Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network

de Souza Batista, Gabriele; Clemente de Lacerda, Mateus; Pires Aragão, Dunfrey; Cabral de Araújo, Marília Marcy; Lima Rodrigues, Andrea Carla

doi:10.1016/j.jwpe.2024.105312

Journal of Water Process Engineering

2024

DOI: 10.1016/j.jwpe.2024.105312

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Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network

Gabriele de Souza Batista,

Mateus Clemente de Lacerda,

Dunfrey Pires Aragão

et al.

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2024

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Cited by 3 publications

References 28 publications

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Applications of machine learning in drinking water quality management: A critical review on water distribution system

Li,

Ma,

Zhong

et al. 2024

Journal of Cleaner Production

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Applications of machine learning in drinking water quality management: A critical review on water distribution system

Li,

Ma,

Zhong

et al. 2024

Journal of Cleaner Production

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Intelligent dosing of sodium hypochlorite in municipal wastewater treatment plants: Experimental and modeling studies

Li,

Cui,

Gao

et al. 2024

Journal of Water Process Engineering

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Study of the combined effect of temperature and free chlorine concentration on the chlorine bulk decay coefficient in water supply systems

Mompremier,

León Santiesteban,

Ramírez Muñoz

et al. 2024

Water Supply

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The chlorine bulk decay coefficient (kb) is a crucial parameter for modeling the chlorine decay process within drinking water distribution systems. In this study, the combined effect of temperature and chlorine concentration on kb was investigated. A central composite design (CCD) was employed. Ten scenarios were conducted on a pilot-scale pipeline network with initial chlorine concentrations from 0.38 to 4.62 mg/L and water temperature from 22.93 to 37.07 °C. A statistical analysis of the kb obtained in the experiments was conducted using STATGRAPHICS software. An analysis of variance (ANOVA) for Log10(kb) was also performed to segregate the combined contributions of chlorine and temperature effects. Chlorine concentration and temperature are factors that significantly affect kb. However, the temperature effect is marginal compared to that of the chlorine concentration. A quadratic model obtained from CCD data effectively predicted the kb within the proposed experimental zone, although its utility may be limited at lower temperatures. The combined effect of chlorine concentration and temperature on the Log10(kb) is shown on the response surface obtained from the quadratic prediction model of kb. Analysis of the response surface revealed that the operating conditions of 22.3 °C and 3.33 mg/L, respectively, yield the minimal value of kb = 0.0347 h−1.

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Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network

Cited by 3 publications

References 28 publications

Applications of machine learning in drinking water quality management: A critical review on water distribution system

Applications of machine learning in drinking water quality management: A critical review on water distribution system

Intelligent dosing of sodium hypochlorite in municipal wastewater treatment plants: Experimental and modeling studies

Study of the combined effect of temperature and free chlorine concentration on the chlorine bulk decay coefficient in water supply systems

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