Mathematical Programming Model for Procurement Selection in Water Irrigation Systems. A Case Study

[EN] The development tools to optimize the process and helping management to get margin are used inside of the industrial manufacture. Water networks management are not alien to this need. The optimization of the water resource is currently done in big basins, but it is not a general practice in irrigation networks that operate as water distribution companies to supply the farmers¿ demand. Nowadays, this management is not optimized and the costs are not minimized. This research introduces a mathematical programming model to optimize the replenishment process in a local irrigation network with the aim to decide what volume is procured (source, quantity and timetable) as well as what volume is stored while minimising the involved total costs. The final objective is to improve the sustainability of the water systems. The use of this tool reduces the water costs in 52.2% as well as enables to define the necessary source and the electrical schedule along the year. This definition optimizes the operating of the water system and enables to reduce the water price from 0.23 €/m3 (current water management) to 0.11 €/m3 (proposed model).Pérez-Sánchez, M.; Díaz-Madroñero Boluda, FM.; López Jiménez, PA.; Mula, J. (2017). Mathematical Programming Model for Procurement Selection in Water Irrigation Systems. A Case Study. Journal of Engineering Science and Technology Review (Online). 10(6):146-153. doi:10.25103/jestr.106.17S14615310

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, 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.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

Overseas Chinese Environmental Engineers and Scientists Association (OCEESA) Report, Regular Issue, February 2020

This OCEESA report, which is regular issue of OCEESA Journal (Overseas Chinese Environmental Engineers and Scientists Association Journal). This report is OCEESA report number: OCEESA/JL-2020/3701, February 2020, ISSN 1072 -7248. This report is also OCEESA Journal, Volume 37, Number 1, February 2020. Yung-Tse Hung, Permanent Executive Director, OCEESA, is editor of this report. This issue includes: (A) 10 OCEESA Best Papers (B) 6 OCEESA Papers; 21.Zhang-Zhi Charlie Huang 黄长志 , Implementing Compensation System for Environmental Damages: Challenges and Solutions, 22. Hanlu Yan, Kaimin Shih施凱閔 , Quantitative X-Ray Diffraction for Characterizing P Recovery Products from Wastewater, 23. Kaimin Shih 施凱閔 , Material Mineralogical Technology for Pollution Prevention and Resource Recovery材料礦物學技術於污染防治與資源回收的應用, 24. Kuo-Kunag Hsu 許國光 , Cleanup of MSW-Gasified Synthesis Gas, 25. Pao-Chiang Yuan 袁保強 , End of Useful Life Computer Recycling Program at Jackson State University, Jackson, Mississippi, USA, 26. Qin Qian钱琴, Bo Sun, Xianchang Li, Frank Sun, Che-Jen Lin, Water quality modeling with data collected by wireless sensor networks (WSNs) in an experimental pond: A case study; (C) 3 technical papers; 28. Abdulkarim Alorayfij, Yung-Tse Hung, Anaerobic digestion of agricultural waste, 29. Abdullah Alshati, Yung-Tse Hung, Methane Gas Production from Animal Waste, 30. Abdulmajeed Alshatti, Yung-Tse Hung, Treatment of Timber Industry Wastes, 31. OCEESA Constitutions By-Laws (5 November 2000 edition), 32. OCEESA Constitutions By-Laws (14 February 2006 edition), 33. OCEESA Constitutions By-Laws (27 October 2013 edition), 34. Lawrence Kong-Pu Wang letter of support Yung-Tse Hung Permanent Executive Director OCEESA 12-30-20, 35. Wen-Chi Ku letter of support Yung-Tse Hung Permanent Executive Director OCEESA 12-03-20, 36. OCEESA Member Application Form and Information, 37. Mailing Address Pag

