Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models

Metal pollutants such as copper released into the aqueous environment have been increasing as a result of anthropogenic activities. Adsorption-based treatment technologies offer opportunities to remediate metal pollutants from municipal and industrial wastewater effluent. The aim of this work was to...

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Main Authors: A. Hamid, Hazren, Harun‬, ‪Hairuddin, Mohamed Sunar, ‪Norshuhaila, Ahmad, Faridah Hanim, Jasmani, Latifah, Suleiman, Norhidayah
Format: Article
Published: Science Publishing Corporation 2018
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Online Access:http://eprints.uthm.edu.my/2372/
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author A. Hamid, Hazren
Harun‬, ‪Hairuddin
Mohamed Sunar, ‪Norshuhaila
Ahmad, Faridah Hanim
Jasmani, Latifah
Suleiman, Norhidayah
author_facet A. Hamid, Hazren
Harun‬, ‪Hairuddin
Mohamed Sunar, ‪Norshuhaila
Ahmad, Faridah Hanim
Jasmani, Latifah
Suleiman, Norhidayah
author_sort A. Hamid, Hazren
building UTHM Institutional Repository
collection Online Access
description Metal pollutants such as copper released into the aqueous environment have been increasing as a result of anthropogenic activities. Adsorption-based treatment technologies offer opportunities to remediate metal pollutants from municipal and industrial wastewater effluent. The aim of this work was to evaluate the capability of modified cellulose nanowhisker (CNW) adsorbents for the remediation of copper from water matrices under realistic conditions using response surface methodology (RSM) and artificial neural network (ANN) models. Considerations for design and application to remediate Cu(II) from wastewater by developing a continuous flow experiment are described in this study. However, the physical structure of modified CNW adsorbents renders them unsuitable for use in column operation. Therefore, a more detailed study of the mechanical properties of CNW adsorbents would be necessary in order to improve the strength and stability of the adsorbents. This work has demonstrated that modified CNW are promising adsorbents to remediate copper from water matrices under realistic conditions including wastewater complexity and variability. The use of models to predict the test parameter system and account for matrix variability when evaluating CNW adsorbents for remediating Cu from a real-world wastewater matrix may also provide the foundation for assessing other treatment technologies in the future.
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spelling uthm-23722021-10-20T02:05:28Z http://eprints.uthm.edu.my/2372/ Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models A. Hamid, Hazren Harun‬, ‪Hairuddin Mohamed Sunar, ‪Norshuhaila Ahmad, Faridah Hanim Jasmani, Latifah Suleiman, Norhidayah TD511-780 Sewage collection and disposal systems. Sewerage Metal pollutants such as copper released into the aqueous environment have been increasing as a result of anthropogenic activities. Adsorption-based treatment technologies offer opportunities to remediate metal pollutants from municipal and industrial wastewater effluent. The aim of this work was to evaluate the capability of modified cellulose nanowhisker (CNW) adsorbents for the remediation of copper from water matrices under realistic conditions using response surface methodology (RSM) and artificial neural network (ANN) models. Considerations for design and application to remediate Cu(II) from wastewater by developing a continuous flow experiment are described in this study. However, the physical structure of modified CNW adsorbents renders them unsuitable for use in column operation. Therefore, a more detailed study of the mechanical properties of CNW adsorbents would be necessary in order to improve the strength and stability of the adsorbents. This work has demonstrated that modified CNW are promising adsorbents to remediate copper from water matrices under realistic conditions including wastewater complexity and variability. The use of models to predict the test parameter system and account for matrix variability when evaluating CNW adsorbents for remediating Cu from a real-world wastewater matrix may also provide the foundation for assessing other treatment technologies in the future. Science Publishing Corporation 2018 Article PeerReviewed A. Hamid, Hazren and Harun‬, ‪Hairuddin and Mohamed Sunar, ‪Norshuhaila and Ahmad, Faridah Hanim and Jasmani, Latifah and Suleiman, Norhidayah (2018) Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models. International Journal of Engineering and Technology, 7 (4.30). pp. 264-268. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/22279
spellingShingle TD511-780 Sewage collection and disposal systems. Sewerage
A. Hamid, Hazren
Harun‬, ‪Hairuddin
Mohamed Sunar, ‪Norshuhaila
Ahmad, Faridah Hanim
Jasmani, Latifah
Suleiman, Norhidayah
Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
title Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
title_full Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
title_fullStr Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
title_full_unstemmed Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
title_short Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
title_sort predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models
topic TD511-780 Sewage collection and disposal systems. Sewerage
url http://eprints.uthm.edu.my/2372/
http://eprints.uthm.edu.my/2372/