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Arash Asfaram,Mehrorang Ghaedi,Mohammad Hossein Ahmadi Azqhandi,Alireza Goudarzi,Shaaker Hajati 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.54 No.-
Response surface methodology (RSM), Artificial Neural Network (ANN) and Radial Basis Function Neural Network (RBFNN) were applied to model and predict the efficiency of two carcinogenic dyes (Methylene blue (MB) and Malachite green (MG)) adsorption onto Mn@ CuS/ZnS nanocomposite-loaded activated carbon (Mn@ CuS/ZnS-NC-AC) as a novel adsorbent. The properties of Mn@ CuS/ZnS-NC-AC were identified by XRD; FE-SEM and EDS. The parameters such as pH, Mn@ CuS/ZnS-NC-AC mass, sonication time, MB concentration and MG concentration involved in the adsorption process were set within the ranges 4.0–8.0, 0.010–0.030 g, 1–5 min, 5–25 mg L1 and 5–25 mg L1, respectively. The applicability of the RBFNN, ANN and RSM models for the description of experimental data was examined using four statistical criteria (coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD)). Compared to RSM, the RBFNN and ANN exhibited better performance for modeling the process of both dyes adsorption. The significant factors were evaluated followed by the optimization of the process. The adsorption of MB and MG was found to be mostly affected by the concentration of MB and MG dyes. The equilibrium adsorption data were analyzed by Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherm models. The best fit to the data was obtained by applying the Langmuir model. Meanwhile, the maximum adsorption capacity for MB and MG was estimated to be 126.42 and 115.08 mg g1, respectively.
Fardin Sadeghfar,Mehrorang Ghaedi,Arash Asfaram,Ramin Jannesar,Hamedreza Javadian,Vahid Pezeshkpour 한국공업화학회 2018 Journal of Industrial and Engineering Chemistry Vol.65 No.-
In this research study, the polyvinyl alcohol/Fe3O4@carbon nanotubes (PVA/Fe3O4@CNTs) nanocomposite was prepared by electrochemical-assisted synthesis method, characterized by FT-IR, UV–vis, FE-SEM, TEM, BET and XRD techniques, and subsequently applied for the ultrasound-assisted removal of methylene blue (MB) dye from aqueous solution and as antibacterial agent in vitro investigation against Proteus mirabilis (PM), Methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli (E. Coli) and Pseudomonas aeruginosa (PAO1) bacteria. The effects of important variables such as the initial concentration of MB, adsorbent mass, sonication time and pH on the removal percentage of MB were investigated and optimized by central composite design (CCD). The experimental results were applied for the construction of a quadratic model to predict the response following the analysis of variance (ANOVA) and to obtain useful information about the possible interaction between the variables and their main effects. The high F-value (207.38), low P-value (<0.0001) and non-significant lack of fit (P-value > 0.05) along with the reasonable value of the coefficient of determination (R2 = 0.99452) confirmed a good correlation between the experimental and predicted values. The highest removal percentage of 99.5% was attained at the optimum conditions of 0.035 g of the adsorbent, 25.0 mg L−1 of dye concentration, 6 min sonication time and pH = 5.5. The adsorption process of MB by PVA/3 wt% Fe3O4@CNTs was well described by pseudo-second-order kinetic and Langmuir isotherm models. A low dose of PVA/Fe3O4@CNTs nanocomposite (0.005 g) was successfully used for the adsorption of MB (R% > 90.0%) within a short time (6.0 min) with the highest monolayer adsorption capacity of 250.10 mg g−1 at 25 °C.
Ebrahim Alipanahpour Dil,Mehrorang Ghaedi,Arash Asfaram,Fatemeh Mehrabi,Fardin Sadeghfar 한국공업화학회 2019 Journal of Industrial and Engineering Chemistry Vol.74 No.-
This work devoted to the investigation of adsorption Azure B (Az-B) onto CNTs/Zn:ZnO@Ni2P-NCs, whiledependency of process efficiency to variables was optimized by the application response surfacemethodology. This adsorbent successfully using small amount 0.031 g is applicable to the adsorption ofthe high amount of Az-B in a short time. The optimum variables for achievement of maximum Az-Badsorption percentage (98.84%) were achieved using 0.031 g of CNTs/Zn:ZnO@Ni2P-NCs, 26.1 mg L 1initial Az-B concentration, 8.2 min sonication time and pH 6.3. The kinetic of Az-B adsorption followedpseudo-second-order kinetic and equilibrium data strongly follow the Langmuir model.
Ebrahim Alipanahpour Dil,Mehrorang Ghaedi,Gholam Reza Ghezelbash,Arash Asfaram,Mihir Kumar Purkait 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.48 No.-
In this research, biosorption of Hg2+, Pb2+ and Cu2+ from aqueous solution via Yarrowia lipolytica 70562living mass was described. Correction and dependency of biosorption efficiency to effective variables likepH, initial Hg2+, Pb2+ and Cu2+ concentration, contact time and temperature was studied by centralcomposite design under response surface methodology. Three responses were simultaneously studied bynumerical optimization methodology. The optimum biosorption efficiency of Hg2+, Pb2+ and Cu2+ ions at18, 22 and 25 mg L 1 onto Y. lipolytica 70562 was found to be 99.26%, 101.15% and 99.74% at pH of 6.4 and25 C after around 8 h under well mixing. Finally, optimum condition for acceptable and repeatableagreement among experimental and real data was explored. The artificial neural network (ANN) modelwas used for predicting simultaneous biosorption of Hg2+, Pb2+ and Cu2+ ions based on experimental data. It was found that using ANN model with 12 neurons at hidden layer for all three ions, the R2 of 0.989 andMSE of 993 for Hg2+ ion were obtained. The R2 of 0.981 and MSE of 1.155 for Pb2+ ion were obtained andalso for Cu2+ ion the values of the R2 and MSE were found to be 0.971 and 1.589, respectively. Langmuirisotherm model was bestfitted with the equilibrium experimental. The experimental kinetic data wasrepresented well by using pseudo-second-order model and intraparticle diffusion model.