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    Water quality sensor placement: a multi-objective and multi-criteria approach

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    [EN] To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.Brentan, B.; Carpitella, S.; Barros, D.; Meirelles, G.; Certa, A.; Izquierdo Sebastián, J. (2021). Water quality sensor placement: a multi-objective and multi-criteria approach. Water Resources Management. 35(1):225-241. https://doi.org/10.1007/s11269-020-02720-3S225241351Barak S, Mokfi T (2019) Evaluation and selection of clustering methods using a hybrid group mcdm. Expert Syst Appl 138:112817Berry JW, Fleischer L, Hart WE, Phillips CA, Watson JP (2005) Sensor placement in municipal water networks. J Water Resour Plan Manag 131 (3):237–243Bouyssou D, Marchant T (2015) On the relations between electre tri-b and electre tri-c and on a new variant of electre tri-b. Eur J Oper Res 242(1):201–211Brentan B, Carpitella S, Izquierdo J, Luvizotto E Jr, Meirelles G (2019) A multi-objective and multi-criteria approach for district metered area design: water operation and quality analysis. In: International conference on mathematical modeling in engineering & human behaviour, vol 2019, pp 110–117Brito AJ, de Almeida AT, Mota CM (2010) A multicriteria model for risk sorting of natural gas pipelines based on electre tri integrating utility theory. Eur J Oper Res 200(3):812–821Broad DR, Maier HR, Dandy GC, Nixon JB (2008) Optimal design of water distribution systems including water quality and system uncertainty. In: Water distribution systems analysis symposium, vol 2006, pp 1–17Candelieri A, Conti D, Archetti F (2014) A graph based analysis of leak localization in urban water networks. Procedia Eng 70:228–237Carpitella S, Brentan B, Montalvo I, Izquierdo J, Certa A (2018a) Multi-objective and multi-criteria analysis for optimal pump scheduling in water systems. EPiC Series Eng 3:364–371Carpitella S, Certa A, Izquierdo J, La Fata CM (2018b) k-out-of-n systems: an exact formula for the stationary availability and multi-objective configuration design based on mathematical programming and topsis. J Comput Appl Math 330:1007–1015Carpitella S, Ocaña-Levario SJ, Benítez J, Certa A, Izquierdo J (2018c) A hybrid multi-criteria approach to gpr image mining applied to water supply system maintenance. J Appl Geophy 159:754–764Certa A, Enea M, Galante GM, La Fata CM (2017) Electre tri-based approach to the failure modes classification on the basis of risk parameters: an alternative to the risk priority number. Comput Indust Eng 108:100–110Cheung P, Piller O, Propato M (2005) Optimal location of water quality sensors in supply systems by multiobjective genetic algorithms. In: Eight international conference on computing and control in the water industry CCWI05, vol 1, p 2Christodoulou SE, Gagatsis A, Xanthos S, Kranioti S, Agathokleous A, Fragiadakis M (2013) Entropy-based sensor placement optimization for waterloss detection in water distribution networks. Water Resour Manag 27 (13):4443–4468Corrente S, Greco S, Słowiński R (2016) Multiple criteria hierarchy process for electre tri methods. Eur J Oper Res 252(1):191–203Costa AS, Govindan K, Figueira JR (2018) Supplier classification in emerging economies using the electre tri-nc method: a case study considering sustainability aspects. J Clean Prod 201:925–947De Schaetzen W, Walters G, Savic D (2000) Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms. Urban Water 