Abstract

This contribution serves two purposes. (1) It presents an updated version of the Astlingen example developed by the working group ‘Integral Real Time Control’ of the German Water Association (DWA), which serves as a benchmark example for the setup and evaluation of real time control strategies. As this benchmark is also intended for educational use, it demonstrates a simple RTC algorithm, illustrating the main concepts of RTC of drainage system. (2) The paper also encourages the preliminary analysis of the potential feasibility and benefit of a temporal increase of inflow to the wastewater treatment plant (WWTP) before analysing the WWTP behaviour in detail. For the present example, RTC within the sewer system alone led to almost the same reduction of overflow volume as permitting the inflow to the WWTP to be increased for 6 h within any 24 h, if at all permitted.

INTRODUCTION

As can be seen from many implementations in practice, but also from theoretical and application-oriented research, real time control (RTC) of sewer systems has many benefits which are also visible in practice (see Schütze et al. 2004; Beeneken et al. 2013; García et al. 2015; and many other publications). Despite this, there are hardly any comprehensively worked examples which serve the need of explaining the core principles and methods of RTC to the interested practitioner. Having realised this necessity, the working group ‘Integral Real Time Control’ of the German Water Association (DWA) has set up a worked example, called Astlingen, which has been updated following discussions at earlier conferences and within the working group and which now is presented in its updated version (a draft version has been presented previously, Schütze et al. 2015). The Astlingen example network serves as an example in a (German-language) publication of DWA currently under preparation (DWA in preparation). It complements the DWA-M180 guideline document ‘Framework for Planning of Real Time Control of Sewer Networks’ (DWA 2005) by providing the worked example ‘Astlingen’. The Astlingen sewer system is a hypothetical network, meeting the German design guidelines (ATV 1992), but is yet typical for Central European conditions. Furthermore, this sewer system can serve as a benchmark for RTC strategies and, thus, encourages systematise research on the development of RTC strategies. The present paper makes the Astlingen benchmark system accessible also to the non-German speaking world.

A benchmark system for RTC

In particular to the practical engineer considering, planning or integrating an RTC system, it will be helpful to have a completely worked example, which illustrates the important steps of the procedure of (1) checking the control potential of a given system and, if appropriate, (2) to set up an RTC system for this sewer network. However, there are hardly any worked examples detailed in the literature (Schilling (1990) and Messmer et al. (2008) represent notable exceptions). Also, most publications on RTC of drainage systems do not provide many details about the control algorithm employed; hence, such publications are of limited use for the engineer entrusted with the task of developing a control algorithm for a given system. Therefore, the working group ‘Integrated Real Time Control’ of DWA has defined the Astlingen example – representing an example drainage system, which everybody can model on their sewer system simulator and develop, evaluate and compare RTC strategies under fully described conditions.

Such a benchmark example, however, will also benefit the scientific community as it allows researchers to develop and to test their control algorithms on a common example. So far, most research on how to best define RTC algorithms is done for specific sites, under specific framework conditions, and can usually not be easily compared with findings of other research groups. A similar effort to set up a benchmark model has been made in the field of wastewater treatment plant (WWTP) modelling and control: The ‘Benchmark Simulation Model’ (IWA Benchmark plant, Gernaey et al. 2014) is now widely used in the wastewater treatment and control community as a benchmark to develop, to discuss and to improve control strategies for WWTPs.

There have been earlier attempts to define benchmark sewer systems for analysis of RTC strategies (for example, Borsányi et al. 2008; Saagi et al. 2017). However, these are based on proprietary simulation systems (sewer simulators or general-purpose simulation systems, such as Matlab-Simulink), which are not easily accessible to the practitioner. The Astlingen system, which is presented in this paper and, in more detail, in DWA (in preparation), provides complete information on system characteristics, modelling approaches, rainfall input data and boundary conditions, which thus enables anyone to implement it in any sewer system simulator capable of hydrologic sewer system simulation with control options. Hence, the Astlingen benchmark example is not limited to any specific simulator. Whilst the present paper illustrates it using the Simba# simulator (Alex et al. 2013; Schütze et al. 2017), other simulators could also be used.

