Predicting the Removal Amount of Aqueous Thiocyanate Anions by Titanium Dioxide Nanoparticles Using Novel Artificial Neural Network Methods | ||||
| Egyptian Journal of Chemistry | ||||
| Article 24, Volume 63, Issue 2, February 2020, Page 633-652 PDF (1.91 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ejchem.2019.6409.1540 | ||||
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| Authors | ||||
Rashin Andayesh  1; Mehran Zargaran2
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| 1Departemant of chemistry, Islamic Azad University of Ahvaz, Iran | ||||
| 2Department 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. | ||||
| Keywords | ||||
| Thiocyanate; Titanium dioxide nanoparticles; Fe-SCN complex; artificial neural network; Pearson's Chi–square | ||||
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