Application of artificial neural network for comparison and modeling of the ultrasonic and stirrer assisted removal of anionic dye using activated carbon supported with nanostructure material. (25th September 2017)

• Record Type: Journal Article
• Authors: Ghaedi, Abdol Mohammad; Karami, Parisa; Ghaedi, Mehrorang; Vafaei, Azam; Alipanahpour Dil, Ebrahim; Mehrabi, Fatemeh
• Is Part Of: Applied organometallic chemistry. Volume 32:Number 2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-25
• Subjects: artificial neural network -- copper sulfide nanoparticles -- sunset yellow (SY) -- ultrasonic; Organometallic chemistry -- Periodicals; Organometallic compounds -- Periodicals; 547.05
• DOI: 10.1002/aoc.4050
• Languages: English
• ISSNs: 0268-2605

Abstract

Abstract : In this study, a green approach has been described for the synthesis of copper sulfide nanoparticles loaded on activated carbon (CuS‐NP‐AC) and usability of it for the removal of sunset yellow (SY) dye by ultrasound‐assisted and stirrer has been compared. In addition, the artificial neural network (ANN) model has been employed for a forecasting removal percentage of SY dye using the results obtained. This material was characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The impact of variables, including initial dye concentration (mg/L), pH, adsorbent dosage (g), sonication time (min) and temperature (°C) on SY removal was studied. Fitting the experimental equilibrium data of different isotherm models such as Langmuir, Freundlich, Temkin and Dubinin–Radushkevich models display the suitability and applicability of the Langmuir model. Analysis of experimental adsorption data of different kinetic models including pseudo‐first and second order, Elovich and intraparticle diffusion models indicate the applicability of the second‐order equation model. The adsorbent (0.005 g) is applicable for successful removal of SY dye (> 98%) in short time (9 min) under ultrasound condition. A three layer ANN models with 8 and 6 neurons at hidden layer was selected as optimal models using stirrer and ultrasonic, respectively. These models displayed a good agreement between forecasted data and experimental data with the determination coefficient (R 2 ) of 0.9948 and 0.9907 and mean squared error (MSE) of 0.0001 and 0.0002 for training set using stirrer and ultrasonic, respectively. Abstract : CuS‐NP‐AC was synthesized and usability of it for the removal of sunset yellow dye by ultrasound‐assisted and stirrer has been compared. In addition, the artificial neural network model has been employed for a forecasting removal percentage of sunset yellow dye using the results obtained. For testing data set, the optimal ANN models showed a good agreement between forecasted data and experimental data. The data display that the adsorption process follow the pseudo‐second‐order kinetic and Langmuir isotherm.