Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology

被引：16
|
作者：
Ashan, Saber Khodaei [1 ]
Ziaeifar, Nasim [1 ]
Khalilnezhad, Rana [2 ]
机构：
[1] Islamic Azad Univ, Maragheh Branch, Dept Sci, Maragheh, Iran
[2] Payame Noor Univ, Dept Appl Chem, Tehran, Iran
来源：
NEURAL COMPUTING & APPLICATIONS | 2018年 / 29卷 / 10期
关键词：
Adsorption; Cr(VI); NiO nanoparticles; Sol-gel method; Artificial neural network (ANN); AQUEOUS-SOLUTION; WASTE-WATER; TEXTILE DYE; REMOVAL; ANN; EXTRACTION; PREDICTION; CADMIUM;
D O I：
10.1007/s00521-017-3172-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, the nanoparticles of sol-gel-synthesized NiO were used as effective adsorbents for removing Cr(VI) from aqueous solutions. To do so, the effect of four initial parameters including Cr(VI) concentration, the amount of NiO adsorbent, contact time, and pH on removing Cr(VI) with sol-gel-synthesized NiO was studied. Using the results of designing the experiment, the process of surface adsorption by ANN was modelled. For modelling the results of Cr(VI) removal process with NiO nanoparticles, a three-layered ANN of feed-forward back-propagation having 4:10:1 topology was used. The findings indicated that the results obtained from ANN correspond well with the data obtained from response surface methodology and experimental data.
引用
收藏
页码：969 / 979
页数：11
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