Solar light assistance for remediation of hypersaline petrochemical wastewater: kinetics and artificial neural network models

Abstract

ABSTRACT Nowadays, treatment of xenobiotic and refractory hypersaline petrochemical wastewater (HSPW) has been widely pointed out as one of the main environmental challenges. In the current study, the effectiveness of solar light and sulphate radical advanced oxidation process is evaluated for the remediation of a real phenolic-HSPW. Therefore, the effect of persulphate (PS) concentration, Fe(II) dosage, pH, initial total phenol (TP) concentration, reaction time, temperature and solar radiation intensity were examined. An artificial neural network system also investigated to model the processes. The results showed that solar radiation enhanced dramatically the performance of processes and >99% TP removal was achieved. The efficiency of the processes was followed: summertime solar‒Fe(II)/PS (99.8%) > wintertime solar‒Fe(II)/PS (70.0%) > Fe(II)/PS (26.3%) > sole PS (<5%). The corresponding values for the removal of COD (with the initial concentration of 1016 mg/L) were found 61, 31, 24, and <3%, respectively. According to artificial neural network model, pH and temperature played the most important role for removal of the TP from the HSPW. Finally, our results proved that solar-induced AOP systems are promising novel solution to combat emerging contaminants in petrochemical effluents.