2(2):141–152de Winter C, Palleti VR, Worm D, Kooij R (2019) Optimal placement of imperfect water quality sensors in water distribution networks. Comput Chem Eng 121:200–211Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6 (2):182–197Dias LC, Antunes CH, Dantas G, de Castro N, Zamboni L (2018) A multi-criteria approach to sort and rank policies based on delphi qualitative assessments and electre tri: the case of smart grids in brazil. Omega 76:100–111Eliades DG, Kyriakou M, Vrachimis S, Polycarpou MM (2016) Epanet-matlab toolkit: An open-source software for interfacing epanet with matlab. In: Proceedings of the 14th international conference on computing and control for the water industry, CCWIFernandez E, Navarro J (2011) A new approach to multi-criteria sorting based on fuzzy outranking relations: the theseus method. Eur J Oper Res 213 (2):405–413Fernández E, Figueira JR, Navarro J, Roy B (2017) Electre tri-nb: a new multiple criteria ordinal classification method. Eur J Oper Res 263 (1):214–224Figueira JR, Greco S, Roy B, Słowiński R (2010) Electre methods: main features and recent developments. In: Handbook of multicriteria analysis. Springer, New York, pp 51–89Figueira JR, Greco S, Roy B, Słowiński R (2013) An overview of electre methods and their recent extensions. J Multi-Criteria Dec Anal 20 (1-2):61–85Francés-Chust J, Brentan BM, Carpitella S, Izquierdo J, Montalvo I (2020) Optimal placement of pressure sensors using fuzzy dematel-based sensor influence. Water 12(2):493Gandy M (2004) Rethinking urban metabolism: water, space and the modern city. City 8(3):363–379Giudicianni C, Herrera M, Di Nardo A, Greco R, Creaco E, Scala A (2020) Topological placement of quality sensors in water-distribution networks without the recourse to hydraulic modeling. J Water Resour Plan Manag 146 (6):04020030Hart WE, Murray R (2010) Review of sensor placement strategies for contamination warning systems in drinking water distribution systems. J Water Resour Plan Manag 136(6):611–619Herrera M, Abraham E, Stoianov I (2016) A graph-theoretic framework for assessing the resilience of sectorised water distribution networks. Water Resour Manag 30(5):1685–1699Huang JJ, McBean EA, James W (2008) Multi-objective optimization for monitoring sensor placement in water distribution systems. In: Water distribution systems analysis symposium, vol 2006, pp 1–14Kapelan ZS, Savic DA, Walters GA (2003) A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks. J Hydraul Res 41(5):481–492Lee JH (2013) Determination of optimal water quality monitoring points in sewer systems using entropy theory. Entropy 15(9):3419–3434Liu Z, Ming X (2019) A methodological framework with rough-entropy-electre tri to classify failure modes for co-implementation of smart pss. Adv Eng Inform 42:100968Marchi A, Salomons E, Ostfeld A, Kapelan Z, Simpson AR, Zecchin AC, Maier HR, Wu ZY, Elsayed SM, Song Y et al (2013) Battle of the water networks ii. J Water Resour Plan Manag 140(7):04014009Mohammed A, Harris I, Soroka A, Nujoom R (2019) A hybrid mcdm-fuzzy multi-objective programming approach for a g-resilient supply chain network design. Comput Indust Eng 127:297–312Montalvo I, Izquierdo J, Pérez-garcía R, Herrera M (2014) Water distribution system computer-aided design by agent swarm optimization. Comput-Aided Civ Inf Eng 29(6):433–448Mousseau V, Slowinski R, Zielniewicz P (2000) A user-oriented implementation of the electre-tri method integrating preference elicitation support. Comput