Aplicación del método de jerarquías analíticas (AHP) a la gestión de pérdidas de agua en redes de abastecimiento

Las fugas en los sistemas de distribución de agua están provocadas por todos aquellos fallos en las tuberías, accesorios o tanques de almacenamiento que provocan una pérdida de agua. La cantidad de agua que se pierde por fugas en las redes de distribución de agua potable representa para los gestores de las empresas de abastecimiento uno de los mayores desafíos a los que deben enfrentarse, no solamente por el coste del agua que se pierde, sino porque esa agua lleva implícita una serie de costes adicionales, e incluso conlleva impactos en la sociedad y el medio ambiente. Las decisiones tomadas por la compañía para la gestión de las fugas, tienen efecto no sólo en la propia compañía, sino también en la sociedad y en el medio que la rodea. Por ello, estas decisiones deberían estar basadas en un ejercicio de toma de decisión que incluya un mayor número de criterios, además de los meramente técnicos y económicos que normalmente suelen ser tomados en cuenta en una evaluación de proyectos. La toma de decisiones respecto a la política de gestión de fugas a ejecutar en una empresa representa un reto para los gestores, en vista de la complejidad que ello implica. Si el problema se observa desde el punto de vista económico, es posible que la opción que se tome esté encaminada a reparar solo las fugas evidentes o reportadas, ya que la inversión que se debe realizar para detectar y reparar suele ser mayor que el valor del agua recuperada, tomando en cuenta que en la gran mayoría de los casos, la tasa de recuperación o la tarifa cobrada al abonado no llega a cubrir siquiera los costes variables. Sin embargo, en vista de que se trata de un servicio público del cual todos somos usuarios, y que además las decisiones tomadas por la compañía tienen un impacto más allá de la empresa, se recomienda considerar una serie de criterios, adicionales a los aspectos técnicos y económicos, para que la decisión que se tome sea lo más acertada para la compañía y su entorno.Delgado Galván, XV. (2011). Aplicación del método de jerarquías analíticas (AHP) a la gestión de pérdidas de agua en redes de abastecimiento [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11238Palanci

