Reducing the Environmental Impact of Sewer Network Overflows Using Model Predictive Control Strategy.

This paper proposes a method for reducing the environmental impact of sewer network (SN) overflows. The main objective of the paper is to minimize the wastewater quantity and the pollutant loads that overflow from the SN. The proposed algorithm to achieve this goal is Model Predictive Control using Particle Swarm Optimization as optimization method. It was tested in simulation using a simplified model of the network based on Benchmark Simulation Modelsewer as prediction model, and a forecasted influent. Three cases have been considered: (a) the fitness function is defined as the global yearly overflow volume calculated using equal weights for each tank; (b) the fitness function uses different weights for each tank depending on the medium loads and (c) integrating a penalty term related to the system state at the end of the prediction horizon in the previous fitness function. The simplified model determined a significant reduction of the integration time minimizing the optimization time. Key Points: Model Predictive Control is used to reduce pollution caused by sewer network (SN) overflowsSimplified SN flow model decreases the optimization time of the control algorithmPollutant loads are assessed by the control algorithm by weighting overflow volumes with values calculated based on offline measurements [ABSTRACT FROM AUTHOR]