MULTI-OBJECTIVE OPTIMIZATION OF INDOOR AIR QUALITY FOR DISPLACEMENT VENTILATION USING GENETIC ALGORITHMS

摘要

The effects of supply air temperature, air inlet and exhaust position on energy utilization factor, air diffusion performance index, mean air age in the working area and the concentration of fine particle on the breathing zone were studied using response surface methodology (RSM) under displacement ventilation mode (DV) in this study, and two second-order polynomial models were obtained with regard to the effect of the three factors as stated above. Based on the prediction models, the genetic algorithm was adopted to study the multi-objective optimization. In addition, the evaluation indexes of the optimal condition were compared with those of the typical condition. The results show that the optimal condition (the supply temperature is 23.0°C, the distance that the air inlet away from the south wall is 0.84 m and the distance that the air exhaust away from the east wall is 0.18 m) can provide better indoor air quality (IAQ) than the typical condition.