| Abstrakt: |
In the present approach, we investigate the sustainable extraction of biosilica from sugarcane bagasse ash and its subsequent utilization as bioadsorbent in the remediation of methylene blue (MB) dye in aqueous solutions, as well as using artificial neural network (ANN) as an auxiliary tool and the experimental data were compared with the theoretical models of kinetics and equilibrium of adsorption. The biosilica extraction was carried out using sodium hydroxide as an extractor, so the biosilica was completely characterized by different characterization techniques, such as Fourier transform infrared spectroscopy (FTIR), nitrogen adsorption/desorption isotherms, diffuse reflectance spectroscopy (DRS), thermogravimetric analysis (TG), and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDS). The physical–chemical characterization revealed that the extracted biosilica presents good thermal stability, porosity, and absorption bands analogous to silica gel. The effects of the initial MB concentration, pH, adsorbent dosage, and time were evaluated in the MB adsorption. Biosilica showed a maximum MB adsorption capacity of 9.95 mg g−1 (MB concentration = 50 mg L−1, biosilica mass = 100 mg, pH 12, and time = 10 min). The experimental data were suitable with the Freundlich isotherm and pseudo-second-order models, suggesting that the adsorptive process of the MB dye by the biosilica is favorable. Response surface methodology (RSM) and artificial neural networks hybridizing with genetic algorithm (ANN-GA) showed an excellent fit to the behavior of the experimental data. Both models obtained a correlation coefficient (R2) above 0.99, proving that they can potentially be used in the simulation and optimization of the adsorption process of MB dye by the extracted biosilica. [ABSTRACT FROM AUTHOR] |
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