National Information and Documentation Centre (NIDOC), Academy of Scientific Research and Technology, ASRTEgyptian Journal of Chemistry0449-2285Articles in Press20190626Predicting the Removal Amount of Aqueous Thiocyanate Anions by Titanium Dioxide Nanoparticles Using Novel Artificial Neural Network Methods3722010.21608/ejchem.2019.6409.1540ENRashin AndayeshDepartemant of chemistry, Islamic Azad University of Ahvaz, IranMehran ZargaranDepartment of chemistry, Islamic Azad University of Ahvaz, IranJournal Article20181127In 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.