Pressure-dependent Demand and Leakage Modelling with an EPANET Extension – WaterNetGen

Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qualities are examined at the treatment plant. However, its quality can substantially be contaminated during transportation from the plant to the consumers' taps. Contamination in water distribution networks (WDNs) is a danger that can have severe consequences on public health as well as an economic and social instability. Water distribution networks are immensely susceptible to deliberate or accidental attacks due to the complex nature of the system. Hence, contamination source identification (CSI) is a topical issue in water distribution systems that require immediate attention of researchers in order to protect mankind from the adverse effect of consuming contaminated water. Usually, a contaminant event can be detected by the water quality monitoring sensors or the contaminant warning system (CWS) installed on the network. Nevertheless, how to derive the source of the contamination from the collected information is a difficult task that must be tackled in order to evaluate the spread of the contamination and for immediate remedial strategies. In the past two decades, considerable efforts and advancement have been made by researchers applying various techniques in order to locate the source of the contamination in WDNs. Each of the techniques has certain limitations and applicability as reported in the literature. This paper presents a comprehensive review of the existing techniques with emphasis on their importance and technical challenges. Despite a series of investigations in this domain, the field is yet to be unified. Hence, open research areas are still available to explore. Consequently, improvement on the existing techniques is necessary and hereby suggested. More importantly, practical application of these techniques offer a major research gap that must be addressed.

Section: Epanet Msxmentioning

confidence: 99%

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

“…Equations (3) and (4) are widely used in many research studies [16][17][18][19][20][21][22][23][24] to model and assesses network leakage outflows. However, the research attempts in [16,25] have proved that the use of the orifice flow equation can lead to misleading results based on some specific pipe conditions.…”

Section: Background and Related Workmentioning

confidence: 99%

“…That is, q = q norm +q nleak (20) where q norm denotes the vector of the normal nodal demand andq nleak is the vector of the nodal leakage flow.…”

Section: Wdn Topology and Model Formulationmentioning

confidence: 99%

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Adedeji

Hamam

Abe

et al. 2017

Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact leaking pipelines connected to this segment(s) where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.

“…PDA, on the other hand, provides more realistic hydraulics avoiding negative pressures. WaterGEMS [47], WDNetXL [48], and WaterNetGen [49] are well-known programs equipped with PDA option based on HOR. However, the system-specific HOR should be provided, which is spatially and temporally inconsistent and also operational dependent.…”

Section: Negative Pressure Treatmentmentioning

confidence: 99%

Seismic-Reliability-Based Optimal Layout of a Water Distribution Network

Yoo

Jung

Kang

et al. 2016

Abstract:We proposed an economic, cost-constrained optimal design of a water distribution system (WDS) that maximizes seismic reliability while satisfying pressure constraints. The model quantifies the seismic reliability of a WDS through a series of procedures: stochastic earthquake generation, seismic intensity attenuation, determination of the pipe failure status (normal, leakage, and breakage), pipe failure modeling in hydraulic simulation, and negative pressure treatment. The network's seismic reliability is defined as the ratio of the available quantity of water to the required water demand under stochastic earthquakes. The proposed model allows no pipe option in decisions, making it possible to identify seismic-reliability-based optimal layout for a WDS. The model takes into account the physical impact of earthquake events on the WDS, which ultimately affects the network's boundary conditions (e.g., failure level of pipes). A well-known benchmark network, the Anytown network, is used to demonstrate the proposed model. The network's optimal topology and pipe layouts are determined from a series of optimizations. The results show that installing large redundant pipes degrades the system's seismic reliability because the pipes will cause a large rupture opening under failure. Our model is a useful tool to find the optimal pipe layout that maximizes system reliability under earthquakes.