Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review

Ardila, Andres; Rodrı́guez, Manuel J.; Pelletier, Geneviève

doi:10.1016/j.jenvman.2023.117734

Cited by 7 publications

(1 citation statement)

References 62 publications

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“…The conventional treatment applied at the DCTA Water Treatment Plant (WTP) uses Vidoca Brook as the raw water source, pumping water from the source to the station for treatment at an approximate elevation difference of 70 m. The WTP has a full-cycle treatment system that includes processes such as prealkalization, coagulation, flocculation (three chambers with horizontal baffles), sedimentation through conventional settlers, rapid filtration with prechlorination of the raw water from Vidoca Brook, supplementation by postchlorination and pH adjustment for groundwater from other well sources. It has an average production of 3,028 m³/day of drinking water and serves approximately 20,000 people, including 5,000 permanent residents and 15,000 transient people [31]; thus, it is classified as a small-scale station [32].…”

Section: Proposed Model Applicationmentioning

confidence: 99%

Using the flocculation index to optimise velocity gradient during slow mixing in drinking water treatment

Pereira,

Silva,

Pimentel

2024

Preprint

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This article aimed to study the influence of the velocity gradient on the flocculation process by aluminum sulfate (AS) in raw water samples under the action of a sweep mechanism at a temperature of 20°C and, with the help of continuous flocculation monitoring equipment (CFME), to verify the potential use of the flocculation index (FI) as a way to choose an adequate velocity gradient to achieve higher sedimentation rates and to obtain kinetic flocculation aggregation (KA) and breakup constant (KB) data. KA and KB helped to explain why the best tapered velocity gradient (G) conditions (G0 − 5 min = 80 s− 1, G5 − 10 min = 50 s− 1 and G10 − 15 min = 20 s− 1: 83.4%) promoted greater turbidity removal efficiency than did the fixed gradient (G0 − 15 min = 20 s− 1: 78.6%), highlighting the compartmentalization benefits. It was also observed that floc size was the most relevant factor for selecting velocity gradients that generated greater removal efficiencies for turbidity and apparent color. Finally, the model developed through kinetic constants was applied to water treatment plant conditions, and the mean absolute errors were 1.5% and 0.1 s− 1, considering the turbidity removal efficiencies and the best estimated velocity gradients, respectively, allowing us to improve the quality of the treated waters.

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Section: Proposed Model Applicationmentioning

confidence: 99%