Study of acid orange 7 removal from aqueous solutions by powdered activated carbon and modeling of experimental results by artificial neural network

被引:91
作者
Aber, Soheil
Daneshvar, Nezameddin
Soroureddin, Saeed Mohammad
Chabok, Ammar
Asadpour-Zeynali, Karim
机构
[1] Univ Tabriz, Fac Chem, Dept Appl Chem, Tabriz, Iran
[2] Univ Tabriz, Fac Chem, Dept Analyt Chem, Tabriz, Iran
关键词
adsorption; acid orange 7; decolourization; artificial neural network; activated carbon; aqueous solution;
D O I
10.1016/j.desal.2006.03.592
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
In this work, removal of acid orange 7 (AO7) by powdered activated carbon, from aqueous solutions with initial concentrations of 150 ppm to 350 ppm and initial pH values of 2.8, 5.8, 8.0 and 10.5 at 25 degrees C was studied. Experiments were done in batch mode and the experimental solutions were agitated periodically. All concentrations were measured spectrophotometrically at 483 nm and three times replicated. In most cases, after 75 min contact time, the most of AO7 removal is performed. The maximum equilibrium removal of acid orange 7 (AO7) was 96.24% for its initial concentration of 150 ppm at pH(i) = 2.8, and minimum equilibrium removal was 48.05% for initial concentration of 350 ppm at pH(i) = 5.8. At the similar experimental conditions, application of different initial. pH values altered the AO7 removal percent no more than 9.06%. It is found that the adsorption system follows the second-order adsorption rate expression and the constants of the rate expression at different conditions were calculated which are comparable and often higher than other adsorbents in adsorption of other dyes. The constants of Langmuir equation, Q and b, and constants of Freundlich equation, K-f and l/n, were calculated and results show that the adsorption process is favorable. Comparison of R-2 values shows that fitting of Freundlich equation to experimental data is better than Langmuir equation. The experimental results were also modeled by artificial neural network with. mean relative error of 5.81%. This model was developed in Matlab 6.5 environment using a 3-layer feed forward. backpropagation network with 3, 2 and 1 neurons in first, second and third layers, respectively.
引用
收藏
页码:87 / 95
页数:9
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