Experimental Formulation of a Reynolds-Number- and Wall-Reaction- Based Correction Factor for Chlorine Transport in Pressurized PVC Pipes
DOI:
https://doi.org/10.51988/jtsc.v7i4.736Keywords:
Reynolds number, Correction factor, Chlorine transport, Wall reaction, Pipe networkAbstract
Accurate residual-chlorine prediction in pressurized water systems can be affected by the way one-dimensional models aggregate radial transport and pipe-wall processes. This study develops an empirical Reynolds-number-dependent correction for chlorine transport using controlled loop experiments. A 20 m PVC loop with a nominal diameter of 2 in was operated at eleven Reynolds numbers from 500 to 30,000. Each condition was tested in triplicate; chlorine was restored to 1.5 mg/L before each run and residual concentration was recorded hourly for 72 h. The analysis used a dimensionless wall-related term Z and a worksheet-defined percentage error E. For each Reynolds-number group, the normalized response was calculated as ? = E?/Z? and fitted with a power law in logarithmic space. Parameter uncertainty and predictive performance were assessed using 95% confidence intervals, MAE, RMSE, MAPE, and leave-one-Reynolds-group-out cross-validation. ? decreased from 0.300650 at Re = 500 to 0.003466 at Re = 30,000. The fitted relationship, ? = 0.1306(Re/1000)?1.063, produced R² = 0.9967, RMSE = 0.0094, and MAPE = 5.69%; cross-validation yielded RMSE = 0.0140 and MAPE = 6.95%. The formulation is therefore proposed as a compact diagnostic correction within the tested PVC-loop range and should not be used as a substitute for system-specific hydraulic and water-quality calibration.
References
[1]. 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
[2]. Angassa, K., Feyisa, M., Tessema, I., & Abewaa, M. (2025). Residual chlorine modelling in drinking water distribution system of Bishoftu Town, Ethiopia. Results in Engineering, 25, 104075. https://doi.org/10.1016/j.rineng.2025.104075
[3]. Batista, G. S., de Lacerda, M. C., Aragão, D. P., de Araújo, M. M. C., & Rodrigues, A. C. L. (2024). Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network. Journal of Water Process Engineering, 61, 105312. https://doi.org/10.1016/j.jwpe.2024.105312
[4]. Brazil, G. R. M., Arai, Y., Iwamoto, T., Caronge, M. A., Kunizane, T., & Koizumi, A. (2025). Long duration forecasting and its performance capability for seasonal variation modelling of residual chlorine concentrations: A comparative evaluation of two small-scale water distribution systems in Japan. Water Research, 268, 122766. https://doi.org/10.1016/j.watres.2024.122766
[5]. Costa, J., Mesquita, E., Ferreira, F., Figueiredo, D., Rosa, M. J., & Viegas, R. M. C. (2023). Modeling chlorine decay in reclaimed water distribution systems—A Lisbon area case study. Sustainability, 15(23), 16211. https://doi.org/10.3390/su152316211
[6]. Fisher, I., Kastl, G., & Sathasivan, A. (2023). Validity of chlorine-wall reaction models for drinking water distribution systems. Urban Water Journal, 20(9), 1157–1168. https://doi.org/10.1080/1573062X.2023.2241437
[7]. Hallam, N. B., West, J. R., Forster, C. F., Powell, J. C., & Spencer, I. (2002). The decay of chlorine associated with the pipe wall in water distribution systems. Water Research, 36(14), 3479–3488. https://doi.org/10.1016/S0043-1354(02)00056-8
[8]. Jafari, I., Luo, R., Lim, F. Y., Ng, S. H., & Hu, J. (2023). Machine-learning-assisted prediction and optimized kinetic modelling of residual chlorine decay for enhanced water quality management. Chemosphere, 341, 140011. https://doi.org/10.1016/j.chemosphere.2023.140011
[9]. Jafari, I., Luo, R., Ng, E., Corral, F., Jr., Chua, Y., Ng, S. H., & Hu, J. (2024). Robust prediction of residual chlorine decay in a drinking water distribution system integrating water quality sensing and predictive tools. ACS ES&T Water, 4(12), 5506–5521. https://doi.org/10.1021/acsestwater.4c00586
[10]. Jeong, G., Kang, D., & Hwang, T. (2022). Reaction coefficient assessment and rechlorination optimization for chlorine residual equalization in water distribution networks. Journal of Korea Water Resources Association, 55(S-1), 1197–1210. https://doi.org/10.3741/JKWRA.2022.55.S-1.1197
[11]. Kwio-Tamale, J. C., & Onyutha, C. (2024). Influence of physical and water quality parameters on residual chlorine decay in water distribution network. Heliyon, 10(10), e30892. https://doi.org/10.1016/j.heliyon.2024.e30892
[12]. Maleki, M., Pelletier, G., & Rodriguez, M. (2023). Identifying key pipe attributes and locations to best determine chlorine decay coefficients within a water distribution system. Water Supply, 23(10), 4333–4345. https://doi.org/10.2166/ws.2023.251
[13]. Maphanga, D., Moropeng, M. L., Masindi, V., Akinwekomi, V., & Foteinis, S. (2024). Experimental appraisal and numerical modelling of chlorine demand and decay in a typical drinking water distribution network in South Africa. Ecotoxicology and Environmental Safety, 286, 117153. https://doi.org/10.1016/j.ecoenv.2024.117153
[14]. Moeini, M., & Abokifa, A. (2024). Chlorine dosage management in drinking water systems: Comparing Bayesian optimization to evolutionary algorithms. Journal of Hydroinformatics, 26(11), 2720–2738. https://doi.org/10.2166/hydro.2024.090
[15]. Obaid, S., Hosoi, K., Ngoc, N. M., Inoue, T., & Yokota, K. (2025). Modelling free residual chlorine decay in a residential district water distribution network using smart water meter data. International Journal of Environmental Science and Technology, 22, 11809–11816. https://doi.org/10.1007/s13762-025-06378-5
[16]. Onyutha, C., & Kwio-Tamale, J. C. (2022). Modelling chlorine residuals in drinking water: A review. International Journal of Environmental Science and Technology, 19(11), 11613–11630. https://doi.org/10.1007/s13762-022-03924-3
[17]. Ozdemir, O. N., & Ger, A. M. (1998). Realistic numerical simulation of chlorine decay in pipes. Water Research, 32(11), 3307–3312. https://doi.org/10.1016/S0043-1354(98)00107-9
[18]. Ozdemir, O. N., Buyruk, T., & Ucaner, E. (2021). Calculation of chlorine and fluoride diffusion/dispersion coefficients in water supply pipes. Journal of Water Resources Planning and Management, 147(7), 04021041. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001396
[19]. Riyadh, A., Zayat, A., Chaaban, A., & Peleato, N. M. (2024). Improving chlorine residual predictions in water distribution systems using recurrent neural networks. Environmental Science: Water Research & Technology, 10, 2533–2545. https://doi.org/10.1039/D4EW00329B
[20]. Ucak, A., & Ozdemir, O. N. (2002). Simulation of chlorine decay in drinking-water distribution systems. Journal of Environmental Engineering, 128(1), 31–39. https://doi.org/10.1061/(ASCE)0733-9372(2002)128:1(31)
[21]. Wei, S., Richard, R., Hogue, D., Mondal, I., Xu, T., Boyer, T. H., & Hamilton, K. A. (2024). High resolution data visualization and machine learning prediction of free chlorine residual in a green building water system. Water Research X, 24, 100244. https://doi.org/10.1016/j.wroa.2024.100244
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