A probability-based optimal ventilation control strategy for non-unidirectional cleanrooms considering particle spatial distribution and measurement uncertainty

Ventilation systems in non-unidirectional cleanrooms require a large volume of clean air to maintain cleanliness, resulting in high energy consumption. The primary challenges in implementing energy-efficient control strategies include uneven spatial distribution of particle and measurement uncertainty, which are not adequately quantified or addressed by conventional methods. To overcome these challenges, a cleanliness violation probability-based optimal ventilation control strategy for non-unidirectional cleanrooms is proposed. At the core of this strategy is a model for cleanliness violation probability, designed to identify and quantify risks arising from particle emission variation, uneven spatial distribution and measurement noise in particle concentration. The strategy targets dual optimization objectives: minimizing fan energy consumption and cleanliness violation risks, while also considering the practical constraint of maintaining the pressure hierarchy. The effectiveness of the proposed model and strategy is tested in a simulated building and air-conditioning environment based on a typical pharmaceutical cleanroom. Results indicate that the proposed method can rapidly detect dynamic changes in emission rates and effectively determine energy-saving yet sufficient airflow rates under varying conditions. The strategy achieves a 50 % reduction in fan energy consumption compared to the occupant schedule-based control strategy and reduces the spatial violation risk by 26 % compared to the particle concentration-based control strategy.