Predicting the Removal Amount of Aqueous Thiocyanate Anions by Titanium Dioxide Nanoparticles Using Novel Artificial Neural Network Methods

Document Type : Original Article

Authors

1 Departemant of chemistry, Islamic Azad University of Ahvaz, Iran

2 Department of chemistry, Islamic Azad University of Ahvaz, Iran

Abstract

In this work, the adsorbent method is performed using artificial neural network (ANN) modeling. The adsorbent is applied for removal of Thiocyanate in water samples using Titanium Dioxide (TiO2) nanoparticles as effective sorbent. Prediction amount of Thiocyanate removal was investigated with novel algorithms of neural network. For this purpose, six parameters were chosen as training input data of neural network functions including pH, time of stirring, the mass of adsorbent, volume of TiO2, volume of Fe (III), and volume of buffer. Performances of the suggested methods were examined using statistical parameters and found that it is an efficient, effective modeling satisfactory outputs. The radial basis function (RBF) and Levenberg-Marquardt (LM) algorithm could accurately predict the experimental data with correlation coefficient of 0.997939 and 0.99931, respectively. The Pearson's Chi–square measure was found to be 29.00 for most variables, indicating that these variables are likely to be dependent in some way.

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History

  • Receive Date: 27 November 2018
  • Revise Date: 09 June 2019
  • Accept Date: 25 June 2019

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