Modeling of the Removal of Arsenic Species from Simulated Groundwater Containing As,Fe, and Mn: A Neural Network Based Approach |
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| Authors: | Prasenjit Mondal Bikash Mohanty Chandrajit Balomajumder Samir Saraswati |
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| Institution: | 1. Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, India;2. Department of Mechanical Engineering, Motital Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India |
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| Abstract: | The present paper deals with the modeling of the removal of total arsenic As(T), trivalent arsenic As(III), and pentavalent arsenic As(V) from synthetic solutions containing total arsenic (0.167–2.0 mg/L), Fe (0.9–2.7 mg/L), and Mn (0.2–0.6 mg/L) in a batch reactor using Fe impregnated granular activated charcoal (GAC‐Fe). Mass ratio of As(III) and As(V) in the solution was 1:1. Multi‐layer neural network (MLNN) has been used and full factorial design technique has been applied for the selection of input data set. The developed models are able to predict the adsorption of arsenic species with an error limit of ?0.3 to +1.7%. Combination of MLNN with design of experiment has been able to generalize the MLNN with less number of experimental points. |
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| Keywords: | Adsorption Artificial neural network GAC‐Fe Granular activated charcoal Surface modification |
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