step of receiving a measured pressure pm at or downstream of an outlet of the at least pump.
Optionally, the method may further comprise a step of receiving a measured flow qm or processing an estimated wastewater flow qe through the at least one pump.
Optionally, the method may further comprise a step of applying a low-pass filtering to the at least one load-dependent pump variable and/or the at least one model-based pipe parameter before selecting an operating scenario dependent on at least one first criterion and/or second criterion, respectively.
Optionally, the method may further comprise a step of sequentially processing a multitude of samples of the at least one load-dependent pump variable, wherein the at least one first criterion is based on whether a cumulative sum of deviations between the actual sample and an average of past samples of the at least one load-dependent pump variable exceeds a predetermined maximum or falls below a predetermined minimum.
Optionally, the method may further comprise a step of sequentially processing a multitude of samples of the at least one model-based pipe parameter, wherein the at least one second criterion is based on whether a cumulative sum of deviations between the actual sample and an average of past samples of the at least one model-based pipe parameter exceeds a predetermined maximum or falls below a predetermined minimum.
Optionally, the method may further comprise the steps of
The monitoring module described above and/or some or all of the steps of the method described above may be implemented in form of compiled or uncompiled software code that is stored on a computer readable medium with instructions for executing the method. Alternatively or in addition, some or all method steps may be executed by software in a cloud-based system, in particular the monitoring module may be partly or in full implemented on a computer and/or in a cloud-based system.
Embodiments of the present disclosure will now be described by way of example with reference to the following figures. The various features of novelty which characterize the invention are pointed out with particularity in the claims annexed to and forming a part of this disclosure. For a better understanding of the invention, its operating advantages and specific objects attained by its uses, reference is made to the accompanying drawings and descriptive matter in which preferred embodiments of the invention are illustrated.
In the drawings:
The wastewater pumping station further comprises an outflow port 7 near the bottom of the wastewater pit 1, wherein the outflow port 7 is in fluid connection with two pumps 9a, 9b for pumping wastewater out of the wastewater pit into a pipe 11. The pumps 9a, 9b may be arranged, as shown in
The monitoring module 13 is configured to identify an operating scenario in the wastewater pumping station by selecting an operating scenario from a group of predefined operating scenarios dependent on at least one first criterion that is based on at least one load-dependent pump variable and at least one second criterion that is based on at least one model-based pipe parameter. In order to do this, as shown in
The at least one load-dependent pump variable may be a specific energy consumption Esp of each of the two pumps 9a, 9b. There are different ways to determine the specific energy consumption Esp for each pump. For example, the specific energy consumption Esp for one pump may be defined by Esp=E/V, wherein E is an average energy consumed by said pump during a defined time period and V is the volume of wastewater pumped during said defined time period by said pump. The average energy consumption may be determined by integrating or summing the current power consumption P(t) over the time t between an end of a delay period after pump start and pump stop:
Analogously, the pumped wastewater volume may be determined by integrating or summing the current flow q(t) over the same time period:
Alternatively or in addition, a current specific energy consumption Esp(t) of each one of the two pumps may be defined by Esp(t)=P(t)/q(t), wherein P(t) is a current power consumption of said pump and q(t) is a current flow of wastewater pumped by said pump. If the current specific energy consumption Esp(t) fluctuates too much to the at least one first criterion on it, a low-pass filtering may be applied as explained later herein. Even in case of a specific energy consumption Esp that is averaged for each pump cycle, it can fluctuate between the pump cycles so much that a low-pass filtering may be advantageous.
In order to process the specific energy consumption Esp for each pump as the load-dependent pump variables, the monitoring module 13 receives, firstly, a power signal indicative of a power consumption of each of the pumps 9a, 9b via the signal connection 15 and, secondly, a pressure signal from the pressure sensor 19 via the signal connection 17 and/or a flow signal from the flow meter 25 via the signal connection 23. As a flow meter may be quite expensive and may require regular maintenance, it may be preferable to estimate the flow q of wastewater through the pumps 9a,9b based on the pressure signal and the power signal. For instance, the outflow q of wastewater through the pumps 9a, 9b may be estimated by
wherein s is the number of running pumps, ω is the pump speed (e.g. constant), Δp is the measured pressure differential, P is the power consumption of the running pump(s), and λ0, λ1, λ2 and λ3 are pump parameters that may be known from the pump manufacturer or determined by calibration.
