ABSTRACT
Prepared material-supported Fe/Ni particles (PM-Fe/Ni) were produced and applied as an adsorbent, reductant and Fenton-like catalyst for removing methylene blue (MB) and crystal violet (CV) from aqueous solutions. Fe/Ni particles were prepared by reducing ferric chloride with sodium borohydride and supported on the produced porous material. Various techniques including X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy analysis (SEM) were employed to characterize the crystal phase, surface area, surface morphology and functional groups. Removal experiments were conducted to study the effects of different factors such as PM-Fe/Ni dosage, initial pH, H2O2 concentration, initial concentrations and temperature on MB and CV removal. The removal efficiency of CV and MB by PM-Fe/Ni/H2O2 were 91.86% and 61.41% under the conditions of dye concentration of 1000 mg L-1, H2O2 concentration of 50 mM, PM-Fe/Ni dosage of 0.20 g and temperature of 293 K. The analysis of the degradation kinetics showed that the degradation of MB and CV followed well pseudo-first-order kinetics. A possible mechanism of removal of MB and CV was proposed, including the adsorption, reduction and dominating Fenton oxidation. The regeneration experiments of PM-Fe/Ni demonstrated that PM-Fe/Ni with H2O2 still showed a high removal efficiency after six reaction cycles.
ABSTRACT
Graphene oxide (GO), as an emerging material, exhibits extraordinary performance in terms of water treatment. Adsorption is a process that is influenced by multiple factors and is difficult to simulate by traditional statistical models. Artificial neural networks (ANNs) can establish highly accurate nonlinear functional relationships between multiple variables; hence, we constructed a three-layered ANN model to predict the removal performance of Cu(ii) metal ions by the prepared GO. In the present research work, GO was prepared and characterized by FT-IR spectroscopy, SEM, and XRD analysis techniques. In ANN modeling, the Levenberg-Marquardt learning algorithm (LMA) was applied by comparing 13 different back-propagation (BP) learning algorithms. The network structure and parameters were optimized according to various error indicators between the predicted and experimental data. The hidden layer neurons were set to be 12, and optimal network learning rate was 0.08. Contour and 3-D diagrams were used to illustrate the interactions of different influencing factors on the adsorption efficiency. Based on the results of batch adsorption experiments combined with the optimization of influencing factors by ANN, the optimum pH, initial Cu(ii) ion concentration and temperature were anticipated to be 5.5, 15 mg L-1 and 318 K, respectively. Moreover, the adsorption experiments reached equilibrium at about 120 min. Combined with sensitivity analysis, the degree of influence of each factor could be ranked as: pH > initial concentration > temperature > contact time.
