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Kinetics, equilibrium isotherm and neural network modeling studies for the sorption of hexavalent chromium from aqueous solution by quartz/feldspar/wollastonite
A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of Cr( vi ) in aqueous solution using riverbed sand containing Quartz/Feldspar/Wollastonite (QFW) as adsorbent. The effect of operational parameters such...
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Published in: | RSC advances 2016-01, Vol.6 (7), p.5837-5847 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of Cr(
vi
) in aqueous solution using riverbed sand containing Quartz/Feldspar/Wollastonite (QFW) as adsorbent. The effect of operational parameters such as adsorbent dosage, initial concentration of Cr(
vi
) ions, initial pH, agitation speed and contact time was studied to optimize the conditions for maximum removal of Cr(
vi
) ions in the laboratory batch adsorption experiment. The maximum adsorption efficiency was found at an initial concentration of 10 mg L
−1
, an adsorbent dosage of 0.75 g L
−1
and pH of the solution of 2. Experimental results revealed that a contact time of 90 min was generally sufficient to accomplish equilibrium. The experimental equilibrium data were fitted to various isotherm models. The maximum adsorption capacity of Cr(
vi
) was found to be 9.812 mg g
−1
. The kinetic data agreed well with the pseudo-second order model with rate constant value of 4.8 × 10
−2
. Ninety one experimental data were used to construct an ANN model to predict removal efficiency of Cr(
vi
). A three-layer ANN, an input layer with five neurons, a hidden layer with 15 neurons and an output layer with one neuron is constructed. The Levenberg-Marquardt algorithm (LMA) was found as the best algorithms with a minimum mean squared error (MSE) of 0.0056. The linear regression between the network outputs and the resultant targets were established to be reasonable with a correlation coefficient of about 0.985 and the experimental data were best fitted to the artificial neural network model.
A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of Cr(
vi
) in aqueous solution using riverbed sand containing quartz/feldspar/wollastonite (QFW) as adsorbent. |
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ISSN: | 2046-2069 2046-2069 |
DOI: | 10.1039/c5ra22851d |