Application of the Taguchi and ANOVA Methods to Optimize Ventilation Parameters for Infection Risk Based on the Wells-Riley Model

Abstract

The coronavirus pandemic has caused many deaths and affected societies with social and economic problems as a consequence of its effect. Many different measures were taken to stop or reduce the spread of the virus like wearing a face mask and reorganizing school activities, transportation, and meetings. As an alternative to these measures, ventilation is a critical engineering solution that can help reduce the infection risk in the indoor environment. In this study, the effects of ventilation parameters (volume, ACH) and breathing rates on the Wells-Riley method-based infection risk probability were investigated by the Taguchi method. The orthogonal array was used to create the experimental design. Then, each parameter was analyzed according to the performance criterion (infection risk probability) using signal-to-noise (S/N) ratios and the order of importance of the parameters was calculated. Consequently, these data were used to identify worst-case and best-case scenarios to minimize the risk of infection in the indoor environment.