Hybrid regression model for near real-time urban water demand forecasting

[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.This work has been partially supported by CAPES Foundation of Brazil’s Ministry of Education. The data were provided by SABESP, São Paulo state water management company.Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics. 309:532-541. doi:10.1016/j.cam.2016.02.009S53254130

Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization

[EN] Management of large water distribution systems can be improved by dividing their networks into so-called district metered areas (DMAs). However, such divisions must be based on appropriated technical criteria. Considering the importance of deeply understanding the relationship between DMA creation and these criteria, this work proposes a performance analysis of DMA generation that takes into account such indicators as resilience index, demand similarity, pressure uniformity, water age (and thus water quality), solution implantation costs, and electrical consumption. To cope with the complexity of the problem, suitable mathematical techniques are proposed in this paper. We use a social community detection technique to define the sectors, and then a multilevel particle swarm optimization approach is applied to find the optimal placement and operating point of the necessary devices. The results obtained by implementing themethodology in a real water supply network show its validity and the meaningful influence on the final result of, especially, elevation and pipe length.Brentan, BM.; Campbell-Gonzalez, E.; Meirelles, GL.; Luvizotto, EJ.; Izquierdo Sebastián, J. (2017). Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization. Mathematical Problems in Engineering. (9053238):1-12. doi:10.1155/2017/9053238S112905323

Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems

[EN] With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results. © 2018 Elsevier Ltd. All rights reserved.This work is partially supported by Capes and CNPq, Brazilian research agencies. The use of English was revised by John Rawlins.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems. Environmental Modelling & Software. 106:77-88. https://doi.org/10.1016/j.envsoft.2018.02.013S778810

Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems

[EN] New environmental paradigms imposed by climate change and urbanization processes are leading cities to rethink urban management services. Propelled by technological development and the internet of things, an increasingly smart management of cities has favored the emergence of a new research field, namely, the smart city. Within this new way of considering cities, smart water systems are emerging for the planning, operation, and management of water distribution networks (WDNs) with maximum efficiency derived from the application of data analysis and other information technology tools. Considering the possibility of improving WDN operation using available demand data, this work proposes a hybrid and near-real-time optimization algorithm to jointly manage pumps working with variable speed drives and pressure-reducing valves for maximum operational efficiency. A near-real-time demand forecasting model is coupled with an optimization algorithm that updates in real time the water demand of the hydraulic model and can be used to define optimal operations. The D-town WDN is used to validate the proposal. The number of control devices in this WDN makes real time control especially complex. Warm solutions are proposed to cope with this feature as they reduce the computational effort needed if suitably tuned. In addition to energy savings of around 50%, the methodology proposed in this paper enables an efficient system pressure management, leading to significant leakage reduction.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, 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):04018055-1-04018055-12. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000974S04018055-104018055-12144

Trunk Network Rehabilitation for Resilience Improvement and Energy Recovery in Water Distribution Networks

