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Type: Artigo
Title: Efficiency maximization of fixed-bed adsorption by applying hybrid statistical-phenomenological modeling
Author: Sausen, Mateus Gustavo
Scheufele, Fabiano Bisinella
Alves, Helton Jose
Adeodato Vieira, Melissa Gurgel
Carlos da Silva, Meuris Gurgel
Borba, Fernando Henrique
Borba, Carlos Eduardo
Abstract: The objective of this work was to maximize the efficiency of the ciprofloxacin (CIP) removal in fixed-bed column, packed with SGC650H resin, by the optimization of the volumetric flow rate (Q) and bed height (H-bed). For this, a hybrid procedure based on statistical and phenomenological modeling was used. Based on a central composite rotatable design (CCRD) the conditions of Q and H-bed were defined to obtain breakthrough curves, wherein only 8 of a total of 11 assays were experimentally obtained. Based on the resin textural characterization and molecular dimensions analysis of the ciprofloxacin, phenomenological models were employed to describe the experimental data. The most accurate mathematical model for the breakthrough curves description, namely, adsorption on the sites model (AS) was validated with the experimental results of the CCRD and further used to predict other experimental conditions. For this, independent experiments were performed to evaluate the proposed methodology, which demonstrated a predictive capacity of the model and allowed the fixed-bed column performance optimization, achieving a maximum efficiency of 81.5%. In general, the presented data may be useful for the development of fixed-bed systems. In addition, the results evidenced the potential of the adsorption process for trace-level pollutants treatment, due to elevated CIP adsorption capacities and high affinity to the SGC650H resin as well as favorable kinetics. Besides, by applying predictive phenomenological modeling, the hybrid proposed approach presents a perspective to support the optimization and scale-up of fixed-bed column operation, thus allowing the application of the treatment method in industrial scale
Subject: Adsorção
Country: Reino Unido
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.seppur.2018.07.002
Date Issue: 2018
Appears in Collections:FEQ - Artigos e Outros Documentos

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