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

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A Correction to this article was published on 10 January 2018

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Abstract

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.

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Change history

  • 10 January 2018

    The author list in the original publication included Mohammad A. Behnajady as second author.

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Acknowledgements

The authors gratefully acknowledge their appreciation to the Islamic Azad University, Maragheh, for providing facilities.

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Authors and Affiliations

  1. Department of Science, Maragheh Branch, Islamic Azad University, Maragheh, Iran

    Saber Khodaei Ashan & Nasim Ziaeifar

  2. Departemant of Applied Chemistry, Payame Noor University, Tehran, Iran

    Rana Khalilnezhad

Authors
  1. Saber Khodaei Ashan
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  2. Nasim Ziaeifar
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  3. Rana Khalilnezhad
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Correspondence to Saber Khodaei Ashan.

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Additional information

The original version of this article was revised: The author, Mohammad A. Behnajady was wrongly included as second author in the author list. This was incorrect. The correct author list is as follows: Saber Khodaei Ashan, Nasim Ziaeifar and Rana Khalilnezhad. Now, it has been corrected.

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Ashan, S.K., Ziaeifar, N. & Khalilnezhad, R. Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology. Neural Comput & Applic 29, 969–979 (2018). https://doi.org/10.1007/s00521-017-3172-8

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