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 - Brossura
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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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Predicting the Removal Amount of SCN- by TiO2 NPs Using ANN Methods
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LAP LAMBERT Academic Publishing Mrz 2020, 2020
ISBN 10: 6202514078
ISBN 13: 9786202514071
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 52 pp. Englisch. Codice articolo 9786202514071
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Predicting the Removal Amount of SCN- by TiO2 NPs Using ANN Methods
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202514078
ISBN 13: 9786202514071
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Predicting the Removal Amount of SCN- by TiO2 NPs Using ANN Methods
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LAP LAMBERT Academic Publishing Mär 2020, 2020
ISBN 10: 6202514078
ISBN 13: 9786202514071
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this work, the adsorbent method is performed using arti¿cial 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 ef¿cient, 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. Codice articolo 9786202514071
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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
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202514078
ISBN 13: 9786202514071
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9786202514071
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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
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LAP LAMBERT Academic Publishing, 2020
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ISBN 13: 9786202514071
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Taschenbuch. Condizione: Neu. 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. Codice articolo 118209851
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