Strategy of screening and optimization of process parameters using experimental design: application to amoxicillin elimination by adsorption on activated carbon
Autor: | N. Doufene, T. Berrama, S. Benredouane |
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Rok vydání: | 2016 |
Předmět: |
Central composite design
Process Chemistry and Technology Coefficient of variation Analytical chemistry Fractional factorial design 02 engineering and technology 010501 environmental sciences 021001 nanoscience & nanotechnology 01 natural sciences Computer Science Applications Analytical Chemistry Adsorption Yield (chemistry) Statistics Analytic element method medicine Response surface methodology 0210 nano-technology Spectroscopy Software 0105 earth and related environmental sciences Mathematics Activated carbon medicine.drug |
Zdroj: | Chemometrics and Intelligent Laboratory Systems. 155:128-137 |
ISSN: | 0169-7439 |
DOI: | 10.1016/j.chemolab.2016.04.010 |
Popis: | A fractional factorial design (FFD) 2 7 − 3 was used as screening technique to verify the influence of the seven factors on the response variable (yield of Amoxicillin elimination: y e ) by adsorption on activated carbon prepared from pedicels of dates. Factors studied are the impregnation ratio (r = 1.0–2.0), carbonization temperature (Tc = 300–500 °C), carbonization time (tc = 1.5–2.5 h), stirring rate (v = 200–400 rpm), dose of activated carbon ([AC] = 1–2 g·L − 1 ), pollutant concentration ([AMX] = 50–100 mg·L − 1 ), and medium temperature (T = 25–35 °C). As a result of FFD evaluation, the main influent factors are the Tc, [AC], [AMX] and T. The fitted quadratic regression model, including these parameters was developed using the central composite design (CCD) as the response surface methodology. Analysis of variance (ANOVA) of the preliminary fitted model was validated with a confidence level of > 95%. The final model is established on the basis of additional statistical analysis using other statistical criteria (coefficient of variation, adequate precision, Mallow's Cp statistic), the convenient results obtained, ensuring a satisfactory quadratic regression model, including all main parameters and their quadratic terms. Optimal conditions were determined using an analytic method, stationary point of model was determined, and the determinant values of Hessian matrix showed that the response (yield of AMX elimination) agrees a maximum solution corresponding to optimal conditions, Tc = 488 °C, T = 33.6 °C, [AMX] = 58.5 mg·L-1 and [AC] 1.178 g·L-1, at which the predictive value of the response is y e = 100 ± 3%. |
Databáze: | OpenAIRE |
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