Computational Determination of Air Valves Capacity Using CFD Techniques

[EN] The analysis of transient flow is necessary to design adequate protection systems that support the oscillations of pressure produced in the operation of motor elements and regulation. Air valves are generally used in pressurized water pipes to manage the air inside them. Under certain circumstances, they can be used as an indirect control mechanism of the hydraulic transient. Unfortunately, one of the major limitations is the reliability of information provided by manufacturers and vendors, which is why experimental trials are usually used to characterize such devices. The realization of these tests is not simple since they require an enormous volume of previously stored air to be used in such experiments. Additionally, the costs are expensive. Consequently, it is necessary to develop models that represent the behaviour of these devices. Although computational fluid dynamics (CFD) techniques cannot completely replace measurements, the amount of experimentation and the overall cost can be reduced significantly. This work approaches the characterization of air valves using CFD techniques, including some experimental tests to calibrate and validate the results. A mesh convergence analysis was made. The results show how the CFD models are an efficient alternative to represent the behavior of air valves during the entry and exit of air to the system, implying a better knowledge of the system to improve it.This research was funded by the Program Fondecyt Regular, grant number 1180660.García-Todolí, S.; Iglesias Rey, PL.; Mora Melia, D.; Martínez-Solano, FJ.; Fuertes-Miquel, VS. (2018). Computational Determination of Air Valves Capacity Using CFD Techniques. Water. 10(10):1-16. https://doi.org/10.3390/w10101433S1161010Liou, C. P., & Hunt, W. A. (1996). Filling of Pipelines with Undulating Elevation Profiles. Journal of Hydraulic Engineering, 122(10), 534-539. doi:10.1061/(asce)0733-9429(1996)122:10(534)Zhou, F., Hicks, F. E., & Steffler, P. M. (2002). Transient Flow in a Rapidly Filling Horizontal Pipe Containing Trapped Air. Journal of Hydraulic Engineering, 128(6), 625-634. doi:10.1061/(asce)0733-9429(2002)128:6(625)Laanearu, J., Annus, I., Koppel, T., Bergant, A., Vučković, S., Hou, Q., … van’t Westende, J. M. C. (2012). Emptying of Large-Scale Pipeline by Pressurized Air. Journal of Hydraulic Engineering, 138(12), 1090-1100. doi:10.1061/(asce)hy.1943-7900.0000631Apollonio, C., Balacco, G., Fontana, N., Giugni, M., Marini, G., & Piccinni, A. (2016). Hydraulic Transients Caused by Air Expulsion During Rapid Filling of Undulating Pipelines. Water, 8(1), 25. doi:10.3390/w8010025Zhou, F., Hicks, F. E., & Steffler, P. M. (2002). Observations of Air–Water Interaction in a Rapidly Filling Horizontal Pipe. Journal of Hydraulic Engineering, 128(6), 635-639. doi:10.1061/(asce)0733-9429(2002)128:6(635)Vasconcelos, J. G., Wright, S. J., & Roe, P. L. (2006). Improved Simulation of Flow Regime Transition in Sewers: Two-Component Pressure Approach. Journal of Hydraulic Engineering, 132(6), 553-562. doi:10.1061/(asce)0733-9429(2006)132:6(553)Li, J., & McCorquodale, A. (1999). Modeling Mixed Flow in Storm Sewers. Journal of Hydraulic Engineering, 125(11), 1170-1180. doi:10.1061/(asce)0733-9429(1999)125:11(1170)Ramezani, L., Karney, B., & Malekpour, A. (2015). The Challenge of Air Valves: A Selective Critical Literature Review. Journal of Water Resources Planning and Management, 141(10), 04015017. doi:10.1061/(asce)wr.1943-5452.0000530Stephenson, D. (1997). Effects of Air Valves and Pipework on Water Hammer Pressures. Journal of Transportation Engineering, 