Pollution-based Model Predictive Control of Combined Sewer Networks, Considering Uncertainty Propagation

Pollution-based Model Predictive Control of Combined Sewer Networks, Considering Uncertainty Propagation

Mahmood Mahmoodian Orianne Delmont Georges Schutz

Luxembourg Institute of Science and Technology (LIST), Luxembourg.

Centre de Recherche en Automatique de Nancy (CRAN), France.

RTC4Water, Luxembourg

1 February 2017
| Citation



In this paper, two types of controllers are proposed and compared to attain a better understanding of the potential of a real-time control (RTC) approach based on wastewater quantity and quality for combined sewer networks. The first controller is solely based on a wastewater quantity control approach, whilst, the second one is also considering wastewater quality modelling, as well as its uncertainty propagation. In fact, we have developed the first controller to achieve the second one without the requirement of adding new measurement devices to the given system. Model Predictive Control (MPC) approach is selected as the underlying control method. The control model in this study is a simple but fast model which is developed based on volume balance and mass balance laws in the tanks and time delay concept in the pipes. This model is described and applied to a simple case study to illustrate and discuss the results. The uncertainty propagation in the second controller is based on Taylor series of first order approximation. Based on the simulations and the achieved results, it was observed that the introduced pollution-based MPC approach is able to reduce significantly the volume of combined sewer overflows (CSOs) as well as the pollution load caused by them. Finally, it is concluded that considering the pollution load and its uncertainty propagation in the objective function of the optimization problem has a significant effect on the system performance improvement. This is a very important achievement because it can reduce the released pollution load to the environment without the requirement of additional equipment in the system. Because such elements (e.g. sensors) are normally expensive to purchase and maintain.


combined sewer overflow (CSO), model predictive control (MPC), real-time control (RTC), uncertainty propagation, wastewater quality


[1] Campisano, A.P., Creaco, E. & Modica, C., Improving combined sewer overflow and treatment plant performance by real-time control operation. Enhancing Urban Environment by Environmental Upgrading and Restoration, 43, pp. 122–138, 2005. http://dx.doi.org/10.1007/1-4020-2694-3_11

[2] Fuchs, L., Günther, H. & Lindenberg, M., Minimizing the water pollution load by means of real-time control – the dresden example. Proceeding 6th International Conference Urban Drainage Model, 2004.

[3] Schütze, M., Campisano, A., Colas, H., Schilling, W. & Vanrolleghem, P.A., Real time control of urban wastewater systems—where do we stand today? Journal of Hydrology, 299(3–4), pp. 335–348, 2004. http://dx.doi.org/10.1016/S0022-1694(04)00375-0

[4] Dirckx, G., Schütze, M., Kroll, S., Thoeye, C., De Gueldre, G. & Van De Steene, B., Cost-efficiency of RTC for CSO impact mitigation. Urban Water Journal, 8(6), pp. 367–377, 2011. http://dx.doi.org/10.1080/1573062X.2011.630092

[5] Langeveld, J.G., Benedetti, L., de Klein, J.J.M., Nopens, I., Amerlinck, Y., van Nieuwenhuijzen, A., Flameling, T., van Zanten, O. & Weijers, S., Impact-based integrated real-time control for improvement of the Dommel River water quality. Urban Water Journal, 10(5), pp. 312–329, 2013. http://dx.doi.org/10.1080/1573062X.2013.820332 

[6] Weinreich, G., Schilling, W., Birkely, A. & Moland, T., Pollution based real time control strategies for combined sewer systems. Water Science Technology, 36(8–9), pp. 331–336, 1997. http://dx.doi.org/10.1016/s0273-1223(97)00577-5

[7] Schilperoort, R.P.S., Gruber, G., Flamink, C.M.L., Clemens, F.H.L.R. & van der Graaf, J.H.L.R., Temperature and conductivity as control parameters for pollution-based realtime control. Water Science Technology, 54(11–12), pp. 257–263, 2006. http://dx.doi.org/10.2166/wst.2006.744

