Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization

La necesidad de hacer eficientes y económicamente viables las grandes inversiones relacionadas con la construcción y el mantenimiento de las redes de abastecimiento de agua, hace que se preste especial atención al diseño de este tipo de redes. Concebir soluciones económicamente optimizadas y que garanticen un adecuado funcionamiento de los sistemas de distribución de agua (SDA), tomando en cuenta la fiabilidad de la red para ofrecer sus servicios, incluso ante posibles condiciones de fallo, es uno de los grandes retos que han tenido desde hace muchos años varios hombres y mujeres de ciencias que han trabajado el tema. Se impone obtener los mayores beneficios con los menores costes. En el diseño óptimo de sistemas de distribución de agua, como muchos otros problemas de optimización, los objetivos a optimizar están frecuentemente en conflicto unos con otros. Ante este hecho, más conveniente que encontrar una única solución, es elaborar un conjunto de soluciones que representen el mejor compromiso posible entre todos los objetivos involucrados. En los últimos 15 años, varios investigadores se han desviado de las técnicas tradicionales de optimización basadas en la programación lineal y no lineal, para dirigirse hacia la implementación de Algoritmos Evolutivos. En esta investigación se proponen soluciones para el diseño óptimo de SDA basadas en el empleo de una generalización del algoritmo Particle Swarm Optimization (PSO) orientada a la inteligencia artificial distribuida tomando como base a los sistemas multi-agente (MA). El algoritmo final propuesto recibió la denominación de Agent Swarm Optimization (ASO) El algoritmo ASO se aprovecha de las ventajas de la computación paralela y distribuida para hacer interactuar diversas poblaciones de agentes que pueden tener comportamientos diferentes. Su versatilidad da origen a su principal fortaleza: la introducción de agentes con reglas de comportamiento específicas para la mejor solución de un problema, que problema, que trabajan de manera conjunta con algoritmos evolutivos de carácter general como PSO, Algoritmos Genéticos, Ant Colony Optimization, etcétera.Montalvo Arango, I. (2011). Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14858Palanci

