Modeling Phenol Adsorption in Water Environment Using Artificial Neural Network

In the present work removal of phenol from aqueous solution using peat soil as adsorbent dose was studied. The initial phenol concentration was varied from 5 mg/L to 20 mg/L with varying amount of peat soil (5-20 gm) in laboratory batch adsorption experiment. The maximum adsorption efficiency was found at initial phenol concentration of 10 mg/L, adsorption dose of 200 g/L and pH of the solution of 6.0. The equilibrium contact time was found at 6 hour. A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of phenol in aqueous solution using peat soil as adsorbent. The neural network architecture consisted of tangent sigmoid transfer function (tansig) at hidden layer with 20 hidden neurons, linear transfer function (purelin) at output layer and Lavenberg-Marquardt (LM) backpropagation training algorithm. The neural network model predicted values are found in close agreement with the batch experiment result with correlation coefficient (R) of 0.993 and mean squared error (MSE) 0.00105996.