Statistical optimization of process parameters for the extraction of chromium(VI) from pharmaceutical wastewater by emulsion liquid membrane

Rajasimman, M.; Sangeetha, R.; Karthik, P. N.

doi:10.1016/j.cej.2008.12.026

Cited by 85 publications

(35 citation statements)

References 18 publications

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“…The experimental design is shown in Table-2. (2) where Y is the predicted response, Xi is the uncoded value of the i-th test variable, β0, βj and βij are coefficients estimated from the regression according to Rajasimman et al 7 A statistical program package Design-Expert version 9.0.4.1 (State-Ease Inc., Minneapolis, MN, USA) was used for regression analysis of the data and for estimating the coefficient of the regression equation. The equations were validated by the analysis of variance (ANOVA) test.…”

Section: Design Of Experimentsmentioning

confidence: 99%

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Optimization of Microwave Hydrodistillation of Dried Patchouli Leaves by Response Surface Methodology

2017

RJC

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This paper deals with the optimization of patchouli oil yield in microwave hydrodistillation using response surface methodology (RSM). The factors considered were microwave power (A), feed to the solvent ratio (B) and extraction time (C). These parameters were varied at two levels. The conditions of optimum patchouli oil yield predicted were microwave power of 467.517 W, feed to solvent ratio of 0.432 g/mL and extraction time of 152.122 min. These factors gave an optimum patchouli oil yield of 4.862%. The significant model terms are extraction time. Analysis of variance (ANOVA) indicates that the model was significant as evidenced from R 2 of 0.9036 and the model F-value of 7.29. The patchouli oil yield predicted by the model was closed to the experimentally determined values (1.85 % and 1.87 % respectively); hence the model can be used for prediction of patchouli oil yield in essential oil extraction from dried patchouli leaves using microwave hydrodistillation method.

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Section: Design Of Experimentsmentioning

confidence: 99%

“…Therefore the model can be used to navigate the design space. The response surface curves are plotted to understand the interaction of the variables and the optimum level of each variable for maximum response 7 . The response surface curves for extraction of patchouli oil using microwave hydrodistillation are presented in Fig.-2.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Optimization of Microwave Hydrodistillation of Dried Patchouli Leaves by Response Surface Methodology

2017

RJC

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“…This multivariate approach also improves statistical interpretation possibilities and evaluates the relative significance of several contributing factors even in the presence of complex interactions. RSM is widely used for multivariable optimization studies in several biotechnological processes such as the optimization of media and process conditions, hydrogen production, food processing, fermentation, metal extraction by emulsion liquid membranes, and biosorption of metals and dyes [17][18][19][20][21][22][23][24][25]. In the present work, the optimization of inulinase production by streptomyces sp.…”

Section: Introductionmentioning

confidence: 99%

Application of statistical design for the production of inulinase by streptomyces sp. using pressmud

Dilipkumar

Rajamohan

2011

Front. Chem. Sci. Eng.

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A Plackett-Burman design was employed for screening 18 nutrient components for the production of inulinase using streptomyces sp. and pressmud as the substrate via solid-state fermentation (SSF). From the experiments, three nutrients viz. yeast extract, FeSO 4 $7H 2 O, and NH 4 NO 3 were found to be the most significant components. Hence these three components were selected and optimized using Response Surface Methodology (RSM). The optimum conditions are: yeast extract 0.00274 g/gds, FeSO 4 $7H 2 O 0.00011 g/gds and NH 4 NO 3 0.00772 g/gds. The effect of the substrate concentration and initial moisture content were also studied. A substrate concentration of 12 g and an initial moisture content of 65% are optimum for the maximum production of inulinase (89 U/gds).

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“…with five replicates at the center point was used to optimize the selected key variables (X 1 , X 2 , X 3, and X 4 ) (Singh et al 2013). The coded values of the process parameters were determined by the following equation (Rajasimman et al 2009;Satapathy and Das 2014;De Sales et al 2013 …”

Section: Experimental Design and Proceduresmentioning

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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Statistical optimization of process parameters for the extraction of chromium(VI) from pharmaceutical wastewater by emulsion liquid membrane

Cited by 85 publications

References 18 publications

Optimization of Microwave Hydrodistillation of Dried Patchouli Leaves by Response Surface Methodology

Optimization of Microwave Hydrodistillation of Dried Patchouli Leaves by Response Surface Methodology

Application of statistical design for the production of inulinase by streptomyces sp. using pressmud

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

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