Haas, U.

Pabst, M.

Schuetze, M.

I-Revues

NOVATECH 2016 
1 
Urban Drainage Systems – Static throttle flows or 
real time control? A systematic approach to answer 
this 
Systèmes d'assainissement - Régulation statique de 
débits ou gestion en temps réel ? Réponse par une 
approche systématique 
 
M. Schütze*, M. Pabst**, U. Haas*** 
 
*  ifak - Institut für Automation und Kommunikation e.V. Magdeburg, Werner-
Heisenberg-Str. 1, 39106 Magdeburg, Germany, 
manfred.schuetze@ifak.eu 
*** bpi-Hannover Verworn Consultant Engineers, 30177 Hannover, Germany 
Email: pabst@bpi-hannover.de 
*** InfraConsult GmbH, Schaiblestr. 1, 70499 Stuttgart, Germany, 
ulrich.haas@infraconsult.de 
 
RÉSUMÉ 
Parmi les options multiples permettant de parvenir à une gestion durable des eaux pluviales, le 
contrôle en temps réel (CTR) suscite une attention de plus en plus grande. Dans le cadre de la 
procédure d’évaluation du potentiel du contrôle en temps réel, d’autres mesures devraient être 
également prises en considération (par exemple le contrôle à la source, les différents réglages 
statiques des débits maximum dans les dispositifs à goulot). Cet article présente et démontre une 
méthodologie visant à inclure l’analyse des débits régulés de façon optimisée dans l’analyse du 
potentiel du contrôle en temps réel. Afin de réduire le lourd temps de calcul d’une application brutale 
de la procédure d’optimisation, une analyse de la variance est suggérée sous la forme d’une simple 
analyse de sensibilité comme étape préalable, dont le traitement est encore possible. La 
méthodologie est démontrée en utilisant comme exemple un projet de réseau d’assainissement à 
Astlingen, mis au point par le groupe de travail sur le contrôle intégré de la Compagnie des Eaux 
Allemande (DWA). 
 
ABSTRACT 
Among the manifold options to arrive at sustainable stormwater management, real time control (RTC) 
is receiving increased attention. As part of the procedure to assess the potential of RTC, also 
alternative measures should be taken into consideration (e.g. source control, different static settings of 
maximum flows  in throttle devices). This paper outlines and demonstrates a methodology to include 
the analysis of optimised throttle flows in the analysis of RTC potential. In order to reduce the 
computational burden of a brute-force application of optimisation procedure, an Analysis of Variance is 
suggested as a simple form of sensitivity analysis as preceding step, which is yet computationally 
feasible. The methodology is demonstrated using a draft of the Astlingen drainage network set up by 
the working group on Integrated control of the German Water Association (DWA) as an example. 
 