On the Complexities of the Design of Water Distribution Networks

Water supply is one of the most recognizable and important public services contributing to quality of life. Water distribution networks WDNs are extremely complex assets. A number of complex tasks, such as design, planning, operation, maintenance, and management, are inherently associated with such networks. In this paper, we focus on the design of a WDN, which is a wide and open problem in hydraulic engineering. This problem is a large-scale combinatorial, nonlinear, nonconvex, multiobjective optimization problem, involving various types of decision variables and many complex implicit constraints. To handle this problem, we provide a synergetic association between swarm intelligence and multiagent systems where human interaction is also enabled. This results in a powerful collaborative system for finding solutions to such a complex hydraulic engineering problem. All the ingredients have been integrated into a software tool that has also been shown to efficiently solve problems from other engineering fields.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Educacion y Ciencia, and ACOMP/2010/146 of the Conselleria d'Educacio of the Generalitat Valenciana. The first author is also indebted to the Universitat Politecnica de Valencia for the sabbatical leave granted during the first semester of 2011. The use of English in this paper was revised by John Rawlins.Izquierdo Sebastián, J.; Montalvo Arango, I.; Pérez García, R.; Matías, A. (2012). On the Complexities of the Design of Water Distribution Networks. Mathematical Problems in Engineering. 2012:1-25. https://doi.org/10.1155/2012/9479611252012Goulter, I. C., & Coals, A. V. (1986). Quantitative Approaches to Reliability Assessment in Pipe Networks. Journal of Transportation Engineering, 112(3), 287-301. doi:10.1061/(asce)0733-947x(1986)112:3(287)Goulter, I. C., & Bouchart, F. (1990). Reliability‐Constrained Pipe Network Model. Journal of Hydraulic Engineering, 116(2), 211-229. doi:10.1061/(asce)0733-9429(1990)116:2(211)Kleiner, Y., Adams, B. J., & Rogers, J. S. (2001). Water Distribution Network Renewal Planning. Journal of Computing in Civil Engineering, 15(1), 15-26. doi:10.1061/(asce)0887-3801(2001)15:1(15)Dandy, G. C., & Engelhardt, M. O. (2006). Multi-Objective Trade-Offs between Cost and Reliability in the Replacement of Water Mains. Journal of Water Resources Planning and Management, 132(2), 79-88. doi:10.1061/(asce)0733-9496(2006)132:2(79)Izquierdo, J., Pérez, R., & Iglesias, P. L. (2004). Mathematical models and methods in the water industry. Mathematical and Computer Modelling, 39(11-12), 1353-1374. doi:10.1016/j.mcm.2004.06.012Giustolisi, 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)Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle Swarm Optimization applied to the design of water supply systems. Computers & Mathematics with Applications, 56(3), 769-776. doi:10.1016/j.camwa.2008.02.006Montalvo, I., Izquierdo, J., Pérez, R., & Iglesias, P. L. (2008). A diversity-enriched variant of discrete PSO applied to the design of water distribution networks. Engineering Optimization, 40(7), 655-668. doi:10.1080/03052150802010607Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Martínez, J. B. (2010). Cost and reliability comparison between branched and looped water supply networks. Journal of Hydroinformatics, 12(2), 150-160. doi:10.2166/hydro.2009.080Goulter, I. C. (1992). Systems Analysis in Water‐Distribution Network Design: From Theory to Practice. Journal of Water Resources Planning and Management, 118(3), 238-248. doi:10.1061/(asce)0733-9496(1992)118:3(238)Park, H., & Liebman, J. C. (1993). Redundancy‐Constrained Minimum‐Cost Design of Water‐Distribution Nets. Journal of Water Resources Planning and Management, 119(1), 83-98. doi:10.1061/(asce)0733-9496(1993)119:1(83)Khomsi, D., Walters, G. A., Thorley, A. R. D., & Ouazar, D. (1996). Reliability Tester for Water-Distribution Networks. Journal of Computing in Civil Engineering, 10(1), 10-19. doi:10.1061/(asce)0887-3801(1996)10:1(10)Tanyimboh, T. T., Tabesh, M., & Burrows, R. (2001). Appraisal of Source Head Methods for Calculating Reliability of Water Distribution Networks. Journal of Water Resources Planning and Management, 127(4), 206-213. doi:10.1061/(asce)0733-9496(2001)127:4(206)Kalungi, P., & Tanyimboh, T. T. (2003). Redundancy model for water distribution systems. Reliability Engineering & System Safety, 82(3), 