A MONITORING MODULE AND METHOD FOR IDENTIFYING AN OPERATING SCENARIO IN A WASTEWATER PUMPING STATION

Abstract

A monitoring module (13) identifies an operating scenario in a wastewater pumping station, with at least one pump (9a, 9b) arranged for pumping wastewater out of a wastewater pit (1) into a pipe (11). The monitoring module (13) is configured to process at least one load-dependent pump variable indicative of how the at least one pump (9a, 9b) operates and at least one model-based pipe parameter indicative of how the wastewater flows through the pipe (11) and/or the at least one pump (9a, 9b). 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.

Claims

1. A monitoring module for identifying an operating scenario in a wastewater pumping station, with at least one pump arranged for pumping wastewater out of a wastewater pit in-to 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.

2. A monitoring module of claim 1, wherein the group of operating scenarios is 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.

3. A monitoring module of claim 1, wherein the at least one load-dependent pump variable comprises a specific energy consumption E.sub.sp of the at least one pump.

4. A monitoring module of claim 3, wherein the specific energy consumption E.sub.sp of the at least one pump is defined by E.sub.sp=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.

5. A monitoring module of claim 3, wherein the specific energy consumption E.sub.sp of the at least one pump is defined by E.sub.sp=P/q, wherein P is a power consumption of the at least one pump and q is a flow of wastewater pumped by the at least one pump.

6. A monitoring module of claim 1, wherein one of the at least one model-based pipe parameter is a pipe clogging parameter A in a pipe model polynomial p=Aq.sup.2+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.

7. A monitoring module of claim 1, wherein one of the at least one model-based pipe parameter is a residual r=p.sub.m p.sub.e=p.sub.m Aq.sup.2B between a measured pressure p.sub.m at or downstream of an outlet of the at least pump and an estimated pressure p.sub.e according to a pipe model polynomial p.sub.e=Aq.sup.2+B, wherein A is a pipe clogging parameter, q is a wastewater flow through the pipe and/or the at least one pump and B is a zero-flow offset parameter.

8. A monitoring module of claim 1, wherein the monitoring module is configured to receive a measured pressure p.sub.m at or downstream of an outlet of the at least pump.

9. A monitoring module of claim 1, wherein the monitoring module is configured to receive a measured flow q.sub.m through the pipe or to process an estimated wastewater flow q.sub.e through the at least one pump.

10. A monitoring module of claim 1, wherein the monitoring module is 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 the at least one second criterion, respectively.

11. A monitoring module of claim 1, wherein the monitoring module is 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.

12. A monitoring module of claim 1, wherein the monitoring module is 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.

13. A monitoring module of claim 1, wherein the monitoring module is configured to process a first of at least two model-based pipe parameters and a negative-flow 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 is 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.

14. A method 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: processing 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 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.

15. The method of claim 14, wherein the group of operating scenarios is 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.

16. The method of claim 14, wherein the at least one load-dependent pump variable comprises a specific energy consumption E.sub.sp of the at least one pump.

17. The method of claim 16, wherein the specific energy consumption E.sub.sp of the at least one pump is defined by E.sub.sp=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.

18. The method of claim 16, wherein the specific energy consumption E.sub.sp of the at least one pump is defined by E.sub.sp=P/q, wherein P is a power consumption and q is a flow of wastewater pumped by the at least one pump.

19. The method of claim 14, wherein one of the at least one model-based pipe parameter is a pipe clogging parameter A in a pipe model polynomial p=Aq.sup.2+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.

20. The method of claim 14, wherein one of the at least one model-based pipe parameter is a residual r=p.sub.m p.sub.e=p.sub.m Aq.sup.2B between a measured pressure p.sub.m at or downstream of an outlet of the at least pump and an estimated pressure p.sub.e according to a pipe model polynomial p.sub.e=Aq.sup.2+B, wherein A is a pipe clogging parameter, q is the wastewater flow through the pipe and/or the at least one pump and B is a zero-flow offset parameter.

