Calibration of Hydraulic Model in Rea-Life Water Distribution System

2012 ◽  
Vol 155-156 ◽  
pp. 285-290 ◽  
Author(s):  
Wei Wei Zhang ◽  
Guo Ping Yu ◽  
Miao Shun Bai
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Real Life ◽  
Hydraulic Model ◽  
Calibration Model ◽  
Loading Conditions ◽  
Head Losses ◽  
Real Coded Genetic Algorithm ◽  
Pipe Roughness

The most uncertain input parameters that often considered for calibration in water distribution system hydraulic model are pipe roughness coefficients and nodal demands. Both pipe roughness coefficients and nodal demands are considered to be calibrated in the calibration process, which works alternately. The calibration model was formulated as a constrained optimization problem. The entire head losses under different loading conditions are introduced in the objective function to guide the calibration direction, which can make consistent calibration effects on different loading conditions. The calibration model uses real-coded genetic algorithm along with a general network solver (EPANET 2.0) to adjust pipe roughness coefficients and nodal demands multipliers until the preset criteria are meet. The approach was applied in the calibration of a real-life water distribution system hydraulic model in China, which takes three loading conditions (max, min and average hour) into consider. The results show that the approach works well in achieving good calibration results, which match field observation in a reasonable level and meet engineering requirements.

scholarly journals S-PLACE GA for optimal water quality sensor locations in water distribution network for dual purpose: regular monitoring and early contamination detection- a software tool for academia and practitioner

2020 ◽  
Author(s):  
Shweta Rathi
Keyword(s):  
Water Quality ◽  
Water Distribution ◽  
Real Life ◽  
Software Tool ◽  
Distribution Networks ◽  
Hydraulic Model ◽  
Calibration Model ◽  
Dual Purpose ◽  
Practical Applications ◽  
Sensor Locations

Abstract Security concern of water distribution networks (WDNs) lead to increased interest in optimize sensor locations in WDNs achieved through a calibrated hydraulic model. This paper presents a methodology which consists of two stages. The first stage consists of calibration of a hydraulic model using Genetic Algorithm (GA). A real-life network of one of the hydraulic zones of Nagpur city, India is considered which optimizes settings of a throttled controlled valve in different timings for calibration. In this stage, detailed case study, GA calibration model, methodology and results on calibrated models are discussed. The second stage consists of identifying optimal sensor locations using a newly developed software tool named as ‘S-PLACE GA’ and its efficiency and effectiveness are discussed. It can be used for dual purpose of routine monitoring of water quality and for early detection of contamination. The optimal locations are obtained considering two objective metrics ‘Demand Coverage’ and ‘Time-Constrained Detection Likelihood’. These two objectives are combined into a single objective by using weights. Key features, input data required for the software and their applications on: 1) BWSN network 1 and result comparison with others and 2) Calibrated model of first stage are discussed. Results showed the effectiveness of S-PLACE GA for practical applications.

scholarly journals Evaluation of Hydraulic Performance of Water Distribution System for Sustainable Management

2021 ◽  
Author(s):  
Tarekegn Kuma ◽  
Brook Abate Getahun
Keyword(s):  
Distribution System ◽  
Field Data ◽  
Water Distribution ◽  
Water Distribution System ◽  
Simulated Data ◽  
Water Distribution Network ◽  
Hydraulic Performance ◽  
Hydraulic Model ◽  
Integrated System

Abstract Understanding water distribution system hydraulic performance is crucial for a water supply system management. A case study was conducted evaluating the hydraulic performance of water distribution system of Tulu Bolo town. The hydraulic model of water distribution network was developed using GIS integrated with WaterGEMS hydraulic model. The implementation of the integrated system verified that water to regulate the pressure and velocity in order to sustain. According to the analysis, about 92.6% of nodes have optimized pressure ranged between 15m to 70m and about 1.27% is under permissible pressure. Model calibration was performed by comparing simulated data with field data, the result of pressure calibration has a linear correlation coefficient of 0.93 and the hydraulic model in WaterGEMS was calibrated and optimized with a field data.

scholarly journals A deterministic approach for optimization of booster disinfection placement and operation for a water distribution system in Beijing

2013 ◽  
Vol 15 (3) ◽  
pp. 1042-1058 ◽  
Author(s):  
Fanlin Meng ◽  
Shuming Liu ◽  
Avi Ostfeld ◽  
Chao Chen ◽  
Alejandra Burchard-Levine
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Real Life ◽  
Deterministic Approach ◽  
Study Results ◽  
The Impact ◽  
Beijing China

