MnO2 nanoparticle-loaded activated carbon (MnO2-NP-AC) as an efficient, environmental friendly and cost-effective adsorbent was synthesized and characterized using different techniques such as FE-SEM, EDX, XRD, BET and FTIR. The rapid and simultaneous ultrasound-assisted adsorption of brilliant green (BG), crystal violet (CV) and methylene blue (MB) dyes with severe spectra overlap was investigated onto MnO2-NP-AC as a novel and efficient adsorbent. The dyes in their ternary mixtures were simultaneously determined using third order derivative spectrophotometry. Response surface methodology (RSM) was successfully applied to analyze and optimize the adsorption process. The optimal conditions for pH, adsorbent dosage, initial dye concentration and sonication time were obtained to be 7.0, 0.022 g, 6 mg L−1 and 4 min, respectively. The predicted and experimental data were found to be in good agreement. An artificial neural network (ANN) was applied for the accurate prediction of percentages of dye removal from their ternary solution by the MnO2-NP-AC adsorbent. The experimental equilibrium data were modeled by applying different isotherm models. The Langmuir model was found to be the most applicable for describing the experimental equilibrium data obtained under the optimal conditions. A small amount of MnO2-NP-AC adsorbent (0.005 g) was successfully used for the removal of dyes (RE > 90.0%) in a very short time (4.0 min) with high adsorption capacity in a single component system (206.20, 234.20 and 263.16 mg g−1 for BG, CV and MB, respectively). Kinetic studies showed the applicability of the second-order kinetic model.
کلید واژگان :Artificial neural network, Brilliant green, Crystal Violet, Methylene blue, MnO2-NP-AC, Response surface methodology.
ارزش ریالی : 600000 ریال
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