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A Unified Adsorption Kinetic Model Inspired by Epidemiological Model: Based on Adsorbates "Infect" Adsorbents.

Xuan GuoJianlong Wang
Published in: Langmuir : the ACS journal of surfaces and colloids (2024)
Adsorption is a unit operation used in various fields, including the environmental, chemical, and pharmaceutical industries. Understanding the adsorption kinetics is crucial to designing efficient adsorption systems. However, existing empirical adsorption models are limited in providing insights into the mass transfer mechanisms. Additionally, the absence of a unified adsorption kinetic model hampers the effective comparison of different adsorption systems. Here, we viewed the adsorption as an "infectious process of adsorbates by adsorbents" akin to epidemiology. In epidemiology, individuals can be divided into susceptible, infected, and recovered compartments, ignoring the complexities of movement among individuals. Analogously, we have categorized the adsorbates as adsorbable, adsorbed, and removed compartments. Thus, we proposed a unified adsorption kinetic model (the monolayer-multilayer-adsorbable-adsorbed-removed model) that accommodates monolayer/multilayer adsorption. The model was designed to encompass diverse adsorption setups, including continuous and batch processes with fixed/dispersed adsorbents. The versatility and applicability of the model were demonstrated through validation using a diverse set of experimental data. This validation underscored its effectiveness in water/wastewater treatment, salt reduction, metal recovery, and drug purification. A MATLAB-based program for solving this model was made available to researchers for their utilization and further investigations. Overall, this study developed a versatile adsorption kinetic model that offers a comprehensive and unified understanding of adsorption kinetics across various applications.
Keyphrases
  • aqueous solution
  • randomized controlled trial
  • systematic review
  • machine learning
  • emergency department
  • quality improvement
  • deep learning
  • data analysis
  • human health