Optimizing Sensing: From Water to the Web

Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems

Validation of a Computational Fluid Dynamics Model for a Novel Residence Time Distribution Analysis in Mixing at Cross-Junctions

[EN] In Water Distribution Networks, the chlorine control is feasible with the use of water quality simulation codes. EPANET is a broad domain software and several commercial computer software packages base their models on its methodology. However, EPANET assumes that the solute mixing at cross-junctions is ¿complete and instantaneous¿. Several authors have questioned this model. In this paper, experimental tests are developed while using Copper Sulphate as tracer at different operating conditions, like those of real water distribution networks, in order to obtain the Residence Time Distribution and its behavior in the mixing as a novel analysis for the cross-junctions. Validation tests are developed in Computational Fluid Dynamics, following the k-# turbulence model. It is verified that the mixing phenomenon is dominated by convection, analyzing variation of Turbulent Schmidt Number vs. experimental tests. Having more accurate mixing models will improve the water quality simulations to have an appropriate control for chlorine and possible contaminants in water distribution networks.To CONACYT for the Master and Ph.D. scholarships (417824 and 703220) to D.H.-C. and the Ph.D. scholarship (294038) to M.R.; To Universidad de Guanajuato for the financial support of the project No. 100/2018 of J.L.N.; To Engineering Division, Campus Guanajuato and Geomatics and Hydraulics Engineering Department for the financial support of this project; and finally, to SEP-PRODEP and UG for the financial support to publish this paper.Hernandez Cervantes, D.; Delgado Galván, XV.; Nava, JL.; López Jiménez, PA.; Rosales, M.; Mora Rodríguez, JDJ. (2018). Validation of a Computational Fluid Dynamics Model for a Novel Residence Time Distribution Analysis in Mixing at Cross-Junctions. Water. 10(6):1-18. https://doi.org/10.3390/w10060733S118106Mercier Shanks, C., Sérodes, J.-B., & Rodriguez, M. J. (2013). 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Environment International, 33(2), 219-225. doi:10.1016/j.envint.2006.09.009Parks, S. L. I., & VanBriesen, J. M. (2009). Booster Disinfection for Response to Contamination in a Drinking Water Distribution System. Journal of Water Resources Planning and Management, 135(6), 502-511. doi:10.1061/(asce)0733-9496(2009)135:6(502)Hernández Cervantes, D., Mora Rodríguez, J., Delgado Galván, X., Ortiz Medel, J., & Jiménez Magaña, M. R. (2015). Optimal use of chlorine in water distribution networks based on specific locations of booster chlorination: analyzing conditions in Mexico. Water Supply, 16(2), 493-505. doi:10.2166/ws.2015.161Weickgenannt, M., Kapelan, Z., Blokker, M., & Savic, D. A. (2010). Risk-Based Sensor Placement for Contaminant Detection in Water Distribution Systems. Journal of Water Resources Planning and Management, 136(6), 629-636. doi:10.1061/(asce)wr.1943-5452.0000073Rathi, S., & Gupta, R. (2013). Monitoring stations in water distribution systems to detect contamination events. ISH Journal of Hydraulic Engineering, 20(2), 142-150. doi:10.1080/09715010.2013.857470Seth, A., Klise, K. A., Siirola, J. D., Haxton, T., & Laird, C. D. (2016). Testing Contamination Source Identification Methods for Water Distribution Networks. Journal of Water Resources Planning and Management, 142(4), 04016001. doi:10.1061/(asce)wr.1943-5452.0000619Xuesong, Y., Jie, S., & Chengyu, H. (2017). Research on contaminant sources identification of uncertainty water demand using genetic algorithm. Cluster Computing, 20(2), 1007-1016. doi:10.1007/s10586-017-0787-6Rathi, S., & Gupta, R. (2015). Optimal sensor locations for contamination detection in pressure-deficient water distribution networks using genetic algorithm. Urban Water Journal, 14(2), 160-172. doi:10.1080/1573062x.2015.1080736Sandoval, M. A., Fuentes, R., Walsh, F. C., Nava, J. L., & de León, C. P. (2016). Computational fluid dynamics simulations of single-phase flow in a filter-press flow reactor having a stack of three cells. Electrochimica Acta, 216, 490-498. doi:10.1016/j.electacta.2016.09.045Castañeda, L. (2017). Computational Fluid Dynamic Simulations of Single-Phase Flow in a Spacer-Filled Channel of a Filter-Press Electrolyzer. International Journal of Electrochemical Science, 7351-7364. doi:10.20964/2017.08.09Song, I., Romero-Gomez, P., & Choi, C. Y. (2009). Experimental Verification of Incomplete Solute Mixing in a Pressurized Pipe Network with Multiple Cross Junctions. Journal of Hydraulic Engineering, 135(11), 1005-1011. doi:10.1061/(asce)hy.1943-7900.0000095Romero-Gomez, P., Lansey, K. E., & Choi, C. Y. (2010). Impact of an incomplete solute mixing model on sensor network design. Journal of Hydroinformatics, 13(4), 642-651. doi:10.2166/hydro.2010.123Yu, T. C., Shao, Y., & Shen, C. (2014). Mixing at Cross Joints with Different Pipe Sizes in Water Distribution Systems. 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Contamination source inference in water distribution networks

We study the inference of the origin and the pattern of contamination in water distribution networks. We assume a simplified model for the dyanmics of the contamination spread inside a water distribution network, and assume that at some random location a sensor detects the presence of contaminants. We transform the source location problem into an optimization problem by considering discrete times and a binary contaminated/not contaminated state for the nodes of the network. The resulting problem is solved by Mixed Integer Linear Programming. We test our results on random networks as well as in the Modena city network

A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems

Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach

Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an Evolutionary Algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the Water Sensor Network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensor

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Alternative Strategies For Optimal Water Quality Sensor Placement In Drinking Water Distribution Networks

The most commonly applied strategies for optimal water quality sensor placement in drinking water distribution systems are aimed at contamination early warning systems. These strategies aim to minimize the number of people affected in case of a deliberate contamination of drinking water in the distribution system, and provide a valuable tool. A number of factors which are usually not taken into account, including the response strategy to the identification of a contamination event, the fallibility of sensors and changes in network configuration (valve manipulation) and operation, may affect the results of these strategies. Since the quickness and effectiveness of a response is generally also a function of the location of the contamination event (both source and first detection), knowledge on the response strategy should also be part of the sensor placement optimization methodology. Hydraulic models generally play a central role in the optimization of sensor placement. The validity of their computations strongly depends upon accurate and up to date information on the network, which is often not fully available (e.g. unregistered valve status changes). Therefore, a sensor network configuration which is somewhat robust to these issues is desirable. Besides contamination early warning systems, there are several other reasons for placing water quality sensors in distribution network, including process control and monitoring, regulatory monitoring, etc. These require a different approach to optimization of the sensor network in terms of sensor locations. In this paper, we demonstrate the application of different sensor location optimization strategies in drinking water distribution networks, with aims such as minimization of the number of people affected, maximization of distribution network coverage, optimization of sensor network robustness and optimization of contamination source identification. We present and compare results of these different approaches applied to hydraulic models of a real drinking water distribution network in the Netherlands