Opera Res 27(7-8):757–777Nafi A, Crastes E, Sadiq R, Gilbert D, Piller O (2018) Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments. Stoch Environ Res Risk Assess 32(2):527–544Neto JGD, Machado MAS, Gomes LFAM, Caldeira AM, Sallum FSV (2017) Investments in a new technological infrastructure: Decision making using the electre-tri methodology. Procedia Comput Sci 122:194–199Ohar Z, Lahav O, Ostfeld A (2015) Optimal sensor placement for detecting organophosphate intrusions into water distribution systems. Water Res 73:193–203Oliker N, Ostfeld A (2015) Network hydraulics inclusion in water quality event detection using multiple sensor stations data. Water Res 80:47–58Ostfeld A, Salomons E (2005) Optimal early warning monitoring system layout for water networks security: Inclusion of sensors sensitivities and response delays. Civ Eng Environ Syst 22(3):151–169Ostfeld A, Uber JG, Salomons E, Berry JW, Hart WE, Phillips CA, Watson JP, Dorini G, Jonkergouw P, Kapelan Z et al (2008) The battle of the water sensor networks (bwsn): A design challenge for engineers and algorithms. J Water Resour Plan Manag 134(6):556–568Quiñones-Grueiro M, Verde C, Llanes-santiago O (2019) Multi-objective sensor placement for leakage detection and localization in water distribution networks. In: 2019 4th conference on control and fault tolerant systems (SysTol), IEEE, pp 129–134Ramezanian R (2019) Estimation of the profiles in posteriori electre tri: A mathematical programming model. Comput Indust Eng 128:47–59Rathi S, Gupta R, Kamble S, Sargaonkar A (2016) Risk based analysis for contamination event selection and optimal sensor placement for intermittent water distribution network security. Water Resour Manag 30(8):2671–2685Reginaldo F (2015) Portfolio management in Brazil and a proposal for evaluation and balancing of portfolio projects with electre tri and iris. Procedia Comput Sci 55:1265–1274Roy B (1968) Classement et choix en présence de points de vue multiples. Revue française d’informatique et de recherche opérationnelle 2(8):57–75Roy B (1990) The outranking approach and the foundations of electre methods. In: Readings in multiple criteria decision aid. Springer, New York, pp 155–183Sánchez-Lozano J, García-cascales M, Lamata M (2016) Comparative topsis-electre tri methods for optimal sites for photovoltaic solar farms. case study in spain. J Clean Prod 127:387–398Seiti H, Hafezalkotob A, Najafi SE, Khalaj M (2019) Developing a novel risk-based mcdm approach based on d numbers and fuzzy information axiom and its applications in preventive maintenance planning. Appl Soft Comput: 105559Shang F, Uber JG, Rossman LA et al (2008) Epanet multi-species extension user’s manual. risk reduction engineering laboratory us environmental protection agency. Cincinnati, OhioShannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423Štirbanović Z, Stanujkić D, Miljanović I, Milanović D (2019) Application of mcdm methods for flotation machine selection. Miner Eng 137:140–146Wang H, Jiang Z, Zhang H, Wang Y, Yang Y, Li Y (2019) An integrated mcdm approach considering demands-matching for reverse logistics. J Clean Prod 208:199–210Wéber R, Hős C (2020) Efficient technique for pipe roughness calibration and sensor placement for water distribution systems. J. Water Resour Plan Manag 146(1):04019070Weickgenannt M, Kapelan Z, Blokker M, Savic DA (2010) Risk-based sensor placement for contaminant detection in water distribution systems. J Water Resour Plan Manag 136(6):629–63

