Synergistic Degradation of 4-Chlorophenol by Persulfate and Oxalic Acid Mixture With Heterogeneous Fenton Like System for Wastewater Treatment: Adaptive Neuro-Fuzzy Inference Systems Modeling Publisher Pubmed



Hadi S^{1, 2} ; Taheri E^{2, 3} ; Amin MM^{1, 3} ; Fatehizadeh A^{2, 3} ; Aminabhavi TM⁴ Show Affiliations
Source: Journal of Environmental Management Published:2020

Abstract

The 4-chlorophenol (4-CP) is known to be a highly toxic compound having harmful effects on human health and the environment. Due to adverse effect of 4-CP, a new combination of persulfate (PS) and oxalic acid (OA) with heterogeneous Fenton like (HFL) system was developed and applied for 4-CP degradation as an emerging contaminant from synthetic wastewater. The individual (OA, PS, and HFL) and combined (HFL/OA, HFL/PS, and HFL/OA/PS) systems were investigated under various conditions to synergistic effects verification and determination of degradation mechanism of 4-CP. Compared to individual and combined systems, significant synergetic of 4-CP degradation efficiency was observed by HFL/OA/PS system. The highest 4-CP degradation efficiency by HFL/OA/PS system under optimal conditions (solution pH: 6, H2O2 dose: 275 mg/L, goethite dose: 125 mg/L, OA dose: 50 mg/L and PS dose: 100 mg/L) with an initial 4-CP concentration of 30 mg/L was 99.6 ± 4.9% after 35 min reaction time. 4-CP degradation by HFL/OA/PS system was followed with the first-order kinetic. The application of radical scavengers including ethanol (EtOH) and tert-butyl alcohol (TBA) revealed that the SO4 •− radical was determined as primary produced radical species. The Clˉ ions release was measured during degradation reaction at various 4-CP concentrations and indicating the complete 4-CP degradation. The developing of the adaptive neuro-fuzzy inference system (ANFIS) for 4-CP degradation efficiency prediction was revealed. These results show that prediction of 4-CP degradation efficiency using HFL/OA/PS system is possible by the ANFIS model with a high accuracy (R2: 0.98). © 2020 Elsevier Ltd