The use of artificial neural network (ANN) for modeling of Pb(II) adsorption in batch process

The effects of different operating parameters such as initial pH, initial Pb(II) ion concentration, adsorbent dosages, and contact time are studied to optimize the conditions for maximum batch adsorptive removal of Pb(II) ions from aqueous solution using six different low cost natural bio-sorbents. Applicability of artificial neural network (ANN) analysis is investigated for the removal of Pb(II). Three standard training algorithms (Backpropagation, Levenberg-Marquardt and Scaled Conjugate Gradient) along with four different standard transfer functions in a hidden layer with a linear transfer function in the output layer are used for the analysis. Statistical analysis show that the ANN model with a BP algorithm, using transfer function 1 and 25 processing elements in a single hidden layer gives best predictability of the percentage removal of Pb(II) ions from aqueous solutions.