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

Document Type

Conference Proceeding

Publication Date

1-1-2022

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.

Publication Source (Journal or Book title)

Building Simulation Conference Proceedings

First Page

3473

Last Page

3482

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