This study presents a newly developed pilot-scale water treatment system that integrates oxidation, aeration, and multi-stage filtration (birm, activated carbon, and sand) for efficient iron reduction from drinking water to acceptable levels. Synthetic water samples were prepared with different iron concentrations and passed through different treatment stages. The iron removal efficiency is affected by various variables, including chlorine dose, oxidation time, aeration time, filtration time, and the number of filtering times. A comparison was conducted between different treatment scenarios with different treatment stages. The result showed that the iron removal efficiency could reach 95% at an initial iron concentration of 1.5 mg/L, a chlorine dose of 0.63 mg/L, oxidation time of 240 s, aeration time of 15 min, and double filtration of 60 s each. The results also showed that the iron concentration could reach the allowable limit of 0.4 mg/L when applying a chlorine dose of 0.63 mg/L, oxidation time of 120 s, and single filtration of 60 s. It is estimated that the operation cost of iron removal by the proposed system under the optimal conditions will be about 0.1 USD per cubic meter. An artificial neural network (ANN) was used to predict iron removal efficiency. Modeling results showed that the ANN with R 2 -value of 0.995 is reliable in describing the iron removal. This system can be scaled up to be integrated into any riverbank filtration system.
Refay et al. (Thu,) studied this question.