Degradation and mineralization of phenol compounds with goethite catalyst and mineralization prediction using artificial intelligence

Abstract

The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weight ratio of initial concentration of phenol to that of H 2 O 2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H 2 O 2 (1: 0.3 to 1: 0.7). More than 90%of phenol removal and more than 40% of TOC removal were achieved within 60 minutes of reaction. Two separate models were developed using artificial neural networks to predict degradation percentage by a combination of Fe 3+ and Fe 2+ catalyst. Five operational parameters were employed as inputs while phenol degradation and TOC removal were considered as outputs of the developed models. Satisfactory agreement was observed between testing data and the predicted values (R 2 Phenol = 0.9214 and R 2 TOC= 0.9082).