Statistical optimization and modeling approach for azo dye decolorization: Combined effects of ultrasound waves and nanomaterial‐based adsorbent

Abstract

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.

Synthesis of carbon nanotube‐based nanocomposite and application for wastewater treatment by ultrasonicated adsorption process

“…Green alga Caulerpa scalpelliformis BY28 27.00 [73] Natural kaolinitic clay MB 175.9 [74] Rarasaponin-bentonite MB 163.1 [75] Carrot stem/leaves powder MB 55.50-66.60 [6] MWCNTs MB 32.79 [76] ZnS@Mn-NPs-AC MB 85.03 [77] Chitosan-halloysite nanotube composite hydrogel beads MB 270.3 [78] Ag@ZnO/MWCNT-NC MB 294.2 This work diffusion models. [68,69] Accordingly, given the R 2 value, the pseudo-second-order kinetic model was found to be a better fit than other models for dyes in binary systems and the calculated q e value is mainly close to the experimental adsorption capacity value (Table 8). Hence, this model was chosen as an efficient model, the other models for this application not showing such good performance.…”

“…The experimental data were examined using the pseudo‐first‐order, pseudo‐second‐order, Elovich and intraparticle diffusion models . Accordingly, given the R 2 value, the pseudo‐second‐order kinetic model was found to be a better fit than other models for dyes in binary systems and the calculated q e value is mainly close to the experimental adsorption capacity value (Table ).…”

Process Optimization Studies of Congo Red Dye Adsorption onto Magnesium Aluminium Layered Double Hydroxide Using Response Surface Methodology

“…Thus, in present study, we used solutions of aluminum and magnesium, under the urea decomposition and hydrothermal treatment to synthesis Mg/Al LDHs. Adsorption is considered as a highly appropriate technique because this kind of conventional method features a range of favorability such as easy performance without any toxic additional substances [43][44][45], as a result, many researchers paid much attention to it [46,47]. To study the removal of dyes in water, effect of factors (concentration, adsorbent dosage, pH, contact time, etc.)…”

Combining Experimental Data with Statistical Methods to Evaluate Hydrolyzed Reactive Dye Removal by α-Fe2O3 in a Cellulose-Based Membrane

“…Baneshi et al [30] reported the optimization and modeling by RSM of mixed matrix membranes, P84 polyimide incorporated with metallic organic structures based on cadmium (MOF-Cd), for the high flow of simultaneous dyes and its rejection, revealing a good correlation between the membranes' performance and their different physicochemical properties. Pooralhossini et al [31] used an Artificial Neural Network (ANN) and RSM for modeling the removal of sunset yellow (SY) and disulfine blue (DB) with nanoparticles of tin oxide loaded onto activated carbon and showed that the ANN was much more precise in the modeling analysis when compared to RSM.…”