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Artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems

Authors
Liao, ZiqiaoChoi, KungWonUllah, ZahidSon, MoonAhn, YongtaeKhan, Moonis AliPrabhu, Subbaiah MuthuJeon, Byong-Hun
Issue Date
Aug-2024
Publisher
Elsevier BV
Keywords
Arsenic; Artificial neural network modeling; Kinetics; Manganese; Oxidation
Citation
Water Research, v.259, pp 1 - 9
Pages
9
Indexed
SCIE
SCOPUS
Journal Title
Water Research
Volume
259
Start Page
1
End Page
9
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/197649
DOI
10.1016/j.watres.2024.121876
ISSN
0043-1354
1879-2448
Abstract
This study investigated the coexistence and contamination of manganese (Mn(II)) and arsenite (As(III)) in groundwater and examined their oxidation behavior under different equilibrating parameters, including varying pH, bicarbonate (HCO3−) concentrations, and sodium hypochlorite (NaClO) oxidant concentrations. Results showed that if the molar ratio of NaClO: As(III) was >1, the oxidation of As(III) could be achieved within a minute with an extremely high oxidation rate of 99.7 %. In the binary system, the removal of As(III) prevailed over Mn(II). The As(III) oxidation efficiency increased from 59.8 ± 0.6 % to 70.8 ± 1.9 % when pH rose from 5.7 to 8.0. The oxidation reaction between As(III) and NaClO releases H+ ions, decreasing the pH from 6.77 to 6.19 and reducing the removal efficiency of Mn(II). The presence of HCO3− reduced the oxidation rate of Mn(II) from 63.2 % to 13.9 % within four hours. Instead, the final oxidation rate of Mn(II) increased from 68.1 % to 87.7 %. This increase can be attributed to HCO3− ions competing with the free Mn(II) for the adsorption sites on the sediments, inhibiting the formation of H+. Moreover, kinetic studies revealed that the oxidation reaction between Mn(II) and NaClO followed first-order kinetics based on their R2 values. The significant factors affecting the Mn(II) oxidation efficiency were the initial concentration of NaClO and pH. Applying an artificial neural network (ANN) model for data analysis proved to be an effective tool for predicting Mn(II) oxidation rates under different experimental conditions. The actual Mn(II) oxidation data and the predicted values obtained from the ANN model showed significant consistency. The training and validation data sets yielded R2 values of 0.995 and 0.992, respectively. Moreover, the ANN model highlights the importance of pH and NaClO concentrations in influencing the oxidation rate of Mn(II).
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COLLEGE OF ENGINEERING (DEPARTMENT OF EARTH RESOURCES AND ENVIRONMENTAL ENGINEERING)
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