[EN] Water distribution networks (WDNs) are designed to meet water demand with minimum implementation costs. However, this approach leads to poor long-term results, since system resilience is also minimal, and this requires the rehabilitation of the network if the network is expanded or the demand increases. In addition, in emergency situations, such as pipe bursts, large areas will suffer water shortage. However, the use of resilience as a criterion for WDN design is a difficult task, since its economic value is subjective. Thus, in this paper, it is proposed that trunk networks (TNs) are rehabilitated when considering the generation of electrical energy using pumps as turbines (PATs) to compensate for an increase of resilience derived from increasing pipe diameters. During normal operation, these micro-hydros will control pressure and produce electricity. When an emergency occurs, a by-pass can be used to increase network pressure. The results that were obtained for two hypothetical networks show that a small increase in TN pipe diameters is sufficient to significantly improve the resilience of the WDN. In addition, the value of the energy produced surpasses the investment that is made during rehabilitation.The authors wish to thank the project REDAWN (Reducing Energy Dependency in Atlantic Area Water Networks) EAPA_198/2016 from INTERREG ATLANTIC AREA PROGRAMME 2014-2020.Meirelles, G.; Brentan, BM.; Izquierdo Sebastián, J.; Ramos, HM.; Luvizotto, E. (2018). Trunk Network Rehabilitation for Resilience Improvement and Energy Recovery in Water Distribution Networks. Water. 10(6):1-14. https://doi.org/10.3390/w10060693S114106Zong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Maier, H. R., Simpson, A. R., Zecchin, A. C., Foong, W. K., Phang, K. Y., Seah, H. Y., & Tan, C. L. (2003). Ant Colony Optimization for Design of Water Distribution Systems. Journal of Water Resources Planning and Management, 129(3), 200-209. doi:10.1061/(asce)0733-9496(2003)129:3(200)Suribabu, C. R., & Neelakantan, T. R. (2006). Design of water distribution networks using particle swarm optimization. Urban Water Journal, 3(2), 111-120. doi:10.1080/15730620600855928Baños, R., Reca, J., Martínez, J., Gil, C., & Márquez, A. L. (2011). Resilience Indexes for Water Distribution Network Design: A Performance Analysis Under Demand Uncertainty. Water Resources Management, 25(10), 2351-2366. doi:10.1007/s11269-011-9812-3Shokoohi, M., Tabesh, M., Nazif, S., & Dini, M. (2016). Water Quality Based Multi-objective Optimal Design of Water Distribution Systems. Water Resources Management, 31(1), 93-108. doi:10.1007/s11269-016-1512-6Marques, J., Cunha, M., & Savić, D. (2015). Using Real Options in the Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 141(2), 04014052. doi:10.1061/(asce)wr.1943-5452.0000448Schwartz, R., Housh, M., & Ostfeld, A. (2016). Least-Cost Robust Design Optimization of Water Distribution Systems under Multiple Loading. Journal of Water Resources Planning and Management, 142(9), 04016031. doi:10.1061/(asce)wr.1943-5452.0000670Giustolisi, O., Laucelli, D., & Colombo, A. F. (2009). Deterministic versus Stochastic Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 135(2), 117-127. doi:10.1061/(asce)0733-9496(2009)135:2(117)Lansey, K. E., Duan, N., Mays, L. W., & Tung, Y. (1989). Water Distribution System Design Under Uncertainties. Journal of Water Resources Planning and Management, 115(5), 630-645. doi:10.1061/(asce)0733-9496(1989)115:5(630)Zheng, F., Simpson, A., & Zecchin, A. (2015). Improving the efficiency of multi-objective evolutionary algorithms through decomposition: An application to water distribution network design. Environmental Modelling & Software, 69, 240-252. doi:10.1016/j.envsoft.2014.08.022Geem, Z. (2015). Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search. Water, 7(12), 3613-3625. doi:10.3390/w7073613Prasad, T. D., & Park, N.