4.7 Article

Prediction of breakthrough curves in a fixed-bed column based on normalized Gudermannian and error functions

期刊

JOURNAL OF MOLECULAR LIQUIDS
卷 323, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.molliq.2020.115061

关键词

Adsorption; Fixed bed; Gudermannian function; Fractal-like kinetics; Residual plot

资金

  1. Scientific Research Foundation [10912-KYQD2019-08165]
  2. Natural Science Foundation of Inner Mongolia [2019BS02006]
  3. Research Start-up Funds for the Chifeng University [QDJRCYJ022]

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The breakthrough models discussed in this study can accurately describe the diffusion-limited process on heterogeneous surfaces, with the fractal-like Gudermannian model providing the best fit for adsorption of norfloxacin and methylene blue. These models offer important supplements to existing adsorption model studies.
The curve characteristics of the breakthrough models were constrained by the mathematical laws that the models themselves followed. The existing breakthrough models such as Bohart-Adams, Thomas, Yoon-Nelson, Clark and modified dose-response models may be not enough to describe all adsorption systems. This work established the Gudermannian and error models only containing the two parameters. The fractal-like Gudermannian and fractal-like error models could describe the diffusion-limited process on the heterogeneous surfaces. The applicability of these models was evaluated by the adjusted coefficient of determination (Adj. R-2), reduced chi-square value (chi(2)) and residual plot. The introduction of the fractal-like kinetics made the Gudermannian and error models have the ability to describe the asymmetric curves. Among these models, the fractal-like Gudermannian model provided the best fit for norfloxacin adsorption on granular activated carbon (Adj. R-2 = 0.9991 and chi(2) = 1.26 x 10(-4)) and methylene blue adsorption on porous silica microsphere (Adj. R-2 = 0.9997 and chi(2) = 635 x 10(-5)). The breakthrough models proposed in this work were an important supplement of adsorption model studies. (C) 2020 Elsevier B.V. All rights reserved.

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