Artificial Neural Network (ANN) Modelling for Removal of Arsenate from Groundwater by Impregnated Binary Oxide Adsorbent (IBOA)

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Rajesh M. Dhoble, Prasad Kane, Vaishali P. Kesalkar, Sadhana S. Rayalu


In many developing and developed countries, presence of arsenite [As(III)] more than 10 ppb in groundwater is reported. Many people use the arsenic contaminated groundwater for drinking purposes, which is hazardous to human health. Hence need to be removed below the permissible level before use for drinking purpose. In this batch study, IBOA was used as an adsorbent to remove As(III) from water. From experimental data, optimal values for the dose of IBOA, time of contact and pH were found to be 1.0g/L, 24 hrs and 7.0, respectively. In this paper, ANN was applied to the various parameters and found that the results are satisfied in time study. Best fitting was found in dose study and initial concentration study where as in pH study, the ANN results and experimental results are in good agreement. In effect of coexisting ions study, ANN could not map in all cases and not shown the satisfactory results. From experimental data, it is proved that IBOA having good potential for removal of As(III) from groundwater.

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