Modeling and Control of an HVAC System to Minimize COVID-19 Virus Transmission

Abstract

COVID-19 is one of the high-level infections that have caused considerable damage to the lives of millions of people worldwide. Many studies confirm that indoor environments are the hotspots for the transmission of COVID-19 infectious. Thus, improving indoor air quality in enclosed areas is an efficient and fundamental approach to ease or dilute the concentration of viral aerosols. Heating, ventilation, and air conditioning (HVAC) systems have an integral role in increasing or reducing the infection risk. For example, one of the best ways to control and decrease the infection risk is high ventilation; On the other hand, high ventilation will increase energy consumption and cost. This thesis proposes an intelligent controller of HVAC systems to assess the tradeoff between energy consumption and indoor infection risk. To achieve this goal, a model predictive controller (MPC) is designed for a university building to control HVAC systems to minimize COVID-19 virus transmission and reduce building energy consumption. MPC uses dynamic models to predict the system’s future output while tuning to system constraints. First, a set of dynamic models according to physics-based models, including conduction, convection, radiation heat transfer, and conservation of mass is created to utilize the MPC controller. Then, the developed models were experimentally validated by conducting experiments in the ETLC building at the University of Alberta. One building classroom was equipped with nine sensors measuring indoor and outdoor environmental parameters such as temperature, relative humidity, CO2, and airflow rate. The validation results showed that the model could predict room temperature and CO2 concentration by 0.8%, and 2.4% average errors, respectively. After validation, the MPC controller was implemented on the dynamic models to calculate the optimal airflow and supply air temperature at each moment. The results will show that the MPC controller can mitigate the infection risk of the COVID-19 virus while reducing daily energy consumption by about 54.8% compared to the baseline controller implemented in the current building. The results of testing designed controllers on the ETLC classroom show the potential saving energy for the ETLC classroom by comparing the new control results with those from the building measurements.