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Water Sci Technol. 2017 Nov;76(9-10):2413-2426. doi: 10.2166/wst.2017.393.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

Author information

1
Nanotechnology & Catalysis Research Centre (NANOCAT), University of Malaya, IPS Building, Kuala Lumpur 50603, Malaysia E-mail: mdsd68j@gmail.com.
2
Nanotechnology & Catalysis Research Centre (NANOCAT), University of Malaya, IPS Building, Kuala Lumpur 50603, Malaysia E-mail: mdsd68j@gmail.com; University of Malaya Centre for Ionic Liquids, University of Malaya, Kuala Lumpur 50603, Malaysia.
3
University of Malaya Centre for Ionic Liquids, University of Malaya, Kuala Lumpur 50603, Malaysia; Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
4
Civil Engineering Department, Faculty of Engineering, Komar University of Science and Technology, Sulaymaniyah, Iraq.
5
Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.

Abstract

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

PMID:
29144299
PMCID:
wst_2017_393
DOI:
10.2166/wst.2017.393
[Indexed for MEDLINE]

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