Modelling and optimization of photocatalytic degradation of phenol via TiO2 nanoparticles: An insight into response surface methodology and artificial neural network

Abstract

The epidemic growth of the pharmaceuticals industries over the years in order to meet the human demands had exerted substantial pressure on the global environment, particularly water pollutions crisis. Herein, the photocatalytic degradation of phenol was investigated via commercial TiO2 nanoparticles. The Artificial Neural Network (ANN) and Response Surface Methodology (RSM) was employed for scrutinizing the suitable modelling and optimized condition of the TiO2 nanoparticles in yielding a profound rate of phenol removal. The parameters of investigation involved pH, phenol concentration, catalyst doses and degradation time. The RSM data shows the profound rate of phenol removal ˜ 99.48% was achieved by TiO2 NPs in a designed photocatalytic system that set at 5.42 pH, 15.21 mg/L phenol concentration, 1.75 g/L TiO2 dosage and 540 min irradiation time. The designed system fits well with the Pseudo-First-Order and the Langmuir isotherm model with R2 > 0.999. On the other hand, the ANN study revealed that the predicated model was perfectly fitted with the experimental data giving the highest value of R2. This work provides an insight into two different statistical modelling and optimization which could provide exposure for developing an optimized nanomaterial towards the removal of hazardous pollutant.