Use of response surface methodology for optimization of fluoride adsorption in an aqueous solution by Brushite

Mourabet, M.; Rhilassi, A. El; Boujaady, H. El; Bennani-Ziatni, M.; Taïtaï, A.

doi:10.1016/j.arabjc.2013.12.028

Cited by 229 publications

(123 citation statements)

References 26 publications

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“…The desirability value ranged between 0 and 1, where a value of 1 is perfect, i.e., the ideal parameter value [22]. The optimizing desirability of transfersome luteolin was 0.609.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Luteolin-Loaded Transfersome Using Response Surface Methodology

Setyawati

Surini

Mardliyati³

2017

Int J App Pharm

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Objective: This research was carried out to optimize luteolin-loaded transfersome formula with independent variables such as lipid-surfactant (total lipid) concentration and luteolin concentration.Methods: Luteolin-loaded transfersome was optimized by response surface methodology based on four parameters, namely, particle size (Z-average), polydispersity index, zeta potential, and entrapment efficiency. The transfersome formula was prepared using central composite design, and the selected independent variables were the total lipid (mixture of phospholipid and Tween 80) and luteolin concentrations. 14 formulas of luteolinloaded transfersome were prepared by thin film hydration, followed by the sonication method. Results:The total lipid and luteolin concentration significantly affected the entrapment efficiency only. The other parameters were not affected by a change in these variables. The optimum formula of 4.88% total lipid and 0.5% luteolin with desirability value of 0.609 conformed with the prediction parameters. Vesicle imaging using transmission electron microscopy revealed spherical particles and the occurrence of particle aggregation. The optimum formula of luteolin-loaded transfersome possessed the following characteristics: Particle size of 286.03±8.46 nm, polydispersity index of 0.480±0.013, zeta potential of -18.67±0.379 mV, and entrapment efficiency of 94.97±0.28 %. However, these values did not correspond to the predicted values and were confirmed by the low adjusted and predicted R-squared values. Conclusion:This method can be applied to optimize the entrapment efficiency, and in the future, it can be used for further optimizing formula of transfersome by including more variables.

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“…The desirability value ranged between 0 and 1, where a value of 1 is perfect, i.e., the ideal parameter value [22]. The optimizing desirability of transfersome luteolin was 0.609.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Luteolin-Loaded Transfersome Using Response Surface Methodology

Setyawati

Surini

Mardliyati³

2017

Int J App Pharm

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“…4 and 5 having positive signs, showing that the SP removal increases when the sample dosage increase for DMSP and when left to stir for longer time for ODSP. A positive value represents an effect that favors the optimization, while a negative value indicates an inverse relationship between the factors and the response [25]. For DMSP and ODSP in BRE, significant effect were obtained for linear terms of coagulant dosage ( ), pH ( ) and stirring time ( ) while the interaction effect is on and .…”

Section: Validation Of the Modelmentioning

confidence: 97%

Optimizing bio-coagulants for brewery wastewater treatment using response surface methodology

Okolo¹,

Nnaji²,

Oke³

et al. 2018

Nig. J. Tech.

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Coag-flocculation process was used to treat brewery effluent stream with Detarium Microcarpum seed powder (DMSP) and oyster dried shell powder (ODSP) as coagulants. The proximate analyses of the coagulants were obtained using AOAC standard method. Percentage moisture, ash, fat, crude protein, crude fiber and carbohydrate content were determined for both coagulants. Jar test experiments were employed for the coag-flocculation process and response surface methodology (RSM) optimize the process. A box-Behnken design (BBD) of Design Expert 6.0.8, implementing RSM was used to evaluate the effects and interactions of three factors: coagulant dosage, pH and stirring time on the treatment efficiency. The optimal conditions obtained were coagulant dosage of 100.53mg/L, pH of 2.001 and stirring time of 24.47mins with 90.44% solid particle (SP) removal (desirability value of 1.0) and coagulant dosage of 104.19mg/L, pH of 3.34 and stirring time of 27.54 with 96.55% SP removal (desirability value of 1.0) for DMSP and ODSP, respectively. These agree reasonably with the experimental optimum for both coagulants. A determination coefficient, R 2 , of 97 and 98%; F-value of 45.8056 and 55.3045; and prob.-value of 1.92E-07 and 2.39E-07 for DMSP and ODSP respectively were used to evaluate the model adequacy.RSM has been demonstrated to be appropriate approach for the optimization of this process.

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“…The use of ANOVA enables the comparison between the variations that arise from the treatment of the experimental data and the variation as a result of the random errors that accompanied the measurement of the obtained responses [28]. Moreover, the ANOVA helps to determine the significance and the mathematical model adequacy [28]. Besides ANOVA, other tools such as normality test, regression analysis and lack of fit test can be employed to examine the model adequacy of the RSM optimization.…”

Section: Statistical Approaches With Emphasis On Design Of Experimentmentioning

confidence: 99%

“…This can be achieved by employing the analysis of variance (ANOVA). The use of ANOVA enables the comparison between the variations that arise from the treatment of the experimental data and the variation as a result of the random errors that accompanied the measurement of the obtained responses [28]. Moreover, the ANOVA helps to determine the significance and the mathematical model adequacy [28].…”

Section: Statistical Approaches With Emphasis On Design Of Experimentmentioning

confidence: 99%

An Overview of Response Surface Methodology Approach to Optimization of Hydrogen and Syngas Production by Catalytic Reforming of Greenhouse Gases (CH4 and CO2)

Ayodele¹,

Abdullah²

2018

Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes

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Catalytic reforming of Methane (CH 4 ) and carbon dioxide (CO 2 ) is one of the techniques used for the production of hydrogen and syngas. This technique has dual advantages of mitigation of greenhouse gases and production of hydrogen and syngas which are often used as intermediates for the synthesis of valuable chemical products and oxygenates. This study presented an overview of the application of response surface methodology (RSM) in the optimization of hydrogen and syngas production from catalytic reforming of CH 4 and CO 2 . The different catalytic system that has been employed together with the nature of experimental design, input parameters, responses, the optimum conditions and the maximum values of their responses were examined. The future research direction in the application of RSM to optimization of hydrogen and syngas production by catalytic reforming of CH 4 and CO 2 was recommended.

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Use of response surface methodology for optimization of fluoride adsorption in an aqueous solution by Brushite

Cited by 229 publications

References 26 publications

Optimization of Luteolin-Loaded Transfersome Using Response Surface Methodology

Optimization of Luteolin-Loaded Transfersome Using Response Surface Methodology

Optimizing bio-coagulants for brewery wastewater treatment using response surface methodology

An Overview of Response Surface Methodology Approach to Optimization of Hydrogen and Syngas Production by Catalytic Reforming of Greenhouse Gases (CH4 and CO2)

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