Donghwi Jung

A water distribution system burst from a sudden pipe failure results in water loss and disruption of customer service. Artificial neural networks, state estimation, and statistical process control (SPC) have been applied to detect bursts. However, system operational condition changes such as the set of operating pumps and valve closures greatly complicates the detection problem. Thus, to date applications have been limited to networks that are supplied by gravity or under consistent operation co

Robustness is generally defined as an ability of a system to maintain its function under a defined set of disturbances. To introduce robustness to the water distribution systems (WDSs) design, chance constrained, or so-called reliability-based models have been formulated. Under variations in system parameters, such as nodal demands and pipe roughness, system reliability is generally measured as the probability that the stochastic nodal pressures will be higher than an allowable minimum pressure

A pipe burst is a major water distribution system failure. Water escapes the network through the break increasing the total flow entering the network. These higher flows, in turn, increase the head losses in pipes and result in lower water pressures at customer taps. This study focuses on burst detection by seeking to identify anomalies in net system demand, pipe flow rates, and nodal pressure heads. Three univariate statistical process control (SPC) methods (the Western Electric Company rules,

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized

The water distribution system (WDS) reliability is the ability of a network to provide the required quantity of water to customers under uncertain system conditions. In this study, eight surrogate measures of WDS reliability were investigated: Two hydraulic reliability measures, two robustness measures, single failure reliability (SFRel) with three valve installation levels, and seismic reliability (SeisRel). The eight reliability measures were calculated for 16 study networks of various sizes (

Pipe bursts in water distribution systems (WDS) must be rapidly detected to minimize the loss of system functionality and recovery time. Pipe burst is the most common failure in WDS. It results in water loss out of the system, increased head losses, and low pressure at the customers' taps. Therefore, effective and efficient detection of pipe bursts can improve system resilience. To this end, this study proposes an optimal meter placement model to identify meter locations that maximize detection

This study proposes variable balancing approaches for the exploration (diversification) and exploitation (intensification) of the non-dominated sorting genetic algorithm-II (NSGA-II) with simulated binary crossover (SBX) and polynomial mutation (PM) in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of th

Many surrogate measures for water distribution system (WDS) reliability have been introduced in the last three decades. This study investigated the differences between designs based on topological and hydraulic reliabilities. The former considers network structural redundancy and connectivity, whereas the latter considers system performance under uncertain future conditions. Two topological reliabilities are proposed based on the network theory: the average node degree ratio (ANDr) and meshednes

This study develops a real-time near-optimal pump scheduling model for water transmission networks. The model aims to minimize energy cost by means of optimal pump operation while meeting system demands and satisfying operational constraints. The real-time control is realized using Supervisory Control and Data Acquisition (SCADA) system, by which system monitoring data is collected and the optimal control decisions are sent to the pumping stations for implementation. The model is composed of thr

Statistical process control (SPC) identifies any nonrandom patterns in the system output variables of a water distribution system (WDS) by comparing them to their normal historic mean and variance. While each SPC method has different performance characteristics, there has been little effort expended to develop a hybrid method that combines the different characteristics. This paper proposes a hybrid SPC method that combines a modified Western Electric Company (WECO) method and the cumulative sum

Real-time state estimation is defined as the process of calculating the state variable of interest in real time not being directly measured. In a water distribution system (WDS), nodal demands are often considered as the state variable (i.e., unknown variable) and can be estimated using nodal pressures and pipe flow rates measured at sensors installed throughout the system. Nodes are often grouped for aggregation to decrease the number of unknowns (demands) in the WDS demand estimation problem.

This study examines the impact of advanced metering infrastructure (AMI) end-user demand metering failure on water distribution system (WDS) operation and management. To address this issue, our first step is to develop a burst detection algorithm that compares total end-user demands with system inflow rates. Western Electric Company (WEC) rules are applied to test for anomalies in the time series of normalized differences between supply and withdrawal. Then, hydraulic model prediction and burst

A water distribution system burst from a pipe rupture results in water loss and disruptions of customer service. Numerous methods, including Statistical Process Control, time series modeling, and pattern recognition, have been applied to detect bursts. However, system changes its boundary conditions such as the set of operating pumps and valve closures greatly complicating the detection problem. Thus, to date applications have been limited to the network supplied by gravity or under constant bou