Su Dağıtım Şebekelerinde Basınç Kaybına Neden Olan Boruların Yapay Bağışıklık Sistemleri ile Tespit Edilmesi

Yıl 2023, Cilt: 11 Sayı: 4, 2236 - 2245, 24.10.2023

Mirac Eryiğit

https://doi.org/10.29130/dubited.1158728

Öz

Bu çalışmada, su dağıtım şebekelerinde basınç kaybına neden olan düşük Hazen-Williams pürüzlülük katsayısına sahip eskimiş boruların belirlenmesi için model kalibrasyonuna bağlı Yapay Bağışıklık Sistemlerini kullanan bir optimizasyon modeli önerilmektedir. Sezgisel optimizasyon yöntemi olarak Yapay Bağışıklık Sistemlerinden biri olan modifiye Klonal Seçim Algoritması kullanılmıştır. Modelin performansını test etmek için, sürekli-kararlı koşullar altında dört gözlü farazi (sanal) bir su dağıtım şebekesinde model uygulanmıştır. Elde edilen sonuçlara göre, su dağıtım şebekelerindeki yüksek basınç kayıplarına neden olan eskimiş boruların tespit edilmesinde modelin gelecek vaat ettiği görülmüştür

Anahtar Kelimeler

Basınç kaybı, Su Dağıtım Şebekeleri, Optimizasyon, Model Kalibrasyonu, Yapay Bağışıklık Sistemleri

Detection of Pipes Causing Pressure Loss in Water Distribution Networks via Artificial Immune Systems

Öz

This paper proposes the optimization model using Artificial Immune Systems, depending on a model calibration, in order to determine worn out pipes with low Hazen-Williams roughness coefficient causing pressure loss in the water distribution networks. The modified Clonal Selection Algorithm, a type of Artificial Immune Systems, was used as a heuristic optimization method. In order to evaluate its performance, the model was implemented to the four-loop hypothetical water distribution network under steady-state conditions. According to the results, the model appeared to be promising in the detection of old pipes causing high pressure losses in the water distribution networks

Anahtar Kelimeler

Pressure loss, Water Distribution Networks, Optimization, Model Calibration, Artificial Immune Systems

