Incorporating online monitoring data into fast prediction models towards the development of artificial intelligent ventilation systems

Abstract

It has proved that LLVM (Low-dimensional Linear Ventilation Models)-based ANN (artificial neural network) method is able to realize ventilation online control (modes or airflow rates) based on indoor pollutant response. However, it is challenging to rapidly predict indoor pollutant concentration due to the difficulty of identifying pollutant sources (location and strength). Therefore, we will incorporate monitoring techniques to modelling that is able to efficiently predict pollutant concentration, aiming for ventilation online control. A large database was firstly constructed using experiment-validated CFD (Computational Fluid Dynamics) simulations considering different ACHs (air change rates per hour) and individual pollutant sources. Next, LLVM method was utilized to process CFD data, further yielding low-dimensional database for ANN predictions. We then carried out a series of ANN predictions input with monitored concentration from different sensor layouts (i.e., positions and numbers). It is found that well-deployed sensors would provide satisfying inputs for ANN predictions. Suggestions were also given for the sensors placement that should be located in the well-mix zone (e.g. outlet region), but avoiding along the same or parallel with the main flow stream region and near the inlet zone. These findings will further provide strategies of sensor deployment and move crucial steps forward for ventilation intelligent control.