Previous studies on booster disinfection optimization were commonly based on ‘blank networks’, neglecting the impact of existing disinfection facilities, which could result in misleading solutions. To overcome this limitation, a method, which incorporates the existing disinfection facilities, is developed and demonstrated in this study. A particle backtracking algorithm, which traces the upstream pathways of the disinfection insufficiency nodes, is employed to narrow down the potential positions for booster stations. Deterministic optimization results are then efficiently yielded by the introduction of a ‘coverage matrix’. The proposed method is applied to a real life water distribution system in Beijing, China. Results show the methodology effectiveness in optimizing booster disinfection placement and operation for real life water distribution systems. For the explored case study, results suggest that adding a booster disinfection station at 0.1% of the nodes of the system can satisfy chlorine residual at about 97.5% of all nodes.

A GIS-based water distribution model for Zhengzhou city, China

2011 ◽  
Vol 11 (4) ◽  
pp. 497-503 ◽  
Author(s):  
Hou Yu-Kun ◽  
Zhao Chun-Hui ◽  
Huang Yu-Chung
Keyword(s):  
Water Supply ◽  
Distribution System ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Hydraulic Model ◽  
Distribution Model ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
Pipe Roughness

Many water companies in China are developing GIS as a computer-based tool, for mapping and analyzing objects and events that happen on a water distribution network. However, only a few companies have taken a further step to develop a hydraulic model based on GIS, and Zhengzhou Water Supply Corporation is one of them. The WaterGEMS V8 XM from Bentley is used to develop the hydraulic model for the water distribution network in Zhengzhou city, which has a population of over 3 million. During establishment of the model, some of the data extracted from GIS are missing, abnormal, and redundant and require careful screening, searching, and judging. Model calibration is performed after a sensitivity analysis. Peaking factor and pipe roughness coefficient are key model parameters to calibrate. In calibrating peaking factors, the distribution system is divided into 5 operation districts with different types of water usage. To calibrate pipe roughness coefficients, the system was divided into 4 water supply districts with different attributes of pipelines. Finally, a case study of pipe layout evaluation it shows the hydraulic model to be a powerful tool for water supply management.

Upgrading Water Distribution System Based on GA-RBF Neural Network Model

2011 ◽  
Vol 267 ◽  
pp. 605-608 ◽  
Author(s):  
Hong Xiang Wang ◽  
Wen Xian Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Network Model ◽  
Basis Function ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Calibration Model ◽  
Radial Basis

Hydraulic network calibration model is to minimize the sum of the squares of the differences between the calibrated and initial pipe roughness estimates, under a set of constraints determined from a sensitivity matrix. The upgrading problem of water distribution system was put forward after the preferable network model was obtained. Radial Basis Function neural network (RBF) based on genetic algorithm (GA) was proposed to solve the model. Genetic algorithm was applied to optimize the parameters of the neural network, and overcome the over-fitting problem. Case study concludes that using Radial Basis Function neural network (RBF) based on genetic algorithm (GA) and good results were obtained.

scholarly journals The Sensitivity of a Water Distribution System to Regional State Parameter Variations

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Philip R. Page
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
State Parameter ◽  
Reservoir Water ◽  
Flow Rates ◽  
Parameter Variations ◽  
Pipe Roughness ◽  
Minor Losses ◽  
First Time

The sensitivity of a pressurised water distribution system (WDS) to state parameter variations is studied. A novel local regional sensitivity analysis (LRSA) approach is introduced which applies the same change to a collection of parameters, called a region. For example, sensitivity to suburbs can be studied. General analytical (using algebraic methods) results are derived. They show how sensible conclusions arise from LRSA and state this dependence of the WDS on regions for the first time. For most cases, the WDS is 1.852–2 times more sensitive to pipe roughness coefficients than to pipe lengths. In most cases, when certain pipes do not have minor losses, the WDS is 4.871–5.333 times more sensitive to pipe diameters than to pipe lengths. Hence, the WDS is the most sensitive to pipe diameters, medium sensitive to pipe roughness coefficients, and least sensitive to pipe lengths. For most cases, when all reservoir and tank elevations (and heads) remain the same, changes of other elevations do not change flow rates and change the pressures in a simple additive way. In most cases, when all the reservoir water surface elevations are changed together, the flow rates remain unchanged, and the pressures change in a simple additive way.

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