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Model prediction of the purification time when PM2.5 is removed unevenly by an air purifier
The portable air purifier can't uniformly remove the PM2.5 at each personnel position in a room, which causes the unevenness of PM2.5 distribution. In order to achieve priority purification of PM2.5 at the personnel position, experiments and simulations were conducted here to determine the time required for the purifier to reduce the PM2.5 concentration in a room to 15 μg/m3 (hereafter referred to as the purification time) in each area of the room at different wind speeds. SPSS was used to conduct regression analysis of the simulated data and determine the contribution rate of each impact factor, and assess the prediction model for the purification time. This prediction model can be used to calculate the purification time of any location in rooms of different sizes. The results show that, there was a nonlinear relationship between the purification time and impact factors, however, when the purifier wind speed is determined, PM2.5 purification time shows a linear relationship with the remaining impact factors. The contribution rate of the initial PM2.5 concentration to the purification time was largest. Increasing the wind speed will further increase the contribution rate of the initial PM2.5 concentration, but reduce the contribution rate of the air supply angle, personnel distance, and personnel angle.
Model prediction of the purification time when PM2.5 is removed unevenly by an air purifier
The portable air purifier can't uniformly remove the PM2.5 at each personnel position in a room, which causes the unevenness of PM2.5 distribution. In order to achieve priority purification of PM2.5 at the personnel position, experiments and simulations were conducted here to determine the time required for the purifier to reduce the PM2.5 concentration in a room to 15 μg/m3 (hereafter referred to as the purification time) in each area of the room at different wind speeds. SPSS was used to conduct regression analysis of the simulated data and determine the contribution rate of each impact factor, and assess the prediction model for the purification time. This prediction model can be used to calculate the purification time of any location in rooms of different sizes. The results show that, there was a nonlinear relationship between the purification time and impact factors, however, when the purifier wind speed is determined, PM2.5 purification time shows a linear relationship with the remaining impact factors. The contribution rate of the initial PM2.5 concentration to the purification time was largest. Increasing the wind speed will further increase the contribution rate of the initial PM2.5 concentration, but reduce the contribution rate of the air supply angle, personnel distance, and personnel angle.
Model prediction of the purification time when PM2.5 is removed unevenly by an air purifier
Jin, Wufeng (author) / Wang, Cheng (author) / Choi, Bongsoo (author) / Ma, Jingda (author) / Jing, Jiajun (author) / Wang, Zhiqiang (author)
Science and Technology for the Built Environment ; 28 ; 483-498
2022-05-03
16 pages
Article (Journal)
Electronic Resource
Unknown
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