How Effective is Model Predictive Control in Real-Time Water Quality Regulation? State-Space Modeling and Scalable Control

TL;DR

This paper introduces a new state-space model and a scalable model predictive control algorithm to improve real-time water quality regulation in water distribution networks, addressing modeling complexity and control scalability.

Contribution

It proposes a novel state-space representation for water quality control and develops a scalable MPC algorithm for real-time regulation in complex water networks.

Findings

The proposed model provides explicit input-output relationships for water quality.

The MPC algorithm demonstrates fast response times and resilience to uncertainties.

The approach enhances real-time water quality management in complex networks.

Abstract

Real-time water quality control (WQC) in water distribution networks (WDN), the problem of regulating disinfectant levels, is challenging due to lack of (i) a proper control-oriented modeling considering complicated components (junctions, reservoirs, tanks, pipes, pumps, and valves) for water quality modeling in WDN and (ii) a corresponding scalable control algorithm that performs real-time water quality regulation. In this paper, we solve the WQC problem by (a) proposing a novel state-space representation of the WQC problem that provides an explicit relationship between inputs (chlorine dosage at booster stations) and states/outputs (chlorine concentrations in the entire network) and (b) designing a highly scalable model predictive control (MPC) algorithm that showcases fast response time and resilience against some sources of uncertainty.