The Astlingen case study

Astlingen (see Figure 1) represents a city of 35,000 inhabitants. Its sewer system is a combination of combined and separate sewer systems (thus reflecting typical Central European conditions) with a total dry-weather flow (including infiltration inflow) of 87.9 L/s and a total storage volume of 5,900 m3, resulting in specific storage volume of 32.7 m3/ha, a value well in the typical range according to ATV (1992). Four rainfall gauges provide information about non-uniform rainfall over the catchment. Whilst Astlingen represents a hypothetical network, it includes many features relevant to the evaluation of RTC potential which play a crucial role in its implementation. The city is located along two receiving water bodies – the Main River and the smaller (thus, more sensitive) Park Creek. Figure 2 illustrates the configuration of the Astlingen drainage system and its storage tanks and overflow structures.

Figure 1

The Astlingen benchmark sewer network.

Figure 1

The Astlingen benchmark sewer network.

Figure 2

System configuration of Astlingen sewer network.

Figure 2

System configuration of Astlingen sewer network.

Together with spatially distributed rainfall input (continuous historic long-term rainfall series of 10 years with a time resolution of 5 min, provided by the Erftverband Water Association) and realistic boundary conditions, it represents a benchmark simulation and control example. The average annual rainfall at these four rainfall stations amounts to 705 mm, 723 mm, 699 mm, and 711 mm, respectively. Use of historic rainfall data avoids the uncertainties incurred by synthetically generated rainfall data (Müller et al. 2017).

The DWA publication presents full details, also on modelling approaches and parameters (summarised in this paper), so that anyone can model and simulate it, flows in sewer network and RTC control actions. Therefore, it can be used as a benchmark, for example, for stimulating research on the systematic development of RTC algorithms.

Whilst Figure 2 shows the schematics of the Astlingen sewer network, Figure 3 also provides information on the subcatchments (impervious areas, as well as the domestic dry-weather flows and the infiltration inflow assumed for each subcatchment). Table 1 gives information on the subcatchments, whilst Table 2 summarises the characteristics of the storage tanks and overflow structures. The setup of the system complies with German design rules and has been double-checked by the practitioners in the DWA Working Group on Integrated Real-Time Control’.

Table 1

Subcatchments of the Astlingen network

Sub- catchment Aimperv [ha] Qdomestic [L/s] Qinfiltr [L/s] QDWF [L/s] Rain gauge 
SC01 33.00 6.95 5.56 12.51 
SC02 22.75 5.87 4.69 10.56 
SC03 18.00 4.58 4.13 8.71 
SC04 6.90 1.60 1.28 2.88 
SC05-1 15.60 3.52 3.17 6.69 
SC05-2 0.00 3.75 2.25 6.00 
SC06 32.55 11.19 10.07 21.26 
SC07-1 4.75 1.32 1.45 2.77 
SC07-2 0.00 2.37 1.42 3.79 
SC08 28.00 3.24 1.94 5.18 
SC09 6.90 2.77 1.66 4.43 
SC10 11.75 2.09 1.05 3.14 
Sum 180.20 49.25 38.67 87.92  
Sub- catchment Aimperv [ha] Qdomestic [L/s] Qinfiltr [L/s] QDWF [L/s] Rain gauge 
SC01 33.00 6.95 5.56 12.51 
SC02 22.75 5.87 4.69 10.56 
SC03 18.00 4.58 4.13 8.71 
SC04 6.90 1.60 1.28 2.88 
SC05-1 15.60 3.52 3.17 6.69 
SC05-2 0.00 3.75 2.25 6.00 
SC06 32.55 11.19 10.07 21.26 
SC07-1 4.75 1.32 1.45 2.77 
SC07-2 0.00 2.37 1.42 3.79 
SC08 28.00 3.24 1.94 5.18 
SC09 6.90 2.77 1.66 4.43 
SC10 11.75 2.09 1.05 3.14 
Sum 180.20 49.25 38.67 87.92  
Table 2

Tanks and overflow structures of the Astlingen network

Structure Volume [m³] Specific vol. [m³/ha Aimp.Throttle flows [L/s]
 
Min. Nominal Max. 
Tank 1 700 15.6 271.28 271.28 271.28 
Tank 2 1.000 44.0 15.00 32.00 140.00 
Tank 3 2.600 56.5 60.00 124.00 190.00 
Tank 4 500 42.9 15.00 28.00 80.00 
Tank 5 500 32.1 39.00 39.00 39.00 
Tank 6 600 15.2 30.00 76.00 175.00 
CSO 7  85.50  
CSO 8  485.33  
CSO 9  129.17  
CSO 10  203.67  
Total 5.900 32.7    
Structure Volume [m³] Specific vol. [m³/ha Aimp.Throttle flows [L/s]
 