-S. (2004). Multiobjective Genetic Algorithms for Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 130(1), 73-82. doi:10.1061/(asce)0733-9496(2004)130:1(73)Pérez-Sánchez, M., Sánchez-Romero, F., Ramos, H., & López-Jiménez, P. (2017). Energy Recovery in Existing Water Networks: Towards Greater Sustainability. Water, 9(2), 97. doi:10.3390/w9020097De Marchis, M., & Freni, G. (2015). Pump as turbine implementation in a dynamic numerical model: cost analysis for energy recovery in water distribution network. Journal of Hydroinformatics, 17(3), 347-360. doi:10.2166/hydro.2015.018Carravetta, A., del Giudice, G., Fecarotta, O., & Ramos, H. (2013). PAT Design Strategy for Energy Recovery in Water Distribution Networks by Electrical Regulation. Energies, 6(1), 411-424. doi:10.3390/en6010411Lima, G. M., Luvizotto, E., Brentan, B. M., & Ramos, H. M. (2018). Leakage Control and Energy Recovery Using Variable Speed Pumps as Turbines. Journal of Water Resources Planning and Management, 144(1), 04017077. doi:10.1061/(asce)wr.1943-5452.0000852Carravetta, A., Del Giudice, G., Fecarotta, O., & Ramos, H. M. (2012). Energy Production in Water Distribution Networks: A PAT Design Strategy. Water Resources Management, 26(13), 3947-3959. doi:10.1007/s11269-012-0114-1Lydon, T., Coughlan, P., & McNabola, A. (2017). Pump-As-Turbine: Characterization as an Energy Recovery Device for the Water Distribution Network. Journal of Hydraulic Engineering, 143(8), 04017020. doi:10.1061/(asce)hy.1943-7900.0001316Afshar, A., Jemaa, F. B., & Mariño, M. A. (1990). Optimization of Hydropower Plant Integration in Water Supply System. Journal of Water Resources Planning and Management, 116(5), 665-675. doi:10.1061/(asce)0733-9496(1990)116:5(665)Meirelles Lima, G., Brentan, B. M., & Luvizotto, E. (2018). Optimal design of water supply networks using an energy recovery approach. Renewable Energy, 117, 404-413. doi:10.1016/j.renene.2017.10.080Campbell, E., Izquierdo, J., Montalvo, I., Ilaya-Ayza, A., Pérez-García, R., & Tavera, M. (2015). A flexible methodology to sectorize water supply networks based on social network theory concepts and multi-objective optimization. Journal of Hydroinformatics, 18(1), 62-76. doi:10.2166/hydro.2015.146Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., & Santonastaso, G. F. (2017). Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Supply, 18(3), 767-777. doi:10.2166/ws.2017.124Bragalli, C., D’Ambrosio, C., Lee, J., Lodi, A., & Toth, P. (2011). On the optimal design of water distribution networks: a practical MINLP approach. Optimization and Engineering, 13(2), 219-246. doi:10.1007/s11081-011-9141-7Reca, J., & Martínez, J. (2006). Genetic algorithms for the design of looped irrigation water distribution networks. Water Resources Research, 42(5). doi:10.1029/2005wr004383Di Nardo, A., Di Natale, M., Santonastaso, G. F., Tzatchkov, V. G., & Alcocer-Yamanaka, V. H. (2014). Water Network Sectorization Based on Graph Theory and Energy Performance Indices. Journal of Water Resources Planning and Management, 140(5), 620-629. doi:10.1061/(asce)wr.1943-5452.0000364Hajebi, S., Temate, S., Barrett, S., Clarke, A., & Clarke, S. (2014). Water Distribution Network Sectorisation Using Structural Graph Partitioning and Multi-objective Optimization. Procedia Engineering, 89, 1144-1151. doi:10.1016/j.proeng.2014.11.238Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Brentan, B. M., Campbell, E., Meirelles, G. L., Luvizotto, E., & Izquierdo, J. (2017). Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization. Mathematical Problems in Engineering, 2017, 1-12. doi:10.1155/2017/9053238Lima, 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.2017.03.056Letting, L., Hamam, Y., & Abu-Mahfouz, A. (2017). Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization. Water, 9(8), 593. doi:10.3390/w908059

