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

Page: 
98-111
|
DOI: 
https://doi.org/10.2495/SDP-V12-N1-98-111
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
1 February 2017
| Citation

OPEN ACCESS

Abstract: 

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.

Keywords: 

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

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