  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.
