Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA


A Smart CO2-Based Ventilation Control Framework to Minimize the Infection Risk of COVID-19 In Public Buildings

Zhihong Pang 1, Pingfan Hu 2,3, Xing Lu 1, Qingsheng Wang 2,3, Zheng O'Neill 1
1 J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA
2 Mary Kay O'Connor Process Safety Center, Texas A&M University, College Station, TX, USA
3 Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA

Abstract: This study aims to present a smart ventilation control framework to reduce the infection risk of COVID-19 in indoor spaces of public buildings. To achieve this goal, an artificial neural network (ANN) was trained based on the results from a parametric computational fluid dynamics (CFD) simulation to predict the COVID-19 infection risk according to the zone carbon dioxide (CO2) concentration and other information (e.g., zone dimension). Four sample cases were analyzed to reveal how the CO2 concentration setpoint was varied for a given risk level under different scenarios. A framework of smart ventilation control was briefly discussed based on the ANN model. This framework could automatically adjust the system outdoor airflow rate and variable air volume (VAV) terminal box supply airflow rate to meet the needs of reducing infection risk and achieving a good energy performance.
Keywords: Demand-Controlled Ventilation, COVID-19, Occupant Well-being, Computational Fluid Dynamics, Energy Efficiency
Pages: 3473 - 3482