Abstract
Water quality plays a significant role as a key factor in water resource management. The photocatalytic method is widely used for the removal of recalcitrant pollutants present in seawater. Photocatalysis is a cost-effective technology, sustainable, and environmentally friendly treatment process. In the current approach, a batch reactor was utilized experimentally to study the degradation of contaminants present in seawater by utilizing ZnO as a photocatalyst under natural sunlight. The performance of the process was studied by measuring the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD), and biodegradability with respect to photocatalyst dosage, reaction time and pH of the solution. Biodegradability is defined as the ratio of BOD to COD and this parameter significantly removes pollutants from seawater. The higher the biodegradability, the better the performance of the treatment technology. It also significantly reduces the fouling characteristics of seawater during the desalination process. According to experimental values, the maximum percentage removal efficiencies were found to be TOC = 45.6, COD = 65.4, BOD = 20.01% and biodegradability = 0.038 with respect to the initial values of the seawater sample. The response surface methodology based on Box Behnken design (RSM-BBD) and a predictive model based on the MATLAB adaptive neuro-fuzzy inference system (ANFIS) tool were employed for modeling, optimizing, and evaluating the effects of parameters. According to the RSM-BBD and ANFIS models, the determination coefficients were R2 = 0.959 and R2 = 0.99, respectively, which was very close to 1. The maximum percentage removal efficiencies according to the RSM-BBD design were found to be TOC = 40.3; COD = 61.9; BOD = 18.8% and BOD/COD = 0.0390, whereas for the ANFIS model, the maximum reduction were found to be TOC = 46.5; COD = 65.4; BOD = 20.4% and BOD/COD = 0.040. In process optimization, the ANFIS model was shown better prediction than RSM-BBD in the process's optimization.