Modelling of Copper adsorption from aqueous solution on Jamun seed using Artificial Neural Network“

Abstract

Artificial neural network was employed to develop prediction models for Copper removal efficiency on Jamun seed Powder. This adsorbent was characterized by FT-IR. Batch adsorption studies were carried out for adsorption of Cu by using natural cheap agro waste such as Jamun seed Powder for removal of copper from aqueous solution. The effects of initial metal concentration, Dose, pH, contact time on the removal on Cu have been studied. Results indicate that contact time of 360 min is sufficient to achieve
equilibrium at different concentrations. Determination of Cu was done using Atomic absorption spectrophotometer. The peak percentage adsorption of Cu was attained at pH 7.0.These operating variables were used as the inputs to the constructed neural network to predict the copper uptake at any time as output. The model was developed using multilayer feedforward backpropagation network with levenberg marquardt training algorithm. A comparision between simulated and experimental results shows that the model is able to predict the concentration of residual copper in the solution.