title: Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk.
abstract: The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g(-1) for Au-NP-AC and 3.84 mg g(-1) for Tamarisk-AC.
authors.affiliation: Department of Chemistry, Yasouj University, Yasouj 75918-74831, Iran. Electronic address: m_ghaedi@mail.yu.ac.ir.
authors.forename: M
authors.initials: M
authors.lastname: Ghaedi
authors.affiliation: Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran. Electronic address: abm_ghaedi@yahoo.com.
authors.forename: A M
authors.initials: AM
authors.lastname: Ghaedi
authors.affiliation: Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran.
authors.forename: A
authors.initials: A
authors.lastname: Ansari
authors.affiliation: Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran.
authors.forename: F
authors.initials: F
authors.lastname: Mohammadi
authors.affiliation: Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran.
authors.forename: A
authors.initials: A
authors.lastname: Vafaei
mesh_terms: D000327:Adsorption; D000465:Algorithms; D001391:Azo Compounds / Q000302:isolation & purification*; D002606:Charcoal / Q000737:chemistry*; D004396:Coloring Agents / Q000302:isolation & purification; D006046:Gold / Q000737:chemistry*; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D016014:Linear Models; D053768:Metal Nanoparticles / Q000737:chemistry* / Q000648:ultrastructure; D016571:Neural Networks, Computer; D016062:Porosity; D013056:Spectrophotometry, Ultraviolet; D032363:Tamaricaceae / Q000737:chemistry*; D013696:Temperature; D013997:Time Factors
chemical_list: D001391:Azo Compounds; D004396:Coloring Agents; D002606:Charcoal; C100258:methyl orange; D006046:Gold
keywords: Artificial neural network; Gold nanoparticles; Methyl orange; Partial swarm optimization; Tamarisk