275-286. doi:10.1016/s0951-8320(03)00168-6Morgan, D. R., & Goulter, I. C. (1985). Optimal urban water distribution design. Water Resources Research, 21(5), 642-652. doi:10.1029/wr021i005p00642Walters, G. A., & Knezevic, J. (1989). Discussion of « Reliability‐Based Optimization Model for Water Distribution Systems » by Yu‐Chun Su, Larry W. Mays, Ning Duan, and Kevin E. Lansey (December, 1987, Vol. 113, No. 12). Journal of Hydraulic Engineering, 115(8), 1157-1158. doi:10.1061/(asce)0733-9429(1989)115:8(1157)LOGANATHAN, G. V., SHERALI, H. D., & SHAH, M. P. (1990). A TWO-PHASE NETWORK DESIGN HEURISTIC FOR MINIMUM COST WATER DISTRIBUTION SYSTEMS UNDER A RELIABILITY CONSTRAINT. Engineering Optimization, 15(4), 311-336. doi:10.1080/03052159008941160Bouchart, F., & Goulter, I. (1991). Reliability Improvements in Design of Water Distribution Networks Recognizing Valve Location. Water Resources Research, 27(12), 3029-3040. doi:10.1029/91wr00590Gupta, R., & Bhave, P. R. (1994). Reliability Analysis of Water‐Distribution Systems. Journal of Environmental Engineering, 120(2), 447-461. doi:10.1061/(asce)0733-9372(1994)120:2(447)Xu, C., & Goulter, I. C. (1999). Reliability-Based Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 125(6), 352-362. doi:10.1061/(asce)0733-9496(1999)125:6(352)Su, Y., Mays, L. W., Duan, N., & Lansey, K. E. (1987). Reliability‐Based Optimization Model for Water Distribution Systems. Journal of Hydraulic Engineering, 113(12), 1539-1556. doi:10.1061/(asce)0733-9429(1987)113:12(1539)Cullinane, M. J., Lansey, K. E., & Mays, L. W. (1992). Optimization‐Availability‐Based Design of Water‐Distribution Networks. Journal of Hydraulic Engineering, 118(3), 420-441. doi:10.1061/(asce)0733-9429(1992)118:3(420)Vamvakeridou-Lyroudia, L. S., Walters, G. A., & Savic, D. A. (2005). Fuzzy Multiobjective Optimization of Water Distribution Networks. Journal of Water Resources Planning and Management, 131(6), 467-476. doi:10.1061/(asce)0733-9496(2005)131:6(467)Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Dong, Y., Tang, J., Xu, B., & Wang, D. (2005). An application of swarm optimization to nonlinear programming. Computers & Mathematics with Applications, 49(11-12), 1655-1668. doi:10.1016/j.camwa.2005.02.006Jin, Y.-X., Cheng, H.-Z., Yan, J., & Zhang, L. (2007). New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research, 77(3-4), 227-233. doi:10.1016/j.epsr.2006.02.016Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2009). Forecasting pedestrian evacuation times by using swarm intelligence. Physica A: Statistical Mechanics and its Applications, 388(7), 1213-1220. doi:10.1016/j.physa.2008.12.008Herrera, M., Izquierdo, J., Montalvo, I., García-Armengol, J., & Roig, J. V. (2009). Identification of surgical practice patterns using evolutionary cluster analysis. Mathematical and Computer Modelling, 50(5-6), 705-712. doi:10.1016/j.mcm.2008.12.026Molina, J., Santana, L. V., Hernández-Díaz, A. G., Coello Coello, C. A., & Caballero, R. (2009). g-dominance: Reference point based dominance for multiobjective metaheuristics. European Journal of Operational Research, 197(2), 685-692. doi:10.1016/j.ejor.2008.07.01510.1029/89WR02879. (2010). Water Resources Research. doi:10.1029/89wr02879Savic, D. A., & Walters, G. A. (1997). Genetic Algorithms for Least-Cost Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 123(2), 67-77. doi:10.1061/(asce)0733-9496(1997)123:2(67)Zecchin, A. C., Simpson, A. R., Maier, H. R., & Nixon, J. B. (2005). Parametric Study for an Ant Algorithm Applied to Water Distribution System Optimization. IEEE Transactions on Evolutionary Computation, 9(2), 175-191. doi:10.1109/tevc.2005.844168Yurong Liu, Zidong Wang, Jinling Liang, & Xiaohui Liu. (2009). Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays. IEEE Transactions on Neural Networks, 20(7), 1102-1116. doi:10.1109/tnn.2009.2016210Jinling Liang, Zidong Wang, & Xiaohui Liu. (2009). State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case. IEEE Transactions on Neural Networks, 20(5), 781-793. doi:10.1109/tnn.2009.2013240Zidong Wang, Yao Wang, & Yurong Liu. (2010). Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays. IEEE Transactions on Neural Networks, 21(1), 11-25. doi:10.1109/tnn.2009.2033599Bo Shen, Zidong Wang, & Xiaohui Liu. (2011). Bounded $H_{\infty}$ Synchronization and State Estimation for Discrete Time-Varying Stochastic Complex Networks Over a Finite Horizon. IEEE Transactions on Neural Networks, 22(1), 145-157. doi:10.1109/tnn.2010.209066

