Development and Performance Analysis of Indoor PM2.5 Preemptive Control Algorithm

Purpose: Recently, as the demand for comfortable indoor environments has significantly increased, the importance of the health of occupants has come to the forefront. In response to this societal issue, this study developed an integrated control algorithm for ventilation and air purification system that can proactively manage indoor PMsub 2.5/sub levels. Method: To analyze the performance of the developed indoor PMsub2.5/sub preemptive control algorithm, ventilation and air purification systems installed in two actively used elementary school classrooms were utilized. The control algorithm incorporates an ANN based indoor PM sub2.5/sub prediction model and derives control values using the predicted values and real-time environmental data. Data obtained through empirical studies were analyzed according to occupancy schedules to verify compliance with national recommended standards. Additionally, an analysis of the control algorithm's inherent performance was conducted. Result: The preemptive control algorithm was observed to maintain lower indoor PMsub 2.5/sub concentrations and sustain the comfortable range for extended periods compared to rule based control algorithms. However, the system operating time was longer than that of rule based control algorithms.