Raido Puust

A review of methods for leakage management in pipe networks

Leakage in water distribution systems is an important issue which is affecting water companies and their customers worldwide. It is therefore no surprise that it has attracted a lot of attention by both practitioners and researchers over the past years. Most of the leakage management related methods developed so far can be broadly classified as follows: (1) leakage assessment methods which are focusing on quantifying the amount of water lost; (2) leakage detection methods which are primarily concerned with the detection of leakage hotspots and (3) leakage control models which are focused on the effective control of current and future leakage levels. This paper provides a comprehensive review of the above methods with the objective to identify the current state-of-the-art in the field and to then make recommendations for future work. The review ends with the main conclusion that despite all the advancements made in the past, there is still a lot of scope and need for further work, especially in area of real-time models for pipe networks which should enable fusion of leakage detection, assessment and control methods.

PROBABILISTIC LEAK DETECTION IN PIPE NETWORKS USING THE SCEM-UA ALGORITHM

Several methods have been developed so far which attempt to detect leaks in water distribution systems by taking into account pressure (and possibly other available) measurements. As in many previous approaches, the leak detection problem is formulated and solved here as an inverse problem with unknown leak areas being the calibration parameters. However, unlike the previous approaches, a stochastic (rather than deterministic) leakage detection methodology is developed and used here. This methodology is based on the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm and is capable of estimating the posterior probability density functions of unknown leak areas in a single model run. Baring in mind the accuracy of similar leak detection approaches, it is believed that the stochastic context used here is more appropriate than the deterministic one used before. To achieve the above goal, the Epanet2 hydraulic network modelling software is linked to the SCEM-UA software in the Matlab programming environment. The SCEM-UA algorithm parameters are tuned to achieve an effective and efficient search for unknown leak areas. The methodology developed is tested on two literature case studies.

Controlling Stormwater Runoff from Impermeable Areas by Using Smart Inlets

Climate change and rapid urbanization are driving the need for improved urban stormwater runoff strategies. Urban stormwater drainage systems are severely affected by the changing climate bringing along inter alia more intense rainfall events. The pipeline system, usually having limited capacity, is unable to cope with these excessive flows and becomes surcharged. This may trigger overland flow from the drainage manholes and activate combined sewer overflows. Both events have negative consequences and therefore should be avoided. There are available effective solutions, like low impact development techniques for the catchments under development. However, options for retrofitting the existing drainage facilities are much more limited. Enlarging the pipelines, which has been a traditional response for rising demands is often financially unrealistic due to the large scope of the work. Therefore, recent advances in “smart” water system technologies are considered as an opportunity to meet the future challenges. In this study the concept for controlling stormwater outflow from impervious catchment areas, i.e. parking lots is developed. The target is to find a solution that is affordable and can be implemented with minor disturbances in the area. For that, a novel approach of controlling water flow by regulating the inflow to the manholes is analysed. The adjustable gullies are real time controlled by coupling rule-based algorithms with distributed model predictive control. The concept is successfully tested in a 12 ha impervious catchment area in Tallinn.