Zonal demand-controlled ventilation strategy to minimize infection probability and energy consumption: A coordinated control based on occupant detection

Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no...

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Vydáno v:Environmental pollution (1987) Ročník 345; s. 123550
Hlavní autoři: Ren, Chen, Yu, Hanhui, Wang, Junqi, Zhu, Hao-Cheng, Feng, Zhuangbo, Cao, Shi-Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 15.03.2024
Témata:
Air Conditioning
Air Pollution, Indoor - analysis
Air Pollution, Indoor - prevention & control
airborne transmission
Artificial neural network (ANN)
carbon dioxide
Coordinated control
COVID-19 infection
energy
energy conservation
Energy saving
Environmental Pollutants
Infection prevention
neural networks
Occupant detection
pollutants
pollution
pollution control
prediction
Respiration
risk
Ventilation
viruses
Zonal demand-controlled ventilation (ZDCV)
Coordinated control
Infection prevention
Artificial neural network (ANN)
Energy saving
Occupant detection
Zonal demand-controlled ventilation (ZDCV)
ISSN:0269-7491, 1873-6424, 1873-6424
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Shrnutí:Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no longer mandatory. However, ventilating the space as a whole (e.g., using a unified ventilation rate) may lead to situations where there is either insufficient or excessive ventilation in localized areas, potentially resulting in localized virus accumulation or large energy consumption. It is of urgent need to investigate real-time control of ventilation systems based on local demands of the occupants to strike a balance between infection risk and energy saving. In this work, a zonal demand-controlled ventilation (ZDCV) strategy was proposed to optimize the ventilation rates in sub-zones. A camera-based occupant detection method was developed to detect occupants (with eight possible locations in sub-zones denoted as ‘A’ to ‘H’). Linear ventilation model (LVM), dimension reduction, and artificial neural network (ANN) were integrated for rapid prediction of pollutant concentrations in sub-zones with the identified occupants and ventilation rates as inputs. Coordinated ventilation effects between sub-zones were optimized to improve infection prevention and energy savings. Results showed that rapid prediction models achieved an average prediction error of 6 ppm for CO2 concentration fields compared with the simulation under different occupant scenarios (i.e., occupant locations at ABH, ABCFH, and ABCDEFH). ZDCV largely reduced the infection risk to 2.8% while improved energy-saving efficiency by 34% compared with the system using constant ventilation rate. This work can contribute to the development of building environmental control systems in terms of pollutant removal, infection prevention, and energy sustainability. [Display omitted] •Developed zonal demand-controlled ventilation strategy for public space.•Realized rapid prediction of pollutant levels based on occupant detection algorithm.•Proposed the coordinated ventilation for infection risk control and energy savings.•Reduced the infection probability to 2.8% and improving the energy savings by 34%.•Promoting a safer and more sustainable building environment.
Bibliografie:ObjectType-Article-1
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ISSN:0269-7491
1873-6424
1873-6424
DOI:10.1016/j.envpol.2024.123550