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

The main challenge in the lead removal simulation is the behavior of non-linearity relationships between the process parameters. The conventional modeling technique usually deals with problem by linear method. The substitute modelling technique is artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, the synthesized deep eutectic solvents were used as functionalized agent with carbon nanotubes as adsorbents of (Pb 2+ ). 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 Pb 2+ initial concentration (3 to 60 mg/ L). The number of experimental trials to feed and train the system was 158 runs conveyed in lab scale. Two ANN types were designed in this work, the FFBP 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 (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed determination coefficient (R 2 ) of 0.9956 with MSE 1.66 × 10 −4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.