Study of the Effect of Additives on the Photocatalytic Degradation of a Triphenylmethane Dye in the Presence of Immobilized TiO2/NiO Nanoparticles: Artificial Neural Network Modeling

In the present work, TiO2/NiO coupled nanoparticles were prepared from a powder mixture of the corresponding component solid oxides by using an impregnation technique. Then, the prepared TiO2/NiO nanoparticles were immobilized on glass plate and used as a fixed-bed photocatalytic system for photodegradation of Acid Fuchsin (AF), as a triphenylmethane dye pollutant. The effects of nature and concentration of various additives included inorganic oxidants (such as HSO5-, IO4-, ClO3-, S2O82-, H2O2 and BrO3-), inorganic anions (such as CH3COO-, CO32-, NO3-, Cl-, H2PO4-, and SO42-), and transition-metal ions (such as Co2+, Zn2+, Fe2+, Cu2+, Ni2+, and Mn2+) on photocatalytic degradation of AF, were investigated. It was found that the nature and concentration of studied additives significantly affected the photocatalytic degradation of dye pollutant in fixed-bed systems. The transition-metal ions and inorganic oxidants have a positive effect on the photocatalytic degradation rate of AF dye, whereas inorganic anions have a negative effect. An artificial neural network (ANN) model was designed for modeling of the photocatalytic degradation rate of AF dye. The results showed that the predicted data from designed ANN model were in good agreement with the experimental data. Designed ANN provides a reliable method for modeling the photocatalytic activity of immobilized TiO2/NiO nanoparticles in the presence of various additives.