Predictive Real-Time Control Optimization of a Stormwater Management System

TL;DR

This paper introduces a predictive real-time control approach for stormwater ponds that dynamically optimizes water quantity and quality management, reducing hydraulic shocks and improving discharge quality in urban stormwater systems.

Contribution

It develops a novel predictive control model that adapts to environmental changes, enhancing stormwater pond performance over static management strategies.

Findings

Improved water quantity regulation during wet periods.

Enhanced water quality during dry periods.

Demonstrated superior performance over static control methods.

Abstract

An optimization model of a stormwater pond is developed to improve the performance of the system in terms of water quantity and quality. Nowadays, stormwater management systems play an important role in mitigating the impacts of urbanization on the natural hydrological cycle. These systems can be managed in such a way that they meet smart city needs. An automated dynamically managed system that can adapt itself to ever-changing environmental conditions can be modeled using a mathematical optimization approach. Hence, a Predictive Real-Time Control (PRTC) approach is proposed in this paper to optimize the performance of stormwater management basins in terms of minimizing hydraulic shocks during wet periods. Then some generalized rules are designed to control the sedimentation of trapped water in the pond during dry periods to improve the quality of water discharged to the receiving stream. The combination of these two approaches provides a real-time improved performance in comparison to the static strategy.