Optimization of the process parameters for the adsorption of ternary dyes by Ni doped FeO(OH)-NWs–AC using response surface methodology and an artificial neural network

Azad, F. Nasiri; Ghaedi, Mehrorang; Asfaram, Arash; Jamshidi, Arsalan; Hassani, Ghasem; Goudarzi, Alireza; Azqhandi, Mohammad Hossein Ahmadi; Ghaedi, A.M.

doi:10.1039/c5ra26036a

Cited by 107 publications

(28 citation statements)

References 49 publications

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“…The analysis shows that the form of the model chosen to explain the relationship between the factors and the response is well‐correlated (Figure a). At the same time very low values (1.988 and 0.6527 for R% MO and MB) of coefficient of the variation (CV %) means very high degree of precision and reliability of the experimental values . ‘Adequate Precision’ (signal to noise ratio) value higher than 4 is desirable which in present study corresponding value of 40.32 and 39.11 for MO and MB removal, respectively is good indication of adequate signal.…”

Section: Resultsmentioning

confidence: 99%

Ultrasonic treatment of wastewater contaminated with various dyes using tin oxide hydroxide nanoparticles loaded on activated carbon: Synthesis, performance, mechanism and statistical optimization

Porhemmat

Rezvani

Ghaedi

et al. 2017

Applied Organom Chemis

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Present research describe applicability of tin oxide hydroxide nanoparticles loaded on activated carbon (Sn 6 O 4 (OH) 4 -NPs-AC) which recognized via by Field emission scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD) for the rapid simultaneous removal of Methyl orange (MB) and methylene blue (MB) in mixture in the presence of ultrasonic wave. The central composite design (CCD) pattern as branch of response surface methodology (RSM) give useful information about influence of five most important operating variables is separate and combination nature namely pH (4.0-8.0), initial MO (8-24 mg L −1 ) and MB concentration (4-12 mg l −1 ), sonication time (2-6 min) and adsorbent mass (0.01-0.03 g) on the ultrasound under study dyes assisted adsorption. After surveying the experiment data and finding regression analysis correspond to experimental data, mathematical model and equation was constructed and derived which applied for optimizing ultrasound assisted adsorption and revel serious dependency of response to variables like adsorbent mass and sonication time s. F values of 114.5 and 165.5, coefficient of determination (R 2 ) of 0.9978 and 0.9985, adjusted-R 2 of 0.9891 and 0.9925, adequate precision (AP) of 39.11 and 40.23, coefficient of variation (CV) of 0.653 and 1.988% for MB and MO respectively, implied the satisfactory adjustment of the quadratic model. Based on CCD, the optimum conditions was set as pH of 6.0; 0.02 g adsorbent; 20 and 12 mg l −1 of MO and MB concentration and 2 min sonication time. The predicted removal of MO and MB in binary system were 98.19 and 98.02% which was good agreement with their experimental R% MOand MB value of 98.00% for both at optimum condition. The equilibrium data has good agreement with Langmuir with maximum monolayer adsorption capacity of 30.90 and 25.70 mg g −1 for MO and MB respectively. Investigation of time dependency of adsorption data revel more applicability pseudo-second order in cooperation with intraparticle diffusion description of the mechanism and adsorption rate.KEYWORDS isotherm and kinetic studies, methyl orange, methylene blue, optimization, Sn 6 O 4 (OH) 4 -NPs-AC, ultrasonic wave

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Section: Resultsmentioning

confidence: 99%

Ultrasonic treatment of wastewater contaminated with various dyes using tin oxide hydroxide nanoparticles loaded on activated carbon: Synthesis, performance, mechanism and statistical optimization

Porhemmat

Rezvani

Ghaedi

et al. 2017

Applied Organom Chemis

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“…The final concentration of non‐adsorbed target compounds (MB and BY28) was determined using a UV–visible spectrophotometer at the end of the adsorption procedure. Removal efficiency stated as percent adsorption of dye was determined using the following equation :

