Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models
This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a w...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/35718/ |
| _version_ | 1848795145560915968 |
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| author | Hamid, Hazren A. Jenidi, Youla Thielemans, Wim Somerfield, Christopher Gomes, Rachel L. |
| author_facet | Hamid, Hazren A. Jenidi, Youla Thielemans, Wim Somerfield, Christopher Gomes, Rachel L. |
| author_sort | Hamid, Hazren A. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a wastewater treatment plant. Conductometric titration of CNWs suspensions showed a surface charge of 54 and 410 mmol/kg for the unmodified and modified CNWs, respectively, which indicated that the modified CNWs provide a relatively high surface area per unit mass than the unmodified CNWs. In addition, the stability of the modified CNWs was tested under different conditions and proved that the functional groups were permanent and not degraded. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in order to optimize the system and to create a predictive model to evaluate the Cu(II) removal performance of the modified CNWs. The performance of the ANN and RSM models were statistically evaluated in terms of the coefficient of determination (R2), absolute average deviation (AAD), and the root mean squared error (RMSE) on predicted experiment outcomes. Moreover, to confirm the model suitability, unseen experiments were conducted for 14 new trials not belonging to the training data set and located both inside and outside of the training set boundaries. Result showed that the ANN model (R2 = 0.9925, AAD = 1.15%, RMSE = 1.66) outperformed the RSM model (R2 = 0.9541, AAD = 7.07%, RMSE = 3.99) in terms of the R2, AAD, and RMSE when predicting the Cu(II) removal and is thus more reliable. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and the results revealed that Langmuir isotherm (R2 = 0.9998) had better correlation than the Freundlich isotherm (R2 = 0.9461). Experimental data were also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption. |
| first_indexed | 2025-11-14T19:27:26Z |
| format | Article |
| id | nottingham-35718 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:27:26Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-357182020-05-04T17:57:35Z https://eprints.nottingham.ac.uk/35718/ Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models Hamid, Hazren A. Jenidi, Youla Thielemans, Wim Somerfield, Christopher Gomes, Rachel L. This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a wastewater treatment plant. Conductometric titration of CNWs suspensions showed a surface charge of 54 and 410 mmol/kg for the unmodified and modified CNWs, respectively, which indicated that the modified CNWs provide a relatively high surface area per unit mass than the unmodified CNWs. In addition, the stability of the modified CNWs was tested under different conditions and proved that the functional groups were permanent and not degraded. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in order to optimize the system and to create a predictive model to evaluate the Cu(II) removal performance of the modified CNWs. The performance of the ANN and RSM models were statistically evaluated in terms of the coefficient of determination (R2), absolute average deviation (AAD), and the root mean squared error (RMSE) on predicted experiment outcomes. Moreover, to confirm the model suitability, unseen experiments were conducted for 14 new trials not belonging to the training data set and located both inside and outside of the training set boundaries. Result showed that the ANN model (R2 = 0.9925, AAD = 1.15%, RMSE = 1.66) outperformed the RSM model (R2 = 0.9541, AAD = 7.07%, RMSE = 3.99) in terms of the R2, AAD, and RMSE when predicting the Cu(II) removal and is thus more reliable. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and the results revealed that Langmuir isotherm (R2 = 0.9998) had better correlation than the Freundlich isotherm (R2 = 0.9461). Experimental data were also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption. Elsevier 2016-12-25 Article PeerReviewed Hamid, Hazren A., Jenidi, Youla, Thielemans, Wim, Somerfield, Christopher and Gomes, Rachel L. (2016) Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models. Industrial Crops and Products, 93 . pp. 108-120. ISSN 0926-6690 Artificial neural networks; Adsorption; Cu(II) ions; Cellulose nanowhiskers; Response surface methodology http://dx.doi.org/10.1016/j.indcrop.2016.05.035 doi:10.1016/j.indcrop.2016.05.035 doi:10.1016/j.indcrop.2016.05.035 |
| spellingShingle | Artificial neural networks; Adsorption; Cu(II) ions; Cellulose nanowhiskers; Response surface methodology Hamid, Hazren A. Jenidi, Youla Thielemans, Wim Somerfield, Christopher Gomes, Rachel L. Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models |
| title | Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models |
| title_full | Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models |
| title_fullStr | Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models |
| title_full_unstemmed | Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models |
| title_short | Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models |
| title_sort | predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (rsm) and artificial neural network (ann) models |
| topic | Artificial neural networks; Adsorption; Cu(II) ions; Cellulose nanowhiskers; Response surface methodology |
| url | https://eprints.nottingham.ac.uk/35718/ https://eprints.nottingham.ac.uk/35718/ https://eprints.nottingham.ac.uk/35718/ |