| Abstrakt: |
In the current report, both response surface methodology (RSM) and artificial neural network (ANN) were employed to develop an innovative way for removing crystal violet (CV) from aqueous media using Haloxylon salicornicum (HS) as a cost‐effective, eco‐friendly adsorbent. HS was characterized using scanning electron microscopy (SEM) and Fourier‐transform infrared (FTIR) spectroscopy. The effects of operational parameters such as adsorbent dosage, initial dye concentration, and pH on HS were studied using a central composite design (CCD). A comparative analysis of the model findings and experimental measurements revealed high correlation coefficients (R2ANN = 0.994, R2RSM = 0.971), indicating both models accurately predicted HS. The predictive performance of the ANN and RSM models was evaluated using metrics such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), root mean square error (RMSE), mean square error (MSE), and the correlation coefficient (R2). The results indicate that the ANN model provides greater accuracy compared to the RSM model. The experimental data were analyzed using both linear and nonlinear forms of pseudo‐first and pseudo‐second order kinetic models (LPFO, NLPFO, LPSO, and NLPSO). Statistical error analysis was conducted to identify the best‐fitting kinetic or isotherm models for the adsorption data. The adsorption process of CV/HS was best described by NLPSO and LPSO. Additionally, the adsorption isotherms were analyzed using linear and nonlinear regression methods. The findings indicated that the linear Langmuir and Freundlich isotherms provided a more accurate fit compared to the nonlinear models, demonstrating greater effectiveness in accounting for the adsorption parameters. Thermodynamic investigations clearly demonstrate that the biosorption of CV is spontaneous and exothermic. This cost‐effective adsorbent is highly promising for treating textile wastewater. [ABSTRACT FROM AUTHOR] |
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