%0 Journal Article
%T Taguchi optimization of photodegradation of yellow water of trinitrotoluene production catalyzed by nanoparticles TiO2/N under visible light
%J Iranian Journal of Catalysis
%I Islamic Azad University, Shahreza Branch
%Z 2252-0236
%A Pouretedal, Hamid Reza
%A Fallahgar, Mohammad
%A Sotoudeh Pourhasan, Fahimeh
%A Nasiri, Mohammad
%D 2017
%\ 12/01/2017
%V 7
%N 4
%P 317-326
%! Taguchi optimization of photodegradation of yellow water of trinitrotoluene production catalyzed by nanoparticles TiO2/N under visible light
%K Taguchi method
%K Doped TiO2 nanophotocatalyst
%K Yellow water
%K Photodegradation
%R
%X Taguchi experimental design technique was used for optimization of photodegradation of yellow water sample of trinitrotoluene (TNT) production process. The nanoparticles of doped N-TiO2 were also used as photocatalysts in the photodegradation reaction under visible light. The ranking of data based on signal to noise ratio values showed that the importance order of the factors affecting the degradation efficiency was: the nature of photocatalyst > time of photodegradation > amount of photocatalyst > initial concentration of pollutant. The optimized conditions were photocatalyst of TiO2/N0.1 photocatalyst dosage of 1.5 g L-1 and dilution times of 750 for real samples. The photocatalyst of TiO2/N0.1 was analyzed by BET surface analysis, X-ray diffraction pattern, field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS) and diffuse reflectance spectra (DTS). Relatively high surface area of 150 m2×g-1, anatase/rutile structure, approximately uniform distribution of nanoparticles size and band-gap energy of 2.92 eV were measured for TiO2/N0.1 nanophotocatalyst. A linear model with the regression coefficient (R2) of 0.887 was obtained by the multiple linear regression analysis. The proposed model was "Degradation efficiency (Y) = 20.492 + 1.461 X1 + 6.330 X2 + 0.014 X3 + 2.291 X4". The obtained P-values in the confidence level of 95% were < 0.05, showing a meaningful addition in the model. Therefore, changes in the predictor’s value are due to changes in the response variable.
%U http://ijc.iaush.ac.ir/article_598347_7879e1e91403f6db48247f621d9bcec6.pdf