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

[EN] In Water Distribution Networks, the chlorine control is feasible with the use of water quality simulation codes. EPANET is a broad domain software and several commercial computer software packages base their models on its methodology. However, EPANET assumes that the solute mixing at cross-junctions is ¿complete and instantaneous¿. Several authors have questioned this model. In this paper, experimental tests are developed while using Copper Sulphate as tracer at different operating conditions, like those of real water distribution networks, in order to obtain the Residence Time Distribution and its behavior in the mixing as a novel analysis for the cross-junctions. Validation tests are developed in Computational Fluid Dynamics, following the k-# turbulence model. It is verified that the mixing phenomenon is dominated by convection, analyzing variation of Turbulent Schmidt Number vs. experimental tests. Having more accurate mixing models will improve the water quality simulations to have an appropriate control for chlorine and possible contaminants in water distribution networks.To CONACYT for the Master and Ph.D. scholarships (417824 and 703220) to D.H.-C. and the Ph.D. scholarship (294038) to M.R.; To Universidad de Guanajuato for the financial support of the project No. 100/2018 of J.L.N.; To Engineering Division, Campus Guanajuato and Geomatics and Hydraulics Engineering Department for the financial support of this project; and finally, to SEP-PRODEP and UG for the financial support to publish this paper.Hernandez Cervantes, D.; Delgado Galván, XV.; Nava, JL.; López Jiménez, PA.; Rosales, M.; Mora Rodríguez, JDJ. (2018). Validation of a Computational Fluid Dynamics Model for a Novel Residence Time Distribution Analysis in Mixing at Cross-Junctions. Water. 10(6):1-18. https://doi.org/10.3390/w10060733S118106Mercier Shanks, C., Sérodes, J.-B., & Rodriguez, M. J. (2013). Spatio-temporal variability of non-regulated disinfection by-products within a drinking water distribution network. Water Research, 47(9), 3231-3243. doi:10.1016/j.watres.2013.03.033Vasconcelos, J. J., Rossman, L. A., Grayman, W. M., Boulos, P. F., & Clark, R. M. (1997). Kinetics of chlorine decay. Journal - American Water Works Association, 89(7), 54-65. doi:10.1002/j.1551-8833.1997.tb08259.xOzdemir, O. N., & Ucak, A. (2002). Simulation of Chlorine Decay in Drinking-Water Distribution Systems. Journal of Environmental Engineering, 128(1), 31-39. doi:10.1061/(asce)0733-9372(2002)128:1(31)Knobelsdorf Miranda, J., & Mujeriego Sahuquillo, R. (1997). Crecimiento bacteriano en las redes de distribución de agua potable: una revisión bibliográfica. Ingeniería del agua, 4(2). doi:10.4995/ia.1997.2719Wang, W., Ye, B., Yang, L., Li, Y., & Wang, Y. (2007). Risk assessment on disinfection by-products of drinking water of different water sources and disinfection processes. Environment International, 33(2), 219-225. doi:10.1016/j.envint.2006.09.009Parks, S. L. I., & VanBriesen, J. M. (2009). Booster Disinfection for Response to Contamination in a Drinking Water Distribution System. Journal of Water Resources Planning and Management, 135(6), 502-511. doi:10.1061/(asce)0733-9496(2009)135:6(502)Hernández Cervantes, D., Mora Rodríguez, J., Delgado Galván, X., Ortiz Medel, J., & Jiménez Magaña, M. R. (2015). Optimal use of chlorine in water distribution networks based on specific locations of booster chlorination: analyzing conditions in Mexico. Water Supply, 16(2), 493-505. doi:10.2166/ws.2015.161Weickgenannt, M., Kapelan, Z., Blokker, M., & Savic, D. A. (2010). Risk-Based Sensor Placement for Contaminant Detection in Water Distribution Systems. Journal of Water Resources Planning and Management, 136(6), 629-636. doi:10.1061/(asce)wr.1943-5452.0000073Rathi, S., & Gupta, R. (2013). Monitoring stations in water distribution systems to detect contamination events. ISH Journal of Hydraulic Engineering, 20(2), 142-150. doi:10.1080/09715010.2013.857470Seth, A., Klise, K. A., Siirola, J. D., Haxton, T., & Laird, C. D. (2016). Testing Contamination Source Identification Methods for Water Distribution Networks. Journal of Water Resources Planning and Management, 142(4), 04016001. doi:10.1061/(asce)wr.1943-5452.0000619Xuesong, Y., Jie, S., & Chengyu, H. (2017). Research on contaminant sources identification of uncertainty water demand using genetic algorithm. Cluster Computing, 20(2), 1007-1016. doi:10.1007/s10586-017-0787-6Rathi, S., & Gupta, R. (2015). Optimal sensor locations for contamination detection in pressure-deficient water distribution networks using genetic algorithm. Urban Water Journal, 14(2), 160-172. doi:10.1080/1573062x.2015.1080736Sandoval, M. A., Fuentes, R., Walsh, F. C., Nava, J. L., & de León, C. P. (2016). Computational fluid dynamics simulations of single-phase flow in a filter-press flow reactor having a stack of three cells. Electrochimica Acta, 216, 490-498. doi:10.1016/j.electacta.2016.09.045Castañeda, L. (2017). Computational Fluid Dynamic Simulations of Single-Phase Flow in a Spacer-Filled Channel of a Filter-Press Electrolyzer. International Journal of Electrochemical Science, 7351-7364. doi:10.20964/2017.08.09Song, I., Romero-Gomez, P., & Choi, C. Y. (2009). Experimental Verification of Incomplete Solute Mixing in a Pressurized Pipe Network with Multiple Cross Junctions. Journal of Hydraulic Engineering, 135(11), 1005-1011. doi:10.1061/(asce)hy.1943-7900.0000095Romero-Gomez, P., Lansey, K. E., & Choi, C. Y. (2010). Impact of an incomplete solute mixing model on sensor network design. Journal of Hydroinformatics, 13(4), 642-651. doi:10.2166/hydro.2010.123Yu, T. C., Shao, Y., & Shen, C. (2014). Mixing at Cross Joints with Different Pipe Sizes in Water Distribution Systems. Journal of Water Resources Planning and Management, 140(5), 658-665. doi:10.1061/(asce)wr.1943-5452.0000372Shao, Y., Jeffrey Yang, Y., Jiang, L., Yu, T., & Shen, C. (2014). Experimental testing and modeling analysis of solute mixing at water distribution pipe junctions. Water Research, 56, 133-147. doi:10.1016/j.watres.2014.02.053Mompremier, R., Pelletier, G., Fuentes Mariles, Ó. A., & Ghebremichael, K. (2015). Impact of incomplete mixing in the prediction of chlorine residuals in municipal water distribution systems. Journal of Water Supply: Research and Technology - Aqua, 64(8), 904-914. doi:10.2166/aqua.2015.148McKenna, S. A., O’Hern, T., & Hartenberger, J. (2009). Detailed Investigation of Solute Mixing in Pipe Joints through High Speed Photography. Water Distribution Systems Analysis 2008. doi:10.1061/41024(340)88Ho, C. K., & O’Rear, L. (2009). Evaluation of solute mixing in water distribution pipe junctions. Journal - American Water Works Association, 101(9), 116-127. doi:10.1002/j.1551-8833.2009.tb09964.xChoi, C. Y., Shen, J. Y., & Austin, R. G. (2009). Development of a Comprehensive Solute Mixing Model (AZRED) for Double-Tee, Cross, and Wye Junctions. Water Distribution Systems Analysis 2008. doi:10.1061/41024(340)89Rosales, M., Pérez, T., & Nava, J. L. (2016). Computational fluid dynamic simulations of turbulent flow in a rotating cylinder electrode reactor in continuous mode of operation. Electrochimica Acta, 194, 338-345. doi:10.1016/j.electacta.2016.02.076Moncho-Esteve, I. J., Palau-Salvador, G., Brevis, W., Vaas, M. O., & López-Jiménez, P. A. (2015). Numerical simulation of the hydrodynamics and turbulent mixing process in a drinking water storage tank. Journal of Hydraulic Research, 53(2), 207-217. doi:10.1080/00221686.2014.98945

