Response surface methodology approach for optimization of the removal of chromium(VI) by NH2-MCM-41

Cao, Julin; Wu, Yali; Jin, Yanping; Yilihan, Palizhati; Huang, Wei

doi:10.1016/j.jtice.2013.09.011

Cited by 94 publications

(41 citation statements)

References 51 publications

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“…This result differs to other studies and likely explained due to the literature experiments being conducted under temperatures that are not realistic to a wastewater treatment environment (Bingol et al, 2012;Shojaeimehr et al, 2014). Cao et al (2014) presented the effects of temperature (10-50°C) and showed that the adsorption of Cr(VI) increased with increasing temperature even when the range was higher and unrealistic conditions to a wastewater treatment environment (Cao et al, 2014). …”

Section: Ann Modelcontrasting

confidence: 63%

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Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Hamid

Jenidi

Thielemans

et al. 2016

Industrial Crops and Products

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This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a wastewater treatment plant. Conductometric titration of CNWs suspensions showed a surface charge of 54 and 410 mmol/kg for the unmodified and modified CNWs, respectively, which indicated that the modified CNWs provide a relatively high surface area per unit mass than the unmodified CNWs. In addition, the stability of the modified CNWs was tested under different conditions and proved that the functional groups were permanent and not degraded. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in order to optimize the system and to create a predictive model to evaluate the Cu(II) removal performance of the modified CNWs. The performance of the ANN and RSM models were statistically evaluated in terms of the coefficient of determination (R 2 ), 2 absolute average deviation (AAD), and the root mean squared error (RMSE) on predicted experiment outcomes. Moreover, to confirm the model suitability, unseen experiments were conducted for 14 new trials not belonging to the training data set and located both inside and outside of the training set boundaries. Result showed that the ANN model (R 2 =0.9925, AAD = 1.15%, RMSE = 1.66) outperformed the RSM model (R 2 =0.9541, AAD = 7.07%, RMSE = 3.99) in terms of the R 2 , AAD, and RMSE when predicting the Cu (II) removal and is thus more reliable. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and the results revealed that Langmuir isotherm (R 2 = 0.9998) had better correlation than the Freundlich isotherm (R 2 = 0.9461). Experimental data were also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption.

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Section: Ann Modelcontrasting

confidence: 63%

“…These parameters and their range were selected based on the literature (Cao et al, 2014;Thirumavalavan et al, 2010) and scoping studies undertaken in this study. Fixed conditions for pH and time were chosen to be 6 and 30 min respectively as determined in the scoping studies.…”

Section: Experimental Designmentioning

confidence: 99%

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Hamid

Jenidi

Thielemans

et al. 2016

Industrial Crops and Products

View full text Add to dashboard Cite

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“…The value of the mean squares is calculated by dividing the sum of squares of each variable by their respective degree of freedom. The F (Fishers' variance ratio) value is calculated by taking the and a very low probability value (P B 0.0001) was obtained for both responses Y 1 and Y 2 (Cao et al 2014). The lack of fit F value for response Y 1 (147.32) and Y 2 (185.22) depicts that the lack of fit is significant.…”

Section: Resultsmentioning

confidence: 99%

“…Some insignificant terms of the above model can be neglected based on the statistical analysis for the accurate prediction of response (Cao et al 2014). …”

Section: Model and Statistical Analysismentioning

confidence: 99%

Statistical optimization of process parameters for the simultaneous adsorption of Cr(VI) and phenol onto Fe-treated tea waste biomass

Gupta

Balomajumder

2017

Appl Water Sci

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In this study, simultaneous removal of Cr(VI) and phenol from binary solution was carried out using Fetreated tea waste biomass. The effect of process parameters such as adsorbent dose, pH, initial concentration of Cr(VI) (mg/L), and initial concentration of phenol (mg/L) was optimized. The analysis of variance of the quadratic model demonstrates that the experimental results are in good agreement with the predicted values. Based on experimental design at an initial concentration of 55 mg/L of Cr(VI), 27.50 mg/L of phenol, pH 2.0, 15 g/L adsorbent dose, 99.99% removal of Cr(VI), and phenol was achieved.

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“…Yanıt yüzey metodu (YYM), çeşitli bağımsız değişkenlerin bağımlı değişkenin değerini optimize etmek için istatistiksel ve matematiksel tekniklerin bir arada kullanıldığı bir yöntemdir [11,12]. YYM de bağımlı değişkenler yanıt ve bağımsız değişkenler faktör olarak değerlendirilir.…”

Section: Yanıt Yüzey Metoduunclassified

DEEA-Volkanit Üzerine Toryum(IV) Adsorpsiyonunun Yanıt Yüzey Metodu (YYM) ile Optimizasyonu

Kaynar¹,

Kaynar²

2017

SDÜ Fen Bil Enst Der

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Özet: Bu çalışmada, Th(IV) adsorpsiyonu için ekonomik ve bol miktarda bulunan alkali bazaltik yapıdaki Kula volkanitinin DEEA organik katyonu ile modifiye edilip adsorpsiyon kapasitesinin artırılarak kullanılması amaçlanmıştır. Öncelikle hazırlanan DEEA-volkanitin XRF, FT-IR, SEM ve BET yüzey alanı gibi yapısal karakteristik özellikleri incelendi, daha sonra sulu çözeltilerden Th(IV) adsorpsiyonu endüstriyel araştırmalarda geniş bir kullanıma sahip yanıt yüzey metodu ( Abstract: In this study, the use of Kula volcanic in the alkaline basaltic structure, economical and abundant for Th (IV) adsorption, which is modified with the DEEA organic cation and increased of its adsorption capacity was investigated. Firstly, structural characteristics such as XRF, FT-IR, SEM and BET surface area of prepared DEEA-volcanic were investigated. Then, Th (IV) adsorption from aqueous solutions was optimized with a wide range of the response surface methods (RSM) in industrial investigations. Effective experimental conditions on adsorption efficiency: pH (3-8), Th (IV) concentration (20-100 mg / L), temperature (20-60 °C) and time (15-120 minutes) were selected. The best possible combinations of these parameters were obtained by the response surface method (RSM). Central composite design (CCD) was chosen for the design of experimental conditions. According to the RSM data, it is seen that the model is the second-order (quadratic) and R 2 and R 2 adj values from the parameters which are shown model fitting are 0.99 and 0.99, respectively. The adsorption capacity for DEEA-Volcanite at optimum conditions was found to be 38.94 mg/g. In addition, this work, Th (IV) adsorption was investigated the suitability to the isotherms and thermodynamic parameters were calculated.

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Response surface methodology approach for optimization of the removal of chromium(VI) by NH2-MCM-41

Cited by 94 publications

References 51 publications

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Statistical optimization of process parameters for the simultaneous adsorption of Cr(VI) and phenol onto Fe-treated tea waste biomass

DEEA-Volkanit Üzerine Toryum(IV) Adsorpsiyonunun Yanıt Yüzey Metodu (YYM) ile Optimizasyonu

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