This study is devoted to an investigation of the effects of sonication time, adsorbent mass, pH and sunset yellow (SY) and disulfine blue (DB) concentration on the removal of DB and SY from water. Artificial neural network and response surface methodology approaches were used to optimize an analytical model to calculate the DB and SY removal performance of tin oxide nanoparticles loaded on activated carbon. The performance of both models was statistically evaluated in terms of the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD), and graphical plots were also used for comparison of the models. The obtained results show that the artificial neural network model outperforms the classical statistical model in terms of R2, RMSE, MAE and AAD for both dyes. Various isotherm models were studied for fitting the experimental equilibrium data, and the results confirm the applicability of the Langmuir isotherm for description of the adsorption equilibrium. Various kinetic models were applied to the experimental data and the results reveal that the pseudo‐second‐order model with better correlation is superior to the other kinetic models. The significant factors were optimized using the desirability function approach combined with central composite design. The obtained optimal point is located in the valid region and the experimental confirmation indicates good agreement between the predicted optimal points and the experimental data.
Applied Organometallic Chemistry – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ;
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