Neural network model and isotherm study for removal of phenol from aqueous solution by orange peel ash

Abstract

Artificial Neural Network model and isotherm study were done to predict the removal efficiency of phenol. An inexpensive adsorbent was developed from orange peel ash (OPA) for effective uptake of phenol from aqueous solution. The influence of different experimental parameters (initial concentration, pH, adsorbents dose, contact time, stirring rate and temperature) on phenol uptake efficiency was evaluated. Phenol was adsorbed by the OPA up to maximum of 97.34 %. Adsorption of phenol on OPA correlated well with the Langmuir isotherm model, implying monolayer coverage of phenol onto the surface of the adsorbent. The maximum adsorption capacity was found to be 3.55 mg g−1 at 303 K. Pseudo-second-order kinetic model provided a better correlation for the experimental data. Moreover, the activation energy of the adsorption process (Ea) was found to be −18.001 kJ mol−1 indicating physorption nature of phenol onto OPA. A negative enthalpy (∆H°) value indicated that the adsorption process was exothermic. Again multi-layer Neural Network model was in very good agreement with the experimental results.