Electrocoagulation for Arsenic Removal: Field Trials in Rural West Bengal

Dutta, Neelanjan; Haldar, Arunansu; Gupta, Anirban Das

doi:10.1007/s00244-020-00799-8

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2024

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Cited by 16 publications

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“…A similar trend was observed for the other anode materials and applied currents. Our results showed good agreement with the reported values in literature ( Omwene and Kobya, 2018 ; Dutta et al., 2021 ).…”

Section: Resultssupporting

confidence: 93%

Removal of arsenic in groundwater from western Anatolia, Turkey using an electrocoagulation reactor with different types of iron anodes

Kobya

Dolaz²,

Özaydın-Şenol³

et al. 2022

Heliyon

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Section: Resultssupporting

confidence: 93%

Removal of arsenic in groundwater from western Anatolia, Turkey using an electrocoagulation reactor with different types of iron anodes

Kobya

Dolaz²,

Özaydın-Şenol³

et al. 2022

Heliyon

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Application of machine learning for optimization and modeling of turbidity removal from aqueous solution

Dutta,

Dey,

Pal

2024

Env Prog and Sustain Energy

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Turbidity affects the aesthetic and overall quality of water and therefore, its prediction and modeling are essential for designing treatment strategies. In the present research, the outcomes of altering parameters and optimizing the removal of turbidity using response surface methodology (RSM), artificial neural network (ANN), support vector machine (SVM), and K‐nearest neighbor (KNN) based on a statistically designed set of experiments are examined. pH, coagulant dose, and settling time are considered process variables. The optimum removal of turbidity was obtained at a pH range of 6–8, coagulant dosage of 20–35 mg/L, and settling time of 30–45 min for the coagulants. The best turbidity reduction (60%) was achieved using alum coagulant (30 mg/L), at a pH of 7.5 and settling time for 45 min. All the models proved to be effective in demonstrating how the operating variables being studied influence the removal of turbidity from the aqueous solution. In contrast to the RSM, SVM, and KNN models, the ANN more accurately characterized the parametric impact.

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