Volume 6, Issue 1 (12-2018)
J Environ Health Eng 2018, 6(1): 42-66 |
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Farzan M, Miralinaghi M. Optimization of Removal Efficiency of An Anionic Dye Onto Magnetic Fe3O4-Activated Carbon Nanocomposite Using Artificial Neural Network. J Environ Health Eng 2018; 6 (1) :42-66
URL: http://jehe.abzums.ac.ir/article-1-577-en.html
URL: http://jehe.abzums.ac.ir/article-1-577-en.html
Department of Chemistry, Faculty of Science, Islamic Azad University, Vramin-Pishva Branch,Varamin, Iran
Abstract: (2284 Views)
Background and objective: Wastewaters including dyes produced by various industries have serious
destructive effects on the environment. Therefore, proposing analytical and numerical mathematics
methods simulating dye removal process from industrial wastewaters are great of importance.
Methods: In this research, the Fe3O4-activated carbon magnetic nanocomposite was synthesized and
its crystalline structure, surface, and magnetic properties were characterized by XRD, SEM, and VSM
techniques. Efficiency of the composite adsorbent for decolorization of Reactive Red dye in different
conditions was investigated. Then, an artificial neural network was constructed by using Matlab
program to predict the removal efficiency of dye onto magnetic activated carbon and the number of
neurons in a hidden layer was optimized. pH, contact time, initial dye concentration, and temperature
as input parameters and dye removal percentage as an output parameter were considered.
Performance of network after its training was evaluated based on the correlation factor. The
experimental data were analyzed by pseudo- first- order, pseudo- second- order , and intra-particle
diffusion kinetics models.The Langmuir and Freundlich models were used to describe the sorption
equilibrium isotherms.
Results: . The high correlation factor for testing data showed that artificial neural network model can
estimate the experimental data. The intra-particle diffusion kinetics and Freundlich isotherm models
best describe the experimental data for the uptake of dye. A relatively low activation energy (34.6 kJ
mol-1) suggests that the adsorption involve physio sorption. Maximum adsorption capacity decreased
with increasing temperature.
Conclusion: Use of network prediction resulted to eliminate experiments and to improve dye
removal percentage.
destructive effects on the environment. Therefore, proposing analytical and numerical mathematics
methods simulating dye removal process from industrial wastewaters are great of importance.
Methods: In this research, the Fe3O4-activated carbon magnetic nanocomposite was synthesized and
its crystalline structure, surface, and magnetic properties were characterized by XRD, SEM, and VSM
techniques. Efficiency of the composite adsorbent for decolorization of Reactive Red dye in different
conditions was investigated. Then, an artificial neural network was constructed by using Matlab
program to predict the removal efficiency of dye onto magnetic activated carbon and the number of
neurons in a hidden layer was optimized. pH, contact time, initial dye concentration, and temperature
as input parameters and dye removal percentage as an output parameter were considered.
Performance of network after its training was evaluated based on the correlation factor. The
experimental data were analyzed by pseudo- first- order, pseudo- second- order , and intra-particle
diffusion kinetics models.The Langmuir and Freundlich models were used to describe the sorption
equilibrium isotherms.
Results: . The high correlation factor for testing data showed that artificial neural network model can
estimate the experimental data. The intra-particle diffusion kinetics and Freundlich isotherm models
best describe the experimental data for the uptake of dye. A relatively low activation energy (34.6 kJ
mol-1) suggests that the adsorption involve physio sorption. Maximum adsorption capacity decreased
with increasing temperature.
Conclusion: Use of network prediction resulted to eliminate experiments and to improve dye
removal percentage.
Type of Study: Research |
Subject:
Special
Received: 2018/12/30 | Accepted: 2018/12/30 | Published: 2018/12/30
Received: 2018/12/30 | Accepted: 2018/12/30 | Published: 2018/12/30
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