[8] Lacour, C. & Schutze, M., Real-time control of sewer systems using turbidity measurements. Water Science Technology, 63(11), pp. 2628–2632, 2011. http://dx.doi.org/10.2166/wst.2011.159

[9] Warmink, J.J., Janssen, J.A.E.B., Booij, M.J. & Krol, M.S., Identification and classification of uncertainties in the application of environmental models. Environmental Model Software, 25(12), pp. 1518–1527, 2010. http://dx.doi.org/10.1016/j.envsoft.2010.04.011

[10] Walker, W.E., Harremoes, P., Rotmans, J., Van Der Sluijs, J.P., Van Asselt, M.B., Janssen, P. & Krayer Von Krauss, M.P., A conceptual basis for uncertainty management. Integrated Assessment, 4(1), pp. 5–17, 2003. http://dx.doi.org/10.1076/iaij.

[11] Refsgaard, J.C., van der Sluijs, J.P., Højberg, A.L. & Vanrolleghem, P.A., Uncertainty in the environmental modelling process – a framework and guidance. Environment Model Software, 22(11), pp. 1543–1556, 2007. http://dx.doi.org/10.1016/j.envsoft.2007.02.004

[12] Lei, J.H., Uncertainty Analysis of Urban Rainfall-runoff Modelling, NTNU: Trondheim, Norway, 1996.

[13] Xu, M., van Overloop, P.J. & van de Giesen, N.C., Model reduction in model predictive control of combined water quantity and quality in open channels. Environmental Modelling Software, 42, pp. 72–87, 2013. http://dx.doi.org/10.1016/j.envsoft.2012.12.008

[14] Fallis, A., Model predictive control, 53(9), 2013.

[15] Wang, L., Model Predictive Control System Design and Implementation Using MATLAB®, Springer, Australis, 2009.

[16] Ocampo-Martinez, C., Model Predictive Control of Wastewater Systems, 2010. http://dx.doi.org/10.1007/978-1-84996-353-4

[17] Gillé, S., Fiorelli, D., Henry, E. & Klepiszewski, K., Optimal operation of a sewer network using a simplified hydraulic model. In 11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008.

[18] Seiffert, S. & Klepiszewski, K., Haute-Sure Catchment Measurement Campaigns, Luxembourg Institute of Science and Technology (LIST): Belvaux, 2011.

[19] Regneri, M., Modeling and multi-objective optimal control of integrated wastewater collection and treatment systems in rural areas based on fuzzy decision-making. doctoral thesis at Technischen Universität Graz, 2014.

[20] Vezzaro, L., Christensen, M.L., Thirsing, C., Grum, M. & Mikkelsen, P.S., Water quality-based real time control of integrated urban drainage systems: a preliminary study from Copenhagen, Denmark. Procedia Engineering, 70, pp. 1707–1716, 2014. http://dx.doi.org/10.1016/j.proeng.2014.02.188 

[21] Cabane, P., Incertitudes associées à l’estimation des rejets de temps de pluie des réseaux d’assainissement unitaires, Thèse de Doct. Génie Civil, Lyon: Institut National des Sciences Appliquées de Lyon, 2001.

[22] Garcia Salas, J.C., Evaluation des performances, sources d’erreur et incertitudes dans les modèles de déversoirs d’orage. Thèse de Doct. Génie Civil, Lyon: Institut National des Sciences Appliquées de Lyon, 2003.

[23] Fiorelli, D. & Schutz, G., Real-time control of a sewer network using a multi-goal objective function. IEEE Proceeding 17th Mediterranean Conference on Control and Automation, pp. 676–681, 2009. http://dx.doi.org/10.1109/med.2009.5164621

[24] and G. S. D. Fiorelli, “Sensitivity of an optimal controller of a combined sewer system to the influent flows forecasting accuracy.” In 7th international conference on sustainable techniques and strategies in urban water management (Novatech 2010), 2010.