Antonio Cembellín

This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

This article presents a distributed model predictive control algorithm including fuzzy negotiation among subsystems and a dynamic setpoint generation method, applied to a simulated sewerage network. The methodology considers WWTP as an additional objective of control. To improve the performance of a DMPC using a hydraulic model for prediction, a more detailed model has been considered including suspended solids concentration (TSS). The results obtained with the proposed methodology have been validated on a benchmark simulation model for sewer systems developed to test and compare methodologies, showing good performance.

In this work, a Distributed Model Predictive Control (MPC) methodology with fuzzy negotiation among subsystems has been developed and applied to a simulated sewer network. The wastewater treatment plant (WWTP) receiving this wastewater has also been considered in the methodology by means of an additional objective for the problem. In order to decompose the system into interconnected local subsystems, sectorization techniques have been applied based on structural analysis. In addition, a dynamic setpoint generation method has been added to improve system performance. The results obtained with the proposed methodology are compared to those obtained with standard centralized and decentralized model predictive controllers.