• Daniel Brødbæk

The purpose of this master thesis has been to improve data for real-time control of urban sewer systems. When real-time control is conducted, it is crucial that decisions are based on reliable information about the state of the sewer system.
In order to enhance the reliability of in-situ sensor data, a method capable of conducting a validation in real-time have been developed. The developed method is validated on a case study.

If real-time control is conducted based on a deterministic hydraulic model of a sewer system, then errors on the model prediction will effect the performance. Although in this case the state of the entire sewer system is modelled and can therefore be favourable compared to observations from a finite number of in-situ sensors. In order to get the optimal information about the state of the sewer system the in-situ sensors can be assimilated with a hydraulic model running in real-time.

This can be done by the Ensemble Kalman Filter, which calculate the most probable state of the sewer system based on the uncertainty of the measurements and the model prediction. The Ensemble Kalman filter not only update the hydraulic model locally where the in-situ sensors are installed, but the whole model is updated based on the available in-situ sensors. How the Ensemble Kalman filter can be configured for the assimilation is described in this master thesis
Publication date31 May 2013
Number of pages88
External collaboratorAarhus Vand A/S
Lene Bassø LBA@aarhusvand.dk
Envidan A/S
Mads Uggerby mau@envidan.dk
ID: 76989680