Abstract
This paper presents a MATLAB ® Simulink air-quality model of a commercial building with a heating, ventilation, and air conditioning (HVAC) system in Fairbanks, Alaska. Outdoor and indoor real-time fine particulate matter (PM 2.5) levels were measured at this building during a summer wild-fire smoke episode and then during a winter period. The correlation coefficient between the model-predicted and the measured indoor concentrations was 0.99 for the summer and 0.98 for the winter, justifying the usability of the model for further studies. An HVAC control algorithm was developed that reduces the indoor PM 2.5 levels. The algorithm was tested using the HVAC Simulink model and the outdoor PM 2.5 data from the summer smoke episode. The average indoor PM 2.5 level with this control algorithm was 65% lower than with the regular control. Thanks to the PM 2.5 control strategy being automatically engaged only during episodes, it was shown to have the potential of significantly reducing the indoor PM 2.5 levels without significantly compromising the purpose of the original control strategy.
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