Su Dağıtım Şebekelerinde Bakiye Kloru Azaltan Boruların Cidar Reaksiyon Katsayısına Bağlı Olarak Tespit Edilmesi

Yıl 2024, Cilt: 14 Sayı: 3, 86 - 94, 25.11.2024

Miraç Eryiğit

Öz

Bu çalışmada, su dağıtım şebekelerindeki bakiye kloru azaltan boruların tespit edilmesi için modifiye klonal seçim algoritması (çok bilinen sezgisel optimizasyon tekniklerinden biri) kullanan bir optimizasyon modeli inşa edilmesi amaçlanmıştır. Model, EPANET ile bağlantılı olarak MATLAB yazılım dilinde kodlanmıştır. Modelin performansı, kararlı/sabit akım koşulları altında iki gözlü farazi bir su dağıtım şebekesinde değerlendirilmiştir. Amaç fonksiyonu, model kalibrasyonuna dayalı olduğu için şebekenin düğüm noktalarında serbest klor konsantrasyonlarının ölçüldüğü kabul edilmiştir. Bakiye klor konsantrasyonlarını azaltan borular, her bir düğüm noktasındaki ölçülen ve tahmin edilen serbest klor konsantrasyonları arasındaki farkların toplamının minimize edilmesine bağlı olarak model tarafından belirlenmiştir. Boruların belirlenmesi için boru cidarı reaksiyon hız katsayılarından yararlanılmıştır. Model 10 kez çalıştırılarak su dağıtım şebekesindeki her bir borunun ortalama reaksiyon hız katsayıları elde edilmiştir. Model 10 kez çalıştırıldıktan sonra, ortalama tahmin ve gerçek reaksiyon hız katsayı değerlerinin hemen hemen aynı olduğu sonucuna varılmıştır (R2=0.99). Su dağıtım şebekesindeki bakiye klor kaybına neden olan boruların tespit edilmesi için optimizasyon modelinin uygulanabilir olduğu görülmüştür.

Anahtar Kelimeler

Yapay bağışıklık sistemleri, model kalibrasyonu, boru cidar reaksiyon katsayısı, bakiye klor, su dağıtım şebekesi

Detection of Pipes Decreasing Residual Chlorine Via Wall Reaction Coefficient in Water Distribution Networks

Öz

This paper intended to build an optimization model utilizing the modified clonal selection algorithm (one of the famous heuristic optimization techniques) to detect pipes which reduces a residual chlorine in the water distribution networks (WDNs). MATLAB programming language was used to code the model linked with EPANET. The model performance was evaluated in a two-loop hypothetical WDN under steady-state flow conditions. In nodes of this hypothetical WDN, free chlorine concentrations were assumed to be measured since an objective function depends on model calibration. Pipes decreasing residual chlorine concentrations were determined by running the model which minimizes a total of concentration differences between estimated and measured free chlorine in each node. In order to find these pipes, pipe wall reaction rate coefficients were utilized. The model was run 10 times to obtain average reaction rate coefficient of each pipe in the WDN. After 10 runs, mean estimated and actual/real reaction rate coefficient values were almost equal (R2=0.99). The optimization model appeared to be viable for detecting pipes causing a residual chlorine loss in the WDN.

Anahtar Kelimeler

Artificial immune systems, model calibration, pipe wall reaction coefficient, residual chlorine, water distribution network

