Towards Real-Time Monitoring and Control of Water Networks

TL;DR

This paper presents a novel reduced order modeling approach for water networks that enables real-time monitoring and control by accurately predicting water temperature, flow, and pressure with minimal computational delay.

Contribution

A new parametric model order reduction technique is introduced, preserving spatial resolution and enabling real-time predictions in water network monitoring.

Findings

Achieved mean relative error below 4% for temperature, flow rate, and pressure predictions.

Reduced order model computation time under 2 milliseconds.

Validated the model with experimental data from a 60-meter water circulation network.

Abstract

Water networks are used in numerous applications, all of which have the essential task of real-time monitoring and control of water states. A framework for the generation of efficient models of water networks suitable for real-time monitoring and control purposes is proposed. The proposed models preserve the distributed parameter character of the connected local elements. Hence, the spatial resolution of the property under consideration is recovered. The real-time feasibility of the network model is ensured by means of reduced order modeling of the models constituting components. A novel model order reduction procedure that preserves the model parametric dependency is introduced. The proposed concept is evaluated with the water temperature as the property under consideration. The formulated model is applied for the prediction of the water temperature within an experimental test bench of a 60 meter exemplary circulation network at the company VIEGA. A reduced order model (ROM) with a 50 mm spatial resolution, i.e. 1200 discretization points, is constructed and utilized as the identification model for a single path of the test bench. Afterwards, the ROM is evaluated in a Hardware in the Loop experiment for the prediction of the downstream temperature showing high prediction accuracy with mean relative error below 3.5 \%. The ROM single step computation time did not exceed 2 msec highlighting the real-time potential of the method. Moreover, full network model validation experiments featuring both diffusion and transport dominated parts were conducted. The network model is able to predict the temperature evolution, flow rate, and pressure accurately at the different paths of the network with mean relative errors below 4 \%, 2 \%, and 2 \%, respectively.