  Existing techniques for the cost optimization of water distribution networks
either employ meta-heuristics, or try to develop problem-specific optimization
techniques. Instead, we exploit recent advances in generic NLP solvers and
explore a rich set of model refinement techniques. The networks that we study
contain a single source and multiple demand nodes with residual pressure
constraints. Indeterminism of flow values and flow direction in the network
leads to non-linearity in these constraints making the optimization problem
non-convex. While the physical network is cyclic, flow through the network is
necessarily acyclic and thus enforces an acyclic orientation. We devise
different strategies of finding acyclic orientations and explore the benefit of
enforcing such orientations explicitly as a constraint. Finally, we propose a
parallel link formulation that models flow in each link as two separate flows
with opposing directions. This allows us to tackle numerical difficulties in
optimization when flow in a link is near zero. We find that all our proposed
formulations give results at par with least cost solutions obtained in the
literature on benchmark networks. We also introduce a suite of large test
networks since existing benchmark networks are small in size, and find that the
parallel link approach outperforms all other approaches on these bigger
networks, resulting in a more tractable technique of cost optimization.