Kaynakça

• Abhijith, G.R., Ostfeld, A. 2022. Contaminant Fate and Transport Modeling in Distribution Systems: EPANET-C. Water, 14: 1665.
• Absalan, F., Hatam, F., Barbeau, B., Prévost, M., Bichai, F. 2022. Predicting Chlorine and Trihalomethanes in a Full-Scale Water Distribution System under Changing Operating Conditions. Water, 14(22): 3685. https://doi.org/10.3390/w14223685.
• Ardila, A., Rodriguez, M.J., Pelletier, G. 2023. Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review. Journal of Environmental Management, 337: 117734. Doi: 10.1016/j.jenvman.2023.117734.
• Askenaizer, D. 2003. Drinking Water Quality and Treatment, Editor(s): Robert A. Meyers, Encyclopedia of Physical Science and Technology (Third Edition), Academic Press, 651-671, https://doi.org/10.1016/B0-12-227410-5/00186-1.
• Belcaid, A., Faycal, T., Tounssi, T.A. 2023. Chlorine Decay Modeling in a Water Distribution Network in Mohammedia City, Morocco. Ecological Engineering and Environmental Technology, 24(2): 269-278. Doi: https://doi.org/10.12912/27197050/157538.
• Blokker, M., Vreeburg, J., Speight, V. 2014. Residual Chlorine in the Extremities of the Drinking Water Distribution System: The Influence of Stochastic Water Demands. Procedia Engineering, 70: 172-180. Doi: 10.1016/j.proeng.2014.02.020.
• Chelsea, J. 2016. Operational and Water Quality Analysis for the City of Akron's Water Treatment Plant and Distribution System. Honors Research Projects, 369.
• De Castro L.N., Von Zuben F.J. 2002. Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, 6(3): 239-251. Doi: 10.1109/TEVC.2002.1011539.
• Enriquez, L., Saldarriaga, J., Berardi, L., Laucelli, D., Giustolisi, O. 2023. Using artificial intelligence models to support water quality prediction in water distribution networks. IOP Conference Series: Earth and Environmental Science, 1136: 012009. Doi: 10.1088/1755-1315/1136/1/012009.
• Elsherif, S.M., Wang, S., Taha, A.F., Sela, L., Giacomoni, M.H., Abokifa, A.A. 2023. Control-theoretic modeling of multi-species water quality dynamics in drinking water networks: Survey, methods, and test cases. Annual Reviews in Control, 55: 466-485, doi.org/10.1016/j.arcontrol.2022.08.003.
• Eryiğit M. 2015. Cost optimization of water distribution networks by using artificial immune systems. Journal of Water Supply: Research and Technology-AQUA, 64(1): 47-63. https://doi.org/10.2166/aqua.2014.031.
• Eryiğit, M., Sulaiman, S.O. 2022. Specifying optimum water resources based on cost-benefit relationship for settlements by artificial immune systems: Case study of Rutba City, Iraq. Water Supply, 22(6): 5873–5881. https://doi.org/10.2166/ws.2022.227.
• García-Ávila, F., Avilés-Añazco, A., Ordoñez-Jara, J., Guanuchi-Quezada, C., Pino, L.F., Ramos-Fernández, L. 2021. Modeling of residual chlorine in a drinking water network in times of pandemic of the SARS-CoV-2 (COVID-19). Sustainable Environment Research, 31(12). https://doi.org/10.1186/s42834-021-00084-w.
• Gibbs, M.S., Morgan, N., Maier, H.R., Dandy, G.C., Nixon, J.B., Holmes, M. 2006. Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods. Mathematical and Computer Modelling, 44(5-6): 485-498. Doi:10.1016/j.mcm.2006.01.007.
• Han, K.S., Park, J.H., Park, Y.B., Kim, S.J., Kim, H.D., Choi, Y.J., Choi, I.C., Hong, S.H. 2017. Effect of Residual Chlorine Concentration on Water Pipe Corrosion. ECS Meeting Abstracts, MA2017-02, 687. Doi: 10.1149/MA2017-02/9/687.
• Helbling, D.E., VanBriesen, J.M. 2009. Modeling Residual Chlorine Response to a Microbial Contamination Event in Drinking Water Distribution Systems. Journal of Environmental Engineering, 135(10): 918-927. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000080.
• Helm, W., Zhong, S., Reid, E., Igou, T., Chen, Y. 2024. Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction. Frontiers of Environmental Science and Engineering, 18(17). https://doi.org/10.1007/s11783-024-1777-6.
• Hossain, S., Chow, C.W.K., Cook, D., Sawade, E., Hewa, G.A. 2022. Review of chloramine decay models in drinking water system. Environmental Science: Water Research and Technology, 8(5): 926-948. https://doi.org/10.1039/D1EW00640A.
• Huang, X., Wang, Y. 2023. Optimization of injection costs for water distribution systems under double-sided fuzziness. Urban Water Journal, 20(5): 513-520. Doi: 10.1080/1573062X.2023.2184702.
• Fisher, I., Kastl, G., Sathasivan, A. 2017a. New model of chlorine-wall reaction for simulating chlorine concentration in drinking water distribution systems. Water Research, 125: 427-437. https://doi.org/10.1016/j.watres.2017.08.066.
• Fisher, I., Kastl, G., Sathasivan, A. 2017b. A comprehensive bulk chlorine decay model for simulating residuals in water distribution systems. Urban Water Journal, 14(4): 361-368. https://doi.org/10.1080/1573062X.2016.1148180.
• Fisher, I., Kastl, G., Sathasivan, A., Catling, R. 2021. Modelling chlorine residual and trihalomethane profiles in water distribution systems after treatment including pre-chlorination. Journal of Environmental Chemical Engineering, 9(4): 105686. https://doi.org/10.1016/j.jece.2021.105686.