Enhanced water demand analysis via symbolic approximation within an epidemiology-based forecasting framework

Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand112sem informaçãosem informaçã

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

[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

Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind-Wave Farm

[EN] Offshore floating hybrid wind and wave energy is a young technology yet to be scaled up. A way to reduce the total costs of the energy production process in order to ensure competitiveness in the sustainable energy market is to maximize the farm's efficiency. To do so, an energy generation and costs calculation model was developed with the objective of minimizing the technology's Levelized Cost of Energy (LCOE) of the P80 hybrid wind-wave concept, designed by the company Floating Power Plant A/S. A Particle Swarm Optimization (PSO) algorithm was then implemented on top of other technical and decision-making processes, taking as decision variables the layout, the offshore substation position, and the export cable choice. The process was applied off the west coast of Ireland in a site of interest for the company, and after a quantitative and qualitative optimization process, a minimized LCOE was obtained. It was then found that lower costs of similar to 73% can be reached in the short-term, and the room for improvement in the structure's design and materials was highlighted, with an LCOE reduction potential of up to 32%. The model serves usefully as a preliminary analysis. However, the uncertainty estimate of 11% indicates that further site-specific studies and measurements are essential.Izquierdo-Pérez, J.; Brentan, BM.; Izquierdo Sebastián, J.; Clausen, N.; Pegalajar-Jurado, A.; Ebsen, N. (2020). Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind-Wave Farm. Processes. 8(2):1-23. https://doi.org/10.3390/pr8020139S12382Wind Power Capacity Worldwide Reaches 597 GW, 50.1 GWhttps://wwindea.org/blog/2019/02/25/wind-power-capacity-worldwide-reaches-600-gw-539-gw-added-in-2018/Global Offshore Wind Energy Capacity from 2008 to 2018 (in Megawatts)https://www.statista.com/statistics/476327/global-capacity-of-offshore-wind-energy/Haliade-X Offshore Wind Turbine Platformhttps://www.ge.com/renewableenergy/wind-energy/offshore-wind/haliade-x-offshore-turbinePérez-Collazo, C., Greaves, D., & Iglesias, G. (2015). A review of combined wave and offshore wind energy. Renewable and Sustainable Energy Reviews, 42, 141-153. doi:10.1016/j.rser.2014.09.032Astariz, S., & Iglesias, G. (2015). Enhancing Wave Energy Competitiveness through Co-Located Wind and Wave Energy Farms. A Review on the Shadow Effect. Energies, 8(7), 7344-7366. doi:10.3390/en8077344Floating Power Plant A/Shttp://www.floatingpowerplant.com/González, J. S., Gonzalez Rodriguez, A. G., Mora, J. C., Santos, J. R., & Payan, M. B. (2010). Optimization of wind farm turbines layout using an evolutive algorithm. Renewable Energy, 35(8), 1671-1681. doi:10.1016/j.renene.2010.01.010Lerch, M., De-Prada-Gil, M., Molins, C., & Benveniste, G. (2018). Sensitivity analysis on the levelized cost of energy for floating offshore wind farms. Sustainable Energy Technologies and Assessments, 30, 77-90. doi:10.1016/j.seta.2018.09.005Montalvo, 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.12062Ireland Second Highest in Europe for Wind Energyhttps://www.irishexaminer.com/breakingnews/ireland/ireland-second-highest-in-europe-for-wind-energy-910442.htmlIreland Plans 12GW Renewables Boosthttps://www.windpowermonthly.com/article/1587884/ireland-plans-12gw-renewables-boostDS3 Programme Operational Capability Outlook 2016http://www.eirgridgroup.com/site-files/library/EirGrid/DS3-Operational-Capability-Outlook-2016.pdfDesmond, C., Murphy, J., Blonk, L., & Haans, W. (2016). Description of an 8 MW reference wind turbine. Journal of Physics: Conference Series, 753, 092013. doi:10.1088/1742-6596/753/9/092013Global Wind Atlas (GWA)https://globalwindatlas.info/Bathymetry Viewing and Downloading Service, EMODnethttp://portal.emodnet-bathymetry.eu/?menu=1

Near real time pump optimization and pressure management

[EN] Management of existing systems can be interpreted as sets of decisions to make regarding pumps and valves to create hydraulic conditions able to satisfy the demand without operational problems such as pressures lower or higher than the normative pressure values. However, among the large number of combinations, some of them manage to reduce energy consumption, by finding the best operating point for pumps, and also water losses, by finding the best operating point for pressure reducing valves (PRV). Several works may be found in the literature using recent and advanced optimization techniques to define pump and valve operation. However, the processing time to define operational rules is a limiting factor for real time decision-making. Taking into account the need to improve the models in terms of optimal rules to apply in near real-time operations, this work presents a hybrid model (simulator + optimizer) to find pump speeds and PRV set points, aiming at combining energy savings with pressure control while reducing water losses. PSO is applied as the main optimization algorithm, which can also work in cooperation with other bio-inspired concepts to deploy an effective and fast search algorithm. The results allow comparisons with other techniques and show the ability of PSO to find an optimal point of operationBrentan, BM.; Luvizotto, EJ.; Montalvo, I.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Near real time pump optimization and pressure management. Procedia Engineering. 186:666-675. doi:10.1016/j.proeng.2017.06.248S66667518

Selection and location of pumps as turbines substituting pressure reducing valves

Pressure control is a fundamental component of safe operation of water supply systems, mainly to reduce leakage, risk of disruption, and maintenance costs. System topology and topography can define high-pressure zones, and the use of Pressure Reducing Valves (PRVs) to maintain standard pressures in these zones is common. However, all energy available in the fluid is dissipated trough headloss. A turbine could be used instead of PRVs to produce electrical energy and to control pressure. The use of Pumps as Turbines (PATs) is recommended to reduce investment cost. Due to dynamic operations throughout the day, PATs operate under varying conditions of flow and head. This variation affects efficiency and headloss, which makes difficult the selection of PATs to substitute PRVs through conventional methods; therefore, this paper proposes a method for such selection. The method is based on maximization of energy produced, restricted to the system pressure limits. To solve this selection problem, the optimization technique of Particle Swarm Optimization (PSO) is used, and complete pump curves are used to simulate the PATs. In addition, this method is capable of identifying the best location on the network to install the PATs10939240