Intensified removal of Malachite green by AgOH‐AC nanoparticles combined with ultrasound: Modeling and optimization

Abstract

In this work Random Forest (RF) and Response surface methodology (RSM) were used to model and predict the efficiency of malachite green removal from aqueous solution by ultrasound-assisted adsorption onto the silver hydroxide nanoparticles loaded on activated carbon (AgOH-NPs-AC). The prepared nanoparticles were characterized by SEM, FTIR, XRD and TEM. The parameters such as pH, initial MG concentration, sonication time and adsorbent dosage involved in the adsorption process were set within the ranges 2.0-10, 4-20 mg L −1 , 2-6 min and 0.005-0.025 g, respectively. The performance of the RF and CCD models for the description of experimental data was evaluated in terms of the coefficient of determination (R 2 ), the root mean squared error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD). The obtained results showed that the RF model outperformed in comparison to classical statistical model for modeling the process of dye adsorption. Desirability function approach (DFA) and Genetic algorithm (GA) combined central composite design (CCD) as global optimization technique were used for simultaneous optimization of effective factors. GA and DFA were revealed that 20 mg L −1 MG by 0.025 g AgOH-NPs-AC at pH = 8.0 and sonication time for 6 min with adsorption capacity 40.98 and 41.99 mg g −1 can be removed, respectively. The equilibrium adsorption data were analyzed by Langmuir Freundlich, Temkin and Dubinin-Radushkevich isotherm models. The best fit to the data was obtained from the Langmuir model.Meanwhile, the maximum adsorption capacity for MG by 0.01, 0.02 and 0.025 g of AgOH-NPs-AC was estimated 57.143, 42.735 and 40.980 mg g −1 , respectively.Analysis of experimental adsorption data to various kinetic models shows the applicability of the second-order equation model. The variable importance principle also shows that RF give maximum importance to sonication time for removal of MG.

Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence

“…The Garson method for calculating the influence of each input variable on the output variable using weight and bias is shown in Table 5. The high importance score of variables indicates that the contribution of variables is significant to dye removal prediction [83]. Table 6 gives the factor importance obtained from F value, Garson method, GBRT and RF, which shows that the dosage had the maximum importance to the EV removal by Mn-doped Fe/rGO.…”

Application of AI in Modeling of Real System in Chemistry

“…More recently, our group simplified their MLR model by introducing only four inputs, namely, pH, sonication time, adsorbent dose, and the initial dye concentration. Their results indicated that the proposed method cannot well predict the removal dye percentage [13,[30][31][32][33][34][35].…”

“…In another work by our group [13], random forest (RF) and response surface methodology (RSM) were used to model and predict the efficiency of malachite green removal from aqueous solution by ultrasound-assisted adsorption onto the silver hydroxide nanoparticles loaded on activated carbon (AgOH-NPs-AC). The parameters such as pH, initial MG concentration, sonication time, and adsorbent dosage involved in the adsorption process were set within the ranges 2.0-10, 4-20 mg L −1 , 2-6 min, and 0.005-0.025 g, respectively.…”

“…Commonly used models for describing kinematic and/or equilibrium studies (e.g., second-order models or Langmuir, Freundlich) may be inadequate in determining the relationship between factors and evaluating their effect on the absorption process. Optimization is a way for determining the best solution in terms of certain quality criteria, such as process efficiency, and results in improving the performance of the process or designed system [13,14].…”

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

“…RSM is generally unable to give a suitable model due to the fact that the relation between effective parameters and removal of dye is too complicated and difficult to model by regression analysis. However, recently RSM and nonlinear methods such as support vector machine, random forest, tree regression and decomposition algorithm have been used jointly to solve this issue and for modelling or/and optimization in environmental studies . However, there has been little study of modelling dye removal using RSM and intelligent systems.…”