Artificial neural network approach for modelling of mercury ions removal from water using functionalized CNTs with deep eutectic solvent

Seef Saadi Fiyadh, Mohamed Khalid Alomar, Wan Zurina Binti Jaafar, Mohammed Abdulhakim Alsaadi, Sabah Saadi Fayaed, Suhana Binti Koting, Sai Hin Lai, Ming Fai Chow, Ali Najah Ahmed, Ahmed El-Shafie

Research output: Contribution to journalArticle


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.

Original languageEnglish
Article number4206
JournalInternational journal of molecular sciences
Issue number17
Publication statusPublished - 01 Sep 2019


All Science Journal Classification (ASJC) codes

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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