Introduction of complex chemical compounds in the wastewater treatment plants challenged the biological process efficiency. Photocatalyst oxidation was newly introduced to remove the trace organic contaminations. Since their surface area determined the production of hydroxyl radicals, the pure TiO 2 surface area was increased by the preparation of TiO 2 -SiO 2 nanocomposite using sol-gel technique. Later on, the X-ray diffraction, scanning electron microscopy, energy diffraction spectroscopy, Fourier transform infrared spectroscopy, UV-vis reflective dispersion spectroscopy, and adsorption of nitrogen gas were deployed for its characterization. Anatase phase was detected as the dominant form of nanocomposites made in this study. Furthermore, rise of calcination temperature, increased the nanoparticle pore size. Therefore, 600˚C was selected to gain the optimum nanoparticle pore size (around 10 nm). Finally, the photocatalytic activities of the synthesized nanoparticles were evaluated for removing Acid Blue 9 (AB9) dye. The photocatalytic performance of the most optimized ratio TiO 2 -SiO 2 nanocomposites was three times higher than that of pure TiO 2 and even commercial nanoparticle.

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Feasibility of applying intelligent tools in prediction and optimization of photocatalytic degradation of beta-naphthol using the titanium dioxide (TiO 2 ) nanoparticles were conducted in this study. Biphasic TiO 2 nanoparticles were synthesized using the controlled hydrolysis of TiCl 4 , and their properties were studied using the Xray diffraction and transmission electron microscopy methods. Therefore, factors affecting photocatalytic degradation of beta-naphthol including impurity concentration, catalyst content, acidity, and aeration rate were monitored and controlled. The laboratory data showed that degradation rate of beta-naphthol is a complicated nonlinear function of monitored variables. Two models including artificial network trained with particle swarm optimization (ANN-PSO) and adaptive neuro-fuzzy interference system trained with particle swarm optimization (ANFIS-PSO) were used for prediction of this system. The results showed presence of a significant relation between the real and predicted data of these 2 models. However, ANFIS-PSO can be more efficiently applied for prediction and optimization of photocatalytic behavior of TiO 2 nanoparticles as for degradation of beta-naphthol as compared to ANN-PSO. As an advantage, ANFIS eliminates the problems of fuzzy logic, such as creation of membership functions, and local minima, which should be located in design of ANN, and through PSO algorithm, it could be a very powerful tool for simulating kinds of processes.

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