KEYWORDS 
Astlingen, optimisation, real time control, static throttle flows 
SESSION 
2 
1 INTRODUCTION 
Among the manifold options to arrive at sustainable stormwater management, real time control is 
receiving increased attention. Real time control is understood as taking active influence in the flow 
processes in the drainage system by operating, dependent on the current (and possibly past and 
predicted future states of the systems) control devices, such as movable gates, weirs and pumps (see, 
for example, Schütze et al. (2004) for a review). Whilst control proves to be beneficial for many 
drainage systems, it also involves additional efforts (Beeneken et al., 2013). Although the benefits of 
control, such as adding flexibility and resilience to the system, often outweigh such efforts, a prudent 
assessment of the drainage system should also consider static measures in an evaluation of potential 
options. 
Often, the static optimisation of maximum permissible throttle flows is advocated as a solution simpler 
than time-varying permissible throttle flows (hereafter denoted as control). Despite significant earlier 
work in each of these fields – static throttles (HMULV, 2004), rule-based control, control involving 
online optimisation (“Model-based control”)(for example, Muschalla, 2009,  Joseph et al., 2014, 
Regneri, 2014), real time control using generalised and easy-to-apply controllers (Alex et al., 2008) – a 
concise methodology considering and integrating these various schools and approaches is still 
missing. Thus, the practitioner often gets confused. As a small mosaic stone to structure the 
procedures, this contribution bridges the gap between consideration of static throttle settings and 
dynamic variation of throttle settings. This work is related to present work of the working group 
“Integrated control” of the German Water Association (DWA), which is currently preparing an 
application manual, illustrating to the practitioner the issues related to setting up a real time control 
system on an worked example.   
2 IMPROVING PERFORMANCE OF A DRAINAGE SYSTEM –BUT HOW? 
In order to improve the performance of a drainage system, a multitude of options (including 
combinations thereof) can be considered. Besides measures such as reducing runoff at source 
(Source control, Best Management Practices, Low-Impact Design, see Fletcher et al., 2014) or 
construction of additional storage volume in order to cope better with runoff, also the following 
represent options of potential interest, and on these the present paper is focussing: 
(1) Use of the existing system “as is”, but with different static settings of maximum throttle flows 
(2) Use of the existing system in a dynamic way, i.e. by real time control (time-varying settings of 
maximum flows) 
In order to assess the potential of (2), various methods and procedures have been suggested (DWA, 
2005, and others). The M180 procedure includes, in its Step 2, an analysis of the potential of 
alternative approaches. Here, also a systematic analysis should be carried out as to how far one can 
get with Option (1) for the given drainage system. For this, the following procedure is suggested and 
demonstrated for the Astlingen system. 
3 AN EXAMPLE DRAINAGE SYSTEM - ASTLINGEN 
Whilst this example is still under development by the respective working group of DWA, a draft version 
of this example, as presented and described by Schütze et al. (2015) is used here to illustrate the 
procedure. The system consists of 13 subcatchments, most of which are combined subsystems. 
Overall, domestic and industrial dry-weather flow amounts to 47.4 l/s, to which 17.5 l/s of infiltration 
inflow are added, resulting in a total dry weather flow of 64.9 l/s. The catchment area is 620 ha. Total 
storage volume amounts to 7800 m
3
. 
As “base” case, it is assumed that the Astlingen drainage system is operated in a static way, i.e. the 
throttles in the system allow as maximum flows those designated as “nominal flows” in Table 1. These 
values have been found, for this example, by conventional sewer system design according to the DWA 
guidelines. Many real-life drainage systems, however, have been modified and extended significantly 
after its design. Therefore, the static throttle settings obtained long time ago might not be valid 
anymore. For optimisation of the throttles, a decision has to be made, which throttles are to be 
considered and which ranges might be permissible. In some cases, for certain throttle structures, a 
modification of their settings might not be feasible. A brute-force method would now involve to 
consider all remaining ones in the optimisation process. For these, also their feasible ranges have to 
be defined. Some guidance results from general considerations (minimum throttle flow larger than 
NOVATECH 2016 
3 
peak dry-weather flow; maximum throttle flow not exceeding downstream hydraulic limitations; see 
also HMULV, 2004). Following such considerations, the minimum and maximum throttle flows for the 
devices of the Astlingen draft system as shown in Table 1 have been defined. 
Table 1: Storage tanks and overflow devices in the [draft] Astlingen system (taken from Schütze et al., 2015)  
 
To optimise their static settings, would involve the constrained optimisation of a function of five 
variables (static throttle settings for Tanks 2, 3, 4, 5 and 7; with their respective minimum and 
maximum values as constraints). Optimisation is understood here as “minimisation of the total 
overflow volume”, whilst often several concomitant objectives will have to be met (addressed by multi-
objective optimisation which has been reported vastly in the literature). Function evaluation consists in 
carrying out a simulation run with such settings for a predefined input time series of rainfall (which, 
therefore, should be – in some sense “representative”), Obviously, whilst a range of methods and add-
on tools to simulators have been developed (e.g. Schütze, 2007, Henrichs et al. 2014 and others), 
these are not yet integrated with the analyses outlined above under (2). 
As function evaluation in this context usually is computationally demanding (unless significant system 
simplifications are carried out), care has to be taken in the selection and application of the optimisation 
approach. Instead of “just” applying brute-force optimisation of all throttle devices, it is suggested to do 
a simple global sensitivity analysis first, in order to establish which throttle exert significant influence. A 
wide range of sensitivity analysis methods is available (often themselves characterised by huge 
computational demand); for the present purpose of practical application, a full factorial experiment, 
varying each of the n parameters (throttle settings) between its minimum and maximum values, results 
in a feasible compromise between computational demand (2
n
 function evaluations, which might still be 
computationally feasible) and resulting system information about importance of parameters (derived by 
a formal Analysis of Variance). Application of this method is illustrated in the next paragraph. 
It is assumed that, in the example drainage system, outlined above, the static throttle settings for 
Tanks 2, 3, 4, 5, and 7 are subjected to this analysis. Carrying out the 2
5
 full factorial experiment and 
analysing the variance by F tests applying various degrees of freedom (Box and Draper, 1987), gives 
the results shown in Table 2. 
Table 2: Analysis of Variance of influence of static throttle settings  
Location in parameter space: 
 Parameter  A: T2   Variation from    15.600000 to    39.300000  
 Parameter  B: T3   Variation from    28.900000 to   163.200000  
 Parameter  C: T4   Variation from    15.600000 to    35.800000  
 Parameter  D: T5   Variation from    16.700000 to    45.100000  
 Parameter  E: T7   Variation from    34.200000 to    92.400000   
 
