Removal of Pb(II) ions from aqueous solution using water hyacinth root by fixed-bed column and ANN modeling.

Hyacinth root was used as a biosorbent for generating adsorption data in fixed-bed glass column. The influence of different operating parameters like inlet Pb(II) ion concentration, liquid flow rate and bed height on the breakthrough curves and the performance of the column was studied. The result showed that the adsorption efficiency increased with increase in bed height and decreased with increase in inlet Pb(II) ion concentration and flow rate. Increasing the flow rate resulted in shorter time for bed saturation. The result showed that as the bed height increased the availability of more number of adsorption sites in the bed increased, hence the throughput volume of the aqueous solution also increased. The adsorption kinetics was analyzed using different models. It was observed that maximum adsorption capacity increased with increase in flow rate and initial Pb(II) ion concentration but decreased with increase in bed height. Applicability of artificial neural network (ANN) modeling for the prediction of Pb(II) ion removal was also reported by using multilayer perceptron with backpropagation, Levenberg-Marquardt and scaled conjugate algorithms and four different transfer functions in a hidden layer and a linear output transfer function.