N Golchinpour, N Rastkari, R Nabizadeh Nodehi, M Abtahi, A Azari, E Iravani, K Yaghmaeian,
Volume 10, Issue 4 (3-2018)
Abstract
Background and Objective: Triclosan is one of the substances as anti-microbial that is used in many of these pharmaceutical products. This compound can affect human such as reduction of thyroid hormone levels, antibiotic resistant, and increasing skin cancer. This study evaluated the performance nanophotocatalysis process UV/Xe/TiO2-GO on triclosan removal from aqueous solutions.
Materials and Methods: Synthesis of TiO2@GO and its structure was analyzed by SEM, EDX and FTIR. The effects of pollutant concentration, catalyst dosage, and contact time on the removal of Triclosan were studied by DOE software according to response surface methodology. Analysis of variance test was considered for the influence of parameters. Optimum process condition was determined by desirability factor.
Results: Optimum conditions regarding concentration of pollutant, contact time, and catalyst dosage were determined as 0.205 g/L, 14.898 min, and 0.487 mg/L, respectively. Maximum removal efficiency in optimum condition was 97.542 percent. The catalyst dosage was the most effective parameter in removal of Triclosan.
Conclusion: Using of TiO2@GO and xenon lamp had acceptable efficiency for the removal of Triclosan. The use of Xenon lamps alone was economically affordable.
M Sabonian, Ma Behnajady,
Volume 11, Issue 2 (9-2018)
Abstract
Background and Objective: Chromium is present in two oxidation forms of Cr(III) and Cr(VI). Cr(III) is less toxic than Cr(VI). The aim of this article was to optimize an artificial neural network structure in modeling the photocatalytic reduction of Cr(VI) by TiO2-P25 nanoparticles.
Materials and Methods: In this work, an artificial neural network (ANN) for the modeling photocatalytic reduction Cr(VI) by TiO2-P25 nanoparticles were used and its structure was optimized. The operating parameters were initial concentration of chromium, amount of photocatalyst, ultraviolet light irradiation time and pH. All the experiments were conducted in a batch photoreactor. The Cr(VI) concentration was measured with a UV/Vis spectrophotometer. ANN calculations were performed using Matlab 7 software and the ANN toolbox.
Results: The results show that the optimization of the ANN structure and the use of an appropriate algorithm and transfer function could significantly improve performance. The proposed neural network in modeling the photoactivity of TiO2-P25 nanoparticles in reducing Cr(VI) was acceptable, based on a good correlation coefficient (0.9886) and a small mean square error (0.00018). All the input variables affected the reduction of Cr(VI), however the effect of pH with an impact factor of 34.15 % was more significant than the others. The results indicated that pH = 2 was the best pH for photocatalytic reduction of Cr(VI). Increasing photocatalyst dosage and irradiation time in the investigated range increased Cr(VI) photocatalytic reduction.
Conclusion: Optimized structure of the ANN includes a three-layer feed-forward back propagation network with 4:10:1 topology and the most appropriate algorithm is a scaled conjugate gradient backpropagation algorithm.
