Evaluation of real-life demand-controlled ventilation from the perception of indoor air quality with probable implications

2020 ◽  
Vol 219 ◽  
pp. 110018
Author(s):  
Zakia Afroz ◽  
Gary Higgins ◽  
G.M. Shafiullah ◽  
Tania Urmee
2020 ◽  
Author(s):  
Fritz Nieborowski

<div> <p>Improper ventilation of buildings may lead to an accumulation of pollutants indoors. In the case of a room with forced air ventilation and external air intake like most centralized and some home air conditioning units, this study will show CFD simulations of various indoor air quality conditions based on different forced ventilation AC unit intake conditions like common in housing situations like Hong Kong. Especially when close to roadways or other external pollution sources, the positioning of the air intake shows up to have a high significance for the infiltration rate resulting as influence for the indoor air quality as previous research shows (e.g. Zheming Tong et al., 2016). The same is the case for a forced ventilation case like air conditioning units with outside air intake. Research like earlier referenced paper has not been conducted with higher buildings or forced air intake yet. Parametrized CFD-based air quality models with using OpenFoam will be employed to quantify the impact of the air intake location and rate in a 2-dimensional interface on the indoor air quality of a forced ventilated section of a building. The findings of the CFD simulation will be simplified as average indoor air pollution and other external factors. As an approach to predict the estimate indoor infiltration rate, an ANN (Artificial Neuronal Network) will be used, trained and validated with said data. The neural network is supposed to predict the pollutant intake based on fewer and as easier to obtain meteorological parameters and air pollution data. Finally, the ANN predictions of the models will be verified with real life data from other papers. Results will show that a major part of indoor pollutants may emerge indoors and cannot be neglected. In comparison with real life data, it seems the model lacks significant input to predict with high accuracy. </p> </div>


Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4425
Author(s):  
Wei Wang ◽  
Xiaofang Shan ◽  
Syed Asad Hussain ◽  
Changshan Wang ◽  
Ying Ji

As most residents spend more than 90% of their time in buildings, acceptable and reasonable control of both indoor thermal comfort and air quality is imperative to ensure occupants’ health status and work productivity. However, current control strategies generally take either thermal comfort or indoor air quality as a single loop, rather than the concurrent control of two. To analyze their mutual influence, this study investigated the performance of three multi-control approaches, i.e., proportional integral derivative (PID) control of thermal comfort and a fixed outdoor air ratio, PID control of thermal comfort and design outdoor air rate, and PID control of thermal comfort and occupancy-based demand-controlled ventilation. As a pilot study, three typical control methods were implemented to a multi-zone building via OpenModelica modeling. The results indicate that indoor air temperature can be well-maintained under three control methods, however, the CO2 concentration under the fixed outdoor air ratio was over 1000 ppm, leading to poor indoor air quality. The control strategy with the design outdoor air rate could not properly ensure the CO2 concentration, due to the over-ventilated or under-ventilated phenomena, subsequently resulting in unnecessary energy waste. The occupancy-based demand controlled ventilation could maintain the CO2 concentration under the set-point with an intermediate power energy utilization.


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