A STUDY ON OPTIMIZATION OF PROCESS VARIABLES FOR OSMOTIC DEHYDRATION OF APPLE SLICES BY THE APPLICATION OF RESPONSE SURFACE METHODOLOGY.

Magnetic MXene composite Fe3O4@Ti3C2 was successfully prepared and employed as 17α-ethinylestradiol (EE2) adsorbent from water solution. The response surface methodology was employed to investigate the interactive effects of adsorption parameters (adsorption time, pH of the solution, initial concentration, and the adsorbent dose) and optimize these parameters for obtaining maximum adsorption efficiency of EE2. The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA) and t-test statistics. Optimization of the process variables for maximum adsorption of EE2 by Fe3O4@Ti3C2 was performed using the quadratic model. The model predicted maximum adsorption of 97.08% under the optimum conditions of the independent variables (adsorption time 6.7 h, pH of the solution 6.4, initial EE2 concentration 0.98 mg L−1, and the adsorbent dose 88.9 mg L−1) was very close to the experimental value (95.34%). pH showed the highest level of significance with the percent contribution (63.86%) as compared to other factors. The interactive influences of pH and initial concentration on EE2 adsorption efficiency were significant (p < 0.05). The goodness of fit of the model was checked by the coefficient of determination (R2) between the experimental and predicted values of the response variable. The response surface methodology successfully reflects the impact of various factors and optimized the process variables for EE2 adsorption. The kinetic adsorption data for EE2 fitted well with a pseudo-second-order model, while the equilibrium data followed Langmuir isotherms. Thermodynamic analysis indicated that the adsorption was a spontaneous and endothermic process. Therefore, Fe3O4@Ti3C2 composite present the outstanding capacity to be employed in the remediation of EE2 contaminated wastewaters.

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Statistical optimization of process variables by response surface methodology to enhance phenol degradation by Pseudomonas putida (NCIM 2102)

Advances in Bioscience and Biotechnology ◽

10.4236/abb.2011.24028 ◽

2011 ◽

Vol 02 (04) ◽

pp. 175-181 ◽

Cited By ~ 4

Author(s):

Velluru Sridevi ◽

M. V. V Chandana Lakshmi ◽

A. V. N Swamy ◽

M Narasimha Rao

Keyword(s):

Response Surface Methodology ◽

Pseudomonas Putida ◽

Response Surface ◽

Phenol Degradation ◽

Statistical Optimization ◽

Process Variables

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Optimization of osmotic dehydration of chestnut (Castanea sativa Mill.) slices using Response Surface Methodology

International Journal of Food Studies ◽

10.7455/ijfs.v7i1.433 ◽

2018 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Teresa Delgado ◽

Bruna Paim ◽

José Alberto Pereira ◽

Susana Casal ◽

Elsa Ramalhosa

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Water Loss ◽

Osmotic Dehydration ◽

Sucrose Concentration ◽

Volume Ratio ◽

Castanea Sativa ◽

Color Variation ◽

Process Conditions ◽

Operational Conditions

Osmotic dehydration of chestnut slices in sucrose was optimized for the first time by Response Surface Methodology (RSM). Experiments were planned according to a three-factor central composite design (α=1.68), studying the influence of sucrose concentration, temperature and time, on the following parameters: volume ratio, water activity, color variation, weight reduction, solids gain, water loss and normalized moisture content, as well as total moisture, ash and fat contents. The experimental data was adequately fitted into second-order polynomial models with coefficients of determination (R2) from 0.716 to 0.976, adjusted-R2 values from 0.460 to 0.954, and non-significant lacks of fit. The optimal osmotic dehydration process conditions for maximum water loss and minimum solids gain and color variation were determined by the “Response Optimizer” option: 83% sucrose concentration, 20 °C and 9.2 hours. Thus, the best operational conditions corresponded to high sugar concentration and low temperature, improving energy saving and decreasing the process costs.

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