A Revised Pseudo-Second-Order Kinetic Model for Adsorption, Sensitive to Changes in Adsorbate and Adsorbent...

Area of Science:

• Environmental Science
• Water Treatment Technologies
• Adsorption Science

Background:

• Adsorbent materials are critical for removing toxic contaminants from drinking water, aligning with UN Sustainable Development Goal 6.
• The pseudo-second-order (PSO) model is commonly used for adsorption kinetics but has limitations in predicting performance under varying conditions.
• The PSO model's rate constant is sensitive to experimental conditions, hindering reliable comparisons of literature data.

Purpose of the Study:

• To develop a modified pseudo-second-order (PSO) adsorption kinetics model that accounts for initial adsorbate and adsorbent concentrations.
• To create a more robust model for predicting adsorbent performance and facilitating comparisons across diverse studies.

Main Methods:

• Analysis of 103 kinetic experiments from 47 literature sources.
• Development of a revised pseudo-second-order (rPSO) rate equation.
• Validation of the rPSO model by comparing its performance against the standard PSO model using residual sum of squares.

Main Results:

• The revised PSO (rPSO) model demonstrates first-order and zero-order dependencies on initial adsorbate (C0) and adsorbent (Cs) concentrations, respectively.
• The rPSO model reduced the residual sum of squares by 66% compared to the standard PSO model when fitting data from experiments with varying initial conditions.
• The rPSO rate constant (k') offers a more appropriate metric for comparing adsorption kinetics across different studies and materials.

Conclusions:

• The developed rPSO model provides a more accurate and condition-independent method for characterizing adsorption kinetics.
• The rPSO model's rate constant (k') is suitable for comparing adsorption performance, as seen in the faster kinetics of arsenic adsorption onto alumina versus iron oxides.
• The rPSO model offers improved applicability for engineering design and literature data interpretation in water treatment.