The fluctuations are better visible in the plots shown in
Sup(i+1)=max[0,Sup(i)+Gup(x−nσ)]
Sdown(i+1)=max[0,Sdown(i)−Gdown(x−nσ)],
wherein Sup and Sdown are decision variables summing up deviations using a test variable x. The test variable x may, for instance, be defined as the deviation of the specific energy consumption in the i-th pump cycle from an average specific energy consumption Ēsp, i.e. x=Esp−Ēsp. The average specific energy consumption Ēsp may be a predefined value or a value statistically determined over several previous pump cycles during normal faultless operation. For instance, it may be useful to identify non-faulty operating scenarios to statistically determine an average specific energy consumption Ēsp. Dependent on the variance of x, the decision variables may be tuned by gain parameters Gup and Gdown. Fluctuations below a certain number n, e.g. n=1, 2 or 3, of standard deviations a may be suppressed for the decision variables. Similar to the average specific energy consumption Ēsp, the standard deviation σ may be statistically determined over several previous pump cycles during normal faultless operation. The lower left plot of
However, in order to cope with fluctuations, similar low-pass filtering as described above for the specific energy consumption Esp may be applied to the model-based pipe parameters A, B before selecting an operating scenario dependent on the at least one second criterion. For instance, the evolvement of the pipe clogging parameter A may be monitored by decision variables Sup and Sdown with a test variable x being defined as the deviation of the pipe clogging parameter A in the i-th pump cycle from an average pipe clogging parameter Ā, i.e. x=A−Ā. Kalman filters may be applied to calculate the mean and variance of the pipe clogging parameter A.
Alternatively or in addition, as shown in
is the change in pressure at the outlet of a pump over time, and q is the leakage flow. Following Toricelli's law, the leakage flow may be calculated by q=K√{square root over (p−ρgh−Δp0)}, wherein K is a constant, ρ is the density of the wastewater, p is the measured pressure at an outlet of one of the pumps 9a, 10b, h is the wastewater's height above the level sensor 5, and Δp0 is a hydrostatic pressure of a difference in geodetic elevation between the pump outlet and the level sensor 5. This leads to a differential equation as follows: A{dot over (p)}=K√{square root over (p−ρgh−Δp0)}, which may be approximated by discrete test samples i as follows:
so that a decision variable
can be tested for hypotheses H0 and H1 as shown in the lower plot of
Each of the selection matrices in
Where, in the foregoing description, integers or elements are mentioned which have known, obvious or foreseeable equivalents, then such equivalents are herein incorporated as if individually set forth. Reference should be made to the claims for determining the true scope of the present disclosure, which should be construed so as to encompass any such equivalents. It will also be appreciated by the reader that integers or features of the disclosure that are described as optional, preferable, advantageous, convenient or the like are optional and do not limit the scope of the independent claims.
The above embodiments are to be understood as illustrative examples of the disclosure. It is to be understood that any feature described in relation to any one embodiment may be used alone, or in combination with other features described, and may also be used in combination with one or more features of any other of the embodiments, or any combination of any other of the embodiments. While at least one exemplary embodiment has been shown and described, it should be understood that other modifications, substitutions and alternatives are apparent to one of ordinary skill in the art and may be changed without departing from the scope of the subject matter described herein, and this application is intended to cover any adaptations or variations of the specific embodiments discussed herein.
In addition, “comprising” does not exclude other elements or steps, and “a” or “one” does not exclude a plural number. Furthermore, characteristics or steps which have been described with reference to one of the above exemplary embodiments may also be used in combination with other characteristics or steps of other exemplary embodiments described above. Method steps may be applied in any order or in parallel or may constitute a part or a more detailed version of another method step. It should be understood that there should be embodied within the scope of the patent warranted hereon all such modifications as reasonably and properly come within the scope of the contribution to the art. Such modifications, substitutions and alternatives can be made without departing from the spirit and scope of the disclosure, which should be determined from the appended claims and their legal equivalents.
While specific embodiments of the invention have been shown and described in detail to illustrate the application of the principles of the invention, it will be understood that the invention may be embodied otherwise without departing from such principles.
| Number | Date | Country | Kind |
|---|---|---|---|
| 18171929 | May 2018 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2019/061210 | 5/2/2019 | WO |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2019/215000 | 11/14/2019 | WO | A |
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| 20210215158 A1 | Jul 2021 | US |
