Air filtration in HVAC systems is a key factor in indoor air quality, energy consumption, and sustainable building design. Designing efficient filters requires balancing particle capture efficiency (η), pressure drop (ΔP), and operational energy cost.

HVAC filter design relies on classical filtration models. Darcy’s Law governs laminar airflow through porous media, while the Darcy–Forchheimer equation incorporates inertial effects at higher duct velocities. The Kozeny–Carman relation links fiber diameter, porosity, and packing density to media permeability, optimizing flow resistance. Single-fiber filtration theory quantifies particle capture via Brownian diffusion, interception, and inertial impaction, enabling precise evaluation of filter efficiency for PM2.5 and ultrafine particles.

The energy efficiency of HVAC filters depends on pressure drop, airflow, and fan power, with fan efficiency playing a key role. Filters are rated using MERV, ASHRAE 52.2, and Energy Star standards, balancing particle removal with acceptable pressure drop to ensure efficient system operation. The Filter Quality Factor (QF) combines filtration efficiency and pressure drop into a single metric, allowing direct comparison of energy and performance.

Recent research applies computational and data-driven methods to advance HVAC filter design. Computational Fluid Dynamics (CFD) predicts airflow, pressure drop, and particle trajectories in pleated filters, while machine learning models such as Artificial Neural Networks and Gaussian Process Regression forecast filtration efficiency and pressure drop from media structure and particle size, reducing the need for extensive experiments. Hybrid CFD–AI approaches enable rapid optimization of pleat geometry, surface area, and dust-holding capacity. Emerging digital twins and AI-driven HVAC control strategies dynamically optimize airflow, filtration performance, and energy use in real time.

By integrating mathematical modeling with building energy metrics, HVAC filters can meet indoor air quality (IAQ) targets while supporting sustainability benchmarks. Optimized filters achieve high MERV or ASHRAE ratings with minimal pressure drop, lower fan energy consumption, and contribute to LEED and Energy Star certifications. This review highlights how classical filtration theory, advanced computational modeling, and energy-rating frameworks combine to guide engineers to design next-generation HVAC filters that are both energy-efficient and environmentally sustainable.