A high-efficient batch-recirculated photoreactor packed with immobilized TiO2-P25 nanoparticles onto glass beads for photocatalytic degradation of phenazopyridine as a pharmaceutical contaminant: artificial neural network modeling.

In this study, removal efficiency of phenazopyridine (PhP) as a model pharmaceutical contaminant was investigated in a batch-recirculated photoreactor packed with immobilized TiO2-P25 nanoparticles on glass beads. Influence of various operational parameters such as irradiation time, initial concentration of PhP, volume of solution, volumetric flow rate, pH and power of light source was investigated. Results indicated that removal percentage increases with the rise of irradiation time, volumetric flow rate and power of light source but decreases with the rise of initial concentration of PhP and volume of solution. Highest removal percentage was obtained in the natural pH of PhP solution (pH = 5.9). Results of mineralization studies also showed a decreasing trend of total organic carbon (TOC) and producing mineralization products such as NO3(-), NO2(-) and NH4(+). Modeling of the process using artificial neural network showed that the most effective parameters in the degradation of PhP were volume of solution and power of light source. The packed bed photoreactor with TiO2-P25 nanoparticles coated onto glass beads in consecutive repeats have the proper ability for PhP degradation. Therefore, this system can be a promising alternative for the removal of recalcitrant organic pollutants such as PhP from aqueous solutions.