Hospital Ventilation Optimization: Balancing Thermal Comfort and Energy Efficiency in Nonlinear Building Dynamics

Despite growing interest in AI-driven Heating, Ventilation, and Air Conditioning (HVAC) systems, existing approaches often rely on static control strategies or offline simulations that fail to adapt to real-time environmental changes, especially in high-risk healthcare settings. There remains a critical gap in integrating dynamic, physics-informed control with human-centric design to simultaneously address infection control, energy efficiency, and occupant comfort in hospital environments. This study presents an AI-driven ventilation system integrating BIM, adaptive control, and computational fluid dynamics (CFD) to optimize hospital environments dynamically. The framework features (1) HVAC control using real-time sensor datasets; (2) CFD-validated architectural interventions (1.8 m partitions and the pressure range at a return vent); and (3) patient flow prediction for spatial efficiency. The system reduces airborne pathogen exposure by 61.96% (159 s vs. 418 s residence time) and achieves 51.85% energy savings (0.19 m/s airflow) while maintaining thermal comfort. Key innovations include adaptive energy management, pandemic-resilient design, and human-centric spatial planning. This work establishes a scalable model for sustainable hospitals that manages infection risk, energy use, and occupant comfort. Future directions include waste heat recovery and lifecycle analysis to further enhance dynamic system performance.