21. The method of claim 14, further comprising receiving a measured pressure p.sub.m at or downstream of an outlet of the at least pump.

22. The method of claim 14, further comprising receiving a measured flow q.sub.m through the pipe or processing an estimated wastewater flow q.sub.e through the at least one pump.

23. The method of claim 14, further comprising 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 the at least one first criterion and/or the at least one second criterion, respectively.

24. The method of claim 14, further comprising 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.

25. The method of claim, further comprising 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.

26. The method of claim 14, further comprising: processing a first of at least two model-based pipe parameters, processing a negative-flow 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, and 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.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In the drawings:

[0046] FIG. 1 is a schematic cross-sectional view on a wastewater pit of a wastewater pumping station with two pumps, wherein the wastewater pumping station is connected with an example of the monitoring module according to the present disclosure;

[0047] FIG. 2 is a schematic view on a chain of wastewater pumping stations, wherein each wastewater pumping station is connected with an example of the monitoring module according to the present disclosure;

[0048] FIG. 3 is a schematic diagram of a specific energy consumption E.sub.sp over time for each of two pumps of a wastewater pumping station being connected with an example of the monitoring module according to the present disclosure;

[0049] FIG. 4 is a view showing schematic plots of a specific energy consumption E.sub.sp and an associated decision variable S.sub.up over time for each of two pumps of a wastewater pumping station being connected with an example of the monitoring module according to the present disclosure;

[0050] FIG. 5 is a schematic pq-diagram for each of two pumps of a wastewater pumping station being connected with an example of the monitoring module according to the present disclosure;

[0051] FIG. 6 is a view showing schematic diagrams of a residual r and an associated decision variable S over time for a pipe of a wastewater pumping station being connected with an example of the monitoring module according to the present disclosure;

[0052] FIG. 7 is a view showing schematic diagrams of a pressure and an associated decision variable over time for each of two pumps of a wastewater pumping station being connected with an example of the monitoring module according to the present disclosure;

[0053] FIG. 8 is a view showing a first example of a selection matrix applied by an example of the monitoring module according to the present disclosure; and

[0054] FIG. 9 is a view showing a second example of a selection matrix applied by an example of the monitoring module according to the present disclosure;

DETAILED DESCRIPTION

[0055] FIG. 1 shows a wastewater pit 1 of a wastewater pumping station. The wastewater pit 1 has a certain height H and can be filled through an inflow port 3. The current level of wastewater is denoted as h and may be continuously or regularly monitored by means of a level sensor 5, e.g. a hydrostatic pressure sensor at the bottom of the wastewater pit 1 and/or an ultrasonic distance meter for determining the surface position of the wastewater in the pit 1 by detecting ultrasonic waves being reflected by the wastewater surface. Alternatively or in addition, the wastewater pit 1 may be equipped with one or more photoelectric sensors or other kind of sensors at one or more pre-defined levels for simply indicating whether the wastewater has reached the respective pre-defined level or not.

[0056] 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 FIG. 1, outside of the wastewater pit 1 or submerged at the bottom of the wastewater pit 1 in form of submersible pumps. A non-return valve 10a, 10b at or after each pump 9a, 9b prevents a backflow when one of the pumps 9a, 9b is idle and the other one of the pumps 9b, 9a is running. A monitoring module 13 is configured to identify operating scenarios and to output an according information and/or alarm on an output device 27. The output device 27 may be a display and/or a loudspeaker on a mobile or stationary device for an operator to take notice of a visual and/or acoustic signal as the information and/or alarm.

[0057] FIG. 2 shows a chain of wastewater pumping stations being connected by respective pipes 11 through which a lower level wastewater pumping station is able to pump wastewater to the next higher level wastewater pumping station against gravity. Each of the wastewater pumping stations may be monitored by a monitoring module 13 in order to identify operating scenarios.

