Predicting the Removal Amount of SCN- by TiO2 NPs Using ANN Methods | Using Novel Artificial Neural Network Methods in Removing Aqueous Thiocyanate Anions by Titanium Dioxide Nanoparticles

Rashin Andayesh

ISBN 10: 6202514078 / ISBN 13: 9786202514071

Published by LAP LAMBERT Academic Publishing, 2020

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Predicting the Removal Amount of SCN- by TiO2 NPs Using ANN Methods | Using Novel Artificial Neural Network Methods in Removing Aqueous Thiocyanate Anions by Titanium Dioxide Nanoparticles | Rashin Andayesh | Taschenbuch | 52 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202514071 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Predicting the Removal Amount of SCN- by TiO2 NPs Using ANN Methods | Using Novel Artificial Neural Network Methods in Removing Aqueous Thiocyanate Anions by Titanium Dioxide Nanoparticles
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 6202514078
• ISBN 13: 9786202514071
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams
• Seller catalogs:
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Synopsis

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.