Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

Feasibility of applying intelligent tools in prediction and optimization of photocatalytic degradation of beta-naphthol using the titanium dioxide (TiO2) nanoparticles were conducted in this study. Biphasic TiO2 nanoparticles were synthesized using the controlled hydrolysis of TiCl4, and their properties were studied using the X-ray 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 TiO2 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.