[0058] 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 FIG. 1, the monitoring module 13 is signal connected with the with power electronics of the pumps 9a, 9b and/or power sensors in the pumps 9a, 9b of the wastewater pumping station(s) to receive a power signal indicative of a power consumption of each of the pumps 9a, 9b via wired or wireless signal connection 15. Depending on which sensors are available in the wastewater pumping station, further signal connections between the monitoring module 13 and available sensors are shown in FIG. 1 as options that may be implemented alone or in combination with one or two of other options. The first option is a wired or wireless signal connection 17 with a pressure sensor 19 at or downstream of the pump 9a. The second option is a wired or wireless signal connection 21 with the level sensor 5. The third option is a wired or wireless signal connection 23 with a flow meter 25 at or downstream of the pump 9a. The signal connections 15, 17, 21, 23 may be separate communication channels or combined in a common communication channel or bus. The monitoring module 13 is configured to receive a respective pressure, power and/or flow signal via the signal connections 15, 17, 23 and to process accordingly at least one load-dependent pump variable indicative of how the pumps 9a, 9b operate and at least one model-based pipe parameter indicative of how the wastewater flows through the pipe 11 and/or the pumps 9a, 9b.

[0059] The at least one load-dependent pump variable may be a specific energy consumption E.sub.sp of each of the two pumps 9a, 9b. There are different ways to determine the specific energy consumption E.sub.sp for each pump. For example, the specific energy consumption E.sub.sp for one pump may be defined by E.sub.sp=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: E=.sub.t.sub.start.sub.+t.sub.delay.sup.t.sup.stopP(t)dt. Analogously, the pumped wastewater volume may be determined by integrating or summing the current flow q(t) over the same time period: V=.sub.t.sub.start.sub.+t.sub.delay.sup.t.sup.stopq(t)dt. Alternatively or in addition, a current specific energy consumption E.sub.sp(t) of each one of the two pumps may be defined by E.sub.sp(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 E.sub.sp(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 E.sub.sp that is averaged for each pump cycle, it can fluctuate between the pump cycles so much that a low-pass filtering may be advantageous.

[0060] In order to process the specific energy consumption E.sub.sp 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

[00005]$qs\frac{{}_0}{}+s\frac{{}_1}{}p+s\frac{{}_2}{{}^2}P+s{}_3,$

wherein s is the number of running pumps, is the pump speed (e. g. constant), p is the measured pressure differential, P is the power consumption of the running pump(s), and .sub.0, .sub.1, .sub.2 and .sub.3 are pump parameters that may be known from the pump manufacturer or determined by calibration.

[0061] FIG. 3 shows samples of the specific energy consumption E.sub.sp for each pump cycle over three days of operation. Each data point represents the specific energy consumption E.sub.sp averaged over one pump cycle. Typically, during normal faultless operation, only one of the pumps 9a, 9b is active at a time during a pump cycle and they are used in turns, i.e. in alternating order, to evenly distribute operating hours and corresponding wear among the pumps 9a, 9b. FIG. 3 shows that the first pump 9a has, on average over these three days, a higher specific energy consumption E.sub.sp than the second pump 9b. As can be seen, the specific energy consumptions E.sub.sp fluctuate for both pumps 9a, 9b around a respective average specific energy consumption E.sub.sp indicated by the horizontal lines.

[0062] The fluctuations are better visible in the plots shown in FIG. 4, where the upper left plot shows the specific energy consumption E.sub.sp of the first pump 9a and the upper right plot shows the specific energy consumption E.sub.sp of the first pump 9a. In order to improve the identification of operating scenarios and reduce the rate of misidentifications, the monitoring module 13 is configured to apply a low-pass filtering to the at least one load-dependent pump variable. This is very helpful to cope with fluctuations of the specific energy consumption E.sub.sp. The monitoring module is thus, for each pump 9a, 9b, configured to sequentially process a multitude of samples of the specific energy consumption E.sub.sp and to determine a cumulative sum of deviations between the actual sample and an average of past samples of the specific energy consumption E.sub.sp. Such a low-pass filtering may follow a so-called iterative CUSUM (cumulative sum) algorithm such as:

