Modeling of fixed-bed adsorption of Cs+ and Sr2+ onto clay–iron oxide composite using artificial neural network and constant–pattern wave approach
| Autor: | Abdelkader Bouzidi, Brahim Mohamedi, Abderrahmane Ararem, Omar Bouras |
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| Rok vydání: | 2014 |
| Předmět: |
Aqueous solution
Health Toxicology and Mutagenesis Composite number Public Health Environmental and Occupational Health Iron oxide Analytical chemistry Mineralogy chemistry.chemical_element Pollution Analytical Chemistry Volumetric flow rate chemistry.chemical_compound Adsorption Montmorillonite Nuclear Energy and Engineering chemistry Caesium Radiology Nuclear Medicine and imaging Freundlich equation Spectroscopy |
| Zdroj: | Journal of Radioanalytical and Nuclear Chemistry. 301:881-887 |
| ISSN: | 1588-2780 0236-5731 |
| Popis: | A low-cost, non-toxic and effective adsorbent constituted by a montmorillonite coated by iron oxides (montmorillonite–iron oxide composite) was prepared to assess its effectiveness in the removal of Cs+ and Sr2+ from aqueous solution. Dynamic adsorption experiments were carried out at room temperature under the effect of various operating parameters such as bed depth Z (5–15 cm), initial cation concentration C 0 (2–50 mg L−1) and volumetric flow rate Q (0.5–8 mL min−1). Column performance has been modeled with constant-pattern wave approach combined to the Freundlich isotherm model and artificial neural network (ANN) models. The time, initial cation concentration, bed depth and volumetric flow rate were chosen as the input variables whereas, the outlet concentration C t was considered as the output variable. The developed network was found to be useful in predicting the breakthrough curves. Experimental data for the used system were well fitted with ANN than the combined constant–pattern wave approach. |
| Databáze: | OpenAIRE |
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