Real-Time Predictive Control for Chemical Distribution in Sewer Networks Using Improved Elephant Herding Optimization

Abstract

As a critical infrastructure of urban water systems, sewer networks suffer from serious corrosion and odor problems, which can be controlled by chemical dosing. It is a challenging task to optimize chemical distribution in such a hybrid system with continuous hydraulic flow, discrete pump operations, and dynamic constraints. In this article, we study real-time control of multiple pumps to achieve the desired chemical distribution in a sewer network. A novel hybrid optimization approach is developed, which involves an event-triggered scheme triggered by predicting proper pumping events at uncontrolled pumping stations, and an improved nature-inspired elephant herding optimization (iEHO) algorithm for scheduling pumping at controllable pumping stations. The proposed method is validated through simulation studies of a real-life sewer network using real measured data. Our strategy significantly improves chemical distribution with reduced costs, despite an astronomic searching space. The iEHO algorithm outperforms the genetic algorithm in terms of the quality of solutions and convergence efficiency.