Employing UV/periodate process for degradation of p-chloronitrobenzene in aqueous environment

In this study, the potassium periodate was activated by ultraviolet (UV) light in UV/KPI process, and it was used for degradation of p-chloronitrobenzene (pCNB) in an aqueous environment. The full factorial design (FFD) and artificial neural networks (ANN) were used to investigate the influence of experimental parameters including temperature (T), initial concentration of periodate and pH on the removal of pCNB. The optimum conditions in the treatment of 50 mg/L of the pCNB were achieved at 300 mg/L of KPI, pH of 7, and Temperature at 35 degrees C. At optimum condition, the removal of pCNB was 97.8% (experimental), and the predicted amount by ANN and FFD were 97.79% and 99.02%, respectively. The chemical oxygen demand removal percent was 64.3% after 90 min of reaction. Although, the ANN was better than FFD model, and the root mean square error of ANN was lower than FFD model (1.0268(AAN) < 2.055838(FFD)). The ANN needs larger sets of data and computational time. A high correlation coefficient (R-ANN(2) = 0.9974, R-FFD(2) = 0. 9886) was achieved by an evaluation between the results of the model and experimental. The average percentage error for FFD and ANN were 1.570615 and 0.4328, respectively, signifying the advantage of ANN in taking the nonlinear presentation of the system.