title: 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 H2O2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H2O2 (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 Fe3+ and Fe2+ 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 (R2Phenol = 0.9214 and R2TOC= 0.9082).
authors: Department of Chemical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia.
authors: Farhana
authors: F
authors: Tisa
authors: Department of Chemical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia.
authors: Meysam
authors: M
authors: Davoody
authors: Department of Chemical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia.
authors: Abdul Aziz
authors: AA
authors: Abdul Raman
authors: Department of Chemical Engineering, Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia.
authors: Wan Mohd Ashri Wan
authors: WM
authors: Daud
mesh_terms: D001185:Artificial Intelligence; D002384:Catalysis; D005290:Ferric Compounds / Q000737:chemistry*; D005296:Ferrous Compounds / Q000737:chemistry*; D006861:Hydrogen Peroxide / Q000494:pharmacology; D058085:Iron Compounds / Q000737:chemistry*; D008903:Minerals / Q000737:chemistry*; D016571:Neural Networks, Computer; D016877:Oxidants / Q000494:pharmacology; D010636:Phenols / Q000737:chemistry* / Q000302:isolation & purification / Q000378:metabolism*; D012984:Software; D014873:Water Pollutants / Q000737:chemistry*
chemical_list: D005290:Ferric Compounds; D005296:Ferrous Compounds; D058085:Iron Compounds; D008903:Minerals; D016877:Oxidants; D010636:Phenols; D014873:Water Pollutants; C094886:goethite; D006861:Hydrogen Peroxide