Kinetics, equilibrium isotherm and neural network modeling studies for the sorption of hexavalent chromium from aqueous solution by quartz/feldspar/wollastonite

Abstract

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.