Machine learning-driven strategies for optimal design of heating, ventilation, and air-conditioning (HVAC) filter media

• ML models predicted the filtration efficiency and pressure drop of HVAC filter media. • GPR outperforms ANN and DT, achieving highest R 2 and lowest RMSE for small datasets. • GPR predicted filtration efficiency at Most Penetrating Particle Size (∼300 nm) • Robustness of GPR predictions confirmed via validation with commercial HVAC filters. The COVID-19 pandemic has highlighted the critical need to improve indoor air quality (IAQ) through efficient air filtration, especially in heating, ventilation, and air-conditioning (HVAC) systems. While dedicated high-performance filters are effective, their high-pressure drops result in significant energy consumption when used in HVAC systems. Herein, we report the application of machine learning (ML) models to predict filtration efficiency and pressure drop, enabling the design and optimisation of filter media in HVAC. Specifically, three ML models, Gaussian process regression (GPR), artificial neural network (ANN), and decision tree (DT), have been trained on a dataset obtained from the literature. The dataset comprised key structural parameters of a wide range of filter media. The GPR model emerged as the most reliable predictor, exhibiting the highest coefficient of determination ( R 2 ) and lowest root mean squared error (RMSE) in predicting filtration efficiency and pressure drop, rendering it the most reliable predictor for small and uncertain datasets. The robustness of the GPR model is further confirmed via validation with commercially available filter media. In addition, the ML models accurately capture the established relationship between filtration efficiency and its characteristic drop at the most penetrating particle size (MPPS).