Output-feedback model predictive control of sewer networks through moving horizon estimation

Trabajo presentado a la 53rd IEEE Conference on Decision and Control (CDC 2014), celebrada del 15 al 17 de diciembre en Los Angeles, California (US).Based on a simplified control-oriented hybrid linear delayed model, model predictive control (MPC) of a sewer network designed to reduce pollution during heavy rain events is presented. The lack of measurements at many parts of the system to update the initial conditions of the optimal control problems (OCPs) leads to the need for estimation techniques. A simple modification of the OCP used in the MPC iterations allows to formulate a state estimation problem (SEP) to reconstruct the full system state from a few measurements. Results comparing the system performance under the MPC controller using full-state measurements and a moving horizon estimation (MHE) strategy solving a finite horizon SEP at each time instant are presented. Closed-loop simulations are performed by using a detailed physically-based model of the network as virtual reality.This work has been partially funded by the research project ECOCIS (DPI–2013–48243–C2–1–R).Peer Reviewe

Output-feedback model predictive control of sewer networks through moving horizon estimation

Based on a simplified control-oriented hybrid linear delayed model, model predictive control (MPC) of a sewer network designed to reduce pollution during heavy rain events is presented. The lack of measurements at many parts of the system to update the initial conditions of the optimal control problems (OCPs) leads to the need for estimation techniques. A simple modification of the OCP used in the MPC iterations allows to formulate a state estimation problem (SEP) to reconstruct the full system state from a few measurements. Results comparing the system performance under the MPC controller using full-state measurements and a moving horizon estimation (MHE) strategy solving a finite horizon SEP at each time instant are presented. Closed-loop simulations are performed by using a detailed physically-based model of the network as virtual reality.Peer ReviewedPostprint (author’s final draft

A feedback simulation procedure for real-time control of urban drainage systems

This paper presents a feedback simulation procedure for the real-time control (RTC) of urban drainage systems (UDS) with the aim of providing accurate state evolutions to the RTC optimizer as well as illustrating the optimization performance in a virtual reality. Model predictive control (MPC) has been implemented to generate optimal solutions for the multiple objectives of UDS using a simplified conceptual model. A high-fidelity simulator InfoWorks ICM is used to carry on the simulation based on a high level detailed model of a UDS. Communication between optimizer and simulator is realized in a feedback manner, from which both the state dynamics and the optimal solutions have been implemented through realistic demonstrations. In order to validate the proposed procedure, a real pilot based on Badalona UDS has been applied as the case study.Peer ReviewedPostprint (author's final draft

Conceptual quality modelling and integrated control of combined urban drainage system

This paper presents the first results of conceptual quality modelling approach oriented to the integrated real-time control (RTC) strategy for urban drainage networks (UDN) and wastewater treatment plants (WWTP) developed in the European project LIFE EFFIDRAIN (Efficient Integrated Real-time Control in Urban Drainage and Wastewater Treatment Plants for Environmental Protection). Model predictive control (MPC) has been selected as a proper RTC to minimize the polluting discharge in case of raining events. The simulator SWMM5 was modified to integrate a lumped conceptual model for total suspended solids (TSS) called SWMM-TSS, which has been used as virtual reality for calibration and validation of the proposed modelling approaches in Perinot network, a real case study in Bordeaux.Peer ReviewedPostprint (author's final draft

A learning-based approach towards the data-driven predictive control of combined wastewater networks - An experimental study

Smart control in water systems aims to reduce the cost of infrastructure expansion by better utilizing the available capacity through real-time control. The recent availability of sensors and advanced data processing is expected to transform the view of water system operators, increasing the need for deploying a new generation of data-driven control solutions. To that end, this paper proposes a data-driven control framework for combined wastewater and stormwater networks. We propose to learn the effect of wet- and dry-weather flows through the variation of water levels by deploying a number of level sensors in the network. To tackle the challenges associated with combining hydraulic and hydrologic modelling, we adopt a Gaussian process-based predictive control tool to capture the dynamic effect of rain and wastewater inflows, while applying domain knowledge to preserve the balance of water volumes. To show the practical feasibility of the approach, we test the control performance on a laboratory setup, inspired by the topology of a real-world wastewater network. We compare our method to a rule-based controller currently used by the water utility operating the proposed network. Overall, the controller learns the wastewater load and the temporal dynamics of the network, and therefore significantly outperforms the baseline controller, especially during high-intensity rain periods. Finally, we discuss the benefits and drawbacks of the approach for practical real-time control implementations.Peer ReviewedPostprint (published version

Ouput-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according to different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically-based model of a real case-study network as virtual reality.Peer ReviewedPostprint (author's final draft

A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.Peer ReviewedPostprint (author's final draft

Integrated pollution-based real-time control of sanitation systems

© 2020. ElsevierAn integrated pollution-based real-time control (RTC) approach is proposed for a sewer network (SN) integrated with wastewater treatment plants (WWTPs) in a sanitation system (SS) to mitigate the impacts of pollution from combined sewer overflows (CSOs) on ecosystems. To obtain the optimal solution for the SS while considering both quantity and quality dynamics for multiple objectives, model predictive control (MPC) is selected as the optimal control method. To integrate SN and WWTP management, a feedback coordination algorithm is developed. A closed-loop virtual-reality simulator is used to assess the results of the optimal management approach achieved by applying MPC. The Badalona SS (Spain) provides a pilot case study to assess the efficacy and applicability of the proposed approach. A comparison with local rule-based and volume-based control strategies currently in use indicates that the proposed integrated pollution-based RTC approach can reduce the pollutant loads released to the receiving environment.Peer ReviewedPostprint (author's final draft