(534b) Predictive Strategies for Control of Indoor Air Quality

Ganesh, H. S., McKetta Department of Chemical Engineering, The University of Texas at Austin
Fritz, H. E., University of Texas at Austin
Edgar, T. F., McKetta Department of Chemical Engineering, The University of Texas at Austin
Novoselac, A., The University of Texas at Austin
Baldea, M., The University of Texas at Austin
Americans spend more than 90% of their time indoors. Air inside our homes can contain high concentrations of particulate matter (PM) of different sizes and chemical pollutants such as ozone (O3), formaldehyde (HCHO) and volatile organic compounds (VOCs) [1,2]. Hence, controlling these contaminants of concern (COCs) is an important factor for human health. The American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHARE) prescribes fixed ventilation rates for the operation of heating, ventilation and air conditioning (HVAC) systems, which is suboptimal in terms of pollution control and energy consumption. Moreover, different pollutants react differently to a change in manipulated variables, in this case the ventilation rate. For example, an increase in the ventilation rate might be favorable by reducing PM but could increase the concentration of ozone in the room. The aforementioned issues make indoor air quality control a primary target for model-based analysis, control and optimization. The primary objective of this work is to identify the operating conditions of the HVAC system that minimize the energy usage while keeping the concentration of COCs within acceptable limits.

We develop a first-principles model that relates the concentration of PM, HCHO and O3 in a room to the ventilation rate and energy consumption of the HVAC system. Different pollutants have different penetration factors, deposition velocities and indoor emission rates. Therefore, the concentration of each type of pollutant is described by a separate differential equation with the above mentioned quantities as parameters determined from experiments in the published literature. The model captures real-world situations such as pollutants entering the room from outside and pollutants generated in the room due to human activities like cooking.

Next, we use the model to identify optimal operational settings. We solve two problems: (1) identify “steady-state” ventilation rate for idealized periodic variations in pollutants in the room, and (2) develop and deploy model predictive control as an online optimization strategy to calculate optimal ventilation rate at each time instant based on instantaneous measured fluctuations in indoor and outdoor conditions. The objective function of the optimization problem is the energy consumption of the system and the pollutant concentrations (either instantaneous or time-averaged) are expressed as constraints. The proposed optimization strategies show considerable reduction in energy usage compared to the existing standards, without jeopardizing indoor air quality or posing health concerns.


[1] Boor, B.E., Siegel, J.A., Novoselac, A., “Monolayer and multilayer particle deposits on hard surfaces: literature review and implications for particle resuspension in the indoor environment.” Aerosol Science and Technology, 47(8): 831-847 (2013)

[2] Morawska, L., Afshari, A., Bae, G.N., Buonanno, G., Chao, C.Y.H., Hanninen, O., Hofmann, W., Isaxon, C., Jayaratne, E.R., Pasanen, P., Salthammer, T., “Indoor aerosols: from personal exposure to risk assessment.” Indoor Air, 23(6): 462-487 (2013)