Risk assessment-based particle sensor location optimization for non-unidirectional cleanrooms concerning air distribution uncertainties

Air conditioning systems in cleanrooms require a huge amount of clean air to maintain the desired indoor air cleanliness, resulting in significant energy consumption. A major challenge in achieving energy-efficient control of such systems is obtaining accurate and reliable measurements of particle concentration which is essential for precisely controlling minimum but sufficient airflow rate. Therefore, this paper proposes a risk assessment-based method for optimizing particle sensor locations in non-unidirectional cleanrooms, addressing the limitations of conventional empirical methods for sensor placement. Two sensor performance indexes, "systematic measurement bias" and "spatial violation risk", are formulated to balance measurement accuracy and the risk of unsatisfactory air cleanliness at a sensor location. This optimization method is explored through experimentally validated computational fluid dynamics simulations based on a typical non-unidirectional cleanroom. The results show that the proposed method can be conveniently implemented to optimize the sensor location under various scenarios, and improve the particle monitoring performance by optimizing the number of sensors and the location of source. Compared to a commonly-used practical sensor placement method, the proposed method can reduce the spatial violation risk by 31 %.