Abstract

Energy recovery ventilators (ERVs), which integrate fresh air supply, air purification, and energy recovery, are prominently employed in buildings. Particulate fouling and dynamic climate conditions in real-time scenarios could cause the deviation of operating parameters from the designed values, impacting the long-term performance of ERVs. In this study, a non-linear regression model of energy exchanger involving imbalanced airflow rate conditions was derived based on experimental data, considering the decreased supply air volume caused by dust-loading of fresh air filter. Model validation presented errors within ±10 %. Furthermore, an ERV model was established by integrating the energy exchanger, filter, and fan. Simulation results indicate that particulate fouling could decrease the instant supply airflow rate and recovered energy to maximums of 15 % and 12 % respectively, compared to the ideal performance in the clean state. Analysis throughout the heating season concludes that, the reduced recovered energy together with the unorganized air infiltration consequent on the imbalanced airflow rates could increase the seasonal fresh air load by 10.9 %, 13.1 %, and 24.1 % in three typical cities, Shanghai, Beijing, and Harbin, respectively. The proposed model provides a reliable platform for evaluating the performance and analyzing the maintenance strategies of ERVs, contributing to improving their practical effects.

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