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Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO 2 concentration. The validation results show that the model can predict room temperature and CO 2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller. [ABSTRACT FROM AUTHOR]

The year 2020 has seen the world gripped by the effects of the COVID‐19 pandemic. It is not the first time, nor will it be last, that our increasingly globalized world has been significantly affected by the emergence of a new disease. In much of the Northern Hemisphere, the academic year begins in September, and for many countries, September 2020 marked the return to full schooling after some period of enforced closure due to COVID‐19. In this paper, we focus on the airborne spread of disease and investigate the likelihood of transmission in school environments. It is crucial to understand the risk airborne infection from COVID‐19 might pose to pupils, teachers, and their wider social groups. We use monitored CO2 data from 45 classrooms in 11 different schools from within the UK to estimate the likelihood of infection occurring within classrooms regularly attended by the same staff and pupils. We determine estimates of the number of secondary infections arising via the airborne route over pre/asymptomatic periods on a rolling basis. Results show that, assuming relatively quiet desk‐based work, the number of secondary infections is likely to remain reassuringly below unity; however, it can vary widely between classrooms of the same school even when the same ventilation system is present. Crucially, the data highlight significant variation with the seasons with January being nearly twice as risky as July. We show that such seasonal variations in risk due to changes in ventilation rates are robust and our results hold for wide variations in disease parameterizations, suggesting our results may be applied to a number of different airborne diseases. [ABSTRACT FROM AUTHOR]

The changes of indoor environment and occupant behavior (OB) are two main causes for the gap between predicted and actual airborne infection risk. To improve the accuracy of COVID-19 airborne infection risk assessment, the environment (CO2 concentration) and OBs (occupant area per person (OA) and activity level (AL)) in three typical classrooms of a primary school in Tianjin, China was selected to conduct the on-site measurement. Based on the measured data, a modified Wells-Riley model was proposed to predict the infection risk, and a risk-controlled ventilation strategy was developed to calculate the ventilation demand. Results indicated that classrooms in the breaking time (B-T) showed a lower indoor CO2 concentration (Cin), larger OA, and higher AL than in the teaching time (T-T). The variation tendency of the calculated infection risk increment in T-T was consistent with Cin while in B-T was significantly affected by OA and AL, and the maximum fluctuation extent in B-T was two times of that in T-T. Moreover, to avoid the risk spreading in classrooms, a feasible solution of dynamic ventilation control based on the real-time infection risk was proposed, thus facilitating to provide a healthy and sustainable environment for students in classrooms. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO2 concentration. The validation results show that the model can predict room temperature and CO2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller.

Abstract: This paper discusses the issue of selecting the design solution that best accords with an articulated preference of multiple criteria with an acceptable performance band. The application of a newly developed multi-criteria decision-making tool called RR-PARETO2 is presented. An example of HVAC design is used to illustrate how solutions could be selected within a multi-criteria optimization framework. In this example, five criteria have been selected, namely, power consumption, thermal comfort, risk of airborne infection of influenza and tuberculosis and effective differential temperature (Δt eq) of body parts. The goal is to select the optimal air exchange rate that makes reasonable trade-offs among all the objectives. Two scenarios have been studied. In the first scenario, there is an influenza outbreak and the important objective is to prevent the spread of infection. In the second scenario, energy prices are high and the primary objective is to reduce energy. In both scenarios, RR-PARETO2 algorithm selects solutions that make reasonable trade-offs among conflicting objectives. The example illustrates how objectives such as reduction of airborne disease transmission and maximizing thermal comfort can be incorporated in the design of a practical, full-scale HVAC system. [Copyright &y& Elsevier]