Results of Analysis of Variance:   F value:  Comment: 
      Const  94360658278.1952  1029848.2515  significant at the 0.1 % level             
     B          5629478732.9212  61439.8939  significant at the 0.1 % level             
     A              44037332.2758  480.6216  significant at the 0.1 % level             
SESSION 
4 
     E              19204608.4854  209.5983  significant at the 0.1 % level             
     BE             19204608.4854  209.5983  significant at the 0.1 % level             
     AB             13706477.3428  149.5919  significant at the 0.1 % level             
     BC              5069314.7456   55.3263  significant at the 0.1 % level             
     C               2690142.5849   29.3601  significant at the 0.5 % level             
     BD              2488710.5209   27.1617  significant at the 0.5 % level             
     AE               608788.1718    6.6443  significant at the   5 % level             
     D            296479.4389    3.2358 not significant (not even at the 5 % level) 
These results clearly indicate that the setting of Throttle 5 does not have much influence on the overall 
overflow volume in this simulation experiment. Therefore, it is discarded from the application of a 
formal optimisation routine in the next step. Now, the remaining four static maximum throttle flow 
settings are optimised (for a given rain input time series), i.e. their constant settings within their 
feasible ranges are optimised, applying the Shuffled Complex Evolution algorithm (Duan et al., 1992) 
is applied as an example of a derivative-free evolutionary algorithm for constrained optimisation. If it 
can be ascertained that the objective function is of well-behaved nature without the potential problem 
of getting stuck in local minima, also a more efficient search should be applied in this step. The 
optimisation yielded the following optimum settings: T2: 31.6 l/s, T3: 145.4 l/s, T4: 21.3 l/s, T7: 65.1 l/s. 
In order to compare the performance of the drainage system with these constant throttle flows against 
the base case (nominal settings of the flows) and against performance of real time control algorithms, 
a different rainfall input time series should be used than the one used for optimising the static throttle 
flows (similar to a sound calibration-validation exercise where also different data should be used for 
each of these two steps). 
Therefore, the drainage system is now simulated for a different historic rainfall time series (of one year 
duration), this time also employing non-uniform distribution of rainfall. Here, several scenarios are 
simulated; Simulation of these various scenarios is done using the state-of-the-art simulator Simba# 
(Ogurek et al., 2015), which allows also the convenient setup and analysis of control systems; its 
features with regard to systems analysis techniques (including optimisation) are constantly extended. 
The results of the simulation are summarised in Table 3 
Table 3: Simulation results for different control scenarios over one year 
Control scenario Total overflow volume [m
3
]  
Base case (static throttle flows equal to nominal flows) 908219 
Static throttle flows with optimised values as given above 905540 
Generalised controller (as in Alex et al., 2008) 877239 
Theoretical optimum  835330 
These results indicate that, even though for the rain series for which static throttle flows were 
optimised they yielded good performance (far better than the base case), these do necessarily give 
low overflow values also for another time series, here being only marginally better than the base case. 
Therefore, in this example (as in many others), static throttle settings do not increase as much to the 
flexibility of a system as does real time control. At the same time, for example, a generalised control 
algorithm, set up with very little effort and without optimisation, leads to improvement of the system. 
4 CONCLUSIONS 
This paper outlined a small, yet important step to the methodology of assessment of real time control 
as a potential means to improve a drainage system’s performance by comparing the results of 
optimisation of static (constant) settings of maximum throttle flows against results of real time control. 
A method, involving an analysis of variance prior to the optimisation procedure, has been proposed 
and illustrated for the draft version of the Astlingen example system.   
5 ACKNOWLEDGEMENTS 
Part of this work has been funded by the German Ministry of Education and Research (BMBF) within 
the SaMuWa project (grant reference 033W004L), which is gratefully acknowledged. 
  
 
NOVATECH 2016 
5 
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-23.09.2015 
 


Systèmes d'assainissement - Régulation statique de débits ou gestion en temps réel ? Réponse par une approche systématique

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Systèmes d'assainissement - Régulation statique de débits ou gestion en temps réel ? Réponse par une approche systématique

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