Modelling and computational fluid dynamic behaviour of a biofilter treating benzene

Area of Science:

• Environmental Engineering
• Bioremediation
• Chemical Engineering

Background:

• Air pollution control using biofilters is crucial for removing volatile organic compounds (VOCs).
• Benzene, a common air pollutant, requires effective removal strategies.
• Biofiltration offers a sustainable method for treating benzene-contaminated air streams.

Purpose of the Study:

• To evaluate the performance of a laboratory biofilter for benzene removal.
• To assess the accuracy of computational fluid dynamics (CFD) and convection-diffusion reactor (CDR) models in predicting benzene concentration profiles.
• To analyze the elimination capacity of a biofilter packed with compost, sugar cane bagasse, and granular activated carbon (GAC).

Main Methods:

• Experimental study of benzene biofiltration in a laboratory-scale biofilter.
• Application of 3-D mesh techniques to evaluate overall biofilter performance and elimination capacity.
• Utilizing convection-diffusion reactor (CDR) model and computational fluid dynamic (CFD) technique to determine benzene concentration profiles.

Main Results:

• Excellent agreement was observed between experimental data and model predictions for benzene biofiltration.
• Benzene concentration profiles showed good agreement with the CDR model at low flow rates and concentrations, exhibiting a curved decay.
• CFD model predictions for benzene profiles demonstrated high accuracy, within 5% of experimental results, and accurately predicted linear profiles at high concentrations.

Conclusions:

• The biofilter effectively removes benzene from air streams, with excellent agreement between experimental results and model predictions.
• Both CDR and CFD models are reliable tools for accurately predicting benzene concentration profiles within the biofilter depth.
• The study validates the use of advanced modeling techniques for optimizing biofilter design and performance in air pollution control.