123(2), 101-106. doi:10.1061/(asce)0733-947x(1997)123:2(101)Bianchi, A., Mambretti, S., & Pianta, P. (2007). Practical Formulas for the Dimensioning of Air Valves. Journal of Hydraulic Engineering, 133(10), 1177-1180. doi:10.1061/(asce)0733-9429(2007)133:10(1177)De Martino, G., Fontana, N., & Giugni, M. (2008). Transient Flow Caused by Air Expulsion through an Orifice. Journal of Hydraulic Engineering, 134(9), 1395-1399. doi:10.1061/(asce)0733-9429(2008)134:9(1395)Bhosekar, V. V., Jothiprakash, V., & Deolalikar, P. B. (2012). Orifice Spillway Aerator: Hydraulic Design. Journal of Hydraulic Engineering, 138(6), 563-572. doi:10.1061/(asce)hy.1943-7900.0000548Iglesias-Rey, P. L., Fuertes-Miquel, V. S., García-Mares, F. J., & Martínez-Solano, J. J. (2014). Comparative Study of Intake and Exhaust Air Flows of Different Commercial Air Valves. Procedia Engineering, 89, 1412-1419. doi:10.1016/j.proeng.2014.11.467Martins, N. M. C., Soares, A. K., Ramos, H. M., & Covas, D. I. C. (2016). CFD modeling of transient flow in pressurized pipes. Computers & Fluids, 126, 129-140. doi:10.1016/j.compfluid.2015.12.002Zhou, L., Liu, D., & Ou, C. (2011). Simulation of Flow Transients in a Water Filling Pipe Containing Entrapped Air Pocket with VOF Model. Engineering Applications of Computational Fluid Mechanics, 5(1), 127-140. doi:10.1080/19942060.2011.11015357Davis, J. A., & Stewart, M. (2002). Predicting Globe Control Valve Performance—Part I: CFD Modeling. Journal of Fluids Engineering, 124(3), 772-777. doi:10.1115/1.1490108Stephens, D., Johnson, M. C., & Sharp, Z. B. (2012). Design Considerations for Fixed-Cone Valve with Baffled Hood. Journal of Hydraulic Engineering, 138(2), 204-209. doi:10.1061/(asce)hy.1943-7900.0000496Romero-Gomez, P., Ho, C. K., & Choi, C. Y. (2008). Mixing at Cross Junctions in Water Distribution Systems. I: Numerical Study. Journal of Water Resources Planning and Management, 134(3), 285-294. doi:10.1061/(asce)0733-9496(2008)134:3(285)Austin, R. G., Waanders, B. van B., McKenna, S., & Choi, C. Y. (2008). Mixing at Cross Junctions in Water Distribution Systems. II: Experimental Study. Journal of Water Resources Planning and Management, 134(3), 295-302. doi:10.1061/(asce)0733-9496(2008)134:3(295)Ho, C. K. (2008). Solute Mixing Models for Water-Distribution Pipe Networks. Journal of Hydraulic Engineering, 134(9), 1236-1244. doi:10.1061/(asce)0733-9429(2008)134:9(1236)Huang, J., Weber, L. J., & Lai, Y. G. (2002). Three-Dimensional Numerical Study of Flows in Open-Channel Junctions. Journal of Hydraulic Engineering, 128(3), 268-280. doi:10.1061/(asce)0733-9429(2002)128:3(268)Weber, L. J., Schumate, E. D., & Mawer, N. (2001). Experiments on Flow at a 90° Open-Channel Junction. Journal of Hydraulic Engineering, 127(5), 340-350. doi:10.1061/(asce)0733-9429(2001)127:5(340)Chanel, P. G., & Doering, J. C. (2008). Assessment of spillway modeling using computational fluid dynamics. Canadian Journal of Civil Engineering, 35(12), 1481-1485. doi:10.1139/l08-094Li, S., Cain, S., Wosnik, M., Miller, C., Kocahan, H., & Wyckoff, R. (2011). Numerical Modeling of Probable Maximum Flood Flowing through a System of Spillways. Journal of Hydraulic Engineering, 137(1), 66-74. doi:10.1061/(asce)hy.1943-7900.0000279Castillo, L., García, J., & Carrillo, J. (2017). Influence of Rack Slope and Approaching Conditions in Bottom Intake Systems. Water, 9(1), 65. doi:10.3390/w9010065Regueiro-Picallo, M., Naves, J., Anta, J., Puertas, J., & Suárez, J. (2016). Experimental and Numerical Analysis of Egg-Shaped Sewer Pipes Flow Performance. Water, 8(12), 587. doi:10.3390/w812058

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle