An agglomeration-based model for colloid filtration.

Area of Science:

• Environmental science
• Water treatment
• Colloid and surface chemistry

Background:

• Colloid filtration is crucial for water purification.
• Existing models often overlook particle aggregation, a key factor in removal efficiency.
• Understanding colloid behavior in porous media is essential for predicting contaminant transport.

Purpose of the Study:

• To develop a novel depth filtration model incorporating colloid aggregation.
• To experimentally validate the model's predictions against observed colloid concentration profiles.
• To provide a framework for predicting colloid transport in groundwater.

Main Methods:

• Experimental measurement of colloid concentration profiles (C(x)) in saturated porous media.
• Development of a new depth filtration model based on material balances of colloidal aggregates.
• Parameter estimation techniques for the developed model.

Main Results:

• Log of C(x) significantly deviated from linearity, indicating aggregation effects.
• Preaggregation of colloids correlated with their retention in porous media.
• The new model demonstrated a good match with experimental data for varying cation concentrations.

Conclusions:

• Colloid aggregation significantly influences filtration and retention in porous media.
• The developed model accurately predicts colloid concentration variations, accounting for aggregate size.
• This model offers a valuable tool for predicting colloid transport in environmental systems, including groundwater.