    Optimal placement of pressure sensors using fuzzy DEMATEL-based sensor influence

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    [EN] Nowadays, optimal sensor placement (OSP) for leakage detection in water distribution networks is a lively field of research, and a challenge for water utilities in terms of network control, management, and maintenance. How many sensors to install and where to install them are crucial decisions to make for those utilities to reach a trade-off between efficiency and economy. In this paper, we address the where-to-install-them part of the OSP through the following elements: nodes' sensitivity to leakage, uncertainty of information, and redundancy through conditional entropy maximisation. We evaluate relationships among candidate sensors in a network to get a picture of the mutual influence among the nodes. This analysis is performed within a multi-criteria decision-making approach: specifically, a herein proposed variant of DEMATEL, which uses fuzzy logic and builds comparison matrices derived from information obtained through leakage simulations of the network. We apply the proposal first to a toy example to show how the approach works, and then to a real-world case study.This research has been partially supported by the CNPq grant with number 156213/2018-4.Frances-Chust, J.; Brentan, BM.; Carpitella, S.; Izquierdo Sebastián, J.; Montalvo, I. (2020). Optimal placement of pressure sensors using fuzzy DEMATEL-based sensor influence. Water. 12(2):1-18. https://doi.org/10.3390/w12020493S118122Li, J., Wang, C., Qian, Z., & Lu, C. (2019). Optimal sensor placement for leak localization in water distribution networks based on a novel semi-supervised strategy. Journal of Process Control, 82, 13-21. doi:10.1016/j.jprocont.2019.08.001Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Boatwright, S., Romano, M., Mounce, S., Woodward, K., & Boxall, J. (s. f.). Optimal Sensor Placement and Leak/Burst Localisation in a Water Distribution System Using Spatially-Constrained Inverse-Distance Weighted Interpolation. doi:10.29007/37cpBlesa, J., Nejjari, F., & Sarrate, R. (2015). Robust sensor placement for leak location: analysis and design. Journal of Hydroinformatics, 18(1), 136-148. doi:10.2166/hydro.2015.021Steffelbauer, D. B., & Fuchs-Hanusch, D. (2016). Efficient Sensor Placement for Leak Localization Considering Uncertainties. Water Resources Management, 30(14), 5517-5533. doi:10.1007/s11269-016-1504-6Yoo, D., Chang, D., Song, Y., & Lee, J. (2018). Optimal Placement of Pressure Gauges for Water Distribution Networks Using Entropy Theory Based on Pressure Dependent Hydraulic Simulation. Entropy, 20(8), 576. doi:10.3390/e20080576De Schaetzen, W. B. ., Walters, G. ., & Savic, D. . (2000). Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms. Urban Water, 2(2), 141-152. doi:10.1016/s1462-0758(00)00052-2Cugueró-Escofet, M. À., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1-12. doi:10.1016/j.conengprac.2017.03.003Sela Perelman, L., Abbas, W., Koutsoukos, X., & Amin, S. (2016). Sensor placement for fault location identification in water networks: A minimum test cover approach. Automatica, 72, 166-176. doi:10.1016/j.automatica.2016.06.005Carpitella, S., Carpitella, F., Certa, A., Benítez, J., & Izquierdo, J. (2018). Managing Human Factors to Reduce Organisational Risk in Industry. Mathematical and Computational Applications, 23(4), 67. doi:10.3390/mca23040067Addae, B. A., Zhang, L., Zhou, P., & Wang, F. (2019). Analyzing barriers of Smart Energy City in Accra with two-step fuzzy DEMATEL. Cities, 89, 218-227. doi:10.1016/j.cities.2019.01.043Dalvi-Esfahani, M., Niknafs, A., Kuss, D. J., Nilashi, M., & Afrough, S. (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43, 101250. doi:10.1016/j.tele.2019.101250Quezada, L. E., López-Ospina, H. A., Palominos, P. I., & Oddershede, A. M. (2018). Identifying causal relationships in strategy maps using ANP and DEMATEL. Computers & Industrial Engineering, 118, 170-179. doi:10.1016/j.cie.2018.02.020Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., … Hassan Ahmed, O. (2019). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, 106005. doi:10.1016/j.cie.2019.106005Zhang, L., Sun, X., & Xue, H. (2019). Identifying critical risks in Sponge City PPP projects using DEMATEL method: A case study of China. Journal of Cleaner Production, 226, 949-958. doi:10.1016/j.jclepro.2019.04.067Du, Y.