• Islam, M. R., Chaudhry, M. H., Clark, R.M. 1997. Inverse Modeling of Chlorine Concentration in Pipe Networks under Dynamic Condition. Journal of Environmental Engineering, 123(10): 1033-1040. https://doi.org/10.1061/(ASCE)0733-9372(1997)123:10(1033).
• Kim, H., Kim, S., Koo, J. 2014. Prediction of Chlorine Concentration in Various Hydraulic Conditions for a Pilot Scale Water Distribution System. Procedia Engineering, 70: 934-942. https://doi.org/10.1016/j.proeng.2014.02.104.
• Kim, H., Kim, S., Koo, J. 2015. Modelling Chlorine Decay in a Pilot Scale Water Distribution System Subjected to Transient. Procedia Engineering, 119: 370-378. https://doi.org/10.1016/j.proeng.2015.08.897.
• Kyritsakas, G., Speight, V., Boxall, J. 2023. A data driven model for the prediction of chlorine losses in water distribution trunk mains. IOP Conference Series: Earth and Environmental Science, 1136: 012048. Doi:10.1088/1755-1315/1136/1/012048.
• Li, X., Gu, D.M., Qi, J.Y., M, U., Zhao, H.B. 2003. Modeling of residual chlorine in water distribution system. Journal of Environmental Sciences, 15(1): 136-144.
• Li, Z., Liu, H., Zhang, C., Fu, G. 2024. Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data. Water Research, 250: 121018. https://doi.org/10.1016/j.watres.2023.121018.
• Monteiro, L., Figueiredo, D., Dias, S., Freitas, R., Covas, D., Menaia, J., Coelho, S.T. 2014. Modeling of Chlorine Decay in Drinking Water Supply Systems Using EPANET MSX. Procedia Engineering, 70: 1192-1200. https://doi.org/10.1016/j.proeng.2014.02.132.
• Monteiro, L., Carneiro, J., Covas, D.I.C. 2020. Modelling chlorine wall decay in a full-scale water supply system. Urban Water Journal, 17(8): 754-762. https://doi.org/10.1080/1573062X.2020.1804595.
• Oğur, R., Tekbaş, Ö.F., Hasde, M. 2004. Klorlama Rehberi (İçme ve Kullanma Sularının Klorlanması). Gülhane Askeri Tıp Akademisi, Ankara.
• Onyutha, C. 2022. Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms. Journal of Environmental and Public Health, 7104752. https://doi.org/10.1155/2022/7104752.
• Onyutha, C., Kwio‑Tamale, J.C. 2022. Modelling chlorine residuals in drinking water: a review. International Journal of Environmental Science and Technology, 19: 11613–11630. https://doi.org/10.1007/s13762-022-03924-3.
• Rodriguez, M.J., Sérodes, J.B. 1998. Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems. Environmental Modelling and Software, 14(1): 93-102. https://doi.org/10.1016/S1364-8152(98)00061-9.
• Rossman, L.A., Clark, R.M., Grayman, W.M. 1994. Modeling Chlorine Residuals in Drinking‐Water Distribution Systems. Journal of Environmental Engineering, 120(4): 803-820. https://doi.org/10.1061/(ASCE)0733-9372(1994)120:4(803).
• Rossman, L. 2000. EPANET 2 Users Manual, Technical Report EPA/600/R-00/057. Water Supply and Water Resources Division, National Risk Management Research Laboratory, U.S., Environmental Protection Agency, Cincinnati, OH.
• Rossman, L., Woo, H., Tryby, M., Shang, F., Janke, R., Haxton, T. 2020. EPANET 2.2 User Manual. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-20/133.
• Wang, Y. 2022. Optimization of booster chlorination for water distribution system under dual uncertainties. Urban Water Journal, 19(4): 363-373. https://doi.org/10.1080/1573062X.2021.2016868.
• Wang, H., Wang, B., Peng, Y., Crittenden, J.C., Pan, H., Wang, L. 2023. Improved VRC-3R- model for bulk water residual chlorine decay in the UV/Cl2 process for a water distribution network. Environmental Science: Water Research and Technology, 9: 308-330. https://doi.org/10.1039/D2EW00647B.
• Wongpeerak, K., Charuwimolkul, N., Changklom, J., Lipiwattanakarn, S., Pornprommin, A. 2023. Theoretical Estimation of Disinfectant Mass Balance Components in Drinking Water Distribution Systems. Water, 15(2): 368. https://doi.org/10.3390/w15020368.
• World Health Organization 2022. Guidelines for drinking-water quality: fourth edition incorporating the first and second addenda. Geneva, Licence: CC BY-NC-SA 3.0 IGO.
• Wu, H., Dorea, C.C. 2022. Evaluation and application of chlorine decay models for humanitarian emergency water supply contexts. Environmental Technology, 43(21): 3221-3230. https://doi.org/10.1080/09593330.2021.1920626.
• Xu, J., Huang, C., Shi, X., Dong, S., Yuan, B., Nguyen, T.H. 2018. Role of drinking water biofilms on residual chlorine decay and trihalomethane formation: An experimental and modeling study. Science of The Total Environment, 642: 516-525. https://doi.org/10.1016/j.scitotenv.2018.05.363.
• Yimer, T., Desale, T., Asmare, M., Endris, S., Ali, A., Metaferia, G. 2022. Modeling of Residual Chlorine on Addis Ababa Water Supply Distribution Systems. Water Conservation Science and Engineering, 7: 443-452. https://doi.org/10.1007/s41101-022-00153-0.
• Zaghini, A., Gagliardi, F., Marsili, V., Mazzoni, F., Tirello, L., Alvisi, S., Franchini, M. 2024. A Pragmatic Approach for Chlorine Decay Modeling in Multiple-Source Water Distribution Networks Based on Trace Analysis. Water, 16, 345. https://doi.org/10.3390/w16020345.