Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent
adsorbent; carbon nanotube; deep eutectic solvent; mercury; politef; sodium hydroxide; solvent; unclassified drug; water; carbon nanotube; mercury; solvent; adsorption kinetics; Article; artificial neural network; contact time; correlation coefficient; diffusion; electricity; environmental parameter...
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my.uniten.dspace-244822023-05-29T15:23:54Z Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent Fiyadh S.S. Alomar M.K. Jaafar W.Z.B. Alsaadi M.A. Fayaed S.S. Koting S.B. Lai S.H. Chow M.F. Ahmed A.N. El-Shafie A. 57197765961 57008043000 55006925400 57216181014 54782522900 55839645200 36102664300 57214146115 57214837520 16068189400 adsorbent; carbon nanotube; deep eutectic solvent; mercury; politef; sodium hydroxide; solvent; unclassified drug; water; carbon nanotube; mercury; solvent; adsorption kinetics; Article; artificial neural network; contact time; correlation coefficient; diffusion; electricity; environmental parameters; Fourier transform infrared spectroscopy; inductively coupled plasma mass spectrometry; kinetics; mathematical phenomena; optical spectroscopy; oxidation; performance; pH; Raman spectrometry; zeta potential; adsorption; chemistry; procedures; water management; Adsorption; Mercury; Nanotubes, Carbon; Neural Networks, Computer; Solvents; Water Purification Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 � 10?3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 � 10?3 for the LR model; and 16.4%, 0.9313 and 2.27 � 10?3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models. � 2019 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T07:23:53Z 2023-05-29T07:23:53Z 2019 Article 10.3390/ijms20174206 2-s2.0-85071762876 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071762876&doi=10.3390%2fijms20174206&partnerID=40&md5=6e82043feda8a6fc7ed82b1842292914 https://irepository.uniten.edu.my/handle/123456789/24482 20 17 4206 All Open Access, Gold, Green MDPI AG Scopus |
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adsorbent; carbon nanotube; deep eutectic solvent; mercury; politef; sodium hydroxide; solvent; unclassified drug; water; carbon nanotube; mercury; solvent; adsorption kinetics; Article; artificial neural network; contact time; correlation coefficient; diffusion; electricity; environmental parameters; Fourier transform infrared spectroscopy; inductively coupled plasma mass spectrometry; kinetics; mathematical phenomena; optical spectroscopy; oxidation; performance; pH; Raman spectrometry; zeta potential; adsorption; chemistry; procedures; water management; Adsorption; Mercury; Nanotubes, Carbon; Neural Networks, Computer; Solvents; Water Purification |
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57197765961 Fiyadh S.S. Alomar M.K. Jaafar W.Z.B. Alsaadi M.A. Fayaed S.S. Koting S.B. Lai S.H. Chow M.F. Ahmed A.N. El-Shafie A. |
| format |
Article |
| author |
Fiyadh S.S. Alomar M.K. Jaafar W.Z.B. Alsaadi M.A. Fayaed S.S. Koting S.B. Lai S.H. Chow M.F. Ahmed A.N. El-Shafie A. |
| spellingShingle |
Fiyadh S.S. Alomar M.K. Jaafar W.Z.B. Alsaadi M.A. Fayaed S.S. Koting S.B. Lai S.H. Chow M.F. Ahmed A.N. El-Shafie A. Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent |
| author_sort |
Fiyadh S.S. |
| title |
Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent |
| title_short |
Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent |
| title_full |
Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent |
| title_fullStr |
Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent |
| title_full_unstemmed |
Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent |
| title_sort |
artificial neural network approach for modelling of mercury ions removal from water using functionalized cnts with deep eutectic solvent |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806428128019480576 |