-W., & Zhou, W. (2019). New improved DEMATEL method based on both subjective experience and objective data. Engineering Applications of Artificial Intelligence, 83, 57-71. doi:10.1016/j.engappai.2019.05.001Yazdi, M., Nedjati, A., Zarei, E., & Abbassi, R. (2020). A novel extension of DEMATEL approach for probabilistic safety analysis in process systems. Safety Science, 121, 119-136. doi:10.1016/j.ssci.2019.09.006Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. doi:10.1016/j.eswa.2005.12.005Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xMahmoudi, S., Jalali, A., Ahmadi, M., Abasi, P., & Salari, N. (2019). Identifying critical success factors in Heart Failure Self-Care using fuzzy DEMATEL method. Applied Soft Computing, 84, 105729. doi:10.1016/j.asoc.2019.105729Lin, K.-P., Tseng, M.-L., & Pai, P.-F. (2018). Sustainable supply chain management using approximate fuzzy DEMATEL method. Resources, Conservation and Recycling, 128, 134-142. doi:10.1016/j.resconrec.2016.11.017Vardopoulos, I. (2019). Critical sustainable development factors in the adaptive reuse of urban industrial buildings. A fuzzy DEMATEL approach. Sustainable Cities and Society, 50, 101684. doi:10.1016/j.scs.2019.101684Mirmousa, S., & Dehnavi, H. D. (2016). Development of Criteria of Selecting the Supplier by Using the Fuzzy DEMATEL Method. Procedia - Social and Behavioral Sciences, 230, 281-289. doi:10.1016/j.sbspro.2016.09.036Acuña-Carvajal, F., Pinto-Tarazona, L., López-Ospina, H., Barros-Castro, R., Quezada, L., & Palacio, K. (2019). An integrated method to plan, structure and validate a business strategy using fuzzy DEMATEL and the balanced scorecard. Expert Systems with Applications, 122, 351-368. doi:10.1016/j.eswa.2019.01.030Chou, J.-S., & Ongkowijoyo, C. S. (2019). Hybrid decision-making method for assessing interdependency and priority of critical infrastructure. International Journal of Disaster Risk Reduction, 39, 101134. doi:10.1016/j.ijdrr.2019.101134Winter, C. de, Palleti, V. R., Worm, D., & Kooij, R. (2019). Optimal placement of imperfect water quality sensors in water distribution networks. Computers & Chemical Engineering, 121, 200-211. doi:10.1016/j.compchemeng.2018.10.021Schwaller, J., & van Zyl, J. E. (2015). Modeling the Pressure-Leakage Response of Water Distribution Systems Based on Individual Leak Behavior. Journal of Hydraulic Engineering, 141(5), 04014089. doi:10.1061/(asce)hy.1943-7900.0000984Giustolisi, O., Savic, D., & Kapelan, Z. (2008). Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)EPANET 2: Users Manualhttps://epanet.es/wp-content/uploads/2012/10/EPANET_User_Guide.pdfChristodoulou, S. E., Gagatsis, A., Xanthos, S., Kranioti, S., Agathokleous, A., & Fragiadakis, M. (2013). Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks. Water Resources Management, 27(13), 4443-4468. doi:10.1007/s11269-013-0419-8Falatoonitoosi, E., Leman, Z., Sorooshian, S., & Salimi, M. (2013). Decision-Making Trial and Evaluation Laboratory. Research Journal of Applied Sciences, Engineering and Technology, 5(13), 3476-3480. doi:10.19026/rjaset.5.4475OPRICOVIC, S., & TZENG, G.-H. (2003). DEFUZZIFICATION WITHIN A MULTICRITERIA DECISION MODEL. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635-652. doi:10.1142/s0218488503002387Sara, J., Stikkelman, R. M., & Herder, P. M. (2015). Assessing relative importance and mutual influence of barriers for CCS deployment of the ROAD project using AHP and DEMATEL methods. International Journal of Greenhouse Gas Control, 41, 336-357. doi:10.1016/j.ijggc.2015.07.008Alperovits, E., & Shamir, U. (1977). Design of optimal water distribution systems. Water Resources Research, 13(6), 885-900. doi:10.1029/wr013i006p00885Walski, T., Bezts, W., Posluszny, E. T., Weir, M., & Whitman, B. E. (2006). Modeling leakage reduction through pressure control. Journal - American Water Works Association, 98(4), 147-155. doi:10.1002/j.1551-8833.2006.tb07642.xZheng, F., Du, J., Diao, K., Zhang, T., Yu, T., & Shao, Y. (2018). Investigating Effectiveness of Sensor Placement Strategies in Contamination Detection within Water Distribution Systems. Journal of Water Resources Planning and Management, 144(4), 06018003. doi:10.1061/(asce)wr.1943-5452.0000919Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.1206

