Mercury removal from water using deep eutectic solvents‐functionalized multi walled carbon nanotubes: Nonlinear autoregressive network with an exogenous input neural network approach

2019 ◽  
Vol 38 (6) ◽  
Author(s):  
Seef S. Fiyadh ◽  
Mohammed A. AlSaadi ◽  
Wan Z. Binti Jaafar ◽  
Mohamed K. AlOmar ◽  
Sabah S. Fayaed ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Nanotubes ◽  
Deep Eutectic Solvents ◽  
Mercury Removal ◽  
Network Approach ◽  
Neural Network Approach ◽  
Multi Walled Carbon Nanotubes ◽  
Walled Carbon Nanotubes ◽  
Nonlinear Autoregressive

scholarly journals Response surface methodology, modeling to improve mercury removal from aqueous solutions using L-Cysteine functionalized Multi-walled Carbon Nanotubes

2019 ◽  
Vol 21 (1) ◽  
pp. 64-69
Keyword(s):  
Carbon Nanotubes ◽  
Response Surface Methodology ◽  
Aqueous Solutions ◽  
Response Surface ◽  
Removal Efficiency ◽  
Mercury Concentration ◽  
Contact Time ◽  
Mercury Removal ◽  
Multi Walled Carbon Nanotubes ◽  
Walled Carbon Nanotubes

<p>The aim of this study was to evaluate the mercury removal from aqueous solutions by using L-Cysteine functionalized Multi-walled Carbon Nanotubes. The effect of pH, adsorbent dose, contact time and mercury concentration in removal efficiency was evaluated. Multi -walled carbon nanotubes were functionalized with L-cysteine. The Response Surface Methodology (RSM) was used to find the optimum process parameters. The results showed that an increase in contact time, pH and adsorbent dosage resulted in an increase of the adsorption rate. However, removal efficiency decreases by increasing mercury concentration. The highest and lowest removal efficiencies of mercury were 89% and 17%, respectively. The maximum adsorption rate was occurring at 120 min. It is concluded that L-Cysteine functionalized multi-walled carbon nanotubes is an effective adsorbent for removal from aqueous solutions.</p>

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