A novel water supply network sectorization methodology based on a complete economic analysis, including uncertainties

[EN] The core idea behind sectorization of water supply networks (WSNs) is to establish areas partially isolated from the rest of the network to improve operational control. Besides the benefits associated to sectorization, some drawbacks must be taken into consideration by water operators: the economic investment associated to both boundary valves and flowmeters, and the reduction of both pressure and system resilience. The target of sectorization is to properly balance these negative and positive aspects. Sectorization methodologies addressing the economic aspects mainly consider costs of valves and flowmeters and of energy, and the benefits in terms of water saving linked to pressure reduction. However, sectorization entails other benefits such as reduction of domestic consumption; reduction of bursts frequency; and enhanced capacity to detect and intervene over future leakage events. We implement a development proposed by the International Water Association (IWA) to estimate the aforementioned benefits. Such development is integrated in a novel sectorization methodology based on a Social Network Community Detection Algorithm, combined with a Genetic Algorithm optimization method and Monte Carlo Simulation. The methodology is implemented over a fraction of the WSN of Managua city, capital of Nicaragua, generating a net benefit of 25,518 $/year.Campbell-Gonzalez, E.; Izquierdo Sebastián, J.; Montalvo Arango, I.; Pérez García, R. (2016). A novel water supply network sectorization methodology based on a complete economic analysis, including uncertainties. Water. 8(5). doi:10.3390/w8050179S8

Injecting problem-dependent knowledge to improve evolutionary optimization search ability

The flexibility introduced by evolutionary algorithms (EAs) has allowed the use of virtually arbitrary objective functions and constraints even when evaluations require, as for real-world problems, running complex mathematical and/or procedural simulations of the systems under analysis. Even so, EAs are not a panacea. Traditionally, the solution search process has been totally oblivious of the specific problem being solved, and optimization processes have been applied regardless of the size, complexity, and domain of the problem. In this paper, we justify our claim that far-reaching benefits may be obtained from more directly influencing how searches are performed. We propose using data mining techniques as a step for dynamically generating knowledge that can be used to improve the efficiency of solution search processes. In this paper, we use Kohonen SOMs and show an application for a well-known benchmark problem in the water distribution system design literature. The result crystallizes the conceptual rules for the EA to apply at certain stages of the evolution, which reduces the search space and accelerates convergence. (C) 2015 Elsevier B.V. All rights reserved.Izquierdo Sebastián, J.; Campbell-Gonzalez, E.; Montalvo Arango, I.; Pérez García, R. (2016). Injecting problem-dependent knowledge to improve evolutionary optimization search ability. Journal of Computational and Applied Mathematics. 291:281-292. doi:10.1016/j.cam.2015.03.019S28129229

Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

[EN] This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the non-dominated sorting genetic algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria.Carpitella, S.; Brentan, BM.; Montalvo Arango, I.; Izquierdo Sebastián, J.; Certa, A. (2019). Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems. Water Science & Technology: Water Supply. 19(8):2338-2346. https://doi.org/10.2166/ws.2019.115S23382346198Ancău, M., & Caizar, C. (2010). The computation of Pareto-optimal set in multicriterial optimization of rapid prototyping processes. Computers & Industrial Engineering, 58(4), 696-708. doi:10.1016/j.cie.2010.01.015Aşchilean, I., Badea, G., Giurca, I., Naghiu, G. S., & Iloaie, F. G. (2017). Choosing the Optimal Technology to Rehabilitate the Pipes in Water Distribution Systems Using the AHP Method. Energy Procedia, 112, 19-26. doi:10.1016/j.egypro.2017.03.1109Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Joint Operation of Pressure-Reducing Valves and Pumps for Improving the Efficiency of Water Distribution Systems. Journal of Water Resources Planning and Management, 144(9), 04018055. doi:10.1061/(asce)wr.1943-5452.0000974Certa, A., Enea, M., Galante, G. M., & La Fata, C. M. (2017). ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number. Computers & Industrial Engineering, 108, 100-110. doi:10.1016/j.cie.2017.04.018Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9. doi:10.1016/s0165-0114(97)00377-1Cruz-Reyes, L., Fernandez, E., Sanchez, P., Coello Coello, C. A., & Gomez, C. (2017). Incorporation of implicit decision-maker preferences in multi-objective evolutionary optimization using a multi-criteria classification method. Applied Soft Computing, 50, 48-57. doi:10.1016/j.asoc.2016.10.037Farmani, R., Ingeduld, P., Savic, D., Walters, G., Svitak, Z., & Berka, J. (2007). Real-time modelling of a major water supply system. Proceedings of the Institution of Civil Engineers - Water Management, 160(2), 103-108. doi:10.1680/wama.2007.160.2.103Hadas, Y., & Nahum, O. E. (2016). Urban bus network of priority lanes: A combined multi-objective, multi-criteria and group decision-making approach. Transport Policy, 52, 186-196. doi:10.1016/j.tranpol.2016.08.006Hamdan, S., & Cheaitou, A. (2017). Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach. Computers & Operations Research, 81, 282-304. doi:10.1016/j.cor.2016.11.005Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. doi:10.1016/j.ejor.2007.01.004Jowitt, P. W., & Germanopoulos, G. (1992). Optimal Pump Scheduling in Water‐Supply Networks. Journal of Water Resources Planning and Management, 118(4), 406-422. doi:10.1061/(asce)0733-9496(1992)118:4(406)Jowitt, P. W., & Xu, C. (1990). Optimal Valve Control in Water‐Distribution Networks. Journal of Water Resources Planning and Management, 116(4), 455-472. doi:10.1061/(asce)0733-9496(1990)116:4(455)Kurek, W., & Ostfeld, A. (2013). Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems. Journal of Environmental Management, 115, 189-197. doi:10.1016/j.jenvman.2012.11.030Lima, G. M., Luvizotto, E., & Brentan, B. M. (2017). Selection and location of Pumps as Turbines substituting pressure reducing valves. Renewable Energy, 109, 392-405. doi:10.1016/j.renene.2017.03.056Mala-Jetmarova, H., Sultanova, N., & Savic, D. (2017). Lost in optimisation of water distribution systems? A literature review of system operation. Environmental Modelling & Software, 93, 209-254. doi:10.1016/j.envsoft.2017.02.009Montalvo, 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.12062Odan, F. K., Ribeiro Reis, L. F., & Kapelan, Z. (2015). Real-Time Multiobjective Optimization of Operation of Water Supply Systems. Journal of Water Resources Planning and Management, 141(9), 04015011. doi:10.1061/(asce)wr.1943-5452.0000515Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., … Walski, T. (2008). The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms. 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Water Distribution System Computer-Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDS), including the sizing of components, quality control, reliability, renewal and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly-dimensional, multimodal, non-linear problems, especially given inaccurate, noisy, discrete and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent based systems. It is aimed at supporting decisionmaking processes by solving multi-objective optimization problems. ASO offers robustness through a framework where various population-based algorithms co-exist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert-based proposals.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Spanish Ministry of Education and Science, and ACOMP/2010/146 of the education department of the Generalitat Valenciana. The use of English was revised by John Rawlins.Montalvo Arango, I.; Izquierdo Sebastián, J.; Pérez García, R.; Herrera Fernández, AM. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering. 29(6):433-448. https://doi.org/10.1111/mice.12062433448296Adeli, H., & Kumar, S. (1995). Distributed Genetic Algorithm for Structural Optimization. Journal of Aerospace Engineering, 8(3), 156-163. doi:10.1061/(asce)0893-1321(1995)8:3(156)Afshar, M. H., Akbari, M., & Mariño, M. A. (2005). Simultaneous Layout and Size Optimization of Water Distribution Networks: Engineering Approach. Journal of Infrastructure Systems, 11(4), 221-230. doi:10.1061/(asce)1076-0342(2005)11:4(221)Amini, F., Hazaveh, N. K., & Rad, A. A. (2013). Wavelet PSO-Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers. Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-557. doi:10.1111/mice.12017Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Badawy, R., Yassine, A., Heßler, A., Hirsch, B., & Albayrak, S. (2013). A novel multi-agent system utilizing quantum-inspired evolution for demand side management in the future smart grid. 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A New Alarm Generation Concept For Water Distribution Networks Based On Machine Learning Algorithms

