Multi-objective Optimization Of Indoor Air Quality For Different Ventilation Modes

With the improvement of the living standard and the increase of spending more time indoors, there is a growing concern and awareness over indoor air quality (IAQ). In this study, the effects of supply temperature, the location of air inlet and exhaust position on energy utilization factor (η), air diffusion performance index (ADPI ), mean age of air in work area (Ï„) and the concentration of fine particle on the inhalable surface (C ) were studied using response surface methodology (RSM) by means of the statistical software program (Minitab 15) under displacement ventilation and hybrid ventilation, 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 algorithms was adopted to study the multi-objective optimization. The results showed that:(1) For the displacement ventilation, there were significant impact of X₁ toη, X₃to ADPI , X₁ and X₁² toÏ„and X₁ X₂ to C . In the present paper, when the supply temperature was 23.0℃, the distance that the air inlet is away from the south wall was 0.84 m and the distance that the air exhaust away from the east wall was 0.18 m, it could provide better IAQ.(2) For the hybrid ventilation, there are important effect of X 1, X₂ and X₁ X₂ toη, X 1, X 3, X₂², X₁ X₃and X₂ X₃to ADPI and X₁², X₂², X 3² and X₁ X₃to C. In the present study, when the supply temperature was 21.2℃, the distance that the air inlet is away from the south wall was 3.2² m and the distance that the air exhaust away from the east wall was 0.02 m, the IAQ would be better.(3) There were advance and disadvance between the two optimization methods of displacement ventilation and hybrid ventilation. So it was advised that the ventilation model should be selected according to the demand of people to the indoor environment. when the thermal comfort and the clearness of air are important, the second optimization methods is preferential; otherwise, the efficiency of energy saving and the fresh of the air are considered firstly, the first optimization methods is underlying.(4) The genetic algorithms for multi-objective optimization used in this research considered multi-projectives and avoided the disfigurement of singleobjective optimization. So the use of genetic algorithms for multi-objective optimization was very important for the optimization of IAQ.