    A Global Stochastic Programming Approach for the Optimal Placement of Gas Detectors with Nonuniform Unavailabilities

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    PresentationOptimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem that requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems

    SMaRT-OnlineWDN: A Franco-German Project For The Online Security Management Of Water Distribution Networks

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    Water Distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental chemical, biological or radioactive contamination which need to be detected in due time. However, until now, no monitoring system is capable of protecting a WDN in real time. Powerful online sensor systems are currently developed and the prototypes are able to detect a small change in water quality. In the immediate future, water service utilities will install their networks with water quantity and water quality sensors. For taking appropriate decisions and countermeasures, WDN operators will need to dispose of: 1) a fast and reliable detection of abnormal events in the WDNs; 2) reliable online models both for the hydraulics and water quality predictions; 3) methods for contaminant source identification backtracking from the data history. Actually, in general none of these issues (1) – (3) are available at the water suppliers. Consequently, the main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for WDNs that is based on sensor measurements of water quality as well as water quantity. Its main innovations are the detection of abnormal events with a binary classifier of high accuracy and the generation of real-time, reliable (i) flow and pressure predictions, (ii) water quality indicator predictions of the whole water network. Detailed information regarding contamination sources (localization and intensity) will be explored by means of the online running model, which is automatically calibrated to the measured sensor data. Its field of application ranges from the detection of deliberate contamination including source identification and decision support for effective countermeasures to improved operation and control of a WDN under normal and abnormal conditions (dual benefit).In this project, the technical research work is completed with a sociological, economical and management analysis

    ADVANCED MODELING AND EFFICIENT OPTIMIZATION METHODS FOR REAL-TIME RESPONSE IN WATER NETWORKS

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    In response to a contamination incident in water distribution networks, effective mitigation procedures must be planned. Disinfectant booster stations can be used to neutralize a variety of contaminant and protect the public. In this thesis, two methods are proposed for the optimal placement of booster stations. Since the contaminant species is unknown a priori, these two methods differ in how they model the unknown reaction between the contaminant and the disinfectant. Both methods employ Mixed-Integer Linear Programming to minimize the expected impact over a large set of potential contamination scenarios that consider the uncertainty in the location and time of the incident. To make the optimal booster placement problem tractable for realistic large-scale networks, we exploit the symmetry in the problem structure to drastically reduce the problem size. The results highlight the effectiveness of booster stations in reducing the overall impact on the population, which is measured using two different metrics - mass of contaminant consumed, and population dosed above a cumulative mass threshold. Additionally, we also study the importance of various factors that influence the performance of disinfectant booster stations (e.g., sensor placement, contaminant reactivity and toxicity, etc.)

    Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

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    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

    Strategic Monitoring of Networked Systems with Heterogeneous Security Levels

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    We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components' security levels and formulate a large-scale maximin optimization problem. After analyzing its structure, we propose a three-step approach to approximately solve the problem. First, we solve a generalized covering set problem and run a combinatorial algorithm to compute an approximate solution. Then, we compute approximation bounds by solving a nonlinear set packing problem. To evaluate our solution approach, we implement two classical solution methods based on column generation and multiplicative weights updates, and test them on real-world water distribution and power systems. Our numerical analysis shows that our solution method outperforms the classical methods on large-scale networks, as it efficiently generates solutions that achieve a close to optimal performance and that are simple to implement in practice
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