Comparative photocatalytic degradation of two dyes on immobilized TiO2 nanoparticles: Effect of dye molecular structure and response surface approach

Photocatalytic degradation of pentachlorophenol by visible light Ν–F–TiO2 in the presence of oxalate ions: optimization, modeling, and scavenging studies

“…It is well documented in the literature that various operational parameters affect the degradation rate of target compounds in heterogeneous photocatalysis, including catalyst concentration, initial concentration of the target compound, light intensity, oxygen concentration, temperature, and pH (Konstantinou and Albanis 2004;Antonopoulou et al 2012a;Fathinia et al 2010). As in this study, the photocatalytic degradation of PCP in the presence of oxalates was investigated, the ratio of OA/PCP is also an important parameter.…”

“…1a). The residuals, i.e., the differences between the response values from experiment and prediction, can also be used to evaluate the adequacy of the model (Fathinia et al 2010;Tzikalos et al 2013;. The internally studentized residues in Fig.…”

Degradation of Organic Dyes over Polymeric Photocatalyst C3N3S3

“…Accordingly, generation of the reactive species (•OH and •O 2 -) needed for the degradation of the dye decreased; secondly, more intermediates would be generated at higher dye concentration, which could also be adsorbed on the surface of the solid catalyst. Slow diffusion of the generated intermediates from the catalyst surface could lead to the deactivation of the photocatalyst and consequently, a reduction in the degradation efficiency; thirdly, at higher dye concentration, more absorption of light photon by the dye itself resulted in a lesser availability of photons for reactive species generation [21]. In summary, the characterization results revealed that the obtained sample had the stable structure of C 3 N 3 S 3 polymer with the band gap of 2.67eV, which is one of the characteristics of conductors.…”

Photocatalytic oxidation of treated municipal wastewaters for the removal of phenolic compounds: optimization and modeling using response surface methodology (RSM) and artificial neural networks (ANNs)

“…This means that the error function, which measures the RMS difference between calculated (predicted) and measured (actual) output values, should be minimized. 8,17 In order to determine the optimal number of neurons in the hidden layer, MLPs with different number of hidden layer neurons (varying from 1-14 in our case) were trained. The optimal networks found were afterwards used to identify the optimum regions on the basis of the maximum photocatalytic oxidation rate of TPh.…”

“…Response surface methodology (RSM) and artificial neural networks (ANNs) are powerful mathematical methods suitable for modeling and optimizing chemical reactions and/or industrial processes. 8,13 In fact, these modeling techniques approximate the functional relationships between input variables (experimental operational parameters) and the output (response) of the process using experimental data. Afterwards, the models are used to estimate the optimal settings of input variables to maximize or minimize the response.…”