Affiliations 

  • 1 Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, Kuala Lumpur 50603, Malaysia. saadisaif3@gmail
  • 2 Department of Civil Engineering, Al-Maaref University College, Ramadi 31001, Iraq
  • 3 Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
  • 4 Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, Kuala Lumpur 50603, Malaysia
  • 5 Institute of Energy Infrastructure (IEI), Department of Civil Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia. Chowmf@uniten.edu.my
  • 6 Institute of Energy Infrastructure (IEI), Department of Civil Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia
Int J Mol Sci, 2019 Aug 28;20(17).
PMID: 31466219 DOI: 10.3390/ijms20174206

Abstract

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.