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Cited by 1,206

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Insight into the adsorption kinetics models for the removal of contaminants from aqueous solutions

Tan, K.L., Hameed, B.H.·2017·Journal of the Taiwan Institute of Chemical Engineers

Abstract

Abstract The past decade has seen a boom in environmental adsorption studies on the adsorptive removal of pollutants from the aqueous phase. A large majority of works treat kinetic modeling as a mere routine to describe the macroscopic trend of adsorptive uptake by using common models, often without careful appraisal of the characteristics and validity of the models. This review compiles common kinetic models and discusses their origins, features, modified versions (if any), and applicability with regard to liquid adsorption modeling for both batch adsorption and dynamic adsorption systems. Indiscriminate applications, ambiguities, and controversies are highlighted and clarified. The appropriateness of linear regression for correlating kinetic data is discussed. This review concludes with a note on the current scenario and the future of kinetics modeling of liquid adsorption.

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Metadata

Authors
Tan, K. L., & Hameed, B. H.
Venue
Journal of the Taiwan Institute of Chemical Engineers
Publisher
Elsevier BV
Published
2017-05
Volume
74
Pages
25-48
ISSN
1876-1070 (ISSN-L)
OpenAlex
W2602426269
Cited by
1,206
References
3
FWCI
39.88
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Insight into the adsorption kinetics models for the removal of contaminants from aqueous solutions. https://app.cochat.ai/papers/10.1016/j.jtice.2017.01.024
The paper
Tan, K. L., & Hameed, B. H. (2017). Insight into the adsorption kinetics models for the removal of contaminants from aqueous solutions. Journal of the Taiwan Institute of Chemical Engineers. https://doi.org/10.1016/j.jtice.2017.01.024

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.