محسن شیدایی

In this work, natural clinoptilolite nanaostructures were prepared from natural zeolite and used to remove Cu2 cation from polluted water. In the first section the natural clinoptilolite particles were converted to nanostructures using a ball mill. The natural clinoptilolite and prepared nanostructures samples were characterized by SEM, XRD, and BET. The SEM images approved the development of clinoptilolite nanostructures. The result obtained from BET analysis indicated the increase pore volume of clinoptilolite nanostructures related to the clinoptilolite microparticles. In the second part of this work, the nanostructures were used as adsorbent for removal of Cu2 cation from polluted water. Effect of initial pH of solution, adsorbent dosage and contact time on the Cu2 removal efficiency was investigated, where the 6, 1.2 g/L and 6 were determined as optimum conditions, respectively. Isotherm and kinetics of the adsorption process were analyzed. The equilibrium data were fitted well to the Langmuir isotherm model and pseudo-second-order kinetic model.

In this work, the degradation of Reactive Orange 29 was studied using submerged sono-photocatalytic membrane reactor (SPMR) combining N-TiO2 nanoparticles, visible light lamp(s) and polyvinylidene fluoride (PVDF) membrane. Modeling of experimental results by artificial neural network was done. The effect of operational variables including: catalyst dosage, suspension pH, visible light irradiation intensity, pollutant concentration and reaction time on efficiency of RO29 degradation was investigated. An enhancement in decolorization efficiency with decreasing suspension pH and increasing visible light irradiation intensity was observed. The optimum artificial neural network model yielded a determination coefficient of R2 = 0.9956 and showed that the relative effect of reaction time and catalyst dosage was higher than other investigated variables.