R (() %) = \frac{C_{0} - C_{e}}{C_{normale}} \times 100

where C e and C 0 are the final and initial concentrations of dye (mg l −1 ), respectively.

q_{e} = \frac{((), C_{e} - C_{0}) V}{W}

where V is the volume of dye‐containing solution (l) and W is the mass of the adsorbent (g).…”

Section: Methodsmentioning

confidence: 99%

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

Azqhandi

Shekari

Ghalami‐Choobar

2018

Applied Organom Chemis

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The sorption of methylene blue (MB) and basic yellow 28 (BY28) dyes in water on Ag@ZnO/MWCNT (Ag‐doped ZnO loaded on multiwall carbon nanotubes) nanocomposite is investigated in a batch process, optimizing starting initial dye concentration, sonication time and adsorbent mass. Isotherms and kinetic behaviours of MB and BY28 adsorption onto Ag@ZnO/MWCNT were explained by extended Freundlich and pseudo‐second‐order kinetic models. Ag@ZnO/MWCNT was synthesized and characterized using X‐ray diffraction, energy‐dispersive X‐ray spectroscopy, field emission scanning electron microscopy and Brunauer–Emmett–Teller analysis. According to the experimental data, adaptive neuro‐fuzzy inference system (ANFIS), generalized regression neural network (GRNN), backpropagation neural network (BPNN), radial basic function neural network (RBFNN) and response surface methodology (RSM) were developed, and applied to forecast the removal performance of the sorbent. The influence of process variables (i.e. sonication time, initial dye concentration, adsorbent mass) on the removal of MB and BY28 was considered by central composite rotatable design of RSM, GRNN, ANFIS, BPNN and RBFNN. The performances of the developed ANFIS, GRNN, BPNN and RBFNN models were compared with RSM mathematical models in terms of the root mean square error, coefficient of determination, absolute average deviation and mean absolute error. The coefficients of determination calculated from the validation data for ANFIS, GRNN, BPNN, RBFNN and RSM models were 0.9999, 0.9997, 0.9883, 0.9898 and 0.9608 for MB and 0.9997, 0.9990, 0.9859, 0.9895 and 0.9593 for BY28 dye, respectively. The ANFIS model was found to be more precise compared to the other models. However, the GRNN method is much easier than the ANFIS method and needs less time for analysis. So, it has potential in chemometrics and it is feasible that the GRNN algorithm could be applied to model real systems. The monolayer adsorption capacity of MB and BY28 was 292.20 and 287.02 mg g−1, respectively.

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“…A RSM and ANN have been utilized for removal of ternary toxic dyes onto copper sulfide nanoparticles loaded on activated carbon [24]. ANN and RSM have been proposed for modeling and interaction of the variables for the maximum removal percentage of ternary dyes utilizing the experimental data on the basis of the central composite design [25].…”

Section: Literature Surveymentioning

confidence: 99%

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

Razvarz

Jafari

2017

MCA

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This paper discusses the elimination of Colour Index Acid Yellow 23 (C.I. AY23) using the ultraviolet (UV)/Ag-TiO 2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO 2 nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO 2 , and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme.

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Optimization of the process parameters for the adsorption of ternary dyes by Ni doped FeO(OH)-NWs–AC using response surface methodology and an artificial neural network

Abstract: The present study deals with the simultaneous removal of chrysoidine G (CG), rhodamine B (RB) and disulfine blue (DB) by Ni doped ferric oxyhydroxide FeO(OH) nanowires on activated carbon (Ni doped FeO(OH)-NWs–AC).

Cited by 107 publications

References 49 publications

Ultrasonic treatment of wastewater contaminated with various dyes using tin oxide hydroxide nanoparticles loaded on activated carbon: Synthesis, performance, mechanism and statistical optimization

Ultrasonic treatment of wastewater contaminated with various dyes using tin oxide hydroxide nanoparticles loaded on activated carbon: Synthesis, performance, mechanism and statistical optimization

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

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

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