Min. Nominal Max. 
Tank 1 700 15.6 271.28 271.28 271.28 
Tank 2 1.000 44.0 15.00 32.00 140.00 
Tank 3 2.600 56.5 60.00 124.00 190.00 
Tank 4 500 42.9 15.00 28.00 80.00 
Tank 5 500 32.1 39.00 39.00 39.00 
Tank 6 600 15.2 30.00 76.00 175.00 
CSO 7  85.50  
CSO 8  485.33  
CSO 9  129.17  
CSO 10  203.67  
Total 5.900 32.7    
Figure 3

Example implementation of the Astlingen network using the hydrologic modules of Simba# (Snapshot of flows at t = 14.583 d as Sankey representation).

Figure 3

Example implementation of the Astlingen network using the hydrologic modules of Simba# (Snapshot of flows at t = 14.583 d as Sankey representation).

Assessing/evaluating the RTC potential of the Astlingen network

As the guideline document M180 of the German Water Association (DWA 2005) suggests, one of the first steps to assess control potential of a given drainage system is to apply the M180 scoring table (which is also provided on the web page of DWA: see also https://de.dwa.de/de/PASST.html). For the Astlingen example, a score of 39 is obtained, indicating a considerable storage potential. This encourages a more detailed analysis of the RTC potential according to Step 2 of the M180 procedure, which involves setting up a simple model of the case study in order to allow a more detailed case-specific analysis of the control potential.

The definition of the Astlingen benchmark example includes the general modelling assumptions for this example, including the application of common hydrologic modelling concepts, using standard approaches and parameters for rainfall–runoff modelling (see Verworn 1999), considering evaporation, wetting and depression storage losses in the rainfall–runoff calculations, the application of linear reservoir cascades for flow routing within catchments (with parameters n = 3 and k = 5 min). This example makes use of different types of storage tank configurations as defined by the German A128 guideline, such as online and offline storage tanks (ATV 1992; see also Schütze et al. 2002). Diurnal variation of domestic dry weather flow follows the pattern defined by DWA for 10,000 to 50,000 inhabitants.

As an example of an implementation in a simulator, the Astlingen example has been implemented in the Simba# simulator (see Figure 3), which also allows integration of control algorithms and of dynamic wastewater treatment and river water quality modelling (Alex et al. 2013; Schütze et al. 2017). However, as the Astlingen benchmark is provided with full details, also any other simulator allowing sewer system modelling including control actions can be used here. The Simba# simulator contains, for urban drainage modelling, modules of different degrees of complexity, including modules for simple flow translation, hydrologic modelling approaches and fully hydrodynamic simulation of flows in sewer systems and consideration of pollutants. Furthermore, it contains various options of modelling pollutant transport and any in-sewer biochemical transformation processes and arbitrary control algorithms. Control algorithms can also be expressed in IEC 61131-3 Structured Text language (IEC 2002). This is complemented by scripting (e.g. C# and Python) facilities and an API interface, allowing the simulator to be embedded in other software frameworks.

Having set up a simulation model and following the DWA M180 procedure for establishing the control potential, now a comparative simulation is done. Here, the given system (constant nominal throttle devices as defined in Table 2) is evaluated against the theoretical optimum according to the Central Basin Approach (Einfalt & Stölting 2002). Simulating the case of constant nominal throttle flows – denoted here as base case – over 1 year of non-uniform rainfall distribution (assigning the four raingauges to the subcatchments according to Table 1), results in a total overflow volume of 253,620 m3 (see also Table 3, which provides an evaluation with regard to some commonly applied emission-based criteria, Van Daal et al. 2017). Calculating the theoretical optimum according to the Central Basin Approach, analysing potential control of the throttles of Tanks 2, 3, 4, and 6, results in a total overflow volume of 203,523 m3. The Central Basin Approach just provides a theoretical lower bound for the total overflow volume (i.e. no control strategy can achieve a lower overflow volume). Obviously, the simplifying assumptions of the Central Basin Appraoch have to be taken into account, but this simple and quick calculation provides useful insights into the control potential of a given sewer network.

