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Hybrid Homogeneous and Heterogeneous Photocatalytic Processes for Removal of Triphenylmethane Dyes: Artificial Neural Network Modeling
Author(s) -
Eskandarloo Hamed,
Badiei Alireza,
Behnajady Mohammad A.,
Mohammadi Ziarani Ghodsi
Publication year - 2016
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201400449
Subject(s) - triphenylmethane , homogeneous , photocatalysis , artificial neural network , materials science , process engineering , biological system , biochemical engineering , computer science , environmental science , chemistry , artificial intelligence , engineering , catalysis , organic chemistry , physics , thermodynamics , biology
Removal of two triphenylmethane dyes, Acid Fuchsin (AF) and Malachite green (MG), was studied by hybrid advanced oxidation processes of homogeneous (UV/Fe 2+ /H 2 O 2 ) and heterogeneous (UV/TiO 2 −SiO 2 ) photocatalysis. A comparison of various processes for removal of model pollutants was performed. The results showed that the utilizing hybrid photocatalytic processes in the presence of silica leads to rapid removal of pollutants, which may be ascribable to the synergistic influence of produced various radical species. The effects of operational variables were studied on the efficiency of the UV/Fe 2+ /H 2 O 2 /TiO 2 −SiO 2 hybrid process. An artificial neural network (ANN) model was intended to predict the removal efficiency of the UV/Fe 2+ /H 2 O 2 /TiO 2 −SiO 2 hybrid process under different operational conditions. The results indicated that there is a good concurrence between the ANN predicted values and experimental results with a correlation coefficient of 0.9873 and 0.9774 for removal of AF and MG dyes, respectively. The designed neural network model gives a dependable technique for modeling the removal efficiency of the UV/Fe 2+ /H 2 O 2 /TiO 2 −SiO 2 hybrid process. Moreover, the relative significance of each variable was computed based on the input‐hidden and hidden‐output connection weights of the neural network model. The initial concentration of dyes was the most significant variable in the removal efficiency.