Experimental evidence of diffusion and dispersion impact on optimal positioning of water quality sensors in distribution networks

2021 ◽  
Author(s):  
Stefania Piazza ◽  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Water Quality ◽  
Experimental Evidence ◽  
Distribution Networks ◽  
Water Quality Sensors

scholarly journals Measuring imperfections of water quality sensors in water distribution networks

2019 ◽  
Vol 30 (9) ◽  
pp. 095101 ◽  
Author(s):  
Casper de Winter ◽  
Venkata Reddy Palleti ◽  
Daniel Worm ◽  
Robert Kooij
Keyword(s):  
Water Quality ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Water Quality Sensors

scholarly journals Reducing Impacts of Contamination in Water Distribution Networks: A Combined Strategy Based on Network Partitioning and Installation of Water Quality Sensors

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1315 ◽  
Author(s):  
Carlo Ciaponi ◽  
Enrico Creaco ◽  
Armando Di Nardo ◽  
Michele Di Natale ◽  
Carlo Giudicianni ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Distribution ◽  
Water Quality Monitoring ◽  
Search Space ◽  
Distribution Networks ◽  
Economic Benefits ◽  
Water Distribution Networks ◽  
Network Partitioning ◽  
Combined Strategy ◽  
Water Quality Sensors

This paper proposes a combined management strategy for monitoring water distribution networks (WDNs). This strategy is based on the application of water network partitioning (WNP) for the creation of district metered areas (DMAs) and on the installation of sensors for water quality monitoring. The proposed methodology was tested on a real WDN, showing that boundary pipes, at which flowmeters are installed to monitor flow, are good candidate locations for sensor installation, when considered along with few other nodes detected through topological criteria on the partitioned WDN. The option of considering only these potential locations, instead of all WDN nodes, inside a multi-objective optimization process, helps in reducing the search space of possible solutions and, ultimately, the computational burden. The solutions obtained with the optimization are effective in reducing affected population and detection time in contamination scenarios, and in increasing detection likelihood and redundancy of the monitoring system. Last but most importantly, these solutions offer benefits in terms of management and costs. In fact, installing a sensor alongside the flowmeter present between two adjacent DMAs yields managerial advantages associated with the closeness of the two devices. Furthermore, economic benefits due to the possibility of sharing some electronical components for data acquisition, saving, and transmission are derived.

scholarly journals On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces

2021 ◽  
Author(s):  
Antonio Candelieri ◽  
Andrea Ponti ◽  
Francesco Archetti
Keyword(s):  
Water Quality ◽  
Probability Distribution ◽  
Water Distribution ◽  
Probability Distributions ◽  
Sensor Placement ◽  
Distribution Networks ◽  
Detection Time ◽  
Optimal Sensor Placement ◽  
Discrete Probability ◽  
Water Quality Sensors

This paper is focused on two topics very relevant in water distribution networks (WDNs): vulnerability assessment and the optimal placement of water quality sensors. The main novelty element of this paper is to represent the data of the problem, in this case all objects in a graph underlying a water distribution network, as discrete probability distributions. For vulnerability (and the related issue of re-silience) the metrics from network theory, widely studied and largely adopted in the water research community, reflect connectivity expressed as closeness centrality or, betweenness centrality based on the average values of shortest paths between all pairs of nodes. Also network efficiency and the related vulnerability measures are related to average of inverse distances. In this paper we propose a different approach based on the discrete probability distribution, for each node, of the node-to-node distances. For the optimal sensor placement, the elements to be represented as dis-crete probability distributions are sub-graphs given by the locations of water quality sensors. The objective functions, detection time and its variance as a proxy of risk, are accordingly represented as a discrete e probability distribution over contamination events. This problem is usually dealt with by EA algorithm. We’ll show that a probabilistic distance, specifically the Wasserstein (WST) distance, can naturally allow an effective formulation of genetic operators. Usually, each node is associated to a scalar real number, in the optimal sensor placement considered in the literature, average detection time, but in many applications, node labels are more naturally expressed as histograms or probability distributions: the water demand at each node is naturally seen as a histogram over the 24 hours cycle. The main aim of this paper is twofold: first to show how different problems in WDNs can take advantage of the representational flexibility inherent in WST spaces. Second how this flexibility translates into computational procedures.

SENSE IT - student created water quality sensors

2011 ◽  
Author(s):  
L. Hotaling ◽  
R. Stolkin ◽  
S. Lowes ◽  
P. Chen ◽  
J. Bonner ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Sensors

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

2020 ◽  
Author(s):  
Mark B. Green ◽  
Linda H. Pardo ◽  
Scott W. Bailey ◽  
John L. Campbell ◽  
William H. McDowell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
High Frequency ◽  
Frequency Variation ◽  
Water Quality Sensors

scholarly journals Optimal Water Quality Sensor Placement by Accounting for Possible Contamination Events in Water Distribution Networks

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1999
Author(s):  
Malvin S. Marlim ◽  
Doosun Kang
Keyword(s):  
Water Quality ◽  
Water Distribution ◽  
Sensor Placement ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Fair Value ◽  
Water Distribution Networks ◽  
Optimization Approach ◽  
Protection Measure ◽  
Contamination Events

Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors.

Improving water age estimation in a drinking water distribution network by field study and digital modeling

2021 ◽  
Author(s):  
Jon Kristian Rakstang ◽  
Michael B. Waak ◽  
Marius M. Rokstad ◽  
Cynthia Hallé
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Network Model ◽  
Age Estimation ◽  
Water Distribution ◽  
Distribution Network ◽  
Distribution Networks ◽  
Plate Count ◽  
Water Age ◽  
Drinking Water Distribution

<p>Municipal drinking water distribution networks are complex and dynamic systems often spanning many hundreds of kilometers and serving thousands of consumers. Degradation of water quality within a distribution network can be associated to water age (i.e., time elapsed after treatment). Norwegian distribution networks often consist of an intricate combination of pressure zones, in which the transport path(s) between source and consumer is not easily ascertained. Water age is therefore poorly understood in many Norwegian distribution networks. In this study, simulations obtained from a water network model were used to estimate water age in a Norwegian municipal distribution network. A full-scale tracer study using sodium chloride salt was conducted to assess simulation accuracy. Water conductivity provided empirical estimates of salt arrival time at five monitoring stations. These estimates were consistently higher than simulated peak arrival times. Nevertheless, empirical and simulated water age correlated well, indicating that additional network model calibration will improve accuracy. Subsequently, simulated mean water age also correlated strongly with heterotrophic plate count (HPC) monitoring data from the distribution network (Pearson’s R= 0.78, P= 0.00046), indicating biomass accumulation during distribution—perhaps due to bacterial growth or biofilm interactions—and illustrating the importance of water age for water quality. This study demonstrates that Norwegian network models can be calibrated with simple and cost-effective salt tracer studies to improve water age estimates. Improved water age estimation will increase our understanding of water quality dynamics in distribution networks. This can, through digital tools, be used to monitor and control water age, and its impact on biogrowth in the network.</p>

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