S.sub.up(i+1)=max[0,S.sub.up(i)+G.sub.up(xn)]

S.sub.down(i+1)=max[0,S.sub.down(i)G.sub.down(xn)],

wherein S.sub.up and S.sub.down 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 .sub.sp, i.e. x=E.sub.sp.sub.sp. The average specific energy consumption .sub.sp 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 .sub.sp. Dependent on the variance of x, the decision variables may be tuned by gain parameters G.sub.up and G.sub.down. 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 .sub.sp, the standard deviation a may be statistically determined over several previous pump cycles during normal faultless operation. The lower left plot of FIG. 4 shows the decision variable S.sub.up of the first pump 9a and the lower right plot of FIG. 4 shows the decision variable S.sub.up of the second pump 9b. As can be seen, the decision variable S.sub.up is more robust against fluctuations. A first one of the at least one first criterion based on the specific energy consumption E.sub.sp may be whether the decision variable S.sub.up is above or below an alarm threshold, e.g. 0.8, indicating that the specific energy consumption E.sub.sp is rising. A second one of the at least one first criterion based on the specific energy consumption E.sub.sp may be whether the decision variable S.sub.down is above or below the alarm threshold, e.g. 0.8, indicating that the specific energy consumption E.sub.sp is falling. Although the fluctuations are sometimes above n.Math., the alarm threshold of 0.8 has not been reached in the example shown in FIG. 4, so that the first criterion would not be fulfilled here. Once the alarm threshold of 0.8 has been reached and the first criterion is fulfilled, an alarm reset threshold at 0.2 is useful to reset the first criterion to unfulfilled when the decision variable S.sub.up has dropped again below the alarm reset threshold at 0.2. Thus, a hysteresis effect is achieved in order to reduce the risk of missing short operating scenarios.

[0063] FIG. 5 shows a schematic pq-diagram for each of two pumps 9a, 9b. Analogous to FIG. 3, each data point represents the flow q and the pressure q in one pump cycle. Each of the two clouds of data points correspond to one of the pumps 9a, 9b, which have different performance in this case. The parabola fitted to the data points indicates a pipe model characterized by a pipe model polynomial p=Aq.sup.2+B, wherein A is a pipe clogging parameter, p is the pressure measured at or downstream of an outlet of the at least pump, q is a wastewater flow through the pipe 11 and/or the pumps 9a, 9b, and B is a zero-flow offset parameter. The pipe clogging parameter A and/or the zero-flow offset parameter B may be used as model-based pipe parameters for the at least one second criterion.

[0064] However, in order to cope with fluctuations, similar low-pass filtering as described above for the specific energy consumption E.sub.sp 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 S.sub.up and S.sub.down 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 , i.e. x=A. Kalman filters may be applied to calculate the mean and variance of the pipe clogging parameter A.

[0065] Alternatively or in addition, as shown in FIG. 6, one of the at least one model-based pipe parameter may be a residual r=p.sub.mp.sub.e=p.sub.mAq.sup.2B between a measured pressure p.sub.m at or downstream of an outlet of the at least pump and an estimated pressure p.sub.e according to a pipe model polynomial p.sub.e=Aq.sup.2+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, whereas a non-fulfilled second criterion may mean a model match. As the residual r also fluctuates significantly, a similar low-pass filtering as described above for the specific energy consumption E.sub.sp may be applied to the residual r before selecting an operating scenario dependent on the at least one second criterion. 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 the CUSUM algorithm described above. In this case, a combined decision variable S=S.sub.up+S.sub.down as shown in the lower plot of FIG. 6 may be used to indicate a model mismatch, because there is no need to distinguish between upward and downward fluctuations.

