Neural Computing and Applications

, Volume 29, Issue 10, pp 969–979 | Cite as

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

  • Saber Khodaei Ashan
  • Nasim Ziaeifar
  • Rana Khalilnezhad
Original Article
  • 201 Downloads

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.

Keywords

Adsorption Cr(VI) NiO nanoparticles Sol–gel method Artificial neural network (ANN) 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest.

References

  1. 1.
    Ahmadia F, Valadan Zoeja MJ, Ebadia H, Mokhtarzadea M (2008) The application of neural networks, image processing and cad—based environments facilities in automatic road extraction and vectorization from high resolution satellite images. In: The international archives of the photogrammetry, remote sensing and spatial information sciences, vol XXXVII. Part B3b, pp 585–592, BeijingGoogle Scholar
  2. 2.
    Aleboyeh A, Kasiri MB, Olya ME, Aleboyeh H (2008) Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks. Dyes Pigm 72:288–294. doi: 10.1016/jdyepig.2007.05.014 CrossRefGoogle Scholar
  3. 3.
    Amrouche A, Debyeche M, Taleb-Ahmed A, Rouvaen MJ, Yagoub M (2008) An efficient speech recognition system in adverse conditions using the nonparametric regression. Eng Appl Artif Intell 23:85–94. doi: 10.1016/j.engappai.2009.09.006 CrossRefGoogle Scholar
  4. 4.
    Behin J, Farhadian N (2016) Response surface methodology and artificial neural network modeling of reactive red 33 decolorization by O3/UV in a bubble column reactor. Adv Environ Technol 1(2016):33–44Google Scholar
  5. 5.
    Cheok CY, Chin NL, Yusof YA, Talib RA, Law CL (2012) Optimization of total phenolic content extracted from Garcinia mangostana Linn. Hull using response surface methodology versus artificial neural network. Ind Crops Prod 40:247–253. doi: 10.1016/j.indcrop CrossRefGoogle Scholar
  6. 6.
    Cheung CW, Porter JF, Mckay G (2001) Sorption kinetic analysis for the removal of cadmium ions from effluents using bone char. Water Res 35:605–612. doi: 10.1016/S0043-1354(00)00306-7 CrossRefGoogle Scholar
  7. 7.
    Daneshvar N, Khataee AR, Djafarzadeh N (2006) The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C.I. Basic Yellow 28 by electrcoagulation process. J Hazard Mater B 137:1788–1795. doi: 10.1016/j.jhazmat.2006.05.042 CrossRefGoogle Scholar
  8. 8.
    Ebrahimzadeh H, Tavassoli N, Sadeghi O, Amini MM (2012) Optimization of solid-phase extraction using artificial neural networks and response surface methodology in combination with experimental design for determination of gold by atomic absorption spectrometry in industrial wastewater samples. Talanta 97:211–217. doi: 10.1016/j.talanta.2012.04.019 CrossRefGoogle Scholar
  9. 9.
    Ghafari S, Abdul Aziz H, Hasnain Isa M, Zinatizadeh AA (2009) Application of response surface methodology (RSM) to optimize coagulation–flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum. J Hazard Mater 163:650–656. doi: 10.1016/j.jhazmat.2008.07.090 CrossRefGoogle Scholar
  10. 10.
    Gonen F, Serin S (2012) Adsorption study on orange peel: removal of Ni(II) ions from aqueous solution. Afr J Biotechnol 11:1250–1258. doi: 10.5897/AJB11.1753 Google Scholar
  11. 11.
    Gupta VK, Suhas S (2009) Application of low-cost adsorbents for dye removal—a review. J Environ Manage 90:2313–2342. doi: 10.1016/j.jenvman.2008.11.017 (get rights and content) CrossRefGoogle Scholar
  12. 12.
    Hamed MM, Khalafallah MG, Hassanien EA (2004) Prediction of wastewater treatment plant performance using artificial neural network. Environ Modell Softw 19:919–928. doi: 10.1155/2013/268064 CrossRefGoogle Scholar
  13. 13.
    Hu J, Chen G, Lo IMC (2005) Removal & recovery of Cr(VI) from wastewater by maghemite nanoparticles. Water Res 39:4528–4536. doi: 10.1016/j.watres.2005.05.051 CrossRefGoogle Scholar
  14. 14.
    Khajeh M, Kaykhaii M, Sharafi A (2013) Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles. J Ind Eng Chem 19(5):1624–1630. doi: 10.1016/j.jiec.2013.01.033 CrossRefGoogle Scholar
  15. 15.
    Khataee AR, Zarei M, Pourhassan M (2009) Application of microalga chlamydonas sp. For biosorptive removal of a textile dye from contaminated water: modeling by a neural network. Environ Technol 30:1615–1623. doi: 10.1080/09593330903370018 CrossRefGoogle Scholar
  16. 16.
    Khayet M, Cojocaru C (2013) Artificial neural network model for desalination by sweeping gas membrane distillation. Desalination 308:102–110. doi: 10.1016/j.desal.2012.06.023 CrossRefGoogle Scholar
  17. 17.
    Kia SM Soft Computing in matlab. Kian Rayaneh SabzGoogle Scholar
  18. 18.
    Krisha R, Padma S (2013) Artificial neural network and response surface methodology approach for modeling and optimization of chromium (VI) adsorption from waste water using Ragi husk powder. Indian Chem Eng. doi: 10.1080/00194506.2013.829257 Google Scholar
  19. 19.
    Lakshminarayanan AK, Balasubramanian V (2009) Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Trans Nonferr Metals Soc China 19(1):9–18. doi: 10.1016/S1003-6326(08)60221-6 CrossRefGoogle Scholar
  20. 20.
    Mahmood T, Saddique MT, Naeem A, Mustafa S, Hussain J, Dilara B (2011) Cation exchange removal of Zn from aqueous solution by NiO. J Non Cryst Solids 357:1016–1020. doi: 10.1016/j.jnoncrysol.2010.11.044 CrossRefGoogle Scholar
  21. 21.
    Modirshahla N, Behnajady MA, Shamel A, Eskandari H (2010) Sorption study of C.I. Acid Red 88 from aqueous solutions onto sawdust. J Phys Theor Chem IAU Iran 7(2):77–81Google Scholar
  22. 22.
    Moussavi G, Mahmoudi M (2009) Removal of azo & anthraquinone reactive dyes from industrial wastewaters using MgO nanoparticles. J Hazard Mater 168:806–812. doi: 10.1016/j.jhazmat.2009.02.097 CrossRefGoogle Scholar
  23. 23.
    Nandi BK, Goswami A, Purkai MK (2009) Adsorption characteristics of brilliant green dye on kaolin. J Hazard Mater 161:387–395. doi: 10.1016/j.jhazmat CrossRefGoogle Scholar
  24. 24.
    Niaei A, Towfighi J, Khataee AR, Rostamizadeh K (2007) The use of ANN and mathematical model for prediction of main product yields in the thermal cracking of naphtha. Pet Sci Technol 25:967–982. doi: 10.1080/10916460500423304 CrossRefGoogle Scholar
  25. 25.
    Oubagaranadin JUK and Murthy Z (2009) Modeling of adsorption of chromium (VI) on activated carbons derived from corn (zeamays) cob. Chem Prod Process Model 4, Article 32. doi: 10.2202/1934-2659.1377
  26. 26.
    Ozacar M, Sengil IA (2005) Adsorption of metal complex dyes from aqueous solutions by pine sawdust. Bioresour Technol 96:791–795. doi: 10.1016/j.biortech.2004.07.011 CrossRefGoogle Scholar
  27. 27.
    Qamar M, Gondal MA, Yamani ZH (2011) Synthesis of nanostructured NiO and its application in laser-induced photocatalytic reduction of Cr(VI) from water. J Mol Catal A 341:83–88. doi: 10.1016/j.molcata.2011.03.029 CrossRefGoogle Scholar
  28. 28.
    Rao KS, Anand S, Rout K, Venkatesewarlu P (2012) Response surface optimization for removal of cadmium from aqueous solution by waste agricultural biosorbent Psidium guvajava L. leaf powder. CLEAN Soil Air Water 40:80–86. doi: 10.1002/clen.201000392 CrossRefGoogle Scholar
  29. 29.
    Sinha K, Saha PD, Datta S (2012) Response surface optimization and artificial neural network modeling of microwave assisted natural dye extraction from pomegranate rind. Ind Crops Prod 37(1):408–414. doi: 10.1016/j.indcrop.2011.12.032 CrossRefGoogle Scholar
  30. 30.
    Xiang L, Deng XY, Jin Y (2002) Experimental study on synthesis of NiO nano-particles. Scr Mater 47:219–224. doi: 10.1016/S1359-6462(02)00108-2 CrossRefGoogle Scholar
  31. 31.
    Yetilmezsoy K, Demirel S (2008) Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia vera L.) shells. J Hazard Mater 153(2008):1288–1300. doi: 10.1016/j.jhazmat.2007.09.092 CrossRefGoogle Scholar
  32. 32.
    Ziaeifar N, Khosravi M, Behnajady MA, Mahmood R, Sohrabi MR, Modirshahla N (2015) Optimizing adsorption of Cr(VI) from aqueous solutions by NiO nanoparticles using aguchi and response surface methods. Water Sci Technol. doi: 10.2166/wst.2015.253 Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Saber Khodaei Ashan
    • 1
  • Nasim Ziaeifar
    • 1
  • Rana Khalilnezhad
    • 2
  1. 1.Department of Science, Maragheh BranchIslamic Azad UniversityMaraghehIran
  2. 2.Departemant of Applied ChemistryPayame Noor UniversityTehranIran

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