Prediction of Photocatalytic Activity of TiO2 Thin Films Doped by SiO2 using Artificial Neural Network and Fuzzy Model Approach

Title:Prediction of Photocatalytic Activity of TiO2 Thin Films Doped by SiO2 using Artificial Neural Network and Fuzzy Model Approach

Volume: 10 Issue: 1

Author(s): Ehsan Rahmani*, Dariush Jafari, Hossein Rahmani and Firouzeh Kazemi

Affiliation:

• Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad,Iran

Keywords: Sol-gel, photocatalytic activity, fuzzy logic, artificial neural network, organic materials, TIO2.

Abstract: Background: In the current study, nanocrystalline thin films of TiO2:xSiO2 (x: mole percentage) with high photocatalytic activity were grown on glass surfaces using sol-gel method. The films were then treated under high temperature of 500°C for the crystal growth. The synthesized films presented high photocatalytic activity when they were in contact with methyl orange (MO) solution and UV irradiation. Due to complexity and nonlinearity photocatalytic features of TiO2 films doped by SiO2, Artificial Neural Network (ANN) and Fuzzy Logic (FL) models have been applied to predict and calculate the MO concentration variations with SiO2 concentration and MO degradation time.

Results: The simulations have resulted in accurate and reliable predictive models since the squared correlation coefficient (R2) and the standard squared error (SSE) have been R2>0.99, 0.004, R2 >0.96, and 0.14 for ANN and FL models, respectively. The reported figures have shown that the independent predicted values of MO concentration are extremely close to their corresponding experimental data.

Conclusion: The results of simulations have shown that ANN and Fuzzy models are reliable predictive models to study the photocatalytic activity of TiO2 thin film doped by SiO2.

Cite this article as:

Rahmani Ehsan *, Jafari Dariush , Rahmani Hossein and Kazemi Firouzeh , Prediction of Photocatalytic Activity of TiO2 Thin Films Doped by SiO2 using Artificial Neural Network and Fuzzy Model Approach, Recent Innovations in Chemical Engineering 2017; 10 (1) . https://dx.doi.org/10.2174/2405520410666170614111639