[0066] FIG. 7 shows in the upper plot the pressure p over two pump cycles for a third criterion that may be applied to select an operating scenario. A negative-flow parameter as a basis for the third criterion may be a leakage flow through one of the non-return valves 10a, 10b, which will gradually lead to a pressure decay when the at least one pump 9a, 9b is stopped. This may be formulated by D{dot over (p)}=q, wherein D is the cross-sectional area of the pipe,

[00006]$\stackrel{.}{p}=\frac{\mathrm{dp}}{\mathrm{dt}}$

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 (pghp.sub.0)}, wherein K is a constant, p 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 p.sub.0 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 (pghp.sub.0)}, which may be approximated by discrete test samples i as follows:

[00007]$p_{i+1}-p_i=-h\frac{K}{A}\sqrt{p_i-g{h}_i-p_0},$

so that a decision variable

[00008]$=-h\frac{K}{A}=\frac{\sqrt{p_i-{\mathrm{gh}}_i-p_0}}{p_{i+1}-p_i}$

can be tested for hypotheses H.sub.0 and H.sub.1 as shown in the lower plot of FIG. 7, wherein H.sub.0: =0 and H.sub.1: 0. As long as hypothesis H.sub.0 is rejected, there is probably no leak in the non-return-valve 10a, 10b as shown in FIG. 7. If the decision variable is below a threshold value, for instance 0.1, the hypothesis H.sub.0 cannot be rejected and a leakage in the non-return-valve 10a, 10b is identified. The threshold value may be adjusted to an acceptable compromise between the sensitivity for a leakage in one of the non-return-valves 10a, 10b and a false alarm rate.

[0067] FIGS. 8 and 9 illustrate, by way of selection matrices, how the operating scenario is identified by selecting an operating scenario from a group of seven predefined operating scenarios (seven rows of the selection matrix) dependent on four first criteria (column 1 to 4 of the selection matrix) that are based on the specific energy consumption E.sub.sp, one second criterion (column 5 of the selection matrix) that is based on the residual r, and one third criterion (column 6) based on the decision variable for the negative-flow parameter.

[0068] Each of the selection matrices in FIGS. 8 and 9 unambiguously associate each operating scenario with a unique combination of the four first criteria, the second criterion and the third criterion. An x in the matrices means that the criterion of this column is fulfilled. The difference between the selection matrices in FIGS. 8 and 9 is that the selection matrix of FIG. 8 is applied when a flow q through the pump(s) is estimated and the selection matrix of FIG. 9 is applied when a flow q through the pipe is measured. This is, because the scenario signature depends 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 a non-return valve 10a, 10b may result in a rising specific energy consumption E.sub.sp when the flow q through the pipe is measured. However, if a flow q through the pump(s) is estimated, the specific energy consumption E.sub.sp may turn out to be falling. Therefore, the monitoring module may be configured to apply one of the two predefined selection matrices of FIGS. 8 and 9 dependent on whether a flow q through the pipe is measured or a flow q through the pump(s) is estimated. An estimation of the flow through the pumps 9a, 9b based on pressure p and power consumption P of the pumps 9a, 9b has, compared to a flow q measured by a flow meter 25, not only the advantage that the flow meter 25 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 10a, 10b. In those cases, the specific energy consumption E.sub.sp would appear as falling if the flow through the pump is estimated. If the flow through the pipe 11 is measured, the specific energy consumption E.sub.sp would be rising in case of pipe clogging, pump fault/clogging and leakage of a pump connection or a non-return valve. The number of applied criteria may overdetermine one or more of the selection scenarios, which may provide a beneficial redundancy for better differentiating between the operating scenarios at a lower rate of misidentifications.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

LIST OF REFERENCE NUMERALS

[0073] 1 wastewater pit [0074] 3 inflow port [0075] 5 level sensor [0076] 7 outflow port [0077] 9a,b pumps [0078] 10a,10b non-return valves [0079] 11 pipe [0080] 13 monitoring module [0081] 15 signal connection between pressure sensor and monitoring module [0082] 17 signal connection between pressure sensor and monitoring module [0083] 19 pressure sensor [0084] 21 signal connection between level sensor and monitoring module [0085] 23 signal connection between flow sensor and monitoring module [0086] 25 flow sensor