School of Architecture, Southeast University, Nanjing, 210096, China; Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, Nanjing, 210096, China.
School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
Environ Pollut. 2024 Mar 15;345:123550. doi: 10.1016/j.envpol.2024.123550. Epub 2024 Feb 12.
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 CO 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.
由于 COVID-19 的爆发，建筑物中的空气传播风险增加，尤其是在封闭的公共场所。由于一些基本的预防措施（如戴口罩）不再强制要求，通风作为一种感染预防手段的需求变得更加明显。然而，整体通风（例如，使用统一的通风率）可能导致局部区域通风不足或过度，从而导致局部病毒积聚或大量能源消耗。迫切需要根据居住者的局部需求调查通风系统的实时控制，以在感染风险和节能之间取得平衡。在这项工作中，提出了一种区域需求控制通风（ZDCV）策略，以优化子区域的通风率。开发了一种基于摄像头的居住者检测方法，以检测居住者（子区域中有八个可能的位置，标记为“A”至“H”）。线性通风模型（LVM）、降维和人工神经网络（ANN）被集成在一起，用于快速预测带有识别出的居住者和通风率作为输入的子区域中的污染物浓度。优化了子区域之间的协调通风效果，以提高感染预防和节能效果。结果表明，与不同居住者场景（即 ABH、ABCFH 和 ABCDEFH 处的居住者位置）下的模拟相比，快速预测模型在 CO 浓度场方面的平均预测误差为 6 ppm。与使用恒定通风率的系统相比，ZDCV 将感染风险降低了 2.8%，同时提高了 34%的节能效率。这项工作可以为去除污染物、预防感染和能源可持续性方面的建筑环境控制系统的发展做出贡献。
