Description

Combined sewer networks carry wastewater and storm water together. During normal operation all the water is delivered to wastewater treatment plants, where it is treated before being released to surrounding natural water bodies. However, during heavy rain events, the network capacity may become insufficient leading to untreated water discharges to the receiving environments. To mitigate these undesired effects, combined sewer networks are usually provided with detention tanks and flow redirection elements, managed to fully take advantage of the network capacity. In the last few decades automatic control techniques for the regulation of these storage and redirection elements have been developed, with real-time, global, model-based predictive ones being widely regarded as the most efficient ones due to their capacity to take advantage of instantaneous network measurements and rain intensity forecasts.

In this thesis a complete methodology to develop a real-time, global, model-based predictive controller to minimize pollution effects in combined sewer networks is proposed. The physically-based model for open-channel flow is based on a set of partial differential equations, which must be solved numerically. Since in a real-time predictive control strategy the model equations must be solved many times to evaluate the effect of different control actions, the time needed to solve the equations limits the use of the physically-based model to small network instances with simple topologies. Therefore, it is a common practice to use simplified control-oriented models for real-time control.

The first part of the thesis is focused on the development, calibration and validation of a simplified control-oriented model for water transport in combined sewer networks, taking into account three main features: accuracy, calibration ease and computational speed. The proposed model describes the flows through the most common elements and hydraulic structures present in combined sewer networks, some of which requiring the use of piecewise equations. Once the model equations are presented, calibration procedures to compute all the model parameters are developed. The modelling and calibration methodology is then applied to a real case study and validation results are provided. Finally, sensitivity analysis is conducted with respect to both the most relevant model parameters and the intensity of the considered rain scenarios. The second part of the thesis is devoted to model-based optimal control. First, the piecewise equations of the model are reformulated to obtain a general expression of the system by means of a set of linear equations and inequalities including continuous
and binary variables. Using this general expression, matrix-based procedures for the formulation of Optimal Control Problems and State Estimation Problems are presented.

Using an implementation of the case study network in a commercial sewer network simulator solving the complete physically-based model equations as virtual reality, the proposed model-based controller is evaluated. By iteratively solving State Estimation Problems and Optimal Control Problems and using the simulator to provide network measurements, a Receding Horizon Control strategy is simulated. The inclusion of State Estimation Problems in the control loop allows to perform output feedback control simulations taking into account that in a sewer network the number of available measurements is limited. Finally, a discussion of the results obtained with these simulations corresponding to different measurement availability scenarios is provided.

The work is under the scope of the following projects:

• ITACA: Integración de técnicas avanzadas de modelado, control y supervisión aplicadas a la gestión del ciclo integral del agua (web)
• WATMAN: Analysis and Design of distributed optimal control strategies applied to large-scale WATer systems MANagement (web)