Description

TECHNICAL FIELD

[01 ] 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 leak age.

BACKGROUND

[02] Sewage or wastewater collection systems for wastewater treat ment plants typically comprise one or more wastewater pits, wells or sumps for temporarily collecting and buffering wastewater. Typically, wastewater flows info such pits passively under gravity flow and/or act- ively driven through a force main. One, two or more pumps are usually installed in or at each pi††o pump wastewater out of the pit. If the inflow of wastewater is larger than the outflow for a certain period of time, the wastewater pi† or sump will eventually overflow. Such overflows should be prevented as much as possible in order†o 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 pos sible for maintenance staff to take according action, like cleaning, re pairing or replacing as quickly as possible. [03] US 8,594,851 B1 describes a wastewater treatment system and a

method for reducing energy used in operation of a wastewater treat ment facility.

5 [04] It is a challenge for known wastewater pumping station man agement systems†o 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†o be cleaned, re paired or replaced.

10

SUMMARY

[05] In contras††o known systems, embodiments of the present dis closure provide a monitoring module and method for identifying an

15 operating scenario with more specific and more reliable information.

[06] In accordance with a firs† aspect of the present disclosure, a

monitoring module for identifying an operating scenario in a waste- water pumping station is provided, with a† leas† one pump arranged for

20 pumping wastewater out of a wastewater pit into a pipe, wherein the

monitoring module is configured†o process a† leas† one load-depen dent pump variable indicative of how the a† leas† one pump operates and a† leas† one model-based pipe parameter indicative of how the wastewater flows through the pipe and/or the a† leas† one pump, and

25 wherein the monitoring module is configured†o identify an operating

scenario in the wastewater pumping station by selecting an operating scenario from a group of predefined operating scenarios dependent on a† leas† one firs† criterion that is based on the a† leas† one load-de pendent pump variable and a† leas† one second criterion that is based

30 on the a† leas† one model-based pipe parameter.

Patentanwalte Vollmann & Hemmer GP 3275 WO, 02/05/2019 [07] The group of predefined operating scenarios may include faulty

and/or non-faulfy 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-re- 5 turn 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

10 parameter, may be interpreted by the monitoring module as a“scena

rio signature”.

[08] Optionally, the group of operating scenarios may be predefined in a selection matrix unambiguously associating each operating sce- 15 nario 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 scenar ios may be identified based on the combination of the two criteria as follows:

20

[09] In case of a wastewater pumping station with two or more pumps, a first criterion for each pump may be used to more finely distin guish between operating scenarios in which a specific pump is clogged

Patentanwalte Vollmann & Hemmer GP 3275 WO, 02/05/2019 or pump connection is leaking, for example three different operating scenarios may be identified based on the combination of the two crite ria as follows:

[10] In case of a wastewater pumping station with two or more pumps, only one pump is typically running a† 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 contras††o 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. a† heavy rain incidents, all or several pumps may run simultane- ously in order†o prevent an overflow. For the alternating normal opera tion of only one pump at a time, non-return valves may be installed for each pump†o prevent the active pump from pumping wastewater through the passive pump(s) back info the wastewater pi†. A leak in such a non-return valve of a passive pump may have a different scena rio 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:

[1 1 ] Optionally, the at least one load-dependent pump variable may comprise a specific energy consumption E _sp of the a† leas† one pump. There are different ways†o determine the specific energy consumption E _sp of the a† leas† one pump. For example, the specific energy con sumption E _sp may be defined by E _sp =E/V, wherein E is an average en ergy consumed by the a† leas† one pump during a defined time period and V is the volume of wastewater pumped during said defined time period by the a† leas† one pump. The average energy consumption may be determined by integrating or summing the current power con sumption P(t) over the time† between an end of a delay period after pump start and pump stop: E = f ^tst ° ^p P(t) dt. Analogously, the

^ start t delay

pumped wastewater volume may be determined by integrating or summing the current flow q(t) over the same time period: V =

The delay period may be useful†o skip an initial pe-

riod of high fluctuations after s†ar†-up of the pump(s). The monitoring module may be signal connected wirelessly or via a cable with the pump(s) †o receive a signal indicative of the power or energy con sumption. Furthermore, the monitoring module may be signal con- nected wirelessly or via a cable with a flow sensor to receive a signal indicative of the flow through the pipe.

[12] A current specific energy consumption E _sp (t) of the a† leas† one pump may be defined by E _sp (†)=P(†)/q(t), wherein P(t) is a current power consumption of the a† leas† one pump and q (†) is a current flow of wastewater pumped by the at least one pump. The current specific energy consumption E _sp (t) may be monitored as the at least one load- dependent pump variable as an alternative to the averaged specific energy consumption E _sp as defined above. If the current specific en ergy consumption E _sp (t) fluctuates too much to the at least one first cri terion on it, a low-pass filtering may be applied as explained later herein. Even in case of a specific energy consumption E _sp that is aver aged for each pump cycle, it can fluctuate between the pump cycles so much that a low-pass filtering may be advantageous.

[13] 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 differ ential Dr and power consumption P. For instance, the outflow q of wastewater through the pump(s) may be estimated by q « s^ + s-^A

A ₂

p + s—J ^> + S2 _36O , wherein s is the number of running pumps, w is the pump speed (e. g. constant), Dr is the measured pressure differential, P is the power consumption of the running pump(s), and l _0, l,, l ₂ and l ₃ 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 Dr. So, optionally, the monitoring module may be configured to receive a measured pressure p _m at or downstream of an outlet of the at least pump. Alternatively or in addi tion, the monitoring module may be configured to receive a measured flow q _m through the pipe or to process an estimated wastewater flow q _e through the pump.

[14] 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 E _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 _sp may turn out†o be falling. Therefore, the monitoring module may be configured†o apply one of a† leas† 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 a† leas† two selection matrices unambiguously associate each operating scenario with a unique combination of the a† leas† one firs† criterion and the a† leas† one second criterion.

[15] Optionally, one of the a† leas† one model-based pipe parameter may be a pipe clogging parameter A in a pipe model polynomial p=Aq ² + B, wherein p is a pressure a† or downstream of an outlet of the a† leas† pump, q is a wastewater flow through the pipe and/or the a† leas† one pump, and B is a zero-flow offset parameter. The zero-flow offset parameter B may be a second one of a† leas† two model-based pipe parameters, wherein the pipe clogging parameter A may be a firs† one of the a† leas† two model-based pipe parameters.

[16] Alternatively or in addition, one of the a† leas† one model-based pipe parameter may be a residual r=p _m -p _e =p _m -Aq ² - B between a mea sured pressure p _m a† or downstream of an outlet of the a† leas† pump and an estimated pressure p _e according to a pipe model polynomial p _e =Aq ² + B, wherein A is a pipe clogging parameter of the pipe, q is a wastewater flow through the pipe and/or the a† leas† 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 a† leas† one sec ond criterion may be fulfilled, otherwise no†. 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.

[17] Optionally, the monitoring module may be configured to apply a low-pass filtering†o 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 spe cific energy consumption E _sp, and/or the pipe parameter, e.g. the pipe clogging parameter A or the residual r.

[18] For instance, the monitoring module may be configured to se quentially process a multitude of samples of the at least one load-de pendent 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:

wherein S _up and S _doW n are decision variables summing up deviations us ing a test variable x. The test variable x may, for instance, be defined as the deviation of the specific energy consumption in the i-†h pump cycle from an average specific energy consumption Έ , i.e. x = E _sp - E _sp . The average specific energy consumption Έ 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-faul†y operating scenarios to statistically determine an av erage specific energy consumption Έ Dependent on the variance of x, the decision variables may be tuned by gain parameters G _up and G _down · Fluctuations below a certain number n, e.g. n=l , 2 or 3, of stand ard deviations s may be suppressed for the decision variables. Similar†o the average specific energy consumption Έ , the standard deviation s may be statistically determined over several previous pump cycles dur ing normal faultless operation.

[19] A first one of the at least one first criterion based on the specific energy consumption E _sp may be whether the decision variable S _up is above or below an alarm threshold indicating that the specific energy consumption E _sp is rising. A second one of the at least one first criterion based on the specific energy consumption E _sp may be whether the de cision variable S _doWn is above or below an alarm threshold indicating that the specific energy consumption E _sp 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 con nection or a non-return valve. In those cases, the specific energy con sumption E _sp would appear as falling if the flow through the pump is esti mated. If the flow through pipe is measured, the specific energy con sumption E _sp would be rising in case of pipe clogging, pump fault/clog ging 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.

[20] 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 multi tude of samples of the at least one model-based pipe parameter, wherein the at least one second criterion is based on whether a cumu lative 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.

[21 ] For instance, the evolvemenf of the pipe clogging parameter A may be monitored by decision variables S _up and S _doWn with a test vari able x being defined as the deviation of the pipe clogging parameter A in the i-†h pump cycle from an average pipe clogging parameter A, i.e. x = A - A. Kalman filters may be applied to calculate the mean and variance of the pipe clogging parameter. As an alternative or in addi tion, 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 = S _up + S _doWn may be used to indicate a model mis match, because there is no need to distinguish between upward and downward fluctuations.

[22] 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=Aq ² + B, wherein p is a pres sure at or downstream of an outlet of the at least one pump, q is a wastewater flow through the pipe and/or the a† leas† one pump, and A is a pipe clogging parameter.

[23] Alternatively or in addition, the negative-flow parameter may be a leakage flow through one of the non-return valves or a pump con nection, for instance, which will gradually lead†o a pressure decay when the a† leas† one pump is stopped. This may be formulated by Dp =- q, wherein D is the cross-sectional area of the pipe, p = is the change in pressure a† the outlet of a pump over time, and q is the leak age flow. Following Toricelli’s law, the leakage flow may be calculated by q = K-jp ^~ pgh ^{~ D} Ro, wherein K is a constant, p is the density of the wastewater, p is the measured pressure a† the pump outlet, h is the wastewater's height above a hydrostatic pressure sensor for level meas urement a† the bottom of the pi†, and Dr ₀ is a hydrostatic pressure of a difference in geodetic elevation between the pump outlet and the bottom of the pi†. This leads†o a differential equation as follows: Ap = K ~ P9h ^{~ D} Ro, which may be approximated by discrete test samples i as follows: p _{i + 1 ~} P _i =- h JPi ^~ pgh-i - Dr _{0 so} that a decision variable

K _ί - pa - Dro

g =- h =— _{p p—} ^m ciy be tested as a third criterion for hypotheses H ₀ and Hi, wherein H ₀ : g = 0 and Hp g ¹ 0. If hypothesis H ₀ cannot be re jected, there is probably a leak in the non-re†urn-valve. If the decision variable g is above a threshold value, for instance 0.1 , the hypothesis H ₀ may be rejected. The threshold value for this third criterion may be ad justed†o an acceptable compromise between the sensitivity for a leak age and a false alarm rate.

[24] In accordance with a second aspect of the present disclosure and analogous†o the monitoring module described above, a method is provided for identifying an operating scenario in a wastewater pump ing station with a† leas† one pump arranged for pumping wastewater out of a wastewater pit into a pipe, wherein the method comprises: processing a† leas† one load-dependent pump variable indica tive of how the a† leas† one pump operates and a† leas† one model- based pipe parameter indicative of how the wastewater flows through the pipe and/or the a† leas† one pump, and

selecting an operating scenario from a group of predefined op erating scenarios dependent on a† leas† one firs† criterion that is based on the a† leas† one load-dependent pump variable and a† leas† one second criterion that is based on the a† leas† one pipe parameter.

[25] Optionally, the group of operating scenarios may be predefined in a selection matrix unambiguously associating each operating sce nario with a unique combination of the a† leas† one firs† criterion and the a† leas† one second criterion.

[26] Optionally, the a† leas† one load-dependent pump variable may be a specific energy consumption E _sp of the a† leas† one pump.

[27] Optionally, the specific energy consumption E _sp of the a† leas† one pump may be defined by E _sp =E/V, wherein E is an average energy consumed during a defined time period and V is the volume of waste- water pumped during said defined time period by the a† leas† one pump.

[28] Optionally, the specific energy consumption E _sp of the a† leas† one pump may be defined by E _sp =P/q, wherein P is a power consump tion and q is a flow of wastewater pumped by the a† leas† one pump.

[29] Optionally, the a† leas† one model-based pipe parameter may be a pipe clogging parameter A in a pipe model polynomial p=Aq ² + B, wherein p is a pressure a† or downstream of an outlet of the a† leas† pump, q is the wastewater flow through the pipe and/or the a† leas† one pump, and B is a zero-flow offset parameter. [30] Optionally, the at least one model-based pipe parameter may be a residual r=p _m -p _e =p _m -Aq ² - B between a measured pressure p _m at or downstream of an outlet of the a† leas† pump and an estimated pres sure p _e according to a pipe model polynomial p _e =Aq ² + B, wherein A is a pipe clogging parameter of the pipe, q is the wastewater flow through the pipe and/or the a† leas† one pump and B is a zero-flow off set parameter.

[31 ] Optionally, the method may further comprise a step of receiving a measured pressure p _m a† or downstream of an outlet of the a† leas† pump.

[32] Optionally, the method may further comprise a step of receiving a measured flow q _m or processing an estimated wastewater flow q _e through the a† leas† one pump.

[33] Optionally, the method may further comprise a step of applying a low-pass filtering to the a† leas† one load-dependent pump variable and/or the a† leas† one model-based pipe parameter before selecting an operating scenario dependent on a† leas† one firs† criterion and/or second criterion, respectively.

[34] Optionally, the method may further comprise a step of sequen tially processing a multitude of samples of the a† leas† one load-depen dent pump variable, wherein the a† leas† one firs† criterion is based on whether a cumulative sum of deviations between the actual sample and an average of pas† samples of the a† leas† one load-dependent pump variable exceeds a predetermined maximum or falls below a predetermined minimum. [35] Optionally, the method may further comprise a step of sequen tially processing a multitude of samples of the a† leas† one model- based pipe parameter, wherein the a† leas† one second criterion is based on whether a cumulative sum of deviations between the actual sample and an average of pas† samples of the a† leas† one model- based pipe parameter exceeds a predetermined maximum or falls be low a predetermined minimum.

[36] Optionally, the method may further comprise the steps of

processing a firs† of a† leas† two model-based pipe parameters, processing a negative-flow parameter as a second of the a† leas† two model-based pipe parameters, wherein the negative-flow parameter is indicative of how the wastewater flows through the pipe and/or the a† leas† one pump when the a† leas† one pump is stopped, and

selecting an operating scenario from a group of predefined op erating scenarios further dependent on a† leas† one third criterion that is based on the negative-flow parameter.

[37] 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. Alternat ively or in addition, some or all method steps may be executed by soft ware 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.

SUMMARY OF THE DRAWINGS

[38] Embodiments of the present disclosure will now be described by way of example with reference†o the following figures of which: Fig. 1 shows 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 mod ule according to the present disclosure;

Fig. 2 shows a schematic view on a chain of wastewater pumping sta tions, wherein each wastewater pumping station is connected with an example of the monitoring module according to the present disclosure;

Fig. 3 shows a schematic diagram of a specific energy consumption E _sp over time for each of two pumps of a wastewater pumping station be ing connected with an example of the monitoring module according †o the present disclosure;

Fig. 4 shows schematic plots of a specific energy consumption E _sp and an associated decision variable S _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;

Fig. 5 shows 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;

Fig. 6 shows schematic diagrams of a residual r and an associated de cision variable S over time for a pipe of a wastewater pumping station being connected with an example of the monitoring module accord ing to the present disclosure;

Fig. 7 shows schematic diagrams of a pressure and an associated de cision variable g 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;

Fig. 8 shows a firs† example of a selection matrix applied by an example of the monitoring module according to the present disclosure; and

Fig. 9 shows a second example of a selection matrix applied by an ex ample of the monitoring module according to the present disclosure;

DETAILED DESCRIPTION

[39] Fig. 1 shows a wastewater pit 1 of a wastewater pumping station. The wastewater pit 1 has a certain height FI 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 a† the bottom of the wastewater pit 1 and/or an ultrasonic distance meter for determining the surface posi tion of the wastewater in the pi† 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 a† one or more pre-defined levels for simply indicating whether the wastewater has reached the respective pre-defined level or no†.

[40] 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 1 1 . The pumps 9a, 9b may be arranged, as shown in Fig. 1 , outside of the wastewater pit 1 or submerged a† the bottom of the wastewater pit 1 in form of sub mersible pumps. A non-return valve 10a, 10b a† 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†o identify operating scenarios and†o 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 de vice for an operator†o take notice of a visual and/or acoustic signal as the information and/or alarm.

[41 ] Fig. 2 shows a chain of wastewater pumping stations being con nected by respective pipes 1 1 through which a lower level wastewater pumping station is able to pump wastewater†o the next higher level wastewater pumping station against gravity. Each of the wastewater pumping stations may be monitored by a monitoring module 13 in or der†o identify operating scenarios.

[42] 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-depen- den† pump variable and at least one second criterion that is based on at least one model-based pipe parameter. In order†o 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 connec tions between the monitoring module 13 and available sensors are shown in Fig. 1 as options that may be implemented alone or in com bination 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 down stream 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 wire less signal connection 23 with a flow meter 25 a† or downstream of the pump 9a. The signal connections 15, 1 7, 21 , 23 may be separate com munication channels or combined in a common communication chan nel or bus. The monitoring module 13 is configured†o receive a respect ive pressure, power and/or flow signal via the signal connections 15, 17, 23 and†o process accordingly a† leas† one load-dependent pump vari able indicative of how the pumps 9a, 9b operate and a† leas† one model-based pipe parameter indicative of how the wastewater flows through the pipe 1 1 and/or the pumps 9a, 9b.

[43] The a† leas† one load-dependent pump variable may be a spe cific energy consumption E _sp of each of the two pumps 9a, 9b. There are different ways†o determine the specific energy consumption E _sp for each pump. For example, the specific energy consumption E _sp for one pump may be defined by E _sp =E/V, wherein E is an average energy con sumed 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† between an end of a delay period after pump start and pump stop: E =

Analogously, the pumped wastewater volume may

be determined by integrating or summing the current flow q(t) over the same time period: V = f ^tst ° ^p q(i) dt. Alternatively or in addition, a t start t delay

current specific energy consumption E _sp (t) of each one of the two pumps may be defined by E _sp (†)=P(†)/q(t), wherein P(t) is a current power consumption of said pump and q (†) is a current flow of waste- water pumped by said pump. If the current specific energy consump tion E _sp (t) fluctuates too much to the a† leas† one firs† criterion on it, a low-pass filtering may be applied as explained later herein. Even in case of a specific energy consumption E _sp that is averaged for each pump cycle, i† can fluctuate between the pump cycles so much that a low-pass filtering may be advantageous.

[44] In order to process the specific energy consumption E _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 waste- water 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 q

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

[45] Fig. 3 shows samples of the specific energy consumption E _sp for each pump cycle over three days of operation. Each data point repre sents the specific energy consumption E _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 _sp than the second pump 9b. As can be seen, the specific energy consumptions E _sp fluctuate for both pumps 9a, 9b around a respective average specific energy consumption Έ indi cated by the horizontal lines. [46] The fluctuations are better visible in the plots shown in Fig. 4, where the upper left plot shows the specific energy consumption E _sp of the first pump 9a and the upper right plot shows the specific energy consumption E _sp of the first pump 9a. In order to improve the identifica tion 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 _sp . The mon itoring module is thus, for each pump 9a, 9b, configured to sequentially process a multitude of samples of the specific energy consumption E _sp and to determine a cumulative sum of deviations between the actual sample and an average of past samples of the specific energy con sumption E _sp . Such a low-pass filtering may follow a so-called iterative CUSUM (cumulative sum) algorithm such as:

wherein S _up and S _doWn are decision variables summing up deviations us ing a test variable x. The test variable x may, for instance, be defined as the deviation of the specific energy consumption in the i-†h pump cycle from an average specific energy consumption Έ , i.e. x = E _sp - E _sp . The average specific energy consumption Έ 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 av erage specific energy consumption Έ . Dependent on the variance of x, the decision variables may be tuned by gain parameters G _up and G _down · Fluctuations below a certain number n, e.g. n=l ,2 or 3, of stand ard deviations s may be suppressed for the decision variables. Similar to the average specific energy consumption Έ , the standard deviation s may be statistically determined over several previous pump cycles dur ing normal faultless operation. The lower left plot of Fig. 4 shows the de- cision variable S _up of the firs† pump 9a and fhe lower right plot of Fig. 4 shows fhe decision variable S _up of fhe second pump 9b. As can be seen, fhe decision variable S _up is more robust against fluctuations. A firs† one of fhe af leas† one firs† criterion based on fhe specific energy con sumption E _sp may be whether fhe decision variable S _up is above or be low an alarm threshold, e.g. 0.8, indicating that fhe specific energy consumption E _sp is rising. A second one of fhe af leas† one firs† criterion based on fhe specific energy consumption E _sp may be whether fhe de cision variable S _doWn is above or below fhe alarm threshold, e.g. 0.8, indi cating that fhe specific energy consumption E _sp is falling. Although fhe fluctuations are sometimes above n-s, fhe alarm threshold of 0.8 has no† been reached in fhe example shown in Fig. 4, so that fhe firs† crite rion would no† be fulfilled here. Once fhe alarm threshold of 0.8 has been reached and fhe firs† criterion is fulfilled, an alarm reset threshold af 0.2 is useful†o reset fhe firs† criterion†o“unfulfilled” when fhe decision variable S _up has dropped again below fhe alarm reset threshold af 0.2. Thus, a hysteresis effect is achieved in order†o reduce fhe risk of missing short operating scenarios.

[47] Fig. 5 shows a schematic pq-diagram for each of two pumps 9a, 9b. Analogous†o Fig. 3, each data point represents fhe flow q and fhe pressure q in one pump cycle. Each of fhe two clouds of data points correspond†o one of fhe pumps 9a, 9b, which have different perfor mance in this case. The parabola tiffed†o fhe data points indicates a pipe model characterized by a pipe model polynomial p=Aq ² + B, wherein A is a pipe clogging parameter, p is fhe pressure measured af or downstream of an outlet of fhe af leas† pump, q is a wastewater flow through fhe pipe 1 1 and/or fhe pumps 9a, 9b, and B is a zero-flow offset parameter. The pipe clogging parameter A and/or fhe zero-flow offset parameter B may be used as model-based pipe parameters for fhe af leas† one second criterion. [48] However, in order†o cope with fluctuations, similar low-pass filter ing as described above for the specific energy consumption E _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 evolvemenf of the pipe clogging parameter A may be monitored by decision variables S _up and S _doWn with a test variable x be ing defined as the deviation of the pipe clogging parameter A in the i- †h pump cycle from an average pipe clogging parameter A, i.e. x = A - A. Kalman filters may be applied to calculate the mean and variance of the pipe clogging parameter A.

[49] 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 _m -p _e =p _m -Aq ² - B between a measured pressure p _m at or downstream of an outlet of the at least pump and an estimated pressure p _e according†o a pipe model polynomial p _e =Aq 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 ful filled 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 _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 de cision variable S = S _up + S _doWn as shown in the lower plot of Fig. 6 may be used to indicate a model mismatch, because there is no need to distin guish between upward and downward fluctuations. [50] 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 Dp =- q, wherein D is the cross-sectional area of the pipe, p = 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

jp ^~ P9h ^{~ D} Ro, wherein K is a constant, p is the density of the waste- water, p is the measured pressure at an outlet of one of the pumps 9a, 10b, h is the wastewater's heigh† above the level sensor 5, and Dr 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: Ap = k ^~ P9 ^h - Dr ₀ which may be approximated by dis- crete test samples i as follows: p _{i + 1} - p _t =- h _s jVi ^~ P9 ^h i ^{~ D} Ro , so that a

K ^v i - pa ^h i - ^v o

decision variable y =- h =— _{p p} — can be tested for hypotheses H ₀ and Fli as shown in the lower plot of Fig. 7, wherein H ₀ : g = 0 and Hp g ¹ 0. As long as hypothesis H ₀ is rejected, there is probably no leak in the non-return-valve 10a, 10b as shown in Fig. 7. If the decision variable g is below a threshold value, for instance 0.1 , the hypothesis H 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.

[51 ] Figs. 8 and 9 illustrate, by way of selection matrices, how the op erating scenario is identified by selecting an operating scenario from a group of seven predefined operating scenarios (seven rows of the se lection matrix) dependent on four first criteria (column 1†o 4 of the se- lection matrix) that are based on the specific energy consumption E _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 g for the negative-flow parameter.

[52] Each of the selection matrices in Figs. 8 and 9 unambiguously as sociate each operating scenario with a unique combination of the four firs† criteria, the second criterion and the third criterion. An "x” in the matrices means that the criterion of this column is fulfilled. The differ ence between the selection matrices in Figs. 8 and 9 is that the selec tion matrix of Fig. 8 is applied when a flow q through the pump(s) is esti mated 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 con nection or a non-return valve 10a, 10b may result in a rising specific en ergy consumption E _sp when the flow q through the pipe is measured. Flowever, if a flow q through the pump(s) is estimated, the specific en ergy consumption E _sp may turn out†o be falling. Therefore, the monitor ing module may be configured†o 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 esti mated. An estimation of the flow through the pumps 9a, 9b based on pressure p and power consumption P of the pumps 9a, 9b has, com pared†o a flow q measured by a flow meter 25, no† only the advan tage that the flow meter 25 can be spared with, but also that the sce nario 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 con sumption E _sp would appear as falling if the flow through the pump is esti mated. If the flow through the pipe 1 1 is measured, the specific energy consumption E _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.

[53] 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.

[54] The above embodiments are to be understood as illustrative ex amples of the disclosure. It is to be understood that any feature de scribed 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 embodi ments, 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 altern atives are apparent to one of ordinary skill in the art and may be changed without departing from the scope of the subject matter de scribed herein, and this application is intended to cover any adapta tions or variations of the specific embodiments discussed herein.

[55] 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 com bination with other characteristics or steps of other exemplary embodi- merits described above. Method steps may be applied in any order or in parallel or may constitute a part or a more detailed version of an other method step. It should be understood that there should be em bodied within the scope of the paten† warranted hereon all such modi fications as reasonably and properly come within the scope of the con tribution †o the art. Such modifications, substitutions and alternatives can be made without departing from the spirit and scope of the dis closure, which should be determined from the appended claims and their legal equivalents.

[56] List of reference numerals:

I wastewater pi†

3 inflow port

5 level sensor

7 outflow port

9a, b pumps

10a, 10b non-return valves

I I pipe

13 monitoring module

15 signal connection between pressure sensor and monitor ing module

17 signal connection between pressure sensor and monitor ing module

19 pressure sensor

21 signal connection between level sensor and monitoring module

23 signal connection between flow sensor and monitoring module

25 flow sensor