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Open AccessArticle

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs

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Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
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Nanotechnology & Catalysis Research Centre, University of Malaya, Kuala Lumpur 50603, Malaysia
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Department of Materials Science and Metallurgy, University of Nizwa, Birkat Al Mawz 616, Oman
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Department of Civil Engineering, Al-Maarif University College, Ramadi 31001, Iraq
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Institute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur 50603, Malaysia
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Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
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Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
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Authors to whom correspondence should be addressed.
Academic Editors: Mert Atilhan and Santiago Aparicio
Molecules 2020, 25(7), 1511; https://doi.org/10.3390/molecules25071511 (registering DOI)
Received: 9 July 2019 / Revised: 7 August 2019 / Accepted: 25 August 2019 / Published: 26 March 2020
(This article belongs to the Special Issue Deep Eutectic Solvents)
In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10−5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99. View Full-Text
Keywords: water quality; deep eutectic solvents; carbon nanotubes; feedforward back propagation neural network; adsorption water quality; deep eutectic solvents; carbon nanotubes; feedforward back propagation neural network; adsorption
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MDPI and ACS Style

Ibrahim, R.K.; Fiyadh, S.S.; AlSaadi, M.A.; Hin, L.S.; Mohd, N.S.; Ibrahim, S.; Afan, H.A.; Fai, C.M.; Ahmed, A.N.; Elshafie, A. Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs. Molecules 2020, 25, 1511.

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