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REVISTA DE CHIMIE
Cite as: Rev. Chim.
https://doi.org/10.37358/Rev.Chim.1949

OSIM Nr. R102355
ISSN Print 0034-7752
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Revista de Chimie (Rev. Chim.), Year 2020, Volume 71, Issue 10, 67-80

https://doi.org/10.37358/RC.20.10.8351

Ghaidaa Majeed Jaid, Azhar Jabbar Bohan, Ghaed Khalaf Salman

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances

Abstract:

Adsorption of Pb(II) and Cd(II) from wastewater utilizing three nano-magnetic materials (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2 Fe2O4, and Cu0.7Zn0.3 Fe2O4) were studied. The nano-magnetic materials were prepared from the Cu Frites powder and then the Cu ions were replaced by Zn ions in three concentrations, these materials were characterized by X-ray diffraction (XRD) which has conformed good crystallinity with spinel structure and particle size in the range (26.5-23.9 nm). Artificial neural networks were applying to model the removal of Pb(II) and Cd(II) on three adsorbents from wastewater. The operating conditions that affect on adsorption process are adsorbent dose (0.1, 0.25, and 0.5) g, pH (3, 7, and 9), and contact time (15, 30, and 45) min. Three Multilayered feed-forward neural networks (3:9:2) were successfully used for modeling of removing heavy metals on three adsorbents. The antimicrobial effectiveness of ferrite substances was studied against two types of bacteria. The three adsorbents showed an excellent removal for Cd (II) ions 100% complete removal on Cu0.9Zn0.1 Fe2O4, Cu0.8Zn0.2 Fe2O4, and it was 95% on Cu0.7Zn0.3 Fe2O4, and less removal for Pb (II) ions on Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2 Fe2O4 were 78.4% and 78.8%, and 83.4% on Cu0.7Zn0.3 Fe2O4. ANN models show efficient simulation with a high correlation coefficient (R2 = 0.99) for all three adsorbents, Sensitivity Analysis demonstrated that pH, time, and a dose of the adsorbent have a strong impact on the process of removal.The results for antimicrobial effectiveness showed that Cu0.9Zn0.1 Fe2O4 had the most antibacterial properties against two types of bacteria and the S. aureus killing rate was less than the E. coli killing rate of all ferrite composite nanoparticles.
Keywords:
Cu-ferrites; wastewater; artificial neural network; antimicrobial effectiveness

Issue: 2020, Volume 71, Issue 10
Pages: 67-80
Publication date: 2020/11/3
https://doi.org/10.37358/RC.20.10.8351
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This article is published under the Creative Commons Attribution 4.0 International License
Citation Styles
Cite this article as:
JAID, G.M., BOHAN, A.J., SALMAN, G.K., Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances, Rev. Chim., 71(10), 2020, 67-80.

Vancouver
Jaid GM, Bohan AJ, Salman GK. Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances. Rev. Chim.[internet]. 2020 Oct;71(10):67-80. Available from: https://doi.org/10.37358/RC.20.10.8351


APA 6th edition
Jaid, G.M., Bohan, A.J. & Salman, G.K. (2020). Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances. Revista de Chimie, 71(10), 67-80. https://doi.org/10.37358/RC.20.10.8351


Harvard
Jaid, G.M., Bohan, A.J., Salman, G.K. (2020). 'Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances', Revista de Chimie, 71(10), pp. 67-80. https://doi.org/10.37358/RC.20.10.8351


IEEE
G.M. Jaid, A.J. Bohan, G.K. Salman, "Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances". Revista de Chimie, vol. 71, no. 10, pp. 67-80, 2020. [online]. https://doi.org/10.37358/RC.20.10.8351


Text
Ghaidaa Majeed Jaid, Azhar Jabbar Bohan, Ghaed Khalaf Salman,
Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances,
Revista de Chimie,
Volume 71, Issue 10,
2020,
Pages 67-80,
ISSN 2668-8212,
https://doi.org/10.37358/RC.20.10.8351.
(https://revistadechimie.ro/Articles.asp?ID=8351)
Keywords: Cu-ferrites; wastewater; artificial neural network; antimicrobial effectiveness


RIS
TY - JOUR
T1 - Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances
A1 - Jaid, Ghaidaa Majeed
A2 - Bohan, Azhar Jabbar
A3 - Salman, Ghaed Khalaf
JF - Revista de Chimie
JO - Rev. Chim.
PB - Revista de Chimie SRL
SN - 2668-8212
Y1 - 2020
VL - 71
IS - 10
SP - 67
EP - 80
UR - https://doi.org/10.37358/RC.20.10.8351
KW - Cu-ferrites
KW - wastewater
KW - artificial neural network
KW - antimicrobial effectiveness
ER -


BibTex
@article{RevCh2020P67,
author = {Jaid Ghaidaa Majeed and Bohan Azhar Jabbar and Salman Ghaed Khalaf},
title = {Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances},
journal = {Revista de Chimie},
volume = {71},
number = {10},
pages = {67-80},
year = {2020},
issn = {2668-8212},
doi = {https://doi.org/10.37358/RC.20.10.8351},
url = {https://revistadechimie.ro/Articles.asp?ID=8351}
}
 
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