Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites

Abstract

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R2 value than the pseudo-first-order model.

Recent Advances in Applications of Hybrid Graphene Materials for Metals Removal from Wastewater

“…Dry dichloromethane and phosphorous pentachloride were added to GO and mixed to yield a highly sensitive and stable composite for the adsorption of nickel [122]. [116]. These values compare favorably with reported values for other adsorbents but are not as high as certain ion exchangers.…”

“…Dry dichloromethane and phosphorous pentachloride were added to GO and mixed to yield a highly sensitive and stable composite for the adsorption of nickel [122]. The reported capacities of 76, 104, 181 mg Ni (II)/g GO are linked to the high initial adsorbate concentrations of 51, 60, 100 mg/L, respectively [116]. These values compare favorably with reported values for other adsorbents but are not as high as certain ion exchangers.…”

Spatial heterogeneity of soil organic carbon in a karst region under different land use patterns

“…The ANNs can solve multivariate non‐linear problems with a suitable amount of data and an appropriate training algorithm (Zhang and Pan , Fan et al. , López et al. ).…”

Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm

“…The Langmuir model indicates a monolayer coverage on the surface of the adsorbent, while the Freundlich model is indicative of the surface heterogeneity of the adsorbent [ 53 , 54 ]. In this study, the isotherm plots for the Hg ion adsorption are shown in Figure S11 , and the regression coefficients ( R 2 ) of the Langmuir and the Freundlich models obtained by linear fitting were 0.9049 and 0.9118, and those obtained by nonlinear fitting were 0.8775 and 0.9118 ( Table 2 ), respectively.…”