An overview of leaks and intrusion for different pipe materials and failures

Most leak management methods are focused on quantifying water losses, directly related to energy and resource waste. In this research work, a comprehensive review on relationship and modelling between pipe leaks, materials, and type of failures is presented. Information necessary to study the main defects of pipe materials has been compiled and the different causes of pipe failures were reviewed and analysed. As a result, types of failures were identified depending on the pipe surrounding, pipe material and mechanisms and stresses that support the pipes. A deep focus of the leak problem is presented, analysing intrusion flows and the related pressure variation using the volume through simple orifices with fixed and variable discharge area: Fixed And Variable Area Discharge (FAVAD theory). Finally, a new relationship is proposed between pipe defects and discharge coefficients, depending on the flow through failures (induced by leaks or intrusions).This contribution has been made possible in the frame of the project: DANAIDES: REF. DPI2007-63424. Ministerio de Educacion y Ciencia de Espana.Mora Rodríguez, JDJ.; Delgado Galván, XV.; Ramos, HM.; López Jiménez, PA. (2014). An overview of leaks and intrusion for different pipe materials and failures. Urban Water Journal. 11(1):1-10. https://doi.org/10.1080/1573062X.2012.739630S11011

Pathogen Intrusion flows in WDS: according to orifice equations

[EN] Pathogen intrusion may occur in water pipes when negative pressures allow external flows to enter through failures or leaks and then mix with safe water. It is applied a variation of the Fixed and Variable Area Discharge theory (FAVAD) and the orifice equations to estimate the intrusion flow across defects in pipes considering different types of failure. The equivalent diameter of different round hole failures was considered in order to obtain the dimensions of the split failures that presented the same pressure drop. In addition, experimental scenarios were made with external porous media to model the intrusion flow in buried pipes. An inverse method for the orifice equation is proposed to obtain the intrusion flows generated by the variations of two section failures produced by the pressure drop inside the pipe. The orifice equation properly represents the intrusion flow by adjusting the discharge coefficient. Furthermore, the considerable variations in the failures area with negative pressures should be taken into consideration in the expressions that estimate the intrusion flow.DAIP-UG project 2013: 1101.31A02.42.205000 is acknowledged. The use of English in this paper was revised by the DAIP translation services (Servicios de traduccion del Departamento de Apoyo a la Investigacion y al Posgrado) of the University of Guanajuato.Mora Rodríguez, JDJ.; Delgado Galván, XV.; Ortiz-Medel, J.; Ramos, HM.; Fuertes-Miquel, VS.; López Jiménez, PA. (2015). Pathogen Intrusion flows in WDS: according to orifice equations. Journal of Water Supply Research and Technology. Aqua (Online). 64(8):857-869. https://doi.org/10.2166/aqua.2015.121S85786964