Model Predictive Control for the Self-optimized Operation in Wastewater Treatment Plants

This paper describes a procedure to find the best economically controlled variables for the activated sludge process in a wastewater treatment plant despite the load disturbances. A further controllability analysis of those variables including a nonlinear model predictive controller (NMPC) has been performed. The self-optimizing methodology has been applied, considering the most important measurements of the process. A first pre-screening of those measurements has been done based on the nonlinear model of the process and typical disturbances, in order to avoid non feasible operation. The NMPC performance has been compared with a distributed NMPC-PI structure.