  A state-space representation of water quality dynamics describing
disinfectant (e.g., chlorine) transport dynamics in drinking water distribution
networks has been recently proposed. Such representation is a byproduct of
space- and time-discretization of the PDE modeling transport dynamics. This
results in a large state-space dimension even for small networks with tens of
nodes. Although such a state-space model provides a model-driven approach to
predict water quality dynamics, incorporating it into model-based control
algorithms or state estimators for large networks is challenging and at times
intractable. To that end, this paper investigates model order reduction (MOR)
methods for water quality dynamics. The presented investigation focuses on
reducing state-dimension by orders of magnitude, the stability of the MOR
methods, and the application of these methods to model predictive control.
