Comparison of ultrasonic with stirrer performance for removal of sunset yellow (SY) by activated carbon prepared from wood of orange tree: Artificial neural network modeling

被引:48
|
作者
Ghaedi, A. M. [1 ]
Ghaedi, M. [2 ]
Karami, P. [1 ]
机构
[1] Islamic Azad Univ, Fac Sci, Dept Chem, Gachsaran Branch, Gachsaran, Iran
[2] Univ Yasuj, Dept Chem, Yasuj 7591874831, Iran
关键词
Ultrasonic; Sunset yellow (SY); Activated carbon; Orange tree; Artificial neural network; Modeling; SOLID-PHASE EXTRACTION; ALGORITHM-BASED OPTIMIZATION; METHYLENE-BLUE; AQUEOUS-SOLUTIONS; MALACHITE GREEN; DYE ADSORPTION; WASTE-WATER; NANOTUBES; PRECONCENTRATION; EQUILIBRIUM;
D O I
10.1016/j.saa.2014.11.019
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
The present work focused on the removal of sunset yellow (SY) dye from aqueous solution by ultrasound-assisted adsorption and stirrer by activated carbon prepared from wood of an orange tree. Also, the artificial neural network (ANN) model was used for predicting removal (%) of SY dye based on experimental data. In this study a green approach was described for the synthesis of activated carbon prepared from wood of an orange tree and usability of it for the removal of sunset yellow. This material was characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The impact of variables, including initial dye concentration (mg/L), pH, adsorbent dosage (g), sonication time (min) and temperature (degrees C) on SY removal were studied. Fitting the experimental equilibrium data of different isotherm models such as Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models display the suitability and applicability of the Langmuir model. Analysis of experimental adsorption data by different kinetic models including pseudo-first and second order, Elovich and intraparticle diffusion models indicate the applicability of the second-order equation model. The adsorbent (0.5 g) is applicable for successful removal of SY (>98%) in short time (10 min) under ultrasound condition. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:789 / 799
页数:11
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