The mean age of air (MAA) is one of the most useful parameters in evaluating indoor air quality in natural ventilated buildings. Its evaluation is generally based on the CO2 monitoring within the environment; however, other methods can be found in the literature, but they have not always led to satisfactory results. In this context, the present paper is focused on two main topics: the effect of the windows airtightness and of the environmental conditions on MAA and the application of artificial neural network (ANN) for the CO2 prediction within the room. Two case studies (case study 1 located in Terni and case study 2 located in Perugia) were investigated, which differ in geometric dimensions (useful area, volume, window area) and in airtightness of windows. The indoor and outdoor environmental conditions (air temperature, pressure, relative humidity, air velocity, and indoor CO2 concentration) were monitored in 33 experimental campaigns, in four room configurations: open door-open window (OD-OW); closed door-open window (CD-OW); open door-closed window (OD-CW); closed door-closed window (CD-CW). Tracer decay methodology, according to ISO 16000-8:2007 standard, was compiled during all the experimental campaigns. A feedforward ANN, able to simulate the indoor CO2 concentration within the rooms, was then implemented; the monitored environmental conditions (air temperature, pressure, relative humidity, and air velocity), the geometric dimensions (useful area, volume, window area), and the airtightness of windows were provided as input data, while the CO2 concentration was used as target. In particular, data of 19 experimental campaigns were provided for the training process of the network, while 14 were only used for testing the reliability of ANN. The CO2 concentration predicted by ANN was then used for the MAA calculation in the four room configurations. Experimental results show that MAA of case study 2 is always higher, in all the examined configurations, due to the higher airtightness characteristics of the window and to the higher volume of the room. When the difference between indoor and outdoor temperature increases, the MAA increases too, in almost all the investigated configurations. Finally, the CO2 concentration predicted by ANN was compared with experimental data; results show a good accuracy of the network both in CO2 prediction and in the MAA calculation. The predicted CO2 concentration at the beginning of experimental campaigns (time step 0) always differs less than 2% from experimental data, while a mean percentage difference of-18.8% was found considering the maximum CO2 concentration. The MAA calculated using the predicted CO2 of ANN was greater than the one obtained from experimental data, with a difference in the 0.5-1.3 min range, depending on the configuration. According to the results, the developed ANN can be considered an alternative and valuable tool for a preliminary evaluation of MAA.

Mean age of air in natural ventilated buildings: Experimental evaluation and CO2 prediction by artificial neural networks

Palladino D.
Investigation
2020

Abstract

The mean age of air (MAA) is one of the most useful parameters in evaluating indoor air quality in natural ventilated buildings. Its evaluation is generally based on the CO2 monitoring within the environment; however, other methods can be found in the literature, but they have not always led to satisfactory results. In this context, the present paper is focused on two main topics: the effect of the windows airtightness and of the environmental conditions on MAA and the application of artificial neural network (ANN) for the CO2 prediction within the room. Two case studies (case study 1 located in Terni and case study 2 located in Perugia) were investigated, which differ in geometric dimensions (useful area, volume, window area) and in airtightness of windows. The indoor and outdoor environmental conditions (air temperature, pressure, relative humidity, air velocity, and indoor CO2 concentration) were monitored in 33 experimental campaigns, in four room configurations: open door-open window (OD-OW); closed door-open window (CD-OW); open door-closed window (OD-CW); closed door-closed window (CD-CW). Tracer decay methodology, according to ISO 16000-8:2007 standard, was compiled during all the experimental campaigns. A feedforward ANN, able to simulate the indoor CO2 concentration within the rooms, was then implemented; the monitored environmental conditions (air temperature, pressure, relative humidity, and air velocity), the geometric dimensions (useful area, volume, window area), and the airtightness of windows were provided as input data, while the CO2 concentration was used as target. In particular, data of 19 experimental campaigns were provided for the training process of the network, while 14 were only used for testing the reliability of ANN. The CO2 concentration predicted by ANN was then used for the MAA calculation in the four room configurations. Experimental results show that MAA of case study 2 is always higher, in all the examined configurations, due to the higher airtightness characteristics of the window and to the higher volume of the room. When the difference between indoor and outdoor temperature increases, the MAA increases too, in almost all the investigated configurations. Finally, the CO2 concentration predicted by ANN was compared with experimental data; results show a good accuracy of the network both in CO2 prediction and in the MAA calculation. The predicted CO2 concentration at the beginning of experimental campaigns (time step 0) always differs less than 2% from experimental data, while a mean percentage difference of-18.8% was found considering the maximum CO2 concentration. The MAA calculated using the predicted CO2 of ANN was greater than the one obtained from experimental data, with a difference in the 0.5-1.3 min range, depending on the configuration. According to the results, the developed ANN can be considered an alternative and valuable tool for a preliminary evaluation of MAA.
Artificial neural network
CO2
Environmental conditions effect
Mean age of air
Windows air tightness
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/55637
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