Table 3

Simulation results over 1 year – base case

Structure Overfl. duration [h] Number of overflow events [−] Overflow days [calendar days] Overflow vol. [m3Overflow load [kg COD] 
Tank 1 93.9 65 48 77,339 11,197 
Tank 2 39.8 31 28 31,605 3,950 
Tank 3 28.7 20 20 26,029 3,344 
Tank 4 31.2 25 25 10,058 1,346 
Tank 5 30.1 32 27 14,053 1,859 
Tank 6 74.0 66 50 66,095 8,607 
CSO 7 11.1 26 21 3,920 484 
CSO 8 8.6 24 20 15,862 1,735 
CSO 9 7.8 19 18 3,951 445 
CSO 10 7.9 28 21 4,711 518 
Sum    253,620 33,484 
Structure Overfl. duration [h] Number of overflow events [−] Overflow days [calendar days] Overflow vol. [m3Overflow load [kg COD] 
Tank 1 93.9 65 48 77,339 11,197 
Tank 2 39.8 31 28 31,605 3,950 
Tank 3 28.7 20 20 26,029 3,344 
Tank 4 31.2 25 25 10,058 1,346 
Tank 5 30.1 32 27 14,053 1,859 
Tank 6 74.0 66 50 66,095 8,607 
CSO 7 11.1 26 21 3,920 484 
CSO 8 8.6 24 20 15,862 1,735 
CSO 9 7.8 19 18 3,951 445 
CSO 10 7.9 28 21 4,711 518 
Sum    253,620 33,484 

Following the evaluation of its control potential (according to DWA (2005), and further detailed in Schütze (2017)), and in order to illustrate development of control algorithms, now an algorithm based on the equal-filling degree (EFD) principle (explained in detail by Dirckx et al. (2014)) is applied, with its full code in IEC 61131 Structured Text control language provided in DWA (in preparation). The EFD approach compares the filling degree of the storage tanks in the system and sets the throttleflows leaving the tanks accordingly, aiming at establishing an equal filling degree in all tanks. No claim is made here that this represents the ‘best’ algorithm (see also Kroll et al. (2017) for a critical discussion of the EFD principle). This control algorithm serves only as an illustrative example. In fact, for example, a more sophisticated algorithm, described extensively in the literature (Alex et al. 2008), provides results superior than those shown here. However, due to the illustrative and didactic nature of the Astlingen example, the EFD algorithm is used in this paper. Any researcher is encouraged to use the Astlingen example as a benchmark for developing and testing control algorithms.

The EFD algorithm, as applied here, uses information only on water levels in the storage tanks. Additional aspects, such as sensor and control delays and errors, consideration of additional sensor information or even prediction of future rainfall and systems states, and failures of sensoring and control equipment, whilst of great importance in practical applications, are deliberately not considered in this simple benchmark example, in order to ensure applicability of the benchmark example and also comparability with other simulators.

The EFD algorithm compares the filling degrees fi, i = 1,..,n of the n considered storage tanks with its mean value fmean. In case the filling degree fi of Tank i is very different from the filling degree of all tanks (fmean), its maximum throttle flow qthr,i will be set to its maximum possible (qmax,i) or minimum possible (qmin,i) value, depending on its location within the network. If, however, all filling degrees are lower than a threshold value of 20%, no control is applied, but all throttle flows are set to their nominal values (qthr,i = qnom,i for all i = 1,…,n).

Application of this simple EFD control algorithm for the same rainfall input as used for the simulations of the base case yields a total overflow volume of 232,320 m3, thus a reduction of 8.4% of the overflow volume. Figure 4 also illustrates that application of RTC mainly reduces discharges to the more sensitive Park Creek.

Figure 4

Total overflow volumes into the different receiving water bodies (1 year simulation).

Figure 4

Total overflow volumes into the different receiving water bodies (1 year simulation).

Whilst evaluation of RTC potential has to be based on long-term performance and not on single events (see also Van Daal et al. 2017), Figure 5 illustrates the total overflow volumes of the base case vs the application of the EFD algorithm for the event on Days 14 to 16.

Figure 5

Comparing base case and RTC (EFD algorithm) for a selected time period.

Figure 5

Comparing base case and RTC (EFD algorithm) for a selected time period.

The overflows during this event have been reduced from 1,979 m3 to 1,440 m3 (reduction of 27%; whilst discharges to the Park Creek have been reduced even by 64%). Figure 5 also illustrates that, due to RTC, the start of the overflow event could be delayed by 25 min. Figure 6 shows the filling degrees of the storage tank when the control algorithm is applied. Overall, the storage tanks are used in a more balanced way than in the base case (not shown here).

Figure 6

Utilisation of storage tanks when applying RTC (EFD algorithm) for a selected time period.

