Cadmium and nickel: Assessment of the physiological effects and heavy metal removal using a response surface approach by L. gibba

Demim, S.; Drouiche, Nadjib; Ali, Aidy; Benayad, Tahar; Dendene-Badache, O.; Semsari, S.

doi:10.1016/j.ecoleng.2013.10.016

Cited by 84 publications

(31 citation statements)

References 56 publications

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“…Therefore, combining careful experimental design with response surface methodology (RSM) is an effective strategy for deriving optimized conditions for arsenic removal using minimal experimentation. Demin et al [28,29] demonstrated the application of central composite design (CCD) combined with RSM analysis to investigate the significant factors that influenced the heavy metal removal (Cd 2+ , Cr 6+ , Cu 2+ , Zn 2+ , and Ni 2+ ) from aqueous solution using bioremoval process. Baskan and Pala reported the use of Box-Behnken statistical experiment design (BBD) to evaluate optimal conditions for As(V) removal by ferric ions [12], and aluminum sulfate [13].…”

Section: Desalination and Water Treatmentmentioning

confidence: 99%

Simultaneous removal of As(III) and As(V) from wastewater by co-precipitation using an experimental design approach

Wittayarak

Imyim

Wongravee

2015

Desalination and Water Treatment

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A B S T R A C TResponse surface methodology (RSM) with central composite design was used to determine the significant effects of pH, ferric ion, and initial arsenic concentrations on the removal efficiency of arsenic by a co-precipitation method. The regression function, with coefficients calculated by multiple linear regression, was calibrated and validated using external experimental runs. The correlation coefficients (R 2 ) of the actual vs. predicted arsenic removal percentages were 0.9871 and 0.9478 for As(III) and As(V), respectively. All major factors were determined to be significant by analysis of variance, with p-values < 0.01 and had a district effort on the removal process. Multi-layer response surfaces were developed to determine the highest removal efficiency. The maximum removal efficiencies for arsenic species were approximately 100%, achieved by model prediction with a Fe/As mole fraction of 3.34 at pH 7. These optimized conditions were then applied to remove arsenic from two industrial wastewater samples, giving efficiencies of 93.98 and 91.48%. The results reveal that the chosen conditions from the RSM approach are applicable for arsenic removal from real water samples, without any pretreatment process.

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Section: Desalination and Water Treatmentmentioning

confidence: 99%

Simultaneous removal of As(III) and As(V) from wastewater by co-precipitation using an experimental design approach

Wittayarak

Imyim

Wongravee

2015

Desalination and Water Treatment

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“…1(a)) was evaluated as R 2 = 0.9687 indicates that the factorial model accuracy was satisfactory. The coefficient R 2 adj (0.9218) is more suitable for comparing models with independent variables of different numbers [39]. The color experimental response is given in Parity plot ( Fig.…”

Section: Analysis Of Variancementioning

confidence: 99%

Simultaneous removal of natural organic matter and turbidity from Oued El Harrach River by electrocoagulation using an experimental design approach

Boudjema

Abdi

Grib

et al. 2015

Desalination and Water Treatment

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In this study, the application of full factorial design and a surface response methodology was used to model the two-factor influence. Time and current intensity on bacterial biomass, color, turbidity, and natural organic matter (NOM) removal from Oued El Harrach River were discussed. A factorial experimental design was used to investigate the bacterial biomass and color removal from Oued El Harrach River situated in northern Algeria which is treated by electrocoagulation using iron plate electrodes. However, a response surface methodology (RSM) was used to reduce the turbidity and NOM. The bacteria removal efficiency was determined after 30 min of treatment. The combined effects of operating parameters on removal turbidity and NOM were also analyzed. A regression model was found to fit the experimental data very well. Besides, the results were statistically analyzed using the student's t-test, analysis of variance, F-test, and lack of fit in order to define the most important process variables affecting the removal of bacteria biomass, color turbidity, and NOM from Oued El Harrach River. The predicted results using factorial regression model showed high regression coefficient values (i.e. R 2 bact = 0.9687 and R 2 colo = 0.9983) which are in substantial agreement with experimental data. On the other hand, predicted values of turbidity and NOM obtained from central composite model were in close agreement with the experimental values (i.e. R 2 turb = 0.9442 and R 2 NOM = 0.9432). This study proved that factorial design and RSM could efficiently be applied for the wastewater treatment modeling by electrocoagulation process.

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“…To study the robustness of enzymatic hydrolysis it is necessary to use at least a polynomial of degree 2 only compatible with the presence of an extremum in inside the experimental area. Among the use of polynomials of degree 2, central composite experimental design (Baskar, Muthukumaran, & Renganathan, ; Demim et al, ; Kunamneni & Singh, ; Silva et al, ; Demim, Drouiche, Aouabed, & Semsari, ) was chosen and provides optimum quality for the predicted response calculated at any point in the area. The plan focuses composite is actually a type of factorial design 2k (fractional factorial 2k ‐ p ) with the addition of star points and center points.…”

Section: Theorymentioning

confidence: 99%

Evaluation of the robustness of the enzymatic hydrolysis in batch and continuous mode by a central composite design

Abdi

Kitous

Grib

et al. 2017

J Food Process Preserv

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The main purpose of this work is to study the ability of the enzymatic reaction. For the performance of the experiment, it is desirable to control variations so that they are minimized to the maximum extend and therefore an optimum operating point can be maintained. To achieve this target, the central composite design takes into account all interaction effects that may be useful for creating a mathematical model in agreement with the validity criteria. The model showed to be more suitable for the saccharification experiments in batch mode than in continuous mode. In batch mode, a correlation coefficient close to unity (R2 = .98) was obtained. Moreover, the resulting mathematical model was satisfactory in accordance with the validation criteria. For continuous mode, the coefficient value obtained was R2 = .83 indicating that the model to study the ability of the enzymatic reaction was inferior than the batch mode conditions of the analysis. Practical applications The ability of the enzymatic reaction to maintain its performance at small experimental condition variations was studied. The central composite design conformed to criterion validity. A correlation coefficient close to unity (R2 = .98) was obtained.

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Cadmium and nickel: Assessment of the physiological effects and heavy metal removal using a response surface approach by L. gibba

Cited by 84 publications

References 56 publications

Simultaneous removal of As(III) and As(V) from wastewater by co-precipitation using an experimental design approach

Simultaneous removal of As(III) and As(V) from wastewater by co-precipitation using an experimental design approach

Simultaneous removal of natural organic matter and turbidity from Oued El Harrach River by electrocoagulation using an experimental design approach

Evaluation of the robustness of the enzymatic hydrolysis in batch and continuous mode by a central composite design

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