Experimental validation of adsorption filter model under dynamic VOC concentrations: Prediction of long-term efficiency

Indoor volatile organic compound (VOC) concentrations are often dynamic because the ventilation and emission rates of VOC usually change. Adsorption filters used for air purification may operate with a capacity that fluctuates with unsteady VOC concentrations in buildings. Modeling the dynamic interactions between adsorption filters and indoor air is crucial for predicting their performance under real-world conditions. This study presents a numerical model of partially reversible adsorption equilibrium coupled with a mass transfer model to create a predictive model for adsorption efficiency in environments with dynamic VOC concentrations. A honeycomb adsorption filter for benzene adsorption was simulated and tested, including the breakthrough and purging curve and the long-term efficiency in an experimental chamber with dynamic concentrations. The results reveal that the curve generated with the partially reversible adsorption equilibrium model closely aligns with the measured one. Furthermore, the model was coupled with a chamber model and the simulation results were compared with those calculated using the filter model with a single adsorption isotherm. When VOCs were emitted intermittently in the chamber and there was sufficient ventilation, the concentration peaks in the chamber derived from the models with different assumptions on adsorption reversibility were significantly different from each other. Moreover, it was observed that the reversible adsorption capacity of the filter was crucial for long-term operation in rooms with dynamic concentration. Despite the reversible adsorption capacity constituting only 6.7% of the total adsorption capacity of the tested filter, it contributes to a significant “peak shaving and valley filling” effect, even when the irreversible adsorption capacity is saturated. The adsorption reversibility should be taken as an important parameter for selecting adsorbents for dynamic concentration conditions.