Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh - Seef Saadi , Fiyadh (2019)
Water is a vital nutrient, it is the most valuable resource for the existence and maintenance of life. Heavy metals are the most challenging pollutants that require continues monitoring and creative solutions to be removed from polluted water. The multi-wall carbon nanotubes (MW-CNTs) is a sophisticated adsorbent for heavy metals removal from water, but it needs a functionalization using chemicals and non-environmental friendly acids by a complicated process. This research uses the deep eutectic solvents (DESs) as a novel functionalization agent for CNTs. DESs, as novel solvents involved in chemistry, were recently involved in different applications due to their advantages towards green chemistry. Different molar ratios of salts to hydrogen bond donors (HBDs) were used to prepare the DESs. The selected DESs were used as CNTs functionalization agents to form novel adsorbents for mercury ions (Hg2+), lead ions (Pb2+) and arsenic ions (As3+) removal from water. A screening process was conducted for the removal of the selected heavy metal using the adsorption process. Three kinetic models were used to identify the adsorption rate and mechanism, the pseudo-second order best described the adsorption kinetics. The adsorption process is complicated due to the interactive effect of many parameters. The relationship between the parameters in the adsorption process (i,e; contact time, adsorbent dosage, pH and initial concentration) is nonlinear; thus, there is a necessity for mapping such a complicated process by a powerful modelling technique. Therefore, artificial neural network (ANN) was proposed as a novel modelling technique for this particular nano-adsorption system as a less complicated modelling method within the sophisticated biological networks. This technique is selected for the treatment such a non-linear function relationship among the variables. The ANN techniques do not require any mathematical induction since ANN analysing the dataset and recognize their correlations from inputs and outputs series of the dataset without any presumption about their interrelations and characteristics. Different ANN algorithms have been used in this study such as feed forward backpropagation algorithm, layer recurrent algorithm, adaptive neuro fuzzy inference system and NARX neural network. Moreover, various indicators were implemented to evaluate the ANN model’s productivity including relative root mean square error (RRMSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and relative error (RE). The sensitivity study for the parameters in the experimental work was achieved. Three algorithms are used for the modelling of Pb2+ ions, the maximum relative error (RE) for the layer recurrent is 18.67%, whereby, for the feed-forward back-propagation RE is 11.62%. The best result achieved for Pb2+ removal using ANFIS algorithm is with RE 7.078%. For As3+ removal using different adsorbents, two algorithms were applied for the modelling, the feed-forward back-propagation maximum RE achieved is 5.97% while, the NARX algorithm achieved better accuracy with maximum RE of 5.79%. The NARX algorithm is used for the modelling of Hg2+ removal. The maximum RE obtained is 3.49%. The modelling results revealed that NARX algorithm is the best compared to the used algorithms in term of accuracy.