Optimizing the operation of the Haifa-A water-distribution network

Haifa-A is the first of two case studies relating to the POWADIMA research project. It comprises about 20% of the city's water-distribution network and serves a population of some 60,000 from two sources. The hydraulic simulation model of the network has 126 pipes, 112 nodes, 9 storage tanks, 1 operating valve and 17 pumps in 5 discrete pumping stations. The complex energy tariff structure changes with hours of the day and days of the year. For a dynamically rolling operational horizon of 24 h ahead, the real-time, near-optimal control strategy is calculated by a software package that combines a genetic algorithm (GA) optimizer with an artificial neural network (ANN) predictor, the latter having replaced a conventional hydraulic simulation model to achieve the computational efficiency required for real-time use. This paper describes the Haifa-A hydraulic network, the ANN predictor, the GA optimizer and the demand- forecasting model that were used. Thereafter, it presents and analyses the results obtained for a full (simulated) year of operation in which an energy cost saving of some 25% was achieved in comparison to the corresponding cost of current practice. Conclusions are drawn regarding the achievement of aims and future prospects.

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Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model

Journal of Hydroinformatics ◽

10.2166/hydro.2006.014 ◽

2007 ◽

Vol 9 (1) ◽

pp. 15-24 ◽

Cited By ~ 38

Author(s):

Zhengfu Rao ◽

Fernando Alvarruiz

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Simulation Model ◽

Real Time ◽

Domain Knowledge ◽

Water Distribution ◽

Distribution Network ◽

Water Distribution Network ◽

Hydraulic Simulation ◽

Tank Storage

As part of the POWADIMA research project, this paper describes the technique used to predict the consequences of different control settings on the performance of the water-distribution network, in the context of real-time, near-optimal control. Since the use of a complex hydraulic simulation model is somewhat impractical for real-time operations as a result of the computational burden it imposes, the approach adopted has been to capture its domain knowledge in a far more efficient form by means of an artificial neural network (ANN). The way this is achieved is to run the hydraulic simulation model off-line, with a large number of different combinations of initial tank-storage levels, demands, pump and valve settings, to predict future tank-storage water levels, hydrostatic pressures and flow rates at critical points throughout the network. These input/output data sets are used to train an ANN, which is then verified using testing sets. Thereafter, the ANN is employed in preference to the hydraulic simulation model within the optimization process. For experimental purposes, this technique was initially applied to a small, hypothetical water-distribution network, using EPANET as the hydraulic simulation package. The application to two real networks is described in subsequent papers of this series.

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Optimizing the operation of the Valencia water-distribution network

Journal of Hydroinformatics ◽

10.2166/hydro.2006.018 ◽

2007 ◽

Vol 9 (1) ◽

pp. 65-78 ◽

Cited By ~ 47

Author(s):

Fernando Martínez ◽

Vicente Hernández ◽

José Miguel Alonso ◽

Zhengfu Rao ◽

Stefano Alvisi

Keyword(s):

Control System ◽

Water Distribution ◽

Distribution Network ◽

Water Distribution Network ◽

Production Costs ◽

Operational Cost ◽

Hydraulic Simulation ◽

Pumping Stations ◽

Tariff Structure ◽

The Cost

The second of the two case studies in the POWADIMA research project, the Valencia water-distribution network, serves a population of approximately 1.2 million and is supplied by surface water via two treatment plants which have significantly different production costs. The only storage available is located at the treatment plants, each of which has its own pumping station. The management of the network is a complex operation involving 4 pressure zones and 49 operating valves, 10 of which are routinely adjusted. The electricity tariff structure varies with the hour of the day and month of the year. The EPANET hydraulic simulation model of the network has 725 nodes, 10 operating valves, 2 storage tanks and 17 pumps grouped at the two pumping stations. The control system that has been implemented comprises an artificial neural network predictor in place of the EPANET model and a dynamic genetic algorithm to optimize the control settings of pumps and valves up to a 24 h rolling operating horizon, in response to a highly variable demand. The results indicate a potential operational-cost saving of 17.6% over a complete (simulated) year relative to current practice, which easily justifies the cost of implementing the control system developed.

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Improved Hydraulic Simulation of Valve Layout Effects on Post-Earthquake Restoration of a Water Distribution Network

Sustainability ◽

10.3390/su12083492 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3492

Author(s):

Jeongwook Choi ◽

Doosun Kang

Keyword(s):

Water Distribution ◽

Distribution Network ◽

Water Distribution Network ◽

Seismic Damage ◽

Hydraulic Analysis ◽

Hydraulic Simulation ◽

Restoration Process ◽

Restoration Strategies ◽

The Impact ◽

Pressure Driven Analysis

To restore water pipes damaged by earthquakes, it is common to block the water flow by closing the associated shut-off valves. In this process, water supply suspension in the area connected to the isolated pipes is inevitable, which decreases the serviceability of the water distribution network (WDN). In this study, we identified the impact of valve layout (i.e., number and location) on system serviceability during a seismic damage restoration process. By conducting a pressure-driven-analysis (PDA) using EPANET 3.0, a more realistic hydraulic analysis could be carried out under the seismically damaged condition. Furthermore, by considering the valve-controlled segment in the hydraulic simulation, a more realistic water suspension area was determined, and efficient seismic damage restoration strategies were identified. The developed model was implemented on a WDN to demonstrate the effect of valve layout on the post-earthquake restoration process. Finally, effective restoration strategies were suggested for the application network.

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