Impact of Pressure-Driven Demand on Background Leakage Estimation in Water Supply Networks

Increasing water demand due to urbanization creates a need to develop schemes for managing water supply networks (WSNs). In recent years, hydraulic modeling of WSNs has been used to assess the state of networks in terms of leakage analysis and pressure control. These models are based on demand-driven modeling (DDM) analysis and pressure-driven modeling (PDM) analysis. The former assumes that the nodal demand is fulfilled consistently regardless of the nodal pressure head. The latter appraises the demand as a function of the available pressure head at the nodes. In a previous paper by Adedeji et al. (2017), an algorithm was presented for background leakage detection and estimation in WSNs. The results demonstrated that the algorithm allows the detection of critical pipes and the indication of the nodes where such critical pipes are located for possible pressure control. However, such an algorithm assumes a demand-driven condition of WSNs. In this paper, a pressure-driven modeling is integrated into the developed algorithm with emphasis on its impact on the background leakage estimate. The results obtained are compared to the demand-driven analysis using two WSNs as case studies. The results presented, which consider pipe and node levels, demonstrate that the reliance of the nodal demand on the available pressure head at the node influences the magnitude of the background leakage flow. It is conceived that this investigation might be crucial for the background leakage estimation while considering WSNs operating under pressure-deficient conditions. In this paper, the solution time for both simulation scenarios is also presented.

Real-Time Dynamic Hydraulic Model of Water Distribution Networks

The impact of climate change and increasing urbanisation throughout the world has forced water utility managers to increase the efficiency of water resources. Reduction of real (or physical) water losses plays a crucial role in improving the efficiency of water supply systems. Considering these challenges, it will not be enough to rely only on traditional approaches to solve the problem of water losses. Therefore, more advanced techniques need to be developed and utilized. Recently, a framework for a real-time dynamic hydraulic model for potable water loss reduction was proposed. This paper focuses mainly on the three major components of the proposed real-time dynamic hydraulic model framework for potable water loss reduction, which have been developed recently. These are background leakage detection, pressure management, and water demand forecasting. A background leakage detection algorithm was proposed which, amongst others, permits the localisation of potential critical nodes or pipes with higher leakage flow in the network where such pressure management could be performed. More so, new controllers (algorithms) which perform pressure management by accurately setting the pressure, using either a pressure control valve or variable speed pump, have been constructed. In addition, background leakage flow is greatly affected by demand variations, a water demand forecasting model is constructed with the aim of annexing the demand variation for multi-period leakage analysis. Thus, a short-term water demand forecast utilising the Model Conditional Processor was constructed to forecast the following hour demand and the associated predictive uncertainty. Although each of these components have been tested independently, future work is ongoing for merging these components and integration within the dynamic hydraulic model framework.

Hydraulic Model Calibration of Pressure Reduced Zones with Multiple Input Valves

Pressure management is a widely adopted technique to decrease background leakage or to extend the lifespan of the pipe network [1]. In some cases, it is inevitable to deploy multiple pressure reducing valves to supply a particular zone. In order to supply water to the customers with optimal pressure head, the precise setting of the parallel pressure reducing valves’ (PRV) target pressure is required. Steady-state hydraulic models like EPANET has the functionality to simulate pressure loss of a pressure-reducing valve [1]. This can be simulated by adding minor-loss after the pipe, or by modifying the properties of the next link on the downstream side. Either way, the proper setting of the coefficients is essential to calibrate the hydraulic model. In this paper, two non-linear optimization methods were utilized to calibrate the hydraulic model with multiple input values.