Herein, it is aimed to develop a high-performance monolithic adsorbent to be utilized in methyl orange (MO) adsorption. Therefore, amino-functionalized three-dimensional graphene networks (3D-GNf) fulfilling the requirements of reusability and high capacity have been fabricated via hydrothermal self-assembly approach followed by a double-crosslinking strategy. The potential utilization of 3D-GNf as an adsorbent for removal MO has been assessed using both batch-adsorption studies and an artificial neural network (ANN) approach. Graphene oxide sheets have been amino-functionalized and cross-linked, by ethylenediamine (EDA) during hydrothermal treatment, following the glutaraldehyde has used as a double-crosslinking agent to facilitate the crosslinking of architecture. The successful fabrication of 3D-GNf has been confirmed by field-emission scanning electron microscopy (FESEM), Fourier transform infrared (FT-IR), Raman and X-ray photoelectron spectroscopy (XPS). Moreover, N2 adsorption/desorption isotherms have revealed the high specific surface area (1015 m2 g-1) with high pore volume (1.054 cm3 g-1) and hierarchical porous structure of 3D-GNf. The effect of initial concentration, contact time, and temperature on adsorption capacity have been thoroughly studied, and the kinetics, isotherms, and thermodynamics of MO adsorption have been modelled. The MO adsorption has been well defined by the pseudo-second-order kinetic model and Langmuir isotherm model with a monolayer adsorption capacity of 270.27 mg g-1 at 25 °C. The thermodynamic findings have revealed MO adsorption has occurred spontaneously with an endothermic process. The Levenberg-Marquardt backpropagation algorithm has been implemented to train the ANN model, which has used the activation functions of tansig and purelin functions at the hidden and output layers, respectively. An optimum ANN model with high-performance metrics (coefficient of determination, R2 = 0.9995; mean squared error, MSE = 0.0008) composed of three hidden layers with 5 neurons in each layer was constructed to forecast MO adsorption. The findings have shown that experimental results are consistent with ANN-based data, implying that the suggested ANN model may be used to forecast cationic dye adsorption.
Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl2 and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N2 adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R2 = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.