Water Distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental chemical, biological or radioactive contamination. A monitoring system capable of protecting a WDN against contamination events in real time is a big challenge needed to be accomplished. Powerful online sensor systems are currently developed and the prototypes are able to detect a small change in water quality. 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. A new approach for the fast and reliable detection of abnormal events in the WDNs by an alarm generation module is presented in this paper. Although in the past several approaches have been investigated and implemented (e.g. CANARI of EPA), so far these alarm generation concepts haven\u27t been widely applied in real WDNs. Two reasons for that are: (1) The parameterization of existing alarm generation software products is too complex and time consuming, (2) a lot of abnormalities in the data appear due to special operational actions (e.g. sensor calibrations, flushing of pipes, rapid changes of water quality due to mixing of different water resources). To cope with this difficulties, in our approach the alarm generation module is trained both by historical data and in online mod using OPC technologies. Multi-variate statistical methods which need only a few parameters (e.g. Principal Component) are used. A fingerprint database is built up by the water utility experts and it is used to label known events. Results based on real WDN data of Berlin, Strasbourg and Paris are presented

Mining solution spaces for decision making in water distribution systems

[EN] Data mining solutions can be applied in combination with evolutionary algorithms to extract relevant information from solution spaces analyzed during optimization processes regarding water distribution system (WDS) design. Firstly, results from data mining can be introduced into the evolutionary algorithms to guide the search of solutions. Secondly, data mining techniques can be used not only to help explore the population of potential solutions but also to exploit data regarding the behavior of the WDS under different work conditions. As a result, applications can be developed for supporting decision making regarding WDS models in both offline and online contexts.This work has been supported by project IDAWAS, DPI2009- 11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/ 2011/ 188 of the Conselleria d'Educacio of the Generalitat Valenciana.Izquierdo Sebastián, J.; Montalvo Arango, I.; Pérez García, R.; Campbell, E. (2014). Mining solution spaces for decision making in water distribution systems. Procedia Engineering. 70:864-871. https://doi.org/10.1016/j.proeng.2014.02.095S8648717

Water supply system component evaluation from GPR radargrams using a multi-agent approach

This paper uses a multi-agent approach as a quick and easy tool for the interpretation and analysis of the characteristics of Water Supply System (WSS) components when working on a collection of Ground Penetrating Radar (GPR) survey files. The multi-agent algorithm proposed in this paper has been developed in Matlab and is based on Game Theory. The input is the result of the GPR radargram survey and the output consists of the agent scores in the game proposed in this paper. Useful information can be gained by interpreting the columns of the output matrix that describe the agents’ movements, together with the associated racing times. In effect, this analysis enables a simple determination of the electromagnetic properties of the underground system and provides an accurate classification of these properties. The results of this agent racing algorithm are promising, since it groups, and consequently, decreases the number of points that make up the initial radargrams; while at the same time preserving its main properties, and enabling clearer views of pipes and a better identification of the components in WSS.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, ACOMP/2011/188 of the Conselleria de Educacion of the Generalitat Valenciana, and the FPI-UPV scholarship granted to the first author by the Programa de Ayudas de Investigacion y Desarrollo (PAID) of the Universitat Politecnica de Valencia.Ayala Cabrera, D.; Izquierdo Sebastián, J.; Montalvo Arango, I.; Pérez García, R. (2013). Water supply system component evaluation from GPR radargrams using a multi-agent approach. Mathematical and Computer Modelling. 57(7-8):1927-1932. https://doi.org/10.1016/j.mcm.2011.12.034S19271932577-

Cloud-based decision making in water distribution systems

The evolution of technologies of informatics and telecommunications has improved significantly the way water distribution systems can be managed. The use of quasi real-time information is becoming a desired standard in many water utilities. Nevertheless, tools for water distribution system analysis and for supporting decision making haveńt been integrated with the same speed. This research uses cloud computing capabilities for supporting analysis and decision making in water distribution systems. It includes the development of evolutionary algorithms ready to run in a cloud environment for searching the best possible decisions considering a set of objectives. The evaluation of solutions profits from the scalability of the cloud for making faster the solution search process.Montalvo Arango, I.; Izquierdo Sebastián, J.; Campbell, EOG.; Pérez García, R. (2014). Cloud-based decision making in water distribution systems. Procedia Engineering. 89:488-494. doi:10.1016/j.proeng.2014.11.241S4884948