Kaynakça

• [1] W.B.F. De Schaetzen, G.A. Walters, and D.A. Savic, “Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms,” Urban Water Journal, vol. 2, no. 2, pp. 141-152, 2000.
• [2] T.M. Walski, “Understanding the adjustments for water distribution system model calibration,” Journal of Indian Water Works Association, vol. 33, no. 2, pp. 151-157, 2001.
• [3] Z.Y. Wu, T. Walski, R. Mankowski, J. Cook, M. Tryby, and G. Herrin, “Calibrating water distribution model via genetic algorithms,” Proceedings of the AWWA IMTech Conference, Kansas City, Missouri, US, 2002, pp. 1-10.
• [4] D. Savic, Z. Kapelan, and P.M.R. Jonkergouw, “Quo vadis water distribution model calibration?,” Urban Water Journal, vol. 6, no. 1, pp. 3-22, 2009.
• [5] A. Ostfeld, E. Salomons, L. Ormsbee, J.G. Uber, C.M. Bros, P. Kalungi, R. Burd, B. Zazula-Coetzee, T. Belrain, D. Kang, K. Lansey, H. Shen, E. McBean, Z.Y. Wu, T. Walski, S. Alvisi, M. Franchini, J.P. Johnson, S.R. Ghimire, B.D. Barkdoll, T. Koppel, A. Vassiljev, J.H. Kim, G. Chung, D.G. Yoo, K. Diao, Y. Zhou, J. Li, Z. Liu, K. Chang, J. Gao, S. Qu, Y. Yuan, T.D. Prasad, D. Laucelli, L.S. Vamvakeridou Lyroudia, Z. Kapelan, D. Savic, L. Berardi, G. Barbaro, O. Giustolisi, M. Asadzadeh, B.A. Tolson, and R. McKillop, “Battle of the water calibration networks,” Journal of Water Resources Planning and Management, vol. 138, no. 5, pp. 523-532, 2012.
• [6] Z.S. Kapelan, D.A. Savic, and G.A Walters, “A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks,” Journal of Hydraulic Research, vol. 41, no. 5, pp. 481-492, 2003.
• [7] S. Lingireddy and L.E. Ormsbee, “Hydraulic network calibration using genetic optimization,” Civil Engineering and Environmental Systems, vol. 19, no. 1, pp.13-39, 2002.
• [8] M. Jamasb, M. Tabesh, and M. Rahimi, “Calibration of EPANET using genetic algorithm,” Water Distribution Systems Analysis, Kruger National Park, South Africa, 2008, pp. 881-889.
• [9] T. Boczar, N. Adamikiewicz, and W. Stanisławski, “Calibration of parameters of water supply network model using genetic algorithm,” International Conference Energy, Environment and Material Systems (EEMS 2017), Polanica-Zdrój, Poland, 2017, pp. 1-4.
• [10] T.D. Prasad, “A clonal selection algorithm for the C-Town network calibration,” ASCE Water Distribution Systems Analysis (WDSA), Tucson, AZ, USA, 2010, pp. 1652-1663.
• [11] M. Dini and M. Tabesh, “A new method for simultaneous calibration of demand pattern and Hazen-Williams coefficients in water distribution systems,” Water Resources Management, vol. 28, pp. 2021-2034, 2014.
• [12] D. Kang and K. Lansey, “Demand and roughness estimation in water distribution systems,” Journal of Water Resources Planning and Management, vol. 137, no. 1, pp. 20-30, 2011.
• [13] T. Koppel and A. Vassiljev, “Calibration of a model of an operational water distribution system containing pipes of different age,” Advances in engineering software, vol. 40, no. 8, pp. 659-664, 2009.
• [14] S. Alvisi and M. Franchini, “Pipe roughness calibration in water distribution systems using grey numbers,” Journal of Hydroinformatics, vol. 12, no. 4, pp. 424-445, 2010.
• [15] A. Vassiljev, M. Koor, and T. Koppel, “Real-time demands and calibration of water distribution systems,” Advances in Engineering Software, vol. 89, no. C, pp. 108-113, 2015.
• [16] O. Piller, S. Elhay, J. Deuerlein, and A.R. Simpson, “Local sensitivity of pressure-driven modeling and demand-driven modeling steady-state solutions to variations in parameters,” Journal of Water Resources Planning and Management, vol. 143, no. 2, pp. 1-27, 2017.
• [17] X. Xie, H. Zhang, and D. Hou, “Bayesian approach for joint estimation of demand and roughness in water distribution systems,” Journal of Water Resources Planning and Management, vol. 143, no. 8, pp. 1-10, 2017.
• [18] K. Du, R. Ding, Z. Wang, and Z. Song, “Direct inversion algorithm for pipe resistance coefficient calibration of water distribution systems,” Journal of Water Resources Planning and Management, vol. 144, no. 7, pp. 1-9, 2018.
• [19] R.D. Jadhao and R. Gupta, “Calibration of water distribution network of the Ramnagar zone in Nagpur City using online pressure and flow data,” Applied Water Science, vol. 8, no. 29, pp. 1-10, 2018.
• [20] Q. Zhang, F. Zheng, H.F. Duan, and Y. Jia, “Efficient numerical approach for simultaneous calibration of pipe roughness coefficients and nodal demands for water distribution systems,” Journal of Water Resources Planning and Management, vol. 144, no. 10, pp. 1-12, 2018.
• [21] Bentley Systems. (2022, August 31). Product Documentation [Online]. Available: https://docs.bentley.com/LiveContent/web/Bentley%20WaterGEMS%20SS6-v1/en/9043.html.
• [22] M. Eryiğit, “Cost optimization of water distribution networks by using artificial immune systems,” Journal of Water Supply: Research and Technology-AQUA, vol. 64, no. 1, pp. 47-63, 2015.
• [23] L.N. De Castro and F.J. Von Zuben, “Learning and optimization using the clonal selection principle,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 3, pp. 239-251, 2002.
• [24] L. Rossman, “EPANET 2 Users Manual,” U.S. Environmental Protection Agency, Washington, D.C., U.S., EPA/600/R-00/057, 2000.