Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach
In this paper, the deep eutectic solvent-functionalized carbon nanotube was used for arsenic removal from water solution. The adsorbent used was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) and zeta potential. The effect of the parameters (adsorbent dosage, pH, initial c...
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| Format: | Article |
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IWA Publishing
2018
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| author | Fiyadh, Seef Saadi AlSaadi, Mohammed Abdulhakim AlOmar, Mohamed Khalid Fayaed, Sabah Saadi El-Shafie, Ahmed |
| author_facet | Fiyadh, Seef Saadi AlSaadi, Mohammed Abdulhakim AlOmar, Mohamed Khalid Fayaed, Sabah Saadi El-Shafie, Ahmed |
| author_sort | Fiyadh, Seef Saadi |
| collection | UM |
| description | In this paper, the deep eutectic solvent-functionalized carbon nanotube was used for arsenic removal from water solution. The adsorbent used was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) and zeta potential. The effect of the parameters (adsorbent dosage, pH, initial concentration and contact time) was studied to find the optimum conditions for maximum adsorption capacity of the functionalized carbon nanotube. The pseudo-second-order, the pseudo first-order and intraparticle diffusion kinetic models were applied to identify the adsorption rate and mechanism, the pseudo-second-order model best described the adsorption kinetics of the system. The non-linear autoregressive network with exogenous inputs (NARX) neural network strategy was used for the modelling and predicting of the adsorption capacity of functionalized carbon nanotube. Different indicators were used to determine the efficiency and accuracy of the NARX neural network model which were mean square error (MSE), root mean square error (RMSE), relative root mean square error (RRMSE) and mean absolute percentage error (MAPE). The sensitivity study of the used parameters in the experimental work was completed. Comparison of the NARX model results with the experimental data confirmed that the NARX model was able to predict the arsenic removal from water. |
| first_indexed | 2024-03-06T05:55:57Z |
| format | Article |
| id | um.eprints-22071 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:55:57Z |
| publishDate | 2018 |
| publisher | IWA Publishing |
| record_format | dspace |
| spelling | um.eprints-220712019-08-26T05:33:50Z http://eprints.um.edu.my/22071/ Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach Fiyadh, Seef Saadi AlSaadi, Mohammed Abdulhakim AlOmar, Mohamed Khalid Fayaed, Sabah Saadi El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) TP Chemical technology In this paper, the deep eutectic solvent-functionalized carbon nanotube was used for arsenic removal from water solution. The adsorbent used was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) and zeta potential. The effect of the parameters (adsorbent dosage, pH, initial concentration and contact time) was studied to find the optimum conditions for maximum adsorption capacity of the functionalized carbon nanotube. The pseudo-second-order, the pseudo first-order and intraparticle diffusion kinetic models were applied to identify the adsorption rate and mechanism, the pseudo-second-order model best described the adsorption kinetics of the system. The non-linear autoregressive network with exogenous inputs (NARX) neural network strategy was used for the modelling and predicting of the adsorption capacity of functionalized carbon nanotube. Different indicators were used to determine the efficiency and accuracy of the NARX neural network model which were mean square error (MSE), root mean square error (RMSE), relative root mean square error (RRMSE) and mean absolute percentage error (MAPE). The sensitivity study of the used parameters in the experimental work was completed. Comparison of the NARX model results with the experimental data confirmed that the NARX model was able to predict the arsenic removal from water. IWA Publishing 2018 Article PeerReviewed Fiyadh, Seef Saadi and AlSaadi, Mohammed Abdulhakim and AlOmar, Mohamed Khalid and Fayaed, Sabah Saadi and El-Shafie, Ahmed (2018) Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach. Journal of Water Supply: Research and Technology-Aqua, 67 (6). pp. 531-542. ISSN 0003-7214, DOI https://doi.org/10.2166/aqua.2018.107 <https://doi.org/10.2166/aqua.2018.107>. https://doi.org/10.2166/aqua.2018.107 doi:10.2166/aqua.2018.107 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TP Chemical technology Fiyadh, Seef Saadi AlSaadi, Mohammed Abdulhakim AlOmar, Mohamed Khalid Fayaed, Sabah Saadi El-Shafie, Ahmed Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach |
| title | Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach |
| title_full | Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach |
| title_fullStr | Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach |
| title_full_unstemmed | Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach |
| title_short | Arsenic removal from water using N,N-diethylethanolammonium chloride based DES-functionalized CNTs: (NARX) neural network approach |
| title_sort | arsenic removal from water using n n diethylethanolammonium chloride based des functionalized cnts narx neural network approach |
| topic | TA Engineering (General). Civil engineering (General) TP Chemical technology |
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