SAMPLE-driven Adaptive Management of Pressure and Leakage Estimation in Water Supply

The SAMPLE project develops and demonstrates a pressure management (PM) and leakage isolation (LI) system for large-scale water supply networks. Water scarcity is an increasing problem, and at the same time a huge amount of water is lost due to leaking water inside the distribution networks. It has been proven in practice that improved PM is a cost-efficient way to lower the amount of leaking water and reduce the number of pipe bursts. On top of this, a large amount of energy can be saved by introducing smart pumping station control for PM.
Most water utilities use a substantial effort on LI which is more challenging than
merely detecting a leakage. The methods commercially available in the sector
include detection based on change in the supply flow, LI using acoustic equipment and advanced signal analysis. The drawback with many of the LI methods is the labour needed for either carrying out the leakage search (acoustic), for setting up and maintaining the detection systems (signal analysis), or for expensive equipment (acoustic). These efforts and associated costs can be significantly reduced by utilizing the SAMPLE PM and LI algorithms.
Usage of newly developed reduced order, adaptive network models have shown to be successful in PM for water supply networks with a single water inlet. The
proposers of SAMPLE demonstrated successful results in a test of a pressure
controller performed on a network in Skagen. Furthermore, the same reduced
network models have shown great promise in laboratory tests for usage in isolation of leakages in water supply networks. In SAMPLE, the aim is to establish a similar model framework for development of PM and LI algorithms to water supply networks with multiple inlets.