Investigation of spent caustic wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor

In this research, photocatalytic degradation method was introduced to clean up Spent Caustic of Olefin units of petrochemical industries (neutralized Spent Caustic by means of sulfuric acid). In the next step, an adaptable method and effective parameters in the process performance were investigated. Chemical oxygen demand (COD) was measured by the commercial zinc oxide synthesized with precipitation synthesis method in a two-shell photoreactor. The percent of reduction of COD in the photocatalytic process was modelled using a Box–Behnken design and artificial neural network techniques. It was concluded that the ANN was a more accurate method than the design of experiment was. The effect of important parameters including oxidant dosage, aeration rate, pH, and catalyst loading was investigated. The results showed that all of the parameters, except pH, had positive effects on increasing COD removal. According to the obtained results, adsorption and photolysis phenomena had a negligible effect on COD removal.