Figure 6

Utilisation of storage tanks when applying RTC (EFD algorithm) for a selected time period.

Increasing temporarily the WWTP inflow?

It has been argued that short increases of the inflow to the WWTP might relieve critical situations in the sewer system, for example during heavy rainfall events, without hampering seriously the performance of the WWTP. The German A198 guidelines recognise this issue (ATV-DVWK 2003). In some WWTPs, the hydraulic capacity is deliberately increased during wet weather (see, for example, Sharma et al. 2013). The feasibility of a temporal increase of the inflow to the WWTP, obviously, depends on the particular characteristics of the given WWTP (for example, the capacity of the plant to deal with ammonium peaks, which often are observed after sudden increases of treatment plant inflow). A recent publication of the German Water Association addresses in detail the issue of increased flows towards the WWTPs during rain events (DWA 2016). A means to study such questions in detail obviously consists in the integrated simulation of sewer systems and WWTPs, which is being done by many researchers (e.g. Schütze et al. 2002; Benedetti et al. 2013; Saagi et al. 2017) and for which modern and easy-to-use simulators are available (e.g. Alex et al. 2013; Bach et al. 2014; Schütze et al. 2017). However, in many practical applications, a detailed simulation of the WWTP, albeit desirable, might be out of the scope of the project. In order to address this, a simplified approach is chosen here, allowing for a temporal increase of the WWTP inflow in the controller for the sewer system/regulating WWTP inflow under consideration of the state of the sewer system. A simple controller is implemented in the simulation, allowing an increased inflow to the WWTP for up to a certain time period (e.g. 1 or 3 h) within any 24 h period, thus reflecting, albeit in a simplified way, the WWTP's capacity to deal with short-term increases. Obviously, this controller could also be extended to serve as a predictive controller of the WWTP inflow. It should be noted that variation of the maximum permissible flow into the WWTP also leads to changes in the control algorithm within the sewer system. The approach chosen here allows establishment, as a first step of the analysis, of the benefit for the sewer system of a temporal increase of WWTP inflow, prior to embarking on the second step of such analysis, analysing by detailed dynamic simulation of the WWTP the potentially detrimental effects that temporarily increased WWTP inflows might have on the WWTP. Schütze et al. (2017) provide an example of this analysis, evaluating, by integrated simulation, the effects on WWTP and on river water quality.

RESULTS AND DISCUSSION

Applying this additional control principle to the Astlingen example (permitting increase of WWTP inflow from 271 L/s to 330 L/s), obviously reduces overflow volumes within the sewer system, in particular if this can be combined with RTC in the sewer system (see Figure 7). This is not surprising.

Figure 7

Total annual discharge volume (m3) for various control options and various maximum WWTP inflows of the Astlingen example.

Figure 7

Total annual discharge volume (m3) for various control options and various maximum WWTP inflows of the Astlingen example.

However, considering these two measures individually (RTC in the sewer system; various durations of temporal increase of WWTP inflow), it can be concluded that, for the given example, RTC in the sewer system alone leads to almost the same reduction of overflow volume than permitting the inflow to the WWTP to be increased for 6 h within any 24 h, if at all permitted. Integrated modelling of the sewer system and WWTP, obviously, allows a more detailed analysis of impacts on the WWTP performance (see Schütze et al. 2017). It can also assist in fine-tuning this time-based controller regulating the inflow to the WWTP and the related flow settings within the drainage system. However, whilst the evaluation of RTC potential has to be carried out specifically for a given drainage system, this example shows that RTC in the sewer system alone can already contribute to better urban wastewater management, without necessarily having to upgrade WWTP infrastructure. Application of the EFD algorithm in this example was done for illustration due to its simplicity. However, the reader is encouraged to develop and test more advanced control algorithms, using the Astlingen benchmark as a test example.

CONCLUSION

Besides this technical result, the Astlingen sewer system constitutes a benchmark example developed by the DWA working group. It will serve a multitude of purposes – illustrating RTC principles to practitioners, demonstrating procedures on how to develop RTC to engineers and serving as a benchmark for research on development of RTC algorithms, triggering also efforts to systematise research on development of RTC strategies (‘which RTC gives best results for the given system and boundary conditions?’).

ACKNOWLEDGEMENTS

Part of this work has been funded by the German Ministry of Education and Research (BMBF) within the SaMuWa project (grant reference 033w004 l), which is gratefully acknowledged. Acknowledgements are also given